Merge develop branch into master (#3518)

* 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>

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 @@ 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

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