utils.py

import contextlib
import inspect
import logging
import os
import time
from pathlib import Path

import pandas as pd
import torch

VERBOSE = str(os.getenv("VERBOSE", True)).lower() == "true"


def set_logging(name=None, verbose=VERBOSE):
    rank = int(os.getenv("RANK", -1))  # rank in world for Multi-GPU trainings
    level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR
    log = logging.getLogger(name)
    log.setLevel(level)
    handler = logging.StreamHandler()
    handler.setFormatter(logging.Formatter("%(message)s"))
    handler.setLevel(level)
    log.addHandler(handler)


set_logging()  # run before defining LOGGER
LOGGER = logging.getLogger("convert model")


def export_formats():
    x = [
        ["PyTorch", "-", ".pth", True, True],
        ["TorchScript", "torchscript", ".torchscript", True, True],
        ["ONNX", "onnx", ".onnx", True, True],
        ["TensorRT", "engine", ".engine", False, True],
    ]
    return pd.DataFrame(x, columns=["Format", "Argument", "Suffix", "CPU", "GPU"])


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 Profile(contextlib.ContextDecorator):
    # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager
    def __init__(self, t=0.0):
        self.t = t
        self.cuda = torch.cuda.is_available()

    def __enter__(self):
        self.start = self.time()
        return self

    def __exit__(self, type, value, traceback):
        self.dt = self.time() - self.start  # delta-time
        self.t += self.dt  # accumulate dt

    def time(self):
        if self.cuda:
            torch.cuda.synchronize()
        return time.time()


def file_size(path):
    # Return file/dir size (MB)
    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
    else:
        return 0.0


def get_default_args(func):
    # Get func() default arguments
    signature = inspect.signature(func)
    return {
        k: v.default
        for k, v in signature.parameters.items()
        if v.default is not inspect.Parameter.empty
    }


def try_export(inner_func):
    inner_args = get_default_args(inner_func)

    def outer_func(*args, **kwargs):
        prefix = inner_args["prefix"]
        try:
            with Profile() as dt:
                f, model = inner_func(*args, **kwargs)
            LOGGER.info(
                f"{prefix} export success {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)"
            )
            return f, model
        except Exception as e:
            LOGGER.info(f"{prefix} export failure {dt.t:.1f}s: {e}")

    return outer_func