Mercurial > ift6266

annotate code_tutoriel/utils.py @ 266:1e4e60ddadb1

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Merge. Ah, et dans le dernier commit, j'avais oublié de mentionner que j'ai ajouté du code pour gérer l'isolation de différents clones pour rouler des expériences et modifier le code en même temps.
author fsavard
date Fri, 19 Mar 2010 10:56:16 -0400
parents 4bc5eeec6394
children
rev   line source
165
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1 """ This file contains different utility functions that are not connected
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2 in anyway to the networks presented in the tutorials, but rather help in
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3 processing the outputs into a more understandable way.
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4
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5 For example ``tile_raster_images`` helps in generating a easy to grasp
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6 image from a set of samples or weights.
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7 """
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8
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9
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10 import numpy
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11
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12
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13 def scale_to_unit_interval(ndar,eps=1e-8):
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14 """ Scales all values in the ndarray ndar to be between 0 and 1 """
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15 ndar = ndar.copy()
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16 ndar -= ndar.min()
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17 ndar *= 1.0 / (ndar.max()+eps)
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18 return ndar
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19
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20
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21 def tile_raster_images(X, img_shape, tile_shape,tile_spacing = (0,0),
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22 scale_rows_to_unit_interval = True, output_pixel_vals = True):
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23 """
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24 Transform an array with one flattened image per row, into an array in
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25 which images are reshaped and layed out like tiles on a floor.
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26
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27 This function is useful for visualizing datasets whose rows are images,
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28 and also columns of matrices for transforming those rows
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29 (such as the first layer of a neural net).
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30
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31 :type X: a 2-D ndarray or a tuple of 4 channels, elements of which can
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32 be 2-D ndarrays or None;
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33 :param X: a 2-D array in which every row is a flattened image.
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34
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35 :type img_shape: tuple; (height, width)
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36 :param img_shape: the original shape of each image
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37
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38 :type tile_shape: tuple; (rows, cols)
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39 :param tile_shape: the number of images to tile (rows, cols)
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40
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41 :param output_pixel_vals: if output should be pixel values (i.e. int8
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42 values) or floats
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43
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44 :param scale_rows_to_unit_interval: if the values need to be scaled before
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45 being plotted to [0,1] or not
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46
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47
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48 :returns: array suitable for viewing as an image.
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49 (See:`PIL.Image.fromarray`.)
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50 :rtype: a 2-d array with same dtype as X.
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51
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52 """
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53
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54 assert len(img_shape) == 2
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55 assert len(tile_shape) == 2
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56 assert len(tile_spacing) == 2
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57
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58 # The expression below can be re-written in a more C style as
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59 # follows :
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60 #
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61 # out_shape = [0,0]
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62 # out_shape[0] = (img_shape[0]+tile_spacing[0])*tile_shape[0] -
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63 # tile_spacing[0]
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64 # out_shape[1] = (img_shape[1]+tile_spacing[1])*tile_shape[1] -
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65 # tile_spacing[1]
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66 out_shape = [(ishp + tsp) * tshp - tsp for ishp, tshp, tsp
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67 in zip(img_shape, tile_shape, tile_spacing)]
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68
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69 if isinstance(X, tuple):
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70 assert len(X) == 4
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71 # Create an output numpy ndarray to store the image
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72 if output_pixel_vals:
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73 out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype='uint8')
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74 else:
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75 out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype=X.dtype)
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76
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77 #colors default to 0, alpha defaults to 1 (opaque)
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78 if output_pixel_vals:
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79 channel_defaults = [0,0,0,255]
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80 else:
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81 channel_defaults = [0.,0.,0.,1.]
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82
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83 for i in xrange(4):
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84 if X[i] is None:
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85 # if channel is None, fill it with zeros of the correct
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86 # dtype
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87 out_array[:,:,i] = numpy.zeros(out_shape,
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88 dtype='uint8' if output_pixel_vals else out_array.dtype
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89 )+channel_defaults[i]
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90 else:
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91 # use a recurrent call to compute the channel and store it
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92 # in the output
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93 out_array[:,:,i] = tile_raster_images(X[i], img_shape, tile_shape, tile_spacing, scale_rows_to_unit_interval, output_pixel_vals)
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94 return out_array
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95
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96 else:
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97 # if we are dealing with only one channel
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98 H, W = img_shape
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99 Hs, Ws = tile_spacing
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100
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101 # generate a matrix to store the output
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102 out_array = numpy.zeros(out_shape, dtype='uint8' if output_pixel_vals else X.dtype)
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103
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104
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105 for tile_row in xrange(tile_shape[0]):
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106 for tile_col in xrange(tile_shape[1]):
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107 if tile_row * tile_shape[1] + tile_col < X.shape[0]:
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108 if scale_rows_to_unit_interval:
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109 # if we should scale values to be between 0 and 1
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110 # do this by calling the `scale_to_unit_interval`
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111 # function
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112 this_img = scale_to_unit_interval(X[tile_row * tile_shape[1] + tile_col].reshape(img_shape))
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113 else:
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114 this_img = X[tile_row * tile_shape[1] + tile_col].reshape(img_shape)
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115 # add the slice to the corresponding position in the
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116 # output array
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117 out_array[
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118 tile_row * (H+Hs):tile_row*(H+Hs)+H,
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119 tile_col * (W+Ws):tile_col*(W+Ws)+W
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120 ] \
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121 = this_img * (255 if output_pixel_vals else 1)
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122 return out_array
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123
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124
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125