def extract_image_patches(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 = _preprocess_padding(padding)
if data_format == 'channels_first':
x = KTF.permute_dimensions(x, (0, 2, 3, 1))
bs_i, w_i, h_i, ch_i = KTF.int_shape(x)
patches = tf.extract_image_patches(x, kernel, strides, [1, 1, 1, 1],
padding)
# Reshaping to fit Theano
bs, w, h, ch = KTF.int_shape(patches)
patches = tf.reshape(tf.transpose(tf.reshape(patches, [-1, w, h, tf.floordiv(ch, ch_i), ch_i]), [0, 1, 2, 4, 3]),
[-1, w, h, ch_i, ksizes[0], ksizes[1]])
if data_format == 'channels_last':
patches = KTF.permute_dimensions(patches, [0, 1, 2, 4, 5, 3])
return patches
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