Chiebot-Mirror
/
ultralytics
mirror of https://github.com/ultralytics/ultralytics.git
9
0
Fork 1
Code Issues Packages Projects Releases Wiki Activity

`ultralytics 8.0.20` CLI `yolo` simplifications, DDP and ONNX fixes (#608)

Browse Source 
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Sid Prabhakaran <s2siddhu@gmail.com>
pull/651/head^2 v8.0.20
Glenn Jocher 2 years ago committed by GitHub
parent 59d4335664
commit 15b3b0365a
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
17 changed files with 247 additions and 144 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 2
      ultralytics/__init__.py
  2. 79
      ultralytics/hub/utils.py
  3. 26
      ultralytics/nn/tasks.py
  4. 60
      ultralytics/yolo/cfg/__init__.py
  5. 28
      ultralytics/yolo/cfg/default.yaml
  6. 11
      ultralytics/yolo/utils/__init__.py
  7. 8
      ultralytics/yolo/utils/callbacks/hub.py
  8. 2
      ultralytics/yolo/utils/torch_utils.py
  9. 17
      ultralytics/yolo/v8/classify/predict.py
  10. 27
      ultralytics/yolo/v8/classify/train.py
  11. 17
      ultralytics/yolo/v8/classify/val.py
  12. 17
      ultralytics/yolo/v8/detect/predict.py
  13. 23
      ultralytics/yolo/v8/detect/train.py
  14. 17
      ultralytics/yolo/v8/detect/val.py
  15. 18
      ultralytics/yolo/v8/segment/predict.py
  16. 23
      ultralytics/yolo/v8/segment/train.py
  17. 16
      ultralytics/yolo/v8/segment/val.py

2
ultralytics/__init__.py
Unescape View File

@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
__version__ = "8.0.19"
__version__ = "8.0.20"
from ultralytics.yolo.engine.model import YOLO
from ultralytics.yolo.utils import ops

79
ultralytics/hub/utils.py
Unescape View File

@ -1,14 +1,19 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import os
import platform
import shutil
import sys
import threading
import time
from pathlib import Path
from random import random
import requests
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, LOGGER, RANK, SETTINGS, TryExcept, colorstr, emojis
from ultralytics.yolo.utils import (DEFAULT_CFG_DICT, LOGGER, RANK, SETTINGS, TryExcept, colorstr, emojis,
get_git_origin_url, is_colab, is_docker, is_git_dir, is_github_actions_ci,
is_jupyter, is_kaggle, is_pip_package, is_pytest_running)
PREFIX = colorstr('Ultralytics: ')
HELP_MSG = 'If this issue persists please visit https://github.com/ultralytics/hub/issues for assistance.'
@ -131,21 +136,57 @@ def smart_request(*args, retry=3, timeout=30, thread=True, code=-1, method="post
return func(*args, **kwargs)
@TryExcept(verbose=False)
def traces(cfg, all_keys=False, traces_sample_rate=0.0):
"""
Sync traces data if enabled in the global settings
Args:
cfg (IterableSimpleNamespace): Configuration for the task and mode.
all_keys (bool): Sync all items, not just non-default values.
traces_sample_rate (float): Fraction of traces captured from 0.0 to 1.0
"""
if SETTINGS['sync'] and RANK in {-1, 0} and (random() < traces_sample_rate):
cfg = vars(cfg) # convert type from IterableSimpleNamespace to dict
if not all_keys:
cfg = {k: v for k, v in cfg.items() if v != DEFAULT_CFG_DICT.get(k, None)} # retain non-default values
cfg['uuid'] = SETTINGS['uuid'] # add the device UUID to the configuration data
# Send a request to the HUB API to sync analytics
smart_request(f'{HUB_API_ROOT}/v1/usage/anonymous', json=cfg, headers=None, code=3, retry=0, verbose=False)
class Traces:
def __init__(self):
"""
Initialize Traces for error tracking and reporting if tests are not currently running.
"""
from ultralytics import __version__
env = 'Colab' if is_colab() else 'Kaggle' if is_kaggle() else 'Jupyter' if is_jupyter() else \
'Docker' if is_docker() else platform.system()
self.rate_limit = 3.0 # rate limit (seconds)
self.t = time.time() # rate limit timer (seconds)
self.metadata = {
"sys_argv_name": Path(sys.argv[0]).name,
"install": 'git' if is_git_dir() else 'pip' if is_pip_package() else 'other',
"python": platform.python_version(),
"release": __version__,
"environment": env}
self.enabled = SETTINGS['sync'] and \
RANK in {-1, 0} and \
not is_pytest_running() and \
not is_github_actions_ci() and \
(is_pip_package() or get_git_origin_url() == "https://github.com/ultralytics/ultralytics.git")
@TryExcept(verbose=False)
def __call__(self, cfg, all_keys=False, traces_sample_rate=1.0):
"""
Sync traces data if enabled in the global settings
Args:
cfg (IterableSimpleNamespace): Configuration for the task and mode.
all_keys (bool): Sync all items, not just non-default values.
traces_sample_rate (float): Fraction of traces captured from 0.0 to 1.0
"""
t = time.time() # current time
if self.enabled and random() < traces_sample_rate and (t - self.t) > self.rate_limit:
self.t = t # reset rate limit timer
cfg = vars(cfg) # convert type from IterableSimpleNamespace to dict
if not all_keys: # filter cfg
include_keys = {'task', 'mode'} # always include
cfg = {k: v for k, v in cfg.items() if v != DEFAULT_CFG_DICT.get(k, None) or k in include_keys}
trace = {'uuid': SETTINGS['uuid'], 'cfg': cfg, 'metadata': self.metadata}
# Send a request to the HUB API to sync analytics
smart_request(f'{HUB_API_ROOT}/v1/usage/anonymous',
json=trace,
headers=None,
code=3,
retry=0,
verbose=False)
# Run below code on hub/utils init -------------------------------------------------------------------------------------
traces = Traces()

26
ultralytics/nn/tasks.py
Unescape View File

@ -472,10 +472,13 @@ def guess_model_task(model):
Raises:
SyntaxError: If the task of the model could not be determined.
"""
cfg, task = None, None
cfg = None
if isinstance(model, dict):
cfg = model
elif isinstance(model, nn.Module): # PyTorch model
for x in 'model.args', 'model.model.args', 'model.model.model.args':
with contextlib.suppress(Exception):
return eval(x)['task']
for x in 'model.yaml', 'model.model.yaml', 'model.model.model.yaml':
with contextlib.suppress(Exception):
cfg = eval(x)
@ -485,25 +488,22 @@ def guess_model_task(model):
if cfg:
m = cfg["head"][-1][-2].lower() # output module name
if m in ["classify", "classifier", "cls", "fc"]:
task = "classify"
return "classify"
if m in ["detect"]:
task = "detect"
return "detect"
if m in ["segment"]:
task = "segment"
return "segment"
# Guess from PyTorch model
if task is None and isinstance(model, nn.Module):
if isinstance(model, nn.Module):
for m in model.modules():
if isinstance(m, Detect):
task = "detect"
return "detect"
elif isinstance(m, Segment):
task = "segment"
return "segment"
elif isinstance(m, Classify):
task = "classify"
return "classify"
# Unable to determine task from model
if task is None:
raise SyntaxError("YOLO is unable to automatically guess model task. Explicitly define task for your model, "
"i.e. 'task=detect', 'task=segment' or 'task=classify'.")
else:
return task
raise SyntaxError("YOLO is unable to automatically guess model task. Explicitly define task for your model, "
"i.e. 'task=detect', 'task=segment' or 'task=classify'.")

60
ultralytics/yolo/cfg/__init__.py
Unescape View File

@ -8,8 +8,8 @@ from pathlib import Path
from types import SimpleNamespace
from typing import Dict, List, Union
from ultralytics import __version__, yolo
from ultralytics.yolo.utils import (DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, PREFIX, USER_CONFIG_DIR,
from ultralytics import __version__
from ultralytics.yolo.utils import (DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, PREFIX, ROOT, USER_CONFIG_DIR,
IterableSimpleNamespace, colorstr, yaml_load, yaml_print)
from ultralytics.yolo.utils.checks import check_yolo
@ -211,30 +211,42 @@ def entrypoint(debug=False):
else:
raise argument_error(a)
cfg = get_cfg(DEFAULT_CFG_DICT, overrides) # create CFG instance
# Checks error catch
if cfg.mode == 'checks':
LOGGER.warning(
"WARNING ⚠️ 'yolo mode=checks' is deprecated and will be removed in the future. Use 'yolo checks' instead.")
# Mode
mode = overrides.pop('mode', None)
model = overrides.pop('model', None)
if mode == 'checks':
LOGGER.warning("WARNING ⚠️ 'yolo mode=checks' is deprecated. Use 'yolo checks' instead.")
check_yolo()
return
# Mapping from task to module
module = {"detect": yolo.v8.detect, "segment": yolo.v8.segment, "classify": yolo.v8.classify}.get(cfg.task)
if not module:
raise SyntaxError(f"yolo task={cfg.task} is invalid. Valid tasks are: {', '.join(tasks)}\n{CLI_HELP_MSG}")
# Mapping from mode to function
func = {
"train": module.train,
"val": module.val,
"predict": module.predict,
"export": yolo.engine.exporter.export}.get(cfg.mode)
if not func:
raise SyntaxError(f"yolo mode={cfg.mode} is invalid. Valid modes are: {', '.join(modes)}\n{CLI_HELP_MSG}")
func(cfg)
elif mode is None:
mode = DEFAULT_CFG_DICT['mode'] or 'predict'
LOGGER.warning(f"WARNING ⚠️ 'mode' is missing. Valid modes are {modes}. Using default 'mode={mode}'.")
# Model
if model is None:
model = DEFAULT_CFG_DICT['model'] or 'yolov8n.pt'
LOGGER.warning(f"WARNING ⚠️ 'model' is missing. Using default 'model={model}'.")
from ultralytics.yolo.engine.model import YOLO
model = YOLO(model)
task = model.task
# Task
if mode == 'predict' and 'source' not in overrides:
overrides['source'] = DEFAULT_CFG_DICT['source'] or ROOT / "assets" if (ROOT / "assets").exists() \
else "https://ultralytics.com/images/bus.jpg"
LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using default 'source={overrides['source']}'.")
elif mode in ('train', 'val'):
if 'data' not in overrides:
overrides['data'] = DEFAULT_CFG_DICT['data'] or 'mnist160' if task == 'classify' \
else 'coco128-seg.yaml' if task == 'segment' else 'coco128.yaml'
LOGGER.warning(f"WARNING ⚠️ 'data' is missing. Using default 'data={overrides['data']}'.")
elif mode == 'export':
if 'format' not in overrides:
overrides['format'] = DEFAULT_CFG_DICT['format'] or 'torchscript'
LOGGER.warning(f"WARNING ⚠️ 'format' is missing. Using default 'format={overrides['format']}'.")
# Run command in python
getattr(model, mode)(verbose=True, **overrides)
# Special modes --------------------------------------------------------------------------------------------------------

28
ultralytics/yolo/cfg/default.yaml
Unescape View File

@ -1,26 +1,26 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Default training settings and hyperparameters for medium-augmentation COCO training
task: "detect" # inference task, i.e. detect, segment, classify
mode: "train" # YOLO mode, i.e. train, val, predict, export
task: detect # inference task, i.e. detect, segment, classify
mode: train # YOLO mode, i.e. train, val, predict, export
# Train settings -------------------------------------------------------------------------------------------------------
model: null # path to model file, i.e. yolov8n.pt, yolov8n.yaml
data: null # path to data file, i.e. i.e. coco128.yaml
model: # path to model file, i.e. yolov8n.pt, yolov8n.yaml
data: # path to data file, i.e. i.e. coco128.yaml
epochs: 100 # number of epochs to train for
patience: 50 # epochs to wait for no observable improvement for early stopping of training
batch: 16 # number of images per batch (-1 for AutoBatch)
imgsz: 640 # size of input images as integer or w,h
save: True # save train checkpoints and predict results
cache: False # True/ram, disk or False. Use cache for data loading
device: null # device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
device: # device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
workers: 8 # number of worker threads for data loading (per RANK if DDP)
project: null # project name
name: null # experiment name
project: # project name
name: # experiment name
exist_ok: False # whether to overwrite existing experiment
pretrained: False # whether to use a pretrained model
optimizer: 'SGD' # optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
verbose: False # whether to print verbose output
optimizer: SGD # optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
verbose: True # whether to print verbose output
seed: 0 # random seed for reproducibility
deterministic: True # whether to enable deterministic mode
single_cls: False # train multi-class data as single-class
@ -39,7 +39,7 @@ dropout: 0.0 # use dropout regularization (classify train only)
val: True # validate/test during training
save_json: False # save results to JSON file
save_hybrid: False # save hybrid version of labels (labels + additional predictions)
conf: null # object confidence threshold for detection (default 0.25 predict, 0.001 val)
conf: # object confidence threshold for detection (default 0.25 predict, 0.001 val)
iou: 0.7 # intersection over union (IoU) threshold for NMS
max_det: 300 # maximum number of detections per image
half: False # use half precision (FP16)
@ -47,7 +47,7 @@ dnn: False # use OpenCV DNN for ONNX inference
plots: True # save plots during train/val
# Prediction settings --------------------------------------------------------------------------------------------------
source: null # source directory for images or videos
source: # source directory for images or videos
show: False # show results if possible
save_txt: False # save results as .txt file
save_conf: False # save results with confidence scores
@ -59,7 +59,7 @@ line_thickness: 3 # bounding box thickness (pixels)
visualize: False # visualize model features
augment: False # apply image augmentation to prediction sources
agnostic_nms: False # class-agnostic NMS
classes: null # filter results by class, i.e. class=0, or class=[0,2,3]
classes: # filter results by class, i.e. class=0, or class=[0,2,3]
retina_masks: False # use high-resolution segmentation masks
boxes: True # Show boxes in segmentation predictions
@ -70,7 +70,7 @@ optimize: False # TorchScript: optimize for mobile
int8: False # CoreML/TF INT8 quantization
dynamic: False # ONNX/TF/TensorRT: dynamic axes
simplify: False # ONNX: simplify model
opset: 17 # ONNX: opset version
opset: # ONNX: opset version (optional)
workspace: 4 # TensorRT: workspace size (GB)
nms: False # CoreML: add NMS
@ -103,7 +103,7 @@ mixup: 0.0 # image mixup (probability)
copy_paste: 0.0 # segment copy-paste (probability)
# Custom config.yaml ---------------------------------------------------------------------------------------------------
cfg: null # for overriding defaults.yaml
cfg: # for overriding defaults.yaml
# Debug, do not modify -------------------------------------------------------------------------------------------------
v5loader: False # use legacy YOLOv5 dataloader

11
ultralytics/yolo/utils/__init__.py
Unescape View File

@ -116,6 +116,9 @@ class IterableSimpleNamespace(SimpleNamespace):
# Default configuration
with open(DEFAULT_CFG_PATH, errors='ignore') as f:
DEFAULT_CFG_DICT = yaml.safe_load(f)
for k, v in DEFAULT_CFG_DICT.items():
if isinstance(v, str) and v.lower() == 'none':
DEFAULT_CFG_DICT[k] = None
DEFAULT_CFG_KEYS = DEFAULT_CFG_DICT.keys()
DEFAULT_CFG = IterableSimpleNamespace(**DEFAULT_CFG_DICT)
@ -448,13 +451,13 @@ def set_sentry():
"""
def before_send(event, hint):
oss = 'colab' if is_colab() else 'kaggle' if is_kaggle() else 'jupyter' if is_jupyter() else \
'docker' if is_docker() else platform.system()
env = 'Colab' if is_colab() else 'Kaggle' if is_kaggle() else 'Jupyter' if is_jupyter() else \
'Docker' if is_docker() else platform.system()
event['tags'] = {
"sys_argv": sys.argv[0],
"sys_argv_name": Path(sys.argv[0]).name,
"install": 'git' if is_git_dir() else 'pip' if is_pip_package() else 'other',
"os": oss}
"os": env}
return event
if SETTINGS['sync'] and \
@ -529,7 +532,7 @@ def set_settings(kwargs, file=USER_CONFIG_DIR / 'settings.yaml'):
yaml_save(file, SETTINGS)
# Run below code on utils init -----------------------------------------------------------------------------------------
# Run below code on yolo/utils init ------------------------------------------------------------------------------------
# Set logger
set_logging(LOGGING_NAME) # run before defining LOGGER

8
ultralytics/yolo/utils/callbacks/hub.py
Unescape View File

@ -48,19 +48,19 @@ def on_train_end(trainer):
def on_train_start(trainer):
traces(trainer.args, traces_sample_rate=0.0)
traces(trainer.args, traces_sample_rate=1.0)
def on_val_start(validator):
traces(validator.args, traces_sample_rate=0.0)
traces(validator.args, traces_sample_rate=1.0)
def on_predict_start(predictor):
traces(predictor.args, traces_sample_rate=0.0)
traces(predictor.args, traces_sample_rate=1.0)
def on_export_start(exporter):
traces(exporter.args, traces_sample_rate=0.0)
traces(exporter.args, traces_sample_rate=1.0)
callbacks = {

2
ultralytics/yolo/utils/torch_utils.py
Unescape View File

@ -31,7 +31,7 @@ WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
@contextmanager
def torch_distributed_zero_first(local_rank: int):
# Decorator to make all processes in distributed training wait for each local_master to do something
initialized = torch.distributed.is_initialized() # prevent 'Default process group has not been initialized' errors
initialized = torch.distributed.is_available() and torch.distributed.is_initialized()
if initialized and local_rank not in {-1, 0}:
dist.barrier(device_ids=[local_rank])
yield

17
ultralytics/yolo/v8/classify/predict.py
Unescape View File

@ -1,4 +1,5 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import sys
import torch
@ -63,12 +64,18 @@ class ClassificationPredictor(BasePredictor):
return log_string
def predict(cfg=DEFAULT_CFG):
cfg.model = cfg.model or "yolov8n-cls.pt" # or "resnet18"
cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
def predict(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or "yolov8n-cls.pt" # or "resnet18"
source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
else "https://ultralytics.com/images/bus.jpg"
predictor = ClassificationPredictor(cfg)
predictor.predict_cli()
args = dict(model=model, source=source, verbose=True)
if use_python:
from ultralytics import YOLO
YOLO(model)(**args)
else:
predictor = ClassificationPredictor(args)
predictor.predict_cli()
if __name__ == "__main__":

27
ultralytics/yolo/v8/classify/train.py
Unescape View File

@ -1,4 +1,5 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import sys
import torch
import torchvision
@ -135,22 +136,18 @@ class ClassificationTrainer(BaseTrainer):
# self.run_callbacks('on_fit_epoch_end')
def train(cfg=DEFAULT_CFG):
cfg.model = cfg.model or "yolov8n-cls.pt" # or "resnet18"
cfg.data = cfg.data or "mnist160" # or yolo.ClassificationDataset("mnist")
def train(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or "yolov8n-cls.pt" # or "resnet18"
data = cfg.data or "mnist160" # or yolo.ClassificationDataset("mnist")
device = cfg.device if cfg.device is not None else ''
# Reproduce ImageNet results
# cfg.lr0 = 0.1
# cfg.weight_decay = 5e-5
# cfg.label_smoothing = 0.1
# cfg.warmup_epochs = 0.0
cfg.device = cfg.device if cfg.device is not None else ''
# trainer = ClassificationTrainer(cfg)
# trainer.train()
from ultralytics import YOLO
model = YOLO(cfg.model)
model.train(**vars(cfg))
args = dict(model=model, data=data, device=device, verbose=True)
if use_python:
from ultralytics import YOLO
YOLO(model).train(**args)
else:
trainer = ClassificationTrainer(args)
trainer.train()
if __name__ == "__main__":

17
ultralytics/yolo/v8/classify/val.py
Unescape View File

@ -1,4 +1,5 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import sys
from ultralytics.yolo.data import build_classification_dataloader
from ultralytics.yolo.engine.validator import BaseValidator
@ -45,11 +46,17 @@ class ClassificationValidator(BaseValidator):
self.logger.info(pf % ("all", self.metrics.top1, self.metrics.top5))
def val(cfg=DEFAULT_CFG):
cfg.model = cfg.model or "yolov8n-cls.pt" # or "resnet18"
cfg.data = cfg.data or "mnist160"
validator = ClassificationValidator(args=cfg)
validator(model=cfg.model)
def val(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or "yolov8n-cls.pt" # or "resnet18"
data = cfg.data or "mnist160"
args = dict(model=model, data=data, verbose=True)
if use_python:
from ultralytics import YOLO
YOLO(model).val(**args)
else:
validator = ClassificationValidator(args=args)
validator(model=args['model'])
if __name__ == "__main__":

17
ultralytics/yolo/v8/detect/predict.py
Unescape View File

@ -1,4 +1,5 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import sys
import torch
@ -81,12 +82,18 @@ class DetectionPredictor(BasePredictor):
return log_string
def predict(cfg=DEFAULT_CFG):
cfg.model = cfg.model or "yolov8n.pt"
cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
def predict(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or "yolov8n.pt"
source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
else "https://ultralytics.com/images/bus.jpg"
predictor = DetectionPredictor(cfg)
predictor.predict_cli()
args = dict(model=model, source=source, verbose=True)
if use_python:
from ultralytics import YOLO
YOLO(model)(**args)
else:
predictor = DetectionPredictor(args)
predictor.predict_cli()
if __name__ == "__main__":

23
ultralytics/yolo/v8/detect/train.py
Unescape View File

@ -1,5 +1,5 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import sys
from copy import copy
import torch
@ -194,15 +194,18 @@ class Loss:
return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
def train(cfg=DEFAULT_CFG):
cfg.model = cfg.model or "yolov8n.pt"
cfg.data = cfg.data or "coco128.yaml" # or yolo.ClassificationDataset("mnist")
cfg.device = cfg.device if cfg.device is not None else ''
# trainer = DetectionTrainer(cfg)
# trainer.train()
from ultralytics import YOLO
model = YOLO(cfg.model)
model.train(**vars(cfg))
def train(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or "yolov8n.pt"
data = cfg.data or "coco128.yaml" # or yolo.ClassificationDataset("mnist")
device = cfg.device if cfg.device is not None else ''
args = dict(model=model, data=data, device=device, verbose=True)
if use_python:
from ultralytics import YOLO
YOLO(model).train(**args)
else:
trainer = DetectionTrainer(args)
trainer.train()
if __name__ == "__main__":

17
ultralytics/yolo/v8/detect/val.py
Unescape View File

@ -1,6 +1,7 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import os
import sys
from pathlib import Path
import numpy as np
@ -232,11 +233,17 @@ class DetectionValidator(BaseValidator):
return stats
def val(cfg=DEFAULT_CFG):
cfg.model = cfg.model or "yolov8n.pt"
cfg.data = cfg.data or "coco128.yaml"
validator = DetectionValidator(args=cfg)
validator(model=cfg.model)
def val(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or "yolov8n.pt"
data = cfg.data or "coco128.yaml"
args = dict(model=model, data=data, verbose=True)
if use_python:
from ultralytics import YOLO
YOLO(model).val(**args)
else:
validator = DetectionValidator(args=args)
validator(model=args['model'])
if __name__ == "__main__":

18
ultralytics/yolo/v8/segment/predict.py
Unescape View File

@ -1,5 +1,7 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import sys
import torch
from ultralytics.yolo.engine.results import Results
@ -98,12 +100,18 @@ class SegmentationPredictor(DetectionPredictor):
return log_string
def predict(cfg=DEFAULT_CFG):
cfg.model = cfg.model or "yolov8n-seg.pt"
cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
def predict(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or "yolov8n-seg.pt"
source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
else "https://ultralytics.com/images/bus.jpg"
predictor = SegmentationPredictor(cfg)
predictor.predict_cli()
args = dict(model=model, source=source, verbose=True)
if use_python:
from ultralytics import YOLO
YOLO(model)(**args)
else:
predictor = SegmentationPredictor(args)
predictor.predict_cli()
if __name__ == "__main__":

23
ultralytics/yolo/v8/segment/train.py
Unescape View File

@ -1,5 +1,5 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import sys
from copy import copy
import torch
@ -140,15 +140,18 @@ class SegLoss(Loss):
return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
def train(cfg=DEFAULT_CFG):
cfg.model = cfg.model or "yolov8n-seg.pt"
cfg.data = cfg.data or "coco128-seg.yaml" # or yolo.ClassificationDataset("mnist")
cfg.device = cfg.device if cfg.device is not None else ''
# trainer = SegmentationTrainer(cfg)
# trainer.train()
from ultralytics import YOLO
model = YOLO(cfg.model)
model.train(**vars(cfg))
def train(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or "yolov8n-seg.pt"
data = cfg.data or "coco128-seg.yaml" # or yolo.ClassificationDataset("mnist")
device = cfg.device if cfg.device is not None else ''
args = dict(model=model, data=data, device=device, verbose=True)
if use_python:
from ultralytics import YOLO
YOLO(model).train(**args)
else:
trainer = SegmentationTrainer(args)
trainer.train()
if __name__ == "__main__":

16
ultralytics/yolo/v8/segment/val.py
Unescape View File

@ -1,6 +1,7 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import os
import sys
from multiprocessing.pool import ThreadPool
from pathlib import Path
@ -242,10 +243,17 @@ class SegmentationValidator(DetectionValidator):
return stats
def val(cfg=DEFAULT_CFG):
cfg.data = cfg.data or "coco128-seg.yaml"
validator = SegmentationValidator(args=cfg)
validator(model=cfg.model)
def val(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or "yolov8n-seg.pt"
data = cfg.data or "coco128-seg.yaml"
args = dict(model=model, data=data, verbose=True)
if use_python:
from ultralytics import YOLO
YOLO(model).val(**args)
else:
validator = SegmentationValidator(args=args)
validator(model=args['model'])
if __name__ == "__main__":

Write Preview
Loading…
Cancel
Save