Description
2025-06-03 09:22:16,118 - mmfewshot - INFO - workflow: [('train', 1)], max: 120000 iters
2025-06-03 09:22:16,119 - mmfewshot - INFO - Checkpoints will be saved to /home/dww/OD/mmfewshot/work_dirs/meta-rcnn_r50_c4_8xb4_coco_base-training by HardDiskBackend.
Traceback (most recent call last):
File "tools/detection/train.py", line 236, in
main()
File "tools/detection/train.py", line 225, in main
train_detector(
File "/home/dww/OD/mmfewshot/mmfewshot/detection/apis/train.py", line 206, in train_detector
runner.run(data_loaders, cfg.workflow)
File "/home/dww/anaconda3/envs/mmfewshot/lib/python3.8/site-packages/mmcv/runner/iter_based_runner.py", line 134, in run
iter_runner(iter_loaders[i], **kwargs)
File "/home/dww/anaconda3/envs/mmfewshot/lib/python3.8/site-packages/mmcv/runner/iter_based_runner.py", line 61, in train
outputs = self.model.train_step(data_batch, self.optimizer, **kwargs)
File "/home/dww/anaconda3/envs/mmfewshot/lib/python3.8/site-packages/mmcv/parallel/data_parallel.py", line 60, in train_step
return self.module.train_step(*inputs[0], **kwargs[0])
File "/home/dww/OD/mmfewshot/mmfewshot/detection/models/detectors/query_support_detector.py", line 207, in train_step
losses = self(**data)
File "/home/dww/anaconda3/envs/mmfewshot/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/dww/anaconda3/envs/mmfewshot/lib/python3.8/site-packages/mmcv/runner/fp16_utils.py", line 98, in new_func
return old_func(*args, **kwargs)
File "/home/dww/OD/mmfewshot/mmfewshot/detection/models/detectors/query_support_detector.py", line 169, in forward
return self.forward_train(query_data, support_data, **kwargs)
File "/home/dww/OD/mmfewshot/mmfewshot/detection/models/detectors/query_support_detector.py", line 266, in forward_train
query_feats = self.extract_query_feat(query_img)
File "/home/dww/anaconda3/envs/mmfewshot/lib/python3.8/site-packages/mmcv/runner/fp16_utils.py", line 98, in new_func
return old_func(*args, **kwargs)
File "/home/dww/OD/mmfewshot/mmfewshot/detection/models/detectors/query_support_detector.py", line 103, in extract_query_feat
feats = self.backbone(img)
File "/home/dww/anaconda3/envs/mmfewshot/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727,
4C26
in _call_impl
result = self.forward(*input, **kwargs)
File "/home/dww/OD/mmfewshot/mmfewshot/detection/models/backbones/resnet_with_meta_conv.py", line 55, in forward
x = self.conv1(x)
File "/home/dww/anaconda3/envs/mmfewshot/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/dww/anaconda3/envs/mmfewshot/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 423, in forward
return self._conv_forward(input, self.weight)
File "/home/dww/anaconda3/envs/mmfewshot/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 419, in _conv_forward
return F.conv2d(input, weight, self.bias, self.stride,
RuntimeError: Expected 4-dimensional input for 4-dimensional weight [64, 3, 7, 7], but got 5-dimensional input of size [1, 4, 3, 512, 512] instead