ultralytics 8.0.43 optimized Results class and fixes (#1069)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Alexander Duda <Alexander.Duda@me.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

ultralytics/yolo/utils/benchmarks.py

@@ -27,13 +27,14 @@ import time
 from pathlib import Path
 
 import pandas as pd
-import torch
 
 from ultralytics import YOLO
 from ultralytics.yolo.engine.exporter import export_formats
-from ultralytics.yolo.utils import LOGGER, SETTINGS
+from ultralytics.yolo.utils import LOGGER, ROOT, SETTINGS
 from ultralytics.yolo.utils.checks import check_yolo
+from ultralytics.yolo.utils.downloads import download
 from ultralytics.yolo.utils.files import file_size
+from ultralytics.yolo.utils.torch_utils import select_device
 
 
 def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
@@ -41,8 +42,9 @@ def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
                    half=False,
                    device='cpu',
                    hard_fail=False):
-    device = torch.device(int(device) if device.isnumeric() else device)
-    model = YOLO(model)
+    device = select_device(device, verbose=False)
+    if isinstance(model, (str, Path)):
+        model = YOLO(model)
 
     y = []
     t0 = time.time()
@@ -65,6 +67,11 @@ def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
                 export = YOLO(filename)
             assert suffix in str(filename), 'export failed'
 
+            # Predict
+            if not (ROOT / 'assets/bus.jpg').exists():
+                download(url='https://ultralytics.com/images/bus.jpg', dir=ROOT / 'assets')
+            export.predict(ROOT / 'assets/bus.jpg', imgsz=imgsz, device=device, half=half)  # test
+
             # Validate
             if model.task == 'detect':
                 data, key = 'coco128.yaml', 'metrics/mAP50-95(B)'
@@ -96,6 +103,8 @@ def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
         floor = eval(hard_fail)  # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n
         assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: metric < floor {floor}'
 
+    return df
+
 
 if __name__ == '__main__':
     run_benchmarks()

ultralytics/yolo/utils/plotting.py

@@ -5,19 +5,24 @@ import math
 from pathlib import Path
 
 import cv2
+import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import seaborn as sn
 import torch
 from PIL import Image, ImageDraw, ImageFont
 from PIL import __version__ as pil_version
 
-from ultralytics.yolo.utils import LOGGER, threaded
+from ultralytics.yolo.utils import LOGGER, TryExcept, threaded
 
 from .checks import check_font, check_version, is_ascii
 from .files import increment_path
 from .ops import clip_coords, scale_image, xywh2xyxy, xyxy2xywh
 
+matplotlib.rc('font', **{'size': 11})
+matplotlib.use('Agg')  # for writing to files only
+
 
 class Colors:
     # Ultralytics color palette https://ultralytics.com/
@@ -152,6 +157,52 @@ class Annotator:
         return np.asarray(self.im)
 
 
+@TryExcept()  # known issue https://github.com/ultralytics/yolov5/issues/5395
+def plot_labels(boxes, cls, names=(), save_dir=Path('')):
+    # plot dataset labels
+    LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
+    b = boxes.transpose()  # classes, boxes
+    nc = int(cls.max() + 1)  # number of classes
+    x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
+
+    # seaborn correlogram
+    sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
+    plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
+    plt.close()
+
+    # matplotlib labels
+    matplotlib.use('svg')  # faster
+    ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
+    y = ax[0].hist(cls, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
+    with contextlib.suppress(Exception):  # color histogram bars by class
+        [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)]  # known issue #3195
+    ax[0].set_ylabel('instances')
+    if 0 < len(names) < 30:
+        ax[0].set_xticks(range(len(names)))
+        ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10)
+    else:
+        ax[0].set_xlabel('classes')
+    sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
+    sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
+
+    # rectangles
+    boxes[:, 0:2] = 0.5  # center
+    boxes = xywh2xyxy(boxes) * 2000
+    img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
+    for cls, box in zip(cls[:1000], boxes[:1000]):
+        ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls))  # plot
+    ax[1].imshow(img)
+    ax[1].axis('off')
+
+    for a in [0, 1, 2, 3]:
+        for s in ['top', 'right', 'left', 'bottom']:
+            ax[a].spines[s].set_visible(False)
+
+    plt.savefig(save_dir / 'labels.jpg', dpi=200)
+    matplotlib.use('Agg')
+    plt.close()
+
+
 def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):
     # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
     xyxy = torch.Tensor(xyxy).view(-1, 4)

ultralytics/yolo/utils/torch_utils.py

@@ -59,7 +59,7 @@ def DDP_model(model):
         return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
 
 
-def select_device(device='', batch=0, newline=False):
+def select_device(device='', batch=0, newline=False, verbose=True):
     # device = None or 'cpu' or 0 or '0' or '0,1,2,3'
     s = f'Ultralytics YOLOv{__version__} 🚀 Python-{platform.python_version()} torch-{torch.__version__} '
     device = str(device).lower()
@@ -102,7 +102,7 @@ def select_device(device='', batch=0, newline=False):
         s += 'CPU\n'
         arg = 'cpu'
 
-    if RANK == -1:
+    if verbose and RANK == -1:
         LOGGER.info(s if newline else s.rstrip())
     return torch.device(arg)