def add_gan_model_image_summaries(gan_model,
grid_size=4,
model_summaries=True):
"""Adds image summaries for real and fake images.
Args:
gan_model: A GANModel tuple.
grid_size: The size of an image grid.
model_summaries: Also add summaries of the model.
Raises:
ValueError: If real and generated data aren't images.
"""
if isinstance(gan_model, namedtuples.CycleGANModel):
raise ValueError(
'`add_gan_model_image_summaries` does not take CycleGANModels. Please '
'use `add_cyclegan_image_summaries` instead.')
_assert_is_image(gan_model.real_data)
_assert_is_image(gan_model.generated_data)
num_images = grid_size**2
real_image_shape = gan_model.real_data.shape.as_list()[1:3]
generated_image_shape = gan_model.generated_data.shape.as_list()[1:3]
real_channels = gan_model.real_data.shape.as_list()[3]
generated_channels = gan_model.generated_data.shape.as_list()[3]
tf.compat.v1.summary.image('real_data',
eval_utils.image_grid(
gan_model.real_data[:num_images],
grid_shape=(grid_size, grid_size),
image_shape=real_image_shape,
num_channels=real_channels),
max_outputs=1)
tf.compat.v1.summary.image('generated_data',
eval_utils.image_grid(
gan_model.generated_data[:num_images],
grid_shape=(grid_size, grid_size),
image_shape=generated_image_shape,
num_channels=generated_channels),
max_outputs=1)
if model_summaries:
add_gan_model_summaries(gan_model)
def test_image_grid(self):
eval_utils.image_grid(input_tensor=tf.zeros([25, 32, 32, 3]),
grid_shape=(5, 5))