* Detect() and Segment() fixes for CoreML and Paddle
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* Fix duplicate plots.py
* Fix check_font()
* # torch.use_deterministic_algorithms(True)
* update doc detect->predict
* Resolve precommit for segment/train and segment/val
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* Resolve precommit utils/segment/general
* Align NMS-seg closer to NMS
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* update
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* update
* cleanup
* Remove unused ImageFont import
* Unified NMS
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* DetectMultiBackend compatibility
* segment/predict.py update
* update plot colors
* fix bbox shifted
* sort bbox by confidence
* enable overlap by default
* Merge detect/segment output_to_target() function
* Start segmentation CI
* fix plots
* Update ci-testing.yml
* fix training whitespace
* optimize process mask functions (can we merge both?)
* Update predict/detect
* Update plot_images
* Update plot_images_and_masks
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* fix
* Add train to CI
* fix precommit
* fix precommit CI
* fix precommit pycocotools
* fix val float issues
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* fix masks float float issues
* suppress errors
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* fix no-predictions plotting bug
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* Add CSV Logger
* fix val len(plot_masks)
* speed up evaluation
* fix process_mask
* fix plots
* update segment/utils build_targets
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* optimize utils/segment/general crop()
* optimize utils/segment/general crop() 2
* minor updates
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* torch.where revert
* downsample only if different shape
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* loss cleanup
* loss cleanup 2
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* loss cleanup 3
* update project names
* Rename -seg yamls from _underscore to -dash
* prepare for yolov5n-seg.pt
* precommit space fix
* add coco128-seg.yaml
* update coco128-seg comments
* cleanup val.py
* Major val.py cleanup
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* precommit fix
* precommit fix
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* optional pycocotools
* remove CI pip install pycocotools (auto-installed now)
* seg yaml fix
* optimize mask_iou() and masks_iou()
* threaded fix
* Major train.py update
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* Major segments/val/process_batch() update
* yolov5/val updates from segment
* process_batch numpy/tensor fix
* opt-in to pycocotools with --save-json
* threaded pycocotools ops for 2x speed increase
* Avoid permute contiguous if possible
* Add max_det=300 argument to both val.py and segment/val.py
* fix onnx_dynamic
* speed up pycocotools ops
* faster process_mask(upsample=True) for predict
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* eliminate permutations for process_mask(upsample=True)
* eliminate permute-contiguous in crop(), use native dimension order
* cleanup comment
* Add Proto() module
* fix class count
* fix anchor order
* broadcast mask_gti in loss for speed
* Cleanup seg loss
* faster indexing
* faster indexing fix
* faster indexing fix2
* revert faster indexing
* fix validation plotting
* Loss cleanup and mxyxy simplification
* Loss cleanup and mxyxy simplification 2
* revert validation plotting
* replace missing tanh
* Eliminate last permutation
* delete unneeded .float()
* Remove MaskIOULoss and crop(if HWC)
* Final v6.3 SegmentationModel architecture updates
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* Add support for TF export
* remove debugger trace
* add call
* update
* update
* Merge master
* Merge master
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* Update dataloaders.py
* Restore CI
* Update dataloaders.py
* Fix TF/TFLite export for segmentation model
* Merge master
* Cleanup predict.py mask plotting
* cleanup scale_masks()
* rename scale_masks to scale_image
* cleanup/optimize plot_masks
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* Add Annotator.masks()
* Annotator.masks() fix
* Update plots.py
* Annotator mask optimization
* Rename crop() to crop_mask()
* Do not crop in predict.py
* crop always
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* Merge master
* Add vid-stride from master PR
* Update seg model outputs
* Update seg model outputs
* Add segmentation benchmarks
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* Add segmentation benchmarks
* Add segmentation benchmarks
* Add segmentation benchmarks
* Fix DetectMultiBackend for OpenVINO
* update Annotator.masks
* fix val plot
* revert val plot
* clean up
* revert pil
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix CI error
* fix predict log
* remove upsample
* update interpolate
* fix validation plot logging
* Annotator.masks() cleanup
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* Remove segmentation_model definition
* Restore 0.99999 decimals
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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* added dilate conv support
* added dilate conv support
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* Update common.py
* Update common.py
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* Two dimensional `size=(h,w)` AutoShape support
May resolve https://github.com/ultralytics/yolov5/issues/9039
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Update hubconf.py
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* faster 1 channel to 3 channels image conversion
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* Update hubconf.py
Loading a custom yolov model causes this line to fail. Adding a test to check if the model actually has a model.model field. With this check, I'm able to load the model no prob.
Loading model via
```py
model = torch.hub.load(
'ultralytics/yolov5', 'custom', 'models/frozen_backbone_coco_unlabeled_best.onnx',
autoshape=True, force_reload=False
)
```
Causes traceback:
```
Traceback (most recent call last):
File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/flask/app.py", line 2077, in wsgi_app
response = self.full_dispatch_request()
File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/flask/app.py", line 1525, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/flask/app.py", line 1523, in full_dispatch_request
rv = self.dispatch_request()
File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/flask/app.py", line 1509, in dispatch_request
return self.ensure_sync(self.view_functions[rule.endpoint])(**req.view_args)
File "/Users/jackson/Documents/GitHub/w210-capstone/api/endpoints/predictions.py", line 26, in post_predictions
yolov_predictions = predict_bounding_boxes_for_collection(collection_id)
File "/Users/jackson/Documents/GitHub/w210-capstone/api/predictions/predict_bounding_boxes.py", line 43, in predict_bounding_boxes_for_collection
model = torch.hub.load(
File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/torch/hub.py", line 404, in load
model = _load_local(repo_or_dir, model, *args, **kwargs)
File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/torch/hub.py", line 433, in _load_local
model = entry(*args, **kwargs)
File "/Users/jackson/.cache/torch/hub/ultralytics_yolov5_master/hubconf.py", line 72, in custom
return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
File "/Users/jackson/.cache/torch/hub/ultralytics_yolov5_master/hubconf.py", line 67, in _create
raise Exception(s) from e
Exception: 'DetectMultiBackend' object has no attribute 'model'. Cache may be out of date, try `force_reload=True` or see https://github.com/ultralytics/yolov5/issues/36 for help.
Exception on /api/v1/predictions [POST]
Traceback (most recent call last):
File "/Users/jackson/.cache/torch/hub/ultralytics_yolov5_master/hubconf.py", line 58, in _create
model.model.model[-1].inplace = False # Detect.inplace=False for safe multithread inference
File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1185, in __getattr__
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'DetectMultiBackend' object has no attribute 'model'
```
* Update hubconf.py
* Update common.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Dynamic batch size support for TensorRT
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* Update export.py
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* Fix optimization profile when batch size is 1
* Warn users if they use batch-size=1 with dynamic
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* More descriptive assertion error
* Fix syntax
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* pre-commit formatting sucked
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* Update export.py
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* Apple Metal Performance Shader (MPS) device support
Following https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
Should work with Apple M1 devices with PyTorch nightly installed with command `--device mps`. Usage examples:
```bash
python train.py --device mps
python detect.py --device mps
python val.py --device mps
```
* Update device strategy to fix MPS issue
* Write .yaml file when exporting model to openvino
Write .yaml file automatically when exporting model to openvino to be used during inference
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* Update export.py
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* Load metadata on inference
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* Add PyTorch AMP check
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* Cleanup
* Cleanup
* Cleanup
* Robust for DDP
* Fixes
* Add amp enabled boolean to check_train_batch_size
* Simplify
* space to prefix
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* Add DWConvTranspose2d() module
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* Add DWConvTranspose2d() module
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* Fix
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* Fix
* Fix
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* Uses OpenVINO runtime instead of openvino-dev
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* export with openvino package
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* Revert export.py
* Update common.py
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* New TensorFlow `TFCrossConv()` module
* Move from experimental to common
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* Add C3x
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* Add to C3x to yolo.py
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* TFC3x bug fix
* TFC3x bug fix
* TFC3x bug fix
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* New TensorFlow `TFDWConv()` module
Analog to DWConv() module:
8aa2085a7e/models/common.py (L53-L57)
* Fix and new activations() function
* Update tf.py
* set common default stride as 32
* restore default stride, and set it on argument optional
* fix wrong use of opt
* fix missing parameter of stride
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* fix format of parameters
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* [pre-commit.ci] pre-commit suggestions
updates:
- [github.com/asottile/pyupgrade: v2.31.0 → v2.31.1](https://github.com/asottile/pyupgrade/compare/v2.31.0...v2.31.1)
- [github.com/pre-commit/mirrors-yapf: v0.31.0 → v0.32.0](https://github.com/pre-commit/mirrors-yapf/compare/v0.31.0...v0.32.0)
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* Update yolo.py
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* Update
* Update
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* precommit: yapf
* align isort
* fix
# Conflicts:
# utils/plots.py
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* Update wandb_utils.py
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* simplify colorstr
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* val run fix
* export.py last comma
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* PyTorch Hub tuple fix
* PyTorch Hub tuple fix2
* PyTorch Hub tuple fix3
* Update setup
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* yolo.py profiling updates
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* Sidestep os.path.relpath() Windows bug
os.path.relpath() seems to have a major bug on Windows due to Windows horrible path handling. This fix attempts to sidestep the issue.
```
File "C:\Users\mkokg/.cache\torch\hub\ultralytics_yolov5_master\export.py", line 64, in
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
File "C:\Users\mkokg\AppData\Local\Programs\Python\Python310\lib\ntpath.py", line 718, in relpath
raise ValueError("path is on mount %r, start on mount %r" % (
ValueError: path is on mount 'C:', start on mount 'D:'
```
* Update yolo.py
* Update yolo.py
* Update yolo.py
* Update export.py
* removed transpose op for better edgetpu support
* fix for training case
* enabled experimental new quantizer flag
* precalculate add and mul ops at compile time
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* refactor: use edgetpu flag
* fix: remove bitwise and assignation to tflite
* Cleanup and fix tflite
* Cleanup
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* Fix CoreML P6 inference
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* Fix exporting saved_model: pb exceeds 2GB
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* Replace TF v1.x API with TF v2.x API for saved_model export
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* Clean up
* Remove lambda in tf.function()
* Revert "Remove lambda in tf.function()" to be compatible with TF v2.4
This reverts commit 46c7931f11dfdea6ae340c77287c35c30b9e0779.
* Fix for pre-commit.ci
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* Backwards compatibility update
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Glenn Jocher
Ayush Chaurasia
Katteria
黄不盈
cher-liang
Jackson Argo
Colin Wong
tcmyxc
xylieong
Nanohana
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Nrupatunga
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Mrinal Jain
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