-215 opencv assertion_error ai_generated true

cv::error: (-215:Assertion failed) inputs[0].size[0] == net.getLayer(0)->outputs[0].size[0] in function 'cv::dnn::Net::forward'

ID: opencv/dnn-batch-size-mismatch

Also available as: JSON · Markdown · 中文
88%Fix Rate
85%Confidence
1Evidence
2024-06-20First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
4.7.0 active — — —
4.8.1 active — — —
4.9.0 active — — —

Root Cause

The batch size of the input blob does not match the batch size expected by the first layer of the DNN network.

generic

中文

输入 blob 的批次大小与 DNN 网络第一层期望的批次大小不匹配。

Official Documentation

https://docs.opencv.org/4.x/d6/d0f/group__dnn.html#ga29f34df9366379a6039d2f4f9e0d7d57

Workarounds

  1. 95% success Inspect the expected input shape using net.getLayer(0).outputs[0].size[0] and set the blob batch size accordingly. For single-image inference, use blobFromImage which defaults to batch size 1. 
    Inspect the expected input shape using net.getLayer(0).outputs[0].size[0] and set the blob batch size accordingly. For single-image inference, use blobFromImage which defaults to batch size 1.
  2. 85% success If the model requires batch size > 1, create a blob with multiple images using blobFromImages and ensure the first dimension equals the network's expected batch size. 
    If the model requires batch size > 1, create a blob with multiple images using blobFromImages and ensure the first dimension equals the network's expected batch size.

中文步骤

  1. Inspect the expected input shape using net.getLayer(0).outputs[0].size[0] and set the blob batch size accordingly. For single-image inference, use blobFromImage which defaults to batch size 1.
  2. If the model requires batch size > 1, create a blob with multiple images using blobFromImages and ensure the first dimension equals the network's expected batch size.

Dead Ends

Common approaches that don't work:

  1. Manually reshaping the input blob without checking the network's expected input shape 90% fail

    Reshaping may alter data layout or fail to match the exact batch size dimension; the network expects a specific batch size (e.g., 1).

  2. Setting the blob size to a random large batch (e.g., 32) assuming the network supports dynamic batching 95% fail

    Many pretrained models have a fixed batch size (usually 1) and do not support dynamic batching; this will cause the assertion to fail.