hubconf.py

"""PyTorch Hub models

Usage:
    import torch
    model = torch.hub.load('repo', 'model')
"""

from pathlib import Path

import torch

from models.yolo import Model
from utils.general import check_requirements, set_logging
from utils.google_utils import attempt_download
from utils.torch_utils import select_device

dependencies = ["torch", "yaml"]
check_requirements(
    Path(__file__).parent / "requirements.txt", exclude=("pycocotools", "thop")
)
set_logging()


def create(name, pretrained, channels, classes, autoshape):
    """Creates a specified model

    Arguments:
        name (str): name of model, i.e. 'yolov7'
        pretrained (bool): load pretrained weights into the model
        channels (int): number of input channels
        classes (int): number of model classes

    Returns:
        pytorch model
    """
    try:
        cfg = list((Path(__file__).parent / "cfg").rglob(f"{name}.yaml"))[
            0
        ]  # model.yaml path
        model = Model(cfg, channels, classes)
        if pretrained:
            fname = f"{name}.pt"  # checkpoint filename
            attempt_download(fname)  # download if not found locally
            ckpt = torch.load(fname, map_location=torch.device("cpu"))  # load
            msd = model.state_dict()  # model state_dict
            csd = ckpt["model"].float().state_dict()  # checkpoint state_dict as FP32
            csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape}  # filter
            model.load_state_dict(csd, strict=False)  # load
            if len(ckpt["model"].names) == classes:
                model.names = ckpt["model"].names  # set class names attribute
            if autoshape:
                model = model.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
        device = select_device(
            "0" if torch.cuda.is_available() else "cpu"
        )  # default to GPU if available
        return model.to(device)

    except Exception as e:
        s = "Cache maybe be out of date, try force_reload=True."
        raise Exception(s) from e


def custom(path_or_model="path/to/model.pt", autoshape=True):
    """custom mode

    Arguments (3 options):
        path_or_model (str): 'path/to/model.pt'
        path_or_model (dict): torch.load('path/to/model.pt')
        path_or_model (nn.Module): torch.load('path/to/model.pt')['model']

    Returns:
        pytorch model
    """
    model = (
        torch.load(path_or_model, map_location=torch.device("cpu"))
        if isinstance(path_or_model, str)
        else path_or_model
    )  # load checkpoint
    if isinstance(model, dict):
        model = model["ema" if model.get("ema") else "model"]  # load model

    hub_model = Model(model.yaml).to(next(model.parameters()).device)  # create
    hub_model.load_state_dict(model.float().state_dict())  # load state_dict
    hub_model.names = model.names  # class names
    if autoshape:
        hub_model = hub_model.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
    device = select_device(
        "0" if torch.cuda.is_available() else "cpu"
    )  # default to GPU if available
    return hub_model.to(device)


def yolov7(pretrained=True, channels=3, classes=80, autoshape=True):
    return create("yolov7", pretrained, channels, classes, autoshape)


if __name__ == "__main__":
    model = custom(path_or_model="yolov7.pt")  # custom example
    # model = create(name='yolov7', pretrained=True, channels=3, classes=80, autoshape=True)  # pretrained example

    # Verify inference
    import numpy as np
    from PIL import Image

    imgs = [np.zeros((640, 480, 3))]

    results = model(imgs)  # batched inference
    results.print()
    results.save()