通用物体检测算法 FCOS(目标检测/Pytorch)

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HWCloudAI 发表于 2022/11/23 09:59:07 2022/11/23
【摘要】 通用物体检测算法 FCOS(目标检测/Pytorch)FCOS:Fully Convolutional One-Stage Object Detection本案例代码是FCOS论文复现的体验案例此模型为FCOS论文中所提出算法在ModelArts + PyTorch框架下的实现。该算法使用MS-COCO公共数据集进行训练和评估。本代码支持FCOS + ResNet-101在MS-COCO数...

通用物体检测算法 FCOS(目标检测/Pytorch)

FCOS:Fully Convolutional One-Stage Object Detection

本案例代码是FCOS论文复现的体验案例
此模型为FCOS论文中所提出算法在ModelArts + PyTorch框架下的实现。该算法使用MS-COCO公共数据集进行训练和评估。本代码支持FCOS + ResNet-101在MS-COCO数据集上完整的训练和测试流程

具体的算法介绍:https://marketplace.huaweicloud.com/markets/aihub/modelhub/detail/?id=ce7acc40-0540-45c9-a0c6-e2fda8d1ac7e

注意事项:

1.本案例使用框架: PyTorch1.0.0

2.本案例使用硬件: GPU

3.运行代码方法: 点击本页面顶部菜单栏的三角形运行按钮或按Ctrl+Enter键 运行每个方块中的代码

1.数据和代码下载

import os
import moxing as mox
# 数据代码下载
mox.file.copy_parallel('obs://obs-aigallery-zc/algorithm/FCOS.zip','FCOS.zip')
# 解压缩
os.system('unzip  FCOS.zip -d ./')

2.模型训练

2.1依赖库安装及加载

"""
Basic training script for PyTorch
"""

# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
import os
import argparse
import torch
import shutil

src_dir = './FCOS/'
os.chdir(src_dir)
os.system('pip install -r ./pip-requirements.txt')
os.system('python -m pip install ./trained_model/model/framework-2.0-cp36-cp36m-linux_x86_64.whl')

os.system('python setup.py build develop')

from framework.utils.env import setup_environment
from framework.config import cfg
from framework.data import make_data_loader
from framework.solver import make_lr_scheduler
from framework.solver import make_optimizer
from framework.engine.inference import inference
from framework.engine.trainer import do_train
from framework.modeling.detector import build_detection_model
from framework.utils.checkpoint import DetectronCheckpointer
from framework.utils.collect_env import collect_env_info
from framework.utils.comm import synchronize, \
    get_rank, is_pytorch_1_1_0_or_later
from framework.utils.logger import setup_logger
from framework.utils.miscellaneous import mkdir

2.2训练函数

def train(cfg, local_rank, distributed, new_iteration=False):
    model = build_detection_model(cfg)
    device = torch.device(cfg.MODEL.DEVICE)
    model.to(device)

    if cfg.MODEL.USE_SYNCBN:
        assert is_pytorch_1_1_0_or_later(), \
            "SyncBatchNorm is only available in pytorch >= 1.1.0"
        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)

    optimizer = make_optimizer(cfg, model)
    scheduler = make_lr_scheduler(cfg, optimizer)

    if distributed:
        model = torch.nn.parallel.DistributedDataParallel(
            model, device_ids=[local_rank], output_device=local_rank,
            # this should be removed if we update BatchNorm stats
            broadcast_buffers=False,
        )

    arguments = {}
    arguments["iteration"] = 0

    output_dir = cfg.OUTPUT_DIR

    save_to_disk = get_rank() == 0
    checkpointer = DetectronCheckpointer(
        cfg, model, optimizer, scheduler, output_dir, save_to_disk
    )
    print(cfg.MODEL.WEIGHT)
    extra_checkpoint_data = checkpointer.load_from_file(cfg.MODEL.WEIGHT)
    print(extra_checkpoint_data)
    arguments.update(extra_checkpoint_data)

    if new_iteration:
        arguments["iteration"] = 0

    data_loader = make_data_loader(
        cfg,
        is_train=True,
        is_distributed=distributed,
        start_iter=arguments["iteration"],
    )

    do_train(
        model,
        data_loader,
        optimizer,
        scheduler,
        checkpointer,
        device,
        arguments,
    )
    return model

2.3设置参数,开始训练

def main():
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
    parser.add_argument(
        '--train_url',
        default='./outputs',
        type=str,
        help='the path to save training outputs'
    )
    parser.add_argument(
        "--config-file",
        default="./trained_model/model/fcos_resnet_101_fpn_2x.yaml",
        metavar="FILE",
        help="path to config file",
        type=str,
    )
    parser.add_argument("--local_rank", type=int, default=0)
    parser.add_argument('--train_iterations', default=0, type=int)
    parser.add_argument('--warmup_iterations', default=500, type=int)
    parser.add_argument('--train_batch_size', default=8, type=int)
    parser.add_argument('--solver_lr', default=0.01, type=float)
    parser.add_argument('--decay_steps', default='120000,160000', type=str)
    parser.add_argument('--new_iteration',default=False, action='store_true')

    args, unknown = parser.parse_known_args()

    cfg.merge_from_file(args.config_file)
    # load the model trained on MS-COCO

    if args.train_iterations > 0:
        cfg.SOLVER.MAX_ITER = args.train_iterations
    if args.warmup_iterations > 0:
        cfg.SOLVER.WARMUP_ITERS = args.warmup_iterations

    if args.train_batch_size > 0:
        cfg.SOLVER.IMS_PER_BATCH = args.train_batch_size
    if args.solver_lr > 0:
        cfg.SOLVER.BASE_LR = args.solver_lr

    if len(args.decay_steps) > 0:
        steps = args.decay_steps.replace(' ', ',')
        steps = steps.replace(';', ',')
        steps = steps.replace(';', ',')
        steps = steps.replace(',', ',')
        steps = steps.split(',')
        steps = tuple([int(x) for x in steps])
        cfg.SOLVER.STEPS = steps
    cfg.freeze()

    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
    args.distributed = num_gpus > 1

    if args.distributed:
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(
            backend="nccl", init_method="env://"
        )
        synchronize()
        
    output_dir = args.train_url
    if output_dir:
        mkdir(output_dir)

    logger = setup_logger("framework", output_dir, get_rank())
    logger.info("Using {} GPUs".format(num_gpus))
    logger.info(args)

    logger.info("Loaded configuration file {}".format(args.config_file))
    train(cfg, args.local_rank, args.distributed, args.new_iteration)


if __name__ == "__main__":
    main()

3.模型测试

3.1预测函数

from framework.engine.predictor import Predictor
from PIL import Image,ImageDraw
import numpy as np
import matplotlib.pyplot as plt

def predict(img_path,model_path):    
    config_file = "./trained_model/model/fcos_resnet_101_fpn_2x.yaml"
    
    cfg.merge_from_file(config_file)
    cfg.defrost()
    cfg.MODEL.WEIGHT = model_path
    cfg.OUTPUT_DIR = None
    cfg.freeze()
    predictor = Predictor(cfg=cfg, min_image_size=800)

    src_img = Image.open(img_path)
    img = src_img.convert('RGB')
    img = np.array(img)
    img = img[:, :, ::-1]

    predictions = predictor.compute_prediction(img)
    top_predictions = predictor.select_top_predictions(predictions)

    bboxes = top_predictions.bbox.int().numpy().tolist()

    bboxes = [[x[1], x[0], x[3], x[2]] for x in bboxes]

    scores = top_predictions.get_field("scores").numpy().tolist()
    scores = [round(x, 4) for x in scores]
    labels = top_predictions.get_field("labels").numpy().tolist()
    labels = [predictor.CATEGORIES[x] for x in labels]
    draw = ImageDraw.Draw(src_img)
    for i,bbox in enumerate(bboxes):
        draw.text((bbox[1],bbox[0]),labels[i] + ':'+str(scores[i]),fill=(255,0,0))
        draw.rectangle([bbox[1],bbox[0],bbox[3],bbox[2]],fill=None,outline=(255,0,0))

    return src_img

3.2开始预测

if __name__ == "__main__":
    model_path = "./outputs/weights/fcos_resnet_101_fpn_2x/model_final.pth"  # 训练得到的模型
    image_path = "./trained_model/model/demo_image.jpg"   # 预测的图像

    img = predict(image_path,model_path)
    plt.figure(figsize=(10,10)) #设置窗口大小
    plt.imshow(img)
    plt.show()
2021-06-09 15:33:15,362 framework.utils.checkpoint INFO: Loading checkpoint from ./outputs/weights/fcos_resnet_101_fpn_2x/model_final.pth

png


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