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build_sam.py
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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 os
import torch
from hydra import compose
from hydra.utils import instantiate
from omegaconf import OmegaConf
import sam2
# Check if the user is running Python from the parent directory of the sam2 repo
# (i.e. the directory where this repo is cloned into) -- this is not supported since
# it could shadow the sam2 package and cause issues.
if os.path.isdir(os.path.join(sam2.__path__[0], "sam2")):
# If the user has "sam2/sam2" in their path, they are likey importing the repo itself
# as "sam2" rather than importing the "sam2" python package (i.e. "sam2/sam2" directory).
# This typically happens because the user is running Python from the parent directory
# that contains the sam2 repo they cloned.
raise RuntimeError(
"You're likely running Python from the parent directory of the sam2 repository "
"(i.e. the directory where https://github.com/facebookresearch/sam2 is cloned into). "
"This is not supported since the `sam2` Python package could be shadowed by the "
"repository name (the repository is also named `sam2` and contains the Python package "
"in `sam2/sam2`). Please run Python from another directory (e.g. from the repo dir "
"rather than its parent dir, or from your home directory) after installing SAM 2."
)
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)
missing_keys, unexpected_keys =_load_checkpoint(model, ckpt_path)
model = model.to(device)
if mode == "eval":
model.eval()
return model
def build_sam2_for_self_train(
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)
missing_keys, unexpected_keys =_load_checkpoint(model, ckpt_path)
model = model.to(device)
if mode == "eval":
model.eval()
return model, missing_keys, unexpected_keys
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 我觉得这里非常重要，先加载配置，然后load_checkpoint
cfg = compose(config_name=config_file, overrides=hydra_overrides)
OmegaConf.resolve(cfg)
model = instantiate(cfg.model, _recursive_=True)
missing_keys, unexpected_keys = _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")
if isinstance(sd, dict):
# 尝试从字典中获取 key 'model'
if "model" in sd:
sd = sd["model"]
else:
logging.warning("Checkpoint is a dictionary but does not contain 'model' key.")
else:
logging.warning("Checkpoint is not a dictionary, assuming it's directly the model's state_dict.")
# Load the state_dict with strict=False to ignore missing/unexpected keys
missing_keys, unexpected_keys = model.load_state_dict(sd, strict=False)
# Log missing and unexpected keys if any
if missing_keys:
logging.warning(f"Missing keys in state_dict: {missing_keys}")
if unexpected_keys:
logging.warning(f"Unexpected keys in state_dict: {unexpected_keys}")
logging.info("Loaded checkpoint sucessfully")
return missing_keys, unexpected_keys