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import os
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import os
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from pathlib import Path
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import hydra
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import hydra
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import numpy as np
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import numpy as np
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import torch
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import torch
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from ultralytics.yolo.data import build_dataloader
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from ultralytics.yolo.data import build_dataloader
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from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
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from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
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from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
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from ultralytics.yolo.engine.validator import BaseValidator
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from ultralytics.yolo.engine.validator import BaseValidator
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from ultralytics.yolo.utils import ops
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from ultralytics.yolo.utils import colorstr, ops
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from ultralytics.yolo.utils.checks import check_file
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from ultralytics.yolo.utils.checks import check_file, check_requirements
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from ultralytics.yolo.utils.files import yaml_load
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from ultralytics.yolo.utils.files import yaml_load
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from ultralytics.yolo.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
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from ultralytics.yolo.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
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from ultralytics.yolo.utils.plotting import output_to_target, plot_images
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from ultralytics.yolo.utils.plotting import output_to_target, plot_images
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@ -43,13 +45,11 @@ class DetectionValidator(BaseValidator):
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def init_metrics(self, model):
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def init_metrics(self, model):
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head = model.model[-1] if self.training else model.model.model[-1]
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head = model.model[-1] if self.training else model.model.model[-1]
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if self.data:
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if self.data:
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self.is_coco = isinstance(self.data.get('val'),
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self.is_coco = self.data.get('val', '').endswith(f'coco{os.sep}val2017.txt') # is COCO dataset
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str) and self.data['val'].endswith(f'coco{os.sep}val2017.txt')
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self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
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self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
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self.args.save_json |= self.is_coco and not self.training # run on final val if training COCO
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self.nc = head.nc
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self.nc = head.nc
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self.names = model.names
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self.names = model.names
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if isinstance(self.names, (list, tuple)): # old format
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self.names = dict(enumerate(self.names))
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self.metrics.names = self.names
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self.metrics.names = self.names
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self.confusion_matrix = ConfusionMatrix(nc=self.nc)
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self.confusion_matrix = ConfusionMatrix(nc=self.nc)
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self.seen = 0
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self.seen = 0
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@ -107,11 +107,6 @@ class DetectionValidator(BaseValidator):
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'''
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'''
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if self.args.save_txt:
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if self.args.save_txt:
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save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
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save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
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if self.args.save_json:
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pred_masks = scale_image(im[si].shape[1:],
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pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1])
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save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary
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# callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
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'''
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'''
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def get_stats(self):
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def get_stats(self):
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@ -131,7 +126,7 @@ class DetectionValidator(BaseValidator):
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f'WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels')
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f'WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels')
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# Print results per class
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# Print results per class
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if (self.args.verbose or (self.nc < 50 and not self.training)) and self.nc > 1 and len(self.stats):
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if (self.args.verbose or not self.training) and self.nc > 1 and len(self.stats):
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for i, c in enumerate(self.metrics.ap_class_index):
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for i, c in enumerate(self.metrics.ap_class_index):
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self.logger.info(pf % (self.names[c], self.seen, self.nt_per_class[c], *self.metrics.class_result(i)))
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self.logger.info(pf % (self.names[c], self.seen, self.nt_per_class[c], *self.metrics.class_result(i)))
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@ -167,7 +162,19 @@ class DetectionValidator(BaseValidator):
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# TODO: manage splits differently
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# TODO: manage splits differently
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# calculate stride - check if model is initialized
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# calculate stride - check if model is initialized
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gs = max(int(de_parallel(self.model).stride if self.model else 0), 32)
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gs = max(int(de_parallel(self.model).stride if self.model else 0), 32)
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return build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, mode="val")[0]
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return create_dataloader(path=dataset_path,
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imgsz=self.args.imgsz,
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batch_size=batch_size,
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stride=gs,
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hyp=dict(self.args),
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cache=False,
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pad=0.5,
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rect=self.args.rect,
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workers=self.args.workers,
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prefix=colorstr(f'{val}: '),
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shuffle=False,
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seed=self.args.seed)[0] if self.args.v5loader else \
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build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, mode="val")[0]
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# TODO: align with train loss metrics
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# TODO: align with train loss metrics
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@property
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@property
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@ -175,28 +182,58 @@ class DetectionValidator(BaseValidator):
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return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"]
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return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"]
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def plot_val_samples(self, batch, ni):
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def plot_val_samples(self, batch, ni):
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images = batch["img"]
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plot_images(batch["img"],
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cls = batch["cls"].squeeze(-1)
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batch["batch_idx"],
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bboxes = batch["bboxes"]
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batch["cls"].squeeze(-1),
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paths = batch["im_file"]
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batch["bboxes"],
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batch_idx = batch["batch_idx"]
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paths=batch["im_file"],
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plot_images(images,
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batch_idx,
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cls,
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bboxes,
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paths=paths,
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fname=self.save_dir / f"val_batch{ni}_labels.jpg",
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fname=self.save_dir / f"val_batch{ni}_labels.jpg",
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names=self.names)
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names=self.names)
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def plot_predictions(self, batch, preds, ni):
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def plot_predictions(self, batch, preds, ni):
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images = batch["img"]
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plot_images(batch["img"],
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paths = batch["im_file"]
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plot_images(images,
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*output_to_target(preds, max_det=15),
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*output_to_target(preds, max_det=15),
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paths=paths,
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paths=batch["im_file"],
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fname=self.save_dir / f'val_batch{ni}_pred.jpg',
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fname=self.save_dir / f'val_batch{ni}_pred.jpg',
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names=self.names) # pred
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names=self.names) # pred
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def pred_to_json(self, preds, batch):
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for i, f in enumerate(batch["im_file"]):
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stem = Path(f).stem
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image_id = int(stem) if stem.isnumeric() else stem
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box = ops.xyxy2xywh(preds[i][:, :4]) # xywh
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box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
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for p, b in zip(preds[i].tolist(), box.tolist()):
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self.jdict.append({
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'image_id': image_id,
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'category_id': self.class_map[int(p[5])],
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'bbox': [round(x, 3) for x in b],
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'score': round(p[4], 5)})
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def eval_json(self):
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if self.args.save_json and self.is_coco and len(self.jdict):
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anno_json = self.data['path'] / "annotations/instances_val2017.json" # annotations
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pred_json = self.save_dir / "predictions.json" # predictions
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self.logger.info(f'\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...')
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try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
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check_requirements('pycocotools')
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from pycocotools.coco import COCO # noqa
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from pycocotools.cocoeval import COCOeval # noqa
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for x in anno_json, pred_json:
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assert x.is_file(), f"{x} file not found"
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anno = COCO(str(anno_json)) # init annotations api
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pred = anno.loadRes(str(pred_json)) # init predictions api (must pass string, not Path)
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eval = COCOeval(anno, pred, 'bbox')
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if self.is_coco:
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eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files] # images to eval
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eval.evaluate()
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eval.accumulate()
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eval.summarize()
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self.metrics.metric.map, self.metrics.metric.map50 = eval.stats[:2] # update mAP50-95 and mAP50
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except Exception as e:
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self.logger.warning(f'pycocotools unable to run: {e}')
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
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def val(cfg):
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def val(cfg):
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Glenn Jocher
2 years ago
committed by
GitHub