pokouqiege/models/line_detect/111.py

import os
import time
from datetime import datetime
import torch
from torch.utils.tensorboard import SummaryWriter
from models.base.base_model import BaseModel
from models.base.base_trainer import BaseTrainer
from models.config.config_tool import read_yaml
from models.line_detect.dataset_LD import WirePointDataset
from models.line_detect.postprocess import box_line_, show_
from utils.log_util import show_line, save_last_model, save_best_model
from tools import utils


def _loss(losses):
    total_loss = 0
    for i in losses.keys():
        if i != "loss_wirepoint":
            total_loss += losses[i]
        else:
            loss_labels = losses[i]["losses"]
    loss_labels_k = list(loss_labels[0].keys())
    for j, name in enumerate(loss_labels_k):
        loss = loss_labels[0][name].mean()
        total_loss += loss
    return total_loss


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')


def move_to_device(data, device):
    if isinstance(data, (list, tuple)):
        return type(data)(move_to_device(item, device) for item in data)
    elif isinstance(data, dict):
        return {key: move_to_device(value, device) for key, value in data.items()}
    elif isinstance(data, torch.Tensor):
        return data.to(device)
    else:
        return data  # 对于非张量类型的数据不做任何改变


class Trainer(BaseTrainer):
    def __init__(self, model=None,
                 dataset=None,
                 device='cuda',
                 freeze_config=None,  # 新增：冻结参数配置
                 **kwargs):
        super().__init__(model, dataset, device, **kwargs)
        self.freeze_config = freeze_config or {}  # 默认冻结配置为空
    def move_to_device(self, data, device):
        if isinstance(data, (list, tuple)):
            return type(data)(self.move_to_device(item, device) for item in data)
        elif isinstance(data, dict):
            return {key: self.move_to_device(value, device) for key, value in data.items()}
        elif isinstance(data, torch.Tensor):
            return data.to(device)
        else:
            return data  # 对于非张量类型的数据不做任何改变
    def freeze_params(self, model):
        """根据配置冻结模型参数"""
        default_config = {
            'backbone': True,  # 冻结 backbone
            'rpn': False,      # 不冻结 rpn
            'roi_heads': {
                'box_head': False,
                'box_predictor': False,
                'line_head': False,
                'line_predictor': {
                    'fc1': False,
                    'fc2': {
                        '0': False,
                        '2': False,
                        '4': False
                    }
                }
            }
        }

        # 更新默认配置
        default_config.update(self.freeze_config)
        config = default_config

        print("\n===== Parameter Freezing Configuration =====")
        for name, module in model.named_children():
            if name in config:
                if isinstance(config[name], bool):
                    for param in module.parameters():
                        param.requires_grad = not config[name]
                    print(f"{'Frozen' if config[name] else 'Trainable'} module: {name}")

                elif isinstance(config[name], dict):
                    for subname, submodule in module.named_children():
                        if subname in config[name]:
                            if isinstance(config[name][subname], bool):
                                for param in submodule.parameters():
                                    param.requires_grad = not config[name][subname]
                                print(
                                    f"{'Frozen' if config[name][subname] else 'Trainable'} submodule: {name}.{subname}")

                            elif isinstance(config[name][subname], dict):
                                for subsubname, subsubmodule in submodule.named_children():
                                    if subsubname in config[name][subname]:
                                        for param in subsubmodule.parameters():
                                            param.requires_grad = not config[name][subname][subsubname]
                                        print(
                                            f"{'Frozen' if config[name][subname][subsubname] else 'Trainable'} sub-submodule: {name}.{subname}.{subsubname}")

        # 打印参数统计
        total_params = sum(p.numel() for p in model.parameters())
        trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
        print(f"\nTotal Parameters: {total_params:,}")
        print(f"Trainable Parameters: {trainable_params:,}")
        print(f"Frozen Parameters: {total_params - trainable_params:,}")

    def load_best_model(self, model, optimizer, save_path, device):
        if os.path.exists(save_path):
            checkpoint = torch.load(save_path, map_location=device)
            model.load_state_dict(checkpoint['model_state_dict'])
            if optimizer is not None:
                optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
            epoch = checkpoint['epoch']
            loss = checkpoint['loss']
            print(f"Loaded best model from {save_path} at epoch {epoch} with loss {loss:.4f}")
        else:
            print(f"No saved model found at {save_path}")
        return model, optimizer

    def writer_loss(self, writer, losses, epoch):
        try:
            for key, value in losses.items():
                if key == 'loss_wirepoint':
                    for subdict in losses['loss_wirepoint']['losses']:
                        for subkey, subvalue in subdict.items():
                            writer.add_scalar(f'loss/{subkey}',
                                              subvalue.item() if hasattr(subvalue, 'item') else subvalue,
                                              epoch)
                elif isinstance(value, torch.Tensor):
                    writer.add_scalar(f'loss/{key}', value.item(), epoch)
        except Exception as e:
            print(f"TensorBoard logging error: {e}")

    def train_cfg(self, model: BaseModel, cfg, freeze_config=None):  # 新增：支持传入冻结配置
        cfg = read_yaml(cfg)
        self.freeze_config = freeze_config or {}  # 更新冻结配置
        self.train(model, **cfg)

    def train(self, model, **kwargs):
        dataset_train = WirePointDataset(dataset_path=kwargs['io']['datadir'], dataset_type='train')
        train_sampler = torch.utils.data.RandomSampler(dataset_train)
        train_batch_sampler = torch.utils.data.BatchSampler(train_sampler, batch_size=4, drop_last=True)
        train_collate_fn = utils.collate_fn_wirepoint
        data_loader_train = torch.utils.data.DataLoader(
            dataset_train, batch_sampler=train_batch_sampler, num_workers=1, collate_fn=train_collate_fn
        )

        dataset_val = WirePointDataset(dataset_path=kwargs['io']['datadir'], dataset_type='val')
        val_sampler = torch.utils.data.RandomSampler(dataset_val)
        val_batch_sampler = torch.utils.data.BatchSampler(val_sampler, batch_size=4, drop_last=True)
        val_collate_fn = utils.collate_fn_wirepoint
        data_loader_val = torch.utils.data.DataLoader(
            dataset_val, batch_sampler=val_batch_sampler, num_workers=1, collate_fn=val_collate_fn
        )

        train_result_ptath = os.path.join('train_results', datetime.now().strftime("%Y%m%d_%H%M%S"))
        wts_path = os.path.join(train_result_ptath, 'weights')
        tb_path = os.path.join(train_result_ptath, 'logs')
        writer = SummaryWriter(tb_path)
        model.to(device)
        # # 加载权重
        # save_path =r"F:\BaiduNetdiskDownload\r50fpn_wts_e350\best.pth"
        # model, _ = self.load_best_model(model, None, save_path, device)
        # 冻结参数
        # self.freeze_params(model)

        # 初始化优化器（仅训练未冻结参数）
        optimizer = torch.optim.Adam(
            filter(lambda p: p.requires_grad, model.parameters()),
            lr=kwargs['optim']['lr']
        )

        last_model_path = os.path.join(wts_path, 'last.pth')
        best_model_path = os.path.join(wts_path, 'best.pth')
        global_step = 0

        for epoch in range(kwargs['optim']['max_epoch']):
            print(f"epoch:{epoch}")
            total_train_loss = 0.0
            model.train()
            for imgs, targets in data_loader_train:
                imgs = move_to_device(imgs, device)
                targets = move_to_device(targets, device)
                losses = model(imgs, targets)
                loss = _loss(losses)
                total_train_loss += loss.item()
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
                self.writer_loss(writer, losses, global_step)
                global_step += 1

            avg_train_loss = total_train_loss / len(data_loader_train)
            if epoch == 0:
                best_loss = avg_train_loss
            writer.add_scalar('loss/train', avg_train_loss, epoch)

            if os.path.exists(f'{wts_path}/last.pt'):
                os.remove(f'{wts_path}/last.pt')
            save_last_model(model, last_model_path, epoch, optimizer)
            best_loss = save_best_model(model, best_model_path, epoch, avg_train_loss, best_loss, optimizer)

            model.eval()
            with torch.no_grad():
                for batch_idx, (imgs, targets) in enumerate(data_loader_val):
                    t_start = time.time()
                    print(f'start to predict:{t_start}')
                    pred = model(self.move_to_device(imgs, self.device))
                    # print(f'pred:{pred}')
                    t_end = time.time()
                    print(f'predict used:{t_end - t_start}')
                    if batch_idx == 0:
                        show_line(imgs[0], pred, epoch, writer)
                    break


import torch

from models.line_detect.line_net import linenet_resnet50_fpn, LineNet, linenet_resnet18_fpn
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if __name__ == '__main__':

    # model = LineNet('line_net.yaml')
    model=linenet_resnet50_fpn()
    #model=linenet_resnet18_fpn()
    # trainer = Trainer()
    # trainer.train_cfg(model,cfg='./train.yaml')
    # model.train_by_cfg(cfg='train.yaml')
    trainer = Trainer()
    trainer.train_cfg(model=model, cfg='train.yaml')