-
Notifications
You must be signed in to change notification settings - Fork 56
/
train_p2mpp.py
143 lines (136 loc) · 6.82 KB
/
train_p2mpp.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
# Copyright (C) 2019 Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu
# All rights reserved.
# This code is licensed under BSD 3-Clause License.
import tensorflow as tf
import tflearn
import numpy as np
import pprint
import pickle
import shutil
import os
from modules.models_p2mpp import MeshNet
from modules.config import execute
from utils.dataloader import DataFetcher
from utils.tools import construct_feed_dict
from utils.visualize import plot_scatter
def main(cfg):
os.environ['CUDA_VISIBLE_DEVICES'] = str(cfg.gpu_id)
# ---------------------------------------------------------------
# Set random seed
print('=> pre-porcessing')
seed = 123
np.random.seed(seed)
tf.set_random_seed(seed)
# ---------------------------------------------------------------
num_blocks = 3
num_supports = 2
placeholders = {
'features': tf.placeholder(tf.float32, shape=(None, 3), name='features'),
'img_inp': tf.placeholder(tf.float32, shape=(3, 224, 224, 3), name='img_inp'),
'labels': tf.placeholder(tf.float32, shape=(None, 6), name='labels'),
'support1': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)],
'support2': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)],
'support3': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)],
'faces': [tf.placeholder(tf.int32, shape=(None, 4)) for _ in range(num_blocks)],
'edges': [tf.placeholder(tf.int32, shape=(None, 2)) for _ in range(num_blocks)],
'lape_idx': [tf.placeholder(tf.int32, shape=(None, 10)) for _ in range(num_blocks)], # for laplace term
'pool_idx': [tf.placeholder(tf.int32, shape=(None, 2)) for _ in range(num_blocks - 1)], # for unpooling
'dropout': tf.placeholder_with_default(0., shape=()),
'num_features_nonzero': tf.placeholder(tf.int32),
'sample_coord': tf.placeholder(tf.float32, shape=(43, 3), name='sample_coord'),
'cameras': tf.placeholder(tf.float32, shape=(3, 5), name='Cameras'),
'faces_triangle': [tf.placeholder(tf.int32, shape=(None, 3)) for _ in range(num_blocks)],
'sample_adj': [tf.placeholder(tf.float32, shape=(43, 43)) for _ in range(num_supports)],
}
root_dir = os.path.join(cfg.save_path, cfg.name)
model_dir = os.path.join(cfg.save_path, cfg.name, 'models')
log_dir = os.path.join(cfg.save_path, cfg.name, 'logs')
plt_dir = os.path.join(cfg.save_path, cfg.name, 'plt')
if not os.path.exists(root_dir):
os.mkdir(root_dir)
print('==> make root dir {}'.format(root_dir))
if not os.path.exists(model_dir):
os.mkdir(model_dir)
print('==> make model dir {}'.format(model_dir))
if not os.path.exists(log_dir):
os.mkdir(log_dir)
print('==> make log dir {}'.format(log_dir))
if not os.path.exists(plt_dir):
os.mkdir(plt_dir)
print('==> make plt dir {}'.format(plt_dir))
summaries_dir = os.path.join(cfg.save_path, cfg.name, 'summaries')
train_loss = open('{}/train_loss_record.txt'.format(log_dir), 'a')
train_loss.write('Net {} | Start training | lr = {}\n'.format(cfg.name, cfg.lr))
# -------------------------------------------------------------------
print('=> build model')
# Define model
model = MeshNet(placeholders, logging=True, args=cfg)
# ---------------------------------------------------------------
print('=> load data')
data = DataFetcher(file_list=cfg.train_file_path, data_root=cfg.train_data_path,
image_root=cfg.train_image_path, is_val=False, mesh_root=cfg.train_mesh_root)
data.setDaemon(True)
data.start()
# ---------------------------------------------------------------
print('=> initialize session')
sesscfg = tf.ConfigProto()
sesscfg.gpu_options.allow_growth = True
sesscfg.allow_soft_placement = True
sess = tf.Session(config=sesscfg)
sess.run(tf.global_variables_initializer())
train_writer = tf.summary.FileWriter(summaries_dir, sess.graph, filename_suffix='train')
# ---------------------------------------------------------------
if cfg.load_cnn:
print('=> load pre-trained cnn')
model.loadcnn(sess=sess, ckpt_path=cfg.pre_trained_cnn_path, step=cfg.cnn_step)
if cfg.restore:
print('=> load model')
model.load(sess=sess, ckpt_path=model_dir, step=cfg.init_epoch)
# ---------------------------------------------------------------
# Load init ellipsoid and info about vertices and edges
pkl = pickle.load(open('data/iccv_p2mpp.dat', 'rb'))
# Construct Feed dict
feed_dict = construct_feed_dict(pkl, placeholders)
# ---------------------------------------------------------------
train_number = data.number
step = 0
tflearn.is_training(True, sess)
print('=> start train stage 2')
for epoch in range(cfg.epochs):
current_epoch = epoch + 1 + cfg.init_epoch
epoch_plt_dir = os.path.join(plt_dir, str(current_epoch))
if not os.path.exists(epoch_plt_dir):
os.mkdir(epoch_plt_dir)
mean_loss = 0
all_loss = np.zeros(train_number, dtype='float32')
for iters in range(train_number):
step += 1
# Fetch training data
# need [img, label, pose(camera meta data), dataID]
img_all_view, labels, poses, data_id, mesh = data.fetch()
feed_dict.update({placeholders['features']: mesh})
feed_dict.update({placeholders['img_inp']: img_all_view})
feed_dict.update({placeholders['labels']: labels})
feed_dict.update({placeholders['cameras']: poses})
# ---------------------------------------------------------------
_, dists, summaries, out1l, out2l = sess.run([model.opt_op, model.loss, model.merged_summary_op, model.output1l, model.output2l], feed_dict=feed_dict)
# ---------------------------------------------------------------
all_loss[iters] = dists
mean_loss = np.mean(all_loss[np.where(all_loss)])
print('Epoch {}, Iteration {}, Mean loss = {}, iter loss = {}, {}, data id {}'.format(current_epoch, iters + 1, mean_loss, dists, data.queue.qsize(), data_id))
train_writer.add_summary(summaries, step)
if (iters + 1) % 1000 == 0:
plot_scatter(pt=out2l, data_name=data_id, plt_path=epoch_plt_dir)
# ---------------------------------------------------------------
# Save model
model.save(sess=sess, ckpt_path=model_dir, step=current_epoch)
train_loss.write('Epoch {}, loss {}\n'.format(current_epoch, mean_loss))
train_loss.flush()
# ---------------------------------------------------------------
data.shutdown()
print('CNN-GCN Optimization Finished!')
if __name__ == '__main__':
print('=> set config')
args = execute()
pprint.pprint(vars(args))
main(args)