FCOS论文复现：通用物体检测算法

摘要：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。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

本文分享自华为云社区《通用物体检测算法 FCOS(目标检测/Pytorch)》，作者： HWCloudAI 。

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：AI Gallery_算法_模型_云市场-华为云

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 statsbroadcast_buffers=False,)arguments = {}arguments["iteration"] = 0output_dir = cfg.OUTPUT_DIRsave_to_disk = get_rank() == 0checkpointer = 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"] = 0data_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-COCOif args.train_iterations > 0:cfg.SOLVER.MAX_ITER = args.train_iterationsif args.warmup_iterations > 0:cfg.SOLVER.WARMUP_ITERS = args.warmup_iterationsif args.train_batch_size > 0:cfg.SOLVER.IMS_PER_BATCH = args.train_batch_sizeif args.solver_lr > 0:cfg.SOLVER.BASE_LR = args.solver_lrif 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 = stepscfg.freeze()num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1args.distributed = num_gpus > 1if args.distributed:torch.cuda.set_device(args.local_rank)torch.distributed.init_process_group(backend="nccl", init_method="env://")synchronize()output_dir = args.train_urlif 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_pathcfg.OUTPUT_DIR = Nonecfg.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

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