ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
e795277391
commit
fe27db2f6e
139 changed files with 6870 additions and 5125 deletions
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@ -132,8 +132,11 @@ class BasePredictor:
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def inference(self, im, *args, **kwargs):
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"""Runs inference on a given image using the specified model and arguments."""
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visualize = increment_path(self.save_dir / Path(self.batch[0][0]).stem,
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mkdir=True) if self.args.visualize and (not self.source_type.tensor) else False
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visualize = (
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increment_path(self.save_dir / Path(self.batch[0][0]).stem, mkdir=True)
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if self.args.visualize and (not self.source_type.tensor)
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else False
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)
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return self.model(im, augment=self.args.augment, visualize=visualize, embed=self.args.embed, *args, **kwargs)
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def pre_transform(self, im):
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@ -153,35 +156,38 @@ class BasePredictor:
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def write_results(self, idx, results, batch):
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"""Write inference results to a file or directory."""
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p, im, _ = batch
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log_string = ''
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log_string = ""
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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if self.source_type.webcam or self.source_type.from_img or self.source_type.tensor: # batch_size >= 1
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log_string += f'{idx}: '
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log_string += f"{idx}: "
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frame = self.dataset.count
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else:
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frame = getattr(self.dataset, 'frame', 0)
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frame = getattr(self.dataset, "frame", 0)
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self.data_path = p
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self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
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log_string += '%gx%g ' % im.shape[2:] # print string
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self.txt_path = str(self.save_dir / "labels" / p.stem) + ("" if self.dataset.mode == "image" else f"_{frame}")
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log_string += "%gx%g " % im.shape[2:] # print string
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result = results[idx]
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log_string += result.verbose()
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if self.args.save or self.args.show: # Add bbox to image
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plot_args = {
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'line_width': self.args.line_width,
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'boxes': self.args.show_boxes,
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'conf': self.args.show_conf,
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'labels': self.args.show_labels}
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"line_width": self.args.line_width,
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"boxes": self.args.show_boxes,
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"conf": self.args.show_conf,
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"labels": self.args.show_labels,
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}
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if not self.args.retina_masks:
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plot_args['im_gpu'] = im[idx]
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plot_args["im_gpu"] = im[idx]
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self.plotted_img = result.plot(**plot_args)
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# Write
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if self.args.save_txt:
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result.save_txt(f'{self.txt_path}.txt', save_conf=self.args.save_conf)
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result.save_txt(f"{self.txt_path}.txt", save_conf=self.args.save_conf)
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if self.args.save_crop:
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result.save_crop(save_dir=self.save_dir / 'crops',
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file_name=self.data_path.stem + ('' if self.dataset.mode == 'image' else f'_{frame}'))
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result.save_crop(
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save_dir=self.save_dir / "crops",
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file_name=self.data_path.stem + ("" if self.dataset.mode == "image" else f"_{frame}"),
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)
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return log_string
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@ -210,17 +216,24 @@ class BasePredictor:
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def setup_source(self, source):
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"""Sets up source and inference mode."""
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self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2) # check image size
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self.transforms = getattr(
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self.model.model, 'transforms', classify_transforms(
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self.imgsz[0], crop_fraction=self.args.crop_fraction)) if self.args.task == 'classify' else None
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self.dataset = load_inference_source(source=source,
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imgsz=self.imgsz,
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vid_stride=self.args.vid_stride,
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buffer=self.args.stream_buffer)
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self.transforms = (
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getattr(
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self.model.model,
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"transforms",
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classify_transforms(self.imgsz[0], crop_fraction=self.args.crop_fraction),
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)
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if self.args.task == "classify"
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else None
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)
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self.dataset = load_inference_source(
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source=source, imgsz=self.imgsz, vid_stride=self.args.vid_stride, buffer=self.args.stream_buffer
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)
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self.source_type = self.dataset.source_type
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if not getattr(self, 'stream', True) and (self.dataset.mode == 'stream' or # streams
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len(self.dataset) > 1000 or # images
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any(getattr(self.dataset, 'video_flag', [False]))): # videos
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if not getattr(self, "stream", True) and (
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self.dataset.mode == "stream" # streams
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or len(self.dataset) > 1000 # images
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or any(getattr(self.dataset, "video_flag", [False]))
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): # videos
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LOGGER.warning(STREAM_WARNING)
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self.vid_path = [None] * self.dataset.bs
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self.vid_writer = [None] * self.dataset.bs
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@ -230,7 +243,7 @@ class BasePredictor:
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def stream_inference(self, source=None, model=None, *args, **kwargs):
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"""Streams real-time inference on camera feed and saves results to file."""
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if self.args.verbose:
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LOGGER.info('')
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LOGGER.info("")
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# Setup model
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if not self.model:
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@ -242,7 +255,7 @@ class BasePredictor:
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# Check if save_dir/ label file exists
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if self.args.save or self.args.save_txt:
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(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
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(self.save_dir / "labels" if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
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# Warmup model
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if not self.done_warmup:
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@ -250,10 +263,10 @@ class BasePredictor:
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self.done_warmup = True
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self.seen, self.windows, self.batch, profilers = 0, [], None, (ops.Profile(), ops.Profile(), ops.Profile())
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self.run_callbacks('on_predict_start')
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self.run_callbacks("on_predict_start")
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for batch in self.dataset:
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self.run_callbacks('on_predict_batch_start')
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self.run_callbacks("on_predict_batch_start")
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self.batch = batch
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path, im0s, vid_cap, s = batch
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@ -272,15 +285,16 @@ class BasePredictor:
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with profilers[2]:
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self.results = self.postprocess(preds, im, im0s)
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self.run_callbacks('on_predict_postprocess_end')
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self.run_callbacks("on_predict_postprocess_end")
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# Visualize, save, write results
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n = len(im0s)
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for i in range(n):
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self.seen += 1
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self.results[i].speed = {
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'preprocess': profilers[0].dt * 1E3 / n,
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'inference': profilers[1].dt * 1E3 / n,
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'postprocess': profilers[2].dt * 1E3 / n}
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"preprocess": profilers[0].dt * 1e3 / n,
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"inference": profilers[1].dt * 1e3 / n,
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"postprocess": profilers[2].dt * 1e3 / n,
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}
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p, im0 = path[i], None if self.source_type.tensor else im0s[i].copy()
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p = Path(p)
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@ -293,12 +307,12 @@ class BasePredictor:
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if self.args.save and self.plotted_img is not None:
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self.save_preds(vid_cap, i, str(self.save_dir / p.name))
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self.run_callbacks('on_predict_batch_end')
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self.run_callbacks("on_predict_batch_end")
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yield from self.results
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# Print time (inference-only)
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if self.args.verbose:
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LOGGER.info(f'{s}{profilers[1].dt * 1E3:.1f}ms')
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LOGGER.info(f"{s}{profilers[1].dt * 1E3:.1f}ms")
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# Release assets
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if isinstance(self.vid_writer[-1], cv2.VideoWriter):
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@ -306,25 +320,29 @@ class BasePredictor:
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# Print results
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if self.args.verbose and self.seen:
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t = tuple(x.t / self.seen * 1E3 for x in profilers) # speeds per image
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LOGGER.info(f'Speed: %.1fms preprocess, %.1fms inference, %.1fms postprocess per image at shape '
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f'{(1, 3, *im.shape[2:])}' % t)
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t = tuple(x.t / self.seen * 1e3 for x in profilers) # speeds per image
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LOGGER.info(
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f"Speed: %.1fms preprocess, %.1fms inference, %.1fms postprocess per image at shape "
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f"{(1, 3, *im.shape[2:])}" % t
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)
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if self.args.save or self.args.save_txt or self.args.save_crop:
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nl = len(list(self.save_dir.glob('labels/*.txt'))) # number of labels
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s = f"\n{nl} label{'s' * (nl > 1)} saved to {self.save_dir / 'labels'}" if self.args.save_txt else ''
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nl = len(list(self.save_dir.glob("labels/*.txt"))) # number of labels
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s = f"\n{nl} label{'s' * (nl > 1)} saved to {self.save_dir / 'labels'}" if self.args.save_txt else ""
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LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")
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self.run_callbacks('on_predict_end')
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self.run_callbacks("on_predict_end")
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def setup_model(self, model, verbose=True):
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"""Initialize YOLO model with given parameters and set it to evaluation mode."""
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self.model = AutoBackend(model or self.args.model,
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device=select_device(self.args.device, verbose=verbose),
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dnn=self.args.dnn,
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data=self.args.data,
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fp16=self.args.half,
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fuse=True,
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verbose=verbose)
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self.model = AutoBackend(
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model or self.args.model,
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device=select_device(self.args.device, verbose=verbose),
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dnn=self.args.dnn,
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data=self.args.data,
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fp16=self.args.half,
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fuse=True,
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verbose=verbose,
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)
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self.device = self.model.device # update device
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self.args.half = self.model.fp16 # update half
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@ -333,18 +351,18 @@ class BasePredictor:
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def show(self, p):
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"""Display an image in a window using OpenCV imshow()."""
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im0 = self.plotted_img
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if platform.system() == 'Linux' and p not in self.windows:
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if platform.system() == "Linux" and p not in self.windows:
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self.windows.append(p)
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cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
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cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
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cv2.imshow(str(p), im0)
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cv2.waitKey(500 if self.batch[3].startswith('image') else 1) # 1 millisecond
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cv2.waitKey(500 if self.batch[3].startswith("image") else 1) # 1 millisecond
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def save_preds(self, vid_cap, idx, save_path):
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"""Save video predictions as mp4 at specified path."""
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im0 = self.plotted_img
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# Save imgs
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if self.dataset.mode == 'image':
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if self.dataset.mode == "image":
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cv2.imwrite(save_path, im0)
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else: # 'video' or 'stream'
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frames_path = f'{save_path.split(".", 1)[0]}_frames/'
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@ -361,15 +379,16 @@ class BasePredictor:
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else: # stream
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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suffix, fourcc = ('.mp4', 'avc1') if MACOS else ('.avi', 'WMV2') if WINDOWS else ('.avi', 'MJPG')
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self.vid_writer[idx] = cv2.VideoWriter(str(Path(save_path).with_suffix(suffix)),
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cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
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suffix, fourcc = (".mp4", "avc1") if MACOS else (".avi", "WMV2") if WINDOWS else (".avi", "MJPG")
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self.vid_writer[idx] = cv2.VideoWriter(
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str(Path(save_path).with_suffix(suffix)), cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)
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)
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# Write video
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self.vid_writer[idx].write(im0)
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# Write frame
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if self.args.save_frames:
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cv2.imwrite(f'{frames_path}{self.vid_frame[idx]}.jpg', im0)
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cv2.imwrite(f"{frames_path}{self.vid_frame[idx]}.jpg", im0)
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self.vid_frame[idx] += 1
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def run_callbacks(self, event: str):
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