class BasePredictor:
"""
BasePredictor
A base class for creating predictors.
Attributes:
args (SimpleNamespace): Configuration for the predictor.
save_dir (Path): Directory to save results.
done_setup (bool): Whether the predictor has finished setup.
model (nn.Module): Model used for prediction.
data (dict): Data configuration.
device (torch.device): Device used for prediction.
dataset (Dataset): Dataset used for prediction.
vid_path (str): Path to video file.
vid_writer (cv2.VideoWriter): Video writer for saving video output.
annotator (Annotator): Annotator used for prediction.
data_path (str): Path to data.
"""
def __init__(self, cfg=DEFAULT_CFG, overrides=None):
"""
Initializes the BasePredictor class.
Args:
cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
self.args = get_cfg(cfg, 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)
if self.args.conf is None:
self.args.conf = 0.25 # default conf=0.25
self.done_warmup = False
if self.args.show:
self.args.show = check_imshow(warn=True)
# Usable if setup is done
self.model = None
self.data = self.args.data # data_dict
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
self.data_path = None
self.source_type = None
self.callbacks = defaultdict(list, callbacks.default_callbacks) # add callbacks
callbacks.add_integration_callbacks(self)
def preprocess(self, img):
pass
def get_annotator(self, img):
raise NotImplementedError("get_annotator function needs to be implemented")
def write_results(self, results, batch, print_string):
raise NotImplementedError("print_results function needs to be implemented")
def postprocess(self, preds, img, orig_img, classes=None):
return preds
@smart_inference_mode()
def __call__(self, source=None, model=None, stream=False):
if stream:
return self.stream_inference(source, model)
else:
return list(self.stream_inference(source, model)) # merge list of Result into one
def predict_cli(self, source=None, model=None):
# Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode
gen = self.stream_inference(source, model)
for _ in gen: # running CLI inference without accumulating any outputs (do not modify)
pass
def setup_source(self, source):
self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2) # check image size
self.dataset = load_inference_source(source=source,
transforms=getattr(self.model.model, 'transforms', None),
imgsz=self.imgsz,
vid_stride=self.args.vid_stride,
stride=self.model.stride,
auto=self.model.pt)
self.source_type = self.dataset.source_type
self.vid_path, self.vid_writer = [None] * self.dataset.bs, [None] * self.dataset.bs
def stream_inference(self, source=None, model=None):
self.run_callbacks("on_predict_start")
if self.args.verbose:
LOGGER.info("")
# setup model
if not self.model:
self.setup_model(model)
# setup source every time predict is called
self.setup_source(source if source is not None else self.args.source)
# check if save_dir/ label file exists
if self.args.save or self.args.save_txt:
(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
# warmup model
if not self.done_warmup:
self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.bs, 3, *self.imgsz))
self.done_warmup = True
self.seen, self.windows, self.dt, self.batch = 0, [], (ops.Profile(), ops.Profile(), ops.Profile()), None
for batch in self.dataset:
self.run_callbacks("on_predict_batch_start")
self.batch = batch
path, im, im0s, vid_cap, s = batch
visualize = increment_path(self.save_dir / Path(path).stem, mkdir=True) if self.args.visualize else False
with self.dt[0]:
im = self.preprocess(im)
if len(im.shape) == 3:
im = im[None] # expand for batch dim
# Inference
with self.dt[1]:
preds = self.model(im, augment=self.args.augment, visualize=visualize)
# postprocess
with self.dt[2]:
self.results = self.postprocess(preds, im, im0s, self.classes)
for i in range(len(im)):
p, im0 = (path[i], im0s[i]) if self.source_type.webcam or self.source_type.from_img else (path, im0s)
p = Path(p)
if self.args.verbose or self.args.save or self.args.save_txt or self.args.show:
s += self.write_results(i, self.results, (p, im, im0))
if self.args.show:
self.show(p)
if self.args.save:
self.save_preds(vid_cap, i, str(self.save_dir / p.name))
self.run_callbacks("on_predict_batch_end")
yield from self.results
# 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")
# Release assets
if isinstance(self.vid_writer[-1], cv2.VideoWriter):
self.vid_writer[-1].release() # release final video writer
# Print results
if self.args.verbose and self.seen:
t = tuple(x.t / self.seen * 1E3 for x in self.dt) # speeds per image
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms postprocess per image at shape '
f'{(1, 3, *self.imgsz)}' % t)
if self.args.save_txt or self.args.save:
nl = len(list(self.save_dir.glob('labels/*.txt'))) # number of labels
s = f"\n{nl} label{'s' * (nl > 1)} saved to {self.save_dir / 'labels'}" if self.args.save_txt else ''
LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")
self.run_callbacks("on_predict_end")
def setup_model(self, model):
device = select_device(self.args.device)
model = model or self.args.model
self.args.half &= device.type != 'cpu' # half precision only supported on CUDA
self.model = AutoBackend(model, device=device, dnn=self.args.dnn, data=self.args.data, fp16=self.args.half)
self.device = device
self.model.eval()
def show(self, p):
im0 = self.annotator.result()
if platform.system() == 'Linux' and p not in self.windows:
self.windows.append(p)
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
def save_preds(self, vid_cap, idx, save_path):
im0 = self.annotator.result()
# save imgs
if self.dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if self.vid_path[idx] != save_path: # new video
self.vid_path[idx] = save_path
if isinstance(self.vid_writer[idx], cv2.VideoWriter):
self.vid_writer[idx].release() # release previous video writer
if vid_cap: # video
fps = int(vid_cap.get(cv2.CAP_PROP_FPS)) # integer required, floats produce error in MP4 codec
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
self.vid_writer[idx].write(im0)
def run_callbacks(self, event: str):
for callback in self.callbacks.get(event, []):
callback(self)