-
Notifications
You must be signed in to change notification settings - Fork 0
/
convdata.py
138 lines (116 loc) · 7.48 KB
/
convdata.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
# Copyright (c) 2011, Alex Krizhevsky (akrizhevsky@gmail.com)
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# - Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# - Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from data import *
import numpy.random as nr
import numpy as n
import random as r
class CIFARDataProvider(LabeledMemoryDataProvider):
def __init__(self, data_dir, batch_range, init_epoch=1, init_batchnum=None, dp_params={}, test=False):
LabeledMemoryDataProvider.__init__(self, data_dir, batch_range, init_epoch, init_batchnum, dp_params, test)
self.data_mean = self.batch_meta['data_mean']
self.num_colors = 3
self.img_size = 32
# Subtract the mean from the data and make sure that both data and
# labels are in single-precision floating point.
for d in self.data_dic:
# This converts the data matrix to single precision and makes sure that it is C-ordered
d['data'] = n.require((d['data'] - self.data_mean), dtype=n.single, requirements='C')
d['labels'] = n.require(d['labels'].reshape((1, d['data'].shape[1])), dtype=n.single, requirements='C')
def get_next_batch(self):
epoch, batchnum, datadic = LabeledMemoryDataProvider.get_next_batch(self)
return epoch, batchnum, [datadic['data'], datadic['labels']]
# Returns the dimensionality of the two data matrices returned by get_next_batch
# idx is the index of the matrix.
def get_data_dims(self, idx=0):
return self.img_size**2 * self.num_colors if idx == 0 else 1
# Takes as input an array returned by get_next_batch
# Returns a (numCases, imgSize, imgSize, 3) array which can be
# fed to pylab for plotting.
# This is used by shownet.py to plot test case predictions.
def get_plottable_data(self, data):
return n.require((data + self.data_mean).T.reshape(data.shape[1], 3, self.img_size, self.img_size).swapaxes(1,3).swapaxes(1,2) / 255.0, dtype=n.single)
class CroppedCIFARDataProvider(LabeledMemoryDataProvider):
def __init__(self, data_dir, batch_range=None, init_epoch=1, init_batchnum=None, dp_params=None, test=False):
LabeledMemoryDataProvider.__init__(self, data_dir, batch_range, init_epoch, init_batchnum, dp_params, test)
self.border_size = dp_params['crop_border']
self.inner_size = 32 - self.border_size*2
self.multiview = dp_params['multiview_test'] and test
self.num_views = 5*2
self.data_mult = self.num_views if self.multiview else 1
self.num_colors = 3
for d in self.data_dic:
d['data'] = n.require(d['data'], requirements='C')
d['labels'] = n.require(n.tile(d['labels'].reshape((1, d['data'].shape[1])), (1, self.data_mult)), requirements='C')
self.cropped_data = [n.zeros((self.get_data_dims(), self.data_dic[0]['data'].shape[1]*self.data_mult), dtype=n.single) for x in xrange(2)]
self.batches_generated = 0
self.data_mean = self.batch_meta['data_mean'].reshape((3,32,32))[:,self.border_size:self.border_size+self.inner_size,self.border_size:self.border_size+self.inner_size].reshape((self.get_data_dims(), 1))
def get_next_batch(self):
epoch, batchnum, datadic = LabeledMemoryDataProvider.get_next_batch(self)
cropped = self.cropped_data[self.batches_generated % 2]
self.__trim_borders(datadic['data'], cropped)
cropped -= self.data_mean
self.batches_generated += 1
return epoch, batchnum, [cropped, datadic['labels']]
def get_data_dims(self, idx=0):
return self.inner_size**2 * 3 if idx == 0 else 1
# Takes as input an array returned by get_next_batch
# Returns a (numCases, imgSize, imgSize, 3) array which can be
# fed to pylab for plotting.
# This is used by shownet.py to plot test case predictions.
def get_plottable_data(self, data):
return n.require((data + self.data_mean).T.reshape(data.shape[1], 3, self.inner_size, self.inner_size).swapaxes(1,3).swapaxes(1,2) / 255.0, dtype=n.single)
def __trim_borders(self, x, target):
y = x.reshape(3, 32, 32, x.shape[1])
if self.test: # don't need to loop over cases
if self.multiview:
start_positions = [(0,0), (0, self.border_size*2),
(self.border_size, self.border_size),
(self.border_size*2, 0), (self.border_size*2, self.border_size*2)]
end_positions = [(sy+self.inner_size, sx+self.inner_size) for (sy,sx) in start_positions]
for i in xrange(self.num_views/2):
pic = y[:,start_positions[i][0]:end_positions[i][0],start_positions[i][1]:end_positions[i][1],:]
target[:,i * x.shape[1]:(i+1)* x.shape[1]] = pic.reshape((self.get_data_dims(),x.shape[1]))
target[:,(self.num_views/2 + i) * x.shape[1]:(self.num_views/2 +i+1)* x.shape[1]] = pic[:,:,::-1,:].reshape((self.get_data_dims(),x.shape[1]))
else:
pic = y[:,self.border_size:self.border_size+self.inner_size,self.border_size:self.border_size+self.inner_size, :] # just take the center for now
target[:,:] = pic.reshape((self.get_data_dims(), x.shape[1]))
else:
for c in xrange(x.shape[1]): # loop over cases
startY, startX = nr.randint(0,self.border_size*2 + 1), nr.randint(0,self.border_size*2 + 1)
endY, endX = startY + self.inner_size, startX + self.inner_size
pic = y[:,startY:endY,startX:endX, c]
if nr.randint(2) == 0: # also flip the image with 50% probability
pic = pic[:,:,::-1]
target[:,c] = pic.reshape((self.get_data_dims(),))
class DummyConvNetDataProvider(LabeledDummyDataProvider):
def __init__(self, data_dim):
LabeledDummyDataProvider.__init__(self, data_dim)
def get_next_batch(self):
epoch, batchnum, dic = LabeledDummyDataProvider.get_next_batch(self)
dic['data'] = n.require(dic['data'].T, requirements='C')
dic['labels'] = n.require(dic['labels'].T, requirements='C')
return epoch, batchnum, [dic['data'], dic['labels']]
# Returns the dimensionality of the two data matrices returned by get_next_batch
def get_data_dims(self, idx=0):
return self.batch_meta['num_vis'] if idx == 0 else 1