Keras搭建Efficientdet目标检测平台的实现思路_其它综合_

 from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import json import math import string import collections import numpy as np from keras import backend from six.moves import xrange from nets.layers import BatchNormalization from keras import layers BASE_WEIGHTS_PATH = ( 'https://github.com/Callidior/keras-applications/' 'releases/download/efficientnet/') WEIGHTS_HASHES = { 'efficientnet-b0': ('163292582f1c6eaca8e7dc7b51b01c61' '5b0dbc0039699b4dcd0b975cc21533dc', 'c1421ad80a9fc67c2cc4000f666aa507' '89ce39eedb4e06d531b0c593890ccff3'), 'efficientnet-b1': ('d0a71ddf51ef7a0ca425bab32b7fa7f1' '6043ee598ecee73fc674d9560c8f09b0', '75de265d03ac52fa74f2f510455ba64f' '9c7c5fd96dc923cd4bfefa3d680c4b68'), 'efficientnet-b2': ('bb5451507a6418a574534aa76a91b106' 'f6b605f3b5dde0b21055694319853086', '433b60584fafba1ea3de07443b74cfd3' '2ce004a012020b07ef69e22ba8669333'), 'efficientnet-b3': ('03f1fba367f070bd2545f081cfa7f3e7' '6f5e1aa3b6f4db700f00552901e75ab9', 'c5d42eb6cfae8567b418ad3845cfd63a' 'a48b87f1bd5df8658a49375a9f3135c7'), 'efficientnet-b4': ('98852de93f74d9833c8640474b2c698d' 'b45ec60690c75b3bacb1845e907bf94f', '7942c1407ff1feb34113995864970cd4' 'd9d91ea64877e8d9c38b6c1e0767c411'), 'efficientnet-b5': ('30172f1d45f9b8a41352d4219bf930ee' '3339025fd26ab314a817ba8918fefc7d', '9d197bc2bfe29165c10a2af8c2ebc675' '07f5d70456f09e584c71b822941b1952'), 'efficientnet-b6': ('f5270466747753485a082092ac9939ca' 'a546eb3f09edca6d6fff842cad938720', '1d0923bb038f2f8060faaf0a0449db4b' '96549a881747b7c7678724ac79f427ed'), 'efficientnet-b7': ('876a41319980638fa597acbbf956a82d' '10819531ff2dcb1a52277f10c7aefa1a', '60b56ff3a8daccc8d96edfd40b204c11' '3e51748da657afd58034d54d3cec2bac') } BlockArgs = collections.namedtuple('BlockArgs', [ 'kernel_size', 'num_repeat', 'input_filters', 'output_filters', 'expand_ratio', 'id_skip', 'strides', 'se_ratio' ]) # defaults will be a public argument for namedtuple in Python 3.7 # https://docs.python.org/3/library/collections.html#collections.namedtuple BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields) DEFAULT_BLOCKS_ARGS = [ BlockArgs(kernel_size=3, num_repeat=1, input_filters=32, output_filters=16, expand_ratio=1, id_skip=True, strides=[1, 1], se_ratio=0.25), BlockArgs(kernel_size=3, num_repeat=2, input_filters=16, output_filters=24, expand_ratio=6, id_skip=True, strides=[2, 2], se_ratio=0.25), BlockArgs(kernel_size=5, num_repeat=2, input_filters=24, output_filters=40, expand_ratio=6, id_skip=True, strides=[2, 2], se_ratio=0.25), BlockArgs(kernel_size=3, num_repeat=3, input_filters=40, output_filters=80, expand_ratio=6, id_skip=True, strides=[2, 2], se_ratio=0.25), BlockArgs(kernel_size=5, num_repeat=3, input_filters=80, output_filters=112, expand_ratio=6, id_skip=True, strides=[1, 1], se_ratio=0.25), BlockArgs(kernel_size=5, num_repeat=4, input_filters=112, output_filters=192, expand_ratio=6, id_skip=True, strides=[2, 2], se_ratio=0.25), BlockArgs(kernel_size=3, num_repeat=1, input_filters=192, output_filters=320, expand_ratio=6, id_skip=True, strides=[1, 1], se_ratio=0.25) ] CONV_KERNEL_INITIALIZER = { 'class_name': 'VarianceScaling', 'config': { 'scale': 2.0, 'mode': 'fan_out', # EfficientNet actually uses an untruncated normal distribution for # initializing conv layers, but keras.initializers.VarianceScaling use # a truncated distribution. # We decided against a custom initializer for better serializability. 'distribution': 'normal' } } DENSE_KERNEL_INITIALIZER = { 'class_name': 'VarianceScaling', 'config': { 'scale': 1. / 3., 'mode': 'fan_out', 'distribution': 'uniform' } } def get_swish(): def swish(x): return x * backend.sigmoid(x) return swish def get_dropout(): class FixedDropout(layers.Dropout): def _get_noise_shape(self, inputs): if self.noise_shape is None: return self.noise_shape symbolic_shape = backend.shape(inputs) noise_shape = [symbolic_shape[axis] if shape is None else shape for axis, shape in enumerate(self.noise_shape)] return tuple(noise_shape) return FixedDropout def round_filters(filters, width_coefficient, depth_divisor): filters *= width_coefficient new_filters = int(filters + depth_divisor / 2) // depth_divisor * depth_divisor new_filters = max(depth_divisor, new_filters) if new_filters < 0.9 * filters: new_filters += depth_divisor return int(new_filters) def round_repeats(repeats, depth_coefficient): return int(math.ceil(depth_coefficient * repeats)) def mb_conv_block(inputs, block_args, activation, drop_rate=None, prefix='', freeze_bn=False): has_se = (block_args.se_ratio is not None) and (0 < block_args.se_ratio <= 1) bn_axis = 3 Dropout = get_dropout() filters = block_args.input_filters * block_args.expand_ratio if block_args.expand_ratio != 1: x = layers.Conv2D(filters, 1, padding='same', use_bias=False, kernel_initializer=CONV_KERNEL_INITIALIZER, name=prefix + 'expand_conv')(inputs) x = layers.BatchNormalization(axis=bn_axis, name=prefix + 'expand_bn')(x) x = layers.Activation(activation, name=prefix + 'expand_activation')(x) else: x = inputs # Depthwise Convolution x = layers.DepthwiseConv2D(block_args.kernel_size, strides=block_args.strides, padding='same', use_bias=False, depthwise_initializer=CONV_KERNEL_INITIALIZER, name=prefix + 'dwconv')(x) x = layers.BatchNormalization(axis=bn_axis, name=prefix + 'bn')(x) x = layers.Activation(activation, name=prefix + 'activation')(x) # Squeeze and Excitation phase if has_se: num_reduced_filters = max(1, int( block_args.input_filters * block_args.se_ratio )) se_tensor = layers.GlobalAveragePooling2D(name=prefix + 'se_squeeze')(x) target_shape = (1, 1, filters) if backend.image_data_format() == 'channels_last' else (filters, 1, 1) se_tensor = layers.Reshape(target_shape, name=prefix + 'se_reshape')(se_tensor) se_tensor = layers.Conv2D(num_reduced_filters, 1, activation=activation, padding='same', use_bias=True, kernel_initializer=CONV_KERNEL_INITIALIZER, name=prefix + 'se_reduce')(se_tensor) se_tensor = layers.Conv2D(filters, 1, activation='sigmoid', padding='same', use_bias=True, kernel_initializer=CONV_KERNEL_INITIALIZER, name=prefix + 'se_expand')(se_tensor) if backend.backend() == 'theano': # For the Theano backend, we have to explicitly make # the excitation weights broadcastable. pattern = ([True, True, True, False] if backend.image_data_format() == 'channels_last' else [True, False, True, True]) se_tensor = layers.Lambda( lambda x: backend.pattern_broadcast(x, pattern), name=prefix + 'se_broadcast')(se_tensor) x = layers.multiply([x, se_tensor], name=prefix + 'se_excite') # Output phase x = layers.Conv2D(block_args.output_filters, 1, padding='same', use_bias=False, kernel_initializer=CONV_KERNEL_INITIALIZER, name=prefix + 'project_conv')(x) # x = BatchNormalization(freeze=freeze_bn, axis=bn_axis, name=prefix + 'project_bn')(x) x = layers.BatchNormalization(axis=bn_axis, name=prefix + 'project_bn')(x) if block_args.id_skip and all( s == 1 for s in block_args.strides ) and block_args.input_filters == block_args.output_filters: if drop_rate and (drop_rate > 0): x = Dropout(drop_rate, noise_shape=(None, 1, 1, 1), name=prefix + 'drop')(x) x = layers.add([x, inputs], name=prefix + 'add') return x def EfficientNet(width_coefficient, depth_coefficient, default_resolution, dropout_rate=0.2, drop_connect_rate=0.2, depth_divisor=8, blocks_args=DEFAULT_BLOCKS_ARGS, model_name='efficientnet', include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, freeze_bn=False, **kwargs): features = [] if input_tensor is None: img_
                
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