pytorch summary
1.0.0
model.summary() في Pytorch لدى Keras واجهة برمجة تطبيقات أنيقة لعرض تصور النموذج وهو مفيد للغاية أثناء تصحيح شبكتك. فيما يلي رمز Barebone لمحاولة تقليد الشيء نفسه في Pytorch. الهدف من ذلك هو توفير المعلومات التكميلية لما لا يتم توفيره عن طريق print(your_model) في pytorch.
pip install torchsummary أوgit clone https://github.com/sksq96/pytorch-summary from torchsummary import summary
summary ( your_model , input_size = ( channels , H , W ))input_size مطلوب لإجراء تمرير للأمام عبر الشبكة. 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
----------------------------------------------------------------
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
----------------------------------------------------------------
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
----------------------------------------------------------------
pytorch-summary هو مرخص له معهد ماساتشوستس للتكنولوجيا.
