PyTorch 中的加州理工学院

from torchvision.datasets import Caltech101category_data = Caltech101(    root="data")category_data = Caltech101(    root="data",    target_type="category",    transform=None,    target_transform=None,    download=False)annotation_data = Caltech101(    root="data",    target_type="annotation")all_data = Caltech101(    root="data",    target_type=["category", "annotation"])len(category_data), len(annotation_data), len(all_data)# (8677, 8677, 8677)category_data# Dataset Caltech101#     Number of datapoints: 8677#     Root location: datacaltech101#     Target type: ['category']category_data.root# 'data/caltech101'category_data.target_type# ['category']print(category_data.transform)# Noneprint(category_data.target_transform)# Nonecategory_data.download# <bound method Caltech101.download of Dataset Caltech101#     Number of datapoints: 8677#     Root location: datacaltech101#     Target type: ['category']>len(category_data.categories)# 101category_data.categories# ['Faces', 'Faces_easy', 'Leopards', 'Motorbikes', 'accordion', #  'airplanes', 'anchor', 'ant', 'barrel', 'bass', 'beaver',#  'binocular', 'bonsai', 'brain', 'brontosaurus', 'buddha',#  'butterfly', 'camera', 'cannon', 'car_side', 'ceiling_fan',#  'cellphone', 'chair', 'chandelier', 'cougar_body', 'cougar_face', ...]len(category_data.annotation_categories)# 101category_data.annotation_categories# ['Faces_2', 'Faces_3', 'Leopards', 'Motorbikes_16', 'accordion',#  'Airplanes_Side_2', 'anchor', 'ant', 'barrel', 'bass',#  'beaver', 'binocular', 'bonsai', 'brain', 'brontosaurus',#  'buddha', 'butterfly', 'camera', 'cannon', 'car_side',#  'ceiling_fan', 'cellphone', 'chair', 'chandelier', 'cougar_body', ...]category_data[0]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=510x337>, 0)category_data[1]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=519x343>, 0)category_data[2]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=492x325>, 0)category_data[435]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=290x334>, 1)category_data[870]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=192x128>, 2)annotation_data[0]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=510x337>,#  array([[10.00958466, 8.18210863, 8.18210863, 10.92332268, ...],#         [132.30670927, 120.42811502, 103.52396166, 90.73162939, ...]]))annotation_data[1]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=519x343>,#  array([[15.19298246, 13.71929825, 15.19298246, 19.61403509, ...],#         [121.5877193, 103.90350877, 80.81578947, 64.11403509, ...]]))annotation_data[2]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=492x325>,#  array([[10.40789474, 7.17807018, 5.79385965, 9.02368421, ...],#         [131.30789474, 120.69561404, 102.23947368, 86.09035088, ...]]))annotation_data[435]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=290x334>,#  array([[64.52631579, 95.31578947, 123.26315789, 149.31578947, ...],#         [15.42105263, 8.31578947, 10.21052632, 28.21052632, ...]]))annotation_data[870]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=192x128>,#  array([[2.96536524, 7.55604534, 19.45780856, 33.73992443, ...],#         [23.63413098, 32.13539043, 33.83564232, 8.84193955, ...]]))all_data[0]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=510x337>,#  (0, array([[10.00958466, 8.18210863, 8.18210863, 10.92332268, ...],#             [132.30670927, 120.42811502, 103.52396166, 90.73162939, ...]]))all_data[1]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=519x343>,#  (0, array([[15.19298246, 13.71929825, 15.19298246, 19.61403509, ...],#             [121.5877193, 103.90350877, 80.81578947, 64.11403509, ...]]))all_data[2]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=492x325>,#  (0, array([[10.40789474, 7.17807018, 5.79385965, 9.02368421, ...],#             [131.30789474, 120.69561404, 102.23947368, 86.09035088, ...]]))all_data[3]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=538x355>,#  (0, array([[19.54035088, 18.57894737, 26.27017544, 38.2877193, ...],#             [131.49122807, 100.24561404, 74.2877193, 49.29122807, ...]]))all_data[4]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=528x349>,#  (0, array([[11.87982456, 11.87982456, 13.86578947, 15.35526316, ...],#             [128.34649123, 105.50789474, 91.60614035, 76.71140351, ...]]))import matplotlib.pyplot as pltdef show_images(data, main_title=None):    plt.figure(figsize=(10, 5))    plt.suptitle(t=main_title, y=1.0, fontsize=14)    ims = (0, 1, 2, 435, 870, 1070, 1868, 1923, 2723, 2765, 2807, 2854)    for i, j in enumerate(ims, start=1):        plt.subplot(2, 5, i)        if len(data.target_type) == 1:            if data.target_type[0] == "category":                im, lab = data[j]                plt.title(label=lab)            elif data.target_type[0] == "annotation":                im, (px, py) = data[j]                plt.scatter(x=px, y=py)            plt.imshow(X=im)        elif len(data.target_type) == 2:            if data.target_type[0] == "category":                im, (lab, (px, py)) = data[j]            elif data.target_type[0] == "annotation":                im, ((px, py), lab) = data[j]            plt.title(label=lab)            plt.imshow(X=im)            plt.scatter(x=px, y=py)        if i == 10:            break    plt.tight_layout()    plt.show()show_images(data=category_data, main_title="category_data")show_images(data=annotation_data, main_title="annotation_data")show_images(data=all_data, main_title="all_data")