New YOLOv5 Classification Models (#8956)

* Update * Logger step fix: Increment step with epochs (#8654) * 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 for more information, see https://pre-commit.ci 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 * 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 * 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 for more information, see https://pre-commit.ci * support final model logging * update * update * update * update * remove curses * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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 * Update * Update * Update * Update dataset download * Update dataset download * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Pass imgsz to classify_transforms() * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * 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 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * New smart_inference_mode() * Update README * Refactor into /classify dir * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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 for more information, see https://pre-commit.ci * 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 for more information, see https://pre-commit.ci * 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 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix usage examples * Update README * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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>
2022-08-17 11:59:01 +02:00
1 parent 3456fe65c6
commit d3ea0df8b9
@@ -8,15 +8,21 @@ import random

 import cv2
 import numpy as np
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF

 from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box
 from utils.metrics import bbox_ioa

+IMAGENET_MEAN = 0.485, 0.456, 0.406  # RGB mean
+IMAGENET_STD = 0.229, 0.224, 0.225  # RGB standard deviation
+

 class Albumentations:
    # YOLOv5 Albumentations class (optional, only used if package is installed)
    def __init__(self):
        self.transform = None
+        prefix = colorstr('albumentations: ')
        try:
            import albumentations as A
            check_version(A.__version__, '1.0.3', hard=True)  # version requirement
@@ -31,11 +37,11 @@ class Albumentations:
                A.ImageCompression(quality_lower=75, p=0.0)]  # transforms
            self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))

-            LOGGER.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p))
+            LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
        except ImportError:  # package not installed, skip
            pass
        except Exception as e:
-            LOGGER.info(colorstr('albumentations: ') + f'{e}')
+            LOGGER.info(f'{prefix}{e}')

    def __call__(self, im, labels, p=1.0):
        if self.transform and random.random() < p:
@@ -44,6 +50,18 @@ class Albumentations:
        return im, labels


+def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False):
+    # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std
+    return TF.normalize(x, mean, std, inplace=inplace)
+
+
+def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD):
+    # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean
+    for i in range(3):
+        x[:, i] = x[:, i] * std[i] + mean[i]
+    return x
+
+
 def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
    # HSV color-space augmentation
    if hgain or sgain or vgain:
@@ -282,3 +300,48 @@ def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):
    w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
    ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps))  # aspect ratio
    return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr)  # candidates
+
+
+def classify_albumentations(augment=True,
+                            size=224,
+                            scale=(0.08, 1.0),
+                            hflip=0.5,
+                            vflip=0.0,
+                            jitter=0.4,
+                            mean=IMAGENET_MEAN,
+                            std=IMAGENET_STD,
+                            auto_aug=False):
+    # YOLOv5 classification Albumentations (optional, only used if package is installed)
+    prefix = colorstr('albumentations: ')
+    try:
+        import albumentations as A
+        from albumentations.pytorch import ToTensorV2
+        check_version(A.__version__, '1.0.3', hard=True)  # version requirement
+        if augment:  # Resize and crop
+            T = [A.RandomResizedCrop(height=size, width=size, scale=scale)]
+            if auto_aug:
+                # TODO: implement AugMix, AutoAug & RandAug in albumentation
+                LOGGER.info(f'{prefix}auto augmentations are currently not supported')
+            else:
+                if hflip > 0:
+                    T += [A.HorizontalFlip(p=hflip)]
+                if vflip > 0:
+                    T += [A.VerticalFlip(p=vflip)]
+                if jitter > 0:
+                    color_jitter = (float(jitter),) * 3  # repeat value for brightness, contrast, satuaration, 0 hue
+                    T += [A.ColorJitter(*color_jitter, 0)]
+        else:  # Use fixed crop for eval set (reproducibility)
+            T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
+        T += [A.Normalize(mean=mean, std=std), ToTensorV2()]  # Normalize and convert to Tensor
+        LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
+        return A.Compose(T)
+
+    except ImportError:  # package not installed, skip
+        pass
+    except Exception as e:
+        LOGGER.info(f'{prefix}{e}')
+
+
+def classify_transforms(size=224):
+    # Transforms to apply if albumentations not installed
+    return T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
@@ -22,12 +22,14 @@ from zipfile import ZipFile
 import numpy as np
 import torch
 import torch.nn.functional as F
+import torchvision
 import yaml
 from PIL import ExifTags, Image, ImageOps
 from torch.utils.data import DataLoader, Dataset, dataloader, distributed
 from tqdm import tqdm

-from utils.augmentations import Albumentations, augment_hsv, copy_paste, letterbox, mixup, random_perspective
+from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste,
+                                 letterbox, mixup, random_perspective)
 from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, check_dataset, check_requirements, check_yaml, clean_str,
                           cv2, is_colab, is_kaggle, segments2boxes, xyn2xy, xywh2xyxy, xywhn2xyxy, xyxy2xywhn)
 from utils.torch_utils import torch_distributed_zero_first
@@ -870,7 +872,7 @@ def flatten_recursive(path=DATASETS_DIR / 'coco128'):
 def extract_boxes(path=DATASETS_DIR / 'coco128'):  # from utils.dataloaders import *; extract_boxes()
    # Convert detection dataset into classification dataset, with one directory per class
    path = Path(path)  # images dir
-    shutil.rmtree(path / 'classifier') if (path / 'classifier').is_dir() else None  # remove existing
+    shutil.rmtree(path / 'classification') if (path / 'classification').is_dir() else None  # remove existing
    files = list(path.rglob('*.*'))
    n = len(files)  # number of files
    for im_file in tqdm(files, total=n):
@@ -1090,3 +1092,65 @@ class HUBDatasetStats():
                pass
        print(f'Done. All images saved to {self.im_dir}')
        return self.im_dir
+
+
+# Classification dataloaders -------------------------------------------------------------------------------------------
+class ClassificationDataset(torchvision.datasets.ImageFolder):
+    """
+    YOLOv5 Classification Dataset.
+    Arguments
+        root:  Dataset path
+        transform:  torchvision transforms, used by default
+        album_transform: Albumentations transforms, used if installed
+    """
+
+    def __init__(self, root, augment, imgsz, cache=False):
+        super().__init__(root=root)
+        self.torch_transforms = classify_transforms(imgsz)
+        self.album_transforms = classify_albumentations(augment, imgsz) if augment else None
+        self.cache_ram = cache is True or cache == 'ram'
+        self.cache_disk = cache == 'disk'
+        self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples]  # file, index, npy, im
+
+    def __getitem__(self, i):
+        f, j, fn, im = self.samples[i]  # filename, index, filename.with_suffix('.npy'), image
+        if self.album_transforms:
+            if self.cache_ram and im is None:
+                im = self.samples[i][3] = cv2.imread(f)
+            elif self.cache_disk:
+                if not fn.exists():  # load npy
+                    np.save(fn.as_posix(), cv2.imread(f))
+                im = np.load(fn)
+            else:  # read image
+                im = cv2.imread(f)  # BGR
+            sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"]
+        else:
+            sample = self.torch_transforms(self.loader(f))
+        return sample, j
+
+
+def create_classification_dataloader(path,
+                                     imgsz=224,
+                                     batch_size=16,
+                                     augment=True,
+                                     cache=False,
+                                     rank=-1,
+                                     workers=8,
+                                     shuffle=True):
+    # Returns Dataloader object to be used with YOLOv5 Classifier
+    with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
+        dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache)
+    batch_size = min(batch_size, len(dataset))
+    nd = torch.cuda.device_count()
+    nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])
+    sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
+    generator = torch.Generator()
+    generator.manual_seed(0)
+    return InfiniteDataLoader(dataset,
+                              batch_size=batch_size,
+                              shuffle=shuffle and sampler is None,
+                              num_workers=nw,
+                              sampler=sampler,
+                              pin_memory=True,
+                              worker_init_fn=seed_worker,
+                              generator=generator)  # or DataLoader(persistent_workers=True)
@@ -217,7 +217,11 @@ def print_args(args: Optional[dict] = None, show_file=True, show_fcn=False):
    if args is None:  # get args automatically
        args, _, _, frm = inspect.getargvalues(x)
        args = {k: v for k, v in frm.items() if k in args}
-    s = (f'{Path(file).stem}: ' if show_file else '') + (f'{fcn}: ' if show_fcn else '')
+    try:
+        file = Path(file).resolve().relative_to(ROOT).with_suffix('')
+    except ValueError:
+        file = Path(file).stem
+    s = (f'{file}: ' if show_file else '') + (f'{fcn}: ' if show_fcn else '')
    LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items()))


@@ -345,7 +349,7 @@ def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=Fals

@try_except
 def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=()):
-    # Check installed dependencies meet requirements (pass *.txt file or list of packages)
+    # Check installed dependencies meet YOLOv5 requirements (pass *.txt file or list of packages)
    prefix = colorstr('red', 'bold', 'requirements:')
    check_python()  # check python version
    if isinstance(requirements, (str, Path)):  # requirements.txt file
@@ -549,6 +553,18 @@ def check_amp(model):
        return False


+def yaml_load(file='data.yaml'):
+    # Single-line safe yaml loading
+    with open(file, errors='ignore') as f:
+        return yaml.safe_load(f)
+
+
+def yaml_save(file='data.yaml', data={}):
+    # Single-line safe yaml saving
+    with open(file, 'w') as f:
+        yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)
+
+
 def url2file(url):
    # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt
    url = str(Path(url)).replace(':/', '://')  # Pathlib turns :// -> :/
@@ -5,6 +5,7 @@ Logging utils

 import os
 import warnings
+from pathlib import Path

 import pkg_resources as pkg
 import torch
@@ -76,7 +77,7 @@ class Loggers():
            self.logger.info(s)
        if not clearml:
            prefix = colorstr('ClearML: ')
-            s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 runs in ClearML"
+            s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML"
            self.logger.info(s)

        # TensorBoard
@@ -121,11 +122,8 @@ class Loggers():
        # Callback runs on train batch end
        # ni: number integrated batches (since train start)
        if plots:
-            if ni == 0:
-                if self.tb and not self.opt.sync_bn:  # --sync known issue https://github.com/ultralytics/yolov5/issues/3754
-                    with warnings.catch_warnings():
-                        warnings.simplefilter('ignore')  # suppress jit trace warning
-                        self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
+            if ni == 0 and not self.opt.sync_bn and self.tb:
+                log_tensorboard_graph(self.tb, model, imgsz=list(imgs.shape[2:4]))
            if ni < 3:
                f = self.save_dir / f'train_batch{ni}.jpg'  # filename
                plot_images(imgs, targets, paths, f)
@@ -233,3 +231,78 @@ class Loggers():
        # params: A dict containing {param: value} pairs
        if self.wandb:
            self.wandb.wandb_run.config.update(params, allow_val_change=True)
+
+
+class GenericLogger:
+    """
+    YOLOv5 General purpose logger for non-task specific logging
+    Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...)
+    Arguments
+        opt:             Run arguments
+        console_logger:  Console logger
+        include:         loggers to include
+    """
+
+    def __init__(self, opt, console_logger, include=('tb', 'wandb')):
+        # init default loggers
+        self.save_dir = opt.save_dir
+        self.include = include
+        self.console_logger = console_logger
+        if 'tb' in self.include:
+            prefix = colorstr('TensorBoard: ')
+            self.console_logger.info(
+                f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/")
+            self.tb = SummaryWriter(str(self.save_dir))
+
+        if wandb and 'wandb' in self.include:
+            self.wandb = wandb.init(project="YOLOv5-Classifier" if opt.project == "runs/train" else opt.project,
+                                    name=None if opt.name == "exp" else opt.name,
+                                    config=opt)
+        else:
+            self.wandb = None
+
+    def log_metrics(self, metrics_dict, epoch):
+        # Log metrics dictionary to all loggers
+        if self.tb:
+            for k, v in metrics_dict.items():
+                self.tb.add_scalar(k, v, epoch)
+
+        if self.wandb:
+            self.wandb.log(metrics_dict, step=epoch)
+
+    def log_images(self, files, name='Images', epoch=0):
+        # Log images to all loggers
+        files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])]  # to Path
+        files = [f for f in files if f.exists()]  # filter by exists
+
+        if self.tb:
+            for f in files:
+                self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
+
+        if self.wandb:
+            self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch)
+
+    def log_graph(self, model, imgsz=(640, 640)):
+        # Log model graph to all loggers
+        if self.tb:
+            log_tensorboard_graph(self.tb, model, imgsz)
+
+    def log_model(self, model_path, epoch=0, metadata={}):
+        # Log model to all loggers
+        if self.wandb:
+            art = wandb.Artifact(name=f"run_{wandb.run.id}_model", type="model", metadata=metadata)
+            art.add_file(str(model_path))
+            wandb.log_artifact(art)
+
+
+def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
+    # Log model graph to TensorBoard
+    try:
+        p = next(model.parameters())  # for device, type
+        imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz  # expand
+        im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p)  # input image
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')  # suppress jit trace warning
+            tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), [])
+    except Exception:
+        print('WARNING: TensorBoard graph visualization failure')
@@ -388,6 +388,35 @@ def plot_labels(labels, names=(), save_dir=Path('')):
    plt.close()


+def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')):
+    # Show classification image grid with labels (optional) and predictions (optional)
+    from utils.augmentations import denormalize
+
+    names = names or [f'class{i}' for i in range(1000)]
+    blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im),
+                         dim=0)  # select batch index 0, block by channels
+    n = min(len(blocks), nmax)  # number of plots
+    m = min(8, round(n ** 0.5))  # 8 x 8 default
+    fig, ax = plt.subplots(math.ceil(n / m), m)  # 8 rows x n/8 cols
+    ax = ax.ravel() if m > 1 else [ax]
+    # plt.subplots_adjust(wspace=0.05, hspace=0.05)
+    for i in range(n):
+        ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0))
+        ax[i].axis('off')
+        if labels is not None:
+            s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '')
+            ax[i].set_title(s, fontsize=8, verticalalignment='top')
+    plt.savefig(f, dpi=300, bbox_inches='tight')
+    plt.close()
+    if verbose:
+        LOGGER.info(f"Saving {f}")
+        if labels is not None:
+            LOGGER.info('True:     ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax]))
+        if pred is not None:
+            LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax]))
+    return f
+
+
 def plot_evolve(evolve_csv='path/to/evolve.csv'):  # from utils.plots import *; plot_evolve()
    # Plot evolve.csv hyp evolution results
    evolve_csv = Path(evolve_csv)
@@ -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