Mercurial > ift6266

annotate data_generation/transformations/filetensor.py @ 167:1f5937e9e530

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More moves - transformations into data_generation, added "deep" folder
author Dumitru Erhan <dumitru.erhan@gmail.com>
date Fri, 26 Feb 2010 14:15:38 -0500
parents transformations/filetensor.py@faacc76d21c2
children
rev   line source
10
faacc76d21c2 Basic new pipeline script for the images tranforms
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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