def create_inference_config(self, use_trt=True) -> paddle_infer.Config:
if use_trt:
config = paddle_infer.Config()
config.disable_glog_info()
config.enable_use_gpu(100, 0)
config.set_optim_cache_dir(self.cache_dir)
config.switch_ir_debug()
config.enable_tensorrt_engine(
max_batch_size=self.trt_param.max_batch_size,
workspace_size=self.trt_param.workspace_size,
min_subgraph_size=self.trt_param.min_subgraph_size,
precision_mode=self.trt_param.precision,
use_static=self.trt_param.use_static,
use_calib_mode=self.trt_param.use_calib_mode)
if len(self.dynamic_shape.min_input_shape
) != 0 and self.dynamic_shape.min_input_shape.keys(
) == self.dynamic_shape.max_input_shape.keys(
) and self.dynamic_shape.min_input_shape.keys(
) == self.dynamic_shape.opt_input_shape.keys():
config.set_trt_dynamic_shape_info(
self.dynamic_shape.min_input_shape,
self.dynamic_shape.max_input_shape,
self.dynamic_shape.opt_input_shape,
self.dynamic_shape.disable_trt_plugin_fp16)
return config
else:
config = paddle_infer.Config()
config.switch_ir_debug(True)
config.set_optim_cache_dir(self.cache_dir)
config.disable_glog_info()
return config
def create_trt_inference_config(self) -> paddle_infer.Config:
config = paddle_infer.Config()
config.disable_glog_info()
config.enable_use_gpu(100, 0)
config.set_optim_cache_dir(self.cache_dir)
config.switch_ir_debug()
return config
def create_predictor(cls, args, config=None):
if config is None:
config = inference.Config(
os.path.join(args.inference_model_dir, "transformer.pdmodel"),
os.path.join(args.inference_model_dir,
"transformer.pdiparams"))
if args.use_gpu:
config.enable_use_gpu(100, 0)
elif args.use_xpu:
config.enable_xpu(100)
else:
# CPU
# such as enable_mkldnn, set_cpu_math_library_num_threads
config.disable_gpu()
# Use ZeroCopy.
config.switch_use_feed_fetch_ops(False)
predictor = inference.create_predictor(config)
input_handles = [
predictor.get_input_handle(name)
for name in predictor.get_input_names()
]
output_handles = [
predictor.get_input_handle(name)
for name in predictor.get_output_names()
]
return cls(predictor, input_handles, output_handles)
def get_truth_val_by_inference(self):
try:
import paddle.inference as paddle_infer
except:
# when paddle is not installed, directly return
return
data = np.array(
[0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795,
-0.0332]).astype("float32")[np.newaxis, :]
input_dict = {"x": data}
pd_config = paddle_infer.Config("uci_housing_model/")
pd_config.disable_gpu()
pd_config.switch_ir_optim(False)
predictor = paddle_infer.create_predictor(pd_config)
input_names = predictor.get_input_names()
for i, input_name in enumerate(input_names):
input_handle = predictor.get_input_handle(input_name)
input_handle.copy_from_cpu(input_dict[input_name])
predictor.run()
output_data_dict = {}
output_names = predictor.get_output_names()
for _, output_data_name in enumerate(output_names):
output_handle = predictor.get_output_handle(output_data_name)
output_data = output_handle.copy_to_cpu()
output_data_dict[output_data_name] = output_data
# convert to the same format of Serving output
output_data_dict["prob"] = output_data_dict["fc_0.tmp_1"]
del output_data_dict["fc_0.tmp_1"]
self.truth_val = output_data_dict
def paddle_inference(args):
import paddle.inference as paddle_infer
config = paddle_infer.Config(args.model_file, args.params_file)
predictor = paddle_infer.create_predictor(config)
input_names = predictor.get_input_names()
input_handle = predictor.get_input_handle(input_names[0])
img = cv2.imread(args.image_path)
# normalize to mean 0.5, std 0.5
img = (img - 127.5) * 0.00784313725
# BGR2RGB
img = img[:, :, ::-1]
img = img.transpose((2, 0, 1))
img = np.expand_dims(img, 0)
img = img.astype('float32')
input_handle.copy_from_cpu(img)
predictor.run()
output_names = predictor.get_output_names()
output_handle = predictor.get_output_handle(output_names[0])
output_data = output_handle.copy_to_cpu()
print('paddle inference result: ', output_data.shape)
def __init__(self, model_path, param_path, use_gpu=False):
model_path, param_path = self.check_param(model_path, param_path)
try:
config = paddle_infer.Config(model_path, param_path)
except:
ValueError(" 模型和参数不匹配，请检查模型和参数是否加载错误")
if not use_gpu:
config.enable_mkldnn()
# TODO: fluid要废弃了，研究判断方式
# if paddle.fluid.core.supports_bfloat16():
# config.enable_mkldnn_bfloat16()
config.switch_ir_optim(True)
config.set_cpu_math_library_num_threads(10)
else:
config.enable_use_gpu(500, 0)
config.delete_pass("conv_elementwise_add_act_fuse_pass")
config.delete_pass("conv_elementwise_add2_act_fuse_pass")
config.delete_pass("conv_elementwise_add_fuse_pass")
config.switch_ir_optim()
config.enable_memory_optim()
# use_tensoret = False # TODO: 目前Linux和windows下使用TensorRT报错
# if use_tensoret:
# config.enable_tensorrt_engine(
# workspace_size=1 << 30,
# precision_mode=paddle_infer.PrecisionType.Float32,
# max_batch_size=1,
# min_subgraph_size=5,
# use_static=False,
# use_calib_mode=False,
# )
self.model = paddle_infer.create_predictor(config)
def load(self) -> bool:
def get_model_files(ext: str) -> str:
file_list = []
for filename in os.listdir(model_path):
if filename.endswith(ext):
file_list.append(filename)
if len(file_list) == 0:
raise Exception("Missing {} model file".format(ext))
if len(file_list) > 1:
raise Exception("More than one {} model file".format(ext))
return os.path.join(model_path, file_list[0])
model_path = kserve.Storage.download(self.model_dir)
config = inference.Config(get_model_files('.pdmodel'),
get_model_files('.pdiparams'))
# TODO: add GPU support
config.disable_gpu()
self.predictor = inference.create_predictor(config)
# TODO: add support for multiple input_names/output_names
input_names = self.predictor.get_input_names()
self.input_tensor = self.predictor.get_input_handle(input_names[0])
output_names = self.predictor.get_output_names()
self.output_tensor = self.predictor.get_output_handle(output_names[0])
self.ready = True
return self.ready
def eval(args):
model_file = os.path.join(args.model_path, args.model_filename)
params_file = os.path.join(args.model_path, args.params_filename)
config = paddle_infer.Config(model_file, params_file)
config.enable_mkldnn()
predictor = paddle_infer.create_predictor(config)
input_names = predictor.get_input_names()
input_handle = predictor.get_input_handle(input_names[0])
output_names = predictor.get_output_names()
output_handle = predictor.get_output_handle(output_names[0])
val_dataset = dataset.ImageNetDataset(data_dir=args.data_dir, mode='val')
eval_loader = paddle.io.DataLoader(
val_dataset, batch_size=args.batch_size, drop_last=True)
cost_time = 0.
total_num = 0.
correct_1_num = 0
correct_5_num = 0
for batch_id, data in enumerate(eval_loader()):
img_np = np.array([tensor.numpy() for tensor in data[0]])
label_np = np.array([tensor.numpy() for tensor in data[1]])
input_handle.reshape(img_np.shape)
input_handle.copy_from_cpu(img_np)
t1 = time.time()
predictor.run()
t2 = time.time()
cost_time += (t2 - t1)
output_data = output_handle.copy_to_cpu()
for i in range(len(label_np)):
label = label_np[i][0]
result = output_data[i, :]
index = result.argsort()
total_num += 1
if index[-1] == label:
correct_1_num += 1
if label in index[-5:]:
correct_5_num += 1
if batch_id % 10 == 0:
acc1 = correct_1_num / total_num
acc5 = correct_5_num / total_num
avg_time = cost_time / total_num
print(
"batch_id {}, acc1 {:.3f}, acc5 {:.3f}, avg time {:.5f} sec/img".
format(batch_id, acc1, acc5, avg_time))
if args.test_samples > 0 and \
(batch_id + 1)* args.batch_size >= args.test_samples:
break
acc1 = correct_1_num / total_num
acc5 = correct_5_num / total_num
print("End test: test_acc1 {:.3f}, test_acc5 {:.5f}".format(acc1, acc5))
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)
# 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
def create_predictor(cls,
args,
config=None,
profile=False,
model_name=None):
if config is None:
config = inference.Config(
os.path.join(args.inference_model_dir, "transformer.pdmodel"),
os.path.join(args.inference_model_dir,
"transformer.pdiparams"))
if args.device == "gpu":
config.enable_use_gpu(100, 0)
elif args.device == "xpu":
config.enable_xpu(100)
else:
# CPU
config.disable_gpu()
if args.use_mkl:
config.enable_mkldnn()
config.set_cpu_math_library_num_threads(args.threads)
# Use ZeroCopy.
config.switch_use_feed_fetch_ops(False)
if profile:
if args.mod is recorder:
autolog = args.mod.Recorder(config, args.infer_batch_size,
args.model_name)
else:
pid = os.getpid()
autolog = args.mod.AutoLogger(
model_name=args.model_name,
model_precision="fp32",
batch_size=args.infer_batch_size,
save_path=args.save_log_path,
inference_config=config,
data_shape="dynamic",
pids=pid,
process_name=None,
gpu_ids=0 if args.device == "gpu" else None,
time_keys=[
'preprocess_time', 'inference_time', 'postprocess_time'
],
warmup=0,
logger=logger)
else:
autolog = None
predictor = inference.create_predictor(config)
input_handles = [
predictor.get_input_handle(name)
for name in predictor.get_input_names()
]
output_handles = [
predictor.get_output_handle(name)
for name in predictor.get_output_names()
]
return cls(predictor, input_handles, output_handles, autolog)
def init_resnet50_predictor(model_dir):
model_file = model_dir + '.pdmodel'
params_file = model_dir + '.pdiparams'
config = inference.Config()
config.set_prog_file(model_file)
config.set_params_file(params_file)
config.use_gpu()
config.enable_use_gpu(500, 0)
predictor = inference.create_predictor(config)
return predictor
def init_predictor(model_dir):
# refer https://paddle-inference.readthedocs.io/en/latest/api_reference/python_api_doc/Config/GPUConfig.html
model_file = model_dir+'.pdmodel'
params_file = model_dir + '.pdiparams'
config = inference.Config()
config.set_prog_file(model_file)
config.set_params_file(params_file)
# 启用 GPU 进行预测 - 初始化 GPU 显存 50M, Deivce_ID 为 0
config.enable_use_gpu(50, 0)
predictor = inference.create_predictor(config)
return predictor
def infer(args):
model_name = 'plato-xl'
tokenizer = UnifiedTransformerTokenizer.from_pretrained(model_name)
context = [
"Hi , Becky , what's up ?",
"Not much , except that my mother-in-law is driving me up the wall .",
"What's the problem ?"
]
data = tokenizer.dialogue_encode(
history=context,
add_start_token_as_response=True,
return_length=True,
return_role_ids=args.use_role,
position_style=args.position_style)
# Load FasterTransformer lib.
load("FasterTransformer", verbose=True)
config = paddle_infer.Config(args.inference_model_dir + "plato.pdmodel",
args.inference_model_dir + "plato.pdiparams")
config.enable_use_gpu(100, 0)
config.disable_glog_info()
predictor = paddle_infer.create_predictor(config)
input_handles = {}
for name in predictor.get_input_names():
input_handles[name] = predictor.get_input_handle(name)
if name == "attention_mask":
input_handles[name].copy_from_cpu(
np.expand_dims(
np.asarray(
data[name], dtype="float32"), axis=(0, 1)))
else:
input_handles[name].copy_from_cpu(
np.asarray(
data[name], dtype="int32").reshape([1, -1]))
output_handles = [
predictor.get_output_handle(name)
for name in predictor.get_output_names()
]
predictor.run()
output = [output_handle.copy_to_cpu() for output_handle in output_handles]
for sample in output[0].transpose([1, 0]).tolist():
print(" ".join(postprocess_response(sample, tokenizer)))
def get(self):
# Create predictor, if one doesn't exist, when inference is run
if not self._predictor:
# If model isn't saved, save model to a temp dir
# because predictor init requires the path to a saved model
if self._model_path is None:
self._model_path = tempfile.TemporaryDirectory().name
self._save(self._model_path)
config = paddle_infer.Config(self._model_path + ".pdmodel",
self._model_path + ".pdiparams")
config.enable_memory_optim()
predictor = paddle_infer.create_predictor(config)
self._predictor = predictor
return self._predictor
def create_inference_config(self,
passes: Optional[List[str]] = None,
use_gpu: bool = False,
use_mkldnn: bool = False,
ir_optim: Optional[bool] = None):
config = paddle_infer.Config()
config.switch_ir_debug(True)
config.disable_glog_info()
if ir_optim is not None:
config.switch_ir_optim(ir_optim)
if use_gpu:
config.enable_use_gpu(100, 0)
if use_mkldnn:
config.enable_mkldnn()
if passes is not None:
config.pass_builder().set_passes(passes)
return config
def create_predictor(args, mode, logger):
# if mode == "det":
# model_dir = args[]
# elif mode == 'cls':
# model_dir = args.cls_model_dir
# else:
# model_dir = args.rec_model_dir
model_dir = args['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(8000, 0)
else:
config.disable_gpu()
config.set_cpu_math_library_num_threads(6)
# config.enable_memory_optim()
config.disable_glog_info()
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
config.switch_use_feed_fetch_ops(False)
# 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
def init_paddle_inference_config(args):
import paddle.inference as paddle_infer
config = paddle_infer.Config(args.model_file, args.params_file)
if hasattr(args, 'precision'):
if args.precision == "fp16" and args.use_tensorrt:
precision = paddle_infer.PrecisionType.Half
elif args.precision == "int8":
precision = paddle_infer.PrecisionType.Int8
else:
precision = paddle_infer.PrecisionType.Float32
else:
precision = paddle_infer.PrecisionType.Float32
if args.use_gpu:
gpu_id = get_infer_gpuid()
if gpu_id is None:
raise ValueError(
"Not found GPU in current device. Please check your device or set args.use_gpu as False"
)
config.enable_use_gpu(args.gpu_mem, 0)
if args.use_tensorrt:
config.enable_tensorrt_engine(
precision_mode=precision,
max_batch_size=args.max_batch_size,
min_subgraph_size=args.min_subgraph_size)
# skip the minmum trt subgraph
min_input_shape = {"x": [1, 3, 10, 10]}
max_input_shape = {"x": [1, 3, 1000, 1000]}
opt_input_shape = {"x": [1, 3, 112, 112]}
config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
opt_input_shape)
else:
config.disable_gpu()
cpu_threads = args.cpu_threads if hasattr(args, "cpu_threads") else 10
config.set_cpu_math_library_num_threads(cpu_threads)
if args.enable_mkldnn:
# cache 10 different shapes for mkldnn to avoid memory leak
config.enable_mkldnn()
config.set_mkldnn_cache_capacity(10)
if args.precision == "fp16":
config.enable_mkldnn_bfloat16()
return config
def infer(args):
model_name = 'unimo-text-1.0-lcsts-new'
tokenizer = UNIMOTokenizer.from_pretrained(model_name)
inputs = "深度学习是人工智能的核心技术领域。百度飞桨作为中国首个自主研发、功能丰富、开源开放的产业级深度学习平台,将从多层次技术产品、产业AI人才培养和强大的生态资源支持三方面全面护航企业实现快速AI转型升级。"
data = tokenizer.gen_encode(inputs,
add_start_token_for_decoding=True,
return_length=True,
is_split_into_words=False)
# Load FasterTransformer lib.
load("FasterTransformer", verbose=True)
config = paddle_infer.Config(
args.inference_model_dir + "unimo_text.pdmodel",
args.inference_model_dir + "unimo_text.pdiparams")
config.enable_use_gpu(100, 0)
config.disable_glog_info()
predictor = paddle_infer.create_predictor(config)
input_handles = {}
for name in predictor.get_input_names():
input_handles[name] = predictor.get_input_handle(name)
if name == "attention_mask":
input_handles[name].copy_from_cpu(
np.expand_dims(np.asarray(data[name], dtype="float32"),
axis=(0, 1)))
else:
input_handles[name].copy_from_cpu(
np.asarray(data[name], dtype="int32").reshape([1, -1]))
output_handles = [
predictor.get_output_handle(name)
for name in predictor.get_output_names()
]
predictor.run()
output = [output_handle.copy_to_cpu() for output_handle in output_handles]
for sample in output[0].transpose([1, 0]).tolist():
print("".join(postprocess_response(sample, tokenizer)))
def load_predictor(self, model_file_path, params_file_path):
"""load_predictor
initialize the inference engine
Args:
model_file_path: inference model path (*.pdmodel)
model_file_path: inference parmaeter path (*.pdiparams)
Return:
predictor: Predictor created using Paddle Inference.
config: Configuration of the predictor.
input_tensor: Input tensor of the predictor.
output_tensor: Output tensor of the predictor.
"""
args = self.args
config = inference.Config(model_file_path, params_file_path)
if args.use_gpu:
config.enable_use_gpu(1000, 0)
else:
config.disable_gpu()
# The thread num should not be greater than the number of cores in the CPU.
config.set_cpu_math_library_num_threads(4)
# enable memory optim
config.enable_memory_optim()
config.disable_glog_info()
config.switch_use_feed_fetch_ops(False)
config.switch_ir_optim(True)
# create predictor
predictor = inference.create_predictor(config)
# get input and output tensor property
input_names = predictor.get_input_names()
input_tensor = predictor.get_input_handle(input_names[0])
output_names = predictor.get_output_names()
output_tensor = predictor.get_output_handle(output_names[0])
return predictor, config, input_tensor, output_tensor
def create_predictor(cls,
args,
config=None,
profile=False,
model_name=None):
if config is None:
config = inference.Config(
os.path.join(args.inference_model_dir, "transformer.pdmodel"),
os.path.join(args.inference_model_dir,
"transformer.pdiparams"))
if args.device == "gpu":
config.enable_use_gpu(100, 0)
elif args.device == "xpu":
config.enable_xpu(100)
else:
# CPU
config.disable_gpu()
if args.use_mkl:
config.enable_mkldnn()
config.set_cpu_math_library_num_threads(args.threads)
# Use ZeroCopy.
config.switch_use_feed_fetch_ops(False)
if profile:
recorder = Recorder(config, args.infer_batch_size, model_name)
else:
recorder = None
predictor = inference.create_predictor(config)
input_handles = [
predictor.get_input_handle(name)
for name in predictor.get_input_names()
]
output_handles = [
predictor.get_input_handle(name)
for name in predictor.get_output_names()
]
return cls(predictor, input_handles, output_handles, recorder)
def eval():
# create predictor
model_file = os.path.join(FLAGS.model_path, FLAGS.model_filename)
params_file = os.path.join(FLAGS.model_path, FLAGS.params_filename)
config = paddle_infer.Config(model_file, params_file)
if FLAGS.use_gpu:
config.enable_use_gpu(1000, 0)
if not FLAGS.ir_optim:
config.switch_ir_optim(False)
predictor = paddle_infer.create_predictor(config)
input_names = predictor.get_input_names()
input_handle = predictor.get_input_handle(input_names[0])
output_names = predictor.get_output_names()
output_handle = predictor.get_output_handle(output_names[0])
# prepare data
val_dataset = ImageNetValDataset(FLAGS.data_dir)
eval_loader = paddle.io.DataLoader(val_dataset,
batch_size=FLAGS.batch_size,
num_workers=5)
cost_time = 0.
total_num = 0.
correct_1_num = 0
correct_5_num = 0
for batch_id, data in enumerate(eval_loader()):
# set input
img_np = np.array([tensor.numpy() for tensor in data[0]])
label_np = np.array([tensor.numpy() for tensor in data[1]])
input_handle.reshape(img_np.shape)
input_handle.copy_from_cpu(img_np)
# run
t1 = time.time()
predictor.run()
t2 = time.time()
cost_time += (t2 - t1)
output_data = output_handle.copy_to_cpu()
# calculate accuracy
for i in range(len(label_np)):
label = label_np[i][0]
result = output_data[i, :]
index = result.argsort()
total_num += 1
if index[-1] == label:
correct_1_num += 1
if label in index[-5:]:
correct_5_num += 1
if batch_id % 10 == 0:
acc1 = correct_1_num / total_num
acc5 = correct_5_num / total_num
avg_time = cost_time / total_num
print(
"batch_id {}, acc1 {:.3f}, acc5 {:.3f}, avg time {:.5f} sec/img"
.format(batch_id, acc1, acc5, avg_time))
if FLAGS.test_samples > 0 and \
(batch_id + 1)* FLAGS.batch_size >= FLAGS.test_samples:
break
acc1 = correct_1_num / total_num
acc5 = correct_5_num / total_num
avg_time = cost_time / total_num
print("End test: test image {}".format(total_num))
print("test_acc1 {:.4f}, test_acc5 {:.4f}, avg time {:.5f} sec/img".format(
acc1, acc5, avg_time))
print("\n")
def load_model_config(self,
model_path,
use_gpu=False,
gpu_id=0,
use_profile=False,
thread_num=1,
mem_optim=True,
ir_optim=False,
use_trt=False,
use_lite=False,
use_xpu=False,
precision="fp32",
use_mkldnn=False,
mkldnn_cache_capacity=0,
mkldnn_op_list=None,
mkldnn_bf16_op_list=None,
use_feed_fetch_ops=False,
use_ascend_cl=False,
min_subgraph_size=3,
dynamic_shape_info={},
use_calib=False):
"""
Load model configs and create the paddle predictor by Paddle Inference API.
Args:
model_path: model config path.
use_gpu: calculating with gpu, False default.
gpu_id: gpu id, 0 default.
use_profile: use predictor profiles, False default.
thread_num: thread nums of cpu math library, default 1.
mem_optim: memory optimization, True default.
ir_optim: open calculation chart optimization, False default.
use_trt: use nvidia TensorRT optimization, False default
use_lite: use Paddle-Lite Engint, False default
ir_optim: open calculation chart optimization, False default.
use_trt: use nvidia TensorRT optimization, False default
use_lite: use Paddle-Lite Engint, False default
use_xpu: run predict on Baidu Kunlun, False default
precision: precision mode, "fp32" default
use_mkldnn: use MKLDNN, False default.
mkldnn_cache_capacity: cache capacity for input shapes, 0 default.
mkldnn_op_list: op list accelerated using MKLDNN, None default.
mkldnn_bf16_op_list: op list accelerated using MKLDNN bf16, None default.
use_feed_fetch_ops: use feed/fetch ops, False default.
use_ascend_cl: run predict on Huawei Ascend, False default
min_subgraph_size: the minimal subgraph size for opening tensorrt to optimize, 3 default
dynamic_shape_info: dict including min_input_shape，max_input_shape, opt_input_shape, {} default
use_calib: use TensorRT calibration, False default
"""
gpu_id = int(gpu_id)
client_config = "{}/serving_server_conf.prototxt".format(model_path)
model_conf = m_config.GeneralModelConfig()
f = open(client_config, 'r')
model_conf = google.protobuf.text_format.Merge(
str(f.read()), model_conf)
# Init paddle_infer config
# Paddle's model files and parameter files have multiple naming rules:
# 1) __model__, __params__
# 2) *.pdmodel, *.pdiparams
# 3) __model__, conv2d_1.w_0, conv2d_2.w_0, fc_1.w_0, conv2d_1.b_0, ...
pdmodel_file_list = self.search_suffix_files(model_path, "*.pdmodel")
pdiparams_file_list = self.search_suffix_files(model_path,
"*.pdiparams")
if os.path.exists(os.path.join(model_path, "__params__")):
# case 1) initializing
config = paddle_infer.Config(
os.path.join(model_path, "__model__"),
os.path.join(model_path, "__params__"))
elif pdmodel_file_list and len(
pdmodel_file_list) > 0 and pdiparams_file_list and len(
pdiparams_file_list) > 0:
# case 2) initializing
logger.info("pdmodel_file_list:{}, pdiparams_file_list:{}".format(
pdmodel_file_list, pdiparams_file_list))
config = paddle_infer.Config(pdmodel_file_list[0],
pdiparams_file_list[0])
else:
# case 3) initializing.
config = paddle_infer.Config(model_path)
logger.info(
"LocalPredictor load_model_config params: model_path:{}, use_gpu:{}, "
"gpu_id:{}, use_profile:{}, thread_num:{}, mem_optim:{}, ir_optim:{}, "
"use_trt:{}, use_lite:{}, use_xpu:{}, precision:{}, use_calib:{}, "
"use_mkldnn:{}, mkldnn_cache_capacity:{}, mkldnn_op_list:{}, "
"mkldnn_bf16_op_list:{}, use_feed_fetch_ops:{}, "
"use_ascend_cl:{}, min_subgraph_size:{}, dynamic_shape_info:{}".
format(model_path, use_gpu, gpu_id, use_profile, thread_num,
mem_optim, ir_optim, use_trt, use_lite, use_xpu, precision,
use_calib, use_mkldnn, mkldnn_cache_capacity, mkldnn_op_list,
mkldnn_bf16_op_list, use_feed_fetch_ops, use_ascend_cl,
min_subgraph_size, dynamic_shape_info))
self.feed_names_ = [var.alias_name for var in model_conf.feed_var]
self.fetch_names_ = [var.alias_name for var in model_conf.fetch_var]
self.feed_names_to_idx_ = {}
self.fetch_names_to_idx_ = {}
for i, var in enumerate(model_conf.feed_var):
self.feed_names_to_idx_[var.alias_name] = i
self.feed_types_[var.alias_name] = var.feed_type
self.feed_shapes_[var.alias_name] = var.shape
for i, var in enumerate(model_conf.fetch_var):
self.fetch_names_to_idx_[var.alias_name] = i
self.fetch_types_[var.alias_name] = var.fetch_type
self.fetch_names_to_type_[var.alias_name] = var.shape
# set precision of inference.
precision_type = paddle_infer.PrecisionType.Float32
if precision is not None and precision.lower() in precision_map:
precision_type = precision_map[precision.lower()]
else:
logger.warning("precision error!!! Please check precision:{}".
format(precision))
# set profile
if use_profile:
config.enable_profile()
# set memory optimization
if mem_optim:
config.enable_memory_optim()
# set ir optimization, threads of cpu math library
config.switch_ir_optim(ir_optim)
# use feed & fetch ops
config.switch_use_feed_fetch_ops(use_feed_fetch_ops)
# pass optim
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
# set cpu & mkldnn
config.set_cpu_math_library_num_threads(thread_num)
if use_mkldnn:
config.enable_mkldnn()
if precision_type == "bf16":
config.enable_mkldnn_bfloat16()
if mkldnn_cache_capacity > 0:
config.set_mkldnn_cache_capacity(mkldnn_cache_capacity)
if mkldnn_op_list is not None:
config.set_mkldnn_op(mkldnn_op_list)
# set gpu
if not use_gpu:
config.disable_gpu()
else:
config.enable_use_gpu(100, gpu_id)
if use_trt:
config.enable_tensorrt_engine(
precision_mode=precision_type,
workspace_size=1 << 20,
max_batch_size=32,
min_subgraph_size=min_subgraph_size,
use_static=False,
use_calib_mode=use_calib)
@ErrorCatch
@ParamChecker
def dynamic_shape_info_helper(dynamic_shape_info:lambda dynamic_shape_info: check_dynamic_shape_info(dynamic_shape_info)):
pass
_, resp = dynamic_shape_info_helper(dynamic_shape_info)
if resp.err_no != CustomExceptionCode.OK.value:
print("dynamic_shape_info configure error, it should contain [min_input_shape', 'max_input_shape', 'opt_input_shape' {}".format(resp.err_msg))
kill_stop_process_by_pid("kill", os.getpgid(os.getpid()))
if len(dynamic_shape_info):
config.set_trt_dynamic_shape_info(
dynamic_shape_info['min_input_shape'],
dynamic_shape_info['max_input_shape'],
dynamic_shape_info['opt_input_shape'])
# set lite
if use_lite:
config.enable_lite_engine(
precision_mode=precision_type,
zero_copy=True,
passes_filter=[],
ops_filter=[])
config.switch_ir_optim(True)
# set xpu
if use_xpu:
# 2MB l3 cache
config.enable_xpu(8 * 1024 * 1024)
config.set_xpu_device_id(gpu_id)
# set ascend cl
if use_ascend_cl:
if use_lite:
# for ascend 310
nnadapter_device_names = "huawei_ascend_npu"
nnadapter_context_properties = \
"HUAWEI_ASCEND_NPU_SELECTED_DEVICE_IDS={}".format(gpu_id)
nnadapter_model_cache_dir = ""
config.nnadapter() \
.enable() \
.set_device_names([nnadapter_device_names]) \
.set_context_properties(nnadapter_context_properties) \
.set_model_cache_dir(nnadapter_model_cache_dir)
else:
# for ascend 910
config.enable_npu(gpu_id)
# set cpu low precision
if not use_gpu and not use_lite:
if precision_type == paddle_infer.PrecisionType.Int8:
logger.warning(
"PRECISION INT8 is not supported in CPU right now! Please use fp16 or bf16."
)
#config.enable_quantizer()
if precision is not None and precision.lower() == "bf16":
config.enable_mkldnn_bfloat16()
if mkldnn_bf16_op_list is not None:
config.set_bfloat16_op(mkldnn_bf16_op_list)
@ErrorCatch
def create_predictor_check(config):
predictor = paddle_infer.create_predictor(config)
return predictor
predictor, resp = create_predictor_check(config)
if resp.err_no != CustomExceptionCode.OK.value:
logger.critical(
"failed to create predictor: {}".format(resp.err_msg),
exc_info=False)
print("failed to create predictor: {}".format(resp.err_msg))
kill_stop_process_by_pid("kill", os.getpgid(os.getpid()))
self.predictor = predictor
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
elif mode == 'table':
model_dir = args.table_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)
if args.use_onnx:
import onnxruntime as ort
model_file_path = model_dir
if not os.path.exists(model_file_path):
raise ValueError(
"not find model file path {}".format(model_file_path))
sess = ort.InferenceSession(model_file_path)
return sess, sess.get_inputs()[0], None, None
else:
model_file_path = model_dir + "/inference.pdmodel"
params_file_path = model_dir + "/inference.pdiparams"
if not os.path.exists(model_file_path):
raise ValueError(
"not find model file path {}".format(model_file_path))
if not os.path.exists(params_file_path):
raise ValueError(
"not find params file path {}".format(params_file_path))
config = inference.Config(model_file_path, params_file_path)
if hasattr(args, 'precision'):
if args.precision == "fp16" and args.use_tensorrt:
precision = inference.PrecisionType.Half
elif args.precision == "int8":
precision = inference.PrecisionType.Int8
else:
precision = inference.PrecisionType.Float32
else:
precision = inference.PrecisionType.Float32
if args.use_gpu:
gpu_id = get_infer_gpuid()
if gpu_id is None:
logger.warning(
"GPU is not found in current device by nvidia-smi. Please check your device or ignore it if run on jeston."
)
config.enable_use_gpu(args.gpu_mem, 0)
if args.use_tensorrt:
config.enable_tensorrt_engine(
workspace_size=1 << 30,
precision_mode=precision,
max_batch_size=args.max_batch_size,
min_subgraph_size=args.min_subgraph_size)
# skip the minmum trt subgraph
use_dynamic_shape = True
if mode == "det":
min_input_shape = {
"x": [1, 3, 50, 50],
"conv2d_92.tmp_0": [1, 120, 20, 20],
"conv2d_91.tmp_0": [1, 24, 10, 10],
"conv2d_59.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
"nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
"conv2d_124.tmp_0": [1, 256, 20, 20],
"nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
"elementwise_add_7": [1, 56, 2, 2],
"nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
}
max_input_shape = {
"x": [1, 3, 1536, 1536],
"conv2d_92.tmp_0": [1, 120, 400, 400],
"conv2d_91.tmp_0": [1, 24, 200, 200],
"conv2d_59.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
"conv2d_124.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
"elementwise_add_7": [1, 56, 400, 400],
"nearest_interp_v2_0.tmp_0": [1, 256, 400, 400]
}
opt_input_shape = {
"x": [1, 3, 640, 640],
"conv2d_92.tmp_0": [1, 120, 160, 160],
"conv2d_91.tmp_0": [1, 24, 80, 80],
"conv2d_59.tmp_0": [1, 96, 160, 160],
"nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
"nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
"conv2d_124.tmp_0": [1, 256, 160, 160],
"nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
"elementwise_add_7": [1, 56, 40, 40],
"nearest_interp_v2_0.tmp_0": [1, 256, 40, 40]
}
min_pact_shape = {
"nearest_interp_v2_26.tmp_0": [1, 256, 20, 20],
"nearest_interp_v2_27.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_28.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_29.tmp_0": [1, 64, 20, 20]
}
max_pact_shape = {
"nearest_interp_v2_26.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_27.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_28.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_29.tmp_0": [1, 64, 400, 400]
}
opt_pact_shape = {
"nearest_interp_v2_26.tmp_0": [1, 256, 160, 160],
"nearest_interp_v2_27.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_28.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_29.tmp_0": [1, 64, 160, 160]
}
min_input_shape.update(min_pact_shape)
max_input_shape.update(max_pact_shape)
opt_input_shape.update(opt_pact_shape)
elif mode == "rec":
if args.rec_algorithm != "CRNN":
use_dynamic_shape = False
min_input_shape = {"x": [1, 3, 32, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 1536]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
elif mode == "cls":
min_input_shape = {"x": [1, 3, 48, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 48, 1024]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
else:
use_dynamic_shape = False
if use_dynamic_shape:
config.set_trt_dynamic_shape_info(min_input_shape,
max_input_shape,
opt_input_shape)
else:
config.disable_gpu()
if hasattr(args, "cpu_threads"):
config.set_cpu_math_library_num_threads(args.cpu_threads)
else:
# default cpu threads as 10
config.set_cpu_math_library_num_threads(10)
if args.enable_mkldnn:
# cache 10 different shapes for mkldnn to avoid memory leak
config.set_mkldnn_cache_capacity(10)
config.enable_mkldnn()
if args.precision == "fp16":
config.enable_mkldnn_bfloat16()
# enable memory optim
config.enable_memory_optim()
config.disable_glog_info()
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
if mode == 'table':
config.delete_pass("fc_fuse_pass") # not supported for table
config.switch_use_feed_fetch_ops(False)
config.switch_ir_optim(True)
# 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, config
import os
import cv2
import numpy as np
import paddle.inference as pi
from licsber.cv import parse_img
CHANNEL, HEIGHT, WIDTH = (3, 34, 92)
CHAR_LIST = '12345678ABCDEFHKNPQXYZabcdefhknpxyz'
_now_path = os.path.dirname(__file__)
MODEL_PATH = os.path.join(_now_path, 'models', 'inference.pdmodel')
PARAMS_PATH = os.path.join(_now_path, 'models', 'inference.pdiparams')
_config = pi.Config(MODEL_PATH, PARAMS_PATH)
_predictor = pi.create_predictor(_config)
if CHANNEL == 1:
def pre_process(img):
_, binary = cv2.threshold(img, 0x70, 1, cv2.THRESH_BINARY)
binary = binary[:, :, 0]
return np.array(binary, dtype='float32').reshape((1, HEIGHT, WIDTH))
elif CHANNEL == 3:
def pre_process(img):
return np.array(img, dtype='float32').reshape([CHANNEL, HEIGHT, WIDTH]) / 255
else:
print('error, cannot pre_process img like this.')
def ctc_decode(text, blank=len(CHAR_LIST)):
def run_test(self, quant=False, *args, **kwargs):
status = True
run_flags = []
for prog_config in self.sample_program_configs(*args, **kwargs):
# In CI, only run 10% cases
if np.random.rand() < self.num_percent_cases:
run_flags.append(True)
else:
run_flags.append(False)
for prog_config, run_flags in zip(
self.sample_program_configs(*args, **kwargs), run_flags):
if not run_flags:
continue
# if program is invalid, we should skip that cases.
if not self.is_program_valid(prog_config):
continue
model, params = create_fake_model(prog_config)
if quant:
model, params = create_quant_model(model, params)
feed_data = {}
for name, tensor_config in prog_config.inputs.items():
feed_data[name] = {
'data': tensor_config.data,
'lod': tensor_config.lod
}
results: List[Dict[str, np.ndarray]] = []
# baseline: gpu run
logging.info('RUN program_config: ' + str(prog_config))
gpu_config = self.create_inference_config(use_trt=False)
results.append(
self.run_test_config(model, params, prog_config, gpu_config,
feed_data))
self.success_log('RUN_GPU_BASELINE done')
for pred_config, nodes_num, threshold in self.sample_predictor_configs(
prog_config):
if os.path.exists(self.cache_dir):
shutil.rmtree(self.cache_dir)
if isinstance(threshold, float):
atol = threshold
rtol = 1e-8
elif isinstance(threshold, list) or isinstance(
threshold, tuple):
atol = threshold[0]
rtol = threshold[1]
else:
raise NotImplementedError
if quant and pred_config.tensorrt_precision_mode(
) != paddle_infer.PrecisionType.Int8:
continue
if pred_config.tensorrt_precision_mode(
) == paddle_infer.PrecisionType.Int8 and not quant:
continue
ignore_flag = False
for ignore_info in self.ignore_cases:
if ignore_info[0](prog_config, pred_config):
ignore_flag = True
if ignore_info[1] == IgnoreReasons.TRT_NOT_IMPLEMENTED:
self.ignore_log(
"[TRT_NOT_IMPLEMENTED] " + ignore_info[2] +
' ' + ' vs ' +
self.inference_config_str(pred_config))
elif ignore_info[1] == IgnoreReasons.TRT_NOT_SUPPORT:
self.ignore_log(
"[TRT_NOT_SUPPORT] " + ignore_info[2] + ' ' +
' vs ' +
self.inference_config_str(pred_config))
else:
raise NotImplementedError
break
try:
pred_config_deserialize = paddle_infer.Config(pred_config)
results.append(
self.run_test_config(model, params, prog_config,
pred_config, feed_data))
self.assert_tensors_near(atol, rtol, results[-1],
results[0])
if not ignore_flag:
self.assert_op_size(nodes_num[0], nodes_num[1])
# deserialize test
if nodes_num[0] > 0:
self.run_test_config(model, params, prog_config,
pred_config_deserialize,
feed_data)
except Exception as e:
self.fail_log(
self.inference_config_str(pred_config) +
'\033[1;31m \nERROR INFO: {}\033[0m'.format(str(e)))
if not ignore_flag:
status = False
continue
self.success_log('RUN predictor_config ' +
self.inference_config_str(pred_config) +
' done')
self.assertTrue(status)
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
elif mode == 'table':
model_dir = args.table_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):
raise ValueError("not find model file path {}".format(model_file_path))
if not os.path.exists(params_file_path):
raise ValueError(
"not find params file path {}".format(params_file_path))
config = inference.Config(model_file_path, params_file_path)
if hasattr(args, 'precision'):
if args.precision == "fp16" and args.use_tensorrt:
precision = inference.PrecisionType.Half
elif args.precision == "int8":
precision = inference.PrecisionType.Int8
else:
precision = inference.PrecisionType.Float32
else:
precision = inference.PrecisionType.Float32
if args.use_gpu:
config.enable_use_gpu(args.gpu_mem, 0)
if args.use_tensorrt:
config.enable_tensorrt_engine(
precision_mode=precision,
max_batch_size=args.max_batch_size,
min_subgraph_size=args.min_subgraph_size)
# skip the minmum trt subgraph
if mode == "det":
min_input_shape = {
"x": [1, 3, 50, 50],
"conv2d_92.tmp_0": [1, 96, 20, 20],
"conv2d_91.tmp_0": [1, 96, 10, 10],
"conv2d_59.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_1.tmp_0": [1, 96, 10, 10],
"nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
"conv2d_124.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
"nearest_interp_v2_4.tmp_0": [1, 24, 20, 20],
"nearest_interp_v2_5.tmp_0": [1, 24, 20, 20],
"elementwise_add_7": [1, 56, 2, 2],
"nearest_interp_v2_0.tmp_0": [1, 96, 2, 2]
}
max_input_shape = {
"x": [1, 3, 2000, 2000],
"conv2d_92.tmp_0": [1, 96, 400, 400],
"conv2d_91.tmp_0": [1, 96, 200, 200],
"conv2d_59.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_1.tmp_0": [1, 96, 200, 200],
"conv2d_124.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
"nearest_interp_v2_4.tmp_0": [1, 24, 400, 400],
"nearest_interp_v2_5.tmp_0": [1, 24, 400, 400],
"elementwise_add_7": [1, 56, 400, 400],
"nearest_interp_v2_0.tmp_0": [1, 96, 400, 400]
}
opt_input_shape = {
"x": [1, 3, 640, 640],
"conv2d_92.tmp_0": [1, 96, 160, 160],
"conv2d_91.tmp_0": [1, 96, 80, 80],
"conv2d_59.tmp_0": [1, 96, 160, 160],
"nearest_interp_v2_1.tmp_0": [1, 96, 80, 80],
"nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
"conv2d_124.tmp_0": [1, 256, 160, 160],
"nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
"nearest_interp_v2_4.tmp_0": [1, 24, 160, 160],
"nearest_interp_v2_5.tmp_0": [1, 24, 160, 160],
"elementwise_add_7": [1, 56, 40, 40],
"nearest_interp_v2_0.tmp_0": [1, 96, 40, 40]
}
elif mode == "rec":
min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
elif mode == "cls":
min_input_shape = {"x": [args.rec_batch_num, 3, 48, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
else:
min_input_shape = {"x": [1, 3, 10, 10]}
max_input_shape = {"x": [1, 3, 1000, 1000]}
opt_input_shape = {"x": [1, 3, 500, 500]}
config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
opt_input_shape)
else:
config.disable_gpu()
if hasattr(args, "cpu_threads"):
config.set_cpu_math_library_num_threads(args.cpu_threads)
else:
# default cpu threads as 10
config.set_cpu_math_library_num_threads(10)
if args.enable_mkldnn:
# cache 10 different shapes for mkldnn to avoid memory leak
config.set_mkldnn_cache_capacity(10)
config.enable_mkldnn()
# enable memory optim
config.enable_memory_optim()
#config.disable_glog_info()
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
if mode == 'table':
config.delete_pass("fc_fuse_pass") # not supported for table
config.switch_use_feed_fetch_ops(False)
config.switch_ir_optim(True)
# 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, config
