Python test.Test() Examples

The following are 8 code examples of test.Test(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module test , or try the search function .
Example #1
Source File: main.py    From PyTorch-ENet with MIT License 5 votes vote down vote up 
def test(model, test_loader, class_weights, class_encoding):
    print("\nTesting...\n")

    num_classes = len(class_encoding)

    # We are going to use the CrossEntropyLoss loss function as it's most
    # frequentely used in classification problems with multiple classes which
    # fits the problem. This criterion  combines LogSoftMax and NLLLoss.
    criterion = nn.CrossEntropyLoss(weight=class_weights)

    # Evaluation metric
    if args.ignore_unlabeled:
        ignore_index = list(class_encoding).index('unlabeled')
    else:
        ignore_index = None
    metric = IoU(num_classes, ignore_index=ignore_index)

    # Test the trained model on the test set
    test = Test(model, test_loader, criterion, metric, device)

    print(">>>> Running test dataset")

    loss, (iou, miou) = test.run_epoch(args.print_step)
    class_iou = dict(zip(class_encoding.keys(), iou))

    print(">>>> Avg. loss: {0:.4f} | Mean IoU: {1:.4f}".format(loss, miou))

    # Print per class IoU
    for key, class_iou in zip(class_encoding.keys(), iou):
        print("{0}: {1:.4f}".format(key, class_iou))

    # Show a batch of samples and labels
    if args.imshow_batch:
        print("A batch of predictions from the test set...")
        images, _ = iter(test_loader).next()
        predict(model, images, class_encoding)
Example #2
Source File: main.py    From RPNet-Pytorch with MIT License 5 votes vote down vote up 
def test(model, test_loader, class_weights, class_encoding, step):
    print("\nTesting...\n")

    num_classes = len(class_encoding)

    # We are going to use the CrossEntropyLoss loss function as it's most
    # frequentely used in classification problems with multiple classes which
    # fits the problem. This criterion  combines LogSoftMax and NLLLoss.
    criterion = nn.CrossEntropyLoss(weight=class_weights)
    if use_cuda:
        criterion = criterion.cuda()

    # Evaluation metric
    if args.ignore_unlabeled:
        ignore_index = list(class_encoding).index('unlabeled')
    else:
        ignore_index = None
    metric = IoU(num_classes, ignore_index=ignore_index)

    # Test the trained model on the test set
    test = Test(model, test_loader, criterion, metric, use_cuda, step)

    print(">>>> Running test dataset")

    loss, (iou, miou) = test.run_epoch(args.print_step)
    class_iou = dict(zip(class_encoding.keys(), iou))

    print(">>>> Avg. loss: {0:.4f} | Mean IoU: {1:.4f}".format(loss, miou))

    # Print per class IoU
    for key, class_iou in zip(class_encoding.keys(), iou):
        print("{0}: {1:.4f}".format(key, class_iou))

    # Show a batch of samples and labels
    if args.imshow_batch:
        print("A batch of predictions from the test set...")
        images, _ = iter(test_loader).next()
        predict(model, images, class_encoding)
Example #3
Source File: __init__.py    From quark with Apache License 2.0 5 votes vote down vote up 
def invoke(self, object, args):
        obj = _cast(object, lambda: test.Test);
        (obj).go();
        return None
Example #4
Source File: __init__.py    From quark with Apache License 2.0 5 votes vote down vote up 
def __init__(self):
        super(test_Test, self).__init__(u"test.Test");
        (self).name = u"test.Test"
        (self).parameters = _List([])
        (self).fields = _List([quark.reflect.Field(u"quark.String", u"name")])
        (self).methods = _List([test_Test_go_Method()])
        (self).parents = _List([u"quark.Object"])
Example #5
Source File: __init__.py    From quark with Apache License 2.0 5 votes vote down vote up 
def construct(self, args):
        return test.Test()
Example #6
Source File: __init__.py    From quark with Apache License 2.0 5 votes vote down vote up 
def invoke(self, object, args):
        obj = _cast(object, lambda: test.subtest.Test);
        (obj).go();
        return None
Example #7
Source File: __init__.py    From quark with Apache License 2.0 5 votes vote down vote up 
def __init__(self):
        super(test_subtest_Test, self).__init__(u"test.subtest.Test");
        (self).name = u"test.subtest.Test"
        (self).parameters = _List([])
        (self).fields = _List([quark.reflect.Field(u"quark.int", u"size")])
        (self).methods = _List([test_subtest_Test_go_Method()])
        (self).parents = _List([u"quark.Object"])
Example #8
Source File: main.py    From SMIT with MIT License 5 votes vote down vote up 
def main(config):
    from torch.backends import cudnn
    # For fast training
    cudnn.benchmark = True

    data_loader = get_loader(
        config.mode_data,
        config.image_size,
        config.batch_size,
        config.dataset_fake,
        config.mode,
        num_workers=config.num_workers,
        all_attr=config.ALL_ATTR,
        c_dim=config.c_dim)

    from misc.scores import set_score
    if set_score(config):
        return

    if config.mode == 'train':
        from train import Train
        Train(config, data_loader)
        from test import Test
        test = Test(config, data_loader)
        test(dataset=config.dataset_real)

    elif config.mode == 'test':
        from test import Test
        test = Test(config, data_loader)
        if config.DEMO_PATH:
            test.DEMO(config.DEMO_PATH)
        else:
            test(dataset=config.dataset_real)