local_segment/sam2/build_sam.py

# 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

HF_MODEL_ID_TO_FILENAMES = {
    "facebook/sam2-hiera-tiny": (
        "configs/sam2/sam2_hiera_t.yaml",
        "sam2_hiera_tiny.pt",
    ),
    "facebook/sam2-hiera-small": (
        "configs/sam2/sam2_hiera_s.yaml",
        "sam2_hiera_small.pt",
    ),
    "facebook/sam2-hiera-base-plus": (
        "configs/sam2/sam2_hiera_b+.yaml",
        "sam2_hiera_base_plus.pt",
    ),
    "facebook/sam2-hiera-large": (
        "configs/sam2/sam2_hiera_l.yaml",
        "sam2_hiera_large.pt",
    ),
    "facebook/sam2.1-hiera-tiny": (
        "configs/sam2.1/sam2.1_hiera_t.yaml",
        "sam2.1_hiera_tiny.pt",
    ),
    "facebook/sam2.1-hiera-small": (
        "configs/sam2.1/sam2.1_hiera_s.yaml",
        "sam2.1_hiera_small.pt",
    ),
    "facebook/sam2.1-hiera-base-plus": (
        "configs/sam2.1/sam2.1_hiera_b+.yaml",
        "sam2.1_hiera_base_plus.pt",
    ),
    "facebook/sam2.1-hiera-large": (
        "configs/sam2.1/sam2.1_hiera_l.yaml",
        "sam2.1_hiera_large.pt",
    ),
}


def build_sam2(
    config_file,
    ckpt_path=None,
    device="cuda",
    mode="eval",
    hydra_overrides_extra=[],
    apply_postprocessing=True,
    **kwargs,
):

    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,
    **kwargs,
):
    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 _hf_download(model_id):
    from huggingface_hub import hf_hub_download

    config_name, checkpoint_name = HF_MODEL_ID_TO_FILENAMES[model_id]
    ckpt_path = hf_hub_download(repo_id=model_id, filename=checkpoint_name)
    return config_name, ckpt_path


def build_sam2_hf(model_id, **kwargs):
    config_name, ckpt_path = _hf_download(model_id)
    return build_sam2(config_file=config_name, ckpt_path=ckpt_path, **kwargs)


def build_sam2_video_predictor_hf(model_id, **kwargs):
    config_name, ckpt_path = _hf_download(model_id)
    return build_sam2_video_predictor(
        config_file=config_name, ckpt_path=ckpt_path, **kwargs
    )


def _load_checkpoint(model, ckpt_path):
    if ckpt_path is not None:
        sd = torch.load(ckpt_path, map_location="cpu", weights_only=True)["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")