Update image_processing_deformable_detr.py (#28561)
* Update image_processing_deformable_detr.py * Changes after running make fix-copies
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@ -1414,13 +1414,14 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
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boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
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boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
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# and from relative [0, 1] to absolute [0, height] coordinates
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# and from relative [0, 1] to absolute [0, height] coordinates
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if isinstance(target_sizes, List):
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if target_sizes is not None:
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img_h = torch.Tensor([i[0] for i in target_sizes])
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if isinstance(target_sizes, List):
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img_w = torch.Tensor([i[1] for i in target_sizes])
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img_h = torch.Tensor([i[0] for i in target_sizes])
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else:
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img_w = torch.Tensor([i[1] for i in target_sizes])
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img_h, img_w = target_sizes.unbind(1)
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else:
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scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
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img_h, img_w = target_sizes.unbind(1)
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boxes = boxes * scale_fct[:, None, :]
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scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
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boxes = boxes * scale_fct[:, None, :]
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results = []
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results = []
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for s, l, b in zip(scores, labels, boxes):
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for s, l, b in zip(scores, labels, boxes):
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@ -1411,13 +1411,14 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
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boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
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boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
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# and from relative [0, 1] to absolute [0, height] coordinates
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# and from relative [0, 1] to absolute [0, height] coordinates
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if isinstance(target_sizes, List):
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if target_sizes is not None:
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img_h = torch.Tensor([i[0] for i in target_sizes])
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if isinstance(target_sizes, List):
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img_w = torch.Tensor([i[1] for i in target_sizes])
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img_h = torch.Tensor([i[0] for i in target_sizes])
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else:
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img_w = torch.Tensor([i[1] for i in target_sizes])
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img_h, img_w = target_sizes.unbind(1)
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else:
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scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
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img_h, img_w = target_sizes.unbind(1)
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boxes = boxes * scale_fct[:, None, :]
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scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
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boxes = boxes * scale_fct[:, None, :]
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results = []
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results = []
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for s, l, b in zip(scores, labels, boxes):
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for s, l, b in zip(scores, labels, boxes):
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