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| import os | |
| import torch | |
| from itertools import compress | |
| from detectron2.config import get_cfg | |
| from detectron2.data.detection_utils import read_image | |
| # constants | |
| WINDOW_NAME = "GRiT" | |
| CUR_DIR = os.path.dirname(os.path.abspath(__file__)) | |
| from vbench.utils import CACHE_DIR | |
| # sys.path.insert(0, f"{CUR_DIR}/../") | |
| # print(CUR_DIR) | |
| import sys | |
| sys.path.append(os.path.join(CUR_DIR, './centernet2/')) | |
| from centernet.config import add_centernet_config | |
| from .grit.config import add_grit_config | |
| from .grit.predictor import VisualizationDemo | |
| class ObjDescription: | |
| def __init__(self, object_descriptions): | |
| self.data = object_descriptions | |
| def __getitem__(self, item): | |
| assert type(item) == torch.Tensor | |
| assert item.dim() == 1 | |
| if len(item) > 0: | |
| assert item.dtype == torch.int64 or item.dtype == torch.bool | |
| if item.dtype == torch.int64: | |
| return ObjDescription([self.data[x.item()] for x in item]) | |
| elif item.dtype == torch.bool: | |
| return ObjDescription(list(compress(self.data, item))) | |
| return ObjDescription(list(compress(self.data, item))) | |
| def __len__(self): | |
| return len(self.data) | |
| def __repr__(self): | |
| return "ObjDescription({})".format(self.data) | |
| def dense_pred_to_caption(predictions): | |
| boxes = predictions["instances"].pred_boxes if predictions["instances"].has("pred_boxes") else None | |
| object_description = predictions["instances"].pred_object_descriptions.data | |
| new_caption = "" | |
| for i in range(len(object_description)): | |
| new_caption += (object_description[i] + ": " + str([int(a) for a in boxes[i].tensor.cpu().detach().numpy()[0]])) + "; " | |
| return new_caption | |
| def dense_pred_to_caption_only_name(predictions): | |
| object_description = predictions["instances"].pred_object_descriptions.data | |
| new_caption = ",".join(object_description) | |
| del predictions | |
| return new_caption | |
| def dense_pred_to_caption_tuple(predictions): | |
| boxes = predictions["instances"].pred_boxes if predictions["instances"].has("pred_boxes") else None | |
| object_description = predictions["instances"].pred_object_descriptions.data | |
| object_type = predictions["instances"].det_obj.data | |
| new_caption = [] | |
| for i in range(len(object_description)): | |
| # new_caption += (object_description[i] + ": " + str([int(a) for a in boxes[i].tensor.cpu().detach().numpy()[0]])) + "; " | |
| new_caption.append((object_description[i], [int(a) for a in boxes[i].tensor.cpu().detach().numpy()[0]], object_type)) | |
| return new_caption | |
| def setup_cfg(args): | |
| cfg = get_cfg() | |
| if args["cpu"]: | |
| cfg.MODEL.DEVICE="cpu" | |
| add_centernet_config(cfg) | |
| add_grit_config(cfg) | |
| cfg.merge_from_file(args["config_file"]) | |
| cfg.merge_from_list(args["opts"]) | |
| # Set score_threshold for builtin models | |
| cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args["confidence_threshold"] | |
| cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args["confidence_threshold"] | |
| if args["test_task"]: | |
| cfg.MODEL.TEST_TASK = args["test_task"] | |
| cfg.MODEL.BEAM_SIZE = 1 | |
| cfg.MODEL.ROI_HEADS.SOFT_NMS_ENABLED = False | |
| cfg.USE_ACT_CHECKPOINT = False | |
| cfg.freeze() | |
| return cfg | |
| def get_parser(device, model_weight=f"{CACHE_DIR}/grit_model/grit_b_densecap_objectdet.pth"): | |
| arg_dict = {'config_file': f"{CUR_DIR}/configs/GRiT_B_DenseCap_ObjectDet.yaml", 'cpu': False, 'confidence_threshold': 0.5, 'test_task': 'DenseCap', 'opts': ["MODEL.WEIGHTS", model_weight]} | |
| if device.type == "cpu": | |
| arg_dict["cpu"] = True | |
| return arg_dict | |
| def image_caption_api(image_src, device, model_weight): | |
| args2 = get_parser(device, model_weight) | |
| cfg = setup_cfg(args2) | |
| demo = VisualizationDemo(cfg) | |
| if image_src: | |
| img = read_image(image_src, format="BGR") | |
| predictions, visualized_output = demo.run_on_image(img) | |
| new_caption = dense_pred_to_caption(predictions) | |
| return new_caption | |
| def init_demo(device, model_weight, task="DenseCap"): | |
| args2 = get_parser(device, model_weight) | |
| if task!="DenseCap": | |
| args2["test_task"]=task | |
| cfg = setup_cfg(args2) | |
| demo = VisualizationDemo(cfg) | |
| return demo | |