{"record":{"id":"48b84c586624cbe0","repo":"WZMIAOMIAO/deep-learning-for-image-processing","slug":"expected-target-boxes-to-be-a-tensorof-shape-n-4-48b84c","errorCode":null,"errorMessage":"Expected target boxes to be a tensorof shape [N, 4], got {:}.","messagePattern":"Expected target boxes to be a tensorof shape \\[N, 4\\], got (.+?)\\.","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"pytorch_object_detection/retinaNet/network_files/retinanet.py","lineNumber":464,"sourceCode":"\n        Returns:\n            result (list[BoxList] or dict[Tensor]): the output from the model.\n                During training, it returns a dict[Tensor] which contains the losses.\n                During testing, it returns list[BoxList] contains additional fields\n                like `scores`, `labels` and `mask` (for Mask R-CNN models).\n\n        \"\"\"\n        if self.training and targets is None:\n            raise ValueError(\"In training mode, targets should be passed\")\n\n        if self.training:\n            assert targets is not None\n            # check targets info\n            for target in targets:\n                boxes = target[\"boxes\"]\n                if isinstance(boxes, torch.Tensor):\n                    if len(boxes.shape) != 2 or boxes.shape[-1] != 4:\n                        raise ValueError(\"Expected target boxes to be a tensor\"\n                                         \"of shape [N, 4], got {:}.\".format(boxes.shape))\n                else:\n                    raise ValueError(\"Expected target boxes to be of type \"\n                                     \"Tensor, got {:}.\".format(type(boxes)))\n\n        # get the original images sizes\n        original_img_sizes: List[Tuple[int, int]] = []\n        for img in images:\n            val = img.shape[-2:]\n            assert len(val) == 2\n            original_img_sizes.append((val[0], val[1]))  # h, w\n\n        # transform the input\n        images, targets = self.transform(images, targets)\n\n        # Check for degenerate boxes\n        # TODO: Move this to a function\n        if targets is not None:","sourceCodeStart":446,"sourceCodeEnd":482,"githubUrl":"https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/blob/1ec3fe6f374fc9969973a61f819de25658595afa/pytorch_object_detection/retinaNet/network_files/retinanet.py#L446-L482","documentation":"RetinaNet.forward validates that each target['boxes'] is a torch.Tensor with shape [N, 4]. When it is a tensor but has wrong rank or last dimension (not 4 coordinates), the shape-check raises ValueError including the actual shape. The typo 'tensorof' is in the original message string.","triggerScenarios":"Passing boxes shaped [N] (flat 4*N), [4] (single box, 1-D), [N, 4, 1] or [B, N, 4] (batched); constructing targets from numpy arrays converted with wrong reshape; collate functions stacking boxes into 3-D tensors.","commonSituations":"Custom Dataset returning a single box tensor [4] instead of [1, 4]; concatenating per-image boxes during batching; migrating code from older detection repos with different target layouts; accidental torch.stack of box lists.","solutions":["Reshape to 2-D: boxes.view(-1, 4) or boxes.unsqueeze(0) for a single box.","In your Dataset, return boxes as a [num_objects, 4] tensor (x1, y1, x2, y2 per row).","Fix the collate/stacking logic so per-image targets stay individual dicts, not batched tensors.","Add a pre-forward assert: all(t['boxes'].ndim == 2 and t['boxes'].shape[-1] == 4 ...)."],"exampleFix":"// before\ntarget = {\"boxes\": torch.tensor([10., 20., 100., 120.])}  # shape [4]\n// after\ntarget = {\"boxes\": torch.tensor([[10., 20., 100., 120.]])}  # shape [1, 4]","handlingStrategy":"type-guard","validationCode":"import torch\nfor t in targets:\n    b = t[\"boxes\"]\n    assert isinstance(b, torch.Tensor) and b.ndim == 2 and b.shape[-1] == 4, f\"bad boxes shape {b.shape}\"","typeGuard":"def is_valid_boxes_tensor(boxes) -> bool:\n    import torch\n    return isinstance(boxes, torch.Tensor) and boxes.ndim == 2 and boxes.shape[-1] == 4","tryCatchPattern":"try:\n    outputs = model(images, targets)\nexcept ValueError as e:\n    if \"of shape [N, 4]\" in str(e):\n        targets = [{**t, \"boxes\": torch.as_tensor(t[\"boxes\"]).view(-1, 4)} for t in targets]\n        outputs = model(images, targets)\n    else:\n        raise","preventionTips":["Return boxes as [N, 4] float tensors directly from the Dataset.","Never stack targets across the batch; keep per-image dicts in a list.","Add collate_fn assertions on target structure.","Unsqueeze single boxes to [1, 4]."],"tags":["pytorch","object-detection","shape-mismatch","validation"],"backgroundTag":"tensor-shape-mismatch","analyzedSha":"1ec3fe6f374fc9969973a61f819de25658595afa","analyzedAt":"2026-08-30T09:19:11.901Z","schemaVersion":2},"datasetVersion":"2026-08-30T13:17:10.514Z"}