yolo/model/yolo.py

import os
from typing import Dict, List, Optional, Union

import torch
from loguru import logger
from omegaconf import ListConfig, OmegaConf
from torch import nn

from yolo.config.config import ModelConfig, YOLOLayer
from yolo.tools.dataset_preparation import prepare_weight
from yolo.utils.module_utils import get_layer_map


class YOLO(nn.Module):
    """
    A preliminary YOLO (You Only Look Once) model class still under development.

    Parameters:
        model_cfg: Configuration for the YOLO model. Expected to define the layers,
                   parameters, and any other relevant configuration details.
    """

    def __init__(self, model_cfg: ModelConfig, class_num: int = 80):
        super(YOLO, self).__init__()
        self.num_classes = class_num
        self.layer_map = get_layer_map()  # Get the map Dict[str: Module]
        self.model: List[YOLOLayer] = nn.ModuleList()
        self.reg_max = getattr(model_cfg.anchor, "reg_max", 16)
        self.strides = getattr(model_cfg.anchor, "strides", None)
        self.build_model(model_cfg.model)

    def build_model(self, model_arch: Dict[str, List[Dict[str, Dict[str, Dict]]]]):
        self.layer_index = {}
        output_dim, layer_idx = [3], 1
        logger.info(f"🚜 Building YOLO")
        for arch_name in model_arch:
            if model_arch[arch_name]:
                logger.info(f"  🏗️  Building {arch_name}")
            for layer_idx, layer_spec in enumerate(model_arch[arch_name], start=layer_idx):
                layer_type, layer_info = next(iter(layer_spec.items()))
                layer_args = layer_info.get("args", {})

                # Get input source
                source = self.get_source_idx(layer_info.get("source", -1), layer_idx)

                # Find in channels
                if any(module in layer_type for module in ["Conv", "ELAN", "ADown", "AConv", "CBLinear"]):
                    layer_args["in_channels"] = output_dim[source]
                if "Detection" in layer_type:
                    layer_args["in_channels"] = [output_dim[idx] for idx in source]
                    layer_args["num_classes"] = self.num_classes
                    layer_args["reg_max"] = self.reg_max

                # create layers
                layer = self.create_layer(layer_type, source, layer_info, **layer_args)
                self.model.append(layer)

                if layer.tags:
                    if layer.tags in self.layer_index:
                        raise ValueError(f"Duplicate tag '{layer_info['tags']}' found.")
                    self.layer_index[layer.tags] = layer_idx

                out_channels = self.get_out_channels(layer_type, layer_args, output_dim, source)
                output_dim.append(out_channels)
                setattr(layer, "out_c", out_channels)
            layer_idx += 1

    def forward(self, x):
        y = {0: x}
        output = dict()
        for index, layer in enumerate(self.model, start=1):
            if isinstance(layer.source, list):
                model_input = [y[idx] for idx in layer.source]
            else:
                model_input = y[layer.source]
            x = layer(model_input)
            y[-1] = x
            if layer.usable:
                y[index] = x
            if layer.output:
                output[layer.tags] = x
        return output

    def get_out_channels(self, layer_type: str, layer_args: dict, output_dim: list, source: Union[int, list]):
        # TODO refactor, check out_channels in layer_args, next check source is list, CBFuse|Concat
        if any(module in layer_type for module in ["Conv", "ELAN", "ADown", "AConv"]):
            return layer_args["out_channels"]
        if layer_type == "CBFuse":
            return output_dim[source[-1]]
        if layer_type in ["Pool", "UpSample"]:
            return output_dim[source]
        if layer_type == "Concat":
            return sum(output_dim[idx] for idx in source)
        if layer_type == "IDetect":
            return None

    def get_source_idx(self, source: Union[ListConfig, str, int], layer_idx: int):
        if isinstance(source, ListConfig):
            return [self.get_source_idx(index, layer_idx) for index in source]
        if isinstance(source, str):
            source = self.layer_index[source]
        if source < -1:
            source += layer_idx
        if source > 0:  # Using Previous Layer's Output
            self.model[source - 1].usable = True
        return source

    def create_layer(self, layer_type: str, source: Union[int, list], layer_info: Dict, **kwargs) -> YOLOLayer:
        if layer_type in self.layer_map:
            layer = self.layer_map[layer_type](**kwargs)
            setattr(layer, "layer_type", layer_type)
            setattr(layer, "source", source)
            setattr(layer, "in_c", kwargs.get("in_channels", None))
            setattr(layer, "output", layer_info.get("output", False))
            setattr(layer, "tags", layer_info.get("tags", None))
            setattr(layer, "usable", 0)
            return layer
        else:
            raise ValueError(f"Unsupported layer type: {layer_type}")


def create_model(model_cfg: ModelConfig, weight_path: Union[bool, str] = True, class_num: int = 80) -> YOLO:
    """Constructs and returns a model from a Dictionary configuration file.

    Args:
        config_file (dict): The configuration file of the model.

    Returns:
        YOLO: An instance of the model defined by the given configuration.
    """
    # TODO: "weight_path -> weight = [True|None-False|Path]: True should be default of model name?"
    OmegaConf.set_struct(model_cfg, False)
    model = YOLO(model_cfg, class_num)
    if weight_path:
        if weight_path == True:
            weight_path = os.path.join("weights", f"{model_cfg.name}.pt")
        if not os.path.exists(weight_path):
            logger.info(f"🌐 Weight {weight_path} not found, try downloading")
            prepare_weight(weight_path=weight_path)
        if os.path.exists(weight_path):
            # TODO: fix map_location
            model.model.load_state_dict(torch.load(weight_path, map_location=torch.device("cpu")), strict=False)
            logger.info("✅ Success load model & weight")
    else:
        logger.info("✅ Success load model")
    return model