Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/gsauc3/pytorch-model-details
Package to visualize all the layer parameters of a deep learning model.
https://github.com/gsauc3/pytorch-model-details
cnn-model deep-learning deep-neural-networks model-summary pytorch summary
Last synced: about 4 hours ago
JSON representation
Package to visualize all the layer parameters of a deep learning model.
- Host: GitHub
- URL: https://github.com/gsauc3/pytorch-model-details
- Owner: GSAUC3
- License: mit
- Created: 2022-08-07T10:45:51.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2022-09-03T18:09:10.000Z (about 2 years ago)
- Last Synced: 2024-10-06T12:47:24.217Z (about 1 month ago)
- Topics: cnn-model, deep-learning, deep-neural-networks, model-summary, pytorch, summary
- Language: Python
- Homepage: https://pypi.org/project/pytorchsummary/
- Size: 49.8 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: license.txt
Awesome Lists containing this project
README
# PyTorch Model Parameters Summary
#### Install using pip
```
pip install pytorchsummary
```
## WORKS ON CNNs and MLPs
NOTE: `summary()` function returns a Tuple like `(Total_trainable_params, Total_parameters, Total_non_trainable_params)`
## Example 1
```python
from torch import nn
from pytorchsummary import summary
class CNNET(nn.Module):
def __init__(self):
super(CNNET,self).__init__()
self.layer = nn.Sequential(
nn.Conv2d(3,16,5), # 28-5+1
nn.ReLU(), #24
nn.MaxPool2d(2,2), # 12
nn.Conv2d(16,32,3), # 12+1-3
nn.ReLU(), # 10
nn.MaxPool2d(2,2), # 5
nn.Conv2d(32,64,5), # 11-3+1
nn.ReLU(),
nn.Conv2d(64,10,1)
)
def forward(self,x):
x = self.layer(x)
return x
m = CNNET()
summary((3,128,128),m)
```
### Output
``` Layer Output Shape Kernal Shape #params #(weights + bias) requires_grad
------------------------------------------------------------------------------------------------------------------------------------------------------
Conv2d-1 [1, 16, 124, 124] [16, 3, 5, 5] 1216 (1200 + 16) True True
ReLU-2 [1, 16, 124, 124]
MaxPool2d-3 [1, 16, 62, 62]
Conv2d-4 [1, 32, 60, 60] [32, 16, 3, 3] 4640 (4608 + 32) True True
ReLU-5 [1, 32, 60, 60]
MaxPool2d-6 [1, 32, 30, 30]
Conv2d-7 [1, 64, 26, 26] [64, 32, 5, 5] 51264 (51200 + 64) True True
ReLU-8 [1, 64, 26, 26]
Conv2d-9 [1, 10, 26, 26] [10, 64, 1, 1] 650 (640 + 10) True True
______________________________________________________________________________________________________________________________________________________
Total parameters 57,770
Total Non-Trainable parameters 0
Total Trainable parameters 57,770
(57770, 57770, 0)
```
```python
for i,j in enumerate(m.parameters()):
if i==2:
break
j.requires_grad=False
summary((3,128,128),m,border=True)
```
```
Layer Output Shape Kernal Shape #params #(weights + bias) requires_grad
------------------------------------------------------------------------------------------------------------------------------------------------------
Conv2d-1 [1, 16, 124, 124] [16, 3, 5, 5] 1216 (1200 + 16) False False
______________________________________________________________________________________________________________________________________________________
ReLU-2 [1, 16, 124, 124]
______________________________________________________________________________________________________________________________________________________
MaxPool2d-3 [1, 16, 62, 62]
______________________________________________________________________________________________________________________________________________________
Conv2d-4 [1, 32, 60, 60] [32, 16, 3, 3] 4640 (4608 + 32) True True
______________________________________________________________________________________________________________________________________________________
ReLU-5 [1, 32, 60, 60]
______________________________________________________________________________________________________________________________________________________
MaxPool2d-6 [1, 32, 30, 30]
______________________________________________________________________________________________________________________________________________________
Conv2d-7 [1, 64, 26, 26] [64, 32, 5, 5] 51264 (51200 + 64) True True
______________________________________________________________________________________________________________________________________________________
ReLU-8 [1, 64, 26, 26]
______________________________________________________________________________________________________________________________________________________
Conv2d-9 [1, 10, 26, 26] [10, 64, 1, 1] 650 (640 + 10) True True
______________________________________________________________________________________________________________________________________________________
______________________________________________________________________________________________________________________________________________________
Total parameters 57,770
Total Non-Trainable parameters 1,216
Total Trainable parameters 56,554
(56554, 57770, 1216)
```
## Example 2
```python
from torchvision import models
from pytorchsummary import summary
m = models.alexnet(False)
summary((3,224,224),m)
# this function returns the total number of
# parameters (int) in a model
```
### ouput
```
Layer Output Shape Kernal Shape #params #(weights + bias) requires_grad
------------------------------------------------------------------------------------------------------------------------------------------------------
Conv2d-1 [1, 64, 55, 55] [64, 3, 11, 11] 23296 (23232 + 64) True True
ReLU-2 [1, 64, 55, 55]
MaxPool2d-3 [1, 64, 27, 27]
Conv2d-4 [1, 192, 27, 27] [192, 64, 5, 5] 307392 (307200 + 192) True True
ReLU-5 [1, 192, 27, 27]
MaxPool2d-6 [1, 192, 13, 13]
Conv2d-7 [1, 384, 13, 13] [384, 192, 3, 3] 663936 (663552 + 384) True True
ReLU-8 [1, 384, 13, 13]
Conv2d-9 [1, 256, 13, 13] [256, 384, 3, 3] 884992 (884736 + 256) True True
ReLU-10 [1, 256, 13, 13]
Conv2d-11 [1, 256, 13, 13] [256, 256, 3, 3] 590080 (589824 + 256) True True
ReLU-12 [1, 256, 13, 13]
MaxPool2d-13 [1, 256, 6, 6]
AdaptiveAvgPool2d-14 [1, 256, 6, 6]
Dropout-15 [1, 9216]
Linear-16 [1, 4096] [4096, 9216] 37752832 (37748736 + 4096) True True
ReLU-17 [1, 4096]
Dropout-18 [1, 4096]
Linear-19 [1, 4096] [4096, 4096] 16781312 (16777216 + 4096) True True
ReLU-20 [1, 4096]
Linear-21 [1, 1000] [1000, 4096] 4097000 (4096000 + 1000) True True
______________________________________________________________________________________________________________________________________________________
Total parameters 61,100,840
Total Non-Trainable parameters 0
Total Trainable parameters 61,100,840
(61100840, 61100840, 0)
```
### Calculating the number of specific layer, or layer frequencies
```python
from pytorchsummary import get_num_layers
print(get_num_layers(m)) # alexnet model
```
Output:
```
{'Conv2d': 5,
'ReLU': 7,
'MaxPool2d': 3,
'AdaptiveAvgPool2d': 1,
'Dropout': 2,
'Linear': 3}
```
# Example For MLPs
```python
from pytorchsummary import summary
from torch import nn
class Net(nn.Module):
def __init__(self):
super(Net,self).__init__()
self.l = nn.Sequential(
nn.Linear(18,16),
nn.ReLU(),
nn.Linear(16,8),
nn.ReLU(),
nn.Linear(8,4)
)
def forward(self,x):
return self.l(x)
model = Net()
summary((18,),model)
```
`summary()` function takes inputsize as a tuple so
**if len(input_size)==1**
you have to use `,` like this
`((input_size,))`
Otherwise it will throw an error
### output
```
Layer Output Shape Kernal Shape #params #(weights + bias) requires_grad
------------------------------------------------------------------------------------------------------------------------------------------------------
Linear-1 [1, 16] [16, 18] 304 (288 + 16) True True
ReLU-2 [1, 16]
Linear-3 [1, 8] [8, 16] 136 (128 + 8) True True
ReLU-4 [1, 8]
Linear-5 [1, 4] [4, 8] 36 (32 + 4) True True
______________________________________________________________________________________________________________________________________________________
Total parameters 476
Total Non-Trainable parameters 0
Total Trainable parameters 476
(476, 476, 0)
```