import datasets import numpy as np import os from PIL import Image # URLs (you already uploaded to Huggingface Hub!) TRAIN_URLS = { "dat": "https://huggingface.co/datasets/randall-lab/small-norb/resolve/main/smallnorb-5x46789x9x18x6x2x96x96-training-dat.mat", "cat": "https://huggingface.co/datasets/randall-lab/small-norb/resolve/main/smallnorb-5x46789x9x18x6x2x96x96-training-cat.mat", "info": "https://huggingface.co/datasets/randall-lab/small-norb/resolve/main/smallnorb-5x46789x9x18x6x2x96x96-training-info.mat", } TEST_URLS = { "dat": "https://huggingface.co/datasets/randall-lab/small-norb/resolve/main/smallnorb-5x01235x9x18x6x2x96x96-testing-dat.mat", "cat": "https://huggingface.co/datasets/randall-lab/small-norb/resolve/main/smallnorb-5x01235x9x18x6x2x96x96-testing-cat.mat", "info": "https://huggingface.co/datasets/randall-lab/small-norb/resolve/main/smallnorb-5x01235x9x18x6x2x96x96-testing-info.mat", } class SmallNORB(datasets.GeneratorBasedBuilder): """SmallNORB dataset: 96x96 stereo images with 5 known factors.""" VERSION = datasets.Version("1.0.0") def _info(self): return datasets.DatasetInfo( description=( "SmallNORB dataset: stereo pair images of 3D toy objects, used for learning object recognition " "robust to pose and lighting. Each image pair corresponds to a combination of 5 factors: " "category, instance, elevation, azimuth, lighting. " "Unlike dSprites or MPI3D, SmallNORB does NOT follow a full cartesian product over factors. " "Instances are sampled per category." ), features=datasets.Features( { "left_image": datasets.Image(), # (96, 96), grayscale "right_image": datasets.Image(), # (96, 96), grayscale "index": datasets.Value("int32"), "label": datasets.Sequence(datasets.Value("int32")), # 5 factor indices "category": datasets.Value("int32"), # [0-4] "instance": datasets.Value("int32"), # [0-9] "elevation": datasets.Value("int32"), # [0-8] "azimuth": datasets.Value("int32"), # [0-17], after /2 correction "lighting": datasets.Value("int32"), # [0-5] } ), supervised_keys=("left_image", "label"), homepage="https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/", license="apache-2.0", citation="""@inproceedings{lecun2004learning, title={Learning methods for generic object recognition with invariance to pose and lighting}, author={LeCun, Yann and Huang, Fu Jie and Bottou, Leon}, booktitle={Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.}, volume={2}, pages={II--104}, year={2004}, organization={IEEE} }""", ) def _split_generators(self, dl_manager): # Download (no extract needed since .mat already!) train_files = dl_manager.download(TRAIN_URLS) test_files = dl_manager.download(TEST_URLS) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "dat_file": train_files["dat"], "cat_file": train_files["cat"], "info_file": train_files["info"], }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "dat_file": test_files["dat"], "cat_file": test_files["cat"], "info_file": test_files["info"], }, ), ] def _generate_examples(self, dat_file, cat_file, info_file): # Use your functions to load all (left_images, right_images, features) images_left, images_right, features = _load_small_norb_chunks( path_template=os.path.join(os.path.dirname(dat_file), "{}-{}.mat"), chunk_names=[os.path.basename(dat_file).replace("-dat.mat", "")] ) for idx in range(len(images_left)): left_img = Image.fromarray(images_left[idx].astype(np.uint8), mode="L") right_img = Image.fromarray(images_right[idx].astype(np.uint8), mode="L") factors = features[idx].tolist() # [category, instance, elevation, azimuth, lighting] yield idx, { "left_image": left_img, "right_image": right_img, "index": idx, "label": factors, "category": factors[0], "instance": factors[1], "elevation": factors[2], "azimuth": factors[3], "lighting": factors[4], } # ------------------------------------------------- # Main function: _load_small_norb_chunks # ------------------------------------------------- def _load_small_norb_chunks(path_template, chunk_names): """Loads several chunks of the small NORB dataset for final use.""" list_of_images_left, list_of_images_right, list_of_features = _load_chunks(path_template, chunk_names) features = np.concatenate(list_of_features, axis=0) features[:, 3] = features[:, 3] / 2 # azimuth values are 0, 2, 4, ..., 34 return ( np.concatenate(list_of_images_left, axis=0), np.concatenate(list_of_images_right, axis=0), features ) # ------------------------------------------------- # Helper function: _load_chunks # ------------------------------------------------- def _load_chunks(path_template, chunk_names): """Loads several chunks of the small NORB dataset into lists.""" list_of_images_left = [] list_of_images_right = [] list_of_features = [] for chunk_name in chunk_names: # Read .dat norb = _read_binary_matrix(path_template.format(chunk_name, "dat")) list_of_images_left.append(norb[:, 0]) # left view list_of_images_right.append(norb[:, 1]) # right view # Read .cat norb_class = _read_binary_matrix(path_template.format(chunk_name, "cat")) # Read .info norb_info = _read_binary_matrix(path_template.format(chunk_name, "info")) # Combine features list_of_features.append(np.column_stack((norb_class, norb_info))) return list_of_images_left, list_of_images_right, list_of_features # ------------------------------------------------- # Helper function: _read_binary_matrix # ------------------------------------------------- def _read_binary_matrix(filename): """Reads and returns binary formatted matrix stored in filename.""" with open(filename, "rb") as f: s = f.read() magic = int(np.frombuffer(s, "int32", 1)) ndim = int(np.frombuffer(s, "int32", 1, 4)) eff_dim = max(3, ndim) raw_dims = np.frombuffer(s, "int32", eff_dim, 8) dims = [] for i in range(0, ndim): dims.append(raw_dims[i]) dtype_map = { 507333717: "int8", 507333716: "int32", 507333713: "float", 507333715: "double" } dtype = dtype_map[magic] data = np.frombuffer(s, dtype, offset=8 + eff_dim * 4) data = data.reshape(tuple(dims)) return data