ultralytics 8.0.136 refactor and simplify package (#3748)
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:
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@ -1,809 +1,15 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import contextlib
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import inspect
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import logging.config
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import os
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import platform
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import re
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import subprocess
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import importlib
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import sys
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import threading
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import urllib
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import uuid
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from pathlib import Path
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from types import SimpleNamespace
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from typing import Union
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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import yaml
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from ultralytics.utils import LOGGER
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from ultralytics import __version__
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# Set modules in sys.modules under their old name
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sys.modules['ultralytics.yolo.utils'] = importlib.import_module('ultralytics.utils')
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# PyTorch Multi-GPU DDP Constants
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RANK = int(os.getenv('RANK', -1))
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LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
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WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
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# Other Constants
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[2] # YOLO
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DEFAULT_CFG_PATH = ROOT / 'yolo/cfg/default.yaml'
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NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads
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AUTOINSTALL = str(os.getenv('YOLO_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode
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VERBOSE = str(os.getenv('YOLO_VERBOSE', True)).lower() == 'true' # global verbose mode
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TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}' # tqdm bar format
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LOGGING_NAME = 'ultralytics'
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MACOS, LINUX, WINDOWS = (platform.system() == x for x in ['Darwin', 'Linux', 'Windows']) # environment booleans
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ARM64 = platform.machine() in ('arm64', 'aarch64') # ARM64 booleans
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HELP_MSG = \
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"""
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Usage examples for running YOLOv8:
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1. Install the ultralytics package:
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pip install ultralytics
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2. Use the Python SDK:
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.yaml') # build a new model from scratch
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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# Use the model
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results = model.train(data="coco128.yaml", epochs=3) # train the model
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results = model.val() # evaluate model performance on the validation set
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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success = model.export(format='onnx') # export the model to ONNX format
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3. Use the command line interface (CLI):
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YOLOv8 'yolo' CLI commands use the following syntax:
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yolo TASK MODE ARGS
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Where TASK (optional) is one of [detect, segment, classify]
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MODE (required) is one of [train, val, predict, export]
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ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
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See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'
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- Train a detection model for 10 epochs with an initial learning_rate of 0.01
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
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- Predict a YouTube video using a pretrained segmentation model at image size 320:
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yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320
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- Val a pretrained detection model at batch-size 1 and image size 640:
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yolo detect val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
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- Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)
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yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128
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- Run special commands:
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yolo help
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yolo checks
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yolo version
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yolo settings
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yolo copy-cfg
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yolo cfg
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Docs: https://docs.ultralytics.com
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Community: https://community.ultralytics.com
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GitHub: https://github.com/ultralytics/ultralytics
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"""
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# Settings
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torch.set_printoptions(linewidth=320, precision=4, profile='default')
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np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
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cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
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os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS) # NumExpr max threads
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os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' # for deterministic training
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # suppress verbose TF compiler warnings in Colab
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class SimpleClass:
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"""
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Ultralytics SimpleClass is a base class providing helpful string representation, error reporting, and attribute
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access methods for easier debugging and usage.
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"""
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def __str__(self):
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"""Return a human-readable string representation of the object."""
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attr = []
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for a in dir(self):
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v = getattr(self, a)
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if not callable(v) and not a.startswith('_'):
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if isinstance(v, SimpleClass):
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# Display only the module and class name for subclasses
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s = f'{a}: {v.__module__}.{v.__class__.__name__} object'
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else:
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s = f'{a}: {repr(v)}'
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attr.append(s)
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return f'{self.__module__}.{self.__class__.__name__} object with attributes:\n\n' + '\n'.join(attr)
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def __repr__(self):
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"""Return a machine-readable string representation of the object."""
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return self.__str__()
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def __getattr__(self, attr):
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"""Custom attribute access error message with helpful information."""
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name = self.__class__.__name__
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raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
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class IterableSimpleNamespace(SimpleNamespace):
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"""
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Ultralytics IterableSimpleNamespace is an extension class of SimpleNamespace that adds iterable functionality and
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enables usage with dict() and for loops.
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"""
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def __iter__(self):
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"""Return an iterator of key-value pairs from the namespace's attributes."""
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return iter(vars(self).items())
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def __str__(self):
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"""Return a human-readable string representation of the object."""
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return '\n'.join(f'{k}={v}' for k, v in vars(self).items())
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def __getattr__(self, attr):
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"""Custom attribute access error message with helpful information."""
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name = self.__class__.__name__
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raise AttributeError(f"""
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'{name}' object has no attribute '{attr}'. This may be caused by a modified or out of date ultralytics
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'default.yaml' file.\nPlease update your code with 'pip install -U ultralytics' and if necessary replace
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{DEFAULT_CFG_PATH} with the latest version from
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https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/cfg/default.yaml
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""")
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def get(self, key, default=None):
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"""Return the value of the specified key if it exists; otherwise, return the default value."""
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return getattr(self, key, default)
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def plt_settings(rcparams=None, backend='Agg'):
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"""
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Decorator to temporarily set rc parameters and the backend for a plotting function.
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Usage:
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decorator: @plt_settings({"font.size": 12})
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context manager: with plt_settings({"font.size": 12}):
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Args:
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rcparams (dict): Dictionary of rc parameters to set.
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backend (str, optional): Name of the backend to use. Defaults to 'Agg'.
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Returns:
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(Callable): Decorated function with temporarily set rc parameters and backend. This decorator can be
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applied to any function that needs to have specific matplotlib rc parameters and backend for its execution.
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"""
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if rcparams is None:
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rcparams = {'font.size': 11}
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def decorator(func):
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"""Decorator to apply temporary rc parameters and backend to a function."""
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def wrapper(*args, **kwargs):
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"""Sets rc parameters and backend, calls the original function, and restores the settings."""
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original_backend = plt.get_backend()
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plt.switch_backend(backend)
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with plt.rc_context(rcparams):
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result = func(*args, **kwargs)
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plt.switch_backend(original_backend)
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return result
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return wrapper
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return decorator
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def set_logging(name=LOGGING_NAME, verbose=True):
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"""Sets up logging for the given name."""
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rank = int(os.getenv('RANK', -1)) # rank in world for Multi-GPU trainings
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level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR
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logging.config.dictConfig({
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'version': 1,
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'disable_existing_loggers': False,
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'formatters': {
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name: {
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'format': '%(message)s'}},
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'handlers': {
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name: {
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'class': 'logging.StreamHandler',
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'formatter': name,
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'level': level}},
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'loggers': {
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name: {
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'level': level,
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'handlers': [name],
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'propagate': False}}})
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def emojis(string=''):
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"""Return platform-dependent emoji-safe version of string."""
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return string.encode().decode('ascii', 'ignore') if WINDOWS else string
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class EmojiFilter(logging.Filter):
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"""
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A custom logging filter class for removing emojis in log messages.
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This filter is particularly useful for ensuring compatibility with Windows terminals
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that may not support the display of emojis in log messages.
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"""
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def filter(self, record):
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"""Filter logs by emoji unicode characters on windows."""
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record.msg = emojis(record.msg)
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return super().filter(record)
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# Set logger
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set_logging(LOGGING_NAME, verbose=VERBOSE) # run before defining LOGGER
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LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.)
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if WINDOWS: # emoji-safe logging
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LOGGER.addFilter(EmojiFilter())
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class ThreadingLocked:
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"""
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A decorator class for ensuring thread-safe execution of a function or method.
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This class can be used as a decorator to make sure that if the decorated function
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is called from multiple threads, only one thread at a time will be able to execute the function.
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Attributes:
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lock (threading.Lock): A lock object used to manage access to the decorated function.
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Usage:
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@ThreadingLocked()
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def my_function():
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# Your code here
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pass
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"""
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def __init__(self):
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self.lock = threading.Lock()
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def __call__(self, f):
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from functools import wraps
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@wraps(f)
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def decorated(*args, **kwargs):
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with self.lock:
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return f(*args, **kwargs)
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return decorated
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def yaml_save(file='data.yaml', data=None):
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"""
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Save YAML data to a file.
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Args:
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file (str, optional): File name. Default is 'data.yaml'.
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data (dict): Data to save in YAML format.
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Returns:
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(None): Data is saved to the specified file.
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"""
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if data is None:
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data = {}
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file = Path(file)
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if not file.parent.exists():
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# Create parent directories if they don't exist
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file.parent.mkdir(parents=True, exist_ok=True)
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# Convert Path objects to strings
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for k, v in data.items():
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if isinstance(v, Path):
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data[k] = str(v)
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# Dump data to file in YAML format
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with open(file, 'w') as f:
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yaml.safe_dump(data, f, sort_keys=False, allow_unicode=True)
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def yaml_load(file='data.yaml', append_filename=False):
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"""
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Load YAML data from a file.
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Args:
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file (str, optional): File name. Default is 'data.yaml'.
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append_filename (bool): Add the YAML filename to the YAML dictionary. Default is False.
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Returns:
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(dict): YAML data and file name.
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"""
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with open(file, errors='ignore', encoding='utf-8') as f:
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s = f.read() # string
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# Remove special characters
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if not s.isprintable():
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s = re.sub(r'[^\x09\x0A\x0D\x20-\x7E\x85\xA0-\uD7FF\uE000-\uFFFD\U00010000-\U0010ffff]+', '', s)
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# Add YAML filename to dict and return
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return {**yaml.safe_load(s), 'yaml_file': str(file)} if append_filename else yaml.safe_load(s)
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def yaml_print(yaml_file: Union[str, Path, dict]) -> None:
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"""
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Pretty prints a yaml file or a yaml-formatted dictionary.
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Args:
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yaml_file: The file path of the yaml file or a yaml-formatted dictionary.
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Returns:
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None
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"""
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yaml_dict = yaml_load(yaml_file) if isinstance(yaml_file, (str, Path)) else yaml_file
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dump = yaml.dump(yaml_dict, sort_keys=False, allow_unicode=True)
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LOGGER.info(f"Printing '{colorstr('bold', 'black', yaml_file)}'\n\n{dump}")
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# Default configuration
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DEFAULT_CFG_DICT = yaml_load(DEFAULT_CFG_PATH)
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for k, v in DEFAULT_CFG_DICT.items():
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if isinstance(v, str) and v.lower() == 'none':
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DEFAULT_CFG_DICT[k] = None
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DEFAULT_CFG_KEYS = DEFAULT_CFG_DICT.keys()
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DEFAULT_CFG = IterableSimpleNamespace(**DEFAULT_CFG_DICT)
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def is_colab():
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"""
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Check if the current script is running inside a Google Colab notebook.
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Returns:
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(bool): True if running inside a Colab notebook, False otherwise.
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"""
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return 'COLAB_RELEASE_TAG' in os.environ or 'COLAB_BACKEND_VERSION' in os.environ
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def is_kaggle():
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"""
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Check if the current script is running inside a Kaggle kernel.
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Returns:
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(bool): True if running inside a Kaggle kernel, False otherwise.
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"""
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return os.environ.get('PWD') == '/kaggle/working' and os.environ.get('KAGGLE_URL_BASE') == 'https://www.kaggle.com'
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def is_jupyter():
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"""
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Check if the current script is running inside a Jupyter Notebook.
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Verified on Colab, Jupyterlab, Kaggle, Paperspace.
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Returns:
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(bool): True if running inside a Jupyter Notebook, False otherwise.
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"""
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with contextlib.suppress(Exception):
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from IPython import get_ipython
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return get_ipython() is not None
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return False
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def is_docker() -> bool:
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"""
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Determine if the script is running inside a Docker container.
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Returns:
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(bool): True if the script is running inside a Docker container, False otherwise.
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"""
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file = Path('/proc/self/cgroup')
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if file.exists():
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with open(file) as f:
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return 'docker' in f.read()
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else:
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return False
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def is_online() -> bool:
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"""
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Check internet connectivity by attempting to connect to a known online host.
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Returns:
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(bool): True if connection is successful, False otherwise.
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"""
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import socket
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for host in '1.1.1.1', '8.8.8.8', '223.5.5.5': # Cloudflare, Google, AliDNS:
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try:
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test_connection = socket.create_connection(address=(host, 53), timeout=2)
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except (socket.timeout, socket.gaierror, OSError):
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continue
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else:
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# If the connection was successful, close it to avoid a ResourceWarning
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test_connection.close()
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return True
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return False
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ONLINE = is_online()
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def is_pip_package(filepath: str = __name__) -> bool:
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"""
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Determines if the file at the given filepath is part of a pip package.
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Args:
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filepath (str): The filepath to check.
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Returns:
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(bool): True if the file is part of a pip package, False otherwise.
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"""
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import importlib.util
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# Get the spec for the module
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spec = importlib.util.find_spec(filepath)
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# Return whether the spec is not None and the origin is not None (indicating it is a package)
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return spec is not None and spec.origin is not None
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def is_dir_writeable(dir_path: Union[str, Path]) -> bool:
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"""
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Check if a directory is writeable.
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Args:
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dir_path (str | Path): The path to the directory.
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Returns:
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(bool): True if the directory is writeable, False otherwise.
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"""
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return os.access(str(dir_path), os.W_OK)
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def is_pytest_running():
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"""
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Determines whether pytest is currently running or not.
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Returns:
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(bool): True if pytest is running, False otherwise.
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"""
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return ('PYTEST_CURRENT_TEST' in os.environ) or ('pytest' in sys.modules) or ('pytest' in Path(sys.argv[0]).stem)
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def is_github_actions_ci() -> bool:
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"""
|
||||
Determine if the current environment is a GitHub Actions CI Python runner.
|
||||
|
||||
Returns:
|
||||
(bool): True if the current environment is a GitHub Actions CI Python runner, False otherwise.
|
||||
"""
|
||||
return 'GITHUB_ACTIONS' in os.environ and 'RUNNER_OS' in os.environ and 'RUNNER_TOOL_CACHE' in os.environ
|
||||
|
||||
|
||||
def is_git_dir():
|
||||
"""
|
||||
Determines whether the current file is part of a git repository.
|
||||
If the current file is not part of a git repository, returns None.
|
||||
|
||||
Returns:
|
||||
(bool): True if current file is part of a git repository.
|
||||
"""
|
||||
return get_git_dir() is not None
|
||||
|
||||
|
||||
def get_git_dir():
|
||||
"""
|
||||
Determines whether the current file is part of a git repository and if so, returns the repository root directory.
|
||||
If the current file is not part of a git repository, returns None.
|
||||
|
||||
Returns:
|
||||
(Path | None): Git root directory if found or None if not found.
|
||||
"""
|
||||
for d in Path(__file__).parents:
|
||||
if (d / '.git').is_dir():
|
||||
return d
|
||||
return None # no .git dir found
|
||||
|
||||
|
||||
def get_git_origin_url():
|
||||
"""
|
||||
Retrieves the origin URL of a git repository.
|
||||
|
||||
Returns:
|
||||
(str | None): The origin URL of the git repository.
|
||||
"""
|
||||
if is_git_dir():
|
||||
with contextlib.suppress(subprocess.CalledProcessError):
|
||||
origin = subprocess.check_output(['git', 'config', '--get', 'remote.origin.url'])
|
||||
return origin.decode().strip()
|
||||
return None # if not git dir or on error
|
||||
|
||||
|
||||
def get_git_branch():
|
||||
"""
|
||||
Returns the current git branch name. If not in a git repository, returns None.
|
||||
|
||||
Returns:
|
||||
(str | None): The current git branch name.
|
||||
"""
|
||||
if is_git_dir():
|
||||
with contextlib.suppress(subprocess.CalledProcessError):
|
||||
origin = subprocess.check_output(['git', 'rev-parse', '--abbrev-ref', 'HEAD'])
|
||||
return origin.decode().strip()
|
||||
return None # if not git dir or on error
|
||||
|
||||
|
||||
def get_default_args(func):
|
||||
"""Returns a dictionary of default arguments for a function.
|
||||
|
||||
Args:
|
||||
func (callable): The function to inspect.
|
||||
|
||||
Returns:
|
||||
(dict): A dictionary where each key is a parameter name, and each value is the default value of that parameter.
|
||||
"""
|
||||
signature = inspect.signature(func)
|
||||
return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty}
|
||||
|
||||
|
||||
def get_user_config_dir(sub_dir='Ultralytics'):
|
||||
"""
|
||||
Get the user config directory.
|
||||
|
||||
Args:
|
||||
sub_dir (str): The name of the subdirectory to create.
|
||||
|
||||
Returns:
|
||||
(Path): The path to the user config directory.
|
||||
"""
|
||||
# Return the appropriate config directory for each operating system
|
||||
if WINDOWS:
|
||||
path = Path.home() / 'AppData' / 'Roaming' / sub_dir
|
||||
elif MACOS: # macOS
|
||||
path = Path.home() / 'Library' / 'Application Support' / sub_dir
|
||||
elif LINUX:
|
||||
path = Path.home() / '.config' / sub_dir
|
||||
else:
|
||||
raise ValueError(f'Unsupported operating system: {platform.system()}')
|
||||
|
||||
# GCP and AWS lambda fix, only /tmp is writeable
|
||||
if not is_dir_writeable(str(path.parent)):
|
||||
path = Path('/tmp') / sub_dir
|
||||
LOGGER.warning(f"WARNING ⚠️ user config directory is not writeable, defaulting to '{path}'.")
|
||||
|
||||
# Create the subdirectory if it does not exist
|
||||
path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
return path
|
||||
|
||||
|
||||
USER_CONFIG_DIR = Path(os.getenv('YOLO_CONFIG_DIR', get_user_config_dir())) # Ultralytics settings dir
|
||||
SETTINGS_YAML = USER_CONFIG_DIR / 'settings.yaml'
|
||||
|
||||
|
||||
def colorstr(*input):
|
||||
"""Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world')."""
|
||||
*args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string
|
||||
colors = {
|
||||
'black': '\033[30m', # basic colors
|
||||
'red': '\033[31m',
|
||||
'green': '\033[32m',
|
||||
'yellow': '\033[33m',
|
||||
'blue': '\033[34m',
|
||||
'magenta': '\033[35m',
|
||||
'cyan': '\033[36m',
|
||||
'white': '\033[37m',
|
||||
'bright_black': '\033[90m', # bright colors
|
||||
'bright_red': '\033[91m',
|
||||
'bright_green': '\033[92m',
|
||||
'bright_yellow': '\033[93m',
|
||||
'bright_blue': '\033[94m',
|
||||
'bright_magenta': '\033[95m',
|
||||
'bright_cyan': '\033[96m',
|
||||
'bright_white': '\033[97m',
|
||||
'end': '\033[0m', # misc
|
||||
'bold': '\033[1m',
|
||||
'underline': '\033[4m'}
|
||||
return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
|
||||
|
||||
|
||||
class TryExcept(contextlib.ContextDecorator):
|
||||
"""YOLOv8 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager."""
|
||||
|
||||
def __init__(self, msg='', verbose=True):
|
||||
"""Initialize TryExcept class with optional message and verbosity settings."""
|
||||
self.msg = msg
|
||||
self.verbose = verbose
|
||||
|
||||
def __enter__(self):
|
||||
"""Executes when entering TryExcept context, initializes instance."""
|
||||
pass
|
||||
|
||||
def __exit__(self, exc_type, value, traceback):
|
||||
"""Defines behavior when exiting a 'with' block, prints error message if necessary."""
|
||||
if self.verbose and value:
|
||||
print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
|
||||
return True
|
||||
|
||||
|
||||
def threaded(func):
|
||||
"""Multi-threads a target function and returns thread. Usage: @threaded decorator."""
|
||||
|
||||
def wrapper(*args, **kwargs):
|
||||
"""Multi-threads a given function and returns the thread."""
|
||||
thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)
|
||||
thread.start()
|
||||
return thread
|
||||
|
||||
return wrapper
|
||||
|
||||
|
||||
def set_sentry():
|
||||
"""
|
||||
Initialize the Sentry SDK for error tracking and reporting. Only used if sentry_sdk package is installed and
|
||||
sync=True in settings. Run 'yolo settings' to see and update settings YAML file.
|
||||
|
||||
Conditions required to send errors (ALL conditions must be met or no errors will be reported):
|
||||
- sentry_sdk package is installed
|
||||
- sync=True in YOLO settings
|
||||
- pytest is not running
|
||||
- running in a pip package installation
|
||||
- running in a non-git directory
|
||||
- running with rank -1 or 0
|
||||
- online environment
|
||||
- CLI used to run package (checked with 'yolo' as the name of the main CLI command)
|
||||
|
||||
The function also configures Sentry SDK to ignore KeyboardInterrupt and FileNotFoundError
|
||||
exceptions and to exclude events with 'out of memory' in their exception message.
|
||||
|
||||
Additionally, the function sets custom tags and user information for Sentry events.
|
||||
"""
|
||||
|
||||
def before_send(event, hint):
|
||||
"""
|
||||
Modify the event before sending it to Sentry based on specific exception types and messages.
|
||||
|
||||
Args:
|
||||
event (dict): The event dictionary containing information about the error.
|
||||
hint (dict): A dictionary containing additional information about the error.
|
||||
|
||||
Returns:
|
||||
dict: The modified event or None if the event should not be sent to Sentry.
|
||||
"""
|
||||
if 'exc_info' in hint:
|
||||
exc_type, exc_value, tb = hint['exc_info']
|
||||
if exc_type in (KeyboardInterrupt, FileNotFoundError) \
|
||||
or 'out of memory' in str(exc_value):
|
||||
return None # do not send event
|
||||
|
||||
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': ENVIRONMENT}
|
||||
return event
|
||||
|
||||
if SETTINGS['sync'] and \
|
||||
RANK in (-1, 0) and \
|
||||
Path(sys.argv[0]).name == 'yolo' and \
|
||||
not TESTS_RUNNING and \
|
||||
ONLINE and \
|
||||
is_pip_package() and \
|
||||
not is_git_dir():
|
||||
|
||||
# If sentry_sdk package is not installed then return and do not use Sentry
|
||||
try:
|
||||
import sentry_sdk # noqa
|
||||
except ImportError:
|
||||
return
|
||||
|
||||
sentry_sdk.init(
|
||||
dsn='https://5ff1556b71594bfea135ff0203a0d290@o4504521589325824.ingest.sentry.io/4504521592406016',
|
||||
debug=False,
|
||||
traces_sample_rate=1.0,
|
||||
release=__version__,
|
||||
environment='production', # 'dev' or 'production'
|
||||
before_send=before_send,
|
||||
ignore_errors=[KeyboardInterrupt, FileNotFoundError])
|
||||
sentry_sdk.set_user({'id': SETTINGS['uuid']}) # SHA-256 anonymized UUID hash
|
||||
|
||||
# Disable all sentry logging
|
||||
for logger in 'sentry_sdk', 'sentry_sdk.errors':
|
||||
logging.getLogger(logger).setLevel(logging.CRITICAL)
|
||||
|
||||
|
||||
def get_settings(file=SETTINGS_YAML, version='0.0.3'):
|
||||
"""
|
||||
Loads a global Ultralytics settings YAML file or creates one with default values if it does not exist.
|
||||
|
||||
Args:
|
||||
file (Path): Path to the Ultralytics settings YAML file. Defaults to 'settings.yaml' in the USER_CONFIG_DIR.
|
||||
version (str): Settings version. If min settings version not met, new default settings will be saved.
|
||||
|
||||
Returns:
|
||||
(dict): Dictionary of settings key-value pairs.
|
||||
"""
|
||||
import hashlib
|
||||
|
||||
from ultralytics.yolo.utils.checks import check_version
|
||||
from ultralytics.yolo.utils.torch_utils import torch_distributed_zero_first
|
||||
|
||||
git_dir = get_git_dir()
|
||||
root = git_dir or Path()
|
||||
datasets_root = (root.parent if git_dir and is_dir_writeable(root.parent) else root).resolve()
|
||||
defaults = {
|
||||
'datasets_dir': str(datasets_root / 'datasets'), # default datasets directory.
|
||||
'weights_dir': str(root / 'weights'), # default weights directory.
|
||||
'runs_dir': str(root / 'runs'), # default runs directory.
|
||||
'uuid': hashlib.sha256(str(uuid.getnode()).encode()).hexdigest(), # SHA-256 anonymized UUID hash
|
||||
'sync': True, # sync analytics to help with YOLO development
|
||||
'api_key': '', # Ultralytics HUB API key (https://hub.ultralytics.com/)
|
||||
'settings_version': version} # Ultralytics settings version
|
||||
|
||||
with torch_distributed_zero_first(RANK):
|
||||
if not file.exists():
|
||||
yaml_save(file, defaults)
|
||||
settings = yaml_load(file)
|
||||
|
||||
# Check that settings keys and types match defaults
|
||||
correct = \
|
||||
settings \
|
||||
and settings.keys() == defaults.keys() \
|
||||
and all(type(a) == type(b) for a, b in zip(settings.values(), defaults.values())) \
|
||||
and check_version(settings['settings_version'], version)
|
||||
if not correct:
|
||||
LOGGER.warning('WARNING ⚠️ Ultralytics settings reset to defaults. This is normal and may be due to a '
|
||||
'recent ultralytics package update, but may have overwritten previous settings. '
|
||||
f"\nView and update settings with 'yolo settings' or at '{file}'")
|
||||
settings = defaults # merge **defaults with **settings (prefer **settings)
|
||||
yaml_save(file, settings) # save updated defaults
|
||||
|
||||
return settings
|
||||
|
||||
|
||||
def set_settings(kwargs, file=SETTINGS_YAML):
|
||||
"""
|
||||
Function that runs on a first-time ultralytics package installation to set up global settings and create necessary
|
||||
directories.
|
||||
"""
|
||||
SETTINGS.update(kwargs)
|
||||
yaml_save(file, SETTINGS)
|
||||
|
||||
|
||||
def deprecation_warn(arg, new_arg, version=None):
|
||||
"""Issue a deprecation warning when a deprecated argument is used, suggesting an updated argument."""
|
||||
if not version:
|
||||
version = float(__version__[:3]) + 0.2 # deprecate after 2nd major release
|
||||
LOGGER.warning(f"WARNING ⚠️ '{arg}' is deprecated and will be removed in 'ultralytics {version}' in the future. "
|
||||
f"Please use '{new_arg}' instead.")
|
||||
|
||||
|
||||
def clean_url(url):
|
||||
"""Strip auth from URL, i.e. https://url.com/file.txt?auth -> https://url.com/file.txt."""
|
||||
url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/
|
||||
return urllib.parse.unquote(url).split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth
|
||||
|
||||
|
||||
def url2file(url):
|
||||
"""Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt."""
|
||||
return Path(clean_url(url)).name
|
||||
|
||||
|
||||
# Run below code on yolo/utils init ------------------------------------------------------------------------------------
|
||||
|
||||
# Check first-install steps
|
||||
PREFIX = colorstr('Ultralytics: ')
|
||||
SETTINGS = get_settings()
|
||||
DATASETS_DIR = Path(SETTINGS['datasets_dir']) # global datasets directory
|
||||
ENVIRONMENT = 'Colab' if is_colab() else 'Kaggle' if is_kaggle() else 'Jupyter' if is_jupyter() else \
|
||||
'Docker' if is_docker() else platform.system()
|
||||
TESTS_RUNNING = is_pytest_running() or is_github_actions_ci()
|
||||
set_sentry()
|
||||
|
||||
# Apply monkey patches if the script is being run from within the parent directory of the script's location
|
||||
from .patches import imread, imshow, imwrite
|
||||
|
||||
# torch.save = torch_save
|
||||
if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename:
|
||||
cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow
|
||||
UTILS_WARNING = """WARNING ⚠️ 'ultralytics.yolo.utils' is deprecated since '8.0.136' and will be removed in '8.1.0'. Please use 'ultralytics.utils' instead.
|
||||
Note this warning may be related to loading older models. You can update your model to current structure with:
|
||||
import torch
|
||||
ckpt = torch.load("model.pt") # applies to both official and custom models
|
||||
torch.save(ckpt, "updated-model.pt")
|
||||
"""
|
||||
LOGGER.warning(UTILS_WARNING)
|
||||
|
||||
@ -1,90 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
"""
|
||||
Functions for estimating the best YOLO batch size to use a fraction of the available CUDA memory in PyTorch.
|
||||
"""
|
||||
|
||||
from copy import deepcopy
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, colorstr
|
||||
from ultralytics.yolo.utils.torch_utils import profile
|
||||
|
||||
|
||||
def check_train_batch_size(model, imgsz=640, amp=True):
|
||||
"""
|
||||
Check YOLO training batch size using the autobatch() function.
|
||||
|
||||
Args:
|
||||
model (torch.nn.Module): YOLO model to check batch size for.
|
||||
imgsz (int): Image size used for training.
|
||||
amp (bool): If True, use automatic mixed precision (AMP) for training.
|
||||
|
||||
Returns:
|
||||
(int): Optimal batch size computed using the autobatch() function.
|
||||
"""
|
||||
|
||||
with torch.cuda.amp.autocast(amp):
|
||||
return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size
|
||||
|
||||
|
||||
def autobatch(model, imgsz=640, fraction=0.67, batch_size=DEFAULT_CFG.batch):
|
||||
"""
|
||||
Automatically estimate the best YOLO batch size to use a fraction of the available CUDA memory.
|
||||
|
||||
Args:
|
||||
model (torch.nn.module): YOLO model to compute batch size for.
|
||||
imgsz (int, optional): The image size used as input for the YOLO model. Defaults to 640.
|
||||
fraction (float, optional): The fraction of available CUDA memory to use. Defaults to 0.67.
|
||||
batch_size (int, optional): The default batch size to use if an error is detected. Defaults to 16.
|
||||
|
||||
Returns:
|
||||
(int): The optimal batch size.
|
||||
"""
|
||||
|
||||
# Check device
|
||||
prefix = colorstr('AutoBatch: ')
|
||||
LOGGER.info(f'{prefix}Computing optimal batch size for imgsz={imgsz}')
|
||||
device = next(model.parameters()).device # get model device
|
||||
if device.type == 'cpu':
|
||||
LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
|
||||
return batch_size
|
||||
if torch.backends.cudnn.benchmark:
|
||||
LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}')
|
||||
return batch_size
|
||||
|
||||
# Inspect CUDA memory
|
||||
gb = 1 << 30 # bytes to GiB (1024 ** 3)
|
||||
d = str(device).upper() # 'CUDA:0'
|
||||
properties = torch.cuda.get_device_properties(device) # device properties
|
||||
t = properties.total_memory / gb # GiB total
|
||||
r = torch.cuda.memory_reserved(device) / gb # GiB reserved
|
||||
a = torch.cuda.memory_allocated(device) / gb # GiB allocated
|
||||
f = t - (r + a) # GiB free
|
||||
LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free')
|
||||
|
||||
# Profile batch sizes
|
||||
batch_sizes = [1, 2, 4, 8, 16]
|
||||
try:
|
||||
img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]
|
||||
results = profile(img, model, n=3, device=device)
|
||||
|
||||
# Fit a solution
|
||||
y = [x[2] for x in results if x] # memory [2]
|
||||
p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit
|
||||
b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size)
|
||||
if None in results: # some sizes failed
|
||||
i = results.index(None) # first fail index
|
||||
if b >= batch_sizes[i]: # y intercept above failure point
|
||||
b = batch_sizes[max(i - 1, 0)] # select prior safe point
|
||||
if b < 1 or b > 1024: # b outside of safe range
|
||||
b = batch_size
|
||||
LOGGER.info(f'{prefix}WARNING ⚠️ CUDA anomaly detected, using default batch-size {batch_size}.')
|
||||
|
||||
fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted
|
||||
LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')
|
||||
return b
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'{prefix}WARNING ⚠️ error detected: {e}, using default batch-size {batch_size}.')
|
||||
return batch_size
|
||||
@ -1,358 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
"""
|
||||
Benchmark a YOLO model formats for speed and accuracy
|
||||
|
||||
Usage:
|
||||
from ultralytics.yolo.utils.benchmarks import ProfileModels, benchmark
|
||||
ProfileModels(['yolov8n.yaml', 'yolov8s.yaml']).profile()
|
||||
run_benchmarks(model='yolov8n.pt', imgsz=160)
|
||||
|
||||
Format | `format=argument` | Model
|
||||
--- | --- | ---
|
||||
PyTorch | - | yolov8n.pt
|
||||
TorchScript | `torchscript` | yolov8n.torchscript
|
||||
ONNX | `onnx` | yolov8n.onnx
|
||||
OpenVINO | `openvino` | yolov8n_openvino_model/
|
||||
TensorRT | `engine` | yolov8n.engine
|
||||
CoreML | `coreml` | yolov8n.mlmodel
|
||||
TensorFlow SavedModel | `saved_model` | yolov8n_saved_model/
|
||||
TensorFlow GraphDef | `pb` | yolov8n.pb
|
||||
TensorFlow Lite | `tflite` | yolov8n.tflite
|
||||
TensorFlow Edge TPU | `edgetpu` | yolov8n_edgetpu.tflite
|
||||
TensorFlow.js | `tfjs` | yolov8n_web_model/
|
||||
PaddlePaddle | `paddle` | yolov8n_paddle_model/
|
||||
ncnn | `ncnn` | yolov8n_ncnn_model/
|
||||
"""
|
||||
|
||||
import glob
|
||||
import platform
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch.cuda
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics import YOLO
|
||||
from ultralytics.yolo.cfg import TASK2DATA, TASK2METRIC
|
||||
from ultralytics.yolo.engine.exporter import export_formats
|
||||
from ultralytics.yolo.utils import LINUX, LOGGER, MACOS, ROOT, SETTINGS
|
||||
from ultralytics.yolo.utils.checks import check_requirements, check_yolo
|
||||
from ultralytics.yolo.utils.downloads import download
|
||||
from ultralytics.yolo.utils.files import file_size
|
||||
from ultralytics.yolo.utils.torch_utils import select_device
|
||||
|
||||
|
||||
def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
|
||||
imgsz=160,
|
||||
half=False,
|
||||
int8=False,
|
||||
device='cpu',
|
||||
hard_fail=False):
|
||||
"""
|
||||
Benchmark a YOLO model across different formats for speed and accuracy.
|
||||
|
||||
Args:
|
||||
model (str | Path | optional): Path to the model file or directory. Default is
|
||||
Path(SETTINGS['weights_dir']) / 'yolov8n.pt'.
|
||||
imgsz (int, optional): Image size for the benchmark. Default is 160.
|
||||
half (bool, optional): Use half-precision for the model if True. Default is False.
|
||||
int8 (bool, optional): Use int8-precision for the model if True. Default is False.
|
||||
device (str, optional): Device to run the benchmark on, either 'cpu' or 'cuda'. Default is 'cpu'.
|
||||
hard_fail (bool | float | optional): If True or a float, assert benchmarks pass with given metric.
|
||||
Default is False.
|
||||
|
||||
Returns:
|
||||
df (pandas.DataFrame): A pandas DataFrame with benchmark results for each format, including file size,
|
||||
metric, and inference time.
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
pd.options.display.max_columns = 10
|
||||
pd.options.display.width = 120
|
||||
device = select_device(device, verbose=False)
|
||||
if isinstance(model, (str, Path)):
|
||||
model = YOLO(model)
|
||||
|
||||
y = []
|
||||
t0 = time.time()
|
||||
for i, (name, format, suffix, cpu, gpu) in export_formats().iterrows(): # index, (name, format, suffix, CPU, GPU)
|
||||
emoji, filename = '❌', None # export defaults
|
||||
try:
|
||||
assert i != 9 or LINUX, 'Edge TPU export only supported on Linux'
|
||||
if i == 10:
|
||||
assert MACOS or LINUX, 'TF.js export only supported on macOS and Linux'
|
||||
if 'cpu' in device.type:
|
||||
assert cpu, 'inference not supported on CPU'
|
||||
if 'cuda' in device.type:
|
||||
assert gpu, 'inference not supported on GPU'
|
||||
|
||||
# Export
|
||||
if format == '-':
|
||||
filename = model.ckpt_path or model.cfg
|
||||
export = model # PyTorch format
|
||||
else:
|
||||
filename = model.export(imgsz=imgsz, format=format, half=half, int8=int8, device=device, verbose=False)
|
||||
export = YOLO(filename, task=model.task)
|
||||
assert suffix in str(filename), 'export failed'
|
||||
emoji = '❎' # indicates export succeeded
|
||||
|
||||
# Predict
|
||||
assert model.task != 'pose' or i != 7, 'GraphDef Pose inference is not supported'
|
||||
assert i not in (9, 10), 'inference not supported' # Edge TPU and TF.js are unsupported
|
||||
assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13' # CoreML
|
||||
if not (ROOT / 'assets/bus.jpg').exists():
|
||||
download(url='https://ultralytics.com/images/bus.jpg', dir=ROOT / 'assets')
|
||||
export.predict(ROOT / 'assets/bus.jpg', imgsz=imgsz, device=device, half=half)
|
||||
|
||||
# Validate
|
||||
data = TASK2DATA[model.task] # task to dataset, i.e. coco8.yaml for task=detect
|
||||
key = TASK2METRIC[model.task] # task to metric, i.e. metrics/mAP50-95(B) for task=detect
|
||||
results = export.val(data=data,
|
||||
batch=1,
|
||||
imgsz=imgsz,
|
||||
plots=False,
|
||||
device=device,
|
||||
half=half,
|
||||
int8=int8,
|
||||
verbose=False)
|
||||
metric, speed = results.results_dict[key], results.speed['inference']
|
||||
y.append([name, '✅', round(file_size(filename), 1), round(metric, 4), round(speed, 2)])
|
||||
except Exception as e:
|
||||
if hard_fail:
|
||||
assert type(e) is AssertionError, f'Benchmark hard_fail for {name}: {e}'
|
||||
LOGGER.warning(f'ERROR ❌️ Benchmark failure for {name}: {e}')
|
||||
y.append([name, emoji, round(file_size(filename), 1), None, None]) # mAP, t_inference
|
||||
|
||||
# Print results
|
||||
check_yolo(device=device) # print system info
|
||||
df = pd.DataFrame(y, columns=['Format', 'Status❔', 'Size (MB)', key, 'Inference time (ms/im)'])
|
||||
|
||||
name = Path(model.ckpt_path).name
|
||||
s = f'\nBenchmarks complete for {name} on {data} at imgsz={imgsz} ({time.time() - t0:.2f}s)\n{df}\n'
|
||||
LOGGER.info(s)
|
||||
with open('benchmarks.log', 'a', errors='ignore', encoding='utf-8') as f:
|
||||
f.write(s)
|
||||
|
||||
if hard_fail and isinstance(hard_fail, float):
|
||||
metrics = df[key].array # values to compare to floor
|
||||
floor = hard_fail # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n
|
||||
assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: one or more metric(s) < floor {floor}'
|
||||
|
||||
return df
|
||||
|
||||
|
||||
class ProfileModels:
|
||||
"""
|
||||
ProfileModels class for profiling different models on ONNX and TensorRT.
|
||||
|
||||
This class profiles the performance of different models, provided their paths. The profiling includes parameters such as
|
||||
model speed and FLOPs.
|
||||
|
||||
Attributes:
|
||||
paths (list): Paths of the models to profile.
|
||||
num_timed_runs (int): Number of timed runs for the profiling. Default is 100.
|
||||
num_warmup_runs (int): Number of warmup runs before profiling. Default is 10.
|
||||
min_time (float): Minimum number of seconds to profile for. Default is 60.
|
||||
imgsz (int): Image size used in the models. Default is 640.
|
||||
|
||||
Methods:
|
||||
profile(): Profiles the models and prints the result.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
paths: list,
|
||||
num_timed_runs=100,
|
||||
num_warmup_runs=10,
|
||||
min_time=60,
|
||||
imgsz=640,
|
||||
trt=True,
|
||||
device=None):
|
||||
self.paths = paths
|
||||
self.num_timed_runs = num_timed_runs
|
||||
self.num_warmup_runs = num_warmup_runs
|
||||
self.min_time = min_time
|
||||
self.imgsz = imgsz
|
||||
self.trt = trt # run TensorRT profiling
|
||||
self.device = device or torch.device(0 if torch.cuda.is_available() else 'cpu')
|
||||
|
||||
def profile(self):
|
||||
files = self.get_files()
|
||||
|
||||
if not files:
|
||||
print('No matching *.pt or *.onnx files found.')
|
||||
return
|
||||
|
||||
table_rows = []
|
||||
output = []
|
||||
for file in files:
|
||||
engine_file = file.with_suffix('.engine')
|
||||
if file.suffix in ('.pt', '.yaml'):
|
||||
model = YOLO(str(file))
|
||||
model.fuse() # to report correct params and GFLOPs in model.info()
|
||||
model_info = model.info()
|
||||
if self.trt and self.device.type != 'cpu' and not engine_file.is_file():
|
||||
engine_file = model.export(format='engine',
|
||||
half=True,
|
||||
imgsz=self.imgsz,
|
||||
device=self.device,
|
||||
verbose=False)
|
||||
onnx_file = model.export(format='onnx',
|
||||
half=True,
|
||||
imgsz=self.imgsz,
|
||||
simplify=True,
|
||||
device=self.device,
|
||||
verbose=False)
|
||||
elif file.suffix == '.onnx':
|
||||
model_info = self.get_onnx_model_info(file)
|
||||
onnx_file = file
|
||||
else:
|
||||
continue
|
||||
|
||||
t_engine = self.profile_tensorrt_model(str(engine_file))
|
||||
t_onnx = self.profile_onnx_model(str(onnx_file))
|
||||
table_rows.append(self.generate_table_row(file.stem, t_onnx, t_engine, model_info))
|
||||
output.append(self.generate_results_dict(file.stem, t_onnx, t_engine, model_info))
|
||||
|
||||
self.print_table(table_rows)
|
||||
return output
|
||||
|
||||
def get_files(self):
|
||||
files = []
|
||||
for path in self.paths:
|
||||
path = Path(path)
|
||||
if path.is_dir():
|
||||
extensions = ['*.pt', '*.onnx', '*.yaml']
|
||||
files.extend([file for ext in extensions for file in glob.glob(str(path / ext))])
|
||||
elif path.suffix in {'.pt', '.yaml'}: # add non-existing
|
||||
files.append(str(path))
|
||||
else:
|
||||
files.extend(glob.glob(str(path)))
|
||||
|
||||
print(f'Profiling: {sorted(files)}')
|
||||
return [Path(file) for file in sorted(files)]
|
||||
|
||||
def get_onnx_model_info(self, onnx_file: str):
|
||||
# return (num_layers, num_params, num_gradients, num_flops)
|
||||
return 0.0, 0.0, 0.0, 0.0
|
||||
|
||||
def iterative_sigma_clipping(self, data, sigma=2, max_iters=3):
|
||||
data = np.array(data)
|
||||
for _ in range(max_iters):
|
||||
mean, std = np.mean(data), np.std(data)
|
||||
clipped_data = data[(data > mean - sigma * std) & (data < mean + sigma * std)]
|
||||
if len(clipped_data) == len(data):
|
||||
break
|
||||
data = clipped_data
|
||||
return data
|
||||
|
||||
def profile_tensorrt_model(self, engine_file: str):
|
||||
if not self.trt or not Path(engine_file).is_file():
|
||||
return 0.0, 0.0
|
||||
|
||||
# Model and input
|
||||
model = YOLO(engine_file)
|
||||
input_data = np.random.rand(self.imgsz, self.imgsz, 3).astype(np.float32) # must be FP32
|
||||
|
||||
# Warmup runs
|
||||
elapsed = 0.0
|
||||
for _ in range(3):
|
||||
start_time = time.time()
|
||||
for _ in range(self.num_warmup_runs):
|
||||
model(input_data, imgsz=self.imgsz, verbose=False)
|
||||
elapsed = time.time() - start_time
|
||||
|
||||
# Compute number of runs as higher of min_time or num_timed_runs
|
||||
num_runs = max(round(self.min_time / elapsed * self.num_warmup_runs), self.num_timed_runs * 50)
|
||||
|
||||
# Timed runs
|
||||
run_times = []
|
||||
for _ in tqdm(range(num_runs), desc=engine_file):
|
||||
results = model(input_data, imgsz=self.imgsz, verbose=False)
|
||||
run_times.append(results[0].speed['inference']) # Convert to milliseconds
|
||||
|
||||
run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=3) # sigma clipping
|
||||
return np.mean(run_times), np.std(run_times)
|
||||
|
||||
def profile_onnx_model(self, onnx_file: str):
|
||||
check_requirements('onnxruntime')
|
||||
import onnxruntime as ort
|
||||
|
||||
# Session with either 'TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'
|
||||
sess_options = ort.SessionOptions()
|
||||
sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
|
||||
sess_options.intra_op_num_threads = 8 # Limit the number of threads
|
||||
sess = ort.InferenceSession(onnx_file, sess_options, providers=['CPUExecutionProvider'])
|
||||
|
||||
input_tensor = sess.get_inputs()[0]
|
||||
input_type = input_tensor.type
|
||||
|
||||
# Mapping ONNX datatype to numpy datatype
|
||||
if 'float16' in input_type:
|
||||
input_dtype = np.float16
|
||||
elif 'float' in input_type:
|
||||
input_dtype = np.float32
|
||||
elif 'double' in input_type:
|
||||
input_dtype = np.float64
|
||||
elif 'int64' in input_type:
|
||||
input_dtype = np.int64
|
||||
elif 'int32' in input_type:
|
||||
input_dtype = np.int32
|
||||
else:
|
||||
raise ValueError(f'Unsupported ONNX datatype {input_type}')
|
||||
|
||||
input_data = np.random.rand(*input_tensor.shape).astype(input_dtype)
|
||||
input_name = input_tensor.name
|
||||
output_name = sess.get_outputs()[0].name
|
||||
|
||||
# Warmup runs
|
||||
elapsed = 0.0
|
||||
for _ in range(3):
|
||||
start_time = time.time()
|
||||
for _ in range(self.num_warmup_runs):
|
||||
sess.run([output_name], {input_name: input_data})
|
||||
elapsed = time.time() - start_time
|
||||
|
||||
# Compute number of runs as higher of min_time or num_timed_runs
|
||||
num_runs = max(round(self.min_time / elapsed * self.num_warmup_runs), self.num_timed_runs)
|
||||
|
||||
# Timed runs
|
||||
run_times = []
|
||||
for _ in tqdm(range(num_runs), desc=onnx_file):
|
||||
start_time = time.time()
|
||||
sess.run([output_name], {input_name: input_data})
|
||||
run_times.append((time.time() - start_time) * 1000) # Convert to milliseconds
|
||||
|
||||
run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=5) # sigma clipping
|
||||
return np.mean(run_times), np.std(run_times)
|
||||
|
||||
def generate_table_row(self, model_name, t_onnx, t_engine, model_info):
|
||||
layers, params, gradients, flops = model_info
|
||||
return f'| {model_name:18s} | {self.imgsz} | - | {t_onnx[0]:.2f} ± {t_onnx[1]:.2f} ms | {t_engine[0]:.2f} ± {t_engine[1]:.2f} ms | {params / 1e6:.1f} | {flops:.1f} |'
|
||||
|
||||
def generate_results_dict(self, model_name, t_onnx, t_engine, model_info):
|
||||
layers, params, gradients, flops = model_info
|
||||
return {
|
||||
'model/name': model_name,
|
||||
'model/parameters': params,
|
||||
'model/GFLOPs': round(flops, 3),
|
||||
'model/speed_ONNX(ms)': round(t_onnx[0], 3),
|
||||
'model/speed_TensorRT(ms)': round(t_engine[0], 3)}
|
||||
|
||||
def print_table(self, table_rows):
|
||||
gpu = torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'GPU'
|
||||
header = f'| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>{gpu} TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |'
|
||||
separator = '|-------------|---------------------|--------------------|------------------------------|-----------------------------------|------------------|-----------------|'
|
||||
|
||||
print(f'\n\n{header}')
|
||||
print(separator)
|
||||
for row in table_rows:
|
||||
print(row)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# Benchmark all export formats
|
||||
benchmark()
|
||||
|
||||
# Profiling models on ONNX and TensorRT
|
||||
ProfileModels(['yolov8n.yaml', 'yolov8s.yaml'])
|
||||
@ -1,5 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
from .base import add_integration_callbacks, default_callbacks, get_default_callbacks
|
||||
|
||||
__all__ = 'add_integration_callbacks', 'default_callbacks', 'get_default_callbacks'
|
||||
@ -1,212 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
"""
|
||||
Base callbacks
|
||||
"""
|
||||
|
||||
from collections import defaultdict
|
||||
from copy import deepcopy
|
||||
|
||||
# Trainer callbacks ----------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
def on_pretrain_routine_start(trainer):
|
||||
"""Called before the pretraining routine starts."""
|
||||
pass
|
||||
|
||||
|
||||
def on_pretrain_routine_end(trainer):
|
||||
"""Called after the pretraining routine ends."""
|
||||
pass
|
||||
|
||||
|
||||
def on_train_start(trainer):
|
||||
"""Called when the training starts."""
|
||||
pass
|
||||
|
||||
|
||||
def on_train_epoch_start(trainer):
|
||||
"""Called at the start of each training epoch."""
|
||||
pass
|
||||
|
||||
|
||||
def on_train_batch_start(trainer):
|
||||
"""Called at the start of each training batch."""
|
||||
pass
|
||||
|
||||
|
||||
def optimizer_step(trainer):
|
||||
"""Called when the optimizer takes a step."""
|
||||
pass
|
||||
|
||||
|
||||
def on_before_zero_grad(trainer):
|
||||
"""Called before the gradients are set to zero."""
|
||||
pass
|
||||
|
||||
|
||||
def on_train_batch_end(trainer):
|
||||
"""Called at the end of each training batch."""
|
||||
pass
|
||||
|
||||
|
||||
def on_train_epoch_end(trainer):
|
||||
"""Called at the end of each training epoch."""
|
||||
pass
|
||||
|
||||
|
||||
def on_fit_epoch_end(trainer):
|
||||
"""Called at the end of each fit epoch (train + val)."""
|
||||
pass
|
||||
|
||||
|
||||
def on_model_save(trainer):
|
||||
"""Called when the model is saved."""
|
||||
pass
|
||||
|
||||
|
||||
def on_train_end(trainer):
|
||||
"""Called when the training ends."""
|
||||
pass
|
||||
|
||||
|
||||
def on_params_update(trainer):
|
||||
"""Called when the model parameters are updated."""
|
||||
pass
|
||||
|
||||
|
||||
def teardown(trainer):
|
||||
"""Called during the teardown of the training process."""
|
||||
pass
|
||||
|
||||
|
||||
# Validator callbacks --------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
def on_val_start(validator):
|
||||
"""Called when the validation starts."""
|
||||
pass
|
||||
|
||||
|
||||
def on_val_batch_start(validator):
|
||||
"""Called at the start of each validation batch."""
|
||||
pass
|
||||
|
||||
|
||||
def on_val_batch_end(validator):
|
||||
"""Called at the end of each validation batch."""
|
||||
pass
|
||||
|
||||
|
||||
def on_val_end(validator):
|
||||
"""Called when the validation ends."""
|
||||
pass
|
||||
|
||||
|
||||
# Predictor callbacks --------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
def on_predict_start(predictor):
|
||||
"""Called when the prediction starts."""
|
||||
pass
|
||||
|
||||
|
||||
def on_predict_batch_start(predictor):
|
||||
"""Called at the start of each prediction batch."""
|
||||
pass
|
||||
|
||||
|
||||
def on_predict_batch_end(predictor):
|
||||
"""Called at the end of each prediction batch."""
|
||||
pass
|
||||
|
||||
|
||||
def on_predict_postprocess_end(predictor):
|
||||
"""Called after the post-processing of the prediction ends."""
|
||||
pass
|
||||
|
||||
|
||||
def on_predict_end(predictor):
|
||||
"""Called when the prediction ends."""
|
||||
pass
|
||||
|
||||
|
||||
# Exporter callbacks ---------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
def on_export_start(exporter):
|
||||
"""Called when the model export starts."""
|
||||
pass
|
||||
|
||||
|
||||
def on_export_end(exporter):
|
||||
"""Called when the model export ends."""
|
||||
pass
|
||||
|
||||
|
||||
default_callbacks = {
|
||||
# Run in trainer
|
||||
'on_pretrain_routine_start': [on_pretrain_routine_start],
|
||||
'on_pretrain_routine_end': [on_pretrain_routine_end],
|
||||
'on_train_start': [on_train_start],
|
||||
'on_train_epoch_start': [on_train_epoch_start],
|
||||
'on_train_batch_start': [on_train_batch_start],
|
||||
'optimizer_step': [optimizer_step],
|
||||
'on_before_zero_grad': [on_before_zero_grad],
|
||||
'on_train_batch_end': [on_train_batch_end],
|
||||
'on_train_epoch_end': [on_train_epoch_end],
|
||||
'on_fit_epoch_end': [on_fit_epoch_end], # fit = train + val
|
||||
'on_model_save': [on_model_save],
|
||||
'on_train_end': [on_train_end],
|
||||
'on_params_update': [on_params_update],
|
||||
'teardown': [teardown],
|
||||
|
||||
# Run in validator
|
||||
'on_val_start': [on_val_start],
|
||||
'on_val_batch_start': [on_val_batch_start],
|
||||
'on_val_batch_end': [on_val_batch_end],
|
||||
'on_val_end': [on_val_end],
|
||||
|
||||
# Run in predictor
|
||||
'on_predict_start': [on_predict_start],
|
||||
'on_predict_batch_start': [on_predict_batch_start],
|
||||
'on_predict_postprocess_end': [on_predict_postprocess_end],
|
||||
'on_predict_batch_end': [on_predict_batch_end],
|
||||
'on_predict_end': [on_predict_end],
|
||||
|
||||
# Run in exporter
|
||||
'on_export_start': [on_export_start],
|
||||
'on_export_end': [on_export_end]}
|
||||
|
||||
|
||||
def get_default_callbacks():
|
||||
"""
|
||||
Return a copy of the default_callbacks dictionary with lists as default values.
|
||||
|
||||
Returns:
|
||||
(defaultdict): A defaultdict with keys from default_callbacks and empty lists as default values.
|
||||
"""
|
||||
return defaultdict(list, deepcopy(default_callbacks))
|
||||
|
||||
|
||||
def add_integration_callbacks(instance):
|
||||
"""
|
||||
Add integration callbacks from various sources to the instance's callbacks.
|
||||
|
||||
Args:
|
||||
instance (Trainer, Predictor, Validator, Exporter): An object with a 'callbacks' attribute that is a dictionary
|
||||
of callback lists.
|
||||
"""
|
||||
from .clearml import callbacks as clearml_cb
|
||||
from .comet import callbacks as comet_cb
|
||||
from .dvc import callbacks as dvc_cb
|
||||
from .hub import callbacks as hub_cb
|
||||
from .mlflow import callbacks as mlflow_cb
|
||||
from .neptune import callbacks as neptune_cb
|
||||
from .raytune import callbacks as tune_cb
|
||||
from .tensorboard import callbacks as tensorboard_cb
|
||||
from .wb import callbacks as wb_cb
|
||||
|
||||
for x in clearml_cb, comet_cb, hub_cb, mlflow_cb, neptune_cb, tune_cb, tensorboard_cb, wb_cb, dvc_cb:
|
||||
for k, v in x.items():
|
||||
if v not in instance.callbacks[k]: # prevent duplicate callbacks addition
|
||||
instance.callbacks[k].append(v) # callback[name].append(func)
|
||||
@ -1,143 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import re
|
||||
|
||||
import matplotlib.image as mpimg
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING
|
||||
from ultralytics.yolo.utils.torch_utils import model_info_for_loggers
|
||||
|
||||
try:
|
||||
import clearml
|
||||
from clearml import Task
|
||||
from clearml.binding.frameworks.pytorch_bind import PatchPyTorchModelIO
|
||||
from clearml.binding.matplotlib_bind import PatchedMatplotlib
|
||||
|
||||
assert hasattr(clearml, '__version__') # verify package is not directory
|
||||
assert not TESTS_RUNNING # do not log pytest
|
||||
except (ImportError, AssertionError):
|
||||
clearml = None
|
||||
|
||||
|
||||
def _log_debug_samples(files, title='Debug Samples') -> None:
|
||||
"""
|
||||
Log files (images) as debug samples in the ClearML task.
|
||||
|
||||
Args:
|
||||
files (list): A list of file paths in PosixPath format.
|
||||
title (str): A title that groups together images with the same values.
|
||||
"""
|
||||
task = Task.current_task()
|
||||
if task:
|
||||
for f in files:
|
||||
if f.exists():
|
||||
it = re.search(r'_batch(\d+)', f.name)
|
||||
iteration = int(it.groups()[0]) if it else 0
|
||||
task.get_logger().report_image(title=title,
|
||||
series=f.name.replace(it.group(), ''),
|
||||
local_path=str(f),
|
||||
iteration=iteration)
|
||||
|
||||
|
||||
def _log_plot(title, plot_path) -> None:
|
||||
"""
|
||||
Log an image as a plot in the plot section of ClearML.
|
||||
|
||||
Args:
|
||||
title (str): The title of the plot.
|
||||
plot_path (str): The path to the saved image file.
|
||||
"""
|
||||
img = mpimg.imread(plot_path)
|
||||
fig = plt.figure()
|
||||
ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect='auto', xticks=[], yticks=[]) # no ticks
|
||||
ax.imshow(img)
|
||||
|
||||
Task.current_task().get_logger().report_matplotlib_figure(title=title,
|
||||
series='',
|
||||
figure=fig,
|
||||
report_interactive=False)
|
||||
|
||||
|
||||
def on_pretrain_routine_start(trainer):
|
||||
"""Runs at start of pretraining routine; initializes and connects/ logs task to ClearML."""
|
||||
try:
|
||||
task = Task.current_task()
|
||||
if task:
|
||||
# Make sure the automatic pytorch and matplotlib bindings are disabled!
|
||||
# We are logging these plots and model files manually in the integration
|
||||
PatchPyTorchModelIO.update_current_task(None)
|
||||
PatchedMatplotlib.update_current_task(None)
|
||||
else:
|
||||
task = Task.init(project_name=trainer.args.project or 'YOLOv8',
|
||||
task_name=trainer.args.name,
|
||||
tags=['YOLOv8'],
|
||||
output_uri=True,
|
||||
reuse_last_task_id=False,
|
||||
auto_connect_frameworks={
|
||||
'pytorch': False,
|
||||
'matplotlib': False})
|
||||
LOGGER.warning('ClearML Initialized a new task. If you want to run remotely, '
|
||||
'please add clearml-init and connect your arguments before initializing YOLO.')
|
||||
task.connect(vars(trainer.args), name='General')
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'WARNING ⚠️ ClearML installed but not initialized correctly, not logging this run. {e}')
|
||||
|
||||
|
||||
def on_train_epoch_end(trainer):
|
||||
task = Task.current_task()
|
||||
|
||||
if task:
|
||||
"""Logs debug samples for the first epoch of YOLO training."""
|
||||
if trainer.epoch == 1:
|
||||
_log_debug_samples(sorted(trainer.save_dir.glob('train_batch*.jpg')), 'Mosaic')
|
||||
"""Report the current training progress."""
|
||||
for k, v in trainer.validator.metrics.results_dict.items():
|
||||
task.get_logger().report_scalar('train', k, v, iteration=trainer.epoch)
|
||||
|
||||
|
||||
def on_fit_epoch_end(trainer):
|
||||
"""Reports model information to logger at the end of an epoch."""
|
||||
task = Task.current_task()
|
||||
if task:
|
||||
# You should have access to the validation bboxes under jdict
|
||||
task.get_logger().report_scalar(title='Epoch Time',
|
||||
series='Epoch Time',
|
||||
value=trainer.epoch_time,
|
||||
iteration=trainer.epoch)
|
||||
if trainer.epoch == 0:
|
||||
for k, v in model_info_for_loggers(trainer).items():
|
||||
task.get_logger().report_single_value(k, v)
|
||||
|
||||
|
||||
def on_val_end(validator):
|
||||
"""Logs validation results including labels and predictions."""
|
||||
if Task.current_task():
|
||||
# Log val_labels and val_pred
|
||||
_log_debug_samples(sorted(validator.save_dir.glob('val*.jpg')), 'Validation')
|
||||
|
||||
|
||||
def on_train_end(trainer):
|
||||
"""Logs final model and its name on training completion."""
|
||||
task = Task.current_task()
|
||||
if task:
|
||||
# Log final results, CM matrix + PR plots
|
||||
files = [
|
||||
'results.png', 'confusion_matrix.png', 'confusion_matrix_normalized.png',
|
||||
*(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
|
||||
files = [(trainer.save_dir / f) for f in files if (trainer.save_dir / f).exists()] # filter
|
||||
for f in files:
|
||||
_log_plot(title=f.stem, plot_path=f)
|
||||
# Report final metrics
|
||||
for k, v in trainer.validator.metrics.results_dict.items():
|
||||
task.get_logger().report_single_value(k, v)
|
||||
# Log the final model
|
||||
task.update_output_model(model_path=str(trainer.best), model_name=trainer.args.name, auto_delete_file=False)
|
||||
|
||||
|
||||
callbacks = {
|
||||
'on_pretrain_routine_start': on_pretrain_routine_start,
|
||||
'on_train_epoch_end': on_train_epoch_end,
|
||||
'on_fit_epoch_end': on_fit_epoch_end,
|
||||
'on_val_end': on_val_end,
|
||||
'on_train_end': on_train_end} if clearml else {}
|
||||
@ -1,368 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
from ultralytics.yolo.utils import LOGGER, RANK, TESTS_RUNNING, ops
|
||||
from ultralytics.yolo.utils.torch_utils import model_info_for_loggers
|
||||
|
||||
try:
|
||||
import comet_ml
|
||||
|
||||
assert not TESTS_RUNNING # do not log pytest
|
||||
assert hasattr(comet_ml, '__version__') # verify package is not directory
|
||||
except (ImportError, AssertionError):
|
||||
comet_ml = None
|
||||
|
||||
# Ensures certain logging functions only run for supported tasks
|
||||
COMET_SUPPORTED_TASKS = ['detect']
|
||||
|
||||
# Names of plots created by YOLOv8 that are logged to Comet
|
||||
EVALUATION_PLOT_NAMES = 'F1_curve', 'P_curve', 'R_curve', 'PR_curve', 'confusion_matrix'
|
||||
LABEL_PLOT_NAMES = 'labels', 'labels_correlogram'
|
||||
|
||||
_comet_image_prediction_count = 0
|
||||
|
||||
|
||||
def _get_comet_mode():
|
||||
return os.getenv('COMET_MODE', 'online')
|
||||
|
||||
|
||||
def _get_comet_model_name():
|
||||
return os.getenv('COMET_MODEL_NAME', 'YOLOv8')
|
||||
|
||||
|
||||
def _get_eval_batch_logging_interval():
|
||||
return int(os.getenv('COMET_EVAL_BATCH_LOGGING_INTERVAL', 1))
|
||||
|
||||
|
||||
def _get_max_image_predictions_to_log():
|
||||
return int(os.getenv('COMET_MAX_IMAGE_PREDICTIONS', 100))
|
||||
|
||||
|
||||
def _scale_confidence_score(score):
|
||||
scale = float(os.getenv('COMET_MAX_CONFIDENCE_SCORE', 100.0))
|
||||
return score * scale
|
||||
|
||||
|
||||
def _should_log_confusion_matrix():
|
||||
return os.getenv('COMET_EVAL_LOG_CONFUSION_MATRIX', 'false').lower() == 'true'
|
||||
|
||||
|
||||
def _should_log_image_predictions():
|
||||
return os.getenv('COMET_EVAL_LOG_IMAGE_PREDICTIONS', 'true').lower() == 'true'
|
||||
|
||||
|
||||
def _get_experiment_type(mode, project_name):
|
||||
"""Return an experiment based on mode and project name."""
|
||||
if mode == 'offline':
|
||||
return comet_ml.OfflineExperiment(project_name=project_name)
|
||||
|
||||
return comet_ml.Experiment(project_name=project_name)
|
||||
|
||||
|
||||
def _create_experiment(args):
|
||||
"""Ensures that the experiment object is only created in a single process during distributed training."""
|
||||
if RANK not in (-1, 0):
|
||||
return
|
||||
try:
|
||||
comet_mode = _get_comet_mode()
|
||||
_project_name = os.getenv('COMET_PROJECT_NAME', args.project)
|
||||
experiment = _get_experiment_type(comet_mode, _project_name)
|
||||
experiment.log_parameters(vars(args))
|
||||
experiment.log_others({
|
||||
'eval_batch_logging_interval': _get_eval_batch_logging_interval(),
|
||||
'log_confusion_matrix_on_eval': _should_log_confusion_matrix(),
|
||||
'log_image_predictions': _should_log_image_predictions(),
|
||||
'max_image_predictions': _get_max_image_predictions_to_log(), })
|
||||
experiment.log_other('Created from', 'yolov8')
|
||||
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'WARNING ⚠️ Comet installed but not initialized correctly, not logging this run. {e}')
|
||||
|
||||
|
||||
def _fetch_trainer_metadata(trainer):
|
||||
"""Returns metadata for YOLO training including epoch and asset saving status."""
|
||||
curr_epoch = trainer.epoch + 1
|
||||
|
||||
train_num_steps_per_epoch = len(trainer.train_loader.dataset) // trainer.batch_size
|
||||
curr_step = curr_epoch * train_num_steps_per_epoch
|
||||
final_epoch = curr_epoch == trainer.epochs
|
||||
|
||||
save = trainer.args.save
|
||||
save_period = trainer.args.save_period
|
||||
save_interval = curr_epoch % save_period == 0
|
||||
save_assets = save and save_period > 0 and save_interval and not final_epoch
|
||||
|
||||
return dict(
|
||||
curr_epoch=curr_epoch,
|
||||
curr_step=curr_step,
|
||||
save_assets=save_assets,
|
||||
final_epoch=final_epoch,
|
||||
)
|
||||
|
||||
|
||||
def _scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad):
|
||||
"""YOLOv8 resizes images during training and the label values
|
||||
are normalized based on this resized shape. This function rescales the
|
||||
bounding box labels to the original image shape.
|
||||
"""
|
||||
|
||||
resized_image_height, resized_image_width = resized_image_shape
|
||||
|
||||
# Convert normalized xywh format predictions to xyxy in resized scale format
|
||||
box = ops.xywhn2xyxy(box, h=resized_image_height, w=resized_image_width)
|
||||
# Scale box predictions from resized image scale back to original image scale
|
||||
box = ops.scale_boxes(resized_image_shape, box, original_image_shape, ratio_pad)
|
||||
# Convert bounding box format from xyxy to xywh for Comet logging
|
||||
box = ops.xyxy2xywh(box)
|
||||
# Adjust xy center to correspond top-left corner
|
||||
box[:2] -= box[2:] / 2
|
||||
box = box.tolist()
|
||||
|
||||
return box
|
||||
|
||||
|
||||
def _format_ground_truth_annotations_for_detection(img_idx, image_path, batch, class_name_map=None):
|
||||
"""Format ground truth annotations for detection."""
|
||||
indices = batch['batch_idx'] == img_idx
|
||||
bboxes = batch['bboxes'][indices]
|
||||
if len(bboxes) == 0:
|
||||
LOGGER.debug(f'COMET WARNING: Image: {image_path} has no bounding boxes labels')
|
||||
return None
|
||||
|
||||
cls_labels = batch['cls'][indices].squeeze(1).tolist()
|
||||
if class_name_map:
|
||||
cls_labels = [str(class_name_map[label]) for label in cls_labels]
|
||||
|
||||
original_image_shape = batch['ori_shape'][img_idx]
|
||||
resized_image_shape = batch['resized_shape'][img_idx]
|
||||
ratio_pad = batch['ratio_pad'][img_idx]
|
||||
|
||||
data = []
|
||||
for box, label in zip(bboxes, cls_labels):
|
||||
box = _scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad)
|
||||
data.append({
|
||||
'boxes': [box],
|
||||
'label': f'gt_{label}',
|
||||
'score': _scale_confidence_score(1.0), })
|
||||
|
||||
return {'name': 'ground_truth', 'data': data}
|
||||
|
||||
|
||||
def _format_prediction_annotations_for_detection(image_path, metadata, class_label_map=None):
|
||||
"""Format YOLO predictions for object detection visualization."""
|
||||
stem = image_path.stem
|
||||
image_id = int(stem) if stem.isnumeric() else stem
|
||||
|
||||
predictions = metadata.get(image_id)
|
||||
if not predictions:
|
||||
LOGGER.debug(f'COMET WARNING: Image: {image_path} has no bounding boxes predictions')
|
||||
return None
|
||||
|
||||
data = []
|
||||
for prediction in predictions:
|
||||
boxes = prediction['bbox']
|
||||
score = _scale_confidence_score(prediction['score'])
|
||||
cls_label = prediction['category_id']
|
||||
if class_label_map:
|
||||
cls_label = str(class_label_map[cls_label])
|
||||
|
||||
data.append({'boxes': [boxes], 'label': cls_label, 'score': score})
|
||||
|
||||
return {'name': 'prediction', 'data': data}
|
||||
|
||||
|
||||
def _fetch_annotations(img_idx, image_path, batch, prediction_metadata_map, class_label_map):
|
||||
"""Join the ground truth and prediction annotations if they exist."""
|
||||
ground_truth_annotations = _format_ground_truth_annotations_for_detection(img_idx, image_path, batch,
|
||||
class_label_map)
|
||||
prediction_annotations = _format_prediction_annotations_for_detection(image_path, prediction_metadata_map,
|
||||
class_label_map)
|
||||
|
||||
annotations = [
|
||||
annotation for annotation in [ground_truth_annotations, prediction_annotations] if annotation is not None]
|
||||
return [annotations] if annotations else None
|
||||
|
||||
|
||||
def _create_prediction_metadata_map(model_predictions):
|
||||
"""Create metadata map for model predictions by groupings them based on image ID."""
|
||||
pred_metadata_map = {}
|
||||
for prediction in model_predictions:
|
||||
pred_metadata_map.setdefault(prediction['image_id'], [])
|
||||
pred_metadata_map[prediction['image_id']].append(prediction)
|
||||
|
||||
return pred_metadata_map
|
||||
|
||||
|
||||
def _log_confusion_matrix(experiment, trainer, curr_step, curr_epoch):
|
||||
"""Log the confusion matrix to Comet experiment."""
|
||||
conf_mat = trainer.validator.confusion_matrix.matrix
|
||||
names = list(trainer.data['names'].values()) + ['background']
|
||||
experiment.log_confusion_matrix(
|
||||
matrix=conf_mat,
|
||||
labels=names,
|
||||
max_categories=len(names),
|
||||
epoch=curr_epoch,
|
||||
step=curr_step,
|
||||
)
|
||||
|
||||
|
||||
def _log_images(experiment, image_paths, curr_step, annotations=None):
|
||||
"""Logs images to the experiment with optional annotations."""
|
||||
if annotations:
|
||||
for image_path, annotation in zip(image_paths, annotations):
|
||||
experiment.log_image(image_path, name=image_path.stem, step=curr_step, annotations=annotation)
|
||||
|
||||
else:
|
||||
for image_path in image_paths:
|
||||
experiment.log_image(image_path, name=image_path.stem, step=curr_step)
|
||||
|
||||
|
||||
def _log_image_predictions(experiment, validator, curr_step):
|
||||
"""Logs predicted boxes for a single image during training."""
|
||||
global _comet_image_prediction_count
|
||||
|
||||
task = validator.args.task
|
||||
if task not in COMET_SUPPORTED_TASKS:
|
||||
return
|
||||
|
||||
jdict = validator.jdict
|
||||
if not jdict:
|
||||
return
|
||||
|
||||
predictions_metadata_map = _create_prediction_metadata_map(jdict)
|
||||
dataloader = validator.dataloader
|
||||
class_label_map = validator.names
|
||||
|
||||
batch_logging_interval = _get_eval_batch_logging_interval()
|
||||
max_image_predictions = _get_max_image_predictions_to_log()
|
||||
|
||||
for batch_idx, batch in enumerate(dataloader):
|
||||
if (batch_idx + 1) % batch_logging_interval != 0:
|
||||
continue
|
||||
|
||||
image_paths = batch['im_file']
|
||||
for img_idx, image_path in enumerate(image_paths):
|
||||
if _comet_image_prediction_count >= max_image_predictions:
|
||||
return
|
||||
|
||||
image_path = Path(image_path)
|
||||
annotations = _fetch_annotations(
|
||||
img_idx,
|
||||
image_path,
|
||||
batch,
|
||||
predictions_metadata_map,
|
||||
class_label_map,
|
||||
)
|
||||
_log_images(
|
||||
experiment,
|
||||
[image_path],
|
||||
curr_step,
|
||||
annotations=annotations,
|
||||
)
|
||||
_comet_image_prediction_count += 1
|
||||
|
||||
|
||||
def _log_plots(experiment, trainer):
|
||||
"""Logs evaluation plots and label plots for the experiment."""
|
||||
plot_filenames = [trainer.save_dir / f'{plots}.png' for plots in EVALUATION_PLOT_NAMES]
|
||||
_log_images(experiment, plot_filenames, None)
|
||||
|
||||
label_plot_filenames = [trainer.save_dir / f'{labels}.jpg' for labels in LABEL_PLOT_NAMES]
|
||||
_log_images(experiment, label_plot_filenames, None)
|
||||
|
||||
|
||||
def _log_model(experiment, trainer):
|
||||
"""Log the best-trained model to Comet.ml."""
|
||||
model_name = _get_comet_model_name()
|
||||
experiment.log_model(
|
||||
model_name,
|
||||
file_or_folder=str(trainer.best),
|
||||
file_name='best.pt',
|
||||
overwrite=True,
|
||||
)
|
||||
|
||||
|
||||
def on_pretrain_routine_start(trainer):
|
||||
"""Creates or resumes a CometML experiment at the start of a YOLO pre-training routine."""
|
||||
experiment = comet_ml.get_global_experiment()
|
||||
is_alive = getattr(experiment, 'alive', False)
|
||||
if not experiment or not is_alive:
|
||||
_create_experiment(trainer.args)
|
||||
|
||||
|
||||
def on_train_epoch_end(trainer):
|
||||
"""Log metrics and save batch images at the end of training epochs."""
|
||||
experiment = comet_ml.get_global_experiment()
|
||||
if not experiment:
|
||||
return
|
||||
|
||||
metadata = _fetch_trainer_metadata(trainer)
|
||||
curr_epoch = metadata['curr_epoch']
|
||||
curr_step = metadata['curr_step']
|
||||
|
||||
experiment.log_metrics(
|
||||
trainer.label_loss_items(trainer.tloss, prefix='train'),
|
||||
step=curr_step,
|
||||
epoch=curr_epoch,
|
||||
)
|
||||
|
||||
if curr_epoch == 1:
|
||||
_log_images(experiment, trainer.save_dir.glob('train_batch*.jpg'), curr_step)
|
||||
|
||||
|
||||
def on_fit_epoch_end(trainer):
|
||||
"""Logs model assets at the end of each epoch."""
|
||||
experiment = comet_ml.get_global_experiment()
|
||||
if not experiment:
|
||||
return
|
||||
|
||||
metadata = _fetch_trainer_metadata(trainer)
|
||||
curr_epoch = metadata['curr_epoch']
|
||||
curr_step = metadata['curr_step']
|
||||
save_assets = metadata['save_assets']
|
||||
|
||||
experiment.log_metrics(trainer.metrics, step=curr_step, epoch=curr_epoch)
|
||||
experiment.log_metrics(trainer.lr, step=curr_step, epoch=curr_epoch)
|
||||
if curr_epoch == 1:
|
||||
experiment.log_metrics(model_info_for_loggers(trainer), step=curr_step, epoch=curr_epoch)
|
||||
|
||||
if not save_assets:
|
||||
return
|
||||
|
||||
_log_model(experiment, trainer)
|
||||
if _should_log_confusion_matrix():
|
||||
_log_confusion_matrix(experiment, trainer, curr_step, curr_epoch)
|
||||
if _should_log_image_predictions():
|
||||
_log_image_predictions(experiment, trainer.validator, curr_step)
|
||||
|
||||
|
||||
def on_train_end(trainer):
|
||||
"""Perform operations at the end of training."""
|
||||
experiment = comet_ml.get_global_experiment()
|
||||
if not experiment:
|
||||
return
|
||||
|
||||
metadata = _fetch_trainer_metadata(trainer)
|
||||
curr_epoch = metadata['curr_epoch']
|
||||
curr_step = metadata['curr_step']
|
||||
plots = trainer.args.plots
|
||||
|
||||
_log_model(experiment, trainer)
|
||||
if plots:
|
||||
_log_plots(experiment, trainer)
|
||||
|
||||
_log_confusion_matrix(experiment, trainer, curr_step, curr_epoch)
|
||||
_log_image_predictions(experiment, trainer.validator, curr_step)
|
||||
experiment.end()
|
||||
|
||||
global _comet_image_prediction_count
|
||||
_comet_image_prediction_count = 0
|
||||
|
||||
|
||||
callbacks = {
|
||||
'on_pretrain_routine_start': on_pretrain_routine_start,
|
||||
'on_train_epoch_end': on_train_epoch_end,
|
||||
'on_fit_epoch_end': on_fit_epoch_end,
|
||||
'on_train_end': on_train_end} if comet_ml else {}
|
||||
@ -1,136 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
import os
|
||||
|
||||
import pkg_resources as pkg
|
||||
|
||||
from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING
|
||||
from ultralytics.yolo.utils.torch_utils import model_info_for_loggers
|
||||
|
||||
try:
|
||||
from importlib.metadata import version
|
||||
|
||||
import dvclive
|
||||
|
||||
assert not TESTS_RUNNING # do not log pytest
|
||||
|
||||
ver = version('dvclive')
|
||||
if pkg.parse_version(ver) < pkg.parse_version('2.11.0'):
|
||||
LOGGER.debug(f'DVCLive is detected but version {ver} is incompatible (>=2.11 required).')
|
||||
dvclive = None # noqa: F811
|
||||
except (ImportError, AssertionError, TypeError):
|
||||
dvclive = None
|
||||
|
||||
# DVCLive logger instance
|
||||
live = None
|
||||
_processed_plots = {}
|
||||
|
||||
# `on_fit_epoch_end` is called on final validation (probably need to be fixed)
|
||||
# for now this is the way we distinguish final evaluation of the best model vs
|
||||
# last epoch validation
|
||||
_training_epoch = False
|
||||
|
||||
|
||||
def _logger_disabled():
|
||||
return os.getenv('ULTRALYTICS_DVC_DISABLED', 'false').lower() == 'true'
|
||||
|
||||
|
||||
def _log_images(image_path, prefix=''):
|
||||
if live:
|
||||
live.log_image(os.path.join(prefix, image_path.name), image_path)
|
||||
|
||||
|
||||
def _log_plots(plots, prefix=''):
|
||||
for name, params in plots.items():
|
||||
timestamp = params['timestamp']
|
||||
if _processed_plots.get(name) != timestamp:
|
||||
_log_images(name, prefix)
|
||||
_processed_plots[name] = timestamp
|
||||
|
||||
|
||||
def _log_confusion_matrix(validator):
|
||||
targets = []
|
||||
preds = []
|
||||
matrix = validator.confusion_matrix.matrix
|
||||
names = list(validator.names.values())
|
||||
if validator.confusion_matrix.task == 'detect':
|
||||
names += ['background']
|
||||
|
||||
for ti, pred in enumerate(matrix.T.astype(int)):
|
||||
for pi, num in enumerate(pred):
|
||||
targets.extend([names[ti]] * num)
|
||||
preds.extend([names[pi]] * num)
|
||||
|
||||
live.log_sklearn_plot('confusion_matrix', targets, preds, name='cf.json', normalized=True)
|
||||
|
||||
|
||||
def on_pretrain_routine_start(trainer):
|
||||
try:
|
||||
global live
|
||||
if not _logger_disabled():
|
||||
live = dvclive.Live(save_dvc_exp=True, cache_images=True)
|
||||
LOGGER.info(
|
||||
'DVCLive is detected and auto logging is enabled (can be disabled with `ULTRALYTICS_DVC_DISABLED=true`).'
|
||||
)
|
||||
else:
|
||||
LOGGER.debug('DVCLive is detected and auto logging is disabled via `ULTRALYTICS_DVC_DISABLED`.')
|
||||
live = None
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'WARNING ⚠️ DVCLive installed but not initialized correctly, not logging this run. {e}')
|
||||
|
||||
|
||||
def on_pretrain_routine_end(trainer):
|
||||
_log_plots(trainer.plots, 'train')
|
||||
|
||||
|
||||
def on_train_start(trainer):
|
||||
if live:
|
||||
live.log_params(trainer.args)
|
||||
|
||||
|
||||
def on_train_epoch_start(trainer):
|
||||
global _training_epoch
|
||||
_training_epoch = True
|
||||
|
||||
|
||||
def on_fit_epoch_end(trainer):
|
||||
global _training_epoch
|
||||
if live and _training_epoch:
|
||||
all_metrics = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics, **trainer.lr}
|
||||
for metric, value in all_metrics.items():
|
||||
live.log_metric(metric, value)
|
||||
|
||||
if trainer.epoch == 0:
|
||||
for metric, value in model_info_for_loggers(trainer).items():
|
||||
live.log_metric(metric, value, plot=False)
|
||||
|
||||
_log_plots(trainer.plots, 'train')
|
||||
_log_plots(trainer.validator.plots, 'val')
|
||||
|
||||
live.next_step()
|
||||
_training_epoch = False
|
||||
|
||||
|
||||
def on_train_end(trainer):
|
||||
if live:
|
||||
# At the end log the best metrics. It runs validator on the best model internally.
|
||||
all_metrics = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics, **trainer.lr}
|
||||
for metric, value in all_metrics.items():
|
||||
live.log_metric(metric, value, plot=False)
|
||||
|
||||
_log_plots(trainer.plots, 'eval')
|
||||
_log_plots(trainer.validator.plots, 'eval')
|
||||
_log_confusion_matrix(trainer.validator)
|
||||
|
||||
if trainer.best.exists():
|
||||
live.log_artifact(trainer.best, copy=True)
|
||||
|
||||
live.end()
|
||||
|
||||
|
||||
callbacks = {
|
||||
'on_pretrain_routine_start': on_pretrain_routine_start,
|
||||
'on_pretrain_routine_end': on_pretrain_routine_end,
|
||||
'on_train_start': on_train_start,
|
||||
'on_train_epoch_start': on_train_epoch_start,
|
||||
'on_fit_epoch_end': on_fit_epoch_end,
|
||||
'on_train_end': on_train_end} if dvclive else {}
|
||||
@ -1,87 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import json
|
||||
from time import time
|
||||
|
||||
from ultralytics.hub.utils import PREFIX, events
|
||||
from ultralytics.yolo.utils import LOGGER
|
||||
from ultralytics.yolo.utils.torch_utils import model_info_for_loggers
|
||||
|
||||
|
||||
def on_pretrain_routine_end(trainer):
|
||||
"""Logs info before starting timer for upload rate limit."""
|
||||
session = getattr(trainer, 'hub_session', None)
|
||||
if session:
|
||||
# Start timer for upload rate limit
|
||||
LOGGER.info(f'{PREFIX}View model at https://hub.ultralytics.com/models/{session.model_id} 🚀')
|
||||
session.timers = {'metrics': time(), 'ckpt': time()} # start timer on session.rate_limit
|
||||
|
||||
|
||||
def on_fit_epoch_end(trainer):
|
||||
"""Uploads training progress metrics at the end of each epoch."""
|
||||
session = getattr(trainer, 'hub_session', None)
|
||||
if session:
|
||||
# Upload metrics after val end
|
||||
all_plots = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics}
|
||||
if trainer.epoch == 0:
|
||||
all_plots = {**all_plots, **model_info_for_loggers(trainer)}
|
||||
session.metrics_queue[trainer.epoch] = json.dumps(all_plots)
|
||||
if time() - session.timers['metrics'] > session.rate_limits['metrics']:
|
||||
session.upload_metrics()
|
||||
session.timers['metrics'] = time() # reset timer
|
||||
session.metrics_queue = {} # reset queue
|
||||
|
||||
|
||||
def on_model_save(trainer):
|
||||
"""Saves checkpoints to Ultralytics HUB with rate limiting."""
|
||||
session = getattr(trainer, 'hub_session', None)
|
||||
if session:
|
||||
# Upload checkpoints with rate limiting
|
||||
is_best = trainer.best_fitness == trainer.fitness
|
||||
if time() - session.timers['ckpt'] > session.rate_limits['ckpt']:
|
||||
LOGGER.info(f'{PREFIX}Uploading checkpoint https://hub.ultralytics.com/models/{session.model_id}')
|
||||
session.upload_model(trainer.epoch, trainer.last, is_best)
|
||||
session.timers['ckpt'] = time() # reset timer
|
||||
|
||||
|
||||
def on_train_end(trainer):
|
||||
"""Upload final model and metrics to Ultralytics HUB at the end of training."""
|
||||
session = getattr(trainer, 'hub_session', None)
|
||||
if session:
|
||||
# Upload final model and metrics with exponential standoff
|
||||
LOGGER.info(f'{PREFIX}Syncing final model...')
|
||||
session.upload_model(trainer.epoch, trainer.best, map=trainer.metrics.get('metrics/mAP50-95(B)', 0), final=True)
|
||||
session.alive = False # stop heartbeats
|
||||
LOGGER.info(f'{PREFIX}Done ✅\n'
|
||||
f'{PREFIX}View model at https://hub.ultralytics.com/models/{session.model_id} 🚀')
|
||||
|
||||
|
||||
def on_train_start(trainer):
|
||||
"""Run events on train start."""
|
||||
events(trainer.args)
|
||||
|
||||
|
||||
def on_val_start(validator):
|
||||
"""Runs events on validation start."""
|
||||
events(validator.args)
|
||||
|
||||
|
||||
def on_predict_start(predictor):
|
||||
"""Run events on predict start."""
|
||||
events(predictor.args)
|
||||
|
||||
|
||||
def on_export_start(exporter):
|
||||
"""Run events on export start."""
|
||||
events(exporter.args)
|
||||
|
||||
|
||||
callbacks = {
|
||||
'on_pretrain_routine_end': on_pretrain_routine_end,
|
||||
'on_fit_epoch_end': on_fit_epoch_end,
|
||||
'on_model_save': on_model_save,
|
||||
'on_train_end': on_train_end,
|
||||
'on_train_start': on_train_start,
|
||||
'on_val_start': on_val_start,
|
||||
'on_predict_start': on_predict_start,
|
||||
'on_export_start': on_export_start}
|
||||
@ -1,71 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import os
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING, colorstr
|
||||
|
||||
try:
|
||||
import mlflow
|
||||
|
||||
assert not TESTS_RUNNING # do not log pytest
|
||||
assert hasattr(mlflow, '__version__') # verify package is not directory
|
||||
except (ImportError, AssertionError):
|
||||
mlflow = None
|
||||
|
||||
|
||||
def on_pretrain_routine_end(trainer):
|
||||
"""Logs training parameters to MLflow."""
|
||||
global mlflow, run, run_id, experiment_name
|
||||
|
||||
if os.environ.get('MLFLOW_TRACKING_URI') is None:
|
||||
mlflow = None
|
||||
|
||||
if mlflow:
|
||||
mlflow_location = os.environ['MLFLOW_TRACKING_URI'] # "http://192.168.xxx.xxx:5000"
|
||||
mlflow.set_tracking_uri(mlflow_location)
|
||||
|
||||
experiment_name = os.environ.get('MLFLOW_EXPERIMENT_NAME') or trainer.args.project or '/Shared/YOLOv8'
|
||||
run_name = os.environ.get('MLFLOW_RUN') or trainer.args.name
|
||||
experiment = mlflow.get_experiment_by_name(experiment_name)
|
||||
if experiment is None:
|
||||
mlflow.create_experiment(experiment_name)
|
||||
mlflow.set_experiment(experiment_name)
|
||||
|
||||
prefix = colorstr('MLFlow: ')
|
||||
try:
|
||||
run, active_run = mlflow, mlflow.active_run()
|
||||
if not active_run:
|
||||
active_run = mlflow.start_run(experiment_id=experiment.experiment_id, run_name=run_name)
|
||||
run_id = active_run.info.run_id
|
||||
LOGGER.info(f'{prefix}Using run_id({run_id}) at {mlflow_location}')
|
||||
run.log_params(vars(trainer.model.args))
|
||||
except Exception as err:
|
||||
LOGGER.error(f'{prefix}Failing init - {repr(err)}')
|
||||
LOGGER.warning(f'{prefix}Continuing without Mlflow')
|
||||
|
||||
|
||||
def on_fit_epoch_end(trainer):
|
||||
"""Logs training metrics to Mlflow."""
|
||||
if mlflow:
|
||||
metrics_dict = {f"{re.sub('[()]', '', k)}": float(v) for k, v in trainer.metrics.items()}
|
||||
run.log_metrics(metrics=metrics_dict, step=trainer.epoch)
|
||||
|
||||
|
||||
def on_train_end(trainer):
|
||||
"""Called at end of train loop to log model artifact info."""
|
||||
if mlflow:
|
||||
root_dir = Path(__file__).resolve().parents[3]
|
||||
run.log_artifact(trainer.last)
|
||||
run.log_artifact(trainer.best)
|
||||
run.pyfunc.log_model(artifact_path=experiment_name,
|
||||
code_path=[str(root_dir)],
|
||||
artifacts={'model_path': str(trainer.save_dir)},
|
||||
python_model=run.pyfunc.PythonModel())
|
||||
|
||||
|
||||
callbacks = {
|
||||
'on_pretrain_routine_end': on_pretrain_routine_end,
|
||||
'on_fit_epoch_end': on_fit_epoch_end,
|
||||
'on_train_end': on_train_end} if mlflow else {}
|
||||
@ -1,103 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import matplotlib.image as mpimg
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING
|
||||
from ultralytics.yolo.utils.torch_utils import model_info_for_loggers
|
||||
|
||||
try:
|
||||
import neptune
|
||||
from neptune.types import File
|
||||
|
||||
assert not TESTS_RUNNING # do not log pytest
|
||||
assert hasattr(neptune, '__version__')
|
||||
except (ImportError, AssertionError):
|
||||
neptune = None
|
||||
|
||||
run = None # NeptuneAI experiment logger instance
|
||||
|
||||
|
||||
def _log_scalars(scalars, step=0):
|
||||
"""Log scalars to the NeptuneAI experiment logger."""
|
||||
if run:
|
||||
for k, v in scalars.items():
|
||||
run[k].append(value=v, step=step)
|
||||
|
||||
|
||||
def _log_images(imgs_dict, group=''):
|
||||
"""Log scalars to the NeptuneAI experiment logger."""
|
||||
if run:
|
||||
for k, v in imgs_dict.items():
|
||||
run[f'{group}/{k}'].upload(File(v))
|
||||
|
||||
|
||||
def _log_plot(title, plot_path):
|
||||
"""Log plots to the NeptuneAI experiment logger."""
|
||||
"""
|
||||
Log image as plot in the plot section of NeptuneAI
|
||||
|
||||
arguments:
|
||||
title (str) Title of the plot
|
||||
plot_path (PosixPath or str) Path to the saved image file
|
||||
"""
|
||||
img = mpimg.imread(plot_path)
|
||||
fig = plt.figure()
|
||||
ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect='auto', xticks=[], yticks=[]) # no ticks
|
||||
ax.imshow(img)
|
||||
run[f'Plots/{title}'].upload(fig)
|
||||
|
||||
|
||||
def on_pretrain_routine_start(trainer):
|
||||
"""Callback function called before the training routine starts."""
|
||||
try:
|
||||
global run
|
||||
run = neptune.init_run(project=trainer.args.project or 'YOLOv8', name=trainer.args.name, tags=['YOLOv8'])
|
||||
run['Configuration/Hyperparameters'] = {k: '' if v is None else v for k, v in vars(trainer.args).items()}
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'WARNING ⚠️ NeptuneAI installed but not initialized correctly, not logging this run. {e}')
|
||||
|
||||
|
||||
def on_train_epoch_end(trainer):
|
||||
"""Callback function called at end of each training epoch."""
|
||||
_log_scalars(trainer.label_loss_items(trainer.tloss, prefix='train'), trainer.epoch + 1)
|
||||
_log_scalars(trainer.lr, trainer.epoch + 1)
|
||||
if trainer.epoch == 1:
|
||||
_log_images({f.stem: str(f) for f in trainer.save_dir.glob('train_batch*.jpg')}, 'Mosaic')
|
||||
|
||||
|
||||
def on_fit_epoch_end(trainer):
|
||||
"""Callback function called at end of each fit (train+val) epoch."""
|
||||
if run and trainer.epoch == 0:
|
||||
run['Configuration/Model'] = model_info_for_loggers(trainer)
|
||||
_log_scalars(trainer.metrics, trainer.epoch + 1)
|
||||
|
||||
|
||||
def on_val_end(validator):
|
||||
"""Callback function called at end of each validation."""
|
||||
if run:
|
||||
# Log val_labels and val_pred
|
||||
_log_images({f.stem: str(f) for f in validator.save_dir.glob('val*.jpg')}, 'Validation')
|
||||
|
||||
|
||||
def on_train_end(trainer):
|
||||
"""Callback function called at end of training."""
|
||||
if run:
|
||||
# Log final results, CM matrix + PR plots
|
||||
files = [
|
||||
'results.png', 'confusion_matrix.png', 'confusion_matrix_normalized.png',
|
||||
*(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
|
||||
files = [(trainer.save_dir / f) for f in files if (trainer.save_dir / f).exists()] # filter
|
||||
for f in files:
|
||||
_log_plot(title=f.stem, plot_path=f)
|
||||
# Log the final model
|
||||
run[f'weights/{trainer.args.name or trainer.args.task}/{str(trainer.best.name)}'].upload(File(str(
|
||||
trainer.best)))
|
||||
|
||||
|
||||
callbacks = {
|
||||
'on_pretrain_routine_start': on_pretrain_routine_start,
|
||||
'on_train_epoch_end': on_train_epoch_end,
|
||||
'on_fit_epoch_end': on_fit_epoch_end,
|
||||
'on_val_end': on_val_end,
|
||||
'on_train_end': on_train_end} if neptune else {}
|
||||
@ -1,20 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
try:
|
||||
import ray
|
||||
from ray import tune
|
||||
from ray.air import session
|
||||
except (ImportError, AssertionError):
|
||||
tune = None
|
||||
|
||||
|
||||
def on_fit_epoch_end(trainer):
|
||||
"""Sends training metrics to Ray Tune at end of each epoch."""
|
||||
if ray.tune.is_session_enabled():
|
||||
metrics = trainer.metrics
|
||||
metrics['epoch'] = trainer.epoch
|
||||
session.report(metrics)
|
||||
|
||||
|
||||
callbacks = {
|
||||
'on_fit_epoch_end': on_fit_epoch_end, } if tune else {}
|
||||
@ -1,47 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING, colorstr
|
||||
|
||||
try:
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
|
||||
assert not TESTS_RUNNING # do not log pytest
|
||||
except (ImportError, AssertionError):
|
||||
SummaryWriter = None
|
||||
|
||||
writer = None # TensorBoard SummaryWriter instance
|
||||
|
||||
|
||||
def _log_scalars(scalars, step=0):
|
||||
"""Logs scalar values to TensorBoard."""
|
||||
if writer:
|
||||
for k, v in scalars.items():
|
||||
writer.add_scalar(k, v, step)
|
||||
|
||||
|
||||
def on_pretrain_routine_start(trainer):
|
||||
"""Initialize TensorBoard logging with SummaryWriter."""
|
||||
if SummaryWriter:
|
||||
try:
|
||||
global writer
|
||||
writer = SummaryWriter(str(trainer.save_dir))
|
||||
prefix = colorstr('TensorBoard: ')
|
||||
LOGGER.info(f"{prefix}Start with 'tensorboard --logdir {trainer.save_dir}', view at http://localhost:6006/")
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'WARNING ⚠️ TensorBoard not initialized correctly, not logging this run. {e}')
|
||||
|
||||
|
||||
def on_batch_end(trainer):
|
||||
"""Logs scalar statistics at the end of a training batch."""
|
||||
_log_scalars(trainer.label_loss_items(trainer.tloss, prefix='train'), trainer.epoch + 1)
|
||||
|
||||
|
||||
def on_fit_epoch_end(trainer):
|
||||
"""Logs epoch metrics at end of training epoch."""
|
||||
_log_scalars(trainer.metrics, trainer.epoch + 1)
|
||||
|
||||
|
||||
callbacks = {
|
||||
'on_pretrain_routine_start': on_pretrain_routine_start,
|
||||
'on_fit_epoch_end': on_fit_epoch_end,
|
||||
'on_batch_end': on_batch_end}
|
||||
@ -1,60 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
from ultralytics.yolo.utils import TESTS_RUNNING
|
||||
from ultralytics.yolo.utils.torch_utils import model_info_for_loggers
|
||||
|
||||
try:
|
||||
import wandb as wb
|
||||
|
||||
assert hasattr(wb, '__version__')
|
||||
assert not TESTS_RUNNING # do not log pytest
|
||||
except (ImportError, AssertionError):
|
||||
wb = None
|
||||
|
||||
_processed_plots = {}
|
||||
|
||||
|
||||
def _log_plots(plots, step):
|
||||
for name, params in plots.items():
|
||||
timestamp = params['timestamp']
|
||||
if _processed_plots.get(name, None) != timestamp:
|
||||
wb.run.log({name.stem: wb.Image(str(name))}, step=step)
|
||||
_processed_plots[name] = timestamp
|
||||
|
||||
|
||||
def on_pretrain_routine_start(trainer):
|
||||
"""Initiate and start project if module is present."""
|
||||
wb.run or wb.init(project=trainer.args.project or 'YOLOv8', name=trainer.args.name, config=vars(trainer.args))
|
||||
|
||||
|
||||
def on_fit_epoch_end(trainer):
|
||||
"""Logs training metrics and model information at the end of an epoch."""
|
||||
wb.run.log(trainer.metrics, step=trainer.epoch + 1)
|
||||
_log_plots(trainer.plots, step=trainer.epoch + 1)
|
||||
_log_plots(trainer.validator.plots, step=trainer.epoch + 1)
|
||||
if trainer.epoch == 0:
|
||||
wb.run.log(model_info_for_loggers(trainer), step=trainer.epoch + 1)
|
||||
|
||||
|
||||
def on_train_epoch_end(trainer):
|
||||
"""Log metrics and save images at the end of each training epoch."""
|
||||
wb.run.log(trainer.label_loss_items(trainer.tloss, prefix='train'), step=trainer.epoch + 1)
|
||||
wb.run.log(trainer.lr, step=trainer.epoch + 1)
|
||||
if trainer.epoch == 1:
|
||||
_log_plots(trainer.plots, step=trainer.epoch + 1)
|
||||
|
||||
|
||||
def on_train_end(trainer):
|
||||
"""Save the best model as an artifact at end of training."""
|
||||
_log_plots(trainer.validator.plots, step=trainer.epoch + 1)
|
||||
_log_plots(trainer.plots, step=trainer.epoch + 1)
|
||||
art = wb.Artifact(type='model', name=f'run_{wb.run.id}_model')
|
||||
if trainer.best.exists():
|
||||
art.add_file(trainer.best)
|
||||
wb.run.log_artifact(art, aliases=['best'])
|
||||
|
||||
|
||||
callbacks = {
|
||||
'on_pretrain_routine_start': on_pretrain_routine_start,
|
||||
'on_train_epoch_end': on_train_epoch_end,
|
||||
'on_fit_epoch_end': on_fit_epoch_end,
|
||||
'on_train_end': on_train_end} if wb else {}
|
||||
@ -1,459 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
import contextlib
|
||||
import glob
|
||||
import inspect
|
||||
import math
|
||||
import os
|
||||
import platform
|
||||
import re
|
||||
import shutil
|
||||
import subprocess
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import pkg_resources as pkg
|
||||
import psutil
|
||||
import requests
|
||||
import torch
|
||||
from matplotlib import font_manager
|
||||
|
||||
from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ONLINE, ROOT, USER_CONFIG_DIR, ThreadingLocked, TryExcept,
|
||||
clean_url, colorstr, downloads, emojis, is_colab, is_docker, is_jupyter, is_kaggle,
|
||||
is_online, is_pip_package, url2file)
|
||||
|
||||
|
||||
def is_ascii(s) -> bool:
|
||||
"""
|
||||
Check if a string is composed of only ASCII characters.
|
||||
|
||||
Args:
|
||||
s (str): String to be checked.
|
||||
|
||||
Returns:
|
||||
bool: True if the string is composed only of ASCII characters, False otherwise.
|
||||
"""
|
||||
# Convert list, tuple, None, etc. to string
|
||||
s = str(s)
|
||||
|
||||
# Check if the string is composed of only ASCII characters
|
||||
return all(ord(c) < 128 for c in s)
|
||||
|
||||
|
||||
def check_imgsz(imgsz, stride=32, min_dim=1, max_dim=2, floor=0):
|
||||
"""
|
||||
Verify image size is a multiple of the given stride in each dimension. If the image size is not a multiple of the
|
||||
stride, update it to the nearest multiple of the stride that is greater than or equal to the given floor value.
|
||||
|
||||
Args:
|
||||
imgsz (int | cList[int]): Image size.
|
||||
stride (int): Stride value.
|
||||
min_dim (int): Minimum number of dimensions.
|
||||
floor (int): Minimum allowed value for image size.
|
||||
|
||||
Returns:
|
||||
(List[int]): Updated image size.
|
||||
"""
|
||||
# Convert stride to integer if it is a tensor
|
||||
stride = int(stride.max() if isinstance(stride, torch.Tensor) else stride)
|
||||
|
||||
# Convert image size to list if it is an integer
|
||||
if isinstance(imgsz, int):
|
||||
imgsz = [imgsz]
|
||||
elif isinstance(imgsz, (list, tuple)):
|
||||
imgsz = list(imgsz)
|
||||
else:
|
||||
raise TypeError(f"'imgsz={imgsz}' is of invalid type {type(imgsz).__name__}. "
|
||||
f"Valid imgsz types are int i.e. 'imgsz=640' or list i.e. 'imgsz=[640,640]'")
|
||||
|
||||
# Apply max_dim
|
||||
if len(imgsz) > max_dim:
|
||||
msg = "'train' and 'val' imgsz must be an integer, while 'predict' and 'export' imgsz may be a [h, w] list " \
|
||||
"or an integer, i.e. 'yolo export imgsz=640,480' or 'yolo export imgsz=640'"
|
||||
if max_dim != 1:
|
||||
raise ValueError(f'imgsz={imgsz} is not a valid image size. {msg}')
|
||||
LOGGER.warning(f"WARNING ⚠️ updating to 'imgsz={max(imgsz)}'. {msg}")
|
||||
imgsz = [max(imgsz)]
|
||||
# Make image size a multiple of the stride
|
||||
sz = [max(math.ceil(x / stride) * stride, floor) for x in imgsz]
|
||||
|
||||
# Print warning message if image size was updated
|
||||
if sz != imgsz:
|
||||
LOGGER.warning(f'WARNING ⚠️ imgsz={imgsz} must be multiple of max stride {stride}, updating to {sz}')
|
||||
|
||||
# Add missing dimensions if necessary
|
||||
sz = [sz[0], sz[0]] if min_dim == 2 and len(sz) == 1 else sz[0] if min_dim == 1 and len(sz) == 1 else sz
|
||||
|
||||
return sz
|
||||
|
||||
|
||||
def check_version(current: str = '0.0.0',
|
||||
minimum: str = '0.0.0',
|
||||
name: str = 'version ',
|
||||
pinned: bool = False,
|
||||
hard: bool = False,
|
||||
verbose: bool = False) -> bool:
|
||||
"""
|
||||
Check current version against the required minimum version.
|
||||
|
||||
Args:
|
||||
current (str): Current version.
|
||||
minimum (str): Required minimum version.
|
||||
name (str): Name to be used in warning message.
|
||||
pinned (bool): If True, versions must match exactly. If False, minimum version must be satisfied.
|
||||
hard (bool): If True, raise an AssertionError if the minimum version is not met.
|
||||
verbose (bool): If True, print warning message if minimum version is not met.
|
||||
|
||||
Returns:
|
||||
(bool): True if minimum version is met, False otherwise.
|
||||
"""
|
||||
current, minimum = (pkg.parse_version(x) for x in (current, minimum))
|
||||
result = (current == minimum) if pinned else (current >= minimum) # bool
|
||||
warning_message = f'WARNING ⚠️ {name}{minimum} is required by YOLOv8, but {name}{current} is currently installed'
|
||||
if hard:
|
||||
assert result, emojis(warning_message) # assert min requirements met
|
||||
if verbose and not result:
|
||||
LOGGER.warning(warning_message)
|
||||
return result
|
||||
|
||||
|
||||
def check_latest_pypi_version(package_name='ultralytics'):
|
||||
"""
|
||||
Returns the latest version of a PyPI package without downloading or installing it.
|
||||
|
||||
Parameters:
|
||||
package_name (str): The name of the package to find the latest version for.
|
||||
|
||||
Returns:
|
||||
(str): The latest version of the package.
|
||||
"""
|
||||
with contextlib.suppress(Exception):
|
||||
requests.packages.urllib3.disable_warnings() # Disable the InsecureRequestWarning
|
||||
response = requests.get(f'https://pypi.org/pypi/{package_name}/json', timeout=3)
|
||||
if response.status_code == 200:
|
||||
return response.json()['info']['version']
|
||||
return None
|
||||
|
||||
|
||||
def check_pip_update_available():
|
||||
"""
|
||||
Checks if a new version of the ultralytics package is available on PyPI.
|
||||
|
||||
Returns:
|
||||
(bool): True if an update is available, False otherwise.
|
||||
"""
|
||||
if ONLINE and is_pip_package():
|
||||
with contextlib.suppress(Exception):
|
||||
from ultralytics import __version__
|
||||
latest = check_latest_pypi_version()
|
||||
if pkg.parse_version(__version__) < pkg.parse_version(latest): # update is available
|
||||
LOGGER.info(f'New https://pypi.org/project/ultralytics/{latest} available 😃 '
|
||||
f"Update with 'pip install -U ultralytics'")
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
@ThreadingLocked()
|
||||
def check_font(font='Arial.ttf'):
|
||||
"""
|
||||
Find font locally or download to user's configuration directory if it does not already exist.
|
||||
|
||||
Args:
|
||||
font (str): Path or name of font.
|
||||
|
||||
Returns:
|
||||
file (Path): Resolved font file path.
|
||||
"""
|
||||
name = Path(font).name
|
||||
|
||||
# Check USER_CONFIG_DIR
|
||||
file = USER_CONFIG_DIR / name
|
||||
if file.exists():
|
||||
return file
|
||||
|
||||
# Check system fonts
|
||||
matches = [s for s in font_manager.findSystemFonts() if font in s]
|
||||
if any(matches):
|
||||
return matches[0]
|
||||
|
||||
# Download to USER_CONFIG_DIR if missing
|
||||
url = f'https://ultralytics.com/assets/{name}'
|
||||
if downloads.is_url(url):
|
||||
downloads.safe_download(url=url, file=file)
|
||||
return file
|
||||
|
||||
|
||||
def check_python(minimum: str = '3.7.0') -> bool:
|
||||
"""
|
||||
Check current python version against the required minimum version.
|
||||
|
||||
Args:
|
||||
minimum (str): Required minimum version of python.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
return check_version(platform.python_version(), minimum, name='Python ', hard=True)
|
||||
|
||||
|
||||
@TryExcept()
|
||||
def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=(), install=True, cmds=''):
|
||||
"""
|
||||
Check if installed dependencies meet YOLOv8 requirements and attempt to auto-update if needed.
|
||||
|
||||
Args:
|
||||
requirements (Union[Path, str, List[str]]): Path to a requirements.txt file, a single package requirement as a
|
||||
string, or a list of package requirements as strings.
|
||||
exclude (Tuple[str]): Tuple of package names to exclude from checking.
|
||||
install (bool): If True, attempt to auto-update packages that don't meet requirements.
|
||||
cmds (str): Additional commands to pass to the pip install command when auto-updating.
|
||||
"""
|
||||
prefix = colorstr('red', 'bold', 'requirements:')
|
||||
check_python() # check python version
|
||||
check_torchvision() # check torch-torchvision compatibility
|
||||
if isinstance(requirements, Path): # requirements.txt file
|
||||
file = requirements.resolve()
|
||||
assert file.exists(), f'{prefix} {file} not found, check failed.'
|
||||
with file.open() as f:
|
||||
requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude]
|
||||
elif isinstance(requirements, str):
|
||||
requirements = [requirements]
|
||||
|
||||
s = '' # console string
|
||||
pkgs = []
|
||||
for r in requirements:
|
||||
r_stripped = r.split('/')[-1].replace('.git', '') # replace git+https://org/repo.git -> 'repo'
|
||||
try:
|
||||
pkg.require(r_stripped)
|
||||
except (pkg.VersionConflict, pkg.DistributionNotFound): # exception if requirements not met
|
||||
try: # attempt to import (slower but more accurate)
|
||||
import importlib
|
||||
importlib.import_module(next(pkg.parse_requirements(r_stripped)).name)
|
||||
except ImportError:
|
||||
s += f'"{r}" '
|
||||
pkgs.append(r)
|
||||
|
||||
if s:
|
||||
if install and AUTOINSTALL: # check environment variable
|
||||
n = len(pkgs) # number of packages updates
|
||||
LOGGER.info(f"{prefix} Ultralytics requirement{'s' * (n > 1)} {pkgs} not found, attempting AutoUpdate...")
|
||||
try:
|
||||
t = time.time()
|
||||
assert is_online(), 'AutoUpdate skipped (offline)'
|
||||
LOGGER.info(subprocess.check_output(f'pip install --no-cache {s} {cmds}', shell=True).decode())
|
||||
dt = time.time() - t
|
||||
LOGGER.info(
|
||||
f"{prefix} AutoUpdate success ✅ {dt:.1f}s, installed {n} package{'s' * (n > 1)}: {pkgs}\n"
|
||||
f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n")
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'{prefix} ❌ {e}')
|
||||
return False
|
||||
else:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def check_torchvision():
|
||||
"""
|
||||
Checks the installed versions of PyTorch and Torchvision to ensure they're compatible.
|
||||
|
||||
This function checks the installed versions of PyTorch and Torchvision, and warns if they're incompatible according
|
||||
to the provided compatibility table based on https://github.com/pytorch/vision#installation. The
|
||||
compatibility table is a dictionary where the keys are PyTorch versions and the values are lists of compatible
|
||||
Torchvision versions.
|
||||
"""
|
||||
|
||||
import torchvision
|
||||
|
||||
# Compatibility table
|
||||
compatibility_table = {'2.0': ['0.15'], '1.13': ['0.14'], '1.12': ['0.13']}
|
||||
|
||||
# Extract only the major and minor versions
|
||||
v_torch = '.'.join(torch.__version__.split('+')[0].split('.')[:2])
|
||||
v_torchvision = '.'.join(torchvision.__version__.split('+')[0].split('.')[:2])
|
||||
|
||||
if v_torch in compatibility_table:
|
||||
compatible_versions = compatibility_table[v_torch]
|
||||
if all(pkg.parse_version(v_torchvision) != pkg.parse_version(v) for v in compatible_versions):
|
||||
print(f'WARNING ⚠️ torchvision=={v_torchvision} is incompatible with torch=={v_torch}.\n'
|
||||
f"Run 'pip install torchvision=={compatible_versions[0]}' to fix torchvision or "
|
||||
"'pip install -U torch torchvision' to update both.\n"
|
||||
'For a full compatibility table see https://github.com/pytorch/vision#installation')
|
||||
|
||||
|
||||
def check_suffix(file='yolov8n.pt', suffix='.pt', msg=''):
|
||||
"""Check file(s) for acceptable suffix."""
|
||||
if file and suffix:
|
||||
if isinstance(suffix, str):
|
||||
suffix = (suffix, )
|
||||
for f in file if isinstance(file, (list, tuple)) else [file]:
|
||||
s = Path(f).suffix.lower().strip() # file suffix
|
||||
if len(s):
|
||||
assert s in suffix, f'{msg}{f} acceptable suffix is {suffix}, not {s}'
|
||||
|
||||
|
||||
def check_yolov5u_filename(file: str, verbose: bool = True):
|
||||
"""Replace legacy YOLOv5 filenames with updated YOLOv5u filenames."""
|
||||
if ('yolov3' in file or 'yolov5' in file) and 'u' not in file:
|
||||
original_file = file
|
||||
file = re.sub(r'(.*yolov5([nsmlx]))\.pt', '\\1u.pt', file) # i.e. yolov5n.pt -> yolov5nu.pt
|
||||
file = re.sub(r'(.*yolov5([nsmlx])6)\.pt', '\\1u.pt', file) # i.e. yolov5n6.pt -> yolov5n6u.pt
|
||||
file = re.sub(r'(.*yolov3(|-tiny|-spp))\.pt', '\\1u.pt', file) # i.e. yolov3-spp.pt -> yolov3-sppu.pt
|
||||
if file != original_file and verbose:
|
||||
LOGGER.info(f"PRO TIP 💡 Replace 'model={original_file}' with new 'model={file}'.\nYOLOv5 'u' models are "
|
||||
f'trained with https://github.com/ultralytics/ultralytics and feature improved performance vs '
|
||||
f'standard YOLOv5 models trained with https://github.com/ultralytics/yolov5.\n')
|
||||
return file
|
||||
|
||||
|
||||
def check_file(file, suffix='', download=True, hard=True):
|
||||
"""Search/download file (if necessary) and return path."""
|
||||
check_suffix(file, suffix) # optional
|
||||
file = str(file).strip() # convert to string and strip spaces
|
||||
file = check_yolov5u_filename(file) # yolov5n -> yolov5nu
|
||||
if not file or ('://' not in file and Path(file).exists()): # exists ('://' check required in Windows Python<3.10)
|
||||
return file
|
||||
elif download and file.lower().startswith(('https://', 'http://', 'rtsp://', 'rtmp://')): # download
|
||||
url = file # warning: Pathlib turns :// -> :/
|
||||
file = url2file(file) # '%2F' to '/', split https://url.com/file.txt?auth
|
||||
if Path(file).exists():
|
||||
LOGGER.info(f'Found {clean_url(url)} locally at {file}') # file already exists
|
||||
else:
|
||||
downloads.safe_download(url=url, file=file, unzip=False)
|
||||
return file
|
||||
else: # search
|
||||
files = []
|
||||
for d in 'models', 'datasets', 'tracker/cfg', 'yolo/cfg': # search directories
|
||||
files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True)) # find file
|
||||
if not files and hard:
|
||||
raise FileNotFoundError(f"'{file}' does not exist")
|
||||
elif len(files) > 1 and hard:
|
||||
raise FileNotFoundError(f"Multiple files match '{file}', specify exact path: {files}")
|
||||
return files[0] if len(files) else [] # return file
|
||||
|
||||
|
||||
def check_yaml(file, suffix=('.yaml', '.yml'), hard=True):
|
||||
"""Search/download YAML file (if necessary) and return path, checking suffix."""
|
||||
return check_file(file, suffix, hard=hard)
|
||||
|
||||
|
||||
def check_imshow(warn=False):
|
||||
"""Check if environment supports image displays."""
|
||||
try:
|
||||
assert not any((is_colab(), is_kaggle(), is_docker()))
|
||||
cv2.imshow('test', np.zeros((1, 1, 3)))
|
||||
cv2.waitKey(1)
|
||||
cv2.destroyAllWindows()
|
||||
cv2.waitKey(1)
|
||||
return True
|
||||
except Exception as e:
|
||||
if warn:
|
||||
LOGGER.warning(f'WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}')
|
||||
return False
|
||||
|
||||
|
||||
def check_yolo(verbose=True, device=''):
|
||||
"""Return a human-readable YOLO software and hardware summary."""
|
||||
from ultralytics.yolo.utils.torch_utils import select_device
|
||||
|
||||
if is_jupyter():
|
||||
if check_requirements('wandb', install=False):
|
||||
os.system('pip uninstall -y wandb') # uninstall wandb: unwanted account creation prompt with infinite hang
|
||||
if is_colab():
|
||||
shutil.rmtree('sample_data', ignore_errors=True) # remove colab /sample_data directory
|
||||
|
||||
if verbose:
|
||||
# System info
|
||||
gib = 1 << 30 # bytes per GiB
|
||||
ram = psutil.virtual_memory().total
|
||||
total, used, free = shutil.disk_usage('/')
|
||||
s = f'({os.cpu_count()} CPUs, {ram / gib:.1f} GB RAM, {(total - free) / gib:.1f}/{total / gib:.1f} GB disk)'
|
||||
with contextlib.suppress(Exception): # clear display if ipython is installed
|
||||
from IPython import display
|
||||
display.clear_output()
|
||||
else:
|
||||
s = ''
|
||||
|
||||
select_device(device=device, newline=False)
|
||||
LOGGER.info(f'Setup complete ✅ {s}')
|
||||
|
||||
|
||||
def check_amp(model):
|
||||
"""
|
||||
This function checks the PyTorch Automatic Mixed Precision (AMP) functionality of a YOLOv8 model.
|
||||
If the checks fail, it means there are anomalies with AMP on the system that may cause NaN losses or zero-mAP
|
||||
results, so AMP will be disabled during training.
|
||||
|
||||
Args:
|
||||
model (nn.Module): A YOLOv8 model instance.
|
||||
|
||||
Returns:
|
||||
(bool): Returns True if the AMP functionality works correctly with YOLOv8 model, else False.
|
||||
|
||||
Raises:
|
||||
AssertionError: If the AMP checks fail, indicating anomalies with the AMP functionality on the system.
|
||||
"""
|
||||
device = next(model.parameters()).device # get model device
|
||||
if device.type in ('cpu', 'mps'):
|
||||
return False # AMP only used on CUDA devices
|
||||
|
||||
def amp_allclose(m, im):
|
||||
"""All close FP32 vs AMP results."""
|
||||
a = m(im, device=device, verbose=False)[0].boxes.data # FP32 inference
|
||||
with torch.cuda.amp.autocast(True):
|
||||
b = m(im, device=device, verbose=False)[0].boxes.data # AMP inference
|
||||
del m
|
||||
return a.shape == b.shape and torch.allclose(a, b.float(), atol=0.5) # close to 0.5 absolute tolerance
|
||||
|
||||
f = ROOT / 'assets/bus.jpg' # image to check
|
||||
im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if ONLINE else np.ones((640, 640, 3))
|
||||
prefix = colorstr('AMP: ')
|
||||
LOGGER.info(f'{prefix}running Automatic Mixed Precision (AMP) checks with YOLOv8n...')
|
||||
warning_msg = "Setting 'amp=True'. If you experience zero-mAP or NaN losses you can disable AMP with amp=False."
|
||||
try:
|
||||
from ultralytics import YOLO
|
||||
assert amp_allclose(YOLO('yolov8n.pt'), im)
|
||||
LOGGER.info(f'{prefix}checks passed ✅')
|
||||
except ConnectionError:
|
||||
LOGGER.warning(f'{prefix}checks skipped ⚠️, offline and unable to download YOLOv8n. {warning_msg}')
|
||||
except (AttributeError, ModuleNotFoundError):
|
||||
LOGGER.warning(
|
||||
f'{prefix}checks skipped ⚠️. Unable to load YOLOv8n due to possible Ultralytics package modifications. {warning_msg}'
|
||||
)
|
||||
except AssertionError:
|
||||
LOGGER.warning(f'{prefix}checks failed ❌. Anomalies were detected with AMP on your system that may lead to '
|
||||
f'NaN losses or zero-mAP results, so AMP will be disabled during training.')
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def git_describe(path=ROOT): # path must be a directory
|
||||
"""Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe."""
|
||||
try:
|
||||
assert (Path(path) / '.git').is_dir()
|
||||
return subprocess.check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1]
|
||||
except AssertionError:
|
||||
return ''
|
||||
|
||||
|
||||
def print_args(args: Optional[dict] = None, show_file=True, show_func=False):
|
||||
"""Print function arguments (optional args dict)."""
|
||||
|
||||
def strip_auth(v):
|
||||
"""Clean longer Ultralytics HUB URLs by stripping potential authentication information."""
|
||||
return clean_url(v) if (isinstance(v, str) and v.startswith('http') and len(v) > 100) else v
|
||||
|
||||
x = inspect.currentframe().f_back # previous frame
|
||||
file, _, func, _, _ = inspect.getframeinfo(x)
|
||||
if args is None: # get args automatically
|
||||
args, _, _, frm = inspect.getargvalues(x)
|
||||
args = {k: v for k, v in frm.items() if k in args}
|
||||
try:
|
||||
file = Path(file).resolve().relative_to(ROOT).with_suffix('')
|
||||
except ValueError:
|
||||
file = Path(file).stem
|
||||
s = (f'{file}: ' if show_file else '') + (f'{func}: ' if show_func else '')
|
||||
LOGGER.info(colorstr(s) + ', '.join(f'{k}={strip_auth(v)}' for k, v in args.items()))
|
||||
@ -1,67 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import os
|
||||
import re
|
||||
import shutil
|
||||
import socket
|
||||
import sys
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
|
||||
from . import USER_CONFIG_DIR
|
||||
from .torch_utils import TORCH_1_9
|
||||
|
||||
|
||||
def find_free_network_port() -> int:
|
||||
"""Finds a free port on localhost.
|
||||
|
||||
It is useful in single-node training when we don't want to connect to a real main node but have to set the
|
||||
`MASTER_PORT` environment variable.
|
||||
"""
|
||||
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
|
||||
s.bind(('127.0.0.1', 0))
|
||||
return s.getsockname()[1] # port
|
||||
|
||||
|
||||
def generate_ddp_file(trainer):
|
||||
"""Generates a DDP file and returns its file name."""
|
||||
module, name = f'{trainer.__class__.__module__}.{trainer.__class__.__name__}'.rsplit('.', 1)
|
||||
|
||||
content = f'''overrides = {vars(trainer.args)} \nif __name__ == "__main__":
|
||||
from {module} import {name}
|
||||
from ultralytics.yolo.utils import DEFAULT_CFG_DICT
|
||||
|
||||
cfg = DEFAULT_CFG_DICT.copy()
|
||||
cfg.update(save_dir='') # handle the extra key 'save_dir'
|
||||
trainer = {name}(cfg=cfg, overrides=overrides)
|
||||
trainer.train()'''
|
||||
(USER_CONFIG_DIR / 'DDP').mkdir(exist_ok=True)
|
||||
with tempfile.NamedTemporaryFile(prefix='_temp_',
|
||||
suffix=f'{id(trainer)}.py',
|
||||
mode='w+',
|
||||
encoding='utf-8',
|
||||
dir=USER_CONFIG_DIR / 'DDP',
|
||||
delete=False) as file:
|
||||
file.write(content)
|
||||
return file.name
|
||||
|
||||
|
||||
def generate_ddp_command(world_size, trainer):
|
||||
"""Generates and returns command for distributed training."""
|
||||
import __main__ # noqa local import to avoid https://github.com/Lightning-AI/lightning/issues/15218
|
||||
if not trainer.resume:
|
||||
shutil.rmtree(trainer.save_dir) # remove the save_dir
|
||||
file = str(Path(sys.argv[0]).resolve())
|
||||
safe_pattern = re.compile(r'^[a-zA-Z0-9_. /\\-]{1,128}$') # allowed characters and maximum of 100 characters
|
||||
if not (safe_pattern.match(file) and Path(file).exists() and file.endswith('.py')): # using CLI
|
||||
file = generate_ddp_file(trainer)
|
||||
dist_cmd = 'torch.distributed.run' if TORCH_1_9 else 'torch.distributed.launch'
|
||||
port = find_free_network_port()
|
||||
cmd = [sys.executable, '-m', dist_cmd, '--nproc_per_node', f'{world_size}', '--master_port', f'{port}', file]
|
||||
return cmd, file
|
||||
|
||||
|
||||
def ddp_cleanup(trainer, file):
|
||||
"""Delete temp file if created."""
|
||||
if f'{id(trainer)}.py' in file: # if temp_file suffix in file
|
||||
os.remove(file)
|
||||
@ -1,271 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import contextlib
|
||||
import shutil
|
||||
import subprocess
|
||||
from itertools import repeat
|
||||
from multiprocessing.pool import ThreadPool
|
||||
from pathlib import Path
|
||||
from urllib import parse, request
|
||||
from zipfile import BadZipFile, ZipFile, is_zipfile
|
||||
|
||||
import requests
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics.yolo.utils import LOGGER, checks, clean_url, emojis, is_online, url2file
|
||||
|
||||
GITHUB_ASSET_NAMES = [f'yolov8{k}{suffix}.pt' for k in 'nsmlx' for suffix in ('', '6', '-cls', '-seg', '-pose')] + \
|
||||
[f'yolov5{k}u.pt' for k in 'nsmlx'] + \
|
||||
[f'yolov3{k}u.pt' for k in ('', '-spp', '-tiny')] + \
|
||||
[f'yolo_nas_{k}.pt' for k in 'sml'] + \
|
||||
[f'sam_{k}.pt' for k in 'bl'] + \
|
||||
[f'FastSAM-{k}.pt' for k in 'sx'] + \
|
||||
[f'rtdetr-{k}.pt' for k in 'lx'] + \
|
||||
['mobile_sam.pt']
|
||||
GITHUB_ASSET_STEMS = [Path(k).stem for k in GITHUB_ASSET_NAMES]
|
||||
|
||||
|
||||
def is_url(url, check=True):
|
||||
"""Check if string is URL and check if URL exists."""
|
||||
with contextlib.suppress(Exception):
|
||||
url = str(url)
|
||||
result = parse.urlparse(url)
|
||||
assert all([result.scheme, result.netloc]) # check if is url
|
||||
if check:
|
||||
with request.urlopen(url) as response:
|
||||
return response.getcode() == 200 # check if exists online
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX'), exist_ok=False):
|
||||
"""
|
||||
Unzips a *.zip file to the specified path, excluding files containing strings in the exclude list.
|
||||
|
||||
If the zipfile does not contain a single top-level directory, the function will create a new
|
||||
directory with the same name as the zipfile (without the extension) to extract its contents.
|
||||
If a path is not provided, the function will use the parent directory of the zipfile as the default path.
|
||||
|
||||
Args:
|
||||
file (str): The path to the zipfile to be extracted.
|
||||
path (str, optional): The path to extract the zipfile to. Defaults to None.
|
||||
exclude (tuple, optional): A tuple of filename strings to be excluded. Defaults to ('.DS_Store', '__MACOSX').
|
||||
exist_ok (bool, optional): Whether to overwrite existing contents if they exist. Defaults to False.
|
||||
|
||||
Raises:
|
||||
BadZipFile: If the provided file does not exist or is not a valid zipfile.
|
||||
|
||||
Returns:
|
||||
(Path): The path to the directory where the zipfile was extracted.
|
||||
"""
|
||||
if not (Path(file).exists() and is_zipfile(file)):
|
||||
raise BadZipFile(f"File '{file}' does not exist or is a bad zip file.")
|
||||
if path is None:
|
||||
path = Path(file).parent # default path
|
||||
|
||||
# Unzip the file contents
|
||||
with ZipFile(file) as zipObj:
|
||||
file_list = [f for f in zipObj.namelist() if all(x not in f for x in exclude)]
|
||||
top_level_dirs = {Path(f).parts[0] for f in file_list}
|
||||
|
||||
if len(top_level_dirs) > 1 or not file_list[0].endswith('/'):
|
||||
path = Path(path) / Path(file).stem # define new unzip directory
|
||||
|
||||
# Check if destination directory already exists and contains files
|
||||
extract_path = Path(path) / list(top_level_dirs)[0]
|
||||
if extract_path.exists() and any(extract_path.iterdir()) and not exist_ok:
|
||||
# If it exists and is not empty, return the path without unzipping
|
||||
LOGGER.info(f'Skipping {file} unzip (already unzipped)')
|
||||
return path
|
||||
|
||||
for f in file_list:
|
||||
zipObj.extract(f, path=path)
|
||||
|
||||
return path # return unzip dir
|
||||
|
||||
|
||||
def check_disk_space(url='https://ultralytics.com/assets/coco128.zip', sf=1.5, hard=True):
|
||||
"""
|
||||
Check if there is sufficient disk space to download and store a file.
|
||||
|
||||
Args:
|
||||
url (str, optional): The URL to the file. Defaults to 'https://ultralytics.com/assets/coco128.zip'.
|
||||
sf (float, optional): Safety factor, the multiplier for the required free space. Defaults to 2.0.
|
||||
hard (bool, optional): Whether to throw an error or not on insufficient disk space. Defaults to True.
|
||||
|
||||
Returns:
|
||||
(bool): True if there is sufficient disk space, False otherwise.
|
||||
"""
|
||||
with contextlib.suppress(Exception):
|
||||
gib = 1 << 30 # bytes per GiB
|
||||
data = int(requests.head(url).headers['Content-Length']) / gib # file size (GB)
|
||||
total, used, free = (x / gib for x in shutil.disk_usage('/')) # bytes
|
||||
if data * sf < free:
|
||||
return True # sufficient space
|
||||
|
||||
# Insufficient space
|
||||
text = (f'WARNING ⚠️ Insufficient free disk space {free:.1f} GB < {data * sf:.3f} GB required, '
|
||||
f'Please free {data * sf - free:.1f} GB additional disk space and try again.')
|
||||
if hard:
|
||||
raise MemoryError(text)
|
||||
else:
|
||||
LOGGER.warning(text)
|
||||
return False
|
||||
|
||||
# Pass if error
|
||||
return True
|
||||
|
||||
|
||||
def safe_download(url,
|
||||
file=None,
|
||||
dir=None,
|
||||
unzip=True,
|
||||
delete=False,
|
||||
curl=False,
|
||||
retry=3,
|
||||
min_bytes=1E0,
|
||||
progress=True):
|
||||
"""
|
||||
Downloads files from a URL, with options for retrying, unzipping, and deleting the downloaded file.
|
||||
|
||||
Args:
|
||||
url (str): The URL of the file to be downloaded.
|
||||
file (str, optional): The filename of the downloaded file.
|
||||
If not provided, the file will be saved with the same name as the URL.
|
||||
dir (str, optional): The directory to save the downloaded file.
|
||||
If not provided, the file will be saved in the current working directory.
|
||||
unzip (bool, optional): Whether to unzip the downloaded file. Default: True.
|
||||
delete (bool, optional): Whether to delete the downloaded file after unzipping. Default: False.
|
||||
curl (bool, optional): Whether to use curl command line tool for downloading. Default: False.
|
||||
retry (int, optional): The number of times to retry the download in case of failure. Default: 3.
|
||||
min_bytes (float, optional): The minimum number of bytes that the downloaded file should have, to be considered
|
||||
a successful download. Default: 1E0.
|
||||
progress (bool, optional): Whether to display a progress bar during the download. Default: True.
|
||||
"""
|
||||
f = dir / url2file(url) if dir else Path(file) # URL converted to filename
|
||||
if '://' not in str(url) and Path(url).is_file(): # URL exists ('://' check required in Windows Python<3.10)
|
||||
f = Path(url) # filename
|
||||
elif not f.is_file(): # URL and file do not exist
|
||||
assert dir or file, 'dir or file required for download'
|
||||
f = dir / url2file(url) if dir else Path(file)
|
||||
desc = f'Downloading {clean_url(url)} to {f}'
|
||||
LOGGER.info(f'{desc}...')
|
||||
f.parent.mkdir(parents=True, exist_ok=True) # make directory if missing
|
||||
check_disk_space(url)
|
||||
for i in range(retry + 1):
|
||||
try:
|
||||
if curl or i > 0: # curl download with retry, continue
|
||||
s = 'sS' * (not progress) # silent
|
||||
r = subprocess.run(['curl', '-#', f'-{s}L', url, '-o', f, '--retry', '3', '-C', '-']).returncode
|
||||
assert r == 0, f'Curl return value {r}'
|
||||
else: # urllib download
|
||||
method = 'torch'
|
||||
if method == 'torch':
|
||||
torch.hub.download_url_to_file(url, f, progress=progress)
|
||||
else:
|
||||
from ultralytics.yolo.utils import TQDM_BAR_FORMAT
|
||||
with request.urlopen(url) as response, tqdm(total=int(response.getheader('Content-Length', 0)),
|
||||
desc=desc,
|
||||
disable=not progress,
|
||||
unit='B',
|
||||
unit_scale=True,
|
||||
unit_divisor=1024,
|
||||
bar_format=TQDM_BAR_FORMAT) as pbar:
|
||||
with open(f, 'wb') as f_opened:
|
||||
for data in response:
|
||||
f_opened.write(data)
|
||||
pbar.update(len(data))
|
||||
|
||||
if f.exists():
|
||||
if f.stat().st_size > min_bytes:
|
||||
break # success
|
||||
f.unlink() # remove partial downloads
|
||||
except Exception as e:
|
||||
if i == 0 and not is_online():
|
||||
raise ConnectionError(emojis(f'❌ Download failure for {url}. Environment is not online.')) from e
|
||||
elif i >= retry:
|
||||
raise ConnectionError(emojis(f'❌ Download failure for {url}. Retry limit reached.')) from e
|
||||
LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...')
|
||||
|
||||
if unzip and f.exists() and f.suffix in ('', '.zip', '.tar', '.gz'):
|
||||
unzip_dir = dir or f.parent # unzip to dir if provided else unzip in place
|
||||
LOGGER.info(f'Unzipping {f} to {unzip_dir.absolute()}...')
|
||||
if is_zipfile(f):
|
||||
unzip_dir = unzip_file(file=f, path=unzip_dir) # unzip
|
||||
elif f.suffix == '.tar':
|
||||
subprocess.run(['tar', 'xf', f, '--directory', unzip_dir], check=True) # unzip
|
||||
elif f.suffix == '.gz':
|
||||
subprocess.run(['tar', 'xfz', f, '--directory', unzip_dir], check=True) # unzip
|
||||
if delete:
|
||||
f.unlink() # remove zip
|
||||
return unzip_dir
|
||||
|
||||
|
||||
def get_github_assets(repo='ultralytics/assets', version='latest'):
|
||||
"""Return GitHub repo tag and assets (i.e. ['yolov8n.pt', 'yolov8s.pt', ...])."""
|
||||
if version != 'latest':
|
||||
version = f'tags/{version}' # i.e. tags/v6.2
|
||||
response = requests.get(f'https://api.github.com/repos/{repo}/releases/{version}').json() # github api
|
||||
return response['tag_name'], [x['name'] for x in response['assets']] # tag, assets
|
||||
|
||||
|
||||
def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'):
|
||||
"""Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v6.2', etc."""
|
||||
from ultralytics.yolo.utils import SETTINGS # scoped for circular import
|
||||
|
||||
# YOLOv3/5u updates
|
||||
file = str(file)
|
||||
file = checks.check_yolov5u_filename(file)
|
||||
file = Path(file.strip().replace("'", ''))
|
||||
if file.exists():
|
||||
return str(file)
|
||||
elif (SETTINGS['weights_dir'] / file).exists():
|
||||
return str(SETTINGS['weights_dir'] / file)
|
||||
else:
|
||||
# URL specified
|
||||
name = Path(parse.unquote(str(file))).name # decode '%2F' to '/' etc.
|
||||
if str(file).startswith(('http:/', 'https:/')): # download
|
||||
url = str(file).replace(':/', '://') # Pathlib turns :// -> :/
|
||||
file = url2file(name) # parse authentication https://url.com/file.txt?auth...
|
||||
if Path(file).is_file():
|
||||
LOGGER.info(f'Found {clean_url(url)} locally at {file}') # file already exists
|
||||
else:
|
||||
safe_download(url=url, file=file, min_bytes=1E5)
|
||||
return file
|
||||
|
||||
# GitHub assets
|
||||
assets = GITHUB_ASSET_NAMES
|
||||
try:
|
||||
tag, assets = get_github_assets(repo, release)
|
||||
except Exception:
|
||||
try:
|
||||
tag, assets = get_github_assets(repo) # latest release
|
||||
except Exception:
|
||||
try:
|
||||
tag = subprocess.check_output(['git', 'tag']).decode().split()[-1]
|
||||
except Exception:
|
||||
tag = release
|
||||
|
||||
file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required)
|
||||
if name in assets:
|
||||
safe_download(url=f'https://github.com/{repo}/releases/download/{tag}/{name}', file=file, min_bytes=1E5)
|
||||
|
||||
return str(file)
|
||||
|
||||
|
||||
def download(url, dir=Path.cwd(), unzip=True, delete=False, curl=False, threads=1, retry=3):
|
||||
"""Downloads and unzips files concurrently if threads > 1, else sequentially."""
|
||||
dir = Path(dir)
|
||||
dir.mkdir(parents=True, exist_ok=True) # make directory
|
||||
if threads > 1:
|
||||
with ThreadPool(threads) as pool:
|
||||
pool.map(
|
||||
lambda x: safe_download(
|
||||
url=x[0], dir=x[1], unzip=unzip, delete=delete, curl=curl, retry=retry, progress=threads <= 1),
|
||||
zip(url, repeat(dir)))
|
||||
pool.close()
|
||||
pool.join()
|
||||
else:
|
||||
for u in [url] if isinstance(url, (str, Path)) else url:
|
||||
safe_download(url=u, dir=dir, unzip=unzip, delete=delete, curl=curl, retry=retry)
|
||||
@ -1,10 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
from ultralytics.yolo.utils import emojis
|
||||
|
||||
|
||||
class HUBModelError(Exception):
|
||||
|
||||
def __init__(self, message='Model not found. Please check model URL and try again.'):
|
||||
"""Create an exception for when a model is not found."""
|
||||
super().__init__(emojis(message))
|
||||
@ -1,100 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import contextlib
|
||||
import glob
|
||||
import os
|
||||
import shutil
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
class WorkingDirectory(contextlib.ContextDecorator):
|
||||
"""Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager."""
|
||||
|
||||
def __init__(self, new_dir):
|
||||
"""Sets the working directory to 'new_dir' upon instantiation."""
|
||||
self.dir = new_dir # new dir
|
||||
self.cwd = Path.cwd().resolve() # current dir
|
||||
|
||||
def __enter__(self):
|
||||
"""Changes the current directory to the specified directory."""
|
||||
os.chdir(self.dir)
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
"""Restore the current working directory on context exit."""
|
||||
os.chdir(self.cwd)
|
||||
|
||||
|
||||
def increment_path(path, exist_ok=False, sep='', mkdir=False):
|
||||
"""
|
||||
Increments a file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
|
||||
|
||||
If the path exists and exist_ok is not set to True, the path will be incremented by appending a number and sep to
|
||||
the end of the path. If the path is a file, the file extension will be preserved. If the path is a directory, the
|
||||
number will be appended directly to the end of the path. If mkdir is set to True, the path will be created as a
|
||||
directory if it does not already exist.
|
||||
|
||||
Args:
|
||||
path (str, pathlib.Path): Path to increment.
|
||||
exist_ok (bool, optional): If True, the path will not be incremented and returned as-is. Defaults to False.
|
||||
sep (str, optional): Separator to use between the path and the incrementation number. Defaults to ''.
|
||||
mkdir (bool, optional): Create a directory if it does not exist. Defaults to False.
|
||||
|
||||
Returns:
|
||||
(pathlib.Path): Incremented path.
|
||||
"""
|
||||
path = Path(path) # os-agnostic
|
||||
if path.exists() and not exist_ok:
|
||||
path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '')
|
||||
|
||||
# Method 1
|
||||
for n in range(2, 9999):
|
||||
p = f'{path}{sep}{n}{suffix}' # increment path
|
||||
if not os.path.exists(p): #
|
||||
break
|
||||
path = Path(p)
|
||||
|
||||
if mkdir:
|
||||
path.mkdir(parents=True, exist_ok=True) # make directory
|
||||
|
||||
return path
|
||||
|
||||
|
||||
def file_age(path=__file__):
|
||||
"""Return days since last file update."""
|
||||
dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime)) # delta
|
||||
return dt.days # + dt.seconds / 86400 # fractional days
|
||||
|
||||
|
||||
def file_date(path=__file__):
|
||||
"""Return human-readable file modification date, i.e. '2021-3-26'."""
|
||||
t = datetime.fromtimestamp(Path(path).stat().st_mtime)
|
||||
return f'{t.year}-{t.month}-{t.day}'
|
||||
|
||||
|
||||
def file_size(path):
|
||||
"""Return file/dir size (MB)."""
|
||||
if isinstance(path, (str, Path)):
|
||||
mb = 1 << 20 # bytes to MiB (1024 ** 2)
|
||||
path = Path(path)
|
||||
if path.is_file():
|
||||
return path.stat().st_size / mb
|
||||
elif path.is_dir():
|
||||
return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / mb
|
||||
return 0.0
|
||||
|
||||
|
||||
def get_latest_run(search_dir='.'):
|
||||
"""Return path to most recent 'last.pt' in /runs (i.e. to --resume from)."""
|
||||
last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
|
||||
return max(last_list, key=os.path.getctime) if last_list else ''
|
||||
|
||||
|
||||
def make_dirs(dir='new_dir/'):
|
||||
"""Create directories."""
|
||||
dir = Path(dir)
|
||||
if dir.exists():
|
||||
shutil.rmtree(dir) # delete dir
|
||||
for p in dir, dir / 'labels', dir / 'images':
|
||||
p.mkdir(parents=True, exist_ok=True) # make dir
|
||||
return dir
|
||||
@ -1,392 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
from collections import abc
|
||||
from itertools import repeat
|
||||
from numbers import Number
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
|
||||
from .ops import ltwh2xywh, ltwh2xyxy, resample_segments, xywh2ltwh, xywh2xyxy, xyxy2ltwh, xyxy2xywh
|
||||
|
||||
|
||||
def _ntuple(n):
|
||||
"""From PyTorch internals."""
|
||||
|
||||
def parse(x):
|
||||
"""Parse bounding boxes format between XYWH and LTWH."""
|
||||
return x if isinstance(x, abc.Iterable) else tuple(repeat(x, n))
|
||||
|
||||
return parse
|
||||
|
||||
|
||||
to_2tuple = _ntuple(2)
|
||||
to_4tuple = _ntuple(4)
|
||||
|
||||
# `xyxy` means left top and right bottom
|
||||
# `xywh` means center x, center y and width, height(yolo format)
|
||||
# `ltwh` means left top and width, height(coco format)
|
||||
_formats = ['xyxy', 'xywh', 'ltwh']
|
||||
|
||||
__all__ = 'Bboxes', # tuple or list
|
||||
|
||||
|
||||
class Bboxes:
|
||||
"""Now only numpy is supported."""
|
||||
|
||||
def __init__(self, bboxes, format='xyxy') -> None:
|
||||
assert format in _formats, f'Invalid bounding box format: {format}, format must be one of {_formats}'
|
||||
bboxes = bboxes[None, :] if bboxes.ndim == 1 else bboxes
|
||||
assert bboxes.ndim == 2
|
||||
assert bboxes.shape[1] == 4
|
||||
self.bboxes = bboxes
|
||||
self.format = format
|
||||
# self.normalized = normalized
|
||||
|
||||
# def convert(self, format):
|
||||
# assert format in _formats
|
||||
# if self.format == format:
|
||||
# bboxes = self.bboxes
|
||||
# elif self.format == "xyxy":
|
||||
# if format == "xywh":
|
||||
# bboxes = xyxy2xywh(self.bboxes)
|
||||
# else:
|
||||
# bboxes = xyxy2ltwh(self.bboxes)
|
||||
# elif self.format == "xywh":
|
||||
# if format == "xyxy":
|
||||
# bboxes = xywh2xyxy(self.bboxes)
|
||||
# else:
|
||||
# bboxes = xywh2ltwh(self.bboxes)
|
||||
# else:
|
||||
# if format == "xyxy":
|
||||
# bboxes = ltwh2xyxy(self.bboxes)
|
||||
# else:
|
||||
# bboxes = ltwh2xywh(self.bboxes)
|
||||
#
|
||||
# return Bboxes(bboxes, format)
|
||||
|
||||
def convert(self, format):
|
||||
"""Converts bounding box format from one type to another."""
|
||||
assert format in _formats, f'Invalid bounding box format: {format}, format must be one of {_formats}'
|
||||
if self.format == format:
|
||||
return
|
||||
elif self.format == 'xyxy':
|
||||
bboxes = xyxy2xywh(self.bboxes) if format == 'xywh' else xyxy2ltwh(self.bboxes)
|
||||
elif self.format == 'xywh':
|
||||
bboxes = xywh2xyxy(self.bboxes) if format == 'xyxy' else xywh2ltwh(self.bboxes)
|
||||
else:
|
||||
bboxes = ltwh2xyxy(self.bboxes) if format == 'xyxy' else ltwh2xywh(self.bboxes)
|
||||
self.bboxes = bboxes
|
||||
self.format = format
|
||||
|
||||
def areas(self):
|
||||
"""Return box areas."""
|
||||
self.convert('xyxy')
|
||||
return (self.bboxes[:, 2] - self.bboxes[:, 0]) * (self.bboxes[:, 3] - self.bboxes[:, 1])
|
||||
|
||||
# def denormalize(self, w, h):
|
||||
# if not self.normalized:
|
||||
# return
|
||||
# assert (self.bboxes <= 1.0).all()
|
||||
# self.bboxes[:, 0::2] *= w
|
||||
# self.bboxes[:, 1::2] *= h
|
||||
# self.normalized = False
|
||||
#
|
||||
# def normalize(self, w, h):
|
||||
# if self.normalized:
|
||||
# return
|
||||
# assert (self.bboxes > 1.0).any()
|
||||
# self.bboxes[:, 0::2] /= w
|
||||
# self.bboxes[:, 1::2] /= h
|
||||
# self.normalized = True
|
||||
|
||||
def mul(self, scale):
|
||||
"""
|
||||
Args:
|
||||
scale (tuple | list | int): the scale for four coords.
|
||||
"""
|
||||
if isinstance(scale, Number):
|
||||
scale = to_4tuple(scale)
|
||||
assert isinstance(scale, (tuple, list))
|
||||
assert len(scale) == 4
|
||||
self.bboxes[:, 0] *= scale[0]
|
||||
self.bboxes[:, 1] *= scale[1]
|
||||
self.bboxes[:, 2] *= scale[2]
|
||||
self.bboxes[:, 3] *= scale[3]
|
||||
|
||||
def add(self, offset):
|
||||
"""
|
||||
Args:
|
||||
offset (tuple | list | int): the offset for four coords.
|
||||
"""
|
||||
if isinstance(offset, Number):
|
||||
offset = to_4tuple(offset)
|
||||
assert isinstance(offset, (tuple, list))
|
||||
assert len(offset) == 4
|
||||
self.bboxes[:, 0] += offset[0]
|
||||
self.bboxes[:, 1] += offset[1]
|
||||
self.bboxes[:, 2] += offset[2]
|
||||
self.bboxes[:, 3] += offset[3]
|
||||
|
||||
def __len__(self):
|
||||
"""Return the number of boxes."""
|
||||
return len(self.bboxes)
|
||||
|
||||
@classmethod
|
||||
def concatenate(cls, boxes_list: List['Bboxes'], axis=0) -> 'Bboxes':
|
||||
"""
|
||||
Concatenate a list of Bboxes objects into a single Bboxes object.
|
||||
|
||||
Args:
|
||||
boxes_list (List[Bboxes]): A list of Bboxes objects to concatenate.
|
||||
axis (int, optional): The axis along which to concatenate the bounding boxes.
|
||||
Defaults to 0.
|
||||
|
||||
Returns:
|
||||
Bboxes: A new Bboxes object containing the concatenated bounding boxes.
|
||||
|
||||
Note:
|
||||
The input should be a list or tuple of Bboxes objects.
|
||||
"""
|
||||
assert isinstance(boxes_list, (list, tuple))
|
||||
if not boxes_list:
|
||||
return cls(np.empty(0))
|
||||
assert all(isinstance(box, Bboxes) for box in boxes_list)
|
||||
|
||||
if len(boxes_list) == 1:
|
||||
return boxes_list[0]
|
||||
return cls(np.concatenate([b.bboxes for b in boxes_list], axis=axis))
|
||||
|
||||
def __getitem__(self, index) -> 'Bboxes':
|
||||
"""
|
||||
Retrieve a specific bounding box or a set of bounding boxes using indexing.
|
||||
|
||||
Args:
|
||||
index (int, slice, or np.ndarray): The index, slice, or boolean array to select
|
||||
the desired bounding boxes.
|
||||
|
||||
Returns:
|
||||
Bboxes: A new Bboxes object containing the selected bounding boxes.
|
||||
|
||||
Raises:
|
||||
AssertionError: If the indexed bounding boxes do not form a 2-dimensional matrix.
|
||||
|
||||
Note:
|
||||
When using boolean indexing, make sure to provide a boolean array with the same
|
||||
length as the number of bounding boxes.
|
||||
"""
|
||||
if isinstance(index, int):
|
||||
return Bboxes(self.bboxes[index].view(1, -1))
|
||||
b = self.bboxes[index]
|
||||
assert b.ndim == 2, f'Indexing on Bboxes with {index} failed to return a matrix!'
|
||||
return Bboxes(b)
|
||||
|
||||
|
||||
class Instances:
|
||||
|
||||
def __init__(self, bboxes, segments=None, keypoints=None, bbox_format='xywh', normalized=True) -> None:
|
||||
"""
|
||||
Args:
|
||||
bboxes (ndarray): bboxes with shape [N, 4].
|
||||
segments (list | ndarray): segments.
|
||||
keypoints (ndarray): keypoints(x, y, visible) with shape [N, 17, 3].
|
||||
"""
|
||||
if segments is None:
|
||||
segments = []
|
||||
self._bboxes = Bboxes(bboxes=bboxes, format=bbox_format)
|
||||
self.keypoints = keypoints
|
||||
self.normalized = normalized
|
||||
|
||||
if len(segments) > 0:
|
||||
# list[np.array(1000, 2)] * num_samples
|
||||
segments = resample_segments(segments)
|
||||
# (N, 1000, 2)
|
||||
segments = np.stack(segments, axis=0)
|
||||
else:
|
||||
segments = np.zeros((0, 1000, 2), dtype=np.float32)
|
||||
self.segments = segments
|
||||
|
||||
def convert_bbox(self, format):
|
||||
"""Convert bounding box format."""
|
||||
self._bboxes.convert(format=format)
|
||||
|
||||
@property
|
||||
def bbox_areas(self):
|
||||
"""Calculate the area of bounding boxes."""
|
||||
return self._bboxes.areas()
|
||||
|
||||
def scale(self, scale_w, scale_h, bbox_only=False):
|
||||
"""this might be similar with denormalize func but without normalized sign."""
|
||||
self._bboxes.mul(scale=(scale_w, scale_h, scale_w, scale_h))
|
||||
if bbox_only:
|
||||
return
|
||||
self.segments[..., 0] *= scale_w
|
||||
self.segments[..., 1] *= scale_h
|
||||
if self.keypoints is not None:
|
||||
self.keypoints[..., 0] *= scale_w
|
||||
self.keypoints[..., 1] *= scale_h
|
||||
|
||||
def denormalize(self, w, h):
|
||||
"""Denormalizes boxes, segments, and keypoints from normalized coordinates."""
|
||||
if not self.normalized:
|
||||
return
|
||||
self._bboxes.mul(scale=(w, h, w, h))
|
||||
self.segments[..., 0] *= w
|
||||
self.segments[..., 1] *= h
|
||||
if self.keypoints is not None:
|
||||
self.keypoints[..., 0] *= w
|
||||
self.keypoints[..., 1] *= h
|
||||
self.normalized = False
|
||||
|
||||
def normalize(self, w, h):
|
||||
"""Normalize bounding boxes, segments, and keypoints to image dimensions."""
|
||||
if self.normalized:
|
||||
return
|
||||
self._bboxes.mul(scale=(1 / w, 1 / h, 1 / w, 1 / h))
|
||||
self.segments[..., 0] /= w
|
||||
self.segments[..., 1] /= h
|
||||
if self.keypoints is not None:
|
||||
self.keypoints[..., 0] /= w
|
||||
self.keypoints[..., 1] /= h
|
||||
self.normalized = True
|
||||
|
||||
def add_padding(self, padw, padh):
|
||||
"""Handle rect and mosaic situation."""
|
||||
assert not self.normalized, 'you should add padding with absolute coordinates.'
|
||||
self._bboxes.add(offset=(padw, padh, padw, padh))
|
||||
self.segments[..., 0] += padw
|
||||
self.segments[..., 1] += padh
|
||||
if self.keypoints is not None:
|
||||
self.keypoints[..., 0] += padw
|
||||
self.keypoints[..., 1] += padh
|
||||
|
||||
def __getitem__(self, index) -> 'Instances':
|
||||
"""
|
||||
Retrieve a specific instance or a set of instances using indexing.
|
||||
|
||||
Args:
|
||||
index (int, slice, or np.ndarray): The index, slice, or boolean array to select
|
||||
the desired instances.
|
||||
|
||||
Returns:
|
||||
Instances: A new Instances object containing the selected bounding boxes,
|
||||
segments, and keypoints if present.
|
||||
|
||||
Note:
|
||||
When using boolean indexing, make sure to provide a boolean array with the same
|
||||
length as the number of instances.
|
||||
"""
|
||||
segments = self.segments[index] if len(self.segments) else self.segments
|
||||
keypoints = self.keypoints[index] if self.keypoints is not None else None
|
||||
bboxes = self.bboxes[index]
|
||||
bbox_format = self._bboxes.format
|
||||
return Instances(
|
||||
bboxes=bboxes,
|
||||
segments=segments,
|
||||
keypoints=keypoints,
|
||||
bbox_format=bbox_format,
|
||||
normalized=self.normalized,
|
||||
)
|
||||
|
||||
def flipud(self, h):
|
||||
"""Flips the coordinates of bounding boxes, segments, and keypoints vertically."""
|
||||
if self._bboxes.format == 'xyxy':
|
||||
y1 = self.bboxes[:, 1].copy()
|
||||
y2 = self.bboxes[:, 3].copy()
|
||||
self.bboxes[:, 1] = h - y2
|
||||
self.bboxes[:, 3] = h - y1
|
||||
else:
|
||||
self.bboxes[:, 1] = h - self.bboxes[:, 1]
|
||||
self.segments[..., 1] = h - self.segments[..., 1]
|
||||
if self.keypoints is not None:
|
||||
self.keypoints[..., 1] = h - self.keypoints[..., 1]
|
||||
|
||||
def fliplr(self, w):
|
||||
"""Reverses the order of the bounding boxes and segments horizontally."""
|
||||
if self._bboxes.format == 'xyxy':
|
||||
x1 = self.bboxes[:, 0].copy()
|
||||
x2 = self.bboxes[:, 2].copy()
|
||||
self.bboxes[:, 0] = w - x2
|
||||
self.bboxes[:, 2] = w - x1
|
||||
else:
|
||||
self.bboxes[:, 0] = w - self.bboxes[:, 0]
|
||||
self.segments[..., 0] = w - self.segments[..., 0]
|
||||
if self.keypoints is not None:
|
||||
self.keypoints[..., 0] = w - self.keypoints[..., 0]
|
||||
|
||||
def clip(self, w, h):
|
||||
"""Clips bounding boxes, segments, and keypoints values to stay within image boundaries."""
|
||||
ori_format = self._bboxes.format
|
||||
self.convert_bbox(format='xyxy')
|
||||
self.bboxes[:, [0, 2]] = self.bboxes[:, [0, 2]].clip(0, w)
|
||||
self.bboxes[:, [1, 3]] = self.bboxes[:, [1, 3]].clip(0, h)
|
||||
if ori_format != 'xyxy':
|
||||
self.convert_bbox(format=ori_format)
|
||||
self.segments[..., 0] = self.segments[..., 0].clip(0, w)
|
||||
self.segments[..., 1] = self.segments[..., 1].clip(0, h)
|
||||
if self.keypoints is not None:
|
||||
self.keypoints[..., 0] = self.keypoints[..., 0].clip(0, w)
|
||||
self.keypoints[..., 1] = self.keypoints[..., 1].clip(0, h)
|
||||
|
||||
def remove_zero_area_boxes(self):
|
||||
"""Remove zero-area boxes, i.e. after clipping some boxes may have zero width or height. This removes them."""
|
||||
good = self.bbox_areas > 0
|
||||
if not all(good):
|
||||
self._bboxes = self._bboxes[good]
|
||||
if len(self.segments):
|
||||
self.segments = self.segments[good]
|
||||
if self.keypoints is not None:
|
||||
self.keypoints = self.keypoints[good]
|
||||
return good
|
||||
|
||||
def update(self, bboxes, segments=None, keypoints=None):
|
||||
"""Updates instance variables."""
|
||||
self._bboxes = Bboxes(bboxes, format=self._bboxes.format)
|
||||
if segments is not None:
|
||||
self.segments = segments
|
||||
if keypoints is not None:
|
||||
self.keypoints = keypoints
|
||||
|
||||
def __len__(self):
|
||||
"""Return the length of the instance list."""
|
||||
return len(self.bboxes)
|
||||
|
||||
@classmethod
|
||||
def concatenate(cls, instances_list: List['Instances'], axis=0) -> 'Instances':
|
||||
"""
|
||||
Concatenates a list of Instances objects into a single Instances object.
|
||||
|
||||
Args:
|
||||
instances_list (List[Instances]): A list of Instances objects to concatenate.
|
||||
axis (int, optional): The axis along which the arrays will be concatenated. Defaults to 0.
|
||||
|
||||
Returns:
|
||||
Instances: A new Instances object containing the concatenated bounding boxes,
|
||||
segments, and keypoints if present.
|
||||
|
||||
Note:
|
||||
The `Instances` objects in the list should have the same properties, such as
|
||||
the format of the bounding boxes, whether keypoints are present, and if the
|
||||
coordinates are normalized.
|
||||
"""
|
||||
assert isinstance(instances_list, (list, tuple))
|
||||
if not instances_list:
|
||||
return cls(np.empty(0))
|
||||
assert all(isinstance(instance, Instances) for instance in instances_list)
|
||||
|
||||
if len(instances_list) == 1:
|
||||
return instances_list[0]
|
||||
|
||||
use_keypoint = instances_list[0].keypoints is not None
|
||||
bbox_format = instances_list[0]._bboxes.format
|
||||
normalized = instances_list[0].normalized
|
||||
|
||||
cat_boxes = np.concatenate([ins.bboxes for ins in instances_list], axis=axis)
|
||||
cat_segments = np.concatenate([b.segments for b in instances_list], axis=axis)
|
||||
cat_keypoints = np.concatenate([b.keypoints for b in instances_list], axis=axis) if use_keypoint else None
|
||||
return cls(cat_boxes, cat_segments, cat_keypoints, bbox_format, normalized)
|
||||
|
||||
@property
|
||||
def bboxes(self):
|
||||
"""Return bounding boxes."""
|
||||
return self._bboxes.bboxes
|
||||
@ -1,392 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from ultralytics.yolo.utils.metrics import OKS_SIGMA
|
||||
from ultralytics.yolo.utils.ops import crop_mask, xywh2xyxy, xyxy2xywh
|
||||
from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
|
||||
|
||||
from .metrics import bbox_iou
|
||||
from .tal import bbox2dist
|
||||
|
||||
|
||||
class VarifocalLoss(nn.Module):
|
||||
"""Varifocal loss by Zhang et al. https://arxiv.org/abs/2008.13367."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the VarifocalLoss class."""
|
||||
super().__init__()
|
||||
|
||||
def forward(self, pred_score, gt_score, label, alpha=0.75, gamma=2.0):
|
||||
"""Computes varfocal loss."""
|
||||
weight = alpha * pred_score.sigmoid().pow(gamma) * (1 - label) + gt_score * label
|
||||
with torch.cuda.amp.autocast(enabled=False):
|
||||
loss = (F.binary_cross_entropy_with_logits(pred_score.float(), gt_score.float(), reduction='none') *
|
||||
weight).mean(1).sum()
|
||||
return loss
|
||||
|
||||
|
||||
# Losses
|
||||
class FocalLoss(nn.Module):
|
||||
"""Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)."""
|
||||
|
||||
def __init__(self, ):
|
||||
super().__init__()
|
||||
|
||||
def forward(self, pred, label, gamma=1.5, alpha=0.25):
|
||||
"""Calculates and updates confusion matrix for object detection/classification tasks."""
|
||||
loss = F.binary_cross_entropy_with_logits(pred, label, reduction='none')
|
||||
# p_t = torch.exp(-loss)
|
||||
# loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
|
||||
|
||||
# TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
|
||||
pred_prob = pred.sigmoid() # prob from logits
|
||||
p_t = label * pred_prob + (1 - label) * (1 - pred_prob)
|
||||
modulating_factor = (1.0 - p_t) ** gamma
|
||||
loss *= modulating_factor
|
||||
if alpha > 0:
|
||||
alpha_factor = label * alpha + (1 - label) * (1 - alpha)
|
||||
loss *= alpha_factor
|
||||
return loss.mean(1).sum()
|
||||
|
||||
|
||||
class BboxLoss(nn.Module):
|
||||
|
||||
def __init__(self, reg_max, use_dfl=False):
|
||||
"""Initialize the BboxLoss module with regularization maximum and DFL settings."""
|
||||
super().__init__()
|
||||
self.reg_max = reg_max
|
||||
self.use_dfl = use_dfl
|
||||
|
||||
def forward(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask):
|
||||
"""IoU loss."""
|
||||
weight = target_scores.sum(-1)[fg_mask].unsqueeze(-1)
|
||||
iou = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask], xywh=False, CIoU=True)
|
||||
loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum
|
||||
|
||||
# DFL loss
|
||||
if self.use_dfl:
|
||||
target_ltrb = bbox2dist(anchor_points, target_bboxes, self.reg_max)
|
||||
loss_dfl = self._df_loss(pred_dist[fg_mask].view(-1, self.reg_max + 1), target_ltrb[fg_mask]) * weight
|
||||
loss_dfl = loss_dfl.sum() / target_scores_sum
|
||||
else:
|
||||
loss_dfl = torch.tensor(0.0).to(pred_dist.device)
|
||||
|
||||
return loss_iou, loss_dfl
|
||||
|
||||
@staticmethod
|
||||
def _df_loss(pred_dist, target):
|
||||
"""Return sum of left and right DFL losses."""
|
||||
# Distribution Focal Loss (DFL) proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
|
||||
tl = target.long() # target left
|
||||
tr = tl + 1 # target right
|
||||
wl = tr - target # weight left
|
||||
wr = 1 - wl # weight right
|
||||
return (F.cross_entropy(pred_dist, tl.view(-1), reduction='none').view(tl.shape) * wl +
|
||||
F.cross_entropy(pred_dist, tr.view(-1), reduction='none').view(tl.shape) * wr).mean(-1, keepdim=True)
|
||||
|
||||
|
||||
class KeypointLoss(nn.Module):
|
||||
|
||||
def __init__(self, sigmas) -> None:
|
||||
super().__init__()
|
||||
self.sigmas = sigmas
|
||||
|
||||
def forward(self, pred_kpts, gt_kpts, kpt_mask, area):
|
||||
"""Calculates keypoint loss factor and Euclidean distance loss for predicted and actual keypoints."""
|
||||
d = (pred_kpts[..., 0] - gt_kpts[..., 0]) ** 2 + (pred_kpts[..., 1] - gt_kpts[..., 1]) ** 2
|
||||
kpt_loss_factor = (torch.sum(kpt_mask != 0) + torch.sum(kpt_mask == 0)) / (torch.sum(kpt_mask != 0) + 1e-9)
|
||||
# e = d / (2 * (area * self.sigmas) ** 2 + 1e-9) # from formula
|
||||
e = d / (2 * self.sigmas) ** 2 / (area + 1e-9) / 2 # from cocoeval
|
||||
return kpt_loss_factor * ((1 - torch.exp(-e)) * kpt_mask).mean()
|
||||
|
||||
|
||||
# Criterion class for computing Detection training losses
|
||||
class v8DetectionLoss:
|
||||
|
||||
def __init__(self, model): # model must be de-paralleled
|
||||
|
||||
device = next(model.parameters()).device # get model device
|
||||
h = model.args # hyperparameters
|
||||
|
||||
m = model.model[-1] # Detect() module
|
||||
self.bce = nn.BCEWithLogitsLoss(reduction='none')
|
||||
self.hyp = h
|
||||
self.stride = m.stride # model strides
|
||||
self.nc = m.nc # number of classes
|
||||
self.no = m.no
|
||||
self.reg_max = m.reg_max
|
||||
self.device = device
|
||||
|
||||
self.use_dfl = m.reg_max > 1
|
||||
|
||||
self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
|
||||
self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device)
|
||||
self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)
|
||||
|
||||
def preprocess(self, targets, batch_size, scale_tensor):
|
||||
"""Preprocesses the target counts and matches with the input batch size to output a tensor."""
|
||||
if targets.shape[0] == 0:
|
||||
out = torch.zeros(batch_size, 0, 5, device=self.device)
|
||||
else:
|
||||
i = targets[:, 0] # image index
|
||||
_, counts = i.unique(return_counts=True)
|
||||
counts = counts.to(dtype=torch.int32)
|
||||
out = torch.zeros(batch_size, counts.max(), 5, device=self.device)
|
||||
for j in range(batch_size):
|
||||
matches = i == j
|
||||
n = matches.sum()
|
||||
if n:
|
||||
out[j, :n] = targets[matches, 1:]
|
||||
out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
|
||||
return out
|
||||
|
||||
def bbox_decode(self, anchor_points, pred_dist):
|
||||
"""Decode predicted object bounding box coordinates from anchor points and distribution."""
|
||||
if self.use_dfl:
|
||||
b, a, c = pred_dist.shape # batch, anchors, channels
|
||||
pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
|
||||
# pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype))
|
||||
# pred_dist = (pred_dist.view(b, a, c // 4, 4).softmax(2) * self.proj.type(pred_dist.dtype).view(1, 1, -1, 1)).sum(2)
|
||||
return dist2bbox(pred_dist, anchor_points, xywh=False)
|
||||
|
||||
def __call__(self, preds, batch):
|
||||
"""Calculate the sum of the loss for box, cls and dfl multiplied by batch size."""
|
||||
loss = torch.zeros(3, device=self.device) # box, cls, dfl
|
||||
feats = preds[1] if isinstance(preds, tuple) else preds
|
||||
pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
|
||||
(self.reg_max * 4, self.nc), 1)
|
||||
|
||||
pred_scores = pred_scores.permute(0, 2, 1).contiguous()
|
||||
pred_distri = pred_distri.permute(0, 2, 1).contiguous()
|
||||
|
||||
dtype = pred_scores.dtype
|
||||
batch_size = pred_scores.shape[0]
|
||||
imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] # image size (h,w)
|
||||
anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
|
||||
|
||||
# targets
|
||||
targets = torch.cat((batch['batch_idx'].view(-1, 1), batch['cls'].view(-1, 1), batch['bboxes']), 1)
|
||||
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
|
||||
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
|
||||
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
|
||||
|
||||
# pboxes
|
||||
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
|
||||
|
||||
_, target_bboxes, target_scores, fg_mask, _ = self.assigner(
|
||||
pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
|
||||
anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt)
|
||||
|
||||
target_scores_sum = max(target_scores.sum(), 1)
|
||||
|
||||
# cls loss
|
||||
# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
|
||||
loss[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
|
||||
|
||||
# bbox loss
|
||||
if fg_mask.sum():
|
||||
target_bboxes /= stride_tensor
|
||||
loss[0], loss[2] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
|
||||
target_scores_sum, fg_mask)
|
||||
|
||||
loss[0] *= self.hyp.box # box gain
|
||||
loss[1] *= self.hyp.cls # cls gain
|
||||
loss[2] *= self.hyp.dfl # dfl gain
|
||||
|
||||
return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
|
||||
|
||||
|
||||
# Criterion class for computing training losses
|
||||
class v8SegmentationLoss(v8DetectionLoss):
|
||||
|
||||
def __init__(self, model): # model must be de-paralleled
|
||||
super().__init__(model)
|
||||
self.nm = model.model[-1].nm # number of masks
|
||||
self.overlap = model.args.overlap_mask
|
||||
|
||||
def __call__(self, preds, batch):
|
||||
"""Calculate and return the loss for the YOLO model."""
|
||||
loss = torch.zeros(4, device=self.device) # box, cls, dfl
|
||||
feats, pred_masks, proto = preds if len(preds) == 3 else preds[1]
|
||||
batch_size, _, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width
|
||||
pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
|
||||
(self.reg_max * 4, self.nc), 1)
|
||||
|
||||
# b, grids, ..
|
||||
pred_scores = pred_scores.permute(0, 2, 1).contiguous()
|
||||
pred_distri = pred_distri.permute(0, 2, 1).contiguous()
|
||||
pred_masks = pred_masks.permute(0, 2, 1).contiguous()
|
||||
|
||||
dtype = pred_scores.dtype
|
||||
imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] # image size (h,w)
|
||||
anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
|
||||
|
||||
# targets
|
||||
try:
|
||||
batch_idx = batch['batch_idx'].view(-1, 1)
|
||||
targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes']), 1)
|
||||
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
|
||||
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
|
||||
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
|
||||
except RuntimeError as e:
|
||||
raise TypeError('ERROR ❌ segment dataset incorrectly formatted or not a segment dataset.\n'
|
||||
"This error can occur when incorrectly training a 'segment' model on a 'detect' dataset, "
|
||||
"i.e. 'yolo train model=yolov8n-seg.pt data=coco128.yaml'.\nVerify your dataset is a "
|
||||
"correctly formatted 'segment' dataset using 'data=coco128-seg.yaml' "
|
||||
'as an example.\nSee https://docs.ultralytics.com/tasks/segment/ for help.') from e
|
||||
|
||||
# pboxes
|
||||
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
|
||||
|
||||
_, target_bboxes, target_scores, fg_mask, target_gt_idx = self.assigner(
|
||||
pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
|
||||
anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt)
|
||||
|
||||
target_scores_sum = max(target_scores.sum(), 1)
|
||||
|
||||
# cls loss
|
||||
# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
|
||||
loss[2] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
|
||||
|
||||
if fg_mask.sum():
|
||||
# bbox loss
|
||||
loss[0], loss[3] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes / stride_tensor,
|
||||
target_scores, target_scores_sum, fg_mask)
|
||||
# masks loss
|
||||
masks = batch['masks'].to(self.device).float()
|
||||
if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
|
||||
masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0]
|
||||
|
||||
for i in range(batch_size):
|
||||
if fg_mask[i].sum():
|
||||
mask_idx = target_gt_idx[i][fg_mask[i]]
|
||||
if self.overlap:
|
||||
gt_mask = torch.where(masks[[i]] == (mask_idx + 1).view(-1, 1, 1), 1.0, 0.0)
|
||||
else:
|
||||
gt_mask = masks[batch_idx.view(-1) == i][mask_idx]
|
||||
xyxyn = target_bboxes[i][fg_mask[i]] / imgsz[[1, 0, 1, 0]]
|
||||
marea = xyxy2xywh(xyxyn)[:, 2:].prod(1)
|
||||
mxyxy = xyxyn * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)
|
||||
loss[1] += self.single_mask_loss(gt_mask, pred_masks[i][fg_mask[i]], proto[i], mxyxy, marea) # seg
|
||||
|
||||
# WARNING: lines below prevents Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
|
||||
else:
|
||||
loss[1] += (proto * 0).sum() + (pred_masks * 0).sum() # inf sums may lead to nan loss
|
||||
|
||||
# WARNING: lines below prevent Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
|
||||
else:
|
||||
loss[1] += (proto * 0).sum() + (pred_masks * 0).sum() # inf sums may lead to nan loss
|
||||
|
||||
loss[0] *= self.hyp.box # box gain
|
||||
loss[1] *= self.hyp.box / batch_size # seg gain
|
||||
loss[2] *= self.hyp.cls # cls gain
|
||||
loss[3] *= self.hyp.dfl # dfl gain
|
||||
|
||||
return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
|
||||
|
||||
def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
|
||||
"""Mask loss for one image."""
|
||||
pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n, 32) @ (32,80,80) -> (n,80,80)
|
||||
loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none')
|
||||
return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
|
||||
|
||||
|
||||
# Criterion class for computing training losses
|
||||
class v8PoseLoss(v8DetectionLoss):
|
||||
|
||||
def __init__(self, model): # model must be de-paralleled
|
||||
super().__init__(model)
|
||||
self.kpt_shape = model.model[-1].kpt_shape
|
||||
self.bce_pose = nn.BCEWithLogitsLoss()
|
||||
is_pose = self.kpt_shape == [17, 3]
|
||||
nkpt = self.kpt_shape[0] # number of keypoints
|
||||
sigmas = torch.from_numpy(OKS_SIGMA).to(self.device) if is_pose else torch.ones(nkpt, device=self.device) / nkpt
|
||||
self.keypoint_loss = KeypointLoss(sigmas=sigmas)
|
||||
|
||||
def __call__(self, preds, batch):
|
||||
"""Calculate the total loss and detach it."""
|
||||
loss = torch.zeros(5, device=self.device) # box, cls, dfl, kpt_location, kpt_visibility
|
||||
feats, pred_kpts = preds if isinstance(preds[0], list) else preds[1]
|
||||
pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
|
||||
(self.reg_max * 4, self.nc), 1)
|
||||
|
||||
# b, grids, ..
|
||||
pred_scores = pred_scores.permute(0, 2, 1).contiguous()
|
||||
pred_distri = pred_distri.permute(0, 2, 1).contiguous()
|
||||
pred_kpts = pred_kpts.permute(0, 2, 1).contiguous()
|
||||
|
||||
dtype = pred_scores.dtype
|
||||
imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] # image size (h,w)
|
||||
anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
|
||||
|
||||
# targets
|
||||
batch_size = pred_scores.shape[0]
|
||||
batch_idx = batch['batch_idx'].view(-1, 1)
|
||||
targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes']), 1)
|
||||
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
|
||||
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
|
||||
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
|
||||
|
||||
# pboxes
|
||||
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
|
||||
pred_kpts = self.kpts_decode(anchor_points, pred_kpts.view(batch_size, -1, *self.kpt_shape)) # (b, h*w, 17, 3)
|
||||
|
||||
_, target_bboxes, target_scores, fg_mask, target_gt_idx = self.assigner(
|
||||
pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
|
||||
anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt)
|
||||
|
||||
target_scores_sum = max(target_scores.sum(), 1)
|
||||
|
||||
# cls loss
|
||||
# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
|
||||
loss[3] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
|
||||
|
||||
# bbox loss
|
||||
if fg_mask.sum():
|
||||
target_bboxes /= stride_tensor
|
||||
loss[0], loss[4] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
|
||||
target_scores_sum, fg_mask)
|
||||
keypoints = batch['keypoints'].to(self.device).float().clone()
|
||||
keypoints[..., 0] *= imgsz[1]
|
||||
keypoints[..., 1] *= imgsz[0]
|
||||
for i in range(batch_size):
|
||||
if fg_mask[i].sum():
|
||||
idx = target_gt_idx[i][fg_mask[i]]
|
||||
gt_kpt = keypoints[batch_idx.view(-1) == i][idx] # (n, 51)
|
||||
gt_kpt[..., 0] /= stride_tensor[fg_mask[i]]
|
||||
gt_kpt[..., 1] /= stride_tensor[fg_mask[i]]
|
||||
area = xyxy2xywh(target_bboxes[i][fg_mask[i]])[:, 2:].prod(1, keepdim=True)
|
||||
pred_kpt = pred_kpts[i][fg_mask[i]]
|
||||
kpt_mask = gt_kpt[..., 2] != 0
|
||||
loss[1] += self.keypoint_loss(pred_kpt, gt_kpt, kpt_mask, area) # pose loss
|
||||
# kpt_score loss
|
||||
if pred_kpt.shape[-1] == 3:
|
||||
loss[2] += self.bce_pose(pred_kpt[..., 2], kpt_mask.float()) # keypoint obj loss
|
||||
|
||||
loss[0] *= self.hyp.box # box gain
|
||||
loss[1] *= self.hyp.pose / batch_size # pose gain
|
||||
loss[2] *= self.hyp.kobj / batch_size # kobj gain
|
||||
loss[3] *= self.hyp.cls # cls gain
|
||||
loss[4] *= self.hyp.dfl # dfl gain
|
||||
|
||||
return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
|
||||
|
||||
def kpts_decode(self, anchor_points, pred_kpts):
|
||||
"""Decodes predicted keypoints to image coordinates."""
|
||||
y = pred_kpts.clone()
|
||||
y[..., :2] *= 2.0
|
||||
y[..., 0] += anchor_points[:, [0]] - 0.5
|
||||
y[..., 1] += anchor_points[:, [1]] - 0.5
|
||||
return y
|
||||
|
||||
|
||||
class v8ClassificationLoss:
|
||||
|
||||
def __call__(self, preds, batch):
|
||||
"""Compute the classification loss between predictions and true labels."""
|
||||
loss = torch.nn.functional.cross_entropy(preds, batch['cls'], reduction='sum') / 64
|
||||
loss_items = loss.detach()
|
||||
return loss, loss_items
|
||||
@ -1,977 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
"""
|
||||
Model validation metrics
|
||||
"""
|
||||
import math
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from ultralytics.yolo.utils import LOGGER, SimpleClass, TryExcept, plt_settings
|
||||
|
||||
OKS_SIGMA = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89]) / 10.0
|
||||
|
||||
|
||||
# Boxes
|
||||
def box_area(box):
|
||||
"""Return box area, where box shape is xyxy(4,n)."""
|
||||
return (box[2] - box[0]) * (box[3] - box[1])
|
||||
|
||||
|
||||
def bbox_ioa(box1, box2, eps=1e-7):
|
||||
"""
|
||||
Calculate the intersection over box2 area given box1 and box2. Boxes are in x1y1x2y2 format.
|
||||
|
||||
Args:
|
||||
box1 (np.array): A numpy array of shape (n, 4) representing n bounding boxes.
|
||||
box2 (np.array): A numpy array of shape (m, 4) representing m bounding boxes.
|
||||
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.
|
||||
|
||||
Returns:
|
||||
(np.array): A numpy array of shape (n, m) representing the intersection over box2 area.
|
||||
"""
|
||||
|
||||
# Get the coordinates of bounding boxes
|
||||
b1_x1, b1_y1, b1_x2, b1_y2 = box1.T
|
||||
b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
|
||||
|
||||
# Intersection area
|
||||
inter_area = (np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1)).clip(0) * \
|
||||
(np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)).clip(0)
|
||||
|
||||
# box2 area
|
||||
box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
|
||||
|
||||
# Intersection over box2 area
|
||||
return inter_area / box2_area
|
||||
|
||||
|
||||
def box_iou(box1, box2, eps=1e-7):
|
||||
"""
|
||||
Calculate intersection-over-union (IoU) of boxes.
|
||||
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
|
||||
Based on https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
|
||||
|
||||
Args:
|
||||
box1 (torch.Tensor): A tensor of shape (N, 4) representing N bounding boxes.
|
||||
box2 (torch.Tensor): A tensor of shape (M, 4) representing M bounding boxes.
|
||||
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): An NxM tensor containing the pairwise IoU values for every element in box1 and box2.
|
||||
"""
|
||||
|
||||
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
|
||||
(a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
|
||||
inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp_(0).prod(2)
|
||||
|
||||
# IoU = inter / (area1 + area2 - inter)
|
||||
return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
|
||||
|
||||
|
||||
def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
|
||||
"""
|
||||
Calculate Intersection over Union (IoU) of box1(1, 4) to box2(n, 4).
|
||||
|
||||
Args:
|
||||
box1 (torch.Tensor): A tensor representing a single bounding box with shape (1, 4).
|
||||
box2 (torch.Tensor): A tensor representing n bounding boxes with shape (n, 4).
|
||||
xywh (bool, optional): If True, input boxes are in (x, y, w, h) format. If False, input boxes are in
|
||||
(x1, y1, x2, y2) format. Defaults to True.
|
||||
GIoU (bool, optional): If True, calculate Generalized IoU. Defaults to False.
|
||||
DIoU (bool, optional): If True, calculate Distance IoU. Defaults to False.
|
||||
CIoU (bool, optional): If True, calculate Complete IoU. Defaults to False.
|
||||
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): IoU, GIoU, DIoU, or CIoU values depending on the specified flags.
|
||||
"""
|
||||
|
||||
# Get the coordinates of bounding boxes
|
||||
if xywh: # transform from xywh to xyxy
|
||||
(x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
|
||||
w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
|
||||
b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
|
||||
b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
|
||||
else: # x1, y1, x2, y2 = box1
|
||||
b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
|
||||
b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
|
||||
w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
|
||||
w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
|
||||
|
||||
# Intersection area
|
||||
inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp_(0) * \
|
||||
(b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)).clamp_(0)
|
||||
|
||||
# Union Area
|
||||
union = w1 * h1 + w2 * h2 - inter + eps
|
||||
|
||||
# IoU
|
||||
iou = inter / union
|
||||
if CIoU or DIoU or GIoU:
|
||||
cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width
|
||||
ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height
|
||||
if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
|
||||
c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared
|
||||
rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2
|
||||
if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
|
||||
v = (4 / math.pi ** 2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2)
|
||||
with torch.no_grad():
|
||||
alpha = v / (v - iou + (1 + eps))
|
||||
return iou - (rho2 / c2 + v * alpha) # CIoU
|
||||
return iou - rho2 / c2 # DIoU
|
||||
c_area = cw * ch + eps # convex area
|
||||
return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf
|
||||
return iou # IoU
|
||||
|
||||
|
||||
def mask_iou(mask1, mask2, eps=1e-7):
|
||||
"""
|
||||
Calculate masks IoU.
|
||||
|
||||
Args:
|
||||
mask1 (torch.Tensor): A tensor of shape (N, n) where N is the number of ground truth objects and n is the
|
||||
product of image width and height.
|
||||
mask2 (torch.Tensor): A tensor of shape (M, n) where M is the number of predicted objects and n is the
|
||||
product of image width and height.
|
||||
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): A tensor of shape (N, M) representing masks IoU.
|
||||
"""
|
||||
intersection = torch.matmul(mask1, mask2.T).clamp_(0)
|
||||
union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection
|
||||
return intersection / (union + eps)
|
||||
|
||||
|
||||
def kpt_iou(kpt1, kpt2, area, sigma, eps=1e-7):
|
||||
"""
|
||||
Calculate Object Keypoint Similarity (OKS).
|
||||
|
||||
Args:
|
||||
kpt1 (torch.Tensor): A tensor of shape (N, 17, 3) representing ground truth keypoints.
|
||||
kpt2 (torch.Tensor): A tensor of shape (M, 17, 3) representing predicted keypoints.
|
||||
area (torch.Tensor): A tensor of shape (N,) representing areas from ground truth.
|
||||
sigma (list): A list containing 17 values representing keypoint scales.
|
||||
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): A tensor of shape (N, M) representing keypoint similarities.
|
||||
"""
|
||||
d = (kpt1[:, None, :, 0] - kpt2[..., 0]) ** 2 + (kpt1[:, None, :, 1] - kpt2[..., 1]) ** 2 # (N, M, 17)
|
||||
sigma = torch.tensor(sigma, device=kpt1.device, dtype=kpt1.dtype) # (17, )
|
||||
kpt_mask = kpt1[..., 2] != 0 # (N, 17)
|
||||
e = d / (2 * sigma) ** 2 / (area[:, None, None] + eps) / 2 # from cocoeval
|
||||
# e = d / ((area[None, :, None] + eps) * sigma) ** 2 / 2 # from formula
|
||||
return (torch.exp(-e) * kpt_mask[:, None]).sum(-1) / (kpt_mask.sum(-1)[:, None] + eps)
|
||||
|
||||
|
||||
def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
|
||||
# return positive, negative label smoothing BCE targets
|
||||
return 1.0 - 0.5 * eps, 0.5 * eps
|
||||
|
||||
|
||||
class ConfusionMatrix:
|
||||
"""
|
||||
A class for calculating and updating a confusion matrix for object detection and classification tasks.
|
||||
|
||||
Attributes:
|
||||
task (str): The type of task, either 'detect' or 'classify'.
|
||||
matrix (np.array): The confusion matrix, with dimensions depending on the task.
|
||||
nc (int): The number of classes.
|
||||
conf (float): The confidence threshold for detections.
|
||||
iou_thres (float): The Intersection over Union threshold.
|
||||
"""
|
||||
|
||||
def __init__(self, nc, conf=0.25, iou_thres=0.45, task='detect'):
|
||||
"""Initialize attributes for the YOLO model."""
|
||||
self.task = task
|
||||
self.matrix = np.zeros((nc + 1, nc + 1)) if self.task == 'detect' else np.zeros((nc, nc))
|
||||
self.nc = nc # number of classes
|
||||
self.conf = conf
|
||||
self.iou_thres = iou_thres
|
||||
|
||||
def process_cls_preds(self, preds, targets):
|
||||
"""
|
||||
Update confusion matrix for classification task
|
||||
|
||||
Args:
|
||||
preds (Array[N, min(nc,5)]): Predicted class labels.
|
||||
targets (Array[N, 1]): Ground truth class labels.
|
||||
"""
|
||||
preds, targets = torch.cat(preds)[:, 0], torch.cat(targets)
|
||||
for p, t in zip(preds.cpu().numpy(), targets.cpu().numpy()):
|
||||
self.matrix[p][t] += 1
|
||||
|
||||
def process_batch(self, detections, labels):
|
||||
"""
|
||||
Update confusion matrix for object detection task.
|
||||
|
||||
Args:
|
||||
detections (Array[N, 6]): Detected bounding boxes and their associated information.
|
||||
Each row should contain (x1, y1, x2, y2, conf, class).
|
||||
labels (Array[M, 5]): Ground truth bounding boxes and their associated class labels.
|
||||
Each row should contain (class, x1, y1, x2, y2).
|
||||
"""
|
||||
if detections is None:
|
||||
gt_classes = labels.int()
|
||||
for gc in gt_classes:
|
||||
self.matrix[self.nc, gc] += 1 # background FN
|
||||
return
|
||||
|
||||
detections = detections[detections[:, 4] > self.conf]
|
||||
gt_classes = labels[:, 0].int()
|
||||
detection_classes = detections[:, 5].int()
|
||||
iou = box_iou(labels[:, 1:], detections[:, :4])
|
||||
|
||||
x = torch.where(iou > self.iou_thres)
|
||||
if x[0].shape[0]:
|
||||
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
|
||||
if x[0].shape[0] > 1:
|
||||
matches = matches[matches[:, 2].argsort()[::-1]]
|
||||
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
|
||||
matches = matches[matches[:, 2].argsort()[::-1]]
|
||||
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
|
||||
else:
|
||||
matches = np.zeros((0, 3))
|
||||
|
||||
n = matches.shape[0] > 0
|
||||
m0, m1, _ = matches.transpose().astype(int)
|
||||
for i, gc in enumerate(gt_classes):
|
||||
j = m0 == i
|
||||
if n and sum(j) == 1:
|
||||
self.matrix[detection_classes[m1[j]], gc] += 1 # correct
|
||||
else:
|
||||
self.matrix[self.nc, gc] += 1 # true background
|
||||
|
||||
if n:
|
||||
for i, dc in enumerate(detection_classes):
|
||||
if not any(m1 == i):
|
||||
self.matrix[dc, self.nc] += 1 # predicted background
|
||||
|
||||
def matrix(self):
|
||||
"""Returns the confusion matrix."""
|
||||
return self.matrix
|
||||
|
||||
def tp_fp(self):
|
||||
"""Returns true positives and false positives."""
|
||||
tp = self.matrix.diagonal() # true positives
|
||||
fp = self.matrix.sum(1) - tp # false positives
|
||||
# fn = self.matrix.sum(0) - tp # false negatives (missed detections)
|
||||
return (tp[:-1], fp[:-1]) if self.task == 'detect' else (tp, fp) # remove background class if task=detect
|
||||
|
||||
@TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')
|
||||
@plt_settings()
|
||||
def plot(self, normalize=True, save_dir='', names=(), on_plot=None):
|
||||
"""
|
||||
Plot the confusion matrix using seaborn and save it to a file.
|
||||
|
||||
Args:
|
||||
normalize (bool): Whether to normalize the confusion matrix.
|
||||
save_dir (str): Directory where the plot will be saved.
|
||||
names (tuple): Names of classes, used as labels on the plot.
|
||||
on_plot (func): An optional callback to pass plots path and data when they are rendered.
|
||||
"""
|
||||
import seaborn as sn
|
||||
|
||||
array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1) # normalize columns
|
||||
array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)
|
||||
|
||||
fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)
|
||||
nc, nn = self.nc, len(names) # number of classes, names
|
||||
sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size
|
||||
labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels
|
||||
ticklabels = (list(names) + ['background']) if labels else 'auto'
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered
|
||||
sn.heatmap(array,
|
||||
ax=ax,
|
||||
annot=nc < 30,
|
||||
annot_kws={
|
||||
'size': 8},
|
||||
cmap='Blues',
|
||||
fmt='.2f' if normalize else '.0f',
|
||||
square=True,
|
||||
vmin=0.0,
|
||||
xticklabels=ticklabels,
|
||||
yticklabels=ticklabels).set_facecolor((1, 1, 1))
|
||||
title = 'Confusion Matrix' + ' Normalized' * normalize
|
||||
ax.set_xlabel('True')
|
||||
ax.set_ylabel('Predicted')
|
||||
ax.set_title(title)
|
||||
plot_fname = Path(save_dir) / f'{title.lower().replace(" ", "_")}.png'
|
||||
fig.savefig(plot_fname, dpi=250)
|
||||
plt.close(fig)
|
||||
if on_plot:
|
||||
on_plot(plot_fname)
|
||||
|
||||
def print(self):
|
||||
"""
|
||||
Print the confusion matrix to the console.
|
||||
"""
|
||||
for i in range(self.nc + 1):
|
||||
LOGGER.info(' '.join(map(str, self.matrix[i])))
|
||||
|
||||
|
||||
def smooth(y, f=0.05):
|
||||
"""Box filter of fraction f."""
|
||||
nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd)
|
||||
p = np.ones(nf // 2) # ones padding
|
||||
yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded
|
||||
return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed
|
||||
|
||||
|
||||
@plt_settings()
|
||||
def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=(), on_plot=None):
|
||||
"""Plots a precision-recall curve."""
|
||||
fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
|
||||
py = np.stack(py, axis=1)
|
||||
|
||||
if 0 < len(names) < 21: # display per-class legend if < 21 classes
|
||||
for i, y in enumerate(py.T):
|
||||
ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision)
|
||||
else:
|
||||
ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision)
|
||||
|
||||
ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
|
||||
ax.set_xlabel('Recall')
|
||||
ax.set_ylabel('Precision')
|
||||
ax.set_xlim(0, 1)
|
||||
ax.set_ylim(0, 1)
|
||||
ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left')
|
||||
ax.set_title('Precision-Recall Curve')
|
||||
fig.savefig(save_dir, dpi=250)
|
||||
plt.close(fig)
|
||||
if on_plot:
|
||||
on_plot(save_dir)
|
||||
|
||||
|
||||
@plt_settings()
|
||||
def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric', on_plot=None):
|
||||
"""Plots a metric-confidence curve."""
|
||||
fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
|
||||
|
||||
if 0 < len(names) < 21: # display per-class legend if < 21 classes
|
||||
for i, y in enumerate(py):
|
||||
ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric)
|
||||
else:
|
||||
ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric)
|
||||
|
||||
y = smooth(py.mean(0), 0.05)
|
||||
ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
|
||||
ax.set_xlabel(xlabel)
|
||||
ax.set_ylabel(ylabel)
|
||||
ax.set_xlim(0, 1)
|
||||
ax.set_ylim(0, 1)
|
||||
ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left')
|
||||
ax.set_title(f'{ylabel}-Confidence Curve')
|
||||
fig.savefig(save_dir, dpi=250)
|
||||
plt.close(fig)
|
||||
if on_plot:
|
||||
on_plot(save_dir)
|
||||
|
||||
|
||||
def compute_ap(recall, precision):
|
||||
"""
|
||||
Compute the average precision (AP) given the recall and precision curves.
|
||||
|
||||
Arguments:
|
||||
recall (list): The recall curve.
|
||||
precision (list): The precision curve.
|
||||
|
||||
Returns:
|
||||
(float): Average precision.
|
||||
(np.ndarray): Precision envelope curve.
|
||||
(np.ndarray): Modified recall curve with sentinel values added at the beginning and end.
|
||||
"""
|
||||
|
||||
# Append sentinel values to beginning and end
|
||||
mrec = np.concatenate(([0.0], recall, [1.0]))
|
||||
mpre = np.concatenate(([1.0], precision, [0.0]))
|
||||
|
||||
# Compute the precision envelope
|
||||
mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
|
||||
|
||||
# Integrate area under curve
|
||||
method = 'interp' # methods: 'continuous', 'interp'
|
||||
if method == 'interp':
|
||||
x = np.linspace(0, 1, 101) # 101-point interp (COCO)
|
||||
ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate
|
||||
else: # 'continuous'
|
||||
i = np.where(mrec[1:] != mrec[:-1])[0] # points where x-axis (recall) changes
|
||||
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve
|
||||
|
||||
return ap, mpre, mrec
|
||||
|
||||
|
||||
def ap_per_class(tp,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=False,
|
||||
on_plot=None,
|
||||
save_dir=Path(),
|
||||
names=(),
|
||||
eps=1e-16,
|
||||
prefix=''):
|
||||
"""
|
||||
Computes the average precision per class for object detection evaluation.
|
||||
|
||||
Args:
|
||||
tp (np.ndarray): Binary array indicating whether the detection is correct (True) or not (False).
|
||||
conf (np.ndarray): Array of confidence scores of the detections.
|
||||
pred_cls (np.ndarray): Array of predicted classes of the detections.
|
||||
target_cls (np.ndarray): Array of true classes of the detections.
|
||||
plot (bool, optional): Whether to plot PR curves or not. Defaults to False.
|
||||
on_plot (func, optional): A callback to pass plots path and data when they are rendered. Defaults to None.
|
||||
save_dir (Path, optional): Directory to save the PR curves. Defaults to an empty path.
|
||||
names (tuple, optional): Tuple of class names to plot PR curves. Defaults to an empty tuple.
|
||||
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-16.
|
||||
prefix (str, optional): A prefix string for saving the plot files. Defaults to an empty string.
|
||||
|
||||
Returns:
|
||||
(tuple): A tuple of six arrays and one array of unique classes, where:
|
||||
tp (np.ndarray): True positive counts for each class.
|
||||
fp (np.ndarray): False positive counts for each class.
|
||||
p (np.ndarray): Precision values at each confidence threshold.
|
||||
r (np.ndarray): Recall values at each confidence threshold.
|
||||
f1 (np.ndarray): F1-score values at each confidence threshold.
|
||||
ap (np.ndarray): Average precision for each class at different IoU thresholds.
|
||||
unique_classes (np.ndarray): An array of unique classes that have data.
|
||||
|
||||
"""
|
||||
|
||||
# Sort by objectness
|
||||
i = np.argsort(-conf)
|
||||
tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
|
||||
|
||||
# Find unique classes
|
||||
unique_classes, nt = np.unique(target_cls, return_counts=True)
|
||||
nc = unique_classes.shape[0] # number of classes, number of detections
|
||||
|
||||
# Create Precision-Recall curve and compute AP for each class
|
||||
px, py = np.linspace(0, 1, 1000), [] # for plotting
|
||||
ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
|
||||
for ci, c in enumerate(unique_classes):
|
||||
i = pred_cls == c
|
||||
n_l = nt[ci] # number of labels
|
||||
n_p = i.sum() # number of predictions
|
||||
if n_p == 0 or n_l == 0:
|
||||
continue
|
||||
|
||||
# Accumulate FPs and TPs
|
||||
fpc = (1 - tp[i]).cumsum(0)
|
||||
tpc = tp[i].cumsum(0)
|
||||
|
||||
# Recall
|
||||
recall = tpc / (n_l + eps) # recall curve
|
||||
r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases
|
||||
|
||||
# Precision
|
||||
precision = tpc / (tpc + fpc) # precision curve
|
||||
p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score
|
||||
|
||||
# AP from recall-precision curve
|
||||
for j in range(tp.shape[1]):
|
||||
ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
|
||||
if plot and j == 0:
|
||||
py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5
|
||||
|
||||
# Compute F1 (harmonic mean of precision and recall)
|
||||
f1 = 2 * p * r / (p + r + eps)
|
||||
names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data
|
||||
names = dict(enumerate(names)) # to dict
|
||||
if plot:
|
||||
plot_pr_curve(px, py, ap, save_dir / f'{prefix}PR_curve.png', names, on_plot=on_plot)
|
||||
plot_mc_curve(px, f1, save_dir / f'{prefix}F1_curve.png', names, ylabel='F1', on_plot=on_plot)
|
||||
plot_mc_curve(px, p, save_dir / f'{prefix}P_curve.png', names, ylabel='Precision', on_plot=on_plot)
|
||||
plot_mc_curve(px, r, save_dir / f'{prefix}R_curve.png', names, ylabel='Recall', on_plot=on_plot)
|
||||
|
||||
i = smooth(f1.mean(0), 0.1).argmax() # max F1 index
|
||||
p, r, f1 = p[:, i], r[:, i], f1[:, i]
|
||||
tp = (r * nt).round() # true positives
|
||||
fp = (tp / (p + eps) - tp).round() # false positives
|
||||
return tp, fp, p, r, f1, ap, unique_classes.astype(int)
|
||||
|
||||
|
||||
class Metric(SimpleClass):
|
||||
"""
|
||||
Class for computing evaluation metrics for YOLOv8 model.
|
||||
|
||||
Attributes:
|
||||
p (list): Precision for each class. Shape: (nc,).
|
||||
r (list): Recall for each class. Shape: (nc,).
|
||||
f1 (list): F1 score for each class. Shape: (nc,).
|
||||
all_ap (list): AP scores for all classes and all IoU thresholds. Shape: (nc, 10).
|
||||
ap_class_index (list): Index of class for each AP score. Shape: (nc,).
|
||||
nc (int): Number of classes.
|
||||
|
||||
Methods:
|
||||
ap50(): AP at IoU threshold of 0.5 for all classes. Returns: List of AP scores. Shape: (nc,) or [].
|
||||
ap(): AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: List of AP scores. Shape: (nc,) or [].
|
||||
mp(): Mean precision of all classes. Returns: Float.
|
||||
mr(): Mean recall of all classes. Returns: Float.
|
||||
map50(): Mean AP at IoU threshold of 0.5 for all classes. Returns: Float.
|
||||
map75(): Mean AP at IoU threshold of 0.75 for all classes. Returns: Float.
|
||||
map(): Mean AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: Float.
|
||||
mean_results(): Mean of results, returns mp, mr, map50, map.
|
||||
class_result(i): Class-aware result, returns p[i], r[i], ap50[i], ap[i].
|
||||
maps(): mAP of each class. Returns: Array of mAP scores, shape: (nc,).
|
||||
fitness(): Model fitness as a weighted combination of metrics. Returns: Float.
|
||||
update(results): Update metric attributes with new evaluation results.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.p = [] # (nc, )
|
||||
self.r = [] # (nc, )
|
||||
self.f1 = [] # (nc, )
|
||||
self.all_ap = [] # (nc, 10)
|
||||
self.ap_class_index = [] # (nc, )
|
||||
self.nc = 0
|
||||
|
||||
@property
|
||||
def ap50(self):
|
||||
"""
|
||||
Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes.
|
||||
|
||||
Returns:
|
||||
(np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available.
|
||||
"""
|
||||
return self.all_ap[:, 0] if len(self.all_ap) else []
|
||||
|
||||
@property
|
||||
def ap(self):
|
||||
"""
|
||||
Returns the Average Precision (AP) at an IoU threshold of 0.5-0.95 for all classes.
|
||||
|
||||
Returns:
|
||||
(np.ndarray, list): Array of shape (nc,) with AP50-95 values per class, or an empty list if not available.
|
||||
"""
|
||||
return self.all_ap.mean(1) if len(self.all_ap) else []
|
||||
|
||||
@property
|
||||
def mp(self):
|
||||
"""
|
||||
Returns the Mean Precision of all classes.
|
||||
|
||||
Returns:
|
||||
(float): The mean precision of all classes.
|
||||
"""
|
||||
return self.p.mean() if len(self.p) else 0.0
|
||||
|
||||
@property
|
||||
def mr(self):
|
||||
"""
|
||||
Returns the Mean Recall of all classes.
|
||||
|
||||
Returns:
|
||||
(float): The mean recall of all classes.
|
||||
"""
|
||||
return self.r.mean() if len(self.r) else 0.0
|
||||
|
||||
@property
|
||||
def map50(self):
|
||||
"""
|
||||
Returns the mean Average Precision (mAP) at an IoU threshold of 0.5.
|
||||
|
||||
Returns:
|
||||
(float): The mAP50 at an IoU threshold of 0.5.
|
||||
"""
|
||||
return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0
|
||||
|
||||
@property
|
||||
def map75(self):
|
||||
"""
|
||||
Returns the mean Average Precision (mAP) at an IoU threshold of 0.75.
|
||||
|
||||
Returns:
|
||||
(float): The mAP50 at an IoU threshold of 0.75.
|
||||
"""
|
||||
return self.all_ap[:, 5].mean() if len(self.all_ap) else 0.0
|
||||
|
||||
@property
|
||||
def map(self):
|
||||
"""
|
||||
Returns the mean Average Precision (mAP) over IoU thresholds of 0.5 - 0.95 in steps of 0.05.
|
||||
|
||||
Returns:
|
||||
(float): The mAP over IoU thresholds of 0.5 - 0.95 in steps of 0.05.
|
||||
"""
|
||||
return self.all_ap.mean() if len(self.all_ap) else 0.0
|
||||
|
||||
def mean_results(self):
|
||||
"""Mean of results, return mp, mr, map50, map."""
|
||||
return [self.mp, self.mr, self.map50, self.map]
|
||||
|
||||
def class_result(self, i):
|
||||
"""class-aware result, return p[i], r[i], ap50[i], ap[i]."""
|
||||
return self.p[i], self.r[i], self.ap50[i], self.ap[i]
|
||||
|
||||
@property
|
||||
def maps(self):
|
||||
"""mAP of each class."""
|
||||
maps = np.zeros(self.nc) + self.map
|
||||
for i, c in enumerate(self.ap_class_index):
|
||||
maps[c] = self.ap[i]
|
||||
return maps
|
||||
|
||||
def fitness(self):
|
||||
"""Model fitness as a weighted combination of metrics."""
|
||||
w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
|
||||
return (np.array(self.mean_results()) * w).sum()
|
||||
|
||||
def update(self, results):
|
||||
"""
|
||||
Args:
|
||||
results (tuple): A tuple of (p, r, ap, f1, ap_class)
|
||||
"""
|
||||
self.p, self.r, self.f1, self.all_ap, self.ap_class_index = results
|
||||
|
||||
|
||||
class DetMetrics(SimpleClass):
|
||||
"""
|
||||
This class is a utility class for computing detection metrics such as precision, recall, and mean average precision
|
||||
(mAP) of an object detection model.
|
||||
|
||||
Args:
|
||||
save_dir (Path): A path to the directory where the output plots will be saved. Defaults to current directory.
|
||||
plot (bool): A flag that indicates whether to plot precision-recall curves for each class. Defaults to False.
|
||||
on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None.
|
||||
names (tuple of str): A tuple of strings that represents the names of the classes. Defaults to an empty tuple.
|
||||
|
||||
Attributes:
|
||||
save_dir (Path): A path to the directory where the output plots will be saved.
|
||||
plot (bool): A flag that indicates whether to plot the precision-recall curves for each class.
|
||||
on_plot (func): An optional callback to pass plots path and data when they are rendered.
|
||||
names (tuple of str): A tuple of strings that represents the names of the classes.
|
||||
box (Metric): An instance of the Metric class for storing the results of the detection metrics.
|
||||
speed (dict): A dictionary for storing the execution time of different parts of the detection process.
|
||||
|
||||
Methods:
|
||||
process(tp, conf, pred_cls, target_cls): Updates the metric results with the latest batch of predictions.
|
||||
keys: Returns a list of keys for accessing the computed detection metrics.
|
||||
mean_results: Returns a list of mean values for the computed detection metrics.
|
||||
class_result(i): Returns a list of values for the computed detection metrics for a specific class.
|
||||
maps: Returns a dictionary of mean average precision (mAP) values for different IoU thresholds.
|
||||
fitness: Computes the fitness score based on the computed detection metrics.
|
||||
ap_class_index: Returns a list of class indices sorted by their average precision (AP) values.
|
||||
results_dict: Returns a dictionary that maps detection metric keys to their computed values.
|
||||
"""
|
||||
|
||||
def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None:
|
||||
self.save_dir = save_dir
|
||||
self.plot = plot
|
||||
self.on_plot = on_plot
|
||||
self.names = names
|
||||
self.box = Metric()
|
||||
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
|
||||
|
||||
def process(self, tp, conf, pred_cls, target_cls):
|
||||
"""Process predicted results for object detection and update metrics."""
|
||||
results = ap_per_class(tp,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
on_plot=self.on_plot)[2:]
|
||||
self.box.nc = len(self.names)
|
||||
self.box.update(results)
|
||||
|
||||
@property
|
||||
def keys(self):
|
||||
"""Returns a list of keys for accessing specific metrics."""
|
||||
return ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
|
||||
|
||||
def mean_results(self):
|
||||
"""Calculate mean of detected objects & return precision, recall, mAP50, and mAP50-95."""
|
||||
return self.box.mean_results()
|
||||
|
||||
def class_result(self, i):
|
||||
"""Return the result of evaluating the performance of an object detection model on a specific class."""
|
||||
return self.box.class_result(i)
|
||||
|
||||
@property
|
||||
def maps(self):
|
||||
"""Returns mean Average Precision (mAP) scores per class."""
|
||||
return self.box.maps
|
||||
|
||||
@property
|
||||
def fitness(self):
|
||||
"""Returns the fitness of box object."""
|
||||
return self.box.fitness()
|
||||
|
||||
@property
|
||||
def ap_class_index(self):
|
||||
"""Returns the average precision index per class."""
|
||||
return self.box.ap_class_index
|
||||
|
||||
@property
|
||||
def results_dict(self):
|
||||
"""Returns dictionary of computed performance metrics and statistics."""
|
||||
return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness]))
|
||||
|
||||
|
||||
class SegmentMetrics(SimpleClass):
|
||||
"""
|
||||
Calculates and aggregates detection and segmentation metrics over a given set of classes.
|
||||
|
||||
Args:
|
||||
save_dir (Path): Path to the directory where the output plots should be saved. Default is the current directory.
|
||||
plot (bool): Whether to save the detection and segmentation plots. Default is False.
|
||||
on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None.
|
||||
names (list): List of class names. Default is an empty list.
|
||||
|
||||
Attributes:
|
||||
save_dir (Path): Path to the directory where the output plots should be saved.
|
||||
plot (bool): Whether to save the detection and segmentation plots.
|
||||
on_plot (func): An optional callback to pass plots path and data when they are rendered.
|
||||
names (list): List of class names.
|
||||
box (Metric): An instance of the Metric class to calculate box detection metrics.
|
||||
seg (Metric): An instance of the Metric class to calculate mask segmentation metrics.
|
||||
speed (dict): Dictionary to store the time taken in different phases of inference.
|
||||
|
||||
Methods:
|
||||
process(tp_m, tp_b, conf, pred_cls, target_cls): Processes metrics over the given set of predictions.
|
||||
mean_results(): Returns the mean of the detection and segmentation metrics over all the classes.
|
||||
class_result(i): Returns the detection and segmentation metrics of class `i`.
|
||||
maps: Returns the mean Average Precision (mAP) scores for IoU thresholds ranging from 0.50 to 0.95.
|
||||
fitness: Returns the fitness scores, which are a single weighted combination of metrics.
|
||||
ap_class_index: Returns the list of indices of classes used to compute Average Precision (AP).
|
||||
results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score.
|
||||
"""
|
||||
|
||||
def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None:
|
||||
self.save_dir = save_dir
|
||||
self.plot = plot
|
||||
self.on_plot = on_plot
|
||||
self.names = names
|
||||
self.box = Metric()
|
||||
self.seg = Metric()
|
||||
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
|
||||
|
||||
def process(self, tp_b, tp_m, conf, pred_cls, target_cls):
|
||||
"""
|
||||
Processes the detection and segmentation metrics over the given set of predictions.
|
||||
|
||||
Args:
|
||||
tp_b (list): List of True Positive boxes.
|
||||
tp_m (list): List of True Positive masks.
|
||||
conf (list): List of confidence scores.
|
||||
pred_cls (list): List of predicted classes.
|
||||
target_cls (list): List of target classes.
|
||||
"""
|
||||
|
||||
results_mask = ap_per_class(tp_m,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
on_plot=self.on_plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
prefix='Mask')[2:]
|
||||
self.seg.nc = len(self.names)
|
||||
self.seg.update(results_mask)
|
||||
results_box = ap_per_class(tp_b,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
on_plot=self.on_plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
prefix='Box')[2:]
|
||||
self.box.nc = len(self.names)
|
||||
self.box.update(results_box)
|
||||
|
||||
@property
|
||||
def keys(self):
|
||||
"""Returns a list of keys for accessing metrics."""
|
||||
return [
|
||||
'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)',
|
||||
'metrics/precision(M)', 'metrics/recall(M)', 'metrics/mAP50(M)', 'metrics/mAP50-95(M)']
|
||||
|
||||
def mean_results(self):
|
||||
"""Return the mean metrics for bounding box and segmentation results."""
|
||||
return self.box.mean_results() + self.seg.mean_results()
|
||||
|
||||
def class_result(self, i):
|
||||
"""Returns classification results for a specified class index."""
|
||||
return self.box.class_result(i) + self.seg.class_result(i)
|
||||
|
||||
@property
|
||||
def maps(self):
|
||||
"""Returns mAP scores for object detection and semantic segmentation models."""
|
||||
return self.box.maps + self.seg.maps
|
||||
|
||||
@property
|
||||
def fitness(self):
|
||||
"""Get the fitness score for both segmentation and bounding box models."""
|
||||
return self.seg.fitness() + self.box.fitness()
|
||||
|
||||
@property
|
||||
def ap_class_index(self):
|
||||
"""Boxes and masks have the same ap_class_index."""
|
||||
return self.box.ap_class_index
|
||||
|
||||
@property
|
||||
def results_dict(self):
|
||||
"""Returns results of object detection model for evaluation."""
|
||||
return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness]))
|
||||
|
||||
|
||||
class PoseMetrics(SegmentMetrics):
|
||||
"""
|
||||
Calculates and aggregates detection and pose metrics over a given set of classes.
|
||||
|
||||
Args:
|
||||
save_dir (Path): Path to the directory where the output plots should be saved. Default is the current directory.
|
||||
plot (bool): Whether to save the detection and segmentation plots. Default is False.
|
||||
on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None.
|
||||
names (list): List of class names. Default is an empty list.
|
||||
|
||||
Attributes:
|
||||
save_dir (Path): Path to the directory where the output plots should be saved.
|
||||
plot (bool): Whether to save the detection and segmentation plots.
|
||||
on_plot (func): An optional callback to pass plots path and data when they are rendered.
|
||||
names (list): List of class names.
|
||||
box (Metric): An instance of the Metric class to calculate box detection metrics.
|
||||
pose (Metric): An instance of the Metric class to calculate mask segmentation metrics.
|
||||
speed (dict): Dictionary to store the time taken in different phases of inference.
|
||||
|
||||
Methods:
|
||||
process(tp_m, tp_b, conf, pred_cls, target_cls): Processes metrics over the given set of predictions.
|
||||
mean_results(): Returns the mean of the detection and segmentation metrics over all the classes.
|
||||
class_result(i): Returns the detection and segmentation metrics of class `i`.
|
||||
maps: Returns the mean Average Precision (mAP) scores for IoU thresholds ranging from 0.50 to 0.95.
|
||||
fitness: Returns the fitness scores, which are a single weighted combination of metrics.
|
||||
ap_class_index: Returns the list of indices of classes used to compute Average Precision (AP).
|
||||
results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score.
|
||||
"""
|
||||
|
||||
def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None:
|
||||
super().__init__(save_dir, plot, names)
|
||||
self.save_dir = save_dir
|
||||
self.plot = plot
|
||||
self.on_plot = on_plot
|
||||
self.names = names
|
||||
self.box = Metric()
|
||||
self.pose = Metric()
|
||||
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
|
||||
|
||||
def __getattr__(self, attr):
|
||||
"""Raises an AttributeError if an invalid attribute is accessed."""
|
||||
name = self.__class__.__name__
|
||||
raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
|
||||
|
||||
def process(self, tp_b, tp_p, conf, pred_cls, target_cls):
|
||||
"""
|
||||
Processes the detection and pose metrics over the given set of predictions.
|
||||
|
||||
Args:
|
||||
tp_b (list): List of True Positive boxes.
|
||||
tp_p (list): List of True Positive keypoints.
|
||||
conf (list): List of confidence scores.
|
||||
pred_cls (list): List of predicted classes.
|
||||
target_cls (list): List of target classes.
|
||||
"""
|
||||
|
||||
results_pose = ap_per_class(tp_p,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
on_plot=self.on_plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
prefix='Pose')[2:]
|
||||
self.pose.nc = len(self.names)
|
||||
self.pose.update(results_pose)
|
||||
results_box = ap_per_class(tp_b,
|
||||
conf,
|
||||
pred_cls,
|
||||
target_cls,
|
||||
plot=self.plot,
|
||||
on_plot=self.on_plot,
|
||||
save_dir=self.save_dir,
|
||||
names=self.names,
|
||||
prefix='Box')[2:]
|
||||
self.box.nc = len(self.names)
|
||||
self.box.update(results_box)
|
||||
|
||||
@property
|
||||
def keys(self):
|
||||
"""Returns list of evaluation metric keys."""
|
||||
return [
|
||||
'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)',
|
||||
'metrics/precision(P)', 'metrics/recall(P)', 'metrics/mAP50(P)', 'metrics/mAP50-95(P)']
|
||||
|
||||
def mean_results(self):
|
||||
"""Return the mean results of box and pose."""
|
||||
return self.box.mean_results() + self.pose.mean_results()
|
||||
|
||||
def class_result(self, i):
|
||||
"""Return the class-wise detection results for a specific class i."""
|
||||
return self.box.class_result(i) + self.pose.class_result(i)
|
||||
|
||||
@property
|
||||
def maps(self):
|
||||
"""Returns the mean average precision (mAP) per class for both box and pose detections."""
|
||||
return self.box.maps + self.pose.maps
|
||||
|
||||
@property
|
||||
def fitness(self):
|
||||
"""Computes classification metrics and speed using the `targets` and `pred` inputs."""
|
||||
return self.pose.fitness() + self.box.fitness()
|
||||
|
||||
|
||||
class ClassifyMetrics(SimpleClass):
|
||||
"""
|
||||
Class for computing classification metrics including top-1 and top-5 accuracy.
|
||||
|
||||
Attributes:
|
||||
top1 (float): The top-1 accuracy.
|
||||
top5 (float): The top-5 accuracy.
|
||||
speed (Dict[str, float]): A dictionary containing the time taken for each step in the pipeline.
|
||||
|
||||
Properties:
|
||||
fitness (float): The fitness of the model, which is equal to top-5 accuracy.
|
||||
results_dict (Dict[str, Union[float, str]]): A dictionary containing the classification metrics and fitness.
|
||||
keys (List[str]): A list of keys for the results_dict.
|
||||
|
||||
Methods:
|
||||
process(targets, pred): Processes the targets and predictions to compute classification metrics.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.top1 = 0
|
||||
self.top5 = 0
|
||||
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
|
||||
|
||||
def process(self, targets, pred):
|
||||
"""Target classes and predicted classes."""
|
||||
pred, targets = torch.cat(pred), torch.cat(targets)
|
||||
correct = (targets[:, None] == pred).float()
|
||||
acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy
|
||||
self.top1, self.top5 = acc.mean(0).tolist()
|
||||
|
||||
@property
|
||||
def fitness(self):
|
||||
"""Returns top-5 accuracy as fitness score."""
|
||||
return self.top5
|
||||
|
||||
@property
|
||||
def results_dict(self):
|
||||
"""Returns a dictionary with model's performance metrics and fitness score."""
|
||||
return dict(zip(self.keys + ['fitness'], [self.top1, self.top5, self.fitness]))
|
||||
|
||||
@property
|
||||
def keys(self):
|
||||
"""Returns a list of keys for the results_dict property."""
|
||||
return ['metrics/accuracy_top1', 'metrics/accuracy_top5']
|
||||
@ -1,739 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import contextlib
|
||||
import math
|
||||
import re
|
||||
import time
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torchvision
|
||||
|
||||
from ultralytics.yolo.utils import LOGGER
|
||||
|
||||
from .metrics import box_iou
|
||||
|
||||
|
||||
class Profile(contextlib.ContextDecorator):
|
||||
"""
|
||||
YOLOv8 Profile class.
|
||||
Usage: as a decorator with @Profile() or as a context manager with 'with Profile():'
|
||||
"""
|
||||
|
||||
def __init__(self, t=0.0):
|
||||
"""
|
||||
Initialize the Profile class.
|
||||
|
||||
Args:
|
||||
t (float): Initial time. Defaults to 0.0.
|
||||
"""
|
||||
self.t = t
|
||||
self.cuda = torch.cuda.is_available()
|
||||
|
||||
def __enter__(self):
|
||||
"""
|
||||
Start timing.
|
||||
"""
|
||||
self.start = self.time()
|
||||
return self
|
||||
|
||||
def __exit__(self, type, value, traceback):
|
||||
"""
|
||||
Stop timing.
|
||||
"""
|
||||
self.dt = self.time() - self.start # delta-time
|
||||
self.t += self.dt # accumulate dt
|
||||
|
||||
def time(self):
|
||||
"""
|
||||
Get current time.
|
||||
"""
|
||||
if self.cuda:
|
||||
torch.cuda.synchronize()
|
||||
return time.time()
|
||||
|
||||
|
||||
def coco80_to_coco91_class(): #
|
||||
"""
|
||||
Converts 80-index (val2014) to 91-index (paper).
|
||||
For details see https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/.
|
||||
|
||||
Example:
|
||||
a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
|
||||
b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
|
||||
x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
|
||||
x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
|
||||
"""
|
||||
return [
|
||||
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
|
||||
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
|
||||
64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
|
||||
|
||||
|
||||
def segment2box(segment, width=640, height=640):
|
||||
"""
|
||||
Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
|
||||
|
||||
Args:
|
||||
segment (torch.Tensor): the segment label
|
||||
width (int): the width of the image. Defaults to 640
|
||||
height (int): The height of the image. Defaults to 640
|
||||
|
||||
Returns:
|
||||
(np.ndarray): the minimum and maximum x and y values of the segment.
|
||||
"""
|
||||
# Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
|
||||
x, y = segment.T # segment xy
|
||||
inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
|
||||
x, y, = x[inside], y[inside]
|
||||
return np.array([x.min(), y.min(), x.max(), y.max()], dtype=segment.dtype) if any(x) else np.zeros(
|
||||
4, dtype=segment.dtype) # xyxy
|
||||
|
||||
|
||||
def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None, padding=True):
|
||||
"""
|
||||
Rescales bounding boxes (in the format of xyxy) from the shape of the image they were originally specified in
|
||||
(img1_shape) to the shape of a different image (img0_shape).
|
||||
|
||||
Args:
|
||||
img1_shape (tuple): The shape of the image that the bounding boxes are for, in the format of (height, width).
|
||||
boxes (torch.Tensor): the bounding boxes of the objects in the image, in the format of (x1, y1, x2, y2)
|
||||
img0_shape (tuple): the shape of the target image, in the format of (height, width).
|
||||
ratio_pad (tuple): a tuple of (ratio, pad) for scaling the boxes. If not provided, the ratio and pad will be
|
||||
calculated based on the size difference between the two images.
|
||||
padding (bool): If True, assuming the boxes is based on image augmented by yolo style. If False then do regular
|
||||
rescaling.
|
||||
|
||||
Returns:
|
||||
boxes (torch.Tensor): The scaled bounding boxes, in the format of (x1, y1, x2, y2)
|
||||
"""
|
||||
if ratio_pad is None: # calculate from img0_shape
|
||||
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
|
||||
pad = round((img1_shape[1] - img0_shape[1] * gain) / 2 - 0.1), round(
|
||||
(img1_shape[0] - img0_shape[0] * gain) / 2 - 0.1) # wh padding
|
||||
else:
|
||||
gain = ratio_pad[0][0]
|
||||
pad = ratio_pad[1]
|
||||
|
||||
if padding:
|
||||
boxes[..., [0, 2]] -= pad[0] # x padding
|
||||
boxes[..., [1, 3]] -= pad[1] # y padding
|
||||
boxes[..., :4] /= gain
|
||||
clip_boxes(boxes, img0_shape)
|
||||
return boxes
|
||||
|
||||
|
||||
def make_divisible(x, divisor):
|
||||
"""
|
||||
Returns the nearest number that is divisible by the given divisor.
|
||||
|
||||
Args:
|
||||
x (int): The number to make divisible.
|
||||
divisor (int | torch.Tensor): The divisor.
|
||||
|
||||
Returns:
|
||||
(int): The nearest number divisible by the divisor.
|
||||
"""
|
||||
if isinstance(divisor, torch.Tensor):
|
||||
divisor = int(divisor.max()) # to int
|
||||
return math.ceil(x / divisor) * divisor
|
||||
|
||||
|
||||
def non_max_suppression(
|
||||
prediction,
|
||||
conf_thres=0.25,
|
||||
iou_thres=0.45,
|
||||
classes=None,
|
||||
agnostic=False,
|
||||
multi_label=False,
|
||||
labels=(),
|
||||
max_det=300,
|
||||
nc=0, # number of classes (optional)
|
||||
max_time_img=0.05,
|
||||
max_nms=30000,
|
||||
max_wh=7680,
|
||||
):
|
||||
"""
|
||||
Perform non-maximum suppression (NMS) on a set of boxes, with support for masks and multiple labels per box.
|
||||
|
||||
Arguments:
|
||||
prediction (torch.Tensor): A tensor of shape (batch_size, num_classes + 4 + num_masks, num_boxes)
|
||||
containing the predicted boxes, classes, and masks. The tensor should be in the format
|
||||
output by a model, such as YOLO.
|
||||
conf_thres (float): The confidence threshold below which boxes will be filtered out.
|
||||
Valid values are between 0.0 and 1.0.
|
||||
iou_thres (float): The IoU threshold below which boxes will be filtered out during NMS.
|
||||
Valid values are between 0.0 and 1.0.
|
||||
classes (List[int]): A list of class indices to consider. If None, all classes will be considered.
|
||||
agnostic (bool): If True, the model is agnostic to the number of classes, and all
|
||||
classes will be considered as one.
|
||||
multi_label (bool): If True, each box may have multiple labels.
|
||||
labels (List[List[Union[int, float, torch.Tensor]]]): A list of lists, where each inner
|
||||
list contains the apriori labels for a given image. The list should be in the format
|
||||
output by a dataloader, with each label being a tuple of (class_index, x1, y1, x2, y2).
|
||||
max_det (int): The maximum number of boxes to keep after NMS.
|
||||
nc (int, optional): The number of classes output by the model. Any indices after this will be considered masks.
|
||||
max_time_img (float): The maximum time (seconds) for processing one image.
|
||||
max_nms (int): The maximum number of boxes into torchvision.ops.nms().
|
||||
max_wh (int): The maximum box width and height in pixels
|
||||
|
||||
Returns:
|
||||
(List[torch.Tensor]): A list of length batch_size, where each element is a tensor of
|
||||
shape (num_boxes, 6 + num_masks) containing the kept boxes, with columns
|
||||
(x1, y1, x2, y2, confidence, class, mask1, mask2, ...).
|
||||
"""
|
||||
|
||||
# Checks
|
||||
assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
|
||||
assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
|
||||
if isinstance(prediction, (list, tuple)): # YOLOv8 model in validation model, output = (inference_out, loss_out)
|
||||
prediction = prediction[0] # select only inference output
|
||||
|
||||
device = prediction.device
|
||||
mps = 'mps' in device.type # Apple MPS
|
||||
if mps: # MPS not fully supported yet, convert tensors to CPU before NMS
|
||||
prediction = prediction.cpu()
|
||||
bs = prediction.shape[0] # batch size
|
||||
nc = nc or (prediction.shape[1] - 4) # number of classes
|
||||
nm = prediction.shape[1] - nc - 4
|
||||
mi = 4 + nc # mask start index
|
||||
xc = prediction[:, 4:mi].amax(1) > conf_thres # candidates
|
||||
|
||||
# Settings
|
||||
# min_wh = 2 # (pixels) minimum box width and height
|
||||
time_limit = 0.5 + max_time_img * bs # seconds to quit after
|
||||
redundant = True # require redundant detections
|
||||
multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
|
||||
merge = False # use merge-NMS
|
||||
|
||||
prediction = prediction.transpose(-1, -2) # shape(1,84,6300) to shape(1,6300,84)
|
||||
prediction[..., :4] = xywh2xyxy(prediction[..., :4]) # xywh to xyxy
|
||||
|
||||
t = time.time()
|
||||
output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs
|
||||
for xi, x in enumerate(prediction): # image index, image inference
|
||||
# Apply constraints
|
||||
# x[((x[:, 2:4] < min_wh) | (x[:, 2:4] > max_wh)).any(1), 4] = 0 # width-height
|
||||
x = x[xc[xi]] # confidence
|
||||
|
||||
# Cat apriori labels if autolabelling
|
||||
if labels and len(labels[xi]):
|
||||
lb = labels[xi]
|
||||
v = torch.zeros((len(lb), nc + nm + 5), device=x.device)
|
||||
v[:, :4] = lb[:, 1:5] # box
|
||||
v[range(len(lb)), lb[:, 0].long() + 4] = 1.0 # cls
|
||||
x = torch.cat((x, v), 0)
|
||||
|
||||
# If none remain process next image
|
||||
if not x.shape[0]:
|
||||
continue
|
||||
|
||||
# Detections matrix nx6 (xyxy, conf, cls)
|
||||
box, cls, mask = x.split((4, nc, nm), 1)
|
||||
|
||||
if multi_label:
|
||||
i, j = torch.where(cls > conf_thres)
|
||||
x = torch.cat((box[i], x[i, 4 + j, None], j[:, None].float(), mask[i]), 1)
|
||||
else: # best class only
|
||||
conf, j = cls.max(1, keepdim=True)
|
||||
x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]
|
||||
|
||||
# Filter by class
|
||||
if classes is not None:
|
||||
x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
|
||||
|
||||
# Apply finite constraint
|
||||
# if not torch.isfinite(x).all():
|
||||
# x = x[torch.isfinite(x).all(1)]
|
||||
|
||||
# Check shape
|
||||
n = x.shape[0] # number of boxes
|
||||
if not n: # no boxes
|
||||
continue
|
||||
if n > max_nms: # excess boxes
|
||||
x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes
|
||||
|
||||
# Batched NMS
|
||||
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
|
||||
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
|
||||
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
|
||||
i = i[:max_det] # limit detections
|
||||
if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
|
||||
# Update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
|
||||
iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
|
||||
weights = iou * scores[None] # box weights
|
||||
x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
|
||||
if redundant:
|
||||
i = i[iou.sum(1) > 1] # require redundancy
|
||||
|
||||
output[xi] = x[i]
|
||||
if mps:
|
||||
output[xi] = output[xi].to(device)
|
||||
if (time.time() - t) > time_limit:
|
||||
LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded')
|
||||
break # time limit exceeded
|
||||
|
||||
return output
|
||||
|
||||
|
||||
def clip_boxes(boxes, shape):
|
||||
"""
|
||||
It takes a list of bounding boxes and a shape (height, width) and clips the bounding boxes to the
|
||||
shape
|
||||
|
||||
Args:
|
||||
boxes (torch.Tensor): the bounding boxes to clip
|
||||
shape (tuple): the shape of the image
|
||||
"""
|
||||
if isinstance(boxes, torch.Tensor): # faster individually
|
||||
boxes[..., 0].clamp_(0, shape[1]) # x1
|
||||
boxes[..., 1].clamp_(0, shape[0]) # y1
|
||||
boxes[..., 2].clamp_(0, shape[1]) # x2
|
||||
boxes[..., 3].clamp_(0, shape[0]) # y2
|
||||
else: # np.array (faster grouped)
|
||||
boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2
|
||||
boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2
|
||||
|
||||
|
||||
def clip_coords(coords, shape):
|
||||
"""
|
||||
Clip line coordinates to the image boundaries.
|
||||
|
||||
Args:
|
||||
coords (torch.Tensor | numpy.ndarray): A list of line coordinates.
|
||||
shape (tuple): A tuple of integers representing the size of the image in the format (height, width).
|
||||
|
||||
Returns:
|
||||
(None): The function modifies the input `coordinates` in place, by clipping each coordinate to the image boundaries.
|
||||
"""
|
||||
if isinstance(coords, torch.Tensor): # faster individually
|
||||
coords[..., 0].clamp_(0, shape[1]) # x
|
||||
coords[..., 1].clamp_(0, shape[0]) # y
|
||||
else: # np.array (faster grouped)
|
||||
coords[..., 0] = coords[..., 0].clip(0, shape[1]) # x
|
||||
coords[..., 1] = coords[..., 1].clip(0, shape[0]) # y
|
||||
|
||||
|
||||
def scale_image(masks, im0_shape, ratio_pad=None):
|
||||
"""
|
||||
Takes a mask, and resizes it to the original image size
|
||||
|
||||
Args:
|
||||
masks (torch.Tensor): resized and padded masks/images, [h, w, num]/[h, w, 3].
|
||||
im0_shape (tuple): the original image shape
|
||||
ratio_pad (tuple): the ratio of the padding to the original image.
|
||||
|
||||
Returns:
|
||||
masks (torch.Tensor): The masks that are being returned.
|
||||
"""
|
||||
# Rescale coordinates (xyxy) from im1_shape to im0_shape
|
||||
im1_shape = masks.shape
|
||||
if im1_shape[:2] == im0_shape[:2]:
|
||||
return masks
|
||||
if ratio_pad is None: # calculate from im0_shape
|
||||
gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new
|
||||
pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding
|
||||
else:
|
||||
gain = ratio_pad[0][0]
|
||||
pad = ratio_pad[1]
|
||||
top, left = int(pad[1]), int(pad[0]) # y, x
|
||||
bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])
|
||||
|
||||
if len(masks.shape) < 2:
|
||||
raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}')
|
||||
masks = masks[top:bottom, left:right]
|
||||
# masks = masks.permute(2, 0, 1).contiguous()
|
||||
# masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]
|
||||
# masks = masks.permute(1, 2, 0).contiguous()
|
||||
masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))
|
||||
if len(masks.shape) == 2:
|
||||
masks = masks[:, :, None]
|
||||
|
||||
return masks
|
||||
|
||||
|
||||
def xyxy2xywh(x):
|
||||
"""
|
||||
Convert bounding box coordinates from (x1, y1, x2, y2) format to (x, y, width, height) format.
|
||||
|
||||
Args:
|
||||
x (np.ndarray | torch.Tensor): The input bounding box coordinates in (x1, y1, x2, y2) format.
|
||||
Returns:
|
||||
y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height) format.
|
||||
"""
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center
|
||||
y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center
|
||||
y[..., 2] = x[..., 2] - x[..., 0] # width
|
||||
y[..., 3] = x[..., 3] - x[..., 1] # height
|
||||
return y
|
||||
|
||||
|
||||
def xywh2xyxy(x):
|
||||
"""
|
||||
Convert bounding box coordinates from (x, y, width, height) format to (x1, y1, x2, y2) format where (x1, y1) is the
|
||||
top-left corner and (x2, y2) is the bottom-right corner.
|
||||
|
||||
Args:
|
||||
x (np.ndarray | torch.Tensor): The input bounding box coordinates in (x, y, width, height) format.
|
||||
Returns:
|
||||
y (np.ndarray | torch.Tensor): The bounding box coordinates in (x1, y1, x2, y2) format.
|
||||
"""
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x
|
||||
y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y
|
||||
y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x
|
||||
y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y
|
||||
return y
|
||||
|
||||
|
||||
def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
|
||||
"""
|
||||
Convert normalized bounding box coordinates to pixel coordinates.
|
||||
|
||||
Args:
|
||||
x (np.ndarray | torch.Tensor): The bounding box coordinates.
|
||||
w (int): Width of the image. Defaults to 640
|
||||
h (int): Height of the image. Defaults to 640
|
||||
padw (int): Padding width. Defaults to 0
|
||||
padh (int): Padding height. Defaults to 0
|
||||
Returns:
|
||||
y (np.ndarray | torch.Tensor): The coordinates of the bounding box in the format [x1, y1, x2, y2] where
|
||||
x1,y1 is the top-left corner, x2,y2 is the bottom-right corner of the bounding box.
|
||||
"""
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x
|
||||
y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y
|
||||
y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x
|
||||
y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y
|
||||
return y
|
||||
|
||||
|
||||
def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
|
||||
"""
|
||||
Convert bounding box coordinates from (x1, y1, x2, y2) format to (x, y, width, height, normalized) format.
|
||||
x, y, width and height are normalized to image dimensions
|
||||
|
||||
Args:
|
||||
x (np.ndarray | torch.Tensor): The input bounding box coordinates in (x1, y1, x2, y2) format.
|
||||
w (int): The width of the image. Defaults to 640
|
||||
h (int): The height of the image. Defaults to 640
|
||||
clip (bool): If True, the boxes will be clipped to the image boundaries. Defaults to False
|
||||
eps (float): The minimum value of the box's width and height. Defaults to 0.0
|
||||
Returns:
|
||||
y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height, normalized) format
|
||||
"""
|
||||
if clip:
|
||||
clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center
|
||||
y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center
|
||||
y[..., 2] = (x[..., 2] - x[..., 0]) / w # width
|
||||
y[..., 3] = (x[..., 3] - x[..., 1]) / h # height
|
||||
return y
|
||||
|
||||
|
||||
def xyn2xy(x, w=640, h=640, padw=0, padh=0):
|
||||
"""
|
||||
Convert normalized coordinates to pixel coordinates of shape (n,2)
|
||||
|
||||
Args:
|
||||
x (np.ndarray | torch.Tensor): The input tensor of normalized bounding box coordinates
|
||||
w (int): The width of the image. Defaults to 640
|
||||
h (int): The height of the image. Defaults to 640
|
||||
padw (int): The width of the padding. Defaults to 0
|
||||
padh (int): The height of the padding. Defaults to 0
|
||||
Returns:
|
||||
y (np.ndarray | torch.Tensor): The x and y coordinates of the top left corner of the bounding box
|
||||
"""
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[..., 0] = w * x[..., 0] + padw # top left x
|
||||
y[..., 1] = h * x[..., 1] + padh # top left y
|
||||
return y
|
||||
|
||||
|
||||
def xywh2ltwh(x):
|
||||
"""
|
||||
Convert the bounding box format from [x, y, w, h] to [x1, y1, w, h], where x1, y1 are the top-left coordinates.
|
||||
|
||||
Args:
|
||||
x (np.ndarray | torch.Tensor): The input tensor with the bounding box coordinates in the xywh format
|
||||
Returns:
|
||||
y (np.ndarray | torch.Tensor): The bounding box coordinates in the xyltwh format
|
||||
"""
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
|
||||
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
|
||||
return y
|
||||
|
||||
|
||||
def xyxy2ltwh(x):
|
||||
"""
|
||||
Convert nx4 bounding boxes from [x1, y1, x2, y2] to [x1, y1, w, h], where xy1=top-left, xy2=bottom-right
|
||||
|
||||
Args:
|
||||
x (np.ndarray | torch.Tensor): The input tensor with the bounding boxes coordinates in the xyxy format
|
||||
Returns:
|
||||
y (np.ndarray | torch.Tensor): The bounding box coordinates in the xyltwh format.
|
||||
"""
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[:, 2] = x[:, 2] - x[:, 0] # width
|
||||
y[:, 3] = x[:, 3] - x[:, 1] # height
|
||||
return y
|
||||
|
||||
|
||||
def ltwh2xywh(x):
|
||||
"""
|
||||
Convert nx4 boxes from [x1, y1, w, h] to [x, y, w, h] where xy1=top-left, xy=center
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): the input tensor
|
||||
"""
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[:, 0] = x[:, 0] + x[:, 2] / 2 # center x
|
||||
y[:, 1] = x[:, 1] + x[:, 3] / 2 # center y
|
||||
return y
|
||||
|
||||
|
||||
def ltwh2xyxy(x):
|
||||
"""
|
||||
It converts the bounding box from [x1, y1, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
|
||||
|
||||
Args:
|
||||
x (np.ndarray | torch.Tensor): the input image
|
||||
|
||||
Returns:
|
||||
y (np.ndarray | torch.Tensor): the xyxy coordinates of the bounding boxes.
|
||||
"""
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[:, 2] = x[:, 2] + x[:, 0] # width
|
||||
y[:, 3] = x[:, 3] + x[:, 1] # height
|
||||
return y
|
||||
|
||||
|
||||
def segments2boxes(segments):
|
||||
"""
|
||||
It converts segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
|
||||
|
||||
Args:
|
||||
segments (list): list of segments, each segment is a list of points, each point is a list of x, y coordinates
|
||||
|
||||
Returns:
|
||||
(np.ndarray): the xywh coordinates of the bounding boxes.
|
||||
"""
|
||||
boxes = []
|
||||
for s in segments:
|
||||
x, y = s.T # segment xy
|
||||
boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy
|
||||
return xyxy2xywh(np.array(boxes)) # cls, xywh
|
||||
|
||||
|
||||
def resample_segments(segments, n=1000):
|
||||
"""
|
||||
Inputs a list of segments (n,2) and returns a list of segments (n,2) up-sampled to n points each.
|
||||
|
||||
Args:
|
||||
segments (list): a list of (n,2) arrays, where n is the number of points in the segment.
|
||||
n (int): number of points to resample the segment to. Defaults to 1000
|
||||
|
||||
Returns:
|
||||
segments (list): the resampled segments.
|
||||
"""
|
||||
for i, s in enumerate(segments):
|
||||
s = np.concatenate((s, s[0:1, :]), axis=0)
|
||||
x = np.linspace(0, len(s) - 1, n)
|
||||
xp = np.arange(len(s))
|
||||
segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)],
|
||||
dtype=np.float32).reshape(2, -1).T # segment xy
|
||||
return segments
|
||||
|
||||
|
||||
def crop_mask(masks, boxes):
|
||||
"""
|
||||
It takes a mask and a bounding box, and returns a mask that is cropped to the bounding box
|
||||
|
||||
Args:
|
||||
masks (torch.Tensor): [n, h, w] tensor of masks
|
||||
boxes (torch.Tensor): [n, 4] tensor of bbox coordinates in relative point form
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): The masks are being cropped to the bounding box.
|
||||
"""
|
||||
n, h, w = masks.shape
|
||||
x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(n,1,1)
|
||||
r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,1,w)
|
||||
c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(1,h,1)
|
||||
|
||||
return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2))
|
||||
|
||||
|
||||
def process_mask_upsample(protos, masks_in, bboxes, shape):
|
||||
"""
|
||||
It takes the output of the mask head, and applies the mask to the bounding boxes. This produces masks of higher
|
||||
quality but is slower.
|
||||
|
||||
Args:
|
||||
protos (torch.Tensor): [mask_dim, mask_h, mask_w]
|
||||
masks_in (torch.Tensor): [n, mask_dim], n is number of masks after nms
|
||||
bboxes (torch.Tensor): [n, 4], n is number of masks after nms
|
||||
shape (tuple): the size of the input image (h,w)
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): The upsampled masks.
|
||||
"""
|
||||
c, mh, mw = protos.shape # CHW
|
||||
masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
|
||||
masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
|
||||
masks = crop_mask(masks, bboxes) # CHW
|
||||
return masks.gt_(0.5)
|
||||
|
||||
|
||||
def process_mask(protos, masks_in, bboxes, shape, upsample=False):
|
||||
"""
|
||||
Apply masks to bounding boxes using the output of the mask head.
|
||||
|
||||
Args:
|
||||
protos (torch.Tensor): A tensor of shape [mask_dim, mask_h, mask_w].
|
||||
masks_in (torch.Tensor): A tensor of shape [n, mask_dim], where n is the number of masks after NMS.
|
||||
bboxes (torch.Tensor): A tensor of shape [n, 4], where n is the number of masks after NMS.
|
||||
shape (tuple): A tuple of integers representing the size of the input image in the format (h, w).
|
||||
upsample (bool): A flag to indicate whether to upsample the mask to the original image size. Default is False.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): A binary mask tensor of shape [n, h, w], where n is the number of masks after NMS, and h and w
|
||||
are the height and width of the input image. The mask is applied to the bounding boxes.
|
||||
"""
|
||||
|
||||
c, mh, mw = protos.shape # CHW
|
||||
ih, iw = shape
|
||||
masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW
|
||||
|
||||
downsampled_bboxes = bboxes.clone()
|
||||
downsampled_bboxes[:, 0] *= mw / iw
|
||||
downsampled_bboxes[:, 2] *= mw / iw
|
||||
downsampled_bboxes[:, 3] *= mh / ih
|
||||
downsampled_bboxes[:, 1] *= mh / ih
|
||||
|
||||
masks = crop_mask(masks, downsampled_bboxes) # CHW
|
||||
if upsample:
|
||||
masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
|
||||
return masks.gt_(0.5)
|
||||
|
||||
|
||||
def process_mask_native(protos, masks_in, bboxes, shape):
|
||||
"""
|
||||
It takes the output of the mask head, and crops it after upsampling to the bounding boxes.
|
||||
|
||||
Args:
|
||||
protos (torch.Tensor): [mask_dim, mask_h, mask_w]
|
||||
masks_in (torch.Tensor): [n, mask_dim], n is number of masks after nms
|
||||
bboxes (torch.Tensor): [n, 4], n is number of masks after nms
|
||||
shape (tuple): the size of the input image (h,w)
|
||||
|
||||
Returns:
|
||||
masks (torch.Tensor): The returned masks with dimensions [h, w, n]
|
||||
"""
|
||||
c, mh, mw = protos.shape # CHW
|
||||
masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
|
||||
masks = scale_masks(masks[None], shape)[0] # CHW
|
||||
masks = crop_mask(masks, bboxes) # CHW
|
||||
return masks.gt_(0.5)
|
||||
|
||||
|
||||
def scale_masks(masks, shape, padding=True):
|
||||
"""
|
||||
Rescale segment masks to shape.
|
||||
|
||||
Args:
|
||||
masks (torch.Tensor): (N, C, H, W).
|
||||
shape (tuple): Height and width.
|
||||
padding (bool): If True, assuming the boxes is based on image augmented by yolo style. If False then do regular
|
||||
rescaling.
|
||||
"""
|
||||
mh, mw = masks.shape[2:]
|
||||
gain = min(mh / shape[0], mw / shape[1]) # gain = old / new
|
||||
pad = [mw - shape[1] * gain, mh - shape[0] * gain] # wh padding
|
||||
if padding:
|
||||
pad[0] /= 2
|
||||
pad[1] /= 2
|
||||
top, left = (int(pad[1]), int(pad[0])) if padding else (0, 0) # y, x
|
||||
bottom, right = (int(mh - pad[1]), int(mw - pad[0]))
|
||||
masks = masks[..., top:bottom, left:right]
|
||||
|
||||
masks = F.interpolate(masks, shape, mode='bilinear', align_corners=False) # NCHW
|
||||
return masks
|
||||
|
||||
|
||||
def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None, normalize=False, padding=True):
|
||||
"""
|
||||
Rescale segment coordinates (xyxy) from img1_shape to img0_shape
|
||||
|
||||
Args:
|
||||
img1_shape (tuple): The shape of the image that the coords are from.
|
||||
coords (torch.Tensor): the coords to be scaled
|
||||
img0_shape (tuple): the shape of the image that the segmentation is being applied to
|
||||
ratio_pad (tuple): the ratio of the image size to the padded image size.
|
||||
normalize (bool): If True, the coordinates will be normalized to the range [0, 1]. Defaults to False
|
||||
padding (bool): If True, assuming the boxes is based on image augmented by yolo style. If False then do regular
|
||||
rescaling.
|
||||
|
||||
Returns:
|
||||
coords (torch.Tensor): the segmented image.
|
||||
"""
|
||||
if ratio_pad is None: # calculate from img0_shape
|
||||
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
|
||||
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
|
||||
else:
|
||||
gain = ratio_pad[0][0]
|
||||
pad = ratio_pad[1]
|
||||
|
||||
if padding:
|
||||
coords[..., 0] -= pad[0] # x padding
|
||||
coords[..., 1] -= pad[1] # y padding
|
||||
coords[..., 0] /= gain
|
||||
coords[..., 1] /= gain
|
||||
clip_coords(coords, img0_shape)
|
||||
if normalize:
|
||||
coords[..., 0] /= img0_shape[1] # width
|
||||
coords[..., 1] /= img0_shape[0] # height
|
||||
return coords
|
||||
|
||||
|
||||
def masks2segments(masks, strategy='largest'):
|
||||
"""
|
||||
It takes a list of masks(n,h,w) and returns a list of segments(n,xy)
|
||||
|
||||
Args:
|
||||
masks (torch.Tensor): the output of the model, which is a tensor of shape (batch_size, 160, 160)
|
||||
strategy (str): 'concat' or 'largest'. Defaults to largest
|
||||
|
||||
Returns:
|
||||
segments (List): list of segment masks
|
||||
"""
|
||||
segments = []
|
||||
for x in masks.int().cpu().numpy().astype('uint8'):
|
||||
c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
|
||||
if c:
|
||||
if strategy == 'concat': # concatenate all segments
|
||||
c = np.concatenate([x.reshape(-1, 2) for x in c])
|
||||
elif strategy == 'largest': # select largest segment
|
||||
c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2)
|
||||
else:
|
||||
c = np.zeros((0, 2)) # no segments found
|
||||
segments.append(c.astype('float32'))
|
||||
return segments
|
||||
|
||||
|
||||
def clean_str(s):
|
||||
"""
|
||||
Cleans a string by replacing special characters with underscore _
|
||||
|
||||
Args:
|
||||
s (str): a string needing special characters replaced
|
||||
|
||||
Returns:
|
||||
(str): a string with special characters replaced by an underscore _
|
||||
"""
|
||||
return re.sub(pattern='[|@#!¡·$€%&()=?¿^*;:,¨´><+]', repl='_', string=s)
|
||||
@ -1,45 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
"""
|
||||
Monkey patches to update/extend functionality of existing functions
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
# OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------
|
||||
_imshow = cv2.imshow # copy to avoid recursion errors
|
||||
|
||||
|
||||
def imread(filename, flags=cv2.IMREAD_COLOR):
|
||||
return cv2.imdecode(np.fromfile(filename, np.uint8), flags)
|
||||
|
||||
|
||||
def imwrite(filename, img):
|
||||
try:
|
||||
cv2.imencode(Path(filename).suffix, img)[1].tofile(filename)
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
|
||||
def imshow(path, im):
|
||||
_imshow(path.encode('unicode_escape').decode(), im)
|
||||
|
||||
|
||||
# PyTorch functions ----------------------------------------------------------------------------------------------------
|
||||
_torch_save = torch.save # copy to avoid recursion errors
|
||||
|
||||
|
||||
def torch_save(*args, **kwargs):
|
||||
"""Use dill (if exists) to serialize the lambda functions where pickle does not do this."""
|
||||
try:
|
||||
import dill as pickle
|
||||
except ImportError:
|
||||
import pickle
|
||||
|
||||
if 'pickle_module' not in kwargs:
|
||||
kwargs['pickle_module'] = pickle
|
||||
return _torch_save(*args, **kwargs)
|
||||
@ -1,527 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import contextlib
|
||||
import math
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
from PIL import __version__ as pil_version
|
||||
from scipy.ndimage import gaussian_filter1d
|
||||
|
||||
from ultralytics.yolo.utils import LOGGER, TryExcept, plt_settings, threaded
|
||||
|
||||
from .checks import check_font, check_version, is_ascii
|
||||
from .files import increment_path
|
||||
from .ops import clip_boxes, scale_image, xywh2xyxy, xyxy2xywh
|
||||
|
||||
|
||||
class Colors:
|
||||
"""Ultralytics color palette https://ultralytics.com/."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize colors as hex = matplotlib.colors.TABLEAU_COLORS.values()."""
|
||||
hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
|
||||
'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
|
||||
self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
|
||||
self.n = len(self.palette)
|
||||
self.pose_palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], [230, 230, 0], [255, 153, 255],
|
||||
[153, 204, 255], [255, 102, 255], [255, 51, 255], [102, 178, 255], [51, 153, 255],
|
||||
[255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102],
|
||||
[51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], [255, 255, 255]],
|
||||
dtype=np.uint8)
|
||||
|
||||
def __call__(self, i, bgr=False):
|
||||
"""Converts hex color codes to rgb values."""
|
||||
c = self.palette[int(i) % self.n]
|
||||
return (c[2], c[1], c[0]) if bgr else c
|
||||
|
||||
@staticmethod
|
||||
def hex2rgb(h): # rgb order (PIL)
|
||||
return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
|
||||
|
||||
|
||||
colors = Colors() # create instance for 'from utils.plots import colors'
|
||||
|
||||
|
||||
class Annotator:
|
||||
"""YOLOv8 Annotator for train/val mosaics and jpgs and detect/hub inference annotations."""
|
||||
|
||||
def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
|
||||
"""Initialize the Annotator class with image and line width along with color palette for keypoints and limbs."""
|
||||
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
|
||||
non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
|
||||
self.pil = pil or non_ascii
|
||||
if self.pil: # use PIL
|
||||
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
|
||||
self.draw = ImageDraw.Draw(self.im)
|
||||
try:
|
||||
font = check_font('Arial.Unicode.ttf' if non_ascii else font)
|
||||
size = font_size or max(round(sum(self.im.size) / 2 * 0.035), 12)
|
||||
self.font = ImageFont.truetype(str(font), size)
|
||||
except Exception:
|
||||
self.font = ImageFont.load_default()
|
||||
# Deprecation fix for w, h = getsize(string) -> _, _, w, h = getbox(string)
|
||||
if check_version(pil_version, '9.2.0'):
|
||||
self.font.getsize = lambda x: self.font.getbbox(x)[2:4] # text width, height
|
||||
else: # use cv2
|
||||
self.im = im
|
||||
self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
|
||||
# Pose
|
||||
self.skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13], [6, 7], [6, 8], [7, 9],
|
||||
[8, 10], [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
|
||||
|
||||
self.limb_color = colors.pose_palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]
|
||||
self.kpt_color = colors.pose_palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]
|
||||
|
||||
def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
|
||||
"""Add one xyxy box to image with label."""
|
||||
if isinstance(box, torch.Tensor):
|
||||
box = box.tolist()
|
||||
if self.pil or not is_ascii(label):
|
||||
self.draw.rectangle(box, width=self.lw, outline=color) # box
|
||||
if label:
|
||||
w, h = self.font.getsize(label) # text width, height
|
||||
outside = box[1] - h >= 0 # label fits outside box
|
||||
self.draw.rectangle(
|
||||
(box[0], box[1] - h if outside else box[1], box[0] + w + 1,
|
||||
box[1] + 1 if outside else box[1] + h + 1),
|
||||
fill=color,
|
||||
)
|
||||
# self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
|
||||
self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
|
||||
else: # cv2
|
||||
p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
|
||||
cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
|
||||
if label:
|
||||
tf = max(self.lw - 1, 1) # font thickness
|
||||
w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
|
||||
outside = p1[1] - h >= 3
|
||||
p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
|
||||
cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
|
||||
cv2.putText(self.im,
|
||||
label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
|
||||
0,
|
||||
self.lw / 3,
|
||||
txt_color,
|
||||
thickness=tf,
|
||||
lineType=cv2.LINE_AA)
|
||||
|
||||
def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
|
||||
"""Plot masks at once.
|
||||
Args:
|
||||
masks (tensor): predicted masks on cuda, shape: [n, h, w]
|
||||
colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]
|
||||
im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]
|
||||
alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
|
||||
"""
|
||||
if self.pil:
|
||||
# Convert to numpy first
|
||||
self.im = np.asarray(self.im).copy()
|
||||
if len(masks) == 0:
|
||||
self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
|
||||
if im_gpu.device != masks.device:
|
||||
im_gpu = im_gpu.to(masks.device)
|
||||
colors = torch.tensor(colors, device=masks.device, dtype=torch.float32) / 255.0 # shape(n,3)
|
||||
colors = colors[:, None, None] # shape(n,1,1,3)
|
||||
masks = masks.unsqueeze(3) # shape(n,h,w,1)
|
||||
masks_color = masks * (colors * alpha) # shape(n,h,w,3)
|
||||
|
||||
inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1)
|
||||
mcs = masks_color.max(dim=0).values # shape(n,h,w,3)
|
||||
|
||||
im_gpu = im_gpu.flip(dims=[0]) # flip channel
|
||||
im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3)
|
||||
im_gpu = im_gpu * inv_alph_masks[-1] + mcs
|
||||
im_mask = (im_gpu * 255)
|
||||
im_mask_np = im_mask.byte().cpu().numpy()
|
||||
self.im[:] = im_mask_np if retina_masks else scale_image(im_mask_np, self.im.shape)
|
||||
if self.pil:
|
||||
# Convert im back to PIL and update draw
|
||||
self.fromarray(self.im)
|
||||
|
||||
def kpts(self, kpts, shape=(640, 640), radius=5, kpt_line=True):
|
||||
"""Plot keypoints on the image.
|
||||
|
||||
Args:
|
||||
kpts (tensor): Predicted keypoints with shape [17, 3]. Each keypoint has (x, y, confidence).
|
||||
shape (tuple): Image shape as a tuple (h, w), where h is the height and w is the width.
|
||||
radius (int, optional): Radius of the drawn keypoints. Default is 5.
|
||||
kpt_line (bool, optional): If True, the function will draw lines connecting keypoints
|
||||
for human pose. Default is True.
|
||||
|
||||
Note: `kpt_line=True` currently only supports human pose plotting.
|
||||
"""
|
||||
if self.pil:
|
||||
# Convert to numpy first
|
||||
self.im = np.asarray(self.im).copy()
|
||||
nkpt, ndim = kpts.shape
|
||||
is_pose = nkpt == 17 and ndim == 3
|
||||
kpt_line &= is_pose # `kpt_line=True` for now only supports human pose plotting
|
||||
for i, k in enumerate(kpts):
|
||||
color_k = [int(x) for x in self.kpt_color[i]] if is_pose else colors(i)
|
||||
x_coord, y_coord = k[0], k[1]
|
||||
if x_coord % shape[1] != 0 and y_coord % shape[0] != 0:
|
||||
if len(k) == 3:
|
||||
conf = k[2]
|
||||
if conf < 0.5:
|
||||
continue
|
||||
cv2.circle(self.im, (int(x_coord), int(y_coord)), radius, color_k, -1, lineType=cv2.LINE_AA)
|
||||
|
||||
if kpt_line:
|
||||
ndim = kpts.shape[-1]
|
||||
for i, sk in enumerate(self.skeleton):
|
||||
pos1 = (int(kpts[(sk[0] - 1), 0]), int(kpts[(sk[0] - 1), 1]))
|
||||
pos2 = (int(kpts[(sk[1] - 1), 0]), int(kpts[(sk[1] - 1), 1]))
|
||||
if ndim == 3:
|
||||
conf1 = kpts[(sk[0] - 1), 2]
|
||||
conf2 = kpts[(sk[1] - 1), 2]
|
||||
if conf1 < 0.5 or conf2 < 0.5:
|
||||
continue
|
||||
if pos1[0] % shape[1] == 0 or pos1[1] % shape[0] == 0 or pos1[0] < 0 or pos1[1] < 0:
|
||||
continue
|
||||
if pos2[0] % shape[1] == 0 or pos2[1] % shape[0] == 0 or pos2[0] < 0 or pos2[1] < 0:
|
||||
continue
|
||||
cv2.line(self.im, pos1, pos2, [int(x) for x in self.limb_color[i]], thickness=2, lineType=cv2.LINE_AA)
|
||||
if self.pil:
|
||||
# Convert im back to PIL and update draw
|
||||
self.fromarray(self.im)
|
||||
|
||||
def rectangle(self, xy, fill=None, outline=None, width=1):
|
||||
"""Add rectangle to image (PIL-only)."""
|
||||
self.draw.rectangle(xy, fill, outline, width)
|
||||
|
||||
def text(self, xy, text, txt_color=(255, 255, 255), anchor='top', box_style=False):
|
||||
"""Adds text to an image using PIL or cv2."""
|
||||
if anchor == 'bottom': # start y from font bottom
|
||||
w, h = self.font.getsize(text) # text width, height
|
||||
xy[1] += 1 - h
|
||||
if self.pil:
|
||||
if box_style:
|
||||
w, h = self.font.getsize(text)
|
||||
self.draw.rectangle((xy[0], xy[1], xy[0] + w + 1, xy[1] + h + 1), fill=txt_color)
|
||||
# Using `txt_color` for background and draw fg with white color
|
||||
txt_color = (255, 255, 255)
|
||||
if '\n' in text:
|
||||
lines = text.split('\n')
|
||||
_, h = self.font.getsize(text)
|
||||
for line in lines:
|
||||
self.draw.text(xy, line, fill=txt_color, font=self.font)
|
||||
xy[1] += h
|
||||
else:
|
||||
self.draw.text(xy, text, fill=txt_color, font=self.font)
|
||||
else:
|
||||
if box_style:
|
||||
tf = max(self.lw - 1, 1) # font thickness
|
||||
w, h = cv2.getTextSize(text, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
|
||||
outside = xy[1] - h >= 3
|
||||
p2 = xy[0] + w, xy[1] - h - 3 if outside else xy[1] + h + 3
|
||||
cv2.rectangle(self.im, xy, p2, txt_color, -1, cv2.LINE_AA) # filled
|
||||
# Using `txt_color` for background and draw fg with white color
|
||||
txt_color = (255, 255, 255)
|
||||
tf = max(self.lw - 1, 1) # font thickness
|
||||
cv2.putText(self.im, text, xy, 0, self.lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA)
|
||||
|
||||
def fromarray(self, im):
|
||||
"""Update self.im from a numpy array."""
|
||||
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
|
||||
self.draw = ImageDraw.Draw(self.im)
|
||||
|
||||
def result(self):
|
||||
"""Return annotated image as array."""
|
||||
return np.asarray(self.im)
|
||||
|
||||
|
||||
@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395
|
||||
@plt_settings()
|
||||
def plot_labels(boxes, cls, names=(), save_dir=Path(''), on_plot=None):
|
||||
"""Save and plot image with no axis or spines."""
|
||||
import pandas as pd
|
||||
import seaborn as sn
|
||||
|
||||
# Filter matplotlib>=3.7.2 warning
|
||||
warnings.filterwarnings('ignore', category=UserWarning, message='The figure layout has changed to tight')
|
||||
|
||||
# Plot dataset labels
|
||||
LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
|
||||
b = boxes.transpose() # classes, boxes
|
||||
nc = int(cls.max() + 1) # number of classes
|
||||
x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
|
||||
|
||||
# Seaborn correlogram
|
||||
sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
|
||||
plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
|
||||
plt.close()
|
||||
|
||||
# Matplotlib labels
|
||||
ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
|
||||
y = ax[0].hist(cls, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
|
||||
with contextlib.suppress(Exception): # color histogram bars by class
|
||||
[y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195
|
||||
ax[0].set_ylabel('instances')
|
||||
if 0 < len(names) < 30:
|
||||
ax[0].set_xticks(range(len(names)))
|
||||
ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10)
|
||||
else:
|
||||
ax[0].set_xlabel('classes')
|
||||
sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
|
||||
sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
|
||||
|
||||
# Rectangles
|
||||
boxes[:, 0:2] = 0.5 # center
|
||||
boxes = xywh2xyxy(boxes) * 1000
|
||||
img = Image.fromarray(np.ones((1000, 1000, 3), dtype=np.uint8) * 255)
|
||||
for cls, box in zip(cls[:500], boxes[:500]):
|
||||
ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot
|
||||
ax[1].imshow(img)
|
||||
ax[1].axis('off')
|
||||
|
||||
for a in [0, 1, 2, 3]:
|
||||
for s in ['top', 'right', 'left', 'bottom']:
|
||||
ax[a].spines[s].set_visible(False)
|
||||
|
||||
fname = save_dir / 'labels.jpg'
|
||||
plt.savefig(fname, dpi=200)
|
||||
plt.close()
|
||||
if on_plot:
|
||||
on_plot(fname)
|
||||
|
||||
|
||||
def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):
|
||||
"""Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop."""
|
||||
b = xyxy2xywh(xyxy.view(-1, 4)) # boxes
|
||||
if square:
|
||||
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
|
||||
b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
|
||||
xyxy = xywh2xyxy(b).long()
|
||||
clip_boxes(xyxy, im.shape)
|
||||
crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
|
||||
if save:
|
||||
file.parent.mkdir(parents=True, exist_ok=True) # make directory
|
||||
f = str(increment_path(file).with_suffix('.jpg'))
|
||||
# cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue
|
||||
Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB
|
||||
return crop
|
||||
|
||||
|
||||
@threaded
|
||||
def plot_images(images,
|
||||
batch_idx,
|
||||
cls,
|
||||
bboxes=np.zeros(0, dtype=np.float32),
|
||||
masks=np.zeros(0, dtype=np.uint8),
|
||||
kpts=np.zeros((0, 51), dtype=np.float32),
|
||||
paths=None,
|
||||
fname='images.jpg',
|
||||
names=None,
|
||||
on_plot=None):
|
||||
"""Plot image grid with labels."""
|
||||
if isinstance(images, torch.Tensor):
|
||||
images = images.cpu().float().numpy()
|
||||
if isinstance(cls, torch.Tensor):
|
||||
cls = cls.cpu().numpy()
|
||||
if isinstance(bboxes, torch.Tensor):
|
||||
bboxes = bboxes.cpu().numpy()
|
||||
if isinstance(masks, torch.Tensor):
|
||||
masks = masks.cpu().numpy().astype(int)
|
||||
if isinstance(kpts, torch.Tensor):
|
||||
kpts = kpts.cpu().numpy()
|
||||
if isinstance(batch_idx, torch.Tensor):
|
||||
batch_idx = batch_idx.cpu().numpy()
|
||||
|
||||
max_size = 1920 # max image size
|
||||
max_subplots = 16 # max image subplots, i.e. 4x4
|
||||
bs, _, h, w = images.shape # batch size, _, height, width
|
||||
bs = min(bs, max_subplots) # limit plot images
|
||||
ns = np.ceil(bs ** 0.5) # number of subplots (square)
|
||||
if np.max(images[0]) <= 1:
|
||||
images *= 255 # de-normalise (optional)
|
||||
|
||||
# Build Image
|
||||
mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
|
||||
for i, im in enumerate(images):
|
||||
if i == max_subplots: # if last batch has fewer images than we expect
|
||||
break
|
||||
x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
|
||||
im = im.transpose(1, 2, 0)
|
||||
mosaic[y:y + h, x:x + w, :] = im
|
||||
|
||||
# Resize (optional)
|
||||
scale = max_size / ns / max(h, w)
|
||||
if scale < 1:
|
||||
h = math.ceil(scale * h)
|
||||
w = math.ceil(scale * w)
|
||||
mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
|
||||
|
||||
# Annotate
|
||||
fs = int((h + w) * ns * 0.01) # font size
|
||||
annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
|
||||
for i in range(i + 1):
|
||||
x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
|
||||
annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
|
||||
if paths:
|
||||
annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
|
||||
if len(cls) > 0:
|
||||
idx = batch_idx == i
|
||||
classes = cls[idx].astype('int')
|
||||
|
||||
if len(bboxes):
|
||||
boxes = xywh2xyxy(bboxes[idx, :4]).T
|
||||
labels = bboxes.shape[1] == 4 # labels if no conf column
|
||||
conf = None if labels else bboxes[idx, 4] # check for confidence presence (label vs pred)
|
||||
|
||||
if boxes.shape[1]:
|
||||
if boxes.max() <= 1.01: # if normalized with tolerance 0.01
|
||||
boxes[[0, 2]] *= w # scale to pixels
|
||||
boxes[[1, 3]] *= h
|
||||
elif scale < 1: # absolute coords need scale if image scales
|
||||
boxes *= scale
|
||||
boxes[[0, 2]] += x
|
||||
boxes[[1, 3]] += y
|
||||
for j, box in enumerate(boxes.T.tolist()):
|
||||
c = classes[j]
|
||||
color = colors(c)
|
||||
c = names.get(c, c) if names else c
|
||||
if labels or conf[j] > 0.25: # 0.25 conf thresh
|
||||
label = f'{c}' if labels else f'{c} {conf[j]:.1f}'
|
||||
annotator.box_label(box, label, color=color)
|
||||
elif len(classes):
|
||||
for c in classes:
|
||||
color = colors(c)
|
||||
c = names.get(c, c) if names else c
|
||||
annotator.text((x, y), f'{c}', txt_color=color, box_style=True)
|
||||
|
||||
# Plot keypoints
|
||||
if len(kpts):
|
||||
kpts_ = kpts[idx].copy()
|
||||
if len(kpts_):
|
||||
if kpts_[..., 0].max() <= 1.01 or kpts_[..., 1].max() <= 1.01: # if normalized with tolerance .01
|
||||
kpts_[..., 0] *= w # scale to pixels
|
||||
kpts_[..., 1] *= h
|
||||
elif scale < 1: # absolute coords need scale if image scales
|
||||
kpts_ *= scale
|
||||
kpts_[..., 0] += x
|
||||
kpts_[..., 1] += y
|
||||
for j in range(len(kpts_)):
|
||||
if labels or conf[j] > 0.25: # 0.25 conf thresh
|
||||
annotator.kpts(kpts_[j])
|
||||
|
||||
# Plot masks
|
||||
if len(masks):
|
||||
if idx.shape[0] == masks.shape[0]: # overlap_masks=False
|
||||
image_masks = masks[idx]
|
||||
else: # overlap_masks=True
|
||||
image_masks = masks[[i]] # (1, 640, 640)
|
||||
nl = idx.sum()
|
||||
index = np.arange(nl).reshape((nl, 1, 1)) + 1
|
||||
image_masks = np.repeat(image_masks, nl, axis=0)
|
||||
image_masks = np.where(image_masks == index, 1.0, 0.0)
|
||||
|
||||
im = np.asarray(annotator.im).copy()
|
||||
for j, box in enumerate(boxes.T.tolist()):
|
||||
if labels or conf[j] > 0.25: # 0.25 conf thresh
|
||||
color = colors(classes[j])
|
||||
mh, mw = image_masks[j].shape
|
||||
if mh != h or mw != w:
|
||||
mask = image_masks[j].astype(np.uint8)
|
||||
mask = cv2.resize(mask, (w, h))
|
||||
mask = mask.astype(bool)
|
||||
else:
|
||||
mask = image_masks[j].astype(bool)
|
||||
with contextlib.suppress(Exception):
|
||||
im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6
|
||||
annotator.fromarray(im)
|
||||
annotator.im.save(fname) # save
|
||||
if on_plot:
|
||||
on_plot(fname)
|
||||
|
||||
|
||||
@plt_settings()
|
||||
def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False, classify=False, on_plot=None):
|
||||
"""Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')."""
|
||||
import pandas as pd
|
||||
save_dir = Path(file).parent if file else Path(dir)
|
||||
if classify:
|
||||
fig, ax = plt.subplots(2, 2, figsize=(6, 6), tight_layout=True)
|
||||
index = [1, 4, 2, 3]
|
||||
elif segment:
|
||||
fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True)
|
||||
index = [1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]
|
||||
elif pose:
|
||||
fig, ax = plt.subplots(2, 9, figsize=(21, 6), tight_layout=True)
|
||||
index = [1, 2, 3, 4, 5, 6, 7, 10, 11, 14, 15, 16, 17, 18, 8, 9, 12, 13]
|
||||
else:
|
||||
fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
|
||||
index = [1, 2, 3, 4, 5, 8, 9, 10, 6, 7]
|
||||
ax = ax.ravel()
|
||||
files = list(save_dir.glob('results*.csv'))
|
||||
assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
|
||||
for f in files:
|
||||
try:
|
||||
data = pd.read_csv(f)
|
||||
s = [x.strip() for x in data.columns]
|
||||
x = data.values[:, 0]
|
||||
for i, j in enumerate(index):
|
||||
y = data.values[:, j].astype('float')
|
||||
# y[y == 0] = np.nan # don't show zero values
|
||||
ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8) # actual results
|
||||
ax[i].plot(x, gaussian_filter1d(y, sigma=3), ':', label='smooth', linewidth=2) # smoothing line
|
||||
ax[i].set_title(s[j], fontsize=12)
|
||||
# if j in [8, 9, 10]: # share train and val loss y axes
|
||||
# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'WARNING: Plotting error for {f}: {e}')
|
||||
ax[1].legend()
|
||||
fname = save_dir / 'results.png'
|
||||
fig.savefig(fname, dpi=200)
|
||||
plt.close()
|
||||
if on_plot:
|
||||
on_plot(fname)
|
||||
|
||||
|
||||
def output_to_target(output, max_det=300):
|
||||
"""Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting."""
|
||||
targets = []
|
||||
for i, o in enumerate(output):
|
||||
box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
|
||||
j = torch.full((conf.shape[0], 1), i)
|
||||
targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))
|
||||
targets = torch.cat(targets, 0).numpy()
|
||||
return targets[:, 0], targets[:, 1], targets[:, 2:]
|
||||
|
||||
|
||||
def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
|
||||
"""
|
||||
Visualize feature maps of a given model module during inference.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Features to be visualized.
|
||||
module_type (str): Module type.
|
||||
stage (int): Module stage within the model.
|
||||
n (int, optional): Maximum number of feature maps to plot. Defaults to 32.
|
||||
save_dir (Path, optional): Directory to save results. Defaults to Path('runs/detect/exp').
|
||||
"""
|
||||
for m in ['Detect', 'Pose', 'Segment']:
|
||||
if m in module_type:
|
||||
return
|
||||
batch, channels, height, width = x.shape # batch, channels, height, width
|
||||
if height > 1 and width > 1:
|
||||
f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename
|
||||
|
||||
blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels
|
||||
n = min(n, channels) # number of plots
|
||||
fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols
|
||||
ax = ax.ravel()
|
||||
plt.subplots_adjust(wspace=0.05, hspace=0.05)
|
||||
for i in range(n):
|
||||
ax[i].imshow(blocks[i].squeeze()) # cmap='gray'
|
||||
ax[i].axis('off')
|
||||
|
||||
LOGGER.info(f'Saving {f}... ({n}/{channels})')
|
||||
plt.savefig(f, dpi=300, bbox_inches='tight')
|
||||
plt.close()
|
||||
np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save
|
||||
@ -1,276 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from .checks import check_version
|
||||
from .metrics import bbox_iou
|
||||
|
||||
TORCH_1_10 = check_version(torch.__version__, '1.10.0')
|
||||
|
||||
|
||||
def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
|
||||
"""select the positive anchor center in gt
|
||||
|
||||
Args:
|
||||
xy_centers (Tensor): shape(h*w, 4)
|
||||
gt_bboxes (Tensor): shape(b, n_boxes, 4)
|
||||
Return:
|
||||
(Tensor): shape(b, n_boxes, h*w)
|
||||
"""
|
||||
n_anchors = xy_centers.shape[0]
|
||||
bs, n_boxes, _ = gt_bboxes.shape
|
||||
lt, rb = gt_bboxes.view(-1, 1, 4).chunk(2, 2) # left-top, right-bottom
|
||||
bbox_deltas = torch.cat((xy_centers[None] - lt, rb - xy_centers[None]), dim=2).view(bs, n_boxes, n_anchors, -1)
|
||||
# return (bbox_deltas.min(3)[0] > eps).to(gt_bboxes.dtype)
|
||||
return bbox_deltas.amin(3).gt_(eps)
|
||||
|
||||
|
||||
def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
|
||||
"""if an anchor box is assigned to multiple gts,
|
||||
the one with the highest iou will be selected.
|
||||
|
||||
Args:
|
||||
mask_pos (Tensor): shape(b, n_max_boxes, h*w)
|
||||
overlaps (Tensor): shape(b, n_max_boxes, h*w)
|
||||
Return:
|
||||
target_gt_idx (Tensor): shape(b, h*w)
|
||||
fg_mask (Tensor): shape(b, h*w)
|
||||
mask_pos (Tensor): shape(b, n_max_boxes, h*w)
|
||||
"""
|
||||
# (b, n_max_boxes, h*w) -> (b, h*w)
|
||||
fg_mask = mask_pos.sum(-2)
|
||||
if fg_mask.max() > 1: # one anchor is assigned to multiple gt_bboxes
|
||||
mask_multi_gts = (fg_mask.unsqueeze(1) > 1).expand(-1, n_max_boxes, -1) # (b, n_max_boxes, h*w)
|
||||
max_overlaps_idx = overlaps.argmax(1) # (b, h*w)
|
||||
|
||||
is_max_overlaps = torch.zeros(mask_pos.shape, dtype=mask_pos.dtype, device=mask_pos.device)
|
||||
is_max_overlaps.scatter_(1, max_overlaps_idx.unsqueeze(1), 1)
|
||||
|
||||
mask_pos = torch.where(mask_multi_gts, is_max_overlaps, mask_pos).float() # (b, n_max_boxes, h*w)
|
||||
fg_mask = mask_pos.sum(-2)
|
||||
# Find each grid serve which gt(index)
|
||||
target_gt_idx = mask_pos.argmax(-2) # (b, h*w)
|
||||
return target_gt_idx, fg_mask, mask_pos
|
||||
|
||||
|
||||
class TaskAlignedAssigner(nn.Module):
|
||||
"""
|
||||
A task-aligned assigner for object detection.
|
||||
|
||||
This class assigns ground-truth (gt) objects to anchors based on the task-aligned metric,
|
||||
which combines both classification and localization information.
|
||||
|
||||
Attributes:
|
||||
topk (int): The number of top candidates to consider.
|
||||
num_classes (int): The number of object classes.
|
||||
alpha (float): The alpha parameter for the classification component of the task-aligned metric.
|
||||
beta (float): The beta parameter for the localization component of the task-aligned metric.
|
||||
eps (float): A small value to prevent division by zero.
|
||||
"""
|
||||
|
||||
def __init__(self, topk=13, num_classes=80, alpha=1.0, beta=6.0, eps=1e-9):
|
||||
"""Initialize a TaskAlignedAssigner object with customizable hyperparameters."""
|
||||
super().__init__()
|
||||
self.topk = topk
|
||||
self.num_classes = num_classes
|
||||
self.bg_idx = num_classes
|
||||
self.alpha = alpha
|
||||
self.beta = beta
|
||||
self.eps = eps
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(self, pd_scores, pd_bboxes, anc_points, gt_labels, gt_bboxes, mask_gt):
|
||||
"""
|
||||
Compute the task-aligned assignment.
|
||||
Reference https://github.com/Nioolek/PPYOLOE_pytorch/blob/master/ppyoloe/assigner/tal_assigner.py
|
||||
|
||||
Args:
|
||||
pd_scores (Tensor): shape(bs, num_total_anchors, num_classes)
|
||||
pd_bboxes (Tensor): shape(bs, num_total_anchors, 4)
|
||||
anc_points (Tensor): shape(num_total_anchors, 2)
|
||||
gt_labels (Tensor): shape(bs, n_max_boxes, 1)
|
||||
gt_bboxes (Tensor): shape(bs, n_max_boxes, 4)
|
||||
mask_gt (Tensor): shape(bs, n_max_boxes, 1)
|
||||
|
||||
Returns:
|
||||
target_labels (Tensor): shape(bs, num_total_anchors)
|
||||
target_bboxes (Tensor): shape(bs, num_total_anchors, 4)
|
||||
target_scores (Tensor): shape(bs, num_total_anchors, num_classes)
|
||||
fg_mask (Tensor): shape(bs, num_total_anchors)
|
||||
target_gt_idx (Tensor): shape(bs, num_total_anchors)
|
||||
"""
|
||||
self.bs = pd_scores.size(0)
|
||||
self.n_max_boxes = gt_bboxes.size(1)
|
||||
|
||||
if self.n_max_boxes == 0:
|
||||
device = gt_bboxes.device
|
||||
return (torch.full_like(pd_scores[..., 0], self.bg_idx).to(device), torch.zeros_like(pd_bboxes).to(device),
|
||||
torch.zeros_like(pd_scores).to(device), torch.zeros_like(pd_scores[..., 0]).to(device),
|
||||
torch.zeros_like(pd_scores[..., 0]).to(device))
|
||||
|
||||
mask_pos, align_metric, overlaps = self.get_pos_mask(pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points,
|
||||
mask_gt)
|
||||
|
||||
target_gt_idx, fg_mask, mask_pos = select_highest_overlaps(mask_pos, overlaps, self.n_max_boxes)
|
||||
|
||||
# Assigned target
|
||||
target_labels, target_bboxes, target_scores = self.get_targets(gt_labels, gt_bboxes, target_gt_idx, fg_mask)
|
||||
|
||||
# Normalize
|
||||
align_metric *= mask_pos
|
||||
pos_align_metrics = align_metric.amax(axis=-1, keepdim=True) # b, max_num_obj
|
||||
pos_overlaps = (overlaps * mask_pos).amax(axis=-1, keepdim=True) # b, max_num_obj
|
||||
norm_align_metric = (align_metric * pos_overlaps / (pos_align_metrics + self.eps)).amax(-2).unsqueeze(-1)
|
||||
target_scores = target_scores * norm_align_metric
|
||||
|
||||
return target_labels, target_bboxes, target_scores, fg_mask.bool(), target_gt_idx
|
||||
|
||||
def get_pos_mask(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points, mask_gt):
|
||||
"""Get in_gts mask, (b, max_num_obj, h*w)."""
|
||||
mask_in_gts = select_candidates_in_gts(anc_points, gt_bboxes)
|
||||
# Get anchor_align metric, (b, max_num_obj, h*w)
|
||||
align_metric, overlaps = self.get_box_metrics(pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_in_gts * mask_gt)
|
||||
# Get topk_metric mask, (b, max_num_obj, h*w)
|
||||
mask_topk = self.select_topk_candidates(align_metric, topk_mask=mask_gt.expand(-1, -1, self.topk).bool())
|
||||
# Merge all mask to a final mask, (b, max_num_obj, h*w)
|
||||
mask_pos = mask_topk * mask_in_gts * mask_gt
|
||||
|
||||
return mask_pos, align_metric, overlaps
|
||||
|
||||
def get_box_metrics(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_gt):
|
||||
"""Compute alignment metric given predicted and ground truth bounding boxes."""
|
||||
na = pd_bboxes.shape[-2]
|
||||
mask_gt = mask_gt.bool() # b, max_num_obj, h*w
|
||||
overlaps = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_bboxes.dtype, device=pd_bboxes.device)
|
||||
bbox_scores = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_scores.dtype, device=pd_scores.device)
|
||||
|
||||
ind = torch.zeros([2, self.bs, self.n_max_boxes], dtype=torch.long) # 2, b, max_num_obj
|
||||
ind[0] = torch.arange(end=self.bs).view(-1, 1).expand(-1, self.n_max_boxes) # b, max_num_obj
|
||||
ind[1] = gt_labels.squeeze(-1) # b, max_num_obj
|
||||
# Get the scores of each grid for each gt cls
|
||||
bbox_scores[mask_gt] = pd_scores[ind[0], :, ind[1]][mask_gt] # b, max_num_obj, h*w
|
||||
|
||||
# (b, max_num_obj, 1, 4), (b, 1, h*w, 4)
|
||||
pd_boxes = pd_bboxes.unsqueeze(1).expand(-1, self.n_max_boxes, -1, -1)[mask_gt]
|
||||
gt_boxes = gt_bboxes.unsqueeze(2).expand(-1, -1, na, -1)[mask_gt]
|
||||
overlaps[mask_gt] = bbox_iou(gt_boxes, pd_boxes, xywh=False, CIoU=True).squeeze(-1).clamp_(0)
|
||||
|
||||
align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta)
|
||||
return align_metric, overlaps
|
||||
|
||||
def select_topk_candidates(self, metrics, largest=True, topk_mask=None):
|
||||
"""
|
||||
Select the top-k candidates based on the given metrics.
|
||||
|
||||
Args:
|
||||
metrics (Tensor): A tensor of shape (b, max_num_obj, h*w), where b is the batch size,
|
||||
max_num_obj is the maximum number of objects, and h*w represents the
|
||||
total number of anchor points.
|
||||
largest (bool): If True, select the largest values; otherwise, select the smallest values.
|
||||
topk_mask (Tensor): An optional boolean tensor of shape (b, max_num_obj, topk), where
|
||||
topk is the number of top candidates to consider. If not provided,
|
||||
the top-k values are automatically computed based on the given metrics.
|
||||
|
||||
Returns:
|
||||
(Tensor): A tensor of shape (b, max_num_obj, h*w) containing the selected top-k candidates.
|
||||
"""
|
||||
|
||||
# (b, max_num_obj, topk)
|
||||
topk_metrics, topk_idxs = torch.topk(metrics, self.topk, dim=-1, largest=largest)
|
||||
if topk_mask is None:
|
||||
topk_mask = (topk_metrics.max(-1, keepdim=True)[0] > self.eps).expand_as(topk_idxs)
|
||||
# (b, max_num_obj, topk)
|
||||
topk_idxs.masked_fill_(~topk_mask, 0)
|
||||
|
||||
# (b, max_num_obj, topk, h*w) -> (b, max_num_obj, h*w)
|
||||
count_tensor = torch.zeros(metrics.shape, dtype=torch.int8, device=topk_idxs.device)
|
||||
ones = torch.ones_like(topk_idxs[:, :, :1], dtype=torch.int8, device=topk_idxs.device)
|
||||
for k in range(self.topk):
|
||||
# Expand topk_idxs for each value of k and add 1 at the specified positions
|
||||
count_tensor.scatter_add_(-1, topk_idxs[:, :, k:k + 1], ones)
|
||||
# count_tensor.scatter_add_(-1, topk_idxs, torch.ones_like(topk_idxs, dtype=torch.int8, device=topk_idxs.device))
|
||||
# filter invalid bboxes
|
||||
count_tensor.masked_fill_(count_tensor > 1, 0)
|
||||
|
||||
return count_tensor.to(metrics.dtype)
|
||||
|
||||
def get_targets(self, gt_labels, gt_bboxes, target_gt_idx, fg_mask):
|
||||
"""
|
||||
Compute target labels, target bounding boxes, and target scores for the positive anchor points.
|
||||
|
||||
Args:
|
||||
gt_labels (Tensor): Ground truth labels of shape (b, max_num_obj, 1), where b is the
|
||||
batch size and max_num_obj is the maximum number of objects.
|
||||
gt_bboxes (Tensor): Ground truth bounding boxes of shape (b, max_num_obj, 4).
|
||||
target_gt_idx (Tensor): Indices of the assigned ground truth objects for positive
|
||||
anchor points, with shape (b, h*w), where h*w is the total
|
||||
number of anchor points.
|
||||
fg_mask (Tensor): A boolean tensor of shape (b, h*w) indicating the positive
|
||||
(foreground) anchor points.
|
||||
|
||||
Returns:
|
||||
(Tuple[Tensor, Tensor, Tensor]): A tuple containing the following tensors:
|
||||
- target_labels (Tensor): Shape (b, h*w), containing the target labels for
|
||||
positive anchor points.
|
||||
- target_bboxes (Tensor): Shape (b, h*w, 4), containing the target bounding boxes
|
||||
for positive anchor points.
|
||||
- target_scores (Tensor): Shape (b, h*w, num_classes), containing the target scores
|
||||
for positive anchor points, where num_classes is the number
|
||||
of object classes.
|
||||
"""
|
||||
|
||||
# Assigned target labels, (b, 1)
|
||||
batch_ind = torch.arange(end=self.bs, dtype=torch.int64, device=gt_labels.device)[..., None]
|
||||
target_gt_idx = target_gt_idx + batch_ind * self.n_max_boxes # (b, h*w)
|
||||
target_labels = gt_labels.long().flatten()[target_gt_idx] # (b, h*w)
|
||||
|
||||
# Assigned target boxes, (b, max_num_obj, 4) -> (b, h*w)
|
||||
target_bboxes = gt_bboxes.view(-1, 4)[target_gt_idx]
|
||||
|
||||
# Assigned target scores
|
||||
target_labels.clamp_(0)
|
||||
|
||||
# 10x faster than F.one_hot()
|
||||
target_scores = torch.zeros((target_labels.shape[0], target_labels.shape[1], self.num_classes),
|
||||
dtype=torch.int64,
|
||||
device=target_labels.device) # (b, h*w, 80)
|
||||
target_scores.scatter_(2, target_labels.unsqueeze(-1), 1)
|
||||
|
||||
fg_scores_mask = fg_mask[:, :, None].repeat(1, 1, self.num_classes) # (b, h*w, 80)
|
||||
target_scores = torch.where(fg_scores_mask > 0, target_scores, 0)
|
||||
|
||||
return target_labels, target_bboxes, target_scores
|
||||
|
||||
|
||||
def make_anchors(feats, strides, grid_cell_offset=0.5):
|
||||
"""Generate anchors from features."""
|
||||
anchor_points, stride_tensor = [], []
|
||||
assert feats is not None
|
||||
dtype, device = feats[0].dtype, feats[0].device
|
||||
for i, stride in enumerate(strides):
|
||||
_, _, h, w = feats[i].shape
|
||||
sx = torch.arange(end=w, device=device, dtype=dtype) + grid_cell_offset # shift x
|
||||
sy = torch.arange(end=h, device=device, dtype=dtype) + grid_cell_offset # shift y
|
||||
sy, sx = torch.meshgrid(sy, sx, indexing='ij') if TORCH_1_10 else torch.meshgrid(sy, sx)
|
||||
anchor_points.append(torch.stack((sx, sy), -1).view(-1, 2))
|
||||
stride_tensor.append(torch.full((h * w, 1), stride, dtype=dtype, device=device))
|
||||
return torch.cat(anchor_points), torch.cat(stride_tensor)
|
||||
|
||||
|
||||
def dist2bbox(distance, anchor_points, xywh=True, dim=-1):
|
||||
"""Transform distance(ltrb) to box(xywh or xyxy)."""
|
||||
lt, rb = distance.chunk(2, dim)
|
||||
x1y1 = anchor_points - lt
|
||||
x2y2 = anchor_points + rb
|
||||
if xywh:
|
||||
c_xy = (x1y1 + x2y2) / 2
|
||||
wh = x2y2 - x1y1
|
||||
return torch.cat((c_xy, wh), dim) # xywh bbox
|
||||
return torch.cat((x1y1, x2y2), dim) # xyxy bbox
|
||||
|
||||
|
||||
def bbox2dist(anchor_points, bbox, reg_max):
|
||||
"""Transform bbox(xyxy) to dist(ltrb)."""
|
||||
x1y1, x2y2 = bbox.chunk(2, -1)
|
||||
return torch.cat((anchor_points - x1y1, x2y2 - anchor_points), -1).clamp_(0, reg_max - 0.01) # dist (lt, rb)
|
||||
@ -1,512 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import math
|
||||
import os
|
||||
import platform
|
||||
import random
|
||||
import time
|
||||
from contextlib import contextmanager
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
from typing import Union
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torchvision
|
||||
|
||||
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, __version__
|
||||
from ultralytics.yolo.utils.checks import check_requirements, check_version
|
||||
|
||||
try:
|
||||
import thop
|
||||
except ImportError:
|
||||
thop = None
|
||||
|
||||
TORCHVISION_0_10 = check_version(torchvision.__version__, '0.10.0')
|
||||
TORCH_1_9 = check_version(torch.__version__, '1.9.0')
|
||||
TORCH_1_11 = check_version(torch.__version__, '1.11.0')
|
||||
TORCH_1_12 = check_version(torch.__version__, '1.12.0')
|
||||
TORCH_2_0 = check_version(torch.__version__, minimum='2.0')
|
||||
|
||||
|
||||
@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_available() and torch.distributed.is_initialized()
|
||||
if initialized and local_rank not in (-1, 0):
|
||||
dist.barrier(device_ids=[local_rank])
|
||||
yield
|
||||
if initialized and local_rank == 0:
|
||||
dist.barrier(device_ids=[0])
|
||||
|
||||
|
||||
def smart_inference_mode():
|
||||
"""Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator."""
|
||||
|
||||
def decorate(fn):
|
||||
"""Applies appropriate torch decorator for inference mode based on torch version."""
|
||||
return (torch.inference_mode if TORCH_1_9 else torch.no_grad)()(fn)
|
||||
|
||||
return decorate
|
||||
|
||||
|
||||
def get_cpu_info():
|
||||
"""Return a string with system CPU information, i.e. 'Apple M2'."""
|
||||
check_requirements('py-cpuinfo')
|
||||
import cpuinfo # noqa
|
||||
return cpuinfo.get_cpu_info()['brand_raw'].replace('(R)', '').replace('CPU ', '').replace('@ ', '')
|
||||
|
||||
|
||||
def select_device(device='', batch=0, newline=False, verbose=True):
|
||||
"""Selects PyTorch Device. Options are device = None or 'cpu' or 0 or '0' or '0,1,2,3'."""
|
||||
s = f'Ultralytics YOLOv{__version__} 🚀 Python-{platform.python_version()} torch-{torch.__version__} '
|
||||
device = str(device).lower()
|
||||
for remove in 'cuda:', 'none', '(', ')', '[', ']', "'", ' ':
|
||||
device = device.replace(remove, '') # to string, 'cuda:0' -> '0' and '(0, 1)' -> '0,1'
|
||||
cpu = device == 'cpu'
|
||||
mps = device == 'mps' # Apple Metal Performance Shaders (MPS)
|
||||
if cpu or mps:
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False
|
||||
elif device: # non-cpu device requested
|
||||
if device == 'cuda':
|
||||
device = '0'
|
||||
visible = os.environ.get('CUDA_VISIBLE_DEVICES', None)
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available()
|
||||
if not (torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', ''))):
|
||||
LOGGER.info(s)
|
||||
install = 'See https://pytorch.org/get-started/locally/ for up-to-date torch install instructions if no ' \
|
||||
'CUDA devices are seen by torch.\n' if torch.cuda.device_count() == 0 else ''
|
||||
raise ValueError(f"Invalid CUDA 'device={device}' requested."
|
||||
f" Use 'device=cpu' or pass valid CUDA device(s) if available,"
|
||||
f" i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU.\n"
|
||||
f'\ntorch.cuda.is_available(): {torch.cuda.is_available()}'
|
||||
f'\ntorch.cuda.device_count(): {torch.cuda.device_count()}'
|
||||
f"\nos.environ['CUDA_VISIBLE_DEVICES']: {visible}\n"
|
||||
f'{install}')
|
||||
|
||||
if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available
|
||||
devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7
|
||||
n = len(devices) # device count
|
||||
if n > 1 and batch > 0 and batch % n != 0: # check batch_size is divisible by device_count
|
||||
raise ValueError(f"'batch={batch}' must be a multiple of GPU count {n}. Try 'batch={batch // n * n}' or "
|
||||
f"'batch={batch // n * n + n}', the nearest batch sizes evenly divisible by {n}.")
|
||||
space = ' ' * (len(s) + 1)
|
||||
for i, d in enumerate(devices):
|
||||
p = torch.cuda.get_device_properties(i)
|
||||
s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB
|
||||
arg = 'cuda:0'
|
||||
elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available() and TORCH_2_0:
|
||||
# Prefer MPS if available
|
||||
s += f'MPS ({get_cpu_info()})\n'
|
||||
arg = 'mps'
|
||||
else: # revert to CPU
|
||||
s += f'CPU ({get_cpu_info()})\n'
|
||||
arg = 'cpu'
|
||||
|
||||
if verbose and RANK == -1:
|
||||
LOGGER.info(s if newline else s.rstrip())
|
||||
return torch.device(arg)
|
||||
|
||||
|
||||
def time_sync():
|
||||
"""PyTorch-accurate time."""
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.synchronize()
|
||||
return time.time()
|
||||
|
||||
|
||||
def fuse_conv_and_bn(conv, bn):
|
||||
"""Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/."""
|
||||
fusedconv = nn.Conv2d(conv.in_channels,
|
||||
conv.out_channels,
|
||||
kernel_size=conv.kernel_size,
|
||||
stride=conv.stride,
|
||||
padding=conv.padding,
|
||||
dilation=conv.dilation,
|
||||
groups=conv.groups,
|
||||
bias=True).requires_grad_(False).to(conv.weight.device)
|
||||
|
||||
# Prepare filters
|
||||
w_conv = conv.weight.clone().view(conv.out_channels, -1)
|
||||
w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
|
||||
fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
|
||||
|
||||
# Prepare spatial bias
|
||||
b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
|
||||
b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
|
||||
fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
|
||||
|
||||
return fusedconv
|
||||
|
||||
|
||||
def fuse_deconv_and_bn(deconv, bn):
|
||||
"""Fuse ConvTranspose2d() and BatchNorm2d() layers."""
|
||||
fuseddconv = nn.ConvTranspose2d(deconv.in_channels,
|
||||
deconv.out_channels,
|
||||
kernel_size=deconv.kernel_size,
|
||||
stride=deconv.stride,
|
||||
padding=deconv.padding,
|
||||
output_padding=deconv.output_padding,
|
||||
dilation=deconv.dilation,
|
||||
groups=deconv.groups,
|
||||
bias=True).requires_grad_(False).to(deconv.weight.device)
|
||||
|
||||
# Prepare filters
|
||||
w_deconv = deconv.weight.clone().view(deconv.out_channels, -1)
|
||||
w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
|
||||
fuseddconv.weight.copy_(torch.mm(w_bn, w_deconv).view(fuseddconv.weight.shape))
|
||||
|
||||
# Prepare spatial bias
|
||||
b_conv = torch.zeros(deconv.weight.size(1), device=deconv.weight.device) if deconv.bias is None else deconv.bias
|
||||
b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
|
||||
fuseddconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
|
||||
|
||||
return fuseddconv
|
||||
|
||||
|
||||
def model_info(model, detailed=False, verbose=True, imgsz=640):
|
||||
"""Model information. imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320]."""
|
||||
if not verbose:
|
||||
return
|
||||
n_p = get_num_params(model) # number of parameters
|
||||
n_g = get_num_gradients(model) # number of gradients
|
||||
n_l = len(list(model.modules())) # number of layers
|
||||
if detailed:
|
||||
LOGGER.info(
|
||||
f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
|
||||
for i, (name, p) in enumerate(model.named_parameters()):
|
||||
name = name.replace('module_list.', '')
|
||||
LOGGER.info('%5g %40s %9s %12g %20s %10.3g %10.3g %10s' %
|
||||
(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std(), p.dtype))
|
||||
|
||||
flops = get_flops(model, imgsz)
|
||||
fused = ' (fused)' if getattr(model, 'is_fused', lambda: False)() else ''
|
||||
fs = f', {flops:.1f} GFLOPs' if flops else ''
|
||||
yaml_file = getattr(model, 'yaml_file', '') or getattr(model, 'yaml', {}).get('yaml_file', '')
|
||||
model_name = Path(yaml_file).stem.replace('yolo', 'YOLO') or 'Model'
|
||||
LOGGER.info(f'{model_name} summary{fused}: {n_l} layers, {n_p} parameters, {n_g} gradients{fs}')
|
||||
return n_l, n_p, n_g, flops
|
||||
|
||||
|
||||
def get_num_params(model):
|
||||
"""Return the total number of parameters in a YOLO model."""
|
||||
return sum(x.numel() for x in model.parameters())
|
||||
|
||||
|
||||
def get_num_gradients(model):
|
||||
"""Return the total number of parameters with gradients in a YOLO model."""
|
||||
return sum(x.numel() for x in model.parameters() if x.requires_grad)
|
||||
|
||||
|
||||
def model_info_for_loggers(trainer):
|
||||
"""
|
||||
Return model info dict with useful model information.
|
||||
|
||||
Example for YOLOv8n:
|
||||
{'model/parameters': 3151904,
|
||||
'model/GFLOPs': 8.746,
|
||||
'model/speed_ONNX(ms)': 41.244,
|
||||
'model/speed_TensorRT(ms)': 3.211,
|
||||
'model/speed_PyTorch(ms)': 18.755}
|
||||
"""
|
||||
if trainer.args.profile: # profile ONNX and TensorRT times
|
||||
from ultralytics.yolo.utils.benchmarks import ProfileModels
|
||||
results = ProfileModels([trainer.last], device=trainer.device).profile()[0]
|
||||
results.pop('model/name')
|
||||
else: # only return PyTorch times from most recent validation
|
||||
results = {
|
||||
'model/parameters': get_num_params(trainer.model),
|
||||
'model/GFLOPs': round(get_flops(trainer.model), 3)}
|
||||
results['model/speed_PyTorch(ms)'] = round(trainer.validator.speed['inference'], 3)
|
||||
return results
|
||||
|
||||
|
||||
def get_flops(model, imgsz=640):
|
||||
"""Return a YOLO model's FLOPs."""
|
||||
try:
|
||||
model = de_parallel(model)
|
||||
p = next(model.parameters())
|
||||
stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # max stride
|
||||
im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
|
||||
flops = thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1E9 * 2 if thop else 0 # stride GFLOPs
|
||||
imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
|
||||
return flops * imgsz[0] / stride * imgsz[1] / stride # 640x640 GFLOPs
|
||||
except Exception:
|
||||
return 0
|
||||
|
||||
|
||||
def get_flops_with_torch_profiler(model, imgsz=640):
|
||||
"""Compute model FLOPs (thop alternative)."""
|
||||
model = de_parallel(model)
|
||||
p = next(model.parameters())
|
||||
stride = (max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32) * 2 # max stride
|
||||
im = torch.zeros((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
|
||||
with torch.profiler.profile(with_flops=True) as prof:
|
||||
model(im)
|
||||
flops = sum(x.flops for x in prof.key_averages()) / 1E9
|
||||
imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
|
||||
flops = flops * imgsz[0] / stride * imgsz[1] / stride # 640x640 GFLOPs
|
||||
return flops
|
||||
|
||||
|
||||
def initialize_weights(model):
|
||||
"""Initialize model weights to random values."""
|
||||
for m in model.modules():
|
||||
t = type(m)
|
||||
if t is nn.Conv2d:
|
||||
pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
||||
elif t is nn.BatchNorm2d:
|
||||
m.eps = 1e-3
|
||||
m.momentum = 0.03
|
||||
elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
|
||||
m.inplace = True
|
||||
|
||||
|
||||
def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416)
|
||||
# Scales img(bs,3,y,x) by ratio constrained to gs-multiple
|
||||
if ratio == 1.0:
|
||||
return img
|
||||
h, w = img.shape[2:]
|
||||
s = (int(h * ratio), int(w * ratio)) # new size
|
||||
img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize
|
||||
if not same_shape: # pad/crop img
|
||||
h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
|
||||
return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean
|
||||
|
||||
|
||||
def make_divisible(x, divisor):
|
||||
"""Returns nearest x divisible by divisor."""
|
||||
if isinstance(divisor, torch.Tensor):
|
||||
divisor = int(divisor.max()) # to int
|
||||
return math.ceil(x / divisor) * divisor
|
||||
|
||||
|
||||
def copy_attr(a, b, include=(), exclude=()):
|
||||
"""Copies attributes from object 'b' to object 'a', with options to include/exclude certain attributes."""
|
||||
for k, v in b.__dict__.items():
|
||||
if (len(include) and k not in include) or k.startswith('_') or k in exclude:
|
||||
continue
|
||||
else:
|
||||
setattr(a, k, v)
|
||||
|
||||
|
||||
def get_latest_opset():
|
||||
"""Return second-most (for maturity) recently supported ONNX opset by this version of torch."""
|
||||
return max(int(k[14:]) for k in vars(torch.onnx) if 'symbolic_opset' in k) - 1 # opset
|
||||
|
||||
|
||||
def intersect_dicts(da, db, exclude=()):
|
||||
"""Returns a dictionary of intersecting keys with matching shapes, excluding 'exclude' keys, using da values."""
|
||||
return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape}
|
||||
|
||||
|
||||
def is_parallel(model):
|
||||
"""Returns True if model is of type DP or DDP."""
|
||||
return isinstance(model, (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel))
|
||||
|
||||
|
||||
def de_parallel(model):
|
||||
"""De-parallelize a model: returns single-GPU model if model is of type DP or DDP."""
|
||||
return model.module if is_parallel(model) else model
|
||||
|
||||
|
||||
def one_cycle(y1=0.0, y2=1.0, steps=100):
|
||||
"""Returns a lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf."""
|
||||
return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
|
||||
|
||||
|
||||
def init_seeds(seed=0, deterministic=False):
|
||||
"""Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html."""
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
torch.cuda.manual_seed(seed)
|
||||
torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe
|
||||
# torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287
|
||||
if deterministic:
|
||||
if TORCH_2_0:
|
||||
torch.use_deterministic_algorithms(True, warn_only=True) # warn if deterministic is not possible
|
||||
torch.backends.cudnn.deterministic = True
|
||||
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
|
||||
os.environ['PYTHONHASHSEED'] = str(seed)
|
||||
else:
|
||||
LOGGER.warning('WARNING ⚠️ Upgrade to torch>=2.0.0 for deterministic training.')
|
||||
else:
|
||||
torch.use_deterministic_algorithms(False)
|
||||
torch.backends.cudnn.deterministic = False
|
||||
|
||||
|
||||
class ModelEMA:
|
||||
"""Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
|
||||
Keeps a moving average of everything in the model state_dict (parameters and buffers)
|
||||
For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
|
||||
To disable EMA set the `enabled` attribute to `False`.
|
||||
"""
|
||||
|
||||
def __init__(self, model, decay=0.9999, tau=2000, updates=0):
|
||||
"""Create EMA."""
|
||||
self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA
|
||||
self.updates = updates # number of EMA updates
|
||||
self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs)
|
||||
for p in self.ema.parameters():
|
||||
p.requires_grad_(False)
|
||||
self.enabled = True
|
||||
|
||||
def update(self, model):
|
||||
"""Update EMA parameters."""
|
||||
if self.enabled:
|
||||
self.updates += 1
|
||||
d = self.decay(self.updates)
|
||||
|
||||
msd = de_parallel(model).state_dict() # model state_dict
|
||||
for k, v in self.ema.state_dict().items():
|
||||
if v.dtype.is_floating_point: # true for FP16 and FP32
|
||||
v *= d
|
||||
v += (1 - d) * msd[k].detach()
|
||||
# assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype}, model {msd[k].dtype}'
|
||||
|
||||
def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
|
||||
"""Updates attributes and saves stripped model with optimizer removed."""
|
||||
if self.enabled:
|
||||
copy_attr(self.ema, model, include, exclude)
|
||||
|
||||
|
||||
def strip_optimizer(f: Union[str, Path] = 'best.pt', s: str = '') -> None:
|
||||
"""
|
||||
Strip optimizer from 'f' to finalize training, optionally save as 's'.
|
||||
|
||||
Args:
|
||||
f (str): file path to model to strip the optimizer from. Default is 'best.pt'.
|
||||
s (str): file path to save the model with stripped optimizer to. If not provided, 'f' will be overwritten.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Usage:
|
||||
from pathlib import Path
|
||||
from ultralytics.yolo.utils.torch_utils import strip_optimizer
|
||||
for f in Path('/Users/glennjocher/Downloads/weights').rglob('*.pt'):
|
||||
strip_optimizer(f)
|
||||
"""
|
||||
# Use dill (if exists) to serialize the lambda functions where pickle does not do this
|
||||
try:
|
||||
import dill as pickle
|
||||
except ImportError:
|
||||
import pickle
|
||||
|
||||
x = torch.load(f, map_location=torch.device('cpu'))
|
||||
args = {**DEFAULT_CFG_DICT, **x['train_args']} if 'train_args' in x else None # combine args
|
||||
if x.get('ema'):
|
||||
x['model'] = x['ema'] # replace model with ema
|
||||
for k in 'optimizer', 'best_fitness', 'ema', 'updates': # keys
|
||||
x[k] = None
|
||||
x['epoch'] = -1
|
||||
x['model'].half() # to FP16
|
||||
for p in x['model'].parameters():
|
||||
p.requires_grad = False
|
||||
x['train_args'] = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS} # strip non-default keys
|
||||
# x['model'].args = x['train_args']
|
||||
torch.save(x, s or f, pickle_module=pickle)
|
||||
mb = os.path.getsize(s or f) / 1E6 # filesize
|
||||
LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")
|
||||
|
||||
|
||||
def profile(input, ops, n=10, device=None):
|
||||
"""
|
||||
YOLOv8 speed/memory/FLOPs profiler
|
||||
|
||||
Usage:
|
||||
input = torch.randn(16, 3, 640, 640)
|
||||
m1 = lambda x: x * torch.sigmoid(x)
|
||||
m2 = nn.SiLU()
|
||||
profile(input, [m1, m2], n=100) # profile over 100 iterations
|
||||
"""
|
||||
results = []
|
||||
if not isinstance(device, torch.device):
|
||||
device = select_device(device)
|
||||
LOGGER.info(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
|
||||
f"{'input':>24s}{'output':>24s}")
|
||||
|
||||
for x in input if isinstance(input, list) else [input]:
|
||||
x = x.to(device)
|
||||
x.requires_grad = True
|
||||
for m in ops if isinstance(ops, list) else [ops]:
|
||||
m = m.to(device) if hasattr(m, 'to') else m # device
|
||||
m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
|
||||
tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward
|
||||
try:
|
||||
flops = thop.profile(m, inputs=[x], verbose=False)[0] / 1E9 * 2 if thop else 0 # GFLOPs
|
||||
except Exception:
|
||||
flops = 0
|
||||
|
||||
try:
|
||||
for _ in range(n):
|
||||
t[0] = time_sync()
|
||||
y = m(x)
|
||||
t[1] = time_sync()
|
||||
try:
|
||||
_ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
|
||||
t[2] = time_sync()
|
||||
except Exception: # no backward method
|
||||
# print(e) # for debug
|
||||
t[2] = float('nan')
|
||||
tf += (t[1] - t[0]) * 1000 / n # ms per op forward
|
||||
tb += (t[2] - t[1]) * 1000 / n # ms per op backward
|
||||
mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB)
|
||||
s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y)) # shapes
|
||||
p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters
|
||||
LOGGER.info(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')
|
||||
results.append([p, flops, mem, tf, tb, s_in, s_out])
|
||||
except Exception as e:
|
||||
LOGGER.info(e)
|
||||
results.append(None)
|
||||
torch.cuda.empty_cache()
|
||||
return results
|
||||
|
||||
|
||||
class EarlyStopping:
|
||||
"""
|
||||
Early stopping class that stops training when a specified number of epochs have passed without improvement.
|
||||
"""
|
||||
|
||||
def __init__(self, patience=50):
|
||||
"""
|
||||
Initialize early stopping object
|
||||
|
||||
Args:
|
||||
patience (int, optional): Number of epochs to wait after fitness stops improving before stopping.
|
||||
"""
|
||||
self.best_fitness = 0.0 # i.e. mAP
|
||||
self.best_epoch = 0
|
||||
self.patience = patience or float('inf') # epochs to wait after fitness stops improving to stop
|
||||
self.possible_stop = False # possible stop may occur next epoch
|
||||
|
||||
def __call__(self, epoch, fitness):
|
||||
"""
|
||||
Check whether to stop training
|
||||
|
||||
Args:
|
||||
epoch (int): Current epoch of training
|
||||
fitness (float): Fitness value of current epoch
|
||||
|
||||
Returns:
|
||||
(bool): True if training should stop, False otherwise
|
||||
"""
|
||||
if fitness is None: # check if fitness=None (happens when val=False)
|
||||
return False
|
||||
|
||||
if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training
|
||||
self.best_epoch = epoch
|
||||
self.best_fitness = fitness
|
||||
delta = epoch - self.best_epoch # epochs without improvement
|
||||
self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch
|
||||
stop = delta >= self.patience # stop training if patience exceeded
|
||||
if stop:
|
||||
LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. '
|
||||
f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n'
|
||||
f'To update EarlyStopping(patience={self.patience}) pass a new patience value, '
|
||||
f'i.e. `patience=300` or use `patience=0` to disable EarlyStopping.')
|
||||
return stop
|
||||
@ -1,120 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
from ultralytics.yolo.cfg import TASK2DATA, TASK2METRIC
|
||||
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, LOGGER, NUM_THREADS
|
||||
|
||||
|
||||
def run_ray_tune(model,
|
||||
space: dict = None,
|
||||
grace_period: int = 10,
|
||||
gpu_per_trial: int = None,
|
||||
max_samples: int = 10,
|
||||
**train_args):
|
||||
"""
|
||||
Runs hyperparameter tuning using Ray Tune.
|
||||
|
||||
Args:
|
||||
model (YOLO): Model to run the tuner on.
|
||||
space (dict, optional): The hyperparameter search space. Defaults to None.
|
||||
grace_period (int, optional): The grace period in epochs of the ASHA scheduler. Defaults to 10.
|
||||
gpu_per_trial (int, optional): The number of GPUs to allocate per trial. Defaults to None.
|
||||
max_samples (int, optional): The maximum number of trials to run. Defaults to 10.
|
||||
train_args (dict, optional): Additional arguments to pass to the `train()` method. Defaults to {}.
|
||||
|
||||
Returns:
|
||||
(dict): A dictionary containing the results of the hyperparameter search.
|
||||
|
||||
Raises:
|
||||
ModuleNotFoundError: If Ray Tune is not installed.
|
||||
"""
|
||||
if train_args is None:
|
||||
train_args = {}
|
||||
|
||||
try:
|
||||
from ray import tune
|
||||
from ray.air import RunConfig
|
||||
from ray.air.integrations.wandb import WandbLoggerCallback
|
||||
from ray.tune.schedulers import ASHAScheduler
|
||||
except ImportError:
|
||||
raise ModuleNotFoundError("Tuning hyperparameters requires Ray Tune. Install with: pip install 'ray[tune]'")
|
||||
|
||||
try:
|
||||
import wandb
|
||||
|
||||
assert hasattr(wandb, '__version__')
|
||||
except (ImportError, AssertionError):
|
||||
wandb = False
|
||||
|
||||
default_space = {
|
||||
# 'optimizer': tune.choice(['SGD', 'Adam', 'AdamW', 'NAdam', 'RAdam', 'RMSProp']),
|
||||
'lr0': tune.uniform(1e-5, 1e-1),
|
||||
'lrf': tune.uniform(0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
|
||||
'momentum': tune.uniform(0.6, 0.98), # SGD momentum/Adam beta1
|
||||
'weight_decay': tune.uniform(0.0, 0.001), # optimizer weight decay 5e-4
|
||||
'warmup_epochs': tune.uniform(0.0, 5.0), # warmup epochs (fractions ok)
|
||||
'warmup_momentum': tune.uniform(0.0, 0.95), # warmup initial momentum
|
||||
'box': tune.uniform(0.02, 0.2), # box loss gain
|
||||
'cls': tune.uniform(0.2, 4.0), # cls loss gain (scale with pixels)
|
||||
'hsv_h': tune.uniform(0.0, 0.1), # image HSV-Hue augmentation (fraction)
|
||||
'hsv_s': tune.uniform(0.0, 0.9), # image HSV-Saturation augmentation (fraction)
|
||||
'hsv_v': tune.uniform(0.0, 0.9), # image HSV-Value augmentation (fraction)
|
||||
'degrees': tune.uniform(0.0, 45.0), # image rotation (+/- deg)
|
||||
'translate': tune.uniform(0.0, 0.9), # image translation (+/- fraction)
|
||||
'scale': tune.uniform(0.0, 0.9), # image scale (+/- gain)
|
||||
'shear': tune.uniform(0.0, 10.0), # image shear (+/- deg)
|
||||
'perspective': tune.uniform(0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
|
||||
'flipud': tune.uniform(0.0, 1.0), # image flip up-down (probability)
|
||||
'fliplr': tune.uniform(0.0, 1.0), # image flip left-right (probability)
|
||||
'mosaic': tune.uniform(0.0, 1.0), # image mixup (probability)
|
||||
'mixup': tune.uniform(0.0, 1.0), # image mixup (probability)
|
||||
'copy_paste': tune.uniform(0.0, 1.0)} # segment copy-paste (probability)
|
||||
|
||||
def _tune(config):
|
||||
"""
|
||||
Trains the YOLO model with the specified hyperparameters and additional arguments.
|
||||
|
||||
Args:
|
||||
config (dict): A dictionary of hyperparameters to use for training.
|
||||
|
||||
Returns:
|
||||
None.
|
||||
"""
|
||||
model._reset_callbacks()
|
||||
config.update(train_args)
|
||||
model.train(**config)
|
||||
|
||||
# Get search space
|
||||
if not space:
|
||||
space = default_space
|
||||
LOGGER.warning('WARNING ⚠️ search space not provided, using default search space.')
|
||||
|
||||
# Get dataset
|
||||
data = train_args.get('data', TASK2DATA[model.task])
|
||||
space['data'] = data
|
||||
if 'data' not in train_args:
|
||||
LOGGER.warning(f'WARNING ⚠️ data not provided, using default "data={data}".')
|
||||
|
||||
# Define the trainable function with allocated resources
|
||||
trainable_with_resources = tune.with_resources(_tune, {'cpu': NUM_THREADS, 'gpu': gpu_per_trial or 0})
|
||||
|
||||
# Define the ASHA scheduler for hyperparameter search
|
||||
asha_scheduler = ASHAScheduler(time_attr='epoch',
|
||||
metric=TASK2METRIC[model.task],
|
||||
mode='max',
|
||||
max_t=train_args.get('epochs') or DEFAULT_CFG_DICT['epochs'] or 100,
|
||||
grace_period=grace_period,
|
||||
reduction_factor=3)
|
||||
|
||||
# Define the callbacks for the hyperparameter search
|
||||
tuner_callbacks = [WandbLoggerCallback(project='YOLOv8-tune')] if wandb else []
|
||||
|
||||
# Create the Ray Tune hyperparameter search tuner
|
||||
tuner = tune.Tuner(trainable_with_resources,
|
||||
param_space=space,
|
||||
tune_config=tune.TuneConfig(scheduler=asha_scheduler, num_samples=max_samples),
|
||||
run_config=RunConfig(callbacks=tuner_callbacks, storage_path='./runs/tune'))
|
||||
|
||||
# Run the hyperparameter search
|
||||
tuner.fit()
|
||||
|
||||
# Return the results of the hyperparameter search
|
||||
return tuner.get_results()
|
||||
Reference in New Issue
Block a user