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https://github.com/sksq96/pytorch-summary
Model summary in PyTorch similar to `model.summary()` in Keras
https://github.com/sksq96/pytorch-summary
deep-learning keras pytorch summary
Last synced: 6 days ago
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Model summary in PyTorch similar to `model.summary()` in Keras
- Host: GitHub
- URL: https://github.com/sksq96/pytorch-summary
- Owner: sksq96
- License: mit
- Created: 2018-04-23T13:58:04.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-03-02T15:10:25.000Z (11 months ago)
- Last Synced: 2024-12-31T12:09:17.613Z (13 days ago)
- Topics: deep-learning, keras, pytorch, summary
- Language: Python
- Size: 43 KB
- Stars: 4,022
- Watchers: 37
- Forks: 416
- Open Issues: 139
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Use the new and updated [torchinfo](https://github.com/TylerYep/torchinfo).
## Keras style `model.summary()` in PyTorch
[](https://badge.fury.io/py/torchsummary)
Keras has a neat API to view the visualization of the model which is very helpful while debugging your network. Here is a barebone code to try and mimic the same in PyTorch. The aim is to provide information complementary to, what is not provided by `print(your_model)` in PyTorch.
### Usage
- `pip install torchsummary` or
- `git clone https://github.com/sksq96/pytorch-summary`
```python
from torchsummary import summary
summary(your_model, input_size=(channels, H, W))
```
- Note that the `input_size` is required to make a forward pass through the network.
### Examples
#### CNN for MNIST
```python
import 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))
```
```
----------------------------------------------------------------
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
----------------------------------------------------------------
```
#### VGG16
```python
import torch
from torchvision import models
from torchsummary import summary
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
vgg = models.vgg16().to(device)
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
----------------------------------------------------------------
```
#### Multiple Inputs
```python
import torch
import torch.nn as nn
from torchsummary import summary
class SimpleConv(nn.Module):
def __init__(self):
super(SimpleConv, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
)
def forward(self, x, y):
x1 = self.features(x)
x2 = self.features(y)
return x1, x2
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = SimpleConv().to(device)
summary(model, [(1, 16, 16), (1, 28, 28)])
```
```
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 1, 16, 16] 10
ReLU-2 [-1, 1, 16, 16] 0
Conv2d-3 [-1, 1, 28, 28] 10
ReLU-4 [-1, 1, 28, 28] 0
================================================================
Total params: 20
Trainable params: 20
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.77
Forward/backward pass size (MB): 0.02
Params size (MB): 0.00
Estimated Total Size (MB): 0.78
----------------------------------------------------------------
```
### References
- The idea for this package sparked from [this PyTorch issue](https://github.com/pytorch/pytorch/issues/2001).
- Thanks to @ncullen93 and @HTLife.
- For Model Size Estimation @jacobkimmel ([details here](https://github.com/sksq96/pytorch-summary/pull/21))
### License
`pytorch-summary` is MIT-licensed.