CNNCreator_mnist_mnistClassifier_net.py 13.1 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
from caffe2.python import workspace, core, model_helper, brew, optimizer
from caffe2.python.predictor import mobile_exporter
from caffe2.proto import caffe2_pb2
import numpy as np
import math
import datetime
import logging
import os
import sys
import lmdb

class CNNCreator_mnist_mnistClassifier_net:

    module = None
    _current_dir_ = os.path.join('./')
16
17
    _data_dir_    = os.path.join(_current_dir_, 'data/mnist.LeNetNetwork')
    _model_dir_   = os.path.join(_current_dir_, 'model', 'mnist.LeNetNetwork')
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
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271

    _init_net_    = os.path.join(_model_dir_, 'init_net.pb')
    _predict_net_ = os.path.join(_model_dir_, 'predict_net.pb')

    def get_total_num_iter(self, num_epoch, batch_size, dataset_size):
        #Force floating point calculation
        batch_size_float = float(batch_size)
        dataset_size_float = float(dataset_size)

        iterations_float = math.ceil(num_epoch*(dataset_size_float/batch_size_float))
        iterations_int = int(iterations_float)

        return iterations_int

    def get_epoch_as_iter(self, num_epoch, batch_size, dataset_size):   #To print metric durint training process
        #Force floating point calculation
        batch_size_float = float(batch_size)
        dataset_size_float = float(dataset_size)

        epoch_float = math.ceil(dataset_size_float/batch_size_float)
        epoch_int = int(epoch_float)

        return epoch_int

    def add_input(self, model, batch_size, db, db_type, device_opts):
        with core.DeviceScope(device_opts):
            if not os.path.isdir(db):
                logging.error("Data loading failure. Directory '" + os.path.abspath(db) + "' does not exist.")
                sys.exit(1)
            elif not (os.path.isfile(os.path.join(db, 'data.mdb')) and os.path.isfile(os.path.join(db, 'lock.mdb'))):
                logging.error("Data loading failure. Directory '" + os.path.abspath(db) + "' does not contain lmdb files.")
                sys.exit(1)

            # load the data
            data_uint8, label = brew.db_input(
                model,
                blobs_out=["data_uint8", "label"],
                batch_size=batch_size,
                db=db,
                db_type=db_type,
            )
            # cast the data to float
            data = model.Cast(data_uint8, "data", to=core.DataType.FLOAT)

            # scale data from [0,255] down to [0,1]
            data = model.Scale(data, data, scale=float(1./256))

            # don't need the gradient for the backward pass
            data = model.StopGradient(data, data)

            dataset_size = int (lmdb.open(db).stat()['entries'])

            return data, label, dataset_size

    def create_model(self, model, data, device_opts, is_test):
    	with core.DeviceScope(device_opts):

    		image = data
    		# image, output shape: {[1,28,28]}
    		conv1_ = brew.conv(model, image, 'conv1_', dim_in=1, dim_out=20, kernel=5, stride=1)
    		# conv1_, output shape: {[20,24,24]}
    		pool1_ = brew.max_pool(model, conv1_, 'pool1_', kernel=2, stride=2)
    		# pool1_, output shape: {[20,12,12]}
    		conv2_ = brew.conv(model, pool1_, 'conv2_', dim_in=20, dim_out=50, kernel=5, stride=1)
    		# conv2_, output shape: {[50,8,8]}
    		pool2_ = brew.max_pool(model, conv2_, 'pool2_', kernel=2, stride=2)
    		# pool2_, output shape: {[50,4,4]}
    		fc2_ = brew.fc(model, pool2_, 'fc2_', dim_in=50 * 4 * 4, dim_out=500)
    		# fc2_, output shape: {[500,1,1]}
    		relu2_ = brew.relu(model, fc2_, fc2_)
    		fc3_ = brew.fc(model, relu2_, 'fc3_', dim_in=500, dim_out=10)
    		# fc3_, output shape: {[10,1,1]}
    		predictions = brew.softmax(model, fc3_, 'predictions')

    		return predictions

    # this adds the loss and optimizer
    def add_training_operators(self, model, output, label, device_opts, loss, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum) :
    	with core.DeviceScope(device_opts):
    		if loss == 'cross_entropy':
    		    xent = model.LabelCrossEntropy([output, label], 'xent')
    		    loss = model.AveragedLoss(xent, "loss")
    		elif loss == 'euclidean':
    		    dist = model.net.SquaredL2Distance([label, output], 'dist')
    		    loss = dist.AveragedLoss([], ['loss'])

    		model.AddGradientOperators([loss])

    		if opt_type == 'adam':
    		    if policy == 'step':
    		        opt = optimizer.build_adam(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, beta1=beta1, beta2=beta2, epsilon=epsilon)
    		    elif policy == 'fixed' or policy == 'inv':
    		        opt = optimizer.build_adam(model, base_learning_rate=base_learning_rate, policy=policy, beta1=beta1, beta2=beta2, epsilon=epsilon)
    		    print("adam optimizer selected")
    		elif opt_type == 'sgd':
    		    if policy == 'step':
    		        opt = optimizer.build_sgd(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, gamma=gamma, momentum=momentum)
    		    elif policy == 'fixed' or policy == 'inv':
    		        opt = optimizer.build_sgd(model, base_learning_rate=base_learning_rate, policy=policy, gamma=gamma, momentum=momentum)
    		    print("sgd optimizer selected")
    		elif opt_type == 'rmsprop':
    		    if policy == 'step':
    		        opt = optimizer.build_rms_prop(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, decay=gamma, momentum=momentum, epsilon=epsilon)
    		    elif policy == 'fixed' or policy == 'inv':
    		        opt = optimizer.build_rms_prop(model, base_learning_rate=base_learning_rate, policy=policy, decay=gamma, momentum=momentum, epsilon=epsilon)
    		    print("rmsprop optimizer selected")
    		elif opt_type == 'adagrad':
    		    if policy == 'step':
    		        opt = optimizer.build_adagrad(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, decay=gamma, epsilon=epsilon)
    		    elif policy == 'fixed' or policy == 'inv':
    		        opt = optimizer.build_adagrad(model, base_learning_rate=base_learning_rate, policy=policy, decay=gamma, epsilon=epsilon)
    		    print("adagrad optimizer selected")

    def add_accuracy(self, model, output, label, device_opts, eval_metric):
        with core.DeviceScope(device_opts):
            if eval_metric == 'accuracy':
                accuracy = brew.accuracy(model, [output, label], "accuracy")
            elif eval_metric == 'top_k_accuracy':
                accuracy = brew.accuracy(model, [output, label], "accuracy", top_k=3)
            return accuracy

    def train(self, num_epoch=1000, batch_size=64, context='gpu', eval_metric='accuracy', loss='cross_entropy', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
        if context == 'cpu':
            device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
            print("CPU mode selected")
        elif context == 'gpu':
            device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)
            print("GPU mode selected")

    	workspace.ResetWorkspace(self._model_dir_)

    	arg_scope = {"order": "NCHW"}
    	# == Training model ==
    	train_model= model_helper.ModelHelper(name="train_net", arg_scope=arg_scope)
    	data, label, train_dataset_size = self.add_input(train_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'train_lmdb'), db_type='lmdb', device_opts=device_opts)
    	predictions = self.create_model(train_model, data, device_opts=device_opts, is_test=False)
    	self.add_training_operators(train_model, predictions, label, device_opts, loss, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
    	if not loss == 'euclidean':
    		self.add_accuracy(train_model, predictions, label, device_opts, eval_metric)
    	with core.DeviceScope(device_opts):
    		brew.add_weight_decay(train_model, weight_decay)

    	# Initialize and create the training network
    	workspace.RunNetOnce(train_model.param_init_net)
    	workspace.CreateNet(train_model.net, overwrite=True)

    	# Main Training Loop
    	iterations = self.get_total_num_iter(num_epoch, batch_size, train_dataset_size)
        epoch_as_iter = self.get_epoch_as_iter(num_epoch, batch_size, train_dataset_size)
        print("\n*** Starting Training for " + str(num_epoch) + " epochs = " + str(iterations) + " iterations ***")
        start_date = datetime.datetime.now()
    	for i in range(iterations):
    		workspace.RunNet(train_model.net)
    		if i % 50 == 0 or i % epoch_as_iter == 0:
    			if not loss == 'euclidean':
    				print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss")) + ' - ' + 'Accuracy ' + str(workspace.FetchBlob('accuracy'))
    			else:
    				print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss"))

    			current_time = datetime.datetime.now()
    			elapsed_time = current_time - start_date
    			print 'Progress: ' + str(i) + '/' + str(iterations) + ', ' +'Current time spent: ' + str(elapsed_time)
        current_time = datetime.datetime.now()
        elapsed_time = current_time - start_date
        print 'Progress: ' + str(iterations) + '/' + str(iterations) + ' Training done' + ', ' + 'Total time spent: ' + str(elapsed_time)

    	print("\n*** Running Test model ***")
    	# == Testing model. ==
    	test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
    	data, label, test_dataset_size = self.add_input(test_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'test_lmdb'), db_type='lmdb', device_opts=device_opts)
    	predictions = self.create_model(test_model, data, device_opts=device_opts, is_test=True)
    	if not loss == 'euclidean':
    		self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
    	workspace.RunNetOnce(test_model.param_init_net)
    	workspace.CreateNet(test_model.net, overwrite=True)

    	# Main Testing Loop
    	test_accuracy = np.zeros(test_dataset_size/batch_size)
        start_date = datetime.datetime.now()
    	for i in range(test_dataset_size/batch_size):
    		# Run a forward pass of the net on the current batch
    		workspace.RunNet(test_model.net)
    		# Collect the batch accuracy from the workspace
    		if not loss == 'euclidean':
    			test_accuracy[i] = workspace.FetchBlob('accuracy')
    			print 'Iter ' + str(i) + ': ' + 'Accuracy ' + str(workspace.FetchBlob("accuracy"))
    		else:
    			test_accuracy[i] = workspace.FetchBlob("loss")
    			print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss"))

    		current_time = datetime.datetime.now()
    		elapsed_time = current_time - start_date
    		print 'Progress: ' + str(i) + '/' + str(test_dataset_size/batch_size) + ', ' +'Current time spent: ' + str(elapsed_time)
        current_time = datetime.datetime.now()
        elapsed_time = current_time - start_date
        print 'Progress: ' + str(test_dataset_size/batch_size) + '/' + str(test_dataset_size/batch_size) + ' Testing done' + ', ' + 'Total time spent: ' + str(elapsed_time)
    	print('Test accuracy mean: {:.9f}'.format(test_accuracy.mean()))

    	# == Deployment model. ==
    	# We simply need the main AddModel part.
    	deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
    	self.create_model(deploy_model, "data", device_opts, is_test=True)

    	print("\n*** Saving deploy model ***")
    	self.save_net(self._init_net_, self._predict_net_, deploy_model)

    def save_net(self, init_net_path, predict_net_path, model):

    	init_net, predict_net = mobile_exporter.Export(
    		workspace,
    		model.net,
    		model.params
    	)

        try:
            os.makedirs(self._model_dir_)
        except OSError:
            if not os.path.isdir(self._model_dir_):
                raise

    	print("Save the model to init_net.pb and predict_net.pb")
    	with open(predict_net_path, 'wb') as f:
    		f.write(model.net._net.SerializeToString())
    	with open(init_net_path, 'wb') as f:
    		f.write(init_net.SerializeToString())

    	print("Save the model to init_net.pbtxt and predict_net.pbtxt as additional information")

    	with open(init_net_path.replace('.pb','.pbtxt'), 'w') as f:
    		f.write(str(init_net))
    	with open(predict_net_path.replace('.pb','.pbtxt'), 'w') as f:
    		f.write(str(predict_net))
    	print("== Saved init_net and predict_net ==")

    def load_net(self, init_net_path, predict_net_path, device_opts):
        if not os.path.isfile(init_net_path):
            logging.error("Network loading failure. File '" + os.path.abspath(init_net_path) + "' does not exist.")
            sys.exit(1)
        elif not os.path.isfile(predict_net_path):
            logging.error("Network loading failure. File '" + os.path.abspath(predict_net_path) + "' does not exist.")
            sys.exit(1)

        init_def = caffe2_pb2.NetDef()
    	with open(init_net_path, 'rb') as f:
    		init_def.ParseFromString(f.read())
    		init_def.device_option.CopyFrom(device_opts)
    		workspace.RunNetOnce(init_def.SerializeToString())

    	net_def = caffe2_pb2.NetDef()
    	with open(predict_net_path, 'rb') as f:
    		net_def.ParseFromString(f.read())
    		net_def.device_option.CopyFrom(device_opts)
    		workspace.CreateNet(net_def.SerializeToString(), overwrite=True)
    	print("*** Loaded init_net and predict_net ***")