diff --git a/README.md b/README.md
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+# ViLBERT 
+
+Code and pre-trained models for **ViLBERT: Pretraining Task-Agnostic VisiolinguisticRepresentations for Vision-and-Language Tasks**.
+
+
+## Repository Setup
+
+1. Create a fresh conda environment, and install all dependencies.
+
+```text
+conda create -n vilbert python=3.6
+conda activate vilbert
+git clone https://github.com/jiasenlu/ViLBert
+cd ViLBert
+pip install -r requirements.txt
+```
+
+2. Install pytorch
+```
+conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
+```
+
+3. Install apx, follows https://github.com/NVIDIA/apex
+
+4. Install this codebase as a package in this environment.
+```text
+python setup.py develop
+```
+
+## Data Setup
+
+Check `README.md` under `data` for more details.
+
+## Visiolinguistic Pre-training
+
+To train the model:
+
+```
+To be added
+```
+
+For internal use: copy the pre-trained checkpoint from Skynet
+
+```
+cp -a /srv/share3/jlu347/vilbert/save/* #to_your_directory.
+```
+
+## Benchmark Vision-Lanugage Tasks
+
+| Task | Sub-Task | Model | LR | Results (split) |
+| :-----------------------: | :---------------: | :---------: | :--: | :-----------------------------------------------------: |
+| **VQA** | - | **ViLBERT** | 4e-5 | **70.55** (test-dev) |
+| - | - | DFAF | - | 70.22 (test-dev) |
+| **VCR** | Q->A | **ViLBERT** | 2e-5 | **73.3** (test) |
+| - | Q->A | R2C | - | 63.8 (test) |
+| **VCR** | QA->R | **ViLBERT** | 2e-5 | **74.6** (test) |
+| - | QA->R | R2C | - | 67.3 (test) |
+| **VCR** | Q->AR | **ViLBERT** | 2e-5 | **54.8** (test) |
+| - | Q->AR | R2C | - | 44.0 (test) |
+| **Ref Expression** | RefCOCO+ | **ViLBERT** | 4e-5 | **72.34** (val) - **78.52** (testA) - **62.61** (testB) |
+| - | RefCOCO+ | MAttNet | - | 65.33 (val) - 71.62 (testA) - 56.02 (testB) |
+| **Ref Expression** | RefCOCO | **ViLBERT** | 4e-5 | - |
+| - | RefCOCO | MAttNet | - | - |
+| **Ref Expression** | Refg | **ViLBERT** | 4e-5 | - |
+| - | Refg | MAttNet | - | - |
+| **Image Caption Ranking** | Image Retrieval | **ViLBERT** | 2e-5 | **58.20** (R1) - **84.90** (R5) - **91.52** (R10) |
+| - | Image Retrieval | SCAN | - | 48.60 (R1) - 77.70 (R5) - 85.20 (R10) |
+| **Image Caption Ranking** | Caption Retrieval | **ViLBERT** | 2e-5 | - |
+| - | Caption Retrieval | SCAN | - | - |
+
+
+## TASKS
+### VQA
+
+To fintune a 6-layer vilbert model for VQA with 8 GPU. `--tasks 1` means VQA tasks. Check `vlbert_tasks.yml` for more settings for VQA tasks.
+
+```bash
+python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 train_tasks.py --bert_model bert-base-uncased --from_pretrained save/bert_base_6_layer_6_connect_freeze_0/pytorch_model_8.bin --config_file config/bert_base_6layer_6conect.json --learning_rate 4e-5 --num_workers 16 --tasks 1 --save_name pretrained
+```
+
+### VCR
+
+Similarly, to finetune a 6-layer vilbert model for VCR task, run the following commands. Here we joint train `Q->A ` and `QA->R` tasks, so the tasks is specified as `--tasks 6-7`
+
+```
+python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 train_tasks.py --bert_model bert-base-uncased --from_pretrained save/bert_base_6_layer_6_connect_freeze_0/pytorch_model_8.bin --config_file config/bert_base_6layer_6conect.json --learning_rate 2e-5 --num_workers 16 --tasks 6-7 --save_name pretrained
+```
+
+### Refer Expression
+```
+python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 train_tasks.py --bert_model bert-base-uncased --from_pretrained save/bert_base_6_layer_6_connect_freeze_0/pytorch_model_8.bin --config_file config/bert_base_6layer_6conect.json --learning_rate 4e-5 --num_workers 16 --tasks 11 --save_name pretrained
+```
+
+### Image Retrieval
+```
+python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 train_tasks.py --bert_model bert-base-uncased --from_pretrained save/bert_base_6_layer_6_connect_freeze_0/pytorch_model_8.bin --config_file config/bert_base_6layer_6conect.json --learning_rate 4e-5 --num_workers 9 --tasks 11 --save_name pretrained
+```
+
+### Add your own tasks
+```
+
+```
+## Why does ViLBERT look like ?
+
+
+
+
\ No newline at end of file
diff --git a/config/bert-base-uncased_weight_name.json b/config/bert-base-uncased_weight_name.json
new file mode 100644
index 0000000..e144d69
--- /dev/null
+++ b/config/bert-base-uncased_weight_name.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/config/bert-large-uncased_weight_name.json b/config/bert-large-uncased_weight_name.json
new file mode 100644
index 0000000..97daec5
--- /dev/null
+++ b/config/bert-large-uncased_weight_name.json
@@ -0,0 +1 @@
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"encoder.layer.13.attention.self.value.bias", "encoder.layer.13.attention.output.dense.weight", "encoder.layer.13.attention.output.dense.bias", "encoder.layer.13.attention.output.LayerNorm.weight", "encoder.layer.13.attention.output.LayerNorm.bias", "encoder.layer.13.intermediate.dense.weight", "encoder.layer.13.intermediate.dense.bias", "encoder.layer.13.output.dense.weight", "encoder.layer.13.output.dense.bias", "encoder.layer.13.output.LayerNorm.weight", "encoder.layer.13.output.LayerNorm.bias", "encoder.layer.14.attention.self.query.weight", "encoder.layer.14.attention.self.query.bias", "encoder.layer.14.attention.self.key.weight", "encoder.layer.14.attention.self.key.bias", "encoder.layer.14.attention.self.value.weight", "encoder.layer.14.attention.self.value.bias", "encoder.layer.14.attention.output.dense.weight", "encoder.layer.14.attention.output.dense.bias", "encoder.layer.14.attention.output.LayerNorm.weight", "encoder.layer.14.attention.output.LayerNorm.bias", "encoder.layer.14.intermediate.dense.weight", "encoder.layer.14.intermediate.dense.bias", "encoder.layer.14.output.dense.weight", "encoder.layer.14.output.dense.bias", "encoder.layer.14.output.LayerNorm.weight", "encoder.layer.14.output.LayerNorm.bias", "encoder.layer.15.attention.self.query.weight", "encoder.layer.15.attention.self.query.bias", "encoder.layer.15.attention.self.key.weight", "encoder.layer.15.attention.self.key.bias", "encoder.layer.15.attention.self.value.weight", "encoder.layer.15.attention.self.value.bias", "encoder.layer.15.attention.output.dense.weight", "encoder.layer.15.attention.output.dense.bias", "encoder.layer.15.attention.output.LayerNorm.weight", "encoder.layer.15.attention.output.LayerNorm.bias", "encoder.layer.15.intermediate.dense.weight", "encoder.layer.15.intermediate.dense.bias", "encoder.layer.15.output.dense.weight", "encoder.layer.15.output.dense.bias", "encoder.layer.15.output.LayerNorm.weight", "encoder.layer.15.output.LayerNorm.bias", "encoder.layer.16.attention.self.query.weight", "encoder.layer.16.attention.self.query.bias", "encoder.layer.16.attention.self.key.weight", "encoder.layer.16.attention.self.key.bias", "encoder.layer.16.attention.self.value.weight", "encoder.layer.16.attention.self.value.bias", "encoder.layer.16.attention.output.dense.weight", "encoder.layer.16.attention.output.dense.bias", "encoder.layer.16.attention.output.LayerNorm.weight", "encoder.layer.16.attention.output.LayerNorm.bias", "encoder.layer.16.intermediate.dense.weight", "encoder.layer.16.intermediate.dense.bias", "encoder.layer.16.output.dense.weight", "encoder.layer.16.output.dense.bias", "encoder.layer.16.output.LayerNorm.weight", "encoder.layer.16.output.LayerNorm.bias", "encoder.layer.17.attention.self.query.weight", "encoder.layer.17.attention.self.query.bias", "encoder.layer.17.attention.self.key.weight", "encoder.layer.17.attention.self.key.bias", "encoder.layer.17.attention.self.value.weight", "encoder.layer.17.attention.self.value.bias", "encoder.layer.17.attention.output.dense.weight", "encoder.layer.17.attention.output.dense.bias", "encoder.layer.17.attention.output.LayerNorm.weight", "encoder.layer.17.attention.output.LayerNorm.bias", "encoder.layer.17.intermediate.dense.weight", "encoder.layer.17.intermediate.dense.bias", "encoder.layer.17.output.dense.weight", "encoder.layer.17.output.dense.bias", "encoder.layer.17.output.LayerNorm.weight", "encoder.layer.17.output.LayerNorm.bias", "encoder.layer.18.attention.self.query.weight", "encoder.layer.18.attention.self.query.bias", "encoder.layer.18.attention.self.key.weight", "encoder.layer.18.attention.self.key.bias", "encoder.layer.18.attention.self.value.weight", "encoder.layer.18.attention.self.value.bias", "encoder.layer.18.attention.output.dense.weight", "encoder.layer.18.attention.output.dense.bias", "encoder.layer.18.attention.output.LayerNorm.weight", "encoder.layer.18.attention.output.LayerNorm.bias", "encoder.layer.18.intermediate.dense.weight", "encoder.layer.18.intermediate.dense.bias", "encoder.layer.18.output.dense.weight", "encoder.layer.18.output.dense.bias", "encoder.layer.18.output.LayerNorm.weight", "encoder.layer.18.output.LayerNorm.bias", "encoder.layer.19.attention.self.query.weight", "encoder.layer.19.attention.self.query.bias", "encoder.layer.19.attention.self.key.weight", "encoder.layer.19.attention.self.key.bias", "encoder.layer.19.attention.self.value.weight", "encoder.layer.19.attention.self.value.bias", "encoder.layer.19.attention.output.dense.weight", "encoder.layer.19.attention.output.dense.bias", "encoder.layer.19.attention.output.LayerNorm.weight", "encoder.layer.19.attention.output.LayerNorm.bias", "encoder.layer.19.intermediate.dense.weight", "encoder.layer.19.intermediate.dense.bias", "encoder.layer.19.output.dense.weight", "encoder.layer.19.output.dense.bias", "encoder.layer.19.output.LayerNorm.weight", "encoder.layer.19.output.LayerNorm.bias", "encoder.layer.20.attention.self.query.weight", "encoder.layer.20.attention.self.query.bias", "encoder.layer.20.attention.self.key.weight", "encoder.layer.20.attention.self.key.bias", "encoder.layer.20.attention.self.value.weight", "encoder.layer.20.attention.self.value.bias", "encoder.layer.20.attention.output.dense.weight", "encoder.layer.20.attention.output.dense.bias", "encoder.layer.20.attention.output.LayerNorm.weight", "encoder.layer.20.attention.output.LayerNorm.bias", "encoder.layer.20.intermediate.dense.weight", "encoder.layer.20.intermediate.dense.bias", "encoder.layer.20.output.dense.weight", "encoder.layer.20.output.dense.bias", "encoder.layer.20.output.LayerNorm.weight", "encoder.layer.20.output.LayerNorm.bias", "encoder.layer.21.attention.self.query.weight", "encoder.layer.21.attention.self.query.bias", "encoder.layer.21.attention.self.key.weight", "encoder.layer.21.attention.self.key.bias", "encoder.layer.21.attention.self.value.weight", "encoder.layer.21.attention.self.value.bias", "encoder.layer.21.attention.output.dense.weight", "encoder.layer.21.attention.output.dense.bias", "encoder.layer.21.attention.output.LayerNorm.weight", "encoder.layer.21.attention.output.LayerNorm.bias", "encoder.layer.21.intermediate.dense.weight", "encoder.layer.21.intermediate.dense.bias", "encoder.layer.21.output.dense.weight", "encoder.layer.21.output.dense.bias", "encoder.layer.21.output.LayerNorm.weight", "encoder.layer.21.output.LayerNorm.bias", "encoder.layer.22.attention.self.query.weight", "encoder.layer.22.attention.self.query.bias", "encoder.layer.22.attention.self.key.weight", "encoder.layer.22.attention.self.key.bias", "encoder.layer.22.attention.self.value.weight", "encoder.layer.22.attention.self.value.bias", "encoder.layer.22.attention.output.dense.weight", "encoder.layer.22.attention.output.dense.bias", "encoder.layer.22.attention.output.LayerNorm.weight", "encoder.layer.22.attention.output.LayerNorm.bias", "encoder.layer.22.intermediate.dense.weight", "encoder.layer.22.intermediate.dense.bias", "encoder.layer.22.output.dense.weight", "encoder.layer.22.output.dense.bias", "encoder.layer.22.output.LayerNorm.weight", "encoder.layer.22.output.LayerNorm.bias", "encoder.layer.23.attention.self.query.weight", "encoder.layer.23.attention.self.query.bias", "encoder.layer.23.attention.self.key.weight", "encoder.layer.23.attention.self.key.bias", "encoder.layer.23.attention.self.value.weight", "encoder.layer.23.attention.self.value.bias", "encoder.layer.23.attention.output.dense.weight", "encoder.layer.23.attention.output.dense.bias", "encoder.layer.23.attention.output.LayerNorm.weight", "encoder.layer.23.attention.output.LayerNorm.bias", "encoder.layer.23.intermediate.dense.weight", "encoder.layer.23.intermediate.dense.bias", "encoder.layer.23.output.dense.weight", "encoder.layer.23.output.dense.bias", "encoder.layer.23.output.LayerNorm.weight", "encoder.layer.23.output.LayerNorm.bias", "pooler.dense.weight", "pooler.dense.bias", "cls.predictions.bias", "cls.predictions.transform.dense.weight", "cls.predictions.transform.dense.bias", "cls.predictions.transform.LayerNorm.weight", "cls.predictions.transform.LayerNorm.bias", "cls.seq_relationship.weight", "cls.seq_relationship.bias"]
\ No newline at end of file
diff --git a/config/bert_base_2layer_2conect.json b/config/bert_base_2layer_2conect.json
new file mode 100644
index 0000000..cb27489
--- /dev/null
+++ b/config/bert_base_2layer_2conect.json
@@ -0,0 +1,30 @@
+{
+ "attention_probs_dropout_prob": 0.1,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 768,
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "max_position_embeddings": 512,
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "type_vocab_size": 2,
+ "vocab_size": 30522,
+ "v_feature_size": 2048,
+ "v_target_size": 1601,
+ "v_hidden_size": 1024,
+ "v_num_hidden_layers":2,
+ "v_num_attention_heads":8,
+ "v_intermediate_size":1024,
+ "bi_hidden_size":1024,
+ "bi_num_attention_heads":8,
+ "bi_intermediate_size": 1024,
+ "bi_attention_type":1,
+ "v_attention_probs_dropout_prob":0.1,
+ "v_hidden_act":"gelu",
+ "v_hidden_dropout_prob":0.1,
+ "v_initializer_range":0.02,
+ "v_biattention_id":[0, 1],
+ "t_biattention_id":[10, 11],
+ "pooling_method": "mul"
+}
diff --git a/config/bert_base_4layer_4conect.json b/config/bert_base_4layer_4conect.json
new file mode 100644
index 0000000..e0dd01f
--- /dev/null
+++ b/config/bert_base_4layer_4conect.json
@@ -0,0 +1,30 @@
+{
+ "attention_probs_dropout_prob": 0.1,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 768,
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "max_position_embeddings": 512,
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "type_vocab_size": 2,
+ "vocab_size": 30522,
+ "v_feature_size": 2048,
+ "v_target_size": 1601,
+ "v_hidden_size": 1024,
+ "v_num_hidden_layers":4,
+ "v_num_attention_heads":8,
+ "v_intermediate_size":1024,
+ "bi_hidden_size":1024,
+ "bi_num_attention_heads":8,
+ "bi_intermediate_size": 1024,
+ "bi_attention_type":1,
+ "v_attention_probs_dropout_prob":0.1,
+ "v_hidden_act":"gelu",
+ "v_hidden_dropout_prob":0.1,
+ "v_initializer_range":0.02,
+ "v_biattention_id":[0, 1, 2, 3],
+ "t_biattention_id":[8, 9, 10, 11],
+ "pooling_method": "mul"
+}
diff --git a/config/bert_base_6layer_6conect.json b/config/bert_base_6layer_6conect.json
new file mode 100644
index 0000000..e78fc0c
--- /dev/null
+++ b/config/bert_base_6layer_6conect.json
@@ -0,0 +1,30 @@
+{
+ "attention_probs_dropout_prob": 0.1,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 768,
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "max_position_embeddings": 512,
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "type_vocab_size": 2,
+ "vocab_size": 30522,
+ "v_feature_size": 2048,
+ "v_target_size": 1601,
+ "v_hidden_size": 1024,
+ "v_num_hidden_layers":6,
+ "v_num_attention_heads":8,
+ "v_intermediate_size":1024,
+ "bi_hidden_size":1024,
+ "bi_num_attention_heads":8,
+ "bi_intermediate_size": 1024,
+ "bi_attention_type":1,
+ "v_attention_probs_dropout_prob":0.1,
+ "v_hidden_act":"gelu",
+ "v_hidden_dropout_prob":0.1,
+ "v_initializer_range":0.02,
+ "v_biattention_id":[0, 1, 2, 3, 4, 5],
+ "t_biattention_id":[6, 7, 8, 9, 10, 11],
+ "pooling_method": "mul"
+}
diff --git a/config/bert_base_8layer_8conect.json b/config/bert_base_8layer_8conect.json
new file mode 100644
index 0000000..b5f3571
--- /dev/null
+++ b/config/bert_base_8layer_8conect.json
@@ -0,0 +1,30 @@
+{
+ "attention_probs_dropout_prob": 0.1,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 768,
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "max_position_embeddings": 512,
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "type_vocab_size": 2,
+ "vocab_size": 30522,
+ "v_feature_size": 2048,
+ "v_target_size": 1601,
+ "v_hidden_size": 1024,
+ "v_num_hidden_layers":8,
+ "v_num_attention_heads":8,
+ "v_intermediate_size":1024,
+ "bi_hidden_size":1024,
+ "bi_num_attention_heads":8,
+ "bi_intermediate_size": 1024,
+ "bi_attention_type":1,
+ "v_attention_probs_dropout_prob":0.1,
+ "v_hidden_act":"gelu",
+ "v_hidden_dropout_prob":0.1,
+ "v_initializer_range":0.02,
+ "v_biattention_id":[0, 1, 2, 3, 4, 5, 6, 7],
+ "t_biattention_id":[4, 5, 6, 7, 8, 9, 10, 11],
+ "pooling_method": "mul"
+}
diff --git a/config/bert_base_baseline.json b/config/bert_base_baseline.json
new file mode 100644
index 0000000..fca794a
--- /dev/null
+++ b/config/bert_base_baseline.json
@@ -0,0 +1,13 @@
+{
+ "attention_probs_dropout_prob": 0.1,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 768,
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "max_position_embeddings": 512,
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "type_vocab_size": 2,
+ "vocab_size": 30522
+}
diff --git a/config/bert_large_2layer_2conect.json b/config/bert_large_2layer_2conect.json
new file mode 100644
index 0000000..f83a8a9
--- /dev/null
+++ b/config/bert_large_2layer_2conect.json
@@ -0,0 +1,30 @@
+{
+ "attention_probs_dropout_prob": 0.1,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 1024,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "max_position_embeddings": 512,
+ "num_attention_heads": 16,
+ "num_hidden_layers": 24,
+ "type_vocab_size": 2,
+ "vocab_size": 30522,
+ "v_feature_size": 2048,
+ "v_target_size": 1601,
+ "v_hidden_size":1024,
+ "v_num_hidden_layers":2,
+ "v_num_attention_heads":8,
+ "v_intermediate_size":1024,
+ "bi_hidden_size":1024,
+ "bi_num_attention_heads":8,
+ "bi_intermediate_size": 1024,
+ "bi_attention_type":1,
+ "v_attention_probs_dropout_prob":0.1,
+ "v_hidden_act":"gelu",
+ "v_hidden_dropout_prob":0.1,
+ "v_initializer_range":0.02,
+ "v_biattention_id":[0, 1],
+ "t_biattention_id":[22, 23],
+ "pooling_method": "mul"
+}
\ No newline at end of file
diff --git a/config/bert_large_4layer_4conect.json b/config/bert_large_4layer_4conect.json
new file mode 100644
index 0000000..39cbbd5
--- /dev/null
+++ b/config/bert_large_4layer_4conect.json
@@ -0,0 +1,30 @@
+{
+ "attention_probs_dropout_prob": 0.1,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 1024,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "max_position_embeddings": 512,
+ "num_attention_heads": 16,
+ "num_hidden_layers": 24,
+ "type_vocab_size": 2,
+ "vocab_size": 30522,
+ "v_feature_size": 2048,
+ "v_target_size": 1601,
+ "v_hidden_size":1024,
+ "v_num_hidden_layers":4,
+ "v_num_attention_heads":8,
+ "v_intermediate_size":1024,
+ "bi_hidden_size":1024,
+ "bi_num_attention_heads":8,
+ "bi_intermediate_size": 1024,
+ "bi_attention_type":1,
+ "v_attention_probs_dropout_prob":0.1,
+ "v_hidden_act":"gelu",
+ "v_hidden_dropout_prob":0.1,
+ "v_initializer_range":0.02,
+ "v_biattention_id":[0, 1, 2, 3],
+ "t_biattention_id":[20, 21, 22, 23],
+ "pooling_method": "mul"
+}
\ No newline at end of file
diff --git a/config/bert_large_6layer_6conect.json b/config/bert_large_6layer_6conect.json
new file mode 100644
index 0000000..db6c0ea
--- /dev/null
+++ b/config/bert_large_6layer_6conect.json
@@ -0,0 +1,30 @@
+{
+ "attention_probs_dropout_prob": 0.1,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 1024,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "max_position_embeddings": 512,
+ "num_attention_heads": 16,
+ "num_hidden_layers": 24,
+ "type_vocab_size": 2,
+ "vocab_size": 30522,
+ "v_feature_size": 2048,
+ "v_target_size": 1601,
+ "v_hidden_size":1024,
+ "v_num_hidden_layers":6,
+ "v_num_attention_heads":8,
+ "v_intermediate_size":1024,
+ "bi_hidden_size":1024,
+ "bi_num_attention_heads":8,
+ "bi_intermediate_size": 1024,
+ "bi_attention_type":1,
+ "v_attention_probs_dropout_prob":0.1,
+ "v_hidden_act":"gelu",
+ "v_hidden_dropout_prob":0.1,
+ "v_initializer_range":0.02,
+ "v_biattention_id":[0, 1, 2, 3, 4, 5],
+ "t_biattention_id":[18, 19, 20, 21, 22, 23],
+ "pooling_method": "mul"
+}
\ No newline at end of file
diff --git a/config/bert_large_baseline.json b/config/bert_large_baseline.json
new file mode 100644
index 0000000..a7efa97
--- /dev/null
+++ b/config/bert_large_baseline.json
@@ -0,0 +1,13 @@
+{
+ "attention_probs_dropout_prob": 0.1,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 1024,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "max_position_embeddings": 512,
+ "num_attention_heads": 16,
+ "num_hidden_layers": 24,
+ "type_vocab_size": 2,
+ "vocab_size": 30522
+}
diff --git a/eval_tasks.py b/eval_tasks.py
new file mode 100644
index 0000000..3a87c01
--- /dev/null
+++ b/eval_tasks.py
@@ -0,0 +1,235 @@
+import argparse
+import json
+import logging
+import os
+import random
+from io import open
+import numpy as np
+
+from tensorboardX import SummaryWriter
+from tqdm import tqdm
+from bisect import bisect
+import yaml
+from easydict import EasyDict as edict
+import sys
+import pdb
+
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+
+from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule
+
+from vilbert.task_utils import LoadDatasetEval, LoadLosses, ForwardModelsTrain, ForwardModelsVal, EvaluatingModel
+
+import vilbert.utils as utils
+import torch.distributed as dist
+
+logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+def main():
+ parser = argparse.ArgumentParser()
+
+ parser.add_argument(
+ "--bert_model",
+ default="bert-base-uncased",
+ type=str,
+ help="Bert pre-trained model selected in the list: bert-base-uncased, "
+ "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.",
+ )
+ parser.add_argument(
+ "--from_pretrained",
+ default="bert-base-uncased",
+ type=str,
+ help="Bert pre-trained model selected in the list: bert-base-uncased, "
+ "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.",
+ )
+ parser.add_argument(
+ "--output_dir",
+ default="results",
+ type=str,
+ help="The output directory where the model checkpoints will be written.",
+ )
+ parser.add_argument(
+ "--config_file",
+ default="config/bert_config.json",
+ type=str,
+ help="The config file which specified the model details.",
+ )
+ parser.add_argument(
+ "--no_cuda", action="store_true", help="Whether not to use CUDA when available"
+ )
+ parser.add_argument(
+ "--do_lower_case",
+ default=True,
+ type=bool,
+ help="Whether to lower case the input text. True for uncased models, False for cased models.",
+ )
+ parser.add_argument(
+ "--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus"
+ )
+ parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
+ parser.add_argument(
+ "--fp16",
+ action="store_true",
+ help="Whether to use 16-bit float precision instead of 32-bit",
+ )
+ parser.add_argument(
+ "--loss_scale",
+ type=float,
+ default=0,
+ help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
+ "0 (default value): dynamic loss scaling.\n"
+ "Positive power of 2: static loss scaling value.\n",
+ )
+ parser.add_argument(
+ "--num_workers", type=int, default=16, help="Number of workers in the dataloader."
+ )
+ parser.add_argument(
+ "--save_name",
+ default='',
+ type=str,
+ help="save name for training.",
+ )
+ parser.add_argument(
+ "--use_chunk", default=0, type=float, help="whether use chunck for parallel training."
+ )
+ parser.add_argument(
+ "--batch_size", default=1024, type=int, help="what is the batch size?"
+ )
+ parser.add_argument(
+ "--tasks", default='', type=str, help="1-2-3... training task separate by -"
+ )
+ parser.add_argument(
+ "--in_memory", default=False, type=bool, help="whether use chunck for parallel training."
+ )
+ parser.add_argument(
+ "--baseline", action="store_true", help="whether use single stream baseline."
+ )
+ parser.add_argument(
+ "--split", default="", type=str, help="which split to use."
+ )
+
+ args = parser.parse_args()
+ with open('vlbert_tasks.yml', 'r') as f:
+ task_cfg = edict(yaml.load(f))
+
+ random.seed(args.seed)
+ np.random.seed(args.seed)
+ torch.manual_seed(args.seed)
+
+ if args.baseline:
+ from pytorch_pretrained_bert.modeling import BertConfig
+ from vilbert.basebert import BaseBertForVLTasks
+ else:
+ from vilbert.vilbert import BertConfig
+ from vilbert.vilbert import VILBertForVLTasks
+
+ task_names = []
+ for i, task_id in enumerate(args.tasks.split('-')):
+ task = 'TASK' + task_id
+ name = task_cfg[task]['name']
+ task_names.append(name)
+
+ # timeStamp = '-'.join(task_names) + '_' + args.config_file.split('/')[1].split('.')[0]
+ timeStamp = args.from_pretrained.split('/')[1] + '-' + args.save_name
+ savePath = os.path.join(args.output_dir, timeStamp)
+
+ config = BertConfig.from_json_file(args.config_file)
+ bert_weight_name = json.load(open("config/" + args.bert_model + "_weight_name.json", "r"))
+
+ if args.local_rank == -1 or args.no_cuda:
+ device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
+ n_gpu = torch.cuda.device_count()
+ else:
+ torch.cuda.set_device(args.local_rank)
+ device = torch.device("cuda", args.local_rank)
+ n_gpu = 1
+ # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
+ torch.distributed.init_process_group(backend="nccl")
+
+ logger.info(
+ "device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
+ device, n_gpu, bool(args.local_rank != -1), args.fp16
+ )
+ )
+
+ default_gpu = False
+ if dist.is_available() and args.local_rank != -1:
+ rank = dist.get_rank()
+ if rank == 0:
+ default_gpu = True
+ else:
+ default_gpu = True
+
+ if default_gpu and not os.path.exists(savePath):
+ os.makedirs(savePath)
+
+ task_batch_size, task_num_iters, task_ids, task_datasets_val, task_dataloader_val \
+ = LoadDatasetEval(args, task_cfg, args.tasks.split('-'))
+
+ tbLogger = utils.tbLogger(timeStamp, savePath, task_names, task_ids, task_num_iters, 1, save_logger=False, txt_name='eval.txt')
+
+ num_labels = max([dataset.num_labels for dataset in task_datasets_val.values()])
+
+ if args.baseline:
+ model = BaseBertForVLTasks.from_pretrained(
+ args.from_pretrained, config, num_labels=num_labels, default_gpu=default_gpu
+ )
+ else:
+ model = VILBertForVLTasks.from_pretrained(
+ args.from_pretrained, config, num_labels=num_labels, default_gpu=default_gpu
+ )
+
+ task_losses = LoadLosses(args, task_cfg, args.tasks.split('-'))
+ model.to(device)
+ if args.local_rank != -1:
+ try:
+ from apex.parallel import DistributedDataParallel as DDP
+ except ImportError:
+ raise ImportError(
+ "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
+ )
+ model = DDP(model, delay_allreduce=True)
+
+ elif n_gpu > 1:
+ model = nn.DataParallel(model)
+
+ no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
+
+ print("***** Running training *****")
+ print(" Num Iters: ", task_num_iters)
+ print(" Batch size: ", task_batch_size)
+
+ model.eval()
+ # when run evaluate, we run each task sequentially.
+ for task_id in task_ids:
+ results = []
+ others = []
+ for i, batch in enumerate(task_dataloader_val[task_id]):
+ loss, score, batch_size, results, others = EvaluatingModel(args, task_cfg, device, \
+ task_id, batch, model, task_dataloader_val, task_losses, results, others)
+
+ tbLogger.step_val(0, float(loss), float(score), task_id, batch_size, 'val')
+
+ sys.stdout.write('%d/%d\r' % (i, len(task_dataloader_val[task_id])))
+ sys.stdout.flush()
+ # save the result or evaluate the result.
+ ave_score = tbLogger.showLossVal()
+
+ if args.split:
+ json_path = os.path.join(savePath, args.split)
+ else:
+ json_path = os.path.join(savePath, task_cfg[task_id]['val_split'])
+
+ json.dump(results, open(json_path+ '_result.json', 'w'))
+ json.dump(others, open(json_path+ '_others.json', 'w'))
+
+if __name__ == "__main__":
+
+ main()
diff --git a/parallel/data_parallel.py b/parallel/data_parallel.py
new file mode 100644
index 0000000..8553457
--- /dev/null
+++ b/parallel/data_parallel.py
@@ -0,0 +1,226 @@
+import operator
+import torch
+import warnings
+from itertools import chain
+from torch.nn import Module
+from torch.nn.parallel.replicate import replicate
+from torch.nn.parallel.parallel_apply import parallel_apply
+from torch.cuda._utils import _get_device_index
+
+from parallel.scatter_gather import scatter_kwargs, gather
+
+def _check_balance(device_ids):
+ imbalance_warn = """
+ There is an imbalance between your GPUs. You may want to exclude GPU {} which
+ has less than 75% of the memory or cores of GPU {}. You can do so by setting
+ the device_ids argument to DataParallel, or by setting the CUDA_VISIBLE_DEVICES
+ environment variable."""
+ device_ids = list(map(lambda x: _get_device_index(x, True), device_ids))
+ dev_props = [torch.cuda.get_device_properties(i) for i in device_ids]
+
+ def warn_imbalance(get_prop):
+ values = [get_prop(props) for props in dev_props]
+ min_pos, min_val = min(enumerate(values), key=operator.itemgetter(1))
+ max_pos, max_val = max(enumerate(values), key=operator.itemgetter(1))
+ if min_val / max_val < 0.75:
+ warnings.warn(imbalance_warn.format(device_ids[min_pos], device_ids[max_pos]))
+ return True
+ return False
+
+ if warn_imbalance(lambda props: props.total_memory):
+ return
+ if warn_imbalance(lambda props: props.multi_processor_count):
+ return
+
+
+class DataParallel(Module):
+ r"""Implements data parallelism at the module level.
+
+ This container parallelizes the application of the given :attr:`module` by
+ splitting the input across the specified devices by chunking in the batch
+ dimension (other objects will be copied once per device). In the forward
+ pass, the module is replicated on each device, and each replica handles a
+ portion of the input. During the backwards pass, gradients from each replica
+ are summed into the original module.
+
+ The batch size should be larger than the number of GPUs used.
+
+ See also: :ref:`cuda-nn-dataparallel-instead`
+
+ Arbitrary positional and keyword inputs are allowed to be passed into
+ DataParallel but some types are specially handled. tensors will be
+ **scattered** on dim specified (default 0). tuple, list and dict types will
+ be shallow copied. The other types will be shared among different threads
+ and can be corrupted if written to in the model's forward pass.
+
+ The parallelized :attr:`module` must have its parameters and buffers on
+ ``device_ids[0]`` before running this :class:`~torch.nn.DataParallel`
+ module.
+
+ .. warning::
+ In each forward, :attr:`module` is **replicated** on each device, so any
+ updates to the running module in ``forward`` will be lost. For example,
+ if :attr:`module` has a counter attribute that is incremented in each
+ ``forward``, it will always stay at the initial value because the update
+ is done on the replicas which are destroyed after ``forward``. However,
+ :class:`~torch.nn.DataParallel` guarantees that the replica on
+ ``device[0]`` will have its parameters and buffers sharing storage with
+ the base parallelized :attr:`module`. So **in-place** updates to the
+ parameters or buffers on ``device[0]`` will be recorded. E.g.,
+ :class:`~torch.nn.BatchNorm2d` and :func:`~torch.nn.utils.spectral_norm`
+ rely on this behavior to update the buffers.
+
+ .. warning::
+ Forward and backward hooks defined on :attr:`module` and its submodules
+ will be invoked ``len(device_ids)`` times, each with inputs located on
+ a particular device. Particularly, the hooks are only guaranteed to be
+ executed in correct order with respect to operations on corresponding
+ devices. For example, it is not guaranteed that hooks set via
+ :meth:`~torch.nn.Module.register_forward_pre_hook` be executed before
+ `all` ``len(device_ids)`` :meth:`~torch.nn.Module.forward` calls, but
+ that each such hook be executed before the corresponding
+ :meth:`~torch.nn.Module.forward` call of that device.
+
+ .. warning::
+ When :attr:`module` returns a scalar (i.e., 0-dimensional tensor) in
+ :func:`forward`, this wrapper will return a vector of length equal to
+ number of devices used in data parallelism, containing the result from
+ each device.
+
+ .. note::
+ There is a subtlety in using the
+ ``pack sequence -> recurrent network -> unpack sequence`` pattern in a
+ :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`.
+ See :ref:`pack-rnn-unpack-with-data-parallelism` section in FAQ for
+ details.
+
+
+ Args:
+ module (Module): module to be parallelized
+ device_ids (list of int or torch.device): CUDA devices (default: all devices)
+ output_device (int or torch.device): device location of output (default: device_ids[0])
+
+ Attributes:
+ module (Module): the module to be parallelized
+
+ Example::
+
+ >>> net = torch.nn.DataParallel(model, device_ids=[0, 1, 2])
+ >>> output = net(input_var) # input_var can be on any device, including CPU
+ """
+
+ # TODO: update notes/cuda.rst when this class handles 8+ GPUs well
+
+ def __init__(self, module, device_ids=None, output_device=None, dim=0, use_chuncks=0):
+ super(DataParallel, self).__init__()
+
+ if not torch.cuda.is_available():
+ self.module = module
+ self.device_ids = []
+ return
+
+ if device_ids is None:
+ device_ids = list(range(torch.cuda.device_count()))
+ if output_device is None:
+ output_device = device_ids[0]
+
+ self.dim = dim
+ self.module = module
+ self.device_ids = list(map(lambda x: _get_device_index(x, True), device_ids))
+ self.output_device = _get_device_index(output_device, True)
+ self.src_device_obj = torch.device("cuda:{}".format(self.device_ids[0]))
+ self.use_chuncks = use_chuncks
+
+ _check_balance(self.device_ids)
+
+ if len(self.device_ids) == 1:
+ self.module.cuda(device_ids[0])
+
+ def forward(self, *inputs, **kwargs):
+ if not self.device_ids:
+ return self.module(*inputs, **kwargs)
+
+ for t in chain(self.module.parameters(), self.module.buffers()):
+ if t.device != self.src_device_obj:
+ raise RuntimeError("module must have its parameters and buffers "
+ "on device {} (device_ids[0]) but found one of "
+ "them on device: {}".format(self.src_device_obj, t.device))
+
+ if self.use_chuncks > 0:
+ # we want to assign 0.6 of the batch size to first gpu
+ batch_size = inputs[0].size(0)
+ chunk_sizes = [0] * len(self.device_ids)
+ # first gpu is using only 0.6
+ chunk_sizes[0] = int(((batch_size / len(self.device_ids)) * self.use_chuncks))
+ rest_batch = batch_size - chunk_sizes[0]
+ r_bs = int(rest_batch / (len(self.device_ids)-1))+1
+
+ for i in range(1, len(self.device_ids)):
+ if i == len(self.device_ids)-1:
+ chunk_sizes[i] = batch_size - sum(chunk_sizes)
+ else:
+ chunk_sizes[i] = r_bs
+ else:
+ chunk_sizes = None
+
+ inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids, chunk_sizes)
+ if len(self.device_ids) == 1:
+ return self.module(*inputs[0], **kwargs[0])
+ replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
+ outputs = self.parallel_apply(replicas, inputs, kwargs)
+ return self.gather(outputs, self.output_device)
+
+ def replicate(self, module, device_ids):
+ return replicate(module, device_ids)
+
+ def scatter(self, inputs, kwargs, device_ids, chunk_sizes):
+ return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim, chunk_sizes=chunk_sizes)
+
+ def parallel_apply(self, replicas, inputs, kwargs):
+ return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
+
+ def gather(self, outputs, output_device):
+ return gather(outputs, output_device, dim=self.dim)
+
+
+def data_parallel(module, inputs, device_ids=None, output_device=None, dim=0, module_kwargs=None):
+ r"""Evaluates module(input) in parallel across the GPUs given in device_ids.
+
+ This is the functional version of the DataParallel module.
+
+ Args:
+ module (Module): the module to evaluate in parallel
+ inputs (Tensor): inputs to the module
+ device_ids (list of int or torch.device): GPU ids on which to replicate module
+ output_device (list of int or torch.device): GPU location of the output Use -1 to indicate the CPU.
+ (default: device_ids[0])
+ Returns:
+ a Tensor containing the result of module(input) located on
+ output_device
+ """
+ if not isinstance(inputs, tuple):
+ inputs = (inputs,)
+
+ if device_ids is None:
+ device_ids = list(range(torch.cuda.device_count()))
+
+ if output_device is None:
+ output_device = device_ids[0]
+
+ device_ids = list(map(lambda x: _get_device_index(x, True), device_ids))
+ output_device = _get_device_index(output_device, True)
+ src_device_obj = torch.device("cuda:{}".format(device_ids[0]))
+
+ for t in chain(module.parameters(), module.buffers()):
+ if t.device != src_device_obj:
+ raise RuntimeError("module must have its parameters and buffers "
+ "on device {} (device_ids[0]) but found one of "
+ "them on device: {}".format(src_device_obj, t.device))
+
+ inputs, module_kwargs = scatter_kwargs(inputs, module_kwargs, device_ids, dim)
+ if len(device_ids) == 1:
+ return module(*inputs[0], **module_kwargs[0])
+ used_device_ids = device_ids[:len(inputs)]
+ replicas = replicate(module, used_device_ids)
+ outputs = parallel_apply(replicas, inputs, module_kwargs, used_device_ids)
+ return gather(outputs, output_device, dim)
\ No newline at end of file
diff --git a/parallel/parallel.py b/parallel/parallel.py
new file mode 100755
index 0000000..91dd53f
--- /dev/null
+++ b/parallel/parallel.py
@@ -0,0 +1,281 @@
+##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+## Created by: Hang Zhang, Rutgers University, Email: zhang.hang@rutgers.edu
+## Modified by Thomas Wolf, HuggingFace Inc., Email: thomas@huggingface.co
+## Copyright (c) 2017-2018
+##
+## This source code is licensed under the MIT-style license found in the
+## LICENSE file in the root directory of this source tree
+##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+"""Encoding Data Parallel"""
+import threading
+import functools
+import torch
+from torch.autograd import Variable, Function
+import torch.cuda.comm as comm
+from torch.nn.parallel.data_parallel import DataParallel
+
+from torch.nn.parallel.parallel_apply import get_a_var
+from torch.nn.parallel.scatter_gather import gather
+from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
+
+torch_ver = torch.__version__[:3]
+
+__all__ = ['allreduce', 'DataParallelModel', 'DataParallelCriterion',
+ 'patch_replication_callback']
+
+def allreduce(*inputs):
+ """Cross GPU all reduce autograd operation for calculate mean and
+ variance in SyncBN.
+ """
+ return AllReduce.apply(*inputs)
+
+class AllReduce(Function):
+ @staticmethod
+ def forward(ctx, num_inputs, *inputs):
+ ctx.num_inputs = num_inputs
+ ctx.target_gpus = [inputs[i].get_device() for i in range(0, len(inputs), num_inputs)]
+ inputs = [inputs[i:i + num_inputs]
+ for i in range(0, len(inputs), num_inputs)]
+ # sort before reduce sum
+ inputs = sorted(inputs, key=lambda i: i[0].get_device())
+ results = comm.reduce_add_coalesced(inputs, ctx.target_gpus[0])
+ outputs = comm.broadcast_coalesced(results, ctx.target_gpus)
+ return tuple([t for tensors in outputs for t in tensors])
+
+ @staticmethod
+ def backward(ctx, *inputs):
+ inputs = [i.data for i in inputs]
+ inputs = [inputs[i:i + ctx.num_inputs]
+ for i in range(0, len(inputs), ctx.num_inputs)]
+ results = comm.reduce_add_coalesced(inputs, ctx.target_gpus[0])
+ outputs = comm.broadcast_coalesced(results, ctx.target_gpus)
+ return (None,) + tuple([Variable(t) for tensors in outputs for t in tensors])
+
+
+class Reduce(Function):
+ @staticmethod
+ def forward(ctx, *inputs):
+ ctx.target_gpus = [inputs[i].get_device() for i in range(len(inputs))]
+ inputs = sorted(inputs, key=lambda i: i.get_device())
+ return comm.reduce_add(inputs)
+
+ @staticmethod
+ def backward(ctx, gradOutput):
+ return Broadcast.apply(ctx.target_gpus, gradOutput)
+
+# class DistributedDataParallelModel(DistributedDataParallel):
+# """Implements data parallelism at the module level for the DistributedDataParallel module.
+# This container parallelizes the application of the given module by
+# splitting the input across the specified devices by chunking in the
+# batch dimension.
+# In the forward pass, the module is replicated on each device,
+# and each replica handles a portion of the input. During the backwards pass,
+# gradients from each replica are summed into the original module.
+# Note that the outputs are not gathered, please use compatible
+# :class:`encoding.parallel.DataParallelCriterion`.
+# The batch size should be larger than the number of GPUs used. It should
+# also be an integer multiple of the number of GPUs so that each chunk is
+# the same size (so that each GPU processes the same number of samples).
+# Args:
+# module: module to be parallelized
+# device_ids: CUDA devices (default: all devices)
+# Reference:
+# Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi,
+# Amit Agrawal. “Context Encoding for Semantic Segmentation.
+# *The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018*
+# Example::
+# >>> net = encoding.nn.DistributedDataParallelModel(model, device_ids=[0, 1, 2])
+# >>> y = net(x)
+# """
+# def gather(self, outputs, output_device):
+# return outputs
+
+class DataParallelModel(DataParallel):
+ """Implements data parallelism at the module level.
+
+ This container parallelizes the application of the given module by
+ splitting the input across the specified devices by chunking in the
+ batch dimension.
+ In the forward pass, the module is replicated on each device,
+ and each replica handles a portion of the input. During the backwards pass,
+ gradients from each replica are summed into the original module.
+ Note that the outputs are not gathered, please use compatible
+ :class:`encoding.parallel.DataParallelCriterion`.
+
+ The batch size should be larger than the number of GPUs used. It should
+ also be an integer multiple of the number of GPUs so that each chunk is
+ the same size (so that each GPU processes the same number of samples).
+
+ Args:
+ module: module to be parallelized
+ device_ids: CUDA devices (default: all devices)
+
+ Reference:
+ Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi,
+ Amit Agrawal. “Context Encoding for Semantic Segmentation.
+ *The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018*
+
+ Example::
+
+ >>> net = encoding.nn.DataParallelModel(model, device_ids=[0, 1, 2])
+ >>> y = net(x)
+ """
+ def gather(self, outputs, output_device):
+ return outputs
+
+ def replicate(self, module, device_ids):
+ modules = super(DataParallelModel, self).replicate(module, device_ids)
+ execute_replication_callbacks(modules)
+ return modules
+
+
+class DataParallelCriterion(DataParallel):
+ """
+ Calculate loss in multiple-GPUs, which balance the memory usage.
+ The targets are splitted across the specified devices by chunking in
+ the batch dimension. Please use together with :class:`encoding.parallel.DataParallelModel`.
+
+ Reference:
+ Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi,
+ Amit Agrawal. “Context Encoding for Semantic Segmentation.
+ *The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018*
+
+ Example::
+
+ >>> net = encoding.nn.DataParallelModel(model, device_ids=[0, 1, 2])
+ >>> criterion = encoding.nn.DataParallelCriterion(criterion, device_ids=[0, 1, 2])
+ >>> y = net(x)
+ >>> loss = criterion(y, target)
+ """
+ def forward(self, inputs, *targets, **kwargs):
+ # input should be already scatterd
+ # scattering the targets instead
+ if not self.device_ids:
+ return self.module(inputs, *targets, **kwargs)
+ targets, kwargs = self.scatter(targets, kwargs, self.device_ids)
+ if len(self.device_ids) == 1:
+ return self.module(inputs, *targets[0], **kwargs[0])
+ replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
+ outputs = _criterion_parallel_apply(replicas, inputs, targets, kwargs)
+ #return Reduce.apply(*outputs) / len(outputs)
+ #return self.gather(outputs, self.output_device).mean()
+ return self.gather(outputs, self.output_device)
+
+
+def _criterion_parallel_apply(modules, inputs, targets, kwargs_tup=None, devices=None):
+ assert len(modules) == len(inputs)
+ assert len(targets) == len(inputs)
+ if kwargs_tup:
+ assert len(modules) == len(kwargs_tup)
+ else:
+ kwargs_tup = ({},) * len(modules)
+ if devices is not None:
+ assert len(modules) == len(devices)
+ else:
+ devices = [None] * len(modules)
+
+ lock = threading.Lock()
+ results = {}
+ if torch_ver != "0.3":
+ grad_enabled = torch.is_grad_enabled()
+
+ def _worker(i, module, input, target, kwargs, device=None):
+ if torch_ver != "0.3":
+ torch.set_grad_enabled(grad_enabled)
+ if device is None:
+ device = get_a_var(input).get_device()
+ try:
+ with torch.cuda.device(device):
+ # this also avoids accidental slicing of `input` if it is a Tensor
+ if not isinstance(input, (list, tuple)):
+ input = (input,)
+ if not isinstance(target, (list, tuple)):
+ target = (target,)
+ output = module(*(input + target), **kwargs)
+ with lock:
+ results[i] = output
+ except Exception as e:
+ with lock:
+ results[i] = e
+
+ if len(modules) > 1:
+ threads = [threading.Thread(target=_worker,
+ args=(i, module, input, target,
+ kwargs, device),)
+ for i, (module, input, target, kwargs, device) in
+ enumerate(zip(modules, inputs, targets, kwargs_tup, devices))]
+
+ for thread in threads:
+ thread.start()
+ for thread in threads:
+ thread.join()
+ else:
+ _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])
+
+ outputs = []
+ for i in range(len(inputs)):
+ output = results[i]
+ if isinstance(output, Exception):
+ raise output
+ outputs.append(output)
+ return outputs
+
+
+###########################################################################
+# Adapted from Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+#
+class CallbackContext(object):
+ pass
+
+
+def execute_replication_callbacks(modules):
+ """
+ Execute an replication callback `__data_parallel_replicate__` on each module created
+ by original replication.
+
+ The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
+
+ Note that, as all modules are isomorphism, we assign each sub-module with a context
+ (shared among multiple copies of this module on different devices).
+ Through this context, different copies can share some information.
+
+ We guarantee that the callback on the master copy (the first copy) will be called ahead
+ of calling the callback of any slave copies.
+ """
+ master_copy = modules[0]
+ nr_modules = len(list(master_copy.modules()))
+ ctxs = [CallbackContext() for _ in range(nr_modules)]
+
+ for i, module in enumerate(modules):
+ for j, m in enumerate(module.modules()):
+ if hasattr(m, '__data_parallel_replicate__'):
+ m.__data_parallel_replicate__(ctxs[j], i)
+
+
+def patch_replication_callback(data_parallel):
+ """
+ Monkey-patch an existing `DataParallel` object. Add the replication callback.
+ Useful when you have customized `DataParallel` implementation.
+
+ Examples:
+ > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+ > sync_bn = DataParallel(sync_bn, device_ids=[0, 1])
+ > patch_replication_callback(sync_bn)
+ # this is equivalent to
+ > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+ > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
+ """
+
+ assert isinstance(data_parallel, DataParallel)
+
+ old_replicate = data_parallel.replicate
+
+ @functools.wraps(old_replicate)
+ def new_replicate(module, device_ids):
+ modules = old_replicate(module, device_ids)
+ execute_replication_callbacks(modules)
+ return modules
+
+ data_parallel.replicate = new_replicate
diff --git a/parallel/scatter_gather.py b/parallel/scatter_gather.py
new file mode 100644
index 0000000..8900f8d
--- /dev/null
+++ b/parallel/scatter_gather.py
@@ -0,0 +1,70 @@
+import torch
+from torch.nn.parallel._functions import Scatter, Gather
+
+
+def scatter(inputs, target_gpus, dim=0, chunk_sizes=None):
+ r"""
+ Slices tensors into approximately equal chunks and
+ distributes them across given GPUs. Duplicates
+ references to objects that are not tensors.
+ """
+ def scatter_map(obj):
+ if isinstance(obj, torch.Tensor):
+ return Scatter.apply(target_gpus, chunk_sizes, dim, obj)
+ if isinstance(obj, tuple) and len(obj) > 0:
+ return list(zip(*map(scatter_map, obj)))
+ if isinstance(obj, list) and len(obj) > 0:
+ return list(map(list, zip(*map(scatter_map, obj))))
+ if isinstance(obj, dict) and len(obj) > 0:
+ return list(map(type(obj), zip(*map(scatter_map, obj.items()))))
+ return [obj for targets in target_gpus]
+
+ # After scatter_map is called, a scatter_map cell will exist. This cell
+ # has a reference to the actual function scatter_map, which has references
+ # to a closure that has a reference to the scatter_map cell (because the
+ # fn is recursive). To avoid this reference cycle, we set the function to
+ # None, clearing the cell
+ try:
+ return scatter_map(inputs)
+ finally:
+ scatter_map = None
+
+
+def scatter_kwargs(inputs, kwargs, target_gpus, dim=0, chunk_sizes=None):
+ r"""Scatter with support for kwargs dictionary"""
+ inputs = scatter(inputs, target_gpus, dim, chunk_sizes) if inputs else []
+ kwargs = scatter(kwargs, target_gpus, dim, chunk_sizes) if kwargs else []
+ if len(inputs) < len(kwargs):
+ inputs.extend([() for _ in range(len(kwargs) - len(inputs))])
+ elif len(kwargs) < len(inputs):
+ kwargs.extend([{} for _ in range(len(inputs) - len(kwargs))])
+ inputs = tuple(inputs)
+ kwargs = tuple(kwargs)
+
+ return inputs, kwargs
+
+
+def gather(outputs, target_device, dim=0):
+ r"""
+ Gathers tensors from different GPUs on a specified device
+ (-1 means the CPU).
+ """
+ def gather_map(outputs):
+ out = outputs[0]
+ if isinstance(out, torch.Tensor):
+ return Gather.apply(target_device, dim, *outputs)
+ if out is None:
+ return None
+ if isinstance(out, dict):
+ if not all((len(out) == len(d) for d in outputs)):
+ raise ValueError('All dicts must have the same number of keys')
+ return type(out)(((k, gather_map([d[k] for d in outputs]))
+ for k in out))
+ return type(out)(map(gather_map, zip(*outputs)))
+
+ # Recursive function calls like this create reference cycles.
+ # Setting the function to None clears the refcycle.
+ try:
+ return gather_map(outputs)
+ finally:
+ gather_map = None
diff --git a/requirments.txt b/requirments.txt
new file mode 100644
index 0000000..80caf37
--- /dev/null
+++ b/requirments.txt
@@ -0,0 +1,8 @@
+h5py==2.9.0
+pytorch-transformers
+lmdb==0.94
+tensorboardX==1.2
+tensorflow==1.13.1
+tensorpack==0.9.4
+torch==1.1.0
+tqdm==4.31.1
\ No newline at end of file
diff --git a/tools/__init__.py b/tools/__init__.py
new file mode 100644
index 0000000..3f7d85b
--- /dev/null
+++ b/tools/__init__.py
@@ -0,0 +1 @@
+__author__ = 'tylin'
diff --git a/tools/refer/LICENSE b/tools/refer/LICENSE
new file mode 100755
index 0000000..261eeb9
--- /dev/null
+++ b/tools/refer/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
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+
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diff --git a/tools/refer/Makefile b/tools/refer/Makefile
new file mode 100644
index 0000000..7c6ea46
--- /dev/null
+++ b/tools/refer/Makefile
@@ -0,0 +1,6 @@
+all:
+ # install pycocotools/mask locally
+ # copy from https://github.com/pdollar/coco.git
+ python setup.py build_ext --inplace
+ rm -rf build
+
diff --git a/tools/refer/README.md b/tools/refer/README.md
new file mode 100644
index 0000000..f024ca6
--- /dev/null
+++ b/tools/refer/README.md
@@ -0,0 +1,55 @@
+## Note
+This API is able to load all 4 referring expression datasets, i.e., RefClef, RefCOCO, RefCOCO+ and RefCOCOg.
+They are with different train/val/test split by UNC, Google and UC Berkeley respectively. We provide all kinds of splits here.
+
+
+ |
+
+
+
+## Citation
+If you used the following three datasets RefClef, RefCOCO and RefCOCO+ that were collected by UNC, please consider cite our EMNLP2014 paper; if you want to compare with our recent results, please check our ECCV2016 paper.
+```bash
+Kazemzadeh, Sahar, et al. "ReferItGame: Referring to Objects in Photographs of Natural Scenes." EMNLP 2014.
+Yu, Licheng, et al. "Modeling Context in Referring Expressions." ECCV 2016.
+```
+
+## Setup
+Run "make" before using the code.
+It will generate ``_mask.c`` and ``_mask.so`` in ``external/`` folder.
+These mask-related codes are copied from mscoco [API](https://github.com/pdollar/coco).
+
+## Download
+Download the cleaned data and extract them into "data" folder
+- 1) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refclef.zip
+- 2) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip
+- 3) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco+.zip
+- 4) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refcocog.zip
+
+## Prepare Images:
+Besides, add "mscoco" into the ``data/images`` folder, which can be from [mscoco](http://mscoco.org/dataset/#overview)
+COCO's images are used for RefCOCO, RefCOCO+ and refCOCOg.
+For RefCLEF, please add ``saiapr_tc-12`` into ``data/images`` folder. We extracted the related 19997 images to our cleaned RefCLEF dataset, which is a subset of the original [imageCLEF](http://imageclef.org/SIAPRdata). Download the [subset](http://bvisionweb1.cs.unc.edu/licheng/referit/data/images/saiapr_tc-12.zip) and unzip it to ``data/images/saiapr_tc-12``.
+
+## How to use
+The "refer.py" is able to load all 4 datasets with different kinds of data split by UNC, Google, UMD and UC Berkeley.
+**Note for RefCOCOg, we suggest use UMD's split which has train/val/test splits and there is no overlap of images between different split.**
+```bash
+# locate your own data_root, and choose the dataset_splitBy you want to use
+refer = REFER(data_root, dataset='refclef', splitBy='unc')
+refer = REFER(data_root, dataset='refclef', splitBy='berkeley') # 2 train and 1 test images missed
+refer = REFER(data_root, dataset='refcoco', splitBy='unc')
+refer = REFER(data_root, dataset='refcoco', splitBy='google')
+refer = REFER(data_root, dataset='refcoco+', splitBy='unc')
+refer = REFER(data_root, dataset='refcocog', splitBy='google') # test split not released yet
+refer = REFER(data_root, dataset='refcocog', splitBy='umd') # Recommended, including train/val/test
+```
+
+
+
diff --git a/tools/refer/__init__.py b/tools/refer/__init__.py
new file mode 100644
index 0000000..3f7d85b
--- /dev/null
+++ b/tools/refer/__init__.py
@@ -0,0 +1 @@
+__author__ = 'tylin'
diff --git a/tools/refer/data/README.md b/tools/refer/data/README.md
new file mode 100644
index 0000000..2684424
--- /dev/null
+++ b/tools/refer/data/README.md
@@ -0,0 +1,37 @@
+This directory should contain the following data:
+```
+$DATA_PATH
+├── images
+│  ├── mscoco
+│  └── saiaprtc12
+├── refcoco
+│ ├── instances.json
+│ ├── refs(google).p
+│  └── refs(unc).p
+├── refcoco+
+│ ├── instances.json
+│  └── refs(unc).p
+├── refcocog
+│ ├── instances.json
+│  └── refs(google).p
+└── refclef
+ ├── instances.json
+ ├── refs(unc).p
+ └── refs(berkeley).p
+```
+
+Note, each detections/xxx.json contains
+``{'dets': ['box': {x, y, w, h}, 'image_id', 'object_id', 'score']}``. The ``object_id`` and ``score`` might be missing, depending on what proposal/detection technique we are using.
+
+## Download
+Download my cleaned data and extract them into this folder.
+- 1) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refclef.zip
+- 2) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip
+- 3) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco+.zip
+- 4) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refcocog.zip
+
+Besides make a folder named as "images".
+Add "mscoco" into "images/".
+Download MSCOCO from [mscoco](http://mscoco.org/dataset/#overview)
+
+Add "saiapr_tc-12" into "images/". I only extracted the related images as a subset of the original [imageCLEF](http://imageclef.org/SIAPRdata), i.e., 19997 images. Please download the subset from here (http://bvisionweb1.cs.unc.edu/licheng/referit/data/images/saiapr_tc-12.zip).
diff --git a/tools/refer/evaluation/__init__.py b/tools/refer/evaluation/__init__.py
new file mode 100644
index 0000000..66e55e9
--- /dev/null
+++ b/tools/refer/evaluation/__init__.py
@@ -0,0 +1,3 @@
+__author__ = 'licheng'
+
+
diff --git a/tools/refer/evaluation/bleu/LICENSE b/tools/refer/evaluation/bleu/LICENSE
new file mode 100644
index 0000000..9ccf677
--- /dev/null
+++ b/tools/refer/evaluation/bleu/LICENSE
@@ -0,0 +1,19 @@
+Copyright (c) 2015 Xinlei Chen, Hao Fang, Tsung-Yi Lin, and Ramakrishna Vedantam
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
diff --git a/tools/refer/evaluation/bleu/__init__.py b/tools/refer/evaluation/bleu/__init__.py
new file mode 100644
index 0000000..3f7d85b
--- /dev/null
+++ b/tools/refer/evaluation/bleu/__init__.py
@@ -0,0 +1 @@
+__author__ = 'tylin'
diff --git a/tools/refer/evaluation/bleu/bleu.py b/tools/refer/evaluation/bleu/bleu.py
new file mode 100644
index 0000000..b0da5dd
--- /dev/null
+++ b/tools/refer/evaluation/bleu/bleu.py
@@ -0,0 +1,47 @@
+#!/usr/bin/env python
+#
+# File Name : bleu.py
+#
+# Description : Wrapper for BLEU scorer.
+#
+# Creation Date : 06-01-2015
+# Last Modified : Thu 19 Mar 2015 09:13:28 PM PDT
+# Authors : Hao Fang and Tsung-Yi Lin
+
+from bleu_scorer import BleuScorer
+
+
+class Bleu:
+ def __init__(self, n=4):
+ # default compute Blue score up to 4
+ self._n = n
+ self._hypo_for_image = {}
+ self.ref_for_image = {}
+
+ def compute_score(self, gts, res):
+
+ assert(gts.keys() == res.keys())
+ imgIds = gts.keys()
+
+ bleu_scorer = BleuScorer(n=self._n)
+ for id in imgIds:
+ hypo = res[id]
+ ref = gts[id]
+
+ # Sanity check.
+ assert(type(hypo) is list)
+ assert(len(hypo) == 1)
+ assert(type(ref) is list)
+ assert(len(ref) >= 1)
+
+ bleu_scorer += (hypo[0], ref)
+
+ #score, scores = bleu_scorer.compute_score(option='shortest')
+ score, scores = bleu_scorer.compute_score(option='closest', verbose=1)
+ #score, scores = bleu_scorer.compute_score(option='average', verbose=1)
+
+ # return (bleu, bleu_info)
+ return score, scores
+
+ def method(self):
+ return "Bleu"
diff --git a/tools/refer/evaluation/bleu/bleu_scorer.py b/tools/refer/evaluation/bleu/bleu_scorer.py
new file mode 100644
index 0000000..3685e05
--- /dev/null
+++ b/tools/refer/evaluation/bleu/bleu_scorer.py
@@ -0,0 +1,263 @@
+#!/usr/bin/env python
+
+# bleu_scorer.py
+# David Chiang
+
+# Copyright (c) 2004-2006 University of Maryland. All rights
+# reserved. Do not redistribute without permission from the
+# author. Not for commercial use.
+
+# Modified by:
+# Hao Fang
+# Tsung-Yi Lin
+
+'''Provides:
+cook_refs(refs, n=4): Transform a list of reference sentences as strings into a form usable by cook_test().
+cook_test(test, refs, n=4): Transform a test sentence as a string (together with the cooked reference sentences) into a form usable by score_cooked().
+'''
+
+import copy
+import sys, math, re
+from collections import defaultdict
+
+def precook(s, n=4, out=False):
+ """Takes a string as input and returns an object that can be given to
+ either cook_refs or cook_test. This is optional: cook_refs and cook_test
+ can take string arguments as well."""
+ words = s.split()
+ counts = defaultdict(int)
+ for k in xrange(1,n+1):
+ for i in xrange(len(words)-k+1):
+ ngram = tuple(words[i:i+k])
+ counts[ngram] += 1
+ return (len(words), counts)
+
+def cook_refs(refs, eff=None, n=4): ## lhuang: oracle will call with "average"
+ '''Takes a list of reference sentences for a single segment
+ and returns an object that encapsulates everything that BLEU
+ needs to know about them.'''
+
+ reflen = []
+ maxcounts = {}
+ for ref in refs:
+ rl, counts = precook(ref, n)
+ reflen.append(rl)
+ for (ngram,count) in counts.iteritems():
+ maxcounts[ngram] = max(maxcounts.get(ngram,0), count)
+
+ # Calculate effective reference sentence length.
+ if eff == "shortest":
+ reflen = min(reflen)
+ elif eff == "average":
+ reflen = float(sum(reflen))/len(reflen)
+
+ ## lhuang: N.B.: leave reflen computaiton to the very end!!
+
+ ## lhuang: N.B.: in case of "closest", keep a list of reflens!! (bad design)
+
+ return (reflen, maxcounts)
+
+def cook_test(test, (reflen, refmaxcounts), eff=None, n=4):
+ '''Takes a test sentence and returns an object that
+ encapsulates everything that BLEU needs to know about it.'''
+
+ testlen, counts = precook(test, n, True)
+
+ result = {}
+
+ # Calculate effective reference sentence length.
+
+ if eff == "closest":
+ result["reflen"] = min((abs(l-testlen), l) for l in reflen)[1]
+ else: ## i.e., "average" or "shortest" or None
+ result["reflen"] = reflen
+
+ result["testlen"] = testlen
+
+ result["guess"] = [max(0,testlen-k+1) for k in xrange(1,n+1)]
+
+ result['correct'] = [0]*n
+ for (ngram, count) in counts.iteritems():
+ result["correct"][len(ngram)-1] += min(refmaxcounts.get(ngram,0), count)
+
+ return result
+
+class BleuScorer(object):
+ """Bleu scorer.
+ """
+
+ __slots__ = "n", "crefs", "ctest", "_score", "_ratio", "_testlen", "_reflen", "special_reflen"
+ # special_reflen is used in oracle (proportional effective ref len for a node).
+
+ def copy(self):
+ ''' copy the refs.'''
+ new = BleuScorer(n=self.n)
+ new.ctest = copy.copy(self.ctest)
+ new.crefs = copy.copy(self.crefs)
+ new._score = None
+ return new
+
+ def __init__(self, test=None, refs=None, n=4, special_reflen=None):
+ ''' singular instance '''
+
+ self.n = n
+ self.crefs = []
+ self.ctest = []
+ self.cook_append(test, refs)
+ self.special_reflen = special_reflen
+
+ def cook_append(self, test, refs):
+ '''called by constructor and __iadd__ to avoid creating new instances.'''
+
+ if refs is not None:
+ self.crefs.append(cook_refs(refs))
+ if test is not None:
+ cooked_test = cook_test(test, self.crefs[-1])
+ self.ctest.append(cooked_test) ## N.B.: -1
+ else:
+ self.ctest.append(None) # lens of crefs and ctest have to match
+
+ self._score = None ## need to recompute
+
+ def ratio(self, option=None):
+ self.compute_score(option=option)
+ return self._ratio
+
+ def score_ratio(self, option=None):
+ '''return (bleu, len_ratio) pair'''
+ return (self.fscore(option=option), self.ratio(option=option))
+
+ def score_ratio_str(self, option=None):
+ return "%.4f (%.2f)" % self.score_ratio(option)
+
+ def reflen(self, option=None):
+ self.compute_score(option=option)
+ return self._reflen
+
+ def testlen(self, option=None):
+ self.compute_score(option=option)
+ return self._testlen
+
+ def retest(self, new_test):
+ if type(new_test) is str:
+ new_test = [new_test]
+ assert len(new_test) == len(self.crefs), new_test
+ self.ctest = []
+ for t, rs in zip(new_test, self.crefs):
+ self.ctest.append(cook_test(t, rs))
+ self._score = None
+
+ return self
+
+ def rescore(self, new_test):
+ ''' replace test(s) with new test(s), and returns the new score.'''
+
+ return self.retest(new_test).compute_score()
+
+ def size(self):
+ assert len(self.crefs) == len(self.ctest), "refs/test mismatch! %d<>%d" % (len(self.crefs), len(self.ctest))
+ return len(self.crefs)
+
+ def __iadd__(self, other):
+ '''add an instance (e.g., from another sentence).'''
+
+ if type(other) is tuple:
+ ## avoid creating new BleuScorer instances
+ self.cook_append(other[0], other[1])
+ else:
+ assert self.compatible(other), "incompatible BLEUs."
+ self.ctest.extend(other.ctest)
+ self.crefs.extend(other.crefs)
+ self._score = None ## need to recompute
+
+ return self
+
+ def compatible(self, other):
+ return isinstance(other, BleuScorer) and self.n == other.n
+
+ def single_reflen(self, option="average"):
+ return self._single_reflen(self.crefs[0][0], option)
+
+ def _single_reflen(self, reflens, option=None, testlen=None):
+
+ if option == "shortest":
+ reflen = min(reflens)
+ elif option == "average":
+ reflen = float(sum(reflens))/len(reflens)
+ elif option == "closest":
+ reflen = min((abs(l-testlen), l) for l in reflens)[1]
+ else:
+ assert False, "unsupported reflen option %s" % option
+
+ return reflen
+
+ def recompute_score(self, option=None, verbose=0):
+ self._score = None
+ return self.compute_score(option, verbose)
+
+ def compute_score(self, option=None, verbose=0):
+ n = self.n
+ small = 1e-9
+ tiny = 1e-15 ## so that if guess is 0 still return 0
+ bleu_list = [[] for _ in range(n)]
+
+ if self._score is not None:
+ return self._score
+
+ if option is None:
+ option = "average" if len(self.crefs) == 1 else "closest"
+
+ self._testlen = 0
+ self._reflen = 0
+ totalcomps = {'testlen':0, 'reflen':0, 'guess':[0]*n, 'correct':[0]*n}
+
+ # for each sentence
+ for comps in self.ctest:
+ testlen = comps['testlen']
+ self._testlen += testlen
+
+ if self.special_reflen is None: ## need computation
+ reflen = self._single_reflen(comps['reflen'], option, testlen)
+ else:
+ reflen = self.special_reflen
+
+ self._reflen += reflen
+
+ for key in ['guess','correct']:
+ for k in xrange(n):
+ totalcomps[key][k] += comps[key][k]
+
+ # append per image bleu score
+ bleu = 1.
+ for k in xrange(n):
+ bleu *= (float(comps['correct'][k]) + tiny) \
+ /(float(comps['guess'][k]) + small)
+ bleu_list[k].append(bleu ** (1./(k+1)))
+ ratio = (testlen + tiny) / (reflen + small) ## N.B.: avoid zero division
+ if ratio < 1:
+ for k in xrange(n):
+ bleu_list[k][-1] *= math.exp(1 - 1/ratio)
+
+ if verbose > 1:
+ print comps, reflen
+
+ totalcomps['reflen'] = self._reflen
+ totalcomps['testlen'] = self._testlen
+
+ bleus = []
+ bleu = 1.
+ for k in xrange(n):
+ bleu *= float(totalcomps['correct'][k] + tiny) \
+ / (totalcomps['guess'][k] + small)
+ bleus.append(bleu ** (1./(k+1)))
+ ratio = (self._testlen + tiny) / (self._reflen + small) ## N.B.: avoid zero division
+ if ratio < 1:
+ for k in xrange(n):
+ bleus[k] *= math.exp(1 - 1/ratio)
+
+ if verbose > 0:
+ print totalcomps
+ print "ratio:", ratio
+
+ self._score = bleus
+ return self._score, bleu_list
diff --git a/tools/refer/evaluation/cider/__init__.py b/tools/refer/evaluation/cider/__init__.py
new file mode 100644
index 0000000..3f7d85b
--- /dev/null
+++ b/tools/refer/evaluation/cider/__init__.py
@@ -0,0 +1 @@
+__author__ = 'tylin'
diff --git a/tools/refer/evaluation/cider/cider.py b/tools/refer/evaluation/cider/cider.py
new file mode 100644
index 0000000..d0b99ee
--- /dev/null
+++ b/tools/refer/evaluation/cider/cider.py
@@ -0,0 +1,54 @@
+# Filename: cider.py
+#
+# Description: Describes the class to compute the CIDEr (Consensus-Based Image Description Evaluation) Metric
+# by Vedantam, Zitnick, and Parikh (http://arxiv.org/abs/1411.5726)
+#
+# Creation Date: Sun Feb 8 14:16:54 2015
+#
+# Authors: Ramakrishna Vedantam and Tsung-Yi Lin
+
+from cider_scorer import CiderScorer
+import pdb
+
+class Cider:
+ """
+ Main Class to compute the CIDEr metric
+
+ """
+ def __init__(self, test=None, refs=None, n=4, sigma=6.0):
+ # set cider to sum over 1 to 4-grams
+ self._n = n
+ # set the standard deviation parameter for gaussian penalty
+ self._sigma = sigma
+
+ def compute_score(self, gts, res):
+ """
+ Main function to compute CIDEr score
+ :param hypo_for_image (dict) : dictionary with key and value
+ ref_for_image (dict) : dictionary with key and value
+ :return: cider (float) : computed CIDEr score for the corpus
+ """
+
+ assert(gts.keys() == res.keys())
+ imgIds = gts.keys()
+
+ cider_scorer = CiderScorer(n=self._n, sigma=self._sigma)
+
+ for id in imgIds:
+ hypo = res[id]
+ ref = gts[id]
+
+ # Sanity check.
+ assert(type(hypo) is list)
+ assert(len(hypo) == 1)
+ assert(type(ref) is list)
+ assert(len(ref) > 0)
+
+ cider_scorer += (hypo[0], ref)
+
+ (score, scores) = cider_scorer.compute_score()
+
+ return score, scores
+
+ def method(self):
+ return "CIDEr"
\ No newline at end of file
diff --git a/tools/refer/evaluation/cider/cider_scorer.py b/tools/refer/evaluation/cider/cider_scorer.py
new file mode 100644
index 0000000..a73405e
--- /dev/null
+++ b/tools/refer/evaluation/cider/cider_scorer.py
@@ -0,0 +1,192 @@
+#!/usr/bin/env python
+# Tsung-Yi Lin
+# Ramakrishna Vedantam
+
+import copy
+from collections import defaultdict
+import numpy as np
+import pdb
+import math
+
+def precook(s, n=4, out=False):
+ """
+ Takes a string as input and returns an object that can be given to
+ either cook_refs or cook_test. This is optional: cook_refs and cook_test
+ can take string arguments as well.
+ :param s: string : sentence to be converted into ngrams
+ :param n: int : number of ngrams for which representation is calculated
+ :return: term frequency vector for occuring ngrams
+ """
+ words = s.split()
+ counts = defaultdict(int)
+ for k in xrange(1,n+1):
+ for i in xrange(len(words)-k+1):
+ ngram = tuple(words[i:i+k])
+ counts[ngram] += 1
+ return counts
+
+def cook_refs(refs, n=4): ## lhuang: oracle will call with "average"
+ '''Takes a list of reference sentences for a single segment
+ and returns an object that encapsulates everything that BLEU
+ needs to know about them.
+ :param refs: list of string : reference sentences for some image
+ :param n: int : number of ngrams for which (ngram) representation is calculated
+ :return: result (list of dict)
+ '''
+ return [precook(ref, n) for ref in refs]
+
+def cook_test(test, n=4):
+ '''Takes a test sentence and returns an object that
+ encapsulates everything that BLEU needs to know about it.
+ :param test: list of string : hypothesis sentence for some image
+ :param n: int : number of ngrams for which (ngram) representation is calculated
+ :return: result (dict)
+ '''
+ return precook(test, n, True)
+
+class CiderScorer(object):
+ """CIDEr scorer.
+ """
+
+ def copy(self):
+ ''' copy the refs.'''
+ new = CiderScorer(n=self.n)
+ new.ctest = copy.copy(self.ctest)
+ new.crefs = copy.copy(self.crefs)
+ return new
+
+ def __init__(self, test=None, refs=None, n=4, sigma=6.0):
+ ''' singular instance '''
+ self.n = n
+ self.sigma = sigma
+ self.crefs = []
+ self.ctest = []
+ self.document_frequency = defaultdict(float)
+ self.cook_append(test, refs)
+ self.ref_len = None
+
+ def cook_append(self, test, refs):
+ '''called by constructor and __iadd__ to avoid creating new instances.'''
+
+ if refs is not None:
+ self.crefs.append(cook_refs(refs))
+ if test is not None:
+ self.ctest.append(cook_test(test)) ## N.B.: -1
+ else:
+ self.ctest.append(None) # lens of crefs and ctest have to match
+
+ def size(self):
+ assert len(self.crefs) == len(self.ctest), "refs/test mismatch! %d<>%d" % (len(self.crefs), len(self.ctest))
+ return len(self.crefs)
+
+ def __iadd__(self, other):
+ '''add an instance (e.g., from another sentence).'''
+
+ if type(other) is tuple:
+ ## avoid creating new CiderScorer instances
+ self.cook_append(other[0], other[1])
+ else:
+ self.ctest.extend(other.ctest)
+ self.crefs.extend(other.crefs)
+
+ return self
+ def compute_doc_freq(self):
+ '''
+ Compute term frequency for reference data.
+ This will be used to compute idf (inverse document frequency later)
+ The term frequency is stored in the object
+ :return: None
+ '''
+ for refs in self.crefs:
+ # refs, k ref captions of one image
+ for ngram in set([ngram for ref in refs for (ngram,count) in ref.iteritems()]):
+ self.document_frequency[ngram] += 1
+ # maxcounts[ngram] = max(maxcounts.get(ngram,0), count)
+
+ def compute_cider(self):
+ def counts2vec(cnts):
+ """
+ Function maps counts of ngram to vector of tfidf weights.
+ The function returns vec, an array of dictionary that store mapping of n-gram and tf-idf weights.
+ The n-th entry of array denotes length of n-grams.
+ :param cnts:
+ :return: vec (array of dict), norm (array of float), length (int)
+ """
+ vec = [defaultdict(float) for _ in range(self.n)]
+ length = 0
+ norm = [0.0 for _ in range(self.n)]
+ for (ngram,term_freq) in cnts.iteritems():
+ # give word count 1 if it doesn't appear in reference corpus
+ df = np.log(max(1.0, self.document_frequency[ngram]))
+ # ngram index
+ n = len(ngram)-1
+ # tf (term_freq) * idf (precomputed idf) for n-grams
+ vec[n][ngram] = float(term_freq)*(self.ref_len - df)
+ # compute norm for the vector. the norm will be used for computing similarity
+ norm[n] += pow(vec[n][ngram], 2)
+
+ if n == 1:
+ length += term_freq
+ norm = [np.sqrt(n) for n in norm]
+ return vec, norm, length
+
+ def sim(vec_hyp, vec_ref, norm_hyp, norm_ref, length_hyp, length_ref):
+ '''
+ Compute the cosine similarity of two vectors.
+ :param vec_hyp: array of dictionary for vector corresponding to hypothesis
+ :param vec_ref: array of dictionary for vector corresponding to reference
+ :param norm_hyp: array of float for vector corresponding to hypothesis
+ :param norm_ref: array of float for vector corresponding to reference
+ :param length_hyp: int containing length of hypothesis
+ :param length_ref: int containing length of reference
+ :return: array of score for each n-grams cosine similarity
+ '''
+ delta = float(length_hyp - length_ref)
+ # measure consine similarity
+ val = np.array([0.0 for _ in range(self.n)])
+ for n in range(self.n):
+ # ngram
+ for (ngram,count) in vec_hyp[n].iteritems():
+ # vrama91 : added clipping
+ val[n] += min(vec_hyp[n][ngram], vec_ref[n][ngram]) * vec_ref[n][ngram]
+
+ if (norm_hyp[n] != 0) and (norm_ref[n] != 0):
+ val[n] /= (norm_hyp[n]*norm_ref[n])
+
+ assert(not math.isnan(val[n]))
+ # vrama91: added a length based gaussian penalty
+ val[n] *= np.e**(-(delta**2)/(2*self.sigma**2))
+ return val
+
+ # compute log reference length
+ self.ref_len = np.log(float(len(self.crefs)))
+
+ scores = []
+ for test, refs in zip(self.ctest, self.crefs):
+ # compute vector for test captions
+ vec, norm, length = counts2vec(test)
+ # compute vector for ref captions
+ score = np.array([0.0 for _ in range(self.n)])
+ for ref in refs:
+ vec_ref, norm_ref, length_ref = counts2vec(ref)
+ score += sim(vec, vec_ref, norm, norm_ref, length, length_ref)
+ # change by vrama91 - mean of ngram scores, instead of sum
+ score_avg = np.mean(score)
+ # divide by number of references
+ score_avg /= len(refs)
+ # multiply score by 10
+ score_avg *= 10.0
+ # append score of an image to the score list
+ scores.append(score_avg)
+ return scores
+
+ def compute_score(self, option=None, verbose=0):
+ # compute idf
+ self.compute_doc_freq()
+ # assert to check document frequency
+ assert(len(self.ctest) >= max(self.document_frequency.values()))
+ # compute cider score
+ score = self.compute_cider()
+ # debug
+ # print score
+ return np.mean(np.array(score)), np.array(score)
\ No newline at end of file
diff --git a/tools/refer/evaluation/meteor/__init__.py b/tools/refer/evaluation/meteor/__init__.py
new file mode 100644
index 0000000..3f7d85b
--- /dev/null
+++ b/tools/refer/evaluation/meteor/__init__.py
@@ -0,0 +1 @@
+__author__ = 'tylin'
diff --git a/tools/refer/evaluation/meteor/data/paraphrase-en.gz b/tools/refer/evaluation/meteor/data/paraphrase-en.gz
new file mode 100644
index 0000000..88033c8
Binary files /dev/null and b/tools/refer/evaluation/meteor/data/paraphrase-en.gz differ
diff --git a/tools/refer/evaluation/meteor/meteor-1.5.jar b/tools/refer/evaluation/meteor/meteor-1.5.jar
new file mode 100644
index 0000000..a833bc0
Binary files /dev/null and b/tools/refer/evaluation/meteor/meteor-1.5.jar differ
diff --git a/tools/refer/evaluation/meteor/meteor.py b/tools/refer/evaluation/meteor/meteor.py
new file mode 100644
index 0000000..6472948
--- /dev/null
+++ b/tools/refer/evaluation/meteor/meteor.py
@@ -0,0 +1,76 @@
+#!/usr/bin/env python
+
+# Python wrapper for METEOR implementation, by Xinlei Chen
+# Acknowledge Michael Denkowski for the generous discussion and help
+
+import os
+import sys
+import subprocess
+import threading
+
+# Assumes meteor-1.5.jar is in the same directory as meteor.py. Change as needed.
+METEOR_JAR = 'meteor-1.5.jar'
+# print METEOR_JAR
+
+class Meteor:
+
+ def __init__(self):
+ self.meteor_cmd = ['java', '-jar', '-Xmx2G', METEOR_JAR, \
+ '-', '-', '-stdio', '-l', 'en', '-norm']
+ self.meteor_p = subprocess.Popen(self.meteor_cmd, \
+ cwd=os.path.dirname(os.path.abspath(__file__)), \
+ stdin=subprocess.PIPE, \
+ stdout=subprocess.PIPE, \
+ stderr=subprocess.PIPE)
+ # Used to guarantee thread safety
+ self.lock = threading.Lock()
+
+ def compute_score(self, gts, res):
+ assert(gts.keys() == res.keys())
+ imgIds = gts.keys()
+ scores = []
+
+ eval_line = 'EVAL'
+ self.lock.acquire()
+ for i in imgIds:
+ assert(len(res[i]) == 1)
+ stat = self._stat(res[i][0], gts[i])
+ eval_line += ' ||| {}'.format(stat)
+
+ self.meteor_p.stdin.write('{}\n'.format(eval_line))
+ for i in range(0,len(imgIds)):
+ scores.append(float(self.meteor_p.stdout.readline().strip()))
+ score = float(self.meteor_p.stdout.readline().strip())
+ self.lock.release()
+
+ return score, scores
+
+ def method(self):
+ return "METEOR"
+
+ def _stat(self, hypothesis_str, reference_list):
+ # SCORE ||| reference 1 words ||| reference n words ||| hypothesis words
+ hypothesis_str = hypothesis_str.replace('|||','').replace(' ',' ')
+ score_line = ' ||| '.join(('SCORE', ' ||| '.join(reference_list), hypothesis_str))
+ self.meteor_p.stdin.write('{}\n'.format(score_line))
+ return self.meteor_p.stdout.readline().strip()
+
+ def _score(self, hypothesis_str, reference_list):
+ self.lock.acquire()
+ # SCORE ||| reference 1 words ||| reference n words ||| hypothesis words
+ hypothesis_str = hypothesis_str.replace('|||','').replace(' ',' ')
+ score_line = ' ||| '.join(('SCORE', ' ||| '.join(reference_list), hypothesis_str))
+ self.meteor_p.stdin.write('{}\n'.format(score_line))
+ stats = self.meteor_p.stdout.readline().strip()
+ eval_line = 'EVAL ||| {}'.format(stats)
+ # EVAL ||| stats
+ self.meteor_p.stdin.write('{}\n'.format(eval_line))
+ score = float(self.meteor_p.stdout.readline().strip())
+ self.lock.release()
+ return score
+
+ def __exit__(self):
+ self.lock.acquire()
+ self.meteor_p.stdin.close()
+ self.meteor_p.wait()
+ self.lock.release()
diff --git a/tools/refer/evaluation/readme.txt b/tools/refer/evaluation/readme.txt
new file mode 100644
index 0000000..4491b5a
--- /dev/null
+++ b/tools/refer/evaluation/readme.txt
@@ -0,0 +1,11 @@
+This folder contains modified coco-caption evaluation, which is downloaded from https://github.com/tylin/coco-caption.git
+and refEvaluation which is to be called by the refer algorithm.
+
+More specifically, this folder contains:
+1. bleu/
+2. cider/
+3. meteor/
+4. rouge/
+5. tokenizer/
+6. __init__.py
+7. refEvaluation.py
diff --git a/tools/refer/evaluation/refEvaluation.py b/tools/refer/evaluation/refEvaluation.py
new file mode 100644
index 0000000..bfa11ed
--- /dev/null
+++ b/tools/refer/evaluation/refEvaluation.py
@@ -0,0 +1,136 @@
+from tokenizer.ptbtokenizer import PTBTokenizer
+from bleu.bleu import Bleu
+from meteor.meteor import Meteor
+from rouge.rouge import Rouge
+from cider.cider import Cider
+
+"""
+Input: refer and Res = [{ref_id, sent}]
+
+Things of interest
+evalRefs - list of ['ref_id', 'CIDEr', 'Bleu_1', 'Bleu_2', 'Bleu_3', 'Bleu_4', 'ROUGE_L', 'METEOR']
+eval - dict of {metric: score}
+refToEval - dict of {ref_id: ['ref_id', 'CIDEr', 'Bleu_1', 'Bleu_2', 'Bleu_3', 'Bleu_4', 'ROUGE_L', 'METEOR']}
+"""
+
+class RefEvaluation:
+ def __init__ (self, refer, Res):
+ """
+ :param refer: refer class of current dataset
+ :param Res: [{'ref_id', 'sent'}]
+ """
+ self.evalRefs = []
+ self.eval = {}
+ self.refToEval = {}
+ self.refer = refer
+ self.Res = Res
+
+ def evaluate(self):
+
+ evalRefIds = [ann['ref_id'] for ann in self.Res]
+
+ refToGts = {}
+ for ref_id in evalRefIds:
+ ref = self.refer.Refs[ref_id]
+ gt_sents = [sent['sent'].encode('ascii', 'ignore').decode('ascii') for sent in ref['sentences']] # up to 3 expressions
+ refToGts[ref_id] = gt_sents
+ refToRes = {ann['ref_id']: [ann['sent']] for ann in self.Res}
+
+ print 'tokenization...'
+ tokenizer = PTBTokenizer()
+ self.refToRes = tokenizer.tokenize(refToRes)
+ self.refToGts = tokenizer.tokenize(refToGts)
+
+ # =================================================
+ # Set up scorers
+ # =================================================
+ print 'setting up scorers...'
+ scorers = [
+ (Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
+ (Meteor(),"METEOR"),
+ (Rouge(), "ROUGE_L"),
+ (Cider(), "CIDEr")
+ ]
+
+ # =================================================
+ # Compute scores
+ # =================================================
+ for scorer, method in scorers:
+ print 'computing %s score...'%(scorer.method())
+ score, scores = scorer.compute_score(self.refToGts, self.refToRes)
+ if type(method) == list:
+ for sc, scs, m in zip(score, scores, method):
+ self.setEval(sc, m)
+ self.setRefToEvalRefs(scs, self.refToGts.keys(), m)
+ print "%s: %0.3f"%(m, sc)
+ else:
+ self.setEval(score, method)
+ self.setRefToEvalRefs(scores, self.refToGts.keys(), method)
+ print "%s: %0.3f"%(method, score)
+ self.setEvalRefs()
+
+ def setEval(self, score, method):
+ self.eval[method] = score
+
+ def setRefToEvalRefs(self, scores, refIds, method):
+ for refId, score in zip(refIds, scores):
+ if not refId in self.refToEval:
+ self.refToEval[refId] = {}
+ self.refToEval[refId]["ref_id"] = refId
+ self.refToEval[refId][method] = score
+
+ def setEvalRefs(self):
+ self.evalRefs = [eval for refId, eval in self.refToEval.items()]
+
+
+if __name__ == '__main__':
+
+ import os.path as osp
+ import sys
+ ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..'))
+ sys.path.insert(0, osp.join(ROOT_DIR, 'lib', 'datasets'))
+ from refer import REFER
+
+ # load refer of dataset
+ dataset = 'refcoco'
+ refer = REFER(dataset, splitBy = 'google')
+
+ # mimic some Res
+ val_refIds = refer.getRefIds(split='test')
+ ref_id = 49767
+ print "GD: %s" % refer.Refs[ref_id]['sentences']
+ Res = [{'ref_id': ref_id, 'sent': 'left bottle'}]
+
+ # evaluate some refer expressions
+ refEval = RefEvaluation(refer, Res)
+ refEval.evaluate()
+
+ # print output evaluation scores
+ for metric, score in refEval.eval.items():
+ print '%s: %.3f'%(metric, score)
+
+ # demo how to use evalImgs to retrieve low score result
+ # evals = [eva for eva in refEval.evalRefs if eva['CIDEr']<30]
+ # print 'ground truth sents'
+ # refId = evals[0]['ref_id']
+ # print 'refId: %s' % refId
+ # print [sent['sent'] for sent in refer.Refs[refId]['sentences']]
+ #
+ # print 'generated sent (CIDEr score %0.1f)' % (evals[0]['CIDEr'])
+
+ # print refEval.refToEval[8]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/tools/refer/evaluation/rouge/__init__.py b/tools/refer/evaluation/rouge/__init__.py
new file mode 100644
index 0000000..43a773e
--- /dev/null
+++ b/tools/refer/evaluation/rouge/__init__.py
@@ -0,0 +1 @@
+__author__ = 'vrama91'
diff --git a/tools/refer/evaluation/rouge/rouge.py b/tools/refer/evaluation/rouge/rouge.py
new file mode 100644
index 0000000..3a10f5a
--- /dev/null
+++ b/tools/refer/evaluation/rouge/rouge.py
@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+#
+# File Name : rouge.py
+#
+# Description : Computes ROUGE-L metric as described by Lin and Hovey (2004)
+#
+# Creation Date : 2015-01-07 06:03
+# Author : Ramakrishna Vedantam
+
+import numpy as np
+import pdb
+
+def my_lcs(string, sub):
+ """
+ Calculates longest common subsequence for a pair of tokenized strings
+ :param string : list of str : tokens from a string split using whitespace
+ :param sub : list of str : shorter string, also split using whitespace
+ :returns: length (list of int): length of the longest common subsequence between the two strings
+
+ Note: my_lcs only gives length of the longest common subsequence, not the actual LCS
+ """
+ if(len(string)< len(sub)):
+ sub, string = string, sub
+
+ lengths = [[0 for i in range(0,len(sub)+1)] for j in range(0,len(string)+1)]
+
+ for j in range(1,len(sub)+1):
+ for i in range(1,len(string)+1):
+ if(string[i-1] == sub[j-1]):
+ lengths[i][j] = lengths[i-1][j-1] + 1
+ else:
+ lengths[i][j] = max(lengths[i-1][j] , lengths[i][j-1])
+
+ return lengths[len(string)][len(sub)]
+
+class Rouge():
+ '''
+ Class for computing ROUGE-L score for a set of candidate sentences for the MS COCO test set
+
+ '''
+ def __init__(self):
+ # vrama91: updated the value below based on discussion with Hovey
+ self.beta = 1.2
+
+ def calc_score(self, candidate, refs):
+ """
+ Compute ROUGE-L score given one candidate and references for an image
+ :param candidate: str : candidate sentence to be evaluated
+ :param refs: list of str : COCO reference sentences for the particular image to be evaluated
+ :returns score: int (ROUGE-L score for the candidate evaluated against references)
+ """
+ assert(len(candidate)==1)
+ assert(len(refs)>0)
+ prec = []
+ rec = []
+
+ # split into tokens
+ token_c = candidate[0].split(" ")
+
+ for reference in refs:
+ # split into tokens
+ token_r = reference.split(" ")
+ # compute the longest common subsequence
+ lcs = my_lcs(token_r, token_c)
+ prec.append(lcs/float(len(token_c)))
+ rec.append(lcs/float(len(token_r)))
+
+ prec_max = max(prec)
+ rec_max = max(rec)
+
+ if(prec_max!=0 and rec_max !=0):
+ score = ((1 + self.beta**2)*prec_max*rec_max)/float(rec_max + self.beta**2*prec_max)
+ else:
+ score = 0.0
+ return score
+
+ def compute_score(self, gts, res):
+ """
+ Computes Rouge-L score given a set of reference and candidate sentences for the dataset
+ Invoked by evaluate_captions.py
+ :param hypo_for_image: dict : candidate / test sentences with "image name" key and "tokenized sentences" as values
+ :param ref_for_image: dict : reference MS-COCO sentences with "image name" key and "tokenized sentences" as values
+ :returns: average_score: float (mean ROUGE-L score computed by averaging scores for all the images)
+ """
+ assert(gts.keys() == res.keys())
+ imgIds = gts.keys()
+
+ score = []
+ for id in imgIds:
+ hypo = res[id]
+ ref = gts[id]
+
+ score.append(self.calc_score(hypo, ref))
+
+ # Sanity check.
+ assert(type(hypo) is list)
+ assert(len(hypo) == 1)
+ assert(type(ref) is list)
+ assert(len(ref) > 0)
+
+ average_score = np.mean(np.array(score))
+ return average_score, np.array(score)
+
+ def method(self):
+ return "Rouge"
diff --git a/tools/refer/evaluation/tokenizer/__init__.py b/tools/refer/evaluation/tokenizer/__init__.py
new file mode 100644
index 0000000..71357a4
--- /dev/null
+++ b/tools/refer/evaluation/tokenizer/__init__.py
@@ -0,0 +1 @@
+__author__ = 'hfang'
diff --git a/tools/refer/evaluation/tokenizer/ptbtokenizer.py b/tools/refer/evaluation/tokenizer/ptbtokenizer.py
new file mode 100644
index 0000000..346ebe7
--- /dev/null
+++ b/tools/refer/evaluation/tokenizer/ptbtokenizer.py
@@ -0,0 +1,68 @@
+#!/usr/bin/env python
+#
+# File Name : ptbtokenizer.py
+#
+# Description : Do the PTB Tokenization and remove punctuations.
+#
+# Creation Date : 29-12-2014
+# Last Modified : Thu Mar 19 09:53:35 2015
+# Authors : Hao Fang and Tsung-Yi Lin
+
+import os
+import sys
+import subprocess
+import tempfile
+import itertools
+
+# path to the stanford corenlp jar
+STANFORD_CORENLP_3_4_1_JAR = 'stanford-corenlp-3.4.1.jar'
+
+# punctuations to be removed from the sentences
+PUNCTUATIONS = ["''", "'", "``", "`", "-LRB-", "-RRB-", "-LCB-", "-RCB-", \
+ ".", "?", "!", ",", ":", "-", "--", "...", ";"]
+
+class PTBTokenizer:
+ """Python wrapper of Stanford PTBTokenizer"""
+
+ def tokenize(self, captions_for_image):
+ cmd = ['java', '-cp', STANFORD_CORENLP_3_4_1_JAR, \
+ 'edu.stanford.nlp.process.PTBTokenizer', \
+ '-preserveLines', '-lowerCase']
+
+ # ======================================================
+ # prepare data for PTB Tokenizer
+ # ======================================================
+ final_tokenized_captions_for_image = {}
+ image_id = [k for k, v in captions_for_image.items() for _ in range(len(v))]
+ sentences = '\n'.join([c.replace('\n', ' ') for k, v in captions_for_image.items() for c in v])
+
+ # ======================================================
+ # save sentences to temporary file
+ # ======================================================
+ path_to_jar_dirname=os.path.dirname(os.path.abspath(__file__))
+ tmp_file = tempfile.NamedTemporaryFile(delete=False, dir=path_to_jar_dirname)
+ tmp_file.write(sentences)
+ tmp_file.close()
+
+ # ======================================================
+ # tokenize sentence
+ # ======================================================
+ cmd.append(os.path.basename(tmp_file.name))
+ p_tokenizer = subprocess.Popen(cmd, cwd=path_to_jar_dirname, \
+ stdout=subprocess.PIPE)
+ token_lines = p_tokenizer.communicate(input=sentences.rstrip())[0]
+ lines = token_lines.split('\n')
+ # remove temp file
+ os.remove(tmp_file.name)
+
+ # ======================================================
+ # create dictionary for tokenized captions
+ # ======================================================
+ for k, line in zip(image_id, lines):
+ if not k in final_tokenized_captions_for_image:
+ final_tokenized_captions_for_image[k] = []
+ tokenized_caption = ' '.join([w for w in line.rstrip().split(' ') \
+ if w not in PUNCTUATIONS])
+ final_tokenized_captions_for_image[k].append(tokenized_caption)
+
+ return final_tokenized_captions_for_image
diff --git a/tools/refer/evaluation/tokenizer/stanford-corenlp-3.4.1.jar b/tools/refer/evaluation/tokenizer/stanford-corenlp-3.4.1.jar
new file mode 100644
index 0000000..3cfa0a0
Binary files /dev/null and b/tools/refer/evaluation/tokenizer/stanford-corenlp-3.4.1.jar differ
diff --git a/tools/refer/external/.gitignore b/tools/refer/external/.gitignore
new file mode 100644
index 0000000..20d59f9
--- /dev/null
+++ b/tools/refer/external/.gitignore
@@ -0,0 +1,3 @@
+*.pyc
+_mask.so
+_mask.c
diff --git a/tools/refer/external/README.md b/tools/refer/external/README.md
new file mode 100644
index 0000000..0a0a681
--- /dev/null
+++ b/tools/refer/external/README.md
@@ -0,0 +1 @@
+The codes inside this folder are copied from pycocotools: https://github.com/pdollar/coco
\ No newline at end of file
diff --git a/tools/refer/external/__init__.py b/tools/refer/external/__init__.py
new file mode 100644
index 0000000..3f7d85b
--- /dev/null
+++ b/tools/refer/external/__init__.py
@@ -0,0 +1 @@
+__author__ = 'tylin'
diff --git a/tools/refer/external/_mask.cpython-36m-x86_64-linux-gnu.so b/tools/refer/external/_mask.cpython-36m-x86_64-linux-gnu.so
new file mode 100755
index 0000000..4fed2ad
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diff --git a/tools/refer/external/_mask.cpython-37m-x86_64-linux-gnu.so b/tools/refer/external/_mask.cpython-37m-x86_64-linux-gnu.so
new file mode 100755
index 0000000..f1cd270
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diff --git a/tools/refer/external/_mask.pyx b/tools/refer/external/_mask.pyx
new file mode 100644
index 0000000..9f0562c
--- /dev/null
+++ b/tools/refer/external/_mask.pyx
@@ -0,0 +1,291 @@
+# distutils: language = c
+# distutils: sources = external/maskApi.c
+
+#**************************************************************************
+# Microsoft COCO Toolbox. version 2.0
+# Data, paper, and tutorials available at: http://mscoco.org/
+# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
+# Licensed under the Simplified BSD License [see coco/license.txt]
+#**************************************************************************
+
+__author__ = 'tsungyi'
+
+# import both Python-level and C-level symbols of Numpy
+# the API uses Numpy to interface C and Python
+import numpy as np
+cimport numpy as np
+from libc.stdlib cimport malloc, free
+
+# intialized Numpy. must do.
+np.import_array()
+
+# import numpy C function
+# we use PyArray_ENABLEFLAGS to make Numpy ndarray responsible to memoery management
+cdef extern from "numpy/arrayobject.h":
+ void PyArray_ENABLEFLAGS(np.ndarray arr, int flags)
+
+# Declare the prototype of the C functions in MaskApi.h
+cdef extern from "maskApi.h":
+ ctypedef unsigned int uint
+ ctypedef unsigned long siz
+ ctypedef unsigned char byte
+ ctypedef double* BB
+ ctypedef struct RLE:
+ siz h,
+ siz w,
+ siz m,
+ uint* cnts,
+ void rlesInit( RLE **R, siz n )
+ void rleEncode( RLE *R, const byte *M, siz h, siz w, siz n )
+ void rleDecode( const RLE *R, byte *mask, siz n )
+ void rleMerge( const RLE *R, RLE *M, siz n, bint intersect )
+ void rleArea( const RLE *R, siz n, uint *a )
+ void rleIou( RLE *dt, RLE *gt, siz m, siz n, byte *iscrowd, double *o )
+ void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o )
+ void rleToBbox( const RLE *R, BB bb, siz n )
+ void rleFrBbox( RLE *R, const BB bb, siz h, siz w, siz n )
+ void rleFrPoly( RLE *R, const double *xy, siz k, siz h, siz w )
+ char* rleToString( const RLE *R )
+ void rleFrString( RLE *R, char *s, siz h, siz w )
+
+# python class to wrap RLE array in C
+# the class handles the memory allocation and deallocation
+cdef class RLEs:
+ cdef RLE *_R
+ cdef siz _n
+
+ def __cinit__(self, siz n =0):
+ rlesInit(&self._R, n)
+ self._n = n
+
+ # free the RLE array here
+ def __dealloc__(self):
+ if self._R is not NULL:
+ for i in range(self._n):
+ free(self._R[i].cnts)
+ free(self._R)
+ def __getattr__(self, key):
+ if key == 'n':
+ return self._n
+ raise AttributeError(key)
+
+# python class to wrap Mask array in C
+# the class handles the memory allocation and deallocation
+cdef class Masks:
+ cdef byte *_mask
+ cdef siz _h
+ cdef siz _w
+ cdef siz _n
+
+ def __cinit__(self, h, w, n):
+ self._mask = malloc(h*w*n* sizeof(byte))
+ self._h = h
+ self._w = w
+ self._n = n
+ # def __dealloc__(self):
+ # the memory management of _mask has been passed to np.ndarray
+ # it doesn't need to be freed here
+
+ # called when passing into np.array() and return an np.ndarray in column-major order
+ def __array__(self):
+ cdef np.npy_intp shape[1]
+ shape[0] = self._h*self._w*self._n
+ # Create a 1D array, and reshape it to fortran/Matlab column-major array
+ ndarray = np.PyArray_SimpleNewFromData(1, shape, np.NPY_UINT8, self._mask).reshape((self._h, self._w, self._n), order='F')
+ # The _mask allocated by Masks is now handled by ndarray
+ PyArray_ENABLEFLAGS(ndarray, np.NPY_OWNDATA)
+ return ndarray
+
+# internal conversion from Python RLEs object to compressed RLE format
+def _toString(RLEs Rs):
+ cdef siz n = Rs.n
+ cdef bytes py_string
+ cdef char* c_string
+ objs = []
+ for i in range(n):
+ c_string = rleToString( &Rs._R[i] )
+ py_string = c_string
+ objs.append({
+ 'size': [Rs._R[i].h, Rs._R[i].w],
+ 'counts': py_string
+ })
+ free(c_string)
+ return objs
+
+# internal conversion from compressed RLE format to Python RLEs object
+def _frString(rleObjs):
+ cdef siz n = len(rleObjs)
+ Rs = RLEs(n)
+ cdef bytes py_string
+ cdef char* c_string
+ for i, obj in enumerate(rleObjs):
+ py_string = str(obj['counts'])
+ c_string = py_string
+ rleFrString( &Rs._R[i], c_string, obj['size'][0], obj['size'][1] )
+ return Rs
+
+# encode mask to RLEs objects
+# list of RLE string can be generated by RLEs member function
+def encode(np.ndarray[np.uint8_t, ndim=3, mode='fortran'] mask):
+ h, w, n = mask.shape[0], mask.shape[1], mask.shape[2]
+ cdef RLEs Rs = RLEs(n)
+ rleEncode(Rs._R,mask.data,h,w,n)
+ objs = _toString(Rs)
+ return objs
+
+# decode mask from compressed list of RLE string or RLEs object
+def decode(rleObjs):
+ cdef RLEs Rs = _frString(rleObjs)
+ h, w, n = Rs._R[0].h, Rs._R[0].w, Rs._n
+ masks = Masks(h, w, n)
+ rleDecode( Rs._R, masks._mask, n );
+ return np.array(masks)
+
+def merge(rleObjs, bint intersect=0):
+ cdef RLEs Rs = _frString(rleObjs)
+ cdef RLEs R = RLEs(1)
+ rleMerge(Rs._R, R._R, Rs._n, intersect)
+ obj = _toString(R)[0]
+ return obj
+
+def area(rleObjs):
+ cdef RLEs Rs = _frString(rleObjs)
+ cdef uint* _a = malloc(Rs._n* sizeof(uint))
+ rleArea(Rs._R, Rs._n, _a)
+ cdef np.npy_intp shape[1]
+ shape[0] = Rs._n
+ a = np.array((Rs._n, ), dtype=np.uint8)
+ a = np.PyArray_SimpleNewFromData(1, shape, np.NPY_UINT32, _a)
+ PyArray_ENABLEFLAGS(a, np.NPY_OWNDATA)
+ return a
+
+# iou computation. support function overload (RLEs-RLEs and bbox-bbox).
+def iou( dt, gt, pyiscrowd ):
+ def _preproc(objs):
+ if len(objs) == 0:
+ return objs
+ if type(objs) == np.ndarray:
+ if len(objs.shape) == 1:
+ objs = objs.reshape((objs[0], 1))
+ # check if it's Nx4 bbox
+ if not len(objs.shape) == 2 or not objs.shape[1] == 4:
+ raise Exception('numpy ndarray input is only for *bounding boxes* and should have Nx4 dimension')
+ objs = objs.astype(np.double)
+ elif type(objs) == list:
+ # check if list is in box format and convert it to np.ndarray
+ isbox = np.all(np.array([(len(obj)==4) and ((type(obj)==list) or (type(obj)==np.ndarray)) for obj in objs]))
+ isrle = np.all(np.array([type(obj) == dict for obj in objs]))
+ if isbox:
+ objs = np.array(objs, dtype=np.double)
+ if len(objs.shape) == 1:
+ objs = objs.reshape((1,objs.shape[0]))
+ elif isrle:
+ objs = _frString(objs)
+ else:
+ raise Exception('list input can be bounding box (Nx4) or RLEs ([RLE])')
+ else:
+ raise Exception('unrecognized type. The following type: RLEs (rle), np.ndarray (box), and list (box) are supported.')
+ return objs
+ def _rleIou(RLEs dt, RLEs gt, np.ndarray[np.uint8_t, ndim=1] iscrowd, siz m, siz n, np.ndarray[np.double_t, ndim=1] _iou):
+ rleIou( dt._R, gt._R, m, n, iscrowd.data, _iou.data )
+ def _bbIou(np.ndarray[np.double_t, ndim=2] dt, np.ndarray[np.double_t, ndim=2] gt, np.ndarray[np.uint8_t, ndim=1] iscrowd, siz m, siz n, np.ndarray[np.double_t, ndim=1] _iou):
+ bbIou( dt.data, gt.data, m, n, iscrowd.data, _iou.data )
+ def _len(obj):
+ cdef siz N = 0
+ if type(obj) == RLEs:
+ N = obj.n
+ elif len(obj)==0:
+ pass
+ elif type(obj) == np.ndarray:
+ N = obj.shape[0]
+ return N
+ # convert iscrowd to numpy array
+ cdef np.ndarray[np.uint8_t, ndim=1] iscrowd = np.array(pyiscrowd, dtype=np.uint8)
+ # simple type checking
+ cdef siz m, n
+ dt = _preproc(dt)
+ gt = _preproc(gt)
+ m = _len(dt)
+ n = _len(gt)
+ if m == 0 or n == 0:
+ return []
+ if not type(dt) == type(gt):
+ raise Exception('The dt and gt should have the same data type, either RLEs, list or np.ndarray')
+
+ # define local variables
+ cdef double* _iou = 0
+ cdef np.npy_intp shape[1]
+ # check type and assign iou function
+ if type(dt) == RLEs:
+ _iouFun = _rleIou
+ elif type(dt) == np.ndarray:
+ _iouFun = _bbIou
+ else:
+ raise Exception('input data type not allowed.')
+ _iou = malloc(m*n* sizeof(double))
+ iou = np.zeros((m*n, ), dtype=np.double)
+ shape[0] = m*n
+ iou = np.PyArray_SimpleNewFromData(1, shape, np.NPY_DOUBLE, _iou)
+ PyArray_ENABLEFLAGS(iou, np.NPY_OWNDATA)
+ _iouFun(dt, gt, iscrowd, m, n, iou)
+ return iou.reshape((m,n), order='F')
+
+def toBbox( rleObjs ):
+ cdef RLEs Rs = _frString(rleObjs)
+ cdef siz n = Rs.n
+ cdef BB _bb = malloc(4*n* sizeof(double))
+ rleToBbox( Rs._R, _bb, n )
+ cdef np.npy_intp shape[1]
+ shape[0] = 4*n
+ bb = np.array((1,4*n), dtype=np.double)
+ bb = np.PyArray_SimpleNewFromData(1, shape, np.NPY_DOUBLE, _bb).reshape((n, 4))
+ PyArray_ENABLEFLAGS(bb, np.NPY_OWNDATA)
+ return bb
+
+def frBbox(np.ndarray[np.double_t, ndim=2] bb, siz h, siz w ):
+ cdef siz n = bb.shape[0]
+ Rs = RLEs(n)
+ rleFrBbox( Rs._R, bb.data, h, w, n )
+ objs = _toString(Rs)
+ return objs
+
+def frPoly( poly, siz h, siz w ):
+ cdef np.ndarray[np.double_t, ndim=1] np_poly
+ n = len(poly)
+ Rs = RLEs(n)
+ for i, p in enumerate(poly):
+ np_poly = np.array(p, dtype=np.double, order='F')
+ rleFrPoly( &Rs._R[i], np_poly.data, len(np_poly)/2, h, w )
+ objs = _toString(Rs)
+ return objs
+
+def frUncompressedRLE(ucRles, siz h, siz w):
+ cdef np.ndarray[np.uint32_t, ndim=1] cnts
+ cdef RLE R
+ cdef uint *data
+ n = len(ucRles)
+ objs = []
+ for i in range(n):
+ Rs = RLEs(1)
+ cnts = np.array(ucRles[i]['counts'], dtype=np.uint32)
+ # time for malloc can be saved here but it's fine
+ data = malloc(len(cnts)* sizeof(uint))
+ for j in range(len(cnts)):
+ data[j] = cnts[j]
+ R = RLE(ucRles[i]['size'][0], ucRles[i]['size'][1], len(cnts), data)
+ Rs._R[0] = R
+ objs.append(_toString(Rs)[0])
+ return objs
+
+def frPyObjects(pyobj, siz h, w):
+ if type(pyobj) == np.ndarray:
+ objs = frBbox(pyobj, h, w )
+ elif type(pyobj) == list and len(pyobj[0]) == 4:
+ objs = frBbox(pyobj, h, w )
+ elif type(pyobj) == list and len(pyobj[0]) > 4:
+ objs = frPoly(pyobj, h, w )
+ elif type(pyobj) == list and type(pyobj[0]) == dict:
+ objs = frUncompressedRLE(pyobj, h, w)
+ else:
+ raise Exception('input type is not supported.')
+ return objs
diff --git a/tools/refer/external/mask.py b/tools/refer/external/mask.py
new file mode 100644
index 0000000..67ef560
--- /dev/null
+++ b/tools/refer/external/mask.py
@@ -0,0 +1,82 @@
+__author__ = 'tsungyi'
+
+import tools.refer.external._mask as _mask
+
+# Interface for manipulating masks stored in RLE format.
+#
+# RLE is a simple yet efficient format for storing binary masks. RLE
+# first divides a vector (or vectorized image) into a series of piecewise
+# constant regions and then for each piece simply stores the length of
+# that piece. For example, given M=[0 0 1 1 1 0 1] the RLE counts would
+# be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would be [0 6 1]
+# (note that the odd counts are always the numbers of zeros). Instead of
+# storing the counts directly, additional compression is achieved with a
+# variable bitrate representation based on a common scheme called LEB128.
+#
+# Compression is greatest given large piecewise constant regions.
+# Specifically, the size of the RLE is proportional to the number of
+# *boundaries* in M (or for an image the number of boundaries in the y
+# direction). Assuming fairly simple shapes, the RLE representation is
+# O(sqrt(n)) where n is number of pixels in the object. Hence space usage
+# is substantially lower, especially for large simple objects (large n).
+#
+# Many common operations on masks can be computed directly using the RLE
+# (without need for decoding). This includes computations such as area,
+# union, intersection, etc. All of these operations are linear in the
+# size of the RLE, in other words they are O(sqrt(n)) where n is the area
+# of the object. Computing these operations on the original mask is O(n).
+# Thus, using the RLE can result in substantial computational savings.
+#
+# The following API functions are defined:
+# encode - Encode binary masks using RLE.
+# decode - Decode binary masks encoded via RLE.
+# merge - Compute union or intersection of encoded masks.
+# iou - Compute intersection over union between masks.
+# area - Compute area of encoded masks.
+# toBbox - Get bounding boxes surrounding encoded masks.
+# frPyObjects - Convert polygon, bbox, and uncompressed RLE to encoded RLE mask.
+#
+# Usage:
+# Rs = encode( masks )
+# masks = decode( Rs )
+# R = merge( Rs, intersect=false )
+# o = iou( dt, gt, iscrowd )
+# a = area( Rs )
+# bbs = toBbox( Rs )
+# Rs = frPyObjects( [pyObjects], h, w )
+#
+# In the API the following formats are used:
+# Rs - [dict] Run-length encoding of binary masks
+# R - dict Run-length encoding of binary mask
+# masks - [hxwxn] Binary mask(s) (must have type np.ndarray(dtype=uint8) in column-major order)
+# iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore
+# bbs - [nx4] Bounding box(es) stored as [x y w h]
+# poly - Polygon stored as [[x1 y1 x2 y2...],[x1 y1 ...],...] (2D list)
+# dt,gt - May be either bounding boxes or encoded masks
+# Both poly and bbs are 0-indexed (bbox=[0 0 1 1] encloses first pixel).
+#
+# Finally, a note about the intersection over union (iou) computation.
+# The standard iou of a ground truth (gt) and detected (dt) object is
+# iou(gt,dt) = area(intersect(gt,dt)) / area(union(gt,dt))
+# For "crowd" regions, we use a modified criteria. If a gt object is
+# marked as "iscrowd", we allow a dt to match any subregion of the gt.
+# Choosing gt' in the crowd gt that best matches the dt can be done using
+# gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing
+# iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt)
+# For crowd gt regions we use this modified criteria above for the iou.
+#
+# To compile run "python setup.py build_ext --inplace"
+# Please do not contact us for help with compiling.
+#
+# Microsoft COCO Toolbox. version 2.0
+# Data, paper, and tutorials available at: http://mscoco.org/
+# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
+# Licensed under the Simplified BSD License [see coco/license.txt]
+
+encode = _mask.encode
+decode = _mask.decode
+iou = _mask.iou
+merge = _mask.merge
+area = _mask.area
+toBbox = _mask.toBbox
+frPyObjects = _mask.frPyObjects
diff --git a/tools/refer/external/maskApi.c b/tools/refer/external/maskApi.c
new file mode 100644
index 0000000..85e3979
--- /dev/null
+++ b/tools/refer/external/maskApi.c
@@ -0,0 +1,230 @@
+/**************************************************************************
+* Microsoft COCO Toolbox. version 2.0
+* Data, paper, and tutorials available at: http://mscoco.org/
+* Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
+* Licensed under the Simplified BSD License [see coco/license.txt]
+**************************************************************************/
+#include "maskApi.h"
+#include
+#include
+
+uint umin( uint a, uint b ) { return (ab) ? a : b; }
+
+void rleInit( RLE *R, siz h, siz w, siz m, uint *cnts ) {
+ R->h=h; R->w=w; R->m=m; R->cnts=(m==0)?0:malloc(sizeof(uint)*m);
+ siz j; if(cnts) for(j=0; jcnts[j]=cnts[j];
+}
+
+void rleFree( RLE *R ) {
+ free(R->cnts); R->cnts=0;
+}
+
+void rlesInit( RLE **R, siz n ) {
+ siz i; *R = (RLE*) malloc(sizeof(RLE)*n);
+ for(i=0; i0 ) {
+ c=umin(ca,cb); cc+=c; ct=0;
+ ca-=c; if(!ca && a0) {
+ crowd=iscrowd!=NULL && iscrowd[g];
+ if(dt[d].h!=gt[g].h || dt[d].w!=gt[g].w) { o[g*m+d]=-1; continue; }
+ siz ka, kb, a, b; uint c, ca, cb, ct, i, u; int va, vb;
+ ca=dt[d].cnts[0]; ka=dt[d].m; va=vb=0;
+ cb=gt[g].cnts[0]; kb=gt[g].m; a=b=1; i=u=0; ct=1;
+ while( ct>0 ) {
+ c=umin(ca,cb); if(va||vb) { u+=c; if(va&&vb) i+=c; } ct=0;
+ ca-=c; if(!ca && athr) keep[j]=0;
+ }
+ }
+}
+
+void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o ) {
+ double h, w, i, u, ga, da; siz g, d; int crowd;
+ for( g=0; gthr) keep[j]=0;
+ }
+ }
+}
+
+void rleToBbox( const RLE *R, BB bb, siz n ) {
+ siz i; for( i=0; id?1:c=dy && xs>xe) || (dxye);
+ if(flip) { t=xs; xs=xe; xe=t; t=ys; ys=ye; ye=t; }
+ s = dx>=dy ? (double)(ye-ys)/dx : (double)(xe-xs)/dy;
+ if(dx>=dy) for( d=0; d<=dx; d++ ) {
+ t=flip?dx-d:d; u[m]=t+xs; v[m]=(int)(ys+s*t+.5); m++;
+ } else for( d=0; d<=dy; d++ ) {
+ t=flip?dy-d:d; v[m]=t+ys; u[m]=(int)(xs+s*t+.5); m++;
+ }
+ }
+ /* get points along y-boundary and downsample */
+ free(x); free(y); k=m; m=0; double xd, yd;
+ x=malloc(sizeof(int)*k); y=malloc(sizeof(int)*k);
+ for( j=1; jw-1 ) continue;
+ yd=(double)(v[j]h) yd=h; yd=ceil(yd);
+ x[m]=(int) xd; y[m]=(int) yd; m++;
+ }
+ /* compute rle encoding given y-boundary points */
+ k=m; a=malloc(sizeof(uint)*(k+1));
+ for( j=0; j0) b[m++]=a[j++]; else {
+ j++; if(jm, p=0; long x; int more;
+ char *s=malloc(sizeof(char)*m*6);
+ for( i=0; icnts[i]; if(i>2) x-=(long) R->cnts[i-2]; more=1;
+ while( more ) {
+ char c=x & 0x1f; x >>= 5; more=(c & 0x10) ? x!=-1 : x!=0;
+ if(more) c |= 0x20; c+=48; s[p++]=c;
+ }
+ }
+ s[p]=0; return s;
+}
+
+void rleFrString( RLE *R, char *s, siz h, siz w ) {
+ siz m=0, p=0, k; long x; int more; uint *cnts;
+ while( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0;
+ while( s[p] ) {
+ x=0; k=0; more=1;
+ while( more ) {
+ char c=s[p]-48; x |= (c & 0x1f) << 5*k;
+ more = c & 0x20; p++; k++;
+ if(!more && (c & 0x10)) x |= -1 << 5*k;
+ }
+ if(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;
+ }
+ rleInit(R,h,w,m,cnts); free(cnts);
+}
diff --git a/tools/refer/external/maskApi.h b/tools/refer/external/maskApi.h
new file mode 100644
index 0000000..ebc7892
--- /dev/null
+++ b/tools/refer/external/maskApi.h
@@ -0,0 +1,60 @@
+/**************************************************************************
+* Microsoft COCO Toolbox. version 2.0
+* Data, paper, and tutorials available at: http://mscoco.org/
+* Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
+* Licensed under the Simplified BSD License [see coco/license.txt]
+**************************************************************************/
+#pragma once
+
+typedef unsigned int uint;
+typedef unsigned long siz;
+typedef unsigned char byte;
+typedef double* BB;
+typedef struct { siz h, w, m; uint *cnts; } RLE;
+
+/* Initialize/destroy RLE. */
+void rleInit( RLE *R, siz h, siz w, siz m, uint *cnts );
+void rleFree( RLE *R );
+
+/* Initialize/destroy RLE array. */
+void rlesInit( RLE **R, siz n );
+void rlesFree( RLE **R, siz n );
+
+/* Encode binary masks using RLE. */
+void rleEncode( RLE *R, const byte *mask, siz h, siz w, siz n );
+
+/* Decode binary masks encoded via RLE. */
+void rleDecode( const RLE *R, byte *mask, siz n );
+
+/* Compute union or intersection of encoded masks. */
+void rleMerge( const RLE *R, RLE *M, siz n, int intersect );
+
+/* Compute area of encoded masks. */
+void rleArea( const RLE *R, siz n, uint *a );
+
+/* Compute intersection over union between masks. */
+void rleIou( RLE *dt, RLE *gt, siz m, siz n, byte *iscrowd, double *o );
+
+/* Compute non-maximum suppression between bounding masks */
+void rleNms( RLE *dt, siz n, uint *keep, double thr );
+
+/* Compute intersection over union between bounding boxes. */
+void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o );
+
+/* Compute non-maximum suppression between bounding boxes */
+void bbNms( BB dt, siz n, uint *keep, double thr );
+
+/* Get bounding boxes surrounding encoded masks. */
+void rleToBbox( const RLE *R, BB bb, siz n );
+
+/* Convert bounding boxes to encoded masks. */
+void rleFrBbox( RLE *R, const BB bb, siz h, siz w, siz n );
+
+/* Convert polygon to encoded mask. */
+void rleFrPoly( RLE *R, const double *xy, siz k, siz h, siz w );
+
+/* Get compressed string representation of encoded mask. */
+char* rleToString( const RLE *R );
+
+/* Convert from compressed string representation of encoded mask. */
+void rleFrString( RLE *R, char *s, siz h, siz w );
diff --git a/tools/refer/pyEvalDemo.ipynb b/tools/refer/pyEvalDemo.ipynb
new file mode 100644
index 0000000..5493637
--- /dev/null
+++ b/tools/refer/pyEvalDemo.ipynb
@@ -0,0 +1,308 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "from refer import REFER\n",
+ "import numpy as np\n",
+ "import sys\n",
+ "import os.path as osp\n",
+ "import json\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.patches import Rectangle"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "loading dataset refcoco into memory...\n",
+ "creating index...\n",
+ "index created.\n",
+ "DONE (t=9.47s)\n"
+ ]
+ }
+ ],
+ "source": [
+ "data_root = './data' # contains refclef, refcoco, refcoco+, refcocog and images\n",
+ "dataset = 'refcoco'\n",
+ "splitBy = 'unc'\n",
+ "refer = REFER(data_root, dataset, splitBy)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 1. Evaluate Refering Expressions by Language Metrics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "sys.path.insert(0, './evaluation')\n",
+ "from refEvaluation import RefEvaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{u'sent': u'man in black', u'ref_id': 47}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Here's our example expression file\n",
+ "sample_expr_file = json.load(open('test/sample_expressions_testA.json', 'r'))\n",
+ "sample_exprs = sample_expr_file['predictions']\n",
+ "print sample_exprs[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tokenization...\n",
+ "setting up scorers...\n",
+ "computing Bleu score...\n",
+ "{'reflen': 5356, 'guess': [5009, 3034, 1477, 275], 'testlen': 5009, 'correct': [2576, 580, 112, 2]}\n",
+ "ratio: 0.935212845407\n",
+ "Bleu_1: 0.480\n",
+ "Bleu_2: 0.293\n",
+ "Bleu_3: 0.182\n",
+ "Bleu_4: 0.080\n",
+ "computing METEOR score...\n",
+ "METEOR: 0.172\n",
+ "computing Rouge score...\n",
+ "ROUGE_L: 0.414\n",
+ "computing CIDEr score...\n",
+ "CIDEr: 0.669\n"
+ ]
+ }
+ ],
+ "source": [
+ "refEval = RefEvaluation(refer, sample_exprs)\n",
+ "refEval.evaluate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Evaluate Referring Expressions by Duplicate Rate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "108/750 (14.40%) images have duplicate predicted sentences.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# evalue how many images contain duplicate expressions\n",
+ "pred_refToSent = {int(it['ref_id']): it['sent'] for it in sample_exprs}\n",
+ "pred_imgToSents = {}\n",
+ "for ref_id, pred_sent in pred_refToSent.items():\n",
+ " image_id = refer.Refs[ref_id]['image_id']\n",
+ " pred_imgToSents[image_id] = pred_imgToSents.get(image_id, []) + [pred_sent]\n",
+ "# count duplicate\n",
+ "duplicate = 0\n",
+ "for image_id, sents in pred_imgToSents.items():\n",
+ " if len(set(sents)) < len(sents):\n",
+ " duplicate += 1\n",
+ "ratio = duplicate*100.0 / len(pred_imgToSents)\n",
+ "print '%s/%s (%.2f%%) images have duplicate predicted sentences.' % (duplicate, len(pred_imgToSents), ratio)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "# 3.Evaluate Referring Comprehension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# IoU function\n",
+ "def computeIoU(box1, box2):\n",
+ " # each box is of [x1, y1, w, h]\n",
+ " inter_x1 = max(box1[0], box2[0])\n",
+ " inter_y1 = max(box1[1], box2[1])\n",
+ " inter_x2 = min(box1[0]+box1[2]-1, box2[0]+box2[2]-1)\n",
+ " inter_y2 = min(box1[1]+box1[3]-1, box2[1]+box2[3]-1)\n",
+ "\n",
+ " if inter_x1 < inter_x2 and inter_y1 < inter_y2:\n",
+ " inter = (inter_x2-inter_x1+1)*(inter_y2-inter_y1+1)\n",
+ " else:\n",
+ " inter = 0\n",
+ " union = box1[2]*box1[3] + box2[2]*box2[3] - inter\n",
+ " return float(inter)/union"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# randomly sample one ref\n",
+ "ref_ids = refer.getRefIds()\n",
+ "ref_id = ref_ids[np.random.randint(0, len(ref_ids))]\n",
+ "ref = refer.Refs[ref_id]\n",
+ "\n",
+ "# let's fake one bounding box by randomly picking one instance inside this image\n",
+ "image_id = ref['image_id']\n",
+ "anns = refer.imgToAnns[image_id]\n",
+ "ann = anns[np.random.randint(0, len(anns))]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1. person bending\n",
+ "2. man\n",
+ "3. the person bending over\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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dlH6UYKdwbgdWXsoCnznvOC6YGdYvT3EwPoALYzT8JrwLZDHn4cN/RFRDrUZf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\nEeknS58+j0g/Wfr0eUT+H9bjcOnSi+aPAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# draw box of the ref using 'green'\n",
+ "plt.figure()\n",
+ "refer.showRef(ref, seg_box='box')\n",
+ "# draw box of the ann using 'red'\n",
+ "ax = plt.gca()\n",
+ "bbox = ann['bbox']\n",
+ "box_plot = Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3], fill=False, edgecolor='red', linewidth=2)\n",
+ "ax.add_patch(box_plot)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "IoU=[0.09], wrong comprehension!\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Is the ann actually our ref?\n",
+ "# i.e., IoU >= 0.5?\n",
+ "ref_box = refer.refToAnn[ref_id]['bbox']\n",
+ "ann_box = ann['bbox']\n",
+ "IoU = computeIoU(ref_box, ann_box)\n",
+ "if IoU >= 0.5:\n",
+ " print 'IoU=[%.2f], correct comprehension!' % IoU\n",
+ "else:\n",
+ " print 'IoU=[%.2f], wrong comprehension!' % IoU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/tools/refer/pyReferDemo.ipynb b/tools/refer/pyReferDemo.ipynb
new file mode 100644
index 0000000..5e0acca
--- /dev/null
+++ b/tools/refer/pyReferDemo.ipynb
@@ -0,0 +1,229 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "from refer import REFER\n",
+ "import numpy as np\n",
+ "import skimage.io as io\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Load Refer Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "loading dataset refcoco into memory...\n",
+ "creating index...\n",
+ "index created.\n",
+ "DONE (t=9.88s)\n"
+ ]
+ }
+ ],
+ "source": [
+ "data_root = './data' # contains refclef, refcoco, refcoco+, refcocog and images\n",
+ "dataset = 'refcoco'\n",
+ "splitBy = 'unc'\n",
+ "refer = REFER(data_root, dataset, splitBy)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Stats about the Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dataset [refcoco_unc] contains: \n",
+ "142210 expressions for 50000 refs in 19994 images.\n",
+ "\n",
+ "Among them:\n",
+ "42404 refs are in split [train].\n",
+ "3811 refs are in split [val].\n",
+ "3785 refs are in split [test].\n"
+ ]
+ }
+ ],
+ "source": [
+ "# print stats about the given dataset\n",
+ "print 'dataset [%s_%s] contains: ' % (dataset, splitBy)\n",
+ "ref_ids = refer.getRefIds()\n",
+ "image_ids = refer.getImgIds()\n",
+ "print '%s expressions for %s refs in %s images.' % (len(refer.Sents), len(ref_ids), len(image_ids))\n",
+ "\n",
+ "print '\\nAmong them:'\n",
+ "if dataset == 'refclef':\n",
+ " if splitBy == 'unc':\n",
+ " splits = ['train', 'val', 'testA', 'testB', 'testC']\n",
+ " else:\n",
+ " splits = ['train', 'val', 'test']\n",
+ "elif dataset == 'refcoco':\n",
+ " splits = ['train', 'val', 'test']\n",
+ "elif dataset == 'refcoco+':\n",
+ " splits = ['train', 'val', 'test']\n",
+ "elif dataset == 'refcocog':\n",
+ " splits = ['train', 'val'] # we don't have test split for refcocog right now.\n",
+ " \n",
+ "for split in splits:\n",
+ " ref_ids = refer.getRefIds(split=split)\n",
+ " print '%s refs are in split [%s].' % (len(ref_ids), split)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Show Refered Object and its Expressions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ref_id [22758] (ann_id [540661])\n",
+ "1. woman in front\n",
+ "2. lady smiling\n",
+ "3. woman\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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h3+fadMrb3/koV6/fJLuxTUvVvO+RcwgZsn5si6qqqKuKXm/AbLoL1mCKCiUg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esn6enpxQlbCNEhFDtArGpsuv71FC6+GRSbLkXH1CwKo6PM+T69bVjnC/pDAyVQqLRjQqkSxwlR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\nI4MMFirkY4uyLDNamOD0LZNUcy7XXHw/vBX4GNx99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D5jyfLYocOorKevE1auVypMNUaD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\n2WXanrPs2Z5dpu05y57t2WXa/wVjO2xV5Ti03wAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# randomly sample one ref\n",
+ "ref_ids = refer.getRefIds()\n",
+ "ref_id = ref_ids[np.random.randint(0, len(ref_ids))]\n",
+ "ref = refer.Refs[ref_id]\n",
+ "print 'ref_id [%s] (ann_id [%s])' % (ref_id, refer.refToAnn[ref_id]['id'])\n",
+ "# show the segmentation of the referred object\n",
+ "plt.figure()\n",
+ "refer.showRef(ref, seg_box='seg')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1. woman in front\n",
+ "2. lady smiling\n",
+ "3. woman\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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J0okoJxNiDJRH3JrNeO/7nuXm7bsUd/boKMOH3nEZIWO2zuzSNA2maRgMRsxn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\nKSVlWVLXNU3bIoyhe0ZbaPARowzW2sUxLux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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# or show the bounding box of the referred object\n",
+ "refer.showRef(ref, seg_box='box')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "sent_id[64727]: woman in front\n",
+ "sent_id[64728]: lady smiling\n",
+ "sent_id[64729]: woman\n"
+ ]
+ }
+ ],
+ "source": [
+ "# let's look at the details of each ref\n",
+ "for sent in ref['sentences']:\n",
+ " print 'sent_id[%s]: %s' % (sent['sent_id'], sent['sent'])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/tools/refer/refer.py b/tools/refer/refer.py
new file mode 100644
index 0000000..75cb316
--- /dev/null
+++ b/tools/refer/refer.py
@@ -0,0 +1,359 @@
+__author__ = 'licheng'
+
+"""
+This interface provides access to four datasets:
+1) refclef
+2) refcoco
+3) refcoco+
+4) refcocog
+split by unc and google
+
+The following API functions are defined:
+REFER - REFER api class
+getRefIds - get ref ids that satisfy given filter conditions.
+getAnnIds - get ann ids that satisfy given filter conditions.
+getImgIds - get image ids that satisfy given filter conditions.
+getCatIds - get category ids that satisfy given filter conditions.
+loadRefs - load refs with the specified ref ids.
+loadAnns - load anns with the specified ann ids.
+loadImgs - load images with the specified image ids.
+loadCats - load category names with the specified category ids.
+getRefBox - get ref's bounding box [x, y, w, h] given the ref_id
+showRef - show image, segmentation or box of the referred object with the ref
+getMask - get mask and area of the referred object given ref
+showMask - show mask of the referred object given ref
+"""
+
+import sys
+import os.path as osp
+import json
+# import cPickle as pickle
+import _pickle as pickle
+
+import time
+import itertools
+# import skimage.io as io
+import matplotlib.pyplot as plt
+from matplotlib.collections import PatchCollection
+from matplotlib.patches import Polygon, Rectangle
+from pprint import pprint
+import numpy as np
+from .external import mask
+# import cv2
+# from skimage.measure import label, regionprops
+
+class REFER:
+
+ def __init__(self, data_root, dataset='refcoco', splitBy='unc'):
+ # provide data_root folder which contains refclef, refcoco, refcoco+ and refcocog
+ # also provide dataset name and splitBy information
+ # e.g., dataset = 'refcoco', splitBy = 'unc'
+ print('loading dataset %s into memory...' % dataset)
+ self.ROOT_DIR = osp.abspath(osp.dirname(__file__))
+ self.DATA_DIR = osp.join(data_root, dataset)
+ if dataset in ['refcoco', 'refcoco+', 'refcocog']:
+ self.IMAGE_DIR = osp.join(data_root, 'images/mscoco/images/train2014')
+ elif dataset == 'refclef':
+ self.IMAGE_DIR = osp.join(data_root, 'images/saiapr_tc-12')
+ else:
+ print('No refer dataset is called [%s]' % dataset)
+ sys.exit()
+
+ # load refs from data/dataset/refs(dataset).json
+ tic = time.time()
+ ref_file = osp.join(self.DATA_DIR, 'refs('+splitBy+').p')
+ self.data = {}
+ self.data['dataset'] = dataset
+ self.data['refs'] = pickle.load(open(ref_file, 'rb'))
+
+ # load annotations from data/dataset/instances.json
+ instances_file = osp.join(self.DATA_DIR, 'instances.json')
+ instances = json.load(open(instances_file, 'r'))
+ self.data['images'] = instances['images']
+ self.data['annotations'] = instances['annotations']
+ self.data['categories'] = instances['categories']
+
+ # create index
+ self.createIndex()
+ print('DONE (t=%.2fs)' % (time.time()-tic))
+
+ def createIndex(self):
+ # create sets of mapping
+ # 1) Refs: {ref_id: ref}
+ # 2) Anns: {ann_id: ann}
+ # 3) Imgs: {image_id: image}
+ # 4) Cats: {category_id: category_name}
+ # 5) Sents: {sent_id: sent}
+ # 6) imgToRefs: {image_id: refs}
+ # 7) imgToAnns: {image_id: anns}
+ # 8) refToAnn: {ref_id: ann}
+ # 9) annToRef: {ann_id: ref}
+ # 10) catToRefs: {category_id: refs}
+ # 11) sentToRef: {sent_id: ref}
+ # 12) sentToTokens: {sent_id: tokens}
+ print('creating index...')
+ # fetch info from instances
+ Anns, Imgs, Cats, imgToAnns = {}, {}, {}, {}
+ for ann in self.data['annotations']:
+ Anns[ann['id']] = ann
+ imgToAnns[ann['image_id']] = imgToAnns.get(ann['image_id'], []) + [ann]
+ for img in self.data['images']:
+ Imgs[img['id']] = img
+ for cat in self.data['categories']:
+ Cats[cat['id']] = cat['name']
+
+ # fetch info from refs
+ Refs, imgToRefs, refToAnn, annToRef, catToRefs = {}, {}, {}, {}, {}
+ Sents, sentToRef, sentToTokens = {}, {}, {}
+ for ref in self.data['refs']:
+ # ids
+ ref_id = ref['ref_id']
+ ann_id = ref['ann_id']
+ category_id = ref['category_id']
+ image_id = ref['image_id']
+
+ # add mapping related to ref
+ Refs[ref_id] = ref
+ imgToRefs[image_id] = imgToRefs.get(image_id, []) + [ref]
+ catToRefs[category_id] = catToRefs.get(category_id, []) + [ref]
+ refToAnn[ref_id] = Anns[ann_id]
+ annToRef[ann_id] = ref
+
+ # add mapping of sent
+ for sent in ref['sentences']:
+ Sents[sent['sent_id']] = sent
+ sentToRef[sent['sent_id']] = ref
+ sentToTokens[sent['sent_id']] = sent['tokens']
+
+ # create class members
+ self.Refs = Refs
+ self.Anns = Anns
+ self.Imgs = Imgs
+ self.Cats = Cats
+ self.Sents = Sents
+ self.imgToRefs = imgToRefs
+ self.imgToAnns = imgToAnns
+ self.refToAnn = refToAnn
+ self.annToRef = annToRef
+ self.catToRefs = catToRefs
+ self.sentToRef = sentToRef
+ self.sentToTokens = sentToTokens
+ print('index created.')
+
+ def getRefIds(self, image_ids=[], cat_ids=[], ref_ids=[], split=''):
+ image_ids = image_ids if type(image_ids) == list else [image_ids]
+ cat_ids = cat_ids if type(cat_ids) == list else [cat_ids]
+ ref_ids = ref_ids if type(ref_ids) == list else [ref_ids]
+
+ if len(image_ids)==len(cat_ids)==len(ref_ids)==len(split)==0:
+ refs = self.data['refs']
+ else:
+ if not len(image_ids) == 0:
+ refs = [self.imgToRefs[image_id] for image_id in image_ids]
+ else:
+ refs = self.data['refs']
+ if not len(cat_ids) == 0:
+ refs = [ref for ref in refs if ref['category_id'] in cat_ids]
+ if not len(ref_ids) == 0:
+ refs = [ref for ref in refs if ref['ref_id'] in ref_ids]
+ if not len(split) == 0:
+ if split in ['testA', 'testB', 'testC']:
+ refs = [ref for ref in refs if split[-1] in ref['split']] # we also consider testAB, testBC, ...
+ elif split in ['testAB', 'testBC', 'testAC']:
+ refs = [ref for ref in refs if ref['split'] == split] # rarely used I guess...
+ elif split == 'test':
+ refs = [ref for ref in refs if 'test' in ref['split']]
+ elif split == 'train' or split == 'val':
+ refs = [ref for ref in refs if ref['split'] == split]
+ else:
+ print('No such split [%s]' % split)
+ sys.exit()
+ ref_ids = [ref['ref_id'] for ref in refs]
+ return ref_ids
+
+ def getAnnIds(self, image_ids=[], cat_ids=[], ref_ids=[]):
+ image_ids = image_ids if type(image_ids) == list else [image_ids]
+ cat_ids = cat_ids if type(cat_ids) == list else [cat_ids]
+ ref_ids = ref_ids if type(ref_ids) == list else [ref_ids]
+
+ if len(image_ids) == len(cat_ids) == len(ref_ids) == 0:
+ ann_ids = [ann['id'] for ann in self.data['annotations']]
+ else:
+ if not len(image_ids) == 0:
+ lists = [self.imgToAnns[image_id] for image_id in image_ids if image_id in self.imgToAnns] # list of [anns]
+ anns = list(itertools.chain.from_iterable(lists))
+ else:
+ anns = self.data['annotations']
+ if not len(cat_ids) == 0:
+ anns = [ann for ann in anns if ann['category_id'] in cat_ids]
+ ann_ids = [ann['id'] for ann in anns]
+ if not len(ref_ids) == 0:
+ ids = set(ann_ids).intersection(set([self.Refs[ref_id]['ann_id'] for ref_id in ref_ids]))
+ return ann_ids
+
+ def getImgIds(self, ref_ids=[]):
+ ref_ids = ref_ids if type(ref_ids) == list else [ref_ids]
+
+ if not len(ref_ids) == 0:
+ image_ids = list(set([self.Refs[ref_id]['image_id'] for ref_id in ref_ids]))
+ else:
+ image_ids = self.Imgs.keys()
+ return image_ids
+
+ def getCatIds(self):
+ return self.Cats.keys()
+
+ def loadRefs(self, ref_ids=[]):
+ if type(ref_ids) == list:
+ return [self.Refs[ref_id] for ref_id in ref_ids]
+ elif type(ref_ids) == int:
+ return [self.Refs[ref_ids]]
+
+ def loadAnns(self, ann_ids=[]):
+ if type(ann_ids) == list:
+ return [self.Anns[ann_id] for ann_id in ann_ids]
+ elif type(ann_ids) == int or type(ann_ids) == unicode:
+ return [self.Anns[ann_ids]]
+
+ def loadImgs(self, image_ids=[]):
+ if type(image_ids) == list:
+ return [self.Imgs[image_id] for image_id in image_ids]
+ elif type(image_ids) == int:
+ return [self.Imgs[image_ids]]
+
+ def loadCats(self, cat_ids=[]):
+ if type(cat_ids) == list:
+ return [self.Cats[cat_id] for cat_id in cat_ids]
+ elif type(cat_ids) == int:
+ return [self.Cats[cat_ids]]
+
+ def getRefBox(self, ref_id):
+ ref = self.Refs[ref_id]
+ ann = self.refToAnn[ref_id]
+ return ann['bbox'] # [x, y, w, h]
+
+ def showRef(self, ref, seg_box='seg'):
+ ax = plt.gca()
+ # show image
+ image = self.Imgs[ref['image_id']]
+ I = io.imread(osp.join(self.IMAGE_DIR, image['file_name']))
+ ax.imshow(I)
+ # show refer expression
+ for sid, sent in enumerate(ref['sentences']):
+ print('%s. %s' % (sid+1, sent['sent']))
+ # show segmentations
+ if seg_box == 'seg':
+ ann_id = ref['ann_id']
+ ann = self.Anns[ann_id]
+ polygons = []
+ color = []
+ c = 'none'
+ if type(ann['segmentation'][0]) == list:
+ # polygon used for refcoco*
+ for seg in ann['segmentation']:
+ poly = np.array(seg).reshape((len(seg)/2, 2))
+ polygons.append(Polygon(poly, True, alpha=0.4))
+ color.append(c)
+ p = PatchCollection(polygons, facecolors=color, edgecolors=(1,1,0,0), linewidths=3, alpha=1)
+ ax.add_collection(p) # thick yellow polygon
+ p = PatchCollection(polygons, facecolors=color, edgecolors=(1,0,0,0), linewidths=1, alpha=1)
+ ax.add_collection(p) # thin red polygon
+ else:
+ # mask used for refclef
+ rle = ann['segmentation']
+ m = mask.decode(rle)
+ img = np.ones( (m.shape[0], m.shape[1], 3) )
+ color_mask = np.array([2.0,166.0,101.0])/255
+ for i in range(3):
+ img[:,:,i] = color_mask[i]
+ ax.imshow(np.dstack( (img, m*0.5) ))
+ # show bounding-box
+ elif seg_box == 'box':
+ ann_id = ref['ann_id']
+ ann = self.Anns[ann_id]
+ bbox = self.getRefBox(ref['ref_id'])
+ box_plot = Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3], fill=False, edgecolor='green', linewidth=3)
+ ax.add_patch(box_plot)
+
+ def getMask(self, ref):
+ # return mask, area and mask-center
+ ann = self.refToAnn[ref['ref_id']]
+ image = self.Imgs[ref['image_id']]
+ if type(ann['segmentation'][0]) == list: # polygon
+ rle = mask.frPyObjects(ann['segmentation'], image['height'], image['width'])
+ else:
+ rle = ann['segmentation']
+ m = mask.decode(rle)
+ m = np.sum(m, axis=2) # sometimes there are multiple binary map (corresponding to multiple segs)
+ m = m.astype(np.uint8) # convert to np.uint8
+ # compute area
+ area = sum(mask.area(rle)) # should be close to ann['area']
+ return {'mask': m, 'area': area}
+ # # position
+ # position_x = np.mean(np.where(m==1)[1]) # [1] means columns (matlab style) -> x (c style)
+ # position_y = np.mean(np.where(m==1)[0]) # [0] means rows (matlab style) -> y (c style)
+ # # mass position (if there were multiple regions, we use the largest one.)
+ # label_m = label(m, connectivity=m.ndim)
+ # regions = regionprops(label_m)
+ # if len(regions) > 0:
+ # largest_id = np.argmax(np.array([props.filled_area for props in regions]))
+ # largest_props = regions[largest_id]
+ # mass_y, mass_x = largest_props.centroid
+ # else:
+ # mass_x, mass_y = position_x, position_y
+ # # if centroid is not in mask, we find the closest point to it from mask
+ # if m[mass_y, mass_x] != 1:
+ # print 'Finding closes mask point ...'
+ # kernel = np.ones((10, 10),np.uint8)
+ # me = cv2.erode(m, kernel, iterations = 1)
+ # points = zip(np.where(me == 1)[0].tolist(), np.where(me == 1)[1].tolist()) # row, col style
+ # points = np.array(points)
+ # dist = np.sum((points - (mass_y, mass_x))**2, axis=1)
+ # id = np.argsort(dist)[0]
+ # mass_y, mass_x = points[id]
+ # # return
+ # return {'mask': m, 'area': area, 'position_x': position_x, 'position_y': position_y, 'mass_x': mass_x, 'mass_y': mass_y}
+ # # show image and mask
+ # I = io.imread(osp.join(self.IMAGE_DIR, image['file_name']))
+ # plt.figure()
+ # plt.imshow(I)
+ # ax = plt.gca()
+ # img = np.ones( (m.shape[0], m.shape[1], 3) )
+ # color_mask = np.array([2.0,166.0,101.0])/255
+ # for i in range(3):
+ # img[:,:,i] = color_mask[i]
+ # ax.imshow(np.dstack( (img, m*0.5) ))
+ # plt.show()
+
+ def showMask(self, ref):
+ M = self.getMask(ref)
+ msk = M['mask']
+ ax = plt.gca()
+ ax.imshow(msk)
+
+
+if __name__ == '__main__':
+ refer = REFER(dataset='refcocog', splitBy='google')
+ ref_ids = refer.getRefIds()
+ print(len(ref_ids))
+
+ print(len(refer.Imgs))
+ print(len(refer.imgToRefs))
+
+ ref_ids = refer.getRefIds(split='train')
+ print('There are %s training referred objects.' % len(ref_ids))
+
+ for ref_id in ref_ids:
+ ref = refer.loadRefs(ref_id)[0]
+ if len(ref['sentences']) < 2:
+ continue
+
+ pprint(ref)
+ print('The label is %s.' % refer.Cats[ref['category_id']])
+ plt.figure()
+ refer.showRef(ref, seg_box='box')
+ plt.show()
+
+ # plt.figure()
+ # refer.showMask(ref)
+ # plt.show()
diff --git a/tools/refer/setup.py b/tools/refer/setup.py
new file mode 100644
index 0000000..e055900
--- /dev/null
+++ b/tools/refer/setup.py
@@ -0,0 +1,25 @@
+"""
+This code is for making mask.so, used to visualize the segmentation of referred object.
+All "mask" related code is copied from https://github.com/pdollar/coco.git
+"""
+from distutils.core import setup
+from Cython.Build import cythonize
+from distutils.extension import Extension
+import numpy as np
+
+ext_modules = [
+ Extension(
+ 'external._mask',
+ sources=['external/maskApi.c', 'external/_mask.pyx'],
+ include_dirs = [np.get_include(), 'external'],
+ extra_compile_args=['-Wno-cpp', '-Wno-unused-function', '-std=c99'],
+ )
+ ]
+
+setup(
+ name='external',
+ packages=['external'],
+ package_dir = {'external': 'external'},
+ version='2.0',
+ ext_modules=cythonize(ext_modules)
+ )
diff --git a/tools/refer/test/sample_expressions_testA.json b/tools/refer/test/sample_expressions_testA.json
new file mode 100644
index 0000000..275e3a3
--- /dev/null
+++ b/tools/refer/test/sample_expressions_testA.json
@@ -0,0 +1 @@
+{"predictions":[{"sent":"man in black","ref_id":47},{"sent":"person on right","ref_id":109},{"sent":"woman in red","ref_id":110},{"sent":"car behind bike","ref_id":111},{"sent":"car on left","ref_id":112},{"sent":"man in blue","ref_id":382},{"sent":"man in white","ref_id":383},{"sent":"left person","ref_id":519},{"sent":"man on right","ref_id":520},{"sent":"person in background","ref_id":525},{"sent":"person on left","ref_id":526},{"sent":"man in white","ref_id":527},{"sent":"guy in white","ref_id":528},{"sent":"guy in red","ref_id":537},{"sent":"white shirt","ref_id":538},{"sent":"player in white","ref_id":539},{"sent":"red shirt","ref_id":557},{"sent":"girl","ref_id":558},{"sent":"baby","ref_id":588},{"sent":"baby","ref_id":589},{"sent":"woman in front","ref_id":640},{"sent":"girl","ref_id":641},{"sent":"right guy","ref_id":732},{"sent":"man in white","ref_id":733},{"sent":"middle guy","ref_id":734},{"sent":"woman","ref_id":756},{"sent":"man on right","ref_id":757},{"sent":"woman","ref_id":814},{"sent":"man in white","ref_id":815},{"sent":"man in white shirt","ref_id":828},{"sent":"woman on right","ref_id":829},{"sent":"man in red","ref_id":931},{"sent":"woman in pink","ref_id":932},{"sent":"girl in pink","ref_id":933},{"sent":"middle guy","ref_id":945},{"sent":"second from right","ref_id":946},{"sent":"left guy","ref_id":947},{"sent":"white jacket","ref_id":954},{"sent":"right guy","ref_id":955},{"sent":"blue jacket","ref_id":956},{"sent":"man in white shirt","ref_id":1023},{"sent":"man","ref_id":1024},{"sent":"man in back","ref_id":1052},{"sent":"left guy","ref_id":1053},{"sent":"woman on right","ref_id":1152},{"sent":"woman on right","ref_id":1153},{"sent":"left guy","ref_id":1154},{"sent":"woman on right","ref_id":1333},{"sent":"man in black shirt","ref_id":1334},{"sent":"man","ref_id":1362},{"sent":"man","ref_id":1363},{"sent":"right guy","ref_id":1371},{"sent":"left guy","ref_id":1372},{"sent":"man in front","ref_id":1406},{"sent":"man on left","ref_id":1407},{"sent":"person on right","ref_id":1568},{"sent":"person in front","ref_id":1569},{"sent":"man in black","ref_id":1582},{"sent":"man in front","ref_id":1583},{"sent":"right skier","ref_id":1623},{"sent":"person in front","ref_id":1624},{"sent":"second from left","ref_id":1679},{"sent":"man on left","ref_id":1680},{"sent":"second from right","ref_id":1681},{"sent":"left guy","ref_id":1682},{"sent":"woman on right","ref_id":1683},{"sent":"girl on right","ref_id":1684},{"sent":"man on right","ref_id":1811},{"sent":"man in front of man in white shirt","ref_id":1812},{"sent":"woman in white shirt","ref_id":1861},{"sent":"man in black","ref_id":1862},{"sent":"groom","ref_id":1882},{"sent":"bride","ref_id":1883},{"sent":"middle guy","ref_id":1977},{"sent":"left guy","ref_id":1978},{"sent":"right guy","ref_id":1979},{"sent":"second from left","ref_id":1980},{"sent":"person on left","ref_id":1990},{"sent":"left person","ref_id":1991},{"sent":"player","ref_id":2001},{"sent":"top left corner","ref_id":2002},{"sent":"girl in white on left","ref_id":2129},{"sent":"white shirt","ref_id":2130},{"sent":"woman in white","ref_id":2131},{"sent":"red jacket","ref_id":2173},{"sent":"red","ref_id":2174},{"sent":"catcher","ref_id":2256},{"sent":"umpire","ref_id":2257},{"sent":"baby","ref_id":2264},{"sent":"man","ref_id":2265},{"sent":"boy in blue","ref_id":2291},{"sent":"boy in red","ref_id":2292},{"sent":"man in black","ref_id":2375},{"sent":"man in black","ref_id":2376},{"sent":"blue jacket","ref_id":2721},{"sent":"bottom left","ref_id":2722},{"sent":"man","ref_id":2767},{"sent":"man","ref_id":2768},{"sent":"batter","ref_id":2805},{"sent":"right guy","ref_id":2806},{"sent":"batter","ref_id":2807},{"sent":"woman in black","ref_id":2981},{"sent":"woman in white","ref_id":2982},{"sent":"left girl","ref_id":3247},{"sent":"man in white","ref_id":3248},{"sent":"man on left","ref_id":3257},{"sent":"woman in middle","ref_id":3258},{"sent":"woman on right","ref_id":3259},{"sent":"man in middle","ref_id":3260},{"sent":"guy on right","ref_id":3366},{"sent":"left person","ref_id":3367},{"sent":"girl in pink","ref_id":3768},{"sent":"girl in pink","ref_id":3769},{"sent":"right guy","ref_id":3772},{"sent":"man","ref_id":3773},{"sent":"man in blue shirt","ref_id":3805},{"sent":"person in blue shirt","ref_id":3806},{"sent":"man in black","ref_id":3807},{"sent":"guy in red","ref_id":4002},{"sent":"second horse from left","ref_id":4003},{"sent":"guy in blue shirt","ref_id":4014},{"sent":"man in blue shirt","ref_id":4015},{"sent":"left person","ref_id":4016},{"sent":"man in blue","ref_id":4017},{"sent":"girl on right","ref_id":4089},{"sent":"girl","ref_id":4090},{"sent":"woman","ref_id":4101},{"sent":"girl","ref_id":4102},{"sent":"woman in black","ref_id":4143},{"sent":"person sitting on left","ref_id":4144},{"sent":"man in black","ref_id":4145},{"sent":"white shirt","ref_id":4159},{"sent":"man on right","ref_id":4160},{"sent":"right girl","ref_id":4174},{"sent":"left girl","ref_id":4175},{"sent":"person on right","ref_id":4176},{"sent":"girl on left","ref_id":4177},{"sent":"woman in red","ref_id":4178},{"sent":"bride","ref_id":4187},{"sent":"right kid","ref_id":4188},{"sent":"person on left","ref_id":4190},{"sent":"woman in black","ref_id":4191},{"sent":"woman in white","ref_id":4192},{"sent":"left person","ref_id":4308},{"sent":"person on left","ref_id":4309},{"sent":"man on left","ref_id":4333},{"sent":"man in blue shirt","ref_id":4334},{"sent":"batter","ref_id":4345},{"sent":"catcher","ref_id":4346},{"sent":"umpire","ref_id":4347},{"sent":"man on right","ref_id":4396},{"sent":"right person","ref_id":4397},{"sent":"woman in blue","ref_id":4461},{"sent":"man in black","ref_id":4462},{"sent":"man on left","ref_id":4463},{"sent":"woman on right","ref_id":4485},{"sent":"woman","ref_id":4486},{"sent":"right horse","ref_id":4487},{"sent":"right horse","ref_id":4488},{"sent":"left horse","ref_id":4489},{"sent":"woman","ref_id":4578},{"sent":"woman","ref_id":4579},{"sent":"left bottom corner","ref_id":4580},{"sent":"woman in blue","ref_id":4581},{"sent":"head bottom right","ref_id":4582},{"sent":"woman","ref_id":4616},{"sent":"man","ref_id":4617},{"sent":"woman in black","ref_id":4711},{"sent":"left horse","ref_id":4712},{"sent":"umpire","ref_id":4765},{"sent":"catcher","ref_id":4766},{"sent":"man on right","ref_id":4868},{"sent":"man on right","ref_id":4869},{"sent":"man","ref_id":4870},{"sent":"white shirt","ref_id":5012},{"sent":"right guy","ref_id":5118},{"sent":"man in white","ref_id":5119},{"sent":"woman","ref_id":5149},{"sent":"man","ref_id":5150},{"sent":"right dog","ref_id":5170},{"sent":"left dog","ref_id":5171},{"sent":"woman","ref_id":5244},{"sent":"woman","ref_id":5245},{"sent":"woman in black","ref_id":5289},{"sent":"woman in black","ref_id":5290},{"sent":"man on right","ref_id":5291},{"sent":"person on left","ref_id":5292},{"sent":"woman in black","ref_id":5293},{"sent":"person on right","ref_id":5309},{"sent":"woman in red","ref_id":5310},{"sent":"girl","ref_id":5311},{"sent":"person on right","ref_id":5389},{"sent":"man","ref_id":5390},{"sent":"man","ref_id":5550},{"sent":"woman on right","ref_id":5551},{"sent":"man on left","ref_id":5552},{"sent":"man in white","ref_id":5615},{"sent":"woman in red","ref_id":5648},{"sent":"woman in blue","ref_id":5649},{"sent":"woman in black","ref_id":5650},{"sent":"catcher","ref_id":5767},{"sent":"batter","ref_id":5768},{"sent":"umpire","ref_id":5769},{"sent":"couch on left","ref_id":5776},{"sent":"couch on right","ref_id":5777},{"sent":"guy in red","ref_id":5782},{"sent":"red shirt","ref_id":5783},{"sent":"batter","ref_id":5784},{"sent":"girl on left","ref_id":5811},{"sent":"woman","ref_id":5812},{"sent":"bowl of white stuff on left","ref_id":5923},{"sent":"bowl of rice in the back","ref_id":5924},{"sent":"top right glass","ref_id":5925},{"sent":"top left corner","ref_id":5926},{"sent":"skier in middle","ref_id":6042},{"sent":"left skier","ref_id":6043},{"sent":"guy in white shirt","ref_id":6073},{"sent":"man","ref_id":6074},{"sent":"man in black","ref_id":6081},{"sent":"guy in white","ref_id":6082},{"sent":"umpire","ref_id":6118},{"sent":"batter","ref_id":6119},{"sent":"right guy","ref_id":6210},{"sent":"guy on right","ref_id":6211},{"sent":"kid","ref_id":6237},{"sent":"kid","ref_id":6238},{"sent":"baby","ref_id":6239},{"sent":"woman","ref_id":6257},{"sent":"person on right","ref_id":6461},{"sent":"woman","ref_id":6462},{"sent":"hand on left","ref_id":6607},{"sent":"left kid","ref_id":6698},{"sent":"player in red","ref_id":6699},{"sent":"player","ref_id":6799},{"sent":"guy in white","ref_id":6800},{"sent":"right person","ref_id":6809},{"sent":"guy in red","ref_id":6810},{"sent":"catcher","ref_id":6811},{"sent":"catcher","ref_id":6812},{"sent":"guy in white shirt","ref_id":6956},{"sent":"girl in blue","ref_id":6957},{"sent":"red 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\ No newline at end of file
diff --git a/tools/refer/test/sample_expressions_testB.json b/tools/refer/test/sample_expressions_testB.json
new file mode 100644
index 0000000..eab973c
--- /dev/null
+++ b/tools/refer/test/sample_expressions_testB.json
@@ -0,0 +1 @@
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front","ref_id":40501},{"sent":"bike on left","ref_id":40502},{"sent":"top bowl","ref_id":40554},{"sent":"right sandwich","ref_id":40555},{"sent":"cow in front","ref_id":40571},{"sent":"cow on right","ref_id":40572},{"sent":"left meter","ref_id":40753},{"sent":"left car","ref_id":40754},{"sent":"bottom left orange","ref_id":40762},{"sent":"orange","ref_id":40763},{"sent":"left giraffe","ref_id":40804},{"sent":"giraffe on right","ref_id":40805},{"sent":"bottom left fruit","ref_id":40810},{"sent":"banana slice in the middle","ref_id":40811},{"sent":"banana on right","ref_id":40812},{"sent":"toilet","ref_id":40909},{"sent":"toilet","ref_id":40910},{"sent":"second from right","ref_id":40945},{"sent":"second row from right","ref_id":40946},{"sent":"second from right","ref_id":40947},{"sent":"second banana from left","ref_id":40948},{"sent":"second row from left","ref_id":40949},{"sent":"left elephant","ref_id":41094},{"sent":"baby elephant","ref_id":41095},{"sent":"right cow","ref_id":41136},{"sent":"giraffe in front","ref_id":41137},{"sent":"chair on right","ref_id":41148},{"sent":"bed","ref_id":41167},{"sent":"right bed","ref_id":41168},{"sent":"left screen","ref_id":41173},{"sent":"right monitor","ref_id":41174},{"sent":"left white chair","ref_id":41197},{"sent":"chair on right","ref_id":41198},{"sent":"UNK","ref_id":41209},{"sent":"blue UNK","ref_id":41351},{"sent":"right giraffe","ref_id":41359},{"sent":"giraffe on left","ref_id":41360},{"sent":"right bed","ref_id":41531},{"sent":"red bed","ref_id":41532},{"sent":"left giraffe","ref_id":41551},{"sent":"right giraffe","ref_id":41552},{"sent":"sheep in back","ref_id":41743},{"sent":"sheep in front","ref_id":41744},{"sent":"broccoli on left","ref_id":41795},{"sent":"broccoli on right","ref_id":41796},{"sent":"broccoli on right","ref_id":41797},{"sent":"broccoli on left","ref_id":41798},{"sent":"broccoli on left","ref_id":41799},{"sent":"bottom right food","ref_id":41805},{"sent":"left bowl","ref_id":41806},{"sent":"white bowl on right","ref_id":41807},{"sent":"right most food","ref_id":41808},{"sent":"bowl of food on left","ref_id":41809},{"sent":"sheep in back","ref_id":41877},{"sent":"right sheep","ref_id":41878},{"sent":"cat on right","ref_id":41938},{"sent":"cat on left","ref_id":41939},{"sent":"left horse","ref_id":42136},{"sent":"left horse","ref_id":42137},{"sent":"bird","ref_id":42284},{"sent":"bird","ref_id":42285},{"sent":"left bird","ref_id":42296},{"sent":"duck","ref_id":42297},{"sent":"red surfboard","ref_id":42329},{"sent":"white boat","ref_id":42330},{"sent":"bottom left toilet","ref_id":42354},{"sent":"white toilet","ref_id":42355},{"sent":"toilet on left","ref_id":42356},{"sent":"toilet on right","ref_id":42357},{"sent":"toilet on left","ref_id":42358},{"sent":"toilet on left","ref_id":42359},{"sent":"bananas on left","ref_id":42428},{"sent":"banana bunch","ref_id":42429},{"sent":"bowl of food on right","ref_id":42658},{"sent":"bowl of food","ref_id":42659},{"sent":"bowl of food","ref_id":42660},{"sent":"left donut","ref_id":42697},{"sent":"bottom right donut","ref_id":42698},{"sent":"train","ref_id":42839},{"sent":"left bird","ref_id":42922},{"sent":"bird on right","ref_id":42923},{"sent":"zebra on left","ref_id":42928},{"sent":"zebra on right","ref_id":42929},{"sent":"zebra in back","ref_id":42930},{"sent":"sandwich on top","ref_id":43160},{"sent":"top left sandwich","ref_id":43161},{"sent":"cake","ref_id":43210},{"sent":"bottom left suitcase","ref_id":43211},{"sent":"bird on the left","ref_id":43396},{"sent":"plate of food","ref_id":43446},{"sent":"top bowl","ref_id":43447},{"sent":"plate with lettuce","ref_id":43448},{"sent":"sandwich on right","ref_id":43449},{"sent":"right sandwich","ref_id":43480},{"sent":"sandwich on left","ref_id":43481},{"sent":"toilet","ref_id":43581},{"sent":"toilet","ref_id":43582},{"sent":"cat on right","ref_id":43596},{"sent":"black chair bottom right","ref_id":43597},{"sent":"giraffe on right","ref_id":43598},{"sent":"left giraffe","ref_id":43599},{"sent":"right sandwich","ref_id":43700},{"sent":"sandwich on left","ref_id":43701},{"sent":"red truck","ref_id":43784},{"sent":"truck","ref_id":43785},{"sent":"banana on right","ref_id":43815},{"sent":"right banana","ref_id":43816},{"sent":"bottom right corner","ref_id":43817},{"sent":"banana on right","ref_id":43818},{"sent":"middle banana","ref_id":43819},{"sent":"cow on left","ref_id":43940},{"sent":"top cow","ref_id":43941},{"sent":"black suitcase on right","ref_id":43987},{"sent":"black suitcase on right","ref_id":43988},{"sent":"red suitcase","ref_id":43989},{"sent":"black suitcase in middle","ref_id":43990},{"sent":"baby","ref_id":44146},{"sent":"teddy bear","ref_id":44147},{"sent":"bottom left food","ref_id":44160},{"sent":"right plate","ref_id":44161},{"sent":"bottom tray","ref_id":44162},{"sent":"top plate","ref_id":44163},{"sent":"right bed","ref_id":44228},{"sent":"bed on left","ref_id":44229},{"sent":"bottom left bowl","ref_id":44296},{"sent":"bottom banana","ref_id":44297},{"sent":"front giraffe","ref_id":44300},{"sent":"giraffe on right","ref_id":44301},{"sent":"plane in front","ref_id":44369},{"sent":"plane on left","ref_id":44370},{"sent":"plane in front","ref_id":44408},{"sent":"plane in front","ref_id":44409},{"sent":"top right cake","ref_id":44426},{"sent":"left half of sandwich","ref_id":44427},{"sent":"left sandwich","ref_id":44428},{"sent":"right half of sandwich","ref_id":44429},{"sent":"bottom left bread","ref_id":44430},{"sent":"bottom left cake","ref_id":44431},{"sent":"right half of sandwich","ref_id":44432},{"sent":"white toilet","ref_id":44454},{"sent":"left sheep","ref_id":44463},{"sent":"sheep in front","ref_id":44464},{"sent":"sandwich on right","ref_id":44467},{"sent":"left sandwich","ref_id":44468},{"sent":"right car","ref_id":44514},{"sent":"car on left","ref_id":44515},{"sent":"yellow cab","ref_id":44516},{"sent":"right toilet","ref_id":44573},{"sent":"toilet on left","ref_id":44574},{"sent":"bottom right bear","ref_id":44661},{"sent":"bear on left","ref_id":44662},{"sent":"bear on right","ref_id":44663},{"sent":"right meter","ref_id":44814},{"sent":"left meter","ref_id":44815},{"sent":"red sauce","ref_id":44856},{"sent":"glass on left","ref_id":44857},{"sent":"bear on right","ref_id":44884},{"sent":"brown bear","ref_id":44885},{"sent":"bear on left","ref_id":44886},{"sent":"bear on right","ref_id":44887},{"sent":"teddy bear on left","ref_id":44888},{"sent":"left meter","ref_id":44941},{"sent":"right meter","ref_id":44942},{"sent":"UNK","ref_id":44989},{"sent":"top right corner","ref_id":44990},{"sent":"bed","ref_id":45056},{"sent":"bottom left suitcase","ref_id":45057},{"sent":"blue umbrella","ref_id":45127},{"sent":"left blue vase","ref_id":45128},{"sent":"blue umbrella","ref_id":45129},{"sent":"left blue umbrella","ref_id":45130},{"sent":"bottom right corner","ref_id":45131},{"sent":"glass of the beer","ref_id":45256},{"sent":"right side of table","ref_id":45257},{"sent":"white bus","ref_id":45498},{"sent":"bus in middle","ref_id":45499},{"sent":"right bunch of bananas","ref_id":45503},{"sent":"bananas on right","ref_id":45504},{"sent":"bananas on the left","ref_id":45505},{"sent":"elephant on right","ref_id":45533},{"sent":"elephant on left","ref_id":45534},{"sent":"elephant in front","ref_id":45535},{"sent":"left zebra","ref_id":45645},{"sent":"right zebra","ref_id":45646},{"sent":"middle row","ref_id":45680},{"sent":"bottom right cake","ref_id":45681},{"sent":"white car in back","ref_id":45957},{"sent":"train","ref_id":45958},{"sent":"pizza","ref_id":45972},{"sent":"pizza slice","ref_id":45973},{"sent":"table in front","ref_id":45974},{"sent":"top right corner","ref_id":45975},{"sent":"sandwich on left","ref_id":45976},{"sent":"right half of sandwich","ref_id":45977},{"sent":"blue thing","ref_id":45984},{"sent":"blue thing","ref_id":45985},{"sent":"chair behind dog","ref_id":46007},{"sent":"chair on right","ref_id":46008},{"sent":"donut on right","ref_id":46423},{"sent":"bottom row second from left","ref_id":46439},{"sent":"bottom row second from left","ref_id":46440},{"sent":"second row from bottom right","ref_id":46441},{"sent":"right giraffe","ref_id":46476},{"sent":"giraffe on left","ref_id":46477},{"sent":"truck on right","ref_id":46501},{"sent":"white truck","ref_id":46502},{"sent":"white truck","ref_id":46503},{"sent":"right elephant","ref_id":46569},{"sent":"middle elephant","ref_id":46570},{"sent":"right zebra","ref_id":46668},{"sent":"zebra in front","ref_id":46669},{"sent":"top pizza","ref_id":46684},{"sent":"pizza","ref_id":46685},{"sent":"car in front","ref_id":46724},{"sent":"car in front","ref_id":46725},{"sent":"sheep in front","ref_id":46744},{"sent":"bottom right bowl","ref_id":46773},{"sent":"bowl of food on left","ref_id":46774},{"sent":"middle bowl","ref_id":46775},{"sent":"left pizza","ref_id":46796},{"sent":"pizza on right","ref_id":46797},{"sent":"pizza on the right","ref_id":46798},{"sent":"bear on left","ref_id":46817},{"sent":"right bear","ref_id":46818},{"sent":"right plant","ref_id":46965},{"sent":"left UNK","ref_id":46966},{"sent":"left suitcase","ref_id":46986},{"sent":"right suitcase","ref_id":46987},{"sent":"right suitcase","ref_id":46988},{"sent":"right suitcase","ref_id":46989},{"sent":"suitcase in middle","ref_id":46990},{"sent":"top carrot","ref_id":47273},{"sent":"top carrot","ref_id":47274},{"sent":"top carrot","ref_id":47275},{"sent":"top carrot","ref_id":47276},{"sent":"bottom left carrot","ref_id":47277},{"sent":"right giraffe","ref_id":47305},{"sent":"left giraffe","ref_id":47306},{"sent":"bowl of food","ref_id":47310},{"sent":"bowl of rice","ref_id":47311},{"sent":"top right bowl","ref_id":47312},{"sent":"train on right","ref_id":47313},{"sent":"train on right","ref_id":47314},{"sent":"train on left","ref_id":47315},{"sent":"chair on left","ref_id":47318},{"sent":"right giraffe","ref_id":47366},{"sent":"left zebra","ref_id":47367},{"sent":"white cow","ref_id":47450},{"sent":"big cow","ref_id":47451},{"sent":"white sheep on right","ref_id":47452},{"sent":"white sheep on right","ref_id":47453},{"sent":"top right sheep","ref_id":47454},{"sent":"white cow","ref_id":47455},{"sent":"top left suitcase","ref_id":47529},{"sent":"white boat on left","ref_id":47530},{"sent":"white boat","ref_id":47531},{"sent":"left giraffe","ref_id":47603},{"sent":"right giraffe","ref_id":47604},{"sent":"white plate","ref_id":47644},{"sent":"top left food","ref_id":47645},{"sent":"bear on right","ref_id":47740},{"sent":"bear on right","ref_id":47741},{"sent":"bear in middle","ref_id":47742},{"sent":"bear on right","ref_id":47743},{"sent":"bed on left","ref_id":47840},{"sent":"bed","ref_id":47841},{"sent":"black dog","ref_id":47875},{"sent":"dog on right","ref_id":47876},{"sent":"giraffe in front","ref_id":47931},{"sent":"giraffe on left","ref_id":47932},{"sent":"left person","ref_id":47957},{"sent":"giraffe on left","ref_id":48055},{"sent":"giraffe","ref_id":48056},{"sent":"bowl of food","ref_id":48175},{"sent":"right bowl","ref_id":48176},{"sent":"zebra in front","ref_id":48302},{"sent":"right zebra","ref_id":48303},{"sent":"bottom left food","ref_id":48441},{"sent":"left piece of food","ref_id":48442},{"sent":"top right food","ref_id":48443},{"sent":"right food","ref_id":48444},{"sent":"left vase","ref_id":48545},{"sent":"right vase","ref_id":48546},{"sent":"second bike from right","ref_id":48585},{"sent":"second from right","ref_id":48586},{"sent":"second from left","ref_id":48587},{"sent":"left bike","ref_id":48588},{"sent":"second bike from right","ref_id":48589},{"sent":"right half of sandwich","ref_id":48623},{"sent":"left sandwich","ref_id":48624},{"sent":"right sandwich","ref_id":48625},{"sent":"bottom right chair","ref_id":48681},{"sent":"right couch","ref_id":48682},{"sent":"bottom left corner","ref_id":48683},{"sent":"couch on right","ref_id":48684},{"sent":"couch","ref_id":48685},{"sent":"left couch","ref_id":48686},{"sent":"left cake","ref_id":48861},{"sent":"right cake","ref_id":48862},{"sent":"left elephant","ref_id":48865},{"sent":"baby elephant","ref_id":48866},{"sent":"right elephant","ref_id":48888},{"sent":"left elephant","ref_id":48889},{"sent":"table in front","ref_id":49111},{"sent":"right truck","ref_id":49165},{"sent":"truck","ref_id":49166},{"sent":"truck","ref_id":49167},{"sent":"right truck","ref_id":49168},{"sent":"right bike","ref_id":49248},{"sent":"right bike","ref_id":49249},{"sent":"right bike","ref_id":49250},{"sent":"donut in middle","ref_id":49288},{"sent":"donut on left","ref_id":49289},{"sent":"donut in middle","ref_id":49290},{"sent":"giraffe on left","ref_id":49291},{"sent":"left giraffe","ref_id":49292},{"sent":"giraffe in front","ref_id":49293},{"sent":"left cup","ref_id":49377},{"sent":"right cup","ref_id":49378},{"sent":"left bottle","ref_id":49429},{"sent":"bottle on right","ref_id":49430},{"sent":"pizza","ref_id":49446},{"sent":"pizza slice on right","ref_id":49447},{"sent":"pizza slice on right","ref_id":49448},{"sent":"broccoli on the right","ref_id":49455},{"sent":"broccoli in the middle","ref_id":49456},{"sent":"second board from right","ref_id":49502},{"sent":"blue board","ref_id":49503},{"sent":"right plant","ref_id":49583},{"sent":"left plant","ref_id":49584},{"sent":"black suitcase","ref_id":49672},{"sent":"blue tie","ref_id":49673},{"sent":"middle bus","ref_id":49701},{"sent":"second bus from right","ref_id":49702},{"sent":"right bus","ref_id":49703},{"sent":"right suitcase","ref_id":49721},{"sent":"right side of pizza","ref_id":49781},{"sent":"right slice","ref_id":49782},{"sent":"dog on right","ref_id":49818},{"sent":"left dog","ref_id":49819},{"sent":"left car","ref_id":49824},{"sent":"white car","ref_id":49825},{"sent":"right cup","ref_id":49949},{"sent":"right cup","ref_id":49950},{"sent":"zebra in back","ref_id":49986},{"sent":"zebra on left","ref_id":49987},{"sent":"car on left","ref_id":25},{"sent":"car on left","ref_id":26},{"sent":"top sandwich","ref_id":27},{"sent":"top left donut","ref_id":28},{"sent":"zebra on left","ref_id":45},{"sent":"right zebra","ref_id":46},{"sent":"chair in front of man","ref_id":164},{"sent":"bottom right corner","ref_id":165},{"sent":"left chair","ref_id":166},{"sent":"top right corner","ref_id":232},{"sent":"pizza in front","ref_id":233},{"sent":"glass in back","ref_id":234},{"sent":"left glass","ref_id":235},{"sent":"yellow fruit on left","ref_id":259}]}
\ No newline at end of file
diff --git a/train_baseline.py b/train_baseline.py
new file mode 100644
index 0000000..54541ae
--- /dev/null
+++ b/train_baseline.py
@@ -0,0 +1,622 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""BERT finetuning runner."""
+
+import argparse
+import json
+import logging
+import os
+import random
+from io import open
+import math
+import sys
+
+from time import gmtime, strftime
+from timeit import default_timer as timer
+
+import numpy as np
+from tensorboardX import SummaryWriter
+from tqdm import tqdm, trange
+
+import torch
+from torch.utils.data import DataLoader, Dataset, RandomSampler
+from torch.utils.data.distributed import DistributedSampler
+
+from pytorch_pretrained_bert.tokenization import BertTokenizer
+from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule
+from pytorch_pretrained_bert import BertModel
+
+from vilbert.datasets import ConceptCapLoaderTrain, ConceptCapLoaderVal
+from vilbert.basebert import BertForMultiModalPreTraining
+from pytorch_pretrained_bert.modeling import BertConfig
+import pdb
+
+logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+
+def main():
+ parser = argparse.ArgumentParser()
+
+ # Required parameters
+ parser.add_argument(
+ "--train_file",
+ default="data/conceptual_caption/training",
+ type=str,
+ # required=True,
+ help="The input train corpus.",
+ )
+ parser.add_argument(
+ "--validation_file",
+ default="data/conceptual_caption/validation",
+ type=str,
+ # required=True,
+ help="The input train corpus.",
+ )
+ parser.add_argument(
+ "--pretrained_weight",
+ default="bert-base-uncased",
+ type=str,
+ help="Bert pre-trained model selected in the list: bert-base-uncased, "
+ "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.",
+ )
+
+ parser.add_argument(
+ "--bert_model",
+ default="bert-base-uncased",
+ type=str,
+ help="Bert pre-trained model selected in the list: bert-base-uncased, "
+ "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.",
+ )
+ parser.add_argument(
+ "--output_dir",
+ default="save",
+ type=str,
+ # required=True,
+ help="The output directory where the model checkpoints will be written.",
+ )
+
+ parser.add_argument(
+ "--config_file",
+ default="config/bert_config.json",
+ type=str,
+ # required=True,
+ help="The config file which specified the model details.",
+ )
+ ## Other parameters
+ parser.add_argument(
+ "--max_seq_length",
+ default=36,
+ type=int,
+ help="The maximum total input sequence length after WordPiece tokenization. \n"
+ "Sequences longer than this will be truncated, and sequences shorter \n"
+ "than this will be padded.",
+ )
+ parser.add_argument("--predict_feature", action="store_true", help="visual target.")
+ parser.add_argument(
+ "--use_location", action="store_true", help="whether use location."
+ )
+ parser.add_argument(
+ "--do_train", action="store_true", help="Whether to run training."
+ )
+ parser.add_argument(
+ "--train_batch_size",
+ default=512,
+ type=int,
+ help="Total batch size for training.",
+ )
+ parser.add_argument(
+ "--learning_rate",
+ default=1e-4,
+ type=float,
+ help="The initial learning rate for Adam.",
+ )
+ parser.add_argument(
+ "--num_train_epochs",
+ default=10.0,
+ type=float,
+ help="Total number of training epochs to perform.",
+ )
+ parser.add_argument(
+ "--warmup_proportion",
+ default=0.1,
+ type=float,
+ help="Proportion of training to perform linear learning rate warmup for. "
+ "E.g., 0.1 = 10%% of training.",
+ )
+ parser.add_argument(
+ "--img_weight", default=1, type=float, help="weight for image loss"
+ )
+ parser.add_argument(
+ "--no_cuda", action="store_true", help="Whether not to use CUDA when available"
+ )
+ parser.add_argument(
+ "--on_memory",
+ action="store_true",
+ help="Whether to load train samples into memory or use disk",
+ )
+ parser.add_argument(
+ "--do_lower_case",
+ action="store_true",
+ help="Whether to lower case the input text. True for uncased models, False for cased models.",
+ )
+ parser.add_argument(
+ "--local_rank",
+ type=int,
+ default=-1,
+ help="local_rank for distributed training on gpus",
+ )
+ parser.add_argument(
+ "--seed", type=int, default=42, help="random seed for initialization"
+ )
+ parser.add_argument(
+ "--gradient_accumulation_steps",
+ type=int,
+ default=1,
+ help="Number of updates steps to accumualte before performing a backward/update pass.",
+ )
+ parser.add_argument(
+ "--fp16",
+ action="store_true",
+ help="Whether to use 16-bit float precision instead of 32-bit",
+ )
+ parser.add_argument(
+ "--loss_scale",
+ type=float,
+ default=0,
+ help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
+ "0 (default value): dynamic loss scaling.\n"
+ "Positive power of 2: static loss scaling value.\n",
+ )
+ parser.add_argument(
+ "--num_workers",
+ type=int,
+ default=20,
+ help="Number of workers in the dataloader.",
+ )
+ parser.add_argument(
+ "--from_pretrained",
+ action="store_true",
+ help="Wheter the tensor is from pretrained.",
+ )
+ parser.add_argument(
+ "--save_name",
+ default='',
+ type=str,
+ help="save name for training.",
+ )
+
+ args = parser.parse_args()
+
+ print(args)
+ if args.save_name is not '':
+ timeStamp = args.save_name
+ else:
+ timeStamp = strftime("%d-%b-%y-%X-%a", gmtime())
+ timeStamp += "_{:0>6d}".format(random.randint(0, 10e6))
+
+ savePath = os.path.join(args.output_dir, timeStamp)
+
+ if not os.path.exists(savePath):
+ os.makedirs(savePath)
+
+ # save all the hidden parameters.
+ with open(os.path.join(savePath, 'command.txt'), 'w') as f:
+ print(args, file=f) # Python 3.x
+ print('\n', file=f)
+
+ if args.local_rank == -1 or args.no_cuda:
+ device = torch.device(
+ "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"
+ )
+ n_gpu = torch.cuda.device_count()
+ else:
+ torch.cuda.set_device(args.local_rank)
+ device = torch.device("cuda", args.local_rank)
+ n_gpu = 1
+ # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
+ torch.distributed.init_process_group(backend="nccl")
+ logger.info(
+ "device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
+ device, n_gpu, bool(args.local_rank != -1), args.fp16
+ )
+ )
+
+ if args.gradient_accumulation_steps < 1:
+ raise ValueError(
+ "Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
+ args.gradient_accumulation_steps
+ )
+ )
+
+ args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
+
+ random.seed(args.seed)
+ np.random.seed(args.seed)
+ torch.manual_seed(args.seed)
+ if n_gpu > 0:
+ torch.cuda.manual_seed_all(args.seed)
+
+ if not args.do_train:
+ raise ValueError(
+ "Training is currently the only implemented execution option. Please set `do_train`."
+ )
+
+ if not os.path.exists(args.output_dir):
+ os.makedirs(args.output_dir)
+
+ tokenizer = BertTokenizer.from_pretrained(
+ args.bert_model, do_lower_case=args.do_lower_case
+ )
+
+ num_train_optimization_steps = None
+ if args.do_train:
+
+ viz = TBlogger("logs", timeStamp)
+
+ train_dataset = ConceptCapLoaderTrain(
+ args.train_file,
+ tokenizer,
+ seq_len=args.max_seq_length,
+ batch_size=args.train_batch_size,
+ predict_feature=args.predict_feature,
+ num_workers=args.num_workers,
+ )
+
+ validation_dataset = ConceptCapLoaderVal(
+ args.validation_file,
+ tokenizer,
+ seq_len=args.max_seq_length,
+ batch_size=args.train_batch_size,
+ predict_feature=args.predict_feature,
+ num_workers=args.num_workers,
+ )
+
+ num_train_optimization_steps = (
+ int(
+ train_dataset.num_dataset
+ / args.train_batch_size
+ / args.gradient_accumulation_steps
+ )
+ * args.num_train_epochs
+ )
+ if args.local_rank != -1:
+ num_train_optimization_steps = (
+ num_train_optimization_steps // torch.distributed.get_world_size()
+ )
+
+ config = BertConfig.from_json_file(args.config_file)
+
+ if args.from_pretrained:
+ model = BertForMultiModalPreTraining.from_pretrained(args.bert_model, config)
+ else:
+ model = BertForMultiModalPreTraining(config)
+
+ if args.fp16:
+ model.half()
+ if args.local_rank != -1:
+ try:
+ from apex.parallel import DistributedDataParallel as DDP
+ except ImportError:
+ raise ImportError(
+ "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
+ )
+ model = DDP(model)
+ elif n_gpu > 1:
+ model = torch.nn.DataParallel(model)
+ # model = torch.nn.parallel.DistributedDataParallel(model)
+ model.cuda()
+ no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
+
+ if not args.from_pretrained:
+ param_optimizer = list(model.named_parameters())
+ optimizer_grouped_parameters = [
+ {
+ "params": [
+ p for n, p in param_optimizer if not any(nd in n for nd in no_decay)
+ ],
+ "weight_decay": 0.01,
+ },
+ {
+ "params": [
+ p for n, p in param_optimizer if any(nd in n for nd in no_decay)
+ ],
+ "weight_decay": 0.0,
+ },
+ ]
+ else:
+ bert_weight_name = json.load(open("config/" + args.pretrained_weight + "_weight_name.json", "r"))
+ optimizer_grouped_parameters = []
+ for key, value in dict(model.named_parameters()).items():
+ if value.requires_grad:
+ if key[12:] in bert_weight_name:
+ lr = args.learning_rate * 0.1
+ else:
+ lr = args.learning_rate
+
+ if any(nd in key for nd in no_decay):
+ optimizer_grouped_parameters += [
+ {"params": [value], "lr": lr, "weight_decay": 0.01}
+ ]
+
+ if not any(nd in key for nd in no_decay):
+ optimizer_grouped_parameters += [
+ {"params": [value], "lr": lr, "weight_decay": 0.0}
+ ]
+
+ # set different parameters for vision branch and lanugage branch.
+ if args.fp16:
+ try:
+ from apex.optimizers import FP16_Optimizer
+ from apex.optimizers import FusedAdam
+ except ImportError:
+ raise ImportError(
+ "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
+ )
+
+ optimizer = FusedAdam(
+ optimizer_grouped_parameters,
+ lr=args.learning_rate,
+ bias_correction=False,
+ max_grad_norm=1.0,
+ )
+ if args.loss_scale == 0:
+ optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
+ else:
+ optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
+
+ else:
+ if args.from_pretrained:
+ optimizer = BertAdam(
+ optimizer_grouped_parameters,
+ warmup=args.warmup_proportion,
+ t_total=num_train_optimization_steps,
+ )
+
+ else:
+ optimizer = BertAdam(
+ optimizer_grouped_parameters,
+ lr=args.learning_rate,
+ warmup=args.warmup_proportion,
+ t_total=num_train_optimization_steps,
+ )
+
+ if args.do_train:
+ logger.info("***** Running training *****")
+ logger.info(" Num examples = %d", train_dataset.num_dataset)
+ logger.info(" Batch size = %d", args.train_batch_size)
+ logger.info(" Num steps = %d", num_train_optimization_steps)
+
+ startIterID = 0
+ global_step = 0
+ masked_loss_v_tmp = 0
+ masked_loss_t_tmp = 0
+ next_sentence_loss_tmp = 0
+ loss_tmp = 0
+ start_t = timer()
+
+ model.train()
+ # t1 = timer()
+ for epochId in trange(int(args.num_train_epochs), desc="Epoch"):
+ tr_loss = 0
+ nb_tr_examples, nb_tr_steps = 0, 0
+
+ # iter_dataloader = iter(train_dataloader)
+ for step, batch in enumerate(train_dataset):
+ iterId = startIterID + step + (epochId * len(train_dataset))
+ batch = tuple(t.cuda(device=device, non_blocking=True) for t in batch)
+
+ input_ids, input_mask, segment_ids, lm_label_ids, is_next, image_feat, image_loc, \
+ image_target, image_label, image_mask, image_ids = (batch)
+
+ masked_loss_t, masked_loss_v, next_sentence_loss = model(
+ input_ids,
+ image_feat,
+ image_target,
+ image_loc,
+ segment_ids,
+ input_mask,
+ image_mask,
+ lm_label_ids,
+ image_label,
+ is_next,
+ )
+
+ masked_loss_v = masked_loss_v * args.img_weight
+ loss = masked_loss_t + masked_loss_v + next_sentence_loss
+
+ if n_gpu > 1:
+ loss = loss.mean() # mean() to average on multi-gpu.
+ masked_loss_t = masked_loss_t.mean()
+ masked_loss_v = masked_loss_v.mean()
+ next_sentence_loss = next_sentence_loss.mean()
+ if args.gradient_accumulation_steps > 1:
+ loss = loss / args.gradient_accumulation_steps
+ if args.fp16:
+ optimizer.backward(loss)
+ else:
+ loss.backward()
+
+ if math.isnan(loss.item()):
+ pdb.set_trace()
+
+ tr_loss += loss.item()
+
+ # print(tr_loss)
+ viz.linePlot(iterId, loss.item(), "loss", "train")
+ viz.linePlot(iterId, masked_loss_t.item(), "masked_loss_t", "train")
+ viz.linePlot(iterId, masked_loss_v.item(), "masked_loss_v", "train")
+ viz.linePlot(
+ iterId, next_sentence_loss.item(), "next_sentence_loss", "train"
+ )
+ # viz.linePlot(iterId, optimizer.get_lr()[0], 'learning_rate', 'train')
+
+ loss_tmp += loss.item()
+ masked_loss_v_tmp += masked_loss_v.item()
+ masked_loss_t_tmp += masked_loss_t.item()
+ next_sentence_loss_tmp += next_sentence_loss.item()
+
+ nb_tr_examples += input_ids.size(0)
+ nb_tr_steps += 1
+ if (step + 1) % args.gradient_accumulation_steps == 0:
+ if args.fp16:
+ # modify learning rate with special warm up BERT uses
+ # if args.fp16 is False, BertAdam is used that handles this automatically
+ lr_this_step = args.learning_rate * warmup_linear(
+ global_step / num_train_optimization_steps,
+ args.warmup_proportion,
+ )
+ for param_group in optimizer.param_groups:
+ param_group["lr"] = lr_this_step
+
+ optimizer.step()
+ optimizer.zero_grad()
+ global_step += 1
+
+ if step % 20 == 0 and step != 0:
+ masked_loss_t_tmp = masked_loss_t_tmp / 20.0
+ masked_loss_v_tmp = masked_loss_v_tmp / 20.0
+ next_sentence_loss_tmp = next_sentence_loss_tmp / 20.0
+ loss_tmp = loss_tmp / 20.0
+
+ end_t = timer()
+ timeStamp = strftime("%a %d %b %y %X", gmtime())
+
+ Ep = epochId + nb_tr_steps / float(len(train_dataset))
+ printFormat = "[%s][Ep: %.2f][Iter: %d][Time: %5.2fs][Loss: %.5g][Loss_v: %.5g][Loss_t: %.5g][Loss_n: %.5g][LR: %.5g]"
+
+ printInfo = [
+ timeStamp,
+ Ep,
+ nb_tr_steps,
+ end_t - start_t,
+ loss_tmp,
+ masked_loss_v_tmp,
+ masked_loss_t_tmp,
+ next_sentence_loss_tmp,
+ optimizer.get_lr()[0],
+ ]
+
+ start_t = end_t
+ print(printFormat % tuple(printInfo))
+
+ masked_loss_v_tmp = 0
+ masked_loss_t_tmp = 0
+ next_sentence_loss_tmp = 0
+ loss_tmp = 0
+
+ # Do the evaluation
+ torch.set_grad_enabled(False)
+ start_t = timer()
+ numBatches = len(validation_dataset)
+ eval_masked_loss_t = 0
+ eval_masked_loss_v = 0
+ eval_next_sentence_loss = 0
+ eval_total_loss = 0
+
+ model.eval()
+ for step, batch in enumerate(validation_dataset):
+ batch = tuple(t.cuda(device=device, non_blocking=True) for t in batch)
+
+ input_ids, input_mask, segment_ids, lm_label_ids, is_next, image_feat, image_loc, image_target, image_label, image_mask, image_ids = (
+ batch
+ )
+
+ masked_loss_t, masked_loss_v, next_sentence_loss = model(
+ input_ids,
+ image_feat,
+ image_target,
+ image_loc,
+ segment_ids,
+ input_mask,
+ image_mask,
+ lm_label_ids,
+ image_label,
+ is_next,
+ )
+
+ masked_loss_v = masked_loss_v * args.img_weight
+ loss = masked_loss_t + masked_loss_v + next_sentence_loss
+
+ if n_gpu > 1:
+ loss = loss.mean() # mean() to average on multi-gpu.
+ masked_loss_t = masked_loss_t.mean()
+ masked_loss_v = masked_loss_v.mean()
+ next_sentence_loss = next_sentence_loss.mean()
+
+
+ eval_masked_loss_t += masked_loss_t.item()
+ eval_masked_loss_v += masked_loss_v.item()
+ eval_next_sentence_loss += next_sentence_loss.item()
+ eval_total_loss += loss.item()
+
+ end_t = timer()
+ delta_t = " Time: %5.2fs" % (end_t - start_t)
+ start_t = end_t
+ progressString = "\r Evaluating split '%s' [%d/%d]\t" + delta_t
+ sys.stdout.write(progressString % ('val', step + 1, numBatches))
+ sys.stdout.flush()
+
+ eval_masked_loss_t = eval_masked_loss_t / float(numBatches)
+ eval_masked_loss_v = eval_masked_loss_v / float(numBatches)
+ eval_next_sentence_loss = eval_next_sentence_loss / float(numBatches)
+ eval_total_loss = eval_total_loss / float(numBatches)
+
+ printFormat = "Evaluation: [Loss: %.5g][Loss_v: %.5g][Loss_t: %.5g][Loss_n: %.5g]"
+ printInfo = [
+ eval_total_loss,
+ eval_masked_loss_t,
+ eval_masked_loss_v,
+ eval_next_sentence_loss]
+
+ print(printFormat % tuple(printInfo))
+
+ torch.set_grad_enabled(True)
+
+ viz.linePlot(epochId, eval_total_loss, "loss", "val")
+ viz.linePlot(epochId, eval_masked_loss_t, "masked_loss_t", "val")
+ viz.linePlot(epochId, eval_masked_loss_v, "masked_loss_v", "val")
+ viz.linePlot(epochId, eval_next_sentence_loss, "next_sentence_loss", "val")
+
+ # Save a trained model
+ logger.info("** ** * Saving fine - tuned model ** ** * ")
+ model_to_save = (
+ model.module if hasattr(model, "module") else model
+ ) # Only save the model it-self
+
+ output_model_file = os.path.join(
+ savePath, "pytorch_model_" + str(epochId) + ".bin"
+ )
+ if args.do_train:
+ torch.save(model_to_save.state_dict(), output_model_file)
+
+class TBlogger:
+ def __init__(self, log_dir, exp_name):
+ log_dir = log_dir + "/" + exp_name
+ print("logging file at: " + log_dir)
+ self.logger = SummaryWriter(log_dir=log_dir)
+
+ def linePlot(self, step, val, split, key, xlabel="None"):
+ self.logger.add_scalar(split + "/" + key, val, step)
+
+
+if __name__ == "__main__":
+
+ main()
diff --git a/train_concap.py b/train_concap.py
new file mode 100644
index 0000000..5a93e41
--- /dev/null
+++ b/train_concap.py
@@ -0,0 +1,672 @@
+import argparse
+import json
+import logging
+import os
+import random
+from io import open
+import math
+import sys
+
+from time import gmtime, strftime
+from timeit import default_timer as timer
+
+import numpy as np
+from tqdm import tqdm, trange
+
+import torch
+from torch.utils.data import DataLoader, Dataset, RandomSampler
+from torch.utils.data.distributed import DistributedSampler
+# from parallel.data_parallel import DataParallel
+from tensorboardX import SummaryWriter
+
+from pytorch_pretrained_bert.tokenization import BertTokenizer
+from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule
+
+from vilbert.datasets import ConceptCapLoaderTrain, ConceptCapLoaderVal
+from vilbert.vilbert import BertForMultiModalPreTraining, BertConfig
+import torch.distributed as dist
+
+import pdb
+
+logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+
+def main():
+ parser = argparse.ArgumentParser()
+
+ # Required parameters
+ parser.add_argument(
+ "--train_file",
+ default="data/conceptual_caption/training",
+ type=str,
+ # required=True,
+ help="The input train corpus.",
+ )
+ parser.add_argument(
+ "--validation_file",
+ default="data/conceptual_caption/validation",
+ type=str,
+ # required=True,
+ help="The input train corpus.",
+ )
+ parser.add_argument(
+ "--from_pretrained",
+ default="",
+ type=str,
+ help="Bert pre-trained model selected in the list: bert-base-uncased, "
+ "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.",
+ )
+ parser.add_argument(
+ "--bert_model",
+ default="bert-base-uncased",
+ type=str,
+ help="Bert pre-trained model selected in the list: bert-base-uncased, "
+ "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.",
+ )
+ parser.add_argument(
+ "--output_dir",
+ default="save",
+ type=str,
+ # required=True,
+ help="The output directory where the model checkpoints will be written.",
+ )
+
+ parser.add_argument(
+ "--config_file",
+ default="config/bert_config.json",
+ type=str,
+ # required=True,
+ help="The config file which specified the model details.",
+ )
+ ## Other parameters
+ parser.add_argument(
+ "--max_seq_length",
+ default=36,
+ type=int,
+ help="The maximum total input sequence length after WordPiece tokenization. \n"
+ "Sequences longer than this will be truncated, and sequences shorter \n"
+ "than this will be padded.",
+ )
+ parser.add_argument("--predict_feature", action="store_true", help="visual target.")
+
+ parser.add_argument(
+ "--train_batch_size",
+ default=512,
+ type=int,
+ help="Total batch size for training.",
+ )
+ parser.add_argument(
+ "--learning_rate",
+ default=1e-4,
+ type=float,
+ help="The initial learning rate for Adam.",
+ )
+ parser.add_argument(
+ "--num_train_epochs",
+ default=10.0,
+ type=float,
+ help="Total number of training epochs to perform.",
+ )
+ parser.add_argument(
+ "--start_epoch",
+ default=0,
+ type=float,
+ help="Total number of training epochs to perform.",
+ )
+ parser.add_argument(
+ "--warmup_proportion",
+ default=0.1,
+ type=float,
+ help="Proportion of training to perform linear learning rate warmup for. "
+ "E.g., 0.1 = 10%% of training.",
+ )
+ parser.add_argument(
+ "--img_weight", default=1, type=float, help="weight for image loss"
+ )
+ parser.add_argument(
+ "--no_cuda", action="store_true", help="Whether not to use CUDA when available"
+ )
+ parser.add_argument(
+ "--on_memory",
+ action="store_true",
+ help="Whether to load train samples into memory or use disk",
+ )
+ parser.add_argument(
+ "--do_lower_case",
+ type=bool,
+ default=True,
+ help="Whether to lower case the input text. True for uncased models, False for cased models.",
+ )
+ parser.add_argument(
+ "--local_rank",
+ type=int,
+ default=-1,
+ help="local_rank for distributed training on gpus",
+ )
+ parser.add_argument(
+ "--seed", type=int, default=42, help="random seed for initialization"
+ )
+ parser.add_argument(
+ "--gradient_accumulation_steps",
+ type=int,
+ default=1,
+ help="Number of updates steps to accumualte before performing a backward/update pass.",
+ )
+ parser.add_argument(
+ "--fp16",
+ action="store_true",
+ help="Whether to use 16-bit float precision instead of 32-bit",
+ )
+ parser.add_argument(
+ "--loss_scale",
+ type=float,
+ default=0,
+ help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
+ "0 (default value): dynamic loss scaling.\n"
+ "Positive power of 2: static loss scaling value.\n",
+ )
+ parser.add_argument(
+ "--num_workers",
+ type=int,
+ default=3,
+ help="Number of workers in the dataloader.",
+ )
+
+ parser.add_argument(
+ "--save_name",
+ default='',
+ type=str,
+ help="save name for training.",
+ )
+ parser.add_argument(
+ "--baseline", action="store_true", help="Wheter to use the baseline model (single bert)."
+ )
+ parser.add_argument(
+ "--freeze", default = -1, type=int,
+ help="till which layer of textual stream of vilbert need to fixed."
+ )
+ parser.add_argument(
+ "--use_chuncks", default=0, type=float, help="whether use chunck for parallel training."
+ )
+ parser.add_argument(
+ "--distributed", action="store_true" , help="whether use chunck for parallel training."
+ )
+ parser.add_argument(
+ "--without_coattention", action="store_true" , help="whether pair loss."
+ )
+ args = parser.parse_args()
+ if args.baseline:
+ from pytorch_pretrained_bert.modeling import BertConfig
+ from vilbert.basebert import BertForMultiModalPreTraining
+ else:
+ from vilbert.vilbert import BertForMultiModalPreTraining, BertConfig
+
+ print(args)
+ if args.save_name is not '':
+ timeStamp = args.save_name
+ else:
+ timeStamp = strftime("%d-%b-%y-%X-%a", gmtime())
+ timeStamp += "_{:0>6d}".format(random.randint(0, 10e6))
+
+ savePath = os.path.join(args.output_dir, timeStamp)
+
+ if not os.path.exists(savePath):
+ os.makedirs(savePath)
+
+ config = BertConfig.from_json_file(args.config_file)
+
+ if args.freeze > config.t_biattention_id[0]:
+ config.fixed_t_layer = config.t_biattention_id[0]
+
+ if args.without_coattention:
+ config.with_coattention = False
+ # save all the hidden parameters.
+ with open(os.path.join(savePath, 'command.txt'), 'w') as f:
+ print(args, file=f) # Python 3.x
+ print('\n', file=f)
+ print(config, file=f)
+
+ bert_weight_name = json.load(open("config/" + args.from_pretrained + "_weight_name.json", "r"))
+ if args.local_rank == -1 or args.no_cuda:
+ device = torch.device(
+ "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"
+ )
+ n_gpu = torch.cuda.device_count()
+ else:
+ torch.cuda.set_device(args.local_rank)
+ device = torch.device("cuda", args.local_rank)
+ n_gpu = 1
+ # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
+ torch.distributed.init_process_group(backend="nccl")
+ logger.info(
+ "device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
+ device, n_gpu, bool(args.local_rank != -1), args.fp16
+ )
+ )
+
+ if args.gradient_accumulation_steps < 1:
+ raise ValueError(
+ "Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
+ args.gradient_accumulation_steps
+ )
+ )
+
+ args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
+
+ random.seed(args.seed)
+ np.random.seed(args.seed)
+ torch.manual_seed(args.seed)
+ if n_gpu > 0:
+ torch.cuda.manual_seed_all(args.seed)
+
+ if not os.path.exists(args.output_dir):
+ os.makedirs(args.output_dir)
+
+ tokenizer = BertTokenizer.from_pretrained(
+ args.bert_model, do_lower_case=args.do_lower_case
+ )
+
+ num_train_optimization_steps = None
+
+ viz = TBlogger("logs", timeStamp)
+
+ train_dataset = ConceptCapLoaderTrain(
+ args.train_file,
+ tokenizer,
+ seq_len=args.max_seq_length,
+ batch_size=args.train_batch_size,
+ predict_feature=args.predict_feature,
+ num_workers=args.num_workers,
+ distributed=args.distributed,
+ )
+
+ validation_dataset = ConceptCapLoaderVal(
+ args.validation_file,
+ tokenizer,
+ seq_len=args.max_seq_length,
+ batch_size=args.train_batch_size,
+ predict_feature=args.predict_feature,
+ num_workers=2,
+ distributed=args.distributed,
+ )
+
+ num_train_optimization_steps = (
+ int(
+ train_dataset.num_dataset
+ / args.train_batch_size
+ / args.gradient_accumulation_steps
+ )
+ * (args.num_train_epochs - args.start_epoch)
+ )
+ # if args.local_rank != -1:
+ # num_train_optimization_steps = (
+ # num_train_optimization_steps // torch.distributed.get_world_size()
+ # )
+
+ default_gpu = False
+ if dist.is_available() and args.distributed:
+ rank = dist.get_rank()
+ if rank == 0:
+ default_gpu = True
+ else:
+ default_gpu = True
+
+ # pdb.set_trace()
+ if args.predict_feature:
+ config.v_target_size = 2048
+ config.predict_feature = True
+ else:
+ config.v_target_size = 1601
+ config.predict_feature = False
+
+ if args.from_pretrained:
+ model = BertForMultiModalPreTraining.from_pretrained(args.from_pretrained, config)
+ else:
+ model = BertForMultiModalPreTraining(config)
+
+ model.cuda()
+
+ if args.fp16:
+ model.half()
+ if args.local_rank != -1:
+ try:
+ from apex.parallel import DistributedDataParallel as DDP
+ except ImportError:
+ raise ImportError(
+ "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
+ )
+ model = DDP(model)
+ elif n_gpu > 1:
+ model = torch.nn.DataParallel(model)
+
+ no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
+
+ if args.freeze != -1:
+ bert_weight_name_filtered = []
+ for name in bert_weight_name:
+ if 'embeddings' in name:
+ bert_weight_name_filtered.append(name)
+ elif 'encoder' in name:
+ layer_num = name.split('.')[2]
+ if int(layer_num) <= args.freeze:
+ bert_weight_name_filtered.append(name)
+
+ optimizer_grouped_parameters = []
+ for key, value in dict(model.named_parameters()).items():
+ if key[12:] in bert_weight_name_filtered:
+ value.requires_grad = False
+
+ if default_gpu:
+ print("filtered weight")
+ print(bert_weight_name_filtered)
+
+ if not args.from_pretrained:
+ param_optimizer = list(model.named_parameters())
+ optimizer_grouped_parameters = [
+ {
+ "params": [
+ p for n, p in param_optimizer if not any(nd in n for nd in no_decay)
+ ],
+ "weight_decay": 0.01,
+ },
+ {
+ "params": [
+ p for n, p in param_optimizer if any(nd in n for nd in no_decay)
+ ],
+ "weight_decay": 0.0,
+ },
+ ]
+ else:
+ optimizer_grouped_parameters = []
+ for key, value in dict(model.named_parameters()).items():
+ if value.requires_grad:
+ if key[12:] in bert_weight_name:
+ lr = args.learning_rate * 0.1
+ else:
+ lr = args.learning_rate
+
+ if any(nd in key for nd in no_decay):
+ optimizer_grouped_parameters += [
+ {"params": [value], "lr": lr, "weight_decay": 0.01}
+ ]
+
+ if not any(nd in key for nd in no_decay):
+ optimizer_grouped_parameters += [
+ {"params": [value], "lr": lr, "weight_decay": 0.0}
+ ]
+ if default_gpu:
+ print(len(list(model.named_parameters())), len(optimizer_grouped_parameters))
+
+ # set different parameters for vision branch and lanugage branch.
+ if args.fp16:
+ try:
+ from apex.optimizers import FP16_Optimizer
+ from apex.optimizers import FusedAdam
+ except ImportError:
+ raise ImportError(
+ "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
+ )
+
+ optimizer = FusedAdam(
+ optimizer_grouped_parameters,
+ lr=args.learning_rate,
+ bias_correction=False,
+ max_grad_norm=1.0,
+ )
+ if args.loss_scale == 0:
+ optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
+ else:
+ optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
+
+ else:
+ if args.from_pretrained:
+ optimizer = BertAdam(
+ optimizer_grouped_parameters,
+ warmup=args.warmup_proportion,
+ t_total=num_train_optimization_steps,
+
+ )
+
+ else:
+ optimizer = BertAdam(
+ optimizer_grouped_parameters,
+ lr=args.learning_rate,
+ warmup=args.warmup_proportion,
+ t_total=num_train_optimization_steps,
+ )
+
+ logger.info("***** Running training *****")
+ logger.info(" Num examples = %d", train_dataset.num_dataset)
+ logger.info(" Batch size = %d", args.train_batch_size)
+ logger.info(" Num steps = %d", num_train_optimization_steps)
+
+ startIterID = 0
+ global_step = 0
+ masked_loss_v_tmp = 0
+ masked_loss_t_tmp = 0
+ next_sentence_loss_tmp = 0
+ loss_tmp = 0
+ start_t = timer()
+
+ # t1 = timer()
+ for epochId in range(int(args.start_epoch), int(args.num_train_epochs)):
+ model.train()
+ tr_loss = 0
+ nb_tr_examples, nb_tr_steps = 0, 0
+
+ # iter_dataloader = iter(train_dataloader)
+ for step, batch in enumerate(train_dataset):
+ iterId = startIterID + step + (epochId * len(train_dataset))
+ # batch = iter_dataloader.next()
+ batch = tuple(t.cuda(device=device, non_blocking=True) for t in batch)
+
+ input_ids, input_mask, segment_ids, lm_label_ids, is_next, image_feat, image_loc, image_target, image_label, image_mask, image_ids = (
+ batch
+ )
+
+ masked_loss_t, masked_loss_v, next_sentence_loss = model(
+ input_ids,
+ image_feat,
+ image_loc,
+ segment_ids,
+ input_mask,
+ image_mask,
+ lm_label_ids,
+ image_label,
+ image_target,
+ is_next,
+ )
+
+ if args.without_coattention:
+ next_sentence_loss = next_sentence_loss * 0
+
+ masked_loss_v = masked_loss_v * args.img_weight
+ loss = masked_loss_t + masked_loss_v + next_sentence_loss
+
+ if n_gpu > 1:
+ loss = loss.mean() # mean() to average on multi-gpu.
+ masked_loss_t = masked_loss_t.mean()
+ masked_loss_v = masked_loss_v.mean()
+ next_sentence_loss = next_sentence_loss.mean()
+ if args.gradient_accumulation_steps > 1:
+ loss = loss / args.gradient_accumulation_steps
+ if args.fp16:
+ optimizer.backward(loss)
+ else:
+ loss.backward()
+
+ if math.isnan(loss.item()):
+ pdb.set_trace()
+
+ tr_loss += loss.item()
+
+ rank = 0
+
+ if dist.is_available() and args.distributed:
+ rank = dist.get_rank()
+ else:
+ rank = 0
+
+ viz.linePlot(iterId, loss.item(), "loss_"+str(rank), "train")
+ viz.linePlot(iterId, masked_loss_t.item(), "masked_loss_t_"+str(rank), "train")
+ viz.linePlot(iterId, masked_loss_v.item(), "masked_loss_v_"+str(rank), "train")
+ viz.linePlot(
+ iterId, next_sentence_loss.item(), "next_sentence_loss_"+str(rank), "train"
+ )
+ # viz.linePlot(iterId, optimizer.get_lr()[0], 'learning_rate', 'train')
+
+ loss_tmp += loss.item()
+ masked_loss_v_tmp += masked_loss_v.item()
+ masked_loss_t_tmp += masked_loss_t.item()
+ next_sentence_loss_tmp += next_sentence_loss.item()
+
+ nb_tr_examples += input_ids.size(0)
+ nb_tr_steps += 1
+ if (step + 1) % args.gradient_accumulation_steps == 0:
+ if args.fp16:
+ # modify learning rate with special warm up BERT uses
+ # if args.fp16 is False, BertAdam is used that handles this automatically
+ lr_this_step = args.learning_rate * warmup_linear(
+ global_step / num_train_optimization_steps,
+ args.warmup_proportion,
+ )
+ for param_group in optimizer.param_groups:
+ param_group["lr"] = lr_this_step
+
+ optimizer.step()
+ optimizer.zero_grad()
+ global_step += 1
+
+ if step % 20 == 0 and step != 0:
+ masked_loss_t_tmp = masked_loss_t_tmp / 20.0
+ masked_loss_v_tmp = masked_loss_v_tmp / 20.0
+ next_sentence_loss_tmp = next_sentence_loss_tmp / 20.0
+ loss_tmp = loss_tmp / 20.0
+
+ end_t = timer()
+ timeStamp = strftime("%a %d %b %y %X", gmtime())
+
+ Ep = epochId + nb_tr_steps / float(len(train_dataset))
+ printFormat = "[%s][Ep: %.2f][Iter: %d][Time: %5.2fs][Loss: %.5g][Loss_v: %.5g][Loss_t: %.5g][Loss_n: %.5g][LR: %.8g]"
+
+ printInfo = [
+ timeStamp,
+ Ep,
+ nb_tr_steps,
+ end_t - start_t,
+ loss_tmp,
+ masked_loss_v_tmp,
+ masked_loss_t_tmp,
+ next_sentence_loss_tmp,
+ optimizer.get_lr()[0],
+ ]
+
+ start_t = end_t
+ print(printFormat % tuple(printInfo))
+
+ masked_loss_v_tmp = 0
+ masked_loss_t_tmp = 0
+ next_sentence_loss_tmp = 0
+ loss_tmp = 0
+
+ # Do the evaluation
+ torch.set_grad_enabled(False)
+ start_t = timer()
+ numBatches = len(validation_dataset)
+ eval_masked_loss_t = 0
+ eval_masked_loss_v = 0
+ eval_next_sentence_loss = 0
+ eval_total_loss = 0
+
+ model.eval()
+ for step, batch in enumerate(validation_dataset):
+ batch = tuple(t.cuda(device=device, non_blocking=True) for t in batch)
+
+ input_ids, input_mask, segment_ids, lm_label_ids, is_next, image_feat, image_loc, image_target, image_label, image_mask, image_ids = (
+ batch
+ )
+
+ masked_loss_t, masked_loss_v, next_sentence_loss = model(
+ input_ids,
+ image_feat,
+ image_loc,
+ segment_ids,
+ input_mask,
+ image_mask,
+ lm_label_ids,
+ image_label,
+ image_target,
+ is_next,
+ )
+
+ masked_loss_v = masked_loss_v * args.img_weight
+ loss = masked_loss_t + masked_loss_v + next_sentence_loss
+
+ if n_gpu > 1:
+ loss = loss.mean() # mean() to average on multi-gpu.
+ masked_loss_t = masked_loss_t.mean()
+ masked_loss_v = masked_loss_v.mean()
+ next_sentence_loss = next_sentence_loss.mean()
+
+
+ eval_masked_loss_t += masked_loss_t.item()
+ eval_masked_loss_v += masked_loss_v.item()
+ eval_next_sentence_loss += next_sentence_loss.item()
+ eval_total_loss += loss.item()
+
+ end_t = timer()
+ delta_t = " Time: %5.2fs" % (end_t - start_t)
+ start_t = end_t
+ progressString = "\r Evaluating split '%s' [%d/%d]\t" + delta_t
+ sys.stdout.write(progressString % ('val', step + 1, numBatches))
+ sys.stdout.flush()
+
+ eval_masked_loss_t = eval_masked_loss_t / float(numBatches)
+ eval_masked_loss_v = eval_masked_loss_v / float(numBatches)
+ eval_next_sentence_loss = eval_next_sentence_loss / float(numBatches)
+ eval_total_loss = eval_total_loss / float(numBatches)
+
+ printFormat = "Evaluation: [Loss: %.5g][Loss_v: %.5g][Loss_t: %.5g][Loss_n: %.5g]"
+ printInfo = [
+ eval_total_loss,
+ eval_masked_loss_v,
+ eval_masked_loss_t,
+ eval_next_sentence_loss]
+
+ print(printFormat % tuple(printInfo))
+ torch.set_grad_enabled(True)
+
+ viz.linePlot(epochId, eval_total_loss, "loss_" + str(rank), "val")
+ viz.linePlot(epochId, eval_masked_loss_t, "masked_loss_t_" + str(rank), "val")
+ viz.linePlot(epochId, eval_masked_loss_v, "masked_loss_v_" + str(rank), "val")
+ viz.linePlot(epochId, eval_next_sentence_loss, "next_sentence_loss_" + str(rank), "val")
+
+ if default_gpu:
+ # Save a trained model
+ logger.info("** ** * Saving fine - tuned model ** ** * ")
+ model_to_save = (
+ model.module if hasattr(model, "module") else model
+ ) # Only save the model it-self
+ output_model_file = os.path.join(
+ savePath, "pytorch_model_" + str(epochId) + ".bin"
+ )
+
+ torch.save(model_to_save.state_dict(), output_model_file)
+
+class TBlogger:
+ def __init__(self, log_dir, exp_name):
+ log_dir = log_dir + "/" + exp_name
+ print("logging file at: " + log_dir)
+ self.logger = SummaryWriter(log_dir=log_dir)
+
+ def linePlot(self, step, val, split, key, xlabel="None"):
+ self.logger.add_scalar(split + "/" + key, val, step)
+
+if __name__ == "__main__":
+
+ main()
diff --git a/train_tasks.py b/train_tasks.py
new file mode 100644
index 0000000..e6ead24
--- /dev/null
+++ b/train_tasks.py
@@ -0,0 +1,432 @@
+import argparse
+import json
+import logging
+import os
+import random
+from io import open
+import numpy as np
+
+from tensorboardX import SummaryWriter
+from tqdm import tqdm
+from bisect import bisect
+import yaml
+from easydict import EasyDict as edict
+
+import pdb
+import sys
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+
+from pytorch_pretrained_bert.optimization import WarmupLinearSchedule
+
+# from parallel.parallel import DataParallelModel, DataParallelCriterion
+
+from vilbert.task_utils import LoadDatasets, LoadLosses, ForwardModelsTrain, ForwardModelsVal
+from vilbert.optimization import BertAdam, Adam, Adamax
+from torch.optim.lr_scheduler import LambdaLR, ReduceLROnPlateau
+
+import vilbert.utils as utils
+import torch.distributed as dist
+
+logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+def main():
+ parser = argparse.ArgumentParser()
+
+ parser.add_argument(
+ "--bert_model",
+ default="bert-base-uncased",
+ type=str,
+ help="Bert pre-trained model selected in the list: bert-base-uncased, "
+ "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.",
+ )
+ parser.add_argument(
+ "--from_pretrained",
+ default="bert-base-uncased",
+ type=str,
+ help="Bert pre-trained model selected in the list: bert-base-uncased, "
+ "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.",
+ )
+ parser.add_argument(
+ "--output_dir",
+ default="save",
+ type=str,
+ help="The output directory where the model checkpoints will be written.",
+ )
+ parser.add_argument(
+ "--config_file",
+ default="config/bert_config.json",
+ type=str,
+ help="The config file which specified the model details.",
+ )
+ parser.add_argument(
+ "--learning_rate", default=2e-5, type=float, help="The initial learning rate for Adam."
+ )
+ parser.add_argument(
+ "--num_train_epochs",
+ default=20,
+ type=int,
+ help="Total number of training epochs to perform.",
+ )
+ parser.add_argument(
+ "--warmup_proportion",
+ default=0.1,
+ type=float,
+ help="Proportion of training to perform linear learning rate warmup for. "
+ "E.g., 0.1 = 10%% of training.",
+ )
+ parser.add_argument(
+ "--no_cuda", action="store_true", help="Whether not to use CUDA when available"
+ )
+ parser.add_argument(
+ "--do_lower_case",
+ default=True,
+ type=bool,
+ help="Whether to lower case the input text. True for uncased models, False for cased models.",
+ )
+ parser.add_argument(
+ "--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus"
+ )
+ parser.add_argument("--seed", type=int, default=0, help="random seed for initialization")
+ parser.add_argument(
+ "--gradient_accumulation_steps",
+ type=int,
+ default=1,
+ help="Number of updates steps to accumualte before performing a backward/update pass.",
+ )
+ parser.add_argument(
+ "--fp16",
+ action="store_true",
+ help="Whether to use 16-bit float precision instead of 32-bit",
+ )
+ parser.add_argument(
+ "--loss_scale",
+ type=float,
+ default=0,
+ help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
+ "0 (default value): dynamic loss scaling.\n"
+ "Positive power of 2: static loss scaling value.\n",
+ )
+ parser.add_argument(
+ "--num_workers", type=int, default=16, help="Number of workers in the dataloader."
+ )
+ parser.add_argument(
+ "--save_name",
+ default='',
+ type=str,
+ help="save name for training.",
+ )
+ parser.add_argument(
+ "--use_chunk", default=0, type=float, help="whether use chunck for parallel training."
+ )
+ parser.add_argument(
+ "--in_memory", default=False, type=bool, help="whether use chunck for parallel training."
+ )
+ parser.add_argument(
+ "--optimizer", default='BertAdam', type=str, help="whether use chunck for parallel training."
+ )
+ parser.add_argument(
+ "--tasks", default='', type=str, help="1-2-3... training task separate by -"
+ )
+ parser.add_argument(
+ "--freeze", default = -1, type=int,
+ help="till which layer of textual stream of vilbert need to fixed."
+ )
+ parser.add_argument(
+ "--vision_scratch", action="store_true", help="whether pre-trained the image or not."
+ )
+ parser.add_argument(
+ "--evaluation_interval", default=1, type=int, help="evaluate very n epoch."
+ )
+ parser.add_argument(
+ "--lr_scheduler", default='mannul', type=str, help="whether use learning rate scheduler."
+ )
+ parser.add_argument(
+ "--baseline", action="store_true", help="whether use single stream baseline."
+ )
+ parser.add_argument(
+ "--compact", action="store_true", help="whether use compact vilbert model."
+ )
+ args = parser.parse_args()
+ with open('vlbert_tasks.yml', 'r') as f:
+ task_cfg = edict(yaml.load(f))
+
+ # random.seed(args.seed)
+ # np.random.seed(args.seed)
+ # torch.manual_seed(args.seed)
+
+ if args.baseline:
+ from pytorch_pretrained_bert.modeling import BertConfig
+ from vilbert.basebert import BaseBertForVLTasks
+ elif args.compact:
+ from vilbert.vilbert_compact import BertConfig
+ from vilbert.vilbert_compact import VILBertForVLTasks
+ else:
+ from vilbert.vilbert import BertConfig
+ from vilbert.vilbert import VILBertForVLTasks
+
+ task_names = []
+ task_lr = []
+ for i, task_id in enumerate(args.tasks.split('-')):
+ task = 'TASK' + task_id
+ name = task_cfg[task]['name']
+ task_names.append(name)
+ task_lr.append(task_cfg[task]['lr'])
+
+ base_lr = min(task_lr)
+ loss_scale = {}
+ for i, task_id in enumerate(args.tasks.split('-')):
+ task = 'TASK' + task_id
+ loss_scale[task] = task_lr[i] / base_lr
+
+ if args.save_name:
+ prefix = '-' + args.save_name
+ else:
+ prefix = ''
+ timeStamp = '-'.join(task_names) + '_' + args.config_file.split('/')[1].split('.')[0] + prefix
+ savePath = os.path.join(args.output_dir, timeStamp)
+
+ bert_weight_name = json.load(open("config/" + args.bert_model + "_weight_name.json", "r"))
+
+ if args.local_rank == -1 or args.no_cuda:
+ device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
+ n_gpu = torch.cuda.device_count()
+ else:
+ torch.cuda.set_device(args.local_rank)
+ device = torch.device("cuda", args.local_rank)
+ n_gpu = 1
+ # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
+ torch.distributed.init_process_group(backend="nccl")
+
+ logger.info(
+ "device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
+ device, n_gpu, bool(args.local_rank != -1), args.fp16
+ )
+ )
+
+ default_gpu = False
+ if dist.is_available() and args.local_rank != -1:
+ rank = dist.get_rank()
+ if rank == 0:
+ default_gpu = True
+ else:
+ default_gpu = True
+
+ if default_gpu:
+ if not os.path.exists(savePath):
+ os.makedirs(savePath)
+
+ config = BertConfig.from_json_file(args.config_file)
+ if default_gpu:
+ # save all the hidden parameters.
+ with open(os.path.join(savePath, 'command.txt'), 'w') as f:
+ print(args, file=f) # Python 3.x
+ print('\n', file=f)
+ print(config, file=f)
+
+ task_batch_size, task_num_iters, task_ids, task_datasets_train, task_datasets_val, \
+ task_dataloader_train, task_dataloader_val = LoadDatasets(args, task_cfg, args.tasks.split('-'))
+
+ tbLogger = utils.tbLogger(timeStamp, savePath, task_names, task_ids, task_num_iters, args.gradient_accumulation_steps)
+
+ # if n_gpu > 0:
+ # torch.cuda.manual_seed_all(args.seed)
+
+ if not os.path.exists(args.output_dir):
+ os.makedirs(args.output_dir)
+
+ num_train_optimization_steps = max(task_num_iters.values()) * args.num_train_epochs // args.gradient_accumulation_steps
+ num_labels = max([dataset.num_labels for dataset in task_datasets_train.values()])
+
+ task_start_iter = {}
+ task_interval = {}
+ for task_id, num_iter in task_num_iters.items():
+ task_start_iter[task_id] = num_train_optimization_steps - (task_cfg[task]['num_epoch'] * num_iter // args.gradient_accumulation_steps)
+ task_interval[task_id] = num_train_optimization_steps // (task_cfg[task]['num_epoch'] * num_iter // args.gradient_accumulation_steps)
+
+ if args.baseline:
+ model = BaseBertForVLTasks.from_pretrained(
+ args.from_pretrained, config, num_labels=num_labels, default_gpu=default_gpu
+ )
+ else:
+ model = VILBertForVLTasks.from_pretrained(
+ args.from_pretrained, config, num_labels=num_labels, default_gpu=default_gpu
+ )
+
+ task_losses = LoadLosses(args, task_cfg, args.tasks.split('-'))
+ model.to(device)
+ if args.local_rank != -1:
+ try:
+ from apex.parallel import DistributedDataParallel as DDP
+ except ImportError:
+ raise ImportError(
+ "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
+ )
+ model = DDP(model, delay_allreduce=True)
+
+ elif n_gpu > 1:
+ model = torch.nn.DataParallel(model)
+
+ no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
+
+ if args.freeze != -1:
+ bert_weight_name_filtered = []
+ for name in bert_weight_name:
+ if 'embeddings' in name:
+ bert_weight_name_filtered.append(name)
+ elif 'encoder' in name:
+ layer_num = name.split('.')[2]
+ if int(layer_num) <= args.freeze:
+ bert_weight_name_filtered.append(name)
+
+ optimizer_grouped_parameters = []
+ for key, value in dict(model.named_parameters()).items():
+ if key[12:] in bert_weight_name_filtered:
+ value.requires_grad = False
+
+ if default_gpu:
+ print("filtered weight")
+ print(bert_weight_name_filtered)
+
+ optimizer_grouped_parameters = []
+ lr = args.learning_rate
+ for key, value in dict(model.named_parameters()).items():
+ if value.requires_grad:
+ if 'vil_prediction' in key:
+ # if args.learning_rate <= 2e-5:
+ lr = 1e-4
+ else:
+ if args.vision_scratch:
+ if key[12:] in bert_weight_name:
+ lr = args.learning_rate
+ else:
+ lr = 1e-4
+ else:
+ lr = args.learning_rate
+ if any(nd in key for nd in no_decay):
+ optimizer_grouped_parameters += [
+ {"params": [value], "lr": lr, "weight_decay": 0.01}
+ ]
+ if not any(nd in key for nd in no_decay):
+ optimizer_grouped_parameters += [
+ {"params": [value], "lr": lr, "weight_decay": 0.0}
+ ]
+
+ if default_gpu:
+ print(len(list(model.named_parameters())), len(optimizer_grouped_parameters))
+
+ max_num_iter = max(task_num_iters.values())
+ max_batch_size = max(task_batch_size.values())
+
+ if args.optimizer == 'BertAdam':
+ optimizer = BertAdam(
+ optimizer_grouped_parameters,
+ lr=args.learning_rate,
+ warmup=args.warmup_proportion,
+ t_total=num_train_optimization_steps,
+ schedule='warmup_constant',
+ )
+ elif args.optimizer == 'Adam':
+ optimizer = Adam(
+ optimizer_grouped_parameters,
+ lr=base_lr,
+ warmup=args.warmup_proportion,
+ t_total=num_train_optimization_steps,
+ schedule='warmup_constant',
+ )
+ elif args.optimizer == 'Adamax':
+ optimizer = Adamax(
+ optimizer_grouped_parameters,
+ lr=base_lr,
+ warmup=args.warmup_proportion,
+ t_total=num_train_optimization_steps,
+ schedule='warmup_constant',
+ )
+
+ if args.lr_scheduler == 'automatic':
+ lr_scheduler = ReduceLROnPlateau(optimizer, \
+ mode='max',
+ factor=0.2,
+ patience=1,
+ cooldown=1,
+ threshold=0.001)
+ elif args.lr_scheduler == 'mannul':
+ lr_reduce_list = np.array([12, 16])
+ # lr_reduce_list = np.array([6, 8, 10])
+ def lr_lambda_fun(epoch):
+ return pow(0.1, np.sum(lr_reduce_list <= epoch))
+ lr_scheduler = LambdaLR(optimizer, lr_lambda=lr_lambda_fun)
+
+ if default_gpu:
+ print("***** Running training *****")
+ print(" Num Iters: ", task_num_iters)
+ print(" Batch size: ", task_batch_size)
+ print(" Num steps: %d" %num_train_optimization_steps)
+
+ startIterID = 0
+ # initialize the data iteration.
+ task_iter_train = {name:None for name in task_ids}
+ task_count = {name:0 for name in task_ids}
+ for epochId in tqdm(range(args.num_train_epochs), desc="Epoch"):
+ model.train()
+ for step in range(max_num_iter):
+ iterId = startIterID + step + (epochId * max_num_iter)
+ for task_id in task_ids:
+ if iterId >= task_start_iter[task_id]:
+ # if iterId % task_interval[task_id] == 0:
+ loss, score = ForwardModelsTrain(args, task_cfg, device, task_id, task_count, task_iter_train, task_dataloader_train, model, task_losses, task_start_iter)
+ loss = loss * loss_scale[task_id]
+ if args.gradient_accumulation_steps > 1:
+ loss = loss / args.gradient_accumulation_steps
+
+ loss.backward()
+ if (step + 1) % args.gradient_accumulation_steps == 0:
+ optimizer.step()
+ model.zero_grad()
+
+ if default_gpu:
+ tbLogger.step_train(epochId, iterId, float(loss), float(score), optimizer.show_lr(), task_id, 'train')
+
+ if step % (20 * args.gradient_accumulation_steps) == 0 and step != 0 and default_gpu:
+ tbLogger.showLossTrain()
+
+ model.eval()
+ # when run evaluate, we run each task sequentially.
+ for task_id in task_ids:
+ for i, batch in enumerate(task_dataloader_val[task_id]):
+ loss, score, batch_size = ForwardModelsVal(args, task_cfg, device, task_id, batch, model, task_losses)
+ tbLogger.step_val(epochId, float(loss), float(score), task_id, batch_size, 'val')
+ if default_gpu:
+ sys.stdout.write('%d/%d\r' % (i, len(task_dataloader_val[task_id])))
+ sys.stdout.flush()
+
+ ave_score = tbLogger.showLossVal()
+ if args.lr_scheduler == 'automatic':
+ lr_scheduler.step(ave_score)
+ logger.info("best average score is %3f" %lr_scheduler.best)
+ else:
+ lr_scheduler.step()
+
+ if default_gpu:
+ # Save a trained model
+ logger.info("** ** * Saving fine - tuned model on " + timeStamp + "** ** * ")
+ model_to_save = (
+ model.module if hasattr(model, "module") else model
+ ) # Only save the model it-self
+
+ if not os.path.exists(savePath):
+ os.makedirs(savePath)
+ output_model_file = os.path.join(savePath, "pytorch_model_" + str(epochId) + ".bin")
+ torch.save(model_to_save.state_dict(), output_model_file)
+
+ tbLogger.txt_close()
+
+if __name__ == "__main__":
+
+ main()
\ No newline at end of file
diff --git a/vilbert/__init__.py b/vilbert/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/vilbert/basebert.py b/vilbert/basebert.py
new file mode 100644
index 0000000..f30b301
--- /dev/null
+++ b/vilbert/basebert.py
@@ -0,0 +1,901 @@
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import copy
+import json
+import logging
+import math
+import os
+import shutil
+import tarfile
+import tempfile
+import sys
+from io import open
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.nn import CrossEntropyLoss
+from vilbert.utils import cached_path
+from pytorch_pretrained_bert.modeling import BertConfig
+import pdb
+from torch.nn.utils.weight_norm import weight_norm
+
+# from .file_utils import cached_path
+logger = logging.getLogger(__name__)
+
+
+PRETRAINED_MODEL_ARCHIVE_MAP = {
+ "bert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
+ "bert-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
+ "bert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
+ "bert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
+ "bert-base-multilingual-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
+ "bert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
+ "bert-base-chinese": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
+}
+CONFIG_NAME = "bert_config.json"
+WEIGHTS_NAME = "pytorch_model.bin"
+TF_WEIGHTS_NAME = "model.ckpt"
+
+def gelu(x):
+ """Implementation of the gelu activation function.
+ For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
+ 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
+ Also see https://arxiv.org/abs/1606.08415
+ """
+ return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
+
+
+def swish(x):
+ return x * torch.sigmoid(x)
+
+ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
+
+
+try:
+ from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
+except ImportError:
+ logger.info("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .")
+ class BertLayerNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-12):
+ """Construct a layernorm module in the TF style (epsilon inside the square root).
+ """
+ super(BertLayerNorm, self).__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.bias = nn.Parameter(torch.zeros(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, x):
+ u = x.mean(-1, keepdim=True)
+ s = (x - u).pow(2).mean(-1, keepdim=True)
+ x = (x - u) / torch.sqrt(s + self.variance_epsilon)
+ return self.weight * x + self.bias
+
+
+class BertPreTrainedModel(nn.Module):
+ """ An abstract class to handle weights initialization and
+ a simple interface for dowloading and loading pretrained models.
+ """
+
+ def __init__(self, config, *inputs, **kwargs):
+ super(BertPreTrainedModel, self).__init__()
+
+ if not isinstance(config, BertConfig):
+ raise ValueError(
+ "Parameter config in `{}(config)` should be an instance of class `BertConfig`. "
+ "To create a model from a Google pretrained model use "
+ "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
+ self.__class__.__name__, self.__class__.__name__
+ )
+ )
+
+ self.config = config
+
+ def init_bert_weights(self, module):
+ """ Initialize the weights.
+ """
+ if isinstance(module, (nn.Linear, nn.Embedding)):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ elif isinstance(module, BertLayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ if isinstance(module, nn.Linear) and module.bias is not None:
+ module.bias.data.zero_()
+
+ @classmethod
+ def from_pretrained(
+ cls,
+ pretrained_model_name_or_path,
+ config,
+ state_dict=None,
+ cache_dir=None,
+ from_tf=False,
+ *inputs,
+ **kwargs
+ ):
+ """
+ Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict.
+ Download and cache the pre-trained model file if needed.
+
+ Params:
+ pretrained_model_name_or_path: either:
+ - a str with the name of a pre-trained model to load selected in the list of:
+ . `bert-base-uncased`
+ . `bert-large-uncased`
+ . `bert-base-cased`
+ . `bert-large-cased`
+ . `bert-base-multilingual-uncased`
+ . `bert-base-multilingual-cased`
+ . `bert-base-chinese`
+ - a path or url to a pretrained model archive containing:
+ . `bert_config.json` a configuration file for the model
+ . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance
+ - a path or url to a pretrained model archive containing:
+ . `bert_config.json` a configuration file for the model
+ . `model.chkpt` a TensorFlow checkpoint
+ from_tf: should we load the weights from a locally saved TensorFlow checkpoint
+ cache_dir: an optional path to a folder in which the pre-trained models will be cached.
+ state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
+ *inputs, **kwargs: additional input for the specific Bert class
+ (ex: num_labels for BertForSequenceClassification)
+ """
+ CONFIG_NAME = "bert_config.json"
+ WEIGHTS_NAME = "pytorch_model.bin"
+ TF_WEIGHTS_NAME = "model.ckpt"
+
+ if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
+ archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
+ else:
+ archive_file = pretrained_model_name_or_path
+ # redirect to the cache, if necessary
+ try:
+ resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
+ except EnvironmentError:
+ logger.error(
+ "Model name '{}' was not found in model name list ({}). "
+ "We assumed '{}' was a path or url but couldn't find any file "
+ "associated to this path or url.".format(
+ pretrained_model_name_or_path,
+ ", ".join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
+ archive_file,
+ )
+ )
+ return None
+
+ if resolved_archive_file == archive_file:
+ logger.info("loading archive file {}".format(archive_file))
+ else:
+ logger.info(
+ "loading archive file {} from cache at {}".format(
+ archive_file, resolved_archive_file
+ )
+ )
+ tempdir = None
+ if os.path.isdir(resolved_archive_file) or from_tf:
+ serialization_dir = resolved_archive_file
+ elif resolved_archive_file[-3:] == 'bin':
+ serialization_dir = '/'.join(resolved_archive_file.split('/')[:-1])
+ WEIGHTS_NAME = resolved_archive_file.split('/')[-1]
+ else:
+ # Extract archive to temp dir
+ tempdir = tempfile.mkdtemp()
+ logger.info(
+ "extracting archive file {} to temp dir {}".format(
+ resolved_archive_file, tempdir
+ )
+ )
+ with tarfile.open(resolved_archive_file, "r:gz") as archive:
+ archive.extractall(tempdir)
+ serialization_dir = tempdir
+ # Load config
+ # config_file = os.path.join(serialization_dir, CONFIG_NAME)
+ # config = BertConfig.from_json_file(config_file)
+ logger.info("Model config {}".format(config))
+ # Instantiate model.
+ model = cls(config, *inputs, **kwargs)
+ if state_dict is None and not from_tf:
+ weights_path = os.path.join(serialization_dir, WEIGHTS_NAME)
+ state_dict = torch.load(
+ weights_path,
+ map_location="cpu",
+ )
+ if tempdir:
+ # Clean up temp dir
+ shutil.rmtree(tempdir)
+ if from_tf:
+ # Directly load from a TensorFlow checkpoint
+ weights_path = os.path.join(serialization_dir, TF_WEIGHTS_NAME)
+ return load_tf_weights_in_bert(model, weights_path)
+ # Load from a PyTorch state_dict
+ old_keys = []
+ new_keys = []
+ for key in state_dict.keys():
+ new_key = None
+ if "gamma" in key:
+ new_key = key.replace("gamma", "weight")
+ if "beta" in key:
+ new_key = key.replace("beta", "bias")
+ if new_key:
+ old_keys.append(key)
+ new_keys.append(new_key)
+ for old_key, new_key in zip(old_keys, new_keys):
+ state_dict[new_key] = state_dict.pop(old_key)
+
+ missing_keys = []
+ unexpected_keys = []
+ error_msgs = []
+ # copy state_dict so _load_from_state_dict can modify it
+ metadata = getattr(state_dict, "_metadata", None)
+ state_dict = state_dict.copy()
+ if metadata is not None:
+ state_dict._metadata = metadata
+
+ def load(module, prefix=""):
+ local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
+ module._load_from_state_dict(
+ state_dict,
+ prefix,
+ local_metadata,
+ True,
+ missing_keys,
+ unexpected_keys,
+ error_msgs,
+ )
+ for name, child in module._modules.items():
+ if child is not None:
+ load(child, prefix + name + ".")
+
+ start_prefix = ""
+ if not hasattr(model, "bert") and any(
+ s.startswith("bert.") for s in state_dict.keys()
+ ):
+ start_prefix = "bert."
+ load(model, prefix=start_prefix)
+ if len(missing_keys) > 0:
+ logger.info(
+ "Weights of {} not initialized from pretrained model: {}".format(
+ model.__class__.__name__, missing_keys
+ )
+ )
+ if len(unexpected_keys) > 0:
+ logger.info(
+ "Weights from pretrained model not used in {}: {}".format(
+ model.__class__.__name__, unexpected_keys
+ )
+ )
+ if len(error_msgs) > 0:
+ raise RuntimeError(
+ "Error(s) in loading state_dict for {}:\n\t{}".format(
+ model.__class__.__name__, "\n\t".join(error_msgs)
+ )
+ )
+ return model
+
+class BertEmbeddings(nn.Module):
+ """Construct the embeddings from word, position and token_type embeddings.
+ """
+ def __init__(self, config):
+ super(BertEmbeddings, self).__init__()
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=0)
+ self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size, padding_idx=0)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size, padding_idx=0)
+
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+ # any TensorFlow checkpoint file
+ self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, input_ids, token_type_ids=None):
+ seq_length = input_ids.size(1)
+ position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+ position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+ if token_type_ids is None:
+ token_type_ids = torch.zeros_like(input_ids)
+
+ words_embeddings = self.word_embeddings(input_ids)
+ position_embeddings = self.position_embeddings(position_ids)
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ embeddings = words_embeddings + position_embeddings + token_type_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+class BertImageEmbeddings(nn.Module):
+ """Construct the embeddings from image, spatial location (omit now) and token_type embeddings.
+ """
+ def __init__(self, config):
+ super(BertImageEmbeddings, self).__init__()
+ self.image_embeddings = nn.Linear(2048, config.hidden_size)
+ # self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size, padding_idx=0)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size, padding_idx=0)
+ self.image_location_embeddings = nn.Linear(5, config.hidden_size)
+
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+ # any TensorFlow checkpoint file
+ self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, input_ids, input_loc, token_type_ids=None):
+ seq_length = input_ids.size(1)
+ # position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+ # position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+ if token_type_ids is None:
+ token_type_ids = torch.zeros_like(input_ids)
+
+ image_embeddings = self.image_embeddings(input_ids)
+ # position_embeddings = self.position_embeddings(position_ids)
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+ loc_embeddings = self.image_location_embeddings(input_loc)
+
+ # embeddings = words_embeddings + position_embeddings + token_type_embeddings
+ embeddings = image_embeddings + token_type_embeddings + loc_embeddings
+
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+class BertSelfAttention(nn.Module):
+ def __init__(self, config):
+ super(BertSelfAttention, self).__init__()
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ "The hidden size (%d) is not a multiple of the number of attention "
+ "heads (%d)" % (config.hidden_size, config.num_attention_heads))
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+ def transpose_for_scores(self, x):
+ new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+ x = x.view(*new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def forward(self, hidden_states, attention_mask):
+ mixed_query_layer = self.query(hidden_states)
+ mixed_key_layer = self.key(hidden_states)
+ mixed_value_layer = self.value(hidden_states)
+
+ query_layer = self.transpose_for_scores(mixed_query_layer)
+ key_layer = self.transpose_for_scores(mixed_key_layer)
+ value_layer = self.transpose_for_scores(mixed_value_layer)
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+ # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(*new_context_layer_shape)
+ return context_layer
+
+
+class BertSelfOutput(nn.Module):
+ def __init__(self, config):
+ super(BertSelfOutput, self).__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class BertAttention(nn.Module):
+ def __init__(self, config):
+ super(BertAttention, self).__init__()
+ self.self = BertSelfAttention(config)
+ self.output = BertSelfOutput(config)
+
+ def forward(self, input_tensor, attention_mask):
+ self_output = self.self(input_tensor, attention_mask)
+ attention_output = self.output(self_output, input_tensor)
+ return attention_output
+
+
+class BertIntermediate(nn.Module):
+ def __init__(self, config):
+ super(BertIntermediate, self).__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+class BertOutput(nn.Module):
+ def __init__(self, config):
+ super(BertOutput, self).__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class BertLayer(nn.Module):
+ def __init__(self, config):
+ super(BertLayer, self).__init__()
+ self.attention = BertAttention(config)
+ self.intermediate = BertIntermediate(config)
+ self.output = BertOutput(config)
+
+ def forward(self, hidden_states, attention_mask):
+ attention_output = self.attention(hidden_states, attention_mask)
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+
+class BertEncoder(nn.Module):
+ def __init__(self, config):
+ super(BertEncoder, self).__init__()
+ layer = BertLayer(config)
+ self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
+
+ def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
+ all_encoder_layers = []
+ for layer_module in self.layer:
+ hidden_states = layer_module(hidden_states, attention_mask)
+ if output_all_encoded_layers:
+ all_encoder_layers.append(hidden_states)
+ if not output_all_encoded_layers:
+ all_encoder_layers.append(hidden_states)
+ return all_encoder_layers
+
+
+class BertPooler(nn.Module):
+ def __init__(self, config):
+ super(BertPooler, self).__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.activation = nn.Tanh()
+
+ def forward(self, hidden_states):
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+class BertPredictionHeadTransform(nn.Module):
+ def __init__(self, config):
+ super(BertPredictionHeadTransform, self).__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
+ self.transform_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.transform_act_fn = config.hidden_act
+ self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+ def __init__(self, config, bert_model_embedding_weights):
+ super(BertLMPredictionHead, self).__init__()
+ self.transform = BertPredictionHeadTransform(config)
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
+ bert_model_embedding_weights.size(0),
+ bias=False)
+ self.decoder.weight = bert_model_embedding_weights
+ self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0)))
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states) + self.bias
+ return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+ def __init__(self, config, bert_model_embedding_weights):
+ super(BertOnlyMLMHead, self).__init__()
+ self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
+
+ def forward(self, sequence_output):
+ prediction_scores = self.predictions(sequence_output)
+ return prediction_scores
+
+
+class BertOnlyNSPHead(nn.Module):
+ def __init__(self, config):
+ super(BertOnlyNSPHead, self).__init__()
+ self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+ def forward(self, pooled_output):
+ seq_relationship_score = self.seq_relationship(pooled_output)
+ return seq_relationship_score
+
+class BertPredictionHeadTransform(nn.Module):
+ def __init__(self, config):
+ super(BertPredictionHeadTransform, self).__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
+ self.transform_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.transform_act_fn = config.hidden_act
+ self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+ def __init__(self, config, bert_model_embedding_weights):
+ super(BertLMPredictionHead, self).__init__()
+ self.transform = BertPredictionHeadTransform(config)
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
+ bert_model_embedding_weights.size(0),
+ bias=False)
+ self.decoder.weight = bert_model_embedding_weights
+ self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0)))
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states) + self.bias
+ return hidden_states
+
+class BertImagePredictionHead(nn.Module):
+ def __init__(self, config, bert_model_embedding_weights):
+ super(BertImagePredictionHead, self).__init__()
+ self.transform = BertPredictionHeadTransform(config)
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
+ 1601)
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states)
+ return hidden_states
+
+
+class BertPreTrainingHeads(nn.Module):
+ def __init__(self, config, bert_model_embedding_weights):
+ super(BertPreTrainingHeads, self).__init__()
+ self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
+ self.seq_relationship = nn.Linear(config.hidden_size, 2)
+ self.imagePredictions = BertImagePredictionHead(config, bert_model_embedding_weights)
+
+ def forward(self, sequence_output_t, sequence_output_v, pooled_output):
+
+ img_prediction_scores = self.imagePredictions(sequence_output_v) #sequence_output[:,txt_len:])
+ prediction_scores = self.predictions(sequence_output_t) #sequence_output[:,:txt_len])
+
+ seq_relationship_score = self.seq_relationship(pooled_output)
+ return img_prediction_scores, prediction_scores, seq_relationship_score
+
+class BertModel(BertPreTrainedModel):
+ """BERT model ("Bidirectional Embedding Representations from a Transformer").
+ Params:
+ config: a BertConfig class instance with the configuration to build a new model
+ Inputs:
+ `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+ with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+ `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+ `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+ types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+ a `sentence B` token (see BERT paper for more details).
+ `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+ selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+ input sequence length in the current batch. It's the mask that we typically use for attention when
+ a batch has varying length sentences.
+ `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
+ Outputs: Tuple of (encoded_layers, pooled_output)
+ `encoded_layers`: controled by `output_all_encoded_layers` argument:
+ - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
+ of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
+ encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
+ - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
+ to the last attention block of shape [batch_size, sequence_length, hidden_size],
+ `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
+ classifier pretrained on top of the hidden state associated to the first character of the
+ input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
+ Example usage:
+ ```python
+ # Already been converted into WordPiece token ids
+ input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+ input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+ token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+ config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+ num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+ model = modeling.BertModel(config=config)
+ all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
+ ```
+ """
+ def __init__(self, config):
+ super(BertModel, self).__init__(config)
+ self.image_embeddings = BertImageEmbeddings(config)
+ self.embeddings = BertEmbeddings(config)
+ self.encoder = BertEncoder(config)
+ self.pooler = BertPooler(config)
+ self.apply(self.init_bert_weights)
+
+ def forward(
+ self,
+ input_txt,
+ input_imgs,
+ image_loc,
+ token_type_ids=None,
+ attention_mask=None,
+ image_attention_mask=None,
+ output_all_encoded_layers=True,
+ ):
+
+ if attention_mask is None:
+ attention_mask = torch.ones_like(input_txt)
+ if token_type_ids is None:
+ token_type_ids = torch.zeros_like(input_txt)
+ if image_attention_mask is None:
+ image_attention_mask = torch.ones(
+ input_imgs.size(0), input_imgs.size(1)
+ ).type_as(input_txt)
+
+ image_token_type_ids = torch.ones(input_imgs.size(0), input_imgs.size(1)).type_as(token_type_ids)
+
+ # We create a 3D attention mask from a 2D tensor mask.
+ # Sizes are [batch_size, 1, 1, to_seq_length]
+ # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+ # this attention mask is more simple than the triangular masking of causal attention
+ # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+ extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+ extended_image_attention_mask = image_attention_mask.unsqueeze(1).unsqueeze(2)
+
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+ # masked positions, this operation will create a tensor which is 0.0 for
+ # positions we want to attend and -10000.0 for masked positions.
+ # Since we are adding it to the raw scores before the softmax, this is
+ # effectively the same as removing these entirely.
+ extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
+ extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+
+ extended_image_attention_mask = extended_image_attention_mask.to(
+ dtype=next(self.parameters()).dtype
+ ) # fp16 compatibility
+ extended_image_attention_mask = (1.0 - extended_image_attention_mask) * -10000.0
+
+ img_embeding_output = self.image_embeddings(input_imgs, image_loc, image_token_type_ids)
+
+ embedding_output = self.embeddings(input_txt, token_type_ids)
+
+ embedding_output = torch.cat([embedding_output, img_embeding_output], dim=1)
+ extended_attention_mask = torch.cat([extended_attention_mask, extended_image_attention_mask], dim=3)
+
+ encoded_layers = self.encoder(embedding_output,
+ extended_attention_mask,
+ output_all_encoded_layers=output_all_encoded_layers)
+
+ sequence_output = encoded_layers[-1]
+ pooled_output = self.pooler(sequence_output)
+ if not output_all_encoded_layers:
+ encoded_layers = encoded_layers[-1]
+ return encoded_layers, pooled_output
+
+class BertForMultiModalPreTraining(BertPreTrainedModel):
+ """BERT model with multi modal pre-training heads.
+ This module comprises the BERT model followed by the two pre-training heads:
+ - the masked multi modal modeling head, and
+ - the image caption classification head.
+
+ Params:
+ config: a BertConfig class instance with the configuration to build a new model.
+
+ Inputs:
+ `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+ with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+ `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+ `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+ types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+ a `sentence B` token (see BERT paper for more details).
+ `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+ selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+ input sequence length in the current batch. It's the mask that we typically use for attention when
+ a batch has varying length sentences.
+ `masked_lm_labels`: optional masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
+ with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
+ is only computed for the labels set in [0, ..., vocab_size]
+ `next_sentence_label`: optional next sentence classification loss: torch.LongTensor of shape [batch_size]
+ with indices selected in [0, 1].
+ 0 => next sentence is the continuation, 1 => next sentence is a random sentence.
+
+ Outputs:
+ if `masked_lm_labels` and `next_sentence_label` are not `None`:
+ Outputs the total_loss which is the sum of the masked language modeling loss and the next
+ sentence classification loss.
+ if `masked_lm_labels` or `next_sentence_label` is `None`:
+ Outputs a tuple comprising
+ - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and
+ - the next sentence classification logits of shape [batch_size, 2].
+
+ Example usage:
+ ```python
+ # Already been converted into WordPiece token ids
+ input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+ input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+ token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+ config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+ num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+ model = BertForPreTraining(config)
+ masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
+ ```
+ """
+ def __init__(self, config):
+ super(BertForMultiModalPreTraining, self).__init__(config)
+ self.bert = BertModel(config)
+ self.cls = BertPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight)
+ self.apply(self.init_bert_weights)
+
+ self.vis_criterion = nn.KLDivLoss(reduction="none")
+ self.loss_fct = CrossEntropyLoss(ignore_index=-1)
+
+ def forward(
+ self,
+ input_ids,
+ image_feat,
+ image_target,
+ image_loc,
+ token_type_ids=None,
+ attention_mask=None,
+ image_attention_mask=None,
+ masked_lm_labels=None,
+ image_label=None,
+ next_sentence_label=None,
+ ):
+ # in this model, we first embed the images.
+
+ sequence_output, pooled_output = self.bert(
+ input_ids,
+ image_feat,
+ image_loc,
+ token_type_ids,
+ attention_mask,
+ image_attention_mask,
+ output_all_encoded_layers=False,
+ )
+
+ prediction_scores_v, prediction_scores_t, seq_relationship_score = self.cls(sequence_output, pooled_output, input_ids.size(1), image_feat.size(1))
+
+ if masked_lm_labels is not None and next_sentence_label is not None:
+
+ prediction_scores_v = prediction_scores_v[:, 1:]
+
+ img_loss = self.vis_criterion(
+ F.log_softmax(prediction_scores_v, dim=2), image_target
+ )
+ masked_img_loss = torch.sum(
+ img_loss * (image_label == 1).unsqueeze(2).float()
+ ) / max(torch.sum((image_label == 1)), 0)
+
+ masked_lm_loss = self.loss_fct(
+ prediction_scores_t.view(-1, self.config.vocab_size),
+ masked_lm_labels.view(-1),
+ )
+
+ next_sentence_loss = self.loss_fct(
+ seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)
+ )
+
+ return masked_lm_loss, masked_img_loss, next_sentence_loss
+ else:
+ return prediction_scores, seq_relationship_score, prediction_scores_v
+
+class BaseBertForVLTasks(BertPreTrainedModel):
+ def __init__(self, config, num_labels, dropout_prob=0.1, default_gpu=True):
+ super(BaseBertForVLTasks, self).__init__(config)
+ self.num_labels = num_labels
+ self.bert = BertModel(config)
+ self.dropout = nn.Dropout(dropout_prob)
+ self.cls = BertPreTrainingHeads(
+ config, self.bert.embeddings.word_embeddings.weight
+ )
+ self.vil_prediction = SimpleClassifier(config.hidden_size, config.hidden_size*2, num_labels, 0.5)
+ # self.vil_prediction = nn.Linear(config.bi_hidden_size, num_labels)
+ self.vil_logit = nn.Linear(config.hidden_size, 1)
+ self.vision_logit = nn.Linear(config.hidden_size, 1)
+ self.linguisic_logit = nn.Linear(config.hidden_size, 1)
+ self.apply(self.init_bert_weights)
+
+ def forward(
+ self,
+ input_txt,
+ input_imgs,
+ image_loc,
+ token_type_ids=None,
+ attention_mask=None,
+ image_attention_mask=None,
+ co_attention_mask=None,
+ output_all_encoded_layers=False,
+ ):
+ sequence_output, pooled_output = self.bert(
+ input_txt,
+ input_imgs,
+ image_loc,
+ token_type_ids,
+ attention_mask,
+ image_attention_mask,
+ output_all_encoded_layers=output_all_encoded_layers,
+ )
+
+ sequence_output_v = sequence_output[:,input_txt.size(1):]
+ sequence_output_t = sequence_output[:,:input_txt.size(1)]
+
+ vil_prediction = 0
+ vil_logit = 0
+ vil_binary_prediction = 0
+ vision_prediction = 0
+ vision_logit = 0
+ linguisic_prediction = 0
+ linguisic_logit = 0
+
+ vision_prediction, linguisic_prediction, vil_binary_prediction = self.cls(sequence_output_t, sequence_output_v, pooled_output)
+
+ vil_prediction = self.vil_prediction(pooled_output)
+ vil_logit = self.vil_logit(pooled_output)
+ vision_logit = self.vision_logit(self.dropout(sequence_output_v)) + ((1.0 - image_attention_mask)* -10000.0).unsqueeze(2).to(dtype=next(self.parameters()).dtype)
+ linguisic_logit = self.linguisic_logit(self.dropout(sequence_output_t))
+
+ return vil_prediction, vil_logit, vil_binary_prediction, vision_prediction, vision_logit, linguisic_prediction, linguisic_logit
+
+class SimpleClassifier(nn.Module):
+ def __init__(self, in_dim, hid_dim, out_dim, dropout):
+ super(SimpleClassifier, self).__init__()
+ layers = [
+ weight_norm(nn.Linear(in_dim, hid_dim), dim=None),
+ nn.ReLU(),
+ nn.Dropout(dropout, inplace=True),
+ weight_norm(nn.Linear(hid_dim, out_dim), dim=None)
+ ]
+ self.main = nn.Sequential(*layers)
+
+ def forward(self, x):
+ logits = self.main(x)
+ return logits
diff --git a/vilbert/datasets/__init__.py b/vilbert/datasets/__init__.py
new file mode 100644
index 0000000..a06c537
--- /dev/null
+++ b/vilbert/datasets/__init__.py
@@ -0,0 +1,36 @@
+from .concept_cap_dataset import ConceptCapLoaderTrain, ConceptCapLoaderVal, ConceptCapLoaderRetrieval
+from .vqa_dataset import VQAClassificationDataset
+from .refer_expression_dataset import ReferExpressionDataset
+from .retreival_dataset import RetreivalDataset, RetreivalDatasetVal
+from .vcr_dataset import VCRDataset
+
+
+
+# from .flickr_retreival_dataset import FlickrRetreivalDatasetTrain, FlickrRetreivalDatasetVal
+
+__all__ = ["FoilClassificationDataset", \
+ "VQAClassificationDataset", \
+ "ConceptCapLoaderTrain", \
+ "ConceptCapLoaderVal", \
+ "ReferExpressionDataset", \
+ "RetreivalDataset", \
+ "RetreivalDatasetVal",\
+ "VCRDataset", \
+ "ConceptCapLoaderRetrieval"]
+
+DatasetMapTrain = {'TASK0': ConceptCapLoaderTrain,
+ 'TASK1': VQAClassificationDataset,
+ 'TASK2': VCRDataset,
+ 'TASK3': VCRDataset,
+ 'TASK4': RetreivalDataset,
+ 'TASK5': ReferExpressionDataset,
+ }
+
+
+DatasetMapEval = {'TASK0': ConceptCapLoaderVal,
+ 'TASK1': VQAClassificationDataset,
+ 'TASK2': VCRDataset,
+ 'TASK3': VCRDataset,
+ 'TASK4': RetreivalDatasetVal,
+ 'TASK5': ReferExpressionDataset,
+ }
diff --git a/vilbert/datasets/_image_features_reader.py b/vilbert/datasets/_image_features_reader.py
new file mode 100644
index 0000000..8735f9d
--- /dev/null
+++ b/vilbert/datasets/_image_features_reader.py
@@ -0,0 +1,138 @@
+from typing import List
+import csv
+import h5py
+import numpy as np
+import copy
+import pickle
+import lmdb # install lmdb by "pip install lmdb"
+import base64
+import pdb
+
+class ImageFeaturesH5Reader(object):
+ """
+ A reader for H5 files containing pre-extracted image features. A typical
+ H5 file is expected to have a column named "image_id", and another column
+ named "features".
+
+ Example of an H5 file:
+ ```
+ faster_rcnn_bottomup_features.h5
+ |--- "image_id" [shape: (num_images, )]
+ |--- "features" [shape: (num_images, num_proposals, feature_size)]
+ +--- .attrs ("split", "train")
+ ```
+ # TODO (kd): Add support to read boxes, classes and scores.
+
+ Parameters
+ ----------
+ features_h5path : str
+ Path to an H5 file containing COCO train / val image features.
+ in_memory : bool
+ Whether to load the whole H5 file in memory. Beware, these files are
+ sometimes tens of GBs in size. Set this to true if you have sufficient
+ RAM - trade-off between speed and memory.
+ """
+ def __init__(self, features_path: str, in_memory: bool = False):
+ self.features_path = features_path
+ self._in_memory = in_memory
+
+ # with h5py.File(self.features_h5path, "r", libver='latest', swmr=True) as features_h5:
+ # self._image_ids = list(features_h5["image_ids"])
+ # If not loaded in memory, then list of None.
+ self.env = lmdb.open(self.features_path, max_readers=1, readonly=True,
+ lock=False, readahead=False, meminit=False)
+
+ with self.env.begin(write=False) as txn:
+ self._image_ids = pickle.loads(txn.get('keys'.encode()))
+
+ self.features = [None] * len(self._image_ids)
+ self.num_boxes = [None] * len(self._image_ids)
+ self.boxes = [None] * len(self._image_ids)
+ self.boxes_ori = [None] * len(self._image_ids)
+
+ def __len__(self):
+ return len(self._image_ids)
+
+ def __getitem__(self, image_id):
+ image_id = str(image_id).encode()
+ index = self._image_ids.index(image_id)
+ if self._in_memory:
+ # Load features during first epoch, all not loaded together as it
+ # has a slow start.
+ if self.features[index] is not None:
+ features = self.features[index]
+ num_boxes = self.num_boxes[index]
+ image_location = self.boxes[index]
+ image_location_ori = self.boxes_ori[index]
+ else:
+ with self.env.begin(write=False) as txn:
+ item = pickle.loads(txn.get(image_id))
+ image_id = item['image_id']
+ image_h = int(item['image_h'])
+ image_w = int(item['image_w'])
+ num_boxes = int(item['num_boxes'])
+
+ features = np.frombuffer(base64.b64decode(item["features"]), dtype=np.float32).reshape(num_boxes, 2048)
+ boxes = np.frombuffer(base64.b64decode(item['boxes']), dtype=np.float32).reshape(num_boxes, 4)
+
+ g_feat = np.sum(features, axis=0) / num_boxes
+ num_boxes = num_boxes + 1
+
+ features = np.concatenate([np.expand_dims(g_feat, axis=0), features], axis=0)
+ self.features[index] = features
+
+ image_location = np.zeros((boxes.shape[0], 5), dtype=np.float32)
+ image_location[:,:4] = boxes
+ image_location[:,4] = (image_location[:,3] - image_location[:,1]) * (image_location[:,2] - image_location[:,0]) / (float(image_w) * float(image_h))
+
+ image_location_ori = copy.deepcopy(image_location)
+
+ image_location[:,0] = image_location[:,0] / float(image_w)
+ image_location[:,1] = image_location[:,1] / float(image_h)
+ image_location[:,2] = image_location[:,2] / float(image_w)
+ image_location[:,3] = image_location[:,3] / float(image_h)
+
+ g_location = np.array([0,0,1,1,1])
+ image_location = np.concatenate([np.expand_dims(g_location, axis=0), image_location], axis=0)
+ self.boxes[index] = image_location
+
+ g_location_ori = np.array([0,0,image_w,image_h,image_w*image_h])
+ image_location_ori = np.concatenate([np.expand_dims(g_location_ori, axis=0), image_location_ori], axis=0)
+ self.boxes_ori[index] = image_location_ori
+ self.num_boxes[index] = num_boxes
+ else:
+ # Read chunk from file everytime if not loaded in memory.
+ with self.env.begin(write=False) as txn:
+ item = pickle.loads(txn.get(image_id))
+ image_id = item['image_id']
+ image_h = int(item['image_h'])
+ image_w = int(item['image_w'])
+ num_boxes = int(item['num_boxes'])
+
+ features = np.frombuffer(base64.b64decode(item["features"]), dtype=np.float32).reshape(num_boxes, 2048)
+ boxes = np.frombuffer(base64.b64decode(item['boxes']), dtype=np.float32).reshape(num_boxes, 4)
+ g_feat = np.sum(features, axis=0) / num_boxes
+ num_boxes = num_boxes + 1
+ features = np.concatenate([np.expand_dims(g_feat, axis=0), features], axis=0)
+
+ image_location = np.zeros((boxes.shape[0], 5), dtype=np.float32)
+ image_location[:,:4] = boxes
+ image_location[:,4] = (image_location[:,3] - image_location[:,1]) * (image_location[:,2] - image_location[:,0]) / (float(image_w) * float(image_h))
+
+ image_location_ori = copy.deepcopy(image_location)
+ image_location[:,0] = image_location[:,0] / float(image_w)
+ image_location[:,1] = image_location[:,1] / float(image_h)
+ image_location[:,2] = image_location[:,2] / float(image_w)
+ image_location[:,3] = image_location[:,3] / float(image_h)
+
+ g_location = np.array([0,0,1,1,1])
+ image_location = np.concatenate([np.expand_dims(g_location, axis=0), image_location], axis=0)
+
+ g_location_ori = np.array([0,0,image_w,image_h,image_w*image_h])
+ image_location_ori = np.concatenate([np.expand_dims(g_location_ori, axis=0), image_location_ori], axis=0)
+
+ return features, num_boxes, image_location, image_location_ori
+
+ def keys(self) -> List[int]:
+ return self._image_ids
+
diff --git a/vilbert/datasets/concept_cap_dataset.py b/vilbert/datasets/concept_cap_dataset.py
new file mode 100644
index 0000000..c332208
--- /dev/null
+++ b/vilbert/datasets/concept_cap_dataset.py
@@ -0,0 +1,872 @@
+import copy
+import json
+import logging
+import os
+import random
+
+import lmdb
+import numpy as np
+import tensorpack.dataflow as td
+
+import torch
+from torch.utils.data import Dataset
+from torch.utils.data.sampler import Sampler
+import torch.distributed as dist
+import sys
+import pdb
+
+logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+
+class InputExample(object):
+ """A single training/test example for the language model."""
+
+ def __init__(
+ self, image_feat=None, image_target=None, caption=None, is_next=None, lm_labels=None, image_loc=None, num_boxes=None
+ ):
+ """Constructs a InputExample.
+ Args:
+ guid: Unique id for the example.
+ tokens_a: string. The untokenized text of the first sequence. For single
+ sequence tasks, only this sequence must be specified.
+ tokens_b: (Optional) string. The untokenized text of the second sequence.
+ Only must be specified for sequence pair tasks.
+ label: (Optional) string. The label of the example. This should be
+ specified for train and dev examples, but not for test examples.
+ """
+ self.image_feat = image_feat
+ self.caption = caption
+ self.is_next = is_next # nextSentence
+ self.lm_labels = lm_labels # masked words for language model
+ self.image_loc = image_loc
+ self.image_target = image_target
+ self.num_boxes = num_boxes
+
+class InputFeatures(object):
+ """A single set of features of data."""
+
+ def __init__(
+ self,
+ input_ids=None,
+ input_mask=None,
+ segment_ids=None,
+ is_next=None,
+ lm_label_ids=None,
+ image_feat=None,
+ image_target=None,
+ image_loc=None,
+ image_label=None,
+ image_mask=None
+ ):
+ self.input_ids = input_ids
+ self.input_mask = input_mask
+ self.segment_ids = segment_ids
+ self.is_next = is_next
+ self.lm_label_ids = lm_label_ids
+ self.image_feat = image_feat
+ self.image_loc = image_loc
+ self.image_label = image_label
+ self.image_target = image_target
+ self.image_mask = image_mask
+
+class ConceptCapLoaderTrain(object):
+ """
+ Data loader. Combines a dataset and a sampler, and provides
+ single- or multi-process iterators over the dataset.
+ Arguments:
+ mode (str, required): mode of dataset to operate in, one of ['train', 'val']
+ batch_size (int, optional): how many samples per batch to load
+ (default: 1).
+ shuffle (bool, optional): set to ``True`` to have the data reshuffled
+ at every epoch (default: False).
+ num_workers (int, optional): how many subprocesses to use for data
+ loading. 0 means that the data will be loaded in the main process
+ (default: 0)
+ cache (int, optional): cache size to use when loading data,
+ drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
+ if the dataset size is not divisible by the batch size. If ``False`` and
+ the size of dataset is not divisible by the batch size, then the last batch
+ will be smaller. (default: False)
+ cuda (bool, optional): set to ``True`` and the PyTorch tensors will get preloaded
+ to the GPU for you (necessary because this lets us to uint8 conversion on the
+ GPU, which is faster).
+ """
+
+ def __init__(
+ self,
+ corpus_path,
+ tokenizer,
+ seq_len,
+ encoding="utf-8",
+ predict_feature=False,
+ hard_negative=False,
+ batch_size=512,
+ shuffle=False,
+ num_workers=25,
+ cache=50000,
+ drop_last=False,
+ cuda=False,
+ distributed=False,
+ visualization=False,
+ ):
+
+ if dist.is_available() and distributed:
+ num_replicas = dist.get_world_size()
+ # assert num_replicas == 8
+ rank = dist.get_rank()
+ lmdb_file = "/coc/dataset/conceptual_caption/training_feat_part_" + str(rank) + ".lmdb"
+ # if not os.path.exists(lmdb_file):
+ # lmdb_file = "/srv/share/datasets/conceptual_caption/training_feat_part_" + str(rank) + ".lmdb"
+ else:
+ # lmdb_file = "/coc/dataset/conceptual_caption/training_feat_all.lmdb"
+ # if not os.path.exists(lmdb_file):
+ lmdb_file = "/coc/pskynet2/jlu347/multi-modal-bert/data/conceptual_caption/training_feat_all.lmdb"
+
+ caption_path = "/coc/pskynet2/jlu347/multi-modal-bert/data/conceptual_caption/caption_train.json"
+ print("Loading from %s" % lmdb_file)
+
+ ds = td.LMDBSerializer.load(lmdb_file, shuffle=False)
+ self.num_dataset = len(ds)
+
+ preprocess_function = BertPreprocessBatch(
+ caption_path,
+ tokenizer,
+ seq_len,
+ 36,
+ self.num_dataset,
+ encoding="utf-8",
+ predict_feature=predict_feature,
+ )
+
+ ds = td.LocallyShuffleData(ds, cache)
+ ds = td.PrefetchData(ds, 5000, 1)
+ ds = td.MapData(ds, preprocess_function)
+ # self.ds = td.PrefetchData(ds, 1)
+ ds = td.PrefetchDataZMQ(ds, num_workers)
+ self.ds = td.BatchData(ds, batch_size)
+ # self.ds = ds
+ self.ds.reset_state()
+
+ self.batch_size = batch_size
+ self.num_workers = num_workers
+
+ def __iter__(self):
+
+ for batch in self.ds.get_data():
+ input_ids, input_mask, segment_ids, lm_label_ids, is_next, image_feat, \
+ image_loc, image_target, image_label, image_mask, image_id = batch
+
+ batch_size = input_ids.shape[0]
+ g_image_feat = np.sum(image_feat, axis=1) / np.sum(image_mask, axis=1, keepdims=True)
+ image_feat = np.concatenate([np.expand_dims(g_image_feat, axis=1), image_feat], axis=1)
+ image_feat = np.array(image_feat, dtype=np.float32)
+
+ g_image_loc = np.repeat(np.array([[0,0,1,1,1]], dtype=np.float32), batch_size, axis=0)
+ image_loc = np.concatenate([np.expand_dims(g_image_loc, axis=1), image_loc], axis=1)
+
+ image_loc = np.array(image_loc, dtype=np.float32)
+ g_image_mask = np.repeat(np.array([[1]]), batch_size, axis=0)
+ image_mask = np.concatenate([g_image_mask, image_mask], axis=1)
+
+ batch = (input_ids, input_mask, segment_ids, lm_label_ids, is_next, image_feat, \
+ image_loc, image_target, image_label, image_mask, image_id)
+
+ yield tuple(torch.tensor(data) for data in batch)
+
+ def __len__(self):
+ return self.ds.size()
+
+class ConceptCapLoaderVal(object):
+ """
+ Data loader. Combines a dataset and a sampler, and provides
+ single- or multi-process iterators over the dataset.
+ Arguments:
+ mode (str, required): mode of dataset to operate in, one of ['train', 'val']
+ batch_size (int, optional): how many samples per batch to load
+ (default: 1).
+ shuffle (bool, optional): set to ``True`` to have the data reshuffled
+ at every epoch (default: False).
+ num_workers (int, optional): how many subprocesses to use for data
+ loading. 0 means that the data will be loaded in the main process
+ (default: 0)
+ cache (int, optional): cache size to use when loading data,
+ drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
+ if the dataset size is not divisible by the batch size. If ``False`` and
+ the size of dataset is not divisible by the batch size, then the last batch
+ will be smaller. (default: False)
+ cuda (bool, optional): set to ``True`` and the PyTorch tensors will get preloaded
+ to the GPU for you (necessary because this lets us to uint8 conversion on the
+ GPU, which is faster).
+ """
+
+ def __init__(
+ self,
+ corpus_path,
+ tokenizer,
+ seq_len,
+ encoding="utf-8",
+ predict_feature=False,
+ batch_size=512,
+ shuffle=False,
+ num_workers=25,
+ cache=50000,
+ drop_last=False,
+ cuda=False,
+ distributed=False,
+ visualization=False,
+ ):
+
+ lmdb_file = "/coc/dataset/conceptual_caption/validation_feat_all.lmdb"
+ if not os.path.exists(lmdb_file):
+ lmdb_file = "/coc/pskynet2/jlu347/multi-modal-bert/data/conceptual_caption/validation_feat_all.lmdb"
+ caption_path = "/coc/pskynet2/jlu347/multi-modal-bert/data/conceptual_caption/caption_val.json"
+
+ print("Loading from %s" % lmdb_file)
+
+ ds = td.LMDBSerializer.load(lmdb_file, shuffle=False)
+ self.num_dataset = len(ds)
+ preprocess_function = BertPreprocessBatch(
+ caption_path,
+ tokenizer,
+ seq_len,
+ 36,
+ self.num_dataset,
+ encoding="utf-8",
+ predict_feature=predict_feature,
+ visualization=visualization,
+ )
+
+ ds = td.MapData(ds, preprocess_function)
+ self.ds = td.BatchData(ds, batch_size)
+ self.ds.reset_state()
+
+ self.batch_size = batch_size
+ self.num_workers = num_workers
+
+ def __iter__(self):
+ for batch in self.ds.get_data():
+ input_ids, input_mask, segment_ids, lm_label_ids, is_next, image_feat, \
+ image_loc, image_target, image_label, image_mask, image_id = batch
+
+ batch_size = input_ids.shape[0]
+ g_image_feat = np.sum(image_feat, axis=1) / np.sum(image_mask, axis=1, keepdims=True)
+ image_feat = np.concatenate([np.expand_dims(g_image_feat, axis=1), image_feat], axis=1)
+ image_feat = np.array(image_feat, dtype=np.float32)
+
+ g_image_loc = np.repeat(np.array([[0,0,1,1,1]], dtype=np.float32), batch_size, axis=0)
+ image_loc = np.concatenate([np.expand_dims(g_image_loc, axis=1), image_loc], axis=1)
+
+ image_loc = np.array(image_loc, dtype=np.float32)
+ g_image_mask = np.repeat(np.array([[1]]), batch_size, axis=0)
+ image_mask = np.concatenate([g_image_mask, image_mask], axis=1)
+
+ # batch = (input_ids, input_mask, segment_ids, lm_label_ids, is_next, image_feat, \
+ # image_loc, image_target, image_label, image_mask, image_id)
+ batch = (input_ids, input_mask, segment_ids, lm_label_ids, is_next, image_feat, \
+ image_loc, image_target, image_label, image_mask)
+
+
+ yield tuple([torch.tensor(data) for data in batch] + [image_id])
+
+ def __len__(self):
+ return self.ds.size()
+
+
+class BertPreprocessBatch(object):
+ def __init__(
+ self,
+ caption_path,
+ tokenizer,
+ seq_len,
+ region_len,
+ data_size,
+ split="Train",
+ encoding="utf-8",
+ predict_feature=False,
+ visualization=False
+ ):
+
+ self.split = split
+ self.seq_len = seq_len
+ self.region_len = region_len
+ self.tokenizer = tokenizer
+ self.predict_feature = predict_feature
+ self.num_caps = data_size
+ self.captions = list(json.load(open(caption_path, 'r')).values())
+ self.visualization = visualization
+
+ def __call__(self, data):
+
+ image_feature_wp, image_target_wp, image_location_wp, num_boxes, image_h, image_w, image_id, caption = data
+
+ image_feature = np.zeros((self.region_len, 2048), dtype=np.float32)
+ image_target = np.zeros((self.region_len, 1601), dtype=np.float32)
+ image_location = np.zeros((self.region_len, 5), dtype=np.float32)
+
+ num_boxes = int(num_boxes)
+ image_feature[:num_boxes] = image_feature_wp
+ image_target[:num_boxes] = image_target_wp
+ image_location[:num_boxes,:4] = image_location_wp
+
+ image_location[:,4] = (image_location[:,3] - image_location[:,1]) * (image_location[:,2] - image_location[:,0]) / (float(image_w) * float(image_h))
+
+ image_location[:,0] = image_location[:,0] / float(image_w)
+ image_location[:,1] = image_location[:,1] / float(image_h)
+ image_location[:,2] = image_location[:,2] / float(image_w)
+ image_location[:,3] = image_location[:,3] / float(image_h)
+
+ if self.predict_feature:
+ image_feature = copy.deepcopy(image_feature)
+ image_target = copy.deepcopy(image_feature)
+ else:
+ image_feature = copy.deepcopy(image_feature)
+ image_target = copy.deepcopy(image_target)
+
+ caption, label = self.random_cap(caption)
+
+ tokens_caption = self.tokenizer.tokenize(caption)
+ cur_example = InputExample(
+ image_feat=image_feature,
+ image_target=image_target,
+ caption=tokens_caption,
+ is_next=label,
+ image_loc=image_location,
+ num_boxes=num_boxes
+ )
+
+ # transform sample to features
+ cur_features = self.convert_example_to_features(cur_example, self.seq_len, self.tokenizer, self.region_len)
+
+ cur_tensors = (
+ cur_features.input_ids,
+ cur_features.input_mask,
+ cur_features.segment_ids,
+ cur_features.lm_label_ids,
+ cur_features.is_next,
+ cur_features.image_feat,
+ cur_features.image_loc,
+ cur_features.image_target,
+ cur_features.image_label,
+ cur_features.image_mask,
+ image_id,
+ )
+ return cur_tensors
+
+ def random_cap(self, caption):
+ """
+ Get one sample from corpus consisting of two sentences. With prob. 50% these are two subsequent sentences
+ from one doc. With 50% the second sentence will be a random one from another doc.
+ :param index: int, index of sample.
+ :return: (str, str, int), sentence 1, sentence 2, isNextSentence Label
+ """
+
+ if self.visualization:
+ return caption, 0
+
+ if random.random() > 0.5:
+ label = 0
+ else:
+ caption = self.get_random_caption()
+ label = 1
+
+ return caption, label
+
+ def get_random_caption(self):
+ """
+ Get random caption from another document for nextSentence task.
+ :return: str, content of one line
+ """
+ # Similar to original tf repo: This outer loop should rarely go for more than one iteration for large
+ # corpora. However, just to be careful, we try to make sure that
+ # the random document is not the same as the document we're processing.
+
+ # add the hard negative mining objective here.
+ rand_doc_idx = random.randint(0, self.num_caps - 1)
+ caption = self.captions[rand_doc_idx]
+
+ return caption
+
+ def convert_example_to_features(self, example, max_seq_length, tokenizer, max_region_length):
+ """
+ Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample with
+ IDs, LM labels, input_mask, CLS and SEP tokens etc.
+ :param example: InputExample, containing sentence input as strings and is_next label
+ :param max_seq_length: int, maximum length of sequence.
+ :param tokenizer: Tokenizer
+ :return: InputFeatures, containing all inputs and labels of one sample as IDs (as used for model training)
+ """
+ image_feat = example.image_feat
+ caption = example.caption
+ image_loc = example.image_loc
+ image_target = example.image_target
+ num_boxes = int(example.num_boxes)
+ self._truncate_seq_pair(caption, max_seq_length - 2)
+ caption, caption_label = self.random_word(caption, tokenizer)
+
+ image_feat, image_loc, image_label = self.random_region(image_feat, image_loc, num_boxes)
+
+ # concatenate lm labels and account for CLS, SEP, SEP
+ # lm_label_ids = ([-1] + caption_label + [-1] + image_label + [-1])
+ lm_label_ids = [-1] + caption_label + [-1]
+ # image_label = ([-1] + image_label)
+
+ # The convention in BERT is:
+ # (a) For sequence pairs:
+ # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
+ # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
+ # (b) For single sequences:
+ # tokens: [CLS] the dog is hairy . [SEP]
+ # type_ids: 0 0 0 0 0 0 0
+ #
+ # Where "type_ids" are used to indicate whether this is the first
+ # sequence or the second sequence. The embedding vectors for `type=0` and
+ # `type=1` were learned during pre-training and are added to the wordpiece
+ # embedding vector (and position vector). This is not *strictly* necessary
+ # since the [SEP] token unambigiously separates the sequences, but it makes
+ # it easier for the model to learn the concept of sequences.
+ #
+ # For classification tasks, the first vector (corresponding to [CLS]) is
+ # used as as the "sentence vector". Note that this only makes sense because
+ # the entire model is fine-tuned.
+ tokens = []
+ segment_ids = []
+
+ tokens.append("[CLS]")
+ segment_ids.append(0)
+ # for i in range(36):
+ # # tokens.append(0)
+ # segment_ids.append(0)
+
+ # tokens.append("[SEP]")
+ # segment_ids.append(0)
+ for token in caption:
+ tokens.append(token)
+ segment_ids.append(0)
+ tokens.append("[SEP]")
+ segment_ids.append(0)
+
+ input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+ # The mask has 1 for real tokens and 0 for padding tokens. Only real
+ # tokens are attended to.
+ # input_ids = input_ids[:1] input_ids[1:]
+ input_mask = [1] * (len(input_ids))
+ image_mask = [1] * (num_boxes)
+ # Zero-pad up to the visual sequence length.
+ while len(image_mask) < max_region_length:
+ image_mask.append(0)
+ image_label.append(-1)
+
+ # Zero-pad up to the sequence length.
+ while len(input_ids) < max_seq_length:
+ input_ids.append(0)
+ input_mask.append(0)
+ segment_ids.append(0)
+ lm_label_ids.append(-1)
+
+ assert len(input_ids) == max_seq_length
+ assert len(input_mask) == max_seq_length
+ assert len(segment_ids) == max_seq_length
+ assert len(lm_label_ids) == max_seq_length
+ assert len(image_mask) == max_region_length
+ assert len(image_label) == max_region_length
+
+ # if example.guid < 5:
+ # logger.info("*** Example ***")
+ # logger.info("guid: %s" % (example.guid))
+ # logger.info("tokens: %s" % " ".join(
+ # [str(x) for x in tokens]))
+ # logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+ # logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
+ # logger.info(
+ # "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
+ # logger.info("LM label: %s " % (lm_label_ids))
+ # logger.info("Is next sentence label: %s " % (example.is_next))
+
+ features = InputFeatures(
+ input_ids=np.array(input_ids),
+ input_mask=np.array(input_mask),
+ segment_ids=np.array(segment_ids),
+ lm_label_ids=np.array(lm_label_ids),
+ is_next=np.array(example.is_next),
+ image_feat=image_feat,
+ image_target=image_target,
+ image_loc=image_loc,
+ image_label=np.array(image_label),
+ image_mask = np.array(image_mask)
+ )
+ return features
+
+ def _truncate_seq_pair(self, tokens_b, max_length):
+ """Truncates a sequence pair in place to the maximum length."""
+
+ # This is a simple heuristic which will always truncate the longer sequence
+ # one token at a time. This makes more sense than truncating an equal percent
+ # of tokens from each, since if one sequence is very short then each token
+ # that's truncated likely contains more information than a longer sequence.
+ while True:
+ total_length = len(tokens_b)
+ if total_length <= max_length:
+ break
+
+ tokens_b.pop()
+
+ def random_word(self, tokens, tokenizer):
+ """
+ Masking some random tokens for Language Model task with probabilities as in the original BERT paper.
+ :param tokens: list of str, tokenized sentence.
+ :param tokenizer: Tokenizer, object used for tokenization (we need it's vocab here)
+ :return: (list of str, list of int), masked tokens and related labels for LM prediction
+ """
+ output_label = []
+
+ for i, token in enumerate(tokens):
+ prob = random.random()
+ # mask token with 15% probability
+
+ if prob < 0.15 and not self.visualization:
+ prob /= 0.15
+
+ # 80% randomly change token to mask token
+ if prob < 0.8:
+ tokens[i] = "[MASK]"
+
+ # 10% randomly change token to random token
+ elif prob < 0.9:
+ tokens[i] = random.choice(list(tokenizer.vocab.items()))[0]
+
+ # -> rest 10% randomly keep current token
+
+ # append current token to output (we will predict these later)
+ try:
+ output_label.append(tokenizer.vocab[token])
+ except KeyError:
+ # For unknown words (should not occur with BPE vocab)
+ output_label.append(tokenizer.vocab["[UNK]"])
+ logger.warning(
+ "Cannot find token '{}' in vocab. Using [UNK] insetad".format(token)
+ )
+ else:
+ # no masking token (will be ignored by loss function later)
+ output_label.append(-1)
+
+ return tokens, output_label
+
+ def random_region(self, image_feat, image_loc, num_boxes):
+ """
+ """
+ output_label = []
+
+ for i in range(num_boxes):
+ prob = random.random()
+ # mask token with 15% probability
+ if prob < 0.15 and not self.visualization:
+ prob /= 0.15
+
+ # 80% randomly change token to mask token
+ if prob < 0.9:
+ image_feat[i] = 0
+ # 10% randomly change token to random token
+ # elif prob < 0.9:
+ # tokens[i] = random.choice(list(tokenizer.vocab.items()))[0]
+
+ # -> rest 10% randomly keep current token
+ # append current token to output (we will predict these later)
+ output_label.append(1)
+ else:
+ # no masking token (will be ignored by loss function later)
+ output_label.append(-1)
+
+ return image_feat, image_loc, output_label
+
+
+
+
+class ConceptCapLoaderRetrieval(object):
+ """
+ Data loader. Combines a dataset and a sampler, and provides
+ single- or multi-process iterators over the dataset.
+ Arguments:
+ mode (str, required): mode of dataset to operate in, one of ['train', 'val']
+ batch_size (int, optional): how many samples per batch to load
+ (default: 1).
+ shuffle (bool, optional): set to ``True`` to have the data reshuffled
+ at every epoch (default: False).
+ num_workers (int, optional): how many subprocesses to use for data
+ loading. 0 means that the data will be loaded in the main process
+ (default: 0)
+ cache (int, optional): cache size to use when loading data,
+ drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
+ if the dataset size is not divisible by the batch size. If ``False`` and
+ the size of dataset is not divisible by the batch size, then the last batch
+ will be smaller. (default: False)
+ cuda (bool, optional): set to ``True`` and the PyTorch tensors will get preloaded
+ to the GPU for you (necessary because this lets us to uint8 conversion on the
+ GPU, which is faster).
+ """
+
+ def __init__(
+ self,
+ corpus_path,
+ tokenizer,
+ seq_len,
+ encoding="utf-8",
+ predict_feature=False,
+ batch_size=512,
+ shuffle=False,
+ num_workers=10,
+ cache=50000,
+ drop_last=False,
+ cuda=False,
+ ):
+
+ lmdb_file = "/coc/dataset/conceptual_caption/validation_feat_all.lmdb"
+ if not os.path.exists(lmdb_file):
+ lmdb_file = "/coc/pskynet2/jlu347/multi-modal-bert/data/conceptual_caption/validation_feat_all.lmdb"
+ caption_path = "/coc/pskynet2/jlu347/multi-modal-bert/data/conceptual_caption/caption_val.json"
+
+ print("Loading from %s" % lmdb_file)
+
+ ds = td.LMDBSerializer.load(lmdb_file, shuffle=False)
+ self.num_dataset = len(ds)
+ preprocess_function = BertPreprocessRetrieval(
+ caption_path,
+ tokenizer,
+ seq_len,
+ 36,
+ 1000,
+ encoding="utf-8",
+ predict_feature=predict_feature,
+ )
+
+ ds = td.MapData(ds, preprocess_function)
+ self.ds = td.BatchData(ds, 1)
+ self.ds.reset_state()
+
+ self.batch_size = 1
+ self.num_workers = num_workers
+ self._entry = []
+
+ self.features_all = np.zeros((1000, 37, 2048), dtype=np.float32)
+ self.spatials_all = np.zeros((1000, 37, 5), dtype=np.float32)
+ self.image_mask_all = np.zeros((1000, 37), dtype=np.float32)
+ self.image_ids = []
+ # load first 1000 file here.
+ for i, batch in enumerate(self.ds.get_data()):
+ if i >= 1000:
+ break
+ input_ids, input_mask, segment_ids, is_next, image_feat, \
+ image_loc, image_mask, image_id, caption = batch
+
+ batch_size = input_ids.shape[0]
+ g_image_feat = np.sum(image_feat, axis=1) / np.sum(image_mask, axis=1, keepdims=True)
+ image_feat = np.concatenate([np.expand_dims(g_image_feat, axis=1), image_feat], axis=1)
+ image_feat = np.array(image_feat, dtype=np.float32)
+
+ g_image_loc = np.repeat(np.array([[0,0,1,1,1]], dtype=np.float32), batch_size, axis=0)
+ image_loc = np.concatenate([np.expand_dims(g_image_loc, axis=1), image_loc], axis=1)
+
+ image_loc = np.array(image_loc, dtype=np.float32)
+ g_image_mask = np.repeat(np.array([[1]]), batch_size, axis=0)
+ image_mask = np.concatenate([g_image_mask, image_mask], axis=1)
+
+ batch = (input_ids, input_mask, segment_ids, image_id, caption)
+ self._entry.append(batch)
+
+ self.features_all[i] = image_feat
+ self.image_mask_all[i] = np.array(image_mask)
+ self.spatials_all[i] = image_loc
+ self.image_ids.append(image_id)
+ sys.stdout.write('%d/%d\r' % (i, 1000))
+ sys.stdout.flush()
+
+
+ def __iter__(self):
+
+ for index in range(self.__len__()):
+ caption_idx = int(index / 2)
+ image_idx = index % 2
+
+ if image_idx == 0:
+ image_entries = self.image_ids[:500]
+ features_all = self.features_all[:500]
+ spatials_all = self.spatials_all[:500]
+ image_mask_all = self.image_mask_all[:500]
+
+ else:
+ image_entries = self.image_ids[500:]
+ features_all = self.features_all[500:]
+ spatials_all = self.spatials_all[500:]
+ image_mask_all = self.image_mask_all[500:]
+
+ caption, input_mask, segment_ids, txt_image_id, caption = self._entry[caption_idx]
+ target_all = np.zeros((500))
+ for i, image_id in enumerate(image_entries):
+ if image_id == txt_image_id:
+ target_all[i] = 1
+
+ batch = (features_all, spatials_all, image_mask_all, caption, input_mask, segment_ids, target_all, caption_idx, image_idx)
+ batch = [torch.tensor(data) for data in batch]
+ batch.append(txt_image_id)
+ batch.append(caption)
+
+ yield batch
+
+
+ def __len__(self):
+ return len(self._entry) * 2
+
+
+class BertPreprocessRetrieval(object):
+ def __init__(
+ self,
+ caption_path,
+ tokenizer,
+ seq_len,
+ region_len,
+ data_size,
+ split="Train",
+ encoding="utf-8",
+ predict_feature=False,
+ ):
+
+ self.split = split
+ self.seq_len = seq_len
+ self.region_len = region_len
+ self.tokenizer = tokenizer
+ self.predict_feature = predict_feature
+ self.num_caps = data_size
+ self.captions = list(json.load(open(caption_path, 'r')).values())[:data_size]
+
+ def __call__(self, data):
+
+ image_feature_wp, image_target_wp, image_location_wp, num_boxes, image_h, image_w, image_id, caption = data
+
+ image_feature = np.zeros((self.region_len, 2048), dtype=np.float32)
+ image_target = np.zeros((self.region_len, 1601), dtype=np.float32)
+ image_location = np.zeros((self.region_len, 5), dtype=np.float32)
+
+ num_boxes = int(num_boxes)
+ image_feature[:num_boxes] = image_feature_wp
+ image_target[:num_boxes] = image_target_wp
+ image_location[:num_boxes,:4] = image_location_wp
+
+ image_location[:,4] = (image_location[:,3] - image_location[:,1]) * (image_location[:,2] - image_location[:,0]) / (float(image_w) * float(image_h))
+
+ image_location[:,0] = image_location[:,0] / float(image_w)
+ image_location[:,1] = image_location[:,1] / float(image_h)
+ image_location[:,2] = image_location[:,2] / float(image_w)
+ image_location[:,3] = image_location[:,3] / float(image_h)
+
+ label = 0
+
+ tokens_caption = self.tokenizer.tokenize(caption)
+ cur_example = InputExample(
+ image_feat=image_feature,
+ image_target=image_target,
+ caption=tokens_caption,
+ is_next=label,
+ image_loc=image_location,
+ num_boxes=num_boxes
+ )
+
+ # transform sample to features
+ cur_features = self.convert_example_to_features(cur_example, self.seq_len, self.tokenizer, self.region_len)
+
+ cur_tensors = (
+ cur_features.input_ids,
+ cur_features.input_mask,
+ cur_features.segment_ids,
+ cur_features.is_next,
+ cur_features.image_feat,
+ cur_features.image_loc,
+ cur_features.image_mask,
+ float(image_id),
+ caption,
+ )
+ return cur_tensors
+
+
+ def convert_example_to_features(self, example, max_seq_length, tokenizer, max_region_length):
+ """
+ Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample with
+ IDs, LM labels, input_mask, CLS and SEP tokens etc.
+ :param example: InputExample, containing sentence input as strings and is_next label
+ :param max_seq_length: int, maximum length of sequence.
+ :param tokenizer: Tokenizer
+ :return: InputFeatures, containing all inputs and labels of one sample as IDs (as used for model training)
+ """
+ image_feat = example.image_feat
+ caption = example.caption
+ image_loc = example.image_loc
+ # image_target = example.image_target
+ num_boxes = int(example.num_boxes)
+ self._truncate_seq_pair(caption, max_seq_length - 2)
+ # caption, caption_label = self.random_word(caption, tokenizer)
+ caption_label = None
+ # image_feat, image_loc, image_label = self.random_region(image_feat, image_loc, num_boxes)
+ image_label = None
+
+ tokens = []
+ segment_ids = []
+
+ tokens.append("[CLS]")
+ segment_ids.append(0)
+
+ for token in caption:
+ tokens.append(token)
+ segment_ids.append(0)
+ tokens.append("[SEP]")
+ segment_ids.append(0)
+
+ input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+ # The mask has 1 for real tokens and 0 for padding tokens. Only real
+ # tokens are attended to.
+ # input_ids = input_ids[:1] input_ids[1:]
+ input_mask = [1] * (len(input_ids))
+ image_mask = [1] * (num_boxes)
+ # Zero-pad up to the visual sequence length.
+ while len(image_mask) < max_region_length:
+ image_mask.append(0)
+
+ # Zero-pad up to the sequence length.
+ while len(input_ids) < max_seq_length:
+ input_ids.append(0)
+ input_mask.append(0)
+ segment_ids.append(0)
+
+ assert len(input_ids) == max_seq_length
+ assert len(input_mask) == max_seq_length
+ assert len(segment_ids) == max_seq_length
+ assert len(image_mask) == max_region_length
+
+ features = InputFeatures(
+ input_ids=np.array(input_ids),
+ input_mask=np.array(input_mask),
+ segment_ids=np.array(segment_ids),
+ is_next=np.array(example.is_next),
+ image_feat=image_feat,
+ image_loc=image_loc,
+ image_mask = np.array(image_mask),
+ )
+ return features
+
+ def _truncate_seq_pair(self, tokens_b, max_length):
+ """Truncates a sequence pair in place to the maximum length."""
+
+ # This is a simple heuristic which will always truncate the longer sequence
+ # one token at a time. This makes more sense than truncating an equal percent
+ # of tokens from each, since if one sequence is very short then each token
+ # that's truncated likely contains more information than a longer sequence.
+ while True:
+ total_length = len(tokens_b)
+ if total_length <= max_length:
+ break
+
+ tokens_b.pop()
diff --git a/vilbert/datasets/refer_expression_dataset.py b/vilbert/datasets/refer_expression_dataset.py
new file mode 100644
index 0000000..04edcbf
--- /dev/null
+++ b/vilbert/datasets/refer_expression_dataset.py
@@ -0,0 +1,222 @@
+import json
+from typing import Any, Dict, List
+import random
+import os
+
+import torch
+from torch.utils.data import Dataset
+import numpy as np
+
+from pytorch_pretrained_bert.tokenization import BertTokenizer
+from ._image_features_reader import ImageFeaturesH5Reader
+import _pickle as cPickle
+
+import sys
+#sys.path.append("tools/refer")
+from tools.refer.refer import REFER
+
+def iou(anchors, gt_boxes):
+ """
+ anchors: (N, 4) ndarray of float
+ gt_boxes: (K, 4) ndarray of float
+ overlaps: (N, K) ndarray of overlap between boxes and query_boxes
+ """
+ N = anchors.size(0)
+ K = gt_boxes.size(0)
+
+ gt_boxes_area = ((gt_boxes[:,2] - gt_boxes[:,0] + 1) *
+ (gt_boxes[:,3] - gt_boxes[:,1] + 1)).view(1, K)
+
+ anchors_area = ((anchors[:,2] - anchors[:,0] + 1) *
+ (anchors[:,3] - anchors[:,1] + 1)).view(N, 1)
+
+ boxes = anchors.view(N, 1, 4).expand(N, K, 4)
+ query_boxes = gt_boxes.view(1, K, 4).expand(N, K, 4)
+
+ iw = (torch.min(boxes[:,:,2], query_boxes[:,:,2]) -
+ torch.max(boxes[:,:,0], query_boxes[:,:,0]) + 1)
+ iw[iw < 0] = 0
+
+ ih = (torch.min(boxes[:,:,3], query_boxes[:,:,3]) -
+ torch.max(boxes[:,:,1], query_boxes[:,:,1]) + 1)
+ ih[ih < 0] = 0
+
+ ua = anchors_area + gt_boxes_area - (iw * ih)
+ overlaps = iw * ih / ua
+
+ return overlaps
+
+def assert_eq(real, expected):
+ assert real == expected, "%s (true) vs %s (expected)" % (real, expected)
+
+class ReferExpressionDataset(Dataset):
+ def __init__(
+ self,
+ task: str,
+ dataroot: str,
+ annotations_jsonpath: str,
+ split: str,
+ image_features_reader: ImageFeaturesH5Reader,
+ gt_image_features_reader: ImageFeaturesH5Reader,
+ tokenizer: BertTokenizer,
+ padding_index: int = 0,
+ max_seq_length: int = 20,
+ max_region_num: int = 60
+ ):
+ self.split = split
+ self.refer = REFER(dataroot, dataset=task, splitBy='unc')
+ self.ref_ids = self.refer.getRefIds(split=split)
+ print('%s refs are in split [%s].' % (len(self.ref_ids), split))
+
+ self.num_labels = 1
+ self._image_features_reader = image_features_reader
+ self._gt_image_features_reader = gt_image_features_reader
+ self._tokenizer = tokenizer
+
+ self._padding_index = padding_index
+ self._max_seq_length = max_seq_length
+ self.entries = self._load_annotations()
+
+ self.max_region_num = max_region_num
+
+ cache_path = os.path.join(dataroot, "cache", task + '_' + split + '_' + str(max_seq_length)+ "_" + str(max_region_num) + '.pkl')
+ if not os.path.exists(cache_path):
+ self.tokenize()
+ self.tensorize()
+ cPickle.dump(self.entries, open(cache_path, 'wb'))
+ else:
+ print('loading entries from %s' %(cache_path))
+ self.entries = cPickle.load(open(cache_path, "rb"))
+
+ def _load_annotations(self):
+
+ # annotations_json: Dict[str, Any] = json.load(open(annotations_jsonpath))
+
+ # Build an index which maps image id with a list of caption annotations.
+ entries = []
+
+ for ref_id in self.ref_ids:
+ ref = self.refer.Refs[ref_id]
+ image_id = ref['image_id']
+ ref_id = ref['ref_id']
+ refBox = self.refer.getRefBox(ref_id)
+ for sent, sent_id in zip(ref['sentences'], ref['sent_ids']):
+ caption = sent['raw']
+ entries.append(
+ {"caption": caption, 'sent_id':sent_id, 'image_id':image_id, \
+ "refBox": refBox, 'ref_id': ref_id}
+ )
+
+ return entries
+
+ def tokenize(self):
+ """Tokenizes the captions.
+
+ This will add caption_tokens in each entry of the dataset.
+ -1 represents nil, and should be treated as padding_idx in embedding.
+ """
+ for entry in self.entries:
+
+ sentence_tokens = self._tokenizer.tokenize(entry["caption"])
+ sentence_tokens = ["[CLS]"] + sentence_tokens + ["[SEP]"]
+
+ tokens = [
+ self._tokenizer.vocab.get(w, self._tokenizer.vocab["[UNK]"])
+ for w in sentence_tokens
+ ]
+
+ tokens = tokens[:self._max_seq_length]
+ segment_ids = [0] * len(tokens)
+ input_mask = [1] * len(tokens)
+
+ if len(tokens) < self._max_seq_length:
+ # Note here we pad in front of the sentence
+ padding = [self._padding_index] * (self._max_seq_length - len(tokens))
+ tokens = tokens + padding
+ input_mask += padding
+ segment_ids += padding
+
+ assert_eq(len(tokens), self._max_seq_length)
+ entry["token"] = tokens
+ entry["input_mask"] = input_mask
+ entry["segment_ids"] = segment_ids
+
+ def tensorize(self):
+
+ for entry in self.entries:
+ token = torch.from_numpy(np.array(entry["token"]))
+ entry["token"] = token
+
+ input_mask = torch.from_numpy(np.array(entry["input_mask"]))
+ entry["input_mask"] = input_mask
+
+ segment_ids = torch.from_numpy(np.array(entry["segment_ids"]))
+ entry["segment_ids"] = segment_ids
+
+
+ def __getitem__(self, index):
+ entry = self.entries[index]
+
+ image_id = entry["image_id"]
+ ref_box = entry["refBox"]
+
+ ref_box = [ref_box[0], ref_box[1], ref_box[0]+ref_box[2], ref_box[1]+ref_box[3]]
+ features, num_boxes, boxes, boxes_ori = self._image_features_reader[image_id]
+
+ boxes_ori = boxes_ori[:num_boxes]
+ boxes = boxes[:num_boxes]
+ features = features[:num_boxes]
+
+ if self.split == 'train':
+ gt_features, gt_num_boxes, gt_boxes, gt_boxes_ori = self._gt_image_features_reader[image_id]
+
+ # merge two boxes, and assign the labels.
+ gt_boxes_ori = gt_boxes_ori[1:gt_num_boxes]
+ gt_boxes = gt_boxes[1:gt_num_boxes]
+ gt_features = gt_features[1:gt_num_boxes]
+
+ # concatenate the boxes
+ mix_boxes_ori = np.concatenate((boxes_ori, gt_boxes_ori), axis=0)
+ mix_boxes = np.concatenate((boxes, gt_boxes), axis=0)
+ mix_features = np.concatenate((features, gt_features), axis=0)
+ mix_num_boxes = min(int(num_boxes + int(gt_num_boxes) - 1), self.max_region_num)
+ # given the mix boxes, and ref_box, calculate the overlap.
+ mix_target = iou(torch.tensor(mix_boxes_ori[:,:4]).float(), torch.tensor([ref_box]).float())
+ mix_target[mix_target<0.5] = 0
+
+ else:
+ mix_boxes_ori = boxes_ori
+ mix_boxes = boxes
+ mix_features = features
+ mix_num_boxes = min(int(num_boxes), self.max_region_num)
+ mix_target = iou(torch.tensor(mix_boxes_ori[:,:4]).float(), torch.tensor([ref_box]).float())
+
+ image_mask = [1] * (mix_num_boxes)
+ while len(image_mask) < self.max_region_num:
+ image_mask.append(0)
+
+ mix_boxes_pad = np.zeros((self.max_region_num, 5))
+ mix_features_pad = np.zeros((self.max_region_num, 2048))
+
+ mix_boxes_pad[:mix_num_boxes] = mix_boxes[:mix_num_boxes]
+ mix_features_pad[:mix_num_boxes] = mix_features[:mix_num_boxes]
+
+ # appending the target feature.
+ features = torch.tensor(mix_features_pad).float()
+ image_mask = torch.tensor(image_mask).long()
+ spatials = torch.tensor(mix_boxes_pad).float()
+
+ target = torch.zeros((self.max_region_num,1)).float()
+ target[:mix_num_boxes] = mix_target
+
+ spatials_ori = torch.tensor(mix_boxes_ori).float()
+ co_attention_mask = torch.zeros((self.max_region_num, self._max_seq_length))
+
+ caption = entry["token"]
+ input_mask = entry["input_mask"]
+ segment_ids = entry["segment_ids"]
+
+ return features, spatials, image_mask, caption, target, input_mask, segment_ids, co_attention_mask, image_id
+
+ def __len__(self):
+ return len(self.entries)
diff --git a/vilbert/datasets/retreival_dataset.py b/vilbert/datasets/retreival_dataset.py
new file mode 100644
index 0000000..332c000
--- /dev/null
+++ b/vilbert/datasets/retreival_dataset.py
@@ -0,0 +1,393 @@
+import json
+from typing import Any, Dict, List
+import random
+import os
+
+import torch
+from torch.utils.data import Dataset
+import numpy as np
+import _pickle as cPickle
+
+from pytorch_pretrained_bert.tokenization import BertTokenizer
+from ._image_features_reader import ImageFeaturesH5Reader
+import jsonlines
+import sys
+import pdb
+
+def assert_eq(real, expected):
+ assert real == expected, "%s (true) vs %s (expected)" % (real, expected)
+
+def _load_annotations(annotations_jsonpath, task):
+
+ with jsonlines.open(annotations_jsonpath) as reader:
+
+ # Build an index which maps image id with a list of caption annotations.
+ entries = []
+ imgid2entry = {}
+ count = 0
+
+ for annotation in reader:
+ if task == 'RetrievalCOCO':
+ image_id = annotation['id']
+ elif task == 'RetrievalFlickr30k':
+ image_id = int(annotation['img_path'].split('.')[0])
+ imgid2entry[image_id] = []
+ for sentences in annotation['sentences']:
+ entries.append({"caption": sentences, 'image_id':image_id})
+ imgid2entry[image_id].append(count)
+ count += 1
+
+ return entries, imgid2entry
+
+
+class RetreivalDataset(Dataset):
+ def __init__(
+ self,
+ task: str,
+ dataroot: str,
+ annotations_jsonpath: str,
+ split: str,
+ image_features_reader: ImageFeaturesH5Reader,
+ gt_image_features_reader: ImageFeaturesH5Reader,
+ tokenizer: BertTokenizer,
+ padding_index: int = 0,
+ max_seq_length: int = 20,
+ max_region_num: int = 37,
+ ):
+ # All the keys in `self._entries` would be present in `self._image_features_reader`
+
+ self._entries, self.imgid2entry = _load_annotations(annotations_jsonpath, task)
+ self.image_id_list = [*self.imgid2entry]
+
+ self._image_features_reader = image_features_reader
+ self._tokenizer = tokenizer
+ self.num_labels = 1
+ self._split = split
+ self._padding_index = padding_index
+ self._max_region_num = max_region_num
+ self._max_seq_length = max_seq_length
+
+ if self._split == 'train':
+ image_info = cPickle.load(open(os.path.join(dataroot, 'hard_negative.pkl'), 'rb'))
+ for key, value in image_info.items():
+ setattr(self, key, value)
+ self.train_imgId2pool = {imageId:i for i, imageId in enumerate(self.train_image_list)}
+
+ cache_path = os.path.join(dataroot, "cache", task + '_' + split + '_' + str(max_seq_length)+'.pkl')
+
+ if not os.path.exists(cache_path):
+ self.tokenize()
+ self.tensorize()
+ cPickle.dump(self._entries, open(cache_path, 'wb'))
+ else:
+ print('loading entries from %s' %(cache_path))
+ self._entries = cPickle.load(open(cache_path, "rb"))
+
+ def tokenize(self):
+ """Tokenizes the captions.
+
+ This will add caption_tokens in each entry of the dataset.
+ -1 represents nil, and should be treated as padding_idx in embedding.
+ """
+ for entry in self._entries:
+ sentence_tokens = self._tokenizer.tokenize(entry["caption"])
+ sentence_tokens = ["[CLS]"] + sentence_tokens + ["[SEP]"]
+
+ tokens = [
+ self._tokenizer.vocab.get(w, self._tokenizer.vocab["[UNK]"])
+ for w in sentence_tokens
+ ]
+ tokens = tokens[:self._max_seq_length]
+ segment_ids = [0] * len(tokens)
+ input_mask = [1] * len(tokens)
+
+ if len(tokens) < self._max_seq_length:
+ # Note here we pad in front of the sentence
+ padding = [self._padding_index] * (self._max_seq_length - len(tokens))
+ tokens = tokens + padding
+ input_mask += padding
+ segment_ids += padding
+
+ assert_eq(len(tokens), self._max_seq_length)
+ entry["token"] = tokens
+ entry["input_mask"] = input_mask
+ entry["segment_ids"] = segment_ids
+
+ def tensorize(self):
+
+ for entry in self._entries:
+ token = torch.from_numpy(np.array(entry["token"]))
+ entry["token"] = token
+
+ input_mask = torch.from_numpy(np.array(entry["input_mask"]))
+ entry["input_mask"] = input_mask
+
+ segment_ids = torch.from_numpy(np.array(entry["segment_ids"]))
+ entry["segment_ids"] = segment_ids
+
+
+ def __getitem__(self, index):
+ entry = self._entries[index]
+ image_id = entry["image_id"]
+
+ features, num_boxes, boxes, _ = self._image_features_reader[image_id]
+
+ mix_num_boxes = min(int(num_boxes), self._max_region_num)
+ mix_boxes_pad = np.zeros((self._max_region_num, 5))
+ mix_features_pad = np.zeros((self._max_region_num, 2048))
+
+ image_mask = [1] * (int(mix_num_boxes))
+ while len(image_mask) < self._max_region_num:
+ image_mask.append(0)
+
+ mix_boxes_pad[:mix_num_boxes] = boxes[:mix_num_boxes]
+ mix_features_pad[:mix_num_boxes] = features[:mix_num_boxes]
+
+ features1 = torch.tensor(mix_features_pad).float()
+ image_mask1 = torch.tensor(image_mask).long()
+ spatials1 = torch.tensor(mix_boxes_pad).float()
+
+ caption1 = entry["token"]
+ input_mask1 = entry["input_mask"]
+ segment_ids1 = entry["segment_ids"]
+ # negative samples.
+ # 1: correct one, 2: random caption wrong, 3: random image wrong. 4: hard image wrong.
+
+ while True:
+ # sample a random image:
+ img_id2 = random.choice(self.image_id_list)
+ if img_id2 != image_id: break
+
+ entry2 = self._entries[random.choice(self.imgid2entry[img_id2])]
+
+ features2 = features1
+ image_mask2 = image_mask1
+ spatials2 = spatials1
+ caption2 = entry2["token"]
+ input_mask2 = entry2["input_mask"]
+ segment_ids2 = entry2["segment_ids"]
+
+ # random image wrong
+ while True:
+ # sample a random image:
+ img_id3 = random.choice(self.image_id_list)
+ if img_id3 != image_id: break
+
+ features3, num_boxes3, boxes3, _ = self._image_features_reader[img_id3]
+ image_mask3 = [1] * (int(num_boxes3))
+
+ mix_num_boxes3 = min(int(num_boxes3), self._max_region_num)
+ mix_boxes_pad3 = np.zeros((self._max_region_num, 5))
+ mix_features_pad3 = np.zeros((self._max_region_num, 2048))
+
+ while len(image_mask3) < self._max_region_num:
+ image_mask3.append(0)
+
+
+ mix_boxes_pad[:mix_num_boxes3] = boxes3[:mix_num_boxes3]
+ mix_features_pad[:mix_num_boxes3] = features3[:mix_num_boxes3]
+
+ features3 = torch.tensor(mix_features_pad).float()
+ image_mask3 = torch.tensor(image_mask3).long()
+ spatials3 = torch.tensor(mix_boxes_pad).float()
+
+ caption3 = caption1
+ input_mask3 = input_mask1
+ segment_ids3 = segment_ids1
+
+
+ if self._split == 'train':
+ # random hard caption.
+ rand_img_id_pool = self.train_hard_pool[self.train_imgId2pool[image_id]]
+ pool_img_idx = int(rand_img_id_pool[np.random.randint(1, len(rand_img_id_pool))])
+ img_id4 = self.train_image_list[pool_img_idx]
+ else:
+ while True:
+ # sample a random image:
+ img_id4 = random.choice(self.image_id_list)
+ if img_id4 != image_id: break
+
+ entry4 = self._entries[random.choice(self.imgid2entry[img_id4])]
+
+ features4 = features1
+ image_mask4 = image_mask1
+ spatials4 = spatials1
+ caption4 = entry4["token"]
+ input_mask4 = entry4["input_mask"]
+ segment_ids4 = entry4["segment_ids"]
+
+ features = torch.stack([features1, features2, features3, features4], dim=0)
+ spatials = torch.stack([spatials1, spatials2, spatials3, spatials4], dim=0)
+ image_mask = torch.stack([image_mask1, image_mask2, image_mask3, image_mask4], dim=0)
+ caption = torch.stack([caption1, caption2, caption3, caption4], dim=0)
+ input_mask = torch.stack([input_mask1, input_mask2, input_mask3, input_mask4], dim=0)
+ segment_ids = torch.stack([segment_ids1, segment_ids2, segment_ids3, segment_ids4], dim=0)
+ co_attention_mask = torch.zeros((4, self._max_region_num, self._max_seq_length))
+ target = 0
+
+ return features, spatials, image_mask, caption, target, input_mask, segment_ids, co_attention_mask, image_id
+
+ def __len__(self):
+ return len(self._entries)
+
+def _load_annotationsVal(annotations_jsonpath, task):
+
+ with jsonlines.open(annotations_jsonpath) as reader:
+
+ # Build an index which maps image id with a list of caption annotations.
+ image_entries = {}
+ caption_entries = []
+
+ for annotation in reader:
+ if task == 'RetrievalCOCO':
+ image_id = annotation['id']
+ elif task == 'RetrievalFlickr30k':
+ image_id = int(annotation['img_path'].split('.')[0])
+
+ image_entries[image_id] = 1
+
+ for sentences in annotation['sentences']:
+ caption_entries.append({"caption": sentences, 'image_id':image_id})
+
+ image_entries = [*image_entries]
+
+ return image_entries, caption_entries
+
+class RetreivalDatasetVal(Dataset):
+ def __init__(
+ self,
+ task: str,
+ dataroot: str,
+ annotations_jsonpath: str,
+ split: str,
+ image_features_reader: ImageFeaturesH5Reader,
+ gt_image_features_reader: ImageFeaturesH5Reader,
+ tokenizer: BertTokenizer,
+ padding_index: int = 0,
+ max_seq_length: int = 20,
+ max_region_num: int = 101,
+ ):
+ # All the keys in `self._entries` would be present in `self._image_features_reader`
+
+ self._image_entries, self._caption_entries = _load_annotationsVal(annotations_jsonpath, task)
+ self._image_features_reader = image_features_reader
+ self._tokenizer = tokenizer
+
+ self._split = split
+ self._padding_index = padding_index
+ self._max_region_num = max_region_num
+ self._max_seq_length = max_seq_length
+ self.num_labels = 1
+
+ # cache file path data/cache/train_ques
+ # cap_cache_path = "data/cocoRetreival/cache/val_cap.pkl"
+ # if not os.path.exists(cap_cache_path):
+ self.tokenize()
+ self.tensorize()
+ # cPickle.dump(self._entries, open(cap_cache_path, 'wb'))
+ # else:
+ # print('loading entries from %s' %(cap_cache_path))
+ # self._entries = cPickle.load(open(cap_cache_path, "rb"))
+#
+ self.features_all = np.zeros((1000, self._max_region_num, 2048))
+ self.spatials_all = np.zeros((1000, self._max_region_num, 5))
+ self.image_mask_all = np.zeros((1000, self._max_region_num))
+
+ for i, image_id in enumerate(self._image_entries):
+ features, num_boxes, boxes, _ = self._image_features_reader[image_id]
+
+ mix_num_boxes = min(int(num_boxes), self._max_region_num)
+ mix_boxes_pad = np.zeros((self._max_region_num, 5))
+ mix_features_pad = np.zeros((self._max_region_num, 2048))
+
+ image_mask = [1] * (int(mix_num_boxes))
+ while len(image_mask) < self._max_region_num:
+ image_mask.append(0)
+
+ mix_boxes_pad[:mix_num_boxes] = boxes[:mix_num_boxes]
+ mix_features_pad[:mix_num_boxes] = features[:mix_num_boxes]
+
+ self.features_all[i] = mix_features_pad
+ self.image_mask_all[i] = np.array(image_mask)
+ self.spatials_all[i] = mix_boxes_pad
+
+ sys.stdout.write('%d/%d\r' % (i, len(self._image_entries)))
+ sys.stdout.flush()
+
+ self.features_all = torch.Tensor(self.features_all).float()
+ self.image_mask_all = torch.Tensor(self.image_mask_all).long()
+ self.spatials_all = torch.Tensor(self.spatials_all).float()
+
+ def tokenize(self):
+ """Tokenizes the captions.
+
+ This will add caption_tokens in each entry of the dataset.
+ -1 represents nil, and should be treated as padding_idx in embedding.
+ """
+ for entry in self._caption_entries:
+ sentence_tokens = self._tokenizer.tokenize(entry["caption"])
+ sentence_tokens = ["[CLS]"] + sentence_tokens + ["[SEP]"]
+
+ tokens = [
+ self._tokenizer.vocab.get(w, self._tokenizer.vocab["[UNK]"])
+ for w in sentence_tokens
+ ]
+ tokens = tokens[:self._max_seq_length]
+ segment_ids = [0] * len(tokens)
+ input_mask = [1] * len(tokens)
+
+ if len(tokens) < self._max_seq_length:
+ # Note here we pad in front of the sentence
+ padding = [self._padding_index] * (self._max_seq_length - len(tokens))
+ tokens = tokens + padding
+ input_mask += padding
+ segment_ids += padding
+
+ assert_eq(len(tokens), self._max_seq_length)
+ entry["token"] = tokens
+ entry["input_mask"] = input_mask
+ entry["segment_ids"] = segment_ids
+
+ def tensorize(self):
+ for entry in self._caption_entries:
+ token = torch.from_numpy(np.array(entry["token"])).long()
+ entry["token"] = token
+
+ input_mask = torch.from_numpy(np.array(entry["input_mask"]))
+ entry["input_mask"] = input_mask
+
+ segment_ids = torch.from_numpy(np.array(entry["segment_ids"])).long()
+ entry["segment_ids"] = segment_ids
+
+ def __getitem__(self, index):
+
+ # we iterate through every caption here.
+ caption_idx = int(index / 2)
+ image_idx = index % 2
+
+ if image_idx == 0:
+ image_entries = self._image_entries[:500]
+ features_all = self.features_all[:500]
+ spatials_all = self.spatials_all[:500]
+ image_mask_all = self.image_mask_all[:500]
+
+ else:
+ image_entries = self._image_entries[500:]
+ features_all = self.features_all[500:]
+ spatials_all = self.spatials_all[500:]
+ image_mask_all = self.image_mask_all[500:]
+
+ entry = self._caption_entries[caption_idx]
+ caption = entry["token"]
+ input_mask = entry["input_mask"]
+ segment_ids = entry["segment_ids"]
+
+ target_all = torch.zeros(500)
+ for i, image_id in enumerate(image_entries):
+ if image_id == entry["image_id"]:
+ target_all[i] = 1
+
+ return features_all, spatials_all, image_mask_all, caption, input_mask, segment_ids, target_all, caption_idx, image_idx
+
+ def __len__(self):
+ return len(self._caption_entries) * 2
diff --git a/vilbert/datasets/vcr_dataset.py b/vilbert/datasets/vcr_dataset.py
new file mode 100644
index 0000000..0c7dfb4
--- /dev/null
+++ b/vilbert/datasets/vcr_dataset.py
@@ -0,0 +1,337 @@
+import json
+from typing import Any, Dict, List
+import random
+import os
+
+import torch
+from torch.utils.data import Dataset
+import numpy as np
+import _pickle as cPickle
+import json_lines
+
+from pytorch_pretrained_bert.tokenization import BertTokenizer
+from ._image_features_reader import ImageFeaturesH5Reader
+import pdb
+import csv
+import sys
+
+def assert_eq(real, expected):
+ assert real == expected, "%s (true) vs %s (expected)" % (real, expected)
+
+def _converId(img_id):
+
+ img_id = img_id.split('-')
+ if 'train' in img_id[0]:
+ new_id = int(img_id[1])
+ elif 'val' in img_id[0]:
+ new_id = int(img_id[1]) + 1000000
+ elif 'test' in img_id[0]:
+ new_id = int(img_id[1]) + 2000000
+ else:
+ pdb.set_trace()
+
+ return new_id
+
+
+def _load_annotationsQ_A(annotations_jsonpath, split):
+ """Build an index out of FOIL annotations, mapping each image ID with its corresponding captions."""
+ entries = []
+ with open(annotations_jsonpath, 'rb') as f: # opening file in binary(rb) mode
+ for annotation in json_lines.reader(f):
+ # metadata_fn = json.load(open(os.path.join('data/VCR/vcr1images', annotation["metadata_fn"]), 'r'))
+ # det_names = metadata_fn["names"]
+ det_names = ""
+ question = annotation["question"]
+ if split == 'test':
+ ans_label = 0
+ else:
+ ans_label = annotation["answer_label"]
+ img_id = _converId(annotation["img_id"])
+ anno_id = int(annotation["annot_id"].split('-')[1])
+ entries.append(
+ {"question": question, 'answers':annotation["answer_choices"], "metadata_fn": annotation["metadata_fn"], 'target':ans_label, 'img_id':img_id, 'anno_id':anno_id}
+ )
+
+ return entries
+
+def _load_annotationsQA_R(annotations_jsonpath, split):
+ """Build an index out of FOIL annotations, mapping each image ID with its corresponding captions."""
+ entries = []
+ with open(annotations_jsonpath, 'rb') as f: # opening file in binary(rb) mode
+ for annotation in json_lines.reader(f):
+ # metadata_fn = json.load(open(os.path.join('data/VCR/vcr1images', annotation["metadata_fn"]), 'r'))
+ # det_names = metadata_fn["names"]
+ if split == 'test':
+ # for each answer
+ for answer in annotation["answer_choices"]:
+ question = annotation["question"] + ["[SEP]"] + answer
+ img_id = _converId(annotation["img_id"])
+ ans_label = 0
+ anno_id = int(annotation["annot_id"].split('-')[1])
+ entries.append(
+ {"question": question, 'answers':annotation["rationale_choices"], "metadata_fn": annotation["metadata_fn"], 'target':ans_label, 'img_id':img_id}
+ )
+ else:
+ det_names = ""
+ question = annotation["question"] + ["[SEP]"] + annotation["answer_choices"][annotation['answer_label']]
+ ans_label = annotation["rationale_label"]
+ # img_fn = annotation["img_fn"]
+ img_id = _converId(annotation["img_id"])
+ anno_id = int(annotation["annot_id"].split('-')[1])
+ entries.append(
+ {"question": question, 'answers':annotation["rationale_choices"], "metadata_fn": annotation["metadata_fn"], 'target':ans_label, 'img_id':img_id, 'anno_id':anno_id}
+ )
+
+ return entries
+
+class VCRDataset(Dataset):
+ def __init__(
+ self,
+ task: str,
+ dataroot: str,
+ annotations_jsonpath: str,
+ split: str,
+ image_features_reader: ImageFeaturesH5Reader,
+ gt_image_features_reader: ImageFeaturesH5Reader,
+ tokenizer: BertTokenizer,
+ padding_index: int = 0,
+ max_seq_length: int = 40,
+ max_region_num: int = 60
+ ):
+ # All the keys in `self._entries` would be present in `self._image_features_reader`
+ if task == 'VCR_Q-A':
+ self._entries = _load_annotationsQ_A(annotations_jsonpath, split)
+ elif task == "VCR_QA-R":
+ self._entries = _load_annotationsQA_R(annotations_jsonpath, split)
+ else:
+ assert False
+ self._split = split
+ self._image_features_reader = image_features_reader
+ self._gt_image_features_reader = gt_image_features_reader
+ self._tokenizer = tokenizer
+
+ self._padding_index = padding_index
+ self._max_caption_length = max_seq_length
+ self._max_region_num = max_region_num
+ self.num_labels = 1
+
+ self._names = []
+ with open('data/VCR/unisex_names_table.csv') as csv_file:
+ csv_reader = csv.reader(csv_file, delimiter=',')
+ for row in csv_reader:
+ if row[1] != 'name':
+ self._names.append(row[1])
+
+ # cache file path data/cache/train_ques
+ cache_path = "data/VCR/cache/" + split + '_' + task + "_" + str(max_seq_length) + "_" + str(max_region_num) + "_vcr.pkl"
+ if not os.path.exists(cache_path):
+ self.tokenize()
+ self.tensorize()
+ cPickle.dump(self._entries, open(cache_path, 'wb'))
+ else:
+ self._entries = cPickle.load(open(cache_path, "rb"))
+
+ def tokenize(self):
+ """Tokenizes the captions.
+
+ This will add caption_tokens in each entry of the dataset.
+ -1 represents nil, and should be treated as padding_idx in embedding.
+ """
+ count = 0
+ for entry in self._entries:
+ metadata_fn = json.load(open(os.path.join('data/VCR/vcr1images', entry["metadata_fn"]), 'r'))
+ det_names = metadata_fn["names"]
+ random_names = self.generate_random_name(det_names)
+ # replace with name
+ tokens_a, mask_a = self.replace_det_with_name(entry["question"], random_names)
+
+ input_ids_all = []
+ co_attention_mask_all = []
+ input_mask_all = []
+ segment_ids_all = []
+
+ for answer in entry["answers"]:
+ tokens_b, mask_b = self.replace_det_with_name(answer, random_names)
+
+ self._truncate_seq_pair(tokens_a, tokens_b, mask_a, mask_b, self._max_caption_length - 3)
+
+ tokens = []
+ segment_ids = []
+ tokens.append("[CLS]")
+ segment_ids.append(0)
+
+ for token in tokens_a:
+ tokens.append(token)
+ segment_ids.append(0)
+
+ tokens.append("[SEP]")
+ segment_ids.append(0)
+
+ assert len(tokens_b) > 0
+ for token in tokens_b:
+ tokens.append(token)
+ segment_ids.append(1)
+ tokens.append("[SEP]")
+ segment_ids.append(1)
+
+ input_ids = self._tokenizer.convert_tokens_to_ids(tokens)
+ co_attention_mask = [-1] + mask_a + [-1] + mask_b + [-1]
+
+ input_mask = [1] * len(input_ids)
+ # Zero-pad up to the sequence length.
+ while len(input_ids) < self._max_caption_length:
+ input_ids.append(0)
+ input_mask.append(0)
+ segment_ids.append(0)
+ co_attention_mask.append(-1)
+
+ assert len(input_ids) == self._max_caption_length
+ assert len(input_mask) == self._max_caption_length
+ assert len(segment_ids) == self._max_caption_length
+
+ co_attention_mask_all.append(co_attention_mask)
+ input_ids_all.append(input_ids)
+ input_mask_all.append(input_mask)
+ segment_ids_all.append(segment_ids)
+
+ entry["co_attention_mask"] = co_attention_mask_all
+ entry["input_ids"] = input_ids_all
+ entry["input_mask"] = input_mask_all
+ entry["segment_ids"] = segment_ids_all
+
+ sys.stdout.write('%d/%d\r' % (count, len(self._entries)))
+ sys.stdout.flush()
+ count += 1
+
+ def tensorize(self):
+
+ for entry in self._entries:
+ input_ids = torch.from_numpy(np.array(entry["input_ids"]))
+ entry["input_ids"] = input_ids
+
+ input_mask = torch.from_numpy(np.array(entry["input_mask"]))
+ entry["input_mask"] = input_mask
+
+ segment_ids = torch.from_numpy(np.array(entry["segment_ids"]))
+ entry["segment_ids"] = segment_ids
+
+ def generate_random_name(self, det_names):
+ random_name = []
+ for name in det_names:
+ if name == 'person':
+ word = random.choice(self._names)
+ else:
+ word = name
+ random_name.append(word)
+
+ return random_name
+
+ def replace_det_with_name(self, inputs, random_names):
+ tokens = []
+ mask = []
+ for w in inputs:
+ if isinstance(w, str):
+ word = w
+ det = -1
+ word_token = self._tokenizer.tokenize(word)
+ mask += [det] * len(word_token)
+ tokens += word_token
+ else:
+ for idx in w:
+ word = random_names[idx]
+ word_token = self._tokenizer.tokenize(word)
+ mask += [idx] * len(word_token)
+ tokens += word_token
+
+ return tokens, mask
+
+ def _truncate_seq_pair(self, tokens_a, tokens_b, mask_a, mask_b, max_length):
+ """Truncates a sequence pair in place to the maximum length."""
+
+ # This is a simple heuristic which will always truncate the longer sequence
+ # one token at a time. This makes more sense than truncating an equal percent
+ # of tokens from each, since if one sequence is very short then each token
+ # that's truncated likely contains more information than a longer sequence.
+ while True:
+ total_length = len(tokens_a) + len(tokens_b)
+ if total_length <= max_length:
+ break
+ if len(tokens_a) > len(tokens_b):
+ tokens_a.pop()
+ mask_a.pop()
+ else:
+ tokens_b.pop()
+ mask_b.pop()
+
+ def __getitem__(self, index):
+
+ entry = self._entries[index]
+
+ image_id = entry["img_id"]
+ features, num_boxes, boxes, _ = self._image_features_reader[image_id]
+
+ boxes = boxes[:num_boxes]
+ features = features[:num_boxes]
+
+ gt_features, gt_num_boxes, gt_boxes, _ = self._gt_image_features_reader[image_id]
+
+ # merge two features.
+ features[0] = (features[0] * num_boxes + gt_features[0] * gt_num_boxes) / (num_boxes + gt_num_boxes)
+
+ # merge two boxes, and assign the labels.
+ gt_boxes = gt_boxes[1:gt_num_boxes]
+ gt_features = gt_features[1:gt_num_boxes]
+ gt_num_boxes = gt_num_boxes - 1
+
+ gt_box_preserve = min(self._max_region_num-1, gt_num_boxes)
+ gt_boxes = gt_boxes[:gt_box_preserve]
+ gt_features = gt_features[:gt_box_preserve]
+ gt_num_boxes = gt_box_preserve
+
+ num_box_preserve = min(self._max_region_num - int(gt_num_boxes), int(num_boxes))
+ boxes = boxes[:num_box_preserve]
+ features = features[:num_box_preserve]
+
+ # concatenate the boxes
+ mix_boxes = np.concatenate((boxes, gt_boxes), axis=0)
+ mix_features = np.concatenate((features, gt_features), axis=0)
+ mix_num_boxes = num_box_preserve + int(gt_num_boxes)
+
+ image_mask = [1] * (mix_num_boxes)
+ while len(image_mask) < self._max_region_num:
+ image_mask.append(0)
+
+ mix_boxes_pad = np.zeros((self._max_region_num, 5))
+ mix_features_pad = np.zeros((self._max_region_num, 2048))
+
+ mix_boxes_pad[:mix_num_boxes] = mix_boxes[:mix_num_boxes]
+ mix_features_pad[:mix_num_boxes] = mix_features[:mix_num_boxes]
+
+ # appending the target feature.
+ features = torch.tensor(mix_features_pad).float()
+ image_mask = torch.tensor(image_mask).long()
+ spatials = torch.tensor(mix_boxes_pad).float()
+
+ input_ids = entry["input_ids"]
+ input_mask = entry["input_mask"]
+ segment_ids = entry["segment_ids"]
+ target = int(entry["target"])
+
+ if self._split == 'test':
+ # anno_id = entry["anno_id"]
+ anno_id = 0#entry["anno_id"]
+ else:
+ anno_id = entry["img_id"]
+
+ co_attention_idxs = entry["co_attention_mask"]
+ co_attention_mask = torch.zeros((len(entry["co_attention_mask"]), self._max_region_num, self._max_caption_length))
+
+ for ii, co_attention_idx in enumerate(co_attention_idxs):
+ for jj, idx in enumerate(co_attention_idx):
+ if idx != -1 and idx+num_box_preserve < self._max_region_num:
+ co_attention_mask[ii, idx+num_box_preserve, jj] = 1
+
+ return features, spatials, image_mask, input_ids, target, input_mask, segment_ids, co_attention_mask, anno_id
+
+ def __len__(self):
+ return len(self._entries)
diff --git a/vilbert/datasets/vqa_dataset.py b/vilbert/datasets/vqa_dataset.py
new file mode 100644
index 0000000..fe5c19a
--- /dev/null
+++ b/vilbert/datasets/vqa_dataset.py
@@ -0,0 +1,223 @@
+import os
+import json
+import _pickle as cPickle
+import logging
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+from pytorch_pretrained_bert.tokenization import BertTokenizer
+
+from ._image_features_reader import ImageFeaturesH5Reader
+import pdb
+logger = logging.getLogger(__name__) # pylint: disable=invalid-name
+os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
+
+def assert_eq(real, expected):
+ assert real == expected, "%s (true) vs %s (expected)" % (real, expected)
+
+def _create_entry(question, answer):
+ answer.pop("image_id")
+ answer.pop("question_id")
+ entry = {
+ "question_id": question["question_id"],
+ "image_id": question["image_id"],
+ "question": question["question"],
+ "answer": answer,
+ }
+ return entry
+
+def _load_dataset(dataroot, name):
+ """Load entries
+
+ dataroot: root path of dataset
+ name: 'train', 'val', 'trainval', 'minsval'
+ """
+ if name == 'train' or name == 'val':
+ question_path = os.path.join(dataroot, "v2_OpenEnded_mscoco_%s2014_questions.json" % name)
+ questions = sorted(json.load(open(question_path))["questions"], key=lambda x: x["question_id"])
+ answer_path = os.path.join(dataroot, "cache", "%s_target.pkl" % name)
+ answers = cPickle.load(open(answer_path, "rb"))
+ answers = sorted(answers, key=lambda x: x["question_id"])
+
+ elif name == 'trainval':
+ question_path_train = os.path.join(dataroot, "v2_OpenEnded_mscoco_%s2014_questions.json" % 'train')
+ questions_train = sorted(json.load(open(question_path_train))["questions"], key=lambda x: x["question_id"])
+ answer_path_train = os.path.join(dataroot, "cache", "%s_target.pkl" % 'train')
+ answers_train = cPickle.load(open(answer_path_train, "rb"))
+ answers_train = sorted(answers_train, key=lambda x: x["question_id"])
+
+ question_path_val = os.path.join(dataroot, "v2_OpenEnded_mscoco_%s2014_questions.json" % 'val')
+ questions_val = sorted(json.load(open(question_path_val))["questions"], key=lambda x: x["question_id"])
+ answer_path_val = os.path.join(dataroot, "cache", "%s_target.pkl" % 'val')
+ answers_val = cPickle.load(open(answer_path_val, "rb"))
+ answers_val = sorted(answers_val, key=lambda x: x["question_id"])
+ questions = questions_train + questions_val[:-3000]
+ answers = answers_train + answers_val[:-3000]
+
+ elif name == 'minval':
+ question_path_val = os.path.join(dataroot, "v2_OpenEnded_mscoco_%s2014_questions.json" % 'val')
+ questions_val = sorted(json.load(open(question_path_val))["questions"], key=lambda x: x["question_id"])
+ answer_path_val = os.path.join(dataroot, "cache", "%s_target.pkl" % 'val')
+ answers_val = cPickle.load(open(answer_path_val, "rb"))
+ answers_val = sorted(answers_val, key=lambda x: x["question_id"])
+ questions = questions_val[-3000:]
+ answers = answers_val[-3000:]
+
+ elif name == 'test':
+ question_path_test = os.path.join(dataroot, "v2_OpenEnded_mscoco_%s2015_questions.json" % 'test')
+ questions_test = sorted(json.load(open(question_path_test))["questions"], key=lambda x: x["question_id"])
+ questions = questions_test
+ else:
+ assert False, "data split is not recognized."
+
+ if 'test' in name:
+ entries = []
+ for question in questions:
+ entries.append(question)
+ else:
+ assert_eq(len(questions), len(answers))
+ entries = []
+ for question, answer in zip(questions, answers):
+ assert_eq(question["question_id"], answer["question_id"])
+ assert_eq(question["image_id"], answer["image_id"])
+ entries.append(_create_entry(question, answer))
+ return entries
+
+class VQAClassificationDataset(Dataset):
+ def __init__(
+ self,
+ task: str,
+ dataroot: str,
+ annotations_jsonpath: str,
+ split: str,
+ image_features_reader: ImageFeaturesH5Reader,
+ gt_image_features_reader: ImageFeaturesH5Reader,
+ tokenizer: BertTokenizer,
+ padding_index: int = 0,
+ max_seq_length: int = 16,
+ max_region_num: int = 37,
+ ):
+ super().__init__()
+ self.split = split
+ ans2label_path = os.path.join('data', task, "cache", "trainval_ans2label.pkl")
+ label2ans_path = os.path.join('data', task, "cache", "trainval_label2ans.pkl")
+ self.ans2label = cPickle.load(open(ans2label_path, "rb"))
+ self.label2ans = cPickle.load(open(label2ans_path, "rb"))
+ self.num_labels = len(self.ans2label)
+ self._max_region_num = max_region_num
+ self._max_seq_length = max_seq_length
+ self._image_features_reader = image_features_reader
+ self._tokenizer = tokenizer
+ self._padding_index = padding_index
+ cache_path = os.path.join('data', task, "cache", task + '_' + split + '_' + str(max_seq_length)+'.pkl')
+ if not os.path.exists(cache_path):
+ self.entries = _load_dataset(dataroot, split)
+ self.tokenize(max_seq_length)
+ self.tensorize()
+ cPickle.dump(self.entries, open(cache_path, 'wb'))
+ else:
+ logger.info("Loading from %s" %cache_path)
+ self.entries = cPickle.load(open(cache_path, "rb"))
+
+ def tokenize(self, max_length=16):
+ """Tokenizes the questions.
+
+ This will add q_token in each entry of the dataset.
+ -1 represent nil, and should be treated as padding_index in embedding
+ """
+ for entry in self.entries:
+ tokens = self._tokenizer.tokenize(entry["question"])
+ tokens = ["[CLS]"] + tokens + ["[SEP]"]
+
+ tokens = [
+ self._tokenizer.vocab.get(w, self._tokenizer.vocab["[UNK]"])
+ for w in tokens
+ ]
+
+ tokens = tokens[:max_length]
+ segment_ids = [0] * len(tokens)
+ input_mask = [1] * len(tokens)
+
+ if len(tokens) < max_length:
+ # Note here we pad in front of the sentence
+ padding = [self._padding_index] * (max_length - len(tokens))
+ tokens = tokens + padding
+ input_mask += padding
+ segment_ids += padding
+
+ assert_eq(len(tokens), max_length)
+ entry["q_token"] = tokens
+ entry["q_input_mask"] = input_mask
+ entry["q_segment_ids"] = segment_ids
+
+ def tensorize(self):
+
+ for entry in self.entries:
+ question = torch.from_numpy(np.array(entry["q_token"]))
+ entry["q_token"] = question
+
+ q_input_mask = torch.from_numpy(np.array(entry["q_input_mask"]))
+ entry["q_input_mask"] = q_input_mask
+
+ q_segment_ids = torch.from_numpy(np.array(entry["q_segment_ids"]))
+ entry["q_segment_ids"] = q_segment_ids
+
+ if 'test' not in self.split:
+ answer = entry["answer"]
+ labels = np.array(answer["labels"])
+ scores = np.array(answer["scores"], dtype=np.float32)
+ if len(labels):
+ labels = torch.from_numpy(labels)
+ scores = torch.from_numpy(scores)
+ entry["answer"]["labels"] = labels
+ entry["answer"]["scores"] = scores
+ else:
+ entry["answer"]["labels"] = None
+ entry["answer"]["scores"] = None
+
+ def __getitem__(self, index):
+ entry = self.entries[index]
+ image_id = entry["image_id"]
+ question_id = entry["question_id"]
+ features, num_boxes, boxes, _ = self._image_features_reader[image_id]
+
+ mix_num_boxes = min(int(num_boxes), self._max_region_num)
+ mix_boxes_pad = np.zeros((self._max_region_num, 5))
+ mix_features_pad = np.zeros((self._max_region_num, 2048))
+
+ image_mask = [1] * (int(mix_num_boxes))
+ while len(image_mask) < self._max_region_num:
+ image_mask.append(0)
+
+ # shuffle the image location here.
+ # img_idx = list(np.random.permutation(num_boxes-1)[:mix_num_boxes]+1)
+ # img_idx.append(0)
+ # mix_boxes_pad[:mix_num_boxes] = boxes[img_idx]
+ # mix_features_pad[:mix_num_boxes] = features[img_idx]
+
+ mix_boxes_pad[:mix_num_boxes] = boxes[:mix_num_boxes]
+ mix_features_pad[:mix_num_boxes] = features[:mix_num_boxes]
+
+ features = torch.tensor(mix_features_pad).float()
+ image_mask = torch.tensor(image_mask).long()
+ spatials = torch.tensor(mix_boxes_pad).float()
+
+ question = entry["q_token"]
+ input_mask = entry["q_input_mask"]
+ segment_ids = entry["q_segment_ids"]
+
+ co_attention_mask = torch.zeros((self._max_region_num, self._max_seq_length))
+ target = torch.zeros(self.num_labels)
+
+ if "test" not in self.split:
+ answer = entry["answer"]
+ labels = answer["labels"]
+ scores = answer["scores"]
+ if labels is not None:
+ target.scatter_(0, labels, scores)
+
+ return features, spatials, image_mask, question, target, input_mask, segment_ids, co_attention_mask, question_id
+
+ def __len__(self):
+ return len(self.entries)
diff --git a/vilbert/optimization.py b/vilbert/optimization.py
new file mode 100644
index 0000000..b0983cc
--- /dev/null
+++ b/vilbert/optimization.py
@@ -0,0 +1,554 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch optimization for BERT model."""
+
+import math
+import torch
+from torch.optim import Optimizer
+from torch.optim.optimizer import required
+from torch.nn.utils import clip_grad_norm_
+import logging
+import abc
+import sys
+
+logger = logging.getLogger(__name__)
+
+
+if sys.version_info >= (3, 4):
+ ABC = abc.ABC
+else:
+ ABC = abc.ABCMeta('ABC', (), {})
+
+
+class _LRSchedule(ABC):
+ """ Parent of all LRSchedules here. """
+ warn_t_total = False # is set to True for schedules where progressing beyond t_total steps doesn't make sense
+ def __init__(self, warmup=0.002, t_total=-1, **kw):
+ """
+ :param warmup: what fraction of t_total steps will be used for linear warmup
+ :param t_total: how many training steps (updates) are planned
+ :param kw:
+ """
+ super(_LRSchedule, self).__init__(**kw)
+ if t_total < 0:
+ logger.warning("t_total value of {} results in schedule not being applied".format(t_total))
+ if not 0.0 <= warmup < 1.0 and not warmup == -1:
+ raise ValueError("Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup))
+ warmup = max(warmup, 0.)
+ self.warmup, self.t_total = float(warmup), float(t_total)
+ self.warned_for_t_total_at_progress = -1
+
+ def get_lr(self, step, nowarn=False):
+ """
+ :param step: which of t_total steps we're on
+ :param nowarn: set to True to suppress warning regarding training beyond specified 't_total' steps
+ :return: learning rate multiplier for current update
+ """
+ if self.t_total < 0:
+ return 1.
+ progress = float(step) / self.t_total
+ ret = self.get_lr_(progress)
+ # warning for exceeding t_total (only active with warmup_linear
+ if not nowarn and self.warn_t_total and progress > 1. and progress > self.warned_for_t_total_at_progress:
+ logger.warning(
+ "Training beyond specified 't_total'. Learning rate multiplier set to {}. Please set 't_total' of {} correctly."
+ .format(ret, self.__class__.__name__))
+ self.warned_for_t_total_at_progress = progress
+ # end warning
+ return ret
+
+ @abc.abstractmethod
+ def get_lr_(self, progress):
+ """
+ :param progress: value between 0 and 1 (unless going beyond t_total steps) specifying training progress
+ :return: learning rate multiplier for current update
+ """
+ return 1.
+
+
+class ConstantLR(_LRSchedule):
+ def get_lr_(self, progress):
+ return 1.
+
+
+class WarmupCosineSchedule(_LRSchedule):
+ """
+ Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
+ Decreases learning rate from 1. to 0. over remaining `1 - warmup` steps following a cosine curve.
+ If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup.
+ """
+ warn_t_total = True
+ def __init__(self, warmup=0.002, t_total=-1, cycles=.5, **kw):
+ """
+ :param warmup: see LRSchedule
+ :param t_total: see LRSchedule
+ :param cycles: number of cycles. Default: 0.5, corresponding to cosine decay from 1. at progress==warmup and 0 at progress==1.
+ :param kw:
+ """
+ super(WarmupCosineSchedule, self).__init__(warmup=warmup, t_total=t_total, **kw)
+ self.cycles = cycles
+
+ def get_lr_(self, progress):
+ if progress < self.warmup:
+ return progress / self.warmup
+ else:
+ progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
+ return 0.5 * (1. + math.cos(math.pi * self.cycles * 2 * progress))
+
+
+class WarmupCosineWithHardRestartsSchedule(WarmupCosineSchedule):
+ """
+ Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
+ If `cycles` (default=1.) is different from default, learning rate follows `cycles` times a cosine decaying
+ learning rate (with hard restarts).
+ """
+ def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
+ super(WarmupCosineWithHardRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
+ assert(cycles >= 1.)
+
+ def get_lr_(self, progress):
+ if progress < self.warmup:
+ return progress / self.warmup
+ else:
+ progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
+ ret = 0.5 * (1. + math.cos(math.pi * ((self.cycles * progress) % 1)))
+ return ret
+
+
+class WarmupCosineWithWarmupRestartsSchedule(WarmupCosineWithHardRestartsSchedule):
+ """
+ All training progress is divided in `cycles` (default=1.) parts of equal length.
+ Every part follows a schedule with the first `warmup` fraction of the training steps linearly increasing from 0. to 1.,
+ followed by a learning rate decreasing from 1. to 0. following a cosine curve.
+ """
+ def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
+ assert(warmup * cycles < 1.)
+ warmup = warmup * cycles if warmup >= 0 else warmup
+ super(WarmupCosineWithWarmupRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
+
+ def get_lr_(self, progress):
+ progress = progress * self.cycles % 1.
+ if progress < self.warmup:
+ return progress / self.warmup
+ else:
+ progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
+ ret = 0.5 * (1. + math.cos(math.pi * progress))
+ return ret
+
+
+class WarmupConstantSchedule(_LRSchedule):
+ """
+ Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
+ Keeps learning rate equal to 1. after warmup.
+ """
+ def get_lr_(self, progress):
+ if progress < self.warmup:
+ return progress / self.warmup
+ return 1.
+
+
+class WarmupLinearSchedule(_LRSchedule):
+ """
+ Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
+ Linearly decreases learning rate from 1. to 0. over remaining `1 - warmup` steps.
+ """
+ warn_t_total = True
+ def get_lr_(self, progress):
+ if progress < self.warmup:
+ return progress / self.warmup
+ return max((progress - 1.) / (self.warmup - 1.), 0.)
+
+
+SCHEDULES = {
+ None: ConstantLR,
+ "none": ConstantLR,
+ "warmup_cosine": WarmupCosineSchedule,
+ "warmup_constant": WarmupConstantSchedule,
+ "warmup_linear": WarmupLinearSchedule
+}
+
+
+class BertAdam(Optimizer):
+ """Implements BERT version of Adam algorithm with weight decay fix.
+ Params:
+ lr: learning rate
+ warmup: portion of t_total for the warmup, -1 means no warmup. Default: -1
+ t_total: total number of training steps for the learning
+ rate schedule, -1 means constant learning rate of 1. (no warmup regardless of warmup setting). Default: -1
+ schedule: schedule to use for the warmup (see above).
+ Can be `'warmup_linear'`, `'warmup_constant'`, `'warmup_cosine'`, `'none'`, `None` or a `_LRSchedule` object (see below).
+ If `None` or `'none'`, learning rate is always kept constant.
+ Default : `'warmup_linear'`
+ b1: Adams b1. Default: 0.9
+ b2: Adams b2. Default: 0.999
+ e: Adams epsilon. Default: 1e-6
+ weight_decay: Weight decay. Default: 0.01
+ max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0
+ """
+ def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear',
+ b1=0.9, b2=0.999, e=1e-6, weight_decay=0.01, max_grad_norm=1.0, **kwargs):
+ if lr is not required and lr < 0.0:
+ raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
+ if not isinstance(schedule, _LRSchedule) and schedule not in SCHEDULES:
+ raise ValueError("Invalid schedule parameter: {}".format(schedule))
+ if not 0.0 <= b1 < 1.0:
+ raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1))
+ if not 0.0 <= b2 < 1.0:
+ raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2))
+ if not e >= 0.0:
+ raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e))
+ # initialize schedule object
+ if not isinstance(schedule, _LRSchedule):
+ schedule_type = SCHEDULES[schedule]
+ schedule = schedule_type(warmup=warmup, t_total=t_total)
+ else:
+ if warmup != -1 or t_total != -1:
+ logger.warning("warmup and t_total on the optimizer are ineffective when _LRSchedule object is provided as schedule. "
+ "Please specify custom warmup and t_total in _LRSchedule object.")
+ defaults = dict(lr=lr, schedule=schedule,
+ b1=b1, b2=b2, e=e, weight_decay=weight_decay,
+ max_grad_norm=max_grad_norm)
+ self.rate = None
+ super(BertAdam, self).__init__(params, defaults)
+
+ def show_lr(self):
+ return self.rate
+
+ def get_lr(self):
+ lr = []
+ for group in self.param_groups:
+ for p in group['params']:
+ state = self.state[p]
+ if len(state) == 0:
+ return [0]
+ lr_scheduled = group['lr']
+ lr_scheduled *= group['schedule'].get_lr(state['step'])
+ lr.append(lr_scheduled)
+ return lr
+
+ def step(self, closure=None):
+ """Performs a single optimization step.
+ Arguments:
+ closure (callable, optional): A closure that reevaluates the model
+ and returns the loss.
+ """
+ loss = None
+ if closure is not None:
+ loss = closure()
+
+ for group in self.param_groups:
+ for p in group['params']:
+ if p.grad is None:
+ continue
+ grad = p.grad.data
+ if grad.is_sparse:
+ raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
+
+ state = self.state[p]
+
+ # State initialization
+ if len(state) == 0:
+ state['step'] = 0
+ # Exponential moving average of gradient values
+ state['next_m'] = torch.zeros_like(p.data)
+ # Exponential moving average of squared gradient values
+ state['next_v'] = torch.zeros_like(p.data)
+
+ next_m, next_v = state['next_m'], state['next_v']
+ beta1, beta2 = group['b1'], group['b2']
+
+ # Add grad clipping
+ if group['max_grad_norm'] > 0:
+ clip_grad_norm_(p, group['max_grad_norm'])
+
+ # Decay the first and second moment running average coefficient
+ # In-place operations to update the averages at the same time
+ next_m.mul_(beta1).add_(1 - beta1, grad)
+ next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+ update = next_m / (next_v.sqrt() + group['e'])
+
+ # Just adding the square of the weights to the loss function is *not*
+ # the correct way of using L2 regularization/weight decay with Adam,
+ # since that will interact with the m and v parameters in strange ways.
+ #
+ # Instead we want to decay the weights in a manner that doesn't interact
+ # with the m/v parameters. This is equivalent to adding the square
+ # of the weights to the loss with plain (non-momentum) SGD.
+ if group['weight_decay'] > 0.0:
+ update += group['weight_decay'] * p.data
+
+ lr_scheduled = group['lr']
+ lr_scheduled *= group['schedule'].get_lr(state['step'])
+
+ self.rate = lr_scheduled
+
+ update_with_lr = lr_scheduled * update
+ p.data.add_(-update_with_lr)
+
+ state['step'] += 1
+
+ # step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1
+ # No bias correction
+ # bias_correction1 = 1 - beta1 ** state['step']
+ # bias_correction2 = 1 - beta2 ** state['step']
+
+ return loss
+
+
+class Adam(Optimizer):
+ """Implements pytorch version of Adam algorithm with weight decay fix.
+ Params:
+ lr: learning rate
+ warmup: portion of t_total for the warmup, -1 means no warmup. Default: -1
+ t_total: total number of training steps for the learning
+ rate schedule, -1 means constant learning rate of 1. (no warmup regardless of warmup setting). Default: -1
+ schedule: schedule to use for the warmup (see above).
+ Can be `'warmup_linear'`, `'warmup_constant'`, `'warmup_cosine'`, `'none'`, `None` or a `_LRSchedule` object (see below).
+ If `None` or `'none'`, learning rate is always kept constant.
+ Default : `'warmup_linear'`
+ b1: Adams b1. Default: 0.9
+ b2: Adams b2. Default: 0.999
+ e: Adams epsilon. Default: 1e-6
+ weight_decay: Weight decay. Default: 0.01
+ max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0
+ """
+ def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear',
+ b1=0.9, b2=0.999, e=1e-8, weight_decay=0, amsgrad=False, max_grad_norm=1.0, **kwargs):
+ if lr is not required and lr < 0.0:
+ raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
+ if not isinstance(schedule, _LRSchedule) and schedule not in SCHEDULES:
+ raise ValueError("Invalid schedule parameter: {}".format(schedule))
+ if not 0.0 <= b1 < 1.0:
+ raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1))
+ if not 0.0 <= b2 < 1.0:
+ raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2))
+ if not e >= 0.0:
+ raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e))
+ # initialize schedule object
+ if not isinstance(schedule, _LRSchedule):
+ schedule_type = SCHEDULES[schedule]
+ schedule = schedule_type(warmup=warmup, t_total=t_total)
+ else:
+ if warmup != -1 or t_total != -1:
+ logger.warning("warmup and t_total on the optimizer are ineffective when _LRSchedule object is provided as schedule. "
+ "Please specify custom warmup and t_total in _LRSchedule object.")
+ defaults = dict(lr=lr, schedule=schedule,
+ b1=b1, b2=b2, e=e, weight_decay=weight_decay,
+ amsgrad=amsgrad, max_grad_norm=max_grad_norm)
+ self.rate = None
+ super(Adam, self).__init__(params, defaults)
+
+ def __setstate__(self, state):
+ super(Adam, self).__setstate__(state)
+ for group in self.param_groups:
+ group.setdefault('amsgrad', False)
+
+ def show_lr(self):
+ return self.rate
+
+ def get_lr(self):
+ lr = []
+ for group in self.param_groups:
+ for p in group['params']:
+ state = self.state[p]
+ if len(state) == 0:
+ return [0]
+ lr_scheduled = group['lr']
+ lr_scheduled *= group['schedule'].get_lr(state['step'])
+ lr.append(lr_scheduled)
+ return lr
+
+ def step(self, closure=None):
+ """Performs a single optimization step.
+ Arguments:
+ closure (callable, optional): A closure that reevaluates the model
+ and returns the loss.
+ """
+ loss = None
+ if closure is not None:
+ loss = closure()
+
+ for group in self.param_groups:
+ for p in group['params']:
+ if p.grad is None:
+ continue
+ grad = p.grad.data
+ if grad.is_sparse:
+ raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
+ amsgrad = group['amsgrad']
+
+ state = self.state[p]
+
+ # State initialization
+ if len(state) == 0:
+ state['step'] = 0
+ # Exponential moving average of gradient values
+ state['exp_avg'] = torch.zeros_like(p.data)
+ # Exponential moving average of squared gradient values
+ state['exp_avg_sq'] = torch.zeros_like(p.data)
+ if amsgrad:
+ # Maintains max of all exp. moving avg. of sq. grad. values
+ state['max_exp_avg_sq'] = torch.zeros_like(p.data)
+
+ exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+ if amsgrad:
+ max_exp_avg_sq = state['max_exp_avg_sq']
+ beta1, beta2 = group['b1'], group['b2']
+
+ # Add grad clipping
+ if group['max_grad_norm'] > 0:
+ clip_grad_norm_(p, group['max_grad_norm'])
+
+ lr_scheduled = group['lr']
+ lr_scheduled *= group['schedule'].get_lr(state['step'])
+ self.rate = lr_scheduled
+
+ state['step'] += 1
+
+ if group['weight_decay'] != 0:
+ grad.add_(group['weight_decay'], p.data)
+
+ # Decay the first and second moment running average coefficient
+ exp_avg.mul_(beta1).add_(1 - beta1, grad)
+ exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+ if amsgrad:
+ # Maintains the maximum of all 2nd moment running avg. till now
+ torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
+ # Use the max. for normalizing running avg. of gradient
+ denom = max_exp_avg_sq.sqrt().add_(group['e'])
+ else:
+ denom = exp_avg_sq.sqrt().add_(group['e'])
+
+ bias_correction1 = 1 - beta1 ** state['step']
+ bias_correction2 = 1 - beta2 ** state['step']
+ step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1
+
+ p.data.addcdiv_(-step_size, exp_avg, denom)
+
+ return loss
+
+class Adamax(Optimizer):
+ """Implements Adamax algorithm (a variant of Adam based on infinity norm).
+ It has been proposed in `Adam: A Method for Stochastic Optimization`__.
+ Arguments:
+ params (iterable): iterable of parameters to optimize or dicts defining
+ parameter groups
+ lr (float, optional): learning rate (default: 2e-3)
+ betas (Tuple[float, float], optional): coefficients used for computing
+ running averages of gradient and its square
+ eps (float, optional): term added to the denominator to improve
+ numerical stability (default: 1e-8)
+ weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+ __ https://arxiv.org/abs/1412.6980
+ """
+
+ def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear',
+ b1=0.9, b2=0.999, e=1e-8, weight_decay=0, max_grad_norm=1.0, **kwargs):
+ if not 0.0 <= lr:
+ raise ValueError("Invalid learning rate: {}".format(lr))
+ if not 0.0 <= e:
+ raise ValueError("Invalid epsilon value: {}".format(e))
+ if not 0.0 <= b1 < 1.0:
+ raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1))
+ if not 0.0 <= b2 < 1.0:
+ raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2))
+ if not 0.0 <= weight_decay:
+ raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+
+ if not isinstance(schedule, _LRSchedule):
+ schedule_type = SCHEDULES[schedule]
+ schedule = schedule_type(warmup=warmup, t_total=t_total)
+ else:
+ if warmup != -1 or t_total != -1:
+ logger.warning("warmup and t_total on the optimizer are ineffective when _LRSchedule object is provided as schedule. "
+ "Please specify custom warmup and t_total in _LRSchedule object.")
+
+ defaults = dict(lr=lr, schedule=schedule,
+ b1=b1, b2=b2, e=e, weight_decay=weight_decay, max_grad_norm=max_grad_norm)
+ self.rate = None
+ super(Adamax, self).__init__(params, defaults)
+
+ def show_lr(self):
+ return self.rate
+
+ def get_lr(self):
+ lr = []
+ for group in self.param_groups:
+ for p in group['params']:
+ state = self.state[p]
+ if len(state) == 0:
+ return [0]
+ lr_scheduled = group['lr']
+ lr_scheduled *= group['schedule'].get_lr(state['step'])
+ lr.append(lr_scheduled)
+ return lr
+
+ def step(self, closure=None):
+ """Performs a single optimization step.
+ Arguments:
+ closure (callable, optional): A closure that reevaluates the model
+ and returns the loss.
+ """
+ loss = None
+ if closure is not None:
+ loss = closure()
+
+ for group in self.param_groups:
+ for p in group['params']:
+ if p.grad is None:
+ continue
+ grad = p.grad.data
+ if grad.is_sparse:
+ raise RuntimeError('Adamax does not support sparse gradients')
+ state = self.state[p]
+
+ # State initialization
+ if len(state) == 0:
+ state['step'] = 0
+ state['exp_avg'] = torch.zeros_like(p.data)
+ state['exp_inf'] = torch.zeros_like(p.data)
+
+ exp_avg, exp_inf = state['exp_avg'], state['exp_inf']
+ beta1, beta2 = group['b1'], group['b2']
+ eps = group['e']
+
+ # Add grad clipping
+ if group['max_grad_norm'] > 0:
+ clip_grad_norm_(p, group['max_grad_norm'])
+
+ lr_scheduled = group['lr']
+ lr_scheduled *= group['schedule'].get_lr(state['step'])
+
+ self.rate = lr_scheduled
+ state['step'] += 1
+
+ if group['weight_decay'] != 0:
+ grad = grad.add(group['weight_decay'], p.data)
+
+ # Update biased first moment estimate.
+ exp_avg.mul_(beta1).add_(1 - beta1, grad)
+ # Update the exponentially weighted infinity norm.
+ norm_buf = torch.cat([
+ exp_inf.mul_(beta2).unsqueeze(0),
+ grad.abs().add_(eps).unsqueeze_(0)
+ ], 0)
+ torch.max(norm_buf, 0, keepdim=False, out=(exp_inf, exp_inf.new().long()))
+
+ bias_correction = 1 - beta1 ** state['step']
+ clr = lr_scheduled / bias_correction
+
+ p.data.addcdiv_(-clr, exp_avg, exp_inf)
+
+ return loss
\ No newline at end of file
diff --git a/vilbert/task_utils.py b/vilbert/task_utils.py
new file mode 100644
index 0000000..27ec9e6
--- /dev/null
+++ b/vilbert/task_utils.py
@@ -0,0 +1,406 @@
+from io import open
+import json
+import logging
+import os
+import sys
+
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+import torch.distributed as dist
+from torch.utils.data import DataLoader, Dataset, RandomSampler
+from torch.utils.data.distributed import DistributedSampler
+from pytorch_pretrained_bert.tokenization import BertTokenizer
+from vilbert.datasets import DatasetMapTrain, DatasetMapEval
+from vilbert.datasets._image_features_reader import ImageFeaturesH5Reader
+import pdb
+
+logger = logging.getLogger(__name__) # pylint: disable=invalid-name
+
+LossMap = {'BCEWithLogitLoss': nn.BCEWithLogitsLoss(reduction='mean'),
+ 'CrossEntropyLoss': nn.CrossEntropyLoss(),
+ }
+
+def ForwardModelsVal(args, task_cfg, device, task_id, batch, model, task_losses):
+ batch = tuple(t.cuda(device=device, non_blocking=True) for t in batch)
+ features, spatials, image_mask, question, target, input_mask, segment_ids, co_attention_mask, question_id = batch
+ batch_size = features.size(0)
+
+ if task_id in ['TASK2', 'TASK3', 'TASK5', 'TASK6', 'TASK7']:
+ max_num_bbox = features.size(1)
+ num_options = question.size(1)
+ features = features.unsqueeze(1).expand(batch_size, num_options, max_num_bbox, 2048).contiguous().view(-1, max_num_bbox, 2048)
+ spatials = spatials.unsqueeze(1).expand(batch_size, num_options, max_num_bbox, 5).contiguous().view(-1, max_num_bbox, 5)
+ image_mask = image_mask.unsqueeze(1).expand(batch_size, num_options, max_num_bbox).contiguous().view(-1, max_num_bbox)
+ question = question.view(-1, question.size(2))
+ input_mask = input_mask.view(-1, input_mask.size(2))
+ segment_ids = segment_ids.view(-1, segment_ids.size(2))
+ co_attention_mask = co_attention_mask.view(-1, co_attention_mask.size(2), co_attention_mask.size(3))
+
+ elif task_id in ['TASK8', 'TASK9']:
+ batch_size = features.size(0)
+ max_num_bbox = features.size(1)
+ num_options = question.size(1)
+ features = features.view(-1, features.size(2), features.size(3))
+ spatials = spatials.view(-1, spatials.size(2), spatials.size(3))
+ image_mask = image_mask.view(-1, image_mask.size(2))
+ question = question.view(-1, question.size(2))
+ input_mask = input_mask.view(-1, input_mask.size(2))
+ segment_ids = segment_ids.view(-1, segment_ids.size(2))
+ co_attention_mask = co_attention_mask.view(-1, co_attention_mask.size(2), co_attention_mask.size(3))
+
+ vil_prediction, vil_logit, vil_binary_prediction, vision_prediction, vision_logit, linguisic_prediction, linguisic_logit = \
+ model(question, features, spatials, segment_ids, input_mask, image_mask, co_attention_mask)
+
+ if task_cfg[task_id]['type'] == 'VL-classifier':
+ loss = task_losses[task_id](vil_prediction, target)
+ loss = loss.mean() * target.size(1)
+ batch_score = compute_score_with_logits(vil_prediction, target).sum()
+
+ elif task_cfg[task_id]['type'] == 'VL-logit':
+ vil_logit = vil_logit.view(batch_size, num_options)
+ loss = task_losses[task_id](vil_logit, target)
+ _, preds = torch.max(vil_logit, 1)
+ batch_score = (preds == target).sum()
+
+ elif task_cfg[task_id]['type'] == 'V-logit':
+ loss = task_losses[task_id](vision_logit, target)
+ loss = loss.mean() * target.size(1)
+ _, select_idx = torch.max(vision_logit, dim=1)
+ select_target = target.squeeze(2).gather(1, select_idx.view(-1,1))
+ batch_score = torch.sum(select_target>0.5).item()
+
+ return float(loss), float(batch_score), batch_size
+
+def ForwardModelsTrain(args, task_cfg, device, task_id, task_count, task_iter_train, task_dataloader_train, model, task_losses, task_start_iter):
+ # given the current task, decided whether to forward the model and forward with specific loss.
+
+ # reset the task iteration when needed.
+ if task_count[task_id] % len(task_dataloader_train[task_id]) == 0:
+ task_iter_train[task_id] = iter(task_dataloader_train[task_id])
+
+ task_count[task_id] += 1
+ # get the batch
+ batch = task_iter_train[task_id].next()
+ batch = tuple(t.cuda(device=device, non_blocking=True) for t in batch)
+ features, spatials, image_mask, question, target, input_mask, segment_ids, co_attention_mask, question_id = batch
+ batch_size = features.size(0)
+
+ if task_id in ['TASK2', 'TASK3', 'TASK5', 'TASK6', 'TASK7']:
+ max_num_bbox = features.size(1)
+ num_options = question.size(1)
+ features = features.unsqueeze(1).expand(batch_size, num_options, max_num_bbox, 2048).contiguous().view(-1, max_num_bbox, 2048)
+ spatials = spatials.unsqueeze(1).expand(batch_size, num_options, max_num_bbox, 5).contiguous().view(-1, max_num_bbox, 5)
+ image_mask = image_mask.unsqueeze(1).expand(batch_size, num_options, max_num_bbox).contiguous().view(-1, max_num_bbox)
+ question = question.view(-1, question.size(2))
+ input_mask = input_mask.view(-1, input_mask.size(2))
+ segment_ids = segment_ids.view(-1, segment_ids.size(2))
+ co_attention_mask = co_attention_mask.view(-1, co_attention_mask.size(2), co_attention_mask.size(3))
+
+ elif task_id in ['TASK8', 'TASK9']:
+ max_num_bbox = features.size(1)
+ num_options = question.size(1)
+ features = features.view(-1, features.size(2), features.size(3))
+ spatials = spatials.view(-1, spatials.size(2), spatials.size(3))
+ image_mask = image_mask.view(-1, image_mask.size(2))
+ question = question.view(-1, question.size(2))
+ input_mask = input_mask.view(-1, input_mask.size(2))
+ segment_ids = segment_ids.view(-1, segment_ids.size(2))
+ co_attention_mask = co_attention_mask.view(-1, co_attention_mask.size(2), co_attention_mask.size(3))
+
+ # get the model output
+ vil_prediction, vil_logit, vil_binary_prediction, vision_prediction, vision_logit, linguisic_prediction, linguisic_logit = \
+ model(question, features, spatials, segment_ids, input_mask, image_mask, co_attention_mask)
+
+ # for different task, we use different output to calculate the loss.
+ if task_cfg[task_id]['type'] == 'VL-classifier':
+ loss = task_losses[task_id](vil_prediction, target)
+ loss = loss.mean() * target.size(1)
+ batch_score = compute_score_with_logits(vil_prediction, target).sum() / float(batch_size)
+
+ elif task_cfg[task_id]['type'] == 'VL-logit':
+ vil_logit = vil_logit.view(batch_size, num_options)
+ loss = task_losses[task_id](vil_logit, target)
+ _, preds = torch.max(vil_logit, 1)
+ batch_score = float((preds == target).sum()) / float(batch_size)
+ loss = task_losses[task_id](vil_logit, target)
+
+ elif task_cfg[task_id]['type'] == 'V-logit':
+ loss = task_losses[task_id](vision_logit, target)
+ loss = loss.mean() * target.size(1)
+ _, select_idx = torch.max(vision_logit, dim=1)
+ select_target = target.squeeze(2).gather(1, select_idx.view(-1,1))
+ batch_score = float(torch.sum(select_target>0.5)) / batch_size
+
+ return loss, batch_score
+
+
+def LoadLosses(args, task_cfg, task_ids):
+
+ losses = {}
+ task_types = []
+ num_labels = 0
+ for i, task_id in enumerate(task_ids):
+ task = 'TASK' + task_id
+ model_type = task_cfg[task]['type']
+ if model_type not in task_types:
+ task_types.append(model_type)
+ losses[task] = LossMap[task_cfg[task]['loss']]
+
+ return losses
+
+def LoadDatasets(args, task_cfg, ids, split='trainval'):
+
+ tokenizer = BertTokenizer.from_pretrained(
+ args.bert_model, do_lower_case=True
+ )
+
+ task_feature_reader1 = {}
+ task_feature_reader2 = {}
+ for i, task_id in enumerate(ids):
+ task = 'TASK' + task_id
+ if task_cfg[task]['features_h5path1'] not in task_feature_reader1:
+ task_feature_reader1[task_cfg[task]['features_h5path1']] = None
+ if task_cfg[task]['features_h5path2'] not in task_feature_reader2:
+ task_feature_reader2[task_cfg[task]['features_h5path2']] = None
+
+ # initilzie the feature reader
+ for features_h5path in task_feature_reader1.keys():
+ if features_h5path != '':
+ task_feature_reader1[features_h5path] = ImageFeaturesH5Reader(features_h5path,
+ args.in_memory)
+
+ for features_h5path in task_feature_reader2.keys():
+ if features_h5path != '':
+ task_feature_reader2[features_h5path] = ImageFeaturesH5Reader(features_h5path, args.in_memory)
+
+ task_datasets_train = {}
+ task_datasets_val = {}
+ task_dataloader_train = {}
+ task_dataloader_val = {}
+ task_ids = []
+ task_batch_size = {}
+ task_num_iters = {}
+
+ for i, task_id in enumerate(ids):
+ task = 'TASK' + task_id
+ task_ids.append(task)
+ batch_size = task_cfg[task]['batch_size'] // args.gradient_accumulation_steps
+ num_workers = args.num_workers
+ if args.local_rank != -1:
+ batch_size = int(batch_size / dist.get_world_size())
+ num_workers = int(num_workers / dist.get_world_size())
+
+ # num_workers = int(num_workers / len(ids))
+ logger.info("Loading %s Dataset with batch size %d" %(task_cfg[task]['name'], batch_size))
+
+ task_datasets_train[task] = None
+ if 'train' in split:
+ task_datasets_train[task] = DatasetMapTrain[task](
+ task=task_cfg[task]['name'],
+ dataroot=task_cfg[task]['dataroot'],
+ annotations_jsonpath=task_cfg[task]['train_annotations_jsonpath'],
+ split=task_cfg[task]['train_split'],
+ image_features_reader= task_feature_reader1[task_cfg[task]['features_h5path1']],
+ gt_image_features_reader= task_feature_reader2[task_cfg[task]['features_h5path2']],
+ tokenizer=tokenizer,
+ padding_index=0,
+ max_seq_length=task_cfg[task]['max_seq_length'],
+ max_region_num=task_cfg[task]['max_region_num'],
+ )
+
+ task_datasets_val[task] = None
+ if 'val' in split:
+ task_datasets_val[task] = DatasetMapTrain[task](
+ task=task_cfg[task]['name'],
+ dataroot=task_cfg[task]['dataroot'],
+ annotations_jsonpath=task_cfg[task]['val_annotations_jsonpath'],
+ split=task_cfg[task]['val_split'],
+ image_features_reader= task_feature_reader1[task_cfg[task]['features_h5path1']],
+ gt_image_features_reader= task_feature_reader2[task_cfg[task]['features_h5path2']],
+ tokenizer=tokenizer,
+ padding_index=0,
+ max_seq_length=task_cfg[task]['max_seq_length'],
+ max_region_num=task_cfg[task]['max_region_num'])
+
+ task_num_iters[task] = 0
+ task_batch_size[task] = 0
+ if 'train' in split:
+ if args.local_rank == -1:
+ train_sampler = RandomSampler(task_datasets_train[task])
+ else:
+ #TODO: check if this works with current data generator from disk that relies on next(file)
+ # (it doesn't return item back by index)
+ train_sampler = DistributedSampler(task_datasets_train[task])
+
+ # num_workers = 1
+ task_dataloader_train[task] = DataLoader(
+ task_datasets_train[task],
+ sampler=train_sampler,
+ # shuffle=False,
+ batch_size=batch_size,
+ num_workers=num_workers,
+ pin_memory=True,
+ )
+ task_num_iters[task] = len(task_dataloader_train[task])
+ task_batch_size[task] = batch_size
+
+ if 'val' in split:
+ task_dataloader_val[task] = DataLoader(
+ task_datasets_val[task],
+ shuffle=False,
+ batch_size=batch_size,
+ num_workers=num_workers,
+ pin_memory=True,
+ )
+
+ return task_batch_size, task_num_iters, task_ids, task_datasets_train, task_datasets_val, task_dataloader_train, task_dataloader_val
+
+
+def LoadDatasetEval(args, task_cfg, ids):
+
+ tokenizer = BertTokenizer.from_pretrained(
+ args.bert_model, do_lower_case=True
+ )
+
+ task_feature_reader1 = {}
+ task_feature_reader2 = {}
+ for i, task_id in enumerate(ids):
+ task = 'TASK' + task_id
+ if task_cfg[task]['features_h5path1'] not in task_feature_reader1:
+ task_feature_reader1[task_cfg[task]['features_h5path1']] = None
+ if task_cfg[task]['features_h5path2'] not in task_feature_reader2:
+ task_feature_reader2[task_cfg[task]['features_h5path2']] = None
+
+ # initilzie the feature reader
+ for features_h5path in task_feature_reader1.keys():
+ if features_h5path != '':
+ task_feature_reader1[features_h5path] = ImageFeaturesH5Reader(features_h5path,
+ args.in_memory)
+
+ for features_h5path in task_feature_reader2.keys():
+ if features_h5path != '':
+ task_feature_reader2[features_h5path] = ImageFeaturesH5Reader(features_h5path, args.in_memory)
+
+ task_datasets_val = {}
+ task_dataloader_val = {}
+ task_ids = []
+ task_batch_size = {}
+ task_num_iters = {}
+
+ for i, task_id in enumerate(ids):
+ task = 'TASK' + task_id
+ task_ids.append(task)
+ batch_size = args.batch_size
+ if args.local_rank != -1:
+ batch_size = int(batch_size / dist.get_world_size())
+
+ num_workers = int(args.num_workers / len(ids))
+ logger.info("Loading %s Dataset with batch size %d" %(task_cfg[task]['name'], batch_size))
+
+ if args.split:
+ eval_split = args.split
+ else:
+ eval_split = task_cfg[task]['val_split']
+
+ task_datasets_val[task] = DatasetMapEval[task](
+ task=task_cfg[task]['name'],
+ dataroot=task_cfg[task]['dataroot'],
+ annotations_jsonpath=task_cfg[task]['val_annotations_jsonpath'],
+ split=eval_split,
+ image_features_reader= task_feature_reader1[task_cfg[task]['features_h5path1']],
+ gt_image_features_reader= task_feature_reader2[task_cfg[task]['features_h5path2']],
+ tokenizer=tokenizer,
+ padding_index=0,
+ max_seq_length=task_cfg[task]['max_seq_length'],
+ max_region_num=task_cfg[task]['max_region_num'])
+
+ task_dataloader_val[task] = DataLoader(
+ task_datasets_val[task],
+ shuffle=False,
+ batch_size=batch_size,
+ num_workers=10,
+ pin_memory=True,
+ )
+
+ task_num_iters[task] = len(task_dataloader_val[task])
+ task_batch_size[task] = batch_size
+
+ return task_batch_size, task_num_iters, task_ids, task_datasets_val, task_dataloader_val
+
+
+def compute_score_with_logits(logits, labels):
+ logits = torch.max(logits, 1)[1].data # argmax
+ one_hots = torch.zeros(*labels.size()).cuda()
+ one_hots.scatter_(1, logits.view(-1, 1), 1)
+ scores = one_hots * labels
+ return scores
+
+def EvaluatingModel(args, task_cfg, device, task_id, batch, model, task_dataloader, task_losses, results, others):
+ batch = tuple(t.cuda(device=device, non_blocking=True) for t in batch)
+ features, spatials, image_mask, question, target, input_mask, segment_ids, co_attention_mask, question_id = batch
+ batch_size = features.size(0)
+
+ if task_id in ['TASK2', 'TASK6', 'TASK7']:
+ max_num_bbox = features.size(1)
+ num_options = question.size(1)
+ features = features.unsqueeze(1).expand(batch_size, num_options, max_num_bbox, 2048).contiguous().view(-1, max_num_bbox, 2048)
+ spatials = spatials.unsqueeze(1).expand(batch_size, num_options, max_num_bbox, 5).contiguous().view(-1, max_num_bbox, 5)
+ image_mask = image_mask.unsqueeze(1).expand(batch_size, num_options, max_num_bbox).contiguous().view(-1, max_num_bbox)
+ question = question.view(-1, question.size(2))
+ input_mask = input_mask.view(-1, input_mask.size(2))
+ segment_ids = segment_ids.view(-1, segment_ids.size(2))
+ co_attention_mask = co_attention_mask.view(-1, co_attention_mask.size(2), co_attention_mask.size(3))
+
+ elif task_id in ['TASK8', 'TASK9']:
+ batch_size = features.size(0)
+ max_num_bbox = features.size(1)
+ num_options = question.size(1)
+ features = features.view(-1, features.size(2), features.size(3))
+ spatials = spatials.view(-1, spatials.size(2), spatials.size(3))
+ image_mask = image_mask.view(-1, image_mask.size(2))
+ question = question.view(-1, question.size(2))
+ input_mask = input_mask.view(-1, input_mask.size(2))
+ segment_ids = segment_ids.view(-1, segment_ids.size(2))
+ co_attention_mask = co_attention_mask.view(-1, co_attention_mask.size(2), co_attention_mask.size(3))
+
+ with torch.no_grad():
+ vil_prediction, vil_logit, vil_binary_prediction, vision_prediction, vision_logit, linguisic_prediction, linguisic_logit \
+ = model(question, features, spatials, segment_ids, input_mask, image_mask, co_attention_mask)
+
+ if task_cfg[task_id]['type'] == 'VL-classifier':
+ logits = torch.max(vil_prediction, 1)[1].data # argmax
+ sorted_score, sorted_idx = torch.sort(-vil_prediction)
+ topk = 8 # top candidate.
+ topkInd = sorted_idx[:,:topk]
+ loss = 0
+ batch_score = 0
+ for i in range(logits.size(0)):
+ results.append({'question_id':question_id[i].item(), \
+ 'answer':task_dataloader[task_id].dataset.label2ans[logits[i].item()]})
+
+ # save top 8 as options.
+ others.append({'question_id':question_id[i].item(), \
+ 'answer':[task_dataloader[task_id].dataset.label2ans[idx.item()] for idx in topkInd[i]]})
+
+ elif task_cfg[task_id]['type'] == 'VL-logit':
+ vil_logit = vil_logit.view(batch_size, num_options)
+ loss = task_losses[task_id](vil_logit, target)
+ _, preds = torch.max(vil_logit, 1)
+ batch_score = (preds == target).sum()
+
+ probs = torch.softmax(vil_logit, dim=1)
+ for i in range(vil_logit.size(0)):
+ results.append({'question_id':question_id[i].item(), 'answer':[prob.item() for prob in probs[i]]})
+
+ elif task_cfg[task_id]['type'] == 'V-logit':
+ loss = task_losses[task_id](vision_logit, target)
+ loss = loss.mean() * target.size(1)
+ _, select_idx = torch.max(vision_logit, dim=1)
+ select_target = target.squeeze(2).gather(1, select_idx.view(-1,1))
+ batch_score = torch.sum(select_target>0.5).item()
+
+ for i in range(select_idx.size(0)):
+ results.append({'id':question_id[i].item(), 'target':select_idx[i].item(), 'IOU': select_target[i].item()})
+
+ return float(loss), float(batch_score), batch_size, results, others
\ No newline at end of file
diff --git a/vilbert/utils.py b/vilbert/utils.py
new file mode 100644
index 0000000..a2ba465
--- /dev/null
+++ b/vilbert/utils.py
@@ -0,0 +1,346 @@
+"""
+"""
+from io import open
+import json
+import logging
+from functools import wraps
+from hashlib import sha256
+from pathlib import Path
+import os
+import shutil
+import sys
+import tempfile
+from urllib.parse import urlparse
+
+import boto3
+import requests
+from botocore.exceptions import ClientError
+from tqdm import tqdm
+from tensorboardX import SummaryWriter
+from time import gmtime, strftime
+from bisect import bisect
+
+import pdb
+
+PYTORCH_PRETRAINED_BERT_CACHE = Path(
+ os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", Path.home() / ".pytorch_pretrained_bert")
+)
+
+
+logger = logging.getLogger(__name__) # pylint: disable=invalid-name
+
+def lr_warmup(step, ):
+ if (
+ cfg["training_parameters"]["use_warmup"] is True
+ and i_iter <= cfg["training_parameters"]["warmup_iterations"]
+ ):
+ alpha = float(i_iter) / float(cfg["training_parameters"]["warmup_iterations"])
+ return cfg["training_parameters"]["warmup_factor"] * (1.0 - alpha) + alpha
+ else:
+ idx = bisect(cfg["training_parameters"]["lr_steps"], i_iter)
+ return pow(cfg["training_parameters"]["lr_ratio"], idx)
+
+class tbLogger(object):
+ def __init__(self, log_dir, txt_dir, task_names, task_ids, task_num_iters, gradient_accumulation_steps, save_logger=True, txt_name='out.txt'):
+ logger.info("logging file at: " + log_dir)
+
+ self.save_logger=save_logger
+ if self.save_logger:
+ self.logger = SummaryWriter(log_dir=log_dir)
+
+ self.txt_f = open(txt_dir + '/' + txt_name, 'w')
+ self.task_id2name = {ids:name.replace('+', 'plus') for ids, name in zip(task_ids, task_names)}
+ self.task_ids = task_ids
+ self.task_loss = {task_id:0 for task_id in task_ids}
+ self.task_loss_tmp = {task_id:0 for task_id in task_ids}
+ self.task_score_tmp = {task_id:0 for task_id in task_ids}
+ self.task_norm_tmp = {task_id:0 for task_id in task_ids}
+ self.task_step = {task_id:0 for task_id in task_ids}
+ self.task_step_tmp = {task_id:0 for task_id in task_ids}
+ self.task_num_iters = task_num_iters
+ self.epochId = 0
+ self.gradient_accumulation_steps = gradient_accumulation_steps
+ self.task_loss_val = {task_id:0 for task_id in task_ids}
+ self.task_score_val = {task_id:0 for task_id in task_ids}
+ self.task_step_val = {task_id:0 for task_id in task_ids}
+ self.task_datasize_val = {task_id:0 for task_id in task_ids}
+
+ def txt_close(self):
+ self.txt_f.close()
+
+ def linePlot(self, step, val, split, key, xlabel="None"):
+ if self.save_logger:
+ self.logger.add_scalar(split + "/" + key, val, step)
+
+ def step_train(self, epochId, stepId, loss, score, norm, task_id, split):
+ self.task_loss[task_id] += loss
+ self.task_loss_tmp[task_id] += loss
+ self.task_score_tmp[task_id] += score
+ self.task_norm_tmp[task_id] += norm
+ self.task_step[task_id] += self.gradient_accumulation_steps
+ self.task_step_tmp[task_id] += self.gradient_accumulation_steps
+ self.epochId = epochId
+
+ # plot on tensorboard.
+ self.linePlot(stepId, loss, split, self.task_id2name[task_id] + '_loss')
+ self.linePlot(stepId, score, split, self.task_id2name[task_id] + '_score')
+
+ def step_val(self, epochId, loss, score, task_id, batch_size, split):
+ self.task_loss_val[task_id] += loss
+ self.task_score_val[task_id] += score
+ self.task_step_val[task_id] += self.gradient_accumulation_steps
+ self.task_datasize_val[task_id] += batch_size
+
+ def showLossVal(self):
+ progressInfo = "Eval Ep: %d " %self.epochId
+ lossInfo = 'Validation '
+ ave_score = 0
+ ave_loss = 0
+ for task_id in self.task_ids:
+ loss = self.task_loss_val[task_id] / float(self.task_step_val[task_id])
+ score = self.task_score_val[task_id] / float(self.task_datasize_val[task_id])
+ ave_score += score
+ ave_loss += loss
+ lossInfo += '[%s]: loss %.3f score %.3f ' %(self.task_id2name[task_id], loss, score * 100.0)
+
+ self.linePlot(self.epochId, loss, 'val', self.task_id2name[task_id] + '_loss')
+ self.linePlot(self.epochId, score, 'val', self.task_id2name[task_id] + '_score')
+
+ ave_score = ave_score / len(self.task_ids)
+ self.task_loss_val = {task_id:0 for task_id in self.task_loss_val}
+ self.task_score_val = {task_id:0 for task_id in self.task_score_val}
+ self.task_datasize_val = {task_id:0 for task_id in self.task_datasize_val}
+ self.task_step_val = {task_id:0 for task_id in self.task_ids}
+ logger.info(lossInfo)
+ print(lossInfo, file=self.txt_f)
+ return ave_score
+
+ def showLossTrain(self):
+ # show the current loss, once showed, reset the loss.
+ lossInfo = ''
+ for task_id in self.task_ids:
+ if self.task_num_iters[task_id] > 0:
+ if self.task_step_tmp[task_id]:
+ lossInfo += '[%s]: iter %d Ep: %.2f loss %.3f score %.3f lr %.6g ' %(self.task_id2name[task_id], \
+ self.task_step[task_id], self.task_step[task_id] / float(self.task_num_iters[task_id]), \
+ self.task_loss_tmp[task_id] / float(self.task_step_tmp[task_id]), \
+ self.task_score_tmp[task_id] / float(self.task_step_tmp[task_id]), \
+ self.task_norm_tmp[task_id] / float(self.task_step_tmp[task_id]))
+
+ logger.info(lossInfo)
+ print(lossInfo, file=self.txt_f)
+
+ self.task_step_tmp = {task_id:0 for task_id in self.task_ids}
+ self.task_loss_tmp = {task_id:0 for task_id in self.task_ids}
+ self.task_score_tmp = {task_id:0 for task_id in self.task_ids}
+ self.task_norm_tmp = {task_id:0 for task_id in self.task_ids}
+
+def url_to_filename(url, etag=None):
+ """
+ Convert `url` into a hashed filename in a repeatable way.
+ If `etag` is specified, append its hash to the url's, delimited
+ by a period.
+ """
+ url_bytes = url.encode("utf-8")
+ url_hash = sha256(url_bytes)
+ filename = url_hash.hexdigest()
+
+ if etag:
+ etag_bytes = etag.encode("utf-8")
+ etag_hash = sha256(etag_bytes)
+ filename += "." + etag_hash.hexdigest()
+
+ return filename
+
+
+def filename_to_url(filename, cache_dir=None):
+ """
+ Return the url and etag (which may be ``None``) stored for `filename`.
+ Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
+ """
+ if cache_dir is None:
+ cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+ if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
+ cache_dir = str(cache_dir)
+
+ cache_path = os.path.join(cache_dir, filename)
+ if not os.path.exists(cache_path):
+ raise EnvironmentError("file {} not found".format(cache_path))
+
+ meta_path = cache_path + ".json"
+ if not os.path.exists(meta_path):
+ raise EnvironmentError("file {} not found".format(meta_path))
+
+ with open(meta_path, encoding="utf-8") as meta_file:
+ metadata = json.load(meta_file)
+ url = metadata["url"]
+ etag = metadata["etag"]
+
+ return url, etag
+
+
+def cached_path(url_or_filename, cache_dir=None):
+ """
+ Given something that might be a URL (or might be a local path),
+ determine which. If it's a URL, download the file and cache it, and
+ return the path to the cached file. If it's already a local path,
+ make sure the file exists and then return the path.
+ """
+ if cache_dir is None:
+ cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+ if sys.version_info[0] == 3 and isinstance(url_or_filename, Path):
+ url_or_filename = str(url_or_filename)
+ if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
+ cache_dir = str(cache_dir)
+
+ parsed = urlparse(url_or_filename)
+
+ if parsed.scheme in ("http", "https", "s3"):
+ # URL, so get it from the cache (downloading if necessary)
+ return get_from_cache(url_or_filename, cache_dir)
+ elif os.path.exists(url_or_filename):
+ # File, and it exists.
+ return url_or_filename
+ elif parsed.scheme == "":
+ # File, but it doesn't exist.
+ raise EnvironmentError("file {} not found".format(url_or_filename))
+ else:
+ # Something unknown
+ raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
+
+def split_s3_path(url):
+ """Split a full s3 path into the bucket name and path."""
+ parsed = urlparse(url)
+ if not parsed.netloc or not parsed.path:
+ raise ValueError("bad s3 path {}".format(url))
+ bucket_name = parsed.netloc
+ s3_path = parsed.path
+ # Remove '/' at beginning of path.
+ if s3_path.startswith("/"):
+ s3_path = s3_path[1:]
+ return bucket_name, s3_path
+
+
+def s3_request(func):
+ """
+ Wrapper function for s3 requests in order to create more helpful error
+ messages.
+ """
+
+ @wraps(func)
+ def wrapper(url, *args, **kwargs):
+ try:
+ return func(url, *args, **kwargs)
+ except ClientError as exc:
+ if int(exc.response["Error"]["Code"]) == 404:
+ raise EnvironmentError("file {} not found".format(url))
+ else:
+ raise
+ return wrapper
+
+@s3_request
+def s3_etag(url):
+ """Check ETag on S3 object."""
+ s3_resource = boto3.resource("s3")
+ bucket_name, s3_path = split_s3_path(url)
+ s3_object = s3_resource.Object(bucket_name, s3_path)
+ return s3_object.e_tag
+
+@s3_request
+def s3_get(url, temp_file):
+ """Pull a file directly from S3."""
+ s3_resource = boto3.resource("s3")
+ bucket_name, s3_path = split_s3_path(url)
+ s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
+
+def http_get(url, temp_file):
+ req = requests.get(url, stream=True)
+ content_length = req.headers.get("Content-Length")
+ total = int(content_length) if content_length is not None else None
+ progress = tqdm(unit="B", total=total)
+ for chunk in req.iter_content(chunk_size=1024):
+ if chunk: # filter out keep-alive new chunks
+ progress.update(len(chunk))
+ temp_file.write(chunk)
+ progress.close()
+
+
+def get_from_cache(url, cache_dir=None):
+ """
+ Given a URL, look for the corresponding dataset in the local cache.
+ If it's not there, download it. Then return the path to the cached file.
+ """
+ if cache_dir is None:
+ cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+ if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
+ cache_dir = str(cache_dir)
+
+ if not os.path.exists(cache_dir):
+ os.makedirs(cache_dir)
+
+ # Get eTag to add to filename, if it exists.
+ if url.startswith("s3://"):
+ etag = s3_etag(url)
+ else:
+ response = requests.head(url, allow_redirects=True)
+ if response.status_code != 200:
+ raise IOError(
+ "HEAD request failed for url {} with status code {}".format(
+ url, response.status_code
+ )
+ )
+ etag = response.headers.get("ETag")
+
+ filename = url_to_filename(url, etag)
+
+ # get cache path to put the file
+ cache_path = os.path.join(cache_dir, filename)
+
+ if not os.path.exists(cache_path):
+ # Download to temporary file, then copy to cache dir once finished.
+ # Otherwise you get corrupt cache entries if the download gets interrupted.
+ with tempfile.NamedTemporaryFile() as temp_file:
+ logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
+
+ # GET file object
+ if url.startswith("s3://"):
+ s3_get(url, temp_file)
+ else:
+ http_get(url, temp_file)
+
+ # we are copying the file before closing it, so flush to avoid truncation
+ temp_file.flush()
+ # shutil.copyfileobj() starts at the current position, so go to the start
+ temp_file.seek(0)
+
+ logger.info("copying %s to cache at %s", temp_file.name, cache_path)
+ with open(cache_path, "wb") as cache_file:
+ shutil.copyfileobj(temp_file, cache_file)
+
+ logger.info("creating metadata file for %s", cache_path)
+ meta = {"url": url, "etag": etag}
+ meta_path = cache_path + ".json"
+ with open(meta_path, "w", encoding="utf-8") as meta_file:
+ json.dump(meta, meta_file)
+
+ logger.info("removing temp file %s", temp_file.name)
+
+ return cache_path
+
+
+def read_set_from_file(filename):
+ """
+ Extract a de-duped collection (set) of text from a file.
+ Expected file format is one item per line.
+ """
+ collection = set()
+ with open(filename, "r", encoding="utf-8") as file_:
+ for line in file_:
+ collection.add(line.rstrip())
+ return collection
+
+def get_file_extension(path, dot=True, lower=True):
+ ext = os.path.splitext(path)[1]
+ ext = ext if dot else ext[1:]
+ return ext.lower() if lower else ext
+
diff --git a/vilbert/vilbert.py b/vilbert/vilbert.py
new file mode 100644
index 0000000..d4e6a62
--- /dev/null
+++ b/vilbert/vilbert.py
@@ -0,0 +1,1581 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch BERT model."""
+
+import copy
+import json
+import logging
+import math
+import os
+import shutil
+import tarfile
+import tempfile
+import sys
+from io import open
+
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+import torch.nn.functional as F
+from torch.nn.utils.weight_norm import weight_norm
+
+from .utils import cached_path
+import pdb
+
+logger = logging.getLogger(__name__)
+
+PRETRAINED_MODEL_ARCHIVE_MAP = {
+ "bert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
+ "bert-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
+ "bert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
+ "bert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
+ "bert-base-multilingual-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
+ "bert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
+ "bert-base-chinese": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
+}
+
+def load_tf_weights_in_bert(model, tf_checkpoint_path):
+ """ Load tf checkpoints in a pytorch model
+ """
+ try:
+ import re
+ import numpy as np
+ import tensorflow as tf
+ except ImportError:
+ print(
+ "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
+ "https://www.tensorflow.org/install/ for installation instructions."
+ )
+ raise
+ tf_path = os.path.abspath(tf_checkpoint_path)
+ print("Converting TensorFlow checkpoint from {}".format(tf_path))
+ # Load weights from TF model
+ init_vars = tf.train.list_variables(tf_path)
+ names = []
+ arrays = []
+ for name, shape in init_vars:
+ print("Loading TF weight {} with shape {}".format(name, shape))
+ array = tf.train.load_variable(tf_path, name)
+ names.append(name)
+ arrays.append(array)
+
+ for name, array in zip(names, arrays):
+ name = name.split("/")
+ # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
+ # which are not required for using pretrained model
+ if any(n in ["adam_v", "adam_m"] for n in name):
+ print("Skipping {}".format("/".join(name)))
+ continue
+ pointer = model
+ for m_name in name:
+ if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
+ l = re.split(r"_(\d+)", m_name)
+ else:
+ l = [m_name]
+ if l[0] == "kernel" or l[0] == "gamma":
+ pointer = getattr(pointer, "weight")
+ elif l[0] == "output_bias" or l[0] == "beta":
+ pointer = getattr(pointer, "bias")
+ elif l[0] == "output_weights":
+ pointer = getattr(pointer, "weight")
+ else:
+ pointer = getattr(pointer, l[0])
+ if len(l) >= 2:
+ num = int(l[1])
+ pointer = pointer[num]
+ if m_name[-11:] == "_embeddings":
+ pointer = getattr(pointer, "weight")
+ elif m_name == "kernel":
+ array = np.transpose(array)
+ try:
+ assert pointer.shape == array.shape
+ except AssertionError as e:
+ e.args += (pointer.shape, array.shape)
+ raise
+ print("Initialize PyTorch weight {}".format(name))
+ pointer.data = torch.from_numpy(array)
+ return model
+
+
+def gelu(x):
+ """Implementation of the gelu activation function.
+ For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
+ 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
+ Also see https://arxiv.org/abs/1606.08415
+ """
+ return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
+
+
+def swish(x):
+ return x * torch.sigmoid(x)
+
+
+ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
+
+
+class BertConfig(object):
+ """Configuration class to store the configuration of a `BertModel`.
+ """
+
+ def __init__(
+ self,
+ vocab_size_or_config_json_file,
+ hidden_size=768,
+ num_hidden_layers=12,
+ num_attention_heads=12,
+ intermediate_size=3072,
+ hidden_act="gelu",
+ hidden_dropout_prob=0.1,
+ attention_probs_dropout_prob=0.1,
+ max_position_embeddings=512,
+ type_vocab_size=2,
+ initializer_range=0.02,
+ v_feature_size=2048,
+ v_target_size=1601,
+ v_hidden_size=768,
+ v_num_hidden_layers=3,
+ v_num_attention_heads=12,
+ v_intermediate_size=3072,
+ bi_hidden_size=1024,
+ bi_num_attention_heads=16,
+ v_attention_probs_dropout_prob=0.1,
+ v_hidden_act="gelu",
+ v_hidden_dropout_prob=0.1,
+ v_initializer_range=0.2,
+ v_biattention_id=[0, 1],
+ t_biattention_id=[10, 11],
+ predict_feature=False,
+ fast_mode=False,
+ fixed_v_layer=0,
+ fixed_t_layer=0,
+ in_batch_pairs=False,
+ fusion_method="mul",
+ intra_gate=False,
+ with_coattention=True
+ ):
+
+ """Constructs BertConfig.
+
+ Args:
+ vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
+ hidden_size: Size of the encoder layers and the pooler layer.
+ num_hidden_layers: Number of hidden layers in the Transformer encoder.
+ num_attention_heads: Number of attention heads for each attention layer in
+ the Transformer encoder.
+ intermediate_size: The size of the "intermediate" (i.e., feed-forward)
+ layer in the Transformer encoder.
+ hidden_act: The non-linear activation function (function or string) in the
+ encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
+ hidden_dropout_prob: The dropout probabilitiy for all fully connected
+ layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob: The dropout ratio for the attention
+ probabilities.
+ max_position_embeddings: The maximum sequence length that this model might
+ ever be used with. Typically set this to something large just in case
+ (e.g., 512 or 1024 or 2048).
+ type_vocab_size: The vocabulary size of the `token_type_ids` passed into
+ `BertModel`.
+ initializer_range: The sttdev of the truncated_normal_initializer for
+ initializing all weight matrices.
+ """
+ assert len(v_biattention_id) == len(t_biattention_id)
+ assert max(v_biattention_id) < v_num_hidden_layers
+ assert max(t_biattention_id) < num_hidden_layers
+
+ if isinstance(vocab_size_or_config_json_file, str) or (
+ sys.version_info[0] == 2
+ and isinstance(vocab_size_or_config_json_file, unicode)
+ ):
+ with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
+ json_config = json.loads(reader.read())
+ for key, value in json_config.items():
+ self.__dict__[key] = value
+ elif isinstance(vocab_size_or_config_json_file, int):
+ self.vocab_size = vocab_size_or_config_json_file
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.hidden_act = hidden_act
+ self.intermediate_size = intermediate_size
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.max_position_embeddings = max_position_embeddings
+ self.type_vocab_size = type_vocab_size
+ self.initializer_range = initializer_range
+ self.v_feature_size = v_feature_size
+ self.v_hidden_size = v_hidden_size
+ self.v_num_hidden_layers = v_num_hidden_layers
+ self.v_num_attention_heads = v_num_attention_heads
+ self.v_intermediate_size = v_intermediate_size
+ self.v_attention_probs_dropout_prob = v_attention_probs_dropout_prob
+ self.v_hidden_act = v_hidden_act
+ self.v_hidden_dropout_prob = v_hidden_dropout_prob
+ self.v_initializer_range = v_initializer_range
+ self.v_biattention_id = v_biattention_id
+ self.t_biattention_id = t_biattention_id
+ self.v_target_size = v_target_size
+ self.bi_hidden_size = bi_hidden_size
+ self.bi_num_attention_heads = bi_num_attention_heads
+ self.predict_feature = predict_feature
+ self.fast_mode = fast_mode
+ self.fixed_v_layer = fixed_v_layer
+ self.fixed_t_layer = fixed_t_layer
+
+ self.in_batch_pairs = in_batch_pairs
+ self.fusion_method = fusion_method
+ self.intra_gate = intra_gate
+ self.with_coattention=with_coattention
+ else:
+ raise ValueError(
+ "First argument must be either a vocabulary size (int)"
+ "or the path to a pretrained model config file (str)"
+ )
+
+ @classmethod
+ def from_dict(cls, json_object):
+ """Constructs a `BertConfig` from a Python dictionary of parameters."""
+ config = BertConfig(vocab_size_or_config_json_file=-1)
+ for key, value in json_object.items():
+ config.__dict__[key] = value
+ return config
+
+ @classmethod
+ def from_json_file(cls, json_file):
+ """Constructs a `BertConfig` from a json file of parameters."""
+ with open(json_file, "r", encoding="utf-8") as reader:
+ text = reader.read()
+ return cls.from_dict(json.loads(text))
+
+ def __repr__(self):
+ return str(self.to_json_string())
+
+ def to_dict(self):
+ """Serializes this instance to a Python dictionary."""
+ output = copy.deepcopy(self.__dict__)
+ return output
+
+ def to_json_string(self):
+ """Serializes this instance to a JSON string."""
+ return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
+
+try:
+ from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
+except ImportError:
+ logger.info(
+ "Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex ."
+ )
+
+ class BertLayerNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-12):
+ """Construct a layernorm module in the TF style (epsilon inside the square root).
+ """
+ super(BertLayerNorm, self).__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.bias = nn.Parameter(torch.zeros(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, x):
+ u = x.mean(-1, keepdim=True)
+ s = (x - u).pow(2).mean(-1, keepdim=True)
+ x = (x - u) / torch.sqrt(s + self.variance_epsilon)
+ return self.weight * x + self.bias
+
+class BertEmbeddings(nn.Module):
+ """Construct the embeddings from word, position and token_type embeddings.
+ """
+
+ def __init__(self, config):
+ super(BertEmbeddings, self).__init__()
+ self.word_embeddings = nn.Embedding(
+ config.vocab_size, config.hidden_size, padding_idx=0
+ )
+ self.position_embeddings = nn.Embedding(
+ config.max_position_embeddings, config.hidden_size
+ )
+ self.token_type_embeddings = nn.Embedding(
+ config.type_vocab_size, config.hidden_size
+ )
+
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+ # any TensorFlow checkpoint file
+ self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, input_ids, token_type_ids=None):
+ seq_length = input_ids.size(1)
+ position_ids = torch.arange(
+ seq_length, dtype=torch.long, device=input_ids.device
+ )
+ position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+ if token_type_ids is None:
+ token_type_ids = torch.zeros_like(input_ids)
+
+ words_embeddings = self.word_embeddings(input_ids)
+ position_embeddings = self.position_embeddings(position_ids)
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ embeddings = words_embeddings + position_embeddings + token_type_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+class BertSelfAttention(nn.Module):
+ def __init__(self, config):
+ super(BertSelfAttention, self).__init__()
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ "The hidden size (%d) is not a multiple of the number of attention "
+ "heads (%d)" % (config.hidden_size, config.num_attention_heads)
+ )
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+ def transpose_for_scores(self, x):
+ new_x_shape = x.size()[:-1] + (
+ self.num_attention_heads,
+ self.attention_head_size,
+ )
+ x = x.view(*new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def forward(self, hidden_states, attention_mask):
+ mixed_query_layer = self.query(hidden_states)
+ mixed_key_layer = self.key(hidden_states)
+ mixed_value_layer = self.value(hidden_states)
+
+ query_layer = self.transpose_for_scores(mixed_query_layer)
+ key_layer = self.transpose_for_scores(mixed_key_layer)
+ value_layer = self.transpose_for_scores(mixed_value_layer)
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+ # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(*new_context_layer_shape)
+
+ return context_layer, attention_probs
+
+
+class BertSelfOutput(nn.Module):
+ def __init__(self, config):
+ super(BertSelfOutput, self).__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class BertAttention(nn.Module):
+ def __init__(self, config):
+ super(BertAttention, self).__init__()
+ self.self = BertSelfAttention(config)
+ self.output = BertSelfOutput(config)
+
+ def forward(self, input_tensor, attention_mask):
+ self_output, attention_probs = self.self(input_tensor, attention_mask)
+ attention_output = self.output(self_output, input_tensor)
+ return attention_output, attention_probs
+
+
+class BertIntermediate(nn.Module):
+ def __init__(self, config):
+ super(BertIntermediate, self).__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str) or (
+ sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)
+ ):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+class BertOutput(nn.Module):
+ def __init__(self, config):
+ super(BertOutput, self).__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class BertLayer(nn.Module):
+ def __init__(self, config):
+ super(BertLayer, self).__init__()
+ self.attention = BertAttention(config)
+ self.intermediate = BertIntermediate(config)
+ self.output = BertOutput(config)
+
+ def forward(self, hidden_states, attention_mask):
+ attention_output, attention_probs = self.attention(hidden_states, attention_mask)
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output, attention_probs
+
+
+class BertImageSelfAttention(nn.Module):
+ def __init__(self, config):
+ super(BertImageSelfAttention, self).__init__()
+ if config.v_hidden_size % config.v_num_attention_heads != 0:
+ raise ValueError(
+ "The hidden size (%d) is not a multiple of the number of attention "
+ "heads (%d)" % (config.v_hidden_size, config.v_num_attention_heads)
+ )
+ self.num_attention_heads = config.v_num_attention_heads
+ self.attention_head_size = int(
+ config.v_hidden_size / config.v_num_attention_heads
+ )
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.v_hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.v_hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.v_hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(config.v_attention_probs_dropout_prob)
+
+ def transpose_for_scores(self, x):
+ new_x_shape = x.size()[:-1] + (
+ self.num_attention_heads,
+ self.attention_head_size,
+ )
+ x = x.view(*new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def forward(self, hidden_states, attention_mask):
+ mixed_query_layer = self.query(hidden_states)
+ mixed_key_layer = self.key(hidden_states)
+ mixed_value_layer = self.value(hidden_states)
+
+ query_layer = self.transpose_for_scores(mixed_query_layer)
+ key_layer = self.transpose_for_scores(mixed_key_layer)
+ value_layer = self.transpose_for_scores(mixed_value_layer)
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+ # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(*new_context_layer_shape)
+
+ return context_layer, attention_probs
+
+class BertImageSelfOutput(nn.Module):
+ def __init__(self, config):
+ super(BertImageSelfOutput, self).__init__()
+ self.dense = nn.Linear(config.v_hidden_size, config.v_hidden_size)
+ self.LayerNorm = BertLayerNorm(config.v_hidden_size, eps=1e-12)
+ self.dropout = nn.Dropout(config.v_hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+class BertImageAttention(nn.Module):
+ def __init__(self, config):
+ super(BertImageAttention, self).__init__()
+ self.self = BertImageSelfAttention(config)
+ self.output = BertImageSelfOutput(config)
+
+ def forward(self, input_tensor, attention_mask):
+ self_output, attention_probs = self.self(input_tensor, attention_mask)
+ attention_output = self.output(self_output, input_tensor)
+ return attention_output, attention_probs
+
+
+class BertImageIntermediate(nn.Module):
+ def __init__(self, config):
+ super(BertImageIntermediate, self).__init__()
+ self.dense = nn.Linear(config.v_hidden_size, config.v_intermediate_size)
+ if isinstance(config.v_hidden_act, str) or (
+ sys.version_info[0] == 2 and isinstance(config.v_hidden_act, unicode)
+ ):
+ self.intermediate_act_fn = ACT2FN[config.v_hidden_act]
+ else:
+ self.intermediate_act_fn = config.v_hidden_act
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+class BertImageOutput(nn.Module):
+ def __init__(self, config):
+ super(BertImageOutput, self).__init__()
+ self.dense = nn.Linear(config.v_intermediate_size, config.v_hidden_size)
+ self.LayerNorm = BertLayerNorm(config.v_hidden_size, eps=1e-12)
+ self.dropout = nn.Dropout(config.v_hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class BertImageLayer(nn.Module):
+ def __init__(self, config):
+ super(BertImageLayer, self).__init__()
+ self.attention = BertImageAttention(config)
+ self.intermediate = BertImageIntermediate(config)
+ self.output = BertImageOutput(config)
+
+ def forward(self, hidden_states, attention_mask):
+ attention_output, attention_probs = self.attention(hidden_states, attention_mask)
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output, attention_probs
+
+
+class BertBiAttention(nn.Module):
+ def __init__(self, config):
+ super(BertBiAttention, self).__init__()
+ if config.bi_hidden_size % config.bi_num_attention_heads != 0:
+ raise ValueError(
+ "The hidden size (%d) is not a multiple of the number of attention "
+ "heads (%d)" % (config.bi_hidden_size, config.bi_num_attention_heads)
+ )
+
+ self.num_attention_heads = config.bi_num_attention_heads
+ self.attention_head_size = int(
+ config.bi_hidden_size / config.bi_num_attention_heads
+ )
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ # self.scale = nn.Linear(1, self.num_attention_heads, bias=False)
+ # self.scale_act_fn = ACT2FN['relu']
+
+ self.query1 = nn.Linear(config.v_hidden_size, self.all_head_size)
+ self.key1 = nn.Linear(config.v_hidden_size, self.all_head_size)
+ self.value1 = nn.Linear(config.v_hidden_size, self.all_head_size)
+ # self.logit1 = nn.Linear(config.hidden_size, self.num_attention_heads)
+
+ self.dropout1 = nn.Dropout(config.v_attention_probs_dropout_prob)
+
+ self.query2 = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key2 = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value2 = nn.Linear(config.hidden_size, self.all_head_size)
+ # self.logit2 = nn.Linear(config.hidden_size, self.num_attention_heads)
+
+ self.dropout2 = nn.Dropout(config.attention_probs_dropout_prob)
+
+ def transpose_for_scores(self, x):
+ new_x_shape = x.size()[:-1] + (
+ self.num_attention_heads,
+ self.attention_head_size,
+ )
+ x = x.view(*new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def forward(self, input_tensor1, attention_mask1, input_tensor2, attention_mask2, co_attention_mask=None, use_co_attention_mask=False):
+
+ # for vision input.
+ mixed_query_layer1 = self.query1(input_tensor1)
+ mixed_key_layer1 = self.key1(input_tensor1)
+ mixed_value_layer1 = self.value1(input_tensor1)
+ # mixed_logit_layer1 = self.logit1(input_tensor1)
+
+ query_layer1 = self.transpose_for_scores(mixed_query_layer1)
+ key_layer1 = self.transpose_for_scores(mixed_key_layer1)
+ value_layer1 = self.transpose_for_scores(mixed_value_layer1)
+ # logit_layer1 = self.transpose_for_logits(mixed_logit_layer1)
+
+ # for text input:
+ mixed_query_layer2 = self.query2(input_tensor2)
+ mixed_key_layer2 = self.key2(input_tensor2)
+ mixed_value_layer2 = self.value2(input_tensor2)
+ # mixed_logit_layer2 = self.logit2(input_tensor2)
+
+ query_layer2 = self.transpose_for_scores(mixed_query_layer2)
+ key_layer2 = self.transpose_for_scores(mixed_key_layer2)
+ value_layer2 = self.transpose_for_scores(mixed_value_layer2)
+ # logit_layer2 = self.transpose_for_logits(mixed_logit_layer2)
+
+ # Take the dot product between "query2" and "key1" to get the raw attention scores for value 1.
+ attention_scores1 = torch.matmul(query_layer2, key_layer1.transpose(-1, -2))
+ attention_scores1 = attention_scores1 / math.sqrt(self.attention_head_size)
+ attention_scores1 = attention_scores1 + attention_mask1
+
+ if use_co_attention_mask:
+ attention_scores1 = attention_scores1 + co_attention_mask.permute(0,1,3,2)
+
+ # Normalize the attention scores to probabilities.
+ attention_probs1 = nn.Softmax(dim=-1)(attention_scores1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs1 = self.dropout1(attention_probs1)
+
+ context_layer1 = torch.matmul(attention_probs1, value_layer1)
+ context_layer1 = context_layer1.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape1 = context_layer1.size()[:-2] + (self.all_head_size,)
+ context_layer1 = context_layer1.view(*new_context_layer_shape1)
+
+ # Take the dot product between "query1" and "key2" to get the raw attention scores for value 2.
+ attention_scores2 = torch.matmul(query_layer1, key_layer2.transpose(-1, -2))
+ attention_scores2 = attention_scores2 / math.sqrt(self.attention_head_size)
+ # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+
+ # we can comment this line for single flow.
+ attention_scores2 = attention_scores2 + attention_mask2
+ if use_co_attention_mask:
+ attention_scores2 = attention_scores2 + co_attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs2 = nn.Softmax(dim=-1)(attention_scores2)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs2 = self.dropout2(attention_probs2)
+
+ context_layer2 = torch.matmul(attention_probs2, value_layer2)
+ context_layer2 = context_layer2.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape2 = context_layer2.size()[:-2] + (self.all_head_size,)
+ context_layer2 = context_layer2.view(*new_context_layer_shape2)
+
+ return context_layer1, context_layer2, (attention_probs1, attention_probs2)
+
+class BertBiOutput(nn.Module):
+ def __init__(self, config):
+ super(BertBiOutput, self).__init__()
+
+ self.dense1 = nn.Linear(config.bi_hidden_size, config.v_hidden_size)
+ self.LayerNorm1 = BertLayerNorm(config.v_hidden_size, eps=1e-12)
+ self.dropout1 = nn.Dropout(config.v_hidden_dropout_prob)
+
+ self.q_dense1 = nn.Linear(config.bi_hidden_size, config.v_hidden_size)
+ self.q_dropout1 = nn.Dropout(config.v_hidden_dropout_prob)
+
+ self.dense2 = nn.Linear(config.bi_hidden_size, config.hidden_size)
+ self.LayerNorm2 = BertLayerNorm(config.hidden_size, eps=1e-12)
+ self.dropout2 = nn.Dropout(config.hidden_dropout_prob)
+
+ self.q_dense2 = nn.Linear(config.bi_hidden_size, config.hidden_size)
+ self.q_dropout2 = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states1, input_tensor1, hidden_states2, input_tensor2):
+
+
+ context_state1 = self.dense1(hidden_states1)
+ context_state1 = self.dropout1(context_state1)
+
+ context_state2 = self.dense2(hidden_states2)
+ context_state2 = self.dropout2(context_state2)
+
+ hidden_states1 = self.LayerNorm1(context_state1 + input_tensor1)
+ hidden_states2 = self.LayerNorm2(context_state2 + input_tensor2)
+
+ return hidden_states1, hidden_states2
+
+class BertConnectionLayer(nn.Module):
+ def __init__(self, config):
+ super(BertConnectionLayer, self).__init__()
+ self.biattention = BertBiAttention(config)
+
+ self.biOutput = BertBiOutput(config)
+
+ self.v_intermediate = BertImageIntermediate(config)
+ self.v_output = BertImageOutput(config)
+
+ self.t_intermediate = BertIntermediate(config)
+ self.t_output = BertOutput(config)
+
+ def forward(self, input_tensor1, attention_mask1, input_tensor2, attention_mask2, co_attention_mask=None, use_co_attention_mask=False):
+
+ bi_output1, bi_output2, co_attention_probs = self.biattention(
+ input_tensor1, attention_mask1, input_tensor2, attention_mask2, co_attention_mask, use_co_attention_mask
+ )
+
+ attention_output1, attention_output2 = self.biOutput(bi_output2, input_tensor1, bi_output1, input_tensor2)
+
+ intermediate_output1 = self.v_intermediate(attention_output1)
+ layer_output1 = self.v_output(intermediate_output1, attention_output1)
+
+ intermediate_output2 = self.t_intermediate(attention_output2)
+ layer_output2 = self.t_output(intermediate_output2, attention_output2)
+
+ return layer_output1, layer_output2, co_attention_probs
+
+class BertEncoder(nn.Module):
+ def __init__(self, config):
+ super(BertEncoder, self).__init__()
+
+ # in the bert encoder, we need to extract three things here.
+ # text bert layer: BertLayer
+ # vision bert layer: BertImageLayer
+ # Bi-Attention: Given the output of two bertlayer, perform bi-directional
+ # attention and add on two layers.
+
+ self.FAST_MODE = config.fast_mode
+ self.with_coattention = config.with_coattention
+ self.v_biattention_id = config.v_biattention_id
+ self.t_biattention_id = config.t_biattention_id
+ self.in_batch_pairs = config.in_batch_pairs
+ self.fixed_t_layer = config.fixed_t_layer
+ self.fixed_v_layer = config.fixed_v_layer
+ layer = BertLayer(config)
+ v_layer = BertImageLayer(config)
+ connect_layer = BertConnectionLayer(config)
+
+ self.layer = nn.ModuleList(
+ [copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]
+ )
+ self.v_layer = nn.ModuleList(
+ [copy.deepcopy(v_layer) for _ in range(config.v_num_hidden_layers)]
+ )
+ self.c_layer = nn.ModuleList(
+ [copy.deepcopy(connect_layer) for _ in range(len(config.v_biattention_id))]
+ )
+
+ def forward(
+ self,
+ txt_embedding,
+ image_embedding,
+ txt_attention_mask,
+ image_attention_mask,
+ co_attention_mask=None,
+ output_all_encoded_layers=True,
+ output_all_attention_masks=False,
+ ):
+
+ v_start = 0
+ t_start = 0
+ count = 0
+ all_encoder_layers_t = []
+ all_encoder_layers_v = []
+
+ all_attention_mask_t = []
+ all_attnetion_mask_v = []
+ all_attention_mask_c = []
+
+ batch_size, num_words, t_hidden_size = txt_embedding.size()
+ _, num_regions, v_hidden_size = image_embedding.size()
+
+ use_co_attention_mask = False
+ for v_layer_id, t_layer_id in zip(self.v_biattention_id, self.t_biattention_id):
+
+ v_end = v_layer_id
+ t_end = t_layer_id
+
+ assert self.fixed_t_layer <= t_end
+ assert self.fixed_v_layer <= v_end
+
+ for idx in range(v_start, self.fixed_v_layer):
+ with torch.no_grad():
+ image_embedding, image_attention_probs = self.v_layer[idx](image_embedding, image_attention_mask)
+ v_start = self.fixed_v_layer
+
+ if output_all_attention_masks:
+ all_attnetion_mask_v.append(image_attention_probs)
+
+ for idx in range(v_start, v_end):
+ image_embedding, image_attention_probs = self.v_layer[idx](image_embedding, image_attention_mask)
+
+ if output_all_attention_masks:
+ all_attnetion_mask_v.append(image_attention_probs)
+
+ for idx in range(t_start, self.fixed_t_layer):
+ with torch.no_grad():
+ txt_embedding, txt_attention_probs = self.layer[idx](txt_embedding, txt_attention_mask)
+ t_start = self.fixed_t_layer
+ if output_all_attention_masks:
+ all_attention_mask_t.append(txt_attention_probs)
+
+ for idx in range(t_start, t_end):
+ txt_embedding, txt_attention_probs = self.layer[idx](txt_embedding, txt_attention_mask)
+ if output_all_attention_masks:
+ all_attention_mask_t.append(txt_attention_probs)
+
+ if count == 0 and self.in_batch_pairs:
+ # new batch size is the batch_size ^2
+ image_embedding = image_embedding.unsqueeze(0).expand(batch_size, batch_size, num_regions, v_hidden_size).contiguous().view(batch_size*batch_size, num_regions, v_hidden_size)
+ image_attention_mask = image_attention_mask.unsqueeze(0).expand(batch_size, batch_size, 1, 1, num_regions).contiguous().view(batch_size*batch_size, 1, 1, num_regions)
+
+ txt_embedding = txt_embedding.unsqueeze(1).expand(batch_size, batch_size, num_words, t_hidden_size).contiguous().view(batch_size*batch_size, num_words, t_hidden_size)
+ txt_attention_mask = txt_attention_mask.unsqueeze(1).expand(batch_size, batch_size, 1, 1, num_words).contiguous().view(batch_size*batch_size, 1, 1, num_words)
+ co_attention_mask = co_attention_mask.unsqueeze(1).expand(batch_size, batch_size, 1, num_regions, num_words).contiguous().view(batch_size*batch_size, 1, num_regions, num_words)
+
+ if count == 0 and self.FAST_MODE:
+ txt_embedding = txt_embedding.expand(image_embedding.size(0), txt_embedding.size(1), txt_embedding.size(2))
+ txt_attention_mask = txt_attention_mask.expand(image_embedding.size(0), txt_attention_mask.size(1), txt_attention_mask.size(2), txt_attention_mask.size(3))
+
+ if self.with_coattention:
+ # do the bi attention.
+ image_embedding, txt_embedding, co_attention_probs = self.c_layer[count](
+ image_embedding, image_attention_mask, txt_embedding, txt_attention_mask, co_attention_mask, use_co_attention_mask)
+
+ # use_co_attention_mask = False
+ if output_all_attention_masks:
+ all_attention_mask_c.append(co_attention_probs)
+
+ v_start = v_end
+ t_start = t_end
+ count += 1
+
+ if output_all_encoded_layers:
+ all_encoder_layers_t.append(txt_embedding)
+ all_encoder_layers_v.append(image_embedding)
+
+ for idx in range(v_start, len(self.v_layer)):
+ image_embedding, image_attention_probs = self.v_layer[idx](image_embedding, image_attention_mask)
+
+ if output_all_attention_masks:
+ all_attnetion_mask_v.append(image_attention_probs)
+
+ for idx in range(t_start, len(self.layer)):
+ txt_embedding, txt_attention_probs = self.layer[idx](txt_embedding, txt_attention_mask)
+
+ if output_all_attention_masks:
+ all_attention_mask_t.append(txt_attention_probs)
+
+ # add the end part to finish.
+ if not output_all_encoded_layers:
+ all_encoder_layers_t.append(txt_embedding)
+ all_encoder_layers_v.append(image_embedding)
+
+ return all_encoder_layers_t, all_encoder_layers_v, (all_attention_mask_t, all_attnetion_mask_v, all_attention_mask_c)
+
+
+class BertTextPooler(nn.Module):
+ def __init__(self, config):
+ super(BertTextPooler, self).__init__()
+ self.dense = nn.Linear(config.hidden_size, config.bi_hidden_size)
+ self.activation = nn.ReLU()
+
+ def forward(self, hidden_states):
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+class BertImagePooler(nn.Module):
+ def __init__(self, config):
+ super(BertImagePooler, self).__init__()
+ self.dense = nn.Linear(config.v_hidden_size, config.bi_hidden_size)
+ self.activation = nn.ReLU()
+
+ def forward(self, hidden_states):
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+class BertPredictionHeadTransform(nn.Module):
+ def __init__(self, config):
+ super(BertPredictionHeadTransform, self).__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ if isinstance(config.hidden_act, str) or (
+ sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)
+ ):
+ self.transform_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.transform_act_fn = config.hidden_act
+ self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+
+class BertImgPredictionHeadTransform(nn.Module):
+ def __init__(self, config):
+ super(BertImgPredictionHeadTransform, self).__init__()
+ self.dense = nn.Linear(config.v_hidden_size, config.v_hidden_size)
+ if isinstance(config.hidden_act, str) or (
+ sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)
+ ):
+ self.transform_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.transform_act_fn = config.v_hidden_act
+ self.LayerNorm = BertLayerNorm(config.v_hidden_size, eps=1e-12)
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+ def __init__(self, config, bert_model_embedding_weights):
+ super(BertLMPredictionHead, self).__init__()
+ self.transform = BertPredictionHeadTransform(config)
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = nn.Linear(
+ bert_model_embedding_weights.size(1),
+ bert_model_embedding_weights.size(0),
+ bias=False,
+ )
+ self.decoder.weight = bert_model_embedding_weights
+ self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0)))
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states) + self.bias
+ return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+ def __init__(self, config, bert_model_embedding_weights):
+ super(BertOnlyMLMHead, self).__init__()
+ self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
+
+ def forward(self, sequence_output):
+ prediction_scores = self.predictions(sequence_output)
+ return prediction_scores
+
+
+class BertOnlyNSPHead(nn.Module):
+ def __init__(self, config):
+ super(BertOnlyNSPHead, self).__init__()
+ self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+ def forward(self, pooled_output):
+ seq_relationship_score = self.seq_relationship(pooled_output)
+ return seq_relationship_score
+
+
+class BertPreTrainingHeads(nn.Module):
+ def __init__(self, config, bert_model_embedding_weights):
+ super(BertPreTrainingHeads, self).__init__()
+ self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
+ self.bi_seq_relationship = nn.Linear(config.bi_hidden_size, 2)
+ self.imagePredictions = BertImagePredictionHead(config)
+ self.fusion_method = config.fusion_method
+ self.dropout = nn.Dropout(0.1)
+
+ def forward(
+ self, sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v
+ ):
+
+ if self.fusion_method == 'sum':
+ pooled_output = self.dropout(pooled_output_t + pooled_output_v)
+ elif self.fusion_method == 'mul':
+ pooled_output = self.dropout(pooled_output_t * pooled_output_v)
+ else:
+ assert False
+
+ prediction_scores_t = self.predictions(sequence_output_t)
+ seq_relationship_score = self.bi_seq_relationship(pooled_output)
+ prediction_scores_v = self.imagePredictions(sequence_output_v)
+
+ return prediction_scores_t, prediction_scores_v, seq_relationship_score
+
+
+class BertImagePredictionHead(nn.Module):
+ def __init__(self, config):
+ super(BertImagePredictionHead, self).__init__()
+ self.transform = BertImgPredictionHeadTransform(config)
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = nn.Linear(config.v_hidden_size, config.v_target_size)
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states)
+ return hidden_states
+
+
+class BertPreTrainedModel(nn.Module):
+ """ An abstract class to handle weights initialization and
+ a simple interface for dowloading and loading pretrained models.
+ """
+
+ def __init__(self, config, default_gpu=True, *inputs, **kwargs):
+ super(BertPreTrainedModel, self).__init__()
+
+ if not isinstance(config, BertConfig):
+ raise ValueError(
+ "Parameter config in `{}(config)` should be an instance of class `BertConfig`. "
+ "To create a model from a Google pretrained model use "
+ "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
+ self.__class__.__name__, self.__class__.__name__
+ )
+ )
+
+ self.config = config
+
+ def init_bert_weights(self, module):
+ """ Initialize the weights.
+ """
+ if isinstance(module, (nn.Linear, nn.Embedding)):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ elif isinstance(module, BertLayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ if isinstance(module, nn.Linear) and module.bias is not None:
+ module.bias.data.zero_()
+
+ @classmethod
+ def from_pretrained(
+ cls,
+ pretrained_model_name_or_path,
+ config,
+ default_gpu=True,
+ state_dict=None,
+ cache_dir=None,
+ from_tf=False,
+ *inputs,
+ **kwargs
+ ):
+ """
+ Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict.
+ Download and cache the pre-trained model file if needed.
+
+ Params:
+ pretrained_model_name_or_path: either:
+ - a str with the name of a pre-trained model to load selected in the list of:
+ . `bert-base-uncased`
+ . `bert-large-uncased`
+ . `bert-base-cased`
+ . `bert-large-cased`
+ . `bert-base-multilingual-uncased`
+ . `bert-base-multilingual-cased`
+ . `bert-base-chinese`
+ - a path or url to a pretrained model archive containing:
+ . `bert_config.json` a configuration file for the model
+ . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance
+ - a path or url to a pretrained model archive containing:
+ . `bert_config.json` a configuration file for the model
+ . `model.chkpt` a TensorFlow checkpoint
+ from_tf: should we load the weights from a locally saved TensorFlow checkpoint
+ cache_dir: an optional path to a folder in which the pre-trained models will be cached.
+ state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
+ *inputs, **kwargs: additional input for the specific Bert class
+ (ex: num_labels for BertForSequenceClassification)
+ """
+ CONFIG_NAME = "bert_config.json"
+ WEIGHTS_NAME = "pytorch_model.bin"
+ TF_WEIGHTS_NAME = "model.ckpt"
+
+ if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
+ archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
+ else:
+ archive_file = pretrained_model_name_or_path
+ # redirect to the cache, if necessary
+ try:
+ resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
+ except EnvironmentError:
+ logger.error(
+ "Model name '{}' was not found in model name list ({}). "
+ "We assumed '{}' was a path or url but couldn't find any file "
+ "associated to this path or url.".format(
+ pretrained_model_name_or_path,
+ ", ".join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
+ archive_file,
+ )
+ )
+ return None
+
+ if default_gpu:
+ if resolved_archive_file == archive_file:
+ logger.info("loading archive file {}".format(archive_file))
+ else:
+ logger.info(
+ "loading archive file {} from cache at {}".format(
+ archive_file, resolved_archive_file
+ )
+ )
+ tempdir = None
+ if os.path.isdir(resolved_archive_file) or from_tf:
+ serialization_dir = resolved_archive_file
+ elif resolved_archive_file[-3:] == 'bin':
+ serialization_dir = '/'.join(resolved_archive_file.split('/')[:-1])
+ WEIGHTS_NAME = resolved_archive_file.split('/')[-1]
+ else:
+ # Extract archive to temp dir
+ tempdir = tempfile.mkdtemp()
+ logger.info(
+ "extracting archive file {} to temp dir {}".format(
+ resolved_archive_file, tempdir
+ )
+ )
+ with tarfile.open(resolved_archive_file, "r:gz") as archive:
+ archive.extractall(tempdir)
+ serialization_dir = tempdir
+ # Load config
+ # config_file = os.path.join(serialization_dir, CONFIG_NAME)
+ # config = BertConfig.from_json_file(config_file)
+ if default_gpu:
+ logger.info("Model config {}".format(config))
+ # Instantiate model.
+ model = cls(config, *inputs, **kwargs)
+ if state_dict is None and not from_tf:
+ weights_path = os.path.join(serialization_dir, WEIGHTS_NAME)
+ state_dict = torch.load(
+ weights_path,
+ map_location="cpu",
+ )
+ if 'state_dict' in dir(state_dict):
+ state_dict = state_dict.state_dict()
+
+ if tempdir:
+ # Clean up temp dir
+ shutil.rmtree(tempdir)
+ if from_tf:
+ # Directly load from a TensorFlow checkpoint
+ weights_path = os.path.join(serialization_dir, TF_WEIGHTS_NAME)
+ return load_tf_weights_in_bert(model, weights_path)
+ # Load from a PyTorch state_dict
+ old_keys = []
+ new_keys = []
+ for key in state_dict.keys():
+ new_key = None
+ if "gamma" in key:
+ new_key = key.replace("gamma", "weight")
+ if "beta" in key:
+ new_key = key.replace("beta", "bias")
+ if new_key:
+ old_keys.append(key)
+ new_keys.append(new_key)
+ for old_key, new_key in zip(old_keys, new_keys):
+ state_dict[new_key] = state_dict.pop(old_key)
+
+ missing_keys = []
+ unexpected_keys = []
+ error_msgs = []
+ # copy state_dict so _load_from_state_dict can modify it
+ metadata = getattr(state_dict, "_metadata", None)
+ state_dict = state_dict.copy()
+ if metadata is not None:
+ state_dict._metadata = metadata
+
+ def load(module, prefix=""):
+ local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
+ module._load_from_state_dict(
+ state_dict,
+ prefix,
+ local_metadata,
+ True,
+ missing_keys,
+ unexpected_keys,
+ error_msgs,
+ )
+ for name, child in module._modules.items():
+ if child is not None:
+ load(child, prefix + name + ".")
+
+ start_prefix = ""
+ if not hasattr(model, "bert") and any(
+ s.startswith("bert.") for s in state_dict.keys()
+ ):
+ start_prefix = "bert."
+ load(model, prefix=start_prefix)
+ if len(missing_keys) > 0 and default_gpu:
+ logger.info(
+ "Weights of {} not initialized from pretrained model: {}".format(
+ model.__class__.__name__, missing_keys
+ )
+ )
+ if len(unexpected_keys) > 0 and default_gpu:
+ logger.info(
+ "Weights from pretrained model not used in {}: {}".format(
+ model.__class__.__name__, unexpected_keys
+ )
+ )
+ if len(error_msgs) > 0 and default_gpu:
+ raise RuntimeError(
+ "Error(s) in loading state_dict for {}:\n\t{}".format(
+ model.__class__.__name__, "\n\t".join(error_msgs)
+ )
+ )
+ return model
+
+
+class BertModel(BertPreTrainedModel):
+ """BERT model ("Bidirectional Embedding Representations from a Transformer").
+
+ Params:
+ config: a BertConfig class instance with the configuration to build a new model
+
+ Inputs:
+ `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+ with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+ `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+ `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+ types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+ a `sentence B` token (see BERT paper for more details).
+ `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+ selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+ input sequence length in the current batch. It's the mask that we typically use for attention when
+ a batch has varying length sentences.
+ `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
+
+ Outputs: Tuple of (encoded_layers, pooled_output)
+ `encoded_layers`: controled by `output_all_encoded_layers` argument:
+ - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
+ of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
+ encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
+ - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
+ to the last attention block of shape [batch_size, sequence_length, hidden_size],
+ `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
+ classifier pretrained on top of the hidden state associated to the first character of the
+ input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
+
+ Example usage:
+ ```python
+ # Already been converted into WordPiece token ids
+ input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+ input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+ token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+ config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+ num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+ model = modeling.BertModel(config=config)
+ all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
+ ```
+ """
+
+ def __init__(self, config):
+ super(BertModel, self).__init__(config)
+
+ # initilize word embedding
+ self.embeddings = BertEmbeddings(config)
+
+ # initlize the vision embedding
+ self.v_embeddings = BertImageEmbeddings(config)
+
+ self.encoder = BertEncoder(config)
+ self.t_pooler = BertTextPooler(config)
+ self.v_pooler = BertImagePooler(config)
+
+ self.apply(self.init_bert_weights)
+
+ def forward(
+ self,
+ input_txt,
+ input_imgs,
+ image_loc,
+ token_type_ids=None,
+ attention_mask=None,
+ image_attention_mask=None,
+ co_attention_mask=None,
+ output_all_encoded_layers=False,
+ output_all_attention_masks=False,
+ ):
+ if attention_mask is None:
+ attention_mask = torch.ones_like(input_txt)
+ if token_type_ids is None:
+ token_type_ids = torch.zeros_like(input_txt)
+ if image_attention_mask is None:
+ image_attention_mask = torch.ones(
+ input_imgs.size(0), input_imgs.size(1)
+ ).type_as(input_txt)
+
+ # We create a 3D attention mask from a 2D tensor mask.
+ # Sizes are [batch_size, 1, 1, to_seq_length]
+ # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+ # this attention mask is more simple than the triangular masking of causal attention
+ # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+ extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+ extended_image_attention_mask = image_attention_mask.unsqueeze(1).unsqueeze(2)
+
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+ # masked positions, this operation will create a tensor which is 0.0 for
+ # positions we want to attend and -10000.0 for masked positions.
+ # Since we are adding it to the raw scores before the softmax, this is
+ # effectively the same as removing these entirely.
+ extended_attention_mask = extended_attention_mask.to(
+ dtype=next(self.parameters()).dtype
+ ) # fp16 compatibility
+ extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+
+ extended_image_attention_mask = extended_image_attention_mask.to(
+ dtype=next(self.parameters()).dtype
+ ) # fp16 compatibility
+ extended_image_attention_mask = (1.0 - extended_image_attention_mask) * -10000.0
+
+ if co_attention_mask is None:
+ co_attention_mask = torch.zeros(input_txt.size(0), input_imgs.size(1), input_txt.size(1)).type_as(extended_image_attention_mask)
+
+ extended_co_attention_mask = co_attention_mask.unsqueeze(1)
+
+ # extended_co_attention_mask = co_attention_mask.unsqueeze(-1)
+ extended_co_attention_mask = extended_co_attention_mask * 5.0
+ extended_co_attention_mask = extended_co_attention_mask.to(
+ dtype=next(self.parameters()).dtype
+ ) # fp16 compatibility
+
+ embedding_output = self.embeddings(input_txt, token_type_ids)
+ v_embedding_output = self.v_embeddings(input_imgs, image_loc)
+
+ encoded_layers_t, encoded_layers_v, all_attention_mask = self.encoder(
+ embedding_output,
+ v_embedding_output,
+ extended_attention_mask,
+ extended_image_attention_mask,
+ extended_co_attention_mask,
+ output_all_encoded_layers=output_all_encoded_layers,
+ output_all_attention_masks=output_all_attention_masks,
+ )
+
+ sequence_output_t = encoded_layers_t[-1]
+ sequence_output_v = encoded_layers_v[-1]
+
+ pooled_output_t = self.t_pooler(sequence_output_t)
+ pooled_output_v = self.v_pooler(sequence_output_v)
+
+ if not output_all_encoded_layers:
+ encoded_layers_t = encoded_layers_t[-1]
+ encoded_layers_v = encoded_layers_v[-1]
+
+ return encoded_layers_t, encoded_layers_v, pooled_output_t, pooled_output_v, all_attention_mask
+
+
+class BertImageEmbeddings(nn.Module):
+ """Construct the embeddings from image, spatial location (omit now) and token_type embeddings.
+ """
+ def __init__(self, config):
+ super(BertImageEmbeddings, self).__init__()
+
+ self.image_embeddings = nn.Linear(config.v_feature_size, config.v_hidden_size)
+ self.image_location_embeddings = nn.Linear(5, config.v_hidden_size)
+ self.LayerNorm = BertLayerNorm(config.v_hidden_size, eps=1e-12)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, input_ids, input_loc):
+
+ img_embeddings = self.image_embeddings(input_ids)
+ loc_embeddings = self.image_location_embeddings(input_loc)
+ embeddings = self.LayerNorm(img_embeddings+loc_embeddings)
+ embeddings = self.dropout(embeddings)
+
+ return embeddings
+
+
+class BertForMultiModalPreTraining(BertPreTrainedModel):
+ """BERT model with multi modal pre-training heads.
+ """
+
+ def __init__(self, config):
+ super(BertForMultiModalPreTraining, self).__init__(config)
+
+ self.bert = BertModel(config)
+ self.cls = BertPreTrainingHeads(
+ config, self.bert.embeddings.word_embeddings.weight
+ )
+
+ self.apply(self.init_bert_weights)
+ self.predict_feature = config.predict_feature
+ self.loss_fct = CrossEntropyLoss(ignore_index=-1)
+
+ print("model's option for predict_feature is ", config.predict_feature)
+
+ if self.predict_feature:
+ self.vis_criterion = nn.MSELoss(reduction="none")
+ else:
+ self.vis_criterion = nn.KLDivLoss(reduction="none")
+
+ def forward(
+ self,
+ input_ids,
+ image_feat,
+ image_loc,
+ token_type_ids=None,
+ attention_mask=None,
+ image_attention_mask=None,
+ masked_lm_labels=None,
+ image_label=None,
+ image_target = None,
+ next_sentence_label=None,
+ output_all_attention_masks=False
+ ):
+
+ # in this model, we first embed the images.
+ sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v, all_attention_mask = self.bert(
+ input_ids,
+ image_feat,
+ image_loc,
+ token_type_ids,
+ attention_mask,
+ image_attention_mask,
+ output_all_encoded_layers=False,
+ output_all_attention_masks=output_all_attention_masks
+ )
+
+ prediction_scores_t, prediction_scores_v, seq_relationship_score = self.cls(
+ sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v
+ )
+
+ if masked_lm_labels is not None and next_sentence_label is not None and image_target is not None:
+
+ prediction_scores_v = prediction_scores_v[:, 1:]
+ if self.predict_feature:
+ img_loss = self.vis_criterion(prediction_scores_v, image_target)
+ masked_img_loss = torch.sum(
+ img_loss * (image_label == 1).unsqueeze(2).float()
+ ) / max(torch.sum((image_label == 1).unsqueeze(2).expand_as(img_loss)),1)
+
+ else:
+ img_loss = self.vis_criterion(
+ F.log_softmax(prediction_scores_v, dim=2), image_target
+ )
+ masked_img_loss = torch.sum(
+ img_loss * (image_label == 1).unsqueeze(2).float()
+ ) / max(torch.sum((image_label == 1)), 0)
+
+ # masked_img_loss = torch.sum(img_loss) / (img_loss.shape[0] * img_loss.shape[1])
+ masked_lm_loss = self.loss_fct(
+ prediction_scores_t.view(-1, self.config.vocab_size),
+ masked_lm_labels.view(-1),
+ )
+ next_sentence_loss = self.loss_fct(
+ seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)
+ )
+ # total_loss = masked_lm_loss + next_sentence_loss + masked_img_loss
+ return masked_lm_loss.unsqueeze(0), masked_img_loss.unsqueeze(0), next_sentence_loss.unsqueeze(0)
+ else:
+ return prediction_scores_t, prediction_scores_v, seq_relationship_score, all_attention_mask
+
+class VILBertForVLTasks(BertPreTrainedModel):
+ def __init__(self, config, num_labels, dropout_prob=0.1, default_gpu=True):
+ super(VILBertForVLTasks, self).__init__(config)
+ self.num_labels = num_labels
+ self.bert = BertModel(config)
+ self.dropout = nn.Dropout(dropout_prob)
+ self.cls = BertPreTrainingHeads(
+ config, self.bert.embeddings.word_embeddings.weight
+ )
+ self.vil_prediction = SimpleClassifier(config.bi_hidden_size, config.bi_hidden_size*2, num_labels, 0.5)
+ # self.vil_prediction = nn.Linear(config.bi_hidden_size, num_labels)
+ self.vil_logit = nn.Linear(config.bi_hidden_size, 1)
+ self.vision_logit = nn.Linear(config.v_hidden_size, 1)
+ self.linguisic_logit = nn.Linear(config.hidden_size, 1)
+ self.fusion_method = config.fusion_method
+ self.apply(self.init_bert_weights)
+
+ def forward(
+ self,
+ input_txt,
+ input_imgs,
+ image_loc,
+ token_type_ids=None,
+ attention_mask=None,
+ image_attention_mask=None,
+ co_attention_mask=None,
+ output_all_encoded_layers=False,
+ ):
+ sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v, _ = self.bert(
+ input_txt,
+ input_imgs,
+ image_loc,
+ token_type_ids,
+ attention_mask,
+ image_attention_mask,
+ co_attention_mask,
+ output_all_encoded_layers=False,
+ )
+
+ vil_prediction = 0
+ vil_logit = 0
+ vil_binary_prediction = 0
+ vision_prediction = 0
+ vision_logit = 0
+ linguisic_prediction = 0
+ linguisic_logit = 0
+
+ linguisic_prediction, vision_prediction, vil_binary_prediction = self.cls(
+ sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v
+ )
+
+ if self.fusion_method == 'sum':
+ pooled_output = self.dropout(pooled_output_t + pooled_output_v)
+ elif self.fusion_method == 'mul':
+ pooled_output = self.dropout(pooled_output_t * pooled_output_v)
+ else:
+ assert False
+
+ vil_prediction = self.vil_prediction(pooled_output)
+ vil_logit = self.vil_logit(pooled_output)
+ vision_logit = self.vision_logit(self.dropout(sequence_output_v)) + ((1.0 - image_attention_mask)* -10000.0).unsqueeze(2).to(dtype=next(self.parameters()).dtype)
+ linguisic_logit = self.linguisic_logit(self.dropout(sequence_output_t))
+
+ return vil_prediction, vil_logit, vil_binary_prediction, vision_prediction, vision_logit, linguisic_prediction, linguisic_logit
+
+class SimpleClassifier(nn.Module):
+ def __init__(self, in_dim, hid_dim, out_dim, dropout):
+ super(SimpleClassifier, self).__init__()
+ layers = [
+ weight_norm(nn.Linear(in_dim, hid_dim), dim=None),
+ nn.ReLU(),
+ nn.Dropout(dropout, inplace=True),
+ weight_norm(nn.Linear(hid_dim, out_dim), dim=None)
+ ]
+ self.main = nn.Sequential(*layers)
+
+ def forward(self, x):
+ logits = self.main(x)
+ return logits
\ No newline at end of file
diff --git a/vlbert_tasks.yml b/vlbert_tasks.yml
new file mode 100644
index 0000000..ec82410
--- /dev/null
+++ b/vlbert_tasks.yml
@@ -0,0 +1,228 @@
+TASK0:
+ name: ConceptualCaption
+ type: L-classifier
+ task_id: 0
+ features_h5path:
+ train_file:
+ max_seq_length: 30
+ batch_size: 128
+ train_split: train
+ eval_split: val
+TASK1:
+ name: VQA
+ type: VL-classifier
+ loss: BCEWithLogitLoss
+ task_id: 1
+ dataroot: data/VQA
+ features_h5path1: data/coco/coco_trainval_resnet101_faster_rcnn_genome.lmdb
+ features_h5path2: ''
+ train_annotations_jsonpath: ''
+ val_annotations_jsonpath: ''
+ max_seq_length: 16
+ max_region_num: 100
+ batch_size: 256
+ eval_batch_size: 1024
+ train_split: trainval
+ val_split: minval
+ lr: 0.00004
+ num_epoch: 20
+TASK2:
+ name: VQA-MC
+ type: VL-logit
+ loss: CrossEntropyLoss
+ task_id: 2
+ dataroot: data/VQA
+ features_h5path1: data/coco/coco_trainval_resnet101_faster_rcnn_genome.lmdb
+ features_h5path2: ''
+ train_annotations_jsonpath: ''
+ val_annotations_jsonpath: ''
+ max_seq_length: 20
+ max_region_num: 100
+ batch_size: 128
+ train_split: trainval
+ val_split: minval
+TASK3:
+ name: GenomeQA
+ type: VL-classifier
+ task_id: 3
+ features_h5path: data/coco/coco_trainval.h5
+ train_file: data/VQA/
+ split: train
+TASK4:
+ name: VisualDialog
+ type: VL-logit
+ loss: CrossEntropyLoss
+ task_id: 4
+ dataroot: data/VisualDialog
+ features_h5path1: data/coco/coco_trainval.h5
+ features_h5path2: ''
+ train_annotations_jsonpath: 'data/VisualDialog/visdial_1.0_train.json'
+ val_annotations_jsonpath: 'data/VisualDialog/visdial_1.0_val.json'
+ max_seq_length: 60
+ max_region_num: 100
+ batch_size: 64
+ train_split: train
+ val_split: val
+ lr: 0.00001
+ num_epoch: 20
+TASK5:
+ name: Vissual7w
+ type: VL-logit
+ task_id: 5
+ split: train
+TASK6:
+ name: VCR_Q-A
+ type: VL-logit
+ loss: CrossEntropyLoss
+ task_id: 6
+ dataroot: data/VCR
+ features_h5path1: /srv/datasets/vilbert/VCR/VCR_resnet101_faster_rcnn_genome.lmdb
+ features_h5path2: /srv/datasets/vilbert/VCR/VCR_gt_resnet101_faster_rcnn_genome.lmdb
+ train_annotations_jsonpath: data/VCR/train.jsonl
+ val_annotations_jsonpath: data/VCR/val.jsonl
+ max_seq_length: 60
+ max_region_num: 100
+ batch_size: 64
+ train_split: train
+ val_split: val
+ lr: 0.00002
+ num_epoch: 20
+TASK7:
+ name: VCR_QA-R
+ type: VL-logit
+ loss: CrossEntropyLoss
+ task_id: 7
+ dataroot: data/VCR
+ features_h5path1: /srv/datasets/vilbert/VCR/VCR_resnet101_faster_rcnn_genome.lmdb
+ features_h5path2: /srv/datasets/vilbert/VCR/VCR_gt_resnet101_faster_rcnn_genome.lmdb
+ train_annotations_jsonpath: data/VCR/train.jsonl
+ val_annotations_jsonpath: data/VCR/val.jsonl
+ max_seq_length: 80
+ max_region_num: 100
+ batch_size: 64
+ train_split: train
+ val_split: val
+ lr: 0.00002
+ num_epoch: 20
+TASK8:
+ name: RetrievalCOCO
+ type: VL-logit
+ loss: CrossEntropyLoss
+ task_id: 8
+ dataroot: data/cocoRetreival
+ features_h5path1: data/coco/coco_trainval_resnet101_faster_rcnn_genome.lmdb
+ features_h5path2: ''
+ train_annotations_jsonpath: data/cocoRetreival/all_data_final_train_2014.jsonline
+ val_annotations_jsonpath: data/cocoRetreival/all_data_final_val_set0_2014.jsonline
+ max_seq_length: 30
+ max_region_num: 100
+ batch_size: 64
+ train_split: train
+ val_split: val
+ lr: 0.00002
+ num_epoch: 20
+TASK9:
+ name: RetrievalFlickr30k
+ type: VL-logit
+ loss: CrossEntropyLoss
+ task_id: 9
+ dataroot: data/flickr30k
+ features_h5path1: data/flickr30k/flickr30k_resnet101_faster_rcnn_genome.lmdb
+ features_h5path2: ''
+ train_annotations_jsonpath: data/flickr30k/all_data_final_train_2014.jsonline
+ val_annotations_jsonpath: data/flickr30k/all_data_final_val_set0_2014.jsonline
+ max_seq_length: 30
+ max_region_num: 100
+ batch_size: 64
+ train_split: train
+ val_split: val
+ lr: 0.00002
+ num_epoch: 20
+TASK10:
+ name: refcoco
+ type: V-logit
+ loss: BCEWithLogitLoss
+ task_id: 10
+ dataroot: data/referExpression
+ features_h5path1: data/referExpression/refcoco.h5
+ features_h5path2: data/referExpression/refcoco_gt.h5
+ train_annotations_jsonpath: ''
+ val_annotations_jsonpath: ''
+ max_seq_length: 20
+ max_region_num: 100
+ batch_size: 256
+ train_split: train
+ val_split: val
+ lr: 0.00004
+ num_epoch: 20
+TASK11:
+ name: refcoco+
+ type: V-logit
+ loss: BCEWithLogitLoss
+ task_id: 11
+ dataroot: data/referExpression
+ features_h5path1: data/referExpression/refcoco+_resnet101_faster_rcnn_genome.lmdb
+ features_h5path2: data/referExpression/refcoco+_gt_resnet101_faster_rcnn_genome.lmdb
+ train_annotations_jsonpath: ''
+ val_annotations_jsonpath: ''
+ max_seq_length: 20
+ max_region_num: 100
+ batch_size: 256
+ eval_batch_size: 1024
+ train_split: train
+ val_split: val
+ lr: 0.00004
+ num_epoch: 20
+TASK12:
+ name: refgoogle
+ type: V-logit
+ loss: BCEWithLogitLoss
+ task_id: 12
+ dataroot: data/referExpression
+ features_h5path1: data/referExpression/refcoco+.h5
+ features_h5path2: data/referExpression/refcoco+_gt.h5
+ train_annotations_jsonpath: ''
+ val_annotations_jsonpath: ''
+ max_seq_length: 20
+ max_region_num: 100
+ batch_size: 256
+ train_split: train
+ val_split: val
+ start_iteration: 0
+ lr: 0.00004
+ num_epoch: 20
+TASK13:
+ name: refDenseCaption
+ type: V-logit
+ loss: BCEWithLogitLoss
+ task_id: 13
+ dataroot: data/visgenome
+ features_h5path1: data/referExpression/refcoco+.h5
+ features_h5path2: ''
+ train_annotations_jsonpath: 'data/visgenome/region_descriptions.json'
+ val_annotations_jsonpath: 'data/visgenome/region_descriptions.json'
+ max_seq_length: 20
+ max_region_num: 60
+ batch_size: 256
+ train_split: train
+ val_split: val
+TASK14:
+ name: Flickr30kGround
+ type: L-logit
+ task_id: 14
+ split: train
+TASK15:
+ name: CocoCaption
+ type: L-classifier
+ task_id: 15
+ split: train
+TASK16:
+ name: Flickr30kCaption
+ type: L-classifier
+ task_id: 16
+ split: train
+TASK17:
+ name: NLVR2
+ type: VL-logit
+ task_id: 17
+ split: train
\ No newline at end of file