Create ref_cap.py

tasks/ref_cap.py

@@ -0,0 +1,68 @@
+# --------------------------------------------------------
+# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
+# Copyright (c) 2022 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Xueyan Zou ([email protected])
+# --------------------------------------------------------
+
+import torch
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+from torchvision import transforms
+from utils.visualizer import Visualizer
+from detectron2.data import MetadataCatalog
+
+t = []
+t.append(transforms.Resize(224, interpolation=Image.BICUBIC))
+transform_ret = transforms.Compose(t)
+t = []
+t.append(transforms.Resize(512, interpolation=Image.BICUBIC))
+transform_grd = transforms.Compose(t)
+
+metedata = MetadataCatalog.get('coco_2017_train_panoptic')
+
+def referring_captioning(model, image, texts, inpainting_text, *args, **kwargs):
+    model_last, model_cap = model
+    with torch.no_grad():
+        image_ori = image
+        image = transform_grd(image)
+        width = image.size[0]
+        height = image.size[1]
+        image = np.asarray(image)
+        image_ori_ = image
+        images = torch.from_numpy(image.copy()).permute(2,0,1).cuda()
+        texts_input = [[texts.strip() if texts.endswith('.') else (texts + '.')]]
+
+        batch_inputs = [{'image': images, 'groundings': {'texts':texts_input}, 'height': height, 'width': width}]
+        outputs = model_last.model.evaluate_grounding(batch_inputs, None)
+
+        grd_mask = (outputs[-1]['grounding_mask'] > 0).float()
+        grd_mask_ = (1 - F.interpolate(grd_mask[None,], (224, 224), mode='nearest')[0]).bool()
+
+        color = [252/255, 91/255, 129/255]
+        visual = Visualizer(image_ori_, metadata=metedata)
+        demo = visual.draw_binary_mask(grd_mask.cpu().numpy()[0], color=color, text=texts)
+        res = demo.get_image()
+
+        if (1 - grd_mask_.float()).sum() < 5:
+            torch.cuda.empty_cache()
+            return Image.fromarray(res), 'n/a', None
+
+        grd_mask_ = grd_mask_ * 0
+        image = transform_ret(image_ori)
+        image_ori = np.asarray(image_ori)
+        image = np.asarray(image)
+        images = torch.from_numpy(image.copy()).permute(2,0,1).cuda()
+        batch_inputs = [{'image': images, 'image_id': 0, 'captioning_mask': grd_mask_}]
+
+        token_text = texts.replace('.','') if texts.endswith('.') else texts
+        token = model_cap.model.sem_seg_head.predictor.lang_encoder.tokenizer.encode(token_text)
+        token = torch.tensor(token)[None,:-1]
+
+        outputs = model_cap.model.evaluate_captioning(batch_inputs, extra={'token': token})
+        # outputs = model_cap.model.evaluate_captioning(batch_inputs, extra={})
+        text = outputs[-1]['captioning_text']
+
+    torch.cuda.empty_cache()
+    return Image.fromarray(res), text, None