from torchvision.datasets import CocoCaptions  cap_train2017_data = CocoCaptions(     root="data/coco/imgs/train2017",     annFile="data/coco/anns/trainval2017/captions_train2017.json" )  ins_train2017_data = CocoCaptions(     root="data/coco/imgs/train2017",     annFile="data/coco/anns/trainval2017/instances_train2017.json" )  pk_train2017_data = CocoCaptions(     root="data/coco/imgs/train2017",     annFile="data/coco/anns/trainval2017/person_keypoints_train2017.json" )  len(cap_train2017_data), len(ins_train2017_data), len(pk_train2017_data) # (118287, 118287, 118287)  cap_val2017_data = CocoCaptions(     root="data/coco/imgs/val2017",     annFile="data/coco/anns/trainval2017/captions_val2017.json" )  ins_val2017_data = CocoCaptions(     root="data/coco/imgs/val2017",     annFile="data/coco/anns/trainval2017/instances_val2017.json" )  pk_val2017_data = CocoCaptions(     root="data/coco/imgs/val2017",     annFile="data/coco/anns/trainval2017/person_keypoints_val2017.json" )  len(cap_val2017_data), len(ins_val2017_data), len(pk_val2017_data) # (5000, 5000, 5000)  test2017_data = CocoCaptions(     root="data/coco/imgs/test2017",     annFile="data/coco/anns/test2017/image_info_test2017.json" )  testdev2017_data = CocoCaptions(     root="data/coco/imgs/test2017",     annFile="data/coco/anns/test2017/image_info_test-dev2017.json" )  len(test2017_data), len(testdev2017_data) # (40670, 20288)  cap_train2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x428>, #  ['A flower vase is sitting on a porch stand.', #   'White vase with different colored flowers sitting inside of it. ', #   'a white vase with many flowers on a stage', #   'A white vase filled with different colored flowers.', #   'A vase with red and white flowers outside on a sunny day.'])  cap_train2017_data[47] # (<PIL.Image.Image image mode=RGB size=640x427>, #  ['A man standing in front of a microwave next to pots and pans.', #   'A man displaying pots and utensils on a wall.', #   'A man stands in a kitchen and motions towards pots and pans. ', #   'a man poses in front of some pots and pans ', #   'A man pointing to pots hanging from a pegboard on a gray wall.'])  cap_train2017_data[64] # (<PIL.Image.Image image mode=RGB size=480x640>, #  ['A little girl holding wet broccoli in her hand. ', #   'The young child is happily holding a fresh vegetable. ', #   'A little girl holds a hand full of wet broccoli. ', #   'A little girl holds a piece of broccoli towards the camera.', #   'a small kid holds on to some vegetables '])  ins_train2017_data[2] # Error  ins_train2017_data[47] # Error  ins_train2017_data[67] # Error  pk_train2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x428>, [])  pk_train2017_data[47] # Error  pk_train2017_data[64] # Error  cap_val2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x483>, #  ['Bedroom scene with a bookcase, blue comforter and window.', #   'A bedroom with a bookshelf full of books.', #   'This room has a bed with blue sheets and a large bookcase', #   'A bed and a mirror in a small room.', #   'a bed room with a neatly made bed a window and a book shelf'])  cap_val2017_data[47] # (<PIL.Image.Image image mode=RGB size=640x480>, #  ['A group of people cutting a ribbon on a street.', #   'A man uses a pair of big scissors to cut a pink ribbon.', #   'A man cutting a ribbon at a ceremony ', #   'A group of people on the sidewalk watching two young children.', #   'A group of people holding a large pair of scissors to a ribbon.'])  cap_val2017_data[64] # (<PIL.Image.Image image mode=RGB size=375x500>, #  ['A man and a women posing next to one another in front of a table.', #   'A man and woman hugging in a restaurant', #   'A man and woman standing next to a table.', #   'A happy man and woman pose for a picture.', #   'A man and woman posing for a picture in a sports bar.'])  ins_val2017_data[2] # Error  ins_val2017_data[47] # Error  ins_val2017_data[64] # Error  pk_val2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x483>, [])  pk_val2017_data[47] # Error  pk_val2017_data[64] # Error  test2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x427>, [])  test2017_data[47] # (<PIL.Image.Image image mode=RGB size=640x406>, [])  test2017_data[64] # (<PIL.Image.Image image mode=RGB size=640x427>, [])  testdev2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x427>, [])  testdev2017_data[47] # (<PIL.Image.Image image mode=RGB size=480x640>, [])  testdev2017_data[64] # (<PIL.Image.Image image mode=RGB size=640x480>, [])  import matplotlib.pyplot as plt  def show_images(data, ims, main_title=None):     file = data.root.split('/')[-1]     fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14, 8))     fig.suptitle(t=main_title, y=0.9, fontsize=14)     x_crd = 0.02     for i, axis in zip(ims, axes.ravel()):         if data[i][1]:             im, anns = data[i]             axis.imshow(X=im)             y_crd = 0.0             for j, ann in enumerate(iterable=anns):                 text_list = ann.split()                 if len(text_list) > 9:                     text = " ".join(text_list[0:10]) + " ..."                 else:                     text = " ".join(text_list)                 plt.figtext(x=x_crd, y=y_crd, fontsize=10,                             s=f'{j}: {text}')                 y_crd -= 0.06             x_crd += 0.325             if i == 2 and file == "val2017":                 x_crd += 0.06         elif not data[i][1]:             im, _ = data[i]             axis.imshow(X=im)     fig.tight_layout()     plt.show()  ims = (2, 47, 64)  show_images(data=cap_train2017_data, ims=ims,              main_title="cap_train2017_data") show_images(data=cap_val2017_data, ims=ims,               main_title="cap_val2017_data") show_images(data=test2017_data, ims=ims,             main_title="test2017_data") show_images(data=testdev2017_data, ims=ims,              main_title="testdev2017_data")