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# ---------------------------------------------------------------------------------------------------
# CLIP-DINOiser
# authors: Monika Wysoczanska, Warsaw University of Technology
# ---------------------------------------------------------------------------------------------------
# modified from TCL
# Copyright (c) 2023 Kakao Brain. All Rights Reserved.
# ---------------------------------------------------------------------------------------------------
import mmcv
from mmseg.datasets import build_dataloader, build_dataset
from mmcv.utils import Registry
from mmcv.cnn import MODELS as MMCV_MODELS
MODELS = Registry('models', parent=MMCV_MODELS)
SEGMENTORS = MODELS
from .clip_dinoiser_eval import DinoCLIP_Infrencer
def build_seg_dataset(config):
"""Build a dataset from config."""
cfg = mmcv.Config.fromfile(config)
dataset = build_dataset(cfg.data.test)
return dataset
def build_seg_dataloader(dataset, dist=True):
# batch size is set to 1 to handle varying image size (due to different aspect ratio)
if dist:
data_loader = build_dataloader(
dataset,
samples_per_gpu=1,
workers_per_gpu=2,
dist=dist,
shuffle=False,
persistent_workers=True,
pin_memory=False,
)
else:
data_loader = build_dataloader(
dataset=dataset,
samples_per_gpu=1,
workers_per_gpu=2,
dist=dist,
shuffle=False,
persistent_workers=True,
pin_memory=False,
)
return data_loader
def build_seg_inference(
model,
dataset,
config,
seg_config,
):
dset_cfg = mmcv.Config.fromfile(seg_config) # dataset config
classnames = dataset.CLASSES
kwargs = dict()
if hasattr(dset_cfg, "test_cfg"):
kwargs["test_cfg"] = dset_cfg.test_cfg
seg_model = DinoCLIP_Infrencer(model, num_classes=len(classnames), **kwargs, **config.evaluate)
seg_model.CLASSES = dataset.CLASSES
seg_model.PALETTE = dataset.PALETTE
return seg_model
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