-
Notifications
You must be signed in to change notification settings - Fork 0
/
convnet.py
204 lines (179 loc) · 10.2 KB
/
convnet.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
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
# 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.
import numpy as n
import numpy.random as nr
from util import *
from data import *
from options import *
from gpumodel import *
import sys
import math as m
import layer as lay
from convdata import *
from os import linesep as NL
#import pylab as pl
class ConvNet(IGPUModel):
def __init__(self, op, load_dic, dp_params={}):
filename_options = []
dp_params['multiview_test'] = op.get_value('multiview_test')
dp_params['crop_border'] = op.get_value('crop_border')
IGPUModel.__init__(self, "ConvNet", op, load_dic, filename_options, dp_params=dp_params)
def import_model(self):
lib_name = "pyconvnet" if is_windows_machine() else "_ConvNet"
print "========================="
print "Importing %s C++ module" % lib_name
self.libmodel = __import__(lib_name)
def init_model_lib(self):
self.libmodel.initModel(self.layers, self.minibatch_size, self.device_ids[0])
def init_model_state(self):
ms = self.model_state
if self.load_file:
ms['layers'] = lay.LayerParser.parse_layers(self.layer_def, self.layer_params, self, ms['layers'])
else:
ms['layers'] = lay.LayerParser.parse_layers(self.layer_def, self.layer_params, self)
self.layers_dic = dict(zip([l['name'] for l in ms['layers']], ms['layers']))
logreg_name = self.op.get_value('logreg_name')
if logreg_name:
self.logreg_idx = self.get_layer_idx(logreg_name, check_type='cost.logreg')
# Convert convolutional layers to local
if len(self.op.get_value('conv_to_local')) > 0:
for i, layer in enumerate(ms['layers']):
if layer['type'] == 'conv' and layer['name'] in self.op.get_value('conv_to_local'):
lay.LocalLayerParser.conv_to_local(ms['layers'], i)
# Decouple weight matrices
if len(self.op.get_value('unshare_weights')) > 0:
for name_str in self.op.get_value('unshare_weights'):
if name_str:
name = lay.WeightLayerParser.get_layer_name(name_str)
if name is not None:
name, idx = name[0], name[1]
if name not in self.layers_dic:
raise ModelStateException("Layer '%s' does not exist; unable to unshare" % name)
layer = self.layers_dic[name]
lay.WeightLayerParser.unshare_weights(layer, ms['layers'], matrix_idx=idx)
else:
raise ModelStateException("Invalid layer name '%s'; unable to unshare." % name_str)
self.op.set_value('conv_to_local', [], parse=False)
self.op.set_value('unshare_weights', [], parse=False)
def get_layer_idx(self, layer_name, check_type=None):
try:
layer_idx = [l['name'] for l in self.model_state['layers']].index(layer_name)
if check_type:
layer_type = self.model_state['layers'][layer_idx]['type']
if layer_type != check_type:
raise ModelStateException("Layer with name '%s' has type '%s'; should be '%s'." % (layer_name, layer_type, check_type))
return layer_idx
except ValueError:
raise ModelStateException("Layer with name '%s' not defined." % layer_name)
def fill_excused_options(self):
if self.op.get_value('check_grads'):
self.op.set_value('save_path', '')
self.op.set_value('train_batch_range', '0')
self.op.set_value('test_batch_range', '0')
self.op.set_value('data_path', '')
# Make sure the data provider returned data in proper format
def parse_batch_data(self, batch_data, train=True):
if max(d.dtype != n.single for d in batch_data[2]):
raise DataProviderException("All matrices returned by data provider must consist of single-precision floats.")
return batch_data
def start_batch(self, batch_data, train=True):
data = batch_data[2]
if self.check_grads:
self.libmodel.checkGradients(data)
elif not train and self.multiview_test:
self.libmodel.startMultiviewTest(data, self.train_data_provider.num_views, self.logreg_idx)
else:
self.libmodel.startBatch(data, not train)
def print_iteration(self):
print "%d.%d..." % (self.epoch, self.batchnum),
def print_train_time(self, compute_time_py):
print "(%.3f sec)" % (compute_time_py)
def print_costs(self, cost_outputs):
costs, num_cases = cost_outputs[0], cost_outputs[1]
for errname in costs.keys():
costs[errname] = [(v/num_cases) for v in costs[errname]]
print "%s: " % errname,
print ", ".join("%6f" % v for v in costs[errname]),
if sum(m.isnan(v) for v in costs[errname]) > 0 or sum(m.isinf(v) for v in costs[errname]):
print "^ got nan or inf!"
sys.exit(1)
def print_train_results(self):
self.print_costs(self.train_outputs[-1])
def print_test_status(self):
pass
def print_test_results(self):
print ""
print "======================Test output======================"
self.print_costs(self.test_outputs[-1])
print ""
print "-------------------------------------------------------",
for i,l in enumerate(self.layers): # This is kind of hacky but will do for now.
if 'weights' in l:
if type(l['weights']) == n.ndarray:
print "%sLayer '%s' weights: %e [%e]" % (NL, l['name'], n.mean(n.abs(l['weights'])), n.mean(n.abs(l['weightsInc']))),
elif type(l['weights']) == list:
print ""
print NL.join("Layer '%s' weights[%d]: %e [%e]" % (l['name'], i, n.mean(n.abs(w)), n.mean(n.abs(wi))) for i,(w,wi) in enumerate(zip(l['weights'],l['weightsInc']))),
print "%sLayer '%s' biases: %e [%e]" % (NL, l['name'], n.mean(n.abs(l['biases'])), n.mean(n.abs(l['biasesInc']))),
print ""
def conditional_save(self):
self.save_state()
print "-------------------------------------------------------"
print "Saved checkpoint to %s" % os.path.join(self.save_path, self.save_file)
print "=======================================================",
def aggregate_test_outputs(self, test_outputs):
num_cases = sum(t[1] for t in test_outputs)
for i in xrange(1 ,len(test_outputs)):
for k,v in test_outputs[i][0].items():
for j in xrange(len(v)):
test_outputs[0][0][k][j] += test_outputs[i][0][k][j]
return (test_outputs[0][0], num_cases)
@classmethod
def get_options_parser(cls):
op = IGPUModel.get_options_parser()
op.add_option("mini", "minibatch_size", IntegerOptionParser, "Minibatch size", default=128)
op.add_option("layer-def", "layer_def", StringOptionParser, "Layer definition file", set_once=True)
op.add_option("layer-params", "layer_params", StringOptionParser, "Layer parameter file")
op.add_option("check-grads", "check_grads", BooleanOptionParser, "Check gradients and quit?", default=0, excuses=['data_path','save_path','train_batch_range','test_batch_range'])
op.add_option("multiview-test", "multiview_test", BooleanOptionParser, "Cropped DP: test on multiple patches?", default=0, requires=['logreg_name'])
op.add_option("crop-border", "crop_border", IntegerOptionParser, "Cropped DP: crop border size", default=4, set_once=True)
op.add_option("logreg-name", "logreg_name", StringOptionParser, "Cropped DP: logreg layer name (for --multiview-test)", default="")
op.add_option("conv-to-local", "conv_to_local", ListOptionParser(StringOptionParser), "Convert given conv layers to unshared local", default=[])
op.add_option("unshare-weights", "unshare_weights", ListOptionParser(StringOptionParser), "Unshare weight matrices in given layers", default=[])
op.add_option("conserve-mem", "conserve_mem", BooleanOptionParser, "Conserve GPU memory (slower)?", default=0)
op.delete_option('max_test_err')
op.options["max_filesize_mb"].default = 0
op.options["testing_freq"].default = 50
op.options["num_epochs"].default = 50000
op.options['dp_type'].default = None
DataProvider.register_data_provider('cifar', 'CIFAR', CIFARDataProvider)
DataProvider.register_data_provider('dummy-cn-n', 'Dummy ConvNet', DummyConvNetDataProvider)
DataProvider.register_data_provider('cifar-cropped', 'Cropped CIFAR', CroppedCIFARDataProvider)
return op
if __name__ == "__main__":
#nr.seed(5)
op = ConvNet.get_options_parser()
op, load_dic = IGPUModel.parse_options(op)
model = ConvNet(op, load_dic)
model.start()