ultralytics 8.0.67 Pose speeds, Comet and ClearML updates (#1871)
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> Co-authored-by: Victor Sonck <victor.sonck@gmail.com> Co-authored-by: Danny Kim <dh031200@gmail.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -53,7 +53,6 @@ import platform
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import subprocess
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import time
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import warnings
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from collections import defaultdict
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from copy import deepcopy
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from pathlib import Path
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@ -130,7 +129,7 @@ class Exporter:
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save_dir (Path): Directory to save results.
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None):
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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"""
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Initializes the Exporter class.
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@ -139,7 +138,7 @@ class Exporter:
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overrides (dict, optional): Configuration overrides. Defaults to None.
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"""
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self.args = get_cfg(cfg, overrides)
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self.callbacks = defaultdict(list, callbacks.default_callbacks) # add callbacks
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self.callbacks = _callbacks if _callbacks else callbacks.get_default_callbacks()
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callbacks.add_integration_callbacks(self)
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@smart_inference_mode()
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@ -854,6 +853,12 @@ class Exporter:
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LOGGER.info(f'{prefix} pipeline success')
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return model
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def add_callback(self, event: str, callback):
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"""
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Appends the given callback.
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"""
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self.callbacks[event].append(callback)
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def run_callbacks(self, event: str):
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for callback in self.callbacks.get(event, []):
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callback(self)
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@ -78,7 +78,7 @@ class YOLO:
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task (Any, optional): Task type for the YOLO model. Defaults to None.
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"""
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self._reset_callbacks()
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self.callbacks = callbacks.get_default_callbacks()
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self.predictor = None # reuse predictor
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self.model = None # model object
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self.trainer = None # trainer object
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@ -238,7 +238,7 @@ class YOLO:
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overrides['save'] = kwargs.get('save', False) # not save files by default
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if not self.predictor:
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self.task = overrides.get('task') or self.task
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self.predictor = TASK_MAP[self.task][3](overrides=overrides)
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self.predictor = TASK_MAP[self.task][3](overrides=overrides, _callbacks=self.callbacks)
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self.predictor.setup_model(model=self.model, verbose=is_cli)
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else: # only update args if predictor is already setup
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self.predictor.args = get_cfg(self.predictor.args, overrides)
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@ -277,7 +277,7 @@ class YOLO:
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args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
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args.imgsz = check_imgsz(args.imgsz, max_dim=1)
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validator = TASK_MAP[self.task][2](args=args)
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validator = TASK_MAP[self.task][2](args=args, _callbacks=self.callbacks)
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validator(model=self.model)
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self.metrics = validator.metrics
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@ -316,7 +316,7 @@ class YOLO:
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args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
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if args.batch == DEFAULT_CFG.batch:
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args.batch = 1 # default to 1 if not modified
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return Exporter(overrides=args)(model=self.model)
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return Exporter(overrides=args, _callbacks=self.callbacks)(model=self.model)
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def train(self, **kwargs):
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"""
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@ -344,7 +344,7 @@ class YOLO:
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overrides['resume'] = self.ckpt_path
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self.task = overrides.get('task') or self.task
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self.trainer = TASK_MAP[self.task][1](overrides=overrides)
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self.trainer = TASK_MAP[self.task][1](overrides=overrides, _callbacks=self.callbacks)
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if not overrides.get('resume'): # manually set model only if not resuming
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self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
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self.model = self.trainer.model
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@ -387,19 +387,17 @@ class YOLO:
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"""
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return self.model.transforms if hasattr(self.model, 'transforms') else None
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@staticmethod
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def add_callback(event: str, func):
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def add_callback(self, event: str, func):
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"""
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Add callback
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"""
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callbacks.default_callbacks[event].append(func)
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self.callbacks[event].append(func)
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@staticmethod
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def _reset_ckpt_args(args):
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include = {'imgsz', 'data', 'task', 'single_cls'} # only remember these arguments when loading a PyTorch model
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return {k: v for k, v in args.items() if k in include}
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@staticmethod
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def _reset_callbacks():
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def _reset_callbacks(self):
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for event in callbacks.default_callbacks.keys():
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callbacks.default_callbacks[event] = [callbacks.default_callbacks[event][0]]
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self.callbacks[event] = [callbacks.default_callbacks[event][0]]
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@ -28,7 +28,6 @@ Usage - formats:
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yolov8n_paddle_model # PaddlePaddle
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"""
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import platform
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from collections import defaultdict
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from pathlib import Path
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import cv2
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@ -75,7 +74,7 @@ class BasePredictor:
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data_path (str): Path to data.
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None):
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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"""
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Initializes the BasePredictor class.
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@ -104,7 +103,7 @@ class BasePredictor:
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self.data_path = None
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self.source_type = None
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self.batch = None
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self.callbacks = defaultdict(list, callbacks.default_callbacks) # add callbacks
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self.callbacks = _callbacks if _callbacks else callbacks.get_default_callbacks()
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callbacks.add_integration_callbacks(self)
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def preprocess(self, img):
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@ -283,3 +282,9 @@ class BasePredictor:
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def run_callbacks(self, event: str):
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for callback in self.callbacks.get(event, []):
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callback(self)
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def add_callback(self, event: str, func):
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"""
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Add callback
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"""
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self.callbacks[event].append(func)
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@ -8,7 +8,6 @@ Usage:
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import os
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import subprocess
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import time
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from collections import defaultdict
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from copy import deepcopy
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from datetime import datetime
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from pathlib import Path
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@ -26,7 +25,7 @@ from ultralytics.nn.tasks import attempt_load_one_weight, attempt_load_weights
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
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from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, ONLINE, RANK, ROOT, SETTINGS, TQDM_BAR_FORMAT, __version__,
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callbacks, colorstr, emojis, yaml_save)
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callbacks, clean_url, colorstr, emojis, yaml_save)
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from ultralytics.yolo.utils.autobatch import check_train_batch_size
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from ultralytics.yolo.utils.checks import check_file, check_imgsz, print_args
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from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
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@ -72,7 +71,7 @@ class BaseTrainer:
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csv (Path): Path to results CSV file.
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None):
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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"""
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Initializes the BaseTrainer class.
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@ -124,7 +123,7 @@ class BaseTrainer:
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if 'yaml_file' in self.data:
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self.args.data = self.data['yaml_file'] # for validating 'yolo train data=url.zip' usage
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except Exception as e:
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raise RuntimeError(emojis(f"Dataset '{self.args.data}' error ❌ {e}")) from e
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raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e
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self.trainset, self.testset = self.get_dataset(self.data)
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self.ema = None
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@ -143,7 +142,7 @@ class BaseTrainer:
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self.plot_idx = [0, 1, 2]
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# Callbacks
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self.callbacks = defaultdict(list, callbacks.default_callbacks) # add callbacks
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self.callbacks = _callbacks if _callbacks else callbacks.get_default_callbacks()
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if RANK in (-1, 0):
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callbacks.add_integration_callbacks(self)
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@ -19,7 +19,6 @@ Usage - formats:
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yolov8n_paddle_model # PaddlePaddle
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"""
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import json
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from collections import defaultdict
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from pathlib import Path
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import torch
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@ -55,7 +54,7 @@ class BaseValidator:
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save_dir (Path): Directory to save results.
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"""
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def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None):
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def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
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"""
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Initializes a BaseValidator instance.
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@ -85,7 +84,7 @@ class BaseValidator:
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if self.args.conf is None:
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self.args.conf = 0.001 # default conf=0.001
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self.callbacks = defaultdict(list, callbacks.default_callbacks) # add callbacks
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self.callbacks = _callbacks if _callbacks else callbacks.get_default_callbacks()
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@smart_inference_mode()
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def __call__(self, trainer=None, model=None):
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@ -195,6 +194,12 @@ class BaseValidator:
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LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}")
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return stats
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def add_callback(self, event: str, callback):
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"""
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Appends the given callback.
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"""
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self.callbacks[event].append(callback)
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def run_callbacks(self, event: str):
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for callback in self.callbacks.get(event, []):
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callback(self)
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