From 15b3b0365ab2f12993a58985f3cb7f2137409a0c Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Wed, 25 Jan 2023 21:21:39 +0100
Subject: [PATCH] `ultralytics 8.0.20` CLI `yolo` simplifications, DDP and ONNX
fixes (#608)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Sid Prabhakaran <s2siddhu@gmail.com>
---
ultralytics/__init__.py | 2 +-
ultralytics/hub/utils.py | 75 +++++++++++++++++++------
ultralytics/nn/tasks.py | 26 ++++-----
ultralytics/yolo/cfg/__init__.py | 54 +++++++++++-------
ultralytics/yolo/cfg/default.yaml | 28 ++++-----
ultralytics/yolo/utils/__init__.py | 11 ++--
ultralytics/yolo/utils/callbacks/hub.py | 8 +--
ultralytics/yolo/utils/torch_utils.py | 2 +-
ultralytics/yolo/v8/classify/predict.py | 17 ++++--
ultralytics/yolo/v8/classify/train.py | 27 ++++-----
ultralytics/yolo/v8/classify/val.py | 17 ++++--
ultralytics/yolo/v8/detect/predict.py | 17 ++++--
ultralytics/yolo/v8/detect/train.py | 23 ++++----
ultralytics/yolo/v8/detect/val.py | 17 ++++--
ultralytics/yolo/v8/segment/predict.py | 18 ++++--
ultralytics/yolo/v8/segment/train.py | 23 ++++----
ultralytics/yolo/v8/segment/val.py | 16 ++++--
17 files changed, 242 insertions(+), 139 deletions(-)
diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index f4298659..6d1cc9f2 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -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
diff --git a/ultralytics/hub/utils.py b/ultralytics/hub/utils.py
index 54fdfed0..50d6f42b 100644
--- a/ultralytics/hub/utils.py
+++ b/ultralytics/hub/utils.py
@@ -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
+class Traces:
- 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
+ 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")
- # 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)
+ @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()
diff --git a/ultralytics/nn/tasks.py b/ultralytics/nn/tasks.py
index 2f18597a..472e1e19 100644
--- a/ultralytics/nn/tasks.py
+++ b/ultralytics/nn/tasks.py
@@ -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'.")
diff --git a/ultralytics/yolo/cfg/__init__.py b/ultralytics/yolo/cfg/__init__.py
index a14d67bb..12934639 100644
--- a/ultralytics/yolo/cfg/__init__.py
+++ b/ultralytics/yolo/cfg/__init__.py
@@ -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 --------------------------------------------------------------------------------------------------------
diff --git a/ultralytics/yolo/cfg/default.yaml b/ultralytics/yolo/cfg/default.yaml
index 55dc6701..789f2b70 100644
--- a/ultralytics/yolo/cfg/default.yaml
+++ b/ultralytics/yolo/cfg/default.yaml
@@ -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
diff --git a/ultralytics/yolo/utils/__init__.py b/ultralytics/yolo/utils/__init__.py
index bf447e12..128f00f3 100644
--- a/ultralytics/yolo/utils/__init__.py
+++ b/ultralytics/yolo/utils/__init__.py
@@ -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
diff --git a/ultralytics/yolo/utils/callbacks/hub.py b/ultralytics/yolo/utils/callbacks/hub.py
index ffea13ed..3376945b 100644
--- a/ultralytics/yolo/utils/callbacks/hub.py
+++ b/ultralytics/yolo/utils/callbacks/hub.py
@@ -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 = {
diff --git a/ultralytics/yolo/utils/torch_utils.py b/ultralytics/yolo/utils/torch_utils.py
index 280ad4fd..4010eca5 100644
--- a/ultralytics/yolo/utils/torch_utils.py
+++ b/ultralytics/yolo/utils/torch_utils.py
@@ -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
diff --git a/ultralytics/yolo/v8/classify/predict.py b/ultralytics/yolo/v8/classify/predict.py
index 917d6064..f6c05cf8 100644
--- a/ultralytics/yolo/v8/classify/predict.py
+++ b/ultralytics/yolo/v8/classify/predict.py
@@ -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__":
diff --git a/ultralytics/yolo/v8/classify/train.py b/ultralytics/yolo/v8/classify/train.py
index 2e2a6b9b..c26524dc 100644
--- a/ultralytics/yolo/v8/classify/train.py
+++ b/ultralytics/yolo/v8/classify/train.py
@@ -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__":
diff --git a/ultralytics/yolo/v8/classify/val.py b/ultralytics/yolo/v8/classify/val.py
index b28b9edf..21cb1594 100644
--- a/ultralytics/yolo/v8/classify/val.py
+++ b/ultralytics/yolo/v8/classify/val.py
@@ -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__":
diff --git a/ultralytics/yolo/v8/detect/predict.py b/ultralytics/yolo/v8/detect/predict.py
index 581b686d..2131b836 100644
--- a/ultralytics/yolo/v8/detect/predict.py
+++ b/ultralytics/yolo/v8/detect/predict.py
@@ -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__":
diff --git a/ultralytics/yolo/v8/detect/train.py b/ultralytics/yolo/v8/detect/train.py
index 6c5d6e98..719568b0 100644
--- a/ultralytics/yolo/v8/detect/train.py
+++ b/ultralytics/yolo/v8/detect/train.py
@@ -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__":
diff --git a/ultralytics/yolo/v8/detect/val.py b/ultralytics/yolo/v8/detect/val.py
index e338964f..ab6e465a 100644
--- a/ultralytics/yolo/v8/detect/val.py
+++ b/ultralytics/yolo/v8/detect/val.py
@@ -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__":
diff --git a/ultralytics/yolo/v8/segment/predict.py b/ultralytics/yolo/v8/segment/predict.py
index 78c48d61..76bd0ebf 100644
--- a/ultralytics/yolo/v8/segment/predict.py
+++ b/ultralytics/yolo/v8/segment/predict.py
@@ -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__":
diff --git a/ultralytics/yolo/v8/segment/train.py b/ultralytics/yolo/v8/segment/train.py
index fbd96f80..8964e0b1 100644
--- a/ultralytics/yolo/v8/segment/train.py
+++ b/ultralytics/yolo/v8/segment/train.py
@@ -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__":
diff --git a/ultralytics/yolo/v8/segment/val.py b/ultralytics/yolo/v8/segment/val.py
index 276a50b0..492a6825 100644
--- a/ultralytics/yolo/v8/segment/val.py
+++ b/ultralytics/yolo/v8/segment/val.py
@@ -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__":