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Running
on
A10G
Running
on
A10G
| import os | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| import torch.backends.cudnn as cudnn | |
| from models_depth.model import EVPDepth | |
| from configs.train_options import TrainOptions | |
| from configs.test_options import TestOptions | |
| import glob | |
| import utils | |
| import torchvision.transforms as transforms | |
| from utils_depth.misc import colorize | |
| from PIL import Image | |
| import torch.nn.functional as F | |
| def main(): | |
| opt = TestOptions().initialize() | |
| opt.add_argument('--img_path', type=str) | |
| args = opt.parse_args() | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = EVPDepth(args=args, caption_aggregation=True) | |
| cudnn.benchmark = True | |
| model.to(device) | |
| model_weight = torch.load(args.ckpt_dir)['model'] | |
| if 'module' in next(iter(model_weight.items()))[0]: | |
| model_weight = OrderedDict((k[7:], v) for k, v in model_weight.items()) | |
| model.load_state_dict(model_weight, strict=False) | |
| model.eval() | |
| img_path = args.img_path | |
| image = cv2.imread(img_path) | |
| image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| transform = transforms.ToTensor() | |
| image = transform(image).unsqueeze(0).to(device) | |
| shape = image.shape | |
| image = torch.nn.functional.interpolate(image, (440,480), mode='bilinear', align_corners=True) | |
| image = F.pad(image, (0, 0, 40, 0)) | |
| with torch.no_grad(): | |
| pred = model(image)['pred_d'] | |
| pred = pred[:,:,40:,:] | |
| pred = torch.nn.functional.interpolate(pred, shape[2:], mode='bilinear', align_corners=True) | |
| pred_d_numpy = pred.squeeze().cpu().numpy() | |
| pred_d_color, _, _ = colorize(pred_d_numpy, cmap='gray_r') | |
| Image.fromarray(pred_d_color).save('res.png') | |
| return 0 | |
| if __name__ == '__main__': | |
| main() | |