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gist-ailab / uoais

Codes of paper "Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling", ICRA 2022
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ORCNNROIHeads raises error if input "proposes" is empty. (when batch size changed to 1) #12

Open YueBro opened 2 years ago

YueBro commented 2 years ago

Issue When I changed the batch_size from 2 to 1 for reducing memory usage, an error occurred in ORCNNROIHeads class at adet/modeling/rcnn/rcnn_heads.py line 508 gt_occludeds = cat(gt_occludeds, dim=0).to(torch.int64). I suspect that the error is caused by no ground truth proposals.

NotImplementedError: There were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema aten::_cat. This usually means that this function requires a non-empty list of Tensors, or that you (the operator writer) forgot to register a fallback function. Available functions are [CPU, CUDA, QuantizedCPU, BackendSelect, Python, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradLazy, AutogradXPU, AutogradMLC, AutogradHPU, AutogradNestedTensor, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, Tracer, AutocastCPU, Autocast, Batched, VmapMode, Functionalize].

CPU: registered at aten/src/ATen/RegisterCPU.cpp:21063 [kernel] CUDA: registered at aten/src/ATen/RegisterCUDA.cpp:29726 [kernel] QuantizedCPU: registered at aten/src/ATen/RegisterQuantizedCPU.cpp:1258 [kernel] BackendSelect: fallthrough registered at ../aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback] Python: registered at ../aten/src/ATen/core/PythonFallbackKernel.cpp:47 [backend fallback] Named: registered at ../aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback] Conjugate: registered at ../aten/src/ATen/ConjugateFallback.cpp:18 [backend fallback] Negative: registered at ../aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback] ZeroTensor: registered at ../aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback] ADInplaceOrView: fallthrough registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:64 [backend fallback] AutogradOther: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:11380 [autograd kernel] AutogradCPU: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:11380 [autograd kernel] AutogradCUDA: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:11380 [autograd kernel] AutogradXLA: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:11380 [autograd kernel] AutogradLazy: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:11380 [autograd kernel] AutogradXPU: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:11380 [autograd kernel] AutogradMLC: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:11380 [autograd kernel] AutogradHPU: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:11380 [autograd kernel] AutogradNestedTensor: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:11380 [autograd kernel] AutogradPrivateUse1: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:11380 [autograd kernel] AutogradPrivateUse2: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:11380 [autograd kernel] AutogradPrivateUse3: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:11380 [autograd kernel] Tracer: registered at ../torch/csrc/autograd/generated/TraceType_3.cpp:11220 [kernel] AutocastCPU: fallthrough registered at ../aten/src/ATen/autocast_mode.cpp:461 [backend fallback] Autocast: fallthrough registered at ../aten/src/ATen/autocast_mode.cpp:305 [backend fallback] Batched: registered at ../aten/src/ATen/BatchingRegistrations.cpp:1059 [backend fallback] VmapMode: fallthrough registered at ../aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback] Functionalize: registered at ../aten/src/ATen/FunctionalizeFallbackKernel.cpp:52 [backend fallback]

Possible solution?: Change line 508-513 by adding an if statement:

if gt_occludeds == []:
    losses["loss_occ_cls"] = torch.tensor(0)
else:
    gt_occludeds = cat(gt_occludeds, dim=0).to(torch.int64)
    n_occ, n_gt = torch.sum(gt_occludeds), gt_occludeds.shape[0]
    n_noocc = n_gt - n_occ
    loss = F.cross_entropy(occ_cls_logits, gt_occludeds, reduction="mean", 
                        weight=torch.Tensor([1, n_noocc/n_occ]).to(device=gt_occludeds.device))
    losses["loss_occ_cls"] = loss

So that losses["loss_occ_cls"] = torch.tensor(0) does not contribute to the gradient.

Hugengyuan commented 1 year ago

have you solve this problem?