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Runtime error
| import gradio as gr | |
| from model import SixDRepNet | |
| import os | |
| import numpy as np | |
| import torch | |
| from torchvision import transforms | |
| import utils | |
| import time | |
| transformations = transforms.Compose([transforms.Resize(224), | |
| transforms.CenterCrop(224), | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) | |
| model = SixDRepNet(backbone_name='RepVGG-A0', | |
| backbone_file='', | |
| deploy=True, | |
| pretrained=False) | |
| saved_state_dict = torch.load(os.path.join( | |
| "weights_ALFW_A0.pth"), map_location='cpu') | |
| if 'model_state_dict' in saved_state_dict: | |
| model.load_state_dict(saved_state_dict['model_state_dict']) | |
| else: | |
| model.load_state_dict(saved_state_dict) | |
| # Test the Model | |
| model.eval() # Change model to 'eval' mode (BN uses moving mean/var). | |
| th = 15 | |
| def predict(img): | |
| img = img.convert('RGB') | |
| img = transformations(img).unsqueeze(0) | |
| with torch.no_grad(): | |
| start = time.time() | |
| R_pred = model(img) | |
| end = time.time() | |
| timemilis = (end - start)*1000 | |
| euler = utils.compute_euler_angles_from_rotation_matrices( | |
| R_pred,use_gpu=False)*180/np.pi | |
| p_pred_deg = euler[:, 0].cpu().item() | |
| y_pred_deg = euler[:, 1].cpu().item() | |
| direction_str = "" | |
| if p_pred_deg > th: | |
| direction_str = "UP " | |
| elif p_pred_deg < th: | |
| direction_str ="DOWN " | |
| if y_pred_deg > th: | |
| direction_str += "LEFT" | |
| elif y_pred_deg < th: | |
| direction_str += "RIGHT" | |
| return f"Yaw: {y_pred_deg:0.1f} \n Pitch: {p_pred_deg:0.1f}\n Direction: {direction_str} \n Time: {timemilis:0.2f}ms" | |
| gr.Interface(fn=predict, | |
| inputs=gr.Image(type="pil"), | |
| outputs=gr.Textbox(), | |
| examples=["face_left.jpg","face_right.jpg","face_up.jpg","face_down.jpg"]).launch(share=True) |