FCOS論文復現：通用物體檢測演算法

華為雲開發者聯盟發表於2022-11-28
演算法
摘要：本案例程式碼是FCOS論文復現的體驗案例，此模型為FCOS論文中所提出演算法在ModelArts + PyTorch框架下的實現。本程式碼支援FCOS + ResNet-101在MS-COCO資料集上完整的訓練和測試流程

本文分享自華為雲社群《通用物體檢測演算法 FCOS(目標檢測/Pytorch)》，作者： HWCloudAI 。

FCOS:Fully Convolutional One-Stage Object Detection

本案例程式碼是FCOS論文復現的體驗案例

此模型為FCOS論文中所提出演算法在ModelArts + PyTorch框架下的實現。該演算法使用MS-COCO公共資料集進行訓練和評估。本程式碼支援FCOS + ResNet-101在MS-COCO資料集上完整的訓練和測試流程

具體的演算法介紹：https://marketplace.huaweicloud.com/markets/aihub/modelhub/detail/?id=ce7acc40-0540-45c9-a0c6-e2fda8d1ac7e

注意事項：

1.本案例使用框架: PyTorch1.0.0

2.本案例使用硬體: GPU

3.執行程式碼方法: 點選本頁面頂部選單欄的三角形執行按鈕或按Ctrl+Enter鍵 執行每個方塊中的程式碼

1.資料和程式碼下載

import os
import moxing as mox
# 資料程式碼下載
mox.file.copy_parallel('obs://obs-aigallery-zc/algorithm/FCOS.zip','FCOS.zip')
# 解壓縮
os.system('unzip  FCOS.zip -d ./')

2.模型訓練

2.1依賴庫安裝及載入

"""
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訓練函式

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設定引數，開始訓練

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.模型測試

3.1預測函式

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開始預測

if __name__ == "__main__":
 model_path = "./outputs/weights/fcos_resnet_101_fpn_2x/model_final.pth" # 訓練得到的模型
 image_path = "./trained_model/model/demo_image.jpg" # 預測的影像
 img = predict(image_path,model_path)
 plt.figure(figsize=(10,10)) #設定視窗大小
 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
FCOS論文復現：通用物體檢測演算法

 

點選關注，第一時間瞭解華為雲新鮮技術~

相關文章