请我喝杯咖啡☕
*我的帖子解释了 celeba。
celeba() 可以使用 celeba 数据集，如下所示：
*备忘录：
- 第一个参数是 root（必需类型：str 或 pathlib.path）。 *绝对或相对路径都是可能的。
- 第二个参数是 split(可选-默认:”trn”-类型:str)。 *可以设置“train”（162,770张图片）、“valid”（19,867张图片）、“test”（19,962张图片）或“all”（202,599张图片）。
- 第三个参数是target_type（可选-默认：“attr”-类型：str或str列表）： *备注：
- 可以为其设置“attr”、“identity”、“bbox”和/或“landmark”。
- 也可以设置空列表。
- 可以设置多个相同的值。
- 如果值的顺序不同，则其元素的顺序也会不同。
- 第四个参数是transform(optional-default:none-type:callable)。
- 第 5 个参数是 target_transform(optional-default:none-type:callable)。
- 第 6 个参数是 download(可选-默认:false-类型:bool): *备注：
- 如果为 true，则从互联网下载数据集并解压（解压）到根目录。
- 如果为 true 并且数据集已下载，则将其提取。
- 如果为 true 并且数据集已下载并提取，则不会发生任何事情。
- 如果数据集已经下载并提取，则应该为 false，因为它速度更快。
- 下载数据集需要 gdown。
- 您可以从这里手动下载并解压数据集（img_align_celeba.zip with identity_celeba.txt、list_attr_celeba.txt、list_bbox_celeba.txt、list_eval_partition.txt 和 list_landmarks_align_celeba.txt）到 data/celeba/。
from torchvision.datasets import CelebA train_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: train train_attr_data.root # 'data' train_attr_data.split # 'train' train_attr_data.target_type # ['attr'] print(train_attr_data.transform) # None print(train_attr_data.target_transform) # None train_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 plt from matplotlib.patches import Rectangle from matplotlib.patches import Circle def 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")
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