def call(self, inputs):
print("xxxx",inputs)
expanded_tensor = ktf.expand_dims(inputs[0], -1)
multiples = [1, self.number_of_transforms, 1, 1, 1]
tiled_tensor = ktf.tile(expanded_tensor, multiples=multiples)
repeated_tensor = ktf.reshape(tiled_tensor, ktf.shape(inputs[0]) * np.array([self.number_of_transforms, 1, 1, 1]))
affine_transforms = inputs[1] / self.affine_mul
affine_transforms = ktf.reshape(affine_transforms, (-1, 8))
tranformed = tf_affine_transform(repeated_tensor, affine_transforms)
res = ktf.reshape(tranformed, [-1, self.number_of_transforms] + self.image_size)
res = ktf.transpose(res, [0, 2, 3, 1, 4])
#Use masks
if len(inputs) == 3:
mask = ktf.transpose(inputs[2], [0, 2, 3, 1])
mask = ktf.image.resize_images(mask, self.image_size[:2], method=ktf.image.ResizeMethod.NEAREST_NEIGHBOR)
res = res * ktf.expand_dims(mask, axis=-1)
if self.aggregation_fn == 'none':
res = ktf.reshape(res, [-1] + self.image_size[:2] + [self.image_size[2] * self.number_of_transforms])
elif self.aggregation_fn == 'max':
res = ktf.reduce_max(res, reduction_indices=[-2])
elif self.aggregation_fn == 'avg':
counts = ktf.reduce_sum(mask, reduction_indices=[-1])
counts = ktf.expand_dims(counts, axis=-1)
res = ktf.reduce_sum(res, reduction_indices=[-2])
res /= counts
res = ktf.where(ktf.is_nan(res), ktf.zeros_like(res), res)
return res
def ns_loss_fake(logits):
labels = ktf.zeros_like(logits)
return ktf.reduce_mean(ktf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits))