You can try with summary, here is an example
from summary import summary vgg = models.vgg16() summary(vgg, (3, 224, 224)) ---------------------------------------------------------------- Layer (type) Output Shpae Param # ================================================================ Conv2d-1 [-1, 64, 224, 224] 1792 ReLU-2 [-1, 64, 224, 224] 0 Conv2d-3 [-1, 64, 224, 224] 36928 ReLU-4 [-1, 64, 224, 224] 0 MaxPool2d-5 [-1, 64, 112, 112] 0 Conv2d-6 [-1, 128, 112, 112] 73856 ReLU-7 [-1, 128, 112, 112] 0 Conv2d-8 [-1, 128, 112, 112] 147584 ReLU-9 [-1, 128, 112, 112] 0 MaxPool2d-10 [-1, 128, 56, 56] 0 Conv2d-11 [-1, 256, 56, 56] 295168 ReLU-12 [-1, 256, 56, 56] 0 Conv2d-13 [-1, 256, 56, 56] 590080 ReLU-14 [-1, 256, 56, 56] 0 Conv2d-15 [-1, 256, 56, 56] 590080 ReLU-16 [-1, 256, 56, 56] 0 MaxPool2d-17 [-1, 256, 28, 28] 0 Conv2d-18 [-1, 512, 28, 28] 1180160 ReLU-19 [-1, 512, 28, 28] 0 Conv2d-20 [-1, 512, 28, 28] 2359808 ReLU-21 [-1, 512, 28, 28] 0 Conv2d-22 [-1, 512, 28, 28] 2359808 ReLU-23 [-1, 512, 28, 28] 0 MaxPool2d-24 [-1, 512, 14, 14] 0 Conv2d-25 [-1, 512, 14, 14] 2359808 ReLU-26 [-1, 512, 14, 14] 0 Conv2d-27 [-1, 512, 14, 14] 2359808 ReLU-28 [-1, 512, 14, 14] 0 Conv2d-29 [-1, 512, 14, 14] 2359808 ReLU-30 [-1, 512, 14, 14] 0 MaxPool2d-31 [-1, 512, 7, 7] 0 Linear-32 [-1, 4096] 102764544 ReLU-33 [-1, 4096] 0 Dropout-34 [-1, 4096] 0 Linear-35 [-1, 4096] 16781312 ReLU-36 [-1, 4096] 0 Dropout-37 [-1, 4096] 0 Linear-38 [-1, 1000] 4097000 ================================================================ Total params: 138357544 Trainable params: 138357544 Non-trainable params: 0 ----------------------------------------------------------------

You can use torchsummary, for instance, for ImageNet dimension(3x224x224):
from torchvision import models from torchsummary import summary
vgg = models.vgg16() summary(vgg, (3, 224, 224)

---------------------------------------------------------------- Layer (type) Output Shape Param # ================================================================ Conv2d-1 [-1, 64, 224, 224] 1,792 ReLU-2 [-1, 64, 224, 224] 0 Conv2d-3 [-1, 64, 224, 224] 36,928 ReLU-4 [-1, 64, 224, 224] 0 MaxPool2d-5 [-1, 64, 112, 112] 0 Conv2d-6 [-1, 128, 112, 112] 73,856 ReLU-7 [-1, 128, 112, 112] 0 Conv2d-8 [-1, 128, 112, 112] 147,584 ReLU-9 [-1, 128, 112, 112] 0 MaxPool2d-10 [-1, 128, 56, 56] 0 Conv2d-11 [-1, 256, 56, 56] 295,168 ReLU-12 [-1, 256, 56, 56] 0 Conv2d-13 [-1, 256, 56, 56] 590,080 ReLU-14 [-1, 256, 56, 56] 0 Conv2d-15 [-1, 256, 56, 56] 590,080 ReLU-16 [-1, 256, 56, 56] 0 MaxPool2d-17 [-1, 256, 28, 28] 0 Conv2d-18 [-1, 512, 28, 28] 1,180,160 ReLU-19 [-1, 512, 28, 28] 0 Conv2d-20 [-1, 512, 28, 28] 2,359,808 ReLU-21 [-1, 512, 28, 28] 0 Conv2d-22 [-1, 512, 28, 28] 2,359,808 ReLU-23 [-1, 512, 28, 28] 0 MaxPool2d-24 [-1, 512, 14, 14] 0 Conv2d-25 [-1, 512, 14, 14] 2,359,808 ReLU-26 [-1, 512, 14, 14] 0 Conv2d-27 [-1, 512, 14, 14] 2,359,808 ReLU-28 [-1, 512, 14, 14] 0 Conv2d-29 [-1, 512, 14, 14] 2,359,808 ReLU-30 [-1, 512, 14, 14] 0 MaxPool2d-31 [-1, 512, 7, 7] 0 Linear-32 [-1, 4096] 102,764,544 ReLU-33 [-1, 4096] 0 Dropout-34 [-1, 4096] 0 Linear-35 [-1, 4096] 16,781,312 ReLU-36 [-1, 4096] 0 Dropout-37 [-1, 4096] 0 Linear-38 [-1, 1000] 4,097,000 ================================================================ Total params: 138,357,544 Trainable params: 138,357,544 Non-trainable params: 0 ---------------------------------------------------------------- Input size (MB): 0.57 Forward/backward pass size (MB): 218.59 Params size (MB): 527.79 Estimated Total Size (MB): 746.96 ----------------------------------------------------------------
Source: [model-summary-in-pytorch]

[model-summary-in-pytorch]: https://stackoverflow.com/questions/42480111/model-summary-in-pytorch