PyTorch 中的位置

from torchvision.datasets import Places365from torchvision.datasets.folder import default_loadertrainstd_large_data = Places365(    root="data")trainstd_large_data = Places365(    root="data",    split="train-standard",    small=False,    download=False,    transform=None,    target_transform=None,    loader=default_loader)trainstd_small_data = Places365(    root="data",    split="train-standard",    small=True)trainchal_large_data = Places365(    root="data",    split="train-challenge",    small=False)trainchal_small_data = Places365(    root="data",    split="train-challenge",    small=True)val_large_data = Places365(    root="data",    split="val",    small=False)val_small_data = Places365(    root="data",    split="val",    small=True)len(trainstd_large_data), len(trainstd_small_data)# (1803460, 1803460)len(trainchal_large_data), len(trainchal_small_data)# (8026628, 8026628)len(val_large_data), len(val_small_data)# (36500, 36500)trainstd_large_data# Dataset Places365#     Number of datapoints: 1803460#     Root location: data#     Split: train-standard#     Small: Falsetrainstd_large_data.root# 'data'trainstd_large_data.split# 'train-standard'trainstd_large_data.small# Falsetrainstd_large_data.download_devkittrainstd_large_data.download_images# <bound method Places365.download_devkit of Dataset Places365#     Number of datapoints: 1803460#     Root location: data#     Split: train-standard#     Small: False>print(trainstd_large_data.transform)# Noneprint(trainstd_large_data.target_transform)# Nonetrainstd_large_data.loader# <function torchvision.datasets.folder.default_loader(path: str) -> Any>len(trainstd_large_data.classes), trainstd_large_data.classes# (365,#  ['/a/airfield', '/a/airplane_cabin', '/a/airport_terminal',#   '/a/alcove', '/a/alley', '/a/amphitheater', '/a/amusement_arcade',#   '/a/amusement_park', '/a/apartment_building/outdoor',#   '/a/aquarium', '/a/aqueduct', '/a/arcade', '/a/arch',#   '/a/archaelogical_excavation', ..., '/y/youth_hostel', '/z/zen_garden'])trainstd_large_data[0]# (<PIL.Image.Image image mode=RGB size=683x512>, 0)trainstd_large_data[1]# (<PIL.Image.Image image mode=RGB size=768x512>, 0)trainstd_large_data[2]# (<PIL.Image.Image image mode=RGB size=718x512>, 0)trainstd_large_data[5000]# (<PIL.Image.Image image mode=RGB size=512x683 at 0x1E7834F4770>, 1)trainstd_large_data[10000]# (<PIL.Image.Image image mode=RGB size=683x512 at 0x1E7834A8110>, 2)trainstd_small_data[0]# (<PIL.Image.Image image mode=RGB size=256x256>, 0)trainstd_small_data[1]# (<PIL.Image.Image image mode=RGB size=256x256>, 0)trainstd_small_data[2]# (<PIL.Image.Image image mode=RGB size=256x256>, 0)trainstd_small_data[5000]# (<PIL.Image.Image image mode=RGB size=256x256>, 1)trainstd_small_data[10000]# (<PIL.Image.Image image mode=RGB size=256x256>, 2)trainchal_large_data[0]# (<PIL.Image.Image image mode=RGB size=683x512 at 0x156E22BB680>, 0)trainchal_large_data[1]# (<PIL.Image.Image image mode=RGB size=768x512 at 0x156DF8213D0>, 0)trainchal_large_data[2]# (<PIL.Image.Image image mode=RGB size=718x512 at 0x156DF8213D0>, 0)trainchal_large_data[38567]# (<PIL.Image.Image image mode=RGB size=512x683 at 0x156DF8213D0>, 1)trainchal_large_data[47891]# (<PIL.Image.Image image mode=RGB size=683x512 at 0x156DF8213D0>, 2)trainchal_small_data[0]# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2955B625CA0>, 0)trainchal_small_data[1]# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2950D2A8350>, 0)trainchal_small_data[2]# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2950D2A82C0>, 0)trainchal_small_data[38567]# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2955B3BF6B0>, 1)trainchal_small_data[47891]# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2955B3DD4F0>, 2)val_large_data[0]# (<PIL.Image.Image image mode=RGB size=512x772 at 0x295408DA750>, 165)val_large_data[1]# (<PIL.Image.Image image mode=RGB size=600x493 at 0x29561D468D0>, 358)val_large_data[2]# (<PIL.Image.Image image mode=RGB size=763x512 at 0x2955E09DD60>, 93)val_large_data[3]# (<PIL.Image.Image image mode=RGB size=827x512 at 0x29540938A70>, 164)val_large_data[4]# (<PIL.Image.Image image mode=RGB size=772x512 at 0x29562600650>, 289)val_small_data[0]# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2950D34C500>, 165)val_small_data[1]# (<PIL.Image.Image image mode=RGB size=256x256 at 0x29540892870>, 358)val_small_data[2]# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2954085DBB0>, 93)val_small_data[3]# (<PIL.Image.Image image mode=RGB size=256x256 at 0x29561E348C0>, 164)val_small_data[4]# (<PIL.Image.Image image mode=RGB size=256x256 at 0x29560A415B0>, 289)import matplotlib.pyplot as pltdef show_images(data, ims, main_title=None):    plt.figure(figsize=(12, 6))    plt.suptitle(t=main_title, y=1.0, fontsize=14)    for i, j in enumerate(iterable=ims, start=1):        plt.subplot(2, 5, i)        im, lab = data[j]        plt.imshow(X=im)        plt.title(label=lab)    plt.tight_layout(h_pad=3.0)    plt.show()trainstd_ims = (0, 1, 2, 5000, 10000, 15000, 20000, 25000, 30000, 35000)trainchal_ims = (0, 1, 2, 38567, 47891, 74902, 98483, 137663, 150035, 161052)val_ims = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)show_images(data=trainstd_large_data, ims=trainstd_ims,            main_title="trainstd_large_data")show_images(data=trainstd_small_data, ims=trainstd_ims,            main_title="trainstd_small_data")show_images(data=trainchal_large_data, ims=trainchal_ims,            main_title="trainchal_large_data")show_images(data=trainchal_small_data, ims=trainchal_ims,            main_title="trainchal_small_data")show_images(data=val_large_data, ims=val_ims,            main_title="val_large_data")show_images(data=val_small_data, ims=val_ims,            main_title="val_small_data")