Mercurial > ift6266

annotate datasets/dsetiter.py @ 166:17ae5a1a4dd1

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Moving the convolutional MLP code into baseline
author Dumitru Erhan <dumitru.erhan@gmail.com>
date Fri, 26 Feb 2010 14:03:24 -0500
parents 4b28d7382dbf
children 938bd350dbf0
rev   line source
163
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1 import numpy
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2
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3 class DummyFile(object):
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4 def __init__(self, size):
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5 self.size = size
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6
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7 def read(self, num):
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8 if num > self.size:
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9 num = self.size
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10 self.size -= num
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11 return numpy.zeros((num, 3, 2))
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12
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13 class DataIterator(object):
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14
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15 def __init__(self, files, batchsize, bufsize=None):
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16 r"""
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17 Makes an iterator which will read examples from `files`
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18 and return them in `batchsize` lots.
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19
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20 Parameters:
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21 files -- list of numpy readers
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22 batchsize -- (int) the size of returned batches
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23 bufsize -- (int, default=None) internal read buffer size.
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24
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25 Tests:
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26 >>> d = DataIterator([DummyFile(930)], 10, 100)
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27 >>> d.batchsize
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28 10
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29 >>> d.bufsize
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30 100
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31 >>> d = DataIterator([DummyFile(1)], 10)
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32 >>> d.batchsize
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33 10
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34 >>> d.bufsize
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35 10000
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36 >>> d = DataIterator([DummyFile(1)], 99)
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37 >>> d.batchsize
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38 99
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39 >>> d.bufsize
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40 9999
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41 >>> d = DataIterator([DummyFile(1)], 10, 121)
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42 >>> d.batchsize
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43 10
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44 >>> d.bufsize
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45 120
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46 >>> d = DataIterator([DummyFile(1)], 10, 1)
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47 >>> d.batchsize
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48 10
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49 >>> d.bufsize
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50 10
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51 >>> d = DataIterator([DummyFile(1)], 2000)
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52 >>> d.batchsize
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53 2000
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54 >>> d.bufsize
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55 20000
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56 >>> d = DataIterator([DummyFile(1)], 2000, 31254)
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57 >>> d.batchsize
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58 2000
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59 >>> d.bufsize
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60 30000
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61 >>> d = DataIterator([DummyFile(1)], 2000, 10)
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62 >>> d.batchsize
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63 2000
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64 >>> d.bufsize
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65 2000
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66 """
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67 self.batchsize = batchsize
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68 if bufsize is None:
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69 self.bufsize = max(10*batchsize, 10000)
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70 else:
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71 self.bufsize = bufsize
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72 self.bufsize -= self.bufsize % self.batchsize
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73 if self.bufsize < self.batchsize:
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74 self.bufsize = self.batchsize
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75 self.files = iter(files)
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76 self.curfile = self.files.next()
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77 self.empty = False
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78 self._fill_buf()
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79
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80 def _fill_buf(self):
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81 r"""
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82 Fill the internal buffer.
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83
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84 Will fill across files in case the current one runs out.
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85
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86 Test:
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87 >>> d = DataIterator([DummyFile(20)], 10, 10)
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88 >>> d._fill_buf()
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89 >>> d.curpos
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90 0
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91 >>> len(d.buffer)
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92 10
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93 >>> d = DataIterator([DummyFile(11), DummyFile(9)], 10, 10)
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94 >>> d._fill_buf()
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95 >>> len(d.buffer)
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96 10
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97 >>> d._fill_buf()
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98 Traceback (most recent call last):
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99 ...
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100 StopIteration
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101 >>> d = DataIterator([DummyFile(10), DummyFile(9)], 10, 10)
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102 >>> d._fill_buf()
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103 >>> len(d.buffer)
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104 9
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105 >>> d._fill_buf()
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106 Traceback (most recent call last):
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107 ...
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108 StopIteration
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109 """
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110 if self.empty:
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111 raise StopIteration
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112 self.buffer = self.curfile.read(self.bufsize)
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113
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114 while len(self.buffer) < self.bufsize:
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115 try:
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116 self.curfile = self.files.next()
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117 except StopIteration:
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118 self.empty = True
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119 if len(self.buffer) == 0:
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120 raise StopIteration
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121 self.curpos = 0
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122 return
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123 tmpbuf = self.curfile.read(self.bufsize - len(self.buffer))
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124 self.buffer = numpy.row_stack((self.buffer, tmpbuf))
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125 self.curpos = 0
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126
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127 def __next__(self):
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128 r"""
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129 Returns the next portion of the dataset.
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130
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131 Test:
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132 >>> d = DataIterator([DummyFile(20)], 10, 20)
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133 >>> len(d.next())
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134 10
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135 >>> len(d.next())
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136 10
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137 >>> d.next()
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138 Traceback (most recent call last):
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139 ...
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140 StopIteration
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141 >>> d.next()
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142 Traceback (most recent call last):
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143 ...
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144 StopIteration
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145
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146 """
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147 if self.curpos >= self.bufsize:
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148 self._fill_buf()
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149 res = self.buffer[self.curpos:self.curpos+self.batchsize]
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150 self.curpos += self.batchsize
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151 return res
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152
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153 next = __next__
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154
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155 def __iter__(self):
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156 return self