Pytorch - How to run inference of a model after thinning the model without the model.py file?
I am having a pytorch model with the following network structure.
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
class lenet_mnist(nn.Module):
def __init__(self):
super(lenet_mnist, self).__init__()
self.cuda()
self.conv1 = nn.Conv2d(1, 16, kernel_size=3,padding=1)
self.relu1 = nn.ReLU(inplace=True)
self.relu2 = nn.ReLU(inplace=True)
self.relu3 = nn.ReLU(inplace=True)
self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=5,padding=2)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(1568, 256)
self.fc2 = nn.Linear(256, 10)
def forward(self,x):
#x = x.to(torch.device("cuda:0"))
out = self.conv1(x)
out = self.maxpool1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.conv2_drop(out)
out = self.maxpool2(out)
out = self.relu2(out)
out = out.view(-1,1568)
out = self.fc1(out)
out = self.fc2(out)
return out
I train this model and I remove few channels in the model, for example I made the self.conv2 as (in=16,out=24). I reloaded the state_dict model and modified the model for the new structure.
Now , I am trying to run an inference of the model, but pytorch still picks up the old model.py file where the original structure is defined. How do I stop pytorch looking for model.py?
Edit: (more information)
For thinning the model, I just get the state_dict of the model, edit the tensors by deleting few channels in it and then use load_state_dict on the model to load the model again. I also change the model._parameters with the updated weights and bias. I use this model to run a forward pass. While running, I get an error saying mismatch in dimensions at out.view layer. And, the backtrace shows that the pytorch is reading the model from model.py file where the above code snippet is present.
python conv-neural-network pytorch
asked Nov 19 '18 at 7:55
vsooryavsoorya
13
add a comment |
I am having a pytorch model with the following network structure.
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
class lenet_mnist(nn.Module):
def __init__(self):
super(lenet_mnist, self).__init__()
self.cuda()
self.conv1 = nn.Conv2d(1, 16, kernel_size=3,padding=1)
self.relu1 = nn.ReLU(inplace=True)
self.relu2 = nn.ReLU(inplace=True)
self.relu3 = nn.ReLU(inplace=True)
self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=5,padding=2)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(1568, 256)
self.fc2 = nn.Linear(256, 10)
def forward(self,x):
#x = x.to(torch.device("cuda:0"))
out = self.conv1(x)
out = self.maxpool1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.conv2_drop(out)
out = self.maxpool2(out)
out = self.relu2(out)
out = out.view(-1,1568)
out = self.fc1(out)
out = self.fc2(out)
return out
I train this model and I remove few channels in the model, for example I made the self.conv2 as (in=16,out=24). I reloaded the state_dict model and modified the model for the new structure.
Now , I am trying to run an inference of the model, but pytorch still picks up the old model.py file where the original structure is defined. How do I stop pytorch looking for model.py?
Edit: (more information)
For thinning the model, I just get the state_dict of the model, edit the tensors by deleting few channels in it and then use load_state_dict on the model to load the model again. I also change the model._parameters with the updated weights and bias. I use this model to run a forward pass. While running, I get an error saying mismatch in dimensions at out.view layer. And, the backtrace shows that the pytorch is reading the model from model.py file where the above code snippet is present.
python conv-neural-network pytorch
asked Nov 19 '18 at 7:55
vsooryavsoorya
13
Do you use a jupyter notebook?
– artona
Nov 19 '18 at 8:19
no. I just use vim
– vsoorya
Nov 19 '18 at 8:20
Please provide the code with removing channels and way you are using the new model.
– artona
Nov 19 '18 at 8:23
+ your project structure of files.
– artona
Nov 19 '18 at 8:41
Without the code of new model I cannot tell much about the error.
– artona
Nov 19 '18 at 8:52
add a comment |
I am having a pytorch model with the following network structure.
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
class lenet_mnist(nn.Module):
def __init__(self):
super(lenet_mnist, self).__init__()
self.cuda()
self.conv1 = nn.Conv2d(1, 16, kernel_size=3,padding=1)
self.relu1 = nn.ReLU(inplace=True)
self.relu2 = nn.ReLU(inplace=True)
self.relu3 = nn.ReLU(inplace=True)
self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=5,padding=2)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(1568, 256)
self.fc2 = nn.Linear(256, 10)
def forward(self,x):
#x = x.to(torch.device("cuda:0"))
out = self.conv1(x)
out = self.maxpool1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.conv2_drop(out)
out = self.maxpool2(out)
out = self.relu2(out)
out = out.view(-1,1568)
out = self.fc1(out)
out = self.fc2(out)
return out
I train this model and I remove few channels in the model, for example I made the self.conv2 as (in=16,out=24). I reloaded the state_dict model and modified the model for the new structure.
Now , I am trying to run an inference of the model, but pytorch still picks up the old model.py file where the original structure is defined. How do I stop pytorch looking for model.py?
Edit: (more information)
For thinning the model, I just get the state_dict of the model, edit the tensors by deleting few channels in it and then use load_state_dict on the model to load the model again. I also change the model._parameters with the updated weights and bias. I use this model to run a forward pass. While running, I get an error saying mismatch in dimensions at out.view layer. And, the backtrace shows that the pytorch is reading the model from model.py file where the above code snippet is present.
python conv-neural-network pytorch
asked Nov 19 '18 at 7:55
vsooryavsoorya
13
I am having a pytorch model with the following network structure.
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
class lenet_mnist(nn.Module):
def __init__(self):
super(lenet_mnist, self).__init__()
self.cuda()
self.conv1 = nn.Conv2d(1, 16, kernel_size=3,padding=1)
self.relu1 = nn.ReLU(inplace=True)
self.relu2 = nn.ReLU(inplace=True)
self.relu3 = nn.ReLU(inplace=True)
self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=5,padding=2)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(1568, 256)
self.fc2 = nn.Linear(256, 10)
def forward(self,x):
#x = x.to(torch.device("cuda:0"))
out = self.conv1(x)
out = self.maxpool1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.conv2_drop(out)
out = self.maxpool2(out)
out = self.relu2(out)
out = out.view(-1,1568)
out = self.fc1(out)
out = self.fc2(out)
return out
I train this model and I remove few channels in the model, for example I made the self.conv2 as (in=16,out=24). I reloaded the state_dict model and modified the model for the new structure.
Now , I am trying to run an inference of the model, but pytorch still picks up the old model.py file where the original structure is defined. How do I stop pytorch looking for model.py?
Edit: (more information)
For thinning the model, I just get the state_dict of the model, edit the tensors by deleting few channels in it and then use load_state_dict on the model to load the model again. I also change the model._parameters with the updated weights and bias. I use this model to run a forward pass. While running, I get an error saying mismatch in dimensions at out.view layer. And, the backtrace shows that the pytorch is reading the model from model.py file where the above code snippet is present.
python conv-neural-network pytorch
python conv-neural-network pytorch
asked Nov 19 '18 at 7:55
vsooryavsoorya
13
asked Nov 19 '18 at 7:55
vsooryavsoorya
13
edited Nov 19 '18 at 8:47
vsoorya
asked Nov 19 '18 at 7:55
vsooryavsoorya
13
asked Nov 19 '18 at 7:55
vsooryavsoorya
13
asked Nov 19 '18 at 7:55
vsooryavsoorya
13
13
Do you use a jupyter notebook?
– artona
Nov 19 '18 at 8:19
no. I just use vim
– vsoorya
Nov 19 '18 at 8:20
Please provide the code with removing channels and way you are using the new model.
– artona
Nov 19 '18 at 8:23
+ your project structure of files.
– artona
Nov 19 '18 at 8:41
Without the code of new model I cannot tell much about the error.
– artona
Nov 19 '18 at 8:52
add a comment |
Do you use a jupyter notebook?
– artona
Nov 19 '18 at 8:19
no. I just use vim
– vsoorya
Nov 19 '18 at 8:20
Please provide the code with removing channels and way you are using the new model.
– artona
Nov 19 '18 at 8:23
+ your project structure of files.
– artona
Nov 19 '18 at 8:41
Without the code of new model I cannot tell much about the error.
– artona
Nov 19 '18 at 8:52
Do you use a jupyter notebook?
– artona
Nov 19 '18 at 8:19
Do you use a jupyter notebook?
– artona
Nov 19 '18 at 8:19
no. I just use vim
– vsoorya
Nov 19 '18 at 8:20
no. I just use vim
– vsoorya
Nov 19 '18 at 8:20
Please provide the code with removing channels and way you are using the new model.
– artona
Nov 19 '18 at 8:23
Please provide the code with removing channels and way you are using the new model.
– artona
Nov 19 '18 at 8:23
+ your project structure of files.
– artona
Nov 19 '18 at 8:41
+ your project structure of files.
– artona
Nov 19 '18 at 8:41
Without the code of new model I cannot tell much about the error.
– artona
Nov 19 '18 at 8:52
Without the code of new model I cannot tell much about the error.
– artona
Nov 19 '18 at 8:52
add a comment |
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Do you use a jupyter notebook?
– artona
Nov 19 '18 at 8:19
no. I just use vim
– vsoorya
Nov 19 '18 at 8:20
Please provide the code with removing channels and way you are using the new model.
– artona
Nov 19 '18 at 8:23
+ your project structure of files.
– artona
Nov 19 '18 at 8:41
Without the code of new model I cannot tell much about the error.
– artona
Nov 19 '18 at 8:52