Mercurial > pylearn
annotate filetensor.py @ 513:6103dc5d2a0d
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| author | James Bergstra <bergstrj@iro.umontreal.ca> |
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| date | Thu, 30 Oct 2008 19:39:26 -0400 |
| parents | 040cb796f4e0 |
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| rev | line source |
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| 33 | 1 """ |
| 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} |
| 33 | 4 |
| 5 The format is for dense tensors: | |
| 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> |
| 33 | 11 |
| 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] |
| 33 | 16 |
| 17 For rank >= 3, the number of dimensions matches the rank exactly. | |
| 18 | |
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19 |
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20 @todo: add complex type support |
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21 |
| 33 | 22 """ |
| 23 import sys | |
| 24 import numpy | |
| 25 | |
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26 def _prod(lst): |
| 33 | 27 p = 1 |
| 28 for l in lst: | |
| 29 p *= l | |
| 30 return p | |
| 31 | |
| 32 _magic_dtype = { | |
| 33 0x1E3D4C51 : ('float32', 4), | |
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34 #0x1E3D4C52 : ('packed matrix', 0), #what is a packed matrix? |
| 33 | 35 0x1E3D4C53 : ('float64', 8), |
| 36 0x1E3D4C54 : ('int32', 4), | |
| 35 | 37 0x1E3D4C55 : ('uint8', 1), |
| 33 | 38 0x1E3D4C56 : ('int16', 2), |
| 39 } | |
| 40 _dtype_magic = { | |
| 41 'float32': 0x1E3D4C51, | |
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42 #'packed matrix': 0x1E3D4C52, |
| 33 | 43 'float64': 0x1E3D4C53, |
| 44 'int32': 0x1E3D4C54, | |
| 35 | 45 'uint8': 0x1E3D4C55, |
| 33 | 46 'int16': 0x1E3D4C56 |
| 47 } | |
| 48 | |
| 49 # | |
| 50 # TODO: implement item selection: | |
| 51 # e.g. load('some mat', subtensor=(:6, 2:5)) | |
| 52 # | |
| 53 # This function should be memory efficient by: | |
| 54 # - allocating an output matrix at the beginning | |
| 55 # - seeking through the file, reading subtensors from multiple places | |
| 56 def read(f, subtensor=None, debug=False): | |
| 57 """Load all or part of file 'f' into a numpy ndarray | |
| 58 | |
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59 @param f: file from which to read |
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60 @type f: file-like object |
| 33 | 61 |
| 62 If subtensor is not None, it should be like the argument to | |
| 63 numpy.ndarray.__getitem__. The following two expressions should return | |
| 64 equivalent ndarray objects, but the one on the left may be faster and more | |
| 65 memory efficient if the underlying file f is big. | |
| 66 | |
| 67 read(f, subtensor) <===> read(f)[*subtensor] | |
| 68 | |
| 69 Support for subtensors is currently spotty, so check the code to see if your | |
| 70 particular type of subtensor is supported. | |
| 71 | |
| 72 """ | |
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73 def _read_int32(f): |
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74 s = f.read(4) |
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75 s_array = numpy.fromstring(s, dtype='int32') |
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76 return s_array.item() |
| 33 | 77 |
| 78 #what is the data type of this matrix? | |
| 79 #magic_s = f.read(4) | |
| 80 #magic = numpy.fromstring(magic_s, dtype='int32') | |
| 81 magic = _read_int32(f) | |
| 82 magic_t, elsize = _magic_dtype[magic] | |
| 83 if debug: | |
| 84 print 'header magic', magic, magic_t, elsize | |
| 85 if magic_t == 'packed matrix': | |
| 86 raise NotImplementedError('packed matrix not supported') | |
| 87 | |
| 88 #what is the rank of the tensor? | |
| 89 ndim = _read_int32(f) | |
| 90 if debug: print 'header ndim', ndim | |
| 91 | |
| 92 #what are the dimensions of the tensor? | |
| 93 dim = numpy.fromfile(f, dtype='int32', count=max(ndim,3))[:ndim] | |
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94 dim_size = _prod(dim) |
| 33 | 95 if debug: print 'header dim', dim, dim_size |
| 96 | |
| 97 rval = None | |
| 98 if subtensor is None: | |
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99 rval = numpy.fromfile(f, dtype=magic_t, count=_prod(dim)).reshape(dim) |
| 33 | 100 elif isinstance(subtensor, slice): |
| 101 if subtensor.step not in (None, 1): | |
| 102 raise NotImplementedError('slice with step', subtensor.step) | |
| 103 if subtensor.start not in (None, 0): | |
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104 bytes_per_row = _prod(dim[1:]) * elsize |
| 33 | 105 raise NotImplementedError('slice with start', subtensor.start) |
| 106 dim[0] = min(dim[0], subtensor.stop) | |
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107 rval = numpy.fromfile(f, dtype=magic_t, count=_prod(dim)).reshape(dim) |
| 33 | 108 else: |
| 109 raise NotImplementedError('subtensor access not written yet:', subtensor) | |
| 110 | |
| 111 return rval | |
| 112 | |
| 113 def write(f, mat): | |
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114 """Write a numpy.ndarray to file. |
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115 |
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116 @param f: file into which to write |
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117 @type f: file-like object |
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118 |
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119 @param mat: array to write to file |
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120 @type mat: numpy ndarray or compatible |
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121 |
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122 """ |
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123 def _write_int32(f, i): |
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124 i_array = numpy.asarray(i, dtype='int32') |
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125 if 0: print 'writing int32', i, i_array |
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126 i_array.tofile(f) |
| 33 | 127 |
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128 try: |
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129 _write_int32(f, _dtype_magic[str(mat.dtype)]) |
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130 except KeyError: |
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131 raise TypeError('Invalid ndarray dtype for filetensor format', mat.dtype) |
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132 |
| 33 | 133 _write_int32(f, len(mat.shape)) |
| 134 shape = mat.shape | |
| 135 if len(shape) < 3: | |
| 136 shape = list(shape) + [1] * (3 - len(shape)) | |
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137 if 0: print 'writing shape =', shape |
| 33 | 138 for sh in shape: |
| 139 _write_int32(f, sh) | |
| 140 mat.tofile(f) | |
| 141 |