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‎.gitignore

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logs/
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weights/
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**/.idea
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**/__pycache__
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.ignore
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run/

‎README.md

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# Config Base pytorch FrameWork
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This Framework

‎clean_log_folder.py

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import os
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import shutil
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def delete_empty_logs():
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for root, d_names, f_names in os.walk('./logs'):
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if os.path.basename(root) == 'weights':
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if len(f_names) <= 1:
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shutil.rmtree(root)
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print(f_names)
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for root, d_names, f_names in os.walk('./logs'):
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if os.path.basename(root) != 'weights':
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if len(f_names) > 1 and not ('weights' in d_names):
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shutil.rmtree(root)
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print(root)
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for root, d_names, f_names in os.walk('./logs', topdown=False):
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if len(d_names) == 0 and len(f_names) == 0:
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shutil.rmtree(root)
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if __name__ == "__main__":
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print('hi')
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delete_empty_logs()

‎config/capsulorhexis/FasterRCNN-mobilenet_v3_large_320_fpn.yml

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TRAIN:
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INPUT_SIZE: None
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BATCH_SIZE: 4
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EPOCHS: 20
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STEP: 'FasterRCNN-mobilenet_v3_large_320_fpn'
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PARAM:
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OPTIMIZER: 'SGD'
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OPTIMIZER_PARAM:
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lr: 0.005
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momentum: 0.9
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weight_decay: 0.0005
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MODEL:
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NAME: 'fasterrcnn_mobilenet_v3_large_320_fpn'
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PARAM:
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pretrained: false
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progress: false,
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num_classes: 3,
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pretrained_backbone: true,
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trainable_backbone_layers: null
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# resnet50_fpn, mobilenet_v3_large_fpn, mobilenet_v3_large_320_fpn,
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DATASET:
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NAME: 'AFCID'

‎config/capsulorhexis/FasterRCNN-mobilenet_v3_large_fpn.yml

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TRAIN:
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INPUT_SIZE: None
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BATCH_SIZE: 4
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EPOCHS: 20
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STEP: 'FasterRCNN-mobilenet_v3_large_fpn'
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PARAM:
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OPTIMIZER: 'SGD'
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OPTIMIZER_PARAM:
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lr: 0.005
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momentum: 0.9
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weight_decay: 0.0005
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MODEL:
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NAME: 'fasterrcnn_mobilenet_v3_large_fpn'
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PARAM:
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pretrained: false
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progress: false,
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num_classes: 3,
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pretrained_backbone: true,
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trainable_backbone_layers: null
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# resnet50_fpn, mobilenet_v3_large_fpn, mobilenet_v3_large_320_fpn,
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DATASET:
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NAME: 'AFCID'

‎config/capsulorhexis/FasterRCNN-resnet50_fpn.yml

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TRAIN:
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INPUT_SIZE: None
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BATCH_SIZE: 4
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EPOCHS: 20
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STEP: 'FasterRCNN-resnet50_fpn'
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PARAM:
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OPTIMIZER: 'SGD'
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OPTIMIZER_PARAM:
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lr: 0.005
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momentum: 0.9
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weight_decay: 0.0005
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MODEL:
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NAME: 'fasterrcnn_resnet50_fpn'
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PARAM:
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pretrained: false
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progress: false,
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num_classes: 3,
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pretrained_backbone: true,
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trainable_backbone_layers: null
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# resnet50_fpn, mobilenet_v3_large_fpn, mobilenet_v3_large_320_fpn,
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DATASET:
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NAME: 'AFCID'

‎config/capsulorhexis/RetinaNet_resnet50_fpn.yml

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TRAIN:
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INPUT_SIZE: None
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BATCH_SIZE: 4
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EPOCHS: 20
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STEP: 'RetinaNet_resnet50_fpn'
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PARAM:
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OPTIMIZER: 'SGD'
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OPTIMIZER_PARAM:
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lr: 0.005
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momentum: 0.9
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weight_decay: 0.0005
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MODEL:
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NAME: 'retinanet_resnet50_fpn'
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PARAM:
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pretrained: false
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progress: false,
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num_classes: 3,
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pretrained_backbone: true,
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trainable_backbone_layers: null
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DATASET:
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NAME: 'AFCID'

‎config/capsulorhexis/SSD300_vgg16.yml

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TRAIN:
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INPUT_SIZE: None
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BATCH_SIZE: 4
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EPOCHS: 20
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STEP: 'SSD300_vgg16'
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PARAM:
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OPTIMIZER: 'SGD'
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OPTIMIZER_PARAM:
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lr: 0.005
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momentum: 0.9
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weight_decay: 0.0005
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MODEL:
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NAME: 'ssd300_vgg16'
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PARAM:
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pretrained: false
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progress: false,
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num_classes: 3,
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pretrained_backbone: true,
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trainable_backbone_layers: null
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DATASET:
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NAME: 'AFCID'

‎config/capsulorhexis/SSDlite320_mobilenet_v3_large.yml

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TRAIN:
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INPUT_SIZE: None
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BATCH_SIZE: 4
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EPOCHS: 20
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STEP: 'SSDlite320_mobilenet_v3_large'
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PARAM:
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OPTIMIZER: 'SGD'
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OPTIMIZER_PARAM:
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lr: 0.005
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momentum: 0.9
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weight_decay: 0.0005
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MODEL:
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NAME: 'ssdlite320_mobilenet_v3_large'
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PARAM:
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pretrained: false
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progress: false,
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num_classes: 3,
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pretrained_backbone: true,
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trainable_backbone_layers: null
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DATASET:
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NAME: 'AFCID'

‎config/system/Colab.yml

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SYSTEM:
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NUM_GPUS: 2
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NUM_WORKERS: 4
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DEVISE: 'cuda'
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CUDA_VISIBLE_DEVICES: "'0'"
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MAX_BATCH_SIZE: 10
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DATASET:
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PATH: '../'
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LOGGING:
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BEST: true
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COMMENT: 'just flip augment'

‎core/config.py

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from yacs.config import CfgNode as CN
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_C = CN()
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_C.LOGGING = CN()
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_C.LOGGING.HOST = ''
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_C.LOGGING.TIME = ''
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_C.LOGGING.WEIGHT_FOLDER = './'
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_C.LOGGING.LOG_DIR = './'
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_C.LOGGING.COMMENT = ''
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_C.LOGGING.BEST = False
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_C.SYSTEM = CN()
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_C.SYSTEM.NUM_GPUS = 1
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_C.SYSTEM.NUM_WORKERS = 4
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_C.SYSTEM.DEVISE = 'cuda'
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_C.SYSTEM.CUDA_VISIBLE_DEVICES = '0,2,3,4,5,6,7,8'
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_C.SYSTEM.MAX_BATCH_SIZE = 16
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_C.MODEL = CN(new_allowed=True)
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_C.MODEL.NAME = 'NAN'
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# _C.MODEL.G1 = CN()
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# _C.MODEL.G1.BACKBONE = 'Unet'
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# _C.MODEL.G1.USE_DISCRIMINATOR = False
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# _C.MODEL.G1.HEAD = False
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# _C.MODEL.G1.HEAD_PARAM = CN(new_allowed=True)
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# _C.MODEL.G1.BACKBONE_PARAM = CN(new_allowed=True)
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# _C.MODEL.G2 = CN()
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# _C.MODEL.G2.BACKBONE = 'Unet'
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# _C.MODEL.G2.USE_DISCRIMINATOR = False
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# _C.MODEL.G2.HEAD = False
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# _C.MODEL.G2.HEAD_PARAM = CN(new_allowed=True)
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# _C.MODEL.G2.BACKBONE_PARAM = CN(new_allowed=True)
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# _C.MODEL.G3 = CN()
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# _C.MODEL.G3.BACKBONE = 'Unet'
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# _C.MODEL.G3.USE_DISCRIMINATOR = False
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# _C.MODEL.G3.BACKBONE_PARAM = CN(new_allowed=True)
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# _C.MODEL.G3.HEAD = 'CoordPlusNum'
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# _C.MODEL.G3.HEAD_PARAM = CN(new_allowed=True)
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_C.TRAIN = CN()
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_C.TRAIN.INPUT_SIZE = (512, 512)
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_C.TRAIN.BATCH_SIZE = 10
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_C.TRAIN.EPOCHS = 50
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_C.TRAIN.STEP = 'e2e' # options ['S1', 'S2', 'S3', 'e2e']
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_C.TRAIN.PARAM = CN(new_allowed=True)
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# _C.TRAIN.G1 = CN(new_allowed=True)
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# _C.TRAIN.G1.OPTIMIZER = 'Adam'
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# _C.TRAIN.G1.OPTIMIZER_PARAM = CN(new_allowed=True)
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# _C.TRAIN.G1.LOSS = 'SmoothL1Loss'
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# _C.TRAIN.G1.LOSS_PARAM = CN(new_allowed=True)
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#
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# _C.TRAIN.G2 = CN(new_allowed=True)
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# _C.TRAIN.G2.OPTIMIZER = 'Adam'
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# _C.TRAIN.G2.OPTIMIZER_PARAM = CN(new_allowed=True)
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# _C.TRAIN.G2.LOSS = 'SmoothL1Loss'
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# _C.TRAIN.G2.LOSS_PARAM = CN(new_allowed=True)
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#
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# _C.TRAIN.G3 = CN(new_allowed=True)
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# _C.TRAIN.G3.OPTIMIZER = 'Adam'
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# _C.TRAIN.G3.OPTIMIZER_PARAM = CN(new_allowed=True)
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# _C.TRAIN.G3.LOSS = 'SmoothL1Loss'
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# _C.TRAIN.G3.LOSS_PARAM = CN(new_allowed=True)
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_C.DATASET = CN()
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_C.DATASET.NAME = 'CIHP'
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_C.DATASET.PATH = './dataset'
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cfg = _C

‎infrence_SR.py

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import time
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from torch.utils.data import DataLoader
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from lib.data.datasets import *
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from lib.core import *
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from lib.utils.environment import init_env
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from core.config import cfg
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import lib.engine.detection.utils as utils
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import lib.engine.detection.transforms as T
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def get_transform():
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transforms = [T.ToTensor()]
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return T.Compose(transforms)
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if __name__ == '__main__':
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device, writer = init_env(cfg, has_writer=True)
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# get the model
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model = eval("{}".format(cfg.MODEL.NAME))(cfg.MODEL.PARAM)
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# move model to the right device
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model.to(device)
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DATASET_PATH = cfg.DATASET.PATH
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test_ds = eval(cfg.DATASET.NAME)(DATASET_PATH, train='test', transforms=get_transform())
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NUM_WORKERS = cfg.SYSTEM.NUM_WORKERS
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test_dl = torch.utils.data.DataLoader(
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test_ds, batch_size=1, shuffle=False, num_workers=NUM_WORKERS,
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collate_fn=utils.collate_fn)
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print("number of test samples :{}".format(len(test_ds)))
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print("number of train batch :{}".format(len(test_dl)))
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model.eval()
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sum_time = 0
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t = tqdm(test_dl, desc='calculating inference time..')
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for image, _ in t:
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image = list(img.to(device) for img in image)
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st = time.time()
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outputs = model(image)
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sum_time += (time.time() - st)
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print("mean fps:", 1/(sum_time/len(test_dl)))
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print("mean inference time:", sum_time/len(test_dl))
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with open('inference_time.txt', 'a') as f:
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f.write("Model: {}\n".format(cfg.MODEL.NAME))
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f.write("mean fps: {}\n".format(1/(sum_time/len(test_dl))))
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f.write("mean inference time: {}\n".format(sum_time/len(test_dl)))
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f.write("*****************************************************\n")
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f.write("*****************************************************\n")
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f.write("*****************************************************\n")
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‎lib/__init__.py

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‎lib/backbone/__init__.py

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from .unet import *

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