pytorch cnn Test result is sticked - deep-learning

Question

TRAINING CODE
if os.path.isfile(PATH):
print("checkpoint training '{}' ...".format(PATH))
checkpoint = torch.load(PATH)
start_epoch = checkpoint['epoch']
start_i = checkpoint['i']
net.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (trained for {} epochs, {} i)".format(PATH, checkpoint['epoch'],
checkpoint['i']))
else:
print('new training')
for epoch in range(num_epochs): # loop over the dataset multiple times
running_loss = 0.0
for i in range(len(train_folder_list2)):
# get the inputs; data is a list of [inputs, labels]
# net.train()
inputs, labels = train_input[i], train_list[i]
inputs = torch.as_tensor(inputs).cuda()
inputs = inputs.transpose(1, 3)
labels = torch.as_tensor(labels).cuda()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
# zero the parameter gradients
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 1:
save_checkpoint({
'epoch': start_epoch + epoch + 1,
'i': start_i + i + 1,
'state_dict': net.state_dict(),
})
TEST CODE
PATH = './checkpoint.pth'
model = Net().cuda()
if os.path.isfile(PATH):
print('checkpoint check!')
checkpoint = torch.load(PATH)
model.load_state_dict(checkpoint['state_dict'])
model.eval()
for k in range(len(train_folder_list2)):
inputs = train_input[k]
inputs = torch.as_tensor(inputs).cuda()
inputs = inputs.transpose(1, 3)
outputs = model(inputs)
result = outputs.cpu().detach().numpy()
This is the code to find the edges of the image.
If I run the training code, train it, and test it with the test code, it doesn't seem to find any edges in the image. The edges are on the same side, whatever image i put.
**ADD
CNN CODE
In addition, we added cnn code to give you information. Data input was put in the list separately from the image and label.
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(293904, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 18)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 293904)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
x = x.view(18)
return x