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

    module = None
13
14
15
    _current_dir_ = os.path.join('./')
    _data_dir_    = os.path.join(_current_dir_, 'data', 'Alexnet')
    _model_dir_   = os.path.join(_current_dir_, 'model', 'Alexnet')
16

17
18
    _init_net_    = os.path.join(_model_dir_, 'init_net.pb')
    _predict_net_ = os.path.join(_model_dir_, 'predict_net.pb')
19

20
21
22
23
24
25
26
27
28
29
30
    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


31
32
    def add_input(self, model, batch_size, db, db_type, device_opts):
        with core.DeviceScope(device_opts):
33
34
35
36
37
38
39
            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)

40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
            # 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)
56
57
58
59

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

            return data, label, dataset_size
60
61
62
63
64
65

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

    		data = data
    		# data, output shape: {[3,224,224]}
66
      		conv1_ = brew.conv(model, data, 'conv1_', dim_in=3, dim_out=96, kernel=11, stride=4)
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
    		# conv1_, output shape: {[96,55,55]}
    		lrn1_ = mx.symbol.LRN(data=conv1_,
    		    alpha=0.0001,
    		    beta=0.75,
    		    knorm=2,
    		    nsize=5,
    		    name="lrn1_")
    		pool1_ = brew.max_pool(model, lrn1_, 'pool1_', kernel=3, stride=2)
    		# pool1_, output shape: {[96,27,27]}
    		relu1_ = brew.relu(model, pool1_, pool1_)
    		split1_ = mx.symbol.split(data=relu1_,
    		    num_outputs=2,
    		    axis=1,
    		    name="split1_")
    		# split1_, output shape: {[48,27,27][48,27,27]}
    		get2_1_ = split1_[0]
      		conv2_1_ = brew.conv(model, get2_1_, 'conv2_1_', dim_in=48, dim_out=128, kernel=5, stride=1)
    		# conv2_1_, output shape: {[128,27,27]}
    		lrn2_1_ = mx.symbol.LRN(data=conv2_1_,
    		    alpha=0.0001,
    		    beta=0.75,
    		    knorm=2,
    		    nsize=5,
    		    name="lrn2_1_")
    		pool2_1_ = brew.max_pool(model, lrn2_1_, 'pool2_1_', kernel=3, stride=2)
    		# pool2_1_, output shape: {[128,13,13]}
    		relu2_1_ = brew.relu(model, pool2_1_, pool2_1_)
    		get2_2_ = split1_[1]
      		conv2_2_ = brew.conv(model, get2_2_, 'conv2_2_', dim_in=48, dim_out=128, kernel=5, stride=1)
    		# conv2_2_, output shape: {[128,27,27]}
    		lrn2_2_ = mx.symbol.LRN(data=conv2_2_,
    		    alpha=0.0001,
    		    beta=0.75,
    		    knorm=2,
    		    nsize=5,
    		    name="lrn2_2_")
    		pool2_2_ = brew.max_pool(model, lrn2_2_, 'pool2_2_', kernel=3, stride=2)
    		# pool2_2_, output shape: {[128,13,13]}
    		relu2_2_ = brew.relu(model, pool2_2_, pool2_2_)
    		concatenate3_ = mx.symbol.concat(relu2_1_, relu2_2_,
    		    dim=1,
    		    name="concatenate3_")
    		# concatenate3_, output shape: {[256,13,13]}
      		conv3_ = brew.conv(model, concatenate3_, 'conv3_', dim_in=256, dim_out=384, kernel=3, stride=1)
    		# conv3_, output shape: {[384,13,13]}
    		relu3_ = brew.relu(model, conv3_, conv3_)
    		split3_ = mx.symbol.split(data=relu3_,
    		    num_outputs=2,
    		    axis=1,
    		    name="split3_")
    		# split3_, output shape: {[192,13,13][192,13,13]}
    		get4_1_ = split3_[0]
      		conv4_1_ = brew.conv(model, get4_1_, 'conv4_1_', dim_in=192, dim_out=192, kernel=3, stride=1)
    		# conv4_1_, output shape: {[192,13,13]}
    		relu4_1_ = brew.relu(model, conv4_1_, conv4_1_)
      		conv5_1_ = brew.conv(model, relu4_1_, 'conv5_1_', dim_in=192, dim_out=128, kernel=3, stride=1)
    		# conv5_1_, output shape: {[128,13,13]}
    		pool5_1_ = brew.max_pool(model, conv5_1_, 'pool5_1_', kernel=3, stride=2)
    		# pool5_1_, output shape: {[128,6,6]}
    		relu5_1_ = brew.relu(model, pool5_1_, pool5_1_)
    		get4_2_ = split3_[1]
      		conv4_2_ = brew.conv(model, get4_2_, 'conv4_2_', dim_in=192, dim_out=192, kernel=3, stride=1)
    		# conv4_2_, output shape: {[192,13,13]}
    		relu4_2_ = brew.relu(model, conv4_2_, conv4_2_)
      		conv5_2_ = brew.conv(model, relu4_2_, 'conv5_2_', dim_in=192, dim_out=128, kernel=3, stride=1)
    		# conv5_2_, output shape: {[128,13,13]}
    		pool5_2_ = brew.max_pool(model, conv5_2_, 'pool5_2_', kernel=3, stride=2)
    		# pool5_2_, output shape: {[128,6,6]}
    		relu5_2_ = brew.relu(model, pool5_2_, pool5_2_)
    		concatenate6_ = mx.symbol.concat(relu5_1_, relu5_2_,
    		    dim=1,
    		    name="concatenate6_")
    		# concatenate6_, output shape: {[256,6,6]}
    		fc6_ = brew.fc(model, concatenate6_, 'fc6_', dim_in=256 * 6 * 6, dim_out=4096)
    		# fc6_, output shape: {[4096,1,1]}
    		relu6_ = brew.relu(model, fc6_, fc6_)
    		dropout6_ = mx.symbol.Dropout(data=relu6_,
    		    p=0.5,
    		    name="dropout6_")
    		fc7_ = brew.fc(model, dropout6_, 'fc7_', dim_in=4096, dim_out=4096)
    		# fc7_, output shape: {[4096,1,1]}
    		relu7_ = brew.relu(model, fc7_, fc7_)
    		dropout7_ = mx.symbol.Dropout(data=relu7_,
    		    p=0.5,
    		    name="dropout7_")
    		fc8_ = brew.fc(model, dropout7_, 'fc8_', dim_in=4096, dim_out=10)
    		# fc8_, output shape: {[10,1,1]}
    		predictions = brew.softmax(model, fc8_, 'predictions')

    		return predictions

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

199
200
    def train(self, num_epoch=1000, batch_size=64, context='gpu', eval_metric='accuracy', 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':
201
202
            device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
            print("CPU mode selected")
203
        elif context == 'gpu':
204
205
206
            device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)
            print("GPU mode selected")

207
    	workspace.ResetWorkspace(self._model_dir_)
208
209
210
211

    	arg_scope = {"order": "NCHW"}
    	# == Training model ==
    	train_model= model_helper.ModelHelper(name="train_net", arg_scope=arg_scope)
212
    	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)
213
214
215
216
217
218
219
220
221
222
223
    	predictions = self.create_model(train_model, data, device_opts=device_opts)
    	self.add_training_operators(train_model, predictions, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
    	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
224
225
226
    	iterations = self.get_total_num_iter(num_epoch, batch_size, train_dataset_size)
        print("** Starting Training for " + str(num_epoch) + " epochs = " + str(iterations) + " iterations **")
    	for i in range(iterations):
227
    		workspace.RunNet(train_model.net)
228
229
    		if i % 50 == 0:
    			print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss")) + ' - ' + 'Accuracy ' + str(workspace.FetchBlob('accuracy'))
230
231
232
233
234
    	print("Training done")

    	print("== Running Test model ==")
    	# == Testing model. ==
    	test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
235
    	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)
236
237
238
239
240
241
    	predictions = self.create_model(test_model, data, device_opts=device_opts)
    	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
242
243
    	test_accuracy = np.zeros(test_dataset_size/batch_size)
    	for i in range(test_dataset_size/batch_size):
244
245
246
247
248
249
250
251
252
253
254
255
256
    		# Run a forward pass of the net on the current batch
    		workspace.RunNet(test_model.net)
    		# Collect the batch accuracy from the workspace
    		test_accuracy[i] = workspace.FetchBlob('accuracy')

    	print('Test_accuracy: {:.4f}'.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)

    	print("Saving deploy model")
257
    	self.save_net(self._init_net_, self._predict_net_, deploy_model)
258
259
260
261
262
263
264
265
266

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

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

267
        try:
268
            os.makedirs(self._model_dir_)
269
        except OSError:
270
            if not os.path.isdir(self._model_dir_):
271
272
                raise

273
    	print("Save the model to init_net.pb and predict_net.pb")
274
    	with open(predict_net_path, 'wb') as f:
275
    		f.write(model.net._net.SerializeToString())
276
    	with open(init_net_path, 'wb') as f:
277
278
279
    		f.write(init_net.SerializeToString())

    	print("Save the model to init_net.pbtxt and predict_net.pbtxt")
280
281

    	with open(init_net_path.replace('.pb','.pbtxt'), 'w') as f:
282
    		f.write(str(init_net))
283
    	with open(predict_net_path.replace('.pb','.pbtxt'), 'w') as f:
284
285
286
287
    		f.write(str(predict_net))
    	print("== Saved init_net and predict_net ==")

    def load_net(self, init_net_path, predict_net_path, device_opts):
288
289
290
291
292
293
294
295
296
        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:
297
298
299
300
301
    		init_def.ParseFromString(f.read())
    		init_def.device_option.CopyFrom(device_opts)
    		workspace.RunNetOnce(init_def.SerializeToString())

    	net_def = caffe2_pb2.NetDef()
302
    	with open(predict_net_path, 'rb') as f:
303
304
305
    		net_def.ParseFromString(f.read())
    		net_def.device_option.CopyFrom(device_opts)
    		workspace.CreateNet(net_def.SerializeToString(), overwrite=True)
306
    	print("== Loaded init_net and predict_net ==")