import torch.nn as nn import torchvision.models as models """ VGG_19 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): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (17): ReLU(inplace=True) (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (19): Conv2d(256, 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): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (24): ReLU(inplace=True) (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (26): ReLU(inplace=True) (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (29): ReLU(inplace=True) (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (31): ReLU(inplace=True) (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (33): ReLU(inplace=True) (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (35): ReLU(inplace=True) (36): 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_19(nn.Module): def __init__(self): super(VGG_19, self).__init__() self.model = models.vgg19(weights=models.VGG19_Weights).features[:30] for i, _ in enumerate(self.model): if i in [4, 9, 18, 27]: 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, 19, 28]: features.append(x) return features if __name__ == '__main__': model = VGG_19() print(model)