def MobileNetSlim(input_shape, alpha, depth_multiplier=1, output_classes=1, dropout=0.4):
input = Input(shape=input_shape, name='flow')
x = _conv_block(input, 32, alpha, strides=(2, 2))
x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1)
x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, strides=(2, 2), block_id=2)
x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3)
x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4)
x1 = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5)
x = GlobalMaxPooling2D()(x)
x = Dense(64, kernel_regularizer=regularizers.l2(0.01))(x)
x = Activation('elu')(x)
output = Dense(1, name='speed')(x)
model = Model(inputs=input, outputs=output, name='optical_flow_encoder')
return model
def create_mobile_net_model(size, alpha):
model_net = MobileNet(input_shape=(size, size, 3),
include_top=False,
alpha=alpha)
x = _depthwise_conv_block(model_net.layers[-1].output,
1024,
alpha,
1,
block_id=14)
x = Conv2D(64, (3, 3), activation='relu')(x)
x = MaxPooling2D(pool_size=(4, 4))(x)
x = Conv2D(8, kernel_size=(1, 1), padding="same")(x)
x = Reshape((8, ))(x)
return Model(inputs=model_net.input, outputs=x)
def create_model(size, alpha):
model_net = MobileNet(input_shape=(size, size, 3),
include_top=False,
weights="imagenet",
alpha=alpha)
# for i in range(len(model_net.layers) - 13):
# model_net.layers[i].trainable=False
x = _depthwise_conv_block(model_net.layers[-1].output,
1024 / 4,
alpha,
1,
block_id=14)
x = MaxPooling2D(pool_size=(3, 3))(x)
x = Conv2D(4, kernel_size=(1, 1), padding="same")(x)
x = Reshape((4, ))(x)
return Model(inputs=model_net.input, outputs=x)
def My_MobileNet(triplet, alpha=1.0, depth_multiplier=1):
img_input = triplet
x = _conv_block(img_input, 32, alpha, strides=(2, 2))
x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1)
x = _depthwise_conv_block(x,
128,
alpha,
depth_multiplier,
strides=(2, 2),
block_id=2)
x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3)
x = _depthwise_conv_block(x,
256,
alpha,
depth_multiplier,
strides=(2, 2),
block_id=4)
x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5)
x = _depthwise_conv_block(x,
512,
alpha,
depth_multiplier,
strides=(2, 2),
block_id=6)
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7)
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8)
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9)
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10)
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11)
x = _depthwise_conv_block(x,
1024,
alpha,
depth_multiplier,
strides=(2, 2),
block_id=12)
x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13)
x = GlobalAveragePooling2D()(x)
x = Flatten()(x)
x = Dense(128)(x)
return x