def __init__()

in src/datasets/folder.py [0:0]

• 38 lines of code
• 7 McCabe index (conditional complexity)

    def __init__(self, root, loader, extensions, transform=None, target_transform=None):

        start = time.time()
        path_cache = CACHE_DATASET[root]
        if not os.path.isfile(path_cache):
            print("Images cache not found in %s, parsing dataset..." % path_cache,
                  file=sys.stderr)
            classes, class_to_idx = self._find_classes(root)
            samples = make_dataset(root, class_to_idx, extensions)
            print("Parsing image folder took %.2f seconds." % (time.time() - start),
                  file=sys.stderr)
            with open(path_cache, "wb") as f:
                pickle.dump((classes, class_to_idx, samples), f)
        else:
            with open(path_cache, "rb") as f:
                classes, class_to_idx, samples = pickle.load(f)
            print("Loading cached images took %.2f seconds." % (time.time() - start),
                  file=sys.stderr)

        print("Dataset contains %i images." % len(samples), file=sys.stderr)

        if len(samples) == 0:
            raise(RuntimeError("Found 0 files in subfolders of: " + root + "\n"
                               "Supported extensions are: " + ",".join(extensions)))

        self.root = root
        self.loader = loader
        self.extensions = extensions

        self.classes = classes
        self.class_to_idx = class_to_idx

        self.samples = samples
        self.targets = np.array([s[1] for s in samples])

        self.transform = transform
        self.target_transform = target_transform

        # Samples are grouped by class contiguously
        assert np.all(0 <= self.targets[1:] - self.targets[:-1])
        assert np.all(self.targets[1:] - self.targets[:-1] <= 1)
        assert np.sum(self.targets[1:] - self.targets[:-1]) == max(self.targets)

        cl_positions = np.nonzero(self.targets[1:] - self.targets[:-1])[0] + 1
        cl_positions = np.insert(cl_positions, 0, 0)
        cl_positions = np.append(cl_positions, len(self.targets))

        self.class2position = {i: np.arange(cl_positions[i], cl_positions[i+1]) for i in range(len(cl_positions) - 1)}
        assert all([all(self.targets[v] == i for v in self.class2position[i]) for i in range(max(self.targets) + 1)])