fjxmlzn / pacgan Goto Github PK

I'm trying to replicate this work for a research I am working on, but there´s been multiple problems that I am trying to fix, but there seems to be no end to it.
Most of them seem to be caused by environment incompatibilities.
here are some of the latest I've found:

• Problems downloading datasets

• PacGAN\stacked_MNIST_experiments\VEEGAN_experiment\model.py", line 156, in train

    writer.writeheader()
  

  ...
  TypeError: a bytes-like object is required, not 'str'

• PacGAN\stacked_MNIST_experiments\VEEGAN_experiment\model.py", line 200, in train      
    }, **feed_dict_z))
  

  TypeError: keywords must be strings

• PacGAN\stacked_MNIST_experiments\VEEGAN_experiment\model.py", line 281, in log_metrics
    if len(results) + len(new_results) > self.num_test_sample:
  

  TypeError: object of type 'zip' has no len()

• PacGAN\stacked_MNIST_experiments\VEEGAN_experiment\utils.py", line 67, in imsave      
    return scipy.misc.imsave(path, image)
  

  AttributeError: module 'scipy.misc' has no attribute 'imsave'

• PacGAN\stacked_MNIST_experiments\VEEGAN_experiment\model.py", line 297, in log_metrics
  p[0:num_mode] = map.values()
  TypeError: float() argument must be a string or a number, not 'dict_values'

Is there any conda environment.yml or requirements.txt file one can use to replicate the original environment?
Or probably a newer/alternative repository for this work?
Just in case, attached you´ll find the last version of my requirements.txt file for the stacked MNIST experiments.
requirements.txt

No errors, no warning. When I tried to reproduce the celebA results, the program exits in two seconds. Does anyone have the same issue?

Here is the log.

(pacgan) ➜ celeba_experiments git:(master) ✗ python main.py --config="./configs/CelebA_128x128_N2M2S64.yaml"
2019-11-23 21:02:41,303 INFO gpu0 receives task packing_num-1,run-0,
2019-11-23 21:02:41,304 INFO gpu0 skips task packing_num-1,run-0,
2019-11-23 21:02:41,305 INFO gpu0 receives task packing_num-2,run-0,
2019-11-23 21:02:41,306 INFO gpu0 skips task packing_num-2,run-0,
2019-11-23 21:02:41,307 INFO gpu0 receives task packing_num-3,run-0,
2019-11-23 21:02:41,307 INFO gpu0 skips task packing_num-3,run-0,
2019-11-23 21:02:41,308 INFO gpu0 receives task packing_num-4,run-0,
2019-11-23 21:02:41,309 INFO gpu0 skips task packing_num-4,run-0,
2019-11-23 21:02:41,310 INFO gpu0 receives task packing_num-1,run-1,
2019-11-23 21:02:41,310 INFO gpu0 skips task packing_num-1,run-1,
2019-11-23 21:02:41,311 INFO gpu0 receives task packing_num-2,run-1,
2019-11-23 21:02:41,311 INFO gpu0 skips task packing_num-2,run-1,
2019-11-23 21:02:41,312 INFO gpu0 receives task packing_num-3,run-1,
2019-11-23 21:02:41,312 INFO gpu0 skips task packing_num-3,run-1,
2019-11-23 21:02:41,313 INFO gpu0 receives task packing_num-4,run-1,
2019-11-23 21:02:41,313 INFO gpu0 skips task packing_num-4,run-1,
2019-11-23 21:02:41,314 INFO gpu0 finished