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NanoDet-m
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@PINTO0309
PINTO0309 committed Sep 9, 2021
1 parent 99d052b commit 71382bb
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270 changes: 270 additions & 0 deletions 072_NanoDet/convert_script.txt
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Expand Up @@ -114,3 +114,273 @@ openvino2tensorflow \
--weight_replacement_config replace.json
mv saved_model/model_full_integer_quant_edgetpu.tflite saved_model saved_model_${MODEL}_${H}x${W}
rm -rf saved_model


=== nanodet_m =====================================

CONFIG_PATH=config/nanodet-m.yml
PYTORCH_MODEL_PATH=nanodet_m.ckpt
python3 tools/export_onnx.py --cfg_path ${CONFIG_PATH} --model_path ${PYTORCH_MODEL_PATH}

CONFIG_PATH=config/nanodet-m-416.yml
PYTORCH_MODEL_PATH=nanodet_m_416.ckpt
python3 tools/export_onnx.py --cfg_path ${CONFIG_PATH} --model_path ${PYTORCH_MODEL_PATH}

CONFIG_PATH=config/nanodet-m-0.5x.yml
PYTORCH_MODEL_PATH=nanodet_m_0.5x.ckpt
python3 tools/export_onnx.py --cfg_path ${CONFIG_PATH} --model_path ${PYTORCH_MODEL_PATH}

CONFIG_PATH=config/nanodet-m-1.5x.yml
PYTORCH_MODEL_PATH=nanodet_m_1.5x.ckpt
python3 tools/export_onnx.py --cfg_path ${CONFIG_PATH} --model_path ${PYTORCH_MODEL_PATH}

CONFIG_PATH=config/nanodet-m-1.5x-416.yml
PYTORCH_MODEL_PATH=nanodet_m_1.5x_416.ckpt
python3 tools/export_onnx.py --cfg_path ${CONFIG_PATH} --model_path ${PYTORCH_MODEL_PATH}




python3 -m onnxsim onnx/nanodet_m_320x320.onnx onnx/nanodet_m_320x320.onnx
python3 -m onnxsim onnx/nanodet_m_416x416.onnx onnx/nanodet_m_416x416.onnx
python3 -m onnxsim onnx/nanodet_m_0.5x_320x320.onnx onnx/nanodet_m_0.5x_320x320.onnx
python3 -m onnxsim onnx/nanodet_m_1.5x_320x320.onnx onnx/nanodet_m_1.5x_320x320.onnx
python3 -m onnxsim onnx/nanodet_m_1.5x_416x416.onnx onnx/nanodet_m_1.5x_416x416.onnx



xhost +local: && \
docker run --gpus all -it --rm \
-v `pwd`:/home/user/workdir \
-v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
--device /dev/video0:/dev/video0:mwr \
--net=host \
-e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \
-e DISPLAY=$DISPLAY \
--privileged \
pinto0309/openvino2tensorflow:latest

cd workdir


H=320
W=320
MODEL=nanodet_m
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}_${H}x${W}/FP32 \
--mean_values [103.53,116.28,123.675] \
--scale_values [57.375,57.12,58.395]
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}_${H}x${W}/FP16
--mean_values [103.53,116.28,123.675] \
--scale_values [57.375,57.12,58.395]
mkdir -p openvino/${MODEL}_${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}_${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}_${H}x${W}/myriad/${MODEL}_${H}x${W}.blob

H=416
W=416
MODEL=nanodet_m
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}_${H}x${W}/FP32 \
--mean_values [103.53,116.28,123.675] \
--scale_values [57.375,57.12,58.395]
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}_${H}x${W}/FP16
--mean_values [103.53,116.28,123.675] \
--scale_values [57.375,57.12,58.395]
mkdir -p openvino/${MODEL}_${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}_${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}_${H}x${W}/myriad/${MODEL}_${H}x${W}.blob

H=320
W=320
MODEL=nanodet_m_0.5x
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}_${H}x${W}/FP32 \
--mean_values [103.53,116.28,123.675] \
--scale_values [57.375,57.12,58.395]
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}_${H}x${W}/FP16
--mean_values [103.53,116.28,123.675] \
--scale_values [57.375,57.12,58.395]
mkdir -p openvino/${MODEL}_${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}_${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}_${H}x${W}/myriad/${MODEL}_${H}x${W}.blob

H=320
W=320
MODEL=nanodet_m_1.5x
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}_${H}x${W}/FP32 \
--mean_values [103.53,116.28,123.675] \
--scale_values [57.375,57.12,58.395]
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}_${H}x${W}/FP16
--mean_values [103.53,116.28,123.675] \
--scale_values [57.375,57.12,58.395]
mkdir -p openvino/${MODEL}_${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}_${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}_${H}x${W}/myriad/${MODEL}_${H}x${W}.blob

H=416
W=416
MODEL=nanodet_m_1.5x
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}_${H}x${W}/FP32 \
--mean_values [103.53,116.28,123.675] \
--scale_values [57.375,57.12,58.395]
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}_${H}x${W}/FP16
--mean_values [103.53,116.28,123.675] \
--scale_values [57.375,57.12,58.395]
mkdir -p openvino/${MODEL}_${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}_${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}_${H}x${W}/myriad/${MODEL}_${H}x${W}.blob

=============================================

H=320
W=320
MODEL=nanodet_m
openvino2tensorflow \
--model_path openvino/${MODEL}_${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_integer_quant_tflite \
--string_formulas_for_normalization 'data * 1' \
--output_integer_quant_type 'uint8' \
--output_tfjs \
--output_coreml \
--output_tftrt \
--weight_replacement_config replace_nanodet_m.json

mv saved_model saved_model_${MODEL}_${H}x${W}


H=416
W=416
MODEL=nanodet_m
openvino2tensorflow \
--model_path openvino/${MODEL}_${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_integer_quant_tflite \
--string_formulas_for_normalization 'data * 1' \
--output_integer_quant_type 'uint8' \
--output_tfjs \
--output_coreml \
--output_tftrt \
--weight_replacement_config replace_nanodet_m.json

mv saved_model saved_model_${MODEL}_${H}x${W}


H=320
W=320
MODEL=nanodet_m_0.5x
openvino2tensorflow \
--model_path openvino/${MODEL}_${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_integer_quant_tflite \
--string_formulas_for_normalization 'data * 1' \
--output_integer_quant_type 'uint8' \
--output_tfjs \
--output_coreml \
--output_tftrt \
--weight_replacement_config replace_nanodet_m.json

mv saved_model saved_model_${MODEL}_${H}x${W}


H=320
W=320
MODEL=nanodet_m_1.5x
openvino2tensorflow \
--model_path openvino/${MODEL}_${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_integer_quant_tflite \
--string_formulas_for_normalization 'data * 1' \
--output_integer_quant_type 'uint8' \
--output_tfjs \
--output_coreml \
--output_tftrt \
--weight_replacement_config replace_nanodet_m.json

mv saved_model saved_model_${MODEL}_${H}x${W}


H=416
W=416
MODEL=nanodet_m_1.5x
openvino2tensorflow \
--model_path openvino/${MODEL}_${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_integer_quant_tflite \
--string_formulas_for_normalization 'data * 1' \
--output_integer_quant_type 'uint8' \
--output_tfjs \
--output_coreml \
--output_tftrt \
--weight_replacement_config replace_nanodet_m.json

mv saved_model saved_model_${MODEL}_${H}x${W}

9 changes: 9 additions & 0 deletions 072_NanoDet/download_nanodet_m.sh
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@@ -0,0 +1,9 @@
#!/bin/bash

curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=1Pfy5WG9YmSIoDJU9LiMfSKQAETbQRRti" > /dev/null
CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
curl -Lb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1Pfy5WG9YmSIoDJU9LiMfSKQAETbQRRti" -o resources.tar.gz
tar -zxvf resources.tar.gz
rm resources.tar.gz

echo Download finished.
65 changes: 65 additions & 0 deletions 072_NanoDet/replace_nanodet_m.json
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@@ -0,0 +1,65 @@
{
"format_version": 2,
"layers": [
{
"layer_id": "558",
"type": "Const",
"replace_mode": "direct",
"values": [
0,
1,
2
]
},
{
"layer_id": "564",
"type": "Const",
"replace_mode": "direct",
"values": [
0,
1,
2
]
},
{
"layer_id": "600",
"type": "Const",
"replace_mode": "direct",
"values": [
0,
1,
2
]
},
{
"layer_id": "606",
"type": "Const",
"replace_mode": "direct",
"values": [
0,
1,
2
]
},
{
"layer_id": "642",
"type": "Const",
"replace_mode": "direct",
"values": [
0,
1,
2
]
},
{
"layer_id": "648",
"type": "Const",
"replace_mode": "direct",
"values": [
0,
1,
2
]
}
]
}

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