I am working on SVHN dataset, and I got this error, only during training phase. During the instantiation of the model, it works.
RuntimeError: Given groups=1, weight of size [64, 3, 3, 3], expected input[64, 1, 32, 32] to have 3 channels, but got 1 channels instead
To be sure of having 3 channels, I wrote that Grayscale(3) transformation.
# Compose transformations
data_transform = transforms.Compose([
transforms.Resize((32,32)),
transforms.Grayscale(num_output_channels=3),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
# Compose transformations
test_transform = transforms.Compose([
transforms.Resize((32,32)),
transforms.Grayscale(num_output_channels=3),
transforms.ToTensor(),
])
# Load MNIST dataset with transforms
train_set_svhn = torchvision.datasets.SVHN(root=base_dir, split='train', download=True, transform=data_transform, target_transform=None)
test_set_svhn = torchvision.datasets.SVHN(root=base_dir, split='test', download=True, transform=test_transform)
class VGG16(nn.Module):
def __init__(self, num_classes):
super(VGG16, self).__init__()
# calculate same padding:
# (w - k + 2*p)/s + 1 = o
# => p = (s(o-1) - w + k)/2
self.block_1 = nn.Sequential(
nn.Conv2d(in_channels=3,
out_channels=64,
kernel_size=(3, 3),
stride=(1, 1),
# (1(32-1)- 32 + 3)/2 = 1
padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(in_channels=64,
out_channels=64,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2),
stride=(2, 2))
.....
.....
.....
)
I have no idea where that 1 comes from
Moreover, this is the shape of the output of the model without classifier (fully connected layers):
output = model1(test_x)
output.shape
torch.Size([1, 256, 4, 4])
And indeed I will pass 256x4x4 as input to the first FC.