No problem.,
The issue is you have defined Conv2d. It expects for [channel, height, width] input at least. But when you passing your input to forward method, you are reshaping it into a [sth, sth] which is not proper.
for instance, just run this code:
x = torch.randn(1, 3, 136, 136)
x = x.view(x.size(0), -1)
print(x.shape)  # (xxx, xxx)

So, that line is changing the shape of tensor to something that Conv2d layers cannot accept.
Now run this code:
x = torch.randn(1, 3, 136, 136)
# x = x.view(x.size(0), -1)
x = nn.Conv2d(3,16,3,stride=1,padding=1)
print(x.shape)  # (1, 16, 136, 136)

which works just fine. But if you uncomment the second line (currently commented), you get your mentioned error.
The whole idea is if you are not going to use Fully Connected layer or you are just using Conv2d, your input has to be in the form I previously explained.