CocoDetection accepts slice as argument to its __getitem__ and returns wrong result #7824
Description
Hi, although CocoDetection declares its __getitem__ argument to be of type int, it actually accepts slice as well. However it returns a wrong result. As this might be confusing for some users, I suggest to add a type check at the beginning of the __getitem__.
vision/torchvision/datasets/coco.py
Lines 46 to 49 in cab01fc
ds[:2](tensor([[[0.3373, 0.3373, 0.3373, ..., 0.2863, 0.2863, 0.2863], [0.3373, 0.3373, 0.3373, ..., 0.2863, 0.2863, 0.2863], [0.3373, 0.3373, 0.3373, ..., 0.2863, 0.2863, 0.2863], ..., [0.4196, 0.4196, 0.4196, ..., 0.4137, 0.3980, 0.3235], [0.4196, 0.4196, 0.4196, ..., 0.4137, 0.3980, 0.3235], [0.4196, 0.4196, 0.4196, ..., 0.4137, 0.3980, 0.3235]], [[0.1608, 0.1608, 0.1608, ..., 0.1216, 0.1216, 0.1216], [0.1608, 0.1608, 0.1608, ..., 0.1216, 0.1216, 0.1216], [0.1608, 0.1608, 0.1608, ..., 0.1216, 0.1216, 0.1216], ..., [0.5686, 0.5686, 0.5686, ..., 0.5333, 0.5137, 0.4353], [0.5686, 0.5686, 0.5686, ..., 0.5333, 0.5137, 0.4353], [0.5686, 0.5686, 0.5686, ..., 0.5333, 0.5137, 0.4353]], [[0.1412, 0.1412, 0.1412, ..., 0.1608, 0.1608, 0.1608], [0.1412, 0.1412, 0.1412, ..., 0.1608, 0.1608, 0.1608], [0.1412, 0.1412, 0.1412, ..., 0.1608, 0.1608, 0.1608], ..., [0.0902, 0.0902, 0.0902, ..., 0.1333, 0.1451, 0.1020], [0.0902, 0.0902, 0.0902, ..., 0.1333, 0.1451, 0.1020], [0.0902, 0.0902, 0.0902, ..., 0.1333, 0.1451, 0.1020]]]), {'category_id': tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 1]), 'bbox': tensor([[ 390, 240, 58, 92], [ 391, 319, 66, 99], [ 663, 331, 54, 106], [ 796, 324, 50, 102], [1245, 276, 35, 117], [ 599, 265, 58, 91], [ 755, 228, 42, 91], [ 788, 261, 61, 97], [ 250, 374, 73, 105], [ 224, 449, 29, 28], [ 12, 85, 13, 32], [ 88, 70, 13, 27], [ 102, 99, 14, 33], [ 100, 132, 14, 32], [ 151, 160, 13, 34], [ 177, 115, 18, 32], [ 214, 73, 12, 28], [ 267, 58, 13, 23], [ 358, 71, 14, 33], [ 356, 160, 17, 36], [ 231, 132, 14, 33], [ 234, 88, 16, 29], [ 248, 90, 13, 25], [ 60, 109, 13, 32], [ 243, 75, 7, 5]]), 'iscrowd': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])}) The image in the returned tuple is that of the ds[0] and the target is the combined annotations of ds[0] and ds[1] which is wrong. This is due to the fact that ds.coco could work with a list of inputs in its getAnnIds().
cc @pmeier