1. 04 Oct, 2022 1 commit
  2. 20 Oct, 2021 1 commit
    • Vasilis Vryniotis's avatar
      Refactor the backbone builders of detection (#4656) · d18c4872
      Vasilis Vryniotis authored
      * Refactoring resnet_fpn backbone building.
      
      * Passing the change to *_rcnn and retinanet.
      
      * Applying for faster_rcnn + mobilenetv3
      
      * Applying for ssdlite + mobilenetv3
      
      * Applying for ssd + vgg16
      
      * Update the expected file of retinanet_resnet50_fpn to fix order of initialization.
      
      * Adding full model weights for the VGG16 features.
      d18c4872
  3. 27 May, 2021 1 commit
    • Vasilis Vryniotis's avatar
      Speedup the slow tests of detection models (#3929) · 4c563846
      Vasilis Vryniotis authored
      * Speed up keypoint and retina.
      
      * Update keypoint expected file
      
      * Speed up fasterrcnn_resnet50_fpn.
      
      * Speed up maskrcnn_resnet50_fpn.
      
      * Updating params to resolve flakiness.
      
      * limit runs to those 4 tests
      
      * Relaxing precision to resolve flakiness
      
      * Undo test filtering
      4c563846
  4. 09 Nov, 2020 1 commit
  5. 06 Nov, 2020 1 commit
    • Vasilis Vryniotis's avatar
      Fix flakiness on detection tests (#2966) · 7f7ff056
      Vasilis Vryniotis authored
      * Simplify the ACCEPT=True logic in assertExpected().
      
      * Separate the expected filename estimation from assertExpected
      
      * Unflatten expected values.
      
      * Assert for duplicate scores if primary check fails.
      
      * Remove custom exceptions for algorithms and add a compact function for shrinking large ouputs.
      
      * Removing unused variables.
      
      * Add warning and comments.
      
      * Re-enable all autocast unit-test for detection and marking the tests as skipped in partial validation.
      
      * Move test skip at the end.
      
      * Changing the warning message.
      7f7ff056
  6. 20 Oct, 2020 1 commit
  7. 16 Oct, 2020 1 commit
  8. 13 Oct, 2020 1 commit
    • Francisco Massa's avatar
      RetinaNet object detection (take 2) (#2784) · 5bb81c8e
      Francisco Massa authored
      
      
      * Add rough implementation of RetinaNet.
      
      * Move AnchorGenerator to a seperate file.
      
      * Move box similarity to Matcher.
      
      * Expose extra blocks in FPN.
      
      * Expose retinanet in __init__.py.
      
      * Use P6 and P7 in FPN for retinanet.
      
      * Use parameters from retinanet for anchor generation.
      
      * General fixes for retinanet model.
      
      * Implement loss for retinanet heads.
      
      * Output reshaped outputs from retinanet heads.
      
      * Add postprocessing of detections.
      
      * Small fixes.
      
      * Remove unused argument.
      
      * Remove python2 invocation of super.
      
      * Add postprocessing for additional outputs.
      
      * Add missing import of ImageList.
      
      * Remove redundant import.
      
      * Simplify class correction.
      
      * Fix pylint warnings.
      
      * Remove the label adjustment for background class.
      
      * Set default score threshold to 0.05.
      
      * Add weight initialization for regression layer.
      
      * Allow training on images with no annotations.
      
      * Use smooth_l1_loss with beta value.
      
      * Add more typehints for TorchScript conversions.
      
      * Fix linting issues.
      
      * Fix type hints in postprocess_detections.
      
      * Fix type annotations for TorchScript.
      
      * Fix inconsistency with matched_idxs.
      
      * Add retinanet model test.
      
      * Add missing JIT annotations.
      
      * Remove redundant model construction
      
      Make tests pass
      
      * Fix bugs during training on newer PyTorch and unused params in DDP
      
      Needs cleanup and to add back support for images with no annotations
      
      * Cleanup resnet_fpn_backbone
      
      * Use L1 loss for regression
      
      Gives 1mAP improvement over smooth l1
      
      * Disable support for images with no annotations
      
      Need to fix distributed first
      
      * Fix retinanet tests
      
      Need to deduplicate those box checks
      
      * Fix Lint
      
      * Add pretrained model
      
      * Add training info for retinanet
      Co-authored-by: default avatarHans Gaiser <hansg91@gmail.com>
      Co-authored-by: default avatarHans Gaiser <hans.gaiser@robovalley.com>
      Co-authored-by: default avatarHans Gaiser <hans.gaiser@robohouse.com>
      5bb81c8e