PyTorch 查看模型结构：输出张量维度、参数个数 |
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PyTorch 查看模型结构：输出张量维度、参数个数¶
使用pytorch-summary实现Keras中model.summary()的类似效果。 在自定义网络结构时，我们可以用print(model)来查看网络的基本信息，但只能看到有哪些层，每一层是什么（BatchNorm2d,、MaxPool2d,、AvgPool2d 等等），并不能看到每一层的输出张量的维数、每一层有多少个参数。 而理解每一层输出张量的维数对于理解网络结构是至关重要的。Keras中的model.summary()可以实现这一效果。但这在PyTorch中不能直接做到，可以用torchsummary包实现类似的效果。 定义模型¶ Pythontest2 = Inception2(combination, combination_rev, index_list_2) print(model)查看简单的模型结构¶ Pythonprint(test2)print(model)只能查看简单的模型结构，不能看出每一层的输出张量的维数、每一层有多少个参数。 Text OnlyInception2( (bc1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (bc2): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (bc3): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (bc4): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (bc5): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (bc6): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (bc7): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (max_pool): MaxPool2d(kernel_size=(1, 10), stride=(1, 10), padding=0, dilation=1, ceil_mode=False) (avg_pool): AvgPool2d(kernel_size=(1, 10), stride=(1, 10), padding=0) (min_pool): MaxPool2d(kernel_size=(1, 10), stride=(1, 10), padding=0, dilation=1, ceil_mode=False) (bc_pool1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (bc_pool2): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (bc_pool3): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) torchsummary.summary(model, input_size)查看层类型、输出维度和参数个数¶ Pythonfrom torchsummary import summary summary(test2, input_size=(1, 9, 30)) Text Only---------------------------------------------------------------- Layer (type) Output Shape Param # ================================================================ BatchNorm2d-1 [-1, 1, 36, 10] 2 BatchNorm2d-2 [-1, 1, 36, 10] 2 BatchNorm2d-3 [-1, 1, 9, 10] 2 BatchNorm2d-4 [-1, 1, 9, 10] 2 BatchNorm2d-5 [-1, 1, 9, 10] 2 BatchNorm2d-6 [-1, 1, 9, 10] 2 BatchNorm2d-7 [-1, 1, 9, 10] 2 MaxPool2d-8 [-1, 1, 117, 1] 0 BatchNorm2d-9 [-1, 1, 117, 1] 2 AvgPool2d-10 [-1, 1, 117, 1] 0 BatchNorm2d-11 [-1, 1, 117, 1] 2 MaxPool2d-12 [-1, 1, 117, 1] 0 BatchNorm2d-13 [-1, 1, 117, 1] 2 ================================================================ Total params: 20 Trainable params: 20 Non-trainable params: 0 ---------------------------------------------------------------- Input size (MB): 0.00 Forward/backward pass size (MB): 0.01 Params size (MB): 0.00 Estimated Total Size (MB): 0.02 ---------------------------------------------------------------- 官方指南¶ 安装¶ Bashpip install torchsummary 导入包¶ Pythonfrom torchsummary import summary 官方示例¶ Pythonimport torch import torch.nn as nn import torch.nn.functional as F from torchsummary import summary class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=1) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # PyTorch v0.4.0 model = Net().to(device) summary(model, (1, 28, 28)) Text Only---------------------------------------------------------------- Layer (type) Output Shape Param # ================================================================ Conv2d-1 [-1, 10, 24, 24] 260 Conv2d-2 [-1, 20, 8, 8] 5,020 Dropout2d-3 [-1, 20, 8, 8] 0 Linear-4 [-1, 50] 16,050 Linear-5 [-1, 10] 510 ================================================================ Total params: 21,840 Trainable params: 21,840 Non-trainable params: 0 ---------------------------------------------------------------- Input size (MB): 0.00 Forward/backward pass size (MB): 0.06 Params size (MB): 0.08 Estimated Total Size (MB): 0.15 ----------------------------------------------------------------更多指南可参考https://github.com/sksq96/pytorch-summary 。 评论 |
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