90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
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# --------------------------------------------------------
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# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
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# Copyright (c) 2022 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Xueyan Zou (xueyan@cs.wisc.edu)
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# --------------------------------------------------------
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import os
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import sys
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import json
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import logging
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pth = '/'.join(sys.path[0].split('/')[:-1])
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sys.path.insert(0, pth)
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from PIL import Image
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import numpy as np
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np.random.seed(27)
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import torch
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from torchvision import transforms
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from utils.arguments import load_opt_command
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from detectron2.data import MetadataCatalog
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from detectron2.utils.colormap import random_color
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from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
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from modeling.BaseModel import BaseModel
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from modeling import build_model
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from utils.visualizer import Visualizer
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from utils.distributed import init_distributed
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# logging.basicConfig(level = logging.INFO)
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logger = logging.getLogger(__name__)
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def main(args=None):
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'''
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Main execution point for PyLearn.
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'''
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opt, cmdline_args = load_opt_command(args)
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if cmdline_args.user_dir:
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absolute_user_dir = os.path.abspath(cmdline_args.user_dir)
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opt['base_path'] = absolute_user_dir
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opt = init_distributed(opt)
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# META DATA
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pretrained_pth = os.path.join(opt['RESUME_FROM'])
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output_root = './output'
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image_pth = 'inference/images/fruit.jpg'
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text = [['The larger watermelon.'], ['The front white flower.'], ['White tea pot.'], ['Flower bunch.'], ['white vase.'], ['The left peach.'], ['The brown knife.']]
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model = BaseModel(opt, build_model(opt)).from_pretrained(pretrained_pth).eval().cuda()
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model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(["background", "background"], is_eval=False)
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t = []
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t.append(transforms.Resize(512, interpolation=Image.BICUBIC))
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transform = transforms.Compose(t)
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metadata = MetadataCatalog.get('ade20k_panoptic_train')
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model.model.metadata = metadata
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with torch.no_grad():
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image_ori = Image.open(image_pth)
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width = image_ori.size[0]
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height = image_ori.size[1]
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image = transform(image_ori)
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image = np.asarray(image)
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image_ori = np.asarray(image_ori)
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images = torch.from_numpy(image.copy()).permute(2,0,1).cuda()
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batch_inputs = [{'image': images, 'height': height, 'width': width, 'groundings': {'texts': text}}]
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outputs = model.model.evaluate_grounding(batch_inputs, None)
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visual = Visualizer(image_ori, metadata=metadata)
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grd_mask = (outputs[0]['grounding_mask'] > 0).float().cpu().numpy()
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for idx, mask in enumerate(grd_mask):
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demo = visual.draw_binary_mask(mask, color=random_color(rgb=True, maximum=1).astype(np.int).tolist(), text=text[idx], alpha=0.3)
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output_folder = os.path.join(os.path.join(output_root))
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if not os.path.exists(output_folder):
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os.makedirs(output_folder)
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demo.save(os.path.join(output_folder, 'refseg.png'))
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if __name__ == "__main__":
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main()
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sys.exit(0)
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