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iconvdata (ubuntu's conflicted copy 2013-09-03).py
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# Copyright (c) 2013, Li Sijin ([email protected])
# 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
#CONV_IMG_SIZE = 128 # 128 for the whole image. Attention
CONV_IMG_SIZE=56
class POSEDataProvider(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 = CONV_IMG_SIZE
# 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]), order='F'), dtype=n.single, requirements='C')
def get_next_batch(self):
epoch, batchnum, datadic = LabeledMemoryDataProvider.get_next_batch(self)
alldata = [ datadic['data']] + [ n.require(l.reshape((1,datadic['data'].shape[1]), order='F'), dtype=n.single, requirements='C') for l in datadic['labels']]
return epoch, batchnum, alldata
# 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) / 255.0, dtype=n.single)
class LargeMultiPOSEDataProvider(LabeledDataProvider):
def __init__(self, data_dir, batch_range, init_epoch=1, init_batchnum=None, dp_params={}, test=False):
LabeledDataProvider.__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 = CONV_IMG_SIZE
def get_next_batch(self):
d = self.get_batch(self.curr_batchnum)
d['data'] = n.require((d['data'] - self.data_mean), dtype=n.single, requirements='C')
d['labels'] = n.c_[n.require(d['labels'], dtype=n.single)].reshape((-1, d['data'].shape[1]), order='F')
epoch, batchnum = self.curr_epoch, self.curr_batchnum
self.advance_batch()
alldata = [ d['data'] ] + [n.require(l.reshape((1,d['data'].shape[1]), order='F'), dtype=n.single, requirements='C') for l in d['labels']]
return epoch, batchnum, alldata
# 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) / 255.0, dtype=n.single)
class MultiPOSEDataProvider(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 = CONV_IMG_SIZE
# 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]), order='F'), dtype=n.single, requirements='C')
def get_next_batch(self):
epoch, batchnum, datadic = LabeledMemoryDataProvider.get_next_batch(self)
alldata = [ datadic['data'] ] + [n.require(l.reshape(1,datadic['data'].shape[1]), requirements='C') for l in datadic['labels']]
return epoch, batchnum, alldata
# 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) / 255.0, dtype=n.single)