From 0eb0a0b08fdf23df43c29e3b9dab9bb7eedf8d89 Mon Sep 17 00:00:00 2001 From: tboquet Date: Sun, 26 Feb 2017 14:39:31 -0500 Subject: [PATCH] * doc and style fix --- keras_contrib/backend/tensorflow_backend.py | 40 +++++++++++---------- 1 file changed, 22 insertions(+), 18 deletions(-) diff --git a/keras_contrib/backend/tensorflow_backend.py b/keras_contrib/backend/tensorflow_backend.py index ec02c69..f33d7b2 100644 --- a/keras_contrib/backend/tensorflow_backend.py +++ b/keras_contrib/backend/tensorflow_backend.py @@ -58,7 +58,8 @@ def deconv3d(x, kernel, output_shape, strides=(1, 1, 1), raise ValueError('Unknown dim_ordering ' + str(dim_ordering)) x = _preprocess_conv3d_input(x, dim_ordering) - output_shape = _preprocess_deconv_output_shape(x, output_shape, dim_ordering) + output_shape = _preprocess_deconv_output_shape(x, output_shape, + dim_ordering) kernel = _preprocess_conv3d_kernel(kernel, dim_ordering) kernel = tf.transpose(kernel, (0, 1, 2, 4, 3)) padding = _preprocess_border_mode(border_mode) @@ -69,32 +70,35 @@ def deconv3d(x, kernel, output_shape, strides=(1, 1, 1), return _postprocess_conv3d_output(x, dim_ordering) -def extract_image_patches(X, ksizes, ssizes, border_mode="same", dim_ordering="tf"): +def extract_image_patches(x, ksizes, ssizes, border_mode="same", + dim_ordering="tf"): ''' 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 - border_mode : 'same' or 'valid' - dim_ordering : 'tf' or 'th' - 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) + # Parameters + + x : The input image + ksizes : 2-d tuple with the kernel size + ssizes : 2-d tuple with the strides size + border_mode : 'same' or 'valid' + dim_ordering : 'tf' or 'th' + + # 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_border_mode(border_mode) if dim_ordering == "th": - 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) + 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, [bs, w, h, -1, ch_i]), [0, 1, 2, 4, 3]), + patches = tf.reshape(tf.transpose( + tf.reshape(patches, [bs, w, h, -1, ch_i]), [0, 1, 2, 4, 3]), [bs, w, h, ch_i, ksizes[0], ksizes[1]]) if dim_ordering == "tf": patches = KTF.permute_dimensions(patches, [0, 1, 2, 4, 5, 3])