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‎options/test/GoPro/test_MAENet_GoPro.yml

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# ------------------------------------------------------------------------
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# Copyright (c) 2021 megvii-model. All Rights Reserved.
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# ------------------------------------------------------------------------
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# Modified from BasicSR (https://github.com/xinntao/BasicSR)
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# Copyright 2018-2020 BasicSR Authors
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# ------------------------------------------------------------------------
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# general settings
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name: GoPro-MAENet-test
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model_type: TestImageEventRestorationModel
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scale: 1
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num_gpu: 1
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manual_seed: 10
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datasets:
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test:
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name: gopro-bestmodel-test
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type: H5ImageDataset
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dataroot: /data/zhijing/GOPRO_rawevent/asynav_test # for debug
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# add
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norm_voxel: true
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return_voxel: true
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return_gt_frame: true
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return_mask: false
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use_mask: false
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crop_size: ~
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use_flip: false
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use_rot: false
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io_backend:
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type: h5
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dataset_name: GoPro
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# network structures
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network_g:
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type: Baseline_Biattention_CrossRecurrent_FC
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in_chn: 3
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ev_chn: 6
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width: 64
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middle_blk_num: 3
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enc_blk_nums: [2,2,2]
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dec_blk_nums: [2,2,2]
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num_heads: [2,4,4]
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# path
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path:
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pretrain_network_g: /model/zhijing/Baseline_Biattention/experiments/Baseline_Biattention_CrossRecurrent_FC/models/net_g_latest.pth
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strict_load_g: true
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resume_state: ~ # save current trainig model states, for resume
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root: /model/zhijing/Baseline_Biattention
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# validation settings
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val:
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save_img: true
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save_img_path: /output
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grids: ~
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crop_size: ~
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rgb2bgr: false
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max_minibatch: 8
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metrics: {
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PSNR: {
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type: calculate_psnr,
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crop_border: 0
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}
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}
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# dist training settings
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dist_params:
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backend: nccl
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port: 29500

‎options/test/HS_ERGB/test_MAENet_ERGB.yml

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# general settings
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name: MAENet_ERGB_test #debug
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model_type: TestImageEventRestorationModel
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scale: 1
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num_gpu: 1 #4
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manual_seed: 10
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datasets:
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test:
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name: ERGB-h5-test
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type: H5ImageDataset
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dataroot: /data/zhijing/GOPRO_rawevent/HS_ERGB_test # for debug
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norm_voxel: true
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return_voxel: true
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return_gt_frame: true
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return_mask: false
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use_mask: false
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io_backend:
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type: h5
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crop_size: ~
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use_flip: false
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use_rot: false
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dataset_name: ERGB
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# network structures
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network_g:
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type: Baseline_Biattention_CrossRecurrent_FC
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in_chn: 3
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ev_chn: 6
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width: 64
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middle_blk_num: 3
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enc_blk_nums: [2,2,2]
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dec_blk_nums: [2,2,2]
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num_heads: [2,4,4]
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# path
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path:
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pretrain_network_g: /model/zhijing/Baseline_Biattention/experiments/Baseline_Biattention_CrossRecurrent_FC_ERGB/models/net_g_latest.pth
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# param_key_g: params
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strict_load_g: true
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resume_state: ~
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# training_states: ~ # save current trainig model states, for resume
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root: /model/zhijing/Baseline_Biattention
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# validation settings
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val:
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save_img: true
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save_img_path: /output
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grids: ~
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crop_size: ~
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rgb2bgr: false
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max_minibatch: 8
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metrics: {
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PSNR: {
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type: calculate_psnr,
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crop_border: 0
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}
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}
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# dist training settings
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dist_params:
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backend: nccl
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port: 29500

‎options/test/REBlur/test_MAENet_REBlur.yml

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# general settings
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name: MAENet_REBlur_test
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model_type: TestImageEventRestorationModel
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scale: 1
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num_gpu: 1 # set num_gpu: 0 for cpu mode
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manual_seed: 10
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# dataset and data loader settings
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datasets:
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test:
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name: gopro-bestmodel-test
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type: H5ImageDataset
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dataroot: /data/zhijing/GOPRO_rawevent/REBlur_testav6 # for debug
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# add
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norm_voxel: true
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return_voxel: true
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return_gt_frame: true
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return_mask: false
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use_mask: false
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crop_size: ~
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use_flip: false
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use_rot: false
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io_backend:
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type: h5
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dataset_name: REBlur
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# network structures
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network_g:
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type: Baseline_Biattention_CrossRecurrent_FC
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in_chn: 3
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ev_chn: 6
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width: 64
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middle_blk_num: 3
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enc_blk_nums: [2,2,2]
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dec_blk_nums: [2,2,2]
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num_heads: [2,4,4]
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# path
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path:
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pretrain_network_g: /model/zhijing/Baseline_Biattention/experiments/Baseline_Biattention_CrossRecurrent_FC_REBlur/models/net_g_latest.pth
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strict_load_g: true
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resume_state: ~
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root: /model/zhijing/Baseline_Biattention # set this option ONLY in TEST!!!
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# validation settings
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val:
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save_img: true
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save_img_path: /output
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grids: ~
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crop_size: ~
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max_minibatch: 8
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rgb2bgr: false # to my h5 data, its false
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metrics: {
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PSNR: {
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type: calculate_psnr,
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crop_border: 0
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}
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}
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# dist training settings
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dist_params:
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backend: nccl
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port: 29500

‎options/train/GoPro/MAENet_GoPro.yml

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# general settings
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name: MAENet_GoPro #debug
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model_type: ImageEventRestorationModel
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scale: 1
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num_gpu: 1 #4
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manual_seed: 10
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datasets:
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train:
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name: gopro-h5-train
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type: H5ImageDataset
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# dataroot: F:/GOPRO_rawevents/av_train_small6
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dataroot: /data/zhijing/GOPRO_rawevent/asynav_train
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# keep true if use events
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norm_voxel: true
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return_voxel: true
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return_mask: false
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use_mask: false
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filename_tmpl: '{}'
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io_backend:
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type: h5
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crop_size: 256
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use_flip: true
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use_rot: true
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# data loader settings
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use_shuffle: true
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num_worker_per_gpu: 8
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batch_size_per_gpu: 2 # 4 for 2080, 8 for titan
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dataset_enlarge_ratio: 1 # accelarate, equals to the num_gpu
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prefetch_mode: cuda
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pin_memory: true
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val:
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name: gopro-h5-test
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type: H5ImageDataset
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# dataroot: F:/GOPRO_rawevents/av_test_small6
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dataroot: /data/zhijing/GOPRO_rawevent/asynav_test # for debug
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norm_voxel: true
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return_voxel: true
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return_mask: false
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use_mask: false
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io_backend:
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type: h5
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crop_size: ~
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use_flip: false
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use_rot: false
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dataset_name: GoPro
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# network structures
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network_g:
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type: Baseline_Biattention_CrossRecurrent_FC
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in_chn: 3
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ev_chn: 6
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width: 64
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middle_blk_num: 3
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enc_blk_nums: [2,2,2]
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dec_blk_nums: [2,2,2]
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num_heads: [2,4,4]
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# path
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path:
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pretrain_network_g: ~
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strict_load_g: true
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resume_state: ~
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training_states: ~ # save current trainig model states, for resume
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root: /output
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# training settings
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train:
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optim_g:
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type: AdamW
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lr: !!float 1e-4
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weight_decay: !!float 1e-5
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betas: [0.9, 0.99]
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scheduler:
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type: TrueCosineAnnealingLR
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T_max: 400000
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eta_min: !!float 1e-7
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total_iter: 400000
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warmup_iter: -1 # no warm up
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# losses
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pixel_opt:
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type: PSNRLoss
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loss_weight: 0.5
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reduction: mean
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# validation settings
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val:
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val_freq: !!float 4e4 # 2e4
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save_img: false
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grids: ~
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crop_size: ~
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max_minibatch: 8
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metrics:
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psnr:
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type: calculate_psnr
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crop_border: 0
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test_y_channel: false
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ssim:
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type: calculate_ssim
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crop_border: 0
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test_y_channel: false
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# logging settings
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logger:
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print_freq: 1000
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save_checkpoint_freq: !!float 2e4
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use_tb_logger: true
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wandb: ~
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# dist training settings
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dist_params:
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backend: nccl
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port: 29500

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