-
Notifications
You must be signed in to change notification settings - Fork 0
/
train.py
293 lines (248 loc) · 12.2 KB
/
train.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
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
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
import argparse
import math
import h5py
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import socket
import importlib
import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR, 'models'))
sys.path.append(os.path.join(BASE_DIR, 'utils'))
import provider
import tf_util
from datetime import datetime
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
parser.add_argument('--model', default='pointnet_cls_basic', help='Model name: pointnet_cls or pointnet_cls_basic [default: pointnet_cls]')
parser.add_argument('--log_dir', default='log', help='Log dir [default: log]')
parser.add_argument('--num_point', type=int, default=2048, help='Point Number [256/512/1024/2048] [default: 1024]')
parser.add_argument('--num_feature', type=int, default=256, help='Point Number [32-2048] [default: 128]')
parser.add_argument('--max_epoch', type=int, default=50, help='Epoch to run [default: 50]')
parser.add_argument('--num_class', type=int, default=3, help='Number of Classes [default: 7]')
parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]')
parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]')
parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]')
parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]')
parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]')
parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.8]')
FLAGS = parser.parse_args()
#NAME_MODEL = '1' + datetime.now().strftime('%Y-%m-%d-%H_%M_%S')
#NAME_MODEL = '1' + datetime.now().strftime('%Y-%m-%d-%H_%M_%S')
NAME_MODEL = ''
NUM_CLASSES = FLAGS.num_class
BATCH_SIZE = FLAGS.batch_size
NUM_POINT = FLAGS.num_point
NUM_FEATURE = FLAGS.num_feature
MAX_EPOCH = FLAGS.max_epoch
BASE_LEARNING_RATE = FLAGS.learning_rate
GPU_INDEX = FLAGS.gpu
MOMENTUM = FLAGS.momentum
OPTIMIZER = FLAGS.optimizer
DECAY_STEP = FLAGS.decay_step
DECAY_RATE = FLAGS.decay_rate
MODEL = importlib.import_module(FLAGS.model) # import network module
MODEL_FILE = os.path.join(BASE_DIR, 'models', FLAGS.model+'.py')
LOG_DIR = FLAGS.log_dir
if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
HOSTNAME = socket.gethostname()
# ModelNet40 official train/test split
TRAIN_FILES = provider.getDataFiles( \
os.path.join(BASE_DIR, 'data/3Classes/train_files.txt'))
TEST_FILES = provider.getDataFiles(\
os.path.join(BASE_DIR, 'data/3Classes/test_files.txt'))
accVec = [];
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
def get_learning_rate(batch):
learning_rate = tf.train.exponential_decay(
BASE_LEARNING_RATE, # Base learning rate.
batch * BATCH_SIZE, # Current index into the dataset.
DECAY_STEP, # Decay step.
DECAY_RATE, # Decay rate.
staircase=True)
learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!
return learning_rate
def get_bn_decay(batch):
bn_momentum = tf.train.exponential_decay(
BN_INIT_DECAY,
batch*BATCH_SIZE,
BN_DECAY_DECAY_STEP,
BN_DECAY_DECAY_RATE,
staircase=True)
bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)
return bn_decay
def train(numFeature = 256):
NUM_FEATURE = numFeature
with tf.Graph().as_default():
with tf.device('/gpu:'+str(GPU_INDEX)):
pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)
is_training_pl = tf.placeholder(tf.bool, shape=())
print(is_training_pl)
# Note the global_step=batch parameter to minimize.
# That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains.
batch = tf.Variable(0)
bn_decay = get_bn_decay(batch)
tf.summary.scalar('bn_decay', bn_decay)
# Get model and loss
pred, end_points,global_feature,global_matrix = MODEL.get_model(
pointclouds_pl, is_training_pl, bn_decay=bn_decay,
num_feature = NUM_FEATURE, num_class = NUM_CLASSES)
loss = MODEL.get_loss(pred, labels_pl, end_points)
tf.summary.scalar('loss', loss)
correct = tf.equal(tf.argmax(pred, 1), tf.to_int64(labels_pl))
accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE)
tf.summary.scalar('accuracy', accuracy)
# Get training operator
learning_rate = get_learning_rate(batch)
tf.summary.scalar('learning_rate', learning_rate)
if OPTIMIZER == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM)
elif OPTIMIZER == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss, global_step=batch)
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Create a session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
sess = tf.Session(config=config)
# Add summary writers
#merged = tf.merge_all_summaries()
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(os.path.join(
LOG_DIR, 'train_'+FLAGS.model+ NAME_MODEL +'_'+str(NUM_FEATURE)),
sess.graph)
test_writer = tf.summary.FileWriter(os.path.join(
LOG_DIR, 'test_'+FLAGS.model+ NAME_MODEL +'_'+str(NUM_FEATURE)),
sess.graph)
# Init variables
init = tf.global_variables_initializer()
# To fix the bug introduced in TF 0.12.1 as in
# http://stackoverflow.com/questions/41543774/invalidargumenterror-for-tensor-bool-tensorflow-0-12-1
#sess.run(init)
sess.run(init, {is_training_pl: True})
ops = {'pointclouds_pl': pointclouds_pl,
'labels_pl': labels_pl,
'is_training_pl': is_training_pl,
'pred': pred,
'loss': loss,
'train_op': train_op,
'merged': merged,
'step': batch}
best_accuracy = 0.0
for epoch in range(MAX_EPOCH):
log_string('**** EPOCH %03d ****' % (epoch))
sys.stdout.flush()
train_one_epoch(sess, ops, train_writer)
accuracy = eval_one_epoch(sess, ops, test_writer)
if best_accuracy < accuracy:
best_accuracy = accuracy
save_path = saver.save(sess, os.path.join(LOG_DIR, FLAGS.model+ NAME_MODEL +'_'+str(NUM_FEATURE)+'_'+"model.ckpt"))
log_string("Model saved in file: %s" % save_path)
def train_one_epoch(sess, ops, train_writer):
""" ops: dict mapping from string to tf ops """
is_training = True
# Shuffle train files
train_file_idxs = np.arange(0, len(TRAIN_FILES))
np.random.shuffle(train_file_idxs)
for fn in range(len(TRAIN_FILES)):
log_string('----' + str(fn) + '-----')
current_data, current_label = provider.loadDataFile(TRAIN_FILES[train_file_idxs[fn]])
current_data = current_data[:,0:NUM_POINT,:]
current_data = np.array(current_data)
current_data = provider.offset_data(current_data)
current_data, current_label, _ = provider.shuffle_data(current_data, np.squeeze(current_label))
current_label = np.squeeze(current_label)
file_size = current_data.shape[0]
num_batches = file_size // BATCH_SIZE
total_correct = 0
total_seen = 0
loss_sum = 0
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
# Augment batched point clouds by rotation and jittering
if FLAGS.model == 'pointnet_cls_basic':
jittered_data = current_data[start_idx:end_idx, :, :] # directional pointNet
else:
jittered_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :]) # pointNet
# jittered_data = provider.jitter_point_cloud(jittered_data)
feed_dict = {ops['pointclouds_pl']: jittered_data,
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training,}
summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
train_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 1)
correct = np.sum(pred_val == current_label[start_idx:end_idx])
total_correct += correct
total_seen += BATCH_SIZE
loss_sum += loss_val
log_string('mean loss: %f' % (loss_sum / float(num_batches)))
log_string('accuracy: %f' % (total_correct / float(total_seen)))
def eval_one_epoch(sess, ops, test_writer):
""" ops: dict mapping from string to tf ops """
is_training = False
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
for fn in range(len(TEST_FILES)):
log_string('----' + str(fn) + '-----')
current_data, current_label = provider.loadDataFile(TEST_FILES[fn])
current_data = current_data[:,0:NUM_POINT,:]
current_data = provider.offset_data(current_data)
current_label = np.squeeze(current_label)
file_size = current_data.shape[0]
num_batches = file_size // BATCH_SIZE
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
feed_dict = {ops['pointclouds_pl']: current_data[start_idx:end_idx, :, :],
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training}
summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
ops['loss'], ops['pred']], feed_dict=feed_dict)
test_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 1)
correct = np.sum(pred_val == current_label[start_idx:end_idx])
total_correct += correct
total_seen += BATCH_SIZE
loss_sum += (loss_val*BATCH_SIZE)
for i in range(start_idx, end_idx):
l = current_label[i]
total_seen_class[l] += 1
total_correct_class[l] += (pred_val[i-start_idx] == l)
log_string('eval mean loss: %f' % (loss_sum / float(total_seen)))
log_string('eval accuracy: %f'% (total_correct / float(total_seen)))
# acc = total_correct / float(total_seen)
log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
# plotAcc(acc)
return total_correct / float(total_seen)
def plotAcc(acc):
accVec.append(acc)
plt.plot(accVec)
plt.ylabel('Evaluated accuracy')
if __name__ == "__main__":
# numFeatureVec = [32,64,128,256,512,1024,2048];
# for numFeature in numFeatureVec:
# train(numFeature)
train(numFeature=256)
LOG_FOUT.close()