-
Notifications
You must be signed in to change notification settings - Fork 0
/
train.py
executable file
·153 lines (127 loc) · 5.87 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
"""
Retrain the YOLO model for your own dataset.
"""
import os
import numpy as np
from PIL import Image
from keras.layers import Input, Lambda
from keras.models import load_model, Model
from keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStopping
from yolo3.model import preprocess_true_boxes, yolo_body, yolo_loss
from yolo3.utils import letterbox_image
# Default anchor boxes
YOLO_ANCHORS = np.array(((10,13), (16,30), (33,23), (30,61),
(62,45), (59,119), (116,90), (156,198), (373,326)))
def _main():
annotation_path = 'train.txt'
data_path = 'train.npz'
output_path = 'model_data/my_yolo.h5'
log_dir = 'logs/000/'
classes_path = 'model_data/voc_classes.txt'
anchors_path = 'model_data/yolo_anchors.txt'
class_names = get_classes(classes_path)
anchors = get_anchors(anchors_path)
input_shape = (416,416) # multiple of 32
image_data, box_data = get_training_data(annotation_path, data_path,
input_shape, max_boxes=100, load_previous=True)
y_true = preprocess_true_boxes(box_data, input_shape, anchors, len(class_names))
infer_model, model = create_model(input_shape, anchors, len(class_names),
load_pretrained=True, freeze_body=True)
train(model, image_data/255., y_true, log_dir=log_dir)
infer_model.save(output_path)
def get_classes(classes_path):
'''loads the classes'''
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def get_anchors(anchors_path):
'''loads the anchors from a file'''
if os.path.isfile(anchors_path):
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
return np.array(anchors).reshape(-1, 2)
else:
Warning("Could not open anchors file, using default.")
return YOLO_ANCHORS
def get_training_data(annotation_path, data_path, input_shape, max_boxes=100, load_previous=True):
'''processes the data into standard shape
annotation row format: image_file_path box1 box2 ... boxN
box format: x_min,y_min,x_max,y_max,class_index (no space)
'''
if load_previous==True and os.path.isfile(data_path):
data = np.load(data_path)
print('Loading training data from ' + data_path)
return data['image_data'], data['box_data']
image_data = []
box_data = []
with open(annotation_path) as f:
for line in f.readlines():
line = line.split(' ')
filename = line[0]
image = Image.open(filename)
boxed_image = letterbox_image(image, tuple(reversed(input_shape)))
image_data.append(np.array(boxed_image,dtype='uint8'))
boxes = np.zeros((max_boxes,5), dtype='int32')
for i, box in enumerate(line[1:]):
if i < max_boxes:
boxes[i] = np.array(list(map(int,box.split(','))))
else:
break
image_size = np.array(image.size)
input_size = np.array(input_shape[::-1])
new_size = (image_size * np.min(input_size/image_size)).astype('int32')
boxes[:i+1, 0:2] = (boxes[:i+1, 0:2]*new_size/image_size + (input_size-new_size)/2).astype('int32')
boxes[:i+1, 2:4] = (boxes[:i+1, 2:4]*new_size/image_size + (input_size-new_size)/2).astype('int32')
box_data.append(boxes)
image_data = np.array(image_data)
box_data = np.array(box_data)
np.savez(data_path, image_data=image_data, box_data=box_data)
print('Saving training data into ' + data_path)
return image_data, box_data
def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=True):
'''create the training model'''
image_input = Input(shape=(None, None, 3))
h, w = input_shape
num_anchors = len(anchors)//3
y_true = [Input(shape=(h//32, w//32, num_anchors, num_classes+5)),
Input(shape=(h//16, w//16, num_anchors, num_classes+5)),
Input(shape=(h//8, w//8, num_anchors, num_classes+5))]
model_body = yolo_body(image_input, num_anchors, num_classes)
if load_pretrained:
weights_path = os.path.join('model_data', 'yolo_weights.h5')
if not os.path.exists(weights_path):
print("CREATING WEIGHTS FILE" + weights_path)
yolo_path = os.path.join('model_data', 'yolo.h5')
orig_model = load_model(yolo_path, compile=False)
orig_model.save_weights(weights_path)
model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
if freeze_body:
# Do not freeze 3 output layers.
for i in range(len(model_body.layers)-3):
model_body.layers[i].trainable = False
model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
arguments={'anchors': anchors, 'num_classes': num_classes})(
[*model_body.output, *y_true])
model = Model([model_body.input, *y_true], model_loss)
return model_body, model
def train(model, image_data, y_true, log_dir='logs/'):
'''retrain/fine-tune the model'''
model.compile(optimizer='adam', loss={
# use custom yolo_loss Lambda layer.
'yolo_loss': lambda y_true, y_pred: y_pred})
logging = TensorBoard(log_dir=log_dir)
checkpoint = ModelCheckpoint(log_dir + "ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5",
monitor='val_loss', save_weights_only=True, save_best_only=True)
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=1, mode='auto')
model.fit([image_data, *y_true],
np.zeros(len(image_data)),
validation_split=.1,
batch_size=32,
epochs=30,
callbacks=[logging, checkpoint, early_stopping])
model.save_weights(log_dir + 'trained_weights.h5')
# Further training.
if __name__ == '__main__':
_main()