Mercurial > pylearn
annotate datasets/smallNorb.py @ 535:a6068dedfbd6
Added squashfn
author | Joseph Turian <turian@gmail.com> |
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date | Wed, 19 Nov 2008 18:14:43 -0500 |
parents | 60b7dd5be860 |
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rev | line source |
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1 import os |
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2 import numpy |
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3 from ..filetensor import read |
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4 from .config import data_root |
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5 |
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6 #Path = '/u/bergstrj/pub/data/smallnorb' |
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7 #Path = '/home/fringant2/lisa/louradoj/data/smallnorb' |
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8 #Path = '/home/louradou/data/norb' |
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9 |
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10 class Paths(object): |
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11 """File-related operations on smallNorb |
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12 """ |
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13 def __init__(self): |
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14 smallnorb = [data_root(), 'smallnorb'] |
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15 self.train_dat = os.path.join(*\ |
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16 smallnorb + ['smallnorb-5x46789x9x18x6x2x96x96-training-dat.mat']) |
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17 self.test_dat = os.path.join(*\ |
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18 smallnorb + ['smallnorb-5x01235x9x18x6x2x96x96-testing-dat.mat']) |
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19 self.train_cat = os.path.join(*\ |
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20 smallnorb + ['smallnorb-5x46789x9x18x6x2x96x96-training-cat.mat']) |
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21 self.test_cat = os.path.join(*\ |
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22 smallnorb + ['smallnorb-5x01235x9x18x6x2x96x96-testing-cat.mat']) |
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23 self.train_info = os.path.join(*\ |
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24 smallnorb + ['smallnorb-5x46789x9x18x6x2x96x96-training-info.mat']) |
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25 self.test_info = os.path.join(*\ |
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26 smallnorb + ['smallnorb-5x01235x9x18x6x2x96x96-testing-info.mat']) |
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27 |
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28 def load_append_train_test(self, normalize_pixels=True, downsample_amt=1, dtype='uint8'): |
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29 """ Load the smallNorb data into numpy matrices. |
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30 |
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31 normalize_pixels True will divide the values by 255, which makes sense in conjunction |
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32 with dtype=float32 or dtype=float64. |
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33 |
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34 """ |
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35 def downsample(dataset): |
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36 return dataset[:, 0, ::downsample_amt, ::downsample_amt] |
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37 |
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38 samples = downsample(read(open(self.train_dat))) |
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39 samples = numpy.vstack((samples, downsample(read(open(self.test_dat))))) |
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40 samples = numpy.asarray(samples, dtype=dtype) |
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41 if normalize_pixels: |
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42 samples *= (1.0 / 255.0) |
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43 |
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44 labels = read(open(self.train_cat)) |
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45 labels = numpy.hstack((labels, read(open(self.test_cat)))) |
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46 |
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47 infos = read(open(self.train_info)) |
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48 infos = numpy.vstack((infos, read(open(self.test_info)))) |
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49 |
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50 return samples, labels, infos |
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51 |
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52 def smallnorb_iid(ntrain=29160, nvalid=9720, ntest=9720, dtype='float64', normalize_pixels=True): |
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53 """Variation of the smallNorb task in which we randomly shuffle all the object instances |
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54 together before dividing into train/valid/test. |
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55 |
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56 The default train/valid/test sizes correspond to 60/20/20 split of the entire dataset. |
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57 |
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58 :returns: 5, (train_x, train_labels), (valid_x, valid_labels), (test_x, test_labels) |
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59 |
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60 """ |
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61 # cut from /u/louradoj/theano/hpu/expcode1.py |
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62 rng = numpy.random.RandomState(1) |
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63 samples, labels, infos = Paths().load_append_train_test(downsample_amt=3, dtype=dtype, normalize_pixels=normalize_pixels) |
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64 |
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65 nsamples = samples.shape[0] |
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66 if ntrain + nvalid + ntest > nsamples: |
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67 raise Exception("ntrain+nvalid+ntest exceeds number of samples (%i)" % nsamples, |
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68 (ntrain, nvalid, ntest)) |
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69 i0 = 0 |
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70 i1 = ntrain |
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71 i2 = ntrain + nvalid |
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72 i3 = ntrain + nvalid + ntest |
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73 |
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74 indices = rng.permutation(nsamples) |
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75 train_rows = indices[i0:i1] |
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76 valid_rows = indices[i1:i2] |
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77 test_rows = indices[i2:i3] |
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78 |
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79 n_labels = 5 |
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80 |
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81 def _pick_rows(rows): |
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82 a = numpy.array([samples[i].flatten() for i in rows]) |
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83 b = numpy.array([labels[i] for i in rows]) |
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84 return a, b |
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85 |
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86 return [_pick_rows(r) for r in (train_rows, valid_rows, test_rows)] |
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87 |
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88 def smallnorb_azSplit(): |
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89 # cut from /u/louradoj/theano/hpu/expcode1.py |
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90 # WARNING NOT NECESSARILY WORKING CODE |
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91 |
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92 samples, labels, infos = _load_append_train_test() |
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93 train_rows, valid_rows, test_rows = [], [], [] |
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94 train_rows_azimuth = [] |
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95 for instance in range(10): |
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96 az_min = 4*instance |
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97 az_max = 4*instance + 18 |
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98 train_rows_azimuth.append( [a % 36 for a in range(az_min,az_max,2)] ) |
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99 #print "train_rows_azimuth", train_rows_azimuth |
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100 for i, info in enumerate(infos): |
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101 if info[2] in train_rows_azimuth[info[0]]: |
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102 train_rows.append(i) |
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103 elif info[2] / 2 % 2 == 0: |
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104 test_rows.append(i) |
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105 else: |
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106 valid_rows.append(i) |
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107 |
74b3e65f5f24
added smallNorb dataset, switched to PYLEARN_DATA_ROOT
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff
changeset
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108 return [_pick_rows(samples, labels, r) for r in (train_rows, valid_rows, test_rows)] |