81 lines
2.9 KiB
Python
81 lines
2.9 KiB
Python
#! /usr/bin/env python
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import argparse
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import os
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import numpy as np
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import json
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from voc import parse_voc_annotation
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from yolo import create_yolov3_model
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from generator import BatchGenerator
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from utils.utils import normalize, evaluate
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from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
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from tensorflow.keras.optimizers import Adam
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from tensorflow.keras.models import load_model
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def _main_(args):
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config_path = args.conf
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with open(config_path) as config_buffer:
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config = json.loads(config_buffer.read())
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###############################
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# Create the validation generator
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###############################
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valid_ints, labels = parse_voc_annotation(
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config['test']['test_annot_folder'],
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config['test']['test_image_folder'],
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config['test']['cache_name'],
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config['model']['labels']
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)
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labels = labels.keys() if len(config['model']['labels']) == 0 else config['model']['labels']
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labels = sorted(labels)
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valid_generator = BatchGenerator(
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instances = valid_ints,
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anchors = config['model']['anchors'],
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labels = labels,
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downsample = 32, # ratio between network input's size and network output's size, 32 for YOLOv3
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max_box_per_image = 0,
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batch_size = config['train']['batch_size'],
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min_net_size = config['model']['min_input_size'],
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max_net_size = config['model']['max_input_size'],
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shuffle = True,
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jitter = 0.0,
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norm = normalize
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)
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###############################
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# Load the model and do evaluation
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###############################
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os.environ['CUDA_VISIBLE_DEVICES'] = config['train']['gpus']
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infer_model = load_model(config['train']['saved_weights_name'])
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# compute mAP for all the classes
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average_precisions = evaluate(infer_model, valid_generator)
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# print the score
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total_instances = []
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precisions = []
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print(average_precisions.items())
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for label, (average_precision, num_annotations) in average_precisions.items():
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print('{:.0f} instances of class'.format(num_annotations),
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labels[label], 'with average precision: {:.4f}'.format(average_precision))
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total_instances.append(num_annotations)
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precisions.append(average_precision)
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if sum(total_instances) == 0:
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print('No test instances found.')
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return
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print('mAP using the weighted average of precisions among classes: {:.4f}'.format(sum([a * b for a, b in zip(total_instances, precisions)]) / sum(total_instances)))
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print('mAP: {:.4f}'.format(sum(precisions) / sum(x > 0 for x in total_instances)))
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if __name__ == '__main__':
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argparser = argparse.ArgumentParser(description='Evaluate YOLO_v3 model on any dataset')
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argparser.add_argument('-c', '--conf', help='path to configuration file')
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args = argparser.parse_args()
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_main_(args)
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