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>
This commit is contained in:
Glenn Jocher
GitHub
parent
8c326a1edf
commit
7b1f7aec46
17
detect.py
17
detect.py
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@ -25,8 +25,7 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from models.experimental import attempt_load
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from utils.datasets import LoadImages, LoadStreams
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from utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \
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increment_path, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \
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strip_optimizer, xyxy2xywh
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increment_path, non_max_suppression, print_args, save_one_box, scale_coords, strip_optimizer, xyxy2xywh, LOGGER
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from utils.plots import Annotator, colors
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from utils.torch_utils import load_classifier, select_device, time_sync
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@ -68,7 +67,6 @@ def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
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(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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# Initialize
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set_logging()
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device = select_device(device)
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half &= device.type != 'cpu' # half precision only supported on CUDA
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@ -132,7 +130,7 @@ def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
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if pt and device.type != 'cpu':
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model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.parameters()))) # run once
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dt, seen = [0.0, 0.0, 0.0], 0
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for path, img, im0s, vid_cap in dataset:
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for path, img, im0s, vid_cap, s in dataset:
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t1 = time_sync()
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if onnx:
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img = img.astype('float32')
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@ -191,9 +189,10 @@ def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
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for i, det in enumerate(pred): # per image
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seen += 1
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if webcam: # batch_size >= 1
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p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
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p, im0, frame = path[i], im0s[i].copy(), dataset.count
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s += f'{i}: '
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else:
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p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
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p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
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p = Path(p) # to Path
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save_path = str(save_dir / p.name) # img.jpg
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@ -227,7 +226,7 @@ def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
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save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
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# Print time (inference-only)
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print(f'{s}Done. ({t3 - t2:.3f}s)')
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LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
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# Stream results
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im0 = annotator.result()
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@ -256,10 +255,10 @@ def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
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# Print results
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t = tuple(x / seen * 1E3 for x in dt) # speeds per image
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print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
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LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
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if save_txt or save_img:
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s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
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print(f"Results saved to {colorstr('bold', save_dir)}{s}")
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LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
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if update:
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strip_optimizer(weights) # update model (to fix SourceChangeWarning)
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59
export.py
59
export.py
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@ -42,23 +42,23 @@ from models.experimental import attempt_load
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from models.yolo import Detect
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from utils.activations import SiLU
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from utils.datasets import LoadImages
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from utils.general import colorstr, check_dataset, check_img_size, check_requirements, file_size, print_args, \
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set_logging, url2file
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from utils.general import check_dataset, check_img_size, check_requirements, colorstr, file_size, print_args, \
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url2file, LOGGER
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from utils.torch_utils import select_device
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def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
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# YOLOv5 TorchScript model export
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try:
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print(f'\n{prefix} starting export with torch {torch.__version__}...')
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LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
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f = file.with_suffix('.torchscript.pt')
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ts = torch.jit.trace(model, im, strict=False)
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(optimize_for_mobile(ts) if optimize else ts).save(f)
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print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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except Exception as e:
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print(f'{prefix} export failure: {e}')
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LOGGER.info(f'{prefix} export failure: {e}')
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def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorstr('ONNX:')):
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@ -67,7 +67,7 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
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check_requirements(('onnx',))
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import onnx
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print(f'\n{prefix} starting export with onnx {onnx.__version__}...')
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LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
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f = file.with_suffix('.onnx')
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torch.onnx.export(model, im, f, verbose=False, opset_version=opset,
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@ -82,7 +82,7 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
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# Checks
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model_onnx = onnx.load(f) # load onnx model
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onnx.checker.check_model(model_onnx) # check onnx model
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# print(onnx.helper.printable_graph(model_onnx.graph)) # print
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# LOGGER.info(onnx.helper.printable_graph(model_onnx.graph)) # print
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# Simplify
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if simplify:
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@ -90,7 +90,7 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
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check_requirements(('onnx-simplifier',))
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import onnxsim
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print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
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LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
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model_onnx, check = onnxsim.simplify(
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model_onnx,
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dynamic_input_shape=dynamic,
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@ -98,11 +98,11 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
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assert check, 'assert check failed'
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onnx.save(model_onnx, f)
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except Exception as e:
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print(f'{prefix} simplifier failure: {e}')
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print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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print(f"{prefix} run --dynamic ONNX model inference with: 'python detect.py --weights {f}'")
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LOGGER.info(f'{prefix} simplifier failure: {e}')
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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LOGGER.info(f"{prefix} run --dynamic ONNX model inference with: 'python detect.py --weights {f}'")
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except Exception as e:
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print(f'{prefix} export failure: {e}')
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LOGGER.info(f'{prefix} export failure: {e}')
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def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
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@ -112,7 +112,7 @@ def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
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check_requirements(('coremltools',))
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import coremltools as ct
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print(f'\n{prefix} starting export with coremltools {ct.__version__}...')
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LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
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f = file.with_suffix('.mlmodel')
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model.train() # CoreML exports should be placed in model.train() mode
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@ -120,9 +120,9 @@ def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
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ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255.0, bias=[0, 0, 0])])
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ct_model.save(f)
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print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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except Exception as e:
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print(f'\n{prefix} export failure: {e}')
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LOGGER.info(f'\n{prefix} export failure: {e}')
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return ct_model
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@ -137,7 +137,7 @@ def export_saved_model(model, im, file, dynamic,
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from tensorflow import keras
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from models.tf import TFModel, TFDetect
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print(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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f = str(file).replace('.pt', '_saved_model')
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batch_size, ch, *imgsz = list(im.shape) # BCHW
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@ -151,9 +151,9 @@ def export_saved_model(model, im, file, dynamic,
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keras_model.summary()
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keras_model.save(f, save_format='tf')
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print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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except Exception as e:
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print(f'\n{prefix} export failure: {e}')
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LOGGER.info(f'\n{prefix} export failure: {e}')
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return keras_model
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@ -164,7 +164,7 @@ def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
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import tensorflow as tf
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from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
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print(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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f = file.with_suffix('.pb')
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m = tf.function(lambda x: keras_model(x)) # full model
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@ -173,9 +173,9 @@ def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
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frozen_func.graph.as_graph_def()
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tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
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print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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except Exception as e:
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print(f'\n{prefix} export failure: {e}')
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LOGGER.info(f'\n{prefix} export failure: {e}')
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def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('TensorFlow Lite:')):
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@ -184,7 +184,7 @@ def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('Te
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import tensorflow as tf
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from models.tf import representative_dataset_gen
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print(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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batch_size, ch, *imgsz = list(im.shape) # BCHW
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f = str(file).replace('.pt', '-fp16.tflite')
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@ -204,10 +204,10 @@ def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('Te
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tflite_model = converter.convert()
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open(f, "wb").write(tflite_model)
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print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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except Exception as e:
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print(f'\n{prefix} export failure: {e}')
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LOGGER.info(f'\n{prefix} export failure: {e}')
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def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
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@ -217,7 +217,7 @@ def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
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import re
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import tensorflowjs as tfjs
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print(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
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LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
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f = str(file).replace('.pt', '_web_model') # js dir
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f_pb = file.with_suffix('.pb') # *.pb path
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f_json = f + '/model.json' # *.json path
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@ -240,9 +240,9 @@ def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
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json)
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j.write(subst)
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print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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except Exception as e:
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print(f'\n{prefix} export failure: {e}')
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LOGGER.info(f'\n{prefix} export failure: {e}')
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@torch.no_grad()
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@ -297,7 +297,7 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
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for _ in range(2):
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y = model(im) # dry runs
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print(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)")
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LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)")
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# Exports
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if 'torchscript' in include:
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@ -322,7 +322,7 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
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export_tfjs(model, im, file)
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# Finish
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print(f'\nExport complete ({time.time() - t:.2f}s)'
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LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)'
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f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
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f'\nVisualize with https://netron.app')
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@ -355,7 +355,6 @@ def parse_opt():
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def main(opt):
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set_logging()
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run(**vars(opt))
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@ -31,11 +31,9 @@ from tensorflow import keras
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from models.common import Bottleneck, BottleneckCSP, Concat, Conv, C3, DWConv, Focus, SPP, SPPF, autopad
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from models.experimental import CrossConv, MixConv2d, attempt_load
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from models.yolo import Detect
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from utils.general import make_divisible, print_args, set_logging
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from utils.general import make_divisible, print_args, LOGGER
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from utils.activations import SiLU
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LOGGER = logging.getLogger(__name__)
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class TFBN(keras.layers.Layer):
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# TensorFlow BatchNormalization wrapper
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@ -336,7 +334,7 @@ class TFModel:
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# Define model
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if nc and nc != self.yaml['nc']:
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print(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}")
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LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}")
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self.yaml['nc'] = nc # override yaml value
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self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)
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@ -457,7 +455,6 @@ def parse_opt():
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def main(opt):
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set_logging()
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run(**vars(opt))
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@ -20,7 +20,7 @@ if str(ROOT) not in sys.path:
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from models.common import *
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from models.experimental import *
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from utils.autoanchor import check_anchor_order
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from utils.general import check_yaml, make_divisible, print_args, set_logging, check_version
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from utils.general import check_version, check_yaml, make_divisible, print_args, LOGGER
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from utils.plots import feature_visualization
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from utils.torch_utils import copy_attr, fuse_conv_and_bn, initialize_weights, model_info, scale_img, \
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select_device, time_sync
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@ -30,8 +30,6 @@ try:
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except ImportError:
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thop = None
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LOGGER = logging.getLogger(__name__)
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class Detect(nn.Module):
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stride = None # strides computed during build
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@ -311,7 +309,6 @@ if __name__ == '__main__':
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opt = parser.parse_args()
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opt.cfg = check_yaml(opt.cfg) # check YAML
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print_args(FILE.stem, opt)
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set_logging()
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device = select_device(opt.device)
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# Create model
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8
train.py
8
train.py
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@ -40,7 +40,7 @@ from utils.autobatch import check_train_batch_size
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from utils.datasets import create_dataloader
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from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
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strip_optimizer, get_latest_run, check_dataset, check_git_status, check_img_size, check_requirements, \
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check_file, check_yaml, check_suffix, print_args, print_mutation, set_logging, one_cycle, colorstr, methods
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check_file, check_yaml, check_suffix, print_args, print_mutation, one_cycle, colorstr, methods, LOGGER
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from utils.downloads import attempt_download
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from utils.loss import ComputeLoss
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from utils.plots import plot_labels, plot_evolve
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@ -51,7 +51,6 @@ from utils.metrics import fitness
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from utils.loggers import Loggers
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from utils.callbacks import Callbacks
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LOGGER = logging.getLogger(__name__)
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LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
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||||
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()
|
||||
|
|
@ -609,7 +607,7 @@ def main(opt, callbacks=Callbacks()):
|
|||
|
||||
# Plot results
|
||||
plot_evolve(evolve_csv)
|
||||
print(f'Hyperparameter evolution finished\n'
|
||||
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}')
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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):
|
||||
|
|
|
|||
23
val.py
23
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
|
||||
|
|
|
|||
Loading…
Reference in New Issue