Mercurial > ift6266

annotate code_tutoriel/utils.py @ 438:a6d339033d03

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added AMT
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Mon, 03 May 2010 07:46:18 -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