torchinfo(以前的torchsummary)包产生类似Keras1的输出(对于给定的输入形状):2

from torchinfo import summary

model = ConvNet()
batch_size = 16
summary(model, input_size=(batch_size, 1, 28, 28))

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
├─Conv2d (conv1): 1-1                    [5, 10, 24, 24]           260
├─Conv2d (conv2): 1-2                    [5, 20, 8, 8]             5,020
├─Dropout2d (conv2_drop): 1-3            [5, 20, 8, 8]             --
├─Linear (fc1): 1-4                      [5, 50]                   16,050
├─Linear (fc2): 1-5                      [5, 10]                   510
==========================================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
Total mult-adds (M): 7.69
==========================================================================================
Input size (MB): 0.05
Forward/backward pass size (MB): 0.91
Params size (MB): 0.09
Estimated Total Size (MB): 1.05
==========================================================================================

Notes: Torchinfo provides information complementary to what is provided by print(your_model) in PyTorch, similar to Tensorflow's model.summary()... Unlike Keras, PyTorch has a dynamic computational graph which can adapt to any compatible input shape across multiple calls e.g. any sufficiently large image size (for a fully convolutional network). As such, it cannot present an inherent set of input/output shapes for each layer, as these are input-dependent, and why in the above package you must specify the input dimensions.

Summary (my_model， (3, 224, 224)， device = 'cpu')可以解决这个问题。

torchinfo(以前的torchsummary)包产生类似Keras1的输出(对于给定的输入形状):2

from torchinfo import summary

model = ConvNet()
batch_size = 16
summary(model, input_size=(batch_size, 1, 28, 28))

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
├─Conv2d (conv1): 1-1                    [5, 10, 24, 24]           260
├─Conv2d (conv2): 1-2                    [5, 20, 8, 8]             5,020
├─Dropout2d (conv2_drop): 1-3            [5, 20, 8, 8]             --
├─Linear (fc1): 1-4                      [5, 50]                   16,050
├─Linear (fc2): 1-5                      [5, 10]                   510
==========================================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
Total mult-adds (M): 7.69
==========================================================================================
Input size (MB): 0.05
Forward/backward pass size (MB): 0.91
Params size (MB): 0.09
Estimated Total Size (MB): 1.05
==========================================================================================

Notes: Torchinfo provides information complementary to what is provided by print(your_model) in PyTorch, similar to Tensorflow's model.summary()... Unlike Keras, PyTorch has a dynamic computational graph which can adapt to any compatible input shape across multiple calls e.g. any sufficiently large image size (for a fully convolutional network). As such, it cannot present an inherent set of input/output shapes for each layer, as these are input-dependent, and why in the above package you must specify the input dimensions.

Keras模型总结使用torchsummary:

from torchsummary import summary
summary(model, input_size=(3, 224, 224))

我喜欢简单一点 那么PIP安装电筒镜了吗

from torchvision.models import resnet18
from torchscope import scope

model = resnet18()
scope(model, input_size=(3, 224, 224))

你可以使用

from torchsummary import summary

你可以指定设备

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

您可以创建一个网络，如果您正在使用MNIST数据集，那么以下命令将工作并显示摘要

model = Network().to(device)
summary(model,(1,28,28))