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backbone.py
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#! /usr/bin/env python
# coding=utf-8
import tensorflow as tf
import core.common as common
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 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