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ultralytics 8.0.14 Hydra removal fixes and cleanup (#542)

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Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Kamlesh Kumar <patelkamleshpatel364@gmail.com>
This commit is contained in:
Glenn Jocher
2023-01-21 21:22:40 +01:00
committed by GitHub
parent cc3be0e223
commit d9a0fba251
30 changed files with 339 additions and 301 deletions
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4
ultralytics/yolo/engine/exporter.py
View File

@ -67,7 +67,7 @@ import torch
import ultralytics
from ultralytics.nn.modules import Detect, Segment
from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
from ultralytics.yolo.data.utils import check_dataset
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, callbacks, colorstr, get_default_args, yaml_save
@ -134,7 +134,7 @@ class Exporter:
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
self.args = get_config(config, overrides)
self.args = get_cfg(config, overrides)
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
callbacks.add_integration_callbacks(self)

10
ultralytics/yolo/engine/model.py
View File

@ -4,7 +4,7 @@ from pathlib import Path
from ultralytics import yolo # noqa
from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.engine.exporter import Exporter
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, yaml_load
from ultralytics.yolo.utils.checks import check_yaml
@ -136,7 +136,7 @@ class YOLO:
self.predictor = self.PredictorClass(overrides=overrides)
self.predictor.setup_model(model=self.model)
else: # only update args if predictor is already setup
self.predictor.args = get_config(self.predictor.args, overrides)
self.predictor.args = get_cfg(self.predictor.args, overrides)
return self.predictor(source=source, stream=stream, verbose=verbose)
@smart_inference_mode()
@ -151,7 +151,7 @@ class YOLO:
overrides = self.overrides.copy()
overrides.update(kwargs)
overrides["mode"] = "val"
args = get_config(config=DEFAULT_CFG_PATH, overrides=overrides)
args = get_cfg(cfg=DEFAULT_CFG_PATH, overrides=overrides)
args.data = data or args.data
args.task = self.task
@ -169,7 +169,7 @@ class YOLO:
overrides = self.overrides.copy()
overrides.update(kwargs)
args = get_config(config=DEFAULT_CFG_PATH, overrides=overrides)
args = get_cfg(cfg=DEFAULT_CFG_PATH, overrides=overrides)
args.task = self.task
print(args)
@ -201,7 +201,7 @@ class YOLO:
self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
self.model = self.trainer.model
self.trainer.train()
# update model and configs after training
# update model and cfg after training
self.model, _ = attempt_load_one_weight(str(self.trainer.best))
self.overrides = self.model.args

9
ultralytics/yolo/engine/predictor.py
View File

@ -33,7 +33,7 @@ from pathlib import Path
import cv2
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadPilAndNumpy, LoadScreenshots, LoadStreams
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, SETTINGS, callbacks, colorstr, ops
@ -70,7 +70,7 @@ class BasePredictor:
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
self.args = get_config(config, overrides)
self.args = get_cfg(config, overrides)
project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
name = self.args.name or f"{self.args.mode}"
self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
@ -84,6 +84,7 @@ class BasePredictor:
self.bs = None
self.imgsz = None
self.device = None
self.classes = self.args.classes
self.dataset = None
self.vid_path, self.vid_writer = None, None
self.annotator = None
@ -100,7 +101,7 @@ class BasePredictor:
def write_results(self, results, batch, print_string):
raise NotImplementedError("print_results function needs to be implemented")
def postprocess(self, preds, img, orig_img):
def postprocess(self, preds, img, orig_img, classes=None):
return preds
def setup_source(self, source=None):
@ -195,7 +196,7 @@ class BasePredictor:
# postprocess
with self.dt[2]:
results = self.postprocess(preds, im, im0s)
results = self.postprocess(preds, im, im0s, self.classes)
for i in range(len(im)):
p, im0 = (path[i], im0s[i]) if self.webcam or self.from_img else (path, im0s)
p = Path(p)

44
ultralytics/yolo/engine/results.py
View File

@ -21,6 +21,8 @@ class Results:
masks (Masks, optional): A Masks object containing the detection masks.
probs (torch.Tensor, optional): A tensor containing the detection class probabilities.
orig_shape (tuple, optional): Original image size.
data (torch.Tensor): The raw masks tensor
"""
def __init__(self, boxes=None, masks=None, probs=None, orig_shape=None) -> None:
@ -81,19 +83,20 @@ class Results:
return len(getattr(self, item))
def __str__(self):
return self.__repr__()
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
def __repr__(self):
s = f'Ultralytics YOLO {self.__class__} instance\n' # string
if self.boxes is not None:
s = s + self.boxes.__repr__() + '\n'
if self.masks is not None:
s = s + self.masks.__repr__() + '\n'
if self.probs is not None:
s = s + self.probs.__repr__()
s += f'original size: {self.orig_shape}\n'
return s
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
def __getattr__(self, attr):
name = self.__class__.__name__
@ -129,6 +132,7 @@ class Boxes:
xywh (torch.Tensor) or (numpy.ndarray): The boxes in xywh format.
xyxyn (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format normalized by original image size.
xywhn (torch.Tensor) or (numpy.ndarray): The boxes in xywh format normalized by original image size.
data (torch.Tensor): The raw bboxes tensor
"""
def __init__(self, boxes, orig_shape) -> None:
@ -198,15 +202,19 @@ class Boxes:
def shape(self):
return self.boxes.shape
@property
def data(self):
return self.boxes
def __len__(self): # override len(results)
return len(self.boxes)
def __str__(self):
return self.__repr__()
return self.boxes.__str__()
def __repr__(self):
return (f"Ultralytics YOLO {self.__class__} masks\n" + f"type: {type(self.boxes)}\n" +
f"shape: {self.boxes.shape}\n" + f"dtype: {self.boxes.dtype}")
f"shape: {self.boxes.shape}\n" + f"dtype: {self.boxes.dtype}\n + {self.boxes.__repr__()}")
def __getitem__(self, idx):
boxes = self.boxes[idx]
@ -257,12 +265,16 @@ class Masks:
def segments(self):
return [
ops.scale_segments(self.masks.shape[1:], x, self.orig_shape, normalize=True)
for x in reversed(ops.masks2segments(self.masks))]
for x in ops.masks2segments(self.masks)]
@property
def shape(self):
return self.masks.shape
@property
def data(self):
return self.masks
def cpu(self):
masks = self.masks.cpu()
return Masks(masks, self.orig_shape)
@ -283,11 +295,11 @@ class Masks:
return len(self.masks)
def __str__(self):
return self.__repr__()
return self.masks.__str__()
def __repr__(self):
return (f"Ultralytics YOLO {self.__class__} masks\n" + f"type: {type(self.masks)}\n" +
f"shape: {self.masks.shape}\n" + f"dtype: {self.masks.dtype}")
f"shape: {self.masks.shape}\n" + f"dtype: {self.masks.dtype}\n + {self.masks.__repr__()}")
def __getitem__(self, idx):
masks = self.masks[idx]

6
ultralytics/yolo/engine/trainer.py
View File

@ -23,7 +23,7 @@ from tqdm import tqdm
import ultralytics.yolo.utils as utils
from ultralytics import __version__
from ultralytics.nn.tasks import attempt_load_one_weight
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import (DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr,
yaml_save)
@ -79,7 +79,7 @@ class BaseTrainer:
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
self.args = get_config(config, overrides)
self.args = get_cfg(config, overrides)
self.device = utils.torch_utils.select_device(self.args.device, self.args.batch)
self.check_resume()
self.console = LOGGER
@ -509,7 +509,7 @@ class BaseTrainer:
assert args_yaml.is_file(), \
FileNotFoundError('Resume checkpoint f{last} not found. '
'Please pass a valid checkpoint to resume from, i.e. yolo resume=path/to/last.pt')
args = get_config(args_yaml) # replace
args = get_cfg(args_yaml) # replace
args.model, resume = str(last), True # reinstate
self.args = args
self.resume = resume

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

@ -8,7 +8,7 @@ import torch
from tqdm import tqdm
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks
from ultralytics.yolo.utils.checks import check_imgsz
@ -52,7 +52,7 @@ class BaseValidator:
self.dataloader = dataloader
self.pbar = pbar
self.logger = logger or LOGGER
self.args = args or get_config(DEFAULT_CFG_PATH)
self.args = args or get_cfg(DEFAULT_CFG_PATH)
self.model = None
self.data = None
self.device = None