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ultralytics 8.0.43 optimized Results class and fixes (#1069)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Alexander Duda <Alexander.Duda@me.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-02-21 20:37:59 +01:00
committed by GitHub
parent f2a7a29e53
commit fe61018975
22 changed files with 180 additions and 102 deletions
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18
ultralytics/yolo/engine/model.py
View File

@ -9,7 +9,7 @@ from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, Segmentat
guess_model_task, nn)
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.engine.exporter import Exporter
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, callbacks, yaml_load
from ultralytics.yolo.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, RANK, callbacks, yaml_load
from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_yaml
from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
from ultralytics.yolo.utils.torch_utils import smart_inference_mode
@ -203,7 +203,7 @@ class YOLO:
@smart_inference_mode()
def track(self, source=None, stream=False, **kwargs):
from ultralytics.tracker.track import register_tracker
from ultralytics.tracker import register_tracker
register_tracker(self)
# ByteTrack-based method needs low confidence predictions as input
conf = kwargs.get('conf') or 0.1
@ -237,6 +237,20 @@ class YOLO:
return validator.metrics
@smart_inference_mode()
def benchmark(self, **kwargs):
"""
Benchmark a model on all export formats.
Args:
**kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
"""
from ultralytics.yolo.utils.benchmarks import run_benchmarks
overrides = self.model.args.copy()
overrides.update(kwargs)
overrides = {**DEFAULT_CFG_DICT, **overrides} # fill in missing overrides keys with defaults
return run_benchmarks(model=self, imgsz=overrides['imgsz'], half=overrides['half'], device=overrides['device'])
def export(self, **kwargs):
"""
Export model.

2
ultralytics/yolo/engine/predictor.py
View File

@ -194,7 +194,7 @@ class BasePredictor:
# Print time (inference-only)
if self.args.verbose:
LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms")
LOGGER.info(f'{s}{self.dt[1].dt * 1E3:.1f}ms')
# Release assets
if isinstance(self.vid_writer[-1], cv2.VideoWriter):

89
ultralytics/yolo/engine/results.py
View File

@ -1,3 +1,10 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
"""
Ultralytics Results, Boxes and Masks classes for handling inference results
Usage: See https://docs.ultralytics.com/predict/
"""
from copy import deepcopy
from functools import lru_cache
@ -36,7 +43,7 @@ class Results:
self.probs = probs if probs is not None else None
self.names = names
self.path = path
self.comp = ['boxes', 'masks', 'probs']
self._keys = (k for k in ('boxes', 'masks', 'probs') if getattr(self, k) is not None)
def pandas(self):
pass
@ -44,10 +51,8 @@ class Results:
def __getitem__(self, idx):
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
for item in self.comp:
if getattr(self, item) is None:
continue
setattr(r, item, getattr(self, item)[idx])
for k in self._keys:
setattr(r, k, getattr(self, k)[idx])
return r
def update(self, boxes=None, masks=None, probs=None):
@ -60,57 +65,37 @@ class Results:
def cpu(self):
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
for item in self.comp:
if getattr(self, item) is None:
continue
setattr(r, item, getattr(self, item).cpu())
for k in self._keys:
setattr(r, k, getattr(self, k).cpu())
return r
def numpy(self):
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
for item in self.comp:
if getattr(self, item) is None:
continue
setattr(r, item, getattr(self, item).numpy())
for k in self._keys:
setattr(r, k, getattr(self, k).numpy())
return r
def cuda(self):
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
for item in self.comp:
if getattr(self, item) is None:
continue
setattr(r, item, getattr(self, item).cuda())
for k in self._keys:
setattr(r, k, getattr(self, k).cuda())
return r
def to(self, *args, **kwargs):
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
for item in self.comp:
if getattr(self, item) is None:
continue
setattr(r, item, getattr(self, item).to(*args, **kwargs))
for k in self._keys:
setattr(r, k, getattr(self, k).to(*args, **kwargs))
return r
def __len__(self):
for item in self.comp:
if getattr(self, item) is None:
continue
return len(getattr(self, item))
for k in self._keys:
return len(getattr(self, k))
def __str__(self):
str_out = ''
for item in self.comp:
if getattr(self, item) is None:
continue
str_out = str_out + getattr(self, item).__str__()
return str_out
return ''.join(getattr(self, k).__str__() for k in self._keys)
def __repr__(self):
str_out = ''
for item in self.comp:
if getattr(self, item) is None:
continue
str_out = str_out + getattr(self, item).__repr__()
return str_out
return ''.join(getattr(self, k).__repr__() for k in self._keys)
def __getattr__(self, attr):
name = self.__class__.__name__
@ -226,20 +211,16 @@ class Boxes:
return self.xywh / self.orig_shape[[1, 0, 1, 0]]
def cpu(self):
boxes = self.boxes.cpu()
return Boxes(boxes, self.orig_shape)
return Boxes(self.boxes.cpu(), self.orig_shape)
def numpy(self):
boxes = self.boxes.numpy()
return Boxes(boxes, self.orig_shape)
return Boxes(self.boxes.numpy(), self.orig_shape)
def cuda(self):
boxes = self.boxes.cuda()
return Boxes(boxes, self.orig_shape)
return Boxes(self.boxes.cuda(), self.orig_shape)
def to(self, *args, **kwargs):
boxes = self.boxes.to(*args, **kwargs)
return Boxes(boxes, self.orig_shape)
return Boxes(self.boxes.to(*args, **kwargs), self.orig_shape)
def pandas(self):
LOGGER.info('results.pandas() method not yet implemented')
@ -272,8 +253,7 @@ class Boxes:
f'shape: {self.boxes.shape}\n' + f'dtype: {self.boxes.dtype}\n + {self.boxes.__repr__()}')
def __getitem__(self, idx):
boxes = self.boxes[idx]
return Boxes(boxes, self.orig_shape)
return Boxes(self.boxes[idx], self.orig_shape)
def __getattr__(self, attr):
name = self.__class__.__name__
@ -331,20 +311,16 @@ class Masks:
return self.masks
def cpu(self):
masks = self.masks.cpu()
return Masks(masks, self.orig_shape)
return Masks(self.masks.cpu(), self.orig_shape)
def numpy(self):
masks = self.masks.numpy()
return Masks(masks, self.orig_shape)
return Masks(self.masks.numpy(), self.orig_shape)
def cuda(self):
masks = self.masks.cuda()
return Masks(masks, self.orig_shape)
return Masks(self.masks.cuda(), self.orig_shape)
def to(self, *args, **kwargs):
masks = self.masks.to(*args, **kwargs)
return Masks(masks, self.orig_shape)
return Masks(self.masks.to(*args, **kwargs), self.orig_shape)
def __len__(self): # override len(results)
return len(self.masks)
@ -357,8 +333,7 @@ class Masks:
f'shape: {self.masks.shape}\n' + f'dtype: {self.masks.dtype}\n + {self.masks.__repr__()}')
def __getitem__(self, idx):
masks = self.masks[idx]
return Masks(masks, self.orig_shape)
return Masks(self.masks[idx], self.orig_shape)
def __getattr__(self, attr):
name = self.__class__.__name__

5
ultralytics/yolo/engine/trainer.py
View File

@ -243,6 +243,8 @@ class BaseTrainer:
metric_keys = self.validator.metrics.keys + self.label_loss_items(prefix='val')
self.metrics = dict(zip(metric_keys, [0] * len(metric_keys))) # TODO: init metrics for plot_results()?
self.ema = ModelEMA(self.model)
if self.args.plots:
self.plot_training_labels()
self.resume_training(ckpt)
self.scheduler.last_epoch = self.start_epoch - 1 # do not move
self.run_callbacks('on_pretrain_routine_end')
@ -501,6 +503,9 @@ class BaseTrainer:
def plot_training_samples(self, batch, ni):
pass
def plot_training_labels(self):
pass
def save_metrics(self, metrics):
keys, vals = list(metrics.keys()), list(metrics.values())
n = len(metrics) + 1 # number of cols

4
ultralytics/yolo/engine/validator.py
View File

@ -28,7 +28,7 @@ from tqdm import tqdm
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, emojis
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr, emojis
from ultralytics.yolo.utils.checks import check_imgsz
from ultralytics.yolo.utils.files import increment_path
from ultralytics.yolo.utils.ops import Profile
@ -194,6 +194,8 @@ class BaseValidator:
self.logger.info(f'Saving {f.name}...')
json.dump(self.jdict, f) # flatten and save
stats = self.eval_json(stats) # update stats
if self.args.plots or self.args.save_json:
LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}")
return stats
def run_callbacks(self, event: str):