def extract_image_patches(self,
x,
ksizes,
ssizes,
padding='same',
data_format='channels_last'):
"""
Extract the patches from an image
# Parameters
x : The input image
ksizes : 2-d tuple with the kernel size
ssizes : 2-d tuple with the strides size
padding : 'same' or 'valid'
data_format : 'channels_last' or 'channels_first'
# Returns
The (k_w, k_h) patches extracted
TF ==> (batch_size, w, h, k_w, k_h, c)
TH ==> (batch_size, w, h, c, k_w, k_h)
"""
kernel = [1, ksizes[0], ksizes[1], 1]
strides = [1, ssizes[0], ssizes[1], 1]
padding = self._preprocess_padding(padding)
if data_format == 'channels_first':
x = K.permute_dimensions(x, (0, 2, 3, 1))
patches = extract_image_patches(x, kernel, strides, [1, 1, 1, 1],
padding)
return patches
def extract_image_patches(self, input_tensor, k_sizes, s_sizes,
padding='same', data_format='channels_last'):
""" Extract the patches from an image.
Parameters
----------
input_tensor: tensor
The input image
k_sizes: tuple
2-d tuple with the kernel size
s_sizes: tuple
2-d tuple with the strides size
padding: str, optional
`"same"` or `"valid"`. Default: `"same"`
data_format: str, optional.
`"channels_last"` or `"channels_first"`. Default: `"channels_last"`
Returns
-------
The (k_w, k_h) patches extracted
Tensorflow ==> (batch_size, w, h, k_w, k_h, c)
Theano ==> (batch_size, w, h, c, k_w, k_h)
"""
kernel = [1, k_sizes[0], k_sizes[1], 1]
strides = [1, s_sizes[0], s_sizes[1], 1]
padding = self._preprocess_padding(padding)
if data_format == 'channels_first':
input_tensor = K.permute_dimensions(input_tensor, (0, 2, 3, 1))
patches = extract_image_patches(input_tensor, kernel, strides, [1, 1, 1, 1], padding)
return patches
