def _extract_backbone(self):
"""extract feature map from backbone.
"""
if self.backbone == 'large':
from model.mobilenet_v3_large import MobileNetV3_Large
model = MobileNetV3_Large(self.shape, self.n_class).build()
layer_name8 = 'batch_normalization_13'
layer_name16 = 'add_5'
elif self.backbone == 'small':
from model.mobilenet_v3_small import MobileNetV3_Small
model = MobileNetV3_Small(self.shape, self.n_class).build()
layer_name8 = 'batch_normalization_7'
layer_name16 = 'add_2'
else:
raise Exception('Invalid backbone: {}'.format(self.backbone))
if self.weights is not None:
model.load_weights(self.weights)
inputs= model.input
# 1/8 feature map.
out_feature8 = model.get_layer(layer_name8).output
# 1/16 feature map.
out_feature16 = model.get_layer(layer_name16).output
return inputs, out_feature8, out_feature16
def build(self, plot=False):
"""build Lite R-ASPP.
# Arguments
plot: Boolean, weather to plot model.
# Returns
model: Model, model.
"""
from model.mobilenet_v3_small import MobileNetV3_Small
model = MobileNetV3_Small(self.shape, 1, alpha=1.0,
include_top=False).build()
inputs = model.input
# x = model.get_layer('activation_25').output
x = model.get_layer('global_average_pooling2d_10').output
x = Dropout(0.5)(x)
# x = Flatten()(x)
x = Dense(100, activation='elu')(x)
x = Dropout(0.5)(x)
x = Dense(50, activation='elu')(x)
x = Dense(10, activation='elu')(x)
x = Dense(1)(x)
model = Model(inputs=inputs, outputs=x)
if plot:
plot_model(model, to_file='images/LR_ASPP.png', show_shapes=True)
return model
def pre(img, save_model = False):
global model
global model_shape
if model is None:
with open('config/config.json', 'r') as f:
cfg = json.load(f)
save_dir = cfg['save_dir']
model_shape = (int(cfg['height']), int(cfg['width']), 3)
n_class = int(cfg['class_number'])
batch = int(cfg['batch'])
if not os.path.exists(save_dir):
os.mkdir(save_dir)
if cfg['model'] == 'large':
from model.mobilenet_v3_large import MobileNetV3_Large
model = MobileNetV3_Large(model_shape, n_class).build()
if cfg['model'] == 'small':
from model.mobilenet_v3_small import MobileNetV3_Small
model = MobileNetV3_Small(model_shape, n_class).build()
if cfg['model'] == 'mymodel':
from model.my_model import MyModel
model = MyModel(model_shape, n_class).build()
if cfg['model'] == 'v2':
from model.mobilenet_v2 import MyModel
model = MyModel(model_shape, n_class).build()
pre_weights = "save/v3_weights0.87-0.87.h5"#cfg['weights']
if pre_weights and os.path.exists(pre_weights):
model.load_weights(pre_weights, by_name=True)
print("------------------load pre model!!!!!")
if(save_model):
print("Finish save.")
model.save('save/model_all.h5')
# 预处理
img = cv2.resize(img, model_shape[:2])
img = img*1. / 255
pre = model.predict(np.array([img]))
#print("pre: ",pre,np.argmax(pre[0]))
#pre_cate = np.argmax(pre[0])
return pre
def train():
with open('config/config.json', 'r') as f:
cfg = json.load(f)
save_dir = cfg['save_dir']
shape = (int(cfg['height']), int(cfg['width']), 3)
n_class = int(cfg['class_number'])
batch = int(cfg['batch'])
if not os.path.exists(save_dir):
os.mkdir(save_dir)
if cfg['model'] == 'large':
from model.mobilenet_v3_large import MobileNetV3_Large
model = MobileNetV3_Large(shape, n_class).build()
if cfg['model'] == 'small':
from model.mobilenet_v3_small import MobileNetV3_Small
model = MobileNetV3_Small(shape, n_class).build()
pre_weights = cfg['weights']
if pre_weights and os.path.exists(pre_weights):
model.load_weights(pre_weights, by_name=True)
opt = Adam(lr=float(cfg['learning_rate']))
earlystop = EarlyStopping(monitor='val_acc',
patience=5,
verbose=0,
mode='auto')
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
train_generator, validation_generator, count1, count2 = generate(
batch, shape[:2], cfg['train_dir'], cfg['eval_dir'])
hist = model.fit_generator(train_generator,
validation_data=validation_generator,
steps_per_epoch=count1 // batch,
validation_steps=count2 // batch,
epochs=cfg['epochs'],
callbacks=[earlystop])
df = pd.DataFrame.from_dict(hist.history)
df.to_csv(os.path.join(save_dir, 'hist.csv'),
encoding='utf-8',
index=False)
model.save_weights(
os.path.join(save_dir, '{}_weights.h5'.format(cfg['model'])))
input_size = 32
model_path = "weights/"
shape = (input_size, input_size, 3)
classes = 10
alpha = 0.2
message = "Input size : 32 x 32"
label = [
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck'
]
# model load
print("V3_32 Model loading...")
model = MobileNetV3_Small(shape, classes, alpha).build()
model.load_weights(path + 'v3_32.h5')
def main():
camera_width = 352
camera_height = 288
fps = ""
flag_score = False
elapsedTime = 0
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FPS, 40)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, camera_width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, camera_height)
def train():
with open('config/config.json', 'r') as f:
cfg = json.load(f)
save_dir = cfg['save_dir']
shape = (int(cfg['height']), int(cfg['width']))
n_class = int(cfg['class_number'])
batch = int(cfg['batch'])
if not os.path.exists(save_dir):
os.mkdir(save_dir)
train_images, train_labels, test_images, test_labels = load_images()
if cfg['model'] == 'large':
from model.mobilenet_v3_large import MobileNetV3_Large
model = MobileNetV3_Large(train_images[0].shape,
n_class).build(shape=shape)
if cfg['model'] == 'small':
from model.mobilenet_v3_small import MobileNetV3_Small
model = MobileNetV3_Small(train_images[0].shape,
n_class).build(shape=shape)
optimizer = build_optimizer(learning_rate=float(cfg['learning_rate']),
momentum=0.9)
# earlystop = EarlyStopping(monitor='val_accuracy', patience=5, verbose=0, mode='auto')
checkpoint = ModelCheckpoint(filepath=os.path.join(
save_dir, '{}_weights.h5'.format(cfg['model'])),
monitor='val_acc',
save_best_only=True,
save_weights_only=True)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
model.summary()
# data augmentation
datagen1 = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=
False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=
15, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=
0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=
0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
datagen1.fit(train_images)
hist = model.fit_generator(datagen1.flow(train_images,
train_labels,
batch_size=batch),
validation_data=(test_images, test_labels),
steps_per_epoch=train_images.shape[0] // batch,
epochs=cfg['epochs'],
callbacks=[checkpoint])
df = pd.DataFrame.from_dict(hist.history)
df.to_csv(os.path.join(save_dir, 'hist.csv'),
encoding='utf-8',
index=False)