def load_model(self,
model_dir,
use_gpu=False,
enable_mkldnn=False,
cpu_threads=1):
model = os.path.join(model_dir, '__model__')
params = os.path.join(model_dir, '__params__')
config = Config(model, params)
# 设置参数
if use_gpu:
config.enable_use_gpu(100, 0)
else:
config.disable_gpu()
config.set_cpu_math_library_num_threads(cpu_threads)
if enable_mkldnn:
config.enable_mkldnn()
config.set_mkldnn_cache_capacity(10)
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_predictor(config)
# 获取模型的输入输出
input_names = predictor.get_input_names()
output_names = predictor.get_output_names()
input_handle = predictor.get_input_handle(input_names[0])
output_handle = predictor.get_output_handle(output_names[0])
return predictor, input_handle, output_handle
def preprocess():
#for i in range(1000):
# _preprocess()
#return 1
#feed = _preprocess()
import numpy as np
from paddle.inference import Config
from paddle.inference import create_predictor
config = Config("serving_server/__model__", "serving_server/__params__")
config.disable_gpu()
config.switch_use_feed_fetch_ops(False)
config.switch_specify_input_names(True)
predictor = create_predictor(config)
for i in range(10):
feed = _preprocess()
# input 0 im shape
input_names = predictor.get_input_names()
input_handle = predictor.get_input_handle(input_names[0])
input_t = feed["im_shape"]
input_handle.reshape(input_t.shape)
input_handle.copy_from_cpu(input_t)
# input 1 image
input_names = predictor.get_input_names()
input_handle = predictor.get_input_handle(input_names[1])
input_t = feed["image"]
input_handle.reshape(input_t.shape)
input_handle.copy_from_cpu(input_t)
# input 2 scale factor
input_names = predictor.get_input_names()
input_handle = predictor.get_input_handle(input_names[2])
input_t = feed["scale_factor"]
input_handle.reshape(input_t.shape)
input_handle.copy_from_cpu(input_t)
predictor.run()
output_names = predictor.get_output_names()
output_handle = predictor.get_output_handle(output_names[0])
output_data = output_handle.copy_to_cpu()
def main():
args = parse_args()
# 配置
config = Config(args.model_file, args.params_file)
config.disable_gpu()
config.switch_use_feed_fetch_ops(False)
config.switch_specify_input_names(True)
# 创建paddlePredictor
predictor = create_predictor(config)
# 获取输入
val_dataset = paddle.vision.datasets.MNIST(mode='test',
transform=transforms.ToTensor())
(image, label) = val_dataset[np.random.randint(10000)]
# fake_input = np.random.randn(1, 1, 28, 28).astype("float32")
# image = np.asndarray(image).astype("float32")
# print(image.shape)
image = image.numpy().reshape([1, 1, 28, 28])
# print(image.shape)
# print(fake_input.shape)
input_names = predictor.get_input_names()
input_handle = predictor.get_input_handle(input_names[0])
input_handle.reshape([1, 1, 28, 28])
input_handle.copy_from_cpu(image)
# 运行predictor
predictor.run()
# 获取输出
output_names = predictor.get_output_names()
output_handle = predictor.get_output_handle(output_names[0])
output = output_handle.copy_to_cpu()
print("True label: ", label.item())
print("Prediction: ", np.argmax(output))
def load_model(self, modelpath, use_gpu, use_mkldnn, combined):
# 对运行位置进行配置
if use_gpu:
try:
int(os.environ.get('CUDA_VISIBLE_DEVICES'))
except Exception:
print(
'Error! Unable to use GPU. Please set the environment variables "CUDA_VISIBLE_DEVICES=GPU_id" to use GPU.'
)
use_gpu = False
# 加载模型参数
if combined:
model = os.path.join(modelpath, "__model__")
params = os.path.join(modelpath, "__params__")
config = Config(model, params)
else:
config = Config(modelpath)
# 设置参数
if use_gpu:
config.enable_use_gpu(100, 0)
else:
config.disable_gpu()
if use_mkldnn:
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_predictor(config)
# 返回预测器
return predictor
