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Add best.pt val and COCO pycocotools val (#98)

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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>
This commit is contained in:
Glenn Jocher
2022-12-27 04:56:24 +01:00
committed by GitHub
parent a1808eeda4
commit 6f0ba81427
12 changed files with 159 additions and 115 deletions
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7
ultralytics/yolo/v8/classify/train.py
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@ -12,8 +12,8 @@ class ClassificationTrainer(BaseTrainer):
def set_model_attributes(self):
self.model.names = self.data["names"]
def load_model(self, model_cfg=None, weights=None):
# TODO: why treat clf models as unique. We should have clf yamls?
def load_model(self, model_cfg=None, weights=None, verbose=True):
# TODO: why treat clf models as unique. We should have clf yamls? YES WE SHOULD!
if isinstance(weights, dict): # yolo ckpt
weights = weights["model"]
if weights and not weights.__class__.__name__.startswith("yolo"): # torchvision
@ -57,6 +57,9 @@ class ClassificationTrainer(BaseTrainer):
def resume_training(self, ckpt):
pass
def final_eval(self):
pass
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def train(cfg):

8
ultralytics/yolo/v8/detect/train.py
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@ -13,7 +13,7 @@ from ultralytics.yolo.utils.metrics import smooth_BCE
from ultralytics.yolo.utils.ops import xywh2xyxy
from ultralytics.yolo.utils.plotting import plot_images, plot_results
from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
from ultralytics.yolo.utils.torch_utils import de_parallel
from ultralytics.yolo.utils.torch_utils import de_parallel, strip_optimizer
# BaseTrainer python usage
@ -54,10 +54,10 @@ class DetectionTrainer(BaseTrainer):
# TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
self.model.names = self.data["names"]
def load_model(self, model_cfg=None, weights=None):
model = DetectionModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"])
def load_model(self, model_cfg=None, weights=None, verbose=True):
model = DetectionModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"], verbose=verbose)
if weights:
model.load(weights)
model.load(weights, verbose)
return model
def get_validator(self):

91
ultralytics/yolo/v8/detect/val.py
View File

@ -1,14 +1,16 @@
import os
from pathlib import Path
import hydra
import numpy as np
import torch
from ultralytics.yolo.data import build_dataloader
from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
from ultralytics.yolo.engine.validator import BaseValidator
from ultralytics.yolo.utils import ops
from ultralytics.yolo.utils.checks import check_file
from ultralytics.yolo.utils import colorstr, ops
from ultralytics.yolo.utils.checks import check_file, check_requirements
from ultralytics.yolo.utils.files import yaml_load
from ultralytics.yolo.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
from ultralytics.yolo.utils.plotting import output_to_target, plot_images
@ -43,13 +45,11 @@ class DetectionValidator(BaseValidator):
def init_metrics(self, model):
head = model.model[-1] if self.training else model.model.model[-1]
if self.data:
self.is_coco = isinstance(self.data.get('val'),
str) and self.data['val'].endswith(f'coco{os.sep}val2017.txt')
self.is_coco = self.data.get('val', '').endswith(f'coco{os.sep}val2017.txt') # is COCO dataset
self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
self.args.save_json |= self.is_coco and not self.training # run on final val if training COCO
self.nc = head.nc
self.names = model.names
if isinstance(self.names, (list, tuple)): # old format
self.names = dict(enumerate(self.names))
self.metrics.names = self.names
self.confusion_matrix = ConfusionMatrix(nc=self.nc)
self.seen = 0
@ -107,11 +107,6 @@ class DetectionValidator(BaseValidator):
'''
if self.args.save_txt:
save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
if self.args.save_json:
pred_masks = scale_image(im[si].shape[1:],
pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1])
save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary
# callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
'''
def get_stats(self):
@ -131,7 +126,7 @@ class DetectionValidator(BaseValidator):
f'WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels')
# Print results per class
if (self.args.verbose or (self.nc < 50 and not self.training)) and self.nc > 1 and len(self.stats):
if (self.args.verbose or not self.training) and self.nc > 1 and len(self.stats):
for i, c in enumerate(self.metrics.ap_class_index):
self.logger.info(pf % (self.names[c], self.seen, self.nt_per_class[c], *self.metrics.class_result(i)))
@ -167,7 +162,19 @@ class DetectionValidator(BaseValidator):
# TODO: manage splits differently
# calculate stride - check if model is initialized
gs = max(int(de_parallel(self.model).stride if self.model else 0), 32)
return build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, mode="val")[0]
return create_dataloader(path=dataset_path,
imgsz=self.args.imgsz,
batch_size=batch_size,
stride=gs,
hyp=dict(self.args),
cache=False,
pad=0.5,
rect=self.args.rect,
workers=self.args.workers,
prefix=colorstr(f'{val}: '),
shuffle=False,
seed=self.args.seed)[0] if self.args.v5loader else \
build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, mode="val")[0]
# TODO: align with train loss metrics
@property
@ -175,28 +182,58 @@ class DetectionValidator(BaseValidator):
return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"]
def plot_val_samples(self, batch, ni):
images = batch["img"]
cls = batch["cls"].squeeze(-1)
bboxes = batch["bboxes"]
paths = batch["im_file"]
batch_idx = batch["batch_idx"]
plot_images(images,
batch_idx,
cls,
bboxes,
paths=paths,
plot_images(batch["img"],
batch["batch_idx"],
batch["cls"].squeeze(-1),
batch["bboxes"],
paths=batch["im_file"],
fname=self.save_dir / f"val_batch{ni}_labels.jpg",
names=self.names)
def plot_predictions(self, batch, preds, ni):
images = batch["img"]
paths = batch["im_file"]
plot_images(images,
plot_images(batch["img"],
*output_to_target(preds, max_det=15),
paths=paths,
paths=batch["im_file"],
fname=self.save_dir / f'val_batch{ni}_pred.jpg',
names=self.names) # pred
def pred_to_json(self, preds, batch):
for i, f in enumerate(batch["im_file"]):
stem = Path(f).stem
image_id = int(stem) if stem.isnumeric() else stem
box = ops.xyxy2xywh(preds[i][:, :4]) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for p, b in zip(preds[i].tolist(), box.tolist()):
self.jdict.append({
'image_id': image_id,
'category_id': self.class_map[int(p[5])],
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5)})
def eval_json(self):
if self.args.save_json and self.is_coco and len(self.jdict):
anno_json = self.data['path'] / "annotations/instances_val2017.json" # annotations
pred_json = self.save_dir / "predictions.json" # predictions
self.logger.info(f'\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...')
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
check_requirements('pycocotools')
from pycocotools.coco import COCO # noqa
from pycocotools.cocoeval import COCOeval # noqa
for x in anno_json, pred_json:
assert x.is_file(), f"{x} file not found"
anno = COCO(str(anno_json)) # init annotations api
pred = anno.loadRes(str(pred_json)) # init predictions api (must pass string, not Path)
eval = COCOeval(anno, pred, 'bbox')
if self.is_coco:
eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files] # images to eval
eval.evaluate()
eval.accumulate()
eval.summarize()
self.metrics.metric.map, self.metrics.metric.map50 = eval.stats[:2] # update mAP50-95 and mAP50
except Exception as e:
self.logger.warning(f'pycocotools unable to run: {e}')
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def val(cfg):

6
ultralytics/yolo/v8/segment/train.py
View File

@ -17,10 +17,10 @@ from ..detect import DetectionTrainer
# BaseTrainer python usage
class SegmentationTrainer(DetectionTrainer):
def load_model(self, model_cfg=None, weights=None):
model = SegmentationModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"])
def load_model(self, model_cfg=None, weights=None, verbose=True):
model = SegmentationModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"], verbose=verbose)
if weights:
model.load(weights)
model.load(weights, verbose)
return model
def get_validator(self):

43
ultralytics/yolo/v8/segment/val.py
View File

@ -7,7 +7,6 @@ import torch.nn.functional as F
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
from ultralytics.yolo.utils import ops
from ultralytics.yolo.utils.checks import check_requirements
from ultralytics.yolo.utils.metrics import ConfusionMatrix, SegmentMetrics, box_iou, mask_iou
from ultralytics.yolo.utils.plotting import output_to_target, plot_images
@ -19,7 +18,6 @@ class SegmentationValidator(DetectionValidator):
def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None):
super().__init__(dataloader, save_dir, pbar, logger, args)
if self.args.save_json:
check_requirements(['pycocotools'])
self.process = ops.process_mask_upsample # more accurate
else:
self.process = ops.process_mask # faster
@ -42,14 +40,12 @@ class SegmentationValidator(DetectionValidator):
def init_metrics(self, model):
head = model.model[-1] if self.training else model.model.model[-1]
if self.data:
self.is_coco = isinstance(self.data.get('val'),
str) and self.data['val'].endswith(f'coco{os.sep}val2017.txt')
self.is_coco = self.data.get('val', '').endswith(f'coco{os.sep}val2017.txt') # is COCO dataset
self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
self.args.save_json |= self.is_coco and not self.training # run on final val if training COCO
self.nc = head.nc
self.nm = head.nm if hasattr(head, "nm") else 32
self.names = model.names
if isinstance(self.names, (list, tuple)): # old format
self.names = dict(enumerate(self.names))
self.metrics.names = self.names
self.confusion_matrix = ConfusionMatrix(nc=self.nc)
self.plot_masks = []
@ -70,7 +66,7 @@ class SegmentationValidator(DetectionValidator):
agnostic=self.args.single_cls,
max_det=self.args.max_det,
nm=self.nm)
return (p, preds[1], preds[2])
return p, preds[1], preds[2]
def update_metrics(self, preds, batch):
# Metrics
@ -117,8 +113,7 @@ class SegmentationValidator(DetectionValidator):
masks=True)
if self.args.plots:
self.confusion_matrix.process_batch(predn, labelsn)
self.stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:,
0])) # (conf, pcls, tcls)
self.stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # conf, pcls, tcls
pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
if self.args.plots and self.batch_i < 3:
@ -186,28 +181,22 @@ class SegmentationValidator(DetectionValidator):
"metrics/mAP50-95(M)",]
def plot_val_samples(self, batch, ni):
images = batch["img"]
masks = batch["masks"]
cls = batch["cls"].squeeze(-1)
bboxes = batch["bboxes"]
paths = batch["im_file"]
batch_idx = batch["batch_idx"]
plot_images(images,
batch_idx,
cls,
bboxes,
masks,
paths=paths,
plot_images(batch["img"],
batch["batch_idx"],
batch["cls"].squeeze(-1),
batch["bboxes"],
batch["masks"],
paths=batch["im_file"],
fname=self.save_dir / f"val_batch{ni}_labels.jpg",
names=self.names)
def plot_predictions(self, batch, preds, ni):
images = batch["img"]
paths = batch["im_file"]
if len(self.plot_masks):
plot_masks = torch.cat(self.plot_masks, dim=0)
plot_images(images, *output_to_target(preds[0], max_det=15), plot_masks, paths,
self.save_dir / f'val_batch{ni}_pred.jpg', self.names) # pred
plot_images(batch["img"],
*output_to_target(preds[0], max_det=15),
torch.cat(self.plot_masks, dim=0) if len(self.plot_masks) else self.plot_masks,
paths=batch["im_file"],
fname=self.save_dir / f'val_batch{ni}_pred.jpg',
names=self.names) # pred
self.plot_masks.clear()