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training with custom data #7

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gokceay opened this issue Apr 22, 2024 · 0 comments
Open

training with custom data #7

gokceay opened this issue Apr 22, 2024 · 0 comments

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@gokceay
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gokceay commented Apr 22, 2024

Hi,
I am trying to train rayonet on custom data. I have prepared my data two times but I am getting the same error while training:

File "trainVTPxy90CLAHE.py", line 139, in
loss = trainer.train_step(batch)
File "/home/ubuntu/ray-onet/im2mesh/rayonet/training.py", line 52, in train_step
loss = self.compute_loss(data)
File "/home/ubuntu/ray-onet/im2mesh/rayonet/training.py", line 202, in compute_loss
occ_pred = self.model.decode(scale_factor, points_xy, c, c_local) # (B, n_points, num_samples)
File "/home/ubuntu/ray-onet/im2mesh/rayonet/models/init.py", line 75, in decode
logits = self.decoder(scale_factor, points_xy, c_global, c_local)
File "/home/ubuntu/anaconda3/envs/rayonet/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/home/ubuntu/ray-onet/im2mesh/rayonet/models/decoder.py", line 166, in forward
net = self.fc_geo1(net)
File "/home/ubuntu/anaconda3/envs/rayonet/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/home/ubuntu/ray-onet/im2mesh/layers.py", line 40, in forward
net = self.fc_0(self.actvn(x))
File "/home/ubuntu/anaconda3/envs/rayonet/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/home/ubuntu/anaconda3/envs/rayonet/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 87, in forward
return F.linear(input, self.weight, self.bias)
File "/home/ubuntu/anaconda3/envs/rayonet/lib/python3.6/site-packages/torch/nn/functional.py", line 1372, in linear
output = input.matmul(weight.t())
RuntimeError: size mismatch, m1: [65536 x 258], m2: [259 x 256] at /tmp/pip-req-build-808afw3c/aten/src/THC/generic/THCTensorMathBlas.cu:290

Maybe I am missing sth during data preparation .
Any help would be greatly appreciated.

@gokceay gokceay closed this as completed Apr 24, 2024
@gokceay gokceay reopened this Apr 24, 2024
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