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Cleanup argument handling in Model class (#4614)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Sebastian Stapf <42514241+Wiqzard@users.noreply.github.com>
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Glenn Jocher 2023-08-28 23:12:32 +02:00 committed by GitHub
parent 53b4f8c713
commit 7e99804263
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6 changed files with 67 additions and 93 deletions
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4
docs/reference/cfg/__init__.md
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@ -17,6 +17,10 @@ keywords: Ultralytics, YOLO, Configuration, cfg2dict, handle_deprecation, merge_
## ::: ultralytics.cfg.get_cfg
<br><br>
---
## ::: ultralytics.cfg.get_save_dir
<br><br>
---
## ::: ultralytics.cfg._handle_deprecation
<br><br>

6
tests/test_python.py
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@ -182,9 +182,11 @@ def test_export_openvino():
def test_export_coreml():
if not WINDOWS: # RuntimeError: BlobWriter not loaded with coremltools 7.0 on windows
f = YOLO(MODEL).export(format='coreml', nms=True)
if MACOS:
YOLO(f)(SOURCE) # model prediction only supported on macOS
f = YOLO(MODEL).export(format='coreml')
YOLO(f)(SOURCE) # model prediction only supported on macOS for nms=False models
else:
YOLO(MODEL).export(format='coreml', nms=True)
def test_export_tflite(enabled=False):

122
ultralytics/engine/model.py
View File

@ -5,12 +5,10 @@ import sys
from pathlib import Path
from typing import Union
from ultralytics.cfg import get_cfg, get_save_dir
from ultralytics.engine.exporter import Exporter
from ultralytics.cfg import TASK2DATA, get_cfg, get_save_dir
from ultralytics.hub.utils import HUB_WEB_ROOT
from ultralytics.nn.tasks import attempt_load_one_weight, guess_model_task, nn, yaml_model_load
from ultralytics.utils import (ASSETS, DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, callbacks, emojis,
yaml_load)
from ultralytics.utils import ASSETS, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, callbacks, emojis, yaml_load
from ultralytics.utils.checks import check_file, check_imgsz, check_pip_update_available, check_yaml
from ultralytics.utils.downloads import GITHUB_ASSETS_STEMS
from ultralytics.utils.torch_utils import smart_inference_mode
@ -118,9 +116,9 @@ class Model:
cfg_dict = yaml_model_load(cfg)
self.cfg = cfg
self.task = task or guess_model_task(cfg_dict)
model = model or self.smart_load('model')
self.model = model(cfg_dict, verbose=verbose and RANK == -1) # build model
self.model = (model or self.smart_load('model'))(cfg_dict, verbose=verbose and RANK == -1) # build model
self.overrides['model'] = self.cfg
self.overrides['task'] = self.task
# Below added to allow export from YAMLs
args = {**DEFAULT_CFG_DICT, **self.overrides} # combine model and default args, preferring model args
@ -220,28 +218,22 @@ class Model:
if source is None:
source = ASSETS
LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
is_cli = (sys.argv[0].endswith('yolo') or sys.argv[0].endswith('ultralytics')) and any(
x in sys.argv for x in ('predict', 'track', 'mode=predict', 'mode=track'))
# Check prompts for SAM/FastSAM
prompts = kwargs.pop('prompts', None)
overrides = self.overrides.copy()
overrides['conf'] = 0.25
overrides.update(kwargs) # prefer kwargs
overrides['mode'] = kwargs.get('mode', 'predict')
assert overrides['mode'] in ['track', 'predict']
if not is_cli:
overrides['save'] = kwargs.get('save', False) # do not save by default if called in Python
custom = {'conf': 0.25, 'save': is_cli} # method defaults
args = {**self.overrides, **custom, **kwargs, 'mode': 'predict'} # highest priority args on the right
prompts = args.pop('prompts', None) # for SAM-type models
if not self.predictor:
self.task = overrides.get('task') or self.task
predictor = predictor or self.smart_load('predictor')
self.predictor = predictor(overrides=overrides, _callbacks=self.callbacks)
self.predictor = (predictor or self.smart_load('predictor'))(overrides=args, _callbacks=self.callbacks)
self.predictor.setup_model(model=self.model, verbose=is_cli)
else: # only update args if predictor is already setup
self.predictor.args = get_cfg(self.predictor.args, overrides)
if 'project' in overrides or 'name' in overrides:
self.predictor.args = get_cfg(self.predictor.args, args)
if 'project' in args or 'name' in args:
self.predictor.save_dir = get_save_dir(self.predictor.args)
# Set prompts for SAM/FastSAM
if len and hasattr(self.predictor, 'set_prompts'):
if prompts and hasattr(self.predictor, 'set_prompts'): # for SAM-type models
self.predictor.set_prompts(prompts)
return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
@ -257,46 +249,31 @@ class Model:
Returns:
(List[ultralytics.engine.results.Results]): The tracking results.
"""
if not hasattr(self.predictor, 'trackers'):
from ultralytics.trackers import register_tracker
register_tracker(self, persist)
# ByteTrack-based method needs low confidence predictions as input
conf = kwargs.get('conf') or 0.1
kwargs['conf'] = conf
kwargs['conf'] = kwargs.get('conf') or 0.1
kwargs['mode'] = 'track'
return self.predict(source=source, stream=stream, **kwargs)
@smart_inference_mode()
def val(self, data=None, validator=None, **kwargs):
def val(self, validator=None, **kwargs):
"""
Validate a model on a given dataset.
Args:
data (str): The dataset to validate on. Accepts all formats accepted by yolo
validator (BaseValidator): Customized validator.
**kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
"""
overrides = self.overrides.copy()
overrides['rect'] = True # rect batches as default
overrides.update(kwargs)
overrides['mode'] = 'val'
if overrides.get('imgsz') is None:
overrides['imgsz'] = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
args.data = data or args.data
if 'task' in overrides:
self.task = args.task
else:
args.task = self.task
validator = validator or self.smart_load('validator')
args.imgsz = check_imgsz(args.imgsz, max_dim=1)
custom = {'rect': True} # method defaults
args = {**self.overrides, **custom, **kwargs, 'mode': 'val'} # highest priority args on the right
args['imgsz'] = check_imgsz(args['imgsz'], max_dim=1)
validator = validator(args=args, _callbacks=self.callbacks)
validator = (validator or self.smart_load('validator'))(args=args, _callbacks=self.callbacks)
validator(model=self.model)
self.metrics = validator.metrics
return validator.metrics
@smart_inference_mode()
@ -309,17 +286,16 @@ class Model:
"""
self._check_is_pytorch_model()
from ultralytics.utils.benchmarks import benchmark
overrides = self.model.args.copy()
overrides.update(kwargs)
overrides['mode'] = 'benchmark'
overrides = {**DEFAULT_CFG_DICT, **overrides} # fill in missing overrides keys with defaults
custom = {'verbose': False} # method defaults
args = {**DEFAULT_CFG_DICT, **self.model.args, **custom, **kwargs, 'mode': 'benchmark'}
return benchmark(
model=self,
data=kwargs.get('data'), # if no 'data' argument passed set data=None for default datasets
imgsz=overrides['imgsz'],
half=overrides['half'],
int8=overrides['int8'],
device=overrides['device'],
imgsz=args['imgsz'],
half=args['half'],
int8=args['int8'],
device=args['device'],
verbose=kwargs.get('verbose'))
def export(self, **kwargs):
@ -327,22 +303,13 @@ class Model:
Export model.
Args:
**kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
**kwargs : Any other args accepted by the Exporter. To see all args check 'configuration' section in docs.
"""
self._check_is_pytorch_model()
overrides = self.overrides.copy()
overrides.update(kwargs)
overrides['mode'] = 'export'
if overrides.get('imgsz') is None:
overrides['imgsz'] = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
if 'batch' not in kwargs:
overrides['batch'] = 1 # default to 1 if not modified
if 'data' not in kwargs:
overrides['data'] = None # default to None if not modified (avoid int8 calibration with coco.yaml)
if 'verbose' not in kwargs:
overrides['verbose'] = False
args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
args.task = self.task
from .exporter import Exporter
custom = {'imgsz': self.model.args['imgsz'], 'batch': 1, 'data': None, 'verbose': False} # method defaults
args = {**self.overrides, **custom, **kwargs, 'mode': 'export'} # highest priority args on the right
return Exporter(overrides=args, _callbacks=self.callbacks)(model=self.model)
def train(self, trainer=None, **kwargs):
@ -359,20 +326,15 @@ class Model:
LOGGER.warning('WARNING ⚠️ using HUB training arguments, ignoring local training arguments.')
kwargs = self.session.train_args
check_pip_update_available()
overrides = self.overrides.copy()
if kwargs.get('cfg'):
LOGGER.info(f"cfg file passed. Overriding default params with {kwargs['cfg']}.")
overrides = yaml_load(check_yaml(kwargs['cfg']))
overrides.update(kwargs)
overrides['mode'] = 'train'
if not overrides.get('data'):
raise AttributeError("Dataset required but missing, i.e. pass 'data=coco128.yaml'")
if overrides.get('resume'):
overrides['resume'] = self.ckpt_path
self.task = overrides.get('task') or self.task
trainer = trainer or self.smart_load('trainer')
self.trainer = trainer(overrides=overrides, _callbacks=self.callbacks)
if not overrides.get('resume'): # manually set model only if not resuming
overrides = yaml_load(check_yaml(kwargs['cfg'])) if kwargs.get('cfg') else self.overrides
custom = {'data': TASK2DATA[self.task]} # method defaults
args = {**overrides, **custom, **kwargs, 'mode': 'train'} # highest priority args on the right
if args.get('resume'):
args['resume'] = self.ckpt_path
self.trainer = (trainer or self.smart_load('trainer'))(overrides=args, _callbacks=self.callbacks)
if not args.get('resume'): # manually set model only if not resuming
self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
self.model = self.trainer.model
self.trainer.hub_session = self.session # attach optional HUB session
@ -455,7 +417,7 @@ class Model:
name = self.__class__.__name__
mode = inspect.stack()[1][3] # get the function name.
raise NotImplementedError(
emojis(f'WARNING ⚠️ `{name}` model does not support `{mode}` mode for `{self.task}` task yet.')) from e
emojis(f"WARNING ⚠️ '{name}' model does not support '{mode}' mode for '{self.task}' task yet.")) from e
@property
def task_map(self):

11
ultralytics/engine/trainer.py
View File

@ -175,8 +175,13 @@ class BaseTrainer:
if world_size > 1 and 'LOCAL_RANK' not in os.environ:
# Argument checks
if self.args.rect:
LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with Multi-GPU training, setting rect=False")
LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with Multi-GPU training, setting 'rect=False'")
self.args.rect = False
if self.args.batch == -1:
LOGGER.warning("WARNING ⚠️ 'batch=-1' for AutoBatch is incompatible with Multi-GPU training, setting "
"default 'batch=16'")
self.args.batch = 16
# Command
cmd, file = generate_ddp_command(world_size, self)
try:
@ -186,6 +191,7 @@ class BaseTrainer:
raise e
finally:
ddp_cleanup(self, str(file))
else:
self._do_train(world_size)
@ -248,9 +254,6 @@ class BaseTrainer:
if self.batch_size == -1:
if RANK == -1: # single-GPU only, estimate best batch size
self.args.batch = self.batch_size = check_train_batch_size(self.model, self.args.imgsz, self.amp)
else:
SyntaxError('batch=-1 to use AutoBatch is only available in Single-GPU training. '
'Please pass a valid batch size value for Multi-GPU DDP training, i.e. batch=16')
# Dataloaders
batch_size = self.batch_size // max(world_size, 1)

11
ultralytics/nn/autobackend.py
View File

@ -18,7 +18,6 @@ from PIL import Image
from ultralytics.utils import ARM64, LINUX, LOGGER, ROOT, yaml_load
from ultralytics.utils.checks import check_requirements, check_suffix, check_version, check_yaml
from ultralytics.utils.downloads import attempt_download_asset, is_url
from ultralytics.utils.ops import xywh2xyxy
def check_class_names(names):
@ -363,9 +362,13 @@ class AutoBackend(nn.Module):
# im = im.resize((192, 320), Image.BILINEAR)
y = self.model.predict({'image': im_pil}) # coordinates are xywh normalized
if 'confidence' in y:
box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
raise TypeError('Ultralytics only supports inference of non-pipelined CoreML models exported with '
f"'nms=False', but 'model={w}' has an NMS pipeline created by an 'nms=True' export.")
# TODO: CoreML NMS inference handling
# from ultralytics.utils.ops import xywh2xyxy
# box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
# conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float32)
# y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
elif len(y) == 1: # classification model
y = list(y.values())
elif len(y) == 2: # segmentation model

6
ultralytics/nn/modules/transformer.py
View File

@ -355,15 +355,15 @@ class DeformableTransformerDecoder(nn.Module):
for i, layer in enumerate(self.layers):
output = layer(output, refer_bbox, feats, shapes, padding_mask, attn_mask, pos_mlp(refer_bbox))
# refine bboxes, (bs, num_queries+num_denoising, 4)
refined_bbox = torch.sigmoid(bbox_head[i](output) + inverse_sigmoid(refer_bbox))
bbox = bbox_head[i](output)
refined_bbox = torch.sigmoid(bbox + inverse_sigmoid(refer_bbox))
if self.training:
dec_cls.append(score_head[i](output))
if i == 0:
dec_bboxes.append(refined_bbox)
else:
dec_bboxes.append(torch.sigmoid(bbox_head[i](output) + inverse_sigmoid(last_refined_bbox)))
dec_bboxes.append(torch.sigmoid(bbox + inverse_sigmoid(last_refined_bbox)))
elif i == self.eval_idx:
dec_cls.append(score_head[i](output))
dec_bboxes.append(refined_bbox)