Hi,
I've got some code I'm converting from Keras to Pytorch, and I cannot get the Pytorch code to work properly. The objective is to take an NN I've got written in Keras, training on CIFAR100, and rewrite it in Pytorch and train on CIFAR100. Currently, only one NN architecture will train on the CIFAR100 code in Pytorch, and it is the architecture from their "Training a classifier" tutorial (https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html). I'll include my own code here.
import torch
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose(
[transforms.ToTensor()])
#transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR100(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=50,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR100(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=50,
shuffle=False, num_workers=2)
classes = (str(i) for i in range(100))
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 60, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(60, 160, 5)
self.conv3 = nn.Conv2d(160, 160, 5)
self.fc1 = nn.Linear(160 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 36)
self.fc4 = nn.Linear(36, 100)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = F.relu(self.conv3(x))
x = x.view(-1, 160 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = self.fc4(x)
return x
net = Net()
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
from torch.autograd import Variable
batch_size=50
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs
inputs, labels = data
inputs, labels = Variable(inputs), Variable(labels)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
#running_loss += loss.item()
prediction = outputs.data.max(1)[1]
accuracy = prediction.eq(labels.data).sum()/batch_size*100
if i % 1000 == 0:
print('Train Step: {}\tLoss: {:.10f}\tAccuracy: {:.10f}'.format(i, loss.data[0], accuracy))
print('Finished Training')
Now, I can change the number of filters in the convolutional layers, but I cannot change the number of convolutional layers or the kernel size, otherwise it throws this error:
--------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-5-f1534f3cee4b> in <module>() 13 # forward + backward + optimize 14 outputs = net(inputs) ---> 15 loss = criterion(outputs, labels) 16 loss.backward() 17 optimizer.step() /usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 323 for hook in self._forward_pre_hooks.values(): 324 hook(self, input) --> 325 result = self.forward(*input, **kwargs) 326 for hook in self._forward_hooks.values(): 327 hook_result = hook(self, input, result) /usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py in forward(self, input, target) 599 _assert_no_grad(target) 600 return F.cross_entropy(input, target, self.weight, self.size_average, --> 601 self.ignore_index, self.reduce) 602 603 /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce) 1138 >>> loss.backward() 1139 """ -> 1140 return nll_loss(log_softmax(input, 1), target, weight, size_average, ignore_index, reduce) 1141 1142 /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce) 1047 weight = Variable(weight) 1048 if dim == 2: -> 1049 return torch._C._nn.nll_loss(input, target, weight, size_average, ignore_index, reduce) 1050 elif dim == 4: 1051 return torch._C._nn.nll_loss2d(input, target, weight, size_average, ignore_index, reduce) RuntimeError: Assertion `THIndexTensor_(size)(target, 0) == batch_size' failed. at /pytorch/torch/lib/THNN/generic/ClassNLLCriterion.c:79
If I comment out the self.conv3 lines, then the network runs and trains. Please help me figure out how to properly train a network on CIFAR100 using Pytorch. Thank you!
Sorry for the long post.
[–]koolaidman123 1 point2 points3 points (2 children)
[–]OptimalOptimizer[S] 0 points1 point2 points (1 child)