cvlab-yonsei / MNAD

An official implementation of "Learning Memory-guided Normality for Anomaly Detection" (CVPR 2020) in PyTorch.
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some questions about Evaluate #48

Open huyi1998 opened 2 years ago

huyi1998 commented 2 years ago

Traceback (most recent call last): File "D:\Pycharm\PyCharm Community Edition 2021.3.2\plugins\python-ce\helpers\pydev\pydevd.py", line 1483, in _exec pydev_imports.execfile(file, globals, locals) # execute the script File "D:\Pycharm\PyCharm Community Edition 2021.3.2\plugins\python-ce\helpers\pydev_pydev_imps_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "E:/huyi/MNAD/Evaluate.py", line 184, in main() File "E:/huyi/MNAD/Evaluate.py", line 142, in main outputs, feas, updated_feas, m_items_test, softmax_score_query, softmax_scorememory, , , , compactness_loss = model.forward(imgs[:, 0:3 4], m_items_test, False) File "E:\huyi\MNAD\model\final_future_prediction_with_memory_spatial_sumonly_weight_ranking_top1.py", line 150, in forward updated_fea, keys, softmax_score_query, softmax_score_memory,query, top1_keys, keys_ind, compactness_loss = self.memory(fea, keys, train) File "D:\Anaconda\envs\MNAD\lib\site-packages\torch\nn\modules\module.py", line 493, in call result = self.forward(input, **kwargs) File "E:\huyi\MNAD\model\memory_final_spatial_sumonly_weight_ranking_top1.py", line 148, in forward compactness_loss, query_re, top1_keys, keys_ind = self.gather_loss(query,keys, train) File "E:\huyi\MNAD\model\memory_final_spatial_sumonly_weight_ranking_top1.py", line 215, in gather_loss softmax_score_query, softmax_score_memory = self.get_score(keys, query) File "E:\huyi\MNAD\model\memory_final_spatial_sumonly_weight_ranking_top1.py", line 120, in get_score score = torch.matmul(query, torch.t(mem))# b X h X w X m RuntimeError: cublas runtime error : the GPU program failed to execute at C:/w/1/s/tmp_conda_3.6_035809/conda/conda-bld/pytorch_1556683229598/work/aten/src/THC/THCBlas.cu:259

my environment is windows+A5000+python 3.6.2+pytorch 1.1.0 Could you tell me how I can solve it?