def create_predictor(args, mode, model_path):
model_dir = model_path
model_file_path = model_dir + "/model"
params_file_path = model_dir + "/params"
assert os.path.exists(model_file_path)
assert os.path.exists(params_file_path)
config = AnalysisConfig(model_file_path, params_file_path)
# use CPU
config.disable_gpu()
config.set_cpu_math_library_num_threads(6)
if args['enable_mkldnn']:
config.enable_mkldnn()
# config.enable_memory_optim()
config.disable_glog_info()
if args['use_zero_copy_run']:
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
config.switch_use_feed_fetch_ops(False)
else:
config.switch_use_feed_fetch_ops(True)
predictor = create_paddle_predictor(config)
input_names = predictor.get_input_names()
for name in input_names:
input_tensor = predictor.get_input_tensor(name)
output_names = predictor.get_output_names()
output_tensors = []
for output_name in output_names:
output_tensor = predictor.get_output_tensor(output_name)
output_tensors.append(output_tensor)
return predictor, input_tensor, output_tensors
def _set_config(self):
"""
predictor config setting
"""
model_file_path = os.path.join(self.pretrained_model_path, 'model')
params_file_path = os.path.join(self.pretrained_model_path, 'params')
config = AnalysisConfig(model_file_path, params_file_path)
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
use_gpu = True
except:
use_gpu = False
if use_gpu:
config.enable_use_gpu(8000, 0)
else:
config.disable_gpu()
config.disable_glog_info()
# use zero copy
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
config.switch_use_feed_fetch_ops(False)
self.predictor = create_paddle_predictor(config)
input_names = self.predictor.get_input_names()
self.input_tensor = self.predictor.get_input_tensor(input_names[0])
output_names = self.predictor.get_output_names()
self.output_tensors = []
for output_name in output_names:
output_tensor = self.predictor.get_output_tensor(output_name)
self.output_tensors.append(output_tensor)
def _set_config(self):
"""
predictor config setting
"""
self.model_file_path = self.default_pretrained_model_path
cpu_config = AnalysisConfig(self.model_file_path)
cpu_config.disable_glog_info()
cpu_config.switch_ir_optim(True)
cpu_config.enable_memory_optim()
cpu_config.switch_use_feed_fetch_ops(False)
cpu_config.switch_specify_input_names(True)
cpu_config.disable_glog_info()
cpu_config.disable_gpu()
self.cpu_predictor = create_paddle_predictor(cpu_config)
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
use_gpu = True
except:
use_gpu = False
if use_gpu:
gpu_config = AnalysisConfig(self.model_file_path)
gpu_config.disable_glog_info()
gpu_config.switch_ir_optim(True)
gpu_config.enable_memory_optim()
gpu_config.switch_use_feed_fetch_ops(False)
gpu_config.switch_specify_input_names(True)
gpu_config.disable_glog_info()
gpu_config.enable_use_gpu(100, 0)
self.gpu_predictor = create_paddle_predictor(gpu_config)
def load_model(self, modelpath, use_gpu):
# 对运行位置进行配置
if use_gpu:
try:
places = os.environ["CUDA_VISIBLE_DEVICES"]
places = int(places[0])
except Exception as e:
print(
'Error: %s. Please set the environment variables "CUDA_VISIBLE_DEVICES".'
% e)
use_gpu = False
# 加载模型参数
config = AnalysisConfig(modelpath)
# 设置参数
if use_gpu:
config.enable_use_gpu(100, places)
else:
config.disable_gpu()
config.enable_mkldnn()
config.disable_glog_info()
config.switch_ir_optim(True)
config.enable_memory_optim()
config.switch_use_feed_fetch_ops(False)
config.switch_specify_input_names(True)
# 通过参数加载模型预测器
predictor = create_paddle_predictor(config)
# 返回预测器
return predictor
def _set_config(self):
"""
predictor config setting
"""
# encoder
cpu_config_enc = AnalysisConfig(self.pretrained_encoder_net)
cpu_config_enc.disable_glog_info()
cpu_config_enc.disable_gpu()
self.cpu_predictor_enc = create_paddle_predictor(cpu_config_enc)
# decoder
cpu_config_dec = AnalysisConfig(self.pretrained_decoder_net)
cpu_config_dec.disable_glog_info()
cpu_config_dec.disable_gpu()
self.cpu_predictor_dec = create_paddle_predictor(cpu_config_dec)
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
use_gpu = True
except:
use_gpu = False
if use_gpu:
# encoder
gpu_config_enc = AnalysisConfig(self.pretrained_encoder_net)
gpu_config_enc.disable_glog_info()
gpu_config_enc.enable_use_gpu(memory_pool_init_size_mb=1000,
device_id=0)
self.gpu_predictor_enc = create_paddle_predictor(gpu_config_enc)
# decoder
gpu_config_dec = AnalysisConfig(self.pretrained_decoder_net)
gpu_config_dec.disable_glog_info()
gpu_config_dec.enable_use_gpu(memory_pool_init_size_mb=1000,
device_id=0)
self.gpu_predictor_dec = create_paddle_predictor(gpu_config_dec)
def _set_config(self):
"""
predictor config setting
"""
self.model_file_path = os.path.join(self.default_pretrained_model_path,
'__model__')
self.params_file_path = os.path.join(
self.default_pretrained_model_path, '__params__')
cpu_config = AnalysisConfig(self.model_file_path,
self.params_file_path)
cpu_config.disable_glog_info()
cpu_config.disable_gpu()
self.cpu_predictor = create_paddle_predictor(cpu_config)
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
use_gpu = True
except:
use_gpu = False
if use_gpu:
gpu_config = AnalysisConfig(self.model_file_path,
self.params_file_path)
gpu_config.disable_glog_info()
gpu_config.enable_use_gpu(memory_pool_init_size_mb=1000,
device_id=0)
self.gpu_predictor = create_paddle_predictor(gpu_config)
def __init__(self, model_dir, label_id_path, vocab_path,
gpu_id=None, gpu_mem=8000, zero_copy=True):
self.tokenizer = ErnieTokenizer.from_pretrained(vocab_path)
self.id_2_token = {v: k for k, v in self.tokenizer.vocab.items()}
label_encoder = LabelEncoder(label_id_info=label_id_path, isFile=True)
self.id_label_dict = label_encoder.id_label_dict
# 设置AnalysisConfig
config = AnalysisConfig(model_dir)
if gpu_id is None:
config.disable_gpu()
else:
config.enable_use_gpu(gpu_mem, gpu_id)
logging.info("gpu id: {}".format(config.gpu_device_id()))
self.zero_copy = zero_copy
if self.zero_copy:
config.switch_use_feed_fetch_ops(False)
# 创建PaddlePredictor
self.predictor = create_paddle_predictor(config)
if self.zero_copy:
input_names = self.predictor.get_input_names()
#logging.info(input_names)
self.input_tensor = self.predictor.get_input_tensor(input_names[0])
output_names = self.predictor.get_output_names()
#logging.info(output_names)
self.output_tensor = self.predictor.get_output_tensor(output_names[0])
def predict(args):
# config AnalysisConfig
config = AnalysisConfig(args.model_file, args.params_file)
if args.gpu_id < 0:
config.disable_gpu()
else:
config.enable_use_gpu(args.gpu_mem, args.gpu_id)
# you can enable tensorrt engine if paddle is installed with tensorrt
# config.enable_tensorrt_engine()
predictor = create_paddle_predictor(config)
# input
inputs = preprocess_image(args.image_path)
inputs = PaddleTensor(inputs)
# predict
outputs = predictor.run([inputs])
# get output
output = outputs[0]
output = output.as_ndarray().flatten()
cls = np.argmax(output)
score = output[cls]
logger.info("class: {0}".format(cls))
logger.info("score: {0}".format(score))
return
def _set_config(self):
# predictor config setting.
cpu_config = AnalysisConfig(self.default_pretrained_model_path)
cpu_config.disable_glog_info()
cpu_config.disable_gpu()
cpu_config.switch_ir_optim(False)
self.cpu_predictor = create_paddle_predictor(cpu_config)
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
use_gpu = True
except:
use_gpu = False
if use_gpu:
gpu_config = AnalysisConfig(self.default_pretrained_model_path)
gpu_config.disable_glog_info()
gpu_config.enable_use_gpu(memory_pool_init_size_mb=500,
device_id=0)
self.gpu_predictor = create_paddle_predictor(gpu_config)
# model config setting.
if not self.model_config:
with open(os.path.join(self.directory, 'config.yml')) as fp:
self.model_config = yaml.load(fp.read(),
Loader=yaml.FullLoader)
self.multi_box_head_config = self.model_config['MultiBoxHead']
self.output_decoder_config = self.model_config['SSDOutputDecoder']
self.mobilenet_config = self.model_config['MobileNet']
def _get_analysis_config(self,
use_gpu=False,
use_trt=False,
use_mkldnn=False):
'''
Return a new object of AnalysisConfig.
'''
config = AnalysisConfig(os.path.join(self.path, "model"),
os.path.join(self.path, "params"))
config.disable_gpu()
config.switch_specify_input_names(True)
config.switch_ir_optim(True)
config.switch_use_feed_fetch_ops(False)
if use_gpu:
config.enable_use_gpu(100, 0)
if use_trt:
config.enable_tensorrt_engine(
self.trt_parameters.workspace_size,
self.trt_parameters.max_batch_size,
self.trt_parameters.min_subgraph_size,
self.trt_parameters.precision,
self.trt_parameters.use_static,
self.trt_parameters.use_calib_mode)
elif use_mkldnn:
config.enable_mkldnn()
return config
def create_predictor(mode):
if mode == "detect":
model_file_path = config.det_model_dir
params_file_path = config.det_param_dir
else:
model_file_path = config.rec_model_dir
params_file_path = config.rec_param_dir
an_config = AnalysisConfig(model_file_path, params_file_path)
if config.use_gpu:
an_config.enable_use_gpu(config.gpu_mem, 0)
else:
an_config.disable_gpu()
an_config.disable_glog_info()
# use zero copy
an_config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
an_config.switch_use_feed_fetch_ops(False)
predictor = create_paddle_predictor(an_config)
input_names = predictor.get_input_names()
input_tensor = predictor.get_input_tensor(input_names[0])
output_names = predictor.get_output_names()
output_tensors = []
for output_name in output_names:
output_tensor = predictor.get_output_tensor(output_name)
output_tensors.append(output_tensor)
return predictor, input_tensor, output_tensors
def create_predictor(mode):
"""
create predictor for inference
:param args: params for prediction engine
:param mode: mode
:return: predictor
"""
if mode == "det":
model_dir = "./src/ai/ocr_paddle/inference/ch_ppocr_mobile_v1.1_det_infer"
elif mode == 'cls':
model_dir = "./src/ai/ocr_paddle/inference/ch_ppocr_mobile_v1.1_cls_infer"
elif mode == 'rec':
model_dir = "./src/ai/ocr_paddle/inference/ch_ppocr_mobile_v1.1_rec_infer"
else:
raise ValueError(
"'mode' of create_predictor() can only be one of ['det', 'cls', 'rec']"
)
if model_dir is None:
logger.info("not find {} model file path {}".format(mode, model_dir))
sys.exit(0)
model_file_path = model_dir + "/model"
params_file_path = model_dir + "/params"
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 = AnalysisConfig(model_file_path, params_file_path)
config.disable_gpu()
config.set_cpu_math_library_num_threads(6)
enable_mkldnn = False
if enable_mkldnn:
# cache 10 different shapes for mkldnn to avoid memory leak
config.set_mkldnn_cache_capacity(10)
config.enable_mkldnn()
# config.enable_memory_optim()
config.disable_glog_info()
use_zero_copy_run = False
if use_zero_copy_run:
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
config.switch_use_feed_fetch_ops(False)
else:
config.switch_use_feed_fetch_ops(True)
predictor = create_paddle_predictor(config)
input_names = predictor.get_input_names()
for name in input_names:
input_tensor = predictor.get_input_tensor(name)
output_names = predictor.get_output_names()
output_tensors = []
for output_name in output_names:
output_tensor = predictor.get_output_tensor(output_name)
output_tensors.append(output_tensor)
return predictor, input_tensor, output_tensors
def main():
args = parse_args()
model_file = args.model_dir + "/__model__"
params_file = args.model_dir + "/params"
config = AnalysisConfig(model_file, params_file)
config.disable_gpu()
predictor = create_paddle_predictor(config)
test_image(predictor, args.image_path)
def create_predictor(args, mode, logger):
if mode == "det":
model_dir = args.det_model_dir
elif mode == 'cls':
model_dir = args.cls_model_dir
else:
model_dir = args.rec_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 = AnalysisConfig(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=AnalysisConfig.Precision.Half
if args.use_fp16 else AnalysisConfig.Precision.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()
# config.enable_memory_optim()
config.disable_glog_info()
if args.use_zero_copy_run:
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
config.switch_use_feed_fetch_ops(False)
else:
config.switch_use_feed_fetch_ops(True)
predictor = create_paddle_predictor(config)
input_names = predictor.get_input_names()
for name in input_names:
input_tensor = predictor.get_input_tensor(name)
output_names = predictor.get_output_names()
output_tensors = []
for output_name in output_names:
output_tensor = predictor.get_output_tensor(output_name)
output_tensors.append(output_tensor)
return predictor, input_tensor, output_tensors
def __init__(self):
"""
create predictor manager
"""
self.get_predictor_timeout = float(
config.get('get.predictor.timeout', default_value=0.5))
predictor_count = 0
enable_mkl = False
gpu_memory = 200
gpu_device_ids = []
model_dir = config.get('model.dir')
device_type = config.get('device.type')
if device_type == PredictorManager.CPU_DEVICE:
cpu_predictor_count = int(
config.getint('cpu.predictor.count', default_value=0))
predictor_count = cpu_predictor_count
enable_mkl = config.getboolean('cpu.enable_mkl',
default_value=False)
elif device_type == PredictorManager.GPU_DEVICE:
gpu_predictor_count = int(
config.getint('gpu.predictor.count', default_value=0))
predictor_count = gpu_predictor_count
gpu_memory = config.getint('gpu.predictor.memory',
default_value=200)
gpu_device_ids = config.get('gpu.predictor.device.id').split(',')
gpu_device_ids = map(int, gpu_device_ids)
if PYTHON_VERSION == 3:
gpu_device_ids = list(gpu_device_ids)
assert len(
gpu_device_ids
) == gpu_predictor_count, "gpu predictor count doesn't match device count"
else:
raise Exception("no device to run predictor!")
assert predictor_count > 0, "no device to predict"
logger.info(
"device type:{} predictor count:{} model dir:{} get predictor timeout:{}s"
.format(device_type, predictor_count, model_dir,
self.get_predictor_timeout))
self.predictor_queue = Queue(maxsize=predictor_count)
for i in range(predictor_count):
# Set config
predictor_config = AnalysisConfig(model_dir)
# predictor_config.specify_input_name()
if device_type == PredictorManager.CPU_DEVICE:
predictor_config.disable_gpu()
if enable_mkl:
predictor_config.enable_mkldnn()
else:
device_id = gpu_device_ids[i]
predictor_config.enable_use_gpu(gpu_memory, device_id)
# Create PaddlePredictor
predictor = create_paddle_predictor(predictor_config)
self.predictor_queue.put(predictor)
def set_config(self, model_flie, params_file, use_feed_fetch_ops,
specify_input_names):
config = AnalysisConfig(model_flie, params_file)
config.disable_gpu()
config.enable_mkldnn()
config.disable_glog_info()
config.switch_ir_optim(True)
config.switch_use_feed_fetch_ops(use_feed_fetch_ops)
config.switch_specify_input_names(specify_input_names)
return config
def load_model(self, model_dir, roll_back=False):
print("load_model==>", model_dir)
config = AnalysisConfig(model_dir)
#不启动cpu
config.disable_gpu()
#把老的模型留存
if self.predictor and roll_back:
self.histroy.push(self.predictor)
#创建预测
self.predictor = create_paddle_predictor(config)
return self.predictor
def __load_inference_model(self, model_path, use_gpu):
"""
:param meta_path:
:return:
"""
check_cuda(use_gpu)
config = AnalysisConfig(model_path + "/" + "model", model_path + "/" + "params")
if use_gpu:
config.enable_use_gpu(1024)
else:
config.disable_gpu()
config.enable_mkldnn()
inference = create_paddle_predictor(config.to_native_config())
return inference
def create_predictor(args, mode):
if mode == "det":
model_dir = args.det_model_dir
else:
model_dir = args.rec_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 + "/model"
params_file_path = model_dir + "/params"
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 = AnalysisConfig(model_file_path, params_file_path)
if args.use_gpu:
config.enable_use_gpu(args.gpu_mem, 0)
else:
config.disable_gpu()
config.set_cpu_math_library_num_threads(6)
if args.enable_mkldnn:
config.enable_mkldnn()
#config.enable_memory_optim()
config.disable_glog_info()
if args.use_zero_copy_run:
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
config.switch_use_feed_fetch_ops(False)
else:
config.switch_use_feed_fetch_ops(True)
predictor = create_paddle_predictor(config)
input_names = predictor.get_input_names()
for name in input_names:
input_tensor = predictor.get_input_tensor(name)
output_names = predictor.get_output_names()
output_tensors = []
for output_name in output_names:
output_tensor = predictor.get_output_tensor(output_name)
output_tensors.append(output_tensor)
return predictor, input_tensor, output_tensors
def create_predictor(args, mode):
if mode == "det":
model_dir = args.det_model_dir
else:
model_dir = args.rec_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 + "/model"
params_file_path = model_dir + "/params"
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 = AnalysisConfig(model_file_path, params_file_path)
if args.use_gpu:
config.enable_use_gpu(args.gpu_mem, 0)
else:
config.disable_gpu()
config.disable_glog_info()
config.switch_ir_optim(args.ir_optim)
# if args.use_tensorrt:
# config.enable_tensorrt_engine(
# precision_mode=AnalysisConfig.Precision.Half
# if args.use_fp16 else AnalysisConfig.Precision.Float32,
# max_batch_size=args.batch_size)
config.enable_memory_optim()
# use zero copy
config.switch_use_feed_fetch_ops(False)
predictor = create_paddle_predictor(config)
input_names = predictor.get_input_names()
input_tensor = predictor.get_input_tensor(input_names[0])
output_names = predictor.get_output_names()
output_tensors = []
for output_name in output_names:
output_tensor = predictor.get_output_tensor(output_name)
output_tensors.append(output_tensor)
return predictor, input_tensor, output_tensors
def _get_analysis_config(self,
use_gpu=False,
use_trt=False,
use_mkldnn=False):
'''
Return a new object of AnalysisConfig.
'''
config = AnalysisConfig(self.path)
config.disable_gpu()
config.switch_specify_input_names(True)
config.switch_ir_optim(True)
config.switch_use_feed_fetch_ops(False)
if use_gpu:
config.enable_use_gpu(100, 0)
if use_trt:
config.enable_tensorrt_engine(
self.trt_parameters.workspace_size,
self.trt_parameters.max_batch_size,
self.trt_parameters.min_subgraph_size,
self.trt_parameters.precision,
self.trt_parameters.use_static,
self.trt_parameters.use_calib_mode)
if self.trt_parameters.use_inspector:
config.enable_tensorrt_inspector()
self.assertTrue(
config.tensorrt_inspector_enabled(),
"The inspector option is not set correctly.")
if self.dynamic_shape_params:
config.set_trt_dynamic_shape_info(
self.dynamic_shape_params.min_input_shape,
self.dynamic_shape_params.max_input_shape,
self.dynamic_shape_params.optim_input_shape,
self.dynamic_shape_params.disable_trt_plugin_fp16)
if self.enable_tensorrt_varseqlen:
config.enable_tensorrt_varseqlen()
elif use_mkldnn:
config.enable_mkldnn()
if self.enable_mkldnn_bfloat16:
config.enable_mkldnn_bfloat16()
print('config summary:', config.summary())
return config
def _set_config(self):
"""
predictor config setting.
"""
cpu_config = AnalysisConfig(self.default_pretrained_model_path)
cpu_config.disable_glog_info()
cpu_config.disable_gpu()
cpu_config.switch_ir_optim(False)
self.cpu_predictor = create_paddle_predictor(cpu_config)
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
use_gpu = True
except:
use_gpu = False
if use_gpu:
gpu_config = AnalysisConfig(self.default_pretrained_model_path)
gpu_config.disable_glog_info()
gpu_config.enable_use_gpu(memory_pool_init_size_mb=500, device_id=0)
self.gpu_predictor = create_paddle_predictor(gpu_config)
def set_config(self,
model_path,
num_threads,
mkldnn_cache_capacity,
warmup_data=None,
use_analysis=False,
enable_ptq=False):
config = AnalysisConfig(model_path)
config.set_cpu_math_library_num_threads(num_threads)
if use_analysis:
config.disable_gpu()
config.switch_use_feed_fetch_ops(True)
config.switch_ir_optim(True)
config.enable_mkldnn()
config.set_mkldnn_cache_capacity(mkldnn_cache_capacity)
if enable_ptq:
# This pass to work properly, must be added before fc_fuse_pass
config.pass_builder().insert_pass(5, "fc_lstm_fuse_pass")
config.enable_quantizer()
config.quantizer_config().set_quant_data(warmup_data)
config.quantizer_config().set_quant_batch_size(1)
return config
def create_predictor(args):
config = AnalysisConfig(args.model_file, args.params_file)
if args.use_gpu:
config.enable_use_gpu(args.gpu_mem, 0)
else:
config.disable_gpu()
config.disable_glog_info()
config.switch_ir_optim(args.ir_optim) # default true
if args.use_tensorrt:
config.enable_tensorrt_engine(
precision_mode=AnalysisConfig.Precision.Half
if args.use_fp16 else AnalysisConfig.Precision.Float32,
max_batch_size=args.batch_size)
config.enable_memory_optim()
# use zero copy
config.switch_use_feed_fetch_ops(False)
predictor = create_paddle_predictor(config)
return predictor
def _load_model_and_set_config(self):
'''
load model from file and set analysis config
'''
if os.path.exists(os.path.join(self.model_path, self.params_file)):
config = AnalysisConfig(
os.path.join(self.model_path, "__model__"),
os.path.join(self.model_path, self.params_file))
else:
config = AnalysisConfig(os.path.join(self.model_path))
if fluid.is_compiled_with_cuda():
config.enable_use_gpu(100, 0)
else:
config.disable_gpu()
config.switch_specify_input_names(True)
config.switch_use_feed_fetch_ops(False)
config.enable_memory_optim()
config.disable_glog_info()
config.switch_ir_optim(True)
return config
def _set_config(self, pretrained_model_path):
"""
predictor config path
"""
model_file_path = os.path.join(pretrained_model_path, 'model')
params_file_path = os.path.join(pretrained_model_path, 'params')
config = AnalysisConfig(model_file_path, params_file_path)
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
use_gpu = True
except:
use_gpu = False
if use_gpu:
config.enable_use_gpu(8000, 0)
else:
config.disable_gpu()
if self.enable_mkldnn:
# cache 10 different shapes for mkldnn to avoid memory leak
config.set_mkldnn_cache_capacity(10)
config.enable_mkldnn()
config.disable_glog_info()
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
config.switch_use_feed_fetch_ops(False)
predictor = create_paddle_predictor(config)
input_names = predictor.get_input_names()
input_tensor = predictor.get_input_tensor(input_names[0])
output_names = predictor.get_output_names()
output_tensors = []
for output_name in output_names:
output_tensor = predictor.get_output_tensor(output_name)
output_tensors.append(output_tensor)
return predictor, input_tensor, output_tensors
def _set_config(self):
"""
predictor config setting
"""
model_file_path = os.path.join(self.infer_model_path, 'model')
params_file_path = os.path.join(self.infer_model_path, 'params')
config = AnalysisConfig(model_file_path, params_file_path)
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
use_gpu = True
except:
use_gpu = False
if use_gpu:
config.enable_use_gpu(8000, 0)
else:
config.disable_gpu()
config.disable_glog_info()
self.predictor = create_paddle_predictor(config)
def main():
"""Predictor main"""
args = parse_args()
config = AnalysisConfig(args.model_files_path)
config.disable_gpu()
config.enable_profile()
# config.enable_mkldnn()
config.set_cpu_math_library_num_threads(args.cpu_num)
predictor = create_paddle_predictor(config)
tdm_model = TdmInferNet(args)
first_layer_node = tdm_model.first_layer_node
first_layer_nums = len(first_layer_node)
first_layer_node = np.array(first_layer_node)
first_layer_node = first_layer_node.reshape((1, -1)).astype('int64')
first_layer_node = first_layer_node.repeat(args.batch_size, axis=0)
first_layer_mask = (np.zeros(
(args.batch_size, first_layer_nums))).astype('int64')
file_list = [
str(args.test_files_path) + "/%s" % x
for x in os.listdir(args.test_files_path)
]
test_reader = TDMDataset().infer_reader(file_list, args.batch_size)
for batch_id, data in enumerate(test_reader()):
input_emb = data2tensor(data)
inputs = tdm_input(input_emb, first_layer_node, first_layer_mask)
outputs = predictor.run(inputs)
output = outputs[0]
output_data = output.as_ndarray()
logger.info("TEST --> batch: {} infer_item {}".format(
batch_id, output_data))
def _load_model_and_set_config(self):
'''
load model from file and set analysis config
'''
if os.path.exists(os.path.join(self.model_path, self.params_file)):
config = AnalysisConfig(
os.path.join(self.model_path, self.model_file),
os.path.join(self.model_path, self.params_file))
else:
config = AnalysisConfig(os.path.join(self.model_path))
if fluid.is_compiled_with_cuda():
config.enable_use_gpu(100, 0)
else:
config.disable_gpu()
config.switch_specify_input_names(True)
config.switch_use_feed_fetch_ops(False)
config.enable_memory_optim()
config.disable_glog_info()
# TODO: set it to True after PaddleInference fix the precision error
# in CUDA11
config.switch_ir_optim(False)
return config
def main(args):
ernie_config = ErnieConfig(args.ernie_config_path)
ernie_config.print_config()
reader = ClassifyReader(
vocab_path=args.vocab_path,
label_map_config=args.label_map_config,
max_seq_len=args.max_seq_len,
do_lower_case=args.do_lower_case,
in_tokens=False,
is_inference=True)
predict_prog = fluid.Program()
predict_startup = fluid.Program()
with fluid.program_guard(predict_prog, predict_startup):
with fluid.unique_name.guard():
predict_pyreader, probs, feed_target_names = create_model(
args,
pyreader_name='predict_reader',
ernie_config=ernie_config,
is_classify=True,
is_prediction=True)
predict_prog = predict_prog.clone(for_test=True)
if args.use_cuda:
place = fluid.CUDAPlace(0)
dev_count = fluid.core.get_cuda_device_count()
else:
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
place = fluid.CUDAPlace(0) if args.use_cuda == True else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(predict_startup)
if args.init_checkpoint:
init_pretraining_params(exe, args.init_checkpoint, predict_prog)
else:
raise ValueError("args 'init_checkpoint' should be set for prediction!")
assert args.save_inference_model_path, "args save_inference_model_path should be set for prediction"
_, ckpt_dir = os.path.split(args.init_checkpoint.rstrip('/'))
dir_name = ckpt_dir + '_inference_model'
model_path = os.path.join(args.save_inference_model_path, dir_name)
log.info("save inference model to %s" % model_path)
fluid.io.save_inference_model(
model_path,
feed_target_names, [probs],
exe,
main_program=predict_prog)
# Set config
#config = AnalysisConfig(args.model_dir)
#config = AnalysisConfig(os.path.join(model_path, "__model__"), os.path.join(model_path, ""))
config = AnalysisConfig(model_path)
if not args.use_cuda:
log.info("disable gpu")
config.disable_gpu()
config.switch_ir_optim(True)
else:
log.info("using gpu")
config.enable_use_gpu(1024)
# Create PaddlePredictor
predictor = create_paddle_predictor(config)
predict_data_generator = reader.data_generator(
input_file=args.predict_set,
batch_size=args.batch_size,
epoch=1,
shuffle=False)
log.info("-------------- prediction results --------------")
np.set_printoptions(precision=4, suppress=True)
index = 0
total_time = 0
for sample in predict_data_generator():
src_ids = sample[0]
sent_ids = sample[1]
pos_ids = sample[2]
task_ids = sample[3]
input_mask = sample[4]
inputs = [array2tensor(ndarray) for ndarray in [src_ids, sent_ids, pos_ids, input_mask]]
begin_time = time.time()
outputs = predictor.run(inputs)
end_time = time.time()
total_time += end_time - begin_time
# parse outputs
output = outputs[0]
batch_result = output.as_ndarray()
for single_example_probs in batch_result:
print('\t'.join(map(str, single_example_probs.tolist())))
index += 1
log.info("qps:{}\ttotal_time:{}\ttotal_example:{}\tbatch_size:{}".format(index/total_time, total_time, index, args.batch_size))