-
Notifications
You must be signed in to change notification settings - Fork 191
/
train_caltech.py
154 lines (135 loc) · 5.83 KB
/
train_caltech.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
from __future__ import division
import random
import sys, os
import time
import numpy as np
import cPickle
from keras.utils import generic_utils
from keras.optimizers import Adam
from keras.layers import Input
from keras.models import Model
from keras_csp import config, data_generators
from keras_csp import losses as losses
# get the config parameters
C = config.Config()
C.gpu_ids = '0'
C.onegpu = 16
C.size_train = (336, 448)
C.init_lr = 1e-4
C.num_epochs = 120
C.offset = True
num_gpu = len(C.gpu_ids.split(','))
batchsize = C.onegpu * num_gpu
os.environ["CUDA_VISIBLE_DEVICES"] = C.gpu_ids
# get the training data
cache_ped = 'data/cache/caltech/train_gt'
cache_emp = 'data/cache/caltech/train_nogt'
with open(cache_ped, 'rb') as fid:
ped_data = cPickle.load(fid)
with open(cache_emp, 'rb') as fid:
emp_data = cPickle.load(fid)
num_imgs_ped = len(ped_data)
num_imgs_emp = len(emp_data)
print ('num of ped and emp samples: {} {}'.format(num_imgs_ped,num_imgs_emp))
data_gen_train = data_generators.get_data_hybrid(ped_data, emp_data, C, batchsize=batchsize, hyratio=0.5)
# define the base network (resnet here, can be MobileNet, etc)
if C.network=='resnet50':
from keras_csp import resnet50 as nn
weight_path = 'data/models/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
elif C.network=='mobilenet':
from keras_csp import mobilenet as nn
weight_path = 'data/models/mobilenet_1_0_224_tf_no_top.h5'
else:
raise NotImplementedError('Not support network: {}'.format(C.network))
input_shape_img = (C.size_train[0], C.size_train[1], 3)
img_input = Input(shape=input_shape_img)
# define the network prediction
preds = nn.nn_p3p4p5(img_input, offset=C.offset, num_scale=C.num_scale, trainable=True)
preds_tea = nn.nn_p3p4p5(img_input, offset=C.offset, num_scale=C.num_scale, trainable=True)
model = Model(img_input, preds)
if num_gpu>1:
from keras_csp.parallel_model import ParallelModel
model = ParallelModel(model, int(num_gpu))
model_stu = Model(img_input, preds)
model_tea = Model(img_input, preds_tea)
model.load_weights(weight_path, by_name=True)
model_tea.load_weights(weight_path, by_name=True)
print 'load weights from {}'.format(weight_path)
if C.offset:
out_path = 'output/valmodels/caltech/%s/off2' % (C.scale)
else:
out_path = 'output/valmodels/caltech/%s/nooff' % (C.scale)
if not os.path.exists(out_path):
os.makedirs(out_path)
res_file = os.path.join(out_path,'records.txt')
optimizer = Adam(lr=C.init_lr)
if C.offset:
model.compile(optimizer=optimizer, loss=[losses.cls_center, losses.regr_h, losses.regr_offset])
else:
if C.scale=='hw':
model.compile(optimizer=optimizer, loss=[losses.cls_center, losses.regr_hw])
else:
model.compile(optimizer=optimizer, loss=[losses.cls_center, losses.regr_h])
epoch_length = int(C.iter_per_epoch/batchsize)
iter_num = 0
add_epoch = 0
losses = np.zeros((epoch_length, 3))
best_loss = np.Inf
print('Starting training with lr {} and alpha {}'.format(C.init_lr, C.alpha))
start_time = time.time()
total_loss_r, cls_loss_r1, regr_loss_r1, offset_loss_r1 = [], [], [], []
for epoch_num in range(C.num_epochs):
progbar = generic_utils.Progbar(epoch_length)
print('Epoch {}/{}'.format(epoch_num + 1 + add_epoch, C.num_epochs + C.add_epoch))
while True:
try:
X, Y = next(data_gen_train)
loss_s1 = model.train_on_batch(X, Y)
for l in model_tea.layers:
weights_tea = l.get_weights()
if len(weights_tea)>0:
if num_gpu > 1:
weights_stu = model_stu.get_layer(name=l.name).get_weights()
else:
weights_stu = model.get_layer(name=l.name).get_weights()
weights_tea = [C.alpha*w_tea + (1-C.alpha)*w_stu for (w_tea, w_stu) in zip(weights_tea, weights_stu)]
l.set_weights(weights_tea)
# print loss_s1
losses[iter_num, 0] = loss_s1[1]
losses[iter_num, 1] = loss_s1[2]
if C.offset:
losses[iter_num, 2] = loss_s1[3]
else:
losses[iter_num, 2] = 0
iter_num += 1
if iter_num % 20 == 0:
progbar.update(iter_num,
[('cls', np.mean(losses[:iter_num, 0])), ('regr_h', np.mean(losses[:iter_num, 1])), ('offset', np.mean(losses[:iter_num, 2]))])
if iter_num == epoch_length:
cls_loss1 = np.mean(losses[:, 0])
regr_loss1 = np.mean(losses[:, 1])
offset_loss1 = np.mean(losses[:, 2])
total_loss = cls_loss1+regr_loss1+offset_loss1
total_loss_r.append(total_loss)
cls_loss_r1.append(cls_loss1)
regr_loss_r1.append(regr_loss1)
offset_loss_r1.append(offset_loss1)
print('Total loss: {}'.format(total_loss))
print('Elapsed time: {}'.format(time.time() - start_time))
iter_num = 0
start_time = time.time()
if total_loss < best_loss:
print('Total loss decreased from {} to {}, saving weights'.format(best_loss, total_loss))
best_loss = total_loss
model_tea.save_weights(os.path.join(out_path, 'net_e{}_l{}.hdf5'.format(epoch_num + 1 + add_epoch, total_loss)))
break
except Exception as e:
print ('Exception: {}'.format(e))
continue
records = np.concatenate((np.asarray(total_loss_r).reshape((-1, 1)),
np.asarray(cls_loss_r1).reshape((-1, 1)),
np.asarray(regr_loss_r1).reshape((-1, 1)),
np.asarray(offset_loss_r1).reshape((-1, 1)),),
axis=-1)
np.savetxt(res_file, np.array(records), fmt='%.6f')
print('Training complete, exiting.')