New YOLOv5 Classification Models (#8956)

* Update

* Logger step fix: Increment step with epochs (#8654)

* enhance

* revert

* allow training from scratch

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* Update --img argument from train.py 

single line

* fix image size from 640 to 128

* suport custom dataloader and augmentation

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* format

* Update dataloaders.py

* Single line return, single line comment, remove unused argument

* address PR comments

* fix spelling

* don't augment eval set

* use fstring

* update augmentations.py

* new maning convention for transforms

* reverse if statement, inline ops

* reverse if statement, inline ops

* updates

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* update dataloaders

* Remove additional if statement

* Remove is_train as redundant

* Cleanup

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* Cleanup2

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* Update classifier.py

* Update augmentations.py

* fix: imshow clip warning

* update

* Revert ToTensorV2 removal

* Update classifier.py

* Update normalize values, revert uint8

* normalize image using cv2

* remove dedundant comment

* Update classifier.py

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* replace print with logger

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

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* Update

* Allow logging models from GenericLogger (#8676)

* enhance

* revert

* allow training from scratch

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update --img argument from train.py 

single line

* fix image size from 640 to 128

* suport custom dataloader and augmentation

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* format

* Update dataloaders.py

* Single line return, single line comment, remove unused argument

* address PR comments

* fix spelling

* don't augment eval set

* use fstring

* update augmentations.py

* new maning convention for transforms

* reverse if statement, inline ops

* reverse if statement, inline ops

* updates

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* update dataloaders

* Remove additional if statement

* Remove is_train as redundant

* Cleanup

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Cleanup2

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update classifier.py

* Update augmentations.py

* fix: imshow clip warning

* update

* Revert ToTensorV2 removal

* Update classifier.py

* Update normalize values, revert uint8

* normalize image using cv2

* remove dedundant comment

* Update classifier.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* replace print with logger

* commit steps

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* support final model logging

* update

* update

* update

* update

* remove curses

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* Update classifier.py

* Update __init__.py

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update

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* Update

* Update dataset download

* Update dataset download

* Update

* Update

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* Update

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* Pass imgsz to classify_transforms()

* Update

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* Cos scheduler

* Cos scheduler

* Remove unused args

* Update

* Add seed

* Add seed

* Update

* Update

* Add run(), main()

* Merge master

* Merge master

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Create YOLOv5 BaseModel class (#8829)

* Create BaseModel

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* fix

* Hub load device fix

* Update

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* Add experiment

* Merge master

* Attach names

* weight decay = 1e-4

* weight decay = 5e-5

* update smart_optimizer console printout

* fashion-mnist fix

* Merge master

* Update Table

* Update Table

* Remove destroy process group

* add kwargs to forward()

* fuse fix for resnet50

* nc, names fix for resnet50

* nc, names fix for resnet50

* ONNX CPU inference fix

* revert

* cuda

* if augment or visualize

* if augment or visualize

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* New smart_inference_mode()

* Update README

* Refactor into /classify dir

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* reset defaults

* reset defaults

* fix gpu predict

* warmup

* ema half fix

* spacing

* remove data

* remove cache

* remove denormalize

* save run settings

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* verbose false on initial plots

* new save_yaml() function

* Update ci-testing.yml

* Path(data) CI fix

* Separate classification CI

* fix val

* fix val

* fix val

* smartCrossEntropyLoss

* skip validation on hub load

* autodownload with working dir root

* str(data)

* Dataset usage example

* im_show normalize

* im_show normalize

* add imagenet simple names to multibackend

* Add validation speeds

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* 24-space names

* Update bash scripts

* Update permissions

* Add bash script arguments

* remove verbose

* TRT data fix

* names generator fix

* optimize if names

* update usage

* Add local loading

* Verbose=False

* update names printing

* Add Usage examples

* Add Usage examples

* Add Usage examples

* Add Usage examples

* named_children

* reshape_classifier_outputs

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* update

* update

* fix CI

* fix incorrect class substitution

* fix incorrect class substitution

* remove denormalize

* ravel fix

* cleanup

* update opt file printing

* update opt file printing

* update defaults

* add opt to checkpoint

* Add warning

* Add comment

* plot half bug fix

* Use NotImplementedError

* fix export shape report

* Fix TRT load

* cleanup CI

* profile comment

* CI fix

* Add cls models

* avoid inplace error

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* Fix usage examples

* Update README

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

utils/torch_utils.py

@@ -42,6 +42,16 @@ def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')):
     return decorate
 
 
+def smartCrossEntropyLoss(label_smoothing=0.0):
+    # Returns nn.CrossEntropyLoss with label smoothing enabled for torch>=1.10.0
+    if check_version(torch.__version__, '1.10.0'):
+        return nn.CrossEntropyLoss(label_smoothing=label_smoothing)  # loss function
+    else:
+        if label_smoothing > 0:
+            LOGGER.warning(f'WARNING: label smoothing {label_smoothing} requires torch>=1.10.0')
+        return nn.CrossEntropyLoss()  # loss function
+
+
 def smart_DDP(model):
     # Model DDP creation with checks
     assert not check_version(torch.__version__, '1.12.0', pinned=True), \
@@ -53,6 +63,28 @@ def smart_DDP(model):
         return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
 
 
+def reshape_classifier_output(model, n=1000):
+    # Update a TorchVision classification model to class count 'n' if required
+    from models.common import Classify
+    name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1]  # last module
+    if isinstance(m, Classify):  # YOLOv5 Classify() head
+        if m.linear.out_features != n:
+            m.linear = nn.Linear(m.linear.in_features, n)
+    elif isinstance(m, nn.Linear):  # ResNet, EfficientNet
+        if m.out_features != n:
+            setattr(model, name, nn.Linear(m.in_features, n))
+    elif isinstance(m, nn.Sequential):
+        types = [type(x) for x in m]
+        if nn.Linear in types:
+            i = types.index(nn.Linear)  # nn.Linear index
+            if m[i].out_features != n:
+                m[i] = nn.Linear(m[i].in_features, n)
+        elif nn.Conv2d in types:
+            i = types.index(nn.Conv2d)  # nn.Conv2d index
+            if m[i].out_channels != n:
+                m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias)
+
+
 @contextmanager
 def torch_distributed_zero_first(local_rank: int):
     # Decorator to make all processes in distributed training wait for each local_master to do something
@@ -117,14 +149,13 @@ def time_sync():
 
 
 def profile(input, ops, n=10, device=None):
-    # YOLOv5 speed/memory/FLOPs profiler
-    #
-    # Usage:
-    #     input = torch.randn(16, 3, 640, 640)
-    #     m1 = lambda x: x * torch.sigmoid(x)
-    #     m2 = nn.SiLU()
-    #     profile(input, [m1, m2], n=100)  # profile over 100 iterations
-
+    """ YOLOv5 speed/memory/FLOPs profiler
+    Usage:
+        input = torch.randn(16, 3, 640, 640)
+        m1 = lambda x: x * torch.sigmoid(x)
+        m2 = nn.SiLU()
+        profile(input, [m1, m2], n=100)  # profile over 100 iterations
+    """
     results = []
     if not isinstance(device, torch.device):
         device = select_device(device)
@@ -313,6 +344,18 @@ def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
     return optimizer
 
 
+def smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs):
+    # YOLOv5 torch.hub.load() wrapper with smart error/issue handling
+    if check_version(torch.__version__, '1.9.1'):
+        kwargs['skip_validation'] = True  # validation causes GitHub API rate limit errors
+    if check_version(torch.__version__, '1.12.0'):
+        kwargs['trust_repo'] = True  # argument required starting in torch 0.12
+    try:
+        return torch.hub.load(repo, model, **kwargs)
+    except Exception:
+        return torch.hub.load(repo, model, force_reload=True, **kwargs)
+
+
 def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True):
     # Resume training from a partially trained checkpoint
     best_fitness = 0.0