Spaces:
Sleeping
Sleeping
File size: 2,691 Bytes
aef7d7f 5107c4c aef7d7f f0542d9 aef7d7f 659a4c2 aef7d7f ab90404 641c00f ab90404 f795b75 ab90404 4f65fde ab90404 aef7d7f 5107c4c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
def get_set_up():
import torch
TORCH_VERSION = ".".join(torch.__version__.split(".")[:2])
CUDA_VERSION = torch.__version__.split("+")[-1]
print("torch: ", TORCH_VERSION, "; cuda: ", CUDA_VERSION)
print(f'GPU available: {torch.cuda.is_available()}')
print(torch.cuda.get_device_capability())
# print("detectron2:", detectron2.__version__)
def load_model():
# def predictor(img):
# return {}
# return predictor
# import some common detectron2 utilities
import torch
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.data.datasets import register_coco_instances
import os
import numpy as np
## define relevant parameters
cfg = get_cfg()
cfg.merge_from_file("./configs/test_model_config.yaml")
if not torch.cuda.is_available():
cfg.MODEL.DEVICE = "cpu"
else:
cfg.MODEL.DEVICE = 'cuda'
predictor = DefaultPredictor(cfg)
return predictor
def mask_nms(masks, scores, nms_threshold=0.5):
import supervision as sv
order = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)
keep = []
while order:
i = order.pop(0)
keep.append(i)
for j in order:
intersection = masks[i] * masks[j]
union = masks[i] + masks[j]
iou = intersection.sum() / union.sum()
# Remove masks with IoU greater than the threshold
if iou > nms_threshold:
order.remove(j)
return keep
def apply_nms(prediction, mask=False, cls_agnostic_nms=0.8):
from torchvision.ops import nms
from detectron2.structures import Instances
if mask:
# print("Applying mask NMS")
nms_indices = mask_nms(prediction["instances"].pred_masks.numpy(),
prediction["instances"]._fields["scores"], cls_agnostic_nms)
else:
# print("Applying box NMS")
nms_indices = nms(prediction["instances"].pred_boxes.tensor,
prediction["instances"].scores, cls_agnostic_nms)
pred = {"instances": Instances(image_size=prediction["instances"].image_size,
pred_boxes=prediction["instances"].pred_boxes[nms_indices],
scores=prediction["instances"].scores[nms_indices],
pred_classes=prediction["instances"].pred_classes[nms_indices],
pred_masks=prediction["instances"].pred_masks[nms_indices])}
return pred
if __name__ == '__main__':
# get_set_up()
load_model() |