Update `get_loggers()` (#4854)

* Update `set_logging()`

* Update export.py

* pre-commit fixes

* Update LoadImages

* Update LoadStreams

* Update print_args

* Single LOGGER definition

* yolo.py fix

Co-authored-by: pre-commit <pre-commit@example.com>

detect.py

@@ -25,8 +25,7 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 from models.experimental import attempt_load
 from utils.datasets import LoadImages, LoadStreams
 from utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \
-    increment_path, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \
-    strip_optimizer, xyxy2xywh
+    increment_path, non_max_suppression, print_args, save_one_box, scale_coords, strip_optimizer, xyxy2xywh, LOGGER
 from utils.plots import Annotator, colors
 from utils.torch_utils import load_classifier, select_device, time_sync
 
@@ -68,7 +67,6 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
     (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
 
     # Initialize
-    set_logging()
     device = select_device(device)
     half &= device.type != 'cpu'  # half precision only supported on CUDA
 
@@ -132,7 +130,7 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
     if pt and device.type != 'cpu':
         model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.parameters())))  # run once
     dt, seen = [0.0, 0.0, 0.0], 0
-    for path, img, im0s, vid_cap in dataset:
+    for path, img, im0s, vid_cap, s in dataset:
         t1 = time_sync()
         if onnx:
             img = img.astype('float32')
@@ -191,9 +189,10 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
         for i, det in enumerate(pred):  # per image
             seen += 1
             if webcam:  # batch_size >= 1
-                p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
+                p, im0, frame = path[i], im0s[i].copy(), dataset.count
+                s += f'{i}: '
             else:
-                p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
+                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
 
             p = Path(p)  # to Path
             save_path = str(save_dir / p.name)  # img.jpg
@@ -227,7 +226,7 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
                             save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
 
             # Print time (inference-only)
-            print(f'{s}Done. ({t3 - t2:.3f}s)')
+            LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
 
             # Stream results
             im0 = annotator.result()
@@ -256,10 +255,10 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
 
     # Print results
     t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
-    print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
+    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
     if save_txt or save_img:
         s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
-        print(f"Results saved to {colorstr('bold', save_dir)}{s}")
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
     if update:
         strip_optimizer(weights)  # update model (to fix SourceChangeWarning)
 

export.py

@@ -42,23 +42,23 @@ from models.experimental import attempt_load
 from models.yolo import Detect
 from utils.activations import SiLU
 from utils.datasets import LoadImages
-from utils.general import colorstr, check_dataset, check_img_size, check_requirements, file_size, print_args, \
-    set_logging, url2file
+from utils.general import check_dataset, check_img_size, check_requirements, colorstr, file_size, print_args, \
+    url2file, LOGGER
 from utils.torch_utils import select_device
 
 
 def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
     # YOLOv5 TorchScript model export
     try:
-        print(f'\n{prefix} starting export with torch {torch.__version__}...')
+        LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
         f = file.with_suffix('.torchscript.pt')
 
         ts = torch.jit.trace(model, im, strict=False)
         (optimize_for_mobile(ts) if optimize else ts).save(f)
 
-        print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
     except Exception as e:
-        print(f'{prefix} export failure: {e}')
+        LOGGER.info(f'{prefix} export failure: {e}')
 
 
 def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorstr('ONNX:')):
@@ -67,7 +67,7 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
         check_requirements(('onnx',))
         import onnx
 
-        print(f'\n{prefix} starting export with onnx {onnx.__version__}...')
+        LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
         f = file.with_suffix('.onnx')
 
         torch.onnx.export(model, im, f, verbose=False, opset_version=opset,
@@ -82,7 +82,7 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
         # Checks
         model_onnx = onnx.load(f)  # load onnx model
         onnx.checker.check_model(model_onnx)  # check onnx model
-        # print(onnx.helper.printable_graph(model_onnx.graph))  # print
+        # LOGGER.info(onnx.helper.printable_graph(model_onnx.graph))  # print
 
         # Simplify
         if simplify:
@@ -90,7 +90,7 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
                 check_requirements(('onnx-simplifier',))
                 import onnxsim
 
-                print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
+                LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
                 model_onnx, check = onnxsim.simplify(
                     model_onnx,
                     dynamic_input_shape=dynamic,
@@ -98,11 +98,11 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
                 assert check, 'assert check failed'
                 onnx.save(model_onnx, f)
             except Exception as e:
-                print(f'{prefix} simplifier failure: {e}')
-        print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
-        print(f"{prefix} run --dynamic ONNX model inference with: 'python detect.py --weights {f}'")
+                LOGGER.info(f'{prefix} simplifier failure: {e}')
+        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+        LOGGER.info(f"{prefix} run --dynamic ONNX model inference with: 'python detect.py --weights {f}'")
     except Exception as e:
-        print(f'{prefix} export failure: {e}')
+        LOGGER.info(f'{prefix} export failure: {e}')
 
 
 def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
@@ -112,7 +112,7 @@ def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
         check_requirements(('coremltools',))
         import coremltools as ct
 
-        print(f'\n{prefix} starting export with coremltools {ct.__version__}...')
+        LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
         f = file.with_suffix('.mlmodel')
 
         model.train()  # CoreML exports should be placed in model.train() mode
@@ -120,9 +120,9 @@ def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
         ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255.0, bias=[0, 0, 0])])
         ct_model.save(f)
 
-        print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
     except Exception as e:
-        print(f'\n{prefix} export failure: {e}')
+        LOGGER.info(f'\n{prefix} export failure: {e}')
 
     return ct_model
 
@@ -137,7 +137,7 @@ def export_saved_model(model, im, file, dynamic,
         from tensorflow import keras
         from models.tf import TFModel, TFDetect
 
-        print(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
+        LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
         f = str(file).replace('.pt', '_saved_model')
         batch_size, ch, *imgsz = list(im.shape)  # BCHW
 
@@ -151,9 +151,9 @@ def export_saved_model(model, im, file, dynamic,
         keras_model.summary()
         keras_model.save(f, save_format='tf')
 
-        print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
     except Exception as e:
-        print(f'\n{prefix} export failure: {e}')
+        LOGGER.info(f'\n{prefix} export failure: {e}')
 
     return keras_model
 
@@ -164,7 +164,7 @@ def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
         import tensorflow as tf
         from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
 
-        print(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
+        LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
         f = file.with_suffix('.pb')
 
         m = tf.function(lambda x: keras_model(x))  # full model
@@ -173,9 +173,9 @@ def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
         frozen_func.graph.as_graph_def()
         tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
 
-        print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
     except Exception as e:
-        print(f'\n{prefix} export failure: {e}')
+        LOGGER.info(f'\n{prefix} export failure: {e}')
 
 
 def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('TensorFlow Lite:')):
@@ -184,7 +184,7 @@ def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('Te
         import tensorflow as tf
         from models.tf import representative_dataset_gen
 
-        print(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
+        LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
         batch_size, ch, *imgsz = list(im.shape)  # BCHW
         f = str(file).replace('.pt', '-fp16.tflite')
 
@@ -204,10 +204,10 @@ def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('Te
 
         tflite_model = converter.convert()
         open(f, "wb").write(tflite_model)
-        print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
 
     except Exception as e:
-        print(f'\n{prefix} export failure: {e}')
+        LOGGER.info(f'\n{prefix} export failure: {e}')
 
 
 def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
@@ -217,7 +217,7 @@ def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
         import re
         import tensorflowjs as tfjs
 
-        print(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
+        LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
         f = str(file).replace('.pt', '_web_model')  # js dir
         f_pb = file.with_suffix('.pb')  # *.pb path
         f_json = f + '/model.json'  # *.json path
@@ -240,9 +240,9 @@ def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
                 json)
             j.write(subst)
 
-        print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
     except Exception as e:
-        print(f'\n{prefix} export failure: {e}')
+        LOGGER.info(f'\n{prefix} export failure: {e}')
 
 
 @torch.no_grad()
@@ -297,7 +297,7 @@ def run(data=ROOT / 'data/coco128.yaml',  # 'dataset.yaml path'
 
     for _ in range(2):
         y = model(im)  # dry runs
-    print(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)")
+    LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)")
 
     # Exports
     if 'torchscript' in include:
@@ -322,9 +322,9 @@ def run(data=ROOT / 'data/coco128.yaml',  # 'dataset.yaml path'
             export_tfjs(model, im, file)
 
     # Finish
-    print(f'\nExport complete ({time.time() - t:.2f}s)'
-          f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
-          f'\nVisualize with https://netron.app')
+    LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)'
+                f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
+                f'\nVisualize with https://netron.app')
 
 
 def parse_opt():
@@ -355,7 +355,6 @@ def parse_opt():
 
 
 def main(opt):
-    set_logging()
     run(**vars(opt))
 
 

models/tf.py

@@ -31,11 +31,9 @@ from tensorflow import keras
 from models.common import Bottleneck, BottleneckCSP, Concat, Conv, C3, DWConv, Focus, SPP, SPPF, autopad
 from models.experimental import CrossConv, MixConv2d, attempt_load
 from models.yolo import Detect
-from utils.general import make_divisible, print_args, set_logging
+from utils.general import make_divisible, print_args, LOGGER
 from utils.activations import SiLU
 
-LOGGER = logging.getLogger(__name__)
-
 
 class TFBN(keras.layers.Layer):
     # TensorFlow BatchNormalization wrapper
@@ -336,7 +334,7 @@ class TFModel:
 
         # Define model
         if nc and nc != self.yaml['nc']:
-            print(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}")
+            LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}")
             self.yaml['nc'] = nc  # override yaml value
         self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)
 
@@ -457,7 +455,6 @@ def parse_opt():
 
 
 def main(opt):
-    set_logging()
     run(**vars(opt))
 
 

models/yolo.py

@@ -20,7 +20,7 @@ if str(ROOT) not in sys.path:
 from models.common import *
 from models.experimental import *
 from utils.autoanchor import check_anchor_order
-from utils.general import check_yaml, make_divisible, print_args, set_logging, check_version
+from utils.general import check_version, check_yaml, make_divisible, print_args, LOGGER
 from utils.plots import feature_visualization
 from utils.torch_utils import copy_attr, fuse_conv_and_bn, initialize_weights, model_info, scale_img, \
     select_device, time_sync
@@ -30,8 +30,6 @@ try:
 except ImportError:
     thop = None
 
-LOGGER = logging.getLogger(__name__)
-
 
 class Detect(nn.Module):
     stride = None  # strides computed during build
@@ -311,7 +309,6 @@ if __name__ == '__main__':
     opt = parser.parse_args()
     opt.cfg = check_yaml(opt.cfg)  # check YAML
     print_args(FILE.stem, opt)
-    set_logging()
     device = select_device(opt.device)
 
     # Create model

train.py

@@ -40,7 +40,7 @@ from utils.autobatch import check_train_batch_size
 from utils.datasets import create_dataloader
 from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
     strip_optimizer, get_latest_run, check_dataset, check_git_status, check_img_size, check_requirements, \
-    check_file, check_yaml, check_suffix, print_args, print_mutation, set_logging, one_cycle, colorstr, methods
+    check_file, check_yaml, check_suffix, print_args, print_mutation, one_cycle, colorstr, methods, LOGGER
 from utils.downloads import attempt_download
 from utils.loss import ComputeLoss
 from utils.plots import plot_labels, plot_evolve
@@ -51,7 +51,6 @@ from utils.metrics import fitness
 from utils.loggers import Loggers
 from utils.callbacks import Callbacks
 
-LOGGER = logging.getLogger(__name__)
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
 RANK = int(os.getenv('RANK', -1))
 WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
@@ -129,7 +128,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     for k, v in model.named_parameters():
         v.requires_grad = True  # train all layers
         if any(x in k for x in freeze):
-            print(f'freezing {k}')
+            LOGGER.info(f'freezing {k}')
             v.requires_grad = False
 
     # Image size
@@ -485,7 +484,6 @@ def parse_opt(known=False):
 
 def main(opt, callbacks=Callbacks()):
     # Checks
-    set_logging(RANK)
     if RANK in [-1, 0]:
         print_args(FILE.stem, opt)
         check_git_status()
@@ -609,9 +607,9 @@ def main(opt, callbacks=Callbacks()):
 
         # Plot results
         plot_evolve(evolve_csv)
-        print(f'Hyperparameter evolution finished\n'
-              f"Results saved to {colorstr('bold', save_dir)}\n"
-              f'Use best hyperparameters example: $ python train.py --hyp {evolve_yaml}')
+        LOGGER.info(f'Hyperparameter evolution finished\n'
+                    f"Results saved to {colorstr('bold', save_dir)}\n"
+                    f'Use best hyperparameters example: $ python train.py --hyp {evolve_yaml}')
 
 
 def run(**kwargs):

utils/datasets.py

@@ -28,7 +28,7 @@ from tqdm import tqdm
 
 from utils.augmentations import Albumentations, augment_hsv, copy_paste, letterbox, mixup, random_perspective
 from utils.general import check_dataset, check_requirements, check_yaml, clean_str, segments2boxes, \
-    xywh2xyxy, xywhn2xyxy, xyxy2xywhn, xyn2xy
+    xywh2xyxy, xywhn2xyxy, xyxy2xywhn, xyn2xy, LOGGER
 from utils.torch_utils import torch_distributed_zero_first
 
 # Parameters
@@ -210,14 +210,14 @@ class LoadImages:
                     ret_val, img0 = self.cap.read()
 
             self.frame += 1
-            print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: ', end='')
+            s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: '
 
         else:
             # Read image
             self.count += 1
             img0 = cv2.imread(path)  # BGR
-            assert img0 is not None, 'Image Not Found ' + path
-            print(f'image {self.count}/{self.nf} {path}: ', end='')
+            assert img0 is not None, f'Image Not Found {path}'
+            s = f'image {self.count}/{self.nf} {path}: '
 
         # Padded resize
         img = letterbox(img0, self.img_size, stride=self.stride, auto=self.auto)[0]
@@ -226,7 +226,7 @@ class LoadImages:
         img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
         img = np.ascontiguousarray(img)
 
-        return path, img, img0, self.cap
+        return path, img, img0, self.cap, s
 
     def new_video(self, path):
         self.frame = 0
@@ -264,7 +264,7 @@ class LoadWebcam:  # for inference
         # Print
         assert ret_val, f'Camera Error {self.pipe}'
         img_path = 'webcam.jpg'
-        print(f'webcam {self.count}: ', end='')
+        s = f'webcam {self.count}: '
 
         # Padded resize
         img = letterbox(img0, self.img_size, stride=self.stride)[0]
@@ -273,7 +273,7 @@ class LoadWebcam:  # for inference
         img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
         img = np.ascontiguousarray(img)
 
-        return img_path, img, img0, None
+        return img_path, img, img0, None, s
 
     def __len__(self):
         return 0
@@ -298,14 +298,14 @@ class LoadStreams:
         self.auto = auto
         for i, s in enumerate(sources):  # index, source
             # Start thread to read frames from video stream
-            print(f'{i + 1}/{n}: {s}... ', end='')
+            st = f'{i + 1}/{n}: {s}... '
             if 'youtube.com/' in s or 'youtu.be/' in s:  # if source is YouTube video
                 check_requirements(('pafy', 'youtube_dl'))
                 import pafy
                 s = pafy.new(s).getbest(preftype="mp4").url  # YouTube URL
             s = eval(s) if s.isnumeric() else s  # i.e. s = '0' local webcam
             cap = cv2.VideoCapture(s)
-            assert cap.isOpened(), f'Failed to open {s}'
+            assert cap.isOpened(), f'{st}Failed to open {s}'
             w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
             h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
             self.fps[i] = max(cap.get(cv2.CAP_PROP_FPS) % 100, 0) or 30.0  # 30 FPS fallback
@@ -313,15 +313,15 @@ class LoadStreams:
 
             _, self.imgs[i] = cap.read()  # guarantee first frame
             self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
-            print(f" success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
+            LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
             self.threads[i].start()
-        print('')  # newline
+        LOGGER.info('')  # newline
 
         # check for common shapes
         s = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0].shape for x in self.imgs])
         self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
         if not self.rect:
-            print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')
+            LOGGER.warning('WARNING: Stream shapes differ. For optimal performance supply similarly-shaped streams.')
 
     def update(self, i, cap, stream):
         # Read stream `i` frames in daemon thread
@@ -335,7 +335,7 @@ class LoadStreams:
                 if success:
                     self.imgs[i] = im
                 else:
-                    print('WARNING: Video stream unresponsive, please check your IP camera connection.')
+                    LOGGER.warn('WARNING: Video stream unresponsive, please check your IP camera connection.')
                     self.imgs[i] *= 0
                     cap.open(stream)  # re-open stream if signal was lost
             time.sleep(1 / self.fps[i])  # wait time
@@ -361,7 +361,7 @@ class LoadStreams:
         img = img[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW
         img = np.ascontiguousarray(img)
 
-        return self.sources, img, img0, None
+        return self.sources, img, img0, None, ''
 
     def __len__(self):
         return len(self.sources)  # 1E12 frames = 32 streams at 30 FPS for 30 years
@@ -666,7 +666,7 @@ def load_image(self, i):
         else:  # read image
             path = self.img_files[i]
             im = cv2.imread(path)  # BGR
-            assert im is not None, 'Image Not Found ' + path
+            assert im is not None, f'Image Not Found {path}'
         h0, w0 = im.shape[:2]  # orig hw
         r = self.img_size / max(h0, w0)  # ratio
         if r != 1:  # if sizes are not equal

utils/general.py

@@ -42,6 +42,16 @@ FILE = Path(__file__).resolve()
 ROOT = FILE.parents[1]  # YOLOv5 root directory
 
 
+def set_logging(name=None, verbose=True):
+    # Sets level and returns logger
+    rank = int(os.getenv('RANK', -1))  # rank in world for Multi-GPU trainings
+    logging.basicConfig(format="%(message)s", level=logging.INFO if (verbose and rank in (-1, 0)) else logging.WARN)
+    return logging.getLogger(name)
+
+
+LOGGER = set_logging(__name__)  # define globally (used in train.py, val.py, detect.py, etc.)
+
+
 class Profile(contextlib.ContextDecorator):
     # Usage: @Profile() decorator or 'with Profile():' context manager
     def __enter__(self):
@@ -87,15 +97,9 @@ def methods(instance):
     return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")]
 
 
-def set_logging(rank=-1, verbose=True):
-    logging.basicConfig(
-        format="%(message)s",
-        level=logging.INFO if (verbose and rank in [-1, 0]) else logging.WARN)
-
-
 def print_args(name, opt):
     # Print argparser arguments
-    print(colorstr(f'{name}: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
+    LOGGER.info(colorstr(f'{name}: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
 
 
 def init_seeds(seed=0):

val.py

@@ -25,9 +25,9 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 from models.experimental import attempt_load
 from utils.datasets import create_dataloader
-from utils.general import coco80_to_coco91_class, check_dataset, check_img_size, check_requirements, \
-    check_suffix, check_yaml, box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, \
-    increment_path, colorstr, print_args
+from utils.general import box_iou, coco80_to_coco91_class, colorstr, check_dataset, check_img_size, \
+    check_requirements, check_suffix, check_yaml, increment_path, non_max_suppression, print_args, scale_coords, \
+    xyxy2xywh, xywh2xyxy, LOGGER
 from utils.metrics import ap_per_class, ConfusionMatrix
 from utils.plots import output_to_target, plot_images, plot_val_study
 from utils.torch_utils import select_device, time_sync
@@ -242,18 +242,18 @@ def run(data,
 
     # Print results
     pf = '%20s' + '%11i' * 2 + '%11.3g' * 4  # print format
-    print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
+    LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
 
     # Print results per class
     if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
         for i, c in enumerate(ap_class):
-            print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
+            LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
 
     # Print speeds
     t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
     if not training:
         shape = (batch_size, 3, imgsz, imgsz)
-        print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
+        LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
 
     # Plots
     if plots:
@@ -265,7 +265,7 @@ def run(data,
         w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
         anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json')  # annotations json
         pred_json = str(save_dir / f"{w}_predictions.json")  # predictions json
-        print(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
+        LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
         with open(pred_json, 'w') as f:
             json.dump(jdict, f)
 
@@ -284,13 +284,13 @@ def run(data,
             eval.summarize()
             map, map50 = eval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)
         except Exception as e:
-            print(f'pycocotools unable to run: {e}')
+            LOGGER.info(f'pycocotools unable to run: {e}')
 
     # Return results
     model.float()  # for training
     if not training:
         s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
-        print(f"Results saved to {colorstr('bold', save_dir)}{s}")
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
     maps = np.zeros(nc) + map
     for i, c in enumerate(ap_class):
         maps[c] = ap[i]
@@ -327,8 +327,7 @@ def parse_opt():
 
 
 def main(opt):
-    set_logging()
-    check_requirements(exclude=('tensorboard', 'thop'))
+    check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
 
     if opt.task in ('train', 'val', 'test'):  # run normally
         run(**vars(opt))
@@ -346,7 +345,7 @@ def main(opt):
             f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt'  # filename to save to
             y = []  # y axis
             for i in x:  # img-size
-                print(f'\nRunning {f} point {i}...')
+                LOGGER.info(f'\nRunning {f} point {i}...')
                 r, _, t = run(opt.data, weights=w, batch_size=opt.batch_size, imgsz=i, conf_thres=opt.conf_thres,
                               iou_thres=opt.iou_thres, device=opt.device, save_json=opt.save_json, plots=False)
                 y.append(r + t)  # results and times