-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtrain_unet.py
189 lines (160 loc) · 9.17 KB
/
train_unet.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
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
187
188
189
#Mitali Shah
# Modified unet model for segmenting images.
# Used a small inter-tidal zone dataset for training.
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras.preprocessing.image import array_to_img, img_to_array, load_img
from keras.utils.vis_utils import plot_model
import cv2
from data import *
import numpy as np
import glob
import time
import os
import matplotlib.pyplot as plt
class modifiedUnet(object):
def __init__(self, img_rows=512, img_cols=512):
self.img_rows = img_rows
self.img_cols = img_cols
self.main_folder = '' # path of main folder
self.data_path = self.main_folder + '' # path of folder containing training and validation images
self.label_path = self.main_folder + '' # path of folder containing training and validation image mask
self.img_type = self.main_folder + '' # using filetype of image
self.test_path = self.main_folder + '' # folder containing test image
self.npy_path = self.main_folder + '' # folder to store npydata
def create_train_arr(self):
i = 0
imgs = glob.glob(self.data_path+"/*."+self.img_type) # images with same filetype
imgdata = np.ndarray((len(imgs), self.img_rows, self.img_cols, 3), dtype=np.uint8)
imglabel = np.ndarray((len(imgs), self.img_rows, self.img_cols, 1), dtype=np.uint8)
for x in range(len(imgs)):
imgpath = imgs[x]
pic_name = imgpath.split('/')[-1]
labelpath = self.label_path + '/' + pic_name
img = load_img(imgpath, grayscale=False, target_size=[512, 512])
label = load_img(labelpath, grayscale=True, target_size=[512, 512])
img = img_to_array(img)
label = img_to_array(label)
imgdata[i] = img
imglabel[i] = label
if i % 100 == 0:
print('Done: {0}/{1} images'.format(i, len(imgs)))
i += 1
np.save(self.npy_path + '/imgs_train.npy', imgdata)
np.save(self.npy_path + '/imgs_mask_train.npy', imglabel)
def load_train_data(self):
imgs_train = np.load(self.npy_path + "/imgs_train.npy")
imgs_mask_train = np.load(self.npy_path + "/imgs_mask_train.npy")
imgs_train = imgs_train.astype('float32')
imgs_mask_train = imgs_mask_train.astype('float32')
imgs_train /= 255
imgs_mask_train /= 255
imgs_mask_train[imgs_mask_train > 0.5] = 1
imgs_mask_train[imgs_mask_train <= 0.5] = 0
return imgs_train, imgs_mask_train
def get_unet(self):
inputs = Input((self.img_rows, self.img_cols, 3))
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(drop5))
merge6 = merge([drop4, up6], mode='concat', concat_axis=3)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv6))
merge7 = merge([conv3, up7], mode='concat', concat_axis=3)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv7))
merge8 = merge([conv2, up8], mode='concat', concat_axis=3)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv8))
merge9 = merge([conv1, up9], mode='concat', concat_axis=3)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
model = Model(input=inputs, output=conv10)
model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])
#model.summary()
plot_model(model, to_file=self.main_folder + 'model.png')
return model
def plot_model_history(self, model_history):
fig, axs = plt.subplots(1,2,figsize=(15,5))
# summarize history for accuracy
axs[0].plot(range(1,len(model_history.history['acc'])+1),model_history.history['acc'])
axs[0].plot(range(1,len(model_history.history['val_acc'])+1),model_history.history['val_acc'])
axs[0].set_title('Model Accuracy')
axs[0].set_ylabel('Accuracy')
axs[0].set_xlabel('Epoch')
axs[0].set_xticks(np.arange(1,len(model_history.history['acc'])+1),len(model_history.history['acc'])/10)
axs[0].legend(['Training', 'Validation'], loc='best')
# summarize history for loss
axs[1].plot(range(1,len(model_history.history['loss'])+1),model_history.history['loss'],'r--')
axs[1].plot(range(1,len(model_history.history['val_loss'])+1),model_history.history['val_loss'], 'b-')
axs[1].set_title('Model Loss')
axs[1].set_ylabel('Loss')
axs[1].set_xlabel('Epoch')
axs[1].set_xticks(np.arange(1,len(model_history.history['loss'])+1),len(model_history.history['loss'])/10)
axs[1].legend(['Training', 'Validation'], loc='best')
fig.savefig(self.main_folder + 'accuracy_vs_loss%s.png'%time.strftime("%Y%m%d-%H%M%S"))
plt.close(fig)
def save_model_to_json(self):
classifier_model_json = model.to_json()
model_filename = self.main_folder + 'model.json'
with open(model_filename, "w") as json_file:
json_file.write(classifier_model_json)
def train(self):
# callbacks for model
model_checkpoint = ModelCheckpoint(saved_model, monitor='val_loss', verbose=1, save_best_only=True) # check change in validation loss
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=5, verbose=1, mode='auto') # stop fitting the model if there no change
print('-'*30)
print("Loading the training data")
print('-'*30)
imgs_train, imgs_mask_train = self.load_train_data()
print('-'*30)
print("Gettting the U-Net model")
print('-'*30)
model = self.get_unet()
print('-'*30)
print("Saving the model")
print('-'*30)
saved_model = self.main_folder + '/unet.hdf5'
print('-'*30)
print('Fitting the model')
print('-'*30)
model_info = model.fit(imgs_train, imgs_mask_train, batch_size=2, epochs=60, verbose=1,
validation_split=0.15, shuffle=True, callbacks=[model_checkpoint])
print('-'*30)
print('Saving the model to a json file')
print('-'*30)
modified_unet.save_model_to_json()
print('-'*30)
print('Saving the model accuracy and loss graph')
print('-'*30)
modified_unet.plot_model_history(model_info)
if __name__ == '__main__':
modified_unet = modifiedUnet()
model = modified_unet.get_unet()
modified_unet.create_train_arr()
modified_unet.train()