RuntimeError: "baddbmm_cuda" not implemented for 'Int'

[fj6833]

Namespace(body_representation='2D', cfg='configs/config_jhmdb_simplebaseline_2D.yaml', dataset_name='jhmdb', estimator='simplebaseline')

Seed value for the experiment is 4321
GPU name -> NVIDIA GeForce RTX 3060 Laptop GPU
GPU feat -> _CudaDeviceProperties(name='NVIDIA GeForce RTX 3060 Laptop GPU', major=8, minor=6, total_memory=6143MB, multi_processor_count=30)
{'BODY_REPRESENTATION': '2D',
'CUDNN': CfgNode({'BENCHMARK': True, 'DETERMINISTIC': False, 'ENABLED': True}),
'DATASET': {'AIST': {'DETECTED_PATH': './data\detected_poses/aist',
'GROUND_TRUTH_PATH': './data\groundtruth_poses/aist',
'KEYPOINT_NUM': 14,
'KEYPOINT_ROOT': [2, 3]},
'H36M': {'DETECTED_PATH': './data\detected_poses/h36m',
'GROUND_TRUTH_PATH': './data\groundtruth_poses/h36m',
'KEYPOINT_NUM': 17,
'KEYPOINT_ROOT': [0]},
'JHMDB': {'DETECTED_PATH': './data\detected_poses/jhmdb',
'GROUND_TRUTH_PATH': './data\groundtruth_poses/jhmdb',
'KEYPOINT_NUM': 15,
'KEYPOINT_ROOT': [2]},
'PW3D': {'DETECTED_PATH': './data\detected_poses/pw3d',
'GROUND_TRUTH_PATH': './data\groundtruth_poses/pw3d',
'KEYPOINT_NUM': 14,
'KEYPOINT_ROOT': [2, 3]}},
'DATASET_NAME': 'jhmdb',
'DEBUG': True,
'DEVICE': 'cuda',
'ESTIMATOR': 'simplebaseline',
'EVALUATE': {'DECODER': False,
'INTERP': 'linear',
'PRETRAINED': 'results/30-08-2022_16-06-59_jhmdb_simplebaseline_N10_10/[email protected]_0.89_checkpoint.pth.tar',
'RELATIVE_IMPROVEMENT': False,
'ROOT_RELATIVE': True,
'SLIDE_WINDOW_STEP_Q': 1,
'SLIDE_WINDOW_STEP_SIZE': 10},
'EXP_NAME': 'jhmdb_simplebaseline_N10_1_256',
'GPUS': ['0'],
'LOG': CfgNode({'NAME': ''}),
'LOGDIR': 'results\29-10-2022_17-15-29_jhmdb_simplebaseline_N10_1_256',
'LOSS': CfgNode({'LAMADA': 5.0, 'W_DECODER': 1.0}),
'MODEL': {'DECODER': 'transformer',
'DECODER_EMBEDDING_DIMENSION': 256,
'DECODER_HEAD': 4,
'DECODER_INTERP': 'linear',
'DECODER_RESIDUAL': True,
'DECODER_TOKEN_WINDOW': 5,
'DECODER_TRANSFORMER_BLOCK': 5,
'DROPOUT': 0.1,
'ENCODER_EMBEDDING_DIMENSION': 256,
'ENCODER_HEAD': 4,
'ENCODER_RESIDUAL': True,
'ENCODER_TRANSFORMER_BLOCK': 5,
'INTERVAL_N': 10,
'NAME': '',
'SAMPLE_TYPE': 'uniform',
'SLIDE_WINDOW': True,
'SLIDE_WINDOW_Q': 1,
'SLIDE_WINDOW_SIZE': 11,
'TYPE': 'network'},
'OUTPUT_DIR': 'results',
'SAMPLE_INTERVAL': 10,
'SEED_VALUE': 4321,
'SMPL_MODEL_DIR': 'data/smpl/',
'TRAIN': {'BATCH_SIZE': 16,
'EPOCH': 70,
'LR': 0.001,
'LRDECAY': 0.95,
'PRE_NORM': False,
'RESUME': None,
'USE_6D_SMPL': False,
'USE_SMPL_LOSS': False,
'VALIDATE': True,
'WORKERS_NUM': 0},
'VIS': {'END': 100,
'INPUT_VIDEO_NUMBER': 160,
'INPUT_VIDEO_PATH': 'data/videos/',
'OUTPUT_VIDEO_PATH': 'demo/',
'START': 0}}
#############################################################
You are loading the [training set] of dataset [jhmdb]
You are using pose esimator [simplebaseline]
The type of the data is [2D]
The frame number is [24372]
The sequence number is [687]
#############################################################
#############################################################
You are loading the [testing set] of dataset [jhmdb]
You are using pose esimator [simplebaseline]
The type of the data is [2D]
The frame number is [9228]
The sequence number is [261]
#############################################################
Slide window: 11
Sample interval: 10

Traceback (most recent call last):
File "train.py", line 109, in
main(cfg)
File "train.py", line 96, in main
Trainer(train_dataloader=train_loader,
File "D:\GitLoadWareHouse\HANet\lib\core\trainer.py", line 71, in run
self.train()
File "D:\GitLoadWareHouse\HANet\lib\core\trainer.py", line 124, in train
predicted_3d_pos, decoderd_3d_pos = self.model(
File "D:\Environment\anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "D:\GitLoadWareHouse\HANet\lib\models\HANet.py", line 175, in forward
self.hierarchical_encoder, self.decoder = self.transformer.forward(
File "D:\GitLoadWareHouse\HANet\lib\models\HANet.py", line 335, in forward
output = self.decode(mem, encoder_mask, encoder_pos_embed[0], trans_tgt,
File "D:\GitLoadWareHouse\HANet\lib\models\HANet.py", line 373, in decode
hs = self.decoder(tgt,
File "D:\Environment\anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "D:\GitLoadWareHouse\HANet\lib\models\HANet.py", line 429, in forward
output = layer(output,
File "D:\Environment\anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "D:\GitLoadWareHouse\HANet\lib\models\HANet.py", line 633, in forward
return self.forward_post(tgt, memory, tgt_mask, memory_mask,
File "D:\GitLoadWareHouse\HANet\lib\models\HANet.py", line 570, in forward_post
tgt2 = self.self_attn(q,
File "D:\Environment\anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "D:\Environment\anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\activation.py", line 1153, in forward
attn_output, attn_output_weights = F.multi_head_attention_forward(
File "D:\Environment\anaconda3\envs\pytorch\lib\site-packages\torch\nn\functional.py", line 5179, in multi_head_attention_forward
attn_output, attn_output_weights = _scaled_dot_product_attention(q, k, v, attn_mask, dropout_p)
File "D:\Environment\anaconda3\envs\pytorch\lib\site-packages\torch\nn\functional.py", line 4852, in _scaled_dot_product_attention
attn = torch.baddbmm(attn_mask, q, k.transpose(-2, -1))
RuntimeError: "baddbmm_cuda" not implemented for 'Int'
�[?25h

Hello, the author. I encountered this problem during training. How can I solve it?

[KyungMinJin]

Hi, @fj6833! Thanks for letting me know!
Try with python3.6 and let me know if it doesn't work.