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convdata.py
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convdata.py
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# 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