Background Information I am trying to create a model (a beginner so please excuse my ignorance). The architecture I am trying to convert is given below as a link as well.
This is the code I came up with. I am using Binder to run the code.
import os
import torch
import torchvision
import tarfile
from torchvision.datasets.utils import download_url
from torch.utils.data import random_split
from torchsummary import summary
# Implementation of CNN/ConvNet Model
class build_unet(torch.nn.Module):
def __init__(self):
super(build_unet, self).__init__()
keep_prob = 0.5
self.layer1 = torch.nn.Sequential(
torch.nn.Conv2d(3, 32, kernel_size=3),
torch.nn.ReLU(),
torch.nn.MaxPool2d(kernel_size=2, padding=1))
self.layer2 = torch.nn.Sequential(
torch.nn.Conv2d(32, 64, kernel_size=3),
torch.nn.ReLU(),
torch.nn.MaxPool2d(kernel_size=2, padding=1))
self.layer3 = torch.nn.Sequential(
torch.nn.Conv2d(64, 128, kernel_size=3),
torch.nn.ReLU(),
torch.nn.MaxPool2d(kernel_size=2, padding=1))
self.dense = torch.nn.Linear(64, 128, bias=True)
torch.nn.init.xavier_uniform_(self.dense.weight)
self.layer4 = torch.nn.Sequential(
self.dense,
torch.nn.ReLU(),
torch.nn.Upsample()
)
self.layer5 = torch.nn.Sequential(
torch.nn.Conv2d(128, 128, kernel_size=3),
torch.nn.Sigmoid(),
torch.nn.Upsample()
)
self.layer6 = torch.nn.Sequential(
torch.nn.Conv2d(128, 64, kernel_size=3),
torch.nn.Sigmoid(),
torch.nn.Upsample()
)
self.layer7 = torch.nn.Sequential(
torch.nn.Conv2d(64, 1, kernel_size=3),
torch.nn.Sigmoid()
)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.layer5(out)
out = self.layer6(out)
out = self.layer7(out)
return out
if __name__ == "__main__":
x = torch.randn((2, 3, 512, 512))
f = build_unet()
y = f(x)
print(y.shape)
How would I resolve this error?
ERROR MESSAGE
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_36/1438699785.py in <module>
87 x = torch.randn((2, 3, 512, 512))
88 f = build_unet()
---> 89 y = f(x)
90 print(y.shape)
/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
/tmp/ipykernel_36/1438699785.py in forward(self, x)
72 out = self.layer3(out)
73
---> 74 out = self.layer4(out)
75 out = self.layer5(out)
76 out = self.layer6(out)
/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
/opt/conda/lib/python3.9/site-packages/torch/nn/modules/container.py in forward(self, input)
137 def forward(self, input):
138 for module in self:
--> 139 input = module(input)
140 return input
141
/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
/opt/conda/lib/python3.9/site-packages/torch/nn/modules/upsampling.py in forward(self, input)
139
140 def forward(self, input: Tensor) -> Tensor:
--> 141 return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners)
142
143 def extra_repr(self) -> str:
/opt/conda/lib/python3.9/site-packages/torch/nn/functional.py in interpolate(input, size, scale_factor, mode, align_corners, recompute_scale_factor)
3647 scale_factors = [scale_factor for _ in range(dim)]
3648 else:
-> 3649 raise ValueError("either size or scale_factor should be defined")
3650
3651 if recompute_scale_factor is None:
ValueError: either size or scale_factor should be defined
CodePudding user response：
nn.Upsample() has following parameters: size, scale_factor, mode, align_corners. By default size=None, mode=nearest and align_corners=None.
torch.nn.Upsample(size=None, scale_factor=None, mode='nearest', align_corners=None)
When you set scale_factor=2 you will get following result:
import torch
import torch.nn as nn
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
keep_prob = 0.5
self.layer1 = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, padding=1))
self.layer2 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=3),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, padding=1))
self.layer3 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, padding=1))
self.dense = nn.Linear(64, 128, bias=True)
nn.init.xavier_uniform_(self.dense.weight)
self.layer4 = nn.Sequential(
self.dense,
nn.ReLU(),
nn.Upsample(scale_factor=2)
)
self.layer5 = nn.Sequential(
nn.Conv2d(128, 128, kernel_size=3),
nn.Sigmoid(),
nn.Upsample(scale_factor=2)
)
self.layer6 = nn.Sequential(
nn.Conv2d(128, 64, kernel_size=3),
nn.Sigmoid(),
nn.Upsample(scale_factor=2)
)
self.layer7 = nn.Sequential(
nn.Conv2d(64, 1, kernel_size=3),
nn.Sigmoid()
)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.layer5(out)
out = self.layer6(out)
out = self.layer7(out)
return out
if __name__ == "__main__":
x = torch.randn((2, 3, 512, 512))
f = Net()
y = f(x)
print(y.shape)
Result:
torch.Size([2, 1, 498, 1010])