Summary

keras-yolo3-master/evaluate.py

@@ -0,0 +1,80 @@
+#! /usr/bin/env python
+
+import argparse
+import os
+import numpy as np
+import json
+from voc import parse_voc_annotation
+from yolo import create_yolov3_model
+from generator import BatchGenerator
+from utils.utils import normalize, evaluate
+from keras.callbacks import EarlyStopping, ModelCheckpoint
+from keras.optimizers import Adam
+from keras.models import load_model
+
+def _main_(args):
+    config_path = args.conf
+
+    with open(config_path) as config_buffer:
+        config = json.loads(config_buffer.read())
+
+    ###############################
+    #   Create the validation generator
+    ###############################
+    valid_ints, labels = parse_voc_annotation(
+        config['test']['test_annot_folder'],
+        config['test']['test_image_folder'],
+        config['test']['cache_name'],
+        config['model']['labels']
+    )
+
+    labels = labels.keys() if len(config['model']['labels']) == 0 else config['model']['labels']
+    labels = sorted(labels)
+
+    valid_generator = BatchGenerator(
+        instances           = valid_ints,
+        anchors             = config['model']['anchors'],
+        labels              = labels,
+        downsample          = 32, # ratio between network input's size and network output's size, 32 for YOLOv3
+        max_box_per_image   = 0,
+        batch_size          = config['train']['batch_size'],
+        min_net_size        = config['model']['min_input_size'],
+        max_net_size        = config['model']['max_input_size'],
+        shuffle             = True,
+        jitter              = 0.0,
+        norm                = normalize
+    )
+
+    ###############################
+    #   Load the model and do evaluation
+    ###############################
+    os.environ['CUDA_VISIBLE_DEVICES'] = config['train']['gpus']
+
+    infer_model = load_model(config['train']['saved_weights_name'])
+
+    # compute mAP for all the classes
+    average_precisions = evaluate(infer_model, valid_generator)
+
+    # print the score
+    total_instances = []
+    precisions = []
+    print(average_precisions.items())
+    for label, (average_precision, num_annotations) in average_precisions.items():
+        print('{:.0f} instances of class'.format(num_annotations),
+              labels[label], 'with average precision: {:.4f}'.format(average_precision))
+        total_instances.append(num_annotations)
+        precisions.append(average_precision)
+
+    if sum(total_instances) == 0:
+        print('No test instances found.')
+        return
+
+    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)))
+    print('mAP: {:.4f}'.format(sum(precisions) / sum(x > 0 for x in total_instances)))
+
+if __name__ == '__main__':
+    argparser = argparse.ArgumentParser(description='Evaluate YOLO_v3 model on any dataset')
+    argparser.add_argument('-c', '--conf', help='path to configuration file')
+
+    args = argparser.parse_args()
+    _main_(args)