Esempio n. 1
0
def main(args=None):
    if len(sys.argv) is 3:
        model_path = str(sys.argv[1])
        dataset_path = str(sys.argv[2])
    else:
        print(
            "Pass model path and dataset path in respectively as command line argument"
        )
        exit()

    from generators.pascal import PascalVocGenerator
    from model import efficientdet
    import os

    os.environ['CUDA_VISIBLE_DEVICES'] = '0'

    phi = 4
    weighted_bifpn = False
    common_args = {
        'batch_size': 1,
        'phi': phi,
    }
    test_generator = PascalVocGenerator(dataset_path,
                                        'test',
                                        shuffle_groups=False,
                                        skip_truncated=False,
                                        skip_difficult=True,
                                        **common_args)
    model_path = model_path
    input_shape = (test_generator.image_size, test_generator.image_size)
    anchors = test_generator.anchors
    num_classes = test_generator.num_classes()
    model, prediction_model = efficientdet(phi=phi,
                                           num_classes=num_classes,
                                           weighted_bifpn=weighted_bifpn)
    prediction_model.load_weights(model_path, by_name=True)
    average_precisions = evaluate(test_generator,
                                  prediction_model,
                                  visualize=False)
    # compute per class average precision
    total_instances = []
    precisions = []
    for label, (average_precision,
                num_annotations) in average_precisions.items():
        print('{:.0f} instances of class'.format(num_annotations),
              test_generator.label_to_name(label),
              'with average precision: {:.4f}'.format(average_precision))
        total_instances.append(num_annotations)
        precisions.append(average_precision)
    mean_ap = sum(precisions) / sum(x > 0 for x in total_instances)
    print('mAP: {:.4f}'.format(mean_ap))
Esempio n. 2
0
    }
    test_generator = PascalVocGenerator('datasets/VOC2007',
                                        'test',
                                        shuffle_groups=False,
                                        skip_truncated=False,
                                        skip_difficult=True,
                                        **common_args)
    model_path = 'checkpoints/2019-12-03/pascal_05_0.6283_1.1975_0.8029.h5'
    input_shape = (test_generator.image_size, test_generator.image_size)
    anchors = test_generator.anchors
    num_classes = test_generator.num_classes()
    model, prediction_model = efficientdet(phi=phi,
                                           num_classes=num_classes,
                                           weighted_bifpn=weighted_bifpn)
    prediction_model.load_weights(model_path, by_name=True)
    average_precisions = evaluate(test_generator,
                                  prediction_model,
                                  visualize=False)
    # compute per class average precision
    total_instances = []
    precisions = []
    for label, (average_precision,
                num_annotations) in average_precisions.items():
        print('{:.0f} instances of class'.format(num_annotations),
              test_generator.label_to_name(label),
              'with average precision: {:.4f}'.format(average_precision))
        total_instances.append(num_annotations)
        precisions.append(average_precision)
    mean_ap = sum(precisions) / sum(x > 0 for x in total_instances)
    print('mAP: {:.4f}'.format(mean_ap))
Esempio n. 3
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    weighted_bifpn = False
    common_args = {
        'batch_size': 1,
        'phi': phi,
    }
    test_generator = PascalVocGenerator(
        'datasets/VOC2007',
        'test',
        shuffle_groups=False,
        skip_truncated=False,
        skip_difficult=True,
        **common_args
    )
    model_path = 'checkpoints/2019-12-03/pascal_05_0.6283_1.1975_0.8029.h5'
    input_shape = (test_generator.image_size, test_generator.image_size)
    anchors = test_generator.anchors
    num_classes = test_generator.num_classes()
    model, prediction_model = efficientdet(phi=phi, num_classes=num_classes, weighted_bifpn=weighted_bifpn)
    prediction_model.load_weights(model_path, by_name=True)
    average_precisions = evaluate(test_generator, prediction_model, visualize=False)
    # compute per class average precision
    total_instances = []
    precisions = []
    for label, (average_precision, num_annotations) in average_precisions.items():
        print('{:.0f} instances of class'.format(num_annotations), test_generator.label_to_name(label),
              'with average precision: {:.4f}'.format(average_precision))
        total_instances.append(num_annotations)
        precisions.append(average_precision)
    mean_ap = sum(precisions) / sum(x > 0 for x in total_instances)
    print('mAP: {:.4f}'.format(mean_ap))