print(output.size())
print(target.size())
print(input.size())
torch.Size([15])
torch.Size([1])
torch.Size([1, 3, 100, 133])
This is my output,target and input shapes.Output and target are different.How to solve this.Should I post my whole code?
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
from torch.autograd import Variable
batchSize = 64
imageSize = 100
# Creating the transformations
transform = transforms.Compose([transforms.Scale(imageSize), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),]) # We create a list of transformations (scaling, tensor conversion, normalization) to apply to the input images.
# Loading the dataset
dataset = dset.ImageFolder(root = 'Dataset/', transform = transform) # We download the training set in the ./data folder and we apply the previous transformations on each image.
dataloader = torch.utils.data.DataLoader(dataset, batch_size = 1, shuffle = True, num_workers = 2) # We use dataLoader to get the images of the training set batch by batch.
# Defining the weights_init function that takes as input a neural network m and that will initialize all its weights.
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
# Defining the generator
class G(nn.Module):
def __init__(self):
super(G, self).__init__()
self.main = nn.Sequential(
nn.ConvTranspose2d(100, 512, 4, 1, 0, bias = False),
nn.BatchNorm2d(512),
nn.ReLU(True),
nn.ConvTranspose2d(512, 256, 4, 2, 1, bias = False),
nn.BatchNorm2d(256),
nn.ReLU(True),
nn.ConvTranspose2d(256, 128, 4, 2, 1, bias = False),
nn.BatchNorm2d(128),
nn.ReLU(True),
nn.ConvTranspose2d(128, 64, 4, 2, 1, bias = False),
nn.BatchNorm2d(64),
nn.ReLU(True),
nn.ConvTranspose2d(64, 3, 4, 2, 1, bias = False),
nn.Tanh()
)
def forward(self, input):
output = self.main(input)
return output
# Creating the generator
netG = G()
netG.apply(weights_init)
# Defining the discriminator
class D(nn.Module):
def __init__(self):
super(D, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(3, 64, 4, 2, 1, bias = False),
nn.LeakyReLU(0.2, inplace = True),
nn.Conv2d(64, 128, 4, 2, 1, bias = False),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2, inplace = True),
nn.Conv2d(128, 256, 4, 2, 1, bias = False),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2, inplace = True),
nn.Conv2d(256, 512, 4, 2, 1, bias = False),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2, inplace = True),
nn.Conv2d(512, 1, 4, 1, 0, bias = False),
nn.Sigmoid()
)
def forward(self, input):
output = self.main(input)
return output.view(-1)
# Creating the discriminator
netD = D()
netD.apply(weights_init)
# Training the DCGANs
criterion = nn.BCELoss()
optimizerD = optim.Adam(netD.parameters(), lr = 0.0002, betas = (0.5, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr = 0.0002, betas = (0.5, 0.999))
for epoch in range(25):
for i, data in enumerate(dataloader, 0):
# 1st Step: Updating the weights of the neural network of the discriminator
netD.zero_grad()
# Training the discriminator with a real image of the dataset
real, _ = data
input = Variable(real)
target = Variable(torch.ones(input.size()[0]))
output = netD(input)
errD_real = criterion(output, target)
# Training the discriminator with a fake image generated by the generator
noise = Variable(torch.randn(input.size()[0], 100, 1, 1))
fake = netG(noise)
target = Variable(torch.zeros(input.size()[0]))
output = netD(fake.detach())
errD_fake = criterion(output, target)
target.size()
input.size()
# Backpropagating the total error
errD = errD_real + errD_fake
errD.backward()
optimizerD.step()
# 2nd Step: Updating the weights of the neural network of the generator
netG.zero_grad()
target = Variable(torch.ones(input.size()[0]))
output = netD(fake)
errG = criterion(output, target)
errG.backward()
optimizerG.step()
# 3rd Step: Printing the losses and saving the real images and the generated images of the minibatch every 100 steps
print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f' % (epoch, 25, i, len(dataloader), errD.data[0], errG.data[0]))
if i % 100 == 0:
vutils.save_image(real, '%s/real_samples.png' % "./results", normalize = True)
fake = netG(noise)
vutils.save_image(fake.data, '%s/fake_samples_epoch_%03d.png' % ("./results", epoch), normalize = True)
I have posted my entire code.Please say me the solution.I have two classes(Dogs and cats).