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HUB setup (#108)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Ayush Chaurasia
2023-01-02 00:51:14 +05:30
committed by GitHub
parent c6eb6720de
commit 2bc9a5c87e
16 changed files with 631 additions and 122 deletions
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25
ultralytics/yolo/engine/exporter.py
View File

@ -71,8 +71,7 @@ from ultralytics.nn.tasks import ClassificationModel, DetectionModel, Segmentati
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
from ultralytics.yolo.data.utils import check_dataset
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, colorstr, get_default_args, yaml_save
from ultralytics.yolo.utils.callbacks import default_callbacks
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, callbacks, colorstr, get_default_args, yaml_save
from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
from ultralytics.yolo.utils.files import file_size, increment_path
from ultralytics.yolo.utils.ops import Profile
@ -138,16 +137,15 @@ class Exporter:
"""
if overrides is None:
overrides = {}
if 'batch_size' not in overrides:
overrides['batch_size'] = 1 # set default export batch size
self.args = get_config(config, overrides)
project = self.args.project or f"runs/{self.args.task}"
name = self.args.name or "exp" # hardcode mode as export doesn't require it
self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
self.save_dir.mkdir(parents=True, exist_ok=True)
# callbacks
self.callbacks = defaultdict([])
for callback, func in default_callbacks.items():
self.add_callback(callback, func)
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
callbacks.add_integration_callbacks(self)
@smart_inference_mode()
def __call__(self, model=None):
@ -173,7 +171,6 @@ class Exporter:
assert self.device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
# Input
self.args.batch_size = 1 # TODO: resolve this issue, default 16 not fit for export
im = torch.zeros(self.args.batch_size, 3, *self.imgsz).to(self.device)
file = Path(getattr(model, 'yaml_file', None) or Path(model.yaml['yaml_file']).name)
@ -765,18 +762,6 @@ class Exporter:
LOGGER.info(f'{prefix} pipeline success')
return model
def add_callback(self, event: str, callback):
"""
appends the given callback
"""
self.callbacks[event].append(callback)
def set_callback(self, event: str, callback):
"""
overrides the existing callbacks with the given callback
"""
self.callbacks[event] = [callback]
def run_callbacks(self, event: str):
for callback in self.callbacks.get(event, []):
callback(self)

22
ultralytics/yolo/engine/predictor.py
View File

@ -35,8 +35,7 @@ from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadScreenshots, LoadStreams
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, colorstr, ops
from ultralytics.yolo.utils.callbacks import default_callbacks
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, callbacks, colorstr, ops
from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_imshow
from ultralytics.yolo.utils.files import increment_path
from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode
@ -90,11 +89,8 @@ class BasePredictor:
self.view_img = None
self.annotator = None
self.data_path = None
# callbacks
self.callbacks = defaultdict([])
for callback, func in default_callbacks.items():
self.add_callback(callback, func)
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
callbacks.add_integration_callbacks(self)
def preprocess(self, img):
pass
@ -227,18 +223,6 @@ class BasePredictor:
self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
self.vid_writer[idx].write(im0)
def add_callback(self, event: str, callback):
"""
appends the given callback
"""
self.callbacks[event].append(callback)
def set_callback(self, event: str, callback):
"""
overrides the existing callbacks with the given callback
"""
self.callbacks[event] = [callback]
def run_callbacks(self, event: str):
for callback in self.callbacks.get(event, []):
callback(self)

19
ultralytics/yolo/engine/trainer.py
View File

@ -21,11 +21,10 @@ from torch.optim import lr_scheduler
from tqdm import tqdm
import ultralytics.yolo.utils as utils
import ultralytics.yolo.utils.callbacks as callbacks
from ultralytics import __version__
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, RANK, TQDM_BAR_FORMAT, colorstr, yaml_save
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, RANK, TQDM_BAR_FORMAT, callbacks, colorstr, yaml_save
from ultralytics.yolo.utils.checks import check_file, print_args
from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
from ultralytics.yolo.utils.files import get_latest_run, increment_path
@ -88,7 +87,7 @@ class BaseTrainer:
self.model = None
self.callbacks = defaultdict(list)
# dirs
# Dirs
project = self.args.project or f"runs/{self.args.task}"
name = self.args.name or f"{self.args.mode}"
self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok if RANK in {-1, 0} else True)
@ -104,7 +103,7 @@ class BaseTrainer:
if RANK == -1:
print_args(dict(self.args))
# device
# Device
self.device = utils.torch_utils.select_device(self.args.device, self.batch_size)
self.amp = self.device.type != 'cpu'
self.scaler = amp.GradScaler(enabled=self.amp)
@ -123,7 +122,7 @@ class BaseTrainer:
self.lf = None
self.scheduler = None
# epoch level metrics
# Epoch level metrics
self.best_fitness = None
self.fitness = None
self.loss = None
@ -131,20 +130,20 @@ class BaseTrainer:
self.loss_names = None
self.csv = self.save_dir / 'results.csv'
for callback, func in callbacks.default_callbacks.items():
self.add_callback(callback, func)
# Callbacks
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
if RANK in {0, -1}:
callbacks.add_integration_callbacks(self)
def add_callback(self, event: str, callback):
"""
appends the given callback
Appends the given callback. TODO: unused, consider removing
"""
self.callbacks[event].append(callback)
def set_callback(self, event: str, callback):
"""
overrides the existing callbacks with the given callback
Overrides the existing callbacks with the given callback. TODO: unused, consider removing
"""
self.callbacks[event] = [callback]
@ -469,7 +468,7 @@ class BaseTrainer:
self.validator.args.save_json = True
self.metrics = self.validator(model=f)
self.metrics.pop('fitness', None)
self.run_callbacks('on_val_end')
self.run_callbacks('on_fit_epoch_end')
def check_resume(self):
resume = self.args.resume

23
ultralytics/yolo/engine/validator.py
View File

@ -8,8 +8,7 @@ from tqdm import tqdm
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, RANK, TQDM_BAR_FORMAT
from ultralytics.yolo.utils.callbacks import default_callbacks
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, RANK, TQDM_BAR_FORMAT, callbacks
from ultralytics.yolo.utils.checks import check_imgsz
from ultralytics.yolo.utils.files import increment_path
from ultralytics.yolo.utils.ops import Profile
@ -66,10 +65,7 @@ class BaseValidator:
exist_ok=self.args.exist_ok if RANK in {-1, 0} else True)
(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
# callbacks
self.callbacks = defaultdict(list)
for callback, func in default_callbacks.items():
self.add_callback(callback, func)
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
@smart_inference_mode()
def __call__(self, trainer=None, model=None):
@ -77,7 +73,6 @@ class BaseValidator:
Supports validation of a pre-trained model if passed or a model being trained
if trainer is passed (trainer gets priority).
"""
self.run_callbacks('on_val_start')
self.training = trainer is not None
if self.training:
self.device = trainer.device
@ -89,6 +84,8 @@ class BaseValidator:
self.loss = torch.zeros_like(trainer.loss_items, device=trainer.device)
self.args.plots = trainer.epoch == trainer.epochs - 1 # always plot final epoch
else:
callbacks.add_integration_callbacks(self)
self.run_callbacks('on_val_start')
assert model is not None, "Either trainer or model is needed for validation"
self.device = select_device(self.args.device, self.args.batch_size)
self.args.half &= self.device.type != 'cpu'
@ -167,18 +164,6 @@ class BaseValidator:
stats = self.eval_json(stats) # update stats
return stats
def add_callback(self, event: str, callback):
"""
appends the given callback
"""
self.callbacks[event].append(callback)
def set_callback(self, event: str, callback):
"""
overrides the existing callbacks with the given callback
"""
self.callbacks[event] = [callback]
def run_callbacks(self, event: str):
for callback in self.callbacks.get(event, []):
callback(self)