CelebA 是 PyTorch

from torchvision.datasets import CelebAtrain_attr_data = CelebA(    root="data")train_attr_data = CelebA(    root="data",    split="train",    target_type="attr",    transform=None,    target_transform=None,    download=False)valid_identity_data = CelebA(    root="data",    split="valid",    target_type="identity")test_bbox_data = CelebA(    root="data",    split="test",    target_type="bbox")all_landmarks_data = CelebA(    root="data",    split="all",    target_type="landmarks")all_empty_data = CelebA(    root="data",    split="all",    target_type=[])all_all_data = CelebA(    root="data",    split="all",    target_type=["attr", "identity", "bbox", "landmarks"])len(train_attr_data), len(valid_identity_data), len(test_bbox_data)# (162770, 19867, 19962)len(all_landmarks_data), len(all_empty_data), len(all_all_data)# (202599, 202599, 202599)train_attr_data# Dataset CelebA#     Number of datapoints: 162770#     Root location: data#     Target type: ['attr']#     Split: traintrain_attr_data.root# 'data'train_attr_data.split# 'train'train_attr_data.target_type# ['attr']print(train_attr_data.transform)# Noneprint(train_attr_data.target_transform)# Nonetrain_attr_data.download# <bound method CelebA.download of Dataset CelebA#     Number of datapoints: 162770#     Root location: data#     Target type: ['attr']#     Split: train>len(train_attr_data.attr), train_attr_data.attr# (162770, tensor([[0, 1, 1, ..., 0, 0, 1],#                  [0, 0, 0, ..., 0, 0, 1],#                  [0, 0, 0, ..., 0, 0, 1],#                  ...,#                  [1, 0, 1, ..., 0, 1, 1],#                  [0, 0, 0, ..., 0, 0, 1],#                  [0, 1, 1, ..., 1, 0, 1]]))len(train_attr_data.attr_names), train_attr_data.attr_names# (41, ['5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive', #       'Bags_Under_Eyes', 'Bald', 'Bangs', 'Big_Lips', 'Big_Nose',#       'Black_Hair', 'Blond_Hair', 'Blurry', 'Brown_Hair',#       ...#       'Wearing_Necklace', 'Wearing_Necktie', 'Young', ''])len(train_attr_data.identity), train_attr_data.identity# (162770, tensor([[2880], [2937], [8692], ..., [7391], [8610], [2304]]))len(train_attr_data.bbox), train_attr_data.bbox# (162770, tensor([[95, 71, 226, 313],#                  [72, 94, 221, 306],#                  [216, 59, 91, 126],#                  ...,#                  [103, 103, 143, 198],#                  [30, 59, 216, 280],#                  [376, 4, 372, 515]]))len(train_attr_data.landmarks_align), train_attr_data.landmarks_align# (162770, tensor([[69, 109, 106, ..., 152, 108, 154],#                  [69, 110, 107, ..., 151, 108, 153],#                  [76, 112, 104, ..., 156, 98, 158],#                  ...,#                  [69, 113, 109, ..., 151, 110, 151],#                  [68, 112, 109, ..., 150, 108, 151],#                  [70, 111, 107, ..., 153, 102, 152]]))train_attr_data[0]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  tensor([0, 1, 1, 0, 0, 0, 0, 0, 0, 0,#          0, 1, 0, 0, 0, 0, 0, 0, 1, 1,#          0, 1, 0, 0, 1, 0, 0, 1, 0, 0,#          0, 1, 1, 0, 1, 0, 1, 0, 0, 1]))train_attr_data[1]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  tensor([0, 0, 0, 1, 0, 0, 0, 1, 0, 0,#          0, 1, 0, 0, 0, 0, 0, 0, 0, 1,#          0, 1, 0, 0, 1, 0, 0, 0, 0, 0,#          0, 1, 0, 0, 0, 0, 0, 0, 0, 1]))train_attr_data[2]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  tensor([0, 0, 0, 0, 0, 0, 1, 0, 0, 0,#          1, 0, 0, 0, 0, 0, 0, 0, 0, 0,#          1, 0, 0, 1, 1, 0, 0, 1, 0, 0,#          0, 0, 0, 1, 0, 0, 0, 0, 0, 1]))valid_identity_data[0]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  tensor(2594))valid_identity_data[1]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  tensor(2795))valid_identity_data[2]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  tensor(947))test_bbox_data[0]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  tensor([147, 82, 120, 166]))test_bbox_data[1]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  tensor([106, 34, 140, 194]))test_bbox_data[2]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  tensor([107, 78, 109, 151]))all_landmarks_data[0]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  tensor([69, 109, 106, 113, 77, 142, 73, 152, 108, 154]))all_landmarks_data[1]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  tensor([69, 110, 107, 112, 81, 135, 70, 151, 108, 153]))all_landmarks_data[2]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  tensor([76, 112, 104, 106, 108, 128, 74, 156, 98, 158]))all_empty_data[0]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>, None)all_empty_data[1]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>, None)all_empty_data[2]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>, None)all_all_data[0]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  (tensor([0, 1, 1, 0, 0, 0, 0, 0, 0, 0,#           0, 1, 0, 0, 0, 0, 0, 0, 1, 1,#           0, 1, 0, 0, 1, 0, 0, 1, 0, 0,#           0, 1, 1, 0, 1, 0, 1, 0, 0, 1]),#   tensor(2880),#   tensor([95, 71, 226, 313]),#   tensor([69, 109, 106, 113, 77, 142, 73, 152, 108, 154])))all_all_data[1]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  (tensor([0, 0, 0, 1, 0, 0, 0, 1, 0, 0,#           0, 1, 0, 0, 0, 0, 0, 0, 0, 1,#           0, 1, 0, 0, 1, 0, 0, 0, 0, 0,#           0, 1, 0, 0, 0, 0, 0, 0, 0, 1]),#   tensor(2937),#   tensor([72, 94, 221, 306]),#   tensor([69, 110, 107, 112, 81, 135, 70, 151, 108, 153])))all_all_data[2]# (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>,#  (tensor([0, 0, 0, 0, 0, 0, 1, 0, 0, 0,#           1, 0, 0, 0, 0, 0, 0, 0, 0, 0,#           1, 0, 0, 1, 1, 0, 0, 1, 0, 0,#           0, 0, 0, 1, 0, 0, 0, 0, 0, 1]),#  tensor(8692),#  tensor([216, 59, 91, 126]),#  tensor([76, 112, 104, 106, 108, 128, 74, 156, 98, 158])))import matplotlib.pyplot as pltfrom matplotlib.patches import Rectanglefrom matplotlib.patches import Circledef show_images(data, main_title=None):    if "attr" in data.target_type and len(data.target_type) == 1         or not data.target_type:        plt.figure(figsize=(12, 6))        plt.suptitle(t=main_title, y=1.0, fontsize=14)        for i, (im, _) in enumerate(data, start=1):            plt.subplot(2, 5, i)            plt.imshow(X=im)            if i == 10:                break        plt.tight_layout(h_pad=3.0)        plt.show()    elif "identity" in data.target_type and len(data.target_type) == 1:        plt.figure(figsize=(12, 6))        plt.suptitle(t=main_title, y=1.0, fontsize=14)        for i, (im, lab) in enumerate(data, start=1):            plt.subplot(2, 5, i)            plt.title(label=lab.item())            plt.imshow(X=im)            if i == 10:                break        plt.tight_layout(h_pad=3.0)        plt.show()    elif "bbox" in data.target_type and len(data.target_type) == 1:        fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(12, 6))        fig.suptitle(t=main_title, y=1.0, fontsize=14)        for (i, (im, (x, y, w, h))), axis             in zip(enumerate(data, start=1), axes.ravel()):            axis.imshow(X=im)            rect = Rectangle(xy=(x, y), width=w, height=h,                             linewidth=3, edgecolor='r',                             facecolor='none')            axis.add_patch(p=rect)            if i == 10:                break        fig.tight_layout(h_pad=3.0)        plt.show()    elif "landmarks" in data.target_type and len(data.target_type) == 1:        plt.figure(figsize=(12, 6))        plt.suptitle(t=main_title, y=1.0, fontsize=14)        for i, (im, lm) in enumerate(data, start=1):            px = []            py = []            for j, v in enumerate(lm):                if j%2 == 0:                    px.append(v)                else:                    py.append(v)            plt.subplot(2, 5, i)            plt.imshow(X=im)            plt.scatter(x=px, y=py)            if i == 10:                break        plt.tight_layout(h_pad=3.0)        plt.show()    elif len(data.target_type) == 4:        fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(12, 6))        fig.suptitle(t=main_title, y=1.0, fontsize=14)        for (i, (im, (_, lab, (x, y, w, h), lm))), axis             in zip(enumerate(data, start=1), axes.ravel()):            axis.set_title(label=lab.item())            axis.imshow(X=im)            rect = Rectangle(xy=(x, y), width=w, height=h,                             linewidth=3, edgecolor='r',                             facecolor='none', clip_on=True)            axis.add_patch(p=rect)            for j, (px, py) in enumerate(lm.split(2)):                axis.add_patch(p=Circle(xy=(px, py)))            # for j, v in enumerate(lm):            #     if j%2 == 0:            #         px.append(v)            #     else:            #         py.append(v)            # axis.scatter(x=px, y=py)            # axis.plot(px, py)# `axis.scatter()` and `axis.plot()` of `plt.subplots()` don't work# properly. They shrink images so use `axis.add_patch()` instead.            if i == 10:                break        fig.tight_layout(h_pad=3.0)        plt.show()show_images(data=train_attr_data, main_title="train_attr_data")show_images(data=valid_identity_data, main_title="valid_identity_data")show_images(data=test_bbox_data, main_title="test_bbox_data")show_images(data=all_landmarks_data, main_title="all_landmarks_data")show_images(data=all_empty_data, main_title="all_empty_data")show_images(data=all_all_data, main_title="all_all_data")