* update ci-testing.yml (#3322) * update ci-testing.yml * update greetings.yml * bring back os matrix * update ci-testing.yml (#3322) * update ci-testing.yml * update greetings.yml * bring back os matrix * Enable direct `--weights URL` definition (#3373) * Enable direct `--weights URL` definition @KalenMike this PR will enable direct --weights URL definition. Example use case: ``` python train.py --weights https://storage.googleapis.com/bucket/dir/model.pt ``` * cleanup * bug fixes * weights = attempt_download(weights) * Update experimental.py * Update hubconf.py * return bug fix * comment mirror * min_bytes * Update tutorial.ipynb (#3368) add Open in Kaggle badge * `cv2.imread(img, -1)` for IMREAD_UNCHANGED (#3379) * Update datasets.py * comment Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * COCO evolution fix (#3388) * COCO evolution fix * cleanup * update print * print fix * Create `is_pip()` function (#3391) Returns `True` if file is part of pip package. Useful for contextual behavior modification. ```python def is_pip(): # Is file in a pip package? return 'site-packages' in Path(__file__).absolute().parts ``` * Revert "`cv2.imread(img, -1)` for IMREAD_UNCHANGED (#3379)" (#3395) This reverts commit 21a9607e00. * Update FLOPs description (#3422) * Update README.md * Changing FLOPS to FLOPs. Co-authored-by: BuildTools <unconfigured@null.spigotmc.org> * Parse URL authentication (#3424) * Parse URL authentication * urllib.parse.unquote() * improved error handling * improved error handling * remove %3F * update check_file() * Add FLOPs title to table (#3453) * Suppress jit trace warning + graph once (#3454) * Suppress jit trace warning + graph once Suppress harmless jit trace warning on TensorBoard add_graph call. Also fix multiple add_graph() calls bug, now only on batch 0. * Update train.py * Update MixUp augmentation `alpha=beta=32.0` (#3455) Per VOC empirical results https://github.com/ultralytics/yolov5/issues/3380#issuecomment-853001307 by @developer0hye * Add `timeout()` class (#3460) * Add `timeout()` class * rearrange order * Faster HSV augmentation (#3462) remove datatype conversion process that can be skipped * Add `check_git_status()` 5 second timeout (#3464) * Add check_git_status() 5 second timeout This should prevent the SSH Git bug that we were discussing @KalenMike * cleanup * replace timeout with check_output built-in timeout * Improved `check_requirements()` offline-handling (#3466) Improve robustness of `check_requirements()` function to offline environments (do not attempt pip installs when offline). * Add `output_names` argument for ONNX export with dynamic axes (#3456) * Add output names & dynamic axes for onnx export Add output_names and dynamic_axes names for all outputs in torch.onnx.export. The first four outputs of the model will have names output0, output1, output2, output3 * use first output only + cleanup Co-authored-by: Samridha Shrestha <samridha.shrestha@g42.ai> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Revert FP16 `test.py` and `detect.py` inference to FP32 default (#3423) * fixed inference bug ,while use half precision * replace --use-half with --half * replace space and PEP8 in detect.py * PEP8 detect.py * update --half help comment * Update test.py * revert space Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add additional links/resources to stale.yml message (#3467) * Update stale.yml * cleanup * Update stale.yml * reformat * Update stale.yml HUB URL (#3468) * Stale `github.actor` bug fix (#3483) * Explicit `model.eval()` call `if opt.train=False` (#3475) * call model.eval() when opt.train is False call model.eval() when opt.train is False * single-line if statement * cleanup Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * check_requirements() exclude `opencv-python` (#3495) Fix for 3rd party or contrib versions of installed OpenCV as in https://github.com/ultralytics/yolov5/issues/3494. * Earlier `assert` for cpu and half option (#3508) * early assert for cpu and half option early assert for cpu and half option * Modified comment Modified comment * Update tutorial.ipynb (#3510) * Reduce test.py results spacing (#3511) * Update README.md (#3512) * Update README.md Minor modifications * 850 width * Update greetings.yml revert greeting change as PRs will now merge to master. Co-authored-by: Piotr Skalski <SkalskiP@users.noreply.github.com> Co-authored-by: SkalskiP <piotr.skalski92@gmail.com> Co-authored-by: Peretz Cohen <pizzaz93@users.noreply.github.com> Co-authored-by: tudoulei <34886368+tudoulei@users.noreply.github.com> Co-authored-by: chocosaj <chocosaj@users.noreply.github.com> Co-authored-by: BuildTools <unconfigured@null.spigotmc.org> Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com> Co-authored-by: Sam_S <SamSamhuns@users.noreply.github.com> Co-authored-by: Samridha Shrestha <samridha.shrestha@g42.ai> Co-authored-by: edificewang <609552430@qq.com>

vor 3 Jahren · f3c3d2ce5d
--- a/.github/workflows/ci-testing.yml
+++ b/.github/workflows/ci-testing.yml
@@ -2,12 +2,10 @@ name: CI CPU testing

 on:  # https://help.github.com/en/actions/reference/events-that-trigger-workflows
  push:
    branches: [ master ]
    branches: [ master, develop ]
  pull_request:
    # The branches below must be a subset of the branches above
    branches: [ master ]
  schedule:
    - cron: '0 0 * * *'  # Runs at 00:00 UTC every day
    branches: [ master, develop ]

 jobs:
  cpu-tests:
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@@ -10,8 +10,26 @@ jobs:
      - uses: actions/stale@v3
        with:
          repo-token: ${{ secrets.GITHUB_TOKEN }}
          stale-issue-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.'
          stale-pr-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.'
          stale-issue-message: |
            👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

            Access additional [YOLOv5](https://ultralytics.com/yolov5) 🚀 resources:
            - **Wiki** – https://github.com/ultralytics/yolov5/wiki
            - **Tutorials** – https://github.com/ultralytics/yolov5#tutorials
            - **Docs** – https://docs.ultralytics.com

            Access additional [Ultralytics](https://ultralytics.com) ⚡ resources:
            - **Ultralytics HUB** – https://ultralytics.com/pricing
            - **Vision API** – https://ultralytics.com/yolov5
            - **About Us** – https://ultralytics.com/about
            - **Join Our Team** – https://ultralytics.com/work
            - **Contact Us** – https://ultralytics.com/contact

            Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed!

            Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!

          stale-pr-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions YOLOv5 🚀 and Vision AI ⭐.'
          days-before-stale: 30
          days-before-close: 5
          exempt-issue-labels: 'documentation,tutorial'
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
 <a align="left" href="https://apps.apple.com/app/id1452689527" target="_blank">
 <img width="800" src="https://user-images.githubusercontent.com/26833433/98699617-a1595a00-2377-11eb-8145-fc674eb9b1a7.jpg"></a>
 <img width="850" src="https://user-images.githubusercontent.com/26833433/121094150-72607500-c7ee-11eb-9f39-1d9e4ce89a9e.jpg"></a>
 &nbsp

 <a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
@@ -30,19 +30,19 @@ This repository represents Ultralytics open-source research into future object d

 [assets]: https://github.com/ultralytics/yolov5/releases

 Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPS<br><sup>640 (B)
 ---   |---  |---        |---         |---             |---                |---|---              |---
 [YOLOv5s][assets]    |640  |36.7     |36.7     |55.4     |**2.0** | |7.3   |17.0
 [YOLOv5m][assets]    |640  |44.5     |44.5     |63.1     |2.7     | |21.4  |51.3
 [YOLOv5l][assets]    |640  |48.2     |48.2     |66.9     |3.8     | |47.0  |115.4
 [YOLOv5x][assets]    |640  |**50.4** |**50.4** |**68.8** |6.1     | |87.7  |218.8
 |Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPs<br><sup>640 (B)
 |---                  |---  |---      |---      |---      |---     |---|--- |---
 |[YOLOv5s][assets]    |640  |36.7     |36.7     |55.4     |**2.0** | |7.3   |17.0
 |[YOLOv5m][assets]    |640  |44.5     |44.5     |63.1     |2.7     | |21.4  |51.3
 |[YOLOv5l][assets]    |640  |48.2     |48.2     |66.9     |3.8     | |47.0  |115.4
 |[YOLOv5x][assets]    |640  |**50.4** |**50.4** |**68.8** |6.1     | |87.7  |218.8
 | | | | | | || |
 [YOLOv5s6][assets]   |1280 |43.3     |43.3     |61.9     |**4.3** | |12.7  |17.4
 [YOLOv5m6][assets]   |1280 |50.5     |50.5     |68.7     |8.4     | |35.9  |52.4
 [YOLOv5l6][assets]   |1280 |53.4     |53.4     |71.1     |12.3    | |77.2  |117.7
 [YOLOv5x6][assets]   |1280 |**54.4** |**54.4** |**72.0** |22.4    | |141.8 |222.9
 |[YOLOv5s6][assets]   |1280 |43.3     |43.3     |61.9     |**4.3** | |12.7  |17.4
 |[YOLOv5m6][assets]   |1280 |50.5     |50.5     |68.7     |8.4     | |35.9  |52.4
 |[YOLOv5l6][assets]   |1280 |53.4     |53.4     |71.1     |12.3    | |77.2  |117.7
 |[YOLOv5x6][assets]   |1280 |**54.4** |**54.4** |**72.0** |22.4    | |141.8 |222.9
 | | | | | | || |
 [YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |-  |-
 |[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |-  |-

 <details>
  <summary>Table Notes (click to expand)</summary>
@@ -112,7 +112,7 @@ Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, devi
 YOLOv5 v4.0-96-g83dc1b4 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)

 Fusing layers... 
 Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS
 Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPs
 image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.010s)
 image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.011s)
 Results saved to runs/detect/exp2
--- a/detect.py
+++ b/detect.py
@@ -28,7 +28,7 @@ def detect(opt):
    # Initialize
    set_logging()
    device = select_device(opt.device)
    half = device.type != 'cpu'  # half precision only supported on CUDA
    half = opt.half and device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
@@ -172,6 +172,7 @@ if __name__ == '__main__':
    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')
    parser.add_argument('--half', type=bool, default=False, help='use FP16 half-precision inference')
    opt = parser.parse_args()
    print(opt)
    check_requirements(exclude=('tensorboard', 'pycocotools', 'thop'))
--- a/hubconf.py
+++ b/hubconf.py
@@ -42,8 +42,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
            cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0]  # model.yaml path
            model = Model(cfg, channels, classes)  # create model
            if pretrained:
                attempt_download(fname)  # download if not found locally
                ckpt = torch.load(fname, map_location=torch.device('cpu'))  # load
                ckpt = torch.load(attempt_download(fname), map_location=torch.device('cpu'))  # 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
--- a/models/experimental.py
+++ b/models/experimental.py
@@ -116,8 +116,7 @@ def attempt_load(weights, map_location=None, inplace=True):
    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
    model = Ensemble()
    for w in weights if isinstance(weights, list) else [weights]:
        attempt_download(w)
        ckpt = torch.load(w, map_location=map_location)  # load
        ckpt = torch.load(attempt_download(w), map_location=map_location)  # load
        model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval())  # FP32 model

    # Compatibility updates
--- a/models/export.py
+++ b/models/export.py
@@ -44,22 +44,19 @@ if __name__ == '__main__':

    # Load PyTorch model
    device = select_device(opt.device)
    assert not (opt.device.lower() == 'cpu' and opt.half), '--half only compatible with GPU export, i.e. use --device 0'
    model = attempt_load(opt.weights, map_location=device)  # load FP32 model
    labels = model.names

    # Checks
    # Input
    gs = int(max(model.stride))  # grid size (max stride)
    opt.img_size = [check_img_size(x, gs) for x in opt.img_size]  # verify img_size are gs-multiples
    assert not (opt.device.lower() == 'cpu' and opt.half), '--half only compatible with GPU export, i.e. use --device 0'

    # Input
    img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device)  # image size(1,3,320,192) iDetection

    # Update model
    if opt.half:
        img, model = img.half(), model.half()  # to FP16
    if opt.train:
        model.train()  # training mode (no grid construction in Detect layer)
    model.train() if opt.train else model.eval()  # training mode = no Detect() layer grid construction
    for k, m in model.named_modules():
        m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
        if isinstance(m, models.common.Conv):  # assign export-friendly activations
@@ -96,11 +93,14 @@ if __name__ == '__main__':

            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=opt.opset_version, input_names=['images'],
            torch.onnx.export(model, img, f, verbose=False, opset_version=opt.opset_version,
                              training=torch.onnx.TrainingMode.TRAINING if opt.train else torch.onnx.TrainingMode.EVAL,
                              do_constant_folding=not opt.train,
                              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)
                              input_names=['images'],
                              output_names=['output'],
                              dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'},  # shape(1,3,640,640)
                                            'output': {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
                                            } if opt.dynamic else None)

            # Checks
            model_onnx = onnx.load(f)  # load onnx model
--- a/models/yolo.py
+++ b/models/yolo.py
@@ -21,7 +21,7 @@ from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, s
    select_device, copy_attr

 try:
    import thop  # for FLOPS computation
    import thop  # for FLOPs computation
 except ImportError:
    thop = None

@@ -140,13 +140,13 @@ class Model(nn.Module):
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers

            if profile:
                o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPS
                o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
                t = time_synchronized()
                for _ in range(10):
                    _ = m(x)
                dt.append((time_synchronized() - t) * 100)
                if m == self.model[0]:
                    logger.info(f"{'time (ms)':>10s} {'GFLOPS':>10s} {'params':>10s}  {'module'}")
                    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
--- a/requirements.txt
+++ b/requirements.txt
@@ -27,4 +27,4 @@ pandas
 # extras --------------------------------------
 # Cython  # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
 pycocotools>=2.0  # COCO mAP
 thop  # FLOPS computation
 thop  # FLOPs computation
--- a/test.py
+++ b/test.py
@@ -95,7 +95,7 @@ def test(data,
    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', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
    s = ('%20s' + '%11s' * 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 = [], [], [], [], []
@@ -228,7 +228,7 @@ def test(data,
        nt = torch.zeros(1)

    # Print results
    pf = '%20s' + '%12i' * 2 + '%12.3g' * 4  # print format
    pf = '%20s' + '%11i' * 2 + '%11.3g' * 4  # print format
    print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))

    # Print results per class
@@ -306,6 +306,7 @@ if __name__ == '__main__':
    parser.add_argument('--project', default='runs/test', help='save to project/name')
    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('--half', type=bool, default=False, help='use FP16 half-precision inference')
    opt = parser.parse_args()
    opt.save_json |= opt.data.endswith('coco.yaml')
    opt.data = check_file(opt.data)  # check file
@@ -326,6 +327,7 @@ if __name__ == '__main__':
             save_txt=opt.save_txt | opt.save_hybrid,
             save_hybrid=opt.save_hybrid,
             save_conf=opt.save_conf,
             half_precision=opt.half,
             opt=opt
             )

--- a/train.py
+++ b/train.py
@@ -4,6 +4,7 @@ import math
 import os
 import random
 import time
 import warnings
 from copy import deepcopy
 from pathlib import Path
 from threading import Thread
@@ -62,7 +63,6 @@ def train(hyp, opt, device, tb_writer=None):
    init_seeds(2 + rank)
    with open(opt.data) as f:
        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
@@ -78,12 +78,13 @@ def train(hyp, opt, device, tb_writer=None):
    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
    is_coco = opt.data.endswith('coco.yaml') and nc == 80  # COCO dataset

    # Model
    pretrained = weights.endswith('.pt')
    if pretrained:
        with torch_distributed_zero_first(rank):
            attempt_download(weights)  # download if not found locally
            weights = attempt_download(weights)  # download if not found locally
        ckpt = torch.load(weights, map_location=device)  # load checkpoint
        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
@@ -323,18 +324,19 @@ def train(hyp, opt, device, tb_writer=None):
                mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
                mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0)  # (GB)
                s = ('%10s' * 2 + '%10.4g' * 6) % (
                    '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
                    f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])
                pbar.set_description(s)

                # 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()
                    if tb_writer:
                        tb_writer.add_graph(torch.jit.trace(de_parallel(model), imgs, strict=False), [])  # model graph
                        # tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
                    if tb_writer and ni == 0:
                        with warnings.catch_warnings():
                            warnings.simplefilter('ignore')  # suppress jit trace warning
                            tb_writer.add_graph(torch.jit.trace(de_parallel(model), imgs, strict=False), [])  # 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
                    wandb_logger.log({'Mosaics': [wandb_logger.wandb.Image(str(x), caption=x.name) for x in
                                                  save_dir.glob('train*.jpg') if x.exists()]})

            # end batch ------------------------------------------------------------------------------------------------
@@ -358,6 +360,7 @@ def train(hyp, opt, device, tb_writer=None):
                                                 single_cls=opt.single_cls,
                                                 dataloader=testloader,
                                                 save_dir=save_dir,
                                                 save_json=is_coco and final_epoch,
                                                 verbose=nc < 50 and final_epoch,
                                                 plots=plots and final_epoch,
                                                 wandb_logger=wandb_logger,
@@ -409,41 +412,38 @@ def train(hyp, opt, device, tb_writer=None):
        # end epoch ----------------------------------------------------------------------------------------------------
    # end training
    if rank in [-1, 0]:
        # Plots
        logger.info(f'{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.\n')
        if plots:
            plot_results(save_dir=save_dir)  # save as results.png
            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 m in [last, best] if best.exists() else [last]:  # speed, mAP tests
                results, _, _ = test.test(opt.data,
                                          batch_size=batch_size * 2,
                                          imgsz=imgsz_test,
                                          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=True,
                                          plots=False,
                                          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=['latest', 'best', 'stripped'])

        if not opt.evolve:
            if is_coco:  # COCO dataset
                for m in [last, best] if best.exists() else [last]:  # speed, mAP tests
                    results, _, _ = test.test(opt.data,
                                              batch_size=batch_size * 2,
                                              imgsz=imgsz_test,
                                              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=True,
                                              plots=False,
                                              is_coco=is_coco)

            # Strip optimizers
            for f in last, best:
                if f.exists():
                    strip_optimizer(f)  # strip optimizers
            if wandb_logger.wandb:  # Log the stripped model
                wandb_logger.wandb.log_artifact(str(best if best.exists() else last), type='model',
                                                name='run_' + wandb_logger.wandb_run.id + '_model',
                                                aliases=['latest', 'best', 'stripped'])
        wandb_logger.finish_run()
    else:
        dist.destroy_process_group()
--- a/tutorial.ipynb
+++ b/tutorial.ipynb
@@ -517,7 +517,8 @@
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
        "<a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
        "<a href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ultralytics/yolov5/blob/master/tutorial.ipynb\" target=\"_parent\"><img alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>"        
      ]
    },
    {
@@ -529,7 +530,7 @@
        "<img src=\"https://user-images.githubusercontent.com/26833433/98702494-b71c4e80-237a-11eb-87ed-17fcd6b3f066.jpg\">\n",
        "\n",
        "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!"
        "For more information please visit https://github.com/ultralytics/yolov5 and https://ultralytics.com. Thank you!"
      ]
    },
    {
@@ -610,7 +611,7 @@
            "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: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS\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",
@@ -733,7 +734,7 @@
            "100% 168M/168M [00:05<00:00, 32.3MB/s]\n",
            "\n",
            "Fusing layers... \n",
            "Model Summary: 476 layers, 87730285 parameters, 0 gradients, 218.8 GFLOPS\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",
@@ -963,7 +964,7 @@
            " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \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, 7276605 parameters, 7276605 gradients, 17.1 GFLOPS\n",
            "Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPs\n",
            "\n",
            "Transferred 362/362 items from yolov5s.pt\n",
            "Scaled weight_decay = 0.0005\n",
@@ -1260,4 +1261,4 @@
      "outputs": []
    }
  ]
 }
 }
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -535,7 +535,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
            # MixUp https://arxiv.org/pdf/1710.09412.pdf
            if random.random() < hyp['mixup']:
                img2, labels2 = load_mosaic(self, random.randint(0, self.n - 1))
                r = np.random.beta(8.0, 8.0)  # mixup ratio, alpha=beta=8.0
                r = np.random.beta(32.0, 32.0)  # mixup ratio, alpha=beta=32.0
                img = (img * r + img2 * (1 - r)).astype(np.uint8)
                labels = np.concatenate((labels, labels2), 0)

@@ -655,12 +655,12 @@ def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
    hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
    dtype = img.dtype  # uint8

    x = np.arange(0, 256, dtype=np.int16)
    x = np.arange(0, 256, dtype=r.dtype)
    lut_hue = ((x * r[0]) % 180).astype(dtype)
    lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
    lut_val = np.clip(x * r[2], 0, 255).astype(dtype)

    img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype)
    img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
    cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)  # no return needed


--- a/utils/general.py
+++ b/utils/general.py
@@ -1,5 +1,6 @@
 # YOLOv5 general utils

 import contextlib
 import glob
 import logging
 import math
@@ -7,11 +8,13 @@ import os
 import platform
 import random
 import re
 import subprocess
 import signal
 import time
 import urllib
 from itertools import repeat
 from multiprocessing.pool import ThreadPool
 from pathlib import Path
 from subprocess import check_output

 import cv2
 import numpy as np
@@ -33,6 +36,26 @@ cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with Py
 os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8))  # NumExpr max threads


 class timeout(contextlib.ContextDecorator):
    # Usage: @timeout(seconds) decorator or 'with timeout(seconds):' context manager
    def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True):
        self.seconds = int(seconds)
        self.timeout_message = timeout_msg
        self.suppress = bool(suppress_timeout_errors)

    def _timeout_handler(self, signum, frame):
        raise TimeoutError(self.timeout_message)

    def __enter__(self):
        signal.signal(signal.SIGALRM, self._timeout_handler)  # Set handler for SIGALRM
        signal.alarm(self.seconds)  # start countdown for SIGALRM to be raised

    def __exit__(self, exc_type, exc_val, exc_tb):
        signal.alarm(0)  # Cancel SIGALRM if it's scheduled
        if self.suppress and exc_type is TimeoutError:  # Suppress TimeoutError
            return True


 def set_logging(rank=-1, verbose=True):
    logging.basicConfig(
        format="%(message)s",
@@ -53,12 +76,12 @@ def get_latest_run(search_dir='.'):


 def is_docker():
    # Is environment a Docker container
    # Is environment a Docker container?
    return Path('/workspace').exists()  # or Path('/.dockerenv').exists()


 def is_colab():
    # Is environment a Google Colab instance
    # Is environment a Google Colab instance?
    try:
        import google.colab
        return True
@@ -66,6 +89,11 @@ def is_colab():
        return False


 def is_pip():
    # Is file in a pip package?
    return 'site-packages' in Path(__file__).absolute().parts


 def emojis(str=''):
    # Return platform-dependent emoji-safe version of string
    return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
@@ -80,13 +108,13 @@ def check_online():
    # Check internet connectivity
    import socket
    try:
        socket.create_connection(("1.1.1.1", 443), 5)  # check host accesability
        socket.create_connection(("1.1.1.1", 443), 5)  # check host accessibility
        return True
    except OSError:
        return False


 def check_git_status():
 def check_git_status(err_msg=', for updates see https://github.com/ultralytics/yolov5'):
    # Recommend 'git pull' if code is out of date
    print(colorstr('github: '), end='')
    try:
@@ -95,9 +123,9 @@ def check_git_status():
        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
        url = check_output(cmd, shell=True, timeout=5).decode().strip().rstrip('.git')  # git fetch
        branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip()  # checked out
        n = int(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."
@@ -105,7 +133,7 @@ def check_git_status():
            s = f'up to date with {url} ✅'
        print(emojis(s))  # emoji-safe
    except Exception as e:
        print(e)
        print(f'{e}{err_msg}')


 def check_python(minimum='3.7.0', required=True):
@@ -135,10 +163,11 @@ def check_requirements(requirements='requirements.txt', exclude=()):
        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...")
            try:
                print(subprocess.check_output(f"pip install '{r}'", shell=True).decode())
                assert check_online(), f"'pip install {r}' skipped (offline)"
                print(check_output(f"pip install '{r}'", shell=True).decode())
                n += 1
            except Exception as e:
                print(f'{prefix} {e}')

@@ -178,7 +207,8 @@ def check_file(file):
    if Path(file).is_file() or file == '':  # exists
        return file
    elif file.startswith(('http://', 'https://')):  # download
        url, file = file, Path(file).name
        url, file = file, Path(urllib.parse.unquote(str(file))).name  # url, file (decode '%2F' to '/' etc.)
        file = file.split('?')[0]  # parse authentication https://url.com/file.txt?auth...
        print(f'Downloading {url} to {file}...')
        torch.hub.download_url_to_file(url, file)
        assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}'  # check
--- a/utils/google_utils.py
+++ b/utils/google_utils.py
@@ -4,6 +4,7 @@ import os
 import platform
 import subprocess
 import time
 import urllib
 from pathlib import Path

 import requests
@@ -16,11 +17,39 @@ def gsutil_getsize(url=''):
    return eval(s.split(' ')[0]) if len(s) else 0  # bytes


 def attempt_download(file, repo='ultralytics/yolov5'):
 def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
    # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes
    file = Path(file)
    assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
    try:  # url1
        print(f'Downloading {url} to {file}...')
        torch.hub.download_url_to_file(url, str(file))
        assert file.exists() and file.stat().st_size > min_bytes, assert_msg  # check
    except Exception as e:  # url2
        file.unlink(missing_ok=True)  # remove partial downloads
        print(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
        os.system(f"curl -L '{url2 or url}' -o '{file}' --retry 3 -C -")  # curl download, retry and resume on fail
    finally:
        if not file.exists() or file.stat().st_size < min_bytes:  # check
            file.unlink(missing_ok=True)  # remove partial downloads
            print(f"ERROR: {assert_msg}\n{error_msg}")
        print('')


 def attempt_download(file, repo='ultralytics/yolov5'):  # from utils.google_utils import *; attempt_download()
    # Attempt file download if does not exist
    file = Path(str(file).strip().replace("'", ''))

    if not file.exists():
        # URL specified
        name = Path(urllib.parse.unquote(str(file))).name  # decode '%2F' to '/' etc.
        if str(file).startswith(('http:/', 'https:/')):  # download
            url = str(file).replace(':/', '://')  # Pathlib turns :// -> :/
            name = name.split('?')[0]  # parse authentication https://url.com/file.txt?auth...
            safe_download(file=name, url=url, min_bytes=1E5)
            return name

        # GitHub assets
        file.parent.mkdir(parents=True, exist_ok=True)  # make parent dir (if required)
        try:
            response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json()  # github api
@@ -34,27 +63,14 @@ def attempt_download(file, repo='ultralytics/yolov5'):
            except:
                tag = 'v5.0'  # current release

        name = file.name
        if name in assets:
            msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/'
            redundant = False  # second download option
            try:  # GitHub
                url = f'https://github.com/{repo}/releases/download/{tag}/{name}'
                print(f'Downloading {url} to {file}...')
                torch.hub.download_url_to_file(url, file)
                assert file.exists() and file.stat().st_size > 1E6  # check
            except Exception as e:  # GCP
                print(f'Download error: {e}')
                assert redundant, 'No secondary mirror'
                url = f'https://storage.googleapis.com/{repo}/ckpt/{name}'
                print(f'Downloading {url} to {file}...')
                os.system(f"curl -L '{url}' -o '{file}' --retry 3 -C -")  # curl download, retry and resume on fail
            finally:
                if not file.exists() or file.stat().st_size < 1E6:  # check
                    file.unlink(missing_ok=True)  # remove partial downloads
                    print(f'ERROR: Download failure: {msg}')
                print('')
                return
            safe_download(file,
                          url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
                          # url2=f'https://storage.googleapis.com/{repo}/ckpt/{name}',  # backup url (optional)
                          min_bytes=1E5,
                          error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/')

    return str(file)


 def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'):
--- a/utils/torch_utils.py
+++ b/utils/torch_utils.py
@@ -18,7 +18,7 @@ import torch.nn.functional as F
 import torchvision

 try:
    import thop  # for FLOPS computation
    import thop  # for FLOPs computation
 except ImportError:
    thop = None
 logger = logging.getLogger(__name__)
@@ -105,13 +105,13 @@ def profile(x, ops, n=100, device=None):
    x = x.to(device)
    x.requires_grad = True
    print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '')
    print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}")
    print(f"\n{'Params':>12s}{'GFLOPs':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}")
    for m in ops if isinstance(ops, list) else [ops]:
        m = m.to(device) if hasattr(m, 'to') else m  # device
        m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m  # type
        dtf, dtb, t = 0., 0., [0., 0., 0.]  # dt forward, backward
        try:
            flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPS
            flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPs
        except:
            flops = 0

@@ -219,13 +219,13 @@ def model_info(model, verbose=False, img_size=640):
            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))

    try:  # FLOPS
    try:  # FLOPs
        from thop import profile
        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
        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
        fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride)  # 640x640 GFLOPS
        fs = ', %.1f GFLOPs' % (flops * img_size[0] / stride * img_size[1] / stride)  # 640x640 GFLOPs
    except (ImportError, Exception):
        fs = ''