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• open-reid-master
  • examples
    • softmax_loss.py
    • oim_loss.py
    • triplet_loss.py
  • LICENSE
  • test
    • datasets
      • test_cuhk01.py
      • test_cuhk03.py
      • test_viper.py
      • test_dukemtmc.py
      • test_market1501.py
    • utils
      • data
        • test_preprocessor.py
    • evaluation_metrics
      • test_cmc.py
    • models
      • test_inception.py
    • feature_extraction
      • test_database.py
    • loss
      • test_oim.py
  • setup.py
  • setup.cfg
  • README.md
  • reid
    • datasets
      • dukemtmc.py
      • cuhk03.py
      • viper.py
      • __init__.py
      • market1501.py
      • cuhk01.py
    • utils
      • meters.py
      • osutils.py
      • serialization.py
      • data
        • sampler.py
        • preprocessor.py
        • __init__.py
        • dataset.py
        • transforms.py
      • __init__.py
      • logging.py
    • evaluation_metrics
      • ranking.py
      • classification.py
      • __init__.py
    • evaluators.py
    • trainers.py
    • models
      • resnet.py
      • __init__.py
      • inception.py
    • dist_metric.py
    • feature_extraction
      • database.py
      • cnn.py
      • __init__.py
    • __init__.py
    • metric_learning
      • kissme.py
      • euclidean.py
      • __init__.py
    • loss
      • triplet.py
      • __init__.py
      • oim.py
  • .gitignore
  • docs
    • datasets.rst
    • Makefile
    • make.bat
    • figures
    • examples
      • training_id.rst
      • benchmarks.rst
    • dist_metric.rst
    • trainers.rst
    • notes
      • overview.rst
      • evaluation_metrics.rst
      • data_modules.rst
    • requirements.txt
    • index.rst
    • conf.py
    • evaluators.rst
    • models.rst
    • _static
      • css
        • openreid_theme.css

from unittest import TestCase


class TestPreprocessor(TestCase):
    def test_getitem(self):
        import torchvision.transforms as t
        from reid.datasets.viper import VIPeR
        from reid.utils.data.preprocessor import Preprocessor

        root, split_id, num_val = '/tmp/open-reid/viper', 0, 100
        dataset = VIPeR(root, split_id=split_id, num_val=num_val, download=True)

        preproc = Preprocessor(dataset.train, root=dataset.images_dir,
                               transform=t.Compose([
                                   t.Scale(256),
                                   t.CenterCrop(224),
                                   t.ToTensor(),
                                   t.Normalize(mean=[0.485, 0.456, 0.406],
                                               std=[0.229, 0.224, 0.225])
                               ]))
        self.assertEquals(len(preproc), len(dataset.train))
        img, pid, camid = preproc[0]
        self.assertEquals(img.size(), (3, 224, 224))