Merge develop branch into master

.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:

.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'

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

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 @@ def detect(opt):
     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'))

hubconf.py

@@ -30,7 +30,8 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
     from utils.google_utils import attempt_download
     from utils.torch_utils import select_device
 
-    check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('tensorboard', 'pycocotools', 'thop'))
+    check_requirements(requirements=Path(__file__).parent / 'requirements.txt',
+                       exclude=('tensorboard', 'pycocotools', 'thop', 'opencv-python'))
     set_logging(verbose=verbose)
 
     fname = Path(name).with_suffix('.pt')  # checkpoint filename
@@ -41,8 +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

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

models/export.py

@@ -44,22 +44,19 @@
 
     # 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 @@
 
             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

models/yolo.py

@@ -21,7 +21,7 @@
     select_device, copy_attr
 
 try:
-    import thop  # for FLOPS computation
+    import thop  # for FLOPs computation
 except ImportError:
     thop = None
 
@@ -140,13 +140,13 @@ def forward_once(self, x, profile=False):
                 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

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

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 @@ def test(data,
     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 @@ def test(data,
              save_txt=opt.save_txt | opt.save_hybrid,
              save_hybrid=opt.save_hybrid,
              save_conf=opt.save_conf,
+             half_precision=opt.half,
              opt=opt
              )