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comparison filetensor.py @ 33:bb92087cb0f6

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added filetensor.py
author bergstrj@iro.umontreal.ca
date Thu, 17 Apr 2008 12:49:33 -0400
parents
children 2508c373cf29
comparison
equal deleted inserted replaced
32:039c0f249859 33:bb92087cb0f6
1 """
2 Read and write the matrix file format described at
3 http://www.cs.nyu.edu/~ylclab/data/norb-v1.0/index.html
4
5 The format is for dense tensors:
6
7 magic number indicating type and endianness - 4bytes
8 rank of tensor - int32
9 dimensions - int32, int32, int32, ...
10 <data>
11
12 The number of dimensions and rank is slightly tricky:
13 for scalar: rank=0, dimensions = [1, 1, 1]
14 for vector: rank=1, dimensions = [?, 1, 1]
15 for matrix: rank=2, dimensions = [?, ?, 1]
16
17 For rank >= 3, the number of dimensions matches the rank exactly.
18
19 """
20 import sys
21 import numpy
22
23 def prod(lst):
24 p = 1
25 for l in lst:
26 p *= l
27 return p
28
29 _magic_dtype = {
30 0x1E3D4C51 : ('float32', 4),
31 0x1E3D4C52 : ('packed matrix', 0), #what is a packed matrix?
32 0x1E3D4C53 : ('float64', 8),
33 0x1E3D4C54 : ('int32', 4),
34 0x1E3D4C55 : ('int8', 1),
35 0x1E3D4C56 : ('int16', 2),
36 }
37 _dtype_magic = {
38 'float32': 0x1E3D4C51,
39 'packed matrix': 0x1E3D4C52,
40 'float64': 0x1E3D4C53,
41 'int32': 0x1E3D4C54,
42 'int8': 0x1E3D4C55,
43 'int16': 0x1E3D4C56
44 }
45
46 def _unused():
47 f.seek(0,2) #seek to end
48 f_len = f.tell()
49 f.seek(f_data_start,0) #seek back to where we were
50
51 if debug: print 'length:', f_len
52
53
54 f_data_bytes = (f_len - f_data_start)
55
56 if debug: print 'data bytes according to header: ', dim_size * elsize
57 if debug: print 'data bytes according to file : ', f_data_bytes
58
59 if debug: print 'reading data...'
60 sys.stdout.flush()
61
62 def _write_int32(f, i):
63 i_array = numpy.asarray(i, dtype='int32')
64 if 0: print 'writing int32', i, i_array
65 i_array.tofile(f)
66 def _read_int32(f):
67 s = f.read(4)
68 s_array = numpy.fromstring(s, dtype='int32')
69 return s_array.item()
70
71 def read_ndarray(f, dim, dtype):
72 return numpy.fromfile(f, dtype=dtype, count=prod(dim)).reshape(dim)
73
74 #
75 # TODO: implement item selection:
76 # e.g. load('some mat', subtensor=(:6, 2:5))
77 #
78 # This function should be memory efficient by:
79 # - allocating an output matrix at the beginning
80 # - seeking through the file, reading subtensors from multiple places
81 def read(f, subtensor=None, debug=False):
82 """Load all or part of file 'f' into a numpy ndarray
83
84 If f is a string, it will be treated as a filename, and opened in read mode.
85
86 If subtensor is not None, it should be like the argument to
87 numpy.ndarray.__getitem__. The following two expressions should return
88 equivalent ndarray objects, but the one on the left may be faster and more
89 memory efficient if the underlying file f is big.
90
91 read(f, subtensor) <===> read(f)[*subtensor]
92
93 Support for subtensors is currently spotty, so check the code to see if your
94 particular type of subtensor is supported.
95
96 """
97
98 if isinstance(f, str):
99 if debug: print 'f', f
100 f = file(f, 'r')
101
102 #what is the data type of this matrix?
103 #magic_s = f.read(4)
104 #magic = numpy.fromstring(magic_s, dtype='int32')
105 magic = _read_int32(f)
106 magic_t, elsize = _magic_dtype[magic]
107 if debug:
108 print 'header magic', magic, magic_t, elsize
109 if magic_t == 'packed matrix':
110 raise NotImplementedError('packed matrix not supported')
111
112 #what is the rank of the tensor?
113 ndim = _read_int32(f)
114 if debug: print 'header ndim', ndim
115
116 #what are the dimensions of the tensor?
117 dim = numpy.fromfile(f, dtype='int32', count=max(ndim,3))[:ndim]
118 dim_size = prod(dim)
119 if debug: print 'header dim', dim, dim_size
120
121 rval = None
122 if subtensor is None:
123 rval = read_ndarray(f, dim, magic_t)
124 elif isinstance(subtensor, slice):
125 if subtensor.step not in (None, 1):
126 raise NotImplementedError('slice with step', subtensor.step)
127 if subtensor.start not in (None, 0):
128 bytes_per_row = prod(dim[1:]) * elsize
129 raise NotImplementedError('slice with start', subtensor.start)
130 dim[0] = min(dim[0], subtensor.stop)
131 rval = read_ndarray(f, dim, magic_t)
132 else:
133 raise NotImplementedError('subtensor access not written yet:', subtensor)
134
135 return rval
136
137 def write(f, mat):
138 if isinstance(f, str):
139 f = file(f, 'w')
140
141 _write_int32(f, _dtype_magic[str(mat.dtype)])
142 _write_int32(f, len(mat.shape))
143 shape = mat.shape
144 if len(shape) < 3:
145 shape = list(shape) + [1] * (3 - len(shape))
146 print 'writing shape =', shape
147 for sh in shape:
148 _write_int32(f, sh)
149 mat.tofile(f)
150
151 if __name__ == '__main__':
152 #a small test script, starts by reading sys.argv[1]
153 rval = read(sys.argv[1], slice(400), debug=True) #load from filename
154 print 'rval', rval.shape, rval.size
155
156 f = file('/tmp/some_mat', 'w');
157 write(f, rval)
158 print ''
159 f.close()
160 f = file('/tmp/some_mat', 'r');
161 rval2 = read(f) #load from file handle
162 print 'rval2', rval2.shape, rval2.size
163
164 assert rval.dtype == rval2.dtype
165 assert rval.shape == rval2.shape
166 assert numpy.all(rval == rval2)
167 print 'ok'
168