def darknet53_tiny(input_data):
input_data = common.convolutional(input_data, (3, 3, 3, 16))
input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
input_data = common.convolutional(input_data, (3, 3, 16, 32))
input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
input_data = common.convolutional(input_data, (3, 3, 32, 64))
input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
input_data = common.convolutional(input_data, (3, 3, 64, 128))
input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
input_data = common.convolutional(input_data, (3, 3, 128, 256))
route_1 = input_data
input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
input_data = common.convolutional(input_data, (3, 3, 256, 512))
input_data = tf.keras.layers.MaxPool2D(2, 1, 'same')(input_data)
input_data = common.convolutional(input_data, (3, 3, 512, 1024))
return route_1, input_data
def YOLOv3_tiny(input_layer, NUM_CLASS):
route_1, conv = backbone.darknet53_tiny(input_layer)
conv = common.convolutional(conv, (1, 1, 1024, 256))
conv_lobj_branch = common.convolutional(conv, (3, 3, 256, 512))
conv_lbbox = common.convolutional(conv_lobj_branch,
(1, 1, 512, 3 * (NUM_CLASS + 5)),
activate=False,
bn=False)
conv = common.convolutional(conv, (1, 1, 256, 128))
conv = common.upsample(conv)
conv = tf.concat([conv, route_1], axis=-1)
conv_mobj_branch = common.convolutional(conv, (3, 3, 128, 256))
conv_mbbox = common.convolutional(conv_mobj_branch,
(1, 1, 256, 3 * (NUM_CLASS + 5)),
activate=False,
bn=False)
return [conv_mbbox, conv_lbbox]
def darknet53(input_data):
input_data = common.convolutional(input_data, (3, 3, 3, 32))
input_data = common.convolutional(input_data, (3, 3, 32, 64),
downsample=True)
for i in range(1):
input_data = common.residual_block(input_data, 64, 32, 64)
input_data = common.convolutional(input_data, (3, 3, 64, 128),
downsample=True)
for i in range(2):
input_data = common.residual_block(input_data, 128, 64, 128)
input_data = common.convolutional(input_data, (3, 3, 128, 256),
downsample=True)
for i in range(8):
input_data = common.residual_block(input_data, 256, 128, 256)
route_1 = input_data
input_data = common.convolutional(input_data, (3, 3, 256, 512),
downsample=True)
for i in range(8):
input_data = common.residual_block(input_data, 512, 256, 512)
route_2 = input_data
input_data = common.convolutional(input_data, (3, 3, 512, 1024),
downsample=True)
for i in range(4):
input_data = common.residual_block(input_data, 1024, 512, 1024)
return route_1, route_2, input_data
def cspdarknet53(input_data):
input_data = common.convolutional(input_data, (3, 3, 3, 32),
activate_type="mish")
input_data = common.convolutional(input_data, (3, 3, 32, 64),
downsample=True,
activate_type="mish")
route = input_data
route = common.convolutional(route, (1, 1, 64, 64), activate_type="mish")
input_data = common.convolutional(input_data, (1, 1, 64, 64),
activate_type="mish")
for i in range(1):
input_data = common.residual_block(input_data,
64,
32,
64,
activate_type="mish")
input_data = common.convolutional(input_data, (1, 1, 64, 64),
activate_type="mish")
input_data = tf.concat([input_data, route], axis=-1)
input_data = common.convolutional(input_data, (1, 1, 128, 64),
activate_type="mish")
input_data = common.convolutional(input_data, (3, 3, 64, 128),
downsample=True,
activate_type="mish")
route = input_data
route = common.convolutional(route, (1, 1, 128, 64), activate_type="mish")
input_data = common.convolutional(input_data, (1, 1, 128, 64),
activate_type="mish")
for i in range(2):
input_data = common.residual_block(input_data,
64,
64,
64,
activate_type="mish")
input_data = common.convolutional(input_data, (1, 1, 64, 64),
activate_type="mish")
input_data = tf.concat([input_data, route], axis=-1)
input_data = common.convolutional(input_data, (1, 1, 128, 128),
activate_type="mish")
input_data = common.convolutional(input_data, (3, 3, 128, 256),
downsample=True,
activate_type="mish")
route = input_data
route = common.convolutional(route, (1, 1, 256, 128), activate_type="mish")
input_data = common.convolutional(input_data, (1, 1, 256, 128),
activate_type="mish")
for i in range(8):
input_data = common.residual_block(input_data,
128,
128,
128,
activate_type="mish")
input_data = common.convolutional(input_data, (1, 1, 128, 128),
activate_type="mish")
input_data = tf.concat([input_data, route], axis=-1)
input_data = common.convolutional(input_data, (1, 1, 256, 256),
activate_type="mish")
route_1 = input_data
input_data = common.convolutional(input_data, (3, 3, 256, 512),
downsample=True,
activate_type="mish")
route = input_data
route = common.convolutional(route, (1, 1, 512, 256), activate_type="mish")
input_data = common.convolutional(input_data, (1, 1, 512, 256),
activate_type="mish")
for i in range(8):
input_data = common.residual_block(input_data,
256,
256,
256,
activate_type="mish")
input_data = common.convolutional(input_data, (1, 1, 256, 256),
activate_type="mish")
input_data = tf.concat([input_data, route], axis=-1)
input_data = common.convolutional(input_data, (1, 1, 512, 512),
activate_type="mish")
route_2 = input_data
input_data = common.convolutional(input_data, (3, 3, 512, 1024),
downsample=True,
activate_type="mish")
route = input_data
route = common.convolutional(route, (1, 1, 1024, 512),
activate_type="mish")
input_data = common.convolutional(input_data, (1, 1, 1024, 512),
activate_type="mish")
for i in range(4):
input_data = common.residual_block(input_data,
512,
512,
512,
activate_type="mish")
input_data = common.convolutional(input_data, (1, 1, 512, 512),
activate_type="mish")
input_data = tf.concat([input_data, route], axis=-1)
input_data = common.convolutional(input_data, (1, 1, 1024, 1024),
activate_type="mish")
input_data = common.convolutional(input_data, (1, 1, 1024, 512))
input_data = common.convolutional(input_data, (3, 3, 512, 1024))
input_data = common.convolutional(input_data, (1, 1, 1024, 512))
input_data = tf.concat([
tf.nn.max_pool(input_data, ksize=13, padding='SAME', strides=1),
tf.nn.max_pool(input_data, ksize=9, padding='SAME', strides=1),
tf.nn.max_pool(input_data, ksize=5, padding='SAME', strides=1),
input_data
],
axis=-1)
input_data = common.convolutional(input_data, (1, 1, 2048, 512))
input_data = common.convolutional(input_data, (3, 3, 512, 1024))
input_data = common.convolutional(input_data, (1, 1, 1024, 512))
return route_1, route_2, input_data
def cspdarknet53_tiny(input_data):
input_data = common.convolutional(input_data, (3, 3, 3, 32),
downsample=True)
input_data = common.convolutional(input_data, (3, 3, 32, 64),
downsample=True)
input_data = common.convolutional(input_data, (3, 3, 64, 64))
route = input_data
input_data = common.route_group(input_data, 2, 1)
input_data = common.convolutional(input_data, (3, 3, 32, 32))
route_1 = input_data
input_data = common.convolutional(input_data, (3, 3, 32, 32))
input_data = tf.concat([input_data, route_1], axis=-1)
input_data = common.convolutional(input_data, (1, 1, 32, 64))
input_data = tf.concat([route, input_data], axis=-1)
input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
input_data = common.convolutional(input_data, (3, 3, 64, 128))
route = input_data
input_data = common.route_group(input_data, 2, 1)
input_data = common.convolutional(input_data, (3, 3, 64, 64))
route_1 = input_data
input_data = common.convolutional(input_data, (3, 3, 64, 64))
input_data = tf.concat([input_data, route_1], axis=-1)
input_data = common.convolutional(input_data, (1, 1, 64, 128))
input_data = tf.concat([route, input_data], axis=-1)
input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
input_data = common.convolutional(input_data, (3, 3, 128, 256))
route = input_data
input_data = common.route_group(input_data, 2, 1)
input_data = common.convolutional(input_data, (3, 3, 128, 128))
route_1 = input_data
input_data = common.convolutional(input_data, (3, 3, 128, 128))
input_data = tf.concat([input_data, route_1], axis=-1)
input_data = common.convolutional(input_data, (1, 1, 128, 256))
route_1 = input_data
input_data = tf.concat([route, input_data], axis=-1)
input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
input_data = common.convolutional(input_data, (3, 3, 512, 512))
return route_1, input_data
def YOLOv4(input_layer, NUM_CLASS):
route_1, route_2, conv = backbone.cspdarknet53(input_layer)
route = conv
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.upsample(conv)
route_2 = common.convolutional(route_2, (1, 1, 512, 256))
conv = tf.concat([route_2, conv], axis=-1)
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.convolutional(conv, (3, 3, 256, 512))
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.convolutional(conv, (3, 3, 256, 512))
conv = common.convolutional(conv, (1, 1, 512, 256))
route_2 = conv
conv = common.convolutional(conv, (1, 1, 256, 128))
conv = common.upsample(conv)
route_1 = common.convolutional(route_1, (1, 1, 256, 128))
conv = tf.concat([route_1, conv], axis=-1)
conv = common.convolutional(conv, (1, 1, 256, 128))
conv = common.convolutional(conv, (3, 3, 128, 256))
conv = common.convolutional(conv, (1, 1, 256, 128))
conv = common.convolutional(conv, (3, 3, 128, 256))
conv = common.convolutional(conv, (1, 1, 256, 128))
route_1 = conv
conv = common.convolutional(conv, (3, 3, 128, 256))
conv_sbbox = common.convolutional(conv, (1, 1, 256, 3 * (NUM_CLASS + 5)),
activate=False,
bn=False)
conv = common.convolutional(route_1, (3, 3, 128, 256), downsample=True)
conv = tf.concat([conv, route_2], axis=-1)
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.convolutional(conv, (3, 3, 256, 512))
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.convolutional(conv, (3, 3, 256, 512))
conv = common.convolutional(conv, (1, 1, 512, 256))
route_2 = conv
conv = common.convolutional(conv, (3, 3, 256, 512))
conv_mbbox = common.convolutional(conv, (1, 1, 512, 3 * (NUM_CLASS + 5)),
activate=False,
bn=False)
conv = common.convolutional(route_2, (3, 3, 256, 512), downsample=True)
conv = tf.concat([conv, route], axis=-1)
conv = common.convolutional(conv, (1, 1, 1024, 512))
conv = common.convolutional(conv, (3, 3, 512, 1024))
conv = common.convolutional(conv, (1, 1, 1024, 512))
conv = common.convolutional(conv, (3, 3, 512, 1024))
conv = common.convolutional(conv, (1, 1, 1024, 512))
conv = common.convolutional(conv, (3, 3, 512, 1024))
conv_lbbox = common.convolutional(conv, (1, 1, 1024, 3 * (NUM_CLASS + 5)),
activate=False,
bn=False)
return [conv_sbbox, conv_mbbox, conv_lbbox]
