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add yolo v10 and modify pipeline

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王庆刚
2025-03-28 13:19:54 +08:00
parent 183299c06b
commit 798c596acc
471 changed files with 19109 additions and 7342 deletions
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ultralytics/engine/validator.py
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@ -17,7 +17,9 @@ Usage - formats:
yolov8n.tflite # TensorFlow Lite
yolov8n_edgetpu.tflite # TensorFlow Edge TPU
yolov8n_paddle_model # PaddlePaddle
yolov8n_ncnn_model # NCNN
"""
import json
import time
from pathlib import Path
@ -36,7 +38,7 @@ from ultralytics.utils.torch_utils import de_parallel, select_device, smart_infe
class BaseValidator:
"""
BaseValidator
BaseValidator.
A base class for creating validators.
@ -77,7 +79,7 @@ class BaseValidator:
self.args = get_cfg(overrides=args)
self.dataloader = dataloader
self.pbar = pbar
self.model = None
self.stride = None
self.data = None
self.device = None
self.batch_i = None
@ -89,20 +91,20 @@ class BaseValidator:
self.nc = None
self.iouv = None
self.jdict = None
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
self.save_dir = save_dir or get_save_dir(self.args)
(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
(self.save_dir / "labels" if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
if self.args.conf is None:
self.args.conf = 0.001 # default conf=0.001
self.args.imgsz = check_imgsz(self.args.imgsz, max_dim=1)
self.plots = {}
self.callbacks = _callbacks or callbacks.get_default_callbacks()
@smart_inference_mode()
def __call__(self, trainer=None, model=None):
"""
Supports validation of a pre-trained model if passed or a model being trained if trainer is passed (trainer
"""Supports validation of a pre-trained model if passed or a model being trained if trainer is passed (trainer
gets priority).
"""
self.training = trainer is not None
@ -110,7 +112,7 @@ class BaseValidator:
if self.training:
self.device = trainer.device
self.data = trainer.data
self.args.half = self.device.type != 'cpu' # force FP16 val during training
# self.args.half = self.device.type != "cpu" # force FP16 val during training
model = trainer.ema.ema or trainer.model
model = model.half() if self.args.half else model.float()
# self.model = model
@ -119,12 +121,13 @@ class BaseValidator:
model.eval()
else:
callbacks.add_integration_callbacks(self)
self.run_callbacks('on_val_start')
model = AutoBackend(model or self.args.model,
device=select_device(self.args.device, self.args.batch),
dnn=self.args.dnn,
data=self.args.data,
fp16=self.args.half)
model = AutoBackend(
weights=model or self.args.model,
device=select_device(self.args.device, self.args.batch),
dnn=self.args.dnn,
data=self.args.data,
fp16=self.args.half,
)
# self.model = model
self.device = model.device # update device
self.args.half = model.fp16 # update half
@ -134,30 +137,37 @@ class BaseValidator:
self.args.batch = model.batch_size
elif not pt and not jit:
self.args.batch = 1 # export.py models default to batch-size 1
LOGGER.info(f'Forcing batch=1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
LOGGER.info(f"Forcing batch=1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models")
if isinstance(self.args.data, str) and self.args.data.split('.')[-1] in ('yaml', 'yml'):
if str(self.args.data).split(".")[-1] in ("yaml", "yml"):
self.data = check_det_dataset(self.args.data)
elif self.args.task == 'classify':
elif self.args.task == "classify":
self.data = check_cls_dataset(self.args.data, split=self.args.split)
else:
raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' for task={self.args.task} not found ❌"))
if self.device.type in ('cpu', 'mps'):
if self.device.type in ("cpu", "mps"):
self.args.workers = 0 # faster CPU val as time dominated by inference, not dataloading
if not pt:
self.args.rect = False
self.stride = model.stride # used in get_dataloader() for padding
self.dataloader = self.dataloader or self.get_dataloader(self.data.get(self.args.split), self.args.batch)
model.eval()
model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz)) # warmup
dt = Profile(), Profile(), Profile(), Profile()
self.run_callbacks("on_val_start")
dt = (
Profile(device=self.device),
Profile(device=self.device),
Profile(device=self.device),
Profile(device=self.device),
)
bar = TQDM(self.dataloader, desc=self.get_desc(), total=len(self.dataloader))
self.init_metrics(de_parallel(model))
self.jdict = [] # empty before each val
for batch_i, batch in enumerate(bar):
self.run_callbacks('on_val_batch_start')
self.run_callbacks("on_val_batch_start")
self.batch_i = batch_i
# Preprocess
with dt[0]:
@ -165,7 +175,7 @@ class BaseValidator:
# Inference
with dt[1]:
preds = model(batch['img'], augment=augment)
preds = model(batch["img"], augment=augment)
# Loss
with dt[2]:
@ -181,23 +191,32 @@ class BaseValidator:
self.plot_val_samples(batch, batch_i)
self.plot_predictions(batch, preds, batch_i)
self.run_callbacks('on_val_batch_end')
self.run_callbacks("on_val_batch_end")
stats = self.get_stats()
self.check_stats(stats)
self.speed = dict(zip(self.speed.keys(), (x.t / len(self.dataloader.dataset) * 1E3 for x in dt)))
self.speed = dict(zip(self.speed.keys(), (x.t / len(self.dataloader.dataset) * 1e3 for x in dt)))
self.finalize_metrics()
self.print_results()
self.run_callbacks('on_val_end')
if not (self.args.save_json and self.is_coco and len(self.jdict)):
self.print_results()
self.run_callbacks("on_val_end")
if self.training:
model.float()
results = {**stats, **trainer.label_loss_items(self.loss.cpu() / len(self.dataloader), prefix='val')}
if self.args.save_json and self.jdict:
with open(str(self.save_dir / "predictions.json"), "w") as f:
LOGGER.info(f"Saving {f.name}...")
json.dump(self.jdict, f) # flatten and save
stats = self.eval_json(stats) # update stats
stats['fitness'] = stats['metrics/mAP50-95(B)']
results = {**stats, **trainer.label_loss_items(self.loss.cpu() / len(self.dataloader), prefix="val")}
return {k: round(float(v), 5) for k, v in results.items()} # return results as 5 decimal place floats
else:
LOGGER.info('Speed: %.1fms preprocess, %.1fms inference, %.1fms loss, %.1fms postprocess per image' %
tuple(self.speed.values()))
LOGGER.info(
"Speed: %.1fms preprocess, %.1fms inference, %.1fms loss, %.1fms postprocess per image"
% tuple(self.speed.values())
)
if self.args.save_json and self.jdict:
with open(str(self.save_dir / 'predictions.json'), 'w') as f:
LOGGER.info(f'Saving {f.name}...')
with open(str(self.save_dir / "predictions.json"), "w") as f:
LOGGER.info(f"Saving {f.name}...")
json.dump(self.jdict, f) # flatten and save
stats = self.eval_json(stats) # update stats
if self.args.plots or self.args.save_json:
@ -227,6 +246,7 @@ class BaseValidator:
if use_scipy:
# WARNING: known issue that reduces mAP in https://github.com/ultralytics/ultralytics/pull/4708
import scipy # scope import to avoid importing for all commands
cost_matrix = iou * (iou >= threshold)
if cost_matrix.any():
labels_idx, detections_idx = scipy.optimize.linear_sum_assignment(cost_matrix, maximize=True)
@ -256,11 +276,11 @@ class BaseValidator:
def get_dataloader(self, dataset_path, batch_size):
"""Get data loader from dataset path and batch size."""
raise NotImplementedError('get_dataloader function not implemented for this validator')
raise NotImplementedError("get_dataloader function not implemented for this validator")
def build_dataset(self, img_path):
"""Build dataset"""
raise NotImplementedError('build_dataset function not implemented in validator')
"""Build dataset."""
raise NotImplementedError("build_dataset function not implemented in validator")
def preprocess(self, batch):
"""Preprocesses an input batch."""
@ -305,7 +325,7 @@ class BaseValidator:
def on_plot(self, name, data=None):
"""Registers plots (e.g. to be consumed in callbacks)"""
self.plots[Path(name)] = {'data': data, 'timestamp': time.time()}
self.plots[Path(name)] = {"data": data, "timestamp": time.time()}
# TODO: may need to put these following functions into callback
def plot_val_samples(self, batch, ni):