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))