29 lines
1.2 KiB
Python
29 lines
1.2 KiB
Python
|
import pdb
|
||
|
|
from pathlib import Path
|
||
|
|
import sys
|
||
|
|
import os
|
||
|
|
import onnxruntime as ort
|
||
|
|
PROJECT_ROOT = Path(__file__).absolute().parents[0].absolute()
|
||
|
|
sys.path.insert(0, str(PROJECT_ROOT))
|
||
|
|
from parsing_api import onnx_inference
|
||
|
|
import torch
|
||
|
|
|
||
|
|
|
||
|
|
class Parsing:
|
||
|
|
def __init__(self, model_root, device):
|
||
|
|
providers = ['CPUExecutionProvider'
|
||
|
|
] if device == 'cpu' else ['CUDAExecutionProvider']
|
||
|
|
session_options = ort.SessionOptions()
|
||
|
|
session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
|
||
|
|
session_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
|
||
|
|
self.session = ort.InferenceSession(os.path.join(model_root, 'humanparsing/parsing_atr.onnx'),
|
||
|
|
sess_options=session_options, providers=providers)
|
||
|
|
self.lip_session = ort.InferenceSession(os.path.join(model_root, 'humanparsing/parsing_lip.onnx'),
|
||
|
|
sess_options=session_options, providers=providers)
|
||
|
|
|
||
|
|
|
||
|
|
def __call__(self, input_image):
|
||
|
|
# torch.cuda.set_device(self.gpu_id)
|
||
|
|
parsed_image, face_mask = onnx_inference(self.session, self.lip_session, input_image)
|
||
|
|
return parsed_image, face_mask
|