Tensor shape mismatch on "space" trained models - test_neural_texture.py

[madhawav]

Hi,

Whenever I run the script "test_neural_texture.py", it fails on trained_models that has the term "space" in the filename (such as ../trained_models/neural_texture/version_468753_neuraltexture_rust_paint_2d_space/). Thus, I had to remove those directories from the trained_model directory to completely run the script. I believe these "space" models synthesize textures that encode style statistics from multiple input images.

Log output

Use pytorch 1.4.0
Load config: ../trained_models/neural_texture/version_468753_neuraltexture_rust_paint_2d_space/logs/config.txt
INFO:lightning:GPU available: True, used: True
INFO:lightning:CUDA_VISIBLE_DEVICES: [0]
checkpoint loaded ../trained_models/neural_texture/version_468753_neuraltexture_rust_paint_2d_space/checkpoints/neural_texture_ckpt_epoch_1.ckpt
[PATH TO CONDA ENV]/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:23: UserWarning: Checkpoint directory ../trained_models/neural_texture/version_468753_neuraltexture_rust_paint_2d_space/checkpoints exists and is not empty with save_top_k != 0.All files in this directory will be deleted when a checkpoint is saved!
  warnings.warn(*args, **kwargs)
Testing:  33%|████████████                        | 1/3 [00:01<00:02,  1.49s/it]Traceback (most recent call last):
  File "[PROJECT ROOT]/code/systems/s_neural_texture.py", line 261, in test_step
    image_out_inter = self.forward(z_texture_interpolated, position, seed)
  File "[PROJECT ROOT]/code/systems/s_neural_texture.py", line 66, in forward
    transform_coeff, z_encoding = torch.split(weights, [self.p.texture.t, self.p.texture.e], dim=1)
  File "[PATH TO CONDA ENV]/lib/python3.8/site-packages/torch/functional.py", line 77, in split
    return tensor.split(split_size_or_sections, dim)
  File "[PATH TO CONDA ENV]/lib/python3.8/site-packages/torch/tensor.py", line 377, in split
    return super(Tensor, self).split_with_sizes(split_size, dim)
RuntimeError: start (32) + length (64) exceeds dimension size (94).

I wonder what I should do to overcome this issue.

P.S.: I am using the pre-trained models and test images provided by you. No train images are placed.

[madhawav]

Sorry, this looks like a duplicate of #1.
I have implemented the solution suggested in #1 and sent a new pull request #4.