Predictor support (#65)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Ayush Chaurasia
GitHub
@ -0,0 +1,201 @@
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# predictor engine by Ultralytics
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"""
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Run prection on images, videos, directories, globs, YouTube, webcam, streams, etc.
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Usage - sources:
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$ yolo task=... mode=predict model=s.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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screen # screenshot
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path/ # directory
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list.txt # list of images
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list.streams # list of streams
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'path/*.jpg' # glob
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'https://youtu.be/Zgi9g1ksQHc' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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Usage - formats:
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$ yolo task=... mode=predict --weights yolov5s.pt # PyTorch
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yolov5s.torchscript # TorchScript
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s_openvino_model # OpenVINO
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yolov5s.engine # TensorRT
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yolov5s.mlmodel # CoreML (macOS-only)
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yolov5s_saved_model # TensorFlow SavedModel
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yolov5s.pb # TensorFlow GraphDef
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yolov5s.tflite # TensorFlow Lite
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU
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yolov5s_paddle_model # PaddlePaddle
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"""
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import platform
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from pathlib import Path
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import cv2
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import torch
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from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadScreenshots, LoadStreams
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from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS, check_dataset, check_dataset_yaml
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from ultralytics.yolo.utils import LOGGER, ROOT, TQDM_BAR_FORMAT, colorstr, ops
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from ultralytics.yolo.utils.checks import check_file, check_imshow
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from ultralytics.yolo.utils.configs import get_config
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from ultralytics.yolo.utils.files import increment_path
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from ultralytics.yolo.utils.modeling.autobackend import AutoBackend
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from ultralytics.yolo.utils.plotting import Annotator
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from ultralytics.yolo.utils.torch_utils import check_img_size, select_device, smart_inference_mode
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DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yaml"
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class BasePredictor:
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def __init__(self, config=DEFAULT_CONFIG, overrides={}):
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self.args = get_config(config, overrides)
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self.save_dir = increment_path(Path(self.args.project) / self.args.name, exist_ok=self.args.exist_ok)
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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.done_setup = False
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# Usable if setup is done
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self.model = None
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self.data = self.args.data # data_dict
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self.device = None
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self.dataset = None
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self.vid_path, self.vid_writer = None, None
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self.view_img = None
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self.annotator = None
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self.data_path = None
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def preprocess(self, img):
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pass
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def get_annotator(self, img):
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raise NotImplementedError("get_annotator function needs to be implemented")
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def write_results(self, pred, batch, print_string):
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raise NotImplementedError("print_results function needs to be implemented")
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def postprocess(self, preds, img, orig_img):
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return preds
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def setup(self, source=None, model=None):
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# source
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source = str(source or self.args.source)
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self.save_img = not self.args.nosave and not source.endswith('.txt')
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is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
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is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
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webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
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screenshot = source.lower().startswith('screen')
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if is_url and is_file:
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source = check_file(source) # download
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# data
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if self.data:
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if self.data.endswith(".yaml"):
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self.data = check_dataset_yaml(self.data)
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else:
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self.data = check_dataset(self.data)
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# model
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device = select_device(self.args.device)
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model = model or self.args.model
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self.args.half &= device.type != 'cpu' # half precision only supported on CUDA
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model = AutoBackend(model, device=device, dnn=self.args.dnn, fp16=self.args.half) # NOTE: not passing data
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stride, pt = model.stride, model.pt
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imgsz = check_img_size(self.args.img_size, s=stride) # check image size
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# Dataloader
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bs = 1 # batch_size
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if webcam:
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self.view_img = check_imshow(warn=True)
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self.dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
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bs = len(self.dataset)
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elif screenshot:
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self.dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
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else:
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self.dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
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self.vid_path, self.vid_writer = [None] * bs, [None] * bs
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model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
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self.model = model
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self.webcam = webcam
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self.screenshot = screenshot
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self.imgsz = imgsz
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self.done_setup = True
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self.device = device
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return model
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@smart_inference_mode()
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def __call__(self, source=None, model=None):
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if not self.done_setup:
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model = self.setup(source, model)
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else:
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model = self.model
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self.seen, self.windows, self.dt = 0, [], (ops.Profile(), ops.Profile(), ops.Profile())
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for batch in self.dataset:
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path, im, im0s, vid_cap, s = batch
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visualize = increment_path(self.save_dir / Path(path).stem, mkdir=True) if self.args.visualize else False
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with self.dt[0]:
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im = self.preprocess(im)
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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# Inference
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with self.dt[1]:
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preds = model(im, augment=self.args.augment, visualize=visualize)
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# postprocess
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with self.dt[2]:
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preds = self.postprocess(preds, im, im0s)
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for i in range(len(im)):
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if self.webcam:
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path, im0s = path[i], im0s[i]
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p = Path(path)
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s += self.write_results(i, preds, (p, im, im0s))
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if self.args.view_img:
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self.show(p)
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if self.save_img:
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self.save_preds(vid_cap, i, str(self.save_dir / p.name))
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# Print time (inference-only)
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LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms")
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# Print results
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t = tuple(x.t / self.seen * 1E3 for x in self.dt) # speeds per image
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LOGGER.info(
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f'Speed: %.1fms pre-process, %.1fms inference, %.1fms postprocess per image at shape {(1, 3, *self.imgsz)}'
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% t)
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if self.args.save_txt or self.save_img:
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s = f"\n{len(list(self.save_dir.glob('labels/*.txt')))} labels 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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def show(self, p):
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im0 = self.annotator.result()
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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(1) # 1 millisecond
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def save_preds(self, vid_cap, idx, save_path):
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im0 = self.annotator.result()
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# save imgs
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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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if self.vid_path[idx] != save_path: # new video
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self.vid_path[idx] = save_path
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if isinstance(self.vid_writer[idx], cv2.VideoWriter):
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self.vid_writer[idx].release() # release previous video writer
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if vid_cap: # video
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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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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save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
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self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
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self.vid_writer[idx].write(im0)
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@ -15,7 +15,7 @@ import torch
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import torch.distributed as dist
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import torch.multiprocessing as mp
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import torch.nn as nn
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from omegaconf import DictConfig, OmegaConf
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from omegaconf import OmegaConf
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from torch.cuda import amp
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.optim import lr_scheduler
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@ -26,7 +26,9 @@ import ultralytics.yolo.utils.callbacks as callbacks
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from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
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from ultralytics.yolo.utils import LOGGER, ROOT, TQDM_BAR_FORMAT, colorstr
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from ultralytics.yolo.utils.checks import check_file, print_args
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from ultralytics.yolo.utils.configs import get_config
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from ultralytics.yolo.utils.files import get_latest_run, increment_path, save_yaml
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from ultralytics.yolo.utils.modeling import get_model
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from ultralytics.yolo.utils.torch_utils import ModelEMA, de_parallel, init_seeds, one_cycle, strip_optimizer
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DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yaml"
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@ -36,7 +38,7 @@ RANK = int(os.getenv('RANK', -1))
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class BaseTrainer:
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def __init__(self, config=DEFAULT_CONFIG, overrides={}):
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self.args = self._get_config(config, overrides)
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self.args = get_config(config, overrides)
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self.check_resume()
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init_seeds(self.args.seed + 1 + RANK, deterministic=self.args.deterministic)
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@ -84,25 +86,6 @@ class BaseTrainer:
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self.add_callback(callback, func)
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callbacks.add_integration_callbacks(self)
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def _get_config(self, config: Union[str, DictConfig], overrides: Union[str, Dict] = {}):
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"""
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Accepts yaml file name or DictConfig containing experiment configuration.
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Returns training args namespace
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:param config: Optional file name or DictConfig object
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"""
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if isinstance(config, (str, Path)):
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config = OmegaConf.load(config)
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elif isinstance(config, Dict):
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config = OmegaConf.create(config)
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# override
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if isinstance(overrides, str):
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overrides = OmegaConf.load(overrides)
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elif isinstance(overrides, Dict):
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overrides = OmegaConf.create(overrides)
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return OmegaConf.merge(config, overrides)
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def add_callback(self, onevent: str, callback):
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"""
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appends the given callback
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@ -46,8 +46,8 @@ class BaseValidator:
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self.args.half &= self.device.type != 'cpu'
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model = model.half() if self.args.half else model.float()
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self.model = model
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loss = torch.zeros_like(trainer.loss_items, device=trainer.device)
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else: # TODO: handle this when detectMultiBackend is supported
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self.loss = torch.zeros_like(trainer.loss_items, device=trainer.device)
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else:
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assert model is not None, "Either trainer or model is needed for validation"
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self.device = select_device(self.args.device, self.args.batch_size)
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self.args.half &= self.device.type != 'cpu'
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@ -90,13 +90,11 @@ class BaseValidator:
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# inference
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with dt[1]:
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preds = model(batch["img"])
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# TODO: remember to add native augmentation support when implementing model, like:
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# preds, train_out = model(im, augment=augment)
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# loss
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with dt[2]:
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if self.training:
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loss += trainer.criterion(preds, batch)[1]
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self.loss += trainer.criterion(preds, batch)[1]
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# pre-process predictions
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with dt[3]:
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@ -123,7 +121,7 @@ class BaseValidator:
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model.float()
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# TODO: implement save json
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return stats | trainer.label_loss_items(loss.cpu() / len(self.dataloader), prefix="val") \
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return stats | trainer.label_loss_items(self.loss.cpu() / len(self.dataloader), prefix="val") \
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if self.training else stats
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def get_dataloader(self, dataset_path, batch_size):
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