我正在研究 SVHN 数据集,但仅在训练阶段出现此错误。在模型的实例化期间,它可以工作。
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
为了确保有 3 个通道,我编写了 Grayscale(3) 转换。
# 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))
)
self.block_2 = nn.Sequential(
nn.Conv2d(in_channels=64,
out_channels=128,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Conv2d(in_channels=128,
out_channels=128,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2),
stride=(2, 2))
)
self.block_3 = nn.Sequential(
nn.Conv2d(in_channels=128,
out_channels=256,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(in_channels=256,
out_channels=256,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(in_channels=256,
out_channels=256,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2),
stride=(2, 2))
)
self.classifier = nn.Sequential(
nn.Linear(4096, 4096),
nn.ReLU(True),
nn.Dropout(p=0.25),
nn.Linear(4096, 4096),
nn.ReLU(True),
nn.Dropout(p=0.25),
nn.Linear(4096, num_classes),
)
for m in self.modules():
if isinstance(m, torch.nn.Conv2d) or isinstance(m, torch.nn.Linear):
nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='leaky_relu')
# nn.init.xavier_normal_(m.weight)
if m.bias is not None:
m.bias.detach().zero_()
# self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
def forward(self, x):
x = self.block_1(x)
x = self.block_2(x)
x = self.block_3(x)
# x = self.avgpool(x)
x = x.view(x.size(0), -1)
logits = self.classifier(x)
probas = F.softmax(logits, dim=1)
# probas = nn.Softmax(logits)
return probas
# return logits
我不知道那个 1 来自哪里
此外,这是没有分类器(全连接层)的模型输出的形状:
output = model1(test_x)
output.shape
torch.Size([1, 256, 4, 4])
事实上,我会将 256x4x4 作为输入传递给第一个 FC。