CUDA out of memory on test.py

[Xu-Justin]

I'm following the installation guide. When running test.py on step 4, I got RuntimeError: CUDA out of memory. Is it okay to proceed (using smaller batch on training or inference), or will it have any effect on the performance?

* True check_forward_equal_with_pytorch_double: max_abs_err 8.67e-19 max_rel_err 2.35e-16
* True check_forward_equal_with_pytorch_float: max_abs_err 4.66e-10 max_rel_err 1.13e-07
* True check_gradient_numerical(D=30)
* True check_gradient_numerical(D=32)
* True check_gradient_numerical(D=64)
* True check_gradient_numerical(D=71)
* True check_gradient_numerical(D=1025)
Traceback (most recent call last):
  File "/home/azureuser/WilliamJustin/DAB-DETR/models/dab_deformable_detr/ops/test.py", line 86, in <module>
    check_gradient_numerical(channels, True, True, True)
  File "/home/azureuser/WilliamJustin/DAB-DETR/models/dab_deformable_detr/ops/test.py", line 76, in check_gradient_numerical
    gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step))
  File "/home/azureuser/miniconda3/envs/jstnxu-DAB-DETR/lib/python3.9/site-packages/torch/autograd/gradcheck.py", line 1400, in gradcheck
    return _gradcheck_helper(**args)
  File "/home/azureuser/miniconda3/envs/jstnxu-DAB-DETR/lib/python3.9/site-packages/torch/autograd/gradcheck.py", line 1414, in _gradcheck_helper
    _gradcheck_real_imag(gradcheck_fn, func, func_out, tupled_inputs, outputs, eps,
  File "/home/azureuser/miniconda3/envs/jstnxu-DAB-DETR/lib/python3.9/site-packages/torch/autograd/gradcheck.py", line 1061, in _gradcheck_real_imag
    gradcheck_fn(func, func_out, tupled_inputs, outputs, eps,
  File "/home/azureuser/miniconda3/envs/jstnxu-DAB-DETR/lib/python3.9/site-packages/torch/autograd/gradcheck.py", line 1097, in _slow_gradcheck
    numerical = _transpose(_get_numerical_jacobian(func, tupled_inputs, outputs, eps=eps, is_forward_ad=use_forward_ad))
  File "/home/azureuser/miniconda3/envs/jstnxu-DAB-DETR/lib/python3.9/site-packages/torch/autograd/gradcheck.py", line 146, in _get_numerical_jacobian
    jacobians += [get_numerical_jacobian_wrt_specific_input(fn, inp_idx, inputs, outputs, eps,
  File "/home/azureuser/miniconda3/envs/jstnxu-DAB-DETR/lib/python3.9/site-packages/torch/autograd/gradcheck.py", line 290, in get_numerical_jacobian_wrt_specific_input
    return _combine_jacobian_cols(jacobian_cols, outputs, input, input.numel())
  File "/home/azureuser/miniconda3/envs/jstnxu-DAB-DETR/lib/python3.9/site-packages/torch/autograd/gradcheck.py", line 230, in _combine_jacobian_cols
    jacobians = _allocate_jacobians_with_outputs(outputs, numel, dtype=input.dtype if input.dtype.is_complex else None)
  File "/home/azureuser/miniconda3/envs/jstnxu-DAB-DETR/lib/python3.9/site-packages/torch/autograd/gradcheck.py", line 45, in _allocate_jacobians_with_outputs
    out.append(t.new_zeros((numel_input, t.numel()), **options))
RuntimeError: CUDA out of memory. Tried to allocate 7.50 GiB (GPU 0; 15.75 GiB total capacity; 7.50 GiB already allocated; 7.30 GiB free; 7.50 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

[SlongLiu]

It seems like there is no severe problem in your installation, hence I think it is okay to continue.
You can try experiments with small batch size.

[Xu-Justin]

Thank you for the fast response.