CelebA in PyTorch

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), len(train_attr_data.identity.unique()) # (162770, 8192)  train_attr_data.identity # 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 zip(range(1, 11), data): 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 zip(range(1, 11), data): plt.subplot(2, 5, i) plt.imshow(X=im) plt.title(label=lab.item()) 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(zip(range(1, 11), data), 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) 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 zip(range(1, 11), data): plt.subplot(2, 5, i) plt.imshow(X=im) for px, py in lm.split(2): plt.scatter(x=px, y=py, c='#1f77b4') 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 (im, (_, lab, (x, y, w, h), lm)), axis in zip(data, axes.ravel()): axis.imshow(X=im) axis.set_title(label=lab.item()) rect = Rectangle(xy=(x, y), width=w, height=h, linewidth=3, edgecolor='r', facecolor='none', clip_on=True) axis.add_patch(p=rect) # ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓  axis.autoscale(enable=False) # This is important otherwise  # the images are shrinked  for px, py in lm.split(2): axis.scatter(x=px, y=py, c='#1f77b4') # ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑  # ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓  # You can also use it  # for px, py in lm.split(2):  # axis.add_patch(p=Circle(xy=(px, py)))  # ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑  # ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓  # You can also use it  # axis.autoscale(enable=False) # This is important otherwise  # # the images are shrinked  # px = []  # py = []  # for j, v in enumerate(lm):  # if j%2 == 0:  # px.append(v)  # else:  # py.append(v)  # axis.plot(px, py)  # ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑  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")