python 查看網(wǎng)絡(luò)模型

if __name__ == '__main__':import torchimport torchvision.models as modelsfrom torchsummary import summarydevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")model = models.resnet50().to(device)summary(model, (3, 224, 224))

Output

Layer (type) Output Shape Param # ================================================================Conv2d-1 [-1, 64, 112, 112] 9,408BatchNorm2d-2 [-1, 64, 112, 112] 128ReLU-3 [-1, 64, 112, 112] 0MaxPool2d-4 [-1, 64, 56, 56] 0Conv2d-5 [-1, 64, 56, 56] 36,864BatchNorm2d-6 [-1, 64, 56, 56] 128ReLU-7 [-1, 64, 56, 56] 0Conv2d-8 [-1, 64, 56, 56] 36,864BatchNorm2d-9 [-1, 64, 56, 56] 128ReLU-10 [-1, 64, 56, 56] 0BasicBlock-11 [-1, 64, 56, 56] 0Conv2d-12 [-1, 64, 56, 56] 36,864BatchNorm2d-13 [-1, 64, 56, 56] 128ReLU-14 [-1, 64, 56, 56] 0Conv2d-15 [-1, 64, 56, 56] 36,864BatchNorm2d-16 [-1, 64, 56, 56] 128ReLU-17 [-1, 64, 56, 56] 0BasicBlock-18 [-1, 64, 56, 56] 0Conv2d-19 [-1, 128, 28, 28] 73,728BatchNorm2d-20 [-1, 128, 28, 28] 256ReLU-21 [-1, 128, 28, 28] 0Conv2d-22 [-1, 128, 28, 28] 147,456BatchNorm2d-23 [-1, 128, 28, 28] 256Conv2d-24 [-1, 128, 28, 28] 8,192BatchNorm2d-25 [-1, 128, 28, 28] 256ReLU-26 [-1, 128, 28, 28] 0BasicBlock-27 [-1, 128, 28, 28] 0Conv2d-28 [-1, 128, 28, 28] 147,456BatchNorm2d-29 [-1, 128, 28, 28] 256ReLU-30 [-1, 128, 28, 28] 0Conv2d-31 [-1, 128, 28, 28] 147,456BatchNorm2d-32 [-1, 128, 28, 28] 256ReLU-33 [-1, 128, 28, 28] 0BasicBlock-34 [-1, 128, 28, 28] 0Conv2d-35 [-1, 256, 14, 14] 294,912BatchNorm2d-36 [-1, 256, 14, 14] 512ReLU-37 [-1, 256, 14, 14] 0Conv2d-38 [-1, 256, 14, 14] 589,824BatchNorm2d-39 [-1, 256, 14, 14] 512Conv2d-40 [-1, 256, 14, 14] 32,768BatchNorm2d-41 [-1, 256, 14, 14] 512ReLU-42 [-1, 256, 14, 14] 0BasicBlock-43 [-1, 256, 14, 14] 0Conv2d-44 [-1, 256, 14, 14] 589,824BatchNorm2d-45 [-1, 256, 14, 14] 512ReLU-46 [-1, 256, 14, 14] 0Conv2d-47 [-1, 256, 14, 14] 589,824BatchNorm2d-48 [-1, 256, 14, 14] 512ReLU-49 [-1, 256, 14, 14] 0BasicBlock-50 [-1, 256, 14, 14] 0Conv2d-51 [-1, 512, 7, 7] 1,179,648BatchNorm2d-52 [-1, 512, 7, 7] 1,024ReLU-53 [-1, 512, 7, 7] 0Conv2d-54 [-1, 512, 7, 7] 2,359,296BatchNorm2d-55 [-1, 512, 7, 7] 1,024Conv2d-56 [-1, 512, 7, 7] 131,072BatchNorm2d-57 [-1, 512, 7, 7] 1,024ReLU-58 [-1, 512, 7, 7] 0BasicBlock-59 [-1, 512, 7, 7] 0Conv2d-60 [-1, 512, 7, 7] 2,359,296BatchNorm2d-61 [-1, 512, 7, 7] 1,024ReLU-62 [-1, 512, 7, 7] 0Conv2d-63 [-1, 512, 7, 7] 2,359,296BatchNorm2d-64 [-1, 512, 7, 7] 1,024ReLU-65 [-1, 512, 7, 7] 0BasicBlock-66 [-1, 512, 7, 7] 0 AdaptiveAvgPool2d-67 [-1, 512, 1, 1] 0Linear-68 [-1, 1000] 513,000 ================================================================ Total params: 11,689,512 Trainable params: 11,689,512 Non-trainable params: 0 ---------------------------------------------------------------- Input size (MB): 0.57 Forward/backward pass size (MB): 62.79 Params size (MB): 44.59 Estimated Total Size (MB): 107.96 ----------------------------------------------------------------Process finished with exit code 0

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