train.py
8.72 KB
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
from __future__ import division, print_function
import tensorflow as tf
import numpy as np
import os
from tqdm import trange
import args
from misc_utils import shuffle_and_overwrite, config_learning_rate, config_optimizer, AverageMeter
from data_utils import get_batch_data
from eval_utils import evaluate_on_cpu, evaluate_on_gpu, get_preds_gpu, voc_eval, parse_gt_rec
from nms_utils import gpu_nms
from model import yolov3
is_training = tf.placeholder(tf.bool, name="phase_train")
handle_flag = tf.placeholder(tf.string, [], name='iterator_handle_flag')
pred_boxes_flag = tf.placeholder(tf.float32, [1, None, None])
pred_scores_flag = tf.placeholder(tf.float32, [1, None, None])
gpu_nms_op = gpu_nms(pred_boxes_flag, pred_scores_flag, args.class_num, args.nms_topk, args.score_threshold, args.nms_threshold)
### tf.data pipeline
train_dataset = tf.data.TFRecordDataset(filenames=args.train_file, compression_type='GZIP')
train_dataset = train_dataset.shuffle(args.train_img_cnt)
train_dataset = train_dataset.batch(args.batch_size)
train_dataset = train_dataset.map(
lambda x: tf.py_func(get_batch_data,
inp=[x, args.class_num, args.img_size, args.anchors, True, args.multi_scale_train, args.use_mix_up, args.letterbox_resize],
Tout=[tf.int64, tf.float32, tf.float32, tf.float32, tf.float32]),
num_parallel_calls=args.num_threads
)
train_dataset = train_dataset.prefetch(args.prefetech_buffer)
val_dataset = tf.data.TFRecordDataset(filenames=args.val_file, compression_type='GZIP')
val_dataset = val_dataset.batch(1)
val_dataset = val_dataset.map(
lambda x: tf.py_func(get_batch_data,
inp=[x, args.class_num, args.img_size, args.anchors, False, False, False, args.letterbox_resize],
Tout=[tf.int64, tf.float32, tf.float32, tf.float32, tf.float32]),
num_parallel_calls=args.num_threads
)
val_dataset.prefetch(args.prefetech_buffer)
iterator = tf.data.Iterator.from_structure(train_dataset.output_types, train_dataset.output_shapes)
train_init_op = iterator.make_initializer(train_dataset)
val_init_op = iterator.make_initializer(val_dataset)
image_ids, image, y_true_13, y_true_26, y_true_52 = iterator.get_next()
y_true = [y_true_13, y_true_26, y_true_52]
image_ids.set_shape([None])
image.set_shape([None, None, None, 3])
for y in y_true:
y.set_shape([None, None, None, None, None])
### Model definition
yolo_model = yolov3(class_num, anchors, use_label_smooth, use_focal_loss, batch_norm_decay, weight_decay, use_static_shape=False)
with tf.variable_scope('yolov3'):
pred_feature_maps = yolo_model.forward(image, is_training=is_training)
loss = yolo_model.compute_loss(pred_feature_maps, y_true)
y_pred = yolo_model.predict(pred_feature_maps)
l2_loss = tf.losses.get_regularization_loss()
saver_to_restore = tf.train.Saver(var_list=tf.contrib.framework.get_variables_to_restore(include=restore_include, exclude=restore_exclude))
update_vars = tf.contrib.framework.get_variables_to_restore(include=update_part)
global_step = tf.Variable(float(args.global_step), trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
if use_warm_up:
learning_rate = tf.cond(tf.less(global_step, train_batch_num * warm_up_epoch),
lambda: learning_rate_init * global_step / (train_batch_num * warm_up_epoch),
lambda: config_learning_rate(args, global_step - args.train_batch_num * args.warm_up_epoch))
else:
learning_rate = config_learning_rate(args, global_step)
optimizer = config_optimizer(args.optimizer_name, learning_rate)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
gvs = optimizer.compute_gradients(loss[0] + l2_loss, var_list=update_vars)
clip_grad_var = [gv if gv[0] is None else [
tf.clip_by_norm(gv[0], 100.), gv[1]] for gv in gvs]
train_op = optimizer.apply_gradients(clip_grad_var, global_step=global_step)
if args.save_optimizer:
print('Saving optimizer parameters: ON')
saver_to_save = tf.train.Saver()
saver_best = tf.train.Saver()
else:
print('Saving optimizer parameters: OFF')
##### Start training
with tf.Session() as sess:
sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
if os.path.exists(args.restore_path):
saver_to_restore.restore(sess, args.restore_path)
print('\nStart training...: Total epoches =', args.total_epoches, '\n')
best_mAP = -np.Inf
for epoch in range(args.total_epoches):
sess.run(train_init_op)
loss_total, loss_xy, loss_wh, loss_conf, loss_class = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
### train part
for i in trange(args.train_batch_num):
_, __y_pred, __y_true, __loss, __global_step, __lr = sess.run(
[train_op, y_pred, y_true, loss, global_step, learning_rate],
feed_dict={is_training: True})
loss_total.update(__loss[0], len(__y_pred[0]))
loss_xy.update(__loss[1], len(__y_pred[0]))
loss_wh.update(__loss[2], len(__y_pred[0]))
loss_conf.update(__loss[3], len(__y_pred[0]))
loss_class.update(__loss[4], len(__y_pred[0]))
if __global_step % args.train_evaluation_step == 0 and __global_step > 0:
recall, precision = evaluate_on_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, __y_pred, __y_true, args.class_num, args.nms_threshold)
info = "Epoch: {}, global_step: {} | loss: total: {:.2f}, xy: {:.2f}, wh: {:.2f}, conf: {:.2f}, class: {:.2f} | ".format(
epoch, int(__global_step), loss_total.average, loss_xy.average, loss_wh.average, loss_conf.average, loss_class.average)
info += 'Last batch: rec: {:.3f}, prec: {:.3f} | lr: {:.5g}'.format(recall, precision, __lr)
print(info)
if np.isnan(loss_total.average):
print('****' * 10)
raise ArithmeticError('Gradient exploded!')
## train end (saving parameters)
if args.save_optimizer and epoch % args.save_epoch == 0 and epoch > 0:
if loss_total.average <= 2.:
saver_to_save.save(sess, args.save_dir + 'model-epoch_{}_step_{}_loss_{:.4f}_lr_{:.5g}'.format(epoch, int(__global_step), loss_total.average, __lr))
### validation part
if epoch % args.val_evaluation_epoch == 0 and epoch >= args.warm_up_epoch:
sess.run(val_init_op)
val_loss_total, val_loss_xy, val_loss_wh, val_loss_conf, val_loss_class = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
val_preds = []
for j in trange(args.val_img_cnt):
__image_ids, __y_pred, __loss = sess.run([image_ids, y_pred, loss],
feed_dict={is_training: False})
pred_content = get_preds_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, __image_ids, __y_pred)
val_preds.extend(pred_content)
val_loss_total.update(__loss[0])
val_loss_xy.update(__loss[1])
val_loss_wh.update(__loss[2])
val_loss_conf.update(__loss[3])
val_loss_class.update(__loss[4])
# calc mAP
rec_total, prec_total, ap_total = AverageMeter(), AverageMeter(), AverageMeter()
gt_dict = parse_gt_rec(args.val_file, 'GZIP', args.img_size, args.letterbox_resize)
info = '======> Epoch: {}, global_step: {}, lr: {:.6g} <======\n'.format(epoch, __global_step, __lr)
for ii in range(args.class_num):
npos, nd, rec, prec, ap = voc_eval(gt_dict, val_preds, ii, iou_thres=args.eval_threshold, use_07_metric=args.use_voc_07_metric)
info += 'EVAL: Class {}: Recall: {:.4f}, Precision: {:.4f}, AP: {:.4f}\n'.format(ii, rec, prec, ap)
rec_total.update(rec, npos)
prec_total.update(prec, nd)
ap_total.update(ap, 1)
mAP = ap_total.average
info += 'EVAL: Recall: {:.4f}, Precison: {:.4f}, mAP: {:.4f}\n'.format(rec_total.average, prec_total.average, mAP)
info += 'EVAL: loss: total: {:.2f}, xy: {:.2f}, wh: {:.2f}, conf: {:.2f}, class: {:.2f}\n'.format(
val_loss_total.average, val_loss_xy.average, val_loss_wh.average, val_loss_conf.average, val_loss_class.average)
print(info)
if args.save_optimizer and mAP > best_mAP:
best_mAP = mAP
saver_best.save(sess, args.save_dir + 'best_model_Epoch_{}_step_{}_mAP_{:.4f}_loss_{:.4f}_lr_{:.7g}'.format(
epoch, int(__global_step), best_mAP, val_loss_total.average, __lr))