From 9402cd1e2a88a39241b40e24e0e34670d972e75b Mon Sep 17 00:00:00 2001 From: Max Kolomeychenko Date: Mon, 17 May 2021 17:11:36 +0300 Subject: [PATCH] pull updates from original repo (release v5) + reimplement UI (#21) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Update yolo.py with yaml.SafeLoader (#1970) * Update autoanchor.py with yaml.SafeLoader (#1971) * Update train.py with yaml.SafeLoader (#1972) * check_git_status() asserts (#1977) * Update Dockerfile (#1982) * Add xywhn2xyxy() (#1983) * verbose on final_epoch (#1997) * check_git_status() Windows fix (#2015) * check_git_status() Windows fix * Update general.py * Update general.py * Update general.py * Update general.py * Update general.py * Update general.py * Update Dockerfile (#2016) * Update google_utils.py (#2017) * Update ci-testing.yml (#2018) * Update inference multiple-counting (#2019) * Update inference multiple-counting * update github check * Update general.py check_git_status() fix (#2020) * Update autoshape .print() and .save() (#2022) * Update requirements.txt (#2021) * Update requirements.txt * Update ci-testing.yml * Update hubconf.py * PyYAML==5.4.1 (#2030) * Docker pyYAML>=5.3.1 fix (#2031) * data-autodownload background tasks (#2034) * Check im.format during dataset caching (#2042) * Check im.format during dataset caching * Update datasets.py * Confusion matrix native image-space fix (#2046) Make sure the labels and predictions are equally scaled on confusion_matrix.process_batch * Add histogram equalization fcn (#2049) * W&B log epoch (#1946) * W&B log epoch * capitalize * W&B log epoch * capitalize * Update train.py New try using https://docs.wandb.ai/library/log#incremental-logging * Update train.py * Update test.py * Update train.py * Update plots.py * Update train.py * Update train.py * label plot step -1 * update * update * update * update * update * update * Update train.py * Update train.py * Add 'exclude' tuple to check_requirements() (#2041) * Update run-once lines (#2058) * Metric-Confidence plots feature addition (#2057) * Metric-Confidence plots feature addition * cleanup * Metric-Confidence plots feature addition * cleanup * Update run-once lines * cleanup * save all 4 curves to wandb * Update to colors.TABLEAU_COLORS (#2069) * W&B epoch logging update (#2073) * GhostConv update (#2082) * Add YOLOv5-P6 models (#2083) * Update tutorial.ipynb * Add Amazon Deep Learning AMI environment (#2085) * Update greetings.yml * Update README.md * Add Kaggle badge (#2090) * Update README.md * Update greetings.yml * Created using Colaboratory * Add Kaggle badge (#2090) * Add variable-stride inference support (#2091) * Update test.py --task speed and study (#2099) * Add --speed benchmark * test range 256 - 1536 * update * update * update * update * Update plot_study() (#2112) * Start setup for improved W&B integration (#1948) * Add helper functions for wandb and artifacts * cleanup * Reorganize files * Update wandb_utils.py * Update log_dataset.py We can remove this code, as the giou hyp has been deprecated for a while now. * Reorganize and update dataloader call * yaml.SafeLoader * PEP8 reformat * remove redundant checks * Add helper functions for wandb and artifacts * cleanup * Reorganize files * Update wandb_utils.py * Update log_dataset.py We can remove this code, as the giou hyp has been deprecated for a while now. * Reorganize and update dataloader call * yaml.SafeLoader * PEP8 reformat * remove redundant checks * Update util files * Update wandb_utils.py * Remove word size * Change path of labels.zip * remove unused imports * remove --rect * log_dataset.py cleanup * log_dataset.py cleanup2 * wandb_utils.py cleanup * remove redundant id_count * wandb_utils.py cleanup2 * rename cls * use pathlib for zip * rename dataloader to dataset * Change import order * Remove redundant code * remove unused import * remove unused imports Co-authored-by: Glenn Jocher * LoadImages() pathlib update (#2140) * Unique *.cache filenames fix (#2134) * fix #2121 * Update test.py * Update train.py * Update autoanchor.py * Update datasets.py * Update log_dataset.py * Update datasets.py Co-authored-by: Glenn Jocher * Update train.py test batch_size (#2148) * Update train.py * Update loss.py * Update train.py (#2149) * Linear LR scheduler option (#2150) * Linear LR scheduler option * Update train.py * Update data-autodownload background tasks (#2154) * Update get_coco.sh * Update get_voc.sh * Update detect.py (#2167) Without this cv2.imshow opens a window but nothing is visible * Update requirements.txt (#2173) * Update utils/datasets.py to support .webp files (#2174) Simply added 'webp' as an image format to the img_formats array so that webp image files can be used as training data. * Changed socket port and added timeout (#2176) * PyTorch Hub results.save('path/to/dir') (#2179) * YOLOv5 Segmentation Dataloader Updates (#2188) * Update C3 module * Update C3 module * Update C3 module * Update C3 module * update * update * update * update * update * update * update * update * update * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * update * update * update * update * updates * updates * updates * updates * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update datasets * update * update * update * update attempt_downlaod() * merge * merge * update * update * update * update * update * update * update * update * update * update * parameterize eps * comments * gs-multiple * update * max_nms implemented * Create one_cycle() function * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * GitHub API rate limit fix * update * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * astuple * epochs * update * update * ComputeLoss() * update * update * update * update * update * update * update * update * update * update * update * merge * merge * merge * merge * update * update * update * update * commit=tag == tags[-1] * Update cudnn.benchmark * update * update * update * updates * updates * updates * updates * updates * updates * updates * update * update * update * update * update * mosaic9 * update * update * update * update * update * update * institute cache versioning * only display on existing cache * reverse cache exists booleans * Created using Colaboratory * YOLOv5 PyTorch Hub results.save() method retains filenames (#2194) * save results with name * debug * save original imgs names * Update common.py Co-authored-by: Glenn Jocher * TTA augument boxes one pixel shifted in de-flip ud and lr (#2219) * TTA augument boxes one pixel shifted in de-flip ud and lr * PEP8 reformat Co-authored-by: Jaap van de Loosdrecht Co-authored-by: Glenn Jocher * LoadStreams() frame loss bug fix (#2222) * Update yolo.py channel array (#2223) * Add check_imshow() (#2231) * Add check_imshow() * Update general.py * Update general.py * Update CI badge (#2230) * Add isdocker() (#2232) * Add isdocker() * Update general.py * Update general.py * YOLOv5 Hub URL inference bug fix (#2250) * Update common.py * Update common.py * Update common.py * Improved hubconf.py CI tests (#2251) * Unified hub and detect.py box and labels plotting (#2243) * reset head * Update inference default to multi_label=False (#2252) * Update inference default to multi_label=False * bug fix * Update plots.py * Update plots.py * Robust objectness loss balancing (#2256) * Created using Colaboratory * Update minimum stride to 32 (#2266) * Dynamic ONNX engine generation (#2208) * add: dynamic onnx export * delete: test onnx inference * fix dynamic output axis * Code reduction * fix: dynamic output axes, dynamic input naming * Remove fixed axes Co-authored-by: Shivam Swanrkar Co-authored-by: Glenn Jocher * Update greetings.yml for auto-rebase on PR (#2272) * Update Dockerfile with apt install zip (#2274) * FLOPS min stride 32 (#2276) Signed-off-by: xiaowo1996 <429740343@qq.com> * Update README.md * Amazon AWS EC2 startup and re-startup scripts (#2185) * Amazon AWS EC2 startup and re-startup scripts * Create resume.py * cleanup * Amazon AWS EC2 startup and re-startup scripts (#2282) * Update train.py (#2290) * Update train.py * Update train.py * Update train.py * Update train.py * Create train.py * Improved model+EMA checkpointing (#2292) * Enhanced model+EMA checkpointing * update * bug fix * bug fix 2 * always save optimizer * ema half * remove model.float() * model half * carry ema/model in fp32 * rm model.float() * both to float always * cleanup * cleanup * Improved model+EMA checkpointing 2 (#2295) * Fix labels being missed when image extension appears twice in filename (#2300) * W&B entity support (#2298) * W&B entity support * shorten wandb_entity to entity Co-authored-by: Jan Hajek Co-authored-by: Glenn Jocher * Update yolo.py (#2120) * Avoid mutable state in Detect * LoadImages() pathlib update (#2140) * Unique *.cache filenames fix (#2134) * fix #2121 * Update test.py * Update train.py * Update autoanchor.py * Update datasets.py * Update log_dataset.py * Update datasets.py Co-authored-by: Glenn Jocher * Update train.py test batch_size (#2148) * Update train.py * Update loss.py * Update train.py (#2149) * Linear LR scheduler option (#2150) * Linear LR scheduler option * Update train.py * Update data-autodownload background tasks (#2154) * Update get_coco.sh * Update get_voc.sh * Update detect.py (#2167) Without this cv2.imshow opens a window but nothing is visible * Update requirements.txt (#2173) * Update utils/datasets.py to support .webp files (#2174) Simply added 'webp' as an image format to the img_formats array so that webp image files can be used as training data. * Changed socket port and added timeout (#2176) * PyTorch Hub results.save('path/to/dir') (#2179) * YOLOv5 Segmentation Dataloader Updates (#2188) * Update C3 module * Update C3 module * Update C3 module * Update C3 module * update * update * update * update * update * update * update * update * update * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * update * update * update * update * updates * updates * updates * updates * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update datasets * update * update * update * update attempt_downlaod() * merge * merge * update * update * update * update * update * update * update * update * update * update * parameterize eps * comments * gs-multiple * update * max_nms implemented * Create one_cycle() function * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * GitHub API rate limit fix * update * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * astuple * epochs * update * update * ComputeLoss() * update * update * update * update * update * update * update * update * update * update * update * merge * merge * merge * merge * update * update * update * update * commit=tag == tags[-1] * Update cudnn.benchmark * update * update * update * updates * updates * updates * updates * updates * updates * updates * update * update * update * update * update * mosaic9 * update * update * update * update * update * update * institute cache versioning * only display on existing cache * reverse cache exists booleans * Created using Colaboratory * YOLOv5 PyTorch Hub results.save() method retains filenames (#2194) * save results with name * debug * save original imgs names * Update common.py Co-authored-by: Glenn Jocher * TTA augument boxes one pixel shifted in de-flip ud and lr (#2219) * TTA augument boxes one pixel shifted in de-flip ud and lr * PEP8 reformat Co-authored-by: Jaap van de Loosdrecht Co-authored-by: Glenn Jocher * LoadStreams() frame loss bug fix (#2222) * Update yolo.py channel array (#2223) * Add check_imshow() (#2231) * Add check_imshow() * Update general.py * Update general.py * Update CI badge (#2230) * Add isdocker() (#2232) * Add isdocker() * Update general.py * Update general.py * YOLOv5 Hub URL inference bug fix (#2250) * Update common.py * Update common.py * Update common.py * Improved hubconf.py CI tests (#2251) * Unified hub and detect.py box and labels plotting (#2243) * reset head * Update inference default to multi_label=False (#2252) * Update inference default to multi_label=False * bug fix * Update plots.py * Update plots.py * Robust objectness loss balancing (#2256) * Created using Colaboratory * Update minimum stride to 32 (#2266) * Dynamic ONNX engine generation (#2208) * add: dynamic onnx export * delete: test onnx inference * fix dynamic output axis * Code reduction * fix: dynamic output axes, dynamic input naming * Remove fixed axes Co-authored-by: Shivam Swanrkar Co-authored-by: Glenn Jocher * Update greetings.yml for auto-rebase on PR (#2272) * Update Dockerfile with apt install zip (#2274) * FLOPS min stride 32 (#2276) Signed-off-by: xiaowo1996 <429740343@qq.com> * Update README.md * Amazon AWS EC2 startup and re-startup scripts (#2185) * Amazon AWS EC2 startup and re-startup scripts * Create resume.py * cleanup * Amazon AWS EC2 startup and re-startup scripts (#2282) * Update train.py (#2290) * Update train.py * Update train.py * Update train.py * Update train.py * Create train.py * Improved model+EMA checkpointing (#2292) * Enhanced model+EMA checkpointing * update * bug fix * bug fix 2 * always save optimizer * ema half * remove model.float() * model half * carry ema/model in fp32 * rm model.float() * both to float always * cleanup * cleanup * Improved model+EMA checkpointing 2 (#2295) * Fix labels being missed when image extension appears twice in filename (#2300) * W&B entity support (#2298) * W&B entity support * shorten wandb_entity to entity Co-authored-by: Jan Hajek Co-authored-by: Glenn Jocher * Avoid mutable state in Detect * Update yolo and remove .to(device) Co-authored-by: Oleg Boiko Co-authored-by: Glenn Jocher Co-authored-by: train255 Co-authored-by: ab-101 <56578530+ab-101@users.noreply.github.com> Co-authored-by: Transigent Co-authored-by: NanoCode012 Co-authored-by: Daniel Khromov Co-authored-by: VdLMV Co-authored-by: Jaap van de Loosdrecht Co-authored-by: Yann Defretin Co-authored-by: Aditya Lohia <64709773+aditya-dl@users.noreply.github.com> Co-authored-by: Shivam Swanrkar Co-authored-by: xiaowo1996 <429740343@qq.com> Co-authored-by: Iden Craven Co-authored-by: Jan Hajek Co-authored-by: Jan Hajek * final_epoch EMA bug fix (#2317) * Update test.py (#2319) * Update Dockerfile install htop (#2320) * remove TTA 1 pixel offset (#2325) * EMA bug fix 2 (#2330) * EMA bug fix 2 * update * FROM nvcr.io/nvidia/pytorch:21.02-py3 (#2341) * Confusion matrix background axis swap (#2114) * Created using Colaboratory * Anchor override (#2350) * Resume with custom anchors fix (#2361) * Resume with custom anchors fix * Update train.py * Faster random index generator for mosaic augmentation (#2345) * faster random index generator for mosaic augementation We don't need to access list to generate random index It makes augmentation slower. * Update datasets.py Co-authored-by: Glenn Jocher * --no-cache notebook (#2381) * ENV HOME=/usr/src/app (#2382) Set HOME environment variable per Binder requirements. https://github.com/binder-examples/minimal-dockerfile * image weights compatible faster random index generator v2 for mosaic augmentation (#2383) image weights compatible faster random index generator v2 for mosaic augmentation * GPU export options (#2297) * option for skip last layer and cuda export support * added parameter device * fix import * cleanup 1 * cleanup 2 * opt-in grid --grid will export with grid computation, default export will skip grid (same as current) * default --device cpu GPU export causes ONNX and CoreML errors. Co-authored-by: Jan Hajek Co-authored-by: Glenn Jocher * bbox_iou() stability and speed improvements (#2385) * AWS wait && echo "All tasks done." (#2391) * GCP sudo docker userdata.sh (#2393) * GCP sudo docker * cleanup * CVPR 2021 Argoverse-HD dataset autodownload support (#2400) * added argoverse-download ability * bugfix * add support for Argoverse dataset * Refactored code * renamed to argoverse-HD * unzip -q and YOLOv5 small cleanup items * add image counts Co-authored-by: Kartikeya Sharma Co-authored-by: Kartikeya Sharma Co-authored-by: Glenn Jocher * CVPR 2021 Argoverse-HD autodownload fix (#2418) * DDP after autoanchor reorder (#2421) * Integer printout (#2450) * Integer printout * test.py 'Labels' * Update train.py * Update test.py --task train val study (#2453) * Update test.py --task train val study * update argparser --task * labels.jpg class names (#2454) * labels.png class names * fontsize=10 * CVPR 2021 Argoverse-HD autodownload curl (#2455) curl preferred over wget for slightly better cross platform compatibility (i.e. out of the box macos compatible). * Add autoShape() speed profiling (#2459) * Add autoShape() speed profiling * Update common.py * Create README.md * Update hubconf.py * cleanuip * autoShape() speed profiling update (#2460) * Update tutorial.ipynb * Created using Colaboratory * Update autosplit() with annotated_only option (#2466) * Be able to create dataset from annotated images only Add the ability to create a dataset/splits only with images that have an annotation file, i.e a .txt file, associated to it. As we talked about this, the absence of a txt file could mean two things: * either the image wasn't yet labelled by someone, * either there is no object to detect. When it's easy to create small datasets, when you have to create datasets with thousands of images (and more coming), it's hard to track where you at and you don't want to wait to have all of them annotated before starting to train. Which means some images would lack txt files and annotations, resulting in label inconsistency as you say in #2313. By adding the annotated_only argument to the function, people could create, if they want to, datasets/splits only with images that were labelled, for sure. * Cleanup and update print() Co-authored-by: Glenn Jocher * Scipy kmeans-robust autoanchor update (#2470) Fix for https://github.com/ultralytics/yolov5/issues/2394 * PyTorch Hub models default to CUDA:0 if available (#2472) * PyTorch Hub models default to CUDA:0 if available * device as string bug fix * Created using Colaboratory * Improved W&B integration (#2125) * Init Commit * new wandb integration * Update * Use data_dict in test * Updates * Update: scope of log_img * Update: scope of log_img * Update * Update: Fix logging conditions * Add tqdm bar, support for .txt dataset format * Improve Result table Logger * Init Commit * new wandb integration * Update * Use data_dict in test * Updates * Update: scope of log_img * Update: scope of log_img * Update * Update: Fix logging conditions * Add tqdm bar, support for .txt dataset format * Improve Result table Logger * Add dataset creation in training script * Change scope: self.wandb_run * Add wandb-artifact:// natively you can now use --resume with wandb run links * Add suuport for logging dataset while training * Cleanup * Fix: Merge conflict * Fix: CI tests * Automatically use wandb config * Fix: Resume * Fix: CI * Enhance: Using val_table * More resume enhancement * FIX : CI * Add alias * Get useful opt config data * train.py cleanup * Cleanup train.py * more cleanup * Cleanup| CI fix * Reformat using PEP8 * FIX:CI * rebase * remove uneccesary changes * remove uneccesary changes * remove uneccesary changes * remove unecessary chage from test.py * FIX: resume from local checkpoint * FIX:resume * FIX:resume * Reformat * Performance improvement * Fix local resume * Fix local resume * FIX:CI * Fix: CI * Imporve image logging * (:(:Redo CI tests:):) * Remember epochs when resuming * Remember epochs when resuming * Update DDP location Potential fix for #2405 * PEP8 reformat * 0.25 confidence threshold * reset train.py plots syntax to previous * reset epochs completed syntax to previous * reset space to previous * remove brackets * reset comment to previous * Update: is_coco check, remove unused code * Remove redundant print statement * Remove wandb imports * remove dsviz logger from test.py * Remove redundant change from test.py * remove redundant changes from train.py * reformat and improvements * Fix typo * Add tqdm tqdm progress when scanning files, naming improvements Co-authored-by: Glenn Jocher * Update Detections() times=None (#2570) Fix for results.tolist() method breaking after YOLOv5 Hub profiling PRshttps://github.com/ultralytics/yolov5/pull/2460 https://github.com/ultralytics/yolov5/pull/2459 and * check_requirements() exclude pycocotools, thop (#2571) Exclude non-critical packages from dependency checks in detect.py. pycocotools and thop in particular are not required for inference. Issue first raised in https://github.com/ultralytics/yolov5/issues/1944 and also raised in https://github.com/ultralytics/yolov5/discussions/2556 * W&B DDP fix (#2574) * Enhanced check_requirements() with auto-install (#2575) * Update check_requirements() with auto-install This PR builds on an idea I had to automatically install missing dependencies rather than simply report an error message. YOLOv5 should now 1) display all dependency issues and not simply display the first missing dependency, and 2) attempt to install/update each missing/VersionConflict package. * cleanup * cleanup 2 * Check requirements.txt file exists * cleanup 3 * Update tensorboard>=2.4.1 (#2576) * Update tensorboard>=2.4.1 Update tensorboard version to attempt to address https://github.com/ultralytics/yolov5/issues/2573 (tensorboard logging fail in Docker image). * cleanup * YOLOv5 PyTorch Hub models >> check_requirements() (#2577) * Update hubconf.py with check_requirements() Dependency checks have been missing from YOLOv5 PyTorch Hub model loading, causing errors in some cases when users are attempting to import hub models in unsupported environments. This should examine the YOLOv5 requirements.txt file and pip install any missing or version-conflict packages encountered. This is highly experimental (!), please let us know if this creates problems in your custom workflows. * Update hubconf.py * W&B DDP fix 2 (#2587) Revert unintentional change to test batch sizes caused by PR https://github.com/ultralytics/yolov5/pull/2125 * YOLOv5 PyTorch Hub models >> check_requirements() (#2588) * YOLOv5 PyTorch Hub models >> check_requirements() Update YOLOv5 PyTorch Hub requirements.txt path to cache path. * Update hubconf.py * YOLOv5 PyTorch Hub models >> check_requirements() (#2591) Prints 'Please restart runtime or rerun command for update to take effect.' following package auto-install to inform users to restart/rerun. * YOLOv5 PyTorch Hub models >> check_requirements() (#2592) Improved user-feedback following requirements auto-update. * Supervisely Ecosystem (#2519) guide describes YOLOv5 apps collection in Supervisely Ecosystem * Save webcam results, add --nosave option (#2598) This updates the default detect.py behavior to automatically save all inference images/videos/webcams unless the new argument --nosave is used (python detect.py --nosave) or unless a list of streaming sources is passed (python detect.py --source streams.txt) * Update segment2box() comment (#2600) * resume.py typo (#2603) * Remove Cython from requirements.txt (#2604) Cython should be a dependency of the remaining packages in requirements.txt, so should be installed anyway even if not a direct requirement. * Update git_describe() for remote dir usage (#2606) * Add '*.mpo' to supported image formats (#2615) Co-authored-by: Max Uppenkamp * Create date_modified() (#2616) Updated device selection string with fallback for non-git directories. ```python def select_device(device='', batch_size=None): # device = 'cpu' or '0' or '0,1,2,3' s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' # string ... ``` * Update detections() self.t = tuple() (#2617) * Update detections() self.t = tuple() Fix multiple results.print() bug. * Update experimental.py * Update yolo.py * Fix Indentation in test.py (#2614) * Fix Indentation in test.py * CI fix * Comply with PEP8: 80 characters per line * Update Detections() self.n comment (#2620) ```python self.n = len(self.pred) # number of images (batch size) ``` * Remove conflicting nvidia-tensorboard package (#2622) Attempt to resolve tensorboard Docker error in https://github.com/ultralytics/yolov5/issues/2573 * FROM nvcr.io/nvidia/pytorch:21.03-py3 (#2623) Update Docker FROM nvcr.io/nvidia/pytorch:21.03-py3 * Improve git_describe() (#2633) Catch 'fatal: not a git repository' returns and return '' instead (observed in GCP Hub checks). * Fix: evolve with wandb (#2634) * W&B resume ddp from run link fix (#2579) * W&B resume ddp from run link fix * Native DDP W&B support for training, resuming * Improve git_describe() fix 1 (#2635) Add stderr=subprocess.STDOUT to catch error messages. * PyTorch Hub custom model to CUDA device fix (#2636) Fix for #2630 raised by @Pro100rus32 * PyTorch Hub amp.autocast() inference (#2641) I think this should help speed up CUDA inference, as currently models may be running in FP32 inference mode on CUDA devices unnecesarily. * Add tqdm pbar.close() (#2644) When using tqdm, sometimes it can't print in one line and roll to next line. * Speed profiling improvements (#2648) * Speed profiling improvements * Update torch_utils.py deepcopy() required to avoid adding elements to model. * Update torch_utils.py * Created using Colaboratory (#2649) * Update requirements.txt (#2564) * Add opencv-contrib-python to requirements.txt * Update requirements.txt Co-authored-by: Glenn Jocher * add option to disable half precision in test.py (#2507) Co-authored-by: Glenn Jocher * Add --label-smoothing eps argument to train.py (default 0.0) (#2344) * Add label smoothing option * Correct data type * add_log * Remove log * Add log * Update loss.py remove comment (too versbose) Co-authored-by: phattran Co-authored-by: Glenn Jocher * Created using Colaboratory * Set resume flag to false (#2657) * Update README.md * Created using Colaboratory * Update README with Tips for Best Results tutorial (#2682) * Update README with Tips for Best Results tutorial * Update README.md * Add TransformerLayer, TransformerBlock, C3TR modules (#2333) * yolotr * transformer block * Remove bias in Transformer * Remove C3T * Remove a deprecated class * put the 2nd LayerNorm into the 2nd residual block * move example model to models/hub, rename to -transformer * Add module comments and TODOs * Remove LN in Transformer * Add comments for Transformer * Solve the problem of MA with DDP * cleanup * cleanup find_unused_parameters * PEP8 reformat Co-authored-by: DingYiwei <846414640@qq.com> Co-authored-by: Glenn Jocher * Fix: #2674 (#2683) * Set resume flag to false * Check existance of val dataset * PyTorch Hub model.save() increment as runs/hub/exp (#2684) * PyTorch Hub model.save() increment as runs/hub/exp This chane will align PyTorch Hub results saving with the existing unified results saving directory structure of runs/ /train /detect /test /hub /exp /exp2 ... * cleanup * autoShape forward im = np.asarray(im) # to numpy (#2689) Slight speedup. * pip install coremltools onnx (#2690) Requested in https://github.com/ultralytics/yolov5/issues/2686 * Updated filename attributes for YOLOv5 Hub results (#2708) Proposed fix for 'Model predict with forward will fail if PIL image does not have filename attribute' #2702 * Updated filename attributes for YOLOv5 Hub BytesIO (#2718) Fix 2 for 'Model predict with forward will fail if PIL image does not have filename attribute' #2702 * Add support for list-of-directory data format for wandb (#2719) * Update README with collapsable notes (#2721) * Update README with collapsable notes. * cleanup * center table * Add Hub results.pandas() method (#2725) * Add Hub results.pandas() method New method converts results from torch tensors to pandas DataFrames with column names. This PR may partially resolve issue https://github.com/ultralytics/yolov5/issues/2703 ```python results = model(imgs) print(results.pandas().xyxy[0]) xmin ymin xmax ymax confidence class name 0 57.068970 391.770599 241.383545 905.797852 0.868964 0 person 1 667.661255 399.303589 810.000000 881.396667 0.851888 0 person 2 222.878387 414.774231 343.804474 857.825073 0.838376 0 person 3 4.205386 234.447678 803.739136 750.023376 0.658006 5 bus 4 0.000000 550.596008 76.681190 878.669922 0.450596 0 person ``` * Update comments torch example input now shown resized to size=640 and also now a multiple of P6 stride 64 (see https://github.com/ultralytics/yolov5/issues/2722#issuecomment-814785930) * apply decorators * PEP8 * Update common.py * pd.options.display.max_columns = 10 * Update common.py * autocast enable=torch.cuda.is_available() (#2748) * torch.cuda.amp bug fix (#2750) PR https://github.com/ultralytics/yolov5/pull/2725 introduced a very specific bug that only affects multi-GPU trainings. Apparently the cause was using the torch.cuda.amp decorator in the autoShape forward method. I've implemented amp more traditionally in this PR, and the bug is resolved. * utils/wandb_logging PEP8 reformat (#2755) * wandb_logging PEP8 reformat * Update wandb_utils.py * Tensorboard model visualization bug fix (#2758) This fix should allow for visualizing YOLOv5 model graphs correctly in Tensorboard by uncommenting line 335 in train.py: ```python if tb_writer: tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) # add model graph ``` The problem was that the detect() layer checks the input size to adapt the grid if required, and tracing does not seem to like this shape check (even if the shape is fine and no grid recomputation is required). The following will warn: https://github.com/ultralytics/yolov5/blob/0cae7576a9241110157cd154fc2237e703c2719e/train.py#L335 Solution is below. This is a YOLOv5s model displayed in TensorBoard. You can see the Detect() layer merging the 3 layers into a single output for example, and everything appears to work and visualize correctly. ```python tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) ``` Screenshot 2021-04-11 at 01 10 09 * Created using Colaboratory * YouTube Livestream Detection (#2752) * Youtube livestream detection * dependancy update to auto install pafy * Remove print * include youtube_dl in deps * PEP8 reformat * youtube url check fix * reduce lines * add comment * update check_requirements * stream framerate fix * Update README.md * cleanup * PEP8 * remove cap.retrieve() failure code Co-authored-by: Glenn Jocher * YOLOv5 v5.0 Release (#2762) * YOLOv5 v5.0 Release patch 1 (#2764) * torch.jit.trace(model, img, strict=False) * Update check_file() * Update hubconf.py * Update README.md * Update tutorial.ipynb * Created using Colaboratory * Update tutorial.ipynb * Created using Colaboratory * Created using Colaboratory * Update README.md * Flask REST API Example (#2732) * add files * Update README.md * Update README.md * Update restapi.py pretrained=True and model.eval() are used by default when loading a model now, so no need to call them manually. * PEP8 reformat * PEP8 reformat Co-authored-by: Glenn Jocher * Update README.md * ONNX Simplifier (#2815) * ONNX Simplifier Add ONNX Simplifier to ONNX export pipeline in export.py. Will auto-install onnx-simplifier if onnx is installed but onnx-simplifier is not. * Update general.py * YouTube Bug Fix (#2818) Fix for #2810 ```shell python detect.py --source 0 ``` introduced by YouTube Livestream Detection PR #2752 * PyTorch Hub cv2 .save() .show() bug fix (#2831) * PyTorch Hub cv2 .save() .show() bug fix cv2.rectangle() was failing on non-contiguous np array inputs. This checks for contiguous arrays and applies is necessary: ```python imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update ``` * Update plots.py ```python assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.' ``` * Update hubconf.py Expand CI tests to OpenCV image. * Create FUNDING.yml (#2832) * Update FUNDING.yml (#2833) * Update FUNDING.yml * move FUNDING.yml to ./github * Fix ONNX dynamic axes export support with onnx simplifier, make onnx simplifier optional (#2856) * Ensure dynamic export works succesfully, onnx simplifier optional * Update export.py * add dashes Co-authored-by: Tim Co-authored-by: Glenn Jocher * Update increment_path() to handle file paths (#2867) * Detection cropping+saving feature addition for detect.py and PyTorch Hub (#2827) * Update detect.py * Update detect.py * Update greetings.yml * Update cropping * cleanup * Update increment_path() * Update common.py * Update detect.py * Update detect.py * Update detect.py * Update common.py * cleanup * Update detect.py Co-authored-by: Glenn Jocher * Implement yaml.safe_load() (#2876) * Implement yaml.safe_load() * yaml.safe_dump() * Cleanup load_image() (#2871) * don't resize up in load_image if augmenting * cleanup Co-authored-by: Glenn Jocher * bug fix: switched rows and cols for correct detections in confusion matrix (#2883) * VisDrone2019-DET Dataset Auto-Download (#2882) * VisDrone Dataset Auto-Download * add visdrone.yaml * cleanup * add VisDrone2019-DET-test-dev * cleanup VOC * Uppercase model filenames enabled (#2890) * ACON activation function (#2893) * ACON Activation Function ## 🚀 Feature There is a new activation function [ACON (CVPR 2021)](https://arxiv.org/pdf/2009.04759.pdf) that unifies ReLU and Swish. ACON is simple but very effective, code is here: https://github.com/nmaac/acon/blob/main/acon.py#L19 ![image](https://user-images.githubusercontent.com/5032208/115676962-a38dfe80-a382-11eb-9883-61fa3216e3e6.png) The improvements are very significant: ![image](https://user-images.githubusercontent.com/5032208/115680180-eac9be80-a385-11eb-9c7a-8643db552c69.png) ## Alternatives It also has an enhanced version meta-ACON that uses a small network to learn beta explicitly, which may influence the speed a bit. ## Additional context [Code](https://github.com/nmaac/acon) and [paper](https://arxiv.org/pdf/2009.04759.pdf). * Update activations.py * Explicit opt function arguments (#2817) * more explicit function arguments * fix typo in detect.py * revert import order * revert import order * remove default value * Update yolo.py (#2899) * Update google_utils.py (#2900) * Add detect.py --hide-conf --hide-labels --line-thickness options (#2658) * command line option for line thickness and hiding labels * command line option for line thickness and hiding labels * command line option for line thickness and hiding labels * command line option for line thickness and hiding labels * command line option for line thickness and hiding labels * command line option for hiding confidence values * Update detect.py Co-authored-by: Glenn Jocher * Default optimize_for_mobile() on TorchScript models (#2908) Per https://pytorch.org/tutorials/recipes/script_optimized.html this should improve performance on torchscript models (and maybe coreml models also since coremltools operates on a torchscript model input, though this still requires testing). * Update export.py (#2909) * Update export.py for 2 dry runs (#2910) * Update export.py for 2 dry runs * Update export.py * Add file_size() function (#2911) * Add file_size() function * Update export.py * Update download() for tar.gz files (#2919) * Update download() for tar.gz files * Update general.py * Update visdrone.yaml (#2921) * Change default value of hide label argument to False (#2923) * Change default value of hide-conf argument to false (#2925) * test.py native --single-cls (#2928) * Add verbose option to pytorch hub models (#2926) * Add verbose and update print to logging * Fix positonal param * Revert auto formatting changes * Update hubconf.py Co-authored-by: Glenn Jocher * ACON Activation batch-size 1 bug patch (#2901) * ACON Activation batch-size 1 bug path This is not a great solution to https://github.com/nmaac/acon/issues/4 but it's all I could think of at the moment. WARNING: YOLOv5 models with MetaAconC() activations are incapable of running inference at batch-size 1 properly due to a known bug in https://github.com/nmaac/acon/issues/4 with no known solution. * Update activations.py * Update activations.py * Update activations.py * Update activations.py * Check_requirements() enclosing apostrophe bug fix (#2929) This fixes a bug where the '>' symbol in python package requirements was not running correctly with subprocess.check_output() commands. * Update README.md (#2934) * Update README.md dependencies: ImportError: libGL.so.1: cannot open shared object file: No such file or directory ImportError: libgthread-2.0.so.0: cannot open shared object file: No such file or directory ImportError: libSM.so.6: cannot open shared object file: No such file or directory ImportError: libXrender.so.1: cannot open shared object file: No such file or directory * replace older apt-get with apt Code commented for now until a better understanding of the issue, and also code is not cross-platform compatible. Co-authored-by: Glenn Jocher * Improved yolo.py profiling (#2940) * Improved yolo.py profiling Improved column order and labelling. * Update yolo.py * Add yolov5/ to sys.path() for *.py subdir exec (#2949) * Add yolov5/ to sys.path() for *.py subdir exec * Update export.py * update UI + latest yolov5 sources (#15) * merge latest version done, not tested * split tabs with radio buttons * models table -wip * models table -wip * start split html template to parts * ui refactoring * compile-template wip - paths confusion * compile wip * train/val splits * keep/ignore unlabeled images * models table * training hyperparameters * UI templates - done * unlabeled count in UI * add adam optimizer * convert_project to detection - works * start train/val splits * splits wip * splits done, only simple tests * splits validation * data preprocessing - not tested * download weights - wip * init_script_arguments - not tested * init_script_arguments - not tested * prepare weights - wip * not tested * add metrics period * set output * artifacts dirs * train_batches_uploaded flag * pre-release for debug * update config * update SDK version * fix imports * change imports * change imports * add UI sources directory to sys.path * new SDK version * new SDK version * fix GIoU smoothing * update smoothing * send metrics for the last epoch * save link to app UI * todo * log train/val size * sly-to-yolov5 format: fix same names in different datasets * fix inference * serve not tested * [serve] modal table stat * [serve] modal tabs * [serve] modal tabs * [serve] modal width * [serve] modal tabs style * [serve] fix pretrained weights URL * [serve] add stride to serv * [train] readme wip * [train] readme wip * [train] readme wip * [serve] change inference_image_id to work with remote storages (s3, azure, ...) * [serve] fix stride initialization * [serve] yolov5 serve - fixed * add additional info logs * [serve] todo * [train] splits - hide notice1 * fix collections readme * train readme - new screenshot * train readme Co-authored-by: Abhiram V <61599526+Anon-Artist@users.noreply.github.com> Co-authored-by: Glenn Jocher Co-authored-by: ramonhollands Co-authored-by: Ayush Chaurasia Co-authored-by: train255 Co-authored-by: ab-101 <56578530+ab-101@users.noreply.github.com> Co-authored-by: Transigent Co-authored-by: NanoCode012 Co-authored-by: Daniel Khromov Co-authored-by: VdLMV Co-authored-by: Jaap van de Loosdrecht Co-authored-by: Yann Defretin Co-authored-by: Aditya Lohia <64709773+aditya-dl@users.noreply.github.com> Co-authored-by: Shivam Swanrkar Co-authored-by: xiaowo1996 <429740343@qq.com> Co-authored-by: Iden Craven Co-authored-by: Jan Hajek Co-authored-by: Jan Hajek Co-authored-by: oleg Co-authored-by: Oleg Boiko Co-authored-by: Ryan Avery Co-authored-by: Yonghye Kwon Co-authored-by: Kartikeya Sharma Co-authored-by: Kartikeya Sharma Co-authored-by: Kartikeya Sharma Co-authored-by: Yann Defretin Co-authored-by: maxupp Co-authored-by: Max Uppenkamp Co-authored-by: zzttqu <80448114+zzttqu@users.noreply.github.com> Co-authored-by: Youngjin Shin Co-authored-by: Benjamin Fineran Co-authored-by: Phat Tran <36766404+ptran1203@users.noreply.github.com> Co-authored-by: phattran Co-authored-by: Ding Yiwei <16083536+dingyiwei@users.noreply.github.com> Co-authored-by: DingYiwei <846414640@qq.com> Co-authored-by: Ben Milanko Co-authored-by: Robin Co-authored-by: Tim Stokman <41363+timstokman@users.noreply.github.com> Co-authored-by: Tim Co-authored-by: Burhan Co-authored-by: JoshSong Co-authored-by: Michael Heilig <75843816+MichHeilig@users.noreply.github.com> Co-authored-by: r-blmnr <77175527+r-blmnr@users.noreply.github.com> Co-authored-by: fcakyon <34196005+fcakyon@users.noreply.github.com> Co-authored-by: Maximilian Peters Co-authored-by: albinxavi <62288451+albinxavi@users.noreply.github.com> Co-authored-by: BZFYS --- .github/FUNDING.yml | 5 + .github/workflows/ci-testing.yml | 9 +- .github/workflows/greetings.yml | 11 +- Dockerfile | 23 +- README.md | 2 +- data/argoverse_hd.yaml | 21 + data/coco.yaml | 4 +- data/coco128.yaml | 2 +- data/scripts/get_argoverse_hd.sh | 62 +++ data/scripts/get_coco.sh | 11 +- data/scripts/get_voc.sh | 119 +++--- data/visdrone.yaml | 65 ++++ data/voc.yaml | 2 +- detect.py | 67 ++-- hubconf.py | 139 +++---- models/common.py | 179 ++++++--- models/experimental.py | 7 +- models/export.py | 82 ++-- models/hub/yolov5l6.yaml | 60 +++ models/hub/yolov5m6.yaml | 60 +++ models/hub/yolov5s-transformer.yaml | 48 +++ models/hub/yolov5s6.yaml | 60 +++ models/hub/yolov5x6.yaml | 60 +++ models/yolo.py | 91 ++--- supervisely/serve/config.json | 113 +++++- supervisely/serve/debug.env | 9 +- supervisely/serve/src/modal.html | 95 ++++- supervisely/serve/src/nn_utils.py | 15 +- supervisely/serve/src/sly_serve.py | 11 +- supervisely/train/README.md | 7 +- supervisely/train/config.json | 2 +- supervisely/train/debug.env | 12 +- supervisely/train/src/gui.html | 355 ++--------------- supervisely/train/src/sly_init_ui.py | 153 -------- supervisely/train/src/sly_metrics.py | 45 +-- supervisely/train/src/sly_metrics_utils.py | 142 ------- supervisely/train/src/sly_train.py | 124 +++--- supervisely/train/src/sly_train_globals.py | 42 +- supervisely/train/src/sly_train_utils.py | 55 +-- supervisely/train/src/sly_train_val_split.py | 55 --- supervisely/train/src/sly_utils.py | 10 + supervisely/train/src/ui/architectures.html | 46 +++ supervisely/train/src/ui/architectures.py | 152 ++++++++ supervisely/train/src/ui/artifacts.html | 14 + supervisely/train/src/ui/artifacts.py | 17 + supervisely/train/src/ui/classes.html | 27 ++ supervisely/train/src/ui/classes.py | 25 ++ supervisely/train/src/ui/hyperparameters.html | 75 ++++ supervisely/train/src/ui/hyperparameters.py | 19 + supervisely/train/src/ui/input_project.html | 8 + supervisely/train/src/ui/input_project.py | 8 + supervisely/train/src/ui/monitoring.html | 115 ++++++ supervisely/train/src/ui/monitoring.py | 44 +++ supervisely/train/src/ui/splits.html | 133 +++++++ supervisely/train/src/ui/splits.py | 72 ++++ supervisely/train/src/ui/ui.py | 18 + .../{sly_prepare_data.py => yolov5_format.py} | 50 ++- test_yolov5.py | 122 +++--- train.py | 248 ++++++------ tutorial.ipynb | 366 ++++++++++-------- utils/activations.py | 60 ++- utils/autoanchor.py | 19 +- utils/aws/__init__.py | 0 utils/aws/mime.sh | 26 ++ utils/aws/resume.py | 37 ++ utils/aws/userdata.sh | 27 ++ utils/datasets.py | 270 +++++++------ utils/flask_rest_api/README.md | 51 +++ utils/flask_rest_api/example_request.py | 13 + utils/flask_rest_api/restapi.py | 38 ++ utils/general.py | 240 +++++++++--- utils/google_utils.py | 10 +- utils/loss.py | 7 +- utils/metrics.py | 63 ++- utils/plots.py | 70 ++-- utils/torch_utils.py | 31 +- utils/wandb_logging/__init__.py | 0 utils/wandb_logging/log_dataset.py | 24 ++ utils/wandb_logging/wandb_utils.py | 306 +++++++++++++++ 79 files changed, 3520 insertions(+), 1765 deletions(-) create mode 100644 .github/FUNDING.yml create mode 100644 data/argoverse_hd.yaml create mode 100644 data/scripts/get_argoverse_hd.sh create mode 100644 data/visdrone.yaml create mode 100644 models/hub/yolov5l6.yaml create mode 100644 models/hub/yolov5m6.yaml create mode 100644 models/hub/yolov5s-transformer.yaml create mode 100644 models/hub/yolov5s6.yaml create mode 100644 models/hub/yolov5x6.yaml delete mode 100644 supervisely/train/src/sly_init_ui.py delete mode 100644 supervisely/train/src/sly_metrics_utils.py delete mode 100644 supervisely/train/src/sly_train_val_split.py create mode 100644 supervisely/train/src/ui/architectures.html create mode 100644 supervisely/train/src/ui/architectures.py create mode 100644 supervisely/train/src/ui/artifacts.html create mode 100644 supervisely/train/src/ui/artifacts.py create mode 100644 supervisely/train/src/ui/classes.html create mode 100644 supervisely/train/src/ui/classes.py create mode 100644 supervisely/train/src/ui/hyperparameters.html create mode 100644 supervisely/train/src/ui/hyperparameters.py create mode 100644 supervisely/train/src/ui/input_project.html create mode 100644 supervisely/train/src/ui/input_project.py create mode 100644 supervisely/train/src/ui/monitoring.html create mode 100644 supervisely/train/src/ui/monitoring.py create mode 100644 supervisely/train/src/ui/splits.html create mode 100644 supervisely/train/src/ui/splits.py create mode 100644 supervisely/train/src/ui/ui.py rename supervisely/train/src/{sly_prepare_data.py => yolov5_format.py} (54%) create mode 100644 utils/aws/__init__.py create mode 100644 utils/aws/mime.sh create mode 100644 utils/aws/resume.py create mode 100644 utils/aws/userdata.sh create mode 100644 utils/flask_rest_api/README.md create mode 100644 utils/flask_rest_api/example_request.py create mode 100644 utils/flask_rest_api/restapi.py create mode 100644 utils/wandb_logging/__init__.py create mode 100644 utils/wandb_logging/log_dataset.py create mode 100644 utils/wandb_logging/wandb_utils.py diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml new file mode 100644 index 000000000000..3da386f7e724 --- /dev/null +++ b/.github/FUNDING.yml @@ -0,0 +1,5 @@ +# These are supported funding model platforms + +github: glenn-jocher +patreon: ultralytics +open_collective: ultralytics diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml index 020658372f3c..df508474a955 100644 --- a/.github/workflows/ci-testing.yml +++ b/.github/workflows/ci-testing.yml @@ -66,14 +66,15 @@ jobs: di=cpu # inference devices # define device # train - python train.py --img 256 --batch 8 --weights weights/${{ matrix.model }}.pt --cfg models/${{ matrix.model }}.yaml --epochs 1 --device $di + python train.py --img 128 --batch 16 --weights weights/${{ matrix.model }}.pt --cfg models/${{ matrix.model }}.yaml --epochs 1 --device $di # detect python detect.py --weights weights/${{ matrix.model }}.pt --device $di python detect.py --weights runs/train/exp/weights/last.pt --device $di # test - python test.py --img 256 --batch 8 --weights weights/${{ matrix.model }}.pt --device $di - python test.py --img 256 --batch 8 --weights runs/train/exp/weights/last.pt --device $di + python test.py --img 128 --batch 16 --weights weights/${{ matrix.model }}.pt --device $di + python test.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di + python hubconf.py # hub python models/yolo.py --cfg models/${{ matrix.model }}.yaml # inspect - python models/export.py --img 256 --batch 1 --weights weights/${{ matrix.model }}.pt # export + python models/export.py --img 128 --batch 1 --weights weights/${{ matrix.model }}.pt # export shell: bash diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml index 2bfa53c14fcd..ee472297107e 100644 --- a/.github/workflows/greetings.yml +++ b/.github/workflows/greetings.yml @@ -11,7 +11,7 @@ jobs: repo-token: ${{ secrets.GITHUB_TOKEN }} pr-message: | 👋 Hello @${{ github.actor }}, thank you for submitting a 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to: - - ✅ Verify your PR is **up-to-date with origin/master.** If your PR is behind origin/master update by running the following, replacing 'feature' with the name of your local branch: + - ✅ Verify your PR is **up-to-date with origin/master.** If your PR is behind origin/master an automatic [GitHub actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) rebase may be attempted by including the /rebase command in a comment body, or by running the following code, replacing 'feature' with the name of your local branch: ```bash git remote add upstream https://github.com/ultralytics/yolov5.git git fetch upstream @@ -42,10 +42,11 @@ jobs: YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): - - **Google Colab Notebook** with free GPU: Open In Colab - - **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5) - - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) - - **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker) + - **Google Colab and Kaggle** notebooks with free GPU: Open In Colab Open In Kaggle + - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) + - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart) + - **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) Docker Pulls + ## Status diff --git a/Dockerfile b/Dockerfile index 24529d2b9415..b47e5bbff194 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,14 +1,14 @@ # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch -FROM nvcr.io/nvidia/pytorch:20.12-py3 +FROM nvcr.io/nvidia/pytorch:21.03-py3 # Install linux packages -RUN apt update && apt install -y screen libgl1-mesa-glx +RUN apt update && apt install -y zip htop screen libgl1-mesa-glx # Install python dependencies -RUN pip install --upgrade pip COPY requirements.txt . -RUN pip install -r requirements.txt -RUN pip install gsutil +RUN python -m pip install --upgrade pip +RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof +RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook # Create working directory RUN mkdir -p /usr/src/app @@ -17,11 +17,8 @@ WORKDIR /usr/src/app # Copy contents COPY . /usr/src/app -# Copy weights -#RUN python3 -c "from models import *; \ -#attempt_download('weights/yolov5s.pt'); \ -#attempt_download('weights/yolov5m.pt'); \ -#attempt_download('weights/yolov5l.pt')" +# Set environment variables +ENV HOME=/usr/src/app # --------------------------------------------------- Extras Below --------------------------------------------------- @@ -40,13 +37,13 @@ COPY . /usr/src/app # sudo docker kill $(sudo docker ps -q) # Kill all image-based -# sudo docker kill $(sudo docker ps -a -q --filter ancestor=ultralytics/yolov5:latest) +# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest) # Bash into running container -# sudo docker container exec -it ba65811811ab bash +# sudo docker exec -it 5a9b5863d93d bash # Bash into stopped container -# sudo docker commit 092b16b25c5b usr/resume && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco --entrypoint=sh usr/resume +# id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash # Send weights to GCP # python -c "from utils.general import *; strip_optimizer('runs/train/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt diff --git a/README.md b/README.md index 2904b18376df..0fa30d3e704d 100644 --- a/README.md +++ b/README.md @@ -74,7 +74,7 @@ YOLOv5 is one of the best available detectors. And we are proud to announce its # For Developers - you can use sources of [Serve YOLOv5 app](https://github.com/supervisely-ecosystem/yolov5/tree/master/supervisely/serve) as example of how to prepare weights, initialize model and apply it to a folder with images (or to images URLs) -- This apps collection is based on the original YOLOv5 [release v4.0](https://github.com/ultralytics/yolov5/releases/tag/v4.0). Once a next official release is available, all apps will be synchronized with it and also released with the new versions. Before running any app you can choose what version to use. Also Supervisely Team will pull updates from original master branch from time to time. +- This apps collection is based on the original YOLOv5 [release v5.0](https://github.com/ultralytics/yolov5/releases/tag/v5.0). Once a next official release is available, all apps will be synchronized with it and also released with the new versions. Before running any app you can choose what version to use. Also Supervisely Team will pull updates from original master branch from time to time. # Contact & Questions & Suggestions diff --git a/data/argoverse_hd.yaml b/data/argoverse_hd.yaml new file mode 100644 index 000000000000..0ba314d82ce1 --- /dev/null +++ b/data/argoverse_hd.yaml @@ -0,0 +1,21 @@ +# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ +# Train command: python train.py --data argoverse_hd.yaml +# Default dataset location is next to YOLOv5: +# /parent_folder +# /argoverse +# /yolov5 + + +# download command/URL (optional) +download: bash data/scripts/get_argoverse_hd.sh + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ../argoverse/Argoverse-1.1/images/train/ # 39384 images +val: ../argoverse/Argoverse-1.1/images/val/ # 15062 iamges +test: ../argoverse/Argoverse-1.1/images/test/ # Submit to: https://eval.ai/web/challenges/challenge-page/800/overview + +# number of classes +nc: 8 + +# class names +names: [ 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign' ] diff --git a/data/coco.yaml b/data/coco.yaml index b9da2bf5919b..f818a49ff0fa 100644 --- a/data/coco.yaml +++ b/data/coco.yaml @@ -1,6 +1,6 @@ # COCO 2017 dataset http://cocodataset.org # Train command: python train.py --data coco.yaml -# Default dataset location is next to /yolov5: +# Default dataset location is next to YOLOv5: # /parent_folder # /coco # /yolov5 @@ -30,6 +30,6 @@ names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', ' # Print classes # with open('data/coco.yaml') as f: -# d = yaml.load(f, Loader=yaml.FullLoader) # dict +# d = yaml.safe_load(f) # dict # for i, x in enumerate(d['names']): # print(i, x) diff --git a/data/coco128.yaml b/data/coco128.yaml index c41bccf2b8d5..83fbc29d3404 100644 --- a/data/coco128.yaml +++ b/data/coco128.yaml @@ -1,6 +1,6 @@ # COCO 2017 dataset http://cocodataset.org - first 128 training images # Train command: python train.py --data coco128.yaml -# Default dataset location is next to /yolov5: +# Default dataset location is next to YOLOv5: # /parent_folder # /coco128 # /yolov5 diff --git a/data/scripts/get_argoverse_hd.sh b/data/scripts/get_argoverse_hd.sh new file mode 100644 index 000000000000..18131a6764d6 --- /dev/null +++ b/data/scripts/get_argoverse_hd.sh @@ -0,0 +1,62 @@ +#!/bin/bash +# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ +# Download command: bash data/scripts/get_argoverse_hd.sh +# Train command: python train.py --data argoverse_hd.yaml +# Default dataset location is next to YOLOv5: +# /parent_folder +# /argoverse +# /yolov5 + +# Download/unzip images +d='../argoverse/' # unzip directory +mkdir $d +url=https://argoverse-hd.s3.us-east-2.amazonaws.com/ +f=Argoverse-HD-Full.zip +curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &# download, unzip, remove in background +wait # finish background tasks + +cd ../argoverse/Argoverse-1.1/ +ln -s tracking images + +cd ../Argoverse-HD/annotations/ + +python3 - "$@" <train.txt cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt >train.all.txt -python3 - "$@" <= 1 p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count else: - p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) + p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # img.jpg @@ -97,7 +98,7 @@ def detect(save_img=False): # Print results for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class - s += f'{n} {names[int(c)]}s, ' # add to string + s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string # Write results for *xyxy, conf, cls in reversed(det): @@ -107,9 +108,13 @@ def detect(save_img=False): with open(txt_path + '.txt', 'a') as f: f.write(('%g ' * len(line)).rstrip() % line + '\n') - if save_img or view_img: # Add bbox to image - label = f'{names[int(cls)]} {conf:.2f}' - plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3) + if save_img or opt.save_crop or view_img: # Add bbox to image + c = int(cls) # integer class + label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}') + + plot_one_box(xyxy, im0, label=label, color=colors[c], line_thickness=opt.line_thickness) + if opt.save_crop: + save_one_box(xyxy, im0s, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) # Print time (inference + NMS) print(f'{s}Done. ({t2 - t1:.3f}s)') @@ -117,22 +122,25 @@ def detect(save_img=False): # Stream results if view_img: cv2.imshow(str(p), im0) + cv2.waitKey(1) # 1 millisecond # Save results (image with detections) if save_img: if dataset.mode == 'image': cv2.imwrite(save_path, im0) - else: # 'video' + else: # 'video' or 'stream' if vid_path != save_path: # new video vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.release() # release previous video writer - - fourcc = 'mp4v' # output video codec - fps = vid_cap.get(cv2.CAP_PROP_FPS) - w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path += '.mp4' + vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) vid_writer.write(im0) if save_txt or save_img: @@ -153,6 +161,8 @@ def detect(save_img=False): parser.add_argument('--view-img', action='store_true', help='display results') parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') + parser.add_argument('--nosave', action='store_true', help='do not save images/videos') parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') parser.add_argument('--augment', action='store_true', help='augmented inference') @@ -160,14 +170,17 @@ def detect(save_img=False): parser.add_argument('--project', default='runs/detect', help='save results to project/name') parser.add_argument('--name', default='exp', help='save results to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') + parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') + parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') opt = parser.parse_args() print(opt) - check_requirements() + check_requirements(exclude=('pycocotools', 'thop')) with torch.no_grad(): if opt.update: # update all models (to fix SourceChangeWarning) for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']: - detect() + detect(opt=opt) strip_optimizer(opt.weights) else: - detect() + detect(opt=opt) diff --git a/hubconf.py b/hubconf.py index c4485a42e335..e42d0b59bd2a 100644 --- a/hubconf.py +++ b/hubconf.py @@ -1,8 +1,8 @@ -"""File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/ +"""YOLOv5 PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/ Usage: import torch - model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, channels=3, classes=80) + model = torch.hub.load('ultralytics/yolov5', 'yolov5s') """ from pathlib import Path @@ -10,14 +10,15 @@ import torch from models.yolo import Model -from utils.general import set_logging +from utils.general import check_requirements, set_logging from utils.google_utils import attempt_download +from utils.torch_utils import select_device dependencies = ['torch', 'yaml'] -set_logging() +check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('pycocotools', 'thop')) -def create(name, pretrained, channels, classes, autoshape): +def create(name, pretrained, channels, classes, autoshape, verbose): """Creates a specified YOLOv5 model Arguments: @@ -29,21 +30,25 @@ def create(name, pretrained, channels, classes, autoshape): Returns: pytorch model """ - config = Path(__file__).parent / 'models' / f'{name}.yaml' # model.yaml path try: - model = Model(config, channels, classes) + set_logging(verbose=verbose) + + cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0] # model.yaml path + model = Model(cfg, channels, classes) if pretrained: fname = f'{name}.pt' # checkpoint filename attempt_download(fname) # download if not found locally ckpt = torch.load(fname, map_location=torch.device('cpu')) # load - state_dict = ckpt['model'].float().state_dict() # to FP32 - state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape} # filter - model.load_state_dict(state_dict, strict=False) # load + msd = model.state_dict() # model state_dict + csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 + csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape} # filter + model.load_state_dict(csd, strict=False) # load if len(ckpt['model'].names) == classes: model.names = ckpt['model'].names # set class names attribute if autoshape: model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS - return model + device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available + return model.to(device) except Exception as e: help_url = 'https://github.com/ultralytics/yolov5/issues/36' @@ -51,91 +56,87 @@ def create(name, pretrained, channels, classes, autoshape): raise Exception(s) from e -def yolov5s(pretrained=False, channels=3, classes=80, autoshape=True): - """YOLOv5-small model from https://github.com/ultralytics/yolov5 +def custom(path_or_model='path/to/model.pt', autoshape=True, verbose=True): + """YOLOv5-custom model https://github.com/ultralytics/yolov5 - Arguments: - pretrained (bool): load pretrained weights into the model, default=False - channels (int): number of input channels, default=3 - classes (int): number of model classes, default=80 + Arguments (3 options): + path_or_model (str): 'path/to/model.pt' + path_or_model (dict): torch.load('path/to/model.pt') + path_or_model (nn.Module): torch.load('path/to/model.pt')['model'] Returns: pytorch model """ - return create('yolov5s', pretrained, channels, classes, autoshape) + set_logging(verbose=verbose) + model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model # load checkpoint + if isinstance(model, dict): + model = model['ema' if model.get('ema') else 'model'] # load model -def yolov5m(pretrained=False, channels=3, classes=80, autoshape=True): - """YOLOv5-medium model from https://github.com/ultralytics/yolov5 + hub_model = Model(model.yaml).to(next(model.parameters()).device) # create + hub_model.load_state_dict(model.float().state_dict()) # load state_dict + hub_model.names = model.names # class names + if autoshape: + hub_model = hub_model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS + device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available + return hub_model.to(device) - Arguments: - pretrained (bool): load pretrained weights into the model, default=False - channels (int): number of input channels, default=3 - classes (int): number of model classes, default=80 - Returns: - pytorch model - """ - return create('yolov5m', pretrained, channels, classes, autoshape) +def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): + # YOLOv5-small model https://github.com/ultralytics/yolov5 + return create('yolov5s', pretrained, channels, classes, autoshape, verbose) -def yolov5l(pretrained=False, channels=3, classes=80, autoshape=True): - """YOLOv5-large model from https://github.com/ultralytics/yolov5 +def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): + # YOLOv5-medium model https://github.com/ultralytics/yolov5 + return create('yolov5m', pretrained, channels, classes, autoshape, verbose) - Arguments: - pretrained (bool): load pretrained weights into the model, default=False - channels (int): number of input channels, default=3 - classes (int): number of model classes, default=80 - Returns: - pytorch model - """ - return create('yolov5l', pretrained, channels, classes, autoshape) +def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): + # YOLOv5-large model https://github.com/ultralytics/yolov5 + return create('yolov5l', pretrained, channels, classes, autoshape, verbose) -def yolov5x(pretrained=False, channels=3, classes=80, autoshape=True): - """YOLOv5-xlarge model from https://github.com/ultralytics/yolov5 +def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): + # YOLOv5-xlarge model https://github.com/ultralytics/yolov5 + return create('yolov5x', pretrained, channels, classes, autoshape, verbose) - Arguments: - pretrained (bool): load pretrained weights into the model, default=False - channels (int): number of input channels, default=3 - classes (int): number of model classes, default=80 - Returns: - pytorch model - """ - return create('yolov5x', pretrained, channels, classes, autoshape) +def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): + # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5 + return create('yolov5s6', pretrained, channels, classes, autoshape, verbose) -def custom(path_or_model='path/to/model.pt', autoshape=True): - """YOLOv5-custom model from https://github.com/ultralytics/yolov5 +def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): + # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5 + return create('yolov5m6', pretrained, channels, classes, autoshape, verbose) - Arguments (3 options): - path_or_model (str): 'path/to/model.pt' - path_or_model (dict): torch.load('path/to/model.pt') - path_or_model (nn.Module): torch.load('path/to/model.pt')['model'] - Returns: - pytorch model - """ - model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model # load checkpoint - if isinstance(model, dict): - model = model['model'] # load model +def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): + # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5 + return create('yolov5l6', pretrained, channels, classes, autoshape, verbose) - hub_model = Model(model.yaml).to(next(model.parameters()).device) # create - hub_model.load_state_dict(model.float().state_dict()) # load state_dict - hub_model.names = model.names # class names - return hub_model.autoshape() if autoshape else hub_model + +def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): + # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5 + return create('yolov5x6', pretrained, channels, classes, autoshape, verbose) if __name__ == '__main__': - model = create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True) # pretrained example - # model = custom(path_or_model='path/to/model.pt') # custom example + model = create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # pretrained + # model = custom(path_or_model='path/to/model.pt') # custom # Verify inference + import cv2 + import numpy as np from PIL import Image - imgs = [Image.open(x) for x in Path('data/images').glob('*.jpg')] - results = model(imgs) - results.show() + imgs = ['data/images/zidane.jpg', # filename + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg', # URI + cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV + Image.open('data/images/bus.jpg'), # PIL + np.zeros((320, 640, 3))] # numpy + + results = model(imgs) # batched inference results.print() + results.save() diff --git a/models/common.py b/models/common.py index 3bfdb3c7dc14..a28621904b0e 100644 --- a/models/common.py +++ b/models/common.py @@ -1,16 +1,21 @@ -# This file contains modules common to various models +# YOLOv5 common modules import math +from copy import copy +from pathlib import Path import numpy as np +import pandas as pd import requests import torch import torch.nn as nn -from PIL import Image, ImageDraw +from PIL import Image +from torch.cuda import amp from utils.datasets import letterbox -from utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh -from utils.plots import color_list +from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box +from utils.plots import color_list, plot_one_box +from utils.torch_utils import time_synchronized def autopad(k, p=None): # kernel, padding @@ -40,6 +45,52 @@ def fuseforward(self, x): return self.act(self.conv(x)) +class TransformerLayer(nn.Module): + # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) + def __init__(self, c, num_heads): + super().__init__() + self.q = nn.Linear(c, c, bias=False) + self.k = nn.Linear(c, c, bias=False) + self.v = nn.Linear(c, c, bias=False) + self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) + self.fc1 = nn.Linear(c, c, bias=False) + self.fc2 = nn.Linear(c, c, bias=False) + + def forward(self, x): + x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x + x = self.fc2(self.fc1(x)) + x + return x + + +class TransformerBlock(nn.Module): + # Vision Transformer https://arxiv.org/abs/2010.11929 + def __init__(self, c1, c2, num_heads, num_layers): + super().__init__() + self.conv = None + if c1 != c2: + self.conv = Conv(c1, c2) + self.linear = nn.Linear(c2, c2) # learnable position embedding + self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)]) + self.c2 = c2 + + def forward(self, x): + if self.conv is not None: + x = self.conv(x) + b, _, w, h = x.shape + p = x.flatten(2) + p = p.unsqueeze(0) + p = p.transpose(0, 3) + p = p.squeeze(3) + e = self.linear(p) + x = p + e + + x = self.tr(x) + x = x.unsqueeze(3) + x = x.transpose(0, 3) + x = x.reshape(b, self.c2, w, h) + return x + + class Bottleneck(nn.Module): # Standard bottleneck def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion @@ -87,6 +138,14 @@ def forward(self, x): return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) +class C3TR(C3): + # C3 module with TransformerBlock() + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = TransformerBlock(c_, c_, 4, n) + + class SPP(nn.Module): # Spatial pyramid pooling layer used in YOLOv3-SPP def __init__(self, c1, c2, k=(5, 9, 13)): @@ -166,7 +225,6 @@ def forward(self, x): class autoShape(nn.Module): # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS - img_size = 640 # inference size (pixels) conf = 0.25 # NMS confidence threshold iou = 0.45 # NMS IoU threshold classes = None # (optional list) filter by class @@ -179,27 +237,33 @@ def autoshape(self): print('autoShape already enabled, skipping... ') # model already converted to model.autoshape() return self + @torch.no_grad() def forward(self, imgs, size=640, augment=False, profile=False): - # Inference from various sources. For height=720, width=1280, RGB images example inputs are: - # filename: imgs = 'data/samples/zidane.jpg' + # Inference from various sources. For height=640, width=1280, RGB images example inputs are: + # filename: imgs = 'data/images/zidane.jpg' # URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg' - # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3) - # PIL: = Image.open('image.jpg') # HWC x(720,1280,3) - # numpy: = np.zeros((720,1280,3)) # HWC - # torch: = torch.zeros(16,3,720,1280) # BCHW + # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) + # PIL: = Image.open('image.jpg') # HWC x(640,1280,3) + # numpy: = np.zeros((640,1280,3)) # HWC + # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images + t = [time_synchronized()] p = next(self.model.parameters()) # for device and type if isinstance(imgs, torch.Tensor): # torch - return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference + with amp.autocast(enabled=p.device.type != 'cpu'): + return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference # Pre-process n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images - shape0, shape1 = [], [] # image and inference shapes + shape0, shape1, files = [], [], [] # image and inference shapes, filenames for i, im in enumerate(imgs): + f = f'image{i}' # filename if isinstance(im, str): # filename or uri - im = Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im) # open - im = np.array(im) # to numpy + im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im + elif isinstance(im, Image.Image): # PIL Image + im, f = np.asarray(im), getattr(im, 'filename', f) or f + files.append(Path(f).with_suffix('.jpg').name) if im.shape[0] < 5: # image in CHW im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input @@ -207,88 +271,115 @@ def forward(self, imgs, size=640, augment=False, profile=False): shape0.append(s) # image shape g = (size / max(s)) # gain shape1.append([y * g for y in s]) - imgs[i] = im # update + imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad x = np.stack(x, 0) if n > 1 else x[0][None] # stack x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32 + t.append(time_synchronized()) - # Inference - with torch.no_grad(): + with amp.autocast(enabled=p.device.type != 'cpu'): + # Inference y = self.model(x, augment, profile)[0] # forward - y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS + t.append(time_synchronized()) - # Post-process - for i in range(n): - scale_coords(shape1, y[i][:, :4], shape0[i]) + # Post-process + y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS + for i in range(n): + scale_coords(shape1, y[i][:, :4], shape0[i]) - return Detections(imgs, y, self.names) + t.append(time_synchronized()) + return Detections(imgs, y, files, t, self.names, x.shape) class Detections: # detections class for YOLOv5 inference results - def __init__(self, imgs, pred, names=None): + def __init__(self, imgs, pred, files, times=None, names=None, shape=None): super(Detections, self).__init__() d = pred[0].device # device gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations self.imgs = imgs # list of images as numpy arrays self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) self.names = names # class names + self.files = files # image filenames self.xyxy = pred # xyxy pixels self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized - self.n = len(self.pred) + self.n = len(self.pred) # number of images (batch size) + self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) + self.s = shape # inference BCHW shape - def display(self, pprint=False, show=False, save=False, render=False): + def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')): colors = color_list() - for i, (img, pred) in enumerate(zip(self.imgs, self.pred)): - str = f'Image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} ' + for i, (im, pred) in enumerate(zip(self.imgs, self.pred)): + str = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' if pred is not None: for c in pred[:, -1].unique(): n = (pred[:, -1] == c).sum() # detections per class - str += f'{n} {self.names[int(c)]}s, ' # add to string - if show or save or render: - img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np + str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string + if show or save or render or crop: for *box, conf, cls in pred: # xyxy, confidence, class - # str += '%s %.2f, ' % (names[int(cls)], conf) # label - ImageDraw.Draw(img).rectangle(box, width=4, outline=colors[int(cls) % 10]) # plot + label = f'{self.names[int(cls)]} {conf:.2f}' + if crop: + save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i]) + else: # all others + plot_one_box(box, im, label=label, color=colors[int(cls) % 10]) + + im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np if pprint: - print(str) + print(str.rstrip(', ')) if show: - img.show(f'Image {i}') # show + im.show(self.files[i]) # show if save: - f = f'results{i}.jpg' - str += f"saved to '{f}'" - img.save(f) # save + f = self.files[i] + im.save(save_dir / f) # save + print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n') if render: - self.imgs[i] = np.asarray(img) + self.imgs[i] = np.asarray(im) def print(self): self.display(pprint=True) # print results + print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t) def show(self): self.display(show=True) # show results - def save(self): - self.display(save=True) # save results + def save(self, save_dir='runs/hub/exp'): + save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True) # increment save_dir + self.display(save=True, save_dir=save_dir) # save results + + def crop(self, save_dir='runs/hub/exp'): + save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True) # increment save_dir + self.display(crop=True, save_dir=save_dir) # crop results + print(f'Saved results to {save_dir}\n') def render(self): self.display(render=True) # render results return self.imgs - def __len__(self): - return self.n + def pandas(self): + # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) + new = copy(self) # return copy + ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns + cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns + for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): + a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update + setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) + return new def tolist(self): # return a list of Detections objects, i.e. 'for result in results.tolist():' - x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)] + x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)] for d in x: for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: setattr(d, k, getattr(d, k)[0]) # pop out of list return x + def __len__(self): + return self.n + class Classify(nn.Module): # Classification head, i.e. x(b,c1,20,20) to x(b,c2) diff --git a/models/experimental.py b/models/experimental.py index 72dc877c83cf..548353c93be0 100644 --- a/models/experimental.py +++ b/models/experimental.py @@ -1,4 +1,4 @@ -# This file contains experimental modules +# YOLOv5 experimental modules import numpy as np import torch @@ -58,7 +58,7 @@ def forward(self, x): class GhostBottleneck(nn.Module): # Ghost Bottleneck https://github.com/huawei-noah/ghostnet - def __init__(self, c1, c2, k, s): + def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride super(GhostBottleneck, self).__init__() c_ = c2 // 2 self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw @@ -115,7 +115,8 @@ def attempt_load(weights, map_location=None): model = Ensemble() for w in weights if isinstance(weights, list) else [weights]: attempt_download(w) - model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval()) # load FP32 model + ckpt = torch.load(w, map_location=map_location) # load + model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model # Compatibility updates for m in model.modules(): diff --git a/models/export.py b/models/export.py index 057658af53dc..da15079149a1 100644 --- a/models/export.py +++ b/models/export.py @@ -1,28 +1,35 @@ """Exports a YOLOv5 *.pt model to ONNX and TorchScript formats Usage: - $ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1 + $ export PYTHONPATH="$PWD" && python models/export.py --weights yolov5s.pt --img 640 --batch 1 """ import argparse import sys import time +from pathlib import Path -sys.path.append('./') # to run '$ python *.py' files in subdirectories +sys.path.append(Path(__file__).parent.parent.absolute().__str__()) # to run '$ python *.py' files in subdirectories import torch import torch.nn as nn +from torch.utils.mobile_optimizer import optimize_for_mobile import models from models.experimental import attempt_load from utils.activations import Hardswish, SiLU -from utils.general import set_logging, check_img_size +from utils.general import colorstr, check_img_size, check_requirements, file_size, set_logging +from utils.torch_utils import select_device if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') # from yolov5/models/ + parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--grid', action='store_true', help='export Detect() layer grid') + parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') # ONNX-only + parser.add_argument('--simplify', action='store_true', help='simplify ONNX model') # ONNX-only opt = parser.parse_args() opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand print(opt) @@ -30,7 +37,8 @@ t = time.time() # Load PyTorch model - model = attempt_load(opt.weights, map_location=torch.device('cpu')) # load FP32 model + device = select_device(opt.device) + model = attempt_load(opt.weights, map_location=device) # load FP32 model labels = model.names # Checks @@ -38,7 +46,7 @@ opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples # Input - img = torch.zeros(opt.batch_size, 3, *opt.img_size) # image size(1,3,320,192) iDetection + img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection # Update model for k, m in model.named_modules(): @@ -50,48 +58,70 @@ m.act = SiLU() # elif isinstance(m, models.yolo.Detect): # m.forward = m.forward_export # assign forward (optional) - model.model[-1].export = True # set Detect() layer export=True - y = model(img) # dry run + model.model[-1].export = not opt.grid # set Detect() layer grid export + for _ in range(2): + y = model(img) # dry runs + print(f"\n{colorstr('PyTorch:')} starting from {opt.weights} ({file_size(opt.weights):.1f} MB)") - # TorchScript export + # TorchScript export ----------------------------------------------------------------------------------------------- + prefix = colorstr('TorchScript:') try: - print('\nStarting TorchScript export with torch %s...' % torch.__version__) + print(f'\n{prefix} starting export with torch {torch.__version__}...') f = opt.weights.replace('.pt', '.torchscript.pt') # filename - ts = torch.jit.trace(model, img) + ts = torch.jit.trace(model, img, strict=False) + ts = optimize_for_mobile(ts) # https://pytorch.org/tutorials/recipes/script_optimized.html ts.save(f) - print('TorchScript export success, saved as %s' % f) + print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') except Exception as e: - print('TorchScript export failure: %s' % e) + print(f'{prefix} export failure: {e}') - # ONNX export + # ONNX export ------------------------------------------------------------------------------------------------------ + prefix = colorstr('ONNX:') try: import onnx - print('\nStarting ONNX export with onnx %s...' % onnx.__version__) + print(f'{prefix} starting export with onnx {onnx.__version__}...') f = opt.weights.replace('.pt', '.onnx') # filename torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'], - output_names=['classes', 'boxes'] if y is None else ['output']) + dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640) + 'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None) # Checks - onnx_model = onnx.load(f) # load onnx model - onnx.checker.check_model(onnx_model) # check onnx model - # print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model - print('ONNX export success, saved as %s' % f) + model_onnx = onnx.load(f) # load onnx model + onnx.checker.check_model(model_onnx) # check onnx model + # print(onnx.helper.printable_graph(model_onnx.graph)) # print + + # Simplify + if opt.simplify: + try: + check_requirements(['onnx-simplifier']) + import onnxsim + + print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...') + model_onnx, check = onnxsim.simplify(model_onnx, + dynamic_input_shape=opt.dynamic, + input_shapes={'images': list(img.shape)} if opt.dynamic else None) + assert check, 'assert check failed' + onnx.save(model_onnx, f) + except Exception as e: + print(f'{prefix} simplifier failure: {e}') + print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') except Exception as e: - print('ONNX export failure: %s' % e) + print(f'{prefix} export failure: {e}') - # CoreML export + # CoreML export ---------------------------------------------------------------------------------------------------- + prefix = colorstr('CoreML:') try: import coremltools as ct - print('\nStarting CoreML export with coremltools %s...' % ct.__version__) + print(f'{prefix} starting export with coremltools {ct.__version__}...') # convert model from torchscript and apply pixel scaling as per detect.py model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) f = opt.weights.replace('.pt', '.mlmodel') # filename model.save(f) - print('CoreML export success, saved as %s' % f) + print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') except Exception as e: - print('CoreML export failure: %s' % e) + print(f'{prefix} export failure: {e}') # Finish - print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t)) + print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.') diff --git a/models/hub/yolov5l6.yaml b/models/hub/yolov5l6.yaml new file mode 100644 index 000000000000..11298b01f479 --- /dev/null +++ b/models/hub/yolov5l6.yaml @@ -0,0 +1,60 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 + [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 + [ -1, 3, C3, [ 128 ] ], + [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 + [ -1, 9, C3, [ 256 ] ], + [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 + [ -1, 9, C3, [ 512 ] ], + [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 + [ -1, 3, C3, [ 768 ] ], + [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 + [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], + [ -1, 3, C3, [ 1024, False ] ], # 11 + ] + +# YOLOv5 head +head: + [ [ -1, 1, Conv, [ 768, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 + [ -1, 3, C3, [ 768, False ] ], # 15 + + [ -1, 1, Conv, [ 512, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 + [ -1, 3, C3, [ 512, False ] ], # 19 + + [ -1, 1, Conv, [ 256, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 + [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) + + [ -1, 1, Conv, [ 256, 3, 2 ] ], + [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 + [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) + + [ -1, 1, Conv, [ 512, 3, 2 ] ], + [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 + [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) + + [ -1, 1, Conv, [ 768, 3, 2 ] ], + [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 + [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) + + [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) + ] diff --git a/models/hub/yolov5m6.yaml b/models/hub/yolov5m6.yaml new file mode 100644 index 000000000000..48afc865593a --- /dev/null +++ b/models/hub/yolov5m6.yaml @@ -0,0 +1,60 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 + [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 + [ -1, 3, C3, [ 128 ] ], + [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 + [ -1, 9, C3, [ 256 ] ], + [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 + [ -1, 9, C3, [ 512 ] ], + [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 + [ -1, 3, C3, [ 768 ] ], + [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 + [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], + [ -1, 3, C3, [ 1024, False ] ], # 11 + ] + +# YOLOv5 head +head: + [ [ -1, 1, Conv, [ 768, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 + [ -1, 3, C3, [ 768, False ] ], # 15 + + [ -1, 1, Conv, [ 512, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 + [ -1, 3, C3, [ 512, False ] ], # 19 + + [ -1, 1, Conv, [ 256, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 + [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) + + [ -1, 1, Conv, [ 256, 3, 2 ] ], + [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 + [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) + + [ -1, 1, Conv, [ 512, 3, 2 ] ], + [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 + [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) + + [ -1, 1, Conv, [ 768, 3, 2 ] ], + [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 + [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) + + [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) + ] diff --git a/models/hub/yolov5s-transformer.yaml b/models/hub/yolov5s-transformer.yaml new file mode 100644 index 000000000000..f2d666722b30 --- /dev/null +++ b/models/hub/yolov5s-transformer.yaml @@ -0,0 +1,48 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 3, C3TR, [1024, False]], # 9 <-------- C3TR() Transformer module + ] + +# YOLOv5 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/models/hub/yolov5s6.yaml b/models/hub/yolov5s6.yaml new file mode 100644 index 000000000000..1df577a2cc97 --- /dev/null +++ b/models/hub/yolov5s6.yaml @@ -0,0 +1,60 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 + [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 + [ -1, 3, C3, [ 128 ] ], + [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 + [ -1, 9, C3, [ 256 ] ], + [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 + [ -1, 9, C3, [ 512 ] ], + [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 + [ -1, 3, C3, [ 768 ] ], + [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 + [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], + [ -1, 3, C3, [ 1024, False ] ], # 11 + ] + +# YOLOv5 head +head: + [ [ -1, 1, Conv, [ 768, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 + [ -1, 3, C3, [ 768, False ] ], # 15 + + [ -1, 1, Conv, [ 512, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 + [ -1, 3, C3, [ 512, False ] ], # 19 + + [ -1, 1, Conv, [ 256, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 + [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) + + [ -1, 1, Conv, [ 256, 3, 2 ] ], + [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 + [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) + + [ -1, 1, Conv, [ 512, 3, 2 ] ], + [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 + [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) + + [ -1, 1, Conv, [ 768, 3, 2 ] ], + [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 + [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) + + [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) + ] diff --git a/models/hub/yolov5x6.yaml b/models/hub/yolov5x6.yaml new file mode 100644 index 000000000000..5ebc02124fe7 --- /dev/null +++ b/models/hub/yolov5x6.yaml @@ -0,0 +1,60 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 + [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 + [ -1, 3, C3, [ 128 ] ], + [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 + [ -1, 9, C3, [ 256 ] ], + [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 + [ -1, 9, C3, [ 512 ] ], + [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 + [ -1, 3, C3, [ 768 ] ], + [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 + [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], + [ -1, 3, C3, [ 1024, False ] ], # 11 + ] + +# YOLOv5 head +head: + [ [ -1, 1, Conv, [ 768, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 + [ -1, 3, C3, [ 768, False ] ], # 15 + + [ -1, 1, Conv, [ 512, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 + [ -1, 3, C3, [ 512, False ] ], # 19 + + [ -1, 1, Conv, [ 256, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 + [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) + + [ -1, 1, Conv, [ 256, 3, 2 ] ], + [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 + [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) + + [ -1, 1, Conv, [ 512, 3, 2 ] ], + [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 + [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) + + [ -1, 1, Conv, [ 768, 3, 2 ] ], + [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 + [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) + + [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) + ] diff --git a/models/yolo.py b/models/yolo.py index 5dc8b57f4d98..cbff70fc83d4 100644 --- a/models/yolo.py +++ b/models/yolo.py @@ -1,14 +1,16 @@ +# YOLOv5 YOLO-specific modules + import argparse import logging import sys from copy import deepcopy from pathlib import Path -sys.path.append('./') # to run '$ python *.py' files in subdirectories +sys.path.append(Path(__file__).parent.parent.absolute().__str__()) # to run '$ python *.py' files in subdirectories logger = logging.getLogger(__name__) from models.common import * -from models.experimental import MixConv2d, CrossConv +from models.experimental import * from utils.autoanchor import check_anchor_order from utils.general import make_divisible, check_file, set_logging from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \ @@ -50,7 +52,7 @@ def forward(self, x): self.grid[i] = self._make_grid(nx, ny).to(x[i].device) y = x[i].sigmoid() - y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy + y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh z.append(y.view(bs, -1, self.no)) @@ -63,7 +65,7 @@ def _make_grid(nx=20, ny=20): class Model(nn.Module): - def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, number of classes + def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes super(Model, self).__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict @@ -71,16 +73,19 @@ def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, import yaml # for torch hub self.yaml_file = Path(cfg).name with open(cfg) as f: - self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict + self.yaml = yaml.safe_load(f) # model dict # Define model ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels if nc and nc != self.yaml['nc']: - logger.info('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc)) + logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") self.yaml['nc'] = nc # override yaml value + if anchors: + logger.info(f'Overriding model.yaml anchors with anchors={anchors}') + self.yaml['anchors'] = round(anchors) # override yaml value self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist self.names = [str(i) for i in range(self.yaml['nc'])] # default names - # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) + # logger.info([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) # Build strides, anchors m = self.model[-1] # Detect() @@ -91,7 +96,7 @@ def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, check_anchor_order(m) self.stride = m.stride self._initialize_biases() # only run once - # print('Strides: %s' % m.stride.tolist()) + # logger.info('Strides: %s' % m.stride.tolist()) # Init weights, biases initialize_weights(self) @@ -107,7 +112,7 @@ def forward(self, x, augment=False, profile=False): for si, fi in zip(s, f): xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) yi = self.forward_once(xi)[0] # forward - # cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1]) # save + # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save yi[..., :4] /= si # de-scale if fi == 2: yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud @@ -130,13 +135,15 @@ def forward_once(self, x, profile=False): for _ in range(10): _ = m(x) dt.append((time_synchronized() - t) * 100) - print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type)) + if m == self.model[0]: + logger.info(f"{'time (ms)':>10s} {'GFLOPS':>10s} {'params':>10s} {'module'}") + logger.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}') x = m(x) # run y.append(x if m.i in self.save else None) # save output if profile: - print('%.1fms total' % sum(dt)) + logger.info('%.1fms total' % sum(dt)) return x def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency @@ -153,15 +160,16 @@ def _print_biases(self): m = self.model[-1] # Detect() module for mi in m.m: # from b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85) - print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean())) + logger.info( + ('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean())) # def _print_weights(self): # for m in self.model.modules(): # if type(m) is Bottleneck: - # print('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights + # logger.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers - print('Fusing layers... ') + logger.info('Fusing layers... ') for m in self.model.modules(): if type(m) is Conv and hasattr(m, 'bn'): m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv @@ -173,19 +181,19 @@ def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers def nms(self, mode=True): # add or remove NMS module present = type(self.model[-1]) is NMS # last layer is NMS if mode and not present: - print('Adding NMS... ') + logger.info('Adding NMS... ') m = NMS() # module m.f = -1 # from m.i = self.model[-1].i + 1 # index self.model.add_module(name='%s' % m.i, module=m) # add self.eval() elif not mode and present: - print('Removing NMS... ') + logger.info('Removing NMS... ') self.model = self.model[:-1] # remove return self def autoshape(self): # add autoShape module - print('Adding autoShape... ') + logger.info('Adding autoShape... ') m = autoShape(self) # wrap model copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes return m @@ -210,45 +218,30 @@ def parse_model(d, ch): # model_dict, input_channels(3) pass n = max(round(n * gd), 1) if n > 1 else n # depth gain - if m in [Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]: + if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, + C3, C3TR]: c1, c2 = ch[f], args[0] - - # Normal - # if i > 0 and args[0] != no: # channel expansion factor - # ex = 1.75 # exponential (default 2.0) - # e = math.log(c2 / ch[1]) / math.log(2) - # c2 = int(ch[1] * ex ** e) - # if m != Focus: - - c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 - - # Experimental - # if i > 0 and args[0] != no: # channel expansion factor - # ex = 1 + gw # exponential (default 2.0) - # ch1 = 32 # ch[1] - # e = math.log(c2 / ch1) / math.log(2) # level 1-n - # c2 = int(ch1 * ex ** e) - # if m != Focus: - # c2 = make_divisible(c2, 8) if c2 != no else c2 + if c2 != no: # if not output + c2 = make_divisible(c2 * gw, 8) args = [c1, c2, *args[1:]] - if m in [BottleneckCSP, C3]: - args.insert(2, n) + if m in [BottleneckCSP, C3, C3TR]: + args.insert(2, n) # number of repeats n = 1 elif m is nn.BatchNorm2d: args = [ch[f]] elif m is Concat: - c2 = sum([ch[x if x < 0 else x + 1] for x in f]) + c2 = sum([ch[x] for x in f]) elif m is Detect: - args.append([ch[x + 1] for x in f]) + args.append([ch[x] for x in f]) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f) elif m is Contract: - c2 = ch[f if f < 0 else f + 1] * args[0] ** 2 + c2 = ch[f] * args[0] ** 2 elif m is Expand: - c2 = ch[f if f < 0 else f + 1] // args[0] ** 2 + c2 = ch[f] // args[0] ** 2 else: - c2 = ch[f if f < 0 else f + 1] + c2 = ch[f] m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module t = str(m)[8:-2].replace('__main__.', '') # module type @@ -257,6 +250,8 @@ def parse_model(d, ch): # model_dict, input_channels(3) logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) + if i == 0: + ch = [] ch.append(c2) return nn.Sequential(*layers), sorted(save) @@ -275,12 +270,12 @@ def parse_model(d, ch): # model_dict, input_channels(3) model.train() # Profile - # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device) + # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 320, 320).to(device) # y = model(img, profile=True) - # Tensorboard + # Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898) # from torch.utils.tensorboard import SummaryWriter - # tb_writer = SummaryWriter() - # print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/") - # tb_writer.add_graph(model.model, img) # add model to tensorboard + # tb_writer = SummaryWriter('.') + # logger.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/") + # tb_writer.add_graph(torch.jit.trace(model, img, strict=False), []) # add model graph # tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard diff --git a/supervisely/serve/config.json b/supervisely/serve/config.json index e5880b477660..8e7ce5035866 100644 --- a/supervisely/serve/config.json +++ b/supervisely/serve/config.json @@ -10,11 +10,120 @@ "docker_image": "supervisely/base-pytorch:6.0.21", "main_script": "supervisely/serve/src/sly_serve.py", "modal_template": "supervisely/serve/src/modal.html", + "modal_width": 700, "modal_template_state": { "modelWeightsOptions": "pretrained", - "modelSize": "yolov5s.pt", + "selectedModel": "YOLOv5s", "device": "0", - "weightsPath": "" + "weightsPath": "", + "models": [ + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5s", + "Size": 640, + "mAP^val": 36.7, + "mAP^test": 36.7, + "mAP^val_0.5": 55.4, + "Speed": 2.0, + "Params": 7.3, + "FLOPS": 17.0 + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5m", + "Size": 640, + "mAP^val": 44.5, + "mAP^test": 44.5, + "mAP^val_0.5": 63.1, + "Speed": 2.7, + "Params": 21.4, + "FLOPS": 51.3 + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5l", + "Size": 640, + "mAP^val": 48.2, + "mAP^test": 48.2, + "mAP^val_0.5": 66.9, + "Speed": 3.8, + "Params": 47.0, + "FLOPS": 115.4 + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5x", + "Size": 640, + "mAP^val": 50.4, + "mAP^test": 50.4, + "mAP^val_0.5": 68.8, + "Speed": 6.1, + "Params": 87.7, + "FLOPS": 218.8 + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5s6", + "Size": 1280, + "mAP^val": 43.3, + "mAP^test": 43.3, + "mAP^val_0.5": 61.9, + "Speed": 4.3, + "Params": 12.7, + "FLOPS": 17.4 + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5m6", + "Size": 1280, + "mAP^val": 50.5, + "mAP^test": 50.5, + "mAP^val_0.5": 68.7, + "Speed": 8.4, + "Params": 35.9, + "FLOPS": 52.4 + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5l6", + "Size": 1280, + "mAP^val": 53.4, + "mAP^test": 53.4, + "mAP^val_0.5": 71.1, + "Speed": 12.3, + "Params": 77.2, + "FLOPS": 117.7 + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5x6", + "Size": 1280, + "mAP^val": 54.4, + "mAP^test": 54.4, + "mAP^val_0.5": 72.0, + "Speed": 22.4, + "Params": 141.8, + "FLOPS": 222.9 + } + ], + "modelColumns": [ + {"key": "Model", "title": "Model", "subtitle": null}, + {"key": "Size", "title": "Size", "subtitle": "(pixels)"}, + {"key": "mAP^val", "title": "mAPval", "subtitle": "0.5:0.95"}, + {"key": "mAP^test", "title": "mAPtest", "subtitle": "0.5:0.95"}, + {"key": "mAP^val_0.5", "title": "mAPval", "subtitle": "0.5"}, + {"key": "Speed", "title": "Speed", "subtitle": "V100 (ms)"}, + {"key": "Params", "title": "Params", "subtitle": "(M)"}, + {"key": "FLOPS", "title": "FLOPS", "subtitle": "640 (B)"} + ] }, "task_location": "application_sessions", "icon": "https://i.imgur.com/2U6HufM.png", diff --git a/supervisely/serve/debug.env b/supervisely/serve/debug.env index 991b0537373d..df15b44000fa 100644 --- a/supervisely/serve/debug.env +++ b/supervisely/serve/debug.env @@ -4,14 +4,15 @@ DEBUG_APP_DIR="/app_debug_data" DEBUG_CACHE_DIR="/app_cache" LOG_LEVEL="trace" -TASK_ID=2635 +TASK_ID=4585 -context.teamId=7 -context.workspaceId=263 +context.teamId=237 +context.workspaceId=311 modal.state.modelWeightsOptions="pretrained" #"pretrained" "custom" -modal.state.modelSize="yolov5s.pt" +#modal.state.modelSize="yolov5s.pt" +modal.state.selectedModel="YOLOv5s" modal.state.weightsPath="/yolov5_train/lemons_annotated/2472/weights/best.pt" modal.state.device="0" diff --git a/supervisely/serve/src/modal.html b/supervisely/serve/src/modal.html index beaccd037238..7caf05aecc95 100644 --- a/supervisely/serve/src/modal.html +++ b/supervisely/serve/src/modal.html @@ -1,24 +1,77 @@ -
- - - Pretrained on COCO - Custom model - -
- - - - - - - - -
-
- - - -
+
+ + #yolov5-train .el-tabs.el-tabs-cards .el-radio { + display: flex; + align-items: start; + /*margin-bottom: 10px;*/ + margin-left: 0; + white-space: normal; + } + + #yolov5-train .el-tabs.el-tabs-cards .el-radio__label div { + color: #7f858e; + font-size: 13px; + } + + .beautiful-table { border-collapse: collapse; } + .beautiful-table tr:nth-child(2n) { background-color: #f6f8fa; } + .beautiful-table td, .beautiful-table th { + border: 1px solid #dfe2e5; + padding: 6px 13px; + text-align: center; + line-height: 20px; + } + + #yolov5-train .el-tabs.el-tabs-cards { border-radius: 4px; box-shadow: none; } + #yolov5-train .el-tabs.el-tabs-cards .el-tabs__header { background-color: #f6fafd; } + #yolov5-train .el-tabs.el-tabs-cards .el-tabs__nav { float: none; display: flex; justify-content: + space-between; } + #yolov5-train .el-tabs.el-tabs-cards .el-tabs__item { flex: 1; margin-bottom: -3px; padding: 9px 16px 13px; + height: auto; line-height: normal; border-radius: 4px; } + + + + + + + Pretrained on COCO + + + + + + + + + + + + +
+
+
+ {{row["subtitle"]}} +
+
+
+ + {{model[column.key]}} + +
+
+ {{model[column.key]}} +
+
+ + + + Custom + + + + + + diff --git a/supervisely/serve/src/nn_utils.py b/supervisely/serve/src/nn_utils.py index e0ce21a8e19a..a4028a8453e5 100644 --- a/supervisely/serve/src/nn_utils.py +++ b/supervisely/serve/src/nn_utils.py @@ -48,24 +48,25 @@ def load_model(weights_path, imgsz=640, device='cpu'): else: sly.logger.warning(f"Image size is not found in model checkpoint. Use default: {IMG_SIZE}") imgsz = IMG_SIZE - imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size + stride = int(model.stride.max()) # model stride + imgsz = check_img_size(imgsz, s=stride) # check img_size if half: model.half() # to FP16 - img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img - _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once + if device.type != 'cpu': + model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once - return model, half, device, imgsz + return model, half, device, imgsz, stride -def inference(model, half, device, imgsz, image: np.ndarray, meta: sly.ProjectMeta, conf_thres=0.25, iou_thres=0.45, +def inference(model, half, device, imgsz, stride, image: np.ndarray, meta: sly.ProjectMeta, conf_thres=0.25, iou_thres=0.45, augment=False, agnostic_nms=False, debug_visualization=False) -> sly.Annotation: names = model.module.names if hasattr(model, 'module') else model.names - img0 = image + img0 = image # RGB # Padded resize - img = letterbox(img0, new_shape=imgsz)[0] + img = letterbox(img0, new_shape=imgsz, stride=stride)[0] img = img.transpose(2, 0, 1) # to 3x416x416 img = np.ascontiguousarray(img) diff --git a/supervisely/serve/src/sly_serve.py b/supervisely/serve/src/sly_serve.py index 9389ddd24114..cbdfbd3f2ed9 100644 --- a/supervisely/serve/src/sly_serve.py +++ b/supervisely/serve/src/sly_serve.py @@ -19,7 +19,7 @@ meta: sly.ProjectMeta = None modelWeightsOptions = os.environ['modal.state.modelWeightsOptions'] -pretrained_weights = os.environ['modal.state.modelSize'] +pretrained_weights = os.environ['modal.state.selectedModel'].lower() custom_weights = os.environ['modal.state.weightsPath'] @@ -29,6 +29,7 @@ half = None device = None imgsz = None +stride = None settings_path = os.path.join(root_source_path, "supervisely/serve/custom_settings.yaml") @@ -98,7 +99,7 @@ def inference_image_path(image_path, context, state, app_logger): } rect = results[0] image = sly.image.crop(image, rect) - ann_json = inference(model, half, device, imgsz, image, meta, + ann_json = inference(model, half, device, imgsz, stride, image, meta, conf_thres=conf_thres, iou_thres=iou_thres, augment=augment, debug_visualization=debug_visualization) return ann_json @@ -168,12 +169,13 @@ def debug_inference(): @sly.timeit def preprocess(api: sly.Api, task_id, context, state, app_logger): global model, half, device, imgsz, meta, final_weights + global stride # download weights progress = sly.Progress("Downloading weights", 1, is_size=True, need_info_log=True) local_path = os.path.join(my_app.data_dir, "weights.pt") if modelWeightsOptions == "pretrained": - url = os.path.join("https://github.com/ultralytics/yolov5/releases/download/v4.0/", pretrained_weights) + url = f"https://github.com/ultralytics/yolov5/releases/download/v5.0/{pretrained_weights}.pt" final_weights = url sly.fs.download(url, local_path, my_app.cache, progress) elif modelWeightsOptions == "custom": @@ -185,7 +187,7 @@ def preprocess(api: sly.Api, task_id, context, state, app_logger): raise ValueError("Unknown weights option {!r}".format(modelWeightsOptions)) # load model on device - model, half, device, imgsz = load_model(local_path, device=DEVICE_STR) + model, half, device, imgsz, stride = load_model(local_path, device=DEVICE_STR) meta = construct_model_meta(model) sly.logger.info("Model has been successfully deployed") @@ -202,6 +204,7 @@ def main(): my_app.run(initial_events=[{"command": "preprocess"}]) +#@TODO: move inference methods to SDK #@TODO: augment inference #@TODO: https://pypi.org/project/cachetools/ if __name__ == "__main__": diff --git a/supervisely/train/README.md b/supervisely/train/README.md index 99fdb494038a..c0f6c87578cc 100644 --- a/supervisely/train/README.md +++ b/supervisely/train/README.md @@ -22,6 +22,11 @@ Train YOLOv5 on your custom data. All annotations will be converted to the bounding boxes automatically. Configure Train / Validation splits, model and training hyperparameters. Run on any agent (with GPU) in your team. Monitor progress, metrics, logs and other visualizations withing a single dashboard. + +Major releases: +- **May 17, 2021**: [v5.0 release](https://github.com/supervisely-ecosystem/yolov5/tree/v5.0.0): merge updates from original YOLOv5 repo (including new model architectures), split data to train/val based on datasets or tags, update UI for settings, other fixes +- **March 3, 2021**: [v4.0 release](https://github.com/supervisely-ecosystem/yolov5/tree/v4.0.9): YOLOv5 is integrated to Supervisely (train / serve / inference) + # How To Use 1. Add app to your team from Ecosystem @@ -45,4 +50,4 @@ Watch short video for more details: # Screenshot - + diff --git a/supervisely/train/config.json b/supervisely/train/config.json index 3dcf3042b0ab..f92022b6b8b9 100644 --- a/supervisely/train/config.json +++ b/supervisely/train/config.json @@ -19,5 +19,5 @@ "context_root": "Neural Networks", "context_category": "YOLO v5" }, - "instance_version": "6.3.2" + "instance_version": "6.4.14" } diff --git a/supervisely/train/debug.env b/supervisely/train/debug.env index 561feaa23f0f..f32ca184fde1 100644 --- a/supervisely/train/debug.env +++ b/supervisely/train/debug.env @@ -2,14 +2,14 @@ PYTHONUNBUFFERED=1 DEBUG_APP_DIR="/app_debug_data" DEBUG_CACHE_DIR="/app_cache" +LOG_LEVEL="debug" -TASK_ID=2391 +TASK_ID=4326 -context.teamId=7 -context.workspaceId=263 -#modal.state.slyProjectId=1843 # coco-128 -modal.state.slyProjectId=1805 # lemons-annotated -#modal.state.slyFile="put your value here" +context.teamId=229 +context.workspaceId=287 +#modal.state.slyProjectId=3529 # lemons-annotated +modal.state.slyProjectId=3643 # lemons with ready train/val tags SERVER_ADDRESS="put your value here" API_TOKEN="put your value here" diff --git a/supervisely/train/src/gui.html b/supervisely/train/src/gui.html index 463bfe2816ab..be2bb8140293 100644 --- a/supervisely/train/src/gui.html +++ b/supervisely/train/src/gui.html @@ -1,317 +1,40 @@ -
- - - {{data.projectName}} ({{data.projectImagesCount}} images) - - - - - - - - - - - - - - - - - - - Random - Based on image tags (not implemented yet) - Train = Val (not implemented yet) - - -
- - - - - - - - - - - -
-
- - - - - - - - -
If image does not have such tags, it will be assigned to training set
-
-
- All images are in both training and validation sets -
- - - - - - Pretrained on COCO - From custom model - - - -
- - - - - - - -
-
- - - -
- - - - - - - - - - - - - - - - - Multi-scale - - - Single class - - - - - - - - - -
-
Training hyperparameters templates:
- - scratch - finetune - - Restore - Defaults - -
-
Edit settings in YAML format:
- - - - - - - - Start training - -
- 0 training classes are selected -
-
- Path to model weights is not defined -
- -
-
{{data.progressName}}: {{data.currentProgressLabel}} / - {{data.totalProgressLabel}} -
- -
- - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - -
-
- - - - -
-
- - - - - - - - - - -
- Link to the directory with training artifacts will be here once training is finished -
- - - {{data.outputName}} - - - - - - +
+ + #yolov5-train .el-tabs.el-tabs-cards .el-radio { + display: flex; + align-items: start; + /*margin-bottom: 10px;*/ + margin-left: 0; + white-space: normal; + } + + #yolov5-train .el-tabs.el-tabs-cards .el-radio__label div { + color: #7f858e; + font-size: 13px; + } + + .beautiful-table { border-collapse: collapse; } + .beautiful-table tr:nth-child(2n) { background-color: #f6f8fa; } + .beautiful-table td, .beautiful-table th { + border: 1px solid #dfe2e5; + padding: 6px 13px; + text-align: center; + line-height: 20px; + } + + #yolov5-train .el-tabs.el-tabs-cards { border-radius: 4px; box-shadow: none; } + #yolov5-train .el-tabs.el-tabs-cards .el-tabs__header { background-color: #f6fafd; } + #yolov5-train .el-tabs.el-tabs-cards .el-tabs__nav { float: none; display: flex; justify-content: + space-between; } + #yolov5-train .el-tabs.el-tabs-cards .el-tabs__item { flex: 1; margin-bottom: -3px; padding: 9px 16px 13px; + height: auto; line-height: normal; border-radius: 4px; } + + + {% include 'supervisely/train/src/ui/input_project.html' %} + {% include 'supervisely/train/src/ui/classes.html' %} + {% include 'supervisely/train/src/ui/splits.html' %} + {% include 'supervisely/train/src/ui/architectures.html' %} + {% include 'supervisely/train/src/ui/hyperparameters.html' %} + {% include 'supervisely/train/src/ui/monitoring.html' %} + {% include 'supervisely/train/src/ui/artifacts.html' %}
\ No newline at end of file diff --git a/supervisely/train/src/sly_init_ui.py b/supervisely/train/src/sly_init_ui.py deleted file mode 100644 index a36940f5a4c2..000000000000 --- a/supervisely/train/src/sly_init_ui.py +++ /dev/null @@ -1,153 +0,0 @@ -import os -import supervisely_lib as sly - -import sly_train_globals as globals -import sly_metrics as metrics - - -empty_gallery = { - "content": { - "projectMeta": sly.ProjectMeta().to_json(), - "annotations": {}, - "layout": [] - } -} - - -def init_input_project(data, project_info): - data["projectId"] = globals.project_id - data["projectName"] = project_info.name - data["projectImagesCount"] = project_info.items_count - data["projectPreviewUrl"] = globals.api.image.preview_url(project_info.reference_image_url, 100, 100) - - -def init_classes_stats(data, state, project_meta): - stats = globals.api.project.get_stats(globals.project_id) - class_images = {} - for item in stats["images"]["objectClasses"]: - class_images[item["objectClass"]["name"]] = item["total"] - class_objects = {} - for item in stats["objects"]["items"]: - class_objects[item["objectClass"]["name"]] = item["total"] - - classes_json = project_meta.obj_classes.to_json() - for obj_class in classes_json: - obj_class["imagesCount"] = class_images[obj_class["title"]] - obj_class["objectsCount"] = class_objects[obj_class["title"]] - - data["classes"] = classes_json - state["selectedClasses"] = [] - - state["classes"] = len(classes_json) * [True] - - -def init_random_split(PROJECT, data, state): - data["randomSplit"] = [ - {"name": "train", "type": "success"}, - {"name": "val", "type": "primary"}, - {"name": "total", "type": "gray"}, - ] - data["totalImagesCount"] = PROJECT.items_count - - train_percent = 80 - train_count = int(PROJECT.items_count / 100 * train_percent) - state["randomSplit"] = { - "count": { - "total": PROJECT.items_count, - "train": train_count, - "val": PROJECT.items_count - train_count - }, - "percent": { - "total": 100, - "train": train_percent, - "val": 100 - train_percent - }, - "shareImagesBetweenSplits": False, - "sliderDisabled": False, - } - - state["splitMethod"] = 1 - state["trainTagName"] = "" - state["valTagName"] = "" - - -def init_model_settings(data, state): - data["modelSizes"] = [ - {"label": "yolov5s", "config": "yolov5s.yaml", "params": "7.3M"}, - {"label": "yolov5m", "config": "yolov5m.yaml", "params": "21.4M"}, - {"label": "yolov5l", "config": "yolov5l.yaml", "params": "47.0M"}, - {"label": "yolov5x", "config": "yolov5x.yaml", "params": "87.7M"}, - ] - state["modelSize"] = data["modelSizes"][0]["label"] - state["modelWeightsOptions"] = 1 - state["pretrainedWeights"] = f'{data["modelSizes"][0]["label"]}.pt' - - # @TODO: for debug - #state["weightsPath"] = "/yolov5_train/coco128_002/2390/weights/best.pt" - state["weightsPath"] = "" - - -def init_training_hyperparameters(state): - state["epochs"] = 10 - state["batchSize"] = 16 - state["imgSize"] = 640 - state["multiScale"] = False - state["singleClass"] = False - state["device"] = '0' - state["workers"] = 8 # 0 - for debug - state["activeTabName"] = "General" - state["hyp"] = { - "scratch": globals.scratch_str, - "finetune": globals.finetune_str, - } - state["hypRadio"] = "scratch" - - -def init_start_state(state): - state["started"] = False - state["activeNames"] = [] - - -def init_galleries(data): - data["vis"] = empty_gallery - data["labelsVis"] = empty_gallery - data["predVis"] = empty_gallery - data["syncBindings"] = [] - - -def init_progress(data): - data["progressName"] = "" - data["currentProgress"] = 0 - data["totalProgress"] = 0 - data["currentProgressLabel"] = "" - data["totalProgressLabel"] = "" - - -def init_output(data): - data["outputUrl"] = "" - data["outputName"] = "" - - -def init(data, state): - init_input_project(data, globals.project_info) - init_classes_stats(data, state, globals.project_meta) - init_random_split(globals.project_info, data, state) - init_model_settings(data, state) - init_training_hyperparameters(state) - init_start_state(state) - init_galleries(data) - init_progress(data) - init_output(data) - metrics.init(data, state) - - -def set_output(): - file_info = globals.api.file.get_info_by_path(globals.team_id, - os.path.join(globals.remote_artifacts_dir, 'results.png')) - fields = [ - {"field": "data.outputUrl", "payload": globals.api.file.get_url(file_info.id)}, - {"field": "data.outputName", "payload": globals.remote_artifacts_dir}, - ] - globals.api.app.set_fields(globals.task_id, fields) - globals.api.task.set_output_directory(globals.task_id, file_info.id, globals.remote_artifacts_dir) - diff --git a/supervisely/train/src/sly_metrics.py b/supervisely/train/src/sly_metrics.py index dd6f3b7ab800..06222df4c6ee 100644 --- a/supervisely/train/src/sly_metrics.py +++ b/supervisely/train/src/sly_metrics.py @@ -24,19 +24,19 @@ def init_chart(title, names, xs, ys, smoothing=None): def init(data, state): demo_x = [[], []] #[[1, 2, 3, 4], [2, 4, 6, 8]] demo_y = [[], []] #[[10, 15, 13, 17], [16, 5, 11, 9]] - data["mBox"] = init_chart("Box Loss", - names=["train", "val"], - xs=demo_x, - ys=demo_y, - smoothing=0.6) + data["mGIoU"] = init_chart("GIoU", + names=["train", "val"], + xs=demo_x, + ys=demo_y, + smoothing=0.6) - data["mObjectness"] = init_chart("Obj Loss", + data["mObjectness"] = init_chart("Objectness", names=["train", "val"], xs=demo_x, ys=demo_y, smoothing=0.6) - data["mClassification"] = init_chart("Cls Loss", + data["mClassification"] = init_chart("Classification", names=["train", "val"], xs=demo_x, ys=demo_y, @@ -54,23 +54,24 @@ def init(data, state): state["smoothing"] = 0.6 -def send_metrics(epoch, epochs, metrics): - sly.logger.debug(f"Metrics: epoch {epoch} / {epochs}", extra={"metrics": metrics}) +def send_metrics(epoch, epochs, metrics, log_period=1): + sly.logger.debug(f"Metrics: epoch {epoch + 1} / {epochs}", extra={"metrics": metrics}) - fields = [ - {"field": "data.mBox.series[0].data", "payload": [[epoch, metrics["train/box_loss"]]], "append": True}, - {"field": "data.mBox.series[1].data", "payload": [[epoch, metrics["val/box_loss"]]], "append": True}, + if epoch % log_period == 0 or epoch + 1 == epochs: + fields = [ + {"field": "data.mGIoU.series[0].data", "payload": [[epoch, metrics["train/box_loss"]]], "append": True}, + {"field": "data.mGIoU.series[1].data", "payload": [[epoch, metrics["val/box_loss"]]], "append": True}, - {"field": "data.mObjectness.series[0].data", "payload": [[epoch, metrics["train/obj_loss"]]], "append": True}, - {"field": "data.mObjectness.series[1].data", "payload": [[epoch, metrics["val/obj_loss"]]], "append": True}, + {"field": "data.mObjectness.series[0].data", "payload": [[epoch, metrics["train/obj_loss"]]], "append": True}, + {"field": "data.mObjectness.series[1].data", "payload": [[epoch, metrics["val/obj_loss"]]], "append": True}, - {"field": "data.mClassification.series[0].data", "payload": [[epoch, metrics["train/cls_loss"]]], "append": True}, - {"field": "data.mClassification.series[1].data", "payload": [[epoch, metrics["val/cls_loss"]]], "append": True}, + {"field": "data.mClassification.series[0].data", "payload": [[epoch, metrics["train/cls_loss"]]], "append": True}, + {"field": "data.mClassification.series[1].data", "payload": [[epoch, metrics["val/cls_loss"]]], "append": True}, - {"field": "data.mPR.series[0].data", "payload": [[epoch, metrics["metrics/precision"]]], "append": True}, - {"field": "data.mPR.series[1].data", "payload": [[epoch, metrics["metrics/recall"]]], "append": True}, + {"field": "data.mPR.series[0].data", "payload": [[epoch, metrics["metrics/precision"]]], "append": True}, + {"field": "data.mPR.series[1].data", "payload": [[epoch, metrics["metrics/recall"]]], "append": True}, - {"field": "data.mMAP.series[0].data", "payload": [[epoch, metrics["metrics/mAP_0.5"]]], "append": True}, - {"field": "data.mMAP.series[1].data", "payload": [[epoch, metrics["metrics/mAP_0.5:0.95"]]], "append": True}, - ] - globals.api.app.set_fields(globals.task_id, fields) + {"field": "data.mMAP.series[0].data", "payload": [[epoch, metrics["metrics/mAP_0.5"]]], "append": True}, + {"field": "data.mMAP.series[1].data", "payload": [[epoch, metrics["metrics/mAP_0.5:0.95"]]], "append": True}, + ] + globals.api.app.set_fields(globals.task_id, fields) diff --git a/supervisely/train/src/sly_metrics_utils.py b/supervisely/train/src/sly_metrics_utils.py deleted file mode 100644 index a5d175ae766f..000000000000 --- a/supervisely/train/src/sly_metrics_utils.py +++ /dev/null @@ -1,142 +0,0 @@ -# old plotly implementation -# ================================================================= -# import supervisely_lib as sly -# import sly_train_globals as globals -# -# -# chart_train_style = { -# "name": "train", -# "mode": "lines+markers", -# "line": { -# "color": "rgb(0, 0, 255)", -# "width": 2 -# } -# } -# -# chart_val_style = { -# "name": "val", -# "mode": "lines+markers", -# "line": { -# "color": "rgb(255, 128, 0)", -# "width": 2 -# } -# } -# -# chart_layout = { -# "xaxis": { -# # "title": "epoch", -# "automargin": True -# }, -# "yaxis": { -# # "title": "value", -# "automargin": True -# }, -# "legend": { -# "orientation": "h", -# "yanchor": "bottom", -# "y": 0.99, -# "xanchor": "right", -# "x": 1 -# } -# } -# -# -# def init_chart(title, names, colors, xs, ys): -# data = [] -# for name, color, x, y in zip(names, colors, xs, ys): -# data.append({ -# "x": x, -# "y": y, -# "name": name, -# "mode": "lines+markers", -# #"type": "scattergl", -# "line": { -# "color": f"rgb({color[0]}, {color[1]}, {color[2]})", -# "width": 2 -# } -# }) -# -# chart = { -# "data": data, -# "layout": { -# "title": { -# "text": f"{title}", -# "xanchor": "left", -# 'y': 0.97, -# 'x': 0.03, -# "font": { -# "size": 14, -# "color": "rgb(96, 96, 96)", -# #"color": "rgb(0, 150, 0)", -# } -# }, -# **chart_layout -# } -# } -# return chart -# -# -# def init(data): -# demo_x = [[], []] #[[1, 2, 3, 4], [2, 4, 6, 8]] -# demo_y = [[], []] #[[10, 15, 13, 17], [16, 5, 11, 9]] -# data["mBox"] = init_chart("Box Loss", -# names=["train", "val"], -# colors=[[0, 0, 255], [255, 128, 0]], -# xs=demo_x, -# ys=demo_y) -# -# data["mObjectness"] = init_chart("Objectness Loss", -# names=["train", "val"], -# colors=[[0, 0, 255], [255, 128, 0]], -# xs=demo_x, -# ys=demo_y) -# -# data["mClassification"] = init_chart("Classification Loss", -# names=["train", "val"], -# colors=[[0, 0, 255], [255, 128, 0]], -# xs=demo_x, -# ys=demo_y) -# -# data["mPR"] = init_chart("Precision / Recall", -# names=["precision", "recall"], -# colors=[[255, 0, 255], [127, 0, 255]], -# xs=demo_x, -# ys=demo_y) -# -# data["mMAP"] = init_chart("mAP", -# names=["mAP@0.5", "mAP@0.5:0.95"], -# colors=[[255, 0, 255], [0, 255, 255]], -# xs=demo_x, -# ys=demo_y) -# -# -# def send_metrics(epoch, epochs, metrics): -# sly.logger.debug(f"Metrics: epoch {epoch} / {epochs}", extra={"metrics": metrics}) -# -# fields = [ -# {"field": "data.mBox.data[0].x", "payload": epoch, "append": True}, -# {"field": "data.mBox.data[1].x", "payload": epoch, "append": True}, -# {"field": "data.mBox.data[0].y", "payload": metrics["train/box_loss"], "append": True}, -# {"field": "data.mBox.data[1].y", "payload": metrics["val/box_loss"], "append": True}, -# -# {"field": "data.mObjectness.data[0].x", "payload": epoch, "append": True}, -# {"field": "data.mObjectness.data[1].x", "payload": epoch, "append": True}, -# {"field": "data.mObjectness.data[0].y", "payload": metrics["train/obj_loss"], "append": True}, -# {"field": "data.mObjectness.data[1].y", "payload": metrics["val/obj_loss"], "append": True}, -# -# {"field": "data.mClassification.data[0].x", "payload": epoch, "append": True}, -# {"field": "data.mClassification.data[1].x", "payload": epoch, "append": True}, -# {"field": "data.mClassification.data[0].y", "payload": metrics["train/cls_loss"], "append": True}, -# {"field": "data.mClassification.data[1].y", "payload": metrics["val/cls_loss"], "append": True}, -# -# {"field": "data.mPR.data[0].x", "payload": epoch, "append": True}, -# {"field": "data.mPR.data[1].x", "payload": epoch, "append": True}, -# {"field": "data.mPR.data[0].y", "payload": metrics["metrics/precision"], "append": True}, -# {"field": "data.mPR.data[1].y", "payload": metrics["metrics/recall"], "append": True}, -# -# {"field": "data.mMAP.data[0].x", "payload": epoch, "append": True}, -# {"field": "data.mMAP.data[1].x", "payload": epoch, "append": True}, -# {"field": "data.mMAP.data[0].y", "payload": metrics["metrics/mAP_0.5"], "append": True}, -# {"field": "data.mMAP.data[1].y", "payload": metrics["metrics/mAP_0.5:0.95"], "append": True}, -# ] -# globals.api.app.set_fields(globals.task_id, fields) diff --git a/supervisely/train/src/sly_train.py b/supervisely/train/src/sly_train.py index 77d77c00c691..91d0fb3d1758 100644 --- a/supervisely/train/src/sly_train.py +++ b/supervisely/train/src/sly_train.py @@ -1,23 +1,20 @@ import os import supervisely_lib as sly -from sly_train_globals import init_project_info_and_meta, \ - my_app, task_id, \ - team_id, workspace_id, project_id, \ - root_source_path, scratch_str, finetune_str - -# to import correct values -# project_info, project_meta, \ -# local_artifacts_dir, remote_artifacts_dir import sly_train_globals as g -from sly_train_val_split import train_val_split -import sly_init_ui as ui -from sly_prepare_data import filter_and_transform_labels +from sly_train_globals import \ + my_app, task_id, \ + team_id, workspace_id, project_id, \ + root_source_dir, scratch_str, finetune_str + +import ui as ui from sly_train_utils import init_script_arguments from sly_utils import get_progress_cb, upload_artifacts - - +from splits import get_train_val_sets, verify_train_val_sets +import yolov5_format as yolov5_format +from architectures import prepare_weights +from artifacts import set_task_output import train as train_yolov5 @@ -33,48 +30,53 @@ def restore_hyp(api: sly.Api, task_id, context, state, app_logger): @my_app.callback("train") @sly.timeit def train(api: sly.Api, task_id, context, state, app_logger): - api.app.set_field(task_id, "state.activeNames", ["labels", "train", "pred", "metrics"]) #"logs", - - # prepare directory for original Supervisely project - project_dir = os.path.join(my_app.data_dir, "sly_project") - sly.fs.mkdir(project_dir) - sly.fs.clean_dir(project_dir) # useful for debug, has no effect in production - - # download Sypervisely project (using cache) - sly.download_project(api, project_id, project_dir, cache=my_app.cache, - progress_cb=get_progress_cb("Download data (using cache)", g.project_info.items_count * 2)) - - # prepare directory for transformed data (nn will use it for training) - yolov5_format_dir = os.path.join(my_app.data_dir, "train_data") - sly.fs.mkdir(yolov5_format_dir) - sly.fs.clean_dir(yolov5_format_dir) # useful for debug, has no effect in production - - # split data to train/val sets, filter objects by classes, convert Supervisely project to YOLOv5 format(COCO) - train_split, val_split = train_val_split(project_dir, state) - train_classes = state["selectedClasses"] - progress_cb = get_progress_cb("Convert Supervisely to YOLOv5 format", g.project_info.items_count) - filter_and_transform_labels(project_dir, train_classes, train_split, val_split, yolov5_format_dir, progress_cb) - - # download initial weights from team files - if state["modelWeightsOptions"] == 2: # transfer learning from custom weights - weights_path_remote = state["weightsPath"] - weights_path_local = os.path.join(my_app.data_dir, sly.fs.get_file_name_with_ext(weights_path_remote)) - file_info = api.file.get_info_by_path(team_id, weights_path_remote) - api.file.download(team_id, weights_path_remote, weights_path_local, my_app.cache, - progress_cb=get_progress_cb("Download weights", file_info.sizeb, is_size=True)) - - # init sys.argv for main training script - init_script_arguments(state, yolov5_format_dir, g.project_info.name) - - # start train script - get_progress_cb("YOLOv5: Scanning data ", 1)(1) - train_yolov5.main() - - # upload artifacts directory to Team Files - upload_artifacts(g.local_artifacts_dir, g.remote_artifacts_dir) - - # show path to the artifacts directory in Team Files - ui.set_output() + try: + prepare_weights(state) + + # prepare directory for original Supervisely project + project_dir = os.path.join(my_app.data_dir, "sly_project") + sly.fs.mkdir(project_dir, remove_content_if_exists=True) # clean content for debug, has no effect in prod + + # download and preprocess Sypervisely project (using cache) + download_progress = get_progress_cb("Download data (using cache)", g.project_info.items_count * 2) + sly.download_project(api, project_id, project_dir, cache=my_app.cache, progress_cb=download_progress) + + # preprocessing: transform labels to bboxes, filter classes, ... + sly.Project.to_detection_task(project_dir, inplace=True) + train_classes = state["selectedClasses"] + sly.Project.remove_classes_except(project_dir, classes_to_keep=train_classes, inplace=True) + if state["unlabeledImages"] == "ignore": + sly.Project.remove_items_without_objects(project_dir, inplace=True) + + # split to train / validation sets (paths to images and annotations) + train_set, val_set = get_train_val_sets(project_dir, state) + verify_train_val_sets(train_set, val_set) + sly.logger.info(f"Train set: {len(train_set)} images") + sly.logger.info(f"Val set: {len(val_set)} images") + + # prepare directory for data in YOLOv5 format (nn will use it for training) + train_data_dir = os.path.join(my_app.data_dir, "train_data") + sly.fs.mkdir(train_data_dir, remove_content_if_exists=True) # clean content for debug, has no effect in prod + + # convert Supervisely project to YOLOv5 format + progress_cb = get_progress_cb("Convert Supervisely to YOLOv5 format", len(train_set) + len(val_set)) + yolov5_format.transform(project_dir, train_data_dir, train_set, val_set, progress_cb) + + # init sys.argv for main training script + init_script_arguments(state, train_data_dir, g.project_info.name) + + # start train script + api.app.set_field(task_id, "state.activeNames", ["labels", "train", "pred", "metrics"]) # "logs", + get_progress_cb("YOLOv5: Scanning data ", 1)(1) + train_yolov5.main() + + # upload artifacts directory to Team Files + upload_artifacts(g.local_artifacts_dir, g.remote_artifacts_dir) + set_task_output() + except Exception as e: + my_app.show_modal_window(f"Oops! Something went wrong, please try again or contact tech support. " + f"Find more info in the app logs. Error: {repr(e)}", level="error") + api.app.set_field(task_id, "state.started", False) # stop application get_progress_cb("Finished, app is stopped automatically", 1)(1) @@ -92,8 +94,7 @@ def main(): state = {} data["taskId"] = task_id - # read project information and meta (classes + tags) - init_project_info_and_meta() + my_app.compile_template(g.root_source_dir) # init data for UI widgets ui.init(data, state) @@ -101,13 +102,10 @@ def main(): my_app.run(data=data, state=state) -# @TODO: change pip requirements to quickly skip them (already installed) -# @TODO: handle soft stop event - +# @TODO: doublecheck inference +# @TODO: add to readme - open_app.lnk # New features: -# @TODO: adam or SGD opt? -# @TODO: train == val - handle case in data_config.yaml to avoid data duplication # @TODO: resume training -# @TODO: repeat dataset (for small lemons) +# @TODO: save checkpoint every N-th epochs if __name__ == "__main__": sly.main_wrapper("main", main) diff --git a/supervisely/train/src/sly_train_globals.py b/supervisely/train/src/sly_train_globals.py index 549a8976c3f9..c89dc9d15535 100644 --- a/supervisely/train/src/sly_train_globals.py +++ b/supervisely/train/src/sly_train_globals.py @@ -14,30 +14,34 @@ local_artifacts_dir = None remote_artifacts_dir = None +project_info = api.project.get_info_by_id(project_id) +project_meta = sly.ProjectMeta.from_json(api.project.get_meta(project_id)) -project_info = None -project_meta = None +root_source_dir = str(Path(sys.argv[0]).parents[3]) +sly.logger.info(f"Root source directory: {root_source_dir}") +sys.path.append(root_source_dir) -root_source_path = str(Path(sys.argv[0]).parents[3]) -sly.logger.info(f"Root source directory: {root_source_path}") -sys.path.append(root_source_path) +source_path = str(Path(sys.argv[0]).parents[0]) +sly.logger.info(f"Source directory: {source_path}") +sys.path.append(source_path) -# script_path = str(Path(sys.argv[0]).parents[3])) -# root_app_dir = script_path.parent.parent.absolute() -# sly.logger.info(f"Root app directory: {root_app_dir}") -# sys.path.append(root_app_dir) +ui_sources_dir = os.path.join(source_path, "ui") +sys.path.append(ui_sources_dir) +sly.logger.info(f"Added to sys.path: {ui_sources_dir}") +with open(os.path.join(root_source_dir, "data/hyp.scratch.yaml"), 'r') as file: + scratch_str = file.read() # yaml.safe_load( -def init_project_info_and_meta(): - global project_info, project_meta - project_info = api.project.get_info_by_id(project_id) - project_meta_json = api.project.get_meta(project_id) - project_meta = sly.ProjectMeta.from_json(project_meta_json) - +with open(os.path.join(root_source_dir, "data/hyp.finetune.yaml"), 'r') as file: + finetune_str = file.read() # yaml.safe_load( -with open(os.path.join(root_source_path, "data/hyp.scratch.yaml"), 'r') as file: - scratch_str = file.read() # yaml.safe_load( -with open(os.path.join(root_source_path, "data/hyp.finetune.yaml"), 'r') as file: - finetune_str = file.read() # yaml.safe_load( \ No newline at end of file +runs_dir = os.path.join(my_app.data_dir, 'runs') +sly.fs.mkdir(runs_dir, remove_content_if_exists=True) # for debug, does nothing in production +experiment_name = str(task_id) +local_artifacts_dir = os.path.join(runs_dir, experiment_name) +sly.logger.info(f"All training artifacts will be saved to local directory {local_artifacts_dir}") +remote_artifacts_dir = os.path.join("/yolov5_train", project_info.name, experiment_name) +remote_artifacts_dir = api.file.get_free_dir_name(team_id, remote_artifacts_dir) +sly.logger.info(f"After training artifacts will be uploaded to Team Files: {remote_artifacts_dir}") \ No newline at end of file diff --git a/supervisely/train/src/sly_train_utils.py b/supervisely/train/src/sly_train_utils.py index 0d7f6a0e3570..80e7db28d4a3 100644 --- a/supervisely/train/src/sly_train_utils.py +++ b/supervisely/train/src/sly_train_utils.py @@ -11,28 +11,23 @@ def init_script_arguments(state, yolov5_format_dir, input_project_name): global local_artifacts_dir, remote_artifacts_dir + sys.argv.append("--sly") + data_path = os.path.join(yolov5_format_dir, 'data_config.yaml') sys.argv.extend(["--data", data_path]) - try: - hyp_content = yaml.safe_load(state["hyp"][state["hypRadio"]]) - hyp = os.path.join(my_app.data_dir, 'hyp.custom.yaml') - with open(hyp, 'w') as f: - f.write(state["hyp"][state["hypRadio"]]) - except yaml.YAMLError as e: - sly.logger.error(repr(e)) - api.app.set_field(task_id, "state.started", False) - return + hyp_content = yaml.safe_load(state["hyp"][state["hypRadio"]]) + hyp = os.path.join(my_app.data_dir, 'hyp.custom.yaml') + with open(hyp, 'w') as f: + f.write(state["hyp"][state["hypRadio"]]) sys.argv.extend(["--hyp", hyp]) - weights = "" # random (not tested) - if state["modelWeightsOptions"] == 1: - weights = state["pretrainedWeights"] - cfg = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../../models', f"{state['modelSize']}.yaml") + if state["weightsInitialization"] == "coco": + model_name = state['selectedModel'].lower() + _sub_path = "models/hub" if model_name.endswith('6') else "models" + cfg = os.path.join(g.root_source_dir, _sub_path, f"{model_name}.yaml") sys.argv.extend(["--cfg", cfg]) - elif state["modelWeightsOptions"] == 2: - weights = state["weightsPath"] - sys.argv.extend(["--weights", weights]) + sys.argv.extend(["--weights", state["weightsPath"]]) sys.argv.extend(["--epochs", str(state["epochs"])]) sys.argv.extend(["--batch-size", str(state["batchSize"])]) @@ -45,30 +40,12 @@ def init_script_arguments(state, yolov5_format_dir, input_project_name): if "workers" in state: sys.argv.extend(["--workers", str(state["workers"])]) + if state["optimizer"] == "Adam": + sys.argv.append("--adam") - training_dir = os.path.join(my_app.data_dir, 'experiment', input_project_name) - experiment_name = str(task_id) - local_artifacts_dir = os.path.join(training_dir, experiment_name) - _exp_index = 1 - while sly.fs.dir_exists(local_artifacts_dir): - experiment_name = "{}_{:03d}".format(task_id, _exp_index) - local_artifacts_dir = os.path.join(training_dir, experiment_name) - _exp_index += 1 - g.local_artifacts_dir = local_artifacts_dir - - sys.argv.extend(["--project", training_dir]) - sys.argv.extend(["--name", experiment_name]) - - sys.argv.append("--sly") - - remote_experiment_name = str(task_id) - remote_artifacts_dir = os.path.join("/yolov5_train", input_project_name, remote_experiment_name) - _exp_index = 1 - while api.file.dir_exists(team_id, remote_artifacts_dir): - remote_experiment_name = "{}_{:03d}".format(task_id, _exp_index) - remote_artifacts_dir = os.path.join("/yolov5_train", input_project_name, remote_experiment_name) - _exp_index += 1 - g.remote_artifacts_dir = remote_artifacts_dir + sys.argv.extend(["--metrics_period", str(state["metricsPeriod"])]) + sys.argv.extend(["--project", g.runs_dir]) + sys.argv.extend(["--name", g.experiment_name]) def send_epoch_log(epoch, epochs, progress): diff --git a/supervisely/train/src/sly_train_val_split.py b/supervisely/train/src/sly_train_val_split.py deleted file mode 100644 index e9807c5c3b0a..000000000000 --- a/supervisely/train/src/sly_train_val_split.py +++ /dev/null @@ -1,55 +0,0 @@ -import random -import supervisely_lib as sly - - -def _list_items(project_dir): - items = [] - project = sly.Project(project_dir, sly.OpenMode.READ) - for dataset in project: - for item_name in dataset: - items.append((dataset.name, item_name)) - return items - - -def _split_random(project_dir, train_count, val_count): - items = _list_items(project_dir) - random.shuffle(items) - train_items = items[:train_count] - val_items = items[train_count:] - if len(val_items) != val_count: - sly.logger.warn("Issue in train/val random split in GUI", extra={ - "train_count": train_count, - "val_count": val_count, - "items_count": len(items), - "train_count + val_count": train_count + val_count - }) - #raise RuntimeError("Incorrect train/val random split") - return train_items, val_items - - -def _split_same(project_dir): - items = _list_items(project_dir) - return items, items.copy() - - -def _split_tags(project_dir, train_tag_name, val_tag_name): - raise NotImplementedError() - - -def train_val_split(project_dir, state): - split_method = state["splitMethod"] - train_count = state["randomSplit"]["count"]["train"] - val_count = state["randomSplit"]["count"]["val"] - - train_split = None - val_split = None - if split_method == 1: # Random - train_split, val_split = _split_random(project_dir, train_count, val_count) - elif split_method == 2: # Based on image tags - train_split, val_split = _split_tags() - elif split_method == 3: # Train = Val - train_split, val_split = _split_same() - else: - raise ValueError(f"Train/val split method: {split_method} unknown") - - return train_split, val_split \ No newline at end of file diff --git a/supervisely/train/src/sly_utils.py b/supervisely/train/src/sly_utils.py index ba652011a4ea..5dd875647fe9 100644 --- a/supervisely/train/src/sly_utils.py +++ b/supervisely/train/src/sly_utils.py @@ -36,10 +36,20 @@ def update_uploading_progress(count, api: sly.Api, task_id, progress: sly.Progre _update_progress_ui(api, task_id, progress, stdout_print=True) +def _save_link_to_ui(local_dir, app_url): + # save report to file *.lnk (link to report) + name = "open_app.lnk" + local_path = os.path.join(local_dir, name) + sly.fs.ensure_base_path(local_path) + with open(local_path, "w") as text_file: + print(app_url, file=text_file) + + def upload_artifacts(local_dir, remote_dir): def _gen_message(current, total): return f"Upload artifacts to Team Files [{current}/{total}] " + _save_link_to_ui(local_dir, globals.my_app.app_url) local_files = sly.fs.list_files_recursively(local_dir) total_size = sum([sly.fs.get_file_size(file_path) for file_path in local_files]) diff --git a/supervisely/train/src/ui/architectures.html b/supervisely/train/src/ui/architectures.html new file mode 100644 index 000000000000..4b31235c18d9 --- /dev/null +++ b/supervisely/train/src/ui/architectures.html @@ -0,0 +1,46 @@ + + + + + Pretrained on COCO +
Default pretrained checkpoints provided by authors of YOLOv5
+ + + + + + + + + + + + +
+
+
+ {{row["subtitle"]}} +
+
+
+ + {{model[column.key]}} + +
+
+ {{model[column.key]}} +
+
+ + + + Custom weights + + + + + + + \ No newline at end of file diff --git a/supervisely/train/src/ui/architectures.py b/supervisely/train/src/ui/architectures.py new file mode 100644 index 000000000000..aa8b09240041 --- /dev/null +++ b/supervisely/train/src/ui/architectures.py @@ -0,0 +1,152 @@ +import errno +import os +import sly_train_globals as g +from sly_utils import get_progress_cb +import supervisely_lib as sly + + +def get_models_list(): + return [ + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5s", + "Size": 640, + "mAP^val": 36.7, + "mAP^test": 36.7, + "mAP^val_0.5": 55.4, + "Speed": 2.0, + "Params": 7.3, + "FLOPS": 17.0, + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5m", + "Size": 640, + "mAP^val": 44.5, + "mAP^test": 44.5, + "mAP^val_0.5": 63.1, + "Speed": 2.7, + "Params": 21.4, + "FLOPS": 51.3, + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5l", + "Size": 640, + "mAP^val": 48.2, + "mAP^test": 48.2, + "mAP^val_0.5": 66.9, + "Speed": 3.8, + "Params": 47.0, + "FLOPS": 115.4, + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5x", + "Size": 640, + "mAP^val": 50.4, + "mAP^test": 50.4, + "mAP^val_0.5": 68.8, + "Speed": 6.1, + "Params": 87.7, + "FLOPS": 218.8, + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5s6", + "Size": 1280, + "mAP^val": 43.3, + "mAP^test": 43.3, + "mAP^val_0.5": 61.9, + "Speed": 4.3, + "Params": 12.7, + "FLOPS": 17.4, + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5m6", + "Size": 1280, + "mAP^val": 50.5, + "mAP^test": 50.5, + "mAP^val_0.5": 68.7, + "Speed": 8.4, + "Params": 35.9, + "FLOPS": 52.4, + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5l6", + "Size": 1280, + "mAP^val": 53.4, + "mAP^test": 53.4, + "mAP^val_0.5": 71.1, + "Speed": 12.3, + "Params": 77.2, + "FLOPS": 117.7, + }, + { + "config": "", + "weightsUrl": "", + "Model": "YOLOv5x6", + "Size": 1280, + "mAP^val": 54.4, + "mAP^test": 54.4, + "mAP^val_0.5": 72.0, + "Speed": 22.4, + "Params": 141.8, + "FLOPS": 222.9, + }, + ] + + +def get_table_columns(): + return [ + {"key": "Model", "title": "Model", "subtitle": None}, + {"key": "Size", "title": "Size", "subtitle": "(pixels)"}, + {"key": "mAP^val", "title": "mAPval", "subtitle": "0.5:0.95"}, + {"key": "mAP^test", "title": "mAPtest", "subtitle": "0.5:0.95"}, + {"key": "mAP^val_0.5", "title": "mAPval", "subtitle": "0.5"}, + {"key": "Speed", "title": "Speed", "subtitle": "V100 (ms)"}, + {"key": "Params", "title": "Params", "subtitle": "(M)"}, + {"key": "FLOPS", "title": "FLOPS", "subtitle": "640 (B)"}, + ] + + +def init(data, state): + data["models"] = get_models_list() + data["modelColumns"] = get_table_columns() + state["selectedModel"] = "YOLOv5s" + state["weightsInitialization"] = "coco" + + # @TODO: for debug + #state["weightsPath"] = "/yolov5_train/coco128_002/2390/weights/best.pt" + state["weightsPath"] = "" + + +def prepare_weights(state): + if state["weightsInitialization"] == "custom": + # download custom weights + weights_path_remote = state["weightsPath"] + if not weights_path_remote.endswith(".pt"): + raise ValueError(f"Weights file has unsupported extension {sly.fs.get_file_ext(weights_path_remote)}. " + f"Supported: '.pt'") + weights_path_local = os.path.join(g.my_app.data_dir, sly.fs.get_file_name_with_ext(weights_path_remote)) + file_info = g.api.file.get_info_by_path(g.team_id, weights_path_remote) + if file_info is None: + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), weights_path_remote) + progress_cb = get_progress_cb("Download weights", file_info.sizeb, is_size=True) + g.api.file.download(g.team_id, weights_path_remote, weights_path_local, g.my_app.cache, progress_cb) + + state["_weightsPath"] = weights_path_remote + state["weightsPath"] = weights_path_local + else: + model_name = state['selectedModel'].lower() + state["weightsPath"] = f"{model_name}.pt" + sly.logger.info("Pretrained COCO weights will be added automatically") diff --git a/supervisely/train/src/ui/artifacts.html b/supervisely/train/src/ui/artifacts.html new file mode 100644 index 000000000000..437aa169e248 --- /dev/null +++ b/supervisely/train/src/ui/artifacts.html @@ -0,0 +1,14 @@ + +
+ Link to the directory with training artifacts will be here once training is finished +
+ + + {{data.outputName}} + + + + + \ No newline at end of file diff --git a/supervisely/train/src/ui/artifacts.py b/supervisely/train/src/ui/artifacts.py new file mode 100644 index 000000000000..cceafcabc13c --- /dev/null +++ b/supervisely/train/src/ui/artifacts.py @@ -0,0 +1,17 @@ +import os +import sly_train_globals as g + + +def init(data): + data["outputUrl"] = None + data["outputName"] = None + + +def set_task_output(): + file_info = g.api.file.get_info_by_path(g.team_id, os.path.join(g.remote_artifacts_dir, 'results.png')) + fields = [ + {"field": "data.outputUrl", "payload": g.api.file.get_url(file_info.id)}, + {"field": "data.outputName", "payload": g.remote_artifacts_dir}, + ] + g.api.app.set_fields(g.task_id, fields) + g.api.task.set_output_directory(g.task_id, file_info.id, g.remote_artifacts_dir) \ No newline at end of file diff --git a/supervisely/train/src/ui/classes.html b/supervisely/train/src/ui/classes.html new file mode 100644 index 000000000000..145d4df23632 --- /dev/null +++ b/supervisely/train/src/ui/classes.html @@ -0,0 +1,27 @@ + + + + + + + + + + + \ No newline at end of file diff --git a/supervisely/train/src/ui/classes.py b/supervisely/train/src/ui/classes.py new file mode 100644 index 000000000000..429e68d945bf --- /dev/null +++ b/supervisely/train/src/ui/classes.py @@ -0,0 +1,25 @@ +import supervisely_lib as sly + + +def init(api: sly.Api, data, state, project_id, project_meta: sly.ProjectMeta): + stats = api.project.get_stats(project_id) + class_images = {} + for item in stats["images"]["objectClasses"]: + class_images[item["objectClass"]["name"]] = item["total"] + class_objects = {} + for item in stats["objects"]["items"]: + class_objects[item["objectClass"]["name"]] = item["total"] + + classes_json = project_meta.obj_classes.to_json() + for obj_class in classes_json: + obj_class["imagesCount"] = class_images[obj_class["title"]] + obj_class["objectsCount"] = class_objects[obj_class["title"]] + + unlabeled_count = 0 + for ds_counter in stats["images"]["datasets"]: + unlabeled_count += ds_counter["imagesNotMarked"] + + data["classes"] = classes_json + state["selectedClasses"] = [] + state["classes"] = len(classes_json) * [True] + data["unlabeledCount"] = unlabeled_count \ No newline at end of file diff --git a/supervisely/train/src/ui/hyperparameters.html b/supervisely/train/src/ui/hyperparameters.html new file mode 100644 index 000000000000..f346fb020f35 --- /dev/null +++ b/supervisely/train/src/ui/hyperparameters.html @@ -0,0 +1,75 @@ + + + + + + + + + + + + Multi-scale + + + Single class + + + + + + + + +
+ Log metrics every + + epochs +
+ + + + + + + + + + + + Scratch mode +
Recommended hyperparameters for training from scratch
+ + + Restore Defaults + + + + + + Finetune mode +
Recommended hyperparameters for model finutuning
+ + + Restore Defaults + + + + + + \ No newline at end of file diff --git a/supervisely/train/src/ui/hyperparameters.py b/supervisely/train/src/ui/hyperparameters.py new file mode 100644 index 000000000000..3b28480d143e --- /dev/null +++ b/supervisely/train/src/ui/hyperparameters.py @@ -0,0 +1,19 @@ +import sly_train_globals as g + + +def init(state): + state["epochs"] = 10 + state["batchSize"] = 16 + state["imgSize"] = 640 + state["multiScale"] = False + state["singleClass"] = False + state["device"] = '0' + state["workers"] = 8 # 0 - for debug @TODO: for debug + state["activeTabName"] = "General" + state["hyp"] = { + "scratch": g.scratch_str, + "finetune": g.finetune_str, + } + state["hypRadio"] = "scratch" + state["optimizer"] = "SGD" + state["metricsPeriod"] = 1 diff --git a/supervisely/train/src/ui/input_project.html b/supervisely/train/src/ui/input_project.html new file mode 100644 index 000000000000..ff0ef021bc63 --- /dev/null +++ b/supervisely/train/src/ui/input_project.html @@ -0,0 +1,8 @@ + + + {{data.projectName}} ({{data.projectImagesCount}} + images) + + + \ No newline at end of file diff --git a/supervisely/train/src/ui/input_project.py b/supervisely/train/src/ui/input_project.py new file mode 100644 index 000000000000..aeda250bd7f5 --- /dev/null +++ b/supervisely/train/src/ui/input_project.py @@ -0,0 +1,8 @@ +import sly_train_globals as g + + +def init(data): + data["projectId"] = g.project_info.id + data["projectName"] = g.project_info.name + data["projectImagesCount"] = g.project_info.items_count + data["projectPreviewUrl"] = g.api.image.preview_url(g.project_info.reference_image_url, 100, 100) diff --git a/supervisely/train/src/ui/monitoring.html b/supervisely/train/src/ui/monitoring.html new file mode 100644 index 000000000000..30fcf6d3c5d8 --- /dev/null +++ b/supervisely/train/src/ui/monitoring.html @@ -0,0 +1,115 @@ + + + Start training + +
+ 0 training classes are selected +
+
+ Path to model weights is not defined +
+
+
{{data.progressName}}: {{data.currentProgressLabel}} / + {{data.totalProgressLabel}} +
+ +
+ + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + +
+
+ + + + +
+
+ + + + + + + + \ No newline at end of file diff --git a/supervisely/train/src/ui/monitoring.py b/supervisely/train/src/ui/monitoring.py new file mode 100644 index 000000000000..ec25c0562b35 --- /dev/null +++ b/supervisely/train/src/ui/monitoring.py @@ -0,0 +1,44 @@ +import supervisely_lib as sly +import sly_metrics as metrics + + +empty_gallery = { + "content": { + "projectMeta": sly.ProjectMeta().to_json(), + "annotations": {}, + "layout": [] + } +} + + +def init(data, state): + _init_start_state(state) + _init_galleries(data) + _init_progress(data) + _init_output(data) + metrics.init(data, state) + + +def _init_start_state(state): + state["started"] = False + state["activeNames"] = [] + + +def _init_galleries(data): + data["vis"] = empty_gallery + data["labelsVis"] = empty_gallery + data["predVis"] = empty_gallery + data["syncBindings"] = [] + + +def _init_progress(data): + data["progressName"] = "" + data["currentProgress"] = 0 + data["totalProgress"] = 0 + data["currentProgressLabel"] = "" + data["totalProgressLabel"] = "" + + +def _init_output(data): + data["outputUrl"] = "" + data["outputName"] = "" \ No newline at end of file diff --git a/supervisely/train/src/ui/splits.html b/supervisely/train/src/ui/splits.html new file mode 100644 index 000000000000..81130cc43b90 --- /dev/null +++ b/supervisely/train/src/ui/splits.html @@ -0,0 +1,133 @@ + + + + + Random +
Shuffle data and split with defined probability
+ + + + + + + + + + + + + + + + Based on image tags +
Images should have assigned train or val tag
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Based on datasets +
Select one or several datasets for every split
+ + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/supervisely/train/src/ui/splits.py b/supervisely/train/src/ui/splits.py new file mode 100644 index 000000000000..6ac50b2f4513 --- /dev/null +++ b/supervisely/train/src/ui/splits.py @@ -0,0 +1,72 @@ +import supervisely_lib as sly + + +def init(project_info, project_meta: sly.ProjectMeta, data, state): + data["randomSplit"] = [ + {"name": "train", "type": "success"}, + {"name": "val", "type": "primary"}, + {"name": "total", "type": "gray"}, + ] + data["totalImagesCount"] = project_info.items_count + + train_percent = 80 + train_count = int(project_info.items_count / 100 * train_percent) + state["randomSplit"] = { + "count": { + "total": project_info.items_count, + "train": train_count, + "val": project_info.items_count - train_count + }, + "percent": { + "total": 100, + "train": train_percent, + "val": 100 - train_percent + }, + "shareImagesBetweenSplits": False, + "sliderDisabled": False, + } + + state["splitMethod"] = "random" + + state["trainTagName"] = "" + if project_meta.tag_metas.get("train") is not None: + state["trainTagName"] = "train" + state["valTagName"] = "" + if project_meta.tag_metas.get("val") is not None: + state["valTagName"] = "val" + + state["trainDatasets"] = [] + state["valDatasets"] = [] + + state["unlabeledImages"] = "keep" + state["untaggedImages"] = "train" + + +def get_train_val_sets(project_dir, state): + split_method = state["splitMethod"] + sly.logger.info(f"Split method for train/val is '{split_method}'") + if split_method == "random": + train_count = state["randomSplit"]["count"]["train"] + val_count = state["randomSplit"]["count"]["val"] + train_set, val_set = sly.Project.get_train_val_splits_by_count(project_dir, train_count, val_count) + return train_set, val_set + elif split_method == "tags": + train_tag_name = state["trainTagName"] + val_tag_name = state["valTagName"] + add_untagged_to = state["untaggedImages"] + train_set, val_set = sly.Project.get_train_val_splits_by_tag(project_dir, train_tag_name, val_tag_name, add_untagged_to) + return train_set, val_set + elif split_method == "datasets": + train_datasets = state["trainDatasets"] + val_datasets = state["valDatasets"] + train_set, val_set = sly.Project.get_train_val_splits_by_dataset(project_dir, train_datasets, val_datasets) + return train_set, val_set + else: + raise ValueError(f"Unknown split method: {split_method}") + + +def verify_train_val_sets(train_set, val_set): + if len(train_set) == 0: + raise ValueError("Train set is empty, check or change split configuration") + if len(val_set) == 0: + raise ValueError("Val set is empty, check or change split configuration") \ No newline at end of file diff --git a/supervisely/train/src/ui/ui.py b/supervisely/train/src/ui/ui.py new file mode 100644 index 000000000000..05c0fe58bb01 --- /dev/null +++ b/supervisely/train/src/ui/ui.py @@ -0,0 +1,18 @@ +import sly_train_globals as g +import input_project as input_project +import classes as training_classes +import splits as train_val_split +import architectures as model_architectures +import hyperparameters as hyperparameters +import monitoring as monitoring +import artifacts as artifacts + + +def init(data, state): + input_project.init(data) + training_classes.init(g.api, data, state, g.project_id, g.project_meta) + train_val_split.init(g.project_info, g.project_meta, data, state) + model_architectures.init(data, state) + hyperparameters.init(state) + monitoring.init(data, state) + artifacts.init(data) diff --git a/supervisely/train/src/sly_prepare_data.py b/supervisely/train/src/yolov5_format.py similarity index 54% rename from supervisely/train/src/sly_prepare_data.py rename to supervisely/train/src/yolov5_format.py index aab0e6671dcb..ea519213eec5 100644 --- a/supervisely/train/src/sly_prepare_data.py +++ b/supervisely/train/src/yolov5_format.py @@ -3,7 +3,7 @@ import supervisely_lib as sly -def transform_label(class_names, img_size, label: sly.Label): +def _transform_label(class_names, img_size, label: sly.Label): class_number = class_names.index(label.obj_class.name) rect_geometry = label.geometry.to_bbox() center = rect_geometry.center @@ -15,13 +15,12 @@ def transform_label(class_names, img_size, label: sly.Label): return result -def _create_data_config(output_dir, meta: sly.ProjectMeta, keep_classes): +def _create_data_config(output_dir, meta: sly.ProjectMeta): class_names = [] class_colors = [] for obj_class in meta.obj_classes: - if obj_class.name in keep_classes: - class_names.append(obj_class.name) - class_colors.append(obj_class.color) + class_names.append(obj_class.name) + class_colors.append(obj_class.color) data_yaml = { "train": os.path.join(output_dir, "images/train"), @@ -44,11 +43,11 @@ def _create_data_config(output_dir, meta: sly.ProjectMeta, keep_classes): return data_yaml -def transform_annotation(ann, class_names, save_path): +def _transform_annotation(ann, class_names, save_path): yolov5_ann = [] for label in ann.labels: if label.obj_class.name in class_names: - yolov5_ann.append(transform_label(class_names, ann.img_size, label)) + yolov5_ann.append(_transform_label(class_names, ann.img_size, label)) with open(save_path, 'w') as file: file.write("\n".join(yolov5_ann)) @@ -67,9 +66,9 @@ def _process_split(project, class_names, images_dir, labels_dir, split, progress ann = sly.Annotation.from_json(ann_json, project.meta) save_ann_path = os.path.join(labels_dir, f"{sly.fs.get_file_name(item_name)}.txt") - empty = transform_annotation(ann, class_names, save_ann_path) + empty = _transform_annotation(ann, class_names, save_ann_path) if empty: - sly.logger.warning(f"Empty annotation dataset={dataset_name} image={item_name}") + sly.logger.warning(f"Empty annotation: dataset={dataset_name}, image={item_name}") img_path = dataset.get_img_path(item_name) save_img_path = os.path.join(images_dir, item_name) @@ -78,12 +77,31 @@ def _process_split(project, class_names, images_dir, labels_dir, split, progress progress_cb(len(batch)) -def filter_and_transform_labels(input_dir, train_classes, - train_split, val_split, - output_dir, progress_cb): - project = sly.Project(input_dir, sly.OpenMode.READ) - data_yaml = _create_data_config(output_dir, project.meta, train_classes) +def _transform_set(set_name, data_yaml, project_meta, items, progress_cb): + res_images_dir = data_yaml[set_name] + res_labels_dir = data_yaml[f"labels_{set_name}"] + classes_names = data_yaml["names"] - _process_split(project, data_yaml["names"], data_yaml["train"], data_yaml["labels_train"], train_split, progress_cb) - _process_split(project, data_yaml["names"], data_yaml["val"], data_yaml["labels_val"], val_split, progress_cb) + used_names = set() + for batch in sly.batched(items, batch_size=max(int(len(items) / 50), 10)): + for item in batch: + ann = sly.Annotation.load_json_file(item.ann_path, project_meta) + _item_name = sly._utils.generate_free_name(used_names, sly.fs.get_file_name(item.name)) + used_names.add(_item_name) + _ann_name = f"{_item_name}.txt" + _img_name = f"{_item_name}{sly.fs.get_file_ext(item.img_path)}" + + save_ann_path = os.path.join(res_labels_dir, _ann_name) + _transform_annotation(ann, classes_names, save_ann_path) + save_img_path = os.path.join(res_images_dir, _img_name) + sly.fs.copy_file(item.img_path, save_img_path) # hardlink not working with yolov5 ds caches + progress_cb(len(batch)) + + +def transform(sly_project_dir, yolov5_output_dir, train_set, val_set, progress_cb): + project = sly.Project(sly_project_dir, sly.OpenMode.READ) + data_yaml = _create_data_config(yolov5_output_dir, project.meta) + + _transform_set("train", data_yaml, project.meta, train_set, progress_cb) + _transform_set("val", data_yaml, project.meta, val_set, progress_cb) \ No newline at end of file diff --git a/test_yolov5.py b/test_yolov5.py index 4308814b31f5..af412356a064 100644 --- a/test_yolov5.py +++ b/test_yolov5.py @@ -17,7 +17,7 @@ from utils.plots import plot_images, output_to_target, plot_study_txt from utils.torch_utils import select_device, time_synchronized -from supervisely.train.src.sly_train_utils import upload_pred_vis +from sly_train_utils import upload_pred_vis import supervisely_lib as sly @@ -38,8 +38,11 @@ def test(data, save_hybrid=False, # for hybrid auto-labelling save_conf=False, # save auto-label confidences plots=True, - log_imgs=0, # number of logged images + wandb_logger=None, compute_loss=None, + half_precision=True, + is_coco=False, + opt=None, opt_sly=False): # Initialize/load model and set device @@ -52,52 +55,51 @@ def test(data, device = select_device(opt.device, batch_size=batch_size) # Directories - save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run + save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model model = attempt_load(weights, map_location=device) # load FP32 model - imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size + gs = max(int(model.stride.max()), 32) # grid size (max stride) + imgsz = check_img_size(imgsz, s=gs) # check img_size # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99 # if device.type != 'cpu' and torch.cuda.device_count() > 1: # model = nn.DataParallel(model) # Half - half = device.type != 'cpu' # half precision only supported on CUDA + half = device.type != 'cpu' and half_precision # half precision only supported on CUDA if half: model.half() # Configure model.eval() - is_coco = data.endswith('coco.yaml') # is COCO dataset - with open(data) as f: - data = yaml.load(f, Loader=yaml.FullLoader) # model dict + if isinstance(data, str): + is_coco = data.endswith('coco.yaml') + with open(data) as f: + data = yaml.safe_load(f) check_dataset(data) # check nc = 1 if single_cls else int(data['nc']) # number of classes iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95 niou = iouv.numel() # Logging - log_imgs, wandb = min(log_imgs, 100), None # ceil - try: - import wandb # Weights & Biases - except ImportError: - log_imgs = 0 - + log_imgs = 0 + if wandb_logger and wandb_logger.wandb: + log_imgs = min(wandb_logger.log_imgs, 100) # Dataloader if not training: - img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img - _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once - path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images - dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True, - prefix=colorstr('test: ' if opt.task == 'test' else 'val: '))[0] + if device.type != 'cpu': + model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once + task = opt.task if opt.task in ('train', 'val', 'test') else 'val' # path to train/val/test images + dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True, + prefix=colorstr(f'{task}: '))[0] seen = 0 confusion_matrix = ConfusionMatrix(nc=nc) names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)} coco91class = coco80_to_coco91_class() - #s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') + #s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0. loss = torch.zeros(3, device=device) jdict, stats, ap, ap_class, wandb_images = [], [], [], [], [] @@ -123,7 +125,7 @@ def test(data, targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling t = time_synchronized() - output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb) + output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True, agnostic=single_cls) t1 += time_synchronized() - t # Statistics per image @@ -140,6 +142,8 @@ def test(data, continue # Predictions + if single_cls: + pred[:, 5] = 0 predn = pred.clone() scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred @@ -152,15 +156,17 @@ def test(data, with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f: f.write(('%g ' * len(line)).rstrip() % line + '\n') - # W&B logging - if plots and len(wandb_images) < log_imgs: - box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, - "class_id": int(cls), - "box_caption": "%s %.3f" % (names[cls], conf), - "scores": {"class_score": conf}, - "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] - boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space - wandb_images.append(wandb.Image(img[si], boxes=boxes, caption=path.name)) + # W&B logging - Media Panel Plots + if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation + if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0: + box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": int(cls), + "box_caption": "%s %.3f" % (names[cls], conf), + "scores": {"class_score": conf}, + "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] + boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space + wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name)) + wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None # Append to pycocotools JSON dictionary if save_json: @@ -184,7 +190,7 @@ def test(data, tbox = xywh2xyxy(labels[:, 1:5]) scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels if plots: - confusion_matrix.process_batch(pred, torch.cat((labels[:, 0:1], tbox), 1)) + confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1)) # Per target class for cls in torch.unique(tcls_tensor): @@ -223,26 +229,26 @@ def test(data, stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy if len(stats) and stats[0].any(): p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) - p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, AP@0.5, AP@0.5:0.95] + ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95 mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class else: nt = torch.zeros(1) # Print results - pf = '%20s' + '%12.3g' * 6 # print format + pf = '%20s' + '%12i' * 2 + '%12.3g' * 4 # print format #print(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) - sly.logger.info("Test", extra={'Class': 'all', - 'Images': '%.3g' % seen, - 'Targets': '%.3g' % nt.sum(), - 'P': '%.3g' % mp, - 'R': '%.3g' % mr, - 'mAP@.5': '%.3g' % map50, - 'mAP@.5:.95': '%.3g' % map}) + sly.logger.info("Test", extra={'Class': '%20s' % 'all', + 'Images': '%12i' % seen, + 'Targets': '%12i' % nt.sum(), + 'P': '%12.3g' % mp, + 'R': '%12.3g' % mr, + 'mAP@.5': '%12.3g' % map50, + 'mAP@.5:.95': '%12.3g' % map}) # Print results per class - if (verbose or (nc <= 20 and not training)) and nc > 1 and len(stats): + if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): for i, c in enumerate(ap_class): print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) @@ -254,11 +260,13 @@ def test(data, # Plots if plots: confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) - if wandb and wandb.run: - wandb.log({"Images": wandb_images}) - wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]}) + if wandb_logger and wandb_logger.wandb: + val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))] + wandb_logger.log({"Validation": val_batches}) if opt_sly: upload_pred_vis() + if wandb_images: + wandb_logger.log({"Bounding Box Debugger/Images": wandb_images}) # Save JSON if save_json and len(jdict): @@ -286,10 +294,10 @@ def test(data, print(f'pycocotools unable to run: {e}') # Return results + model.float() # for training if not training: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' print(f"Results saved to {save_dir}{s}") - model.float() # for training maps = np.zeros(nc) + map for i, c in enumerate(ap_class): maps[c] = ap[i] @@ -304,7 +312,7 @@ def test(data, parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS') - parser.add_argument('--task', default='val', help="'val', 'test', 'study'") + parser.add_argument('--task', default='val', help='train, val, test, speed or study') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') parser.add_argument('--augment', action='store_true', help='augmented inference') @@ -322,7 +330,7 @@ def test(data, print(opt) check_requirements() - if opt.task in ['val', 'test']: # run normally + if opt.task in ('train', 'val', 'test'): # run normally test(opt.data, opt.weights, opt.batch_size, @@ -336,18 +344,24 @@ def test(data, save_txt=opt.save_txt | opt.save_hybrid, save_hybrid=opt.save_hybrid, save_conf=opt.save_conf, + opt=opt ) + elif opt.task == 'speed': # speed benchmarks + for w in opt.weights: + test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False, opt=opt) + elif opt.task == 'study': # run over a range of settings and save/plot - for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']: - f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) # filename to save to - x = list(range(320, 800, 64)) # x axis + # python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt + x = list(range(256, 1536 + 128, 128)) # x axis (image sizes) + for w in opt.weights: + f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt' # filename to save to y = [] # y axis for i in x: # img-size - print('\nRunning %s point %s...' % (f, i)) - r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json, - plots=False) + print(f'\nRunning {f} point {i}...') + r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json, + plots=False, opt=opt) y.append(r + t) # results and times np.savetxt(f, y, fmt='%10.4g') # save os.system('zip -r study.zip study_*.txt') - plot_study_txt(f, x) # plot + plot_study_txt(x=x) # plot diff --git a/train.py b/train.py index af0ec21c5c25..7b356327b846 100644 --- a/train.py +++ b/train.py @@ -4,6 +4,7 @@ import os import random import time +from copy import deepcopy from pathlib import Path from threading import Thread @@ -33,17 +34,20 @@ from utils.google_utils import attempt_download from utils.loss import ComputeLoss from utils.plots import plot_images, plot_labels, plot_results, plot_evolution -from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first +from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel +from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume -from supervisely.train.src.sly_train_utils import send_epoch_log, upload_label_vis, upload_train_data_vis -from supervisely.train.src.sly_metrics import send_metrics +from sly_train_utils import send_epoch_log, upload_label_vis, upload_train_data_vis +from sly_metrics import send_metrics #logger = logging.getLogger(__name__) import supervisely_lib as sly from supervisely_lib import logger -def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): +def train(hyp, opt, device, tb_writer=None): + train_batches_uploaded = False + logger.info('hyperparameters', extra=hyp) save_dir, epochs, batch_size, total_batch_size, weights, rank = \ Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank @@ -57,20 +61,29 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): # Save run settings with open(save_dir / 'hyp.yaml', 'w') as f: - yaml.dump(hyp, f, sort_keys=False) + yaml.safe_dump(hyp, f, sort_keys=False) with open(save_dir / 'opt.yaml', 'w') as f: - yaml.dump(vars(opt), f, sort_keys=False) + yaml.safe_dump(vars(opt), f, sort_keys=False) # Configure plots = not opt.evolve # create plots cuda = device.type != 'cpu' init_seeds(2 + rank) with open(opt.data) as f: - data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict - with torch_distributed_zero_first(rank): - check_dataset(data_dict) # check - train_path = data_dict['train'] - test_path = data_dict['val'] + data_dict = yaml.safe_load(f) # data dict + is_coco = opt.data.endswith('coco.yaml') + + # Logging- Doing this before checking the dataset. Might update data_dict + loggers = {'wandb': None} # loggers dict + if rank in [-1, 0]: + opt.hyp = hyp # add hyperparameters + run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None + wandb_logger = WandbLogger(opt, save_dir.stem, run_id, data_dict) + loggers['wandb'] = wandb_logger.wandb + data_dict = wandb_logger.data_dict + if wandb_logger.wandb: + weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming + nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check @@ -82,16 +95,18 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): with torch_distributed_zero_first(rank): attempt_download(weights) # download if not found locally ckpt = torch.load(weights, map_location=device) # load checkpoint - if hyp.get('anchors'): - ckpt['model'].yaml['anchors'] = round(hyp['anchors']) # force autoanchor - model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create - exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else [] # exclude keys + model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys state_dict = ckpt['model'].float().state_dict() # to FP32 state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(state_dict, strict=False) # load logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report else: - model = Model(opt.cfg, ch=3, nc=nc).to(device) # create + model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + with torch_distributed_zero_first(rank): + check_dataset(data_dict) # check + train_path = data_dict['train'] + test_path = data_dict['val'] # Freeze freeze = [] # parameter names to freeze (full or partial) @@ -128,18 +143,15 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): # Scheduler https://arxiv.org/pdf/1812.01187.pdf # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR - lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] + if opt.linear_lr: + lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear + else: + lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) - # Logging - if rank in [-1, 0] and wandb and wandb.run is None: - opt.hyp = hyp # add hyperparameters - wandb_run = wandb.init(config=opt, resume="allow", - project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, - name=save_dir.stem, - id=ckpt.get('wandb_id') if 'ckpt' in locals() else None) - loggers = {'wandb': wandb} # loggers dict + # EMA + ema = ModelEMA(model) if rank in [-1, 0] else None # Resume start_epoch, best_fitness = 0, 0.0 @@ -149,10 +161,14 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): optimizer.load_state_dict(ckpt['optimizer']) best_fitness = ckpt['best_fitness'] + # EMA + if ema and ckpt.get('ema'): + ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) + ema.updates = ckpt['updates'] + # Results if ckpt.get('training_results') is not None: - with open(results_file, 'w') as file: - file.write(ckpt['training_results']) # write results.txt + results_file.write_text(ckpt['training_results']) # write results.txt # Epochs start_epoch = ckpt['epoch'] + 1 @@ -166,7 +182,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): del ckpt, state_dict # Image sizes - gs = int(model.stride.max()) # grid size (max stride) + gs = max(int(model.stride.max()), 32) # grid size (max stride) nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj']) imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples @@ -179,13 +195,6 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) logger.info('Using SyncBatchNorm()') - # EMA - ema = ModelEMA(model) if rank in [-1, 0] else None - - # DDP mode - if cuda and rank != -1: - model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank) - # Trainloader dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank, @@ -197,19 +206,17 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): # Process 0 if rank in [-1, 0]: - ema.updates = start_epoch * nb // accumulate # set EMA updates - testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, # testloader + testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt, # testloader hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1, world_size=opt.world_size, workers=opt.workers, pad=0.5, prefix=colorstr('val: '))[0] - if not opt.resume: labels = np.concatenate(dataset.labels, 0) c = torch.tensor(labels[:, 0]) # classes # cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency # model._initialize_biases(cf.to(device)) if plots: - plot_labels(labels, save_dir, loggers) + plot_labels(labels, names, save_dir, loggers) if opt.sly: upload_label_vis() if tb_writer: @@ -218,11 +225,19 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): # Anchors if not opt.noautoanchor: check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) + model.half().float() # pre-reduce anchor precision + + # DDP mode + if cuda and rank != -1: + model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank, + # nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698 + find_unused_parameters=any(isinstance(layer, nn.MultiheadAttention) for layer in model.modules())) # Model parameters hyp['box'] *= 3. / nl # scale to layers hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers + hyp['label_smoothing'] = opt.label_smoothing model.nc = nc # attach number of classes to model model.hyp = hyp # attach hyperparameters to model model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou) @@ -270,7 +285,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): if rank != -1: dataloader.sampler.set_epoch(epoch) pbar = enumerate(dataloader) - #logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size')) + #logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size')) if rank in [-1, 0]: #pbar = tqdm(pbar, total=nb) # progress bar pass @@ -325,7 +340,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): s = ('%10s' * 2 + '%10.4g' * 6) % ( '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1]) #pbar.set_description(s) - #'Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size' + #'Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size' logger.info("Training", extra={ "epoch": epoch, "epochs_count": epochs - 1, @@ -334,7 +349,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): "mobj_loss": float(mloss[1].cpu().numpy()), "mcls_loss": float(mloss[2].cpu().numpy()), "mtotal_loss": float(mloss[3].cpu().numpy()), - "targets": targets.shape[0], + "labels": targets.shape[0], "img_size": imgs.shape[-1] }) @@ -342,16 +357,19 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): # Plot if plots and ni < 3: f = save_dir / f'train_batch{ni}.jpg' # filename - Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start() + plot_images(imgs, targets, paths, f) + #Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start() # if tb_writer: # tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) - # tb_writer.add_graph(model, imgs) # add model to tensorboard - elif plots and ni == 10 and wandb: - wandb.log({"Mosaics": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('train*.jpg') - if x.exists()]}) - elif plots and ni == 10 and opt.sly: + # tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) # add model graph + elif plots and ni == 10 and wandb_logger.wandb: + wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in + save_dir.glob('train*.jpg') if x.exists()]}) + if plots and ni == 10 and opt.sly: + train_batches_uploaded = True upload_train_data_vis() + # end batch ------------------------------------------------------------------------------------------------ # end epoch ---------------------------------------------------------------------------------------------------- @@ -362,25 +380,27 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): # DDP process 0 or single-GPU if rank in [-1, 0]: # mAP - if ema: - ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'colors', 'img_size', 'stride', 'class_weights']) + ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights']) final_epoch = epoch + 1 == epochs if not opt.notest or final_epoch: # Calculate mAP - results, maps, times = test.test(opt.data, - batch_size=total_batch_size, + wandb_logger.current_epoch = epoch + 1 + results, maps, times = test.test(data_dict, + batch_size=batch_size * 2, imgsz=imgsz_test, model=ema.ema, single_cls=opt.single_cls, dataloader=testloader, save_dir=save_dir, + verbose=nc < 50 and final_epoch, plots=plots and final_epoch, - log_imgs=opt.log_imgs if wandb else 0, + wandb_logger=wandb_logger, compute_loss=compute_loss, + is_coco=is_coco, opt_sly=opt.sly) # Write with open(results_file, 'a') as f: - f.write(s + '%10.4g' * 7 % results + '\n') # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss if len(opt.name) and opt.bucket: os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name)) @@ -395,80 +415,89 @@ def train(hyp, opt, device, tb_writer=None, wandb=None, opt_sly=False): for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags): if tb_writer: tb_writer.add_scalar(tag, x, epoch) # tensorboard - if wandb: - wandb.log({tag: x}) # W&B + if wandb_logger.wandb: + wandb_logger.log({tag: x}) # W&B if opt.sly: if torch.is_tensor(x): x = float(x.cpu().numpy()) metrics[tag] = x if opt.sly: - send_metrics(epoch, epochs, metrics) + send_metrics(epoch, epochs, metrics, opt.metrics_period) # Update best mAP fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] if fi > best_fitness: best_fitness = fi + wandb_logger.end_epoch(best_result=best_fitness == fi) # Save model - save = (not opt.nosave) or (final_epoch and not opt.evolve) - if save: - with open(results_file, 'r') as f: # create checkpoint - ckpt = {'epoch': epoch, - 'best_fitness': best_fitness, - 'training_results': f.read(), - 'model': ema.ema, - 'optimizer': None if final_epoch else optimizer.state_dict(), - 'wandb_id': wandb_run.id if wandb else None} + if (not opt.nosave) or (final_epoch and not opt.evolve): # if save + ckpt = {'epoch': epoch, + 'best_fitness': best_fitness, + 'training_results': results_file.read_text(), + 'model': deepcopy(model.module if is_parallel(model) else model).half(), + 'ema': deepcopy(ema.ema).half(), + 'updates': ema.updates, + 'optimizer': optimizer.state_dict(), + 'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None} # Save last, best and delete torch.save(ckpt, last) if best_fitness == fi: torch.save(ckpt, best) + if wandb_logger.wandb: + if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1: + wandb_logger.log_model( + last.parent, opt, epoch, fi, best_model=best_fitness == fi) del ckpt + # end epoch ---------------------------------------------------------------------------------------------------- - # end training - if rank in [-1, 0]: - # Strip optimizers - final = best if best.exists() else last # final model - for f in [last, best]: - if f.exists(): - strip_optimizer(f) # strip optimizers - if opt.bucket: - os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload + if plots and opt.sly and train_batches_uploaded is False: + train_batches_uploaded = True + upload_train_data_vis() + # end training + if rank in [-1, 0]: # Plots if plots: plot_results(save_dir=save_dir) # save as results.png - if wandb: - files = ['results.png', 'precision_recall_curve.png', 'confusion_matrix.png'] - wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files - if (save_dir / f).exists()]}) - if opt.log_artifacts: - wandb.log_artifact(artifact_or_path=str(final), type='model', name=save_dir.stem) - + if wandb_logger.wandb: + files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]] + wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files + if (save_dir / f).exists()]}) # Test best.pt logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) if opt.data.endswith('coco.yaml') and nc == 80: # if COCO - for conf, iou, save_json in ([0.25, 0.45, False], [0.001, 0.65, True]): # speed, mAP tests + for m in (last, best) if best.exists() else (last): # speed, mAP tests results, _, _ = test.test(opt.data, - batch_size=total_batch_size, + batch_size=batch_size * 2, imgsz=imgsz_test, - conf_thres=conf, - iou_thres=iou, - model=attempt_load(final, device).half(), + conf_thres=0.001, + iou_thres=0.7, + model=attempt_load(m, device).half(), single_cls=opt.single_cls, dataloader=testloader, save_dir=save_dir, - save_json=save_json, + save_json=True, plots=False, - opt_sly=opt.sly) + is_coco=is_coco) + # Strip optimizers + final = best if best.exists() else last # final model + for f in last, best: + if f.exists(): + strip_optimizer(f) # strip optimizers + if opt.bucket: + os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload + if wandb_logger.wandb and not opt.evolve: # Log the stripped model + wandb_logger.wandb.log_artifact(str(final), type='model', + name='run_' + wandb_logger.wandb_run.id + '_model', + aliases=['last', 'best', 'stripped']) + wandb_logger.finish_run() else: dist.destroy_process_group() - - wandb.run.finish() if wandb and wandb.run else None torch.cuda.empty_cache() return results @@ -497,14 +526,20 @@ def main(): parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer') parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') - parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100') - parser.add_argument('--log-artifacts', action='store_true', help='log artifacts, i.e. final trained model') parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers') parser.add_argument('--project', default='runs/train', help='save to project/name') + parser.add_argument('--entity', default=None, help='W&B entity') parser.add_argument('--name', default='exp', help='save to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--quad', action='store_true', help='quad dataloader') + parser.add_argument('--linear-lr', action='store_true', help='linear LR') + parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') + parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table') + parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B') + parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch') + parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used') parser.add_argument('--sly', action='store_true', help='for Supervisely App integration') + parser.add_argument('--metrics_period', type=int, default=1, help='Log metrics to Supervisely every "metrics_period" epochs') opt = parser.parse_args() print("Input arguments:", opt) @@ -515,16 +550,18 @@ def main(): set_logging(opt.global_rank) if opt.global_rank in [-1, 0]: check_git_status() - #check_requirements(file=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'requirements.txt')) + #check_requirements() # Resume - if opt.resume: # resume an interrupted run + wandb_run = check_wandb_resume(opt) + if opt.resume and not wandb_run: # resume an interrupted run ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist' apriori = opt.global_rank, opt.local_rank with open(Path(ckpt).parent.parent / 'opt.yaml') as f: - opt = argparse.Namespace(**yaml.load(f, Loader=yaml.FullLoader)) # replace - opt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = '', ckpt, True, opt.total_batch_size, *apriori # reinstate + opt = argparse.Namespace(**yaml.safe_load(f)) # replace + opt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = \ + '', ckpt, True, opt.total_batch_size, *apriori # reinstate logger.info('Resuming training from %s' % ckpt) else: # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml') @@ -532,7 +569,7 @@ def main(): assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) opt.name = 'evolve' if opt.evolve else opt.name - opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) # increment run + opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve)) # DDP mode opt.total_batch_size = opt.batch_size @@ -547,22 +584,17 @@ def main(): # Hyperparameters with open(opt.hyp) as f: - hyp = yaml.load(f, Loader=yaml.FullLoader) # load hyps + hyp = yaml.safe_load(f) # load hyps # Train logger.info(opt) - try: - import wandb - except ImportError: - wandb = None - #prefix = colorstr('wandb: ') - #logger.info(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)") if not opt.evolve: tb_writer = None # init loggers if opt.global_rank in [-1, 0]: - #logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.project}", view at http://localhost:6006/') + #prefix = colorstr('tensorboard: ') + #logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/") tb_writer = SummaryWriter(opt.save_dir) # Tensorboard - train(hyp, opt, device, tb_writer, wandb) + train(hyp, opt, device, tb_writer) # Evolve hyperparameters (optional) else: @@ -636,7 +668,7 @@ def main(): hyp[k] = round(hyp[k], 5) # significant digits # Train mutation - results = train(hyp.copy(), opt, device, wandb=wandb) + results = train(hyp.copy(), opt, device) # Write mutation results print_mutation(hyp.copy(), results, yaml_file, opt.bucket) diff --git a/tutorial.ipynb b/tutorial.ipynb index 853f42f196d8..245b46aa7d9f 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -16,7 +16,7 @@ "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { - "02ac0588602847eea00a0205f87bcce2": { + "8815626359d84416a2f44a95500580a4": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "state": { @@ -28,15 +28,15 @@ "_view_count": null, "_view_module_version": "1.5.0", "box_style": "", - "layout": "IPY_MODEL_c472ea49806447a68b5a9221a4ddae85", + "layout": "IPY_MODEL_3b85609c4ce94a74823f2cfe141ce68e", "_model_module": "@jupyter-widgets/controls", "children": [ - "IPY_MODEL_091fdf499bd44a80af7281d16da4aa93", - "IPY_MODEL_c79f69c959de4427ba102a87a9f46d80" + "IPY_MODEL_876609753c2946248890344722963d44", + "IPY_MODEL_8abfdd8778e44b7ca0d29881cb1ada05" ] } }, - "c472ea49806447a68b5a9221a4ddae85": { + "3b85609c4ce94a74823f2cfe141ce68e": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -87,12 +87,12 @@ "left": null } }, - "091fdf499bd44a80af7281d16da4aa93": { + "876609753c2946248890344722963d44": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "state": { "_view_name": "ProgressView", - "style": "IPY_MODEL_c42ae5af74a0491187827d0a1fc259bb", + "style": "IPY_MODEL_78c6c3d97c484916b8ee167c63556800", "_dom_classes": [], "description": "100%", "_model_name": "FloatProgressModel", @@ -107,30 +107,30 @@ "min": 0, "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_5a90f72d3a2d46cb9ad915daa3ead8b4" + "layout": "IPY_MODEL_9dd0f182db5d45378ceafb855e486eb8" } }, - 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"5a90f72d3a2d46cb9ad915daa3ead8b4": { + "9dd0f182db5d45378ceafb855e486eb8": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -196,7 +196,7 @@ "left": null } }, - "2a7ed6611da34662b10e37fd4f4e4438": { + "a3dab28b45c247089a3d1b8b09f327de": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "state": { @@ -210,7 +210,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "fead0160658445bf9e966daa4481cad0": { + "32451332b7a94ba9aacddeaa6ac94d50": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -261,7 +261,7 @@ "left": null } }, - "cf1ab9fde7444d3e874fcd407ba8f0f8": { + "0fffa335322b41658508e06aed0acbf0": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "state": { @@ -273,15 +273,15 @@ "_view_count": null, "_view_module_version": "1.5.0", "box_style": "", - "layout": "IPY_MODEL_9ee03f9c85f34155b2645e89c9211547", + "layout": "IPY_MODEL_a354c6f80ce347e5a3ef64af87c0eccb", "_model_module": "@jupyter-widgets/controls", "children": [ - "IPY_MODEL_933ebc451c09490aadf71afbbb3dff2a", - "IPY_MODEL_8e7c55cbca624432a84fa7ad8f3a4016" + "IPY_MODEL_85823e71fea54c39bd11e2e972348836", + "IPY_MODEL_fb11acd663fa4e71b041d67310d045fd" ] } }, - "9ee03f9c85f34155b2645e89c9211547": { + "a354c6f80ce347e5a3ef64af87c0eccb": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -332,50 +332,50 @@ "left": null } }, - "933ebc451c09490aadf71afbbb3dff2a": { + "85823e71fea54c39bd11e2e972348836": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "state": { "_view_name": "ProgressView", - "style": "IPY_MODEL_dd62d83b35d04a178840772e82bd2f2e", + "style": "IPY_MODEL_8a919053b780449aae5523658ad611fa", "_dom_classes": [], "description": "100%", "_model_name": "FloatProgressModel", "bar_style": "success", - "max": 22090455, + "max": 22091032, "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "value": 22090455, + "value": 22091032, "_view_count": null, "_view_module_version": "1.5.0", "orientation": "horizontal", "min": 0, "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - 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"d5c4f3d1c8b046e3a163faaa6b3a51ab": { + "5bae9393a58b44f7b69fb04816f94f6f": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -441,7 +441,7 @@ "left": null } }, - "78d1da8efb504b03878ca9ce5b404006": { + "d26c6d16c7f24030ab2da5285bf198ee": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "state": { @@ -455,7 +455,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "d28208ba1213436a93926a01d99d97ae": { + "f7767886b2364c8d9efdc79e175ad8eb": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -528,8 +528,8 @@ "source": [ "\n", "\n", - "This notebook was written by Ultralytics LLC, and is freely available for redistribution under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/). \n", - "For more information please visit https://github.com/ultralytics/yolov5 and https://www.ultralytics.com." + "This is the **official YOLOv5 🚀 notebook** authored by **Ultralytics**, and is freely available for redistribution under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/). \n", + "For more information please visit https://github.com/ultralytics/yolov5 and https://www.ultralytics.com. Thank you!" ] }, { @@ -550,7 +550,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "888d5c41-00e9-47d8-d230-dded99325bea" + "outputId": "9b022435-4197-41fc-abea-81f86ce857d0" }, "source": [ "!git clone https://github.com/ultralytics/yolov5 # clone repo\n", @@ -561,14 +561,14 @@ "from IPython.display import Image, clear_output # to display images\n", "\n", "clear_output()\n", - "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))" + "print(f\"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})\")" ], - "execution_count": null, + "execution_count": 31, "outputs": [ { "output_type": "stream", "text": [ - "Setup complete. Using torch 1.7.0+cu101 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)\n" + "Setup complete. Using torch 1.8.1+cu101 (Tesla V100-SXM2-16GB)\n" ], "name": "stdout" } @@ -582,7 +582,9 @@ "source": [ "# 1. Inference\n", "\n", - "`detect.py` runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)." + "`detect.py` runs YOLOv5 inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/detect`. Example inference sources are:\n", + "\n", + " " ] }, { @@ -604,15 +606,15 @@ { "output_type": "stream", "text": [ - "Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, save_conf=False, save_dir='runs/detect', save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt'])\n", - "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n", + "Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt'])\n", + "YOLOv5 🚀 v5.0-1-g0f395b3 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n", "\n", "Fusing layers... \n", - "Model Summary: 232 layers, 7459581 parameters, 0 gradients\n", - "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.012s)\n", - "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.012s)\n", + "Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.008s)\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.008s)\n", "Results saved to runs/detect/exp\n", - "Done. (0.113s)\n" + "Done. (0.087)\n" ], "name": "stdout" }, @@ -634,16 +636,6 @@ } ] }, - { - "cell_type": "markdown", - "metadata": { - "id": "4qbaa3iEcrcE" - }, - "source": [ - "Results are saved to `runs/detect`. A full list of available inference sources:\n", - " " - ] - }, { "cell_type": "markdown", "metadata": { @@ -651,7 +643,7 @@ }, "source": [ "# 2. Test\n", - "Test a model on [COCO](https://cocodataset.org/#home) val or test-dev dataset to evaluate trained accuracy. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be 1-2% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation." + "Test a model's accuracy on [COCO](https://cocodataset.org/#home) val or test-dev datasets. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be ~1% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation." ] }, { @@ -670,19 +662,19 @@ "id": "WQPtK1QYVaD_", "colab": { "base_uri": "https://localhost:8080/", - "height": 66, + "height": 65, "referenced_widgets": [ - "02ac0588602847eea00a0205f87bcce2", - "c472ea49806447a68b5a9221a4ddae85", - "091fdf499bd44a80af7281d16da4aa93", - "c79f69c959de4427ba102a87a9f46d80", - "c42ae5af74a0491187827d0a1fc259bb", - "5a90f72d3a2d46cb9ad915daa3ead8b4", - "2a7ed6611da34662b10e37fd4f4e4438", - "fead0160658445bf9e966daa4481cad0" + "8815626359d84416a2f44a95500580a4", + "3b85609c4ce94a74823f2cfe141ce68e", + "876609753c2946248890344722963d44", + "8abfdd8778e44b7ca0d29881cb1ada05", + "78c6c3d97c484916b8ee167c63556800", + "9dd0f182db5d45378ceafb855e486eb8", + "a3dab28b45c247089a3d1b8b09f327de", + "32451332b7a94ba9aacddeaa6ac94d50" ] }, - "outputId": "780d8f5f-766e-4b99-e370-11f9b884c27a" + "outputId": "81521192-cf67-4a47-a4cc-434cb0ebc363" }, "source": [ "# Download COCO val2017\n", @@ -695,7 +687,7 @@ "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "02ac0588602847eea00a0205f87bcce2", + "model_id": "8815626359d84416a2f44a95500580a4", "version_minor": 0, "version_major": 2 }, @@ -723,7 +715,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "013935a5-ba81-4810-b723-0cb01cf7bc79" + "outputId": "2340b131-9943-4cd6-fd3a-8272aeb0774f" }, "source": [ "# Run YOLOv5x on COCO val2017\n", @@ -734,45 +726,46 @@ { "output_type": "stream", "text": [ - "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n", - "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n", + "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_hybrid=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n", + "YOLOv5 🚀 v5.0-1-g0f395b3 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n", "\n", - "Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5x.pt to yolov5x.pt...\n", - "100% 170M/170M [00:05<00:00, 32.6MB/s]\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5x.pt to yolov5x.pt...\n", + "100% 168M/168M [00:05<00:00, 32.3MB/s]\n", "\n", "Fusing layers... \n", - "Model Summary: 484 layers, 88922205 parameters, 0 gradients\n", - "Scanning labels ../coco/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 5000it [00:00, 14785.71it/s]\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:30<00:00, 1.74it/s]\n", - " all 5e+03 3.63e+04 0.409 0.754 0.672 0.484\n", - "Speed: 5.9/2.1/7.9 ms inference/NMS/total per 640x640 image at batch-size 32\n", + "Model Summary: 476 layers, 87730285 parameters, 0 gradients, 218.8 GFLOPS\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco/val2017' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 3102.29it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../coco/val2017.cache\n", + " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:23<00:00, 1.87it/s]\n", + " all 5000 36335 0.745 0.627 0.68 0.49\n", + "Speed: 5.3/1.6/6.9 ms inference/NMS/total per 640x640 image at batch-size 32\n", "\n", "Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...\n", "loading annotations into memory...\n", - "Done (t=0.43s)\n", + "Done (t=0.48s)\n", "creating index...\n", "index created!\n", "Loading and preparing results...\n", - "DONE (t=4.67s)\n", + "DONE (t=5.08s)\n", "creating index...\n", "index created!\n", "Running per image evaluation...\n", "Evaluate annotation type *bbox*\n", - "DONE (t=92.11s).\n", + "DONE (t=90.51s).\n", "Accumulating evaluation results...\n", - "DONE (t=13.24s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.492\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.676\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.534\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.318\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.633\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.376\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.617\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.670\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.493\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.723\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.812\n", + "DONE (t=15.16s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.504\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.688\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.546\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.351\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.644\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.382\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.629\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.681\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.827\n", "Results saved to runs/test/exp\n" ], "name": "stdout" @@ -786,7 +779,7 @@ }, "source": [ "## COCO test-dev2017\n", - "Download [COCO test2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L15) dataset (7GB - 40,000 images), to test model accuracy on test-dev set (20,000 images). Results are saved to a `*.json` file which can be submitted to the evaluation server at https://competitions.codalab.org/competitions/20794." + "Download [COCO test2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L15) dataset (7GB - 40,000 images), to test model accuracy on test-dev set (**20,000 images, no labels**). Results are saved to a `*.json` file which should be **zipped** and submitted to the evaluation server at https://competitions.codalab.org/competitions/20794." ] }, { @@ -797,9 +790,9 @@ "source": [ "# Download COCO test-dev2017\n", "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip', 'tmp.zip')\n", - "!unzip -q tmp.zip -d ../ && rm tmp.zip # unzip labels\n", + "!unzip -q tmp.zip -d ../ && rm tmp.zip # unzip labels\n", "!f=\"test2017.zip\" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f # 7GB, 41k images\n", - "%mv ./test2017 ./coco/images && mv ./coco ../ # move images to /coco and move /coco next to /yolov5" + "%mv ./test2017 ../coco/images # move to /coco" ], "execution_count": null, "outputs": [] @@ -833,19 +826,19 @@ "id": "Knxi2ncxWffW", "colab": { "base_uri": "https://localhost:8080/", - "height": 66, + "height": 65, "referenced_widgets": [ - "cf1ab9fde7444d3e874fcd407ba8f0f8", - "9ee03f9c85f34155b2645e89c9211547", - "933ebc451c09490aadf71afbbb3dff2a", - "8e7c55cbca624432a84fa7ad8f3a4016", - "dd62d83b35d04a178840772e82bd2f2e", - "d5c4f3d1c8b046e3a163faaa6b3a51ab", - "78d1da8efb504b03878ca9ce5b404006", - "d28208ba1213436a93926a01d99d97ae" + "0fffa335322b41658508e06aed0acbf0", + "a354c6f80ce347e5a3ef64af87c0eccb", + "85823e71fea54c39bd11e2e972348836", + "fb11acd663fa4e71b041d67310d045fd", + "8a919053b780449aae5523658ad611fa", + "5bae9393a58b44f7b69fb04816f94f6f", + "d26c6d16c7f24030ab2da5285bf198ee", + "f7767886b2364c8d9efdc79e175ad8eb" ] }, - "outputId": "59f9a94b-21e1-4626-f36a-a8e1b1e5c8f6" + "outputId": "b41ac253-9e1b-4c26-d78b-700ea0154f43" }, "source": [ "# Download COCO128\n", @@ -858,12 +851,12 @@ "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cf1ab9fde7444d3e874fcd407ba8f0f8", + "model_id": "0fffa335322b41658508e06aed0acbf0", "version_minor": 0, "version_major": 2 }, "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, max=22090455.0), HTML(value='')))" + "HBox(children=(FloatProgress(value=0.0, max=22091032.0), HTML(value='')))" ] }, "metadata": { @@ -923,7 +916,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "138f2d1d-364c-405a-cf13-ea91a2aff915" + "outputId": "e715d09c-5d93-4912-a0df-9da0893f2014" }, "source": [ "# Train YOLOv5s on COCO128 for 3 epochs\n", @@ -934,76 +927,76 @@ { "output_type": "stream", "text": [ - "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n", - "\n", - "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', rect=False, resume=False, save_dir='runs/train/exp', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n", - "Start Tensorboard with \"tensorboard --logdir runs/train\", view at http://localhost:6006/\n", - "2020-11-20 11:45:17.042357: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n", - "Hyperparameters {'lr0': 0.01, 'lrf': 0.2, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1, 'box': 0.05, 'cls': 0.5, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mosaic': 1.0, 'mixup': 0.0}\n", - "Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5s.pt to yolov5s.pt...\n", - "100% 14.5M/14.5M [00:01<00:00, 14.8MB/s]\n", + "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", + "YOLOv5 🚀 v5.0-2-g54d6516 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n", "\n", + "Namespace(adam=False, artifact_alias='latest', batch_size=16, bbox_interval=-1, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', entity=None, epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], label_smoothing=0.0, linear_lr=False, local_rank=-1, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/exp', save_period=-1, single_cls=False, sync_bn=False, total_batch_size=16, upload_dataset=False, weights='yolov5s.pt', workers=8, world_size=1)\n", + "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n", + "2021-04-12 10:29:58.539457: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", + "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0\n", + "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)\n", "\n", " from n params module arguments \n", " 0 -1 1 3520 models.common.Focus [3, 32, 3] \n", " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", - " 2 -1 1 19904 models.common.BottleneckCSP [64, 64, 1] \n", + " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n", " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", - " 4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3] \n", + " 4 -1 1 156928 models.common.C3 [128, 128, 3] \n", " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", - " 6 -1 1 641792 models.common.BottleneckCSP [256, 256, 3] \n", + " 6 -1 1 625152 models.common.C3 [256, 256, 3] \n", " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", " 8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]] \n", - " 9 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n", + " 9 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n", " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 12 [-1, 6] 1 0 models.common.Concat [1] \n", - " 13 -1 1 378624 models.common.BottleneckCSP [512, 256, 1, False] \n", + " 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n", " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 16 [-1, 4] 1 0 models.common.Concat [1] \n", - " 17 -1 1 95104 models.common.BottleneckCSP [256, 128, 1, False] \n", + " 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n", " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", " 19 [-1, 14] 1 0 models.common.Concat [1] \n", - " 20 -1 1 313088 models.common.BottleneckCSP [256, 256, 1, False] \n", + " 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n", " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", " 22 [-1, 10] 1 0 models.common.Concat [1] \n", - " 23 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n", + " 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n", " 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n", - "Model Summary: 283 layers, 7468157 parameters, 7468157 gradients\n", + "Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPS\n", "\n", - "Transferred 370/370 items from yolov5s.pt\n", - "Optimizer groups: 62 .bias, 70 conv.weight, 59 other\n", - "Scanning images: 100% 128/128 [00:00<00:00, 5395.63it/s]\n", - "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 13972.28it/s]\n", - "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 173.55it/s]\n", - "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 8693.98it/s]\n", - "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 133.30it/s]\n", - "NumExpr defaulting to 2 threads.\n", + "Transferred 362/362 items from yolov5s.pt\n", + "Scaled weight_decay = 0.0005\n", + "Optimizer groups: 62 .bias, 62 conv.weight, 59 other\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 796544.38it/s]\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 176.73it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 500812.42it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 134.10it/s]\n", + "Plotting labels... \n", "\n", - "Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n", + "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n", "Image sizes 640 train, 640 test\n", "Using 2 dataloader workers\n", "Logging results to runs/train/exp\n", "Starting training for 3 epochs...\n", "\n", - " Epoch gpu_mem box obj cls total targets img_size\n", - " 0/2 5.24G 0.04202 0.06745 0.01503 0.1245 194 640: 100% 8/8 [00:03<00:00, 2.01it/s]\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:03<00:00, 2.40it/s]\n", - " all 128 929 0.404 0.758 0.701 0.45\n", + " Epoch gpu_mem box obj cls total labels img_size\n", + " 0/2 3.29G 0.04368 0.065 0.02127 0.1299 183 640: 100% 8/8 [00:03<00:00, 2.21it/s]\n", + " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:04<00:00, 1.09s/it]\n", + " all 128 929 0.605 0.657 0.666 0.434\n", "\n", - " Epoch gpu_mem box obj cls total targets img_size\n", - " 1/2 5.12G 0.04461 0.05874 0.0169 0.1202 142 640: 100% 8/8 [00:01<00:00, 4.14it/s]\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:01<00:00, 5.75it/s]\n", - " all 128 929 0.403 0.772 0.703 0.453\n", + " Epoch gpu_mem box obj cls total labels img_size\n", + " 1/2 6.65G 0.04556 0.0651 0.01987 0.1305 166 640: 100% 8/8 [00:01<00:00, 5.18it/s]\n", + " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.72it/s]\n", + " all 128 929 0.61 0.66 0.669 0.438\n", "\n", - " Epoch gpu_mem box obj cls total targets img_size\n", - " 2/2 5.12G 0.04445 0.06545 0.01667 0.1266 149 640: 100% 8/8 [00:01<00:00, 4.15it/s]\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:06<00:00, 1.18it/s]\n", - " all 128 929 0.395 0.767 0.702 0.452\n", - "Optimizer stripped from runs/train/exp/weights/last.pt, 15.2MB\n", - "3 epochs completed in 0.006 hours.\n", - "\n" + " Epoch gpu_mem box obj cls total labels img_size\n", + " 2/2 6.65G 0.04624 0.06923 0.0196 0.1351 182 640: 100% 8/8 [00:01<00:00, 5.19it/s]\n", + " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.27it/s]\n", + " all 128 929 0.618 0.659 0.671 0.438\n", + "3 epochs completed in 0.007 hours.\n", + "\n", + "Optimizer stripped from runs/train/exp/weights/last.pt, 14.8MB\n", + "Optimizer stripped from runs/train/exp/weights/best.pt, 14.8MB\n" ], "name": "stdout" } @@ -1026,9 +1019,9 @@ "source": [ "## Weights & Biases Logging 🌟 NEW\n", "\n", - "[Weights & Biases](https://www.wandb.com/) (W&B) is now integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n", + "[Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_notebook) (W&B) is now integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n", "\n", - "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n", + "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home?utm_campaign=repo_yolo_notebook), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n", "\n", "" ] @@ -1114,10 +1107,23 @@ "\n", "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", "\n", - "- **Google Colab Notebook** with free GPU: \"Open\n", - "- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5)\n", - "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) \n", - "- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker)\n" + "- **Google Colab and Kaggle** notebooks with free GPU: \"Open \"Open\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Qu7Iesl0p54" + }, + "source": [ + "# Status\n", + "\n", + "![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)\n", + "\n", + "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov5/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/models/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.\n" ] }, { @@ -1152,11 +1158,32 @@ }, "source": [ "# Reproduce\n", - "%%shell\n", - "for x in yolov5s yolov5m yolov5l yolov5x; do\n", - " python test.py --weights $x.pt --data coco.yaml --img 640 --conf 0.25 --iou 0.45 # speed\n", - " python test.py --weights $x.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65 # mAP\n", - "done" + "for x in 'yolov5s', 'yolov5m', 'yolov5l', 'yolov5x':\n", + " !python test.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.25 --iou 0.45 # speed\n", + " !python test.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65 # mAP" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "GMusP4OAxFu6" + }, + "source": [ + "# PyTorch Hub\n", + "import torch\n", + "\n", + "# Model\n", + "model = torch.hub.load('ultralytics/yolov5', 'yolov5s')\n", + "\n", + "# Images\n", + "dir = 'https://github.com/ultralytics/yolov5/raw/master/data/images/'\n", + "imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')] # batch of images\n", + "\n", + "# Inference\n", + "results = model(imgs)\n", + "results.print() # or .show(), .save()" ], "execution_count": null, "outputs": [] @@ -1205,6 +1232,19 @@ "execution_count": null, "outputs": [] }, + { + "cell_type": "code", + "metadata": { + "id": "RVRSOhEvUdb5" + }, + "source": [ + "# Evolve\n", + "!python train.py --img 640 --batch 64 --epochs 100 --data coco128.yaml --weights yolov5s.pt --cache --noautoanchor --evolve\n", + "!d=runs/train/evolve && cp evolve.* $d && zip -r evolve.zip $d && gsutil mv evolve.zip gs://bucket # upload results (optional)" + ], + "execution_count": null, + "outputs": [] + }, { "cell_type": "code", "metadata": { diff --git a/utils/activations.py b/utils/activations.py index aa3ddf071d28..92a3b5eaa54b 100644 --- a/utils/activations.py +++ b/utils/activations.py @@ -19,23 +19,6 @@ def forward(x): return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX -class MemoryEfficientSwish(nn.Module): - class F(torch.autograd.Function): - @staticmethod - def forward(ctx, x): - ctx.save_for_backward(x) - return x * torch.sigmoid(x) - - @staticmethod - def backward(ctx, grad_output): - x = ctx.saved_tensors[0] - sx = torch.sigmoid(x) - return grad_output * (sx * (1 + x * (1 - sx))) - - def forward(self, x): - return self.F.apply(x) - - # Mish https://github.com/digantamisra98/Mish -------------------------------------------------------------------------- class Mish(nn.Module): @staticmethod @@ -70,3 +53,46 @@ def __init__(self, c1, k=3): # ch_in, kernel def forward(self, x): return torch.max(x, self.bn(self.conv(x))) + + +# ACON https://arxiv.org/pdf/2009.04759.pdf ---------------------------------------------------------------------------- +class AconC(nn.Module): + r""" ACON activation (activate or not). + AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter + according to "Activate or Not: Learning Customized Activation" . + """ + + def __init__(self, c1): + super().__init__() + self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.beta = nn.Parameter(torch.ones(1, c1, 1, 1)) + + def forward(self, x): + dpx = (self.p1 - self.p2) * x + return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x + + +class MetaAconC(nn.Module): + r""" ACON activation (activate or not). + MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network + according to "Activate or Not: Learning Customized Activation" . + """ + + def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r + super().__init__() + c2 = max(r, c1 // r) + self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True) + self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True) + # self.bn1 = nn.BatchNorm2d(c2) + # self.bn2 = nn.BatchNorm2d(c1) + + def forward(self, x): + y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True) + # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891 + # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable + beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed + dpx = (self.p1 - self.p2) * x + return dpx * torch.sigmoid(beta * dpx) + self.p2 * x diff --git a/utils/autoanchor.py b/utils/autoanchor.py index c00f0382ff71..75b350da729c 100644 --- a/utils/autoanchor.py +++ b/utils/autoanchor.py @@ -37,17 +37,21 @@ def metric(k): # compute metric bpr = (best > 1. / thr).float().mean() # best possible recall return bpr, aat - bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2)) + anchors = m.anchor_grid.clone().cpu().view(-1, 2) # current anchors + bpr, aat = metric(anchors) print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='') if bpr < 0.98: # threshold to recompute print('. Attempting to improve anchors, please wait...') na = m.anchor_grid.numel() // 2 # number of anchors - new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) - new_bpr = metric(new_anchors.reshape(-1, 2))[0] + try: + anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) + except Exception as e: + print(f'{prefix}ERROR: {e}') + new_bpr = metric(anchors)[0] if new_bpr > bpr: # replace anchors - new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors) - m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid) # for inference - m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss + anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) + m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid) # for inference + m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss check_anchor_order(m) print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.') else: @@ -98,7 +102,7 @@ def print_results(k): if isinstance(path, str): # *.yaml file with open(path) as f: - data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict + data_dict = yaml.safe_load(f) # model dict from utils.datasets import LoadImagesAndLabels dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) else: @@ -119,6 +123,7 @@ def print_results(k): print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...') s = wh.std(0) # sigmas for whitening k, dist = kmeans(wh / s, n, iter=30) # points, mean distance + assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}') k *= s wh = torch.tensor(wh, dtype=torch.float32) # filtered wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered diff --git a/utils/aws/__init__.py b/utils/aws/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/utils/aws/mime.sh b/utils/aws/mime.sh new file mode 100644 index 000000000000..c319a83cfbdf --- /dev/null +++ b/utils/aws/mime.sh @@ -0,0 +1,26 @@ +# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ +# This script will run on every instance restart, not only on first start +# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- + +Content-Type: multipart/mixed; boundary="//" +MIME-Version: 1.0 + +--// +Content-Type: text/cloud-config; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="cloud-config.txt" + +#cloud-config +cloud_final_modules: +- [scripts-user, always] + +--// +Content-Type: text/x-shellscript; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="userdata.txt" + +#!/bin/bash +# --- paste contents of userdata.sh here --- +--// diff --git a/utils/aws/resume.py b/utils/aws/resume.py new file mode 100644 index 000000000000..4b0d4246b594 --- /dev/null +++ b/utils/aws/resume.py @@ -0,0 +1,37 @@ +# Resume all interrupted trainings in yolov5/ dir including DDP trainings +# Usage: $ python utils/aws/resume.py + +import os +import sys +from pathlib import Path + +import torch +import yaml + +sys.path.append('./') # to run '$ python *.py' files in subdirectories + +port = 0 # --master_port +path = Path('').resolve() +for last in path.rglob('*/**/last.pt'): + ckpt = torch.load(last) + if ckpt['optimizer'] is None: + continue + + # Load opt.yaml + with open(last.parent.parent / 'opt.yaml') as f: + opt = yaml.safe_load(f) + + # Get device count + d = opt['device'].split(',') # devices + nd = len(d) # number of devices + ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel + + if ddp: # multi-GPU + port += 1 + cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}' + else: # single-GPU + cmd = f'python train.py --resume {last}' + + cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread + print(cmd) + os.system(cmd) diff --git a/utils/aws/userdata.sh b/utils/aws/userdata.sh new file mode 100644 index 000000000000..890606b76a06 --- /dev/null +++ b/utils/aws/userdata.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html +# This script will run only once on first instance start (for a re-start script see mime.sh) +# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir +# Use >300 GB SSD + +cd home/ubuntu +if [ ! -d yolov5 ]; then + echo "Running first-time script." # install dependencies, download COCO, pull Docker + git clone https://github.com/ultralytics/yolov5 && sudo chmod -R 777 yolov5 + cd yolov5 + bash data/scripts/get_coco.sh && echo "Data done." & + sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & + python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & + wait && echo "All tasks done." # finish background tasks +else + echo "Running re-start script." # resume interrupted runs + i=0 + list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' + while IFS= read -r id; do + ((i++)) + echo "restarting container $i: $id" + sudo docker start $id + # sudo docker exec -it $id python train.py --resume # single-GPU + sudo docker exec -d $id python utils/aws/resume.py # multi-scenario + done <<<"$list" +fi diff --git a/utils/datasets.py b/utils/datasets.py index 6e6e3253771b..3fcdddd7c013 100755 --- a/utils/datasets.py +++ b/utils/datasets.py @@ -20,12 +20,13 @@ from torch.utils.data import Dataset from tqdm import tqdm -from utils.general import xyxy2xywh, xywh2xyxy, clean_str +from utils.general import check_requirements, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, \ + resample_segments, clean_str from utils.torch_utils import torch_distributed_zero_first # Parameters help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' -img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng'] # acceptable image suffixes +img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo'] # acceptable image suffixes vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes logger = logging.getLogger(__name__) @@ -119,9 +120,8 @@ def __iter__(self): class LoadImages: # for inference - def __init__(self, path, img_size=640): - p = str(Path(path)) # os-agnostic - p = os.path.abspath(p) # absolute path + def __init__(self, path, img_size=640, stride=32): + p = str(Path(path).absolute()) # os-agnostic absolute path if '*' in p: files = sorted(glob.glob(p, recursive=True)) # glob elif os.path.isdir(p): @@ -136,6 +136,7 @@ def __init__(self, path, img_size=640): ni, nv = len(images), len(videos) self.img_size = img_size + self.stride = stride self.files = images + videos self.nf = ni + nv # number of files self.video_flag = [False] * ni + [True] * nv @@ -181,7 +182,7 @@ def __next__(self): print(f'image {self.count}/{self.nf} {path}: ', end='') # Padded resize - img = letterbox(img0, new_shape=self.img_size)[0] + img = letterbox(img0, self.img_size, stride=self.stride)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 @@ -199,8 +200,9 @@ def __len__(self): class LoadWebcam: # for inference - def __init__(self, pipe='0', img_size=640): + def __init__(self, pipe='0', img_size=640, stride=32): self.img_size = img_size + self.stride = stride if pipe.isnumeric(): pipe = eval(pipe) # local camera @@ -243,7 +245,7 @@ def __next__(self): print(f'webcam {self.count}: ', end='') # Padded resize - img = letterbox(img0, new_shape=self.img_size)[0] + img = letterbox(img0, self.img_size, stride=self.stride)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 @@ -256,9 +258,10 @@ def __len__(self): class LoadStreams: # multiple IP or RTSP cameras - def __init__(self, sources='streams.txt', img_size=640): + def __init__(self, sources='streams.txt', img_size=640, stride=32): self.mode = 'stream' self.img_size = img_size + self.stride = stride if os.path.isfile(sources): with open(sources, 'r') as f: @@ -269,22 +272,28 @@ def __init__(self, sources='streams.txt', img_size=640): n = len(sources) self.imgs = [None] * n self.sources = [clean_str(x) for x in sources] # clean source names for later - for i, s in enumerate(sources): - # Start the thread to read frames from the video stream + for i, s in enumerate(sources): # index, source + # Start thread to read frames from video stream print(f'{i + 1}/{n}: {s}... ', end='') - cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s) + if 'youtube.com/' in s or 'youtu.be/' in s: # if source is YouTube video + check_requirements(('pafy', 'youtube_dl')) + import pafy + s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL + s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam + cap = cv2.VideoCapture(s) assert cap.isOpened(), f'Failed to open {s}' w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - fps = cap.get(cv2.CAP_PROP_FPS) % 100 + self.fps = cap.get(cv2.CAP_PROP_FPS) % 100 + _, self.imgs[i] = cap.read() # guarantee first frame thread = Thread(target=self.update, args=([i, cap]), daemon=True) - print(f' success ({w}x{h} at {fps:.2f} FPS).') + print(f' success ({w}x{h} at {self.fps:.2f} FPS).') thread.start() print('') # newline # check for common shapes - s = np.stack([letterbox(x, new_shape=self.img_size)[0].shape for x in self.imgs], 0) # inference shapes + s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0) # shapes self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal if not self.rect: print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.') @@ -297,9 +306,10 @@ def update(self, index, cap): # _, self.imgs[index] = cap.read() cap.grab() if n == 4: # read every 4th frame - _, self.imgs[index] = cap.retrieve() + success, im = cap.retrieve() + self.imgs[index] = im if success else self.imgs[index] * 0 n = 0 - time.sleep(0.01) # wait time + time.sleep(1 / self.fps) # wait time def __iter__(self): self.count = -1 @@ -313,7 +323,7 @@ def __next__(self): raise StopIteration # Letterbox - img = [letterbox(x, new_shape=self.img_size, auto=self.rect)[0] for x in img0] + img = [letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] for x in img0] # Stack img = np.stack(img, 0) @@ -331,7 +341,7 @@ def __len__(self): def img2label_paths(img_paths): # Define label paths as a function of image paths sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # /images/, /labels/ substrings - return [x.replace(sa, sb, 1).replace('.' + x.split('.')[-1], '.txt') for x in img_paths] + return ['txt'.join(x.replace(sa, sb, 1).rsplit(x.split('.')[-1], 1)) for x in img_paths] class LoadImagesAndLabels(Dataset): # for training/testing @@ -345,6 +355,7 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) self.mosaic_border = [-img_size // 2, -img_size // 2] self.stride = stride + self.path = path try: f = [] # image files @@ -352,37 +363,42 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r p = Path(p) # os-agnostic if p.is_dir(): # dir f += glob.glob(str(p / '**' / '*.*'), recursive=True) + # f = list(p.rglob('**/*.*')) # pathlib elif p.is_file(): # file with open(p, 'r') as t: t = t.read().strip().splitlines() parent = str(p.parent) + os.sep f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path + # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib) else: raise Exception(f'{prefix}{p} does not exist') self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats]) + # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats]) # pathlib assert self.img_files, f'{prefix}No images found' except Exception as e: raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {help_url}') # Check cache self.label_files = img2label_paths(self.img_files) # labels - cache_path = Path(self.label_files[0]).parent.with_suffix('.cache') # cached labels + cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') # cached labels if cache_path.is_file(): - cache = torch.load(cache_path) # load - if cache['hash'] != get_hash(self.label_files + self.img_files) or 'results' not in cache: # changed - cache = self.cache_labels(cache_path, prefix) # re-cache + cache, exists = torch.load(cache_path), True # load + if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache: # changed + cache, exists = self.cache_labels(cache_path, prefix), False # re-cache else: - cache = self.cache_labels(cache_path, prefix) # cache + cache, exists = self.cache_labels(cache_path, prefix), False # cache # Display cache - [nf, nm, ne, nc, n] = cache.pop('results') # found, missing, empty, corrupted, total - desc = f"Scanning '{cache_path}' for images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted" - tqdm(None, desc=prefix + desc, total=n, initial=n) + nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupted, total + if exists: + d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted" + tqdm(None, desc=prefix + d, total=n, initial=n) # display cache results assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}' # Read cache cache.pop('hash') # remove hash - labels, shapes = zip(*cache.values()) + cache.pop('version') # remove version + labels, shapes, self.segments = zip(*cache.values()) self.labels = list(labels) self.shapes = np.array(shapes, dtype=np.float64) self.img_files = list(cache.keys()) # update @@ -433,6 +449,7 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r self.imgs[i], self.img_hw0[i], self.img_hw[i] = x # img, hw_original, hw_resized = load_image(self, i) gb += self.imgs[i].nbytes pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB)' + pbar.close() def cache_labels(self, path=Path('./labels.cache'), prefix=''): # Cache dataset labels, check images and read shapes @@ -445,13 +462,20 @@ def cache_labels(self, path=Path('./labels.cache'), prefix=''): im = Image.open(im_file) im.verify() # PIL verify shape = exif_size(im) # image size - assert (shape[0] > 9) & (shape[1] > 9), 'image size <10 pixels' + segments = [] # instance segments + assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels' + assert im.format.lower() in img_formats, f'invalid image format {im.format}' # verify labels if os.path.isfile(lb_file): nf += 1 # label found with open(lb_file, 'r') as f: - l = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels + l = [x.split() for x in f.read().strip().splitlines()] + if any([len(x) > 8 for x in l]): # is segment + classes = np.array([x[0] for x in l], dtype=np.float32) + segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...) + l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) + l = np.array(l, dtype=np.float32) if len(l): assert l.shape[1] == 5, 'labels require 5 columns each' assert (l >= 0).all(), 'negative labels' @@ -463,19 +487,21 @@ def cache_labels(self, path=Path('./labels.cache'), prefix=''): else: nm += 1 # label missing l = np.zeros((0, 5), dtype=np.float32) - x[im_file] = [l, shape] + x[im_file] = [l, shape, segments] except Exception as e: nc += 1 print(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}') - pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' for images and labels... " \ + pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels... " \ f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted" + pbar.close() if nf == 0: print(f'{prefix}WARNING: No labels found in {path}. See {help_url}') x['hash'] = get_hash(self.label_files + self.img_files) - x['results'] = [nf, nm, ne, nc, i + 1] + x['results'] = nf, nm, ne, nc, i + 1 + x['version'] = 0.1 # cache version torch.save(x, path) # save for next time logging.info(f'{prefix}New cache created: {path}') return x @@ -515,16 +541,9 @@ def __getitem__(self, index): img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling - # Load labels - labels = [] - x = self.labels[index] - if x.size > 0: - # Normalized xywh to pixel xyxy format - labels = x.copy() - labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + pad[0] # pad width - labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + pad[1] # pad height - labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0] - labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1] + labels = self.labels[index].copy() + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) if self.augment: # Augment imagespace @@ -615,10 +634,10 @@ def load_image(self, index): img = cv2.imread(path) # BGR assert img is not None, 'Image Not Found ' + path h0, w0 = img.shape[:2] # orig hw - r = self.img_size / max(h0, w0) # resize image to img_size - if r != 1: # always resize down, only resize up if training with augmentation - interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR - img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp) + r = self.img_size / max(h0, w0) # ratio + if r != 1: # if sizes are not equal + img = cv2.resize(img, (int(w0 * r), int(h0 * r)), + interpolation=cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR) return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized else: return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized @@ -637,19 +656,25 @@ def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype) cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed - # Histogram equalization - # if random.random() < 0.2: - # for i in range(3): - # img[:, :, i] = cv2.equalizeHist(img[:, :, i]) + +def hist_equalize(img, clahe=True, bgr=False): + # Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255 + yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) + if clahe: + c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) + yuv[:, :, 0] = c.apply(yuv[:, :, 0]) + else: + yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram + return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def load_mosaic(self, index): # loads images in a 4-mosaic - labels4 = [] + labels4, segments4 = [], [] s = self.img_size yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y - indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(3)] # 3 additional image indices + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices for i, index in enumerate(indices): # Load image img, _, (h, w) = load_image(self, index) @@ -674,23 +699,21 @@ def load_mosaic(self, index): padh = y1a - y1b # Labels - x = self.labels[index] - labels = x.copy() - if x.size > 0: # Normalized xywh to pixel xyxy format - labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padw - labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + padh - labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padw - labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + padh + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] labels4.append(labels) + segments4.extend(segments) # Concat/clip labels - if len(labels4): - labels4 = np.concatenate(labels4, 0) - np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:]) # use with random_perspective - # img4, labels4 = replicate(img4, labels4) # replicate + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate # Augment - img4, labels4 = random_perspective(img4, labels4, + img4, labels4 = random_perspective(img4, labels4, segments4, degrees=self.hyp['degrees'], translate=self.hyp['translate'], scale=self.hyp['scale'], @@ -704,9 +727,9 @@ def load_mosaic(self, index): def load_mosaic9(self, index): # loads images in a 9-mosaic - labels9 = [] + labels9, segments9 = [], [] s = self.img_size - indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(8)] # 8 additional image indices + indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices for i, index in enumerate(indices): # Load image img, _, (h, w) = load_image(self, index) @@ -737,34 +760,34 @@ def load_mosaic9(self, index): x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords # Labels - x = self.labels[index] - labels = x.copy() - if x.size > 0: # Normalized xywh to pixel xyxy format - labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padx - labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + pady - labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padx - labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + pady + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padx, pady) for x in segments] labels9.append(labels) + segments9.extend(segments) # Image img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] hp, wp = h, w # height, width previous # Offset - yc, xc = [int(random.uniform(0, s)) for x in self.mosaic_border] # mosaic center x, y + yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border] # mosaic center x, y img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] # Concat/clip labels - if len(labels9): - labels9 = np.concatenate(labels9, 0) - labels9[:, [1, 3]] -= xc - labels9[:, [2, 4]] -= yc + labels9 = np.concatenate(labels9, 0) + labels9[:, [1, 3]] -= xc + labels9[:, [2, 4]] -= yc + c = np.array([xc, yc]) # centers + segments9 = [x - c for x in segments9] - np.clip(labels9[:, 1:], 0, 2 * s, out=labels9[:, 1:]) # use with random_perspective - # img9, labels9 = replicate(img9, labels9) # replicate + for x in (labels9[:, 1:], *segments9): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img9, labels9 = replicate(img9, labels9) # replicate # Augment - img9, labels9 = random_perspective(img9, labels9, + img9, labels9 = random_perspective(img9, labels9, segments9, degrees=self.hyp['degrees'], translate=self.hyp['translate'], scale=self.hyp['scale'], @@ -792,8 +815,8 @@ def replicate(img, labels): return img, labels -def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True): - # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232 +def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): + # Resize and pad image while meeting stride-multiple constraints shape = img.shape[:2] # current shape [height, width] if isinstance(new_shape, int): new_shape = (new_shape, new_shape) @@ -808,7 +831,7 @@ def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding if auto: # minimum rectangle - dw, dh = np.mod(dw, 32), np.mod(dh, 32) # wh padding + dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding elif scaleFill: # stretch dw, dh = 0.0, 0.0 new_unpad = (new_shape[1], new_shape[0]) @@ -825,7 +848,8 @@ def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale return img, ratio, (dw, dh) -def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)): +def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, + border=(0, 0)): # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) # targets = [cls, xyxy] @@ -877,37 +901,38 @@ def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shea # Transform label coordinates n = len(targets) if n: - # warp points - xy = np.ones((n * 4, 3)) - xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 - xy = xy @ M.T # transform - if perspective: - xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale - else: # affine - xy = xy[:, :2].reshape(n, 8) - - # create new boxes - x = xy[:, [0, 2, 4, 6]] - y = xy[:, [1, 3, 5, 7]] - xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T - - # # apply angle-based reduction of bounding boxes - # radians = a * math.pi / 180 - # reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5 - # x = (xy[:, 2] + xy[:, 0]) / 2 - # y = (xy[:, 3] + xy[:, 1]) / 2 - # w = (xy[:, 2] - xy[:, 0]) * reduction - # h = (xy[:, 3] - xy[:, 1]) * reduction - # xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T - - # clip boxes - xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width) - xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height) + use_segments = any(x.any() for x in segments) + new = np.zeros((n, 4)) + if use_segments: # warp segments + segments = resample_segments(segments) # upsample + for i, segment in enumerate(segments): + xy = np.ones((len(segment), 3)) + xy[:, :2] = segment + xy = xy @ M.T # transform + xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine + + # clip + new[i] = segment2box(xy, width, height) + + else: # warp boxes + xy = np.ones((n * 4, 3)) + xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = xy @ M.T # transform + xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # clip + new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) + new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) # filter candidates - i = box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T) + i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) targets = targets[i] - targets[:, 1:5] = xy[i] + targets[:, 1:5] = new[i] return img, targets @@ -1016,19 +1041,24 @@ def extract_boxes(path='../coco128/'): # from utils.datasets import *; extract_ assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}' -def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0)): # from utils.datasets import *; autosplit('../coco128') +def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0), annotated_only=False): """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files - # Arguments - path: Path to images directory - weights: Train, val, test weights (list) + Usage: from utils.datasets import *; autosplit('../coco128') + Arguments + path: Path to images directory + weights: Train, val, test weights (list) + annotated_only: Only use images with an annotated txt file """ path = Path(path) # images dir - files = list(path.rglob('*.*')) + files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], []) # image files only n = len(files) # number of files indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split + txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files [(path / x).unlink() for x in txt if (path / x).exists()] # remove existing + + print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only) for i, img in tqdm(zip(indices, files), total=n): - if img.suffix[1:] in img_formats: + if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label with open(path / txt[i], 'a') as f: f.write(str(img) + '\n') # add image to txt file diff --git a/utils/flask_rest_api/README.md b/utils/flask_rest_api/README.md new file mode 100644 index 000000000000..0cdc51be692d --- /dev/null +++ b/utils/flask_rest_api/README.md @@ -0,0 +1,51 @@ +# Flask REST API +[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API created using Flask to expose the `yolov5s` model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/). + +## Requirements + +[Flask](https://palletsprojects.com/p/flask/) is required. Install with: +```shell +$ pip install Flask +``` + +## Run + +After Flask installation run: + +```shell +$ python3 restapi.py --port 5000 +``` + +Then use [curl](https://curl.se/) to perform a request: + +```shell +$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'` +``` + +The model inference results are returned: + +```shell +[{'class': 0, + 'confidence': 0.8197850585, + 'name': 'person', + 'xmax': 1159.1403808594, + 'xmin': 750.912902832, + 'ymax': 711.2583007812, + 'ymin': 44.0350036621}, + {'class': 0, + 'confidence': 0.5667674541, + 'name': 'person', + 'xmax': 1065.5523681641, + 'xmin': 116.0448303223, + 'ymax': 713.8904418945, + 'ymin': 198.4603881836}, + {'class': 27, + 'confidence': 0.5661227107, + 'name': 'tie', + 'xmax': 516.7975463867, + 'xmin': 416.6880187988, + 'ymax': 717.0524902344, + 'ymin': 429.2020568848}] +``` + +An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py` diff --git a/utils/flask_rest_api/example_request.py b/utils/flask_rest_api/example_request.py new file mode 100644 index 000000000000..ff21f30f93ca --- /dev/null +++ b/utils/flask_rest_api/example_request.py @@ -0,0 +1,13 @@ +"""Perform test request""" +import pprint + +import requests + +DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s" +TEST_IMAGE = "zidane.jpg" + +image_data = open(TEST_IMAGE, "rb").read() + +response = requests.post(DETECTION_URL, files={"image": image_data}).json() + +pprint.pprint(response) diff --git a/utils/flask_rest_api/restapi.py b/utils/flask_rest_api/restapi.py new file mode 100644 index 000000000000..9d88f618905d --- /dev/null +++ b/utils/flask_rest_api/restapi.py @@ -0,0 +1,38 @@ +""" +Run a rest API exposing the yolov5s object detection model +""" +import argparse +import io + +import torch +from PIL import Image +from flask import Flask, request + +app = Flask(__name__) + +DETECTION_URL = "/v1/object-detection/yolov5s" + + +@app.route(DETECTION_URL, methods=["POST"]) +def predict(): + if not request.method == "POST": + return + + if request.files.get("image"): + image_file = request.files["image"] + image_bytes = image_file.read() + + img = Image.open(io.BytesIO(image_bytes)) + + results = model(img, size=640) + data = results.pandas().xyxy[0].to_json(orient="records") + return data + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Flask api exposing yolov5 model") + parser.add_argument("--port", default=5000, type=int, help="port number") + args = parser.parse_args() + + model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True).autoshape() # force_reload to recache + app.run(host="0.0.0.0", port=args.port) # debug=True causes Restarting with stat diff --git a/utils/general.py b/utils/general.py index 3247b66da0ce..fbb99b9e7f99 100755 --- a/utils/general.py +++ b/utils/general.py @@ -1,17 +1,21 @@ -# General utils +# YOLOv5 general utils import glob import logging import math import os +import platform import random import re import subprocess import time +from itertools import repeat +from multiprocessing.pool import ThreadPool from pathlib import Path import cv2 import numpy as np +import pandas as pd import torch import torchvision import yaml @@ -23,14 +27,15 @@ # Settings torch.set_printoptions(linewidth=320, precision=5, profile='long') np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 +pd.options.display.max_columns = 10 cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8)) # NumExpr max threads -def set_logging(rank=-1): +def set_logging(rank=-1, verbose=True): logging.basicConfig( format="%(message)s", - level=logging.INFO if rank in [-1, 0] else logging.WARN) + level=logging.INFO if (verbose and rank in [-1, 0]) else logging.WARN) def init_seeds(seed=0): @@ -46,11 +51,26 @@ def get_latest_run(search_dir='.'): return max(last_list, key=os.path.getctime) if last_list else '' +def isdocker(): + # Is environment a Docker container + return Path('/workspace').exists() # or Path('/.dockerenv').exists() + + +def emojis(str=''): + # Return platform-dependent emoji-safe version of string + return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str + + +def file_size(file): + # Return file size in MB + return Path(file).stat().st_size / 1e6 + + def check_online(): # Check internet connectivity import socket try: - socket.create_connection(("1.1.1.1", 53)) # check host accesability + socket.create_connection(("1.1.1.1", 443), 5) # check host accesability return True except OSError: return False @@ -60,26 +80,51 @@ def check_git_status(): # Recommend 'git pull' if code is out of date print(colorstr('github: '), end='') try: - if Path('.git').exists() and check_online(): - url = subprocess.check_output( - 'git fetch && git config --get remote.origin.url', shell=True).decode('utf-8')[:-1] - n = int(subprocess.check_output( - 'git rev-list $(git rev-parse --abbrev-ref HEAD)..origin/master --count', shell=True)) # commits behind - if n > 0: - print(f"⚠️ WARNING: code is out of date by {n} {'commits' if n > 1 else 'commmit'}. " - f"Use 'git pull' to update or 'git clone {url}' to download latest.") - else: - print(f'up to date with {url} ✅') + assert Path('.git').exists(), 'skipping check (not a git repository)' + assert not isdocker(), 'skipping check (Docker image)' + assert check_online(), 'skipping check (offline)' + + cmd = 'git fetch && git config --get remote.origin.url' + url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git') # github repo url + branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out + n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind + if n > 0: + s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " \ + f"Use 'git pull' to update or 'git clone {url}' to download latest." + else: + s = f'up to date with {url} ✅' + print(emojis(s)) # emoji-safe except Exception as e: print(e) -def check_requirements(file='requirements.txt'): - # Check installed dependencies meet requirements - import pkg_resources - requirements = pkg_resources.parse_requirements(Path(file).open()) - requirements = [x.name + ''.join(*x.specs) if len(x.specs) else x.name for x in requirements] - pkg_resources.require(requirements) # DistributionNotFound or VersionConflict exception if requirements not met +def check_requirements(requirements='requirements.txt', exclude=()): + # Check installed dependencies meet requirements (pass *.txt file or list of packages) + import pkg_resources as pkg + prefix = colorstr('red', 'bold', 'requirements:') + if isinstance(requirements, (str, Path)): # requirements.txt file + file = Path(requirements) + if not file.exists(): + print(f"{prefix} {file.resolve()} not found, check failed.") + return + requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude] + else: # list or tuple of packages + requirements = [x for x in requirements if x not in exclude] + + n = 0 # number of packages updates + for r in requirements: + try: + pkg.require(r) + except Exception as e: # DistributionNotFound or VersionConflict if requirements not met + n += 1 + print(f"{prefix} {r} not found and is required by YOLOv5, attempting auto-update...") + print(subprocess.check_output(f"pip install '{r}'", shell=True).decode()) + + if n: # if packages updated + source = file.resolve() if 'file' in locals() else requirements + s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \ + f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n" + print(emojis(s)) # emoji-safe def check_img_size(img_size, s=32): @@ -90,14 +135,28 @@ def check_img_size(img_size, s=32): return new_size +def check_imshow(): + # Check if environment supports image displays + try: + assert not isdocker(), 'cv2.imshow() is disabled in Docker environments' + cv2.imshow('test', np.zeros((1, 1, 3))) + cv2.waitKey(1) + cv2.destroyAllWindows() + cv2.waitKey(1) + return True + except Exception as e: + print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}') + return False + + def check_file(file): # Search for file if not found - if os.path.isfile(file) or file == '': + if Path(file).is_file() or file == '': return file else: files = glob.glob('./**/' + file, recursive=True) # find file - assert len(files), 'File Not Found: %s' % file # assert file was found - assert len(files) == 1, "Multiple files match '%s', specify exact path: %s" % (file, files) # assert unique + assert len(files), f'File Not Found: {file}' # assert file was found + assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique return files[0] # return file @@ -109,18 +168,45 @@ def check_dataset(dict): if not all(x.exists() for x in val): print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()]) if s and len(s): # download script - print('Downloading %s ...' % s) if s.startswith('http') and s.endswith('.zip'): # URL f = Path(s).name # filename + print(f'Downloading {s} ...') torch.hub.download_url_to_file(s, f) - r = os.system('unzip -q %s -d ../ && rm %s' % (f, f)) # unzip - else: # bash script + r = os.system(f'unzip -q {f} -d ../ && rm {f}') # unzip + elif s.startswith('bash '): # bash script + print(f'Running {s} ...') r = os.system(s) - print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure')) # analyze return value + else: # python script + r = exec(s) # return None + print('Dataset autodownload %s\n' % ('success' if r in (0, None) else 'failure')) # print result else: raise Exception('Dataset not found.') +def download(url, dir='.', multi_thread=False): + # Multi-threaded file download and unzip function + def download_one(url, dir): + # Download 1 file + f = dir / Path(url).name # filename + if not f.exists(): + print(f'Downloading {url} to {f}...') + torch.hub.download_url_to_file(url, f, progress=True) # download + if f.suffix in ('.zip', '.gz'): + print(f'Unzipping {f}...') + if f.suffix == '.zip': + os.system(f'unzip -qo {f} -d {dir} && rm {f}') # unzip -quiet -overwrite + elif f.suffix == '.gz': + os.system(f'tar xfz {f} --directory {f.parent} && rm {f}') # unzip + + dir = Path(dir) + dir.mkdir(parents=True, exist_ok=True) # make directory + if multi_thread: + ThreadPool(8).imap(lambda x: download_one(*x), zip(url, repeat(dir))) # 8 threads + else: + for u in tuple(url) if isinstance(url, str) else url: + download_one(u, dir) + + def make_divisible(x, divisor): # Returns x evenly divisible by divisor return math.ceil(x / divisor) * divisor @@ -220,6 +306,50 @@ def xywh2xyxy(x): return y +def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): + # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x + y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y + y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x + y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y + return y + + +def xyn2xy(x, w=640, h=640, padw=0, padh=0): + # Convert normalized segments into pixel segments, shape (n,2) + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = w * x[:, 0] + padw # top left x + y[:, 1] = h * x[:, 1] + padh # top left y + return y + + +def segment2box(segment, width=640, height=640): + # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) + x, y = segment.T # segment xy + inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) + x, y, = x[inside], y[inside] + return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy + + +def segments2boxes(segments): + # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) + boxes = [] + for s in segments: + x, y = s.T # segment xy + boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy + return xyxy2xywh(np.array(boxes)) # cls, xywh + + +def resample_segments(segments, n=1000): + # Up-sample an (n,2) segment + for i, s in enumerate(segments): + x = np.linspace(0, len(s) - 1, n) + xp = np.arange(len(s)) + segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy + return segments + + def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): # Rescale coords (xyxy) from img1_shape to img0_shape if ratio_pad is None: # calculate from img0_shape @@ -244,7 +374,7 @@ def clip_coords(boxes, img_shape): boxes[:, 3].clamp_(0, img_shape[0]) # y2 -def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-9): +def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 box2 = box2.T @@ -280,7 +410,7 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps= elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) with torch.no_grad(): - alpha = v / ((1 + eps) - iou + v) + alpha = v / (v - iou + (1 + eps)) return iou - (rho2 / c2 + v * alpha) # CIoU else: # GIoU https://arxiv.org/pdf/1902.09630.pdf c_area = cw * ch + eps # convex area @@ -322,11 +452,12 @@ def wh_iou(wh1, wh2): return inter / (wh1.prod(2) + wh2.prod(2) - inter) # iou = inter / (area1 + area2 - inter) -def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()): - """Performs Non-Maximum Suppression (NMS) on inference results +def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, + labels=()): + """Runs Non-Maximum Suppression (NMS) on inference results Returns: - detections with shape: nx6 (x1, y1, x2, y2, conf, cls) + list of detections, on (n,6) tensor per image [xyxy, conf, cls] """ nc = prediction.shape[2] - 5 # number of classes @@ -338,7 +469,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() time_limit = 10.0 # seconds to quit after redundant = True # require redundant detections - multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img) + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) merge = False # use merge-NMS t = time.time() @@ -412,18 +543,20 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non return output -def strip_optimizer(f='weights/best.pt', s=''): # from utils.general import *; strip_optimizer() +def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer() # Strip optimizer from 'f' to finalize training, optionally save as 's' x = torch.load(f, map_location=torch.device('cpu')) - for key in 'optimizer', 'training_results', 'wandb_id': - x[key] = None + if x.get('ema'): + x['model'] = x['ema'] # replace model with ema + for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates': # keys + x[k] = None x['epoch'] = -1 x['model'].half() # to FP16 for p in x['model'].parameters(): p.requires_grad = False torch.save(x, s or f) mb = os.path.getsize(s or f) / 1E6 # filesize - print('Optimizer stripped from %s,%s %.1fMB' % (f, (' saved as %s,' % s) if s else '', mb)) + print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB") def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''): @@ -451,14 +584,14 @@ def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''): results = tuple(x[0, :7]) c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n') - yaml.dump(hyp, f, sort_keys=False) + yaml.safe_dump(hyp, f, sort_keys=False) if bucket: os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket)) # upload def apply_classifier(x, model, img, im0): - # applies a second stage classifier to yolo outputs + # Apply a second stage classifier to yolo outputs im0 = [im0] if isinstance(im0, np.ndarray) else im0 for i, d in enumerate(x): # per image if d is not None and len(d): @@ -492,14 +625,31 @@ def apply_classifier(x, model, img, im0): return x -def increment_path(path, exist_ok=True, sep=''): - # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc. +def save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False): + # Save an image crop as {file} with crop size multiplied by {gain} and padded by {pad} pixels + xyxy = torch.tensor(xyxy).view(-1, 4) + b = xyxy2xywh(xyxy) # boxes + if square: + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square + b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad + xyxy = xywh2xyxy(b).long() + clip_coords(xyxy, im.shape) + crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2])] + cv2.imwrite(str(increment_path(file, mkdir=True).with_suffix('.jpg')), crop if BGR else crop[..., ::-1]) + + +def increment_path(path, exist_ok=False, sep='', mkdir=False): + # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc. path = Path(path) # os-agnostic - if (path.exists() and exist_ok) or (not path.exists()): - return str(path) - else: + if path.exists() and not exist_ok: + suffix = path.suffix + path = path.with_suffix('') dirs = glob.glob(f"{path}{sep}*") # similar paths matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs] i = [int(m.groups()[0]) for m in matches if m] # indices n = max(i) + 1 if i else 2 # increment number - return f"{path}{sep}{n}" # update path + path = Path(f"{path}{sep}{n}{suffix}") # update path + dir = path if path.suffix == '' else path.parent # directory + if not dir.exists() and mkdir: + dir.mkdir(parents=True, exist_ok=True) # make directory + return path diff --git a/utils/google_utils.py b/utils/google_utils.py index 024dc7802f15..6a4660bad509 100644 --- a/utils/google_utils.py +++ b/utils/google_utils.py @@ -18,7 +18,7 @@ def gsutil_getsize(url=''): def attempt_download(file, repo='ultralytics/yolov5'): # Attempt file download if does not exist - file = Path(str(file).strip().replace("'", '').lower()) + file = Path(str(file).strip().replace("'", '')) if not file.exists(): try: @@ -26,8 +26,12 @@ def attempt_download(file, repo='ultralytics/yolov5'): assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...] tag = response['tag_name'] # i.e. 'v1.0' except: # fallback plan - assets = ['yolov5.pt', 'yolov5.pt', 'yolov5l.pt', 'yolov5x.pt'] - tag = subprocess.check_output('git tag', shell=True).decode('utf-8').split('\n')[-2] + assets = ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', + 'yolov5s6.pt', 'yolov5m6.pt', 'yolov5l6.pt', 'yolov5x6.pt'] + try: + tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1] + except: + tag = 'v5.0' # current release name = file.name if name in assets: diff --git a/utils/loss.py b/utils/loss.py index 889ddf7295da..9e78df17fdf3 100644 --- a/utils/loss.py +++ b/utils/loss.py @@ -97,7 +97,7 @@ def __init__(self, model, autobalance=False): BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=0.0) + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets # Focal loss g = h['fl_gamma'] # focal loss gamma @@ -105,9 +105,8 @@ def __init__(self, model, autobalance=False): BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module - self.balance = {3: [3.67, 1.0, 0.43], 4: [3.78, 1.0, 0.39, 0.22], 5: [3.88, 1.0, 0.37, 0.17, 0.10]}[det.nl] - # self.balance = [1.0] * det.nl - self.ssi = (det.stride == 16).nonzero(as_tuple=False).item() # stride 16 index + self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance for k in 'na', 'nc', 'nl', 'anchors': setattr(self, k, getattr(det, k)) diff --git a/utils/metrics.py b/utils/metrics.py index 99d5bcfaf2af..323c84b6c873 100644 --- a/utils/metrics.py +++ b/utils/metrics.py @@ -15,7 +15,7 @@ def fitness(x): return (x[:, :4] * w).sum(1) -def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision-recall_curve.png', names=[]): +def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()): """ Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Arguments @@ -35,12 +35,11 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision # Find unique classes unique_classes = np.unique(target_cls) + nc = unique_classes.shape[0] # number of classes, number of detections # Create Precision-Recall curve and compute AP for each class px, py = np.linspace(0, 1, 1000), [] # for plotting - pr_score = 0.1 # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898 - s = [unique_classes.shape[0], tp.shape[1]] # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95) - ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s) + ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) for ci, c in enumerate(unique_classes): i = pred_cls == c n_l = (target_cls == c).sum() # number of labels @@ -55,25 +54,28 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision # Recall recall = tpc / (n_l + 1e-16) # recall curve - r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0]) # r at pr_score, negative x, xp because xp decreases + r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases # Precision precision = tpc / (tpc + fpc) # precision curve - p[ci] = np.interp(-pr_score, -conf[i], precision[:, 0]) # p at pr_score + p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score # AP from recall-precision curve for j in range(tp.shape[1]): ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) - if plot and (j == 0): + if plot and j == 0: py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 - # Compute F1 score (harmonic mean of precision and recall) + # Compute F1 (harmonic mean of precision and recall) f1 = 2 * p * r / (p + r + 1e-16) - if plot: - plot_pr_curve(px, py, ap, save_dir, names) + plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names) + plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1') + plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision') + plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall') - return p, r, ap, f1, unique_classes.astype('int32') + i = f1.mean(0).argmax() # max F1 index + return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32') def compute_ap(recall, precision): @@ -143,14 +145,14 @@ def process_batch(self, detections, labels): for i, gc in enumerate(gt_classes): j = m0 == i if n and sum(j) == 1: - self.matrix[gc, detection_classes[m1[j]]] += 1 # correct + self.matrix[detection_classes[m1[j]], gc] += 1 # correct else: - self.matrix[gc, self.nc] += 1 # background FP + self.matrix[self.nc, gc] += 1 # background FP if n: for i, dc in enumerate(detection_classes): if not any(m1 == i): - self.matrix[self.nc, dc] += 1 # background FN + self.matrix[dc, self.nc] += 1 # background FN def matrix(self): return self.matrix @@ -166,8 +168,8 @@ def plot(self, save_dir='', names=()): sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, - xticklabels=names + ['background FN'] if labels else "auto", - yticklabels=names + ['background FP'] if labels else "auto").set_facecolor((1, 1, 1)) + xticklabels=names + ['background FP'] if labels else "auto", + yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1)) fig.axes[0].set_xlabel('True') fig.axes[0].set_ylabel('Predicted') fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) @@ -181,13 +183,14 @@ def print(self): # Plots ---------------------------------------------------------------------------------------------------------------- -def plot_pr_curve(px, py, ap, save_dir='.', names=()): +def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()): + # Precision-recall curve fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) py = np.stack(py, axis=1) - if 0 < len(names) < 21: # show mAP in legend if < 10 classes + if 0 < len(names) < 21: # display per-class legend if < 21 classes for i, y in enumerate(py.T): - ax.plot(px, y, linewidth=1, label=f'{names[i]} %.3f' % ap[i, 0]) # plot(recall, precision) + ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) else: ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) @@ -197,4 +200,24 @@ def plot_pr_curve(px, py, ap, save_dir='.', names=()): ax.set_xlim(0, 1) ax.set_ylim(0, 1) plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") - fig.savefig(Path(save_dir) / 'precision_recall_curve.png', dpi=250) + fig.savefig(Path(save_dir), dpi=250) + + +def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'): + # Metric-confidence curve + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py): + ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) + else: + ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) + + y = py.mean(0) + ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + fig.savefig(Path(save_dir), dpi=250) diff --git a/utils/plots.py b/utils/plots.py index 47cd70776005..f24513c6998d 100644 --- a/utils/plots.py +++ b/utils/plots.py @@ -15,7 +15,7 @@ import seaborn as sns import torch import yaml -from PIL import Image, ImageDraw +from PIL import Image, ImageDraw, ImageFont from scipy.signal import butter, filtfilt from utils.general import xywh2xyxy, xyxy2xywh @@ -31,7 +31,7 @@ def color_list(): def hex2rgb(h): return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) - return [hex2rgb(h) for h in plt.rcParams['axes.prop_cycle'].by_key()['color']] + return [hex2rgb(h) for h in matplotlib.colors.TABLEAU_COLORS.values()] # or BASE_ (8), CSS4_ (148), XKCD_ (949) def hist2d(x, y, n=100): @@ -54,18 +54,34 @@ def butter_lowpass(cutoff, fs, order): return filtfilt(b, a, data) # forward-backward filter -def plot_one_box(x, img, color=None, label=None, line_thickness=None): - # Plots one bounding box on image img - tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness +def plot_one_box(x, im, color=None, label=None, line_thickness=3): + # Plots one bounding box on image 'im' using OpenCV + assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.' + tl = line_thickness or round(0.002 * (im.shape[0] + im.shape[1]) / 2) + 1 # line/font thickness color = color or [random.randint(0, 255) for _ in range(3)] c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) - cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) + cv2.rectangle(im, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) if label: tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 - cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled - cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) + cv2.rectangle(im, c1, c2, color, -1, cv2.LINE_AA) # filled + cv2.putText(im, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) + + +def plot_one_box_PIL(box, im, color=None, label=None, line_thickness=None): + # Plots one bounding box on image 'im' using PIL + im = Image.fromarray(im) + draw = ImageDraw.Draw(im) + line_thickness = line_thickness or max(int(min(im.size) / 200), 2) + draw.rectangle(box, width=line_thickness, outline=tuple(color)) # plot + if label: + fontsize = max(round(max(im.size) / 40), 12) + font = ImageFont.truetype("Arial.ttf", fontsize) + txt_width, txt_height = font.getsize(label) + draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=tuple(color)) + draw.text((box[0], box[1] - txt_height + 1), label, fill=(255, 255, 255), font=font) + return np.asarray(im) def plot_wh_methods(): # from utils.plots import *; plot_wh_methods() @@ -223,38 +239,39 @@ def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() plt.savefig('targets.jpg', dpi=200) -def plot_study_txt(path='study/', x=None): # from utils.plots import *; plot_study_txt() +def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt() # Plot study.txt generated by test.py fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) - ax = ax.ravel() + # ax = ax.ravel() fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) - for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']]: + # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: + for f in sorted(Path(path).glob('study*.txt')): y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T x = np.arange(y.shape[1]) if x is None else np.array(x) s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)'] - for i in range(7): - ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) - ax[i].set_title(s[i]) + # for i in range(7): + # ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) + # ax[i].set_title(s[i]) j = y[3].argmax() + 1 - ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8, + ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8, label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet') - ax2.grid() - ax2.set_yticks(np.arange(30, 60, 5)) - ax2.set_xlim(0, 30) - ax2.set_ylim(29, 51) + ax2.grid(alpha=0.2) + ax2.set_yticks(np.arange(20, 60, 5)) + ax2.set_xlim(0, 57) + ax2.set_ylim(30, 55) ax2.set_xlabel('GPU Speed (ms/img)') ax2.set_ylabel('COCO AP val') ax2.legend(loc='lower right') - plt.savefig('test_study.png', dpi=300) + plt.savefig(str(Path(path).name) + '.png', dpi=300) -def plot_labels(labels, save_dir=Path(''), loggers=None): +def plot_labels(labels, names=(), save_dir=Path(''), loggers=None): # plot dataset labels print('Plotting labels... ') c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes @@ -271,7 +288,12 @@ def plot_labels(labels, save_dir=Path(''), loggers=None): matplotlib.use('svg') # faster ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) - ax[0].set_xlabel('classes') + ax[0].set_ylabel('instances') + if 0 < len(names) < 30: + ax[0].set_xticks(range(len(names))) + ax[0].set_xticklabels(names, rotation=90, fontsize=10) + else: + ax[0].set_xlabel('classes') sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) @@ -295,13 +317,13 @@ def plot_labels(labels, save_dir=Path(''), loggers=None): # loggers for k, v in loggers.items() or {}: if k == 'wandb' and v: - v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}) + v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}, commit=False) def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.plots import *; plot_evolution() # Plot hyperparameter evolution results in evolve.txt with open(yaml_file) as f: - hyp = yaml.load(f, Loader=yaml.FullLoader) + hyp = yaml.safe_load(f) x = np.loadtxt('evolve.txt', ndmin=2) f = fitness(x) # weights = (f - f.min()) ** 2 # for weighted results diff --git a/utils/torch_utils.py b/utils/torch_utils.py index 2cb09e71ce71..9991e5ec87d8 100644 --- a/utils/torch_utils.py +++ b/utils/torch_utils.py @@ -1,8 +1,10 @@ -# PyTorch utils +# YOLOv5 PyTorch utils +import datetime import logging import math import os +import platform import subprocess import time from contextlib import contextmanager @@ -43,17 +45,24 @@ def init_torch_seeds(seed=0): cudnn.benchmark, cudnn.deterministic = True, False -def git_describe(): +def date_modified(path=__file__): + # return human-readable file modification date, i.e. '2021-3-26' + t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime) + return f'{t.year}-{t.month}-{t.day}' + + +def git_describe(path=Path(__file__).parent): # path must be a directory # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe - if Path('.git').exists(): - return subprocess.check_output('git describe --tags --long --always', shell=True).decode('utf-8')[:-1] - else: - return '' + s = f'git -C {path} describe --tags --long --always' + try: + return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1] + except subprocess.CalledProcessError as e: + return '' # not a git repository def select_device(device='', batch_size=None): # device = 'cpu' or '0' or '0,1,2,3' - s = f'YOLOv5 {git_describe()} torch {torch.__version__} ' # string + s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' # string cpu = device.lower() == 'cpu' if cpu: os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False @@ -73,7 +82,7 @@ def select_device(device='', batch_size=None): else: s += 'CPU\n' - logger.info(s) # skip a line + logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe return torch.device('cuda:0' if cuda else 'cpu') @@ -120,7 +129,7 @@ def profile(x, ops, n=100, device=None): s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list' p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters - print(f'{p:12.4g}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') + print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') def is_parallel(model): @@ -182,7 +191,7 @@ def fuse_conv_and_bn(conv, bn): # prepare filters w_conv = conv.weight.clone().view(conv.out_channels, -1) w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) - fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size())) + fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) # prepare spatial bias b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias @@ -205,7 +214,7 @@ def model_info(model, verbose=False, img_size=640): try: # FLOPS from thop import profile - stride = int(model.stride.max()) if hasattr(model, 'stride') else 32 + stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device) # input flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPS img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float diff --git a/utils/wandb_logging/__init__.py b/utils/wandb_logging/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/utils/wandb_logging/log_dataset.py b/utils/wandb_logging/log_dataset.py new file mode 100644 index 000000000000..f45a23011f15 --- /dev/null +++ b/utils/wandb_logging/log_dataset.py @@ -0,0 +1,24 @@ +import argparse + +import yaml + +from wandb_utils import WandbLogger + +WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' + + +def create_dataset_artifact(opt): + with open(opt.data) as f: + data = yaml.safe_load(f) # data dict + logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') + parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') + parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project') + opt = parser.parse_args() + opt.resume = False # Explicitly disallow resume check for dataset upload job + + create_dataset_artifact(opt) diff --git a/utils/wandb_logging/wandb_utils.py b/utils/wandb_logging/wandb_utils.py new file mode 100644 index 000000000000..d8fbd1ef42aa --- /dev/null +++ b/utils/wandb_logging/wandb_utils.py @@ -0,0 +1,306 @@ +import json +import sys +from pathlib import Path + +import torch +import yaml +from tqdm import tqdm + +sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path +from utils.datasets import LoadImagesAndLabels +from utils.datasets import img2label_paths +from utils.general import colorstr, xywh2xyxy, check_dataset + +try: + import wandb + from wandb import init, finish +except ImportError: + wandb = None + +WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' + + +def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX): + return from_string[len(prefix):] + + +def check_wandb_config_file(data_config_file): + wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path + if Path(wandb_config).is_file(): + return wandb_config + return data_config_file + + +def get_run_info(run_path): + run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX)) + run_id = run_path.stem + project = run_path.parent.stem + model_artifact_name = 'run_' + run_id + '_model' + return run_id, project, model_artifact_name + + +def check_wandb_resume(opt): + process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None + if isinstance(opt.resume, str): + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + if opt.global_rank not in [-1, 0]: # For resuming DDP runs + run_id, project, model_artifact_name = get_run_info(opt.resume) + api = wandb.Api() + artifact = api.artifact(project + '/' + model_artifact_name + ':latest') + modeldir = artifact.download() + opt.weights = str(Path(modeldir) / "last.pt") + return True + return None + + +def process_wandb_config_ddp_mode(opt): + with open(opt.data) as f: + data_dict = yaml.safe_load(f) # data dict + train_dir, val_dir = None, None + if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX): + api = wandb.Api() + train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias) + train_dir = train_artifact.download() + train_path = Path(train_dir) / 'data/images/' + data_dict['train'] = str(train_path) + + if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX): + api = wandb.Api() + val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias) + val_dir = val_artifact.download() + val_path = Path(val_dir) / 'data/images/' + data_dict['val'] = str(val_path) + if train_dir or val_dir: + ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml') + with open(ddp_data_path, 'w') as f: + yaml.safe_dump(data_dict, f) + opt.data = ddp_data_path + + +class WandbLogger(): + def __init__(self, opt, name, run_id, data_dict, job_type='Training'): + # Pre-training routine -- + self.job_type = job_type + self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict + # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call + if isinstance(opt.resume, str): # checks resume from artifact + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + run_id, project, model_artifact_name = get_run_info(opt.resume) + model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name + assert wandb, 'install wandb to resume wandb runs' + # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config + self.wandb_run = wandb.init(id=run_id, project=project, resume='allow') + opt.resume = model_artifact_name + elif self.wandb: + self.wandb_run = wandb.init(config=opt, + resume="allow", + project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, + name=name, + job_type=job_type, + id=run_id) if not wandb.run else wandb.run + if self.wandb_run: + if self.job_type == 'Training': + if not opt.resume: + wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict + # Info useful for resuming from artifacts + self.wandb_run.config.opt = vars(opt) + self.wandb_run.config.data_dict = wandb_data_dict + self.data_dict = self.setup_training(opt, data_dict) + if self.job_type == 'Dataset Creation': + self.data_dict = self.check_and_upload_dataset(opt) + else: + prefix = colorstr('wandb: ') + print(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)") + + def check_and_upload_dataset(self, opt): + assert wandb, 'Install wandb to upload dataset' + check_dataset(self.data_dict) + config_path = self.log_dataset_artifact(opt.data, + opt.single_cls, + 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem) + print("Created dataset config file ", config_path) + with open(config_path) as f: + wandb_data_dict = yaml.safe_load(f) + return wandb_data_dict + + def setup_training(self, opt, data_dict): + self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16 # Logging Constants + self.bbox_interval = opt.bbox_interval + if isinstance(opt.resume, str): + modeldir, _ = self.download_model_artifact(opt) + if modeldir: + self.weights = Path(modeldir) / "last.pt" + config = self.wandb_run.config + opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str( + self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \ + config.opt['hyp'] + data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume + if 'val_artifact' not in self.__dict__: # If --upload_dataset is set, use the existing artifact, don't download + self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'), + opt.artifact_alias) + self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'), + opt.artifact_alias) + self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None + if self.train_artifact_path is not None: + train_path = Path(self.train_artifact_path) / 'data/images/' + data_dict['train'] = str(train_path) + if self.val_artifact_path is not None: + val_path = Path(self.val_artifact_path) / 'data/images/' + data_dict['val'] = str(val_path) + self.val_table = self.val_artifact.get("val") + self.map_val_table_path() + if self.val_artifact is not None: + self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") + self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) + if opt.bbox_interval == -1: + self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 + return data_dict + + def download_dataset_artifact(self, path, alias): + if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX): + dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias) + assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'" + datadir = dataset_artifact.download() + return datadir, dataset_artifact + return None, None + + def download_model_artifact(self, opt): + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest") + assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist' + modeldir = model_artifact.download() + epochs_trained = model_artifact.metadata.get('epochs_trained') + total_epochs = model_artifact.metadata.get('total_epochs') + assert epochs_trained < total_epochs, 'training to %g epochs is finished, nothing to resume.' % ( + total_epochs) + return modeldir, model_artifact + return None, None + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={ + 'original_url': str(path), + 'epochs_trained': epoch + 1, + 'save period': opt.save_period, + 'project': opt.project, + 'total_epochs': opt.epochs, + 'fitness_score': fitness_score + }) + model_artifact.add_file(str(path / 'last.pt'), name='last.pt') + wandb.log_artifact(model_artifact, + aliases=['latest', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) + print("Saving model artifact on epoch ", epoch + 1) + + def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False): + with open(data_file) as f: + data = yaml.safe_load(f) # data dict + nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names']) + names = {k: v for k, v in enumerate(names)} # to index dictionary + self.train_artifact = self.create_dataset_table(LoadImagesAndLabels( + data['train']), names, name='train') if data.get('train') else None + self.val_artifact = self.create_dataset_table(LoadImagesAndLabels( + data['val']), names, name='val') if data.get('val') else None + if data.get('train'): + data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train') + if data.get('val'): + data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val') + path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1)) # updated data.yaml path + data.pop('download', None) + with open(path, 'w') as f: + yaml.safe_dump(data, f) + + if self.job_type == 'Training': # builds correct artifact pipeline graph + self.wandb_run.use_artifact(self.val_artifact) + self.wandb_run.use_artifact(self.train_artifact) + self.val_artifact.wait() + self.val_table = self.val_artifact.get('val') + self.map_val_table_path() + else: + self.wandb_run.log_artifact(self.train_artifact) + self.wandb_run.log_artifact(self.val_artifact) + return path + + def map_val_table_path(self): + self.val_table_map = {} + print("Mapping dataset") + for i, data in enumerate(tqdm(self.val_table.data)): + self.val_table_map[data[3]] = data[0] + + def create_dataset_table(self, dataset, class_to_id, name='dataset'): + # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging + artifact = wandb.Artifact(name=name, type="dataset") + img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None + img_files = tqdm(dataset.img_files) if not img_files else img_files + for img_file in img_files: + if Path(img_file).is_dir(): + artifact.add_dir(img_file, name='data/images') + labels_path = 'labels'.join(dataset.path.rsplit('images', 1)) + artifact.add_dir(labels_path, name='data/labels') + else: + artifact.add_file(img_file, name='data/images/' + Path(img_file).name) + label_file = Path(img2label_paths([img_file])[0]) + artifact.add_file(str(label_file), + name='data/labels/' + label_file.name) if label_file.exists() else None + table = wandb.Table(columns=["id", "train_image", "Classes", "name"]) + class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()]) + for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)): + height, width = shapes[0] + labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * torch.Tensor([width, height, width, height]) + box_data, img_classes = [], {} + for cls, *xyxy in labels[:, 1:].tolist(): + cls = int(cls) + box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": cls, + "box_caption": "%s" % (class_to_id[cls]), + "scores": {"acc": 1}, + "domain": "pixel"}) + img_classes[cls] = class_to_id[cls] + boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space + table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes), + Path(paths).name) + artifact.add(table, name) + return artifact + + def log_training_progress(self, predn, path, names): + if self.val_table and self.result_table: + class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) + box_data = [] + total_conf = 0 + for *xyxy, conf, cls in predn.tolist(): + if conf >= 0.25: + box_data.append( + {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": int(cls), + "box_caption": "%s %.3f" % (names[cls], conf), + "scores": {"class_score": conf}, + "domain": "pixel"}) + total_conf = total_conf + conf + boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space + id = self.val_table_map[Path(path).name] + self.result_table.add_data(self.current_epoch, + id, + wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), + total_conf / max(1, len(box_data)) + ) + + def log(self, log_dict): + if self.wandb_run: + for key, value in log_dict.items(): + self.log_dict[key] = value + + def end_epoch(self, best_result=False): + if self.wandb_run: + wandb.log(self.log_dict) + self.log_dict = {} + if self.result_artifact: + train_results = wandb.JoinedTable(self.val_table, self.result_table, "id") + self.result_artifact.add(train_results, 'result') + wandb.log_artifact(self.result_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch), + ('best' if best_result else '')]) + self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) + self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") + + def finish_run(self): + if self.wandb_run: + if self.log_dict: + wandb.log(self.log_dict) + wandb.run.finish()