File size: 4,786 Bytes
b4bedeb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | from __future__ import annotations
from contextlib import nullcontext
from dataclasses import dataclass
from pathlib import Path
import numpy as np
import torch
from hydra import initialize_config_dir
from hydra.core.global_hydra import GlobalHydra
from detectivesam_inference.checkpoint import (
InferenceConfig,
load_inference_config,
resolve_checkpoint_path,
resolve_repo_path,
)
from detectivesam_inference.dataset import PreparedSample
from detectivesam_inference.models.forgerylocalizer import ForgeryLocalizer
@dataclass(frozen=True)
class PredictionResult:
probability: np.ndarray
pred_mask: np.ndarray
def get_repo_root() -> Path:
return Path(__file__).resolve().parent.parent
def select_device(device: str | None = None) -> torch.device:
if device is not None:
return torch.device(device)
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
def initialize_sam2_config(config_dir: str | Path) -> None:
config_dir = str(Path(config_dir).resolve())
hydra = GlobalHydra.instance()
current_dir = getattr(initialize_sam2_config, "_current_dir", None)
if hydra.is_initialized():
if current_dir == config_dir:
return
hydra.clear()
initialize_config_dir(config_dir=config_dir, version_base=None)
initialize_sam2_config._current_dir = config_dir
class DetectiveSAMRunner:
def __init__(
self,
checkpoint_path: str | Path | None = None,
device: str | None = None,
) -> None:
self.repo_root = get_repo_root()
self.checkpoint_path = resolve_checkpoint_path(checkpoint_path, self.repo_root)
self.device = select_device(device)
self.config = load_inference_config(self.checkpoint_path)
self.model = self._load_model()
def _load_model(self) -> ForgeryLocalizer:
sam_config_path = resolve_repo_path(self.config.sam_config_file, self.repo_root)
sam_checkpoint_path = resolve_repo_path(self.config.sam_checkpoint, self.repo_root)
initialize_sam2_config(sam_config_path.parent)
model = ForgeryLocalizer(
sam_config=sam_config_path.name,
sam_checkpoint=str(sam_checkpoint_path),
prompt_dim=self.config.prompt_dim,
dropout_rate=self.config.dropout_rate,
max_streams=self.config.max_streams,
device=str(self.device),
).to(self.device)
checkpoint = torch.load(self.checkpoint_path, map_location=self.device, weights_only=False)
state_dict = self._select_state_dict(checkpoint)
state_dict = self._clean_state_dict_prefixes(state_dict)
try:
model.load_state_dict(state_dict)
except RuntimeError:
self._load_compatible_state_dict(model, state_dict)
model.eval()
return model
@staticmethod
def _select_state_dict(checkpoint) -> dict[str, torch.Tensor]:
if isinstance(checkpoint, dict):
for key in ("model", "ema", "model_state_dict", "state_dict"):
if key in checkpoint:
return checkpoint[key]
return checkpoint
@staticmethod
def _clean_state_dict_prefixes(state_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
return {
key.removeprefix("module.").removeprefix("_orig_mod."): value
for key, value in state_dict.items()
}
@staticmethod
def _load_compatible_state_dict(
model: ForgeryLocalizer,
state_dict: dict[str, torch.Tensor],
) -> None:
current_state = model.state_dict()
compatible = {
key: value
for key, value in state_dict.items()
if key in current_state and current_state[key].shape == value.shape
}
if not compatible:
raise RuntimeError("Checkpoint did not contain any tensors compatible with the selected architecture.")
model.load_state_dict(compatible, strict=False)
def autocast_context(self):
if self.device.type == "cuda":
return torch.amp.autocast(device_type="cuda")
return nullcontext()
def predict_sample(
self,
sample: PreparedSample,
threshold: float = 0.5,
) -> PredictionResult:
orig = sample.orig.unsqueeze(0).to(self.device)
streams = [stream.unsqueeze(0).to(self.device) for stream in sample.streams]
with torch.inference_mode():
with self.autocast_context():
logits = self.model(orig, streams)
probability = torch.sigmoid(logits).squeeze().detach().cpu().numpy()
pred_mask = (probability > threshold).astype("uint8")
return PredictionResult(probability=probability, pred_mask=pred_mask)
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