265 lines
9.9 KiB
Python
265 lines
9.9 KiB
Python
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#! /usr/bin/env python
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"""
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Reads Darknet config and weights and creates Keras model with TF backend.
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"""
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import argparse
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import configparser
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import io
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import os
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from collections import defaultdict
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import numpy as np
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from keras import backend as K
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from keras.layers import (Conv2D, Input, ZeroPadding2D, Add,
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UpSampling2D, MaxPooling2D, Concatenate)
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#from keras.layers.advanced_activations import LeakyReLU
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from keras.layers import ELU, PReLU, LeakyReLU
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#from keras.layers.normalization import BatchNormalization
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from keras.layers import BatchNormalization
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from keras.models import Model
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from keras.regularizers import l2
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from keras.utils.vis_utils import plot_model as plot
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parser = argparse.ArgumentParser(description='Darknet To Keras Converter.')
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parser.add_argument('config_path', help='Path to Darknet cfg file.')
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parser.add_argument('weights_path', help='Path to Darknet weights file.')
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parser.add_argument('output_path', help='Path to output Keras model file.')
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parser.add_argument(
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'-p',
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'--plot_model',
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help='Plot generated Keras model and save as image.',
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action='store_true')
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parser.add_argument(
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'-w',
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'--weights_only',
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help='Save as Keras weights file instead of model file.',
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action='store_true')
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def unique_config_sections(config_file):
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"""Convert all config sections to have unique names.
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Adds unique suffixes to config sections for compability with configparser.
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"""
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section_counters = defaultdict(int)
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output_stream = io.StringIO()
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with open(config_file) as fin:
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for line in fin:
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if line.startswith('['):
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section = line.strip().strip('[]')
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_section = section + '_' + str(section_counters[section])
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section_counters[section] += 1
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line = line.replace(section, _section)
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output_stream.write(line)
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output_stream.seek(0)
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return output_stream
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# %%
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def _main(args):
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config_path = os.path.expanduser(args.config_path)
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weights_path = os.path.expanduser(args.weights_path)
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assert config_path.endswith('.cfg'), '{} is not a .cfg file'.format(
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config_path)
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assert weights_path.endswith(
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'.weights'), '{} is not a .weights file'.format(weights_path)
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output_path = os.path.expanduser(args.output_path)
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assert output_path.endswith(
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'.h5'), 'output path {} is not a .h5 file'.format(output_path)
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output_root = os.path.splitext(output_path)[0]
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# Load weights and config.
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print('Loading weights.')
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weights_file = open(weights_path, 'rb')
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major, minor, revision = np.ndarray(
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shape=(3, ), dtype='int32', buffer=weights_file.read(12))
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if (major*10+minor)>=2 and major<1000 and minor<1000:
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seen = np.ndarray(shape=(1,), dtype='int64', buffer=weights_file.read(8))
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else:
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seen = np.ndarray(shape=(1,), dtype='int32', buffer=weights_file.read(4))
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print('Weights Header: ', major, minor, revision, seen)
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print('Parsing Darknet config.')
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unique_config_file = unique_config_sections(config_path)
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cfg_parser = configparser.ConfigParser()
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cfg_parser.read_file(unique_config_file)
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print('Creating Keras model.')
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input_layer = Input(shape=(None, None, 3))
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prev_layer = input_layer
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all_layers = []
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weight_decay = float(cfg_parser['net_0']['decay']
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) if 'net_0' in cfg_parser.sections() else 5e-4
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count = 0
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out_index = []
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for section in cfg_parser.sections():
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print('Parsing section {}'.format(section))
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if section.startswith('convolutional'):
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filters = int(cfg_parser[section]['filters'])
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size = int(cfg_parser[section]['size'])
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stride = int(cfg_parser[section]['stride'])
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pad = int(cfg_parser[section]['pad'])
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activation = cfg_parser[section]['activation']
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batch_normalize = 'batch_normalize' in cfg_parser[section]
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padding = 'same' if pad == 1 and stride == 1 else 'valid'
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# Setting weights.
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# Darknet serializes convolutional weights as:
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# [bias/beta, [gamma, mean, variance], conv_weights]
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prev_layer_shape = K.int_shape(prev_layer)
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weights_shape = (size, size, prev_layer_shape[-1], filters)
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darknet_w_shape = (filters, weights_shape[2], size, size)
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weights_size = np.product(weights_shape)
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print('conv2d', 'bn'
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if batch_normalize else ' ', activation, weights_shape)
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conv_bias = np.ndarray(
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shape=(filters, ),
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dtype='float32',
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buffer=weights_file.read(filters * 4))
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count += filters
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if batch_normalize:
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bn_weights = np.ndarray(
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shape=(3, filters),
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dtype='float32',
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buffer=weights_file.read(filters * 12))
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count += 3 * filters
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bn_weight_list = [
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bn_weights[0], # scale gamma
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conv_bias, # shift beta
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bn_weights[1], # running mean
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bn_weights[2] # running var
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]
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conv_weights = np.ndarray(
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shape=darknet_w_shape,
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dtype='float32',
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buffer=weights_file.read(weights_size * 4))
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count += weights_size
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# DarkNet conv_weights are serialized Caffe-style:
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# (out_dim, in_dim, height, width)
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# We would like to set these to Tensorflow order:
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# (height, width, in_dim, out_dim)
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conv_weights = np.transpose(conv_weights, [2, 3, 1, 0])
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conv_weights = [conv_weights] if batch_normalize else [
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conv_weights, conv_bias
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]
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# Handle activation.
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act_fn = None
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if activation == 'leaky':
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pass # Add advanced activation later.
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elif activation != 'linear':
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raise ValueError(
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'Unknown activation function `{}` in section {}'.format(
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activation, section))
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# Create Conv2D layer
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if stride>1:
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# Darknet uses left and top padding instead of 'same' mode
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prev_layer = ZeroPadding2D(((1,0),(1,0)))(prev_layer)
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conv_layer = (Conv2D(
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filters, (size, size),
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strides=(stride, stride),
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kernel_regularizer=l2(weight_decay),
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use_bias=not batch_normalize,
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weights=conv_weights,
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activation=act_fn,
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padding=padding))(prev_layer)
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if batch_normalize:
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conv_layer = (BatchNormalization(
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weights=bn_weight_list))(conv_layer)
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prev_layer = conv_layer
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if activation == 'linear':
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all_layers.append(prev_layer)
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elif activation == 'leaky':
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act_layer = LeakyReLU(alpha=0.1)(prev_layer)
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prev_layer = act_layer
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all_layers.append(act_layer)
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elif section.startswith('route'):
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ids = [int(i) for i in cfg_parser[section]['layers'].split(',')]
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layers = [all_layers[i] for i in ids]
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if len(layers) > 1:
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print('Concatenating route layers:', layers)
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concatenate_layer = Concatenate()(layers)
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all_layers.append(concatenate_layer)
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prev_layer = concatenate_layer
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else:
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skip_layer = layers[0] # only one layer to route
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all_layers.append(skip_layer)
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prev_layer = skip_layer
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elif section.startswith('maxpool'):
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size = int(cfg_parser[section]['size'])
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stride = int(cfg_parser[section]['stride'])
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all_layers.append(
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MaxPooling2D(
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pool_size=(size, size),
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strides=(stride, stride),
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padding='same')(prev_layer))
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prev_layer = all_layers[-1]
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elif section.startswith('shortcut'):
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index = int(cfg_parser[section]['from'])
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activation = cfg_parser[section]['activation']
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assert activation == 'linear', 'Only linear activation supported.'
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all_layers.append(Add()([all_layers[index], prev_layer]))
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prev_layer = all_layers[-1]
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elif section.startswith('upsample'):
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stride = int(cfg_parser[section]['stride'])
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assert stride == 2, 'Only stride=2 supported.'
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all_layers.append(UpSampling2D(stride)(prev_layer))
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prev_layer = all_layers[-1]
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elif section.startswith('yolo'):
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out_index.append(len(all_layers)-1)
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all_layers.append(None)
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prev_layer = all_layers[-1]
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elif section.startswith('net'):
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pass
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else:
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raise ValueError(
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'Unsupported section header type: {}'.format(section))
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# Create and save model.
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if len(out_index)==0: out_index.append(len(all_layers)-1)
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model = Model(inputs=input_layer, outputs=[all_layers[i] for i in out_index])
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print(model.summary())
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if args.weights_only:
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model.save_weights('{}'.format(output_path))
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print('Saved Keras weights to {}'.format(output_path))
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else:
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model.save('{}'.format(output_path))
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print('Saved Keras model to {}'.format(output_path))
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# Check to see if all weights have been read.
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remaining_weights = len(weights_file.read()) / 4
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weights_file.close()
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print('Read {} of {} from Darknet weights.'.format(count, count +
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remaining_weights))
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if remaining_weights > 0:
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print('Warning: {} unused weights'.format(remaining_weights))
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if args.plot_model:
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plot(model, to_file='{}.png'.format(output_root), show_shapes=True)
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print('Saved model plot to {}.png'.format(output_root))
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if __name__ == '__main__':
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_main(parser.parse_args())
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