lcnn/train——line_rcnn.py

# 根据LCNN写的train    2025/2/7
'''
#!/usr/bin/env python3
import datetime
import glob
import os
import os.path as osp
import platform
import pprint
import random
import shlex
import shutil
import subprocess
import sys
import numpy as np
import torch
import torchvision
import yaml
import lcnn
from lcnn.config import C, M
from lcnn.datasets import WireframeDataset, collate
from lcnn.models.line_vectorizer import LineVectorizer
from lcnn.models.multitask_learner import MultitaskHead, MultitaskLearner
from torchvision.models import resnet50

from models.line_detect.line_rcnn import linercnn_resnet50_fpn



def main():

    # 训练配置参数
    config = {
        # 数据集配置
        'datadir': r'D:\python\PycharmProjects\data',  # 数据集目录
        'config_file': 'config/wireframe.yaml',  # 配置文件路径

        # GPU配置
        'devices': '0',  # 使用的GPU设备
        'identifier': 'fasterrcnn_resnet50',  # 训练标识符 stacked_hourglass unet

        # 预训练模型路径
        # 'pretrained_model': './190418-201834-f8934c6-lr4d10-312k.pth',  # 预训练模型路径
    }

    # 更新配置
    C.update(C.from_yaml(filename=config['config_file']))
    M.update(C.model)

    # 设置随机数种子
    random.seed(0)
    np.random.seed(0)
    torch.manual_seed(0)

    # 设备配置
    device_name = "cpu"
    os.environ["CUDA_VISIBLE_DEVICES"] = config['devices']

    if torch.cuda.is_available():
        device_name = "cuda"
        torch.backends.cudnn.deterministic = True
        torch.cuda.manual_seed(0)
        print("Let's use", torch.cuda.device_count(), "GPU(s)!")
    else:
        print("CUDA is not available")

    device = torch.device(device_name)

    # 数据加载
    kwargs = {
        "collate_fn": collate,
        "num_workers": C.io.num_workers if os.name != "nt" else 0,
        "pin_memory": True,
    }

    train_loader = torch.utils.data.DataLoader(
        WireframeDataset(config['datadir'], dataset_type="train"),
        shuffle=True,
        batch_size=M.batch_size,
        **kwargs,
    )

    val_loader = torch.utils.data.DataLoader(
        WireframeDataset(config['datadir'], dataset_type="val"),
        shuffle=False,
        batch_size=M.batch_size_eval,
        **kwargs,
    )

    model = linercnn_resnet50_fpn().to(device)

    # 加载预训练权重

    try:
        # 加载模型权重
        checkpoint = torch.load(config['pretrained_model'], map_location=device)

        # 根据实际的检查点结构选择加载方式
        if 'model_state_dict' in checkpoint:
            # 如果是完整的检查点
            model.load_state_dict(checkpoint['model_state_dict'])
        elif 'state_dict' in checkpoint:
            # 如果是只有状态字典的检查点
            model.load_state_dict(checkpoint['state_dict'])
        else:
            # 直接加载权重字典
            model.load_state_dict(checkpoint)

        print("Successfully loaded pre-trained model weights.")
    except Exception as e:
        print(f"Error loading model weights: {e}")


    # 优化器配置
    if C.optim.name == "Adam":
        optim = torch.optim.Adam(
            filter(lambda p: p.requires_grad, model.parameters()),
            lr=C.optim.lr,
            weight_decay=C.optim.weight_decay,
            amsgrad=C.optim.amsgrad,
        )
    elif C.optim.name == "SGD":
        optim = torch.optim.SGD(
            filter(lambda p: p.requires_grad, model.parameters()),
            lr=C.optim.lr,
            weight_decay=C.optim.weight_decay,
            momentum=C.optim.momentum,
        )
    else:
        raise NotImplementedError

    # 输出目录
    outdir = osp.join(
        osp.expanduser(C.io.logdir),
        f"{datetime.datetime.now().strftime('%y%m%d-%H%M%S')}-{config['identifier']}"
    )
    os.makedirs(outdir, exist_ok=True)

    try:
        trainer = lcnn.trainer.Trainer(
            device=device,
            model=model,
            optimizer=optim,
            train_loader=train_loader,
            val_loader=val_loader,
            out=outdir,
        )

        print("Starting training...")
        trainer.train()
        print("Training completed.")

    except BaseException:
        if len(glob.glob(f"{outdir}/viz/*")) <= 1:
            shutil.rmtree(outdir)
        raise


if __name__ == "__main__":
    main()
'''


# 2025/2/9
import os
from typing import Optional, Any

import cv2
import numpy as np
import torch

from models.config.config_tool import read_yaml
from models.line_detect.dataset_LD import WirePointDataset
from tools import utils

from torch.utils.tensorboard import SummaryWriter
import matplotlib.pyplot as plt
import matplotlib as mpl
from skimage import io

from models.line_detect.line_rcnn import linercnn_resnet50_fpn
from torchvision.utils import draw_bounding_boxes
from models.wirenet.postprocess import postprocess
from torchvision import transforms
from collections import OrderedDict

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


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


cmap = plt.get_cmap("jet")
norm = mpl.colors.Normalize(vmin=0.4, vmax=1.0)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])


def c(x):
    return sm.to_rgba(x)


def imshow(im):
    plt.close()
    plt.tight_layout()
    plt.imshow(im)
    plt.colorbar(sm, fraction=0.046)
    plt.xlim([0, im.shape[0]])
    plt.ylim([im.shape[0], 0])


def show_line(img, pred,  epoch, writer):
    im = img.permute(1, 2, 0)
    writer.add_image("ori", im, epoch, dataformats="HWC")

    boxed_image = draw_bounding_boxes((img * 255).to(torch.uint8), pred[0]["boxes"],
                                      colors="yellow", width=1)
    writer.add_image("boxes", boxed_image.permute(1, 2, 0), epoch, dataformats="HWC")

    PLTOPTS = {"color": "#33FFFF", "s": 15, "edgecolors": "none", "zorder": 5}
    H = pred[1]['wires']
    lines = H["lines"][0].cpu().numpy() / 128 * im.shape[:2]
    scores = H["score"][0].cpu().numpy()
    for i in range(1, len(lines)):
        if (lines[i] == lines[0]).all():
            lines = lines[:i]
            scores = scores[:i]
            break

    # postprocess lines to remove overlapped lines
    diag = (im.shape[0] ** 2 + im.shape[1] ** 2) ** 0.5
    nlines, nscores = postprocess(lines, scores, diag * 0.01, 0, False)

    for i, t in enumerate([0.8]):
        plt.gca().set_axis_off()
        plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
        plt.margins(0, 0)
        for (a, b), s in zip(nlines, nscores):
            if s < t:
                continue
            plt.plot([a[1], b[1]], [a[0], b[0]], c=c(s), linewidth=2, zorder=s)
            plt.scatter(a[1], a[0], **PLTOPTS)
            plt.scatter(b[1], b[0], **PLTOPTS)
        plt.gca().xaxis.set_major_locator(plt.NullLocator())
        plt.gca().yaxis.set_major_locator(plt.NullLocator())
        plt.imshow(im)
        plt.tight_layout()
        fig = plt.gcf()
        fig.canvas.draw()
        image_from_plot = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8).reshape(
            fig.canvas.get_width_height()[::-1] + (3,))
        plt.close()
        img2 = transforms.ToTensor()(image_from_plot)

        writer.add_image("output", img2, epoch)


if __name__ == '__main__':
    cfg = r'./config/wireframe.yaml'
    cfg = read_yaml(cfg)
    print(f'cfg:{cfg}')
    print(cfg['model']['n_dyn_negl'])
    # net = WirepointPredictor()

    dataset_train = WirePointDataset(dataset_path=cfg['io']['datadir'], dataset_type='train')
    train_sampler = torch.utils.data.RandomSampler(dataset_train)
    # test_sampler = torch.utils.data.SequentialSampler(dataset_test)
    train_batch_sampler = torch.utils.data.BatchSampler(train_sampler, batch_size=1, 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=0, collate_fn=train_collate_fn
    )

    dataset_val = WirePointDataset(dataset_path=cfg['io']['datadir'], dataset_type='val')
    val_sampler = torch.utils.data.RandomSampler(dataset_val)
    # test_sampler = torch.utils.data.SequentialSampler(dataset_test)
    val_batch_sampler = torch.utils.data.BatchSampler(val_sampler, batch_size=1, 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=0, collate_fn=val_collate_fn
    )

    model = linercnn_resnet50_fpn().to(device)

    optimizer = torch.optim.Adam(model.parameters(), lr=cfg['optim']['lr'])
    writer = SummaryWriter(cfg['io']['logdir'])

    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


    # def writer_loss(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_wirepoint/{subkey}',
    #                                           subvalue.item() if hasattr(subvalue, 'item') else subvalue,
    #                                           epoch)
    #             elif isinstance(value, torch.Tensor):
    #                 writer.add_scalar(key, value.item(), epoch)
    #     except Exception as e:
    #         print(f"TensorBoard logging error: {e}")
    def writer_loss(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}")


    for epoch in range(cfg['optim']['max_epoch']):
        print(f"epoch:{epoch}")
        model.train()

        for imgs, targets in data_loader_train:
            losses = model(move_to_device(imgs, device), move_to_device(targets, device))
            # print(losses)
            loss = _loss(losses)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            writer_loss(writer, losses, epoch)

        model.eval()
        with torch.no_grad():
            for batch_idx, (imgs, targets) in enumerate(data_loader_val):
                pred = model(move_to_device(imgs, device))
                if batch_idx == 0:
                    show_line(imgs[0], pred, epoch, writer)
                break