pytorch可视化

pytorch快速查看网络结构

直接查看

这里是一个小栗子

import torch
from torch import nn
from torch.utils.data import DataLoader,Dataset
import torchvision.models as models
from torchinfo import summary
##定义模型
model = nn.LSTM(input_size=10,hidden_size=20,batch_first=True)
print(model)

OUTPUT:
LSTM(10, 20, batch_first=True)

下面是resnet 的网络

resnet = models.resnet18()
resnet

#OUTPUT:
ResNet(
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU(inplace=True)
  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  (layer1): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (1): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (layer2): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (downsample): Sequential(
        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (layer3): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (downsample): Sequential(
        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (layer4): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (downsample): Sequential(
        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
  (fc): Linear(in_features=512, out_features=1000, bias=True)
)

使用torch_info

from torchinfo import summary
summary(resnet,(1,3,224,224))

#OUTPUT:
==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
ResNet                                   [1, 1000]                 --
├─Conv2d: 1-1                            [1, 64, 112, 112]         9,408
├─BatchNorm2d: 1-2                       [1, 64, 112, 112]         128
├─ReLU: 1-3                              [1, 64, 112, 112]         --
├─MaxPool2d: 1-4                         [1, 64, 56, 56]           --
├─Sequential: 1-5                        [1, 64, 56, 56]           --
│    └─BasicBlock: 2-1                   [1, 64, 56, 56]           --
│    │    └─Conv2d: 3-1                  [1, 64, 56, 56]           36,864
│    │    └─BatchNorm2d: 3-2             [1, 64, 56, 56]           128
│    │    └─ReLU: 3-3                    [1, 64, 56, 56]           --
│    │    └─Conv2d: 3-4                  [1, 64, 56, 56]           36,864
│    │    └─BatchNorm2d: 3-5             [1, 64, 56, 56]           128
│    │    └─ReLU: 3-6                    [1, 64, 56, 56]           --
│    └─BasicBlock: 2-2                   [1, 64, 56, 56]           --
│    │    └─Conv2d: 3-7                  [1, 64, 56, 56]           36,864
│    │    └─BatchNorm2d: 3-8             [1, 64, 56, 56]           128
│    │    └─ReLU: 3-9                    [1, 64, 56, 56]           --
│    │    └─Conv2d: 3-10                 [1, 64, 56, 56]           36,864
│    │    └─BatchNorm2d: 3-11            [1, 64, 56, 56]           128
│    │    └─ReLU: 3-12                   [1, 64, 56, 56]           --
├─Sequential: 1-6                        [1, 128, 28, 28]          --
│    └─BasicBlock: 2-3                   [1, 128, 28, 28]          --
│    │    └─Conv2d: 3-13                 [1, 128, 28, 28]          73,728
│    │    └─BatchNorm2d: 3-14            [1, 128, 28, 28]          256
│    │    └─ReLU: 3-15                   [1, 128, 28, 28]          --
│    │    └─Conv2d: 3-16                 [1, 128, 28, 28]          147,456
│    │    └─BatchNorm2d: 3-17            [1, 128, 28, 28]          256
│    │    └─Sequential: 3-18             [1, 128, 28, 28]          8,448
│    │    └─ReLU: 3-19                   [1, 128, 28, 28]          --
│    └─BasicBlock: 2-4                   [1, 128, 28, 28]          --
│    │    └─Conv2d: 3-20                 [1, 128, 28, 28]          147,456
│    │    └─BatchNorm2d: 3-21            [1, 128, 28, 28]          256
│    │    └─ReLU: 3-22                   [1, 128, 28, 28]          --
│    │    └─Conv2d: 3-23                 [1, 128, 28, 28]          147,456
│    │    └─BatchNorm2d: 3-24            [1, 128, 28, 28]          256
│    │    └─ReLU: 3-25                   [1, 128, 28, 28]          --
├─Sequential: 1-7                        [1, 256, 14, 14]          --
│    └─BasicBlock: 2-5                   [1, 256, 14, 14]          --
│    │    └─Conv2d: 3-26                 [1, 256, 14, 14]          294,912
│    │    └─BatchNorm2d: 3-27            [1, 256, 14, 14]          512
│    │    └─ReLU: 3-28                   [1, 256, 14, 14]          --
│    │    └─Conv2d: 3-29                 [1, 256, 14, 14]          589,824
│    │    └─BatchNorm2d: 3-30            [1, 256, 14, 14]          512
│    │    └─Sequential: 3-31             [1, 256, 14, 14]          33,280
│    │    └─ReLU: 3-32                   [1, 256, 14, 14]          --
│    └─BasicBlock: 2-6                   [1, 256, 14, 14]          --
│    │    └─Conv2d: 3-33                 [1, 256, 14, 14]          589,824
│    │    └─BatchNorm2d: 3-34            [1, 256, 14, 14]          512
│    │    └─ReLU: 3-35                   [1, 256, 14, 14]          --
│    │    └─Conv2d: 3-36                 [1, 256, 14, 14]          589,824
│    │    └─BatchNorm2d: 3-37            [1, 256, 14, 14]          512
│    │    └─ReLU: 3-38                   [1, 256, 14, 14]          --
├─Sequential: 1-8                        [1, 512, 7, 7]            --
│    └─BasicBlock: 2-7                   [1, 512, 7, 7]            --
│    │    └─Conv2d: 3-39                 [1, 512, 7, 7]            1,179,648
│    │    └─BatchNorm2d: 3-40            [1, 512, 7, 7]            1,024
│    │    └─ReLU: 3-41                   [1, 512, 7, 7]            --
│    │    └─Conv2d: 3-42                 [1, 512, 7, 7]            2,359,296
│    │    └─BatchNorm2d: 3-43            [1, 512, 7, 7]            1,024
│    │    └─Sequential: 3-44             [1, 512, 7, 7]            132,096
│    │    └─ReLU: 3-45                   [1, 512, 7, 7]            --
│    └─BasicBlock: 2-8                   [1, 512, 7, 7]            --
│    │    └─Conv2d: 3-46                 [1, 512, 7, 7]            2,359,296
│    │    └─BatchNorm2d: 3-47            [1, 512, 7, 7]            1,024
│    │    └─ReLU: 3-48                   [1, 512, 7, 7]            --
│    │    └─Conv2d: 3-49                 [1, 512, 7, 7]            2,359,296
│    │    └─BatchNorm2d: 3-50            [1, 512, 7, 7]            1,024
│    │    └─ReLU: 3-51                   [1, 512, 7, 7]            --
├─AdaptiveAvgPool2d: 1-9                 [1, 512, 1, 1]            --
├─Linear: 1-10                           [1, 1000]                 513,000
==========================================================================================
Total params: 11,689,512
Trainable params: 11,689,512
Non-trainable params: 0
Total mult-adds (G): 1.81
==========================================================================================
Input size (MB): 0.60
Forward/backward pass size (MB): 39.75
Params size (MB): 46.76
Estimated Total Size (MB): 87.11
==========================================================================================

LSTM_info

summary(model,(20,2,10))

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
LSTM                                     [20, 2, 20]               2,560
==========================================================================================
Total params: 2,560
Trainable params: 2,560
Non-trainable params: 0
Total mult-adds (M): 0.10
==========================================================================================
Input size (MB): 0.00
Forward/backward pass size (MB): 0.01
Params size (MB): 0.01
Estimated Total Size (MB): 0.02
==========================================================================================

TensorBoard可视化网络结构/loss

from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter('./runs')

LSTM network for time series

class Model(nn.Module):
    def __init__(self, input_size):
        hidden = [400, 300, 200, 100]
        super().__init__()
        self.lstm1 = nn.LSTM(input_size, hidden[0],
                             batch_first=True, bidirectional=True)
        self.lstm2 = nn.LSTM(2 * hidden[0], hidden[1],
                             batch_first=True, bidirectional=True)
        self.lstm3 = nn.LSTM(2 * hidden[1], hidden[2],
                             batch_first=True, bidirectional=True)
        self.lstm4 = nn.LSTM(2 * hidden[2], hidden[3],
                             batch_first=True, bidirectional=True)
        self.fc1 = nn.Linear(2 * hidden[3], 50)
        self.selu = nn.SELU()
        self.fc2 = nn.Linear(50, 1)
        self._reinitialize()

    def _reinitialize(self):
        """
        Tensorflow/Keras-like initialization
        """
        for name, p in self.named_parameters():
            if 'lstm' in name:
                if 'weight_ih' in name:
                    nn.init.xavier_uniform_(p.data)
                elif 'weight_hh' in name:
                    nn.init.orthogonal_(p.data)
                elif 'bias_ih' in name:
                    p.data.fill_(0)
                    # Set forget-gate bias to 1
                    n = p.size(0)
                    p.data[(n // 4):(n // 2)].fill_(1)
                elif 'bias_hh' in name:
                    p.data.fill_(0)
            elif 'fc' in name:
                if 'weight' in name:
                    nn.init.xavier_uniform_(p.data)
                elif 'bias' in name:
                    p.data.fill_(0)

    def forward(self, x):
        x, _ = self.lstm1(x)
        x, _ = self.lstm2(x)
        x, _ = self.lstm3(x)
        x, _ = self.lstm4(x)
        x = self.fc1(x)
        x = self.selu(x)
        x = self.fc2(x)

        return x

#结构参数
==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
Model                                    [10, 80, 1]               --
├─LSTM: 1-1                              [10, 80, 800]             1,318,400
├─LSTM: 1-2                              [10, 80, 600]             2,644,800
├─LSTM: 1-3                              [10, 80, 400]             1,283,200
├─LSTM: 1-4                              [10, 80, 200]             401,600
├─Linear: 1-5                            [10, 80, 50]              10,050
├─SELU: 1-6                              [10, 80, 50]              --
├─Linear: 1-7                            [10, 80, 1]               51
==========================================================================================
Total params: 5,658,101
Trainable params: 5,658,101
Non-trainable params: 0
Total mult-adds (G): 4.52
==========================================================================================
Input size (MB): 0.03
Forward/backward pass size (MB): 13.13
Params size (MB): 22.63
Estimated Total Size (MB): 35.79
==========================================================================================

再使用如下代码

writer.add_graph(model,input_to_model=torch.randn(40,29,10))
writer.close()

#最后在终端输入
tensorboard --logdir=runs
#得到
TensorBoard 1.15.0 at http://Tears:6006/ (Press CTRL+C to quit)

就能得到下图了