reuse variable was being unused. This actually caused some issues when the program was run, causing tensorflow to crash.

Again, this fix may be incorrect. Please merge with caution.

Kwang

I got this error below when running the main.py script. I am using tensorflow 0.12.1. Can somebody help my out. Thanks a lot.

[!] Found images in data/gaze/UnityEyes. [*] # of synthetic data: 7373, # of cropped_data: 7373 [*] Finished preprocessing synthetic gaze data. [*] Save samples images in data/gaze /usr/local/lib/python2.7/dist-packages/numpy/core/fromnumeric.py:2699: VisibleDeprecationWarning: rank is deprecated; use the ndim attribute or function instead. To find the rank of a matrix see numpy.linalg.matrix_rank. VisibleDeprecationWarning) Traceback (most recent call last): File "main.py", line 29, in tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 43, in run sys.exit(main(sys.argv[:1] + flags_passthrough)) File "main.py", line 17, in main trainer = Trainer(config, rng) File "/home/w00378682/Documents/Detection/simulated-unsupervised-tensorflow/trainer.py", line 37, in init self.model = Model(config, self.data_loader) File "/home/w00378682/Documents/Detection/simulated-unsupervised-tensorflow/model.py", line 27, in init self._build_model() File "/home/w00378682/Documents/Detection/simulated-unsupervised-tensorflow/model.py", line 111, in _build_model self.R_x = self._build_refiner(self.normalized_x) File "/home/w00378682/Documents/Detection/simulated-unsupervised-tensorflow/model.py", line 294, in _build_refiner layer = repeat(layer, 4, resnet_block, scope="resnet") File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 177, in func_with_args return func(*args, **current_args) File "/home/w00378682/Documents/Detection/simulated-unsupervised-tensorflow/layers.py", line 47, in repeat outputs = slim.repeat(inputs, repetitions, layer, **kargv) File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1670, in repeat outputs = layer(outputs, *args, **kwargs) File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 177, in func_with_args return func(*args, **current_args) File "/home/w00378682/Documents/Detection/simulated-unsupervised-tensorflow/layers.py", line 37, in resnet_block padding=padding, activation_fn=tf.nn.relu, scope="conv1") File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 177, in func_with_args return func(*args, **current_args) File "/home/w00378682/Documents/Detection/simulated-unsupervised-tensorflow/layers.py", line 61, in conv2d biases_initializer=tf.zeros_initializer(dtype=tf.float32), scope=scope, **kargv) TypeError: zeros_initializer() takes at least 1 argument (1 given)

I found the tf.zero_initializer() function should take another argument named shape, but I have no idea what the shape argument should be set. Besides, when I removed the dtype=tf.float32 argument, errors appeared in other parts.

In the article, it is mentioned that there is a 3x3 conv layer before the resnet blocks, in the refiner. I thought it might help to add this layer as well.

Hi,

In the paper the authors mentioned about local adversarial loss and the fact that the discriminator is a fully convolutional, however I couldn't find that part in your code. Could you point me to that.

Thanks

I tried to refine all images. python2 main.py --is_train=False --synthetic_image_dir="./data/gaze/UnityEyes/" --log_dir="./logs/*******/

But, in output folder, it didn't contain refined images. Where are refined files?Do I have to designate output folder?

@carpedm20 hello, I'm a beginner in image processing. Could you plz tell me the input of simGan is a patch of a synthetic image or a whole synthetic image? If the input of simGan is a patch of a synthetic image， what is the patch size? Thank you very much!

I used 640×480 UnityEyes image data, and check the size of output data is 55×35. Next, I want to correct the value of coordinate in .json files.
I read gaze_data.py, but I only checked correction of coordinate point in crop part. Will .json file be modified to 55x35 when the output image is refined?

In layers.py

def conv2d(inputs, num_outputs, kernel_size, stride,
           layer_dict={}, activation_fn=None,
           #weights_initializer=tf.random_normal_initializer(0, 0.001),
           weights_initializer=tf.contrib.layers.xavier_initializer(),
           scope=None, name="", **kargv):
  outputs = slim.conv2d(
      inputs, num_outputs, kernel_size,
      stride, activation_fn=activation_fn, 
      weights_initializer=weights_initializer,
      biases_initializer=tf.zeros_initializer(dtype=tf.float32), scope=scope, **kargv)
  if name:
    scope = "{}/{}".format(name, scope)
  _update_dict(layer_dict, scope, outputs)
  return outputs

and in model.py

  def _build_refiner(self, layer):
    with tf.variable_scope("refiner") as sc:
      layer = conv2d(layer, 64, 3, 1, scope="conv_1")
      layer = repeat(layer, 4, resnet_block, scope="resnet")
      layer = conv2d(layer, 1, 1, 1, 
                     activation_fn=None, scope="conv_2")
      output = tanh(layer, name="tanh")
      self.refiner_vars = tf.contrib.framework.get_variables(sc)
    return output 

  def _build_discrim(self, layer, name, reuse=False):
    with tf.variable_scope("discriminator", reuse=reuse) as sc:
      layer = conv2d(layer, 96, 3, 2, scope="conv_1", name=name)
      layer = conv2d(layer, 64, 3, 2, scope="conv_2", name=name)
      layer = max_pool2d(layer, 3, 1, scope="max_1", name=name)
      layer = conv2d(layer, 32, 3, 1, scope="conv_3", name=name)
      layer = conv2d(layer, 32, 1, 1, scope="conv_4", name=name)
      logits = conv2d(layer, 2, 1, 1, scope="conv_5", name=name)
      output = tf.nn.softmax(logits, name="softmax")
      self.discrim_vars = tf.contrib.framework.get_variables(sc)
    return output, logits

Activation is None in most convolution layers. Is this OK? I think that gradients do not propagate properly.

I'd like to run the refined photos through a CGI vs. photo CNN and see the results. Does anyone have an already generated set of refined photos by any chance I could use?