I have this problem and cannot solve it.
This is my Dataset:
class Data(Dataset):
def __init__(self, df, data_dir, transform):
super().__init__()
self.df = df
self.data_dir = data_dir
self.transform = transform
def __len__(self):
return len(self.df)
def __getitem__(self, index):
img_name = self.df.id[index]
label = self.df.broken[index]
img_path = os.path.join(str(self.data_dir), str(img_name) + '.png')
img = mpimg.imread(img_path)
img = self.transform(img)
return img, label
This is my model:
class SkullCNN(nn.Module):
def __init__(self):
super(SkullCNN, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 32, 4, 2, 0),
nn.BatchNorm2d(32),
nn.ReLU()
)
# 1 + (32 - 4 + 0)/2 = 15
# 32 * 15 * 15
self.conv2 = nn.Sequential(
nn.Conv2d(32, 64, 3, 2, 0),
nn.BatchNorm2d(64),
nn.ReLU()
)
# 1 + (15 - 3 + 0)/2 = 7
# 64 * 7 * 7
self.conv3 = nn.Sequential(
nn.Conv2d(64, 128, 3, 2, 0),
nn.BatchNorm2d(128),
nn.ReLU()
)
# 1 + (7 - 3 + 0)/2 = 3
# 128 * 3 * 3
self.conv4 = nn.Sequential(
nn.Conv2d(128, 256, 3, 2, 0),
nn.BatchNorm2d(256),
nn.ReLU()
)
# 1 + (3 - 3 + 0)/2 = 1
# 256 * 1 * 1
self.fc = nn.Sequential(
nn.Linear(256*1*1, 1024),
nn.ReLU(),
nn.Dropout(p=0.2),
nn.Linear(1024,2)
)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = x.view(x.shape[0],-1)
x = self.fc(x)
return x
This is the code block where the error is generated:
num_epochs = 25
num_classes = 2
batch_size = 128
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.to(device)
labels = labels.to(device)
# print(images[0].shape)
out = model(images)
loss = criterion(out, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('Epoch: {}/{}, Loss: {}'.format(epoch+1, num_epochs, loss.item()))
The error output is:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-72-15d7d37170f1> in <module>()
9 # print(images[0].shape)
10
---> 11 out = model(images)
12 loss = criterion(out, labels)
13
6 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
348 _pair(0), self.dilation, self.groups)
349 return F.conv2d(input, weight, self.bias, self.stride,
--> 350 self.padding, self.dilation, self.groups)
351
352 def forward(self, input):
RuntimeError: Given groups=1, weight of size [32, 3, 4, 4], expected input[64, 1, 340, 340] to have 3 channels, but got 1 channels instead
Please help me, I’m learning PyTorch. Thank you so much, also I see some similar problems here, but I can’t adapt it to my code.
