-
Notifications
You must be signed in to change notification settings - Fork 37
/
train_shape_human.py
152 lines (118 loc) · 5.2 KB
/
train_shape_human.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
import tensorflow as tf
import numpy as np
import os
import sys
sys.path.append('./utils')
sys.path.append('./models')
import dataset_human as dataset
import model_shape as model
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('train_dir', './train_shape_human',
"""Directory where to write summaries and checkpoint.""")
tf.app.flags.DEFINE_string('base_dir', './data/human_im2avatar',
"""The path containing all the samples.""")
tf.app.flags.DEFINE_string('data_list_path', './data_list',
"""The path containing data lists.""")
tf.app.flags.DEFINE_integer('train_epochs', 501, """""")
tf.app.flags.DEFINE_integer('batch_size', 55, """""")
tf.app.flags.DEFINE_integer('gpu', 1, """""")
tf.app.flags.DEFINE_float('learning_rate', 0.0003, """""")
tf.app.flags.DEFINE_float('wd', 0.00001, """""")
tf.app.flags.DEFINE_integer('epochs_to_save',20, """""")
tf.app.flags.DEFINE_integer('decay_step',20000, """for lr""")
tf.app.flags.DEFINE_float('decay_rate', 0.7, """for lr""")
IM_DIM = 128
VOL_DIM = 64
BATCH_SIZE = FLAGS.batch_size
TRAIN_EPOCHS = FLAGS.train_epochs
GPU_INDEX = FLAGS.gpu
BASE_LEARNING_RATE = FLAGS.learning_rate
DECAY_STEP = FLAGS.decay_step
DECAY_RATE = FLAGS.decay_rate
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
os.environ["CUDA_VISIBLE_DEVICES"] = str(GPU_INDEX)
TRAIN_DIR = FLAGS.train_dir
if not os.path.exists(TRAIN_DIR):
os.makedirs(TRAIN_DIR)
LOG_FOUT = open(os.path.join(TRAIN_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(tf.flags._global_parser.parse_args())+'\n')
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(dataset_):
with tf.Graph().as_default():
with tf.device('/gpu:'+str(GPU_INDEX)):
is_train_pl = tf.placeholder(tf.bool)
img_pl, vol_pl = model.placeholder_inputs(BATCH_SIZE, IM_DIM, VOL_DIM)
# batch
global_step = tf.Variable(0)
bn_decay = get_bn_decay(global_step)
tf.summary.scalar('bn_decay', bn_decay)
# get prediction and loss
pred = model.get_model(img_pl, is_train_pl, weight_decay=FLAGS.wd, bn_decay=bn_decay)
loss = model.get_MSFE_cross_entropy_loss(pred, vol_pl)
tf.summary.scalar('loss', loss)
# Get training operator
learning_rate = get_learning_rate(global_step)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss, global_step=global_step)
summary_op = tf.summary.merge_all()
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC'
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
with tf.Session(config=config) as sess:
model_path = os.path.join(TRAIN_DIR, "trained_models")
if tf.gfile.Exists(os.path.join(model_path, "checkpoint")):
ckpt = tf.train.get_checkpoint_state(model_path)
restorer = tf.train.Saver()
restorer.restore(sess, ckpt.model_checkpoint_path)
print ("Load parameters from checkpoint.")
else:
sess.run(tf.global_variables_initializer())
train_summary_writer = tf.summary.FileWriter(model_path, graph=sess.graph)
train_sample_size = dataset_.getTrainSampleSize()
train_batches = train_sample_size // BATCH_SIZE
for epoch in range(TRAIN_EPOCHS):
dataset_.shuffleTrainNames()
for batch_idx in range(train_batches):
imgs, vols_clr = dataset_.next_batch(batch_idx * BATCH_SIZE, BATCH_SIZE)
vols_occu = np.prod(vols_clr > -0.5, axis=-1, keepdims=True) # (batch, vol_dim, vol_dim, vol_dim, 1)
vols_occu = vols_occu.astype(np.float32)
feed_dict = {img_pl: imgs, vol_pl: vols_occu, is_train_pl: True}
step = sess.run(global_step)
_, loss_val = sess.run([train_op, loss], feed_dict=feed_dict)
log_string("<TRAIN> Epoch {} - Batch {}: loss: {}.".format(epoch, batch_idx, loss_val))
if epoch % FLAGS.epochs_to_save == 0:
checkpoint_path = os.path.join(model_path, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=epoch)
def main():
train_dataset = dataset.Dataset(base_path=FLAGS.base_dir,
data_list_path=FLAGS.data_list_path)
train(train_dataset)
if __name__ == '__main__':
main()