def add_gan_model_image_summaries(gan_model, grid_size=10):
"""Adds image summaries for real and fake images.
Args:
gan_model: A GANModel tuple.
grid_size: The size of an image grid.
Raises:
ValueError: If real and generated data aren't images.
"""
_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]
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)
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)
add_gan_model_summaries(gan_model)
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):
saved_params = locals()
saved_params.pop('gan_model', None)
with ops.name_scope('cyclegan_x2y_image_summaries'):
add_gan_model_image_summaries(gan_model.model_x2y, **saved_params)
with ops.name_scope('cyclegan_y2x_image_summaries'):
add_gan_model_image_summaries(gan_model.model_y2x, **saved_params)
return
_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]
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)
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 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):
saved_params = locals()
saved_params.pop('gan_model', None)
with ops.name_scope('cyclegan_x2y_image_summaries'):
add_gan_model_image_summaries(gan_model.model_x2y, **saved_params)
with ops.name_scope('cyclegan_y2x_image_summaries'):
add_gan_model_image_summaries(gan_model.model_y2x, **saved_params)
return
_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]
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)
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 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]
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)
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 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]
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)
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 build(self, img_a, img_b, labels):
self.__call__(img_a, img_b, labels)
# Add summaries
num_images = self.grid_size**2
image_shape = self.img_a.get_shape().as_list()[1:3]
summary.image('image_a',
eval_utils.image_grid(self.img_a[:num_images, :, :, :3],
grid_shape=(self.grid_size,
self.grid_size),
image_shape=image_shape,
num_channels=3),
max_outputs=1)
summary.image('image_b',
eval_utils.image_grid(self.img_b[:num_images, :, :, :3],
grid_shape=(self.grid_size,
self.grid_size),
image_shape=image_shape,
num_channels=3),
max_outputs=1)
def my_summary_image(name, tensor, grid_size=2):
num_images = grid_size**2
inp_image_shape = tensor.shape.as_list()[1:3]
inp_channels = tensor.shape.as_list()[3]
tensor = (tensor + 1) / 2
tensor = tf.image.convert_image_dtype(tensor,
dtype=tf.uint8,
saturate=True)
tf.summary.image(name,
eval_utils.image_grid(tensor[:num_images],
grid_shape=(grid_size, grid_size),
image_shape=inp_image_shape,
num_channels=inp_channels),
max_outputs=1)
def add_gan_model_image_summaries(gan_model, grid_size=10):
"""Adds image summaries for real and fake images.
Args:
gan_model: A GANModel tuple.
grid_size: The size of an image grid.
Raises:
ValueError: If real and generated data aren't images.
"""
_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]
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)
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)
add_gan_model_summaries(gan_model)