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6ed4a9c | 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 | # Copyright (c) OpenMMLab. All rights reserved.
from collections import defaultdict
from typing import Sequence, Union
import numpy as np
from mmengine.dataset import Compose
from mmengine.model import BaseModel
import torch
import cv2
ImageType = Union[str, np.ndarray, Sequence[str], Sequence[np.ndarray]]
def _preprare_data(imgs: ImageType, model: BaseModel):
cfg = model.cfg
for t in cfg.test_pipeline:
if t.get('type') == 'LoadAnnotations':
cfg.test_pipeline.remove(t)
is_batch = True
if not isinstance(imgs, (list, tuple)):
imgs = [imgs]
is_batch = False
if isinstance(imgs[0], np.ndarray):
cfg.test_pipeline[0]['type'] = 'LoadImageFromNDArray'
# TODO: Consider using the singleton pattern to avoid building
# a pipeline for each inference
pipeline = Compose(cfg.test_pipeline)
data = defaultdict(list)
for img in imgs:
if isinstance(img, np.ndarray):
data_ = dict(img=img)
else:
data_ = dict(img_path=img)
data_ = pipeline(data_)
data['inputs'].append(data_['inputs'])
data['data_samples'].append(data_['data_samples'])
# data['inputs'].append(data_['inputs'].to(dtype=torch.float16))
# data['data_samples'].append(data_['data_samples'].to(dtype=torch.float16))
return data, is_batch
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