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| 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 | |
| import requests | |
| # class Parsing: | |
| # def __init__(self, gpu_id: int): | |
| # self.gpu_id = gpu_id | |
| # torch.cuda.set_device(gpu_id) | |
| # session_options = ort.SessionOptions() | |
| # session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL | |
| # session_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL | |
| # session_options.add_session_config_entry('gpu_id', str(gpu_id)) | |
| # self.session = ort.InferenceSession(os.path.join(Path(__file__).absolute().parents[2].absolute(), 'ckpt/humanparsing/parsing_atr.onnx'), | |
| # sess_options=session_options, providers=['CPUExecutionProvider']) | |
| # self.lip_session = ort.InferenceSession(os.path.join(Path(__file__).absolute().parents[2].absolute(), 'ckpt/humanparsing/parsing_lip.onnx'), | |
| # sess_options=session_options, providers=['CPUExecutionProvider']) | |
| # 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 | |
| class Parsing: | |
| def __init__(self, gpu_id: int): | |
| self.gpu_id = gpu_id | |
| torch.cuda.set_device(gpu_id) | |
| # Define the URLs for the models on Hugging Face | |
| atr_url = 'https://huggingface.co/basso4/humanparsing/resolve/main/parsing_atr.onnx' | |
| lip_url = 'https://huggingface.co/basso4/humanparsing/resolve/main/parsing_lip.onnx' | |
| # Define local paths for storing the models | |
| model_dir = Path.home() / '.cache/humanparsing_models' | |
| model_dir.mkdir(parents=True, exist_ok=True) | |
| atr_path = model_dir / 'parsing_atr.onnx' | |
| lip_path = model_dir / 'parsing_lip.onnx' | |
| # Download models if they don't already exist locally | |
| self.download_model(atr_url, atr_path) | |
| self.download_model(lip_url, lip_path) | |
| # Configure ONNX Runtime session options | |
| session_options = ort.SessionOptions() | |
| session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL | |
| session_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL | |
| session_options.add_session_config_entry('gpu_id', str(gpu_id)) | |
| # Initialize ONNX Inference Sessions | |
| self.session = ort.InferenceSession(str(atr_path), sess_options=session_options, providers=['CPUExecutionProvider']) | |
| self.lip_session = ort.InferenceSession(str(lip_path), sess_options=session_options, providers=['CPUExecutionProvider']) | |
| def download_model(self, url, path): | |
| # Download the model if it doesn't exist at the specified path | |
| if not path.exists(): | |
| print(f"Downloading model from {url}...") | |
| response = requests.get(url, stream=True) | |
| response.raise_for_status() | |
| with open(path, 'wb') as file: | |
| for chunk in response.iter_content(chunk_size=8192): | |
| file.write(chunk) | |
| print(f"Model downloaded and saved to {path}") | |
| def __call__(self, input_image): | |
| # Perform inference using both ONNX models and return the results | |
| parsed_image, face_mask = onnx_inference(self.session, self.lip_session, input_image) | |
| return parsed_image, face_mask |