-2 opencv resource_error ai_generated partial

cv::error: (-2:Unspecified error) Failed to allocate memory for DNN forward pass with CUDA backend

ID: opencv/dnn-forward-cuda-memory

Also available as: JSON · Markdown · 中文
75%Fix Rate
82%Confidence
1Evidence
2023-07-20First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
4.6.0 active — — —
4.7.0 active — — —
4.8.0 active — — —
4.9.0 active — — —
4.10.0 active — — —

Root Cause

CUDA GPU ran out of memory during DNN forward pass, often due to large model or batch size exceeding GPU VRAM capacity.

generic

中文

CUDA GPU 在 DNN 前向传播期间内存不足，通常是因为模型过大或批次大小超过 GPU VRAM 容量。

Official Documentation

https://docs.opencv.org/4.x/d6/d0f/group__dnn.html#ga29f34df9376379a603acd8df581ac8d7

Workarounds

  1. 80% success Reduce batch size in the DNN forward pass: `net.setInput(blob); output = net.forward()` with a smaller blob (e.g., half the batch size). 
    Reduce batch size in the DNN forward pass: `net.setInput(blob); output = net.forward()` with a smaller blob (e.g., half the batch size).
  2. 70% success Use model optimization techniques like quantization or pruning before inference, or switch to a smaller model variant. 
    Use model optimization techniques like quantization or pruning before inference, or switch to a smaller model variant.
  3. 75% success Free unused GPU memory by calling `torch.cuda.empty_cache()` or `cv2.cuda.resetDevice()` before the forward pass. 
    Free unused GPU memory by calling `torch.cuda.empty_cache()` or `cv2.cuda.resetDevice()` before the forward pass.

中文步骤

  1. Reduce batch size in the DNN forward pass: `net.setInput(blob); output = net.forward()` with a smaller blob (e.g., half the batch size).
  2. Use model optimization techniques like quantization or pruning before inference, or switch to a smaller model variant.
  3. Free unused GPU memory by calling `torch.cuda.empty_cache()` or `cv2.cuda.resetDevice()` before the forward pass.

Dead Ends

Common approaches that don't work:

  1. 60% fail

    CPU may also run out of memory or be too slow; the issue is the model/batch size, not the backend alone.

  2. 85% fail

    Environment variables don't change available VRAM; they only select GPU devices.

  3. 95% fail

    CUDA memory is GPU VRAM, not system RAM; adding RAM does not help GPU memory.