def load_nyu(ds='train', n_sp=300, sp='rgb'): # trigger cache..... dataset = NYUSegmentation() file_names = dataset.get_split(ds) if ds == "test": reorder = np.array([2, 0, 3, 1]) else: reorder = None # load image to generate superpixels result = Parallel(n_jobs=-1)(delayed(load_single_file)(dataset, f, n_sp, sp, reorder=reorder) for f in file_names) X, Y, superpixels = zip(*result) return DataBunch(X, Y, file_names, superpixels)
def load_nyu(ds='train', n_sp=300, sp='rgb'): # trigger cache..... dataset = NYUSegmentation() file_names = dataset.get_split(ds) if ds == "test": reorder = np.array([2, 0, 3, 1]) else: reorder = None # load image to generate superpixels result = Parallel(n_jobs=-1)( delayed(load_single_file)(dataset, f, n_sp, sp, reorder=reorder) for f in file_names) X, Y, superpixels = zip(*result) return DataBunch(X, Y, file_names, superpixels)
def load_nyu_pixelwise(ds='train'): if ds == "test": reorder = np.array([2, 0, 3, 1]) else: reorder = np.arange(4) # trigger cache. dataset = NYUSegmentation() file_names, X, Y = [], [], [] for file_name in dataset.get_split(ds): print(file_name) file_names.append(file_name) gt = dataset.get_ground_truth(file_name) prediction = get_probabilities(file_name, dataset.directory) Y.append(gt) X.append(prediction[:, :, reorder]) return DataBunchNoSP(X, Y, file_names)