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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_refer.model import EVPRefer | |
| from args import get_parser | |
| import glob | |
| import utils | |
| import torchvision.transforms as transforms | |
| from PIL import Image | |
| import torch.nn.functional as F | |
| from transformers import CLIPTokenizer | |
| def main(): | |
| parser = get_parser() | |
| parser.add_argument('--img_path', type=str) | |
| parser.add_argument('--prompt', type=str) | |
| args = parser.parse_args() | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") | |
| model = EVPRefer(sd_path='../checkpoints/v1-5-pruned-emaonly.ckpt') | |
| cudnn.benchmark = True | |
| model.to(device) | |
| model_weight = torch.load(args.resume)['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) | |
| image_t = transforms.ToTensor()(image).unsqueeze(0).to(device) | |
| image_t = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])(image_t) | |
| shape = image_t.shape | |
| image_t = torch.nn.functional.interpolate(image_t, (512,512), mode='bilinear', align_corners=True) | |
| input_ids = tokenizer(text=args.prompt, truncation=True, max_length=args.token_length, return_length=True, | |
| return_overflowing_tokens=False, padding="max_length", return_tensors="pt")['input_ids'].to(device) | |
| with torch.no_grad(): | |
| pred = model(image_t, input_ids) | |
| pred = torch.nn.functional.interpolate(pred, shape[2:], mode='bilinear', align_corners=True) | |
| output_mask = pred.cpu().argmax(1).data.numpy().squeeze() | |
| alpha = 0.65 | |
| image[output_mask == 0] = (image[output_mask == 0]*alpha).astype(np.uint8) | |
| contours, _ = cv2.findContours(output_mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) | |
| cv2.drawContours(image, contours, -1, (0, 255, 0), 2) | |
| Image.fromarray(image.astype(np.uint8)).save('res.png') | |
| return 0 | |
| if __name__ == '__main__': | |
| main() | |