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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") | |