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configs.py
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from PIL import Image
model_configurations = {
'resnet50': {
'feature_num': 2048,
'feature_map_channels': 2048,
'policy_conv': False,
'policy_hidden_dim': 1024,
'fc_rnn': True,
'fc_hidden_dim': 1024,
'image_size': 224,
'crop_pct': 0.875,
'dataset_interpolation': Image.BILINEAR,
'prime_interpolation': 'bicubic'
},
'densenet121': {
'feature_num': 1024,
'feature_map_channels': 1024,
'policy_conv': False,
'policy_hidden_dim': 1024,
'fc_rnn': True,
'fc_hidden_dim': 1024,
'image_size': 224,
'crop_pct': 0.875,
'dataset_interpolation': Image.BILINEAR,
'prime_interpolation': 'bilinear'
},
'densenet169': {
'feature_num': 1664,
'feature_map_channels': 1664,
'policy_conv': False,
'policy_hidden_dim': 1024,
'fc_rnn': True,
'fc_hidden_dim': 1024,
'image_size': 224,
'crop_pct': 0.875,
'dataset_interpolation': Image.BILINEAR,
'prime_interpolation': 'bilinear'
},
'densenet201': {
'feature_num': 1920,
'feature_map_channels': 1920,
'policy_conv': False,
'policy_hidden_dim': 1024,
'fc_rnn': True,
'fc_hidden_dim': 1024,
'image_size': 224,
'crop_pct': 0.875,
'dataset_interpolation': Image.BILINEAR,
'prime_interpolation': 'bilinear'
},
'mobilenetv3_large_100': {
'feature_num': 1280,
'feature_map_channels': 960,
'policy_conv': True,
'policy_hidden_dim': 256,
'fc_rnn': False,
'fc_hidden_dim': None,
'image_size': 224,
'crop_pct': 0.875,
'dataset_interpolation': Image.BILINEAR,
'prime_interpolation': 'bicubic'
},
'mobilenetv3_large_125': {
'feature_num': 1280,
'feature_map_channels': 1200,
'policy_conv': True,
'policy_hidden_dim': 256,
'fc_rnn': False,
'fc_hidden_dim': None,
'image_size': 224,
'crop_pct': 0.875,
'dataset_interpolation': Image.BILINEAR,
'prime_interpolation': 'bicubic'
},
'efficientnet_b2': {
'feature_num': 1408,
'feature_map_channels': 1408,
'policy_conv': True,
'policy_hidden_dim': 256,
'fc_rnn': False,
'fc_hidden_dim': None,
'image_size': 260,
'crop_pct': 0.875,
'dataset_interpolation': Image.BICUBIC,
'prime_interpolation': 'bicubic'
},
'efficientnet_b3': {
'feature_num': 1536,
'feature_map_channels': 1536,
'policy_conv': True,
'policy_hidden_dim': 256,
'fc_rnn': False,
'fc_hidden_dim': None,
'image_size': 300,
'crop_pct': 0.904,
'dataset_interpolation': Image.BICUBIC,
'prime_interpolation': 'bicubic'
},
'regnety_600m': {
'feature_num': 608,
'feature_map_channels': 608,
'policy_conv': True,
'policy_hidden_dim': 256,
'fc_rnn': True,
'fc_hidden_dim': 1024,
'image_size': 224,
'crop_pct': 0.875,
'dataset_interpolation': Image.BILINEAR,
'prime_interpolation': 'bilinear',
'cfg_file': 'pycls/cfgs/RegNetY-600MF_dds_8gpu.yaml'
},
'regnety_800m': {
'feature_num': 768,
'feature_map_channels': 768,
'policy_conv': True,
'policy_hidden_dim': 256,
'fc_rnn': True,
'fc_hidden_dim': 1024,
'image_size': 224,
'crop_pct': 0.875,
'dataset_interpolation': Image.BILINEAR,
'prime_interpolation': 'bilinear',
'cfg_file': 'pycls/cfgs/RegNetY-800MF_dds_8gpu.yaml'
},
'regnety_1.6g': {
'feature_num': 888,
'feature_map_channels': 888,
'policy_conv': True,
'policy_hidden_dim': 256,
'fc_rnn': True,
'fc_hidden_dim': 1024,
'image_size': 224,
'crop_pct': 0.875,
'dataset_interpolation': Image.BILINEAR,
'prime_interpolation': 'bilinear',
'cfg_file': 'pycls/cfgs/RegNetY-1.6GF_dds_8gpu.yaml'
}
}
train_configurations = {
'resnet': {
'backbone_lr': 0.01,
'fc_stage_1_lr': 0.1,
'fc_stage_3_lr': 0.01,
'weight_decay': 1e-4,
'momentum': 0.9,
'Nesterov': True,
'batch_size': 256,
'dsn_ratio': 1,
'epoch_num': 60,
'train_model_prime': True
},
'densenet': {
'backbone_lr': 0.01,
'fc_stage_1_lr': 0.1,
'fc_stage_3_lr': 0.01,
'weight_decay': 1e-4,
'momentum': 0.9,
'Nesterov': True,
'batch_size': 256,
'dsn_ratio': 1,
'epoch_num': 60,
'train_model_prime': True
},
'efficientnet': {
'backbone_lr': 0.005,
'fc_stage_1_lr': 0.1,
'fc_stage_3_lr': 0.01,
'weight_decay': 1e-4,
'momentum': 0.9,
'Nesterov': True,
'batch_size': 256,
'dsn_ratio': 5,
'epoch_num': 30,
'train_model_prime': False
},
'mobilenetv3': {
'backbone_lr': 0.005,
'fc_stage_1_lr': 0.1,
'fc_stage_3_lr': 0.01,
'weight_decay': 1e-4,
'momentum': 0.9,
'Nesterov': True,
'batch_size': 256,
'dsn_ratio': 5,
'epoch_num': 90,
'train_model_prime': False
},
'regnet': {
'backbone_lr': 0.02,
'fc_stage_1_lr': 0.1,
'fc_stage_3_lr': 0.01,
'weight_decay': 5e-5,
'momentum': 0.9,
'Nesterov': True,
'batch_size': 256,
'dsn_ratio': 1,
'epoch_num': 60,
'train_model_prime': True
}
}