Mercurial > ift6266

annotate transformations/filetensor.py @ 41:fdb0e0870fb4

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Beaucoup de modifications à pipeline.py pour généraliser et un début de visualisation, et créé un wrapper (run_pipeline.py) pour appeler avec GIMP. - Modifications à pipeline.py - Wrappé la boucle du pipeline dans une classe - Isolé le problème de itérer sur les batches et les complexités dans des itérateurs - Permet d'avoir des ordres compliqués de batch (plusieurs sources), de complexités - Maintenant regenerate_parameters() est appelé pour chaque image. - Command line arguments avec getopt(). On pourra rajouter des options ainsi. - run_pipeline.py - Le but est de permettre de passer des arguments. Pas facile (pas trouvé comment de façon simple) avec la command line pour appeler GIMP en mode batch. C'est un hack ici. - Le but ultime est de permettre de lancer les jobs sur les clusters avec dbidispatch en précisant les options (diff. pour chaque job) sur la ligne de commande.
author fsavard
date Wed, 03 Feb 2010 17:08:27 -0500
parents faacc76d21c2
children
rev   line source
10
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1 """
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2 Read and write the matrix file format described at
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3 U{http://www.cs.nyu.edu/~ylclab/data/norb-v1.0/index.html}
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4
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5 The format is for dense tensors:
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6
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7 - magic number indicating type and endianness - 4bytes
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8 - rank of tensor - int32
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9 - dimensions - int32, int32, int32, ...
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10 - <data>
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11
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12 The number of dimensions and rank is slightly tricky:
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13 - for scalar: rank=0, dimensions = [1, 1, 1]
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14 - for vector: rank=1, dimensions = [?, 1, 1]
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15 - for matrix: rank=2, dimensions = [?, ?, 1]
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16
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17 For rank >= 3, the number of dimensions matches the rank exactly.
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18
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19
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20 @todo: add complex type support
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21
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22 """
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23 import sys
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24 import numpy
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25
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26 def _prod(lst):
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27 p = 1
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28 for l in lst:
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29 p *= l
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30 return p
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31
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32 _magic_dtype = {
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33 0x1E3D4C51 : ('float32', 4),
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34 #0x1E3D4C52 : ('packed matrix', 0), #what is a packed matrix?
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35 0x1E3D4C53 : ('float64', 8),
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36 0x1E3D4C54 : ('int32', 4),
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37 0x1E3D4C55 : ('uint8', 1),
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38 0x1E3D4C56 : ('int16', 2),
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39 }
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40 _dtype_magic = {
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41 'float32': 0x1E3D4C51,
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42 #'packed matrix': 0x1E3D4C52,
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43 'float64': 0x1E3D4C53,
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44 'int32': 0x1E3D4C54,
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45 'uint8': 0x1E3D4C55,
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46 'int16': 0x1E3D4C56
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47 }
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48
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49 def _read_int32(f):
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50 """unpack a 4-byte integer from the current position in file f"""
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51 s = f.read(4)
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52 s_array = numpy.fromstring(s, dtype='int32')
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53 return s_array.item()
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54
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55 def _read_header(f, debug=False):
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56 """
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57 :returns: data type, element size, rank, shape, size
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58 """
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59 #what is the data type of this matrix?
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60 #magic_s = f.read(4)
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61 #magic = numpy.fromstring(magic_s, dtype='int32')
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62 magic = _read_int32(f)
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63 magic_t, elsize = _magic_dtype[magic]
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64 if debug:
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65 print 'header magic', magic, magic_t, elsize
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66 if magic_t == 'packed matrix':
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67 raise NotImplementedError('packed matrix not supported')
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68
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69 #what is the rank of the tensor?
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70 ndim = _read_int32(f)
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71 if debug: print 'header ndim', ndim
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72
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73 #what are the dimensions of the tensor?
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74 dim = numpy.fromfile(f, dtype='int32', count=max(ndim,3))[:ndim]
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75 dim_size = _prod(dim)
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76 if debug: print 'header dim', dim, dim_size
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77
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78 return magic_t, elsize, ndim, dim, dim_size
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79
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80 class arraylike(object):
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81 """Provide an array-like interface to the filetensor in f.
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82
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83 The rank parameter to __init__ controls how this object interprets the underlying tensor.
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84 Its behaviour should be clear from the following example.
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85 Suppose the underlying tensor is MxNxK.
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86
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87 - If rank is 0, self[i] will be a scalar and len(self) == M*N*K.
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88
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89 - If rank is 1, self[i] is a vector of length K, and len(self) == M*N.
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90
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91 - If rank is 3, self[i] is a 3D tensor of size MxNxK, and len(self)==1.
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92
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93 - If rank is 5, self[i] is a 5D tensor of size 1x1xMxNxK, and len(self) == 1.
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94
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95
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96 :note: Objects of this class generally require exclusive use of the underlying file handle, because
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97 they call seek() every time you access an element.
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98 """
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99
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100 f = None
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101 """File-like object"""
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102
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103 magic_t = None
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104 """numpy data type of array"""
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105
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106 elsize = None
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107 """number of bytes per scalar element"""
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108
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109 ndim = None
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110 """Rank of underlying tensor"""
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111
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112 dim = None
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113 """tuple of array dimensions (aka shape)"""
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114
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115 dim_size = None
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116 """number of scalars in the tensor (prod of dim)"""
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117
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118 f_start = None
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119 """The file position of the first element of the tensor"""
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120
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121 readshape = None
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122 """tuple of array dimensions of the block that we read"""
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123
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124 readsize = None
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125 """number of elements we must read for each block"""
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126
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127 def __init__(self, f, rank=0, debug=False):
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128 self.f = f
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129 self.magic_t, self.elsize, self.ndim, self.dim, self.dim_size = _read_header(f,debug)
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130 self.f_start = f.tell()
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131
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132 if rank <= self.ndim:
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133 self.readshape = tuple(self.dim[self.ndim-rank:])
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134 else:
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135 self.readshape = tuple(self.dim)
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136
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137 #self.readshape = tuple(self.dim[self.ndim-rank:]) if rank <= self.ndim else tuple(self.dim)
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138
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139 if rank <= self.ndim:
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140 padding = tuple()
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141 else:
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142 padding = (1,) * (rank - self.ndim)
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143
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144 #padding = tuple() if rank <= self.ndim else (1,) * (rank - self.ndim)
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145 self.returnshape = padding + self.readshape
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146 self.readsize = _prod(self.readshape)
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147 if debug: print 'READ PARAM', self.readshape, self.returnshape, self.readsize
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148
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149 def __len__(self):
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150 return _prod(self.dim[:self.ndim-len(self.readshape)])
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151
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152 def __getitem__(self, idx):
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153 if idx >= len(self):
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154 raise IndexError(idx)
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155 self.f.seek(self.f_start + idx * self.elsize * self.readsize)
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156 return numpy.fromfile(self.f,
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157 dtype=self.magic_t,
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158 count=self.readsize).reshape(self.returnshape)
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159
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160
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161 #
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162 # TODO: implement item selection:
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163 # e.g. load('some mat', subtensor=(:6, 2:5))
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164 #
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165 # This function should be memory efficient by:
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166 # - allocating an output matrix at the beginning
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167 # - seeking through the file, reading subtensors from multiple places
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168 def read(f, subtensor=None, debug=False):
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169 """Load all or part of file 'f' into a numpy ndarray
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170
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171 @param f: file from which to read
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172 @type f: file-like object
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173
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174 If subtensor is not None, it should be like the argument to
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175 numpy.ndarray.__getitem__. The following two expressions should return
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176 equivalent ndarray objects, but the one on the left may be faster and more
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177 memory efficient if the underlying file f is big.
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178
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179 read(f, subtensor) <===> read(f)[*subtensor]
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180
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181 Support for subtensors is currently spotty, so check the code to see if your
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182 particular type of subtensor is supported.
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183
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184 """
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185 magic_t, elsize, ndim, dim, dim_size = _read_header(f,debug)
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186 f_start = f.tell()
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187
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188 rval = None
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189 if subtensor is None:
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190 rval = numpy.fromfile(f, dtype=magic_t, count=_prod(dim)).reshape(dim)
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191 elif isinstance(subtensor, slice):
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192 if subtensor.step not in (None, 1):
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193 raise NotImplementedError('slice with step', subtensor.step)
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194 if subtensor.start not in (None, 0):
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195 bytes_per_row = _prod(dim[1:]) * elsize
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196 f.seek(f_start + subtensor.start * bytes_per_row)
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197 dim[0] = min(dim[0], subtensor.stop) - subtensor.start
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198 rval = numpy.fromfile(f, dtype=magic_t, count=_prod(dim)).reshape(dim)
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199 else:
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200 raise NotImplementedError('subtensor access not written yet:', subtensor)
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201
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202 return rval
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203
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204 def write(f, mat):
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205 """Write a numpy.ndarray to file.
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206
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207 @param f: file into which to write
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208 @type f: file-like object
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209
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210 @param mat: array to write to file
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211 @type mat: numpy ndarray or compatible
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212
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213 """
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214 def _write_int32(f, i):
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215 i_array = numpy.asarray(i, dtype='int32')
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216 if 0: print 'writing int32', i, i_array
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217 i_array.tofile(f)
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218
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219 try:
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220 _write_int32(f, _dtype_magic[str(mat.dtype)])
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221 except KeyError:
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222 raise TypeError('Invalid ndarray dtype for filetensor format', mat.dtype)
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223
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224 _write_int32(f, len(mat.shape))
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225 shape = mat.shape
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226 if len(shape) < 3:
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227 shape = list(shape) + [1] * (3 - len(shape))
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228 if 0: print 'writing shape =', shape
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229 for sh in shape:
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230 _write_int32(f, sh)
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231 mat.tofile(f)
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232