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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 | |