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

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Sid Prabhakaran <s2siddhu@gmail.com>
This commit is contained in:
Glenn Jocher
2023-01-25 21:21:39 +01:00
committed by GitHub
parent 59d4335664
commit 15b3b0365a
17 changed files with 242 additions and 139 deletions
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54
ultralytics/yolo/cfg/__init__.py
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
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}'.")
# 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}")
# 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
# 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}")
# 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']}'.")
func(cfg)
# Run command in python
getattr(model, mode)(verbose=True, **overrides)
# Special modes --------------------------------------------------------------------------------------------------------

28
ultralytics/yolo/cfg/default.yaml
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