xiaolongzhuanshi commented 3 months ago

请提出你的问题 Please ask your question

我是这样调用的 `
from paddle import inference config = inference.Config(model_file_path, params_file_path) predictor = inference.create_predictor(config)

运行预测

    self.predictor.run()
    print(f"After run: {process.memory_info().rss / 1024 ** 2:.2f} MB")
    # 清理中间张量
    self.predictor.clear_intermediate_tensor()
    print(f"After clear_intermediate_tensor: {process.memory_info().rss / 1024 ** 2:.2f} MB")
    # 尝试收缩内存
    self.predictor.try_shrink_memory()
    print(f"After try_shrink_memory: {process.memory_info().rss / 1024 ** 2:.2f} MB")
    # 手动调用垃圾回收
    gc.collect()
    print(f"After gc.collect: {process.memory_info().rss / 1024 ** 2:.2f} MB")`
    执行后
    输出信息是这样
    `nitial memory usage: 292.93 MB

After run: 527.06 MB After clear_intermediate_tensor: 527.06 MB After try_shrink_memory: 527.06 MB After gc.collect: 527.06 MB 127.0.0.1 - - [23/May/2024 15:36:15] "POST /upload HTTP/1.1" 200 -` 也就是说执行预测模型后所增加的300M内存怎么也释放不出来，不知道怎么解决

环境配置： OS:win10 python:3.8 paddlepaddle:paddlepaddle-2.3.2-cp38-cp38-win_amd64.whl

FeixLiu commented 3 months ago

收到您的问题，我联系下推理的同学帮忙看下。

xiaolongzhuanshi commented 3 months ago

好的

Wangzheee commented 3 months ago

predictor执行器在被实例化后，会一直占用一部分内存，只有当它被析构的时候，才会释放这部分资源

xiaolongzhuanshi commented 3 months ago

predictor执行器在被实例化后，会一直占用一部分内存，只有当它被析构的时候，才会释放这部分资源

不好意思，请问如何才能析构它，我使用del self.predictor显式删除好像没有效果

Wangzheee commented 3 months ago

可以将 del config，del predictor都执行一下试试。建议把推理的执行过程封装成一个函数，出了函数作用域就自动析构了，不需要手动管理可以参照 demo https://github.com/PaddlePaddle/Paddle-Inference-Demo/tree/master/python/gpu/resnet50

xiaolongzhuanshi commented 3 months ago

可以将 del config，del predictor都执行一下试试。建议把推理的执行过程封装成一个函数，出了函数作用域就自动析构了，不需要手动管理可以参照 demo https://github.com/PaddlePaddle/Paddle-Inference-Demo/tree/master/python/gpu/resnet50

感谢回答，我再次实验了下发现依然没有解决，del config，del predictor都执行过，且把推理的执行过程封装成一个函数，并且在函数内也消除过，占用的那一部分内存还是没有的到释放。下面是我的代码结构希望能解惑

predict_det.py

def init(self, args):

    self.args = args
    self.det_algorithm = args.det_algorithm
    pre_process_list = [{
        'DetResizeForTest': {
            'limit_side_len': args.det_limit_side_len,
            'limit_type': args.det_limit_type
        }
    }, {
        'NormalizeImage': {
            'std': [0.229, 0.224, 0.225],
            'mean': [0.485, 0.456, 0.406],
            'scale': '1./255.',
            'order': 'hwc'
        }
    }, {
        'ToCHWImage': None
    }, {
        'KeepKeys': {
            'keep_keys': ['image', 'shape']
        }
    }]
    postprocess_params = {}
    if self.det_algorithm == "DB":
        postprocess_params['name'] = 'DBPostProcess'
        postprocess_params["thresh"] = args.det_db_thresh
        postprocess_params["box_thresh"] = args.det_db_box_thresh
        postprocess_params["max_candidates"] = 1000
        postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
        postprocess_params["use_dilation"] = args.use_dilation
        if hasattr(args, "det_db_score_mode"):
            postprocess_params["score_mode"] = args.det_db_score_mode

    elif self.det_algorithm == "EAST":
        postprocess_params['name'] = 'EASTPostProcess'
        postprocess_params["score_thresh"] = args.det_east_score_thresh
        postprocess_params["cover_thresh"] = args.det_east_cover_thresh
        postprocess_params["nms_thresh"] = args.det_east_nms_thresh
    elif self.det_algorithm == "SAST":
        pre_process_list[0] = {
            'DetResizeForTest': {
                'resize_long': args.det_limit_side_len
            }
        }
        postprocess_params['name'] = 'SASTPostProcess'
        postprocess_params["score_thresh"] = args.det_sast_score_thresh
        postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
        self.det_sast_polygon = args.det_sast_polygon
        if self.det_sast_polygon:
            postprocess_params["sample_pts_num"] = 6
            postprocess_params["expand_scale"] = 1.2
            postprocess_params["shrink_ratio_of_width"] = 0.2
        else:
            postprocess_params["sample_pts_num"] = 2
            postprocess_params["expand_scale"] = 1.0
            postprocess_params["shrink_ratio_of_width"] = 0.3
    else:
        logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
        sys.exit(0)

    self.preprocess_op = create_operators(pre_process_list)
    self.postprocess_op = build_post_process(postprocess_params)
    self.predictor, self.input_tensor, self.output_tensors = utility.create_predictor(
        args, 'det', logger)  # paddle.jit.load(args.det_model_dir)
    # self.predictor.eval()

def call(self, img):

    ori_im = img.copy()
    data = {'image': img}
    data = transform(data, self.preprocess_op)
    img, shape_list = data
    if img is None:
        return None, 0
    img = np.expand_dims(img, axis=0)
    shape_list = np.expand_dims(shape_list, axis=0)
    img = img.copy()
    starttime = time.time()

    self.input_tensor.copy_from_cpu(img)
    import gc
    import paddle
    import psutil
    # 检查初始内存使用情况
    process = psutil.Process()
    print(f"Initial memory usage: {process.memory_info().rss / 1024 ** 2:.2f} MB")

    # 运行预测
    run(self.predictor)
    print(f"After run: {process.memory_info().rss / 1024 ** 2:.2f} MB")

    # 清理中间张量
    self.predictor.clear_intermediate_tensor()
    print(f"After clear_intermediate_tensor: {process.memory_info().rss / 1024 ** 2:.2f} MB")

    # 尝试收缩内存
    self.predictor.try_shrink_memory()
    print(f"After try_shrink_memory: {process.memory_info().rss / 1024 ** 2:.2f} MB")

    # 显式删除 predictor
    del self.predictor
    print(f"After del self.predictor: {process.memory_info().rss / 1024 ** 2:.2f} MB")

    # 手动调用垃圾回收
    gc.collect()
    print(f"After gc.collect: {process.memory_info().rss / 1024 ** 2:.2f} MB")

    # 清理 GPU 内存
    paddle.device.cuda.empty_cache()
    print(f"After cuda.empty_cache: {process.memory_info().rss / 1024 ** 2:.2f} MB")

    return [], []

def run(predictor):

predictor.run()
del predictor

return []

utility.py

def create_predictor(args, mode, logger):

if mode == "det":
    model_dir = args.det_model_dir
elif mode == 'cls':
    model_dir = args.cls_model_dir
elif mode == 'rec':
    model_dir = args.rec_model_dir
else:
    model_dir = args.e2e_model_dir

if model_dir is None:
    logger.info("not find {} model file path {}".format(mode, model_dir))
    sys.exit(0)
model_file_path = model_dir + "/inference.pdmodel"
params_file_path = model_dir + "/inference.pdiparams"
if not os.path.exists(model_file_path):
    logger.info("not find model file path {}".format(model_file_path))
    sys.exit(0)
if not os.path.exists(params_file_path):
    logger.info("not find params file path {}".format(params_file_path))
    sys.exit(0)

config = inference.Config(model_file_path, params_file_path)

if args.use_gpu:
    config.enable_use_gpu(args.gpu_mem, 0)
    if args.use_tensorrt:
        config.enable_tensorrt_engine(
            precision_mode=inference.PrecisionType.Half
            if args.use_fp16 else inference.PrecisionType.Float32,
            max_batch_size=args.max_batch_size)
else:
    config.disable_gpu()
    config.set_cpu_math_library_num_threads(6)
    if args.enable_mkldnn:
        # cache 10 different shapes for mkldnn to avoid memory leak
        config.set_mkldnn_cache_capacity(10)
        config.enable_mkldnn()
        #  TODO LDOUBLEV: fix mkldnn bug when bach_size  > 1
        #config.set_mkldnn_op({'conv2d', 'depthwise_conv2d', 'pool2d', 'batch_norm'})
        args.rec_batch_num = 1

# enable memory optim
config.enable_memory_optim()
config.disable_glog_info()

config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
config.switch_use_feed_fetch_ops(False)

config.set_cpu_math_library_num_threads(4)
# 调用 summary() 方法
summary_info = config.summary()

# 打印配置信息
print(summary_info)

# create predictor
predictor = inference.create_predictor(config)
input_names = predictor.get_input_names()
for name in input_names:
    input_tensor = predictor.get_input_handle(name)
output_names = predictor.get_output_names()
output_tensors = []
for output_name in output_names:
    output_tensor = predictor.get_output_handle(output_name)
    output_tensors.append(output_tensor)
return predictor, input_tensor, output_tensors

Initial memory usage: 288.16 MB After run: 382.55 MB After clear_intermediate_tensor: 382.55 MB After try_shrink_memory: 382.55 MB After del self.predictor: 372.36 MB After gc.collect: 372.36 MB After cuda.empty_cache: 372.36 MB

xiaolongzhuanshi commented 3 months ago

可以将 del config，del predictor都执行一下试试。建议把推理的执行过程封装成一个函数，出了函数作用域就自动析构了，不需要手动管理可以参照 demo https://github.com/PaddlePaddle/Paddle-Inference-Demo/tree/master/python/gpu/resnet50

关于这个demo我在本地运行后依旧有上面的问题代码resnet50/infer_resnet.py的改修

if name == 'main':

args = parse_args()
pred = init_predictor(args)
img = cv2.imread('E:/PHPStudy/phpstudy_pro/WWW/php_e-invoice/pdf/11223344.png')
img = preprocess(img)
#img = np.ones((1, 3, 224, 224)).astype(np.float32)
import psutil
import gc
process = psutil.Process()
print(f"Initial memory usage: {process.memory_info().rss / 1024 ** 2:.2f} MB")
result = run(pred, [img])
print(f"After del self.predictor: {process.memory_info().rss / 1024 ** 2:.2f} MB")
# 手动调用垃圾回收
gc.collect()
print(f"After gc.collect: {process.memory_info().rss / 1024 ** 2:.2f} MB")
print("class index: ", np.argmax(result[0][0]))

执行后所显示的信息

I0527 11:09:54.261554 3660 naive_executor.cc:102] --- skip [save_infer_model/scale_0.tmp_1], fetch -> fetch Initial memory usage: 256.06 MB I0527 11:09:54.308559 3660 device_context.cc:737] oneDNN v2.5.4 After del self.predictor: 375.02 MB After gc.collect: 375.02 MB class index: 916

可以看出predictor.run()执行后增加的内存依旧没有释放目前只有停止进程才释放

Wangzheee commented 3 months ago

可以将 del config，del predictor都执行一下试试。建议把推理的执行过程封装成一个函数，出了函数作用域就自动析构了，不需要手动管理可以参照 demo https://github.com/PaddlePaddle/Paddle-Inference-Demo/tree/master/python/gpu/resnet50

关于这个demo我在本地运行后依旧有上面的问题代码resnet50/infer_resnet.py的改修

if name == 'main':
args = parse_args()
pred = init_predictor(args)
img = cv2.imread('E:/PHPStudy/phpstudy_pro/WWW/php_e-invoice/pdf/11223344.png')
img = preprocess(img)
#img = np.ones((1, 3, 224, 224)).astype(np.float32)
import psutil
import gc
process = psutil.Process()
print(f"Initial memory usage: {process.memory_info().rss / 1024 ** 2:.2f} MB")
result = run(pred, [img])
print(f"After del self.predictor: {process.memory_info().rss / 1024 ** 2:.2f} MB")
# 手动调用垃圾回收
gc.collect()
print(f"After gc.collect: {process.memory_info().rss / 1024 ** 2:.2f} MB")
print("class index: ", np.argmax(result[0][0]))
执行后所显示的信息

I0527 11:09:54.261554 3660 naive_executor.cc:102] --- skip [save_infer_model/scale_0.tmp_1], fetch -> fetch Initial memory usage: 256.06 MB I0527 11:09:54.308559 3660 device_context.cc:737] oneDNN v2.5.4 After del self.predictor: 375.02 MB After gc.collect: 375.02 MB class index: 916

可以看出predictor.run()执行后增加的内存依旧没有释放目前只有停止进程才释放

是的呢，因为predictor执行器的生命周期还在，想全部释放资源需要析构predictor

panp4n commented 2 months ago

del predictor没有作用，退出实例作用域后不会释放，结束线程也不会释放，只有在结束进程时才会释放。

magicleo commented 1 month ago

碰到了同样的问题，模型预测作为线上服务，运行一段时间就内存占用从6G逐渐涨到40G。

PaddlePaddle / Paddle

运行预测 self.predictor.run() 后，引擎内存释放不掉 #64547

请提出你的问题 Please ask your question

运行预测

predict_det.py

utility.py