From 7b1f7aec4632d7aa0f04442ef21df0b31ec6390a Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 1 Nov 2021 18:22:13 +0100 Subject: [PATCH] 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 --- detect.py | 17 ++++++------- export.py | 63 +++++++++++++++++++++++------------------------ models/tf.py | 7 ++---- models/yolo.py | 5 +--- train.py | 12 ++++----- utils/datasets.py | 30 +++++++++++----------- utils/general.py | 18 ++++++++------ val.py | 23 +++++++++-------- 8 files changed, 84 insertions(+), 91 deletions(-) diff --git a/detect.py b/detect.py index 70c52dc..c57edba 100644 --- a/detect.py +++ b/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) diff --git a/export.py b/export.py index 2aca0f3..47dbcab 100644 --- a/export.py +++ b/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)) diff --git a/models/tf.py b/models/tf.py index 5599ff5..531c8cc 100644 --- a/models/tf.py +++ b/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)) diff --git a/models/yolo.py b/models/yolo.py index 80ff83e..38a17d9 100644 --- a/models/yolo.py +++ b/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 diff --git a/train.py b/train.py index 292f2da..4886034 100644 --- a/train.py +++ b/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): diff --git a/utils/datasets.py b/utils/datasets.py index fce005b..7fce122 100755 --- a/utils/datasets.py +++ b/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 diff --git a/utils/general.py b/utils/general.py index 667af63..872d5ce 100755 --- a/utils/general.py +++ b/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): diff --git a/val.py b/val.py index 2fc5473..1fc98c7 100644 --- a/val.py +++ b/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