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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import logging
import torch
from hydra import compose
from hydra.utils import instantiate
from omegaconf import OmegaConf
def build_sam2(
config_file,
ckpt_path=None,
device="cuda",
mode="eval",
hydra_overrides_extra=[],
apply_postprocessing=True,
):
if apply_postprocessing:
hydra_overrides_extra = hydra_overrides_extra.copy()
hydra_overrides_extra += [
# dynamically fall back to multi-mask if the single mask is not stable
"++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true",
"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05",
"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98",
]
# Read config and init model
cfg = compose(config_name=config_file, overrides=hydra_overrides_extra)
OmegaConf.resolve(cfg)
model = instantiate(cfg.model, _recursive_=True)
_load_checkpoint(model, ckpt_path)
model = model.to(device)
if mode == "eval":
model.eval()
return model
def build_sam2_video_predictor(
config_file,
ckpt_path=None,
device="cuda",
mode="eval",
hydra_overrides_extra=[],
apply_postprocessing=True,
):
hydra_overrides = [
"++model._target_=sam2.sam2_video_predictor.SAM2VideoPredictor",
]
if apply_postprocessing:
hydra_overrides_extra = hydra_overrides_extra.copy()
hydra_overrides_extra += [
# dynamically fall back to multi-mask if the single mask is not stable
"++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true",
"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05",
"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98",
# the sigmoid mask logits on interacted frames with clicks in the memory encoder so that the encoded masks are exactly as what users see from clicking
"++model.binarize_mask_from_pts_for_mem_enc=true",
# fill small holes in the low-res masks up to `fill_hole_area` (before resizing them to the original video resolution)
"++model.fill_hole_area=8",
]
hydra_overrides.extend(hydra_overrides_extra)
# Read config and init model
cfg = compose(config_name=config_file, overrides=hydra_overrides)
OmegaConf.resolve(cfg)
model = instantiate(cfg.model, _recursive_=True)
_load_checkpoint(model, ckpt_path)
model = model.to(device)
if mode == "eval":
model.eval()
return model
def _load_checkpoint(model, ckpt_path):
if ckpt_path is not None:
sd = torch.load(ckpt_path, map_location="cpu")["model"]
missing_keys, unexpected_keys = model.load_state_dict(sd)
if missing_keys:
logging.error(missing_keys)
raise RuntimeError()
if unexpected_keys:
logging.error(unexpected_keys)
raise RuntimeError()
logging.info("Loaded checkpoint sucessfully")
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