Add files via upload

YOLOv3-custom-training/4_CLASS_test_classes.txt

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+Bus
+Car
+Fire_hydrant
+Traffic_light

YOLOv3-custom-training/convert.py

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