ultralytics 8.0.47 Docker and reformat updates (#1153)

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

ultralytics/yolo/data/__init__.py

@@ -5,12 +5,5 @@ from .build import build_classification_dataloader, build_dataloader, load_infer
 from .dataset import ClassificationDataset, SemanticDataset, YOLODataset
 from .dataset_wrappers import MixAndRectDataset
 
-__all__ = [
-    'BaseDataset',
-    'ClassificationDataset',
-    'MixAndRectDataset',
-    'SemanticDataset',
-    'YOLODataset',
-    'build_classification_dataloader',
-    'build_dataloader',
-    'load_inference_source',]
+__all__ = ('BaseDataset', 'ClassificationDataset', 'MixAndRectDataset', 'SemanticDataset', 'YOLODataset',
+           'build_classification_dataloader', 'build_dataloader', 'load_inference_source')

ultralytics/yolo/data/augment.py

@@ -564,7 +564,7 @@ class Albumentations:
                 A.CLAHE(p=0.01),
                 A.RandomBrightnessContrast(p=0.0),
                 A.RandomGamma(p=0.0),
-                A.ImageCompression(quality_lower=75, p=0.0),]  # transforms
+                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(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
@@ -671,14 +671,14 @@ def v8_transforms(dataset, imgsz, hyp):
             shear=hyp.shear,
             perspective=hyp.perspective,
             pre_transform=LetterBox(new_shape=(imgsz, imgsz)),
-        ),])
+        )])
     return Compose([
         pre_transform,
         MixUp(dataset, pre_transform=pre_transform, p=hyp.mixup),
         Albumentations(p=1.0),
         RandomHSV(hgain=hyp.hsv_h, sgain=hyp.hsv_s, vgain=hyp.hsv_v),
         RandomFlip(direction='vertical', p=hyp.flipud),
-        RandomFlip(direction='horizontal', p=hyp.fliplr),])  # transforms
+        RandomFlip(direction='horizontal', p=hyp.fliplr)])  # transforms
 
 
 # Classification augmentations -----------------------------------------------------------------------------------------
@@ -719,8 +719,8 @@ def classify_albumentations(
                 if vflip > 0:
                     T += [A.VerticalFlip(p=vflip)]
                 if jitter > 0:
-                    color_jitter = (float(jitter),) * 3  # repeat value for brightness, contrast, saturation, 0 hue
-                    T += [A.ColorJitter(*color_jitter, 0)]
+                    jitter = float(jitter)
+                    T += [A.ColorJitter(jitter, jitter, jitter, 0)]  # brightness, contrast, saturation, 0 hue
         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

ultralytics/yolo/data/base.py

@@ -24,20 +24,18 @@ class BaseDataset(Dataset):
         label_path (str): label path, this can also be an ann_file or other custom label path.
     """
 
-    def __init__(
-        self,
-        img_path,
-        imgsz=640,
-        cache=False,
-        augment=True,
-        hyp=None,
-        prefix='',
-        rect=False,
-        batch_size=None,
-        stride=32,
-        pad=0.5,
-        single_cls=False,
-    ):
+    def __init__(self,
+                 img_path,
+                 imgsz=640,
+                 cache=False,
+                 augment=True,
+                 hyp=None,
+                 prefix='',
+                 rect=False,
+                 batch_size=None,
+                 stride=32,
+                 pad=0.5,
+                 single_cls=False):
         super().__init__()
         self.img_path = img_path
         self.imgsz = imgsz

ultralytics/yolo/data/dataloaders/v5augmentations.py

@@ -335,8 +335,8 @@ def classify_albumentations(
                 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)]
+                    jitter = float(jitter)
+                    T += [A.ColorJitter(jitter, jitter, jitter, 0)]  # brightness, contrast, satuaration, 0 hue
         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

ultralytics/yolo/data/dataset.py

@@ -4,13 +4,16 @@ from itertools import repeat
 from multiprocessing.pool import ThreadPool
 from pathlib import Path
 
+import cv2
+import numpy as np
+import torch
 import torchvision
 from tqdm import tqdm
 
 from ..utils import NUM_THREADS, TQDM_BAR_FORMAT, is_dir_writeable
-from .augment import *
+from .augment import Compose, Format, Instances, LetterBox, classify_albumentations, classify_transforms, v8_transforms
 from .base import BaseDataset
-from .utils import HELP_URL, LOCAL_RANK, get_hash, img2label_paths, verify_image_label
+from .utils import HELP_URL, LOCAL_RANK, LOGGER, get_hash, img2label_paths, verify_image_label
 
 
 class YOLODataset(BaseDataset):