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ultralytics 8.0.149 add Open Images V7 training (#4178)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: AdiEcho <30563671+AdiEcho@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-08-07 01:19:51 +02:00
committed by GitHub
parent c751c7f88a
commit 7565210484
110 changed files with 969 additions and 144 deletions
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19
ultralytics/engine/exporter.py
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@ -214,8 +214,8 @@ class Exporter:
self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else \
tuple(tuple(x.shape if isinstance(x, torch.Tensor) else []) for x in y)
self.pretty_name = Path(self.model.yaml.get('yaml_file', self.file)).stem.replace('yolo', 'YOLO')
trained_on = f'trained on {Path(self.args.data).name}' if self.args.data else '(untrained)'
description = f'Ultralytics {self.pretty_name} model {trained_on}'
data = model.args['data'] if hasattr(model, 'args') and isinstance(model.args, dict) else ''
description = f'Ultralytics {self.pretty_name} model {f"trained on {data}" if data else ""}'
self.metadata = {
'description': description,
'author': 'Ultralytics',
@ -269,13 +269,12 @@ class Exporter:
s = '' if square else f"WARNING ⚠️ non-PyTorch val requires square images, 'imgsz={self.imgsz}' will not " \
f"work. Use export 'imgsz={max(self.imgsz)}' if val is required."
imgsz = self.imgsz[0] if square else str(self.imgsz)[1:-1].replace(' ', '')
data = f'data={self.args.data}' if model.task == 'segment' and format == 'pb' else ''
LOGGER.info(
f'\nExport complete ({time.time() - t:.1f}s)'
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
f'\nPredict: yolo predict task={model.task} model={f} imgsz={imgsz} {data}'
f'\nValidate: yolo val task={model.task} model={f} imgsz={imgsz} data={self.args.data} {s}'
f'\nVisualize: https://netron.app')
predict_data = f'data={data}' if model.task == 'segment' and format == 'pb' else ''
LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
f'\nPredict: yolo predict task={model.task} model={f} imgsz={imgsz} {predict_data}'
f'\nValidate: yolo val task={model.task} model={f} imgsz={imgsz} data={data} {s}'
f'\nVisualize: https://netron.app')
self.run_callbacks('on_export_end')
return f # return list of exported files/dirs
@ -612,7 +611,7 @@ class Exporter:
for n, batch in enumerate(dataset):
if n >= n_images:
break
im = batch['img'].permute(1, 2, 0)[None] # list to nparray, CHW to BHWC,
im = batch['img'].permute(1, 2, 0)[None] # list to nparray, CHW to BHWC
images.append(im)
f.mkdir()
images = torch.cat(images, 0).float()