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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
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