def compute_output_shape(self, input_shape):
input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list())
if self._output_shape is None:
if context.executing_eagerly():
raise NotImplementedError
x = K.placeholder(shape=input_shape)
x = self.call(x)
if isinstance(x, list):
return [
tensor_shape.TensorShape(K.int_shape(x_elem))
for x_elem in x
]
else:
return tensor_shape.TensorShape(K.int_shape(x))
elif isinstance(self._output_shape, (tuple, list)):
if isinstance(input_shape, list):
num_samples = input_shape[0][0]
else:
num_samples = input_shape[0] if input_shape else None
return tensor_shape.TensorShape((num_samples, ) +
tuple(self._output_shape))
else:
shape = self._output_shape(input_shape)
if not isinstance(shape, (list, tuple)):
raise ValueError(
'`output_shape` function must return a tuple or a list of tuples.'
)
if isinstance(shape, list):
if isinstance(shape[0], int) or shape[0] is None:
shape = tuple(shape)
return tensor_shape.TensorShape(shape)
def _compute_output_shape(self, input_shape):
input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list())
if self._output_shape is None:
x = K.placeholder(shape=input_shape)
x = self.call(x)
if isinstance(x, list):
return [tensor_shape.TensorShape(K.int_shape(x_elem)) for x_elem in x]
else:
return tensor_shape.TensorShape(K.int_shape(x))
elif isinstance(self._output_shape, (tuple, list)):
if isinstance(input_shape, list):
num_samples = input_shape[0][0]
else:
num_samples = input_shape[0] if input_shape else None
return tensor_shape.TensorShape((num_samples,) +
tuple(self._output_shape))
else:
shape = self._output_shape(input_shape)
if not isinstance(shape, (list, tuple)):
raise ValueError(
'`output_shape` function must return a tuple or a list of tuples.')
if isinstance(shape, list):
if isinstance(shape[0], int) or shape[0] is None:
shape = tuple(shape)
return tensor_shape.TensorShape(shape)
def pooling_func(x):
if pooltype == 1:
return AveragePooling2D((2, 2), strides=(2, 2))(x)
else:
return MaxPooling2D((2, 2), strides = (2,2))(x)
# get tensor representations of our images
base_image = K.variable(preprocess_image(base_image_path, True, read_mode=read_mode))
style_reference_images = []
for style_path in style_image_paths:
style_reference_images.append(K.variable(preprocess_image(style_path)))
# this will contain our generated image
combination_image = K.placeholder((1, img_width, img_height, 3))
image_tensors = [base_image]
for style_image_tensor in style_reference_images:
image_tensors.append(style_image_tensor)
image_tensors.append(combination_image)
nb_tensors = len(image_tensors)
print("nb_tensors", nb_tensors)
nb_style_images = nb_tensors - 2 # Content and Output image not considered
# combine the various image into a single Keras tensor
input_tensor = K.concatenate(image_tensors, axis=0)
shape = (nb_tensors, img_width, img_height, 3)
inp_shape = (img_width, img_height, 3)