| import torch.nn as nn | |
| import torchvision.models as models | |
| """ VGG_16 Architecture | |
| VGG( | |
| (features): Sequential( | |
| (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): ReLU(inplace=True) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (3): ReLU(inplace=True) | |
| (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) | |
| (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (6): ReLU(inplace=True) | |
| (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (8): ReLU(inplace=True) | |
| (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) | |
| (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (11): ReLU(inplace=True) | |
| (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (13): ReLU(inplace=True) | |
| (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (15): ReLU(inplace=True) | |
| (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) | |
| (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (18): ReLU(inplace=True) | |
| (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (20): ReLU(inplace=True) | |
| (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (22): ReLU(inplace=True) | |
| (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) | |
| (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (25): ReLU(inplace=True) | |
| (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (27): ReLU(inplace=True) | |
| (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (29): ReLU(inplace=True) | |
| (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) | |
| ) | |
| (avgpool): AdaptiveAvgPool2d(output_size=(7, 7)) | |
| (classifier): Sequential( | |
| (0): Linear(in_features=25088, out_features=4096, bias=True) | |
| (1): ReLU(inplace=True) | |
| (2): Dropout(p=0.5, inplace=False) | |
| (3): Linear(in_features=4096, out_features=4096, bias=True) | |
| (4): ReLU(inplace=True) | |
| (5): Dropout(p=0.5, inplace=False) | |
| (6): Linear(in_features=4096, out_features=1000, bias=True) | |
| ) | |
| ) | |
| """ | |
| class VGG_16(nn.Module): | |
| def __init__(self): | |
| super(VGG_16, self).__init__() | |
| self.model = models.vgg16(weights=models.VGG16_Weights.IMAGENET1K_V1).features[:30] | |
| for i, _ in enumerate(self.model): | |
| if i in [4, 9, 16, 23]: | |
| self.model[i] = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) | |
| def forward(self, x): | |
| features = [] | |
| for i, layer in enumerate(self.model): | |
| x = layer(x) | |
| if i in [0, 5, 10, 17, 24]: | |
| features.append(x) | |
| return features | |
| if __name__ == '__main__': | |
| model = VGG_16() | |
| print(model) |