littletomatodonkey
GitHub
commit
9d3f36b75e
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@ -0,0 +1,74 @@
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mode: 'train'
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ARCHITECTURE:
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name: 'GhostNet_x0_5'
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pretrained_model: ""
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model_save_dir: "./output/"
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classes_num: 1000
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total_images: 1281167
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save_interval: 1
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validate: True
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valid_interval: 1
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epochs: 360
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topk: 5
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image_shape: [3, 224, 224]
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use_mix: False
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ls_epsilon: 0.1
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LEARNING_RATE:
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function: 'CosineWarmup'
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params:
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lr: 0.8
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OPTIMIZER:
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function: 'Momentum'
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params:
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momentum: 0.9
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regularizer:
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function: 'L2'
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factor: 0.0000400
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TRAIN:
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batch_size: 2048
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num_workers: 4
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file_list: "./dataset/ILSVRC2012/train_list.txt"
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data_dir: "./dataset/ILSVRC2012/"
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shuffle_seed: 0
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transforms:
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- DecodeImage:
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to_rgb: True
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to_np: False
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channel_first: False
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- RandCropImage:
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size: 224
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- RandFlipImage:
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flip_code: 1
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- NormalizeImage:
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scale: 1./255.
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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- ToCHWImage:
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VALID:
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batch_size: 64
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num_workers: 4
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file_list: "./dataset/ILSVRC2012/val_list.txt"
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data_dir: "./dataset/ILSVRC2012/"
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shuffle_seed: 0
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transforms:
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- DecodeImage:
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to_rgb: True
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to_np: False
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channel_first: False
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- ResizeImage:
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resize_short: 256
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- CropImage:
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size: 224
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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- ToCHWImage:
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@ -0,0 +1,74 @@
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mode: 'train'
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ARCHITECTURE:
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name: 'GhostNet_x1_0'
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pretrained_model: ""
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model_save_dir: "./output/"
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classes_num: 1000
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total_images: 1281167
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save_interval: 1
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validate: True
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valid_interval: 1
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epochs: 360
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topk: 5
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image_shape: [3, 224, 224]
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use_mix: False
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ls_epsilon: 0.1
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LEARNING_RATE:
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function: 'CosineWarmup'
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params:
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lr: 0.4
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OPTIMIZER:
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function: 'Momentum'
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params:
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momentum: 0.9
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regularizer:
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function: 'L2'
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factor: 0.0000400
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TRAIN:
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batch_size: 1024
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num_workers: 4
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file_list: "./dataset/ILSVRC2012/train_list.txt"
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data_dir: "./dataset/ILSVRC2012/"
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shuffle_seed: 0
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transforms:
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- DecodeImage:
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to_rgb: True
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to_np: False
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channel_first: False
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- RandCropImage:
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size: 224
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- RandFlipImage:
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flip_code: 1
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- NormalizeImage:
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scale: 1./255.
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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- ToCHWImage:
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VALID:
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batch_size: 64
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num_workers: 4
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file_list: "./dataset/ILSVRC2012/val_list.txt"
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data_dir: "./dataset/ILSVRC2012/"
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shuffle_seed: 0
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transforms:
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- DecodeImage:
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to_rgb: True
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to_np: False
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channel_first: False
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- ResizeImage:
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resize_short: 256
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- CropImage:
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size: 224
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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- ToCHWImage:
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@ -0,0 +1,75 @@
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mode: 'train'
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ARCHITECTURE:
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name: 'GhostNet_x1_3'
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pretrained_model: ""
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model_save_dir: "./output/"
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classes_num: 1000
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total_images: 1281167
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save_interval: 1
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validate: True
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valid_interval: 1
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epochs: 360
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topk: 5
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image_shape: [3, 224, 224]
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use_mix: False
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ls_epsilon: 0.1
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LEARNING_RATE:
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function: 'CosineWarmup'
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params:
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lr: 0.4
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OPTIMIZER:
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function: 'Momentum'
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params:
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momentum: 0.9
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regularizer:
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function: 'L2'
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factor: 0.0000400
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TRAIN:
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batch_size: 1024
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num_workers: 4
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file_list: "./dataset/ILSVRC2012/train_list.txt"
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data_dir: "./dataset/ILSVRC2012/"
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shuffle_seed: 0
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transforms:
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- DecodeImage:
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to_rgb: True
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to_np: False
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channel_first: False
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- RandCropImage:
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size: 224
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- RandFlipImage:
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flip_code: 1
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- AutoAugment:
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- NormalizeImage:
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scale: 1./255.
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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- ToCHWImage:
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VALID:
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batch_size: 64
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num_workers: 4
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file_list: "./dataset/ILSVRC2012/val_list.txt"
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data_dir: "./dataset/ILSVRC2012/"
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shuffle_seed: 0
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transforms:
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- DecodeImage:
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to_rgb: True
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to_np: False
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channel_first: False
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- ResizeImage:
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resize_short: 256
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- CropImage:
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size: 224
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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- ToCHWImage:
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@ -42,8 +42,9 @@ from .res2net_vd import Res2Net50_vd_48w_2s, Res2Net50_vd_26w_4s, Res2Net50_vd_1
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from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W18_C, SE_HRNet_W30_C, SE_HRNet_W32_C, SE_HRNet_W40_C, SE_HRNet_W44_C, SE_HRNet_W48_C, SE_HRNet_W60_C, SE_HRNet_W64_C
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from .darts_gs import DARTS_GS_6M, DARTS_GS_4M
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from .resnet_acnet import ResNet18_ACNet, ResNet34_ACNet, ResNet50_ACNet, ResNet101_ACNet, ResNet152_ACNet
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from .ghostnet import GhostNet_x0_5, GhostNet_x1_0, GhostNet_x1_3
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# distillation model
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from .distillation_models import ResNet50_vd_distill_MobileNetV3_large_x1_0, ResNeXt101_32x16d_wsl_distill_ResNet50_vd
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from .csp_resnet import CSPResNet50_leaky
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from .csp_resnet import CSPResNet50_leaky
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@ -0,0 +1,265 @@
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import paddle.fluid as fluid
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from paddle.fluid.param_attr import ParamAttr
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__all__ = ["GhostNet", "GhostNet_x0_5", "GhostNet_x1_0", "GhostNet_x1_3"]
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class GhostNet():
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def __init__(self, scale):
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cfgs = [
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# k, t, c, SE, s
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[3, 16, 16, 0, 1],
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[3, 48, 24, 0, 2],
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[3, 72, 24, 0, 1],
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[5, 72, 40, 1, 2],
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[5, 120, 40, 1, 1],
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[3, 240, 80, 0, 2],
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[3, 200, 80, 0, 1],
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[3, 184, 80, 0, 1],
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[3, 184, 80, 0, 1],
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[3, 480, 112, 1, 1],
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[3, 672, 112, 1, 1],
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[5, 672, 160, 1, 2],
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[5, 960, 160, 0, 1],
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[5, 960, 160, 1, 1],
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[5, 960, 160, 0, 1],
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[5, 960, 160, 1, 1]
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]
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self.cfgs = cfgs
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self.scale = scale
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def net(self, input, class_dim=1000):
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# build first layer:
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output_channel = int(self._make_divisible(16 * self.scale, 4))
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x = self.conv_bn_layer(input=input,
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num_filters=output_channel,
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filter_size=3,
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stride=2,
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groups=1,
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act="relu",
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name="conv1")
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# build inverted residual blocks
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idx = 0
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for k, exp_size, c, use_se, s in self.cfgs:
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output_channel = int(self._make_divisible(c * self.scale, 4))
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hidden_channel = int(self._make_divisible(exp_size * self.scale, 4))
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x = self.ghost_bottleneck(input=x,
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hidden_dim=hidden_channel,
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output=output_channel,
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kernel_size=k,
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stride=s,
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use_se=use_se,
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name="_ghostbottleneck_" + str(idx))
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idx += 1
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# build last several layers
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output_channel = int(self._make_divisible(exp_size * self.scale, 4))
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x = self.conv_bn_layer(input=x,
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num_filters=output_channel,
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filter_size=1,
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stride=1,
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groups=1,
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act="relu",
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name="conv_last")
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x = fluid.layers.pool2d(input=x, pool_type='avg', global_pooling=True)
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output_channel = 1280
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stdv = 1.0 / math.sqrt(x.shape[1] * 1.0)
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out = self.conv_bn_layer(input=x,
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num_filters=output_channel,
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filter_size=1,
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stride=1,
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act="relu",
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name="fc_0")
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out = fluid.layers.dropout(x=out, dropout_prob=0.2)
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stdv = 1.0 / math.sqrt(out.shape[1] * 1.0)
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out = fluid.layers.fc(input=out,
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size=class_dim,
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param_attr=ParamAttr(name="fc_1_weights",
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initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name="fc_1_offset"))
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return out
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def _make_divisible(self, v, divisor, min_value=None):
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"""
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This function is taken from the original tf repo.
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It ensures that all layers have a channel number that is divisible by 8
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It can be seen here:
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https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
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"""
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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def conv_bn_layer(self,
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input,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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name=None):
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x = fluid.layers.conv2d(input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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act=None,
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param_attr=ParamAttr(
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initializer=fluid.initializer.MSRA(), name=name + "_weights"),
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bias_attr=False)
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bn_name = name + "_bn"
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x = fluid.layers.batch_norm(input=x,
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act=act,
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param_attr=ParamAttr(
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name=bn_name + "_scale",
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=0.0)),
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bias_attr=ParamAttr(
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name=bn_name + "_offset",
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=0.0)),
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moving_mean_name=bn_name + "_mean",
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moving_variance_name=name + "_variance")
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return x
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def se_block(self, input, num_channels, reduction_ratio=4, name=None):
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pool = fluid.layers.pool2d(input=input, pool_type='avg', global_pooling=True, use_cudnn=False)
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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squeeze = fluid.layers.fc(input=pool,
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size=num_channels // reduction_ratio,
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act='relu',
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name=name + '_1_weights'),
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bias_attr=ParamAttr(name=name + '_1_offset'))
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stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
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excitation = fluid.layers.fc(input=squeeze,
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size=num_channels,
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act=None,
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name=name + '_2_weights'),
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bias_attr=ParamAttr(name=name + '_2_offset'))
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#excitation = fluid.layers.clip(x=excitation, min=0, max=1)
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se_scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
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return se_scale
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def depthwise_conv(self,
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input,
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output,
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kernel_size,
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stride=1,
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relu=False,
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name=None):
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return self.conv_bn_layer(input=input,
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num_filters=output,
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filter_size=kernel_size,
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stride=stride,
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groups=input.shape[1],
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act="relu" if relu else None,
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name=name + "_depthwise")
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def ghost_module(self,
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input,
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output,
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kernel_size=1,
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ratio=2,
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dw_size=3,
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stride=1,
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relu=True,
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name=None):
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self.output = output
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init_channels = int(math.ceil(output / ratio))
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new_channels = int(init_channels * (ratio - 1))
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primary_conv = self.conv_bn_layer(input=input,
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num_filters=init_channels,
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filter_size=kernel_size,
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stride=stride,
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groups=1,
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act="relu" if relu else None,
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name=name + "_primary_conv")
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cheap_operation = self.conv_bn_layer(input=primary_conv,
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num_filters=new_channels,
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filter_size=dw_size,
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stride=1,
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groups=init_channels,
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act="relu" if relu else None,
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name=name + "_cheap_operation")
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out = fluid.layers.concat([primary_conv, cheap_operation], axis=1)
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return out
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def ghost_bottleneck(self,
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input,
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hidden_dim,
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output,
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kernel_size,
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stride,
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use_se,
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name=None):
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inp_channels = input.shape[1]
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x = self.ghost_module(input=input,
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output=hidden_dim,
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kernel_size=1,
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stride=1,
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relu=True,
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name=name + "_ghost_module_1")
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if stride == 2:
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x = self.depthwise_conv(input=x,
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output=hidden_dim,
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kernel_size=kernel_size,
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stride=stride,
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relu=False,
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name=name + "_depthwise")
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if use_se:
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x = self.se_block(input=x, num_channels=hidden_dim, name=name + "_se")
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x = self.ghost_module(input=x,
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output=output,
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kernel_size=1,
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relu=False,
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name=name + "_ghost_module_2")
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if stride == 1 and inp_channels == output:
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shortcut = input
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else:
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shortcut = self.depthwise_conv(input=input,
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output=inp_channels,
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kernel_size=kernel_size,
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stride=stride,
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relu=False,
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name=name + "_shortcut_depthwise")
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shortcut = self.conv_bn_layer(input=shortcut,
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num_filters=output,
|
||||
filter_size=1,
|
||||
stride=1,
|
||||
groups=1,
|
||||
act=None,
|
||||
name=name + "_shortcut_conv")
|
||||
return fluid.layers.elementwise_add(x=x,
|
||||
y=shortcut,
|
||||
axis=-1)
|
||||
|
||||
|
||||
def GhostNet_x0_5():
|
||||
model = GhostNet(scale=0.5)
|
||||
return model
|
||||
|
||||
|
||||
def GhostNet_x1_0():
|
||||
model = GhostNet(scale=1.0)
|
||||
return model
|
||||
|
||||
|
||||
def GhostNet_x1_3():
|
||||
model = GhostNet(scale=1.3)
|
||||
return model
|
||||
Loading…
Reference in New Issue