Paddle prediction(c++ version) occur error with message "Tensor holds no memory. Call Tensor::mutable_data firstc"

vs3odd8k 于 2021-12-07 发布在 Java

关注(0)|答案(3)|浏览(350)

System information

ubuntu18.04 ,linux ,nvidia Tesla V100-PCIE,
-PaddlePaddle version （eg.1.1）or CommitID
1.8
-GPU: including CUDA/CUDNN version
cuda 10.0 , cudnn 7
-OS Platform (eg.Mac OS 10.14)
ubuntu
-Python version
python 3.8.3
-API information
train ocr attention with gpu with paddle1.8, then convert into c++ version,when run predict ,occur errors:

I1223 08:05:24.510879 26118 analysis_predictor.cc:496] ======= optimize end =======
I1223 08:05:24.510918 26118 naive_executor.cc:95] --- skip [feed], feed -> init_scores
I1223 08:05:24.510921 26118 naive_executor.cc:95] --- skip [feed], feed -> init_ids
I1223 08:05:24.510923 26118 naive_executor.cc:95] --- skip [feed], feed -> pixel
I1223 08:05:24.511201 26118 naive_executor.cc:95] --- skip [save_infer_model/scale_0.tmp_0], fetch -> fetch
W1223 08:05:24.511250 26118 device_context.cc:252] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 10.2, Runtime API Version: 10.0
W1223 08:05:24.514454 26118 device_context.cc:260] device: 0, cuDNN Version: 7.6.
terminate called after throwing an instance of 'paddle::platform::EnforceNotMet'
what():

C++ Call Stacks (More useful to developers):

0 std::string paddle::platform::GetTraceBackString<std::string const&>(std::string const&, char const*, int)
1 paddle::framework::Tensor::check_memory_size() const
2 paddle::framework::TensorCopy(paddle::framework::Tensor const&, paddle::platform::Place const&, paddle::platform::DeviceContext const&, paddle::framework::Tensor*)
3 paddle::operators::ReadFromArrayOp::RunImpl(paddle::framework::Scope const&, paddle::platform::Place const&) const
4 paddle::framework::OperatorBase::Run(paddle::framework::Scope const&, paddle::platform::Place const&)
5 paddle::framework::Executor::RunPartialPreparedContext(paddle::framework::ExecutorPrepareContext*, paddle::framework::Scope*, long, long, bool, bool, bool)
6 paddle::framework::Executor::RunPreparedContext(paddle::framework::ExecutorPrepareContext*, paddle::framework::Scope*, bool, bool, bool)
7 paddle::operators::WhileOp::RunImpl(paddle::framework::Scope const&, paddle::platform::Place const&) const
8 paddle::framework::OperatorBase::Run(paddle::framework::Scope const&, paddle::platform::Place const&)
9 paddle::framework::NaiveExecutor::Run()
10 paddle::AnalysisPredictor::ZeroCopyRun()

Python Call Stacks (More useful to users):

File "/home/mk/anaconda3/envs/paddle_env/lib/python3.7/site-packages/paddle/fluid/framework.py", line 2610, in append_op
attrs=kwargs.get("attrs", None))
File "/home/mk/anaconda3/envs/paddle_env/lib/python3.7/site-packages/paddle/fluid/layer_helper.py", line 43, in append_op
return self.main_program.current_block().append_op(*args,**kwargs)
File "/home/mk/anaconda3/envs/paddle_env/lib/python3.7/site-packages/paddle/fluid/layers/control_flow.py", line 1794, in array_read
outputs={'Out': [out]})
File "/ssd4/moshuojie/code/ocr_recognition/attention_model.py", line 301, in attention_infer
pre_ids = fluid.layers.array_read(array=ids_array, i=counter)
File "/ssd4/moshuojie/code/ocr_recognition/infer.py", line 67, in inference
ids = infer(images, num_classes, use_cudnn=True if args.use_gpu else False)
File "/ssd4/moshuojie/code/ocr_recognition/infer.py", line 202, in main
inference(args)
File "/ssd4/moshuojie/code/ocr_recognition/infer.py", line 206, in
main()
File "/home/mk/anaconda3/envs/paddle_env/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/mk/anaconda3/envs/paddle_env/lib/python3.7/runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "/home/mk/anaconda3/envs/paddle_env/lib/python3.7/runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "/home/mk/.vscode-server/extensions/ms-python.python-2020.11.371526539/pythonFiles/lib/python/debugpy/../debugpy/server/cli.py", line 267, in run_file
runpy.run_path(options.target, run_name=compat.force_str("main"))
File "/home/mk/.vscode-server/extensions/ms-python.python-2020.11.371526539/pythonFiles/lib/python/debugpy/../debugpy/server/cli.py", line 430, in main
run()
File "/home/mk/.vscode-server/extensions/ms-python.python-2020.11.371526539/pythonFiles/lib/python/debugpy/main.py", line 45, in
cli.main()
File "/home/mk/anaconda3/envs/paddle_env/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/mk/anaconda3/envs/paddle_env/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)

Error Message Summary:

**Error: Tensor holds no memory. Call Tensor::mutable_data first.

[Hint: holder_ should not be null.] at (/home/george/paddle/paddle/fluid/framework/tensor.cc:23)
[operator < read_from_array > error]
Aborted (core dumped)**

Paddle

来源：https://github.com/PaddlePaddle/Paddle/issues/29857

3条答案

按热度按时间

hrysbysz1#

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mcvgt66p2#

麻烦帖一下完整的infer.py代码

赞(0）回复(0）举报 2021-12-07

hrirmatl3#

@WenmuZhou infer.py代码：

def inference(args):
"""OCR inference"""
if args.model == "crnn_ctc":
infer = ctc_infer
get_feeder_data = get_ctc_feeder_for_infer
else:
infer = attention_infer
get_feeder_data = get_attention_feeder_for_infer
eos = 1
sos = 0
num_classes = data_reader.num_classes()
data_shape = data_reader.data_shape()

define network

if len(list(data_shape)) == 3:
data_shape = [None] + list(data_shape)
images = fluid.data(name='pixel', shape=data_shape, dtype='float32')
ids = infer(images, num_classes, use_cudnn=True if args.use_gpu else False)

data reader

infer_reader = data_reader.inference(
batch_size=args.batch_size,
infer_images_dir=args.input_images_dir,
infer_list_file=args.input_images_list,
cycle=True if args.iterations > 0 else False,
model=args.model)

prepare environment

place = fluid.CPUPlace()
if args.use_gpu:
place = fluid.CUDAPlace(0)

exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

# load dictionary

dict_map = None
if args.dict is not None and os.path.isfile(args.dict):
    dict_map = {}
    with open(args.dict) as dict_file:
        for i, word in enumerate(dict_file):
            dict_map[i] = word.strip()
    print("Loaded dict from %s" % args.dict)

# load init model

model_dir = args.model_path
fluid.load(
    program=fluid.default_main_program(),
    model_path=model_dir,
    executor=exe,
    var_list=fluid.io.get_program_parameter(fluid.default_main_program()))
print("Init model from: %s." % args.model_path)

fluid.io.save_inference_model(dirname="./mobilenet/", feeded_var_names=['pixel'],
target_vars=[ids], executor=exe,model_filename='model',params_filename='params')

batch_times = []
iters = 0
for data in infer_reader():
    feed_dict = get_feeder_data(data, place)
    if args.iterations > 0 and iters == args.iterations + args.skip_batch_num:
        break
    if iters < args.skip_batch_num:
        print("Warm-up itaration")
    if iters == args.skip_batch_num:
        profiler.reset_profiler()

    start = time.time()
    result = exe.run(fluid.default_main_program(),
                     feed=feed_dict,
                     fetch_list=[ids],
                     return_numpy=False)
    indexes = prune(np.array(result[0]).flatten(), 0, 1)
    batch_time = time.time() - start
    fps = args.batch_size / batch_time
    batch_times.append(batch_time)
    if dict_map is not None:
        print("Iteration %d, latency: %.5f s, fps: %f, result: %s" % (
            iters,
            batch_time,
            fps,
            [dict_map[index] for index in indexes], ))
    else:
        print("Iteration %d, latency: %.5f s, fps: %f, result: %s" % (
            iters,
            batch_time,
            fps,
            indexes, ))

    iters += 1

latencies = batch_times[args.skip_batch_num:]
latency_avg = np.average(latencies)
latency_pc99 = np.percentile(latencies, 99)
fpses = np.divide(args.batch_size, latencies)
fps_avg = np.average(fpses)
fps_pc99 = np.percentile(fpses, 1)

# Benchmark output

print('\nTotal examples (incl. warm-up): %d' % (iters * args.batch_size))
print('average latency: %.5f s, 99pc latency: %.5f s' % (latency_avg,
                                                         latency_pc99))
print('average fps: %.5f, fps for 99pc latency: %.5f' % (fps_avg, fps_pc99))

def prune(words, sos, eos):
"""Remove unused tokens in prediction result."""
start_index = 0
end_index = len(words)
if sos in words:
start_index = np.where(words == sos)[0][0] + 1
if eos in words:
end_index = np.where(words == eos)[0][0]
return words[start_index:end_index]

def main():
args = parser.parse_args()
print_arguments(args)
check_gpu(args.use_gpu)
if args.profile:
if args.use_gpu:
with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof:
inference(args)
else:
with profiler.profiler("CPU", sorted_key='total') as cpuprof:
inference(args)
else:
inference(args)

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我来回答

Paddle prediction(c++ version) occur error with message "Tensor holds no memory. Call Tensor::mutable_data firstc"

C++ Call Stacks (More useful to developers):

Python Call Stacks (More useful to users):

Error Message Summary:

3条答案

define network

data reader

prepare environment

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