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import tensorflow as tf |
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import core.common as common |
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def darknet53(input_data): |
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input_data = common.convolutional(input_data, (3, 3, 3, 32)) |
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input_data = common.convolutional(input_data, (3, 3, 32, 64), downsample=True) |
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for i in range(1): |
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input_data = common.residual_block(input_data, 64, 32, 64) |
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input_data = common.convolutional(input_data, (3, 3, 64, 128), downsample=True) |
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for i in range(2): |
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input_data = common.residual_block(input_data, 128, 64, 128) |
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input_data = common.convolutional(input_data, (3, 3, 128, 256), downsample=True) |
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for i in range(8): |
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input_data = common.residual_block(input_data, 256, 128, 256) |
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route_1 = input_data |
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input_data = common.convolutional(input_data, (3, 3, 256, 512), downsample=True) |
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for i in range(8): |
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input_data = common.residual_block(input_data, 512, 256, 512) |
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route_2 = input_data |
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input_data = common.convolutional(input_data, (3, 3, 512, 1024), downsample=True) |
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for i in range(4): |
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input_data = common.residual_block(input_data, 1024, 512, 1024) |
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return route_1, route_2, input_data |
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def cspdarknet53(input_data): |
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input_data = common.convolutional(input_data, (3, 3, 3, 32), activate_type="mish") |
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input_data = common.convolutional(input_data, (3, 3, 32, 64), downsample=True, activate_type="mish") |
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route = input_data |
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route = common.convolutional(route, (1, 1, 64, 64), activate_type="mish") |
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input_data = common.convolutional(input_data, (1, 1, 64, 64), activate_type="mish") |
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for i in range(1): |
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input_data = common.residual_block(input_data, 64, 32, 64, activate_type="mish") |
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input_data = common.convolutional(input_data, (1, 1, 64, 64), activate_type="mish") |
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input_data = tf.concat([input_data, route], axis=-1) |
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input_data = common.convolutional(input_data, (1, 1, 128, 64), activate_type="mish") |
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input_data = common.convolutional(input_data, (3, 3, 64, 128), downsample=True, activate_type="mish") |
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route = input_data |
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route = common.convolutional(route, (1, 1, 128, 64), activate_type="mish") |
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input_data = common.convolutional(input_data, (1, 1, 128, 64), activate_type="mish") |
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for i in range(2): |
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input_data = common.residual_block(input_data, 64, 64, 64, activate_type="mish") |
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input_data = common.convolutional(input_data, (1, 1, 64, 64), activate_type="mish") |
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input_data = tf.concat([input_data, route], axis=-1) |
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input_data = common.convolutional(input_data, (1, 1, 128, 128), activate_type="mish") |
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input_data = common.convolutional(input_data, (3, 3, 128, 256), downsample=True, activate_type="mish") |
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route = input_data |
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route = common.convolutional(route, (1, 1, 256, 128), activate_type="mish") |
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input_data = common.convolutional(input_data, (1, 1, 256, 128), activate_type="mish") |
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for i in range(8): |
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input_data = common.residual_block(input_data, 128, 128, 128, activate_type="mish") |
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input_data = common.convolutional(input_data, (1, 1, 128, 128), activate_type="mish") |
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input_data = tf.concat([input_data, route], axis=-1) |
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input_data = common.convolutional(input_data, (1, 1, 256, 256), activate_type="mish") |
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route_1 = input_data |
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input_data = common.convolutional(input_data, (3, 3, 256, 512), downsample=True, activate_type="mish") |
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route = input_data |
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route = common.convolutional(route, (1, 1, 512, 256), activate_type="mish") |
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input_data = common.convolutional(input_data, (1, 1, 512, 256), activate_type="mish") |
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for i in range(8): |
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input_data = common.residual_block(input_data, 256, 256, 256, activate_type="mish") |
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input_data = common.convolutional(input_data, (1, 1, 256, 256), activate_type="mish") |
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input_data = tf.concat([input_data, route], axis=-1) |
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input_data = common.convolutional(input_data, (1, 1, 512, 512), activate_type="mish") |
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route_2 = input_data |
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input_data = common.convolutional(input_data, (3, 3, 512, 1024), downsample=True, activate_type="mish") |
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route = input_data |
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route = common.convolutional(route, (1, 1, 1024, 512), activate_type="mish") |
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input_data = common.convolutional(input_data, (1, 1, 1024, 512), activate_type="mish") |
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for i in range(4): |
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input_data = common.residual_block(input_data, 512, 512, 512, activate_type="mish") |
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input_data = common.convolutional(input_data, (1, 1, 512, 512), activate_type="mish") |
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input_data = tf.concat([input_data, route], axis=-1) |
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input_data = common.convolutional(input_data, (1, 1, 1024, 1024), activate_type="mish") |
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input_data = common.convolutional(input_data, (1, 1, 1024, 512)) |
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input_data = common.convolutional(input_data, (3, 3, 512, 1024)) |
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input_data = common.convolutional(input_data, (1, 1, 1024, 512)) |
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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) |
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, tf.nn.max_pool(input_data, ksize=5, padding='SAME', strides=1), input_data], axis=-1) |
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input_data = common.convolutional(input_data, (1, 1, 2048, 512)) |
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input_data = common.convolutional(input_data, (3, 3, 512, 1024)) |
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input_data = common.convolutional(input_data, (1, 1, 1024, 512)) |
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return route_1, route_2, input_data |
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def cspdarknet53_tiny(input_data): |
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input_data = common.convolutional(input_data, (3, 3, 3, 32), downsample=True) |
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input_data = common.convolutional(input_data, (3, 3, 32, 64), downsample=True) |
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input_data = common.convolutional(input_data, (3, 3, 64, 64)) |
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route = input_data |
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input_data = common.route_group(input_data, 2, 1) |
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input_data = common.convolutional(input_data, (3, 3, 32, 32)) |
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route_1 = input_data |
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input_data = common.convolutional(input_data, (3, 3, 32, 32)) |
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input_data = tf.concat([input_data, route_1], axis=-1) |
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input_data = common.convolutional(input_data, (1, 1, 32, 64)) |
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input_data = tf.concat([route, input_data], axis=-1) |
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input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data) |
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input_data = common.convolutional(input_data, (3, 3, 64, 128)) |
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route = input_data |
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input_data = common.route_group(input_data, 2, 1) |
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input_data = common.convolutional(input_data, (3, 3, 64, 64)) |
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route_1 = input_data |
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input_data = common.convolutional(input_data, (3, 3, 64, 64)) |
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input_data = tf.concat([input_data, route_1], axis=-1) |
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input_data = common.convolutional(input_data, (1, 1, 64, 128)) |
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input_data = tf.concat([route, input_data], axis=-1) |
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input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data) |
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input_data = common.convolutional(input_data, (3, 3, 128, 256)) |
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route = input_data |
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input_data = common.route_group(input_data, 2, 1) |
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input_data = common.convolutional(input_data, (3, 3, 128, 128)) |
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route_1 = input_data |
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input_data = common.convolutional(input_data, (3, 3, 128, 128)) |
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input_data = tf.concat([input_data, route_1], axis=-1) |
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input_data = common.convolutional(input_data, (1, 1, 128, 256)) |
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route_1 = input_data |
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input_data = tf.concat([route, input_data], axis=-1) |
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input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data) |
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input_data = common.convolutional(input_data, (3, 3, 512, 512)) |
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return route_1, input_data |
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def darknet53_tiny(input_data): |
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input_data = common.convolutional(input_data, (3, 3, 3, 16)) |
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input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data) |
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input_data = common.convolutional(input_data, (3, 3, 16, 32)) |
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input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data) |
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input_data = common.convolutional(input_data, (3, 3, 32, 64)) |
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input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data) |
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input_data = common.convolutional(input_data, (3, 3, 64, 128)) |
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input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data) |
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input_data = common.convolutional(input_data, (3, 3, 128, 256)) |
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route_1 = input_data |
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input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data) |
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input_data = common.convolutional(input_data, (3, 3, 256, 512)) |
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input_data = tf.keras.layers.MaxPool2D(2, 1, 'same')(input_data) |
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input_data = common.convolutional(input_data, (3, 3, 512, 1024)) |
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return route_1, input_data |
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