Comments (3)
I found the problem is n_frames_load
didn't match the channel size of real_A_all
from vid2vid.
Traceback (most recent call last):
File "train.py", line 148, in <module>
train()
File "train.py", line 55, in train
fake_B, fake_B_raw, flow, weight, real_A, real_Bp, fake_B_last = modelG(input_A, input_B, inst_A, fake_B_prev_last)
File "/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/vid2vid/models/models.py", line 37, in forward
outputs = self.model(*inputs, **kwargs, dummy_bs=self.pad_bs)
File "/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 159, in forward
return self.module(*inputs[0], **kwargs[0])
File "/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/vid2vid/models/vid2vid_model_G.py", line 133, in forward
fake_B, fake_B_raw, flow, weight = self.generate_frame_train(netG, real_A_all, fake_B_prev, start_gpu, is_first_frame)
File "/vid2vid/models/vid2vid_model_G.py", line 178, in generate_frame_train
fake_B_feat, flow_feat, fake_B_fg_feat, use_raw_only)
File "/vid2vid/models/networks.py", line 204, in forward
downsample = self.model_down_seg(input) + self.model_down_img(img_prev)
File "/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/lib/python3.7/site-packages/torch/nn/modules/container.py", line 117, in forward
input = module(input)
File "/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 423, in forward
return self._conv_forward(input, self.weight)
File "/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 420, in _conv_forward
self.padding, self.dilation, self.groups)
RuntimeError: Given groups=1, weight of size [64, 9, 7, 7], expected input[1, 6, 262, 198] to have 9 channels, but got 6 channels instead
from vid2vid.
I thought the problem is expected input of (1): Conv2d(9, 64, kernel_size=(7, 7), stride=(1, 1))
but my image is a 6-channel image but it doesn't happen all the time in most time my image is in 9-channel.
When printing self.model_down_seg
:
Sequential(
(0): ReflectionPad2d((3, 3, 3, 3))
(1): Conv2d(9, 64, kernel_size=(7, 7), stride=(1, 1))
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(5): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU(inplace=True)
(7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(9): ReLU(inplace=True)
(10): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(11): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(12): ReLU(inplace=True)
(13): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1))
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): ReflectionPad2d((1, 1, 1, 1))
(5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1))
(6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(14): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1))
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): ReflectionPad2d((1, 1, 1, 1))
(5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1))
(6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(15): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1))
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): ReflectionPad2d((1, 1, 1, 1))
(5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1))
(6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(16): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1))
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): ReflectionPad2d((1, 1, 1, 1))
(5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1))
(6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(17): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1))
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): ReflectionPad2d((1, 1, 1, 1))
(5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1))
(6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
from vid2vid.
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from vid2vid.