How to use activate_type method in localstack

Best Python code snippet using localstack_python

backbone.py

Source:backbone.py

1#! /usr/bin/env python2# coding=utf-83import tensorflow as tf4import core.common as common5def darknet53(input_data):6    input_data = common.convolutional(input_data, (3, 3,  3,  32))7    input_data = common.convolutional(input_data, (3, 3, 32,  64), downsample=True)8    for i in range(1):9        input_data = common.residual_block(input_data,  64,  32, 64)10    input_data = common.convolutional(input_data, (3, 3,  64, 128), downsample=True)11    for i in range(2):12        input_data = common.residual_block(input_data, 128,  64, 128)13    input_data = common.convolutional(input_data, (3, 3, 128, 256), downsample=True)14    for i in range(8):15        input_data = common.residual_block(input_data, 256, 128, 256)16    route_1 = input_data17    input_data = common.convolutional(input_data, (3, 3, 256, 512), downsample=True)18    for i in range(8):19        input_data = common.residual_block(input_data, 512, 256, 512)20    route_2 = input_data21    input_data = common.convolutional(input_data, (3, 3, 512, 1024), downsample=True)22    for i in range(4):23        input_data = common.residual_block(input_data, 1024, 512, 1024)24    return route_1, route_2, input_data25def cspdarknet53(input_data):26    input_data = common.convolutional(input_data, (3, 3,  3,  32), activate_type="mish")27    input_data = common.convolutional(input_data, (3, 3, 32,  64), downsample=True, activate_type="mish")28    route = input_data29    route = common.convolutional(route, (1, 1, 64, 64), activate_type="mish")30    input_data = common.convolutional(input_data, (1, 1, 64, 64), activate_type="mish")31    for i in range(1):32        input_data = common.residual_block(input_data,  64,  32, 64, activate_type="mish")33    input_data = common.convolutional(input_data, (1, 1, 64, 64), activate_type="mish")34    input_data = tf.concat([input_data, route], axis=-1)35    input_data = common.convolutional(input_data, (1, 1, 128, 64), activate_type="mish")36    input_data = common.convolutional(input_data, (3, 3, 64, 128), downsample=True, activate_type="mish")37    route = input_data38    route = common.convolutional(route, (1, 1, 128, 64), activate_type="mish")39    input_data = common.convolutional(input_data, (1, 1, 128, 64), activate_type="mish")40    for i in range(2):41        input_data = common.residual_block(input_data, 64,  64, 64, activate_type="mish")42    input_data = common.convolutional(input_data, (1, 1, 64, 64), activate_type="mish")43    input_data = tf.concat([input_data, route], axis=-1)44    input_data = common.convolutional(input_data, (1, 1, 128, 128), activate_type="mish")45    input_data = common.convolutional(input_data, (3, 3, 128, 256), downsample=True, activate_type="mish")46    route = input_data47    route = common.convolutional(route, (1, 1, 256, 128), activate_type="mish")48    input_data = common.convolutional(input_data, (1, 1, 256, 128), activate_type="mish")49    for i in range(8):50        input_data = common.residual_block(input_data, 128, 128, 128, activate_type="mish")51    input_data = common.convolutional(input_data, (1, 1, 128, 128), activate_type="mish")52    input_data = tf.concat([input_data, route], axis=-1)53    input_data = common.convolutional(input_data, (1, 1, 256, 256), activate_type="mish")54    route_1 = input_data55    input_data = common.convolutional(input_data, (3, 3, 256, 512), downsample=True, activate_type="mish")56    route = input_data57    route = common.convolutional(route, (1, 1, 512, 256), activate_type="mish")58    input_data = common.convolutional(input_data, (1, 1, 512, 256), activate_type="mish")59    for i in range(8):60        input_data = common.residual_block(input_data, 256, 256, 256, activate_type="mish")61    input_data = common.convolutional(input_data, (1, 1, 256, 256), activate_type="mish")62    input_data = tf.concat([input_data, route], axis=-1)63    input_data = common.convolutional(input_data, (1, 1, 512, 512), activate_type="mish")64    route_2 = input_data65    input_data = common.convolutional(input_data, (3, 3, 512, 1024), downsample=True, activate_type="mish")66    route = input_data67    route = common.convolutional(route, (1, 1, 1024, 512), activate_type="mish")68    input_data = common.convolutional(input_data, (1, 1, 1024, 512), activate_type="mish")69    for i in range(4):70        input_data = common.residual_block(input_data, 512, 512, 512, activate_type="mish")71    input_data = common.convolutional(input_data, (1, 1, 512, 512), activate_type="mish")72    input_data = tf.concat([input_data, route], axis=-1)73    input_data = common.convolutional(input_data, (1, 1, 1024, 1024), activate_type="mish")74    input_data = common.convolutional(input_data, (1, 1, 1024, 512))75    input_data = common.convolutional(input_data, (3, 3, 512, 1024))76    input_data = common.convolutional(input_data, (1, 1, 1024, 512))77    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)78                            , tf.nn.max_pool(input_data, ksize=5, padding='SAME', strides=1), input_data], axis=-1)79    input_data = common.convolutional(input_data, (1, 1, 2048, 512))80    input_data = common.convolutional(input_data, (3, 3, 512, 1024))81    input_data = common.convolutional(input_data, (1, 1, 1024, 512))82    return route_1, route_2, input_data83def cspdarknet53_tiny(input_data):84    input_data = common.convolutional(input_data, (3, 3, 3, 32), downsample=True)85    input_data = common.convolutional(input_data, (3, 3, 32, 64), downsample=True)86    input_data = common.convolutional(input_data, (3, 3, 64, 64))87    route = input_data88    input_data = common.route_group(input_data, 2, 1)89    input_data = common.convolutional(input_data, (3, 3, 32, 32))90    route_1 = input_data91    input_data = common.convolutional(input_data, (3, 3, 32, 32))92    input_data = tf.concat([input_data, route_1], axis=-1)93    input_data = common.convolutional(input_data, (1, 1, 32, 64))94    input_data = tf.concat([route, input_data], axis=-1)95    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)96    input_data = common.convolutional(input_data, (3, 3, 64, 128))97    route = input_data98    input_data = common.route_group(input_data, 2, 1)99    input_data = common.convolutional(input_data, (3, 3, 64, 64))100    route_1 = input_data101    input_data = common.convolutional(input_data, (3, 3, 64, 64))102    input_data = tf.concat([input_data, route_1], axis=-1)103    input_data = common.convolutional(input_data, (1, 1, 64, 128))104    input_data = tf.concat([route, input_data], axis=-1)105    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)106    input_data = common.convolutional(input_data, (3, 3, 128, 256))107    route = input_data108    input_data = common.route_group(input_data, 2, 1)109    input_data = common.convolutional(input_data, (3, 3, 128, 128))110    route_1 = input_data111    input_data = common.convolutional(input_data, (3, 3, 128, 128))112    input_data = tf.concat([input_data, route_1], axis=-1)113    input_data = common.convolutional(input_data, (1, 1, 128, 256))114    route_1 = input_data115    input_data = tf.concat([route, input_data], axis=-1)116    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)117    input_data = common.convolutional(input_data, (3, 3, 512, 512))118    return route_1, input_data119def darknet53_tiny(input_data):120    input_data = common.convolutional(input_data, (3, 3, 3, 16))121    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)122    input_data = common.convolutional(input_data, (3, 3, 16, 32))123    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)124    input_data = common.convolutional(input_data, (3, 3, 32, 64))125    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)126    input_data = common.convolutional(input_data, (3, 3, 64, 128))127    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)128    input_data = common.convolutional(input_data, (3, 3, 128, 256))129    route_1 = input_data130    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)131    input_data = common.convolutional(input_data, (3, 3, 256, 512))132    input_data = tf.keras.layers.MaxPool2D(2, 1, 'same')(input_data)133    input_data = common.convolutional(input_data, (3, 3, 512, 1024))...

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