def apply_cdna_kernels(image, kernels, dilation_rate=(1, 1)):
"""
Args:
image: A 4-D tensor of shape
`[batch, in_height, in_width, in_channels]`.
kernels: A 4-D of shape
`[batch, kernel_size[0], kernel_size[1], num_transformed_images]`.
Returns:
A list of `num_transformed_images` 4-D tensors, each of shape
`[batch, in_height, in_width, in_channels]`.
"""
batch_size, height, width, color_channels = image.get_shape().as_list()
batch_size, kernel_height, kernel_width, num_transformed_images = kernels.get_shape().as_list()
kernel_size = [kernel_height, kernel_width]
image_padded = pad2d(image, kernel_size, rate=dilation_rate, padding='SAME', mode='SYMMETRIC')
# Treat the color channel dimension as the batch dimension since the same
# transformation is applied to each color channel.
# Treat the batch dimension as the channel dimension so that
# depthwise_conv2d can apply a different transformation to each sample.
kernels = tf.transpose(kernels, [1, 2, 0, 3])
kernels = tf.reshape(kernels, [kernel_size[0], kernel_size[1], batch_size, num_transformed_images])
# Swap the batch and channel dimensions.
image_transposed = tf.transpose(image_padded, [3, 1, 2, 0])
# Transform image.
outputs = tf.nn.depthwise_conv2d(image_transposed, kernels, [1, 1, 1, 1], padding='VALID', rate=dilation_rate)
# Transpose the dimensions to where they belong.
outputs = tf.reshape(outputs, [color_channels, height, width, batch_size, num_transformed_images])
outputs = tf.transpose(outputs, [4, 3, 1, 2, 0])
outputs = tf.unstack(outputs, axis=0)
return outputs
def apply_dna_kernels(image, kernels, dilation_rate=(1, 1)):
"""
Args:
image: A 4-D tensor of shape
`[batch, in_height, in_width, in_channels]`.
kernels: A 6-D of shape
`[batch, in_height, in_width, kernel_size[0], kernel_size[1], num_transformed_images]`.
Returns:
A list of `num_transformed_images` 4-D tensors, each of shape
`[batch, in_height, in_width, in_channels]`.
"""
dilation_rate = list(dilation_rate) if isinstance(dilation_rate, (tuple, list)) else [dilation_rate] * 2
batch_size, height, width, color_channels = image.get_shape().as_list()
batch_size, height, width, kernel_height, kernel_width, num_transformed_images = kernels.get_shape().as_list()
kernel_size = [kernel_height, kernel_width]
# Flatten the spatial dimensions.
kernels_reshaped = tf.reshape(kernels, [batch_size, height, width,
kernel_size[0] * kernel_size[1], num_transformed_images])
image_padded = pad2d(image, kernel_size, rate=dilation_rate, padding='SAME', mode='SYMMETRIC')
# Combine channel and batch dimensions into the first dimension.
image_transposed = tf.transpose(image_padded, [3, 0, 1, 2])
image_reshaped = flatten(image_transposed, 0, 1)[..., None]
patches_reshaped = tf.extract_image_patches(image_reshaped, ksizes=[1] + kernel_size + [1],
strides=[1] * 4, rates=[1] + dilation_rate + [1], padding='VALID')
# Separate channel and batch dimensions, and move channel dimension.
patches_transposed = tf.reshape(patches_reshaped, [color_channels, batch_size, height, width, kernel_size[0] * kernel_size[1]])
patches = tf.transpose(patches_transposed, [1, 2, 3, 0, 4])
# Reduce along the spatial dimensions of the kernel.
outputs = tf.matmul(patches, kernels_reshaped)
outputs = tf.unstack(outputs, axis=-1)
return outputs