diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..3f7bba7c88eb4e774ad0f2f4a3ea7602b24029b3 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text
+build/lib.linux-x86_64-cpython-310/sam2/_C.so filter=lfs diff=lfs merge=lfs -text
+build/temp.linux-x86_64-cpython-310/sam2/csrc/connected_components.o filter=lfs diff=lfs merge=lfs -text
+sam2/_C.so filter=lfs diff=lfs merge=lfs -text
diff --git a/assets/model_diagram.png b/assets/model_diagram.png
new file mode 100644
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diff --git a/assets/sa_v_dataset.jpg b/assets/sa_v_dataset.jpg
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new file mode 100644
index 0000000000000000000000000000000000000000..7b8bf46fd13a73ec08d690c65258b3a33fe2d0e2
--- /dev/null
+++ b/build/lib.linux-x86_64-cpython-310/sam2/_C.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67c0d5588c99e7a7d44c2325a98877c585934c8a1e8cd35be793a6ee266f235a
+size 1873536
diff --git a/build/temp.linux-x86_64-cpython-310/build.ninja b/build/temp.linux-x86_64-cpython-310/build.ninja
new file mode 100644
index 0000000000000000000000000000000000000000..315ded51fda5f5ff489b3351507db85d5b35d7ff
--- /dev/null
+++ b/build/temp.linux-x86_64-cpython-310/build.ninja
@@ -0,0 +1,32 @@
+ninja_required_version = 1.3
+cxx = /mnt/petrelfs/share/gcc/gcc-10.2.0/bin/c++
+nvcc = /mnt/petrelfs/share/cuda-11.8/bin/nvcc
+
+cflags = -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /mnt/cache/dingshuangrui/anaconda3/envs/sam/include -fPIC -O2 -isystem /mnt/cache/dingshuangrui/anaconda3/envs/sam/include -fPIC -I/mnt/cache/dingshuangrui/anaconda3/envs/sam/lib/python3.10/site-packages/torch/include -I/mnt/cache/dingshuangrui/anaconda3/envs/sam/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/mnt/cache/dingshuangrui/anaconda3/envs/sam/lib/python3.10/site-packages/torch/include/TH -I/mnt/cache/dingshuangrui/anaconda3/envs/sam/lib/python3.10/site-packages/torch/include/THC -I/mnt/petrelfs/share/cuda-11.8/include -I/mnt/cache/dingshuangrui/anaconda3/envs/sam/include/python3.10 -c
+post_cflags = -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++17
+cuda_cflags = -I/mnt/cache/dingshuangrui/anaconda3/envs/sam/lib/python3.10/site-packages/torch/include -I/mnt/cache/dingshuangrui/anaconda3/envs/sam/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/mnt/cache/dingshuangrui/anaconda3/envs/sam/lib/python3.10/site-packages/torch/include/TH -I/mnt/cache/dingshuangrui/anaconda3/envs/sam/lib/python3.10/site-packages/torch/include/THC -I/mnt/petrelfs/share/cuda-11.8/include -I/mnt/cache/dingshuangrui/anaconda3/envs/sam/include/python3.10 -c
+cuda_post_cflags = -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_80,code=compute_80 -gencode=arch=compute_80,code=sm_80 -ccbin /mnt/petrelfs/share/gcc/gcc-10.2.0/bin/gcc -std=c++17
+cuda_dlink_post_cflags =
+ldflags =
+
+rule compile
+ command = $cxx -MMD -MF $out.d $cflags -c $in -o $out $post_cflags
+ depfile = $out.d
+ deps = gcc
+
+rule cuda_compile
+ depfile = $out.d
+ deps = gcc
+ command = $nvcc --generate-dependencies-with-compile --dependency-output $out.d $cuda_cflags -c $in -o $out $cuda_post_cflags
+
+
+
+
+
+build /mnt/petrelfs/dingshuangrui/SAM2-Video-Predictor/build/temp.linux-x86_64-cpython-310/sam2/csrc/connected_components.o: cuda_compile /mnt/petrelfs/dingshuangrui/SAM2-Video-Predictor/sam2/csrc/connected_components.cu
+
+
+
+
+
+
diff --git a/build/temp.linux-x86_64-cpython-310/sam2/csrc/connected_components.o b/build/temp.linux-x86_64-cpython-310/sam2/csrc/connected_components.o
new file mode 100644
index 0000000000000000000000000000000000000000..e8be5e81724ad2edf3a45691afa45787323dd68c
--- /dev/null
+++ b/build/temp.linux-x86_64-cpython-310/sam2/csrc/connected_components.o
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ae64fe80f05eca117159083e8ab58fbdd187d8414578c7a99257fda7a5a123e
+size 2734904
diff --git a/checkpoints/sam2.1_hiera_base_plus.pt b/checkpoints/sam2.1_hiera_base_plus.pt
new file mode 100644
index 0000000000000000000000000000000000000000..1466bad0cd2125f1be02ffdc1cf98718592636b4
--- /dev/null
+++ b/checkpoints/sam2.1_hiera_base_plus.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a2345aede8715ab1d5d31b4a509fb160c5a4af1970f199d9054ccfb746c004c5
+size 323606802
diff --git a/checkpoints/sam2.1_hiera_small.pt b/checkpoints/sam2.1_hiera_small.pt
new file mode 100644
index 0000000000000000000000000000000000000000..eb980035dd665a3f45871f79ee945634915bc31e
--- /dev/null
+++ b/checkpoints/sam2.1_hiera_small.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d1aa6f30de5c92224f8172114de081d104bbd23dd9dc5c58996f0cad5dc4d38
+size 184416285
diff --git a/checkpoints/sam2.1_hiera_tiny.pt b/checkpoints/sam2.1_hiera_tiny.pt
new file mode 100644
index 0000000000000000000000000000000000000000..56f8d31a0ff55de24d5f30d11ce0cc19a5e5d10b
--- /dev/null
+++ b/checkpoints/sam2.1_hiera_tiny.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7402e0d864fa82708a20fbd15bc84245c2f26dff0eb43a4b5b93452deb34be69
+size 156008466
diff --git a/sam2/_C.so b/sam2/_C.so
new file mode 100644
index 0000000000000000000000000000000000000000..7b8bf46fd13a73ec08d690c65258b3a33fe2d0e2
--- /dev/null
+++ b/sam2/_C.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67c0d5588c99e7a7d44c2325a98877c585934c8a1e8cd35be793a6ee266f235a
+size 1873536
diff --git a/sam2/__init__.py b/sam2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0712dd03cb280ab94ba04f8a32aa8ddc8aa3db4a
--- /dev/null
+++ b/sam2/__init__.py
@@ -0,0 +1,11 @@
+# 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.
+
+from hydra import initialize_config_module
+from hydra.core.global_hydra import GlobalHydra
+
+if not GlobalHydra.instance().is_initialized():
+ initialize_config_module("sam2", version_base="1.2")
diff --git a/sam2/__pycache__/__init__.cpython-310.pyc b/sam2/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f8b054417a62d0bb79ff7b2e0237250aca32f70a
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diff --git a/sam2/__pycache__/build_sam.cpython-310.pyc b/sam2/__pycache__/build_sam.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5151084c7c66cd1aa115b1e28bb99a551d1749fd
Binary files /dev/null and b/sam2/__pycache__/build_sam.cpython-310.pyc differ
diff --git a/sam2/__pycache__/sam2_image_predictor.cpython-310.pyc b/sam2/__pycache__/sam2_image_predictor.cpython-310.pyc
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index 0000000000000000000000000000000000000000..1696e4abfa6e8f710070c8cca952249528178575
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diff --git a/sam2/__pycache__/sam2_video_predictor.cpython-310.pyc b/sam2/__pycache__/sam2_video_predictor.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..98064a1f73fd19c762c5f4804a988cf0c2d35957
Binary files /dev/null and b/sam2/__pycache__/sam2_video_predictor.cpython-310.pyc differ
diff --git a/sam2/automatic_mask_generator.py b/sam2/automatic_mask_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..065e469e27c2d3af40d51d072031e828692c799b
--- /dev/null
+++ b/sam2/automatic_mask_generator.py
@@ -0,0 +1,454 @@
+# 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.
+
+# Adapted from https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/automatic_mask_generator.py
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+import torch
+from torchvision.ops.boxes import batched_nms, box_area # type: ignore
+
+from sam2.modeling.sam2_base import SAM2Base
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+from sam2.utils.amg import (
+ area_from_rle,
+ batch_iterator,
+ batched_mask_to_box,
+ box_xyxy_to_xywh,
+ build_all_layer_point_grids,
+ calculate_stability_score,
+ coco_encode_rle,
+ generate_crop_boxes,
+ is_box_near_crop_edge,
+ mask_to_rle_pytorch,
+ MaskData,
+ remove_small_regions,
+ rle_to_mask,
+ uncrop_boxes_xyxy,
+ uncrop_masks,
+ uncrop_points,
+)
+
+
+class SAM2AutomaticMaskGenerator:
+ def __init__(
+ self,
+ model: SAM2Base,
+ points_per_side: Optional[int] = 32,
+ points_per_batch: int = 64,
+ pred_iou_thresh: float = 0.8,
+ stability_score_thresh: float = 0.95,
+ stability_score_offset: float = 1.0,
+ mask_threshold: float = 0.0,
+ box_nms_thresh: float = 0.7,
+ crop_n_layers: int = 0,
+ crop_nms_thresh: float = 0.7,
+ crop_overlap_ratio: float = 512 / 1500,
+ crop_n_points_downscale_factor: int = 1,
+ point_grids: Optional[List[np.ndarray]] = None,
+ min_mask_region_area: int = 0,
+ output_mode: str = "binary_mask",
+ use_m2m: bool = False,
+ multimask_output: bool = True,
+ **kwargs,
+ ) -> None:
+ """
+ Using a SAM 2 model, generates masks for the entire image.
+ Generates a grid of point prompts over the image, then filters
+ low quality and duplicate masks. The default settings are chosen
+ for SAM 2 with a HieraL backbone.
+
+ Arguments:
+ model (Sam): The SAM 2 model to use for mask prediction.
+ points_per_side (int or None): The number of points to be sampled
+ along one side of the image. The total number of points is
+ points_per_side**2. If None, 'point_grids' must provide explicit
+ point sampling.
+ points_per_batch (int): Sets the number of points run simultaneously
+ by the model. Higher numbers may be faster but use more GPU memory.
+ pred_iou_thresh (float): A filtering threshold in [0,1], using the
+ model's predicted mask quality.
+ stability_score_thresh (float): A filtering threshold in [0,1], using
+ the stability of the mask under changes to the cutoff used to binarize
+ the model's mask predictions.
+ stability_score_offset (float): The amount to shift the cutoff when
+ calculated the stability score.
+ mask_threshold (float): Threshold for binarizing the mask logits
+ box_nms_thresh (float): The box IoU cutoff used by non-maximal
+ suppression to filter duplicate masks.
+ crop_n_layers (int): If >0, mask prediction will be run again on
+ crops of the image. Sets the number of layers to run, where each
+ layer has 2**i_layer number of image crops.
+ crop_nms_thresh (float): The box IoU cutoff used by non-maximal
+ suppression to filter duplicate masks between different crops.
+ crop_overlap_ratio (float): Sets the degree to which crops overlap.
+ In the first crop layer, crops will overlap by this fraction of
+ the image length. Later layers with more crops scale down this overlap.
+ crop_n_points_downscale_factor (int): The number of points-per-side
+ sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
+ point_grids (list(np.ndarray) or None): A list over explicit grids
+ of points used for sampling, normalized to [0,1]. The nth grid in the
+ list is used in the nth crop layer. Exclusive with points_per_side.
+ min_mask_region_area (int): If >0, postprocessing will be applied
+ to remove disconnected regions and holes in masks with area smaller
+ than min_mask_region_area. Requires opencv.
+ output_mode (str): The form masks are returned in. Can be 'binary_mask',
+ 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
+ For large resolutions, 'binary_mask' may consume large amounts of
+ memory.
+ use_m2m (bool): Whether to add a one step refinement using previous mask predictions.
+ multimask_output (bool): Whether to output multimask at each point of the grid.
+ """
+
+ assert (points_per_side is None) != (
+ point_grids is None
+ ), "Exactly one of points_per_side or point_grid must be provided."
+ if points_per_side is not None:
+ self.point_grids = build_all_layer_point_grids(
+ points_per_side,
+ crop_n_layers,
+ crop_n_points_downscale_factor,
+ )
+ elif point_grids is not None:
+ self.point_grids = point_grids
+ else:
+ raise ValueError("Can't have both points_per_side and point_grid be None.")
+
+ assert output_mode in [
+ "binary_mask",
+ "uncompressed_rle",
+ "coco_rle",
+ ], f"Unknown output_mode {output_mode}."
+ if output_mode == "coco_rle":
+ try:
+ from pycocotools import mask as mask_utils # type: ignore # noqa: F401
+ except ImportError as e:
+ print("Please install pycocotools")
+ raise e
+
+ self.predictor = SAM2ImagePredictor(
+ model,
+ max_hole_area=min_mask_region_area,
+ max_sprinkle_area=min_mask_region_area,
+ )
+ self.points_per_batch = points_per_batch
+ self.pred_iou_thresh = pred_iou_thresh
+ self.stability_score_thresh = stability_score_thresh
+ self.stability_score_offset = stability_score_offset
+ self.mask_threshold = mask_threshold
+ self.box_nms_thresh = box_nms_thresh
+ self.crop_n_layers = crop_n_layers
+ self.crop_nms_thresh = crop_nms_thresh
+ self.crop_overlap_ratio = crop_overlap_ratio
+ self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
+ self.min_mask_region_area = min_mask_region_area
+ self.output_mode = output_mode
+ self.use_m2m = use_m2m
+ self.multimask_output = multimask_output
+
+ @classmethod
+ def from_pretrained(cls, model_id: str, **kwargs) -> "SAM2AutomaticMaskGenerator":
+ """
+ Load a pretrained model from the Hugging Face hub.
+
+ Arguments:
+ model_id (str): The Hugging Face repository ID.
+ **kwargs: Additional arguments to pass to the model constructor.
+
+ Returns:
+ (SAM2AutomaticMaskGenerator): The loaded model.
+ """
+ from sam2.build_sam import build_sam2_hf
+
+ sam_model = build_sam2_hf(model_id, **kwargs)
+ return cls(sam_model, **kwargs)
+
+ @torch.no_grad()
+ def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
+ """
+ Generates masks for the given image.
+
+ Arguments:
+ image (np.ndarray): The image to generate masks for, in HWC uint8 format.
+
+ Returns:
+ list(dict(str, any)): A list over records for masks. Each record is
+ a dict containing the following keys:
+ segmentation (dict(str, any) or np.ndarray): The mask. If
+ output_mode='binary_mask', is an array of shape HW. Otherwise,
+ is a dictionary containing the RLE.
+ bbox (list(float)): The box around the mask, in XYWH format.
+ area (int): The area in pixels of the mask.
+ predicted_iou (float): The model's own prediction of the mask's
+ quality. This is filtered by the pred_iou_thresh parameter.
+ point_coords (list(list(float))): The point coordinates input
+ to the model to generate this mask.
+ stability_score (float): A measure of the mask's quality. This
+ is filtered on using the stability_score_thresh parameter.
+ crop_box (list(float)): The crop of the image used to generate
+ the mask, given in XYWH format.
+ """
+
+ # Generate masks
+ mask_data = self._generate_masks(image)
+
+ # Encode masks
+ if self.output_mode == "coco_rle":
+ mask_data["segmentations"] = [
+ coco_encode_rle(rle) for rle in mask_data["rles"]
+ ]
+ elif self.output_mode == "binary_mask":
+ mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
+ else:
+ mask_data["segmentations"] = mask_data["rles"]
+
+ # Write mask records
+ curr_anns = []
+ for idx in range(len(mask_data["segmentations"])):
+ ann = {
+ "segmentation": mask_data["segmentations"][idx],
+ "area": area_from_rle(mask_data["rles"][idx]),
+ "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
+ "predicted_iou": mask_data["iou_preds"][idx].item(),
+ "point_coords": [mask_data["points"][idx].tolist()],
+ "stability_score": mask_data["stability_score"][idx].item(),
+ "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
+ }
+ curr_anns.append(ann)
+
+ return curr_anns
+
+ def _generate_masks(self, image: np.ndarray) -> MaskData:
+ orig_size = image.shape[:2]
+ crop_boxes, layer_idxs = generate_crop_boxes(
+ orig_size, self.crop_n_layers, self.crop_overlap_ratio
+ )
+
+ # Iterate over image crops
+ data = MaskData()
+ for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
+ crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
+ data.cat(crop_data)
+
+ # Remove duplicate masks between crops
+ if len(crop_boxes) > 1:
+ # Prefer masks from smaller crops
+ scores = 1 / box_area(data["crop_boxes"])
+ scores = scores.to(data["boxes"].device)
+ keep_by_nms = batched_nms(
+ data["boxes"].float(),
+ scores,
+ torch.zeros_like(data["boxes"][:, 0]), # categories
+ iou_threshold=self.crop_nms_thresh,
+ )
+ data.filter(keep_by_nms)
+ data.to_numpy()
+ return data
+
+ def _process_crop(
+ self,
+ image: np.ndarray,
+ crop_box: List[int],
+ crop_layer_idx: int,
+ orig_size: Tuple[int, ...],
+ ) -> MaskData:
+ # Crop the image and calculate embeddings
+ x0, y0, x1, y1 = crop_box
+ cropped_im = image[y0:y1, x0:x1, :]
+ cropped_im_size = cropped_im.shape[:2]
+ self.predictor.set_image(cropped_im)
+
+ # Get points for this crop
+ points_scale = np.array(cropped_im_size)[None, ::-1]
+ points_for_image = self.point_grids[crop_layer_idx] * points_scale
+
+ # Generate masks for this crop in batches
+ data = MaskData()
+ for (points,) in batch_iterator(self.points_per_batch, points_for_image):
+ batch_data = self._process_batch(
+ points, cropped_im_size, crop_box, orig_size, normalize=True
+ )
+ data.cat(batch_data)
+ del batch_data
+ self.predictor.reset_predictor()
+
+ # Remove duplicates within this crop.
+ keep_by_nms = batched_nms(
+ data["boxes"].float(),
+ data["iou_preds"],
+ torch.zeros_like(data["boxes"][:, 0]), # categories
+ iou_threshold=self.box_nms_thresh,
+ )
+ data.filter(keep_by_nms)
+
+ # Return to the original image frame
+ data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
+ data["points"] = uncrop_points(data["points"], crop_box)
+ data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
+
+ return data
+
+ def _process_batch(
+ self,
+ points: np.ndarray,
+ im_size: Tuple[int, ...],
+ crop_box: List[int],
+ orig_size: Tuple[int, ...],
+ normalize=False,
+ ) -> MaskData:
+ orig_h, orig_w = orig_size
+
+ # Run model on this batch
+ points = torch.as_tensor(
+ points, dtype=torch.float32, device=self.predictor.device
+ )
+ in_points = self.predictor._transforms.transform_coords(
+ points, normalize=normalize, orig_hw=im_size
+ )
+ in_labels = torch.ones(
+ in_points.shape[0], dtype=torch.int, device=in_points.device
+ )
+ masks, iou_preds, low_res_masks = self.predictor._predict(
+ in_points[:, None, :],
+ in_labels[:, None],
+ multimask_output=self.multimask_output,
+ return_logits=True,
+ )
+
+ # Serialize predictions and store in MaskData
+ data = MaskData(
+ masks=masks.flatten(0, 1),
+ iou_preds=iou_preds.flatten(0, 1),
+ points=points.repeat_interleave(masks.shape[1], dim=0),
+ low_res_masks=low_res_masks.flatten(0, 1),
+ )
+ del masks
+
+ if not self.use_m2m:
+ # Filter by predicted IoU
+ if self.pred_iou_thresh > 0.0:
+ keep_mask = data["iou_preds"] > self.pred_iou_thresh
+ data.filter(keep_mask)
+
+ # Calculate and filter by stability score
+ data["stability_score"] = calculate_stability_score(
+ data["masks"], self.mask_threshold, self.stability_score_offset
+ )
+ if self.stability_score_thresh > 0.0:
+ keep_mask = data["stability_score"] >= self.stability_score_thresh
+ data.filter(keep_mask)
+ else:
+ # One step refinement using previous mask predictions
+ in_points = self.predictor._transforms.transform_coords(
+ data["points"], normalize=normalize, orig_hw=im_size
+ )
+ labels = torch.ones(
+ in_points.shape[0], dtype=torch.int, device=in_points.device
+ )
+ masks, ious = self.refine_with_m2m(
+ in_points, labels, data["low_res_masks"], self.points_per_batch
+ )
+ data["masks"] = masks.squeeze(1)
+ data["iou_preds"] = ious.squeeze(1)
+
+ if self.pred_iou_thresh > 0.0:
+ keep_mask = data["iou_preds"] > self.pred_iou_thresh
+ data.filter(keep_mask)
+
+ data["stability_score"] = calculate_stability_score(
+ data["masks"], self.mask_threshold, self.stability_score_offset
+ )
+ if self.stability_score_thresh > 0.0:
+ keep_mask = data["stability_score"] >= self.stability_score_thresh
+ data.filter(keep_mask)
+
+ # Threshold masks and calculate boxes
+ data["masks"] = data["masks"] > self.mask_threshold
+ data["boxes"] = batched_mask_to_box(data["masks"])
+
+ # Filter boxes that touch crop boundaries
+ keep_mask = ~is_box_near_crop_edge(
+ data["boxes"], crop_box, [0, 0, orig_w, orig_h]
+ )
+ if not torch.all(keep_mask):
+ data.filter(keep_mask)
+
+ # Compress to RLE
+ data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
+ data["rles"] = mask_to_rle_pytorch(data["masks"])
+ del data["masks"]
+
+ return data
+
+ @staticmethod
+ def postprocess_small_regions(
+ mask_data: MaskData, min_area: int, nms_thresh: float
+ ) -> MaskData:
+ """
+ Removes small disconnected regions and holes in masks, then reruns
+ box NMS to remove any new duplicates.
+
+ Edits mask_data in place.
+
+ Requires open-cv as a dependency.
+ """
+ if len(mask_data["rles"]) == 0:
+ return mask_data
+
+ # Filter small disconnected regions and holes
+ new_masks = []
+ scores = []
+ for rle in mask_data["rles"]:
+ mask = rle_to_mask(rle)
+
+ mask, changed = remove_small_regions(mask, min_area, mode="holes")
+ unchanged = not changed
+ mask, changed = remove_small_regions(mask, min_area, mode="islands")
+ unchanged = unchanged and not changed
+
+ new_masks.append(torch.as_tensor(mask).unsqueeze(0))
+ # Give score=0 to changed masks and score=1 to unchanged masks
+ # so NMS will prefer ones that didn't need postprocessing
+ scores.append(float(unchanged))
+
+ # Recalculate boxes and remove any new duplicates
+ masks = torch.cat(new_masks, dim=0)
+ boxes = batched_mask_to_box(masks)
+ keep_by_nms = batched_nms(
+ boxes.float(),
+ torch.as_tensor(scores),
+ torch.zeros_like(boxes[:, 0]), # categories
+ iou_threshold=nms_thresh,
+ )
+
+ # Only recalculate RLEs for masks that have changed
+ for i_mask in keep_by_nms:
+ if scores[i_mask] == 0.0:
+ mask_torch = masks[i_mask].unsqueeze(0)
+ mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
+ mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly
+ mask_data.filter(keep_by_nms)
+
+ return mask_data
+
+ def refine_with_m2m(self, points, point_labels, low_res_masks, points_per_batch):
+ new_masks = []
+ new_iou_preds = []
+
+ for cur_points, cur_point_labels, low_res_mask in batch_iterator(
+ points_per_batch, points, point_labels, low_res_masks
+ ):
+ best_masks, best_iou_preds, _ = self.predictor._predict(
+ cur_points[:, None, :],
+ cur_point_labels[:, None],
+ mask_input=low_res_mask[:, None, :],
+ multimask_output=False,
+ return_logits=True,
+ )
+ new_masks.append(best_masks)
+ new_iou_preds.append(best_iou_preds)
+ masks = torch.cat(new_masks, dim=0)
+ return masks, torch.cat(new_iou_preds, dim=0)
diff --git a/sam2/build_sam.py b/sam2/build_sam.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cfc451395792350eabf17bbb466e45e3f4a8d49
--- /dev/null
+++ b/sam2/build_sam.py
@@ -0,0 +1,167 @@
+# 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)
+ _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")
diff --git a/sam2/configs/sam2.1/sam2.1_hiera_b+.yaml b/sam2/configs/sam2.1/sam2.1_hiera_b+.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cbee3cf9b3977ebe4cc868797a9bfa9e348cb3a3
--- /dev/null
+++ b/sam2/configs/sam2.1/sam2.1_hiera_b+.yaml
@@ -0,0 +1,116 @@
+# @package _global_
+
+# Model
+model:
+ _target_: sam2.modeling.sam2_base.SAM2Base
+ image_encoder:
+ _target_: sam2.modeling.backbones.image_encoder.ImageEncoder
+ scalp: 1
+ trunk:
+ _target_: sam2.modeling.backbones.hieradet.Hiera
+ embed_dim: 112
+ num_heads: 2
+ neck:
+ _target_: sam2.modeling.backbones.image_encoder.FpnNeck
+ position_encoding:
+ _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
+ num_pos_feats: 256
+ normalize: true
+ scale: null
+ temperature: 10000
+ d_model: 256
+ backbone_channel_list: [896, 448, 224, 112]
+ fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features
+ fpn_interp_model: nearest
+
+ memory_attention:
+ _target_: sam2.modeling.memory_attention.MemoryAttention
+ d_model: 256
+ pos_enc_at_input: true
+ layer:
+ _target_: sam2.modeling.memory_attention.MemoryAttentionLayer
+ activation: relu
+ dim_feedforward: 2048
+ dropout: 0.1
+ pos_enc_at_attn: false
+ self_attention:
+ _target_: sam2.modeling.sam.transformer.RoPEAttention
+ rope_theta: 10000.0
+ feat_sizes: [32, 32]
+ embedding_dim: 256
+ num_heads: 1
+ downsample_rate: 1
+ dropout: 0.1
+ d_model: 256
+ pos_enc_at_cross_attn_keys: true
+ pos_enc_at_cross_attn_queries: false
+ cross_attention:
+ _target_: sam2.modeling.sam.transformer.RoPEAttention
+ rope_theta: 10000.0
+ feat_sizes: [32, 32]
+ rope_k_repeat: True
+ embedding_dim: 256
+ num_heads: 1
+ downsample_rate: 1
+ dropout: 0.1
+ kv_in_dim: 64
+ num_layers: 4
+
+ memory_encoder:
+ _target_: sam2.modeling.memory_encoder.MemoryEncoder
+ out_dim: 64
+ position_encoding:
+ _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
+ num_pos_feats: 64
+ normalize: true
+ scale: null
+ temperature: 10000
+ mask_downsampler:
+ _target_: sam2.modeling.memory_encoder.MaskDownSampler
+ kernel_size: 3
+ stride: 2
+ padding: 1
+ fuser:
+ _target_: sam2.modeling.memory_encoder.Fuser
+ layer:
+ _target_: sam2.modeling.memory_encoder.CXBlock
+ dim: 256
+ kernel_size: 7
+ padding: 3
+ layer_scale_init_value: 1e-6
+ use_dwconv: True # depth-wise convs
+ num_layers: 2
+
+ num_maskmem: 7
+ image_size: 1024
+ # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+ sigmoid_scale_for_mem_enc: 20.0
+ sigmoid_bias_for_mem_enc: -10.0
+ use_mask_input_as_output_without_sam: true
+ # Memory
+ directly_add_no_mem_embed: true
+ no_obj_embed_spatial: true
+ # use high-resolution feature map in the SAM mask decoder
+ use_high_res_features_in_sam: true
+ # output 3 masks on the first click on initial conditioning frames
+ multimask_output_in_sam: true
+ # SAM heads
+ iou_prediction_use_sigmoid: True
+ # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+ use_obj_ptrs_in_encoder: true
+ add_tpos_enc_to_obj_ptrs: true
+ proj_tpos_enc_in_obj_ptrs: true
+ use_signed_tpos_enc_to_obj_ptrs: true
+ only_obj_ptrs_in_the_past_for_eval: true
+ # object occlusion prediction
+ pred_obj_scores: true
+ pred_obj_scores_mlp: true
+ fixed_no_obj_ptr: true
+ # multimask tracking settings
+ multimask_output_for_tracking: true
+ use_multimask_token_for_obj_ptr: true
+ multimask_min_pt_num: 0
+ multimask_max_pt_num: 1
+ use_mlp_for_obj_ptr_proj: true
+ # Compilation flag
+ compile_image_encoder: False
diff --git a/sam2/configs/sam2.1/sam2.1_hiera_l.yaml b/sam2/configs/sam2.1/sam2.1_hiera_l.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..33c9097f34ea90beae52776eb88ad8eb1632ab66
--- /dev/null
+++ b/sam2/configs/sam2.1/sam2.1_hiera_l.yaml
@@ -0,0 +1,120 @@
+# @package _global_
+
+# Model
+model:
+ _target_: sam2.modeling.sam2_base.SAM2Base
+ image_encoder:
+ _target_: sam2.modeling.backbones.image_encoder.ImageEncoder
+ scalp: 1
+ trunk:
+ _target_: sam2.modeling.backbones.hieradet.Hiera
+ embed_dim: 144
+ num_heads: 2
+ stages: [2, 6, 36, 4]
+ global_att_blocks: [23, 33, 43]
+ window_pos_embed_bkg_spatial_size: [7, 7]
+ window_spec: [8, 4, 16, 8]
+ neck:
+ _target_: sam2.modeling.backbones.image_encoder.FpnNeck
+ position_encoding:
+ _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
+ num_pos_feats: 256
+ normalize: true
+ scale: null
+ temperature: 10000
+ d_model: 256
+ backbone_channel_list: [1152, 576, 288, 144]
+ fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features
+ fpn_interp_model: nearest
+
+ memory_attention:
+ _target_: sam2.modeling.memory_attention.MemoryAttention
+ d_model: 256
+ pos_enc_at_input: true
+ layer:
+ _target_: sam2.modeling.memory_attention.MemoryAttentionLayer
+ activation: relu
+ dim_feedforward: 2048
+ dropout: 0.1
+ pos_enc_at_attn: false
+ self_attention:
+ _target_: sam2.modeling.sam.transformer.RoPEAttention
+ rope_theta: 10000.0
+ feat_sizes: [32, 32]
+ embedding_dim: 256
+ num_heads: 1
+ downsample_rate: 1
+ dropout: 0.1
+ d_model: 256
+ pos_enc_at_cross_attn_keys: true
+ pos_enc_at_cross_attn_queries: false
+ cross_attention:
+ _target_: sam2.modeling.sam.transformer.RoPEAttention
+ rope_theta: 10000.0
+ feat_sizes: [32, 32]
+ rope_k_repeat: True
+ embedding_dim: 256
+ num_heads: 1
+ downsample_rate: 1
+ dropout: 0.1
+ kv_in_dim: 64
+ num_layers: 4
+
+ memory_encoder:
+ _target_: sam2.modeling.memory_encoder.MemoryEncoder
+ out_dim: 64
+ position_encoding:
+ _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
+ num_pos_feats: 64
+ normalize: true
+ scale: null
+ temperature: 10000
+ mask_downsampler:
+ _target_: sam2.modeling.memory_encoder.MaskDownSampler
+ kernel_size: 3
+ stride: 2
+ padding: 1
+ fuser:
+ _target_: sam2.modeling.memory_encoder.Fuser
+ layer:
+ _target_: sam2.modeling.memory_encoder.CXBlock
+ dim: 256
+ kernel_size: 7
+ padding: 3
+ layer_scale_init_value: 1e-6
+ use_dwconv: True # depth-wise convs
+ num_layers: 2
+
+ num_maskmem: 7
+ image_size: 1024
+ # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+ sigmoid_scale_for_mem_enc: 20.0
+ sigmoid_bias_for_mem_enc: -10.0
+ use_mask_input_as_output_without_sam: true
+ # Memory
+ directly_add_no_mem_embed: true
+ no_obj_embed_spatial: true
+ # use high-resolution feature map in the SAM mask decoder
+ use_high_res_features_in_sam: true
+ # output 3 masks on the first click on initial conditioning frames
+ multimask_output_in_sam: true
+ # SAM heads
+ iou_prediction_use_sigmoid: True
+ # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+ use_obj_ptrs_in_encoder: true
+ add_tpos_enc_to_obj_ptrs: true
+ proj_tpos_enc_in_obj_ptrs: true
+ use_signed_tpos_enc_to_obj_ptrs: true
+ only_obj_ptrs_in_the_past_for_eval: true
+ # object occlusion prediction
+ pred_obj_scores: true
+ pred_obj_scores_mlp: true
+ fixed_no_obj_ptr: true
+ # multimask tracking settings
+ multimask_output_for_tracking: true
+ use_multimask_token_for_obj_ptr: true
+ multimask_min_pt_num: 0
+ multimask_max_pt_num: 1
+ use_mlp_for_obj_ptr_proj: true
+ # Compilation flag
+ compile_image_encoder: False
diff --git a/sam2/configs/sam2.1/sam2.1_hiera_s.yaml b/sam2/configs/sam2.1/sam2.1_hiera_s.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8e803dfea5904f5eb5e73981918c913197587728
--- /dev/null
+++ b/sam2/configs/sam2.1/sam2.1_hiera_s.yaml
@@ -0,0 +1,119 @@
+# @package _global_
+
+# Model
+model:
+ _target_: sam2.modeling.sam2_base.SAM2Base
+ image_encoder:
+ _target_: sam2.modeling.backbones.image_encoder.ImageEncoder
+ scalp: 1
+ trunk:
+ _target_: sam2.modeling.backbones.hieradet.Hiera
+ embed_dim: 96
+ num_heads: 1
+ stages: [1, 2, 11, 2]
+ global_att_blocks: [7, 10, 13]
+ window_pos_embed_bkg_spatial_size: [7, 7]
+ neck:
+ _target_: sam2.modeling.backbones.image_encoder.FpnNeck
+ position_encoding:
+ _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
+ num_pos_feats: 256
+ normalize: true
+ scale: null
+ temperature: 10000
+ d_model: 256
+ backbone_channel_list: [768, 384, 192, 96]
+ fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features
+ fpn_interp_model: nearest
+
+ memory_attention:
+ _target_: sam2.modeling.memory_attention.MemoryAttention
+ d_model: 256
+ pos_enc_at_input: true
+ layer:
+ _target_: sam2.modeling.memory_attention.MemoryAttentionLayer
+ activation: relu
+ dim_feedforward: 2048
+ dropout: 0.1
+ pos_enc_at_attn: false
+ self_attention:
+ _target_: sam2.modeling.sam.transformer.RoPEAttention
+ rope_theta: 10000.0
+ feat_sizes: [32, 32]
+ embedding_dim: 256
+ num_heads: 1
+ downsample_rate: 1
+ dropout: 0.1
+ d_model: 256
+ pos_enc_at_cross_attn_keys: true
+ pos_enc_at_cross_attn_queries: false
+ cross_attention:
+ _target_: sam2.modeling.sam.transformer.RoPEAttention
+ rope_theta: 10000.0
+ feat_sizes: [32, 32]
+ rope_k_repeat: True
+ embedding_dim: 256
+ num_heads: 1
+ downsample_rate: 1
+ dropout: 0.1
+ kv_in_dim: 64
+ num_layers: 4
+
+ memory_encoder:
+ _target_: sam2.modeling.memory_encoder.MemoryEncoder
+ out_dim: 64
+ position_encoding:
+ _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
+ num_pos_feats: 64
+ normalize: true
+ scale: null
+ temperature: 10000
+ mask_downsampler:
+ _target_: sam2.modeling.memory_encoder.MaskDownSampler
+ kernel_size: 3
+ stride: 2
+ padding: 1
+ fuser:
+ _target_: sam2.modeling.memory_encoder.Fuser
+ layer:
+ _target_: sam2.modeling.memory_encoder.CXBlock
+ dim: 256
+ kernel_size: 7
+ padding: 3
+ layer_scale_init_value: 1e-6
+ use_dwconv: True # depth-wise convs
+ num_layers: 2
+
+ num_maskmem: 7
+ image_size: 1024
+ # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+ sigmoid_scale_for_mem_enc: 20.0
+ sigmoid_bias_for_mem_enc: -10.0
+ use_mask_input_as_output_without_sam: true
+ # Memory
+ directly_add_no_mem_embed: true
+ no_obj_embed_spatial: true
+ # use high-resolution feature map in the SAM mask decoder
+ use_high_res_features_in_sam: true
+ # output 3 masks on the first click on initial conditioning frames
+ multimask_output_in_sam: true
+ # SAM heads
+ iou_prediction_use_sigmoid: True
+ # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+ use_obj_ptrs_in_encoder: true
+ add_tpos_enc_to_obj_ptrs: true
+ proj_tpos_enc_in_obj_ptrs: true
+ use_signed_tpos_enc_to_obj_ptrs: true
+ only_obj_ptrs_in_the_past_for_eval: true
+ # object occlusion prediction
+ pred_obj_scores: true
+ pred_obj_scores_mlp: true
+ fixed_no_obj_ptr: true
+ # multimask tracking settings
+ multimask_output_for_tracking: true
+ use_multimask_token_for_obj_ptr: true
+ multimask_min_pt_num: 0
+ multimask_max_pt_num: 1
+ use_mlp_for_obj_ptr_proj: true
+ # Compilation flag
+ compile_image_encoder: False
diff --git a/sam2/configs/sam2.1/sam2.1_hiera_t.yaml b/sam2/configs/sam2.1/sam2.1_hiera_t.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..983c2ea031b7a17db439fe89fa8b7bd426ecd9bb
--- /dev/null
+++ b/sam2/configs/sam2.1/sam2.1_hiera_t.yaml
@@ -0,0 +1,121 @@
+# @package _global_
+
+# Model
+model:
+ _target_: sam2.modeling.sam2_base.SAM2Base
+ image_encoder:
+ _target_: sam2.modeling.backbones.image_encoder.ImageEncoder
+ scalp: 1
+ trunk:
+ _target_: sam2.modeling.backbones.hieradet.Hiera
+ embed_dim: 96
+ num_heads: 1
+ stages: [1, 2, 7, 2]
+ global_att_blocks: [5, 7, 9]
+ window_pos_embed_bkg_spatial_size: [7, 7]
+ neck:
+ _target_: sam2.modeling.backbones.image_encoder.FpnNeck
+ position_encoding:
+ _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
+ num_pos_feats: 256
+ normalize: true
+ scale: null
+ temperature: 10000
+ d_model: 256
+ backbone_channel_list: [768, 384, 192, 96]
+ fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features
+ fpn_interp_model: nearest
+
+ memory_attention:
+ _target_: sam2.modeling.memory_attention.MemoryAttention
+ d_model: 256
+ pos_enc_at_input: true
+ layer:
+ _target_: sam2.modeling.memory_attention.MemoryAttentionLayer
+ activation: relu
+ dim_feedforward: 2048
+ dropout: 0.1
+ pos_enc_at_attn: false
+ self_attention:
+ _target_: sam2.modeling.sam.transformer.RoPEAttention
+ rope_theta: 10000.0
+ feat_sizes: [32, 32]
+ embedding_dim: 256
+ num_heads: 1
+ downsample_rate: 1
+ dropout: 0.1
+ d_model: 256
+ pos_enc_at_cross_attn_keys: true
+ pos_enc_at_cross_attn_queries: false
+ cross_attention:
+ _target_: sam2.modeling.sam.transformer.RoPEAttention
+ rope_theta: 10000.0
+ feat_sizes: [32, 32]
+ rope_k_repeat: True
+ embedding_dim: 256
+ num_heads: 1
+ downsample_rate: 1
+ dropout: 0.1
+ kv_in_dim: 64
+ num_layers: 4
+
+ memory_encoder:
+ _target_: sam2.modeling.memory_encoder.MemoryEncoder
+ out_dim: 64
+ position_encoding:
+ _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
+ num_pos_feats: 64
+ normalize: true
+ scale: null
+ temperature: 10000
+ mask_downsampler:
+ _target_: sam2.modeling.memory_encoder.MaskDownSampler
+ kernel_size: 3
+ stride: 2
+ padding: 1
+ fuser:
+ _target_: sam2.modeling.memory_encoder.Fuser
+ layer:
+ _target_: sam2.modeling.memory_encoder.CXBlock
+ dim: 256
+ kernel_size: 7
+ padding: 3
+ layer_scale_init_value: 1e-6
+ use_dwconv: True # depth-wise convs
+ num_layers: 2
+
+ num_maskmem: 7
+ image_size: 1024
+ # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+ # SAM decoder
+ sigmoid_scale_for_mem_enc: 20.0
+ sigmoid_bias_for_mem_enc: -10.0
+ use_mask_input_as_output_without_sam: true
+ # Memory
+ directly_add_no_mem_embed: true
+ no_obj_embed_spatial: true
+ # use high-resolution feature map in the SAM mask decoder
+ use_high_res_features_in_sam: true
+ # output 3 masks on the first click on initial conditioning frames
+ multimask_output_in_sam: true
+ # SAM heads
+ iou_prediction_use_sigmoid: True
+ # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+ use_obj_ptrs_in_encoder: true
+ add_tpos_enc_to_obj_ptrs: true
+ proj_tpos_enc_in_obj_ptrs: true
+ use_signed_tpos_enc_to_obj_ptrs: true
+ only_obj_ptrs_in_the_past_for_eval: true
+ # object occlusion prediction
+ pred_obj_scores: true
+ pred_obj_scores_mlp: true
+ fixed_no_obj_ptr: true
+ # multimask tracking settings
+ multimask_output_for_tracking: true
+ use_multimask_token_for_obj_ptr: true
+ multimask_min_pt_num: 0
+ multimask_max_pt_num: 1
+ use_mlp_for_obj_ptr_proj: true
+ # Compilation flag
+ # HieraT does not currently support compilation, should always be set to False
+ compile_image_encoder: False
diff --git a/sam2/configs/sam2.1_training/sam2.1_hiera_b+_MOSE_finetune.yaml b/sam2/configs/sam2.1_training/sam2.1_hiera_b+_MOSE_finetune.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1928c4b9a31a99ddc8b780e29e8a1f58a0df463a
--- /dev/null
+++ b/sam2/configs/sam2.1_training/sam2.1_hiera_b+_MOSE_finetune.yaml
@@ -0,0 +1,339 @@
+# @package _global_
+
+scratch:
+ resolution: 1024
+ train_batch_size: 1
+ num_train_workers: 10
+ num_frames: 8
+ max_num_objects: 3
+ base_lr: 5.0e-6
+ vision_lr: 3.0e-06
+ phases_per_epoch: 1
+ num_epochs: 40
+
+dataset:
+ # PATHS to Dataset
+ img_folder: /fsx-onevision/shared/data/academic_vos_data/MOSE/train/JPEGImages # PATH to MOSE JPEGImages folder
+ gt_folder: /fsx-onevision/shared/data/academic_vos_data/MOSE/train/Annotations/ # PATH to MOSE Annotations folder
+ file_list_txt: training/assets/MOSE_sample_train_list.txt # Optional PATH to filelist containing a subset of videos to be used for training
+ multiplier: 2
+
+# Video transforms
+vos:
+ train_transforms:
+ - _target_: training.dataset.transforms.ComposeAPI
+ transforms:
+ - _target_: training.dataset.transforms.RandomHorizontalFlip
+ consistent_transform: True
+ - _target_: training.dataset.transforms.RandomAffine
+ degrees: 25
+ shear: 20
+ image_interpolation: bilinear
+ consistent_transform: True
+ - _target_: training.dataset.transforms.RandomResizeAPI
+ sizes: ${scratch.resolution}
+ square: true
+ consistent_transform: True
+ - _target_: training.dataset.transforms.ColorJitter
+ consistent_transform: True
+ brightness: 0.1
+ contrast: 0.03
+ saturation: 0.03
+ hue: null
+ - _target_: training.dataset.transforms.RandomGrayscale
+ p: 0.05
+ consistent_transform: True
+ - _target_: training.dataset.transforms.ColorJitter
+ consistent_transform: False
+ brightness: 0.1
+ contrast: 0.05
+ saturation: 0.05
+ hue: null
+ - _target_: training.dataset.transforms.ToTensorAPI
+ - _target_: training.dataset.transforms.NormalizeAPI
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+
+trainer:
+ _target_: training.trainer.Trainer
+ mode: train_only
+ max_epochs: ${times:${scratch.num_epochs},${scratch.phases_per_epoch}}
+ accelerator: cuda
+ seed_value: 123
+
+ model:
+ _target_: training.model.sam2.SAM2Train
+ image_encoder:
+ _target_: sam2.modeling.backbones.image_encoder.ImageEncoder
+ scalp: 1
+ trunk:
+ _target_: sam2.modeling.backbones.hieradet.Hiera
+ embed_dim: 112
+ num_heads: 2
+ drop_path_rate: 0.1
+ neck:
+ _target_: sam2.modeling.backbones.image_encoder.FpnNeck
+ position_encoding:
+ _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
+ num_pos_feats: 256
+ normalize: true
+ scale: null
+ temperature: 10000
+ d_model: 256
+ backbone_channel_list: [896, 448, 224, 112]
+ fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features
+ fpn_interp_model: nearest
+
+ memory_attention:
+ _target_: sam2.modeling.memory_attention.MemoryAttention
+ d_model: 256
+ pos_enc_at_input: true
+ layer:
+ _target_: sam2.modeling.memory_attention.MemoryAttentionLayer
+ activation: relu
+ dim_feedforward: 2048
+ dropout: 0.1
+ pos_enc_at_attn: false
+ self_attention:
+ _target_: sam2.modeling.sam.transformer.RoPEAttention
+ rope_theta: 10000.0
+ feat_sizes: [32, 32]
+ embedding_dim: 256
+ num_heads: 1
+ downsample_rate: 1
+ dropout: 0.1
+ d_model: 256
+ pos_enc_at_cross_attn_keys: true
+ pos_enc_at_cross_attn_queries: false
+ cross_attention:
+ _target_: sam2.modeling.sam.transformer.RoPEAttention
+ rope_theta: 10000.0
+ feat_sizes: [32, 32]
+ rope_k_repeat: True
+ embedding_dim: 256
+ num_heads: 1
+ downsample_rate: 1
+ dropout: 0.1
+ kv_in_dim: 64
+ num_layers: 4
+
+ memory_encoder:
+ _target_: sam2.modeling.memory_encoder.MemoryEncoder
+ out_dim: 64
+ position_encoding:
+ _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
+ num_pos_feats: 64
+ normalize: true
+ scale: null
+ temperature: 10000
+ mask_downsampler:
+ _target_: sam2.modeling.memory_encoder.MaskDownSampler
+ kernel_size: 3
+ stride: 2
+ padding: 1
+ fuser:
+ _target_: sam2.modeling.memory_encoder.Fuser
+ layer:
+ _target_: sam2.modeling.memory_encoder.CXBlock
+ dim: 256
+ kernel_size: 7
+ padding: 3
+ layer_scale_init_value: 1e-6
+ use_dwconv: True # depth-wise convs
+ num_layers: 2
+
+ num_maskmem: 7
+ image_size: ${scratch.resolution}
+ # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+ sigmoid_scale_for_mem_enc: 20.0
+ sigmoid_bias_for_mem_enc: -10.0
+ use_mask_input_as_output_without_sam: true
+ # Memory
+ directly_add_no_mem_embed: true
+ no_obj_embed_spatial: true
+ # use high-resolution feature map in the SAM mask decoder
+ use_high_res_features_in_sam: true
+ # output 3 masks on the first click on initial conditioning frames
+ multimask_output_in_sam: true
+ # SAM heads
+ iou_prediction_use_sigmoid: True
+ # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+ use_obj_ptrs_in_encoder: true
+ add_tpos_enc_to_obj_ptrs: true
+ proj_tpos_enc_in_obj_ptrs: true
+ use_signed_tpos_enc_to_obj_ptrs: true
+ only_obj_ptrs_in_the_past_for_eval: true
+ # object occlusion prediction
+ pred_obj_scores: true
+ pred_obj_scores_mlp: true
+ fixed_no_obj_ptr: true
+ # multimask tracking settings
+ multimask_output_for_tracking: true
+ use_multimask_token_for_obj_ptr: true
+ multimask_min_pt_num: 0
+ multimask_max_pt_num: 1
+ use_mlp_for_obj_ptr_proj: true
+ # Compilation flag
+ # compile_image_encoder: False
+
+ ####### Training specific params #######
+ # box/point input and corrections
+ prob_to_use_pt_input_for_train: 0.5
+ prob_to_use_pt_input_for_eval: 0.0
+ prob_to_use_box_input_for_train: 0.5 # 0.5*0.5 = 0.25 prob to use box instead of points
+ prob_to_use_box_input_for_eval: 0.0
+ prob_to_sample_from_gt_for_train: 0.1 # with a small prob, sampling correction points from GT mask instead of prediction errors
+ num_frames_to_correct_for_train: 2 # iteratively sample on random 1~2 frames (always include the first frame)
+ num_frames_to_correct_for_eval: 1 # only iteratively sample on first frame
+ rand_frames_to_correct_for_train: True # random #init-cond-frame ~ 2
+ add_all_frames_to_correct_as_cond: True # when a frame receives a correction click, it becomes a conditioning frame (even if it's not initially a conditioning frame)
+ # maximum 2 initial conditioning frames
+ num_init_cond_frames_for_train: 2
+ rand_init_cond_frames_for_train: True # random 1~2
+ num_correction_pt_per_frame: 7
+ use_act_ckpt_iterative_pt_sampling: false
+
+
+
+ num_init_cond_frames_for_eval: 1 # only mask on the first frame
+ forward_backbone_per_frame_for_eval: True
+
+
+ data:
+ train:
+ _target_: training.dataset.sam2_datasets.TorchTrainMixedDataset
+ phases_per_epoch: ${scratch.phases_per_epoch}
+ batch_sizes:
+ - ${scratch.train_batch_size}
+
+ datasets:
+ - _target_: training.dataset.utils.RepeatFactorWrapper
+ dataset:
+ _target_: training.dataset.utils.ConcatDataset
+ datasets:
+ - _target_: training.dataset.vos_dataset.VOSDataset
+ transforms: ${vos.train_transforms}
+ training: true
+ video_dataset:
+ _target_: training.dataset.vos_raw_dataset.PNGRawDataset
+ img_folder: ${dataset.img_folder}
+ gt_folder: ${dataset.gt_folder}
+ file_list_txt: ${dataset.file_list_txt}
+ sampler:
+ _target_: training.dataset.vos_sampler.RandomUniformSampler
+ num_frames: ${scratch.num_frames}
+ max_num_objects: ${scratch.max_num_objects}
+ multiplier: ${dataset.multiplier}
+ shuffle: True
+ num_workers: ${scratch.num_train_workers}
+ pin_memory: True
+ drop_last: True
+ collate_fn:
+ _target_: training.utils.data_utils.collate_fn
+ _partial_: true
+ dict_key: all
+
+ optim:
+ amp:
+ enabled: True
+ amp_dtype: bfloat16
+
+ optimizer:
+ _target_: torch.optim.AdamW
+
+ gradient_clip:
+ _target_: training.optimizer.GradientClipper
+ max_norm: 0.1
+ norm_type: 2
+
+ param_group_modifiers:
+ - _target_: training.optimizer.layer_decay_param_modifier
+ _partial_: True
+ layer_decay_value: 0.9
+ apply_to: 'image_encoder.trunk'
+ overrides:
+ - pattern: '*pos_embed*'
+ value: 1.0
+
+ options:
+ lr:
+ - scheduler:
+ _target_: fvcore.common.param_scheduler.CosineParamScheduler
+ start_value: ${scratch.base_lr}
+ end_value: ${divide:${scratch.base_lr},10}
+ - scheduler:
+ _target_: fvcore.common.param_scheduler.CosineParamScheduler
+ start_value: ${scratch.vision_lr}
+ end_value: ${divide:${scratch.vision_lr},10}
+ param_names:
+ - 'image_encoder.*'
+ weight_decay:
+ - scheduler:
+ _target_: fvcore.common.param_scheduler.ConstantParamScheduler
+ value: 0.1
+ - scheduler:
+ _target_: fvcore.common.param_scheduler.ConstantParamScheduler
+ value: 0.0
+ param_names:
+ - '*bias*'
+ module_cls_names: ['torch.nn.LayerNorm']
+
+ loss:
+ all:
+ _target_: training.loss_fns.MultiStepMultiMasksAndIous
+ weight_dict:
+ loss_mask: 20
+ loss_dice: 1
+ loss_iou: 1
+ loss_class: 1
+ supervise_all_iou: true
+ iou_use_l1_loss: true
+ pred_obj_scores: true
+ focal_gamma_obj_score: 0.0
+ focal_alpha_obj_score: -1.0
+
+ distributed:
+ backend: nccl
+ find_unused_parameters: True
+
+ logging:
+ tensorboard_writer:
+ _target_: training.utils.logger.make_tensorboard_logger
+ log_dir: ${launcher.experiment_log_dir}/tensorboard
+ flush_secs: 120
+ should_log: True
+ log_dir: ${launcher.experiment_log_dir}/logs
+ log_freq: 10
+
+ # initialize from a SAM 2 checkpoint
+ checkpoint:
+ save_dir: ${launcher.experiment_log_dir}/checkpoints
+ save_freq: 0 # 0 only last checkpoint is saved.
+ model_weight_initializer:
+ _partial_: True
+ _target_: training.utils.checkpoint_utils.load_state_dict_into_model
+ strict: True
+ ignore_unexpected_keys: null
+ ignore_missing_keys: null
+
+ state_dict:
+ _target_: training.utils.checkpoint_utils.load_checkpoint_and_apply_kernels
+ checkpoint_path: ./checkpoints/sam2.1_hiera_base_plus.pt # PATH to SAM 2.1 checkpoint
+ ckpt_state_dict_keys: ['model']
+
+launcher:
+ num_nodes: 1
+ gpus_per_node: 8
+ experiment_log_dir: null # Path to log directory, defaults to ./sam2_logs/${config_name}
+
+# SLURM args if running on a cluster
+submitit:
+ partition: null
+ account: null
+ qos: null
+ cpus_per_task: 10
+ use_cluster: false
+ timeout_hour: 24
+ name: null
+ port_range: [10000, 65000]
+
diff --git a/sam2/csrc/connected_components.cu b/sam2/csrc/connected_components.cu
new file mode 100644
index 0000000000000000000000000000000000000000..ced21eb32eaaadb818d441c1322b99d1bf068f45
--- /dev/null
+++ b/sam2/csrc/connected_components.cu
@@ -0,0 +1,289 @@
+// 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.
+
+// adapted from https://github.com/zsef123/Connected_components_PyTorch
+// with license found in the LICENSE_cctorch file in the root directory.
+#include <ATen/cuda/CUDAContext.h>
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <torch/extension.h>
+#include <torch/script.h>
+#include <vector>
+
+// 2d
+#define BLOCK_ROWS 16
+#define BLOCK_COLS 16
+
+namespace cc2d {
+
+template <typename T>
+__device__ __forceinline__ unsigned char hasBit(T bitmap, unsigned char pos) {
+ return (bitmap >> pos) & 1;
+}
+
+__device__ int32_t find(const int32_t* s_buf, int32_t n) {
+ while (s_buf[n] != n)
+ n = s_buf[n];
+ return n;
+}
+
+__device__ int32_t find_n_compress(int32_t* s_buf, int32_t n) {
+ const int32_t id = n;
+ while (s_buf[n] != n) {
+ n = s_buf[n];
+ s_buf[id] = n;
+ }
+ return n;
+}
+
+__device__ void union_(int32_t* s_buf, int32_t a, int32_t b) {
+ bool done;
+ do {
+ a = find(s_buf, a);
+ b = find(s_buf, b);
+
+ if (a < b) {
+ int32_t old = atomicMin(s_buf + b, a);
+ done = (old == b);
+ b = old;
+ } else if (b < a) {
+ int32_t old = atomicMin(s_buf + a, b);
+ done = (old == a);
+ a = old;
+ } else
+ done = true;
+
+ } while (!done);
+}
+
+__global__ void
+init_labeling(int32_t* label, const uint32_t W, const uint32_t H) {
+ const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;
+ const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;
+ const uint32_t idx = row * W + col;
+
+ if (row < H && col < W)
+ label[idx] = idx;
+}
+
+__global__ void
+merge(uint8_t* img, int32_t* label, const uint32_t W, const uint32_t H) {
+ const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;
+ const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;
+ const uint32_t idx = row * W + col;
+
+ if (row >= H || col >= W)
+ return;
+
+ uint32_t P = 0;
+
+ if (img[idx])
+ P |= 0x777;
+ if (row + 1 < H && img[idx + W])
+ P |= 0x777 << 4;
+ if (col + 1 < W && img[idx + 1])
+ P |= 0x777 << 1;
+
+ if (col == 0)
+ P &= 0xEEEE;
+ if (col + 1 >= W)
+ P &= 0x3333;
+ else if (col + 2 >= W)
+ P &= 0x7777;
+
+ if (row == 0)
+ P &= 0xFFF0;
+ if (row + 1 >= H)
+ P &= 0xFF;
+
+ if (P > 0) {
+ // If need check about top-left pixel(if flag the first bit) and hit the
+ // top-left pixel
+ if (hasBit(P, 0) && img[idx - W - 1]) {
+ union_(label, idx, idx - 2 * W - 2); // top left block
+ }
+
+ if ((hasBit(P, 1) && img[idx - W]) || (hasBit(P, 2) && img[idx - W + 1]))
+ union_(label, idx, idx - 2 * W); // top bottom block
+
+ if (hasBit(P, 3) && img[idx + 2 - W])
+ union_(label, idx, idx - 2 * W + 2); // top right block
+
+ if ((hasBit(P, 4) && img[idx - 1]) || (hasBit(P, 8) && img[idx + W - 1]))
+ union_(label, idx, idx - 2); // just left block
+ }
+}
+
+__global__ void compression(int32_t* label, const int32_t W, const int32_t H) {
+ const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;
+ const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;
+ const uint32_t idx = row * W + col;
+
+ if (row < H && col < W)
+ find_n_compress(label, idx);
+}
+
+__global__ void final_labeling(
+ const uint8_t* img,
+ int32_t* label,
+ const int32_t W,
+ const int32_t H) {
+ const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;
+ const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;
+ const uint32_t idx = row * W + col;
+
+ if (row >= H || col >= W)
+ return;
+
+ int32_t y = label[idx] + 1;
+
+ if (img[idx])
+ label[idx] = y;
+ else
+ label[idx] = 0;
+
+ if (col + 1 < W) {
+ if (img[idx + 1])
+ label[idx + 1] = y;
+ else
+ label[idx + 1] = 0;
+
+ if (row + 1 < H) {
+ if (img[idx + W + 1])
+ label[idx + W + 1] = y;
+ else
+ label[idx + W + 1] = 0;
+ }
+ }
+
+ if (row + 1 < H) {
+ if (img[idx + W])
+ label[idx + W] = y;
+ else
+ label[idx + W] = 0;
+ }
+}
+
+__global__ void init_counting(
+ const int32_t* label,
+ int32_t* count_init,
+ const int32_t W,
+ const int32_t H) {
+ const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y);
+ const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x);
+ const uint32_t idx = row * W + col;
+
+ if (row >= H || col >= W)
+ return;
+
+ int32_t y = label[idx];
+ if (y > 0) {
+ int32_t count_idx = y - 1;
+ atomicAdd(count_init + count_idx, 1);
+ }
+}
+
+__global__ void final_counting(
+ const int32_t* label,
+ const int32_t* count_init,
+ int32_t* count_final,
+ const int32_t W,
+ const int32_t H) {
+ const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y);
+ const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x);
+ const uint32_t idx = row * W + col;
+
+ if (row >= H || col >= W)
+ return;
+
+ int32_t y = label[idx];
+ if (y > 0) {
+ int32_t count_idx = y - 1;
+ count_final[idx] = count_init[count_idx];
+ } else {
+ count_final[idx] = 0;
+ }
+}
+
+} // namespace cc2d
+
+std::vector<torch::Tensor> get_connected_componnets(
+ const torch::Tensor& inputs) {
+ AT_ASSERTM(inputs.is_cuda(), "inputs must be a CUDA tensor");
+ AT_ASSERTM(inputs.ndimension() == 4, "inputs must be [N, 1, H, W] shape");
+ AT_ASSERTM(
+ inputs.scalar_type() == torch::kUInt8, "inputs must be a uint8 type");
+
+ const uint32_t N = inputs.size(0);
+ const uint32_t C = inputs.size(1);
+ const uint32_t H = inputs.size(2);
+ const uint32_t W = inputs.size(3);
+
+ AT_ASSERTM(C == 1, "inputs must be [N, 1, H, W] shape");
+ AT_ASSERTM((H % 2) == 0, "height must be an even number");
+ AT_ASSERTM((W % 2) == 0, "width must be an even number");
+
+ // label must be uint32_t
+ auto label_options =
+ torch::TensorOptions().dtype(torch::kInt32).device(inputs.device());
+ torch::Tensor labels = torch::zeros({N, C, H, W}, label_options);
+ torch::Tensor counts_init = torch::zeros({N, C, H, W}, label_options);
+ torch::Tensor counts_final = torch::zeros({N, C, H, W}, label_options);
+
+ dim3 grid = dim3(
+ ((W + 1) / 2 + BLOCK_COLS - 1) / BLOCK_COLS,
+ ((H + 1) / 2 + BLOCK_ROWS - 1) / BLOCK_ROWS);
+ dim3 block = dim3(BLOCK_COLS, BLOCK_ROWS);
+ dim3 grid_count =
+ dim3((W + BLOCK_COLS) / BLOCK_COLS, (H + BLOCK_ROWS) / BLOCK_ROWS);
+ dim3 block_count = dim3(BLOCK_COLS, BLOCK_ROWS);
+ cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+ for (int n = 0; n < N; n++) {
+ uint32_t offset = n * H * W;
+
+ cc2d::init_labeling<<<grid, block, 0, stream>>>(
+ labels.data_ptr<int32_t>() + offset, W, H);
+ cc2d::merge<<<grid, block, 0, stream>>>(
+ inputs.data_ptr<uint8_t>() + offset,
+ labels.data_ptr<int32_t>() + offset,
+ W,
+ H);
+ cc2d::compression<<<grid, block, 0, stream>>>(
+ labels.data_ptr<int32_t>() + offset, W, H);
+ cc2d::final_labeling<<<grid, block, 0, stream>>>(
+ inputs.data_ptr<uint8_t>() + offset,
+ labels.data_ptr<int32_t>() + offset,
+ W,
+ H);
+
+ // get the counting of each pixel
+ cc2d::init_counting<<<grid_count, block_count, 0, stream>>>(
+ labels.data_ptr<int32_t>() + offset,
+ counts_init.data_ptr<int32_t>() + offset,
+ W,
+ H);
+ cc2d::final_counting<<<grid_count, block_count, 0, stream>>>(
+ labels.data_ptr<int32_t>() + offset,
+ counts_init.data_ptr<int32_t>() + offset,
+ counts_final.data_ptr<int32_t>() + offset,
+ W,
+ H);
+ }
+
+ // returned values are [labels, counts]
+ std::vector<torch::Tensor> outputs;
+ outputs.push_back(labels);
+ outputs.push_back(counts_final);
+ return outputs;
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+ m.def(
+ "get_connected_componnets",
+ &get_connected_componnets,
+ "get_connected_componnets");
+}
diff --git a/sam2/modeling/__init__.py b/sam2/modeling/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae
--- /dev/null
+++ b/sam2/modeling/__init__.py
@@ -0,0 +1,5 @@
+# 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.
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diff --git a/sam2/modeling/__pycache__/sam2_utils.cpython-310.pyc b/sam2/modeling/__pycache__/sam2_utils.cpython-310.pyc
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diff --git a/sam2/modeling/backbones/__init__.py b/sam2/modeling/backbones/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae
--- /dev/null
+++ b/sam2/modeling/backbones/__init__.py
@@ -0,0 +1,5 @@
+# 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.
diff --git a/sam2/modeling/backbones/__pycache__/__init__.cpython-310.pyc b/sam2/modeling/backbones/__pycache__/__init__.cpython-310.pyc
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diff --git a/sam2/modeling/backbones/hieradet.py b/sam2/modeling/backbones/hieradet.py
new file mode 100644
index 0000000000000000000000000000000000000000..19ac77b61d8e1345a301686d39ef2ab6e4b035fb
--- /dev/null
+++ b/sam2/modeling/backbones/hieradet.py
@@ -0,0 +1,317 @@
+# 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
+from functools import partial
+from typing import List, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from iopath.common.file_io import g_pathmgr
+
+from sam2.modeling.backbones.utils import (
+ PatchEmbed,
+ window_partition,
+ window_unpartition,
+)
+
+from sam2.modeling.sam2_utils import DropPath, MLP
+
+
+def do_pool(x: torch.Tensor, pool: nn.Module, norm: nn.Module = None) -> torch.Tensor:
+ if pool is None:
+ return x
+ # (B, H, W, C) -> (B, C, H, W)
+ x = x.permute(0, 3, 1, 2)
+ x = pool(x)
+ # (B, C, H', W') -> (B, H', W', C)
+ x = x.permute(0, 2, 3, 1)
+ if norm:
+ x = norm(x)
+
+ return x
+
+
+class MultiScaleAttention(nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ dim_out: int,
+ num_heads: int,
+ q_pool: nn.Module = None,
+ ):
+ super().__init__()
+
+ self.dim = dim
+ self.dim_out = dim_out
+ self.num_heads = num_heads
+ self.q_pool = q_pool
+ self.qkv = nn.Linear(dim, dim_out * 3)
+ self.proj = nn.Linear(dim_out, dim_out)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ B, H, W, _ = x.shape
+ # qkv with shape (B, H * W, 3, nHead, C)
+ qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1)
+ # q, k, v with shape (B, H * W, nheads, C)
+ q, k, v = torch.unbind(qkv, 2)
+
+ # Q pooling (for downsample at stage changes)
+ if self.q_pool:
+ q = do_pool(q.reshape(B, H, W, -1), self.q_pool)
+ H, W = q.shape[1:3] # downsampled shape
+ q = q.reshape(B, H * W, self.num_heads, -1)
+
+ # Torch's SDPA expects [B, nheads, H*W, C] so we transpose
+ x = F.scaled_dot_product_attention(
+ q.transpose(1, 2),
+ k.transpose(1, 2),
+ v.transpose(1, 2),
+ )
+ # Transpose back
+ x = x.transpose(1, 2)
+ x = x.reshape(B, H, W, -1)
+
+ x = self.proj(x)
+
+ return x
+
+
+class MultiScaleBlock(nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ dim_out: int,
+ num_heads: int,
+ mlp_ratio: float = 4.0,
+ drop_path: float = 0.0,
+ norm_layer: Union[nn.Module, str] = "LayerNorm",
+ q_stride: Tuple[int, int] = None,
+ act_layer: nn.Module = nn.GELU,
+ window_size: int = 0,
+ ):
+ super().__init__()
+
+ if isinstance(norm_layer, str):
+ norm_layer = partial(getattr(nn, norm_layer), eps=1e-6)
+
+ self.dim = dim
+ self.dim_out = dim_out
+ self.norm1 = norm_layer(dim)
+
+ self.window_size = window_size
+
+ self.pool, self.q_stride = None, q_stride
+ if self.q_stride:
+ self.pool = nn.MaxPool2d(
+ kernel_size=q_stride, stride=q_stride, ceil_mode=False
+ )
+
+ self.attn = MultiScaleAttention(
+ dim,
+ dim_out,
+ num_heads=num_heads,
+ q_pool=self.pool,
+ )
+ self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+ self.norm2 = norm_layer(dim_out)
+ self.mlp = MLP(
+ dim_out,
+ int(dim_out * mlp_ratio),
+ dim_out,
+ num_layers=2,
+ activation=act_layer,
+ )
+
+ if dim != dim_out:
+ self.proj = nn.Linear(dim, dim_out)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ shortcut = x # B, H, W, C
+ x = self.norm1(x)
+
+ # Skip connection
+ if self.dim != self.dim_out:
+ shortcut = do_pool(self.proj(x), self.pool)
+
+ # Window partition
+ window_size = self.window_size
+ if window_size > 0:
+ H, W = x.shape[1], x.shape[2]
+ x, pad_hw = window_partition(x, window_size)
+
+ # Window Attention + Q Pooling (if stage change)
+ x = self.attn(x)
+ if self.q_stride:
+ # Shapes have changed due to Q pooling
+ window_size = self.window_size // self.q_stride[0]
+ H, W = shortcut.shape[1:3]
+
+ pad_h = (window_size - H % window_size) % window_size
+ pad_w = (window_size - W % window_size) % window_size
+ pad_hw = (H + pad_h, W + pad_w)
+
+ # Reverse window partition
+ if self.window_size > 0:
+ x = window_unpartition(x, window_size, pad_hw, (H, W))
+
+ x = shortcut + self.drop_path(x)
+ # MLP
+ x = x + self.drop_path(self.mlp(self.norm2(x)))
+ return x
+
+
+class Hiera(nn.Module):
+ """
+ Reference: https://arxiv.org/abs/2306.00989
+ """
+
+ def __init__(
+ self,
+ embed_dim: int = 96, # initial embed dim
+ num_heads: int = 1, # initial number of heads
+ drop_path_rate: float = 0.0, # stochastic depth
+ q_pool: int = 3, # number of q_pool stages
+ q_stride: Tuple[int, int] = (2, 2), # downsample stride bet. stages
+ stages: Tuple[int, ...] = (2, 3, 16, 3), # blocks per stage
+ dim_mul: float = 2.0, # dim_mul factor at stage shift
+ head_mul: float = 2.0, # head_mul factor at stage shift
+ window_pos_embed_bkg_spatial_size: Tuple[int, int] = (14, 14),
+ # window size per stage, when not using global att.
+ window_spec: Tuple[int, ...] = (
+ 8,
+ 4,
+ 14,
+ 7,
+ ),
+ # global attn in these blocks
+ global_att_blocks: Tuple[int, ...] = (
+ 12,
+ 16,
+ 20,
+ ),
+ weights_path=None,
+ return_interm_layers=True, # return feats from every stage
+ ):
+ super().__init__()
+
+ assert len(stages) == len(window_spec)
+ self.window_spec = window_spec
+
+ depth = sum(stages)
+ self.q_stride = q_stride
+ self.stage_ends = [sum(stages[:i]) - 1 for i in range(1, len(stages) + 1)]
+ assert 0 <= q_pool <= len(self.stage_ends[:-1])
+ self.q_pool_blocks = [x + 1 for x in self.stage_ends[:-1]][:q_pool]
+ self.return_interm_layers = return_interm_layers
+
+ self.patch_embed = PatchEmbed(
+ embed_dim=embed_dim,
+ )
+ # Which blocks have global att?
+ self.global_att_blocks = global_att_blocks
+
+ # Windowed positional embedding (https://arxiv.org/abs/2311.05613)
+ self.window_pos_embed_bkg_spatial_size = window_pos_embed_bkg_spatial_size
+ self.pos_embed = nn.Parameter(
+ torch.zeros(1, embed_dim, *self.window_pos_embed_bkg_spatial_size)
+ )
+ self.pos_embed_window = nn.Parameter(
+ torch.zeros(1, embed_dim, self.window_spec[0], self.window_spec[0])
+ )
+
+ dpr = [
+ x.item() for x in torch.linspace(0, drop_path_rate, depth)
+ ] # stochastic depth decay rule
+
+ cur_stage = 1
+ self.blocks = nn.ModuleList()
+
+ for i in range(depth):
+ dim_out = embed_dim
+ # lags by a block, so first block of
+ # next stage uses an initial window size
+ # of previous stage and final window size of current stage
+ window_size = self.window_spec[cur_stage - 1]
+
+ if self.global_att_blocks is not None:
+ window_size = 0 if i in self.global_att_blocks else window_size
+
+ if i - 1 in self.stage_ends:
+ dim_out = int(embed_dim * dim_mul)
+ num_heads = int(num_heads * head_mul)
+ cur_stage += 1
+
+ block = MultiScaleBlock(
+ dim=embed_dim,
+ dim_out=dim_out,
+ num_heads=num_heads,
+ drop_path=dpr[i],
+ q_stride=self.q_stride if i in self.q_pool_blocks else None,
+ window_size=window_size,
+ )
+
+ embed_dim = dim_out
+ self.blocks.append(block)
+
+ self.channel_list = (
+ [self.blocks[i].dim_out for i in self.stage_ends[::-1]]
+ if return_interm_layers
+ else [self.blocks[-1].dim_out]
+ )
+
+ if weights_path is not None:
+ with g_pathmgr.open(weights_path, "rb") as f:
+ chkpt = torch.load(f, map_location="cpu")
+ logging.info("loading Hiera", self.load_state_dict(chkpt, strict=False))
+
+ def _get_pos_embed(self, hw: Tuple[int, int]) -> torch.Tensor:
+ h, w = hw
+ window_embed = self.pos_embed_window
+ pos_embed = F.interpolate(self.pos_embed, size=(h, w), mode="bicubic")
+ pos_embed = pos_embed + window_embed.tile(
+ [x // y for x, y in zip(pos_embed.shape, window_embed.shape)]
+ )
+ pos_embed = pos_embed.permute(0, 2, 3, 1)
+ return pos_embed
+
+ def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
+ x = self.patch_embed(x)
+ # x: (B, H, W, C)
+
+ # Add pos embed
+ x = x + self._get_pos_embed(x.shape[1:3])
+
+ outputs = []
+ for i, blk in enumerate(self.blocks):
+ x = blk(x)
+ if (i == self.stage_ends[-1]) or (
+ i in self.stage_ends and self.return_interm_layers
+ ):
+ feats = x.permute(0, 3, 1, 2)
+ outputs.append(feats)
+
+ return outputs
+
+ def get_layer_id(self, layer_name):
+ # https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L33
+ num_layers = self.get_num_layers()
+
+ if layer_name.find("rel_pos") != -1:
+ return num_layers + 1
+ elif layer_name.find("pos_embed") != -1:
+ return 0
+ elif layer_name.find("patch_embed") != -1:
+ return 0
+ elif layer_name.find("blocks") != -1:
+ return int(layer_name.split("blocks")[1].split(".")[1]) + 1
+ else:
+ return num_layers + 1
+
+ def get_num_layers(self) -> int:
+ return len(self.blocks)
diff --git a/sam2/modeling/backbones/image_encoder.py b/sam2/modeling/backbones/image_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..37e9266bc98596e97ca303118c910ed24f6cee2c
--- /dev/null
+++ b/sam2/modeling/backbones/image_encoder.py
@@ -0,0 +1,134 @@
+# 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.
+
+from typing import List, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ImageEncoder(nn.Module):
+ def __init__(
+ self,
+ trunk: nn.Module,
+ neck: nn.Module,
+ scalp: int = 0,
+ ):
+ super().__init__()
+ self.trunk = trunk
+ self.neck = neck
+ self.scalp = scalp
+ assert (
+ self.trunk.channel_list == self.neck.backbone_channel_list
+ ), f"Channel dims of trunk and neck do not match. Trunk: {self.trunk.channel_list}, neck: {self.neck.backbone_channel_list}"
+
+ def forward(self, sample: torch.Tensor):
+ # Forward through backbone
+ features, pos = self.neck(self.trunk(sample))
+ if self.scalp > 0:
+ # Discard the lowest resolution features
+ features, pos = features[: -self.scalp], pos[: -self.scalp]
+
+ src = features[-1]
+ output = {
+ "vision_features": src,
+ "vision_pos_enc": pos,
+ "backbone_fpn": features,
+ }
+ return output
+
+
+class FpnNeck(nn.Module):
+ """
+ A modified variant of Feature Pyramid Network (FPN) neck
+ (we remove output conv and also do bicubic interpolation similar to ViT
+ pos embed interpolation)
+ """
+
+ def __init__(
+ self,
+ position_encoding: nn.Module,
+ d_model: int,
+ backbone_channel_list: List[int],
+ kernel_size: int = 1,
+ stride: int = 1,
+ padding: int = 0,
+ fpn_interp_model: str = "bilinear",
+ fuse_type: str = "sum",
+ fpn_top_down_levels: Optional[List[int]] = None,
+ ):
+ """Initialize the neck
+ :param trunk: the backbone
+ :param position_encoding: the positional encoding to use
+ :param d_model: the dimension of the model
+ :param neck_norm: the normalization to use
+ """
+ super().__init__()
+ self.position_encoding = position_encoding
+ self.convs = nn.ModuleList()
+ self.backbone_channel_list = backbone_channel_list
+ self.d_model = d_model
+ for dim in backbone_channel_list:
+ current = nn.Sequential()
+ current.add_module(
+ "conv",
+ nn.Conv2d(
+ in_channels=dim,
+ out_channels=d_model,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ ),
+ )
+
+ self.convs.append(current)
+ self.fpn_interp_model = fpn_interp_model
+ assert fuse_type in ["sum", "avg"]
+ self.fuse_type = fuse_type
+
+ # levels to have top-down features in its outputs
+ # e.g. if fpn_top_down_levels is [2, 3], then only outputs of level 2 and 3
+ # have top-down propagation, while outputs of level 0 and level 1 have only
+ # lateral features from the same backbone level.
+ if fpn_top_down_levels is None:
+ # default is to have top-down features on all levels
+ fpn_top_down_levels = range(len(self.convs))
+ self.fpn_top_down_levels = list(fpn_top_down_levels)
+
+ def forward(self, xs: List[torch.Tensor]):
+
+ out = [None] * len(self.convs)
+ pos = [None] * len(self.convs)
+ assert len(xs) == len(self.convs)
+ # fpn forward pass
+ # see https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/fpn.py
+ prev_features = None
+ # forward in top-down order (from low to high resolution)
+ n = len(self.convs) - 1
+ for i in range(n, -1, -1):
+ x = xs[i]
+ lateral_features = self.convs[n - i](x)
+ if i in self.fpn_top_down_levels and prev_features is not None:
+ top_down_features = F.interpolate(
+ prev_features.to(dtype=torch.float32),
+ scale_factor=2.0,
+ mode=self.fpn_interp_model,
+ align_corners=(
+ None if self.fpn_interp_model == "nearest" else False
+ ),
+ antialias=False,
+ )
+ prev_features = lateral_features + top_down_features
+ if self.fuse_type == "avg":
+ prev_features /= 2
+ else:
+ prev_features = lateral_features
+ x_out = prev_features
+ out[i] = x_out
+ pos[i] = self.position_encoding(x_out).to(x_out.dtype)
+
+ return out, pos
diff --git a/sam2/modeling/backbones/utils.py b/sam2/modeling/backbones/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..32d55c7545f064de133a5ff0200ba1ece9b504b7
--- /dev/null
+++ b/sam2/modeling/backbones/utils.py
@@ -0,0 +1,95 @@
+# 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.
+
+"""Some utilities for backbones, in particular for windowing"""
+
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def window_partition(x, window_size):
+ """
+ Partition into non-overlapping windows with padding if needed.
+ Args:
+ x (tensor): input tokens with [B, H, W, C].
+ window_size (int): window size.
+ Returns:
+ windows: windows after partition with [B * num_windows, window_size, window_size, C].
+ (Hp, Wp): padded height and width before partition
+ """
+ B, H, W, C = x.shape
+
+ pad_h = (window_size - H % window_size) % window_size
+ pad_w = (window_size - W % window_size) % window_size
+ if pad_h > 0 or pad_w > 0:
+ x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+ Hp, Wp = H + pad_h, W + pad_w
+
+ x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+ windows = (
+ x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+ )
+ return windows, (Hp, Wp)
+
+
+def window_unpartition(windows, window_size, pad_hw, hw):
+ """
+ Window unpartition into original sequences and removing padding.
+ Args:
+ x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
+ window_size (int): window size.
+ pad_hw (Tuple): padded height and width (Hp, Wp).
+ hw (Tuple): original height and width (H, W) before padding.
+ Returns:
+ x: unpartitioned sequences with [B, H, W, C].
+ """
+ Hp, Wp = pad_hw
+ H, W = hw
+ B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+ x = windows.view(
+ B, Hp // window_size, Wp // window_size, window_size, window_size, -1
+ )
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+
+ if Hp > H or Wp > W:
+ x = x[:, :H, :W, :].contiguous()
+ return x
+
+
+class PatchEmbed(nn.Module):
+ """
+ Image to Patch Embedding.
+ """
+
+ def __init__(
+ self,
+ kernel_size: Tuple[int, ...] = (7, 7),
+ stride: Tuple[int, ...] = (4, 4),
+ padding: Tuple[int, ...] = (3, 3),
+ in_chans: int = 3,
+ embed_dim: int = 768,
+ ):
+ """
+ Args:
+ kernel_size (Tuple): kernel size of the projection layer.
+ stride (Tuple): stride of the projection layer.
+ padding (Tuple): padding size of the projection layer.
+ in_chans (int): Number of input image channels.
+ embed_dim (int): embed_dim (int): Patch embedding dimension.
+ """
+ super().__init__()
+ self.proj = nn.Conv2d(
+ in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
+ )
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ x = self.proj(x)
+ # B C H W -> B H W C
+ x = x.permute(0, 2, 3, 1)
+ return x
diff --git a/sam2/modeling/memory_attention.py b/sam2/modeling/memory_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..31b0e012dc83ae1e761b1bb9c3a77d50a925f446
--- /dev/null
+++ b/sam2/modeling/memory_attention.py
@@ -0,0 +1,205 @@
+# 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.
+
+from typing import Optional
+
+import torch
+from torch import nn, Tensor
+
+from sam2.modeling.sam.transformer import RoPEAttention
+
+from sam2.modeling.sam2_utils import get_activation_fn, get_clones
+import pdb
+
+class MemoryAttentionLayer(nn.Module):
+
+ def __init__(
+ self,
+ activation: str,
+ cross_attention: nn.Module,
+ d_model: int,
+ dim_feedforward: int,
+ dropout: float,
+ pos_enc_at_attn: bool,
+ pos_enc_at_cross_attn_keys: bool,
+ pos_enc_at_cross_attn_queries: bool,
+ self_attention: nn.Module,
+ ):
+ super().__init__()
+ self.d_model = d_model
+ self.dim_feedforward = dim_feedforward
+ self.dropout_value = dropout
+ self.self_attn = self_attention
+ self.cross_attn_image = cross_attention
+
+ # Implementation of Feedforward model
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.norm1 = nn.LayerNorm(d_model)
+ self.norm2 = nn.LayerNorm(d_model)
+ self.norm3 = nn.LayerNorm(d_model)
+ self.dropout1 = nn.Dropout(dropout)
+ self.dropout2 = nn.Dropout(dropout)
+ self.dropout3 = nn.Dropout(dropout)
+
+ self.activation_str = activation
+ self.activation = get_activation_fn(activation)
+
+ # Where to add pos enc
+ self.pos_enc_at_attn = pos_enc_at_attn
+ self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries
+ self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys
+
+ def _forward_sa(self, tgt, query_pos):
+ # Self-Attention
+ tgt2 = self.norm1(tgt)
+ q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2
+ tgt2 = self.self_attn(q, k, v=tgt2)
+ tgt = tgt + self.dropout1(tgt2)
+ return tgt
+
+ def _forward_ca(self, tgt, memory, query_pos, pos, num_k_exclude_rope=0, object_frame_scores=None, object_ptr_scores=None):
+ kwds = {}
+ if num_k_exclude_rope > 0:
+ assert isinstance(self.cross_attn_image, RoPEAttention)
+ kwds = {"num_k_exclude_rope": num_k_exclude_rope}
+
+ # Cross-Attention
+ tgt2 = self.norm2(tgt)
+ if object_frame_scores is None:
+ key = memory + pos if self.pos_enc_at_cross_attn_keys else memory
+ else: # relative
+ key_original = memory + pos if self.pos_enc_at_cross_attn_keys else memory
+ num_frame, num_ptr = len(object_frame_scores), len(object_ptr_scores)
+ num_frame_ = int(num_frame*4096)
+ num_object = key_original.shape[0]
+ key_frame = key_original[:, :num_frame_].reshape(num_object, num_frame, 4096, -1)
+ key_ptr = key_original[:, num_frame_:].reshape(num_object, num_ptr, 4, -1)
+ scaling_low = 0.95
+ scaling_high = 1.05
+ if num_frame == 1:
+ key = key_original
+ else:
+ weight_frame = torch.stack(object_frame_scores, dim=1) # num_object, num_frame
+ weight_ptr = torch.stack(object_ptr_scores, dim=1) # num_object, num_ptr
+
+ standard_weight_frame = torch.linspace(scaling_low, scaling_high, num_frame).to(weight_frame) # num_frame
+ standard_weight_ptr = torch.linspace(scaling_low, scaling_high, num_ptr).to(weight_ptr) # num_ptr
+
+ new_weight_frame = torch.zeros_like(weight_frame)
+ new_weight_ptr = torch.zeros_like(weight_ptr)
+
+ new_weight_frame.scatter_(1, torch.argsort(weight_frame, dim=1), standard_weight_frame.unsqueeze(0).repeat([num_object, 1]))
+ new_weight_ptr.scatter_(1, torch.argsort(weight_ptr, dim=1), standard_weight_ptr.unsqueeze(0).repeat([num_object, 1]))
+
+ key_frame_scale = (new_weight_frame[:, :, None, None].to(key_frame.device) * key_frame)
+ key_ptr_scale = (new_weight_ptr[:, :, None, None].to(key_ptr.device) * key_ptr)
+ key = torch.cat([key_frame_scale.reshape(num_object, num_frame_, -1), key_ptr_scale.reshape(num_object, int(num_ptr*4), -1)], dim=1)
+ # key = memory + pos if self.pos_enc_at_cross_attn_keys else memory
+ tgt2 = self.cross_attn_image(
+ q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,
+ k=key,
+ v=memory,
+ **kwds,
+ )
+ tgt = tgt + self.dropout2(tgt2)
+ return tgt
+
+ def forward(
+ self,
+ tgt,
+ memory,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ num_k_exclude_rope: int = 0,
+ object_frame_scores = None,
+ object_ptr_scores = None,
+ ) -> torch.Tensor:
+
+ # Self-Attn, Cross-Attn
+ tgt = self._forward_sa(tgt, query_pos)
+ tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope, object_frame_scores, object_ptr_scores)
+ # MLP
+ tgt2 = self.norm3(tgt)
+ tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+ tgt = tgt + self.dropout3(tgt2)
+ return tgt
+
+
+class MemoryAttention(nn.Module):
+ def __init__(
+ self,
+ d_model: int,
+ pos_enc_at_input: bool,
+ layer: nn.Module,
+ num_layers: int,
+ batch_first: bool = True, # Do layers expect batch first input?
+ ):
+ super().__init__()
+ self.d_model = d_model
+ self.layers = get_clones(layer, num_layers)
+ self.num_layers = num_layers
+ self.norm = nn.LayerNorm(d_model)
+ self.pos_enc_at_input = pos_enc_at_input
+ self.batch_first = batch_first
+
+ def forward(
+ self,
+ curr: torch.Tensor, # self-attention inputs
+ memory: torch.Tensor, # cross-attention inputs
+ curr_pos: Optional[Tensor] = None, # pos_enc for self-attention inputs
+ memory_pos: Optional[Tensor] = None, # pos_enc for cross-attention inputs
+ num_obj_ptr_tokens: int = 0, # number of object pointer *tokens*
+ object_frame_scores=None,
+ object_ptr_scores=None,
+ ):
+ if isinstance(curr, list):
+ assert isinstance(curr_pos, list)
+ assert len(curr) == len(curr_pos) == 1
+ curr, curr_pos = (
+ curr[0],
+ curr_pos[0],
+ )
+
+ assert (
+ curr.shape[1] == memory.shape[1]
+ ), "Batch size must be the same for curr and memory"
+
+ output = curr
+ if self.pos_enc_at_input and curr_pos is not None:
+ output = output + 0.1 * curr_pos
+
+ if self.batch_first:
+ # Convert to batch first
+ output = output.transpose(0, 1)
+ curr_pos = curr_pos.transpose(0, 1)
+ memory = memory.transpose(0, 1)
+ memory_pos = memory_pos.transpose(0, 1)
+
+ for layer in self.layers:
+ kwds = {}
+ if isinstance(layer.cross_attn_image, RoPEAttention):
+ kwds = {"num_k_exclude_rope": num_obj_ptr_tokens,
+ "object_frame_scores": object_frame_scores,
+ "object_ptr_scores":object_ptr_scores}
+
+ output = layer(
+ tgt=output,
+ memory=memory,
+ pos=memory_pos,
+ query_pos=curr_pos,
+ **kwds,
+ )
+ normed_output = self.norm(output)
+
+ if self.batch_first:
+ # Convert back to seq first
+ normed_output = normed_output.transpose(0, 1)
+ curr_pos = curr_pos.transpose(0, 1)
+
+ return normed_output
\ No newline at end of file
diff --git a/sam2/modeling/memory_encoder.py b/sam2/modeling/memory_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6439b3410d1c82769fe59389f18e28d4a1712c6
--- /dev/null
+++ b/sam2/modeling/memory_encoder.py
@@ -0,0 +1,181 @@
+# 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 math
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from sam2.modeling.sam2_utils import DropPath, get_clones, LayerNorm2d
+
+
+class MaskDownSampler(nn.Module):
+ """
+ Progressively downsample a mask by total_stride, each time by stride.
+ Note that LayerNorm is applied per *token*, like in ViT.
+
+ With each downsample (by a factor stride**2), channel capacity increases by the same factor.
+ In the end, we linearly project to embed_dim channels.
+ """
+
+ def __init__(
+ self,
+ embed_dim=256,
+ kernel_size=4,
+ stride=4,
+ padding=0,
+ total_stride=16,
+ activation=nn.GELU,
+ ):
+ super().__init__()
+ num_layers = int(math.log2(total_stride) // math.log2(stride))
+ assert stride**num_layers == total_stride
+ self.encoder = nn.Sequential()
+ mask_in_chans, mask_out_chans = 1, 1
+ for _ in range(num_layers):
+ mask_out_chans = mask_in_chans * (stride**2)
+ self.encoder.append(
+ nn.Conv2d(
+ mask_in_chans,
+ mask_out_chans,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ )
+ )
+ self.encoder.append(LayerNorm2d(mask_out_chans))
+ self.encoder.append(activation())
+ mask_in_chans = mask_out_chans
+
+ self.encoder.append(nn.Conv2d(mask_out_chans, embed_dim, kernel_size=1))
+
+ def forward(self, x):
+ return self.encoder(x)
+
+
+# Lightly adapted from ConvNext (https://github.com/facebookresearch/ConvNeXt)
+class CXBlock(nn.Module):
+ r"""ConvNeXt Block. There are two equivalent implementations:
+ (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
+ (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
+ We use (2) as we find it slightly faster in PyTorch
+
+ Args:
+ dim (int): Number of input channels.
+ drop_path (float): Stochastic depth rate. Default: 0.0
+ layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
+ """
+
+ def __init__(
+ self,
+ dim,
+ kernel_size=7,
+ padding=3,
+ drop_path=0.0,
+ layer_scale_init_value=1e-6,
+ use_dwconv=True,
+ ):
+ super().__init__()
+ self.dwconv = nn.Conv2d(
+ dim,
+ dim,
+ kernel_size=kernel_size,
+ padding=padding,
+ groups=dim if use_dwconv else 1,
+ ) # depthwise conv
+ self.norm = LayerNorm2d(dim, eps=1e-6)
+ self.pwconv1 = nn.Linear(
+ dim, 4 * dim
+ ) # pointwise/1x1 convs, implemented with linear layers
+ self.act = nn.GELU()
+ self.pwconv2 = nn.Linear(4 * dim, dim)
+ self.gamma = (
+ nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)
+ if layer_scale_init_value > 0
+ else None
+ )
+ self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+ def forward(self, x):
+ input = x
+ x = self.dwconv(x)
+ x = self.norm(x)
+ x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
+ x = self.pwconv1(x)
+ x = self.act(x)
+ x = self.pwconv2(x)
+ if self.gamma is not None:
+ x = self.gamma * x
+ x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
+
+ x = input + self.drop_path(x)
+ return x
+
+
+class Fuser(nn.Module):
+ def __init__(self, layer, num_layers, dim=None, input_projection=False):
+ super().__init__()
+ self.proj = nn.Identity()
+ self.layers = get_clones(layer, num_layers)
+
+ if input_projection:
+ assert dim is not None
+ self.proj = nn.Conv2d(dim, dim, kernel_size=1)
+
+ def forward(self, x):
+ # normally x: (N, C, H, W)
+ x = self.proj(x)
+ for layer in self.layers:
+ x = layer(x)
+ return x
+
+
+class MemoryEncoder(nn.Module):
+ def __init__(
+ self,
+ out_dim,
+ mask_downsampler,
+ fuser,
+ position_encoding,
+ in_dim=256, # in_dim of pix_feats
+ ):
+ super().__init__()
+
+ self.mask_downsampler = mask_downsampler
+
+ self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1)
+ self.fuser = fuser
+ self.position_encoding = position_encoding
+ self.out_proj = nn.Identity()
+ if out_dim != in_dim:
+ self.out_proj = nn.Conv2d(in_dim, out_dim, kernel_size=1)
+
+ def forward(
+ self,
+ pix_feat: torch.Tensor,
+ masks: torch.Tensor,
+ skip_mask_sigmoid: bool = False,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ ## Process masks
+ # sigmoid, so that less domain shift from gt masks which are bool
+ if not skip_mask_sigmoid:
+ masks = F.sigmoid(masks)
+ masks = self.mask_downsampler(masks)
+
+ ## Fuse pix_feats and downsampled masks
+ # in case the visual features are on CPU, cast them to CUDA
+ pix_feat = pix_feat.to(masks.device)
+
+ x = self.pix_feat_proj(pix_feat)
+ x = x + masks
+ x = self.fuser(x)
+ x = self.out_proj(x)
+
+ pos = self.position_encoding(x).to(x.dtype)
+
+ return {"vision_features": x, "vision_pos_enc": [pos]}
\ No newline at end of file
diff --git a/sam2/modeling/position_encoding.py b/sam2/modeling/position_encoding.py
new file mode 100644
index 0000000000000000000000000000000000000000..52ac22674d5d4fdd9e83b6bdf034bff56d04bc0d
--- /dev/null
+++ b/sam2/modeling/position_encoding.py
@@ -0,0 +1,221 @@
+# 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 math
+from typing import Any, Optional, Tuple
+
+import numpy as np
+
+import torch
+from torch import nn
+
+
+class PositionEmbeddingSine(nn.Module):
+ """
+ This is a more standard version of the position embedding, very similar to the one
+ used by the Attention Is All You Need paper, generalized to work on images.
+ """
+
+ def __init__(
+ self,
+ num_pos_feats,
+ temperature: int = 10000,
+ normalize: bool = True,
+ scale: Optional[float] = None,
+ ):
+ super().__init__()
+ assert num_pos_feats % 2 == 0, "Expecting even model width"
+ self.num_pos_feats = num_pos_feats // 2
+ self.temperature = temperature
+ self.normalize = normalize
+ if scale is not None and normalize is False:
+ raise ValueError("normalize should be True if scale is passed")
+ if scale is None:
+ scale = 2 * math.pi
+ self.scale = scale
+
+ self.cache = {}
+
+ def _encode_xy(self, x, y):
+ # The positions are expected to be normalized
+ assert len(x) == len(y) and x.ndim == y.ndim == 1
+ x_embed = x * self.scale
+ y_embed = y * self.scale
+
+ dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+ dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+ pos_x = x_embed[:, None] / dim_t
+ pos_y = y_embed[:, None] / dim_t
+ pos_x = torch.stack(
+ (pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2
+ ).flatten(1)
+ pos_y = torch.stack(
+ (pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2
+ ).flatten(1)
+ return pos_x, pos_y
+
+ @torch.no_grad()
+ def encode_boxes(self, x, y, w, h):
+ pos_x, pos_y = self._encode_xy(x, y)
+ pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1)
+ return pos
+
+ encode = encode_boxes # Backwards compatibility
+
+ @torch.no_grad()
+ def encode_points(self, x, y, labels):
+ (bx, nx), (by, ny), (bl, nl) = x.shape, y.shape, labels.shape
+ assert bx == by and nx == ny and bx == bl and nx == nl
+ pos_x, pos_y = self._encode_xy(x.flatten(), y.flatten())
+ pos_x, pos_y = pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1)
+ pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2)
+ return pos
+
+ @torch.no_grad()
+ def forward(self, x: torch.Tensor):
+ cache_key = (x.shape[-2], x.shape[-1])
+ if cache_key in self.cache:
+ return self.cache[cache_key][None].repeat(x.shape[0], 1, 1, 1)
+ y_embed = (
+ torch.arange(1, x.shape[-2] + 1, dtype=torch.float32, device=x.device)
+ .view(1, -1, 1)
+ .repeat(x.shape[0], 1, x.shape[-1])
+ )
+ x_embed = (
+ torch.arange(1, x.shape[-1] + 1, dtype=torch.float32, device=x.device)
+ .view(1, 1, -1)
+ .repeat(x.shape[0], x.shape[-2], 1)
+ )
+
+ if self.normalize:
+ eps = 1e-6
+ y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+ x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+ dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+ dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+ pos_x = x_embed[:, :, :, None] / dim_t
+ pos_y = y_embed[:, :, :, None] / dim_t
+ pos_x = torch.stack(
+ (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+ ).flatten(3)
+ pos_y = torch.stack(
+ (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+ ).flatten(3)
+ pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+ self.cache[cache_key] = pos[0]
+ return pos
+
+
+class PositionEmbeddingRandom(nn.Module):
+ """
+ Positional encoding using random spatial frequencies.
+ """
+
+ def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
+ super().__init__()
+ if scale is None or scale <= 0.0:
+ scale = 1.0
+ self.register_buffer(
+ "positional_encoding_gaussian_matrix",
+ scale * torch.randn((2, num_pos_feats)),
+ )
+
+ def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
+ """Positionally encode points that are normalized to [0,1]."""
+ # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
+ coords = 2 * coords - 1
+ coords = coords @ self.positional_encoding_gaussian_matrix
+ coords = 2 * np.pi * coords
+ # outputs d_1 x ... x d_n x C shape
+ return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
+
+ def forward(self, size: Tuple[int, int]) -> torch.Tensor:
+ """Generate positional encoding for a grid of the specified size."""
+ h, w = size
+ device: Any = self.positional_encoding_gaussian_matrix.device
+ grid = torch.ones((h, w), device=device, dtype=torch.float32)
+ y_embed = grid.cumsum(dim=0) - 0.5
+ x_embed = grid.cumsum(dim=1) - 0.5
+ y_embed = y_embed / h
+ x_embed = x_embed / w
+
+ pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
+ return pe.permute(2, 0, 1) # C x H x W
+
+ def forward_with_coords(
+ self, coords_input: torch.Tensor, image_size: Tuple[int, int]
+ ) -> torch.Tensor:
+ """Positionally encode points that are not normalized to [0,1]."""
+ coords = coords_input.clone()
+ coords[:, :, 0] = coords[:, :, 0] / image_size[1]
+ coords[:, :, 1] = coords[:, :, 1] / image_size[0]
+ return self._pe_encoding(coords.to(torch.float)) # B x N x C
+
+
+# Rotary Positional Encoding, adapted from:
+# 1. https://github.com/meta-llama/codellama/blob/main/llama/model.py
+# 2. https://github.com/naver-ai/rope-vit
+# 3. https://github.com/lucidrains/rotary-embedding-torch
+
+
+def init_t_xy(end_x: int, end_y: int):
+ t = torch.arange(end_x * end_y, dtype=torch.float32)
+ t_x = (t % end_x).float()
+ t_y = torch.div(t, end_x, rounding_mode="floor").float()
+ return t_x, t_y
+
+
+def compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float = 10000.0):
+ freqs_x = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))
+ freqs_y = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))
+
+ t_x, t_y = init_t_xy(end_x, end_y)
+ freqs_x = torch.outer(t_x, freqs_x)
+ freqs_y = torch.outer(t_y, freqs_y)
+ freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x)
+ freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y)
+ return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1)
+
+
+def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
+ ndim = x.ndim
+ assert 0 <= 1 < ndim
+ assert freqs_cis.shape == (x.shape[-2], x.shape[-1])
+ shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)]
+ return freqs_cis.view(*shape)
+
+
+def apply_rotary_enc(
+ xq: torch.Tensor,
+ xk: torch.Tensor,
+ freqs_cis: torch.Tensor,
+ repeat_freqs_k: bool = False,
+):
+ xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+ xk_ = (
+ torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+ if xk.shape[-2] != 0
+ else None
+ )
+ freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
+ xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
+ if xk_ is None:
+ # no keys to rotate, due to dropout
+ return xq_out.type_as(xq).to(xq.device), xk
+ # repeat freqs along seq_len dim to match k seq_len
+ if repeat_freqs_k:
+ r = xk_.shape[-2] // xq_.shape[-2]
+ if freqs_cis.is_cuda:
+ freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1)
+ else:
+ # torch.repeat on complex numbers may not be supported on non-CUDA devices
+ # (freqs_cis has 4 dims and we repeat on dim 2) so we use expand + flatten
+ freqs_cis = freqs_cis.unsqueeze(2).expand(-1, -1, r, -1, -1).flatten(2, 3)
+ xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
+ return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device)
diff --git a/sam2/modeling/sam/__init__.py b/sam2/modeling/sam/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae
--- /dev/null
+++ b/sam2/modeling/sam/__init__.py
@@ -0,0 +1,5 @@
+# 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.
diff --git a/sam2/modeling/sam/__pycache__/__init__.cpython-310.pyc b/sam2/modeling/sam/__pycache__/__init__.cpython-310.pyc
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diff --git a/sam2/modeling/sam/__pycache__/transformer.cpython-310.pyc b/sam2/modeling/sam/__pycache__/transformer.cpython-310.pyc
new file mode 100644
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diff --git a/sam2/modeling/sam/mask_decoder.py b/sam2/modeling/sam/mask_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f31fd6041acc0660573995688cb745e409db7a7
--- /dev/null
+++ b/sam2/modeling/sam/mask_decoder.py
@@ -0,0 +1,300 @@
+# 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.
+
+from typing import List, Optional, Tuple, Type
+
+import torch
+from torch import nn
+import pdb
+from fvcore.nn import FlopCountAnalysis
+from sam2.modeling.sam2_utils import LayerNorm2d, MLP
+
+
+class MaskDecoder(nn.Module):
+ def __init__(
+ self,
+ *,
+ transformer_dim: int,
+ transformer: nn.Module,
+ num_multimask_outputs: int = 3,
+ activation: Type[nn.Module] = nn.GELU,
+ iou_head_depth: int = 3,
+ iou_head_hidden_dim: int = 256,
+ use_high_res_features: bool = False,
+ iou_prediction_use_sigmoid=False,
+ dynamic_multimask_via_stability=False,
+ dynamic_multimask_stability_delta=0.05,
+ dynamic_multimask_stability_thresh=0.98,
+ pred_obj_scores: bool = False,
+ pred_obj_scores_mlp: bool = False,
+ use_multimask_token_for_obj_ptr: bool = False,
+ ) -> None:
+ """
+ Predicts masks given an image and prompt embeddings, using a
+ transformer architecture.
+
+ Arguments:
+ transformer_dim (int): the channel dimension of the transformer
+ transformer (nn.Module): the transformer used to predict masks
+ num_multimask_outputs (int): the number of masks to predict
+ when disambiguating masks
+ activation (nn.Module): the type of activation to use when
+ upscaling masks
+ iou_head_depth (int): the depth of the MLP used to predict
+ mask quality
+ iou_head_hidden_dim (int): the hidden dimension of the MLP
+ used to predict mask quality
+ """
+ super().__init__()
+ self.transformer_dim = transformer_dim
+ self.transformer = transformer
+
+ self.num_multimask_outputs = num_multimask_outputs
+
+ self.iou_token = nn.Embedding(1, transformer_dim)
+ self.num_mask_tokens = num_multimask_outputs + 1
+ self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
+
+ self.pred_obj_scores = pred_obj_scores
+ if self.pred_obj_scores:
+ self.obj_score_token = nn.Embedding(1, transformer_dim)
+ self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr
+
+ self.output_upscaling = nn.Sequential(
+ nn.ConvTranspose2d(
+ transformer_dim, transformer_dim // 4, kernel_size=2, stride=2
+ ),
+ LayerNorm2d(transformer_dim // 4),
+ activation(),
+ nn.ConvTranspose2d(
+ transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2
+ ),
+ activation(),
+ )
+ self.use_high_res_features = use_high_res_features
+ if use_high_res_features:
+ self.conv_s0 = nn.Conv2d(
+ transformer_dim, transformer_dim // 8, kernel_size=1, stride=1
+ )
+ self.conv_s1 = nn.Conv2d(
+ transformer_dim, transformer_dim // 4, kernel_size=1, stride=1
+ )
+
+ self.output_hypernetworks_mlps = nn.ModuleList(
+ [
+ MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
+ for i in range(self.num_mask_tokens)
+ ]
+ )
+
+ self.iou_prediction_head = MLP(
+ transformer_dim,
+ iou_head_hidden_dim,
+ self.num_mask_tokens,
+ iou_head_depth,
+ sigmoid_output=iou_prediction_use_sigmoid,
+ )
+ if self.pred_obj_scores:
+ self.pred_obj_score_head = nn.Linear(transformer_dim, 1)
+ if pred_obj_scores_mlp:
+ self.pred_obj_score_head = MLP(transformer_dim, transformer_dim, 1, 3)
+
+ # When outputting a single mask, optionally we can dynamically fall back to the best
+ # multimask output token if the single mask output token gives low stability scores.
+ self.dynamic_multimask_via_stability = dynamic_multimask_via_stability
+ self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta
+ self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh
+
+
+
+ def forward(
+ self,
+ image_embeddings: torch.Tensor,
+ image_pe: torch.Tensor,
+ sparse_prompt_embeddings: torch.Tensor,
+ dense_prompt_embeddings: torch.Tensor,
+ multimask_output: bool,
+ repeat_image: bool,
+ high_res_features: Optional[List[torch.Tensor]] = None,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """
+ Predict masks given image and prompt embeddings.
+
+ Arguments:
+ image_embeddings (torch.Tensor): the embeddings from the image encoder
+ image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
+ sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
+ dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
+ multimask_output (bool): Whether to return multiple masks or a single
+ mask.
+
+ Returns:
+ torch.Tensor: batched predicted masks
+ torch.Tensor: batched predictions of mask quality
+ torch.Tensor: batched SAM token for mask output
+ """
+ masks, iou_pred, mask_tokens_out, object_score_logits = self.predict_masks(
+ image_embeddings=image_embeddings,
+ image_pe=image_pe,
+ sparse_prompt_embeddings=sparse_prompt_embeddings,
+ dense_prompt_embeddings=dense_prompt_embeddings,
+ repeat_image=repeat_image,
+ high_res_features=high_res_features,
+ )
+
+ # Select the correct mask or masks for output
+ if multimask_output:
+ masks = masks[:, 1:, :, :]
+ iou_pred = iou_pred[:, 1:]
+ elif self.dynamic_multimask_via_stability and not self.training:
+ masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred)
+ else:
+ masks = masks[:, 0:1, :, :]
+ iou_pred = iou_pred[:, 0:1]
+
+ if multimask_output and self.use_multimask_token_for_obj_ptr:
+ sam_tokens_out = mask_tokens_out[:, 1:] # [b, 3, c] shape
+ else:
+ # Take the mask output token. Here we *always* use the token for single mask output.
+ # At test time, even if we track after 1-click (and using multimask_output=True),
+ # we still take the single mask token here. The rationale is that we always track
+ # after multiple clicks during training, so the past tokens seen during training
+ # are always the single mask token (and we'll let it be the object-memory token).
+ sam_tokens_out = mask_tokens_out[:, 0:1] # [b, 1, c] shape
+
+ # Prepare output
+ return masks, iou_pred, sam_tokens_out, object_score_logits
+
+ def predict_masks(
+ self,
+ image_embeddings: torch.Tensor,
+ image_pe: torch.Tensor,
+ sparse_prompt_embeddings: torch.Tensor,
+ dense_prompt_embeddings: torch.Tensor,
+ repeat_image: bool,
+ high_res_features: Optional[List[torch.Tensor]] = None,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Predicts masks. See 'forward' for more details."""
+ # Concatenate output tokens
+ s = 0
+ if self.pred_obj_scores:
+ output_tokens = torch.cat(
+ [
+ self.obj_score_token.weight,
+ self.iou_token.weight,
+ self.mask_tokens.weight,
+ ],
+ dim=0,
+ )
+ s = 1
+ else:
+ output_tokens = torch.cat(
+ [self.iou_token.weight, self.mask_tokens.weight], dim=0
+ )
+ output_tokens = output_tokens.unsqueeze(0).expand(
+ sparse_prompt_embeddings.size(0), -1, -1
+ )
+ tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
+
+ # Expand per-image data in batch direction to be per-mask
+ if repeat_image:
+ src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
+ else:
+ assert image_embeddings.shape[0] == tokens.shape[0]
+ src = image_embeddings
+ src = src + dense_prompt_embeddings
+ assert (
+ image_pe.size(0) == 1
+ ), "image_pe should have size 1 in batch dim (from `get_dense_pe()`)"
+ pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
+ b, c, h, w = src.shape
+
+
+
+ # Run the transformer
+ hs, src = self.transformer(src, pos_src, tokens)
+ iou_token_out = hs[:, s, :]
+ mask_tokens_out = hs[:, s + 1 : (s + 1 + self.num_mask_tokens), :]
+
+ # Upscale mask embeddings and predict masks using the mask tokens
+ src = src.transpose(1, 2).view(b, c, h, w)
+ if not self.use_high_res_features:
+ upscaled_embedding = self.output_upscaling(src)
+ else:
+ dc1, ln1, act1, dc2, act2 = self.output_upscaling
+ feat_s0, feat_s1 = high_res_features
+ upscaled_embedding = act1(ln1(dc1(src) + feat_s1))
+ upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0)
+
+ hyper_in_list: List[torch.Tensor] = []
+ for i in range(self.num_mask_tokens):
+ hyper_in_list.append(
+ self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])
+ )
+ hyper_in = torch.stack(hyper_in_list, dim=1)
+ b, c, h, w = upscaled_embedding.shape
+ masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
+
+ # Generate mask quality predictions
+ iou_pred = self.iou_prediction_head(iou_token_out)
+ if self.pred_obj_scores:
+ assert s == 1
+ object_score_logits = self.pred_obj_score_head(hs[:, 0, :])
+ else:
+ # Obj scores logits - default to 10.0, i.e. assuming the object is present, sigmoid(10)=1
+ object_score_logits = 10.0 * iou_pred.new_ones(iou_pred.shape[0], 1)
+
+ return masks, iou_pred, mask_tokens_out, object_score_logits
+
+ def _get_stability_scores(self, mask_logits):
+ """
+ Compute stability scores of the mask logits based on the IoU between upper and
+ lower thresholds.
+ """
+ mask_logits = mask_logits.flatten(-2)
+ stability_delta = self.dynamic_multimask_stability_delta
+ area_i = torch.sum(mask_logits > stability_delta, dim=-1).float()
+ area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float()
+ stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0)
+ return stability_scores
+
+ def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores):
+ """
+ When outputting a single mask, if the stability score from the current single-mask
+ output (based on output token 0) falls below a threshold, we instead select from
+ multi-mask outputs (based on output token 1~3) the mask with the highest predicted
+ IoU score. This is intended to ensure a valid mask for both clicking and tracking.
+ """
+ # The best mask from multimask output tokens (1~3)
+ multimask_logits = all_mask_logits[:, 1:, :, :]
+ multimask_iou_scores = all_iou_scores[:, 1:]
+ best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1)
+ batch_inds = torch.arange(
+ multimask_iou_scores.size(0), device=all_iou_scores.device
+ )
+ best_multimask_logits = multimask_logits[batch_inds, best_scores_inds]
+ best_multimask_logits = best_multimask_logits.unsqueeze(1)
+ best_multimask_iou_scores = multimask_iou_scores[batch_inds, best_scores_inds]
+ best_multimask_iou_scores = best_multimask_iou_scores.unsqueeze(1)
+
+ # The mask from singlemask output token 0 and its stability score
+ singlemask_logits = all_mask_logits[:, 0:1, :, :]
+ singlemask_iou_scores = all_iou_scores[:, 0:1]
+ stability_scores = self._get_stability_scores(singlemask_logits)
+ is_stable = stability_scores >= self.dynamic_multimask_stability_thresh
+
+ # Dynamically fall back to best multimask output upon low stability scores.
+ mask_logits_out = torch.where(
+ is_stable[..., None, None].expand_as(singlemask_logits),
+ singlemask_logits,
+ best_multimask_logits,
+ )
+ iou_scores_out = torch.where(
+ is_stable.expand_as(singlemask_iou_scores),
+ singlemask_iou_scores,
+ best_multimask_iou_scores,
+ )
+ return mask_logits_out, iou_scores_out
diff --git a/sam2/modeling/sam/prompt_encoder.py b/sam2/modeling/sam/prompt_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e820a2dd1e3d4686bd8875ad0f2ef794bfdde49
--- /dev/null
+++ b/sam2/modeling/sam/prompt_encoder.py
@@ -0,0 +1,182 @@
+# 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.
+
+from typing import Optional, Tuple, Type
+
+import torch
+from torch import nn
+
+from sam2.modeling.position_encoding import PositionEmbeddingRandom
+
+from sam2.modeling.sam2_utils import LayerNorm2d
+
+
+class PromptEncoder(nn.Module):
+ def __init__(
+ self,
+ embed_dim: int,
+ image_embedding_size: Tuple[int, int],
+ input_image_size: Tuple[int, int],
+ mask_in_chans: int,
+ activation: Type[nn.Module] = nn.GELU,
+ ) -> None:
+ """
+ Encodes prompts for input to SAM's mask decoder.
+
+ Arguments:
+ embed_dim (int): The prompts' embedding dimension
+ image_embedding_size (tuple(int, int)): The spatial size of the
+ image embedding, as (H, W).
+ input_image_size (int): The padded size of the image as input
+ to the image encoder, as (H, W).
+ mask_in_chans (int): The number of hidden channels used for
+ encoding input masks.
+ activation (nn.Module): The activation to use when encoding
+ input masks.
+ """
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.input_image_size = input_image_size
+ self.image_embedding_size = image_embedding_size
+ self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
+
+ self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners
+ point_embeddings = [
+ nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)
+ ]
+ self.point_embeddings = nn.ModuleList(point_embeddings)
+ self.not_a_point_embed = nn.Embedding(1, embed_dim)
+
+ self.mask_input_size = (
+ 4 * image_embedding_size[0],
+ 4 * image_embedding_size[1],
+ )
+ self.mask_downscaling = nn.Sequential(
+ nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
+ LayerNorm2d(mask_in_chans // 4),
+ activation(),
+ nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
+ LayerNorm2d(mask_in_chans),
+ activation(),
+ nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
+ )
+ self.no_mask_embed = nn.Embedding(1, embed_dim)
+
+ def get_dense_pe(self) -> torch.Tensor:
+ """
+ Returns the positional encoding used to encode point prompts,
+ applied to a dense set of points the shape of the image encoding.
+
+ Returns:
+ torch.Tensor: Positional encoding with shape
+ 1x(embed_dim)x(embedding_h)x(embedding_w)
+ """
+ return self.pe_layer(self.image_embedding_size).unsqueeze(0)
+
+ def _embed_points(
+ self,
+ points: torch.Tensor,
+ labels: torch.Tensor,
+ pad: bool,
+ ) -> torch.Tensor:
+ """Embeds point prompts."""
+ points = points + 0.5 # Shift to center of pixel
+ if pad:
+ padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
+ padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
+ points = torch.cat([points, padding_point], dim=1)
+ labels = torch.cat([labels, padding_label], dim=1)
+ point_embedding = self.pe_layer.forward_with_coords(
+ points, self.input_image_size
+ )
+ point_embedding[labels == -1] = 0.0
+ point_embedding[labels == -1] += self.not_a_point_embed.weight
+ point_embedding[labels == 0] += self.point_embeddings[0].weight
+ point_embedding[labels == 1] += self.point_embeddings[1].weight
+ point_embedding[labels == 2] += self.point_embeddings[2].weight
+ point_embedding[labels == 3] += self.point_embeddings[3].weight
+ return point_embedding
+
+ def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
+ """Embeds box prompts."""
+ boxes = boxes + 0.5 # Shift to center of pixel
+ coords = boxes.reshape(-1, 2, 2)
+ corner_embedding = self.pe_layer.forward_with_coords(
+ coords, self.input_image_size
+ )
+ corner_embedding[:, 0, :] += self.point_embeddings[2].weight
+ corner_embedding[:, 1, :] += self.point_embeddings[3].weight
+ return corner_embedding
+
+ def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
+ """Embeds mask inputs."""
+ mask_embedding = self.mask_downscaling(masks)
+ return mask_embedding
+
+ def _get_batch_size(
+ self,
+ points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+ boxes: Optional[torch.Tensor],
+ masks: Optional[torch.Tensor],
+ ) -> int:
+ """
+ Gets the batch size of the output given the batch size of the input prompts.
+ """
+ if points is not None:
+ return points[0].shape[0]
+ elif boxes is not None:
+ return boxes.shape[0]
+ elif masks is not None:
+ return masks.shape[0]
+ else:
+ return 1
+
+ def _get_device(self) -> torch.device:
+ return self.point_embeddings[0].weight.device
+
+ def forward(
+ self,
+ points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+ boxes: Optional[torch.Tensor],
+ masks: Optional[torch.Tensor],
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """
+ Embeds different types of prompts, returning both sparse and dense
+ embeddings.
+
+ Arguments:
+ points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
+ and labels to embed.
+ boxes (torch.Tensor or none): boxes to embed
+ masks (torch.Tensor or none): masks to embed
+
+ Returns:
+ torch.Tensor: sparse embeddings for the points and boxes, with shape
+ BxNx(embed_dim), where N is determined by the number of input points
+ and boxes.
+ torch.Tensor: dense embeddings for the masks, in the shape
+ Bx(embed_dim)x(embed_H)x(embed_W)
+ """
+ bs = self._get_batch_size(points, boxes, masks)
+ sparse_embeddings = torch.empty(
+ (bs, 0, self.embed_dim), device=self._get_device()
+ )
+ if points is not None:
+ coords, labels = points
+ point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
+ sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
+ if boxes is not None:
+ box_embeddings = self._embed_boxes(boxes)
+ sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
+
+ if masks is not None:
+ dense_embeddings = self._embed_masks(masks)
+ else:
+ dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
+ bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
+ )
+
+ return sparse_embeddings, dense_embeddings
\ No newline at end of file
diff --git a/sam2/modeling/sam/transformer.py b/sam2/modeling/sam/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5b6fa2f87e85a7f222fb2ba0b661734dc57a08a
--- /dev/null
+++ b/sam2/modeling/sam/transformer.py
@@ -0,0 +1,360 @@
+# 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 contextlib
+import math
+import warnings
+from functools import partial
+from typing import Tuple, Type
+
+import torch
+import torch.nn.functional as F
+from torch import nn, Tensor
+
+from sam2.modeling.position_encoding import apply_rotary_enc, compute_axial_cis
+from sam2.modeling.sam2_utils import MLP
+from sam2.utils.misc import get_sdpa_settings
+
+warnings.simplefilter(action="ignore", category=FutureWarning)
+# Check whether Flash Attention is available (and use it by default)
+OLD_GPU, USE_FLASH_ATTN, MATH_KERNEL_ON = get_sdpa_settings()
+# A fallback setting to allow all available kernels if Flash Attention fails
+ALLOW_ALL_KERNELS = False
+
+
+def sdp_kernel_context(dropout_p):
+ """
+ Get the context for the attention scaled dot-product kernel. We use Flash Attention
+ by default, but fall back to all available kernels if Flash Attention fails.
+ """
+ if ALLOW_ALL_KERNELS:
+ return contextlib.nullcontext()
+
+ return torch.backends.cuda.sdp_kernel(
+ enable_flash=USE_FLASH_ATTN,
+ # if Flash attention kernel is off, then math kernel needs to be enabled
+ enable_math=(OLD_GPU and dropout_p > 0.0) or MATH_KERNEL_ON,
+ enable_mem_efficient=OLD_GPU,
+ )
+
+
+class TwoWayTransformer(nn.Module):
+ def __init__(
+ self,
+ depth: int,
+ embedding_dim: int,
+ num_heads: int,
+ mlp_dim: int,
+ activation: Type[nn.Module] = nn.ReLU,
+ attention_downsample_rate: int = 2,
+ ) -> None:
+ """
+ A transformer decoder that attends to an input image using
+ queries whose positional embedding is supplied.
+
+ Args:
+ depth (int): number of layers in the transformer
+ embedding_dim (int): the channel dimension for the input embeddings
+ num_heads (int): the number of heads for multihead attention. Must
+ divide embedding_dim
+ mlp_dim (int): the channel dimension internal to the MLP block
+ activation (nn.Module): the activation to use in the MLP block
+ """
+ super().__init__()
+ self.depth = depth
+ self.embedding_dim = embedding_dim
+ self.num_heads = num_heads
+ self.mlp_dim = mlp_dim
+ self.layers = nn.ModuleList()
+
+ for i in range(depth):
+ self.layers.append(
+ TwoWayAttentionBlock(
+ embedding_dim=embedding_dim,
+ num_heads=num_heads,
+ mlp_dim=mlp_dim,
+ activation=activation,
+ attention_downsample_rate=attention_downsample_rate,
+ skip_first_layer_pe=(i == 0),
+ )
+ )
+
+ self.final_attn_token_to_image = Attention(
+ embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+ )
+ self.norm_final_attn = nn.LayerNorm(embedding_dim)
+
+ def forward(
+ self,
+ image_embedding: Tensor,
+ image_pe: Tensor,
+ point_embedding: Tensor,
+ ) -> Tuple[Tensor, Tensor]:
+ """
+ Args:
+ image_embedding (torch.Tensor): image to attend to. Should be shape
+ B x embedding_dim x h x w for any h and w.
+ image_pe (torch.Tensor): the positional encoding to add to the image. Must
+ have the same shape as image_embedding.
+ point_embedding (torch.Tensor): the embedding to add to the query points.
+ Must have shape B x N_points x embedding_dim for any N_points.
+
+ Returns:
+ torch.Tensor: the processed point_embedding
+ torch.Tensor: the processed image_embedding
+ """
+ # BxCxHxW -> BxHWxC == B x N_image_tokens x C
+ bs, c, h, w = image_embedding.shape
+ image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
+ image_pe = image_pe.flatten(2).permute(0, 2, 1)
+
+ # Prepare queries
+ queries = point_embedding
+ keys = image_embedding
+
+ # Apply transformer blocks and final layernorm
+ for layer in self.layers:
+ queries, keys = layer(
+ queries=queries,
+ keys=keys,
+ query_pe=point_embedding,
+ key_pe=image_pe,
+ )
+
+ # Apply the final attention layer from the points to the image
+ q = queries + point_embedding
+ k = keys + image_pe
+ attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
+ queries = queries + attn_out
+ queries = self.norm_final_attn(queries)
+
+ return queries, keys
+
+
+class TwoWayAttentionBlock(nn.Module):
+ def __init__(
+ self,
+ embedding_dim: int,
+ num_heads: int,
+ mlp_dim: int = 2048,
+ activation: Type[nn.Module] = nn.ReLU,
+ attention_downsample_rate: int = 2,
+ skip_first_layer_pe: bool = False,
+ ) -> None:
+ """
+ A transformer block with four layers: (1) self-attention of sparse
+ inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
+ block on sparse inputs, and (4) cross attention of dense inputs to sparse
+ inputs.
+
+ Arguments:
+ embedding_dim (int): the channel dimension of the embeddings
+ num_heads (int): the number of heads in the attention layers
+ mlp_dim (int): the hidden dimension of the mlp block
+ activation (nn.Module): the activation of the mlp block
+ skip_first_layer_pe (bool): skip the PE on the first layer
+ """
+ super().__init__()
+ self.self_attn = Attention(embedding_dim, num_heads)
+ self.norm1 = nn.LayerNorm(embedding_dim)
+
+ self.cross_attn_token_to_image = Attention(
+ embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+ )
+ self.norm2 = nn.LayerNorm(embedding_dim)
+
+ self.mlp = MLP(
+ embedding_dim, mlp_dim, embedding_dim, num_layers=2, activation=activation
+ )
+ self.norm3 = nn.LayerNorm(embedding_dim)
+
+ self.norm4 = nn.LayerNorm(embedding_dim)
+ self.cross_attn_image_to_token = Attention(
+ embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+ )
+
+ self.skip_first_layer_pe = skip_first_layer_pe
+
+ def forward(
+ self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
+ ) -> Tuple[Tensor, Tensor]:
+ # Self attention block
+ if self.skip_first_layer_pe:
+ queries = self.self_attn(q=queries, k=queries, v=queries)
+ else:
+ q = queries + query_pe
+ attn_out = self.self_attn(q=q, k=q, v=queries)
+ queries = queries + attn_out
+ queries = self.norm1(queries)
+
+ # Cross attention block, tokens attending to image embedding
+ q = queries + query_pe
+ k = keys + key_pe
+ attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
+ queries = queries + attn_out
+ queries = self.norm2(queries)
+
+ # MLP block
+ mlp_out = self.mlp(queries)
+ queries = queries + mlp_out
+ queries = self.norm3(queries)
+
+ # Cross attention block, image embedding attending to tokens
+ q = queries + query_pe
+ k = keys + key_pe
+ attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
+ keys = keys + attn_out
+ keys = self.norm4(keys)
+
+ return queries, keys
+
+
+class Attention(nn.Module):
+ """
+ An attention layer that allows for downscaling the size of the embedding
+ after projection to queries, keys, and values.
+ """
+
+ def __init__(
+ self,
+ embedding_dim: int,
+ num_heads: int,
+ downsample_rate: int = 1,
+ dropout: float = 0.0,
+ kv_in_dim: int = None,
+ ) -> None:
+ super().__init__()
+ self.embedding_dim = embedding_dim
+ self.kv_in_dim = kv_in_dim if kv_in_dim is not None else embedding_dim
+ self.internal_dim = embedding_dim // downsample_rate
+ self.num_heads = num_heads
+ assert (
+ self.internal_dim % num_heads == 0
+ ), "num_heads must divide embedding_dim."
+
+ self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
+ self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim)
+ self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim)
+ self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
+
+ self.dropout_p = dropout
+
+ def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
+ b, n, c = x.shape
+ x = x.reshape(b, n, num_heads, c // num_heads)
+ return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
+
+ def _recombine_heads(self, x: Tensor) -> Tensor:
+ b, n_heads, n_tokens, c_per_head = x.shape
+ x = x.transpose(1, 2)
+ return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C
+
+ def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
+ # Input projections
+ q = self.q_proj(q)
+ k = self.k_proj(k)
+ v = self.v_proj(v)
+
+ # Separate into heads
+ q = self._separate_heads(q, self.num_heads)
+ k = self._separate_heads(k, self.num_heads)
+ v = self._separate_heads(v, self.num_heads)
+
+ dropout_p = self.dropout_p if self.training else 0.0
+ # Attention
+ try:
+ with sdp_kernel_context(dropout_p):
+ out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
+ except Exception as e:
+ # Fall back to all kernels if the Flash attention kernel fails
+ warnings.warn(
+ f"Flash Attention kernel failed due to: {e}\nFalling back to all available "
+ f"kernels for scaled_dot_product_attention (which may have a slower speed).",
+ category=UserWarning,
+ stacklevel=2,
+ )
+ global ALLOW_ALL_KERNELS
+ ALLOW_ALL_KERNELS = True
+ out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
+
+ out = self._recombine_heads(out)
+ out = self.out_proj(out)
+
+ return out
+
+
+class RoPEAttention(Attention):
+ """Attention with rotary position encoding."""
+
+ def __init__(
+ self,
+ *args,
+ rope_theta=10000.0,
+ # whether to repeat q rope to match k length
+ # this is needed for cross-attention to memories
+ rope_k_repeat=False,
+ feat_sizes=(32, 32), # [w, h] for stride 16 feats at 512 resolution
+ **kwargs,
+ ):
+ super().__init__(*args, **kwargs)
+
+ self.compute_cis = partial(
+ compute_axial_cis, dim=self.internal_dim // self.num_heads, theta=rope_theta
+ )
+ freqs_cis = self.compute_cis(end_x=feat_sizes[0], end_y=feat_sizes[1])
+ self.freqs_cis = freqs_cis
+ self.rope_k_repeat = rope_k_repeat
+
+ def forward(
+ self, q: Tensor, k: Tensor, v: Tensor, num_k_exclude_rope: int = 0
+ ) -> Tensor:
+ # Input projections
+ q = self.q_proj(q)
+ k = self.k_proj(k)
+ v = self.v_proj(v)
+
+ # Separate into heads
+ q = self._separate_heads(q, self.num_heads)
+ k = self._separate_heads(k, self.num_heads)
+ v = self._separate_heads(v, self.num_heads)
+
+ # Apply rotary position encoding
+ w = h = math.sqrt(q.shape[-2])
+ self.freqs_cis = self.freqs_cis.to(q.device)
+ if self.freqs_cis.shape[0] != q.shape[-2]:
+ self.freqs_cis = self.compute_cis(end_x=w, end_y=h).to(q.device)
+ if q.shape[-2] != k.shape[-2]:
+ assert self.rope_k_repeat
+
+ num_k_rope = k.size(-2) - num_k_exclude_rope
+ q, k[:, :, :num_k_rope] = apply_rotary_enc(
+ q,
+ k[:, :, :num_k_rope],
+ freqs_cis=self.freqs_cis,
+ repeat_freqs_k=self.rope_k_repeat,
+ )
+
+ dropout_p = self.dropout_p if self.training else 0.0
+ # Attention
+ try:
+ with sdp_kernel_context(dropout_p):
+ out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
+ except Exception as e:
+ # Fall back to all kernels if the Flash attention kernel fails
+ warnings.warn(
+ f"Flash Attention kernel failed due to: {e}\nFalling back to all available "
+ f"kernels for scaled_dot_product_attention (which may have a slower speed).",
+ category=UserWarning,
+ stacklevel=2,
+ )
+ global ALLOW_ALL_KERNELS
+ ALLOW_ALL_KERNELS = True
+ out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
+
+ out = self._recombine_heads(out)
+ out = self.out_proj(out)
+
+ return out
diff --git a/sam2/modeling/sam2_base.py b/sam2/modeling/sam2_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..19ee315960c6de5ae12ff0cee0ee8d4605e85b70
--- /dev/null
+++ b/sam2/modeling/sam2_base.py
@@ -0,0 +1,943 @@
+# 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 torch
+import torch.distributed
+import torch.nn.functional as F
+
+from torch.nn.init import trunc_normal_
+
+from sam2.modeling.sam.mask_decoder import MaskDecoder
+from sam2.modeling.sam.prompt_encoder import PromptEncoder
+from sam2.modeling.sam.transformer import TwoWayTransformer
+from sam2.modeling.sam2_utils import get_1d_sine_pe, MLP, select_closest_cond_frames
+import pdb
+from fvcore.nn import FlopCountAnalysis
+# a large negative value as a placeholder score for missing objects
+NO_OBJ_SCORE = -1024.0
+
+
+class SAM2Base(torch.nn.Module):
+ def __init__(
+ self,
+ image_encoder,
+ memory_attention,
+ memory_encoder,
+ num_maskmem=7, # default 1 input frame + 6 previous frames
+ image_size=512,
+ backbone_stride=16, # stride of the image backbone output
+ sigmoid_scale_for_mem_enc=1.0, # scale factor for mask sigmoid prob
+ sigmoid_bias_for_mem_enc=0.0, # bias factor for mask sigmoid prob
+ # During evaluation, whether to binarize the sigmoid mask logits on interacted frames with clicks
+ binarize_mask_from_pts_for_mem_enc=False,
+ use_mask_input_as_output_without_sam=False, # on frames with mask input, whether to directly output the input mask without using a SAM prompt encoder + mask decoder
+ # The maximum number of conditioning frames to participate in the memory attention (-1 means no limit; if there are more conditioning frames than this limit,
+ # we only cross-attend to the temporally closest `max_cond_frames_in_attn` conditioning frames in the encoder when tracking each frame). This gives the model
+ # a temporal locality when handling a large number of annotated frames (since closer frames should be more important) and also avoids GPU OOM.
+ max_cond_frames_in_attn=-1,
+ # on the first frame, whether to directly add the no-memory embedding to the image feature
+ # (instead of using the transformer encoder)
+ directly_add_no_mem_embed=False,
+ # whether to use high-resolution feature maps in the SAM mask decoder
+ use_high_res_features_in_sam=False,
+ # whether to output multiple (3) masks for the first click on initial conditioning frames
+ multimask_output_in_sam=False,
+ # the minimum and maximum number of clicks to use multimask_output_in_sam (only relevant when `multimask_output_in_sam=True`;
+ # default is 1 for both, meaning that only the first click gives multimask output; also note that a box counts as two points)
+ multimask_min_pt_num=1,
+ multimask_max_pt_num=1,
+ # whether to also use multimask output for tracking (not just for the first click on initial conditioning frames; only relevant when `multimask_output_in_sam=True`)
+ multimask_output_for_tracking=False,
+ # Whether to use multimask tokens for obj ptr; Only relevant when both
+ # use_obj_ptrs_in_encoder=True and multimask_output_for_tracking=True
+ use_multimask_token_for_obj_ptr: bool = False,
+ # whether to use sigmoid to restrict ious prediction to [0-1]
+ iou_prediction_use_sigmoid=False,
+ # The memory bank's temporal stride during evaluation (i.e. the `r` parameter in XMem and Cutie; XMem and Cutie use r=5).
+ # For r>1, the (self.num_maskmem - 1) non-conditioning memory frames consist of
+ # (self.num_maskmem - 2) nearest frames from every r-th frames, plus the last frame.
+ memory_temporal_stride_for_eval=1,
+ # whether to apply non-overlapping constraints on the object masks in the memory encoder during evaluation (to avoid/alleviate superposing masks)
+ non_overlap_masks_for_mem_enc=False,
+ # whether to cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+ use_obj_ptrs_in_encoder=False,
+ # the maximum number of object pointers from other frames in encoder cross attention (only relevant when `use_obj_ptrs_in_encoder=True`)
+ max_obj_ptrs_in_encoder=16,
+ # whether to add temporal positional encoding to the object pointers in the encoder (only relevant when `use_obj_ptrs_in_encoder=True`)
+ add_tpos_enc_to_obj_ptrs=True,
+ # whether to add an extra linear projection layer for the temporal positional encoding in the object pointers to avoid potential interference
+ # with spatial positional encoding (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`)
+ proj_tpos_enc_in_obj_ptrs=False,
+ # whether to use signed distance (instead of unsigned absolute distance) in the temporal positional encoding in the object pointers
+ # (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`)
+ use_signed_tpos_enc_to_obj_ptrs=False,
+ # whether to only attend to object pointers in the past (before the current frame) in the encoder during evaluation
+ # (only relevant when `use_obj_ptrs_in_encoder=True`; this might avoid pointer information too far in the future to distract the initial tracking)
+ only_obj_ptrs_in_the_past_for_eval=False,
+ # Whether to predict if there is an object in the frame
+ pred_obj_scores: bool = False,
+ # Whether to use an MLP to predict object scores
+ pred_obj_scores_mlp: bool = False,
+ # Only relevant if pred_obj_scores=True and use_obj_ptrs_in_encoder=True;
+ # Whether to have a fixed no obj pointer when there is no object present
+ # or to use it as an additive embedding with obj_ptr produced by decoder
+ fixed_no_obj_ptr: bool = False,
+ # Soft no object, i.e. mix in no_obj_ptr softly,
+ # hope to make recovery easier if there is a mistake and mitigate accumulation of errors
+ soft_no_obj_ptr: bool = False,
+ use_mlp_for_obj_ptr_proj: bool = False,
+ # add no obj embedding to spatial frames
+ no_obj_embed_spatial: bool = False,
+ # extra arguments used to construct the SAM mask decoder; if not None, it should be a dict of kwargs to be passed into `MaskDecoder` class.
+ sam_mask_decoder_extra_args=None,
+ compile_image_encoder: bool = False,
+ ):
+ super().__init__()
+ # Part 1: the image backbone
+ self.image_encoder = image_encoder
+ # Use level 0, 1, 2 for high-res setting, or just level 2 for the default setting
+ self.use_high_res_features_in_sam = use_high_res_features_in_sam
+ self.num_feature_levels = 3 if use_high_res_features_in_sam else 1
+ self.use_obj_ptrs_in_encoder = use_obj_ptrs_in_encoder
+ self.max_obj_ptrs_in_encoder = max_obj_ptrs_in_encoder
+ if use_obj_ptrs_in_encoder:
+ # A conv layer to downsample the mask prompt to stride 4 (the same stride as
+ # low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale,
+ # so that it can be fed into the SAM mask decoder to generate a pointer.
+ self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4)
+ self.add_tpos_enc_to_obj_ptrs = add_tpos_enc_to_obj_ptrs
+ if proj_tpos_enc_in_obj_ptrs:
+ assert add_tpos_enc_to_obj_ptrs # these options need to be used together
+ self.proj_tpos_enc_in_obj_ptrs = proj_tpos_enc_in_obj_ptrs
+ self.use_signed_tpos_enc_to_obj_ptrs = use_signed_tpos_enc_to_obj_ptrs
+ self.only_obj_ptrs_in_the_past_for_eval = only_obj_ptrs_in_the_past_for_eval
+
+ # Part 2: memory attention to condition current frame's visual features
+ # with memories (and obj ptrs) from past frames
+ self.memory_attention = memory_attention
+ self.hidden_dim = image_encoder.neck.d_model
+
+ # Part 3: memory encoder for the previous frame's outputs
+ self.memory_encoder = memory_encoder
+ self.mem_dim = self.hidden_dim
+ if hasattr(self.memory_encoder, "out_proj") and hasattr(
+ self.memory_encoder.out_proj, "weight"
+ ):
+ # if there is compression of memories along channel dim
+ self.mem_dim = self.memory_encoder.out_proj.weight.shape[0]
+ self.num_maskmem = num_maskmem # Number of memories accessible
+ # Temporal encoding of the memories
+ self.maskmem_tpos_enc = torch.nn.Parameter(
+ torch.zeros(num_maskmem, 1, 1, self.mem_dim)
+ )
+ trunc_normal_(self.maskmem_tpos_enc, std=0.02)
+ # a single token to indicate no memory embedding from previous frames
+ self.no_mem_embed = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))
+ self.no_mem_pos_enc = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))
+ trunc_normal_(self.no_mem_embed, std=0.02)
+ trunc_normal_(self.no_mem_pos_enc, std=0.02)
+ self.directly_add_no_mem_embed = directly_add_no_mem_embed
+ # Apply sigmoid to the output raw mask logits (to turn them from
+ # range (-inf, +inf) to range (0, 1)) before feeding them into the memory encoder
+ self.sigmoid_scale_for_mem_enc = sigmoid_scale_for_mem_enc
+ self.sigmoid_bias_for_mem_enc = sigmoid_bias_for_mem_enc
+ self.binarize_mask_from_pts_for_mem_enc = binarize_mask_from_pts_for_mem_enc
+ self.non_overlap_masks_for_mem_enc = non_overlap_masks_for_mem_enc
+ self.memory_temporal_stride_for_eval = memory_temporal_stride_for_eval
+ # On frames with mask input, whether to directly output the input mask without
+ # using a SAM prompt encoder + mask decoder
+ self.use_mask_input_as_output_without_sam = use_mask_input_as_output_without_sam
+ self.multimask_output_in_sam = multimask_output_in_sam
+ self.multimask_min_pt_num = multimask_min_pt_num
+ self.multimask_max_pt_num = multimask_max_pt_num
+ self.multimask_output_for_tracking = multimask_output_for_tracking
+ self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr
+ self.iou_prediction_use_sigmoid = iou_prediction_use_sigmoid
+
+ # Part 4: SAM-style prompt encoder (for both mask and point inputs)
+ # and SAM-style mask decoder for the final mask output
+ self.image_size = image_size
+ self.backbone_stride = backbone_stride
+ self.sam_mask_decoder_extra_args = sam_mask_decoder_extra_args
+ self.pred_obj_scores = pred_obj_scores
+ self.pred_obj_scores_mlp = pred_obj_scores_mlp
+ self.fixed_no_obj_ptr = fixed_no_obj_ptr
+ self.soft_no_obj_ptr = soft_no_obj_ptr
+ if self.fixed_no_obj_ptr:
+ assert self.pred_obj_scores
+ assert self.use_obj_ptrs_in_encoder
+ if self.pred_obj_scores and self.use_obj_ptrs_in_encoder:
+ self.no_obj_ptr = torch.nn.Parameter(torch.zeros(1, self.hidden_dim))
+ trunc_normal_(self.no_obj_ptr, std=0.02)
+ self.use_mlp_for_obj_ptr_proj = use_mlp_for_obj_ptr_proj
+ self.no_obj_embed_spatial = None
+ if no_obj_embed_spatial:
+ self.no_obj_embed_spatial = torch.nn.Parameter(torch.zeros(1, self.mem_dim))
+ trunc_normal_(self.no_obj_embed_spatial, std=0.02)
+
+ self._build_sam_heads()
+ self.max_cond_frames_in_attn = max_cond_frames_in_attn
+
+ # Model compilation
+ if compile_image_encoder:
+ # Compile the forward function (not the full module) to allow loading checkpoints.
+ print(
+ "Image encoder compilation is enabled. First forward pass will be slow."
+ )
+ self.image_encoder.forward = torch.compile(
+ self.image_encoder.forward,
+ mode="max-autotune",
+ fullgraph=True,
+ dynamic=False,
+ )
+
+ @property
+ def device(self):
+ return next(self.parameters()).device
+
+ def forward(self, *args, **kwargs):
+ raise NotImplementedError(
+ "Please use the corresponding methods in SAM2VideoPredictor for inference or SAM2Train for training/fine-tuning"
+ "See notebooks/video_predictor_example.ipynb for an inference example."
+ )
+
+ def _build_sam_heads(self):
+ """Build SAM-style prompt encoder and mask decoder."""
+ self.sam_prompt_embed_dim = self.hidden_dim
+ self.sam_image_embedding_size = self.image_size // self.backbone_stride
+
+ # build PromptEncoder and MaskDecoder from SAM
+ # (their hyperparameters like `mask_in_chans=16` are from SAM code)
+ self.sam_prompt_encoder = PromptEncoder(
+ embed_dim=self.sam_prompt_embed_dim,
+ image_embedding_size=(
+ self.sam_image_embedding_size,
+ self.sam_image_embedding_size,
+ ),
+ input_image_size=(self.image_size, self.image_size),
+ mask_in_chans=16,
+ )
+ self.sam_mask_decoder = MaskDecoder(
+ num_multimask_outputs=3,
+ transformer=TwoWayTransformer(
+ depth=2,
+ embedding_dim=self.sam_prompt_embed_dim,
+ mlp_dim=2048,
+ num_heads=8,
+ ),
+ transformer_dim=self.sam_prompt_embed_dim,
+ iou_head_depth=3,
+ iou_head_hidden_dim=256,
+ use_high_res_features=self.use_high_res_features_in_sam,
+ iou_prediction_use_sigmoid=self.iou_prediction_use_sigmoid,
+ pred_obj_scores=self.pred_obj_scores,
+ pred_obj_scores_mlp=self.pred_obj_scores_mlp,
+ use_multimask_token_for_obj_ptr=self.use_multimask_token_for_obj_ptr,
+ **(self.sam_mask_decoder_extra_args or {}),
+ )
+ if self.use_obj_ptrs_in_encoder:
+ # a linear projection on SAM output tokens to turn them into object pointers
+ self.obj_ptr_proj = torch.nn.Linear(self.hidden_dim, self.hidden_dim)
+ if self.use_mlp_for_obj_ptr_proj:
+ self.obj_ptr_proj = MLP(
+ self.hidden_dim, self.hidden_dim, self.hidden_dim, 3
+ )
+ else:
+ self.obj_ptr_proj = torch.nn.Identity()
+ if self.proj_tpos_enc_in_obj_ptrs:
+ # a linear projection on temporal positional encoding in object pointers to
+ # avoid potential interference with spatial positional encoding
+ self.obj_ptr_tpos_proj = torch.nn.Linear(self.hidden_dim, self.mem_dim)
+ else:
+ self.obj_ptr_tpos_proj = torch.nn.Identity()
+
+ def _forward_sam_heads(
+ self,
+ backbone_features,
+ point_inputs=None,
+ mask_inputs=None,
+ high_res_features=None,
+ multimask_output=False,
+ ):
+ """
+ Forward SAM prompt encoders and mask heads.
+
+ Inputs:
+ - backbone_features: image features of [B, C, H, W] shape
+ - point_inputs: a dictionary with "point_coords" and "point_labels", where
+ 1) "point_coords" has [B, P, 2] shape and float32 dtype and contains the
+ absolute pixel-unit coordinate in (x, y) format of the P input points
+ 2) "point_labels" has shape [B, P] and int32 dtype, where 1 means
+ positive clicks, 0 means negative clicks, and -1 means padding
+ - mask_inputs: a mask of [B, 1, H*16, W*16] shape, float or bool, with the
+ same spatial size as the image.
+ - high_res_features: either 1) None or 2) or a list of length 2 containing
+ two feature maps of [B, C, 4*H, 4*W] and [B, C, 2*H, 2*W] shapes respectively,
+ which will be used as high-resolution feature maps for SAM decoder.
+ - multimask_output: if it's True, we output 3 candidate masks and their 3
+ corresponding IoU estimates, and if it's False, we output only 1 mask and
+ its corresponding IoU estimate.
+
+ Outputs:
+ - low_res_multimasks: [B, M, H*4, W*4] shape (where M = 3 if
+ `multimask_output=True` and M = 1 if `multimask_output=False`), the SAM
+ output mask logits (before sigmoid) for the low-resolution masks, with 4x
+ the resolution (1/4 stride) of the input backbone_features.
+ - high_res_multimasks: [B, M, H*16, W*16] shape (where M = 3
+ if `multimask_output=True` and M = 1 if `multimask_output=False`),
+ upsampled from the low-resolution masks, with shape size as the image
+ (stride is 1 pixel).
+ - ious, [B, M] shape, where (where M = 3 if `multimask_output=True` and M = 1
+ if `multimask_output=False`), the estimated IoU of each output mask.
+ - low_res_masks: [B, 1, H*4, W*4] shape, the best mask in `low_res_multimasks`.
+ If `multimask_output=True`, it's the mask with the highest IoU estimate.
+ If `multimask_output=False`, it's the same as `low_res_multimasks`.
+ - high_res_masks: [B, 1, H*16, W*16] shape, the best mask in `high_res_multimasks`.
+ If `multimask_output=True`, it's the mask with the highest IoU estimate.
+ If `multimask_output=False`, it's the same as `high_res_multimasks`.
+ - obj_ptr: [B, C] shape, the object pointer vector for the output mask, extracted
+ based on the output token from the SAM mask decoder.
+ """
+ B = backbone_features.size(0)
+ device = backbone_features.device
+ assert backbone_features.size(1) == self.sam_prompt_embed_dim
+ assert backbone_features.size(2) == self.sam_image_embedding_size
+ assert backbone_features.size(3) == self.sam_image_embedding_size
+
+ # a) Handle point prompts
+ if point_inputs is not None:
+ sam_point_coords = point_inputs["point_coords"]
+ sam_point_labels = point_inputs["point_labels"]
+ assert sam_point_coords.size(0) == B and sam_point_labels.size(0) == B
+ else:
+ # If no points are provide, pad with an empty point (with label -1)
+ sam_point_coords = torch.zeros(B, 1, 2, device=device)
+ sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device)
+
+ # b) Handle mask prompts
+ if mask_inputs is not None:
+ # If mask_inputs is provided, downsize it into low-res mask input if needed
+ # and feed it as a dense mask prompt into the SAM mask encoder
+ assert len(mask_inputs.shape) == 4 and mask_inputs.shape[:2] == (B, 1)
+ if mask_inputs.shape[-2:] != self.sam_prompt_encoder.mask_input_size:
+ sam_mask_prompt = F.interpolate(
+ mask_inputs.float(),
+ size=self.sam_prompt_encoder.mask_input_size,
+ align_corners=False,
+ mode="bilinear",
+ antialias=True, # use antialias for downsampling
+ )
+ else:
+ sam_mask_prompt = mask_inputs
+ else:
+ # Otherwise, simply feed None (and SAM's prompt encoder will add
+ # a learned `no_mask_embed` to indicate no mask input in this case).
+ sam_mask_prompt = None
+
+ sparse_embeddings, dense_embeddings = self.sam_prompt_encoder(
+ points=(sam_point_coords, sam_point_labels),
+ boxes=None,
+ masks=sam_mask_prompt,
+ )
+
+
+
+ (
+ low_res_multimasks,
+ ious,
+ sam_output_tokens,
+ object_score_logits,
+ ) = self.sam_mask_decoder(
+ image_embeddings=backbone_features,
+ image_pe=self.sam_prompt_encoder.get_dense_pe(),
+ sparse_prompt_embeddings=sparse_embeddings,
+ dense_prompt_embeddings=dense_embeddings,
+ multimask_output=multimask_output,
+ repeat_image=False, # the image is already batched
+ high_res_features=high_res_features,
+ )
+ if self.pred_obj_scores:
+ is_obj_appearing = object_score_logits > 0
+
+ # Mask used for spatial memories is always a *hard* choice between obj and no obj,
+ # consistent with the actual mask prediction
+ low_res_multimasks = torch.where(
+ is_obj_appearing[:, None, None],
+ low_res_multimasks,
+ NO_OBJ_SCORE,
+ )
+
+ # convert masks from possibly bfloat16 (or float16) to float32
+ # (older PyTorch versions before 2.1 don't support `interpolate` on bf16)
+ low_res_multimasks = low_res_multimasks.float()
+ high_res_multimasks = F.interpolate(
+ low_res_multimasks,
+ size=(self.image_size, self.image_size),
+ mode="bilinear",
+ align_corners=False,
+ )
+
+ sam_output_token = sam_output_tokens[:, 0]
+ if multimask_output:
+ # take the best mask prediction (with the highest IoU estimation)
+ best_iou_inds = torch.argmax(ious, dim=-1)
+ batch_inds = torch.arange(B, device=device)
+ low_res_masks = low_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)
+ high_res_masks = high_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)
+ if sam_output_tokens.size(1) > 1:
+ sam_output_token = sam_output_tokens[batch_inds, best_iou_inds]
+ else:
+ low_res_masks, high_res_masks = low_res_multimasks, high_res_multimasks
+
+ # Extract object pointer from the SAM output token (with occlusion handling)
+ obj_ptr = self.obj_ptr_proj(sam_output_token)
+ if self.pred_obj_scores:
+ # Allow *soft* no obj ptr, unlike for masks
+ if self.soft_no_obj_ptr:
+ lambda_is_obj_appearing = object_score_logits.sigmoid()
+ else:
+ lambda_is_obj_appearing = is_obj_appearing.float()
+
+ if self.fixed_no_obj_ptr:
+ obj_ptr = lambda_is_obj_appearing * obj_ptr
+ obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr
+
+
+ #######SAM2Long########
+ obj_ptrs = self.obj_ptr_proj(sam_output_tokens)
+ lambda_is_obj_appearing = is_obj_appearing.float()[:, None]
+ obj_ptrs = lambda_is_obj_appearing * obj_ptrs
+ obj_ptrs = obj_ptrs + (1 - lambda_is_obj_appearing) * self.no_obj_ptr
+
+
+ return (
+ low_res_multimasks,
+ high_res_multimasks,
+ ious,
+ low_res_masks,
+ high_res_masks,
+ obj_ptr,
+ object_score_logits,
+ obj_ptrs,
+ )
+
+ def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs):
+ """
+ Directly turn binary `mask_inputs` into a output mask logits without using SAM.
+ (same input and output shapes as in _forward_sam_heads above).
+ """
+ # Use -10/+10 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid).
+ out_scale, out_bias = 20.0, -10.0 # sigmoid(-10.0)=4.5398e-05
+ mask_inputs_float = mask_inputs.float()
+ high_res_masks = mask_inputs_float * out_scale + out_bias
+ low_res_masks = F.interpolate(
+ high_res_masks,
+ size=(high_res_masks.size(-2) // 4, high_res_masks.size(-1) // 4),
+ align_corners=False,
+ mode="bilinear",
+ antialias=True, # use antialias for downsampling
+ )
+ # a dummy IoU prediction of all 1's under mask input
+ ious = mask_inputs.new_ones(mask_inputs.size(0), 1).float()
+ if not self.use_obj_ptrs_in_encoder:
+ # all zeros as a dummy object pointer (of shape [B, C])
+ obj_ptr = torch.zeros(
+ mask_inputs.size(0), self.hidden_dim, device=mask_inputs.device
+ )
+ else:
+ # produce an object pointer using the SAM decoder from the mask input
+ _, _, _, _, _, obj_ptr, _, _ = self._forward_sam_heads(
+ backbone_features=backbone_features,
+ mask_inputs=self.mask_downsample(mask_inputs_float),
+ high_res_features=high_res_features,
+ )
+ # In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem;
+ # Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying
+ # on the object_scores from the SAM decoder.
+ is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1)
+ is_obj_appearing = is_obj_appearing[..., None]
+ lambda_is_obj_appearing = is_obj_appearing.float()
+ object_score_logits = out_scale * lambda_is_obj_appearing + out_bias
+ if self.pred_obj_scores:
+ if self.fixed_no_obj_ptr:
+ obj_ptr = lambda_is_obj_appearing * obj_ptr
+ obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr
+
+ return (
+ low_res_masks,
+ high_res_masks,
+ ious,
+ low_res_masks,
+ high_res_masks,
+ obj_ptr,
+ object_score_logits,
+ None,
+ )
+
+ def forward_image(self, img_batch: torch.Tensor):
+ """Get the image feature on the input batch."""
+ backbone_out = self.image_encoder(img_batch)
+ if self.use_high_res_features_in_sam:
+ # precompute projected level 0 and level 1 features in SAM decoder
+ # to avoid running it again on every SAM click
+ backbone_out["backbone_fpn"][0] = self.sam_mask_decoder.conv_s0(
+ backbone_out["backbone_fpn"][0]
+ )
+ backbone_out["backbone_fpn"][1] = self.sam_mask_decoder.conv_s1(
+ backbone_out["backbone_fpn"][1]
+ )
+ return backbone_out
+
+ def _prepare_backbone_features(self, backbone_out):
+ """Prepare and flatten visual features."""
+ backbone_out = backbone_out.copy()
+ assert len(backbone_out["backbone_fpn"]) == len(backbone_out["vision_pos_enc"])
+ assert len(backbone_out["backbone_fpn"]) >= self.num_feature_levels
+
+ feature_maps = backbone_out["backbone_fpn"][-self.num_feature_levels :]
+ vision_pos_embeds = backbone_out["vision_pos_enc"][-self.num_feature_levels :]
+
+ feat_sizes = [(x.shape[-2], x.shape[-1]) for x in vision_pos_embeds]
+ # flatten NxCxHxW to HWxNxC
+ vision_feats = [x.flatten(2).permute(2, 0, 1) for x in feature_maps]
+ vision_pos_embeds = [x.flatten(2).permute(2, 0, 1) for x in vision_pos_embeds]
+
+ return backbone_out, vision_feats, vision_pos_embeds, feat_sizes
+
+ def _prepare_memory_conditioned_features(
+ self,
+ frame_idx,
+ is_init_cond_frame,
+ current_vision_feats,
+ current_vision_pos_embeds,
+ feat_sizes,
+ output_dict,
+ num_frames,
+ track_in_reverse=False, # tracking in reverse time order (for demo usage)
+ mem_pick_index=0,
+ start_frame_idx=0,
+ iou_thre=0.1,
+ ):
+ """Fuse the current frame's visual feature map with previous memory."""
+ B = current_vision_feats[-1].size(1) # batch size on this frame
+ C = self.hidden_dim
+ H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size
+ device = current_vision_feats[-1].device
+ # The case of `self.num_maskmem == 0` below is primarily used for reproducing SAM on images.
+ # In this case, we skip the fusion with any memory.
+ if self.num_maskmem == 0: # Disable memory and skip fusion
+ pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)
+ return pix_feat
+
+ num_obj_ptr_tokens = 0
+ tpos_sign_mul = -1 if track_in_reverse else 1
+ # Step 1: condition the visual features of the current frame on previous memories
+ if not is_init_cond_frame:
+ # Retrieve the memories encoded with the maskmem backbone
+ to_cat_memory, to_cat_memory_pos_embed = [], []
+ # Add conditioning frames's output first (all cond frames have t_pos=0 for
+ # when getting temporal positional embedding below)
+ assert len(output_dict["cond_frame_outputs"]) > 0
+ # Select a maximum number of temporally closest cond frames for cross attention
+ cond_outputs = output_dict["cond_frame_outputs"]
+ selected_cond_outputs, unselected_cond_outputs = select_closest_cond_frames(
+ frame_idx, cond_outputs, self.max_cond_frames_in_attn
+ )
+ t_pos_and_prevs = [(0, out) for out in selected_cond_outputs.values()]
+ # Add last (self.num_maskmem - 1) frames before current frame for non-conditioning memory
+ # the earliest one has t_pos=1 and the latest one has t_pos=self.num_maskmem-1
+ # We also allow taking the memory frame non-consecutively (with stride>1), in which case
+ # we take (self.num_maskmem - 2) frames among every stride-th frames plus the last frame.
+ stride = 1 if self.training else self.memory_temporal_stride_for_eval
+
+ max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder)
+ num_object = int(cond_outputs[start_frame_idx]['obj_ptr'].shape[0]) ##always one
+
+
+ if frame_idx <= start_frame_idx+1 or mem_pick_index==0:
+ valid_indices = []
+ else:
+ valid_indices = []
+ for i in range(frame_idx - 1, start_frame_idx, -1):
+ object_score = output_dict["non_cond_frame_outputs"][i]['object_score_logits'][...,mem_pick_index[i]]
+ iou = output_dict["non_cond_frame_outputs"][i]['ious'][...,mem_pick_index[i]]
+ # print("threshold", iou_thre)
+ if iou.item() > iou_thre and object_score.item() > 0:
+ valid_indices.insert(0, i)
+ if len(valid_indices) >= max_obj_ptrs_in_encoder - 1:
+ break
+ if frame_idx - 1 not in valid_indices: ##pick last frame
+ valid_indices.append(frame_idx-1)
+
+ prev_idxs = [start_frame_idx]
+ for t_pos in range(1, self.num_maskmem):
+ idx = t_pos - self.num_maskmem
+ if idx < -len(valid_indices):
+ continue
+ out = output_dict["non_cond_frame_outputs"].get(valid_indices[idx], None)
+ if out is None:
+ out = unselected_cond_outputs.get(valid_indices[idx], None)
+ t_pos_and_prevs.append((t_pos, out))
+ prev_idxs.append(valid_indices[idx])
+
+ object_frame_score = [torch.ones(num_object).to(cond_outputs[start_frame_idx]['obj_ptr'].device, torch.bfloat16)*10]
+ for (t_pos, prev), prev_idx in zip(t_pos_and_prevs, prev_idxs):
+ if prev is None:
+ continue # skip padding frames
+ # "maskmem_features" might have been offloaded to CPU in demo use cases,
+ # so we load it back to GPU (it's a no-op if it's already on GPU).
+ if t_pos > 0 and mem_pick_index != 0:
+ object_frame_score.append(prev["object_score_logits"][...,mem_pick_index[prev_idx]].view(-1))
+ feats = prev["maskmem_features"][...,mem_pick_index[prev_idx]].to(device, non_blocking=True)
+ else:
+ feats = prev["maskmem_features"].to(device, non_blocking=True)
+ to_cat_memory.append(feats.flatten(2).permute(2, 0, 1))
+ # Spatial positional encoding (it might have been offloaded to CPU in eval)
+ maskmem_enc = prev["maskmem_pos_enc"][-1].to(device)
+ maskmem_enc = maskmem_enc.flatten(2).permute(2, 0, 1)
+ # Temporal positional encoding
+ maskmem_enc = (
+ maskmem_enc + self.maskmem_tpos_enc[self.num_maskmem - t_pos - 1]
+ )
+ to_cat_memory_pos_embed.append(maskmem_enc)
+
+ # Construct the list of past object pointers
+ if self.use_obj_ptrs_in_encoder:
+ max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder)
+ # First add those object pointers from selected conditioning frames
+ # (optionally, only include object pointers in the past during evaluation)
+ if not self.training and self.only_obj_ptrs_in_the_past_for_eval:
+ ptr_cond_outputs = {
+ t: out
+ for t, out in selected_cond_outputs.items()
+ if (t >= frame_idx if track_in_reverse else t <= frame_idx)
+ }
+ else:
+ ptr_cond_outputs = selected_cond_outputs
+ pos_and_ptrs = [
+ # Temporal pos encoding contains how far away each pointer is from current frame
+ (abs(frame_idx - t), out["obj_ptr"])
+ for t, out in ptr_cond_outputs.items()
+ ]
+ # Add up to (max_obj_ptrs_in_encoder - 1) non-conditioning frames before current frame
+ object_ptr_score = [torch.ones(num_object).to(cond_outputs[start_frame_idx]['obj_ptr'].device, torch.bfloat16)*10]
+ for t_diff in range(1, max_obj_ptrs_in_encoder):
+ if -t_diff <= -len(valid_indices):
+ break
+ out = output_dict["non_cond_frame_outputs"].get(
+ valid_indices[-t_diff], unselected_cond_outputs.get(valid_indices[-t_diff], None))
+ if out is not None:
+ mem_idx = mem_pick_index[valid_indices[-t_diff]]
+ object_ptr_score.append(out['object_score_logits'][...,mem_idx].view(-1))
+ pos_and_ptrs.append((t_diff, out["obj_ptr"][...,mem_idx]))
+ # object_ptr_score.append(output_dict["non_cond_frame_outputs"][valid_indices[-t_diff]]['object_score'].item())
+ # If we have at least one object pointer, add them to the across attention
+ if len(pos_and_ptrs) > 0:
+ pos_list, ptrs_list = zip(*pos_and_ptrs)
+ # stack object pointers along dim=0 into [ptr_seq_len, B, C] shape
+ obj_ptrs = torch.stack(ptrs_list, dim=0)
+ # a temporal positional embedding based on how far each object pointer is from
+ # the current frame (sine embedding normalized by the max pointer num).
+ if self.add_tpos_enc_to_obj_ptrs:
+ t_diff_max = max_obj_ptrs_in_encoder - 1
+ tpos_dim = C if self.proj_tpos_enc_in_obj_ptrs else self.mem_dim
+ obj_pos = torch.tensor(pos_list, device=device)
+ obj_pos = get_1d_sine_pe(obj_pos / t_diff_max, dim=tpos_dim)
+ obj_pos = self.obj_ptr_tpos_proj(obj_pos)
+ obj_pos = obj_pos.unsqueeze(1).expand(-1, B, self.mem_dim)
+ else:
+ obj_pos = obj_ptrs.new_zeros(len(pos_list), B, self.mem_dim)
+ if self.mem_dim < C:
+ # split a pointer into (C // self.mem_dim) tokens for self.mem_dim < C
+ obj_ptrs = obj_ptrs.reshape(
+ -1, B, C // self.mem_dim, self.mem_dim
+ )
+ obj_ptrs = obj_ptrs.permute(0, 2, 1, 3).flatten(0, 1)
+ obj_pos = obj_pos.repeat_interleave(C // self.mem_dim, dim=0)
+ to_cat_memory.append(obj_ptrs)
+ to_cat_memory_pos_embed.append(obj_pos)
+ num_obj_ptr_tokens = obj_ptrs.shape[0]
+ else:
+ num_obj_ptr_tokens = 0
+ else:
+ # for initial conditioning frames, encode them without using any previous memory
+ if self.directly_add_no_mem_embed:
+ # directly add no-mem embedding (instead of using the transformer encoder)
+ pix_feat_with_mem = current_vision_feats[-1] + self.no_mem_embed
+ pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W)
+ return pix_feat_with_mem
+
+ # Use a dummy token on the first frame (to avoid empty memory input to tranformer encoder)
+ to_cat_memory = [self.no_mem_embed.expand(1, B, self.mem_dim)]
+ to_cat_memory_pos_embed = [self.no_mem_pos_enc.expand(1, B, self.mem_dim)]
+
+ # Step 2: Concatenate the memories and forward through the transformer encoder
+ memory = torch.cat(to_cat_memory, dim=0)
+ memory_pos_embed = torch.cat(to_cat_memory_pos_embed, dim=0)
+
+ pix_feat_with_mem = self.memory_attention(
+ curr=current_vision_feats,
+ curr_pos=current_vision_pos_embeds,
+ memory=memory,
+ memory_pos=memory_pos_embed,
+ num_obj_ptr_tokens=num_obj_ptr_tokens,
+ object_frame_scores=object_frame_score,
+ object_ptr_scores=object_ptr_score,
+ )
+ # reshape the output (HW)BC => BCHW
+ pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W)
+
+ return pix_feat_with_mem
+
+ def _encode_new_memory(
+ self,
+ current_vision_feats,
+ feat_sizes,
+ pred_masks_high_res,
+ object_score_logits,
+ is_mask_from_pts,
+ ):
+ """Encode the current image and its prediction into a memory feature."""
+ B = current_vision_feats[-1].size(1) # batch size on this frame
+ C = self.hidden_dim
+ H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size
+ # top-level feature, (HW)BC => BCHW
+ pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)
+ if self.non_overlap_masks_for_mem_enc and not self.training:
+ # optionally, apply non-overlapping constraints to the masks (it's applied
+ # in the batch dimension and should only be used during eval, where all
+ # the objects come from the same video under batch size 1).
+ pred_masks_high_res = self._apply_non_overlapping_constraints(
+ pred_masks_high_res
+ )
+ # scale the raw mask logits with a temperature before applying sigmoid
+ binarize = self.binarize_mask_from_pts_for_mem_enc and is_mask_from_pts
+ if binarize and not self.training:
+ mask_for_mem = (pred_masks_high_res > 0).float()
+ else:
+ # apply sigmoid on the raw mask logits to turn them into range (0, 1)
+ mask_for_mem = torch.sigmoid(pred_masks_high_res)
+ # apply scale and bias terms to the sigmoid probabilities
+ if self.sigmoid_scale_for_mem_enc != 1.0:
+ mask_for_mem = mask_for_mem * self.sigmoid_scale_for_mem_enc
+ if self.sigmoid_bias_for_mem_enc != 0.0:
+ mask_for_mem = mask_for_mem + self.sigmoid_bias_for_mem_enc
+
+ maskmem_out = self.memory_encoder(
+ pix_feat, mask_for_mem, skip_mask_sigmoid=True # sigmoid already applied
+ )
+ maskmem_features = maskmem_out["vision_features"]
+ maskmem_pos_enc = maskmem_out["vision_pos_enc"]
+ # add a no-object embedding to the spatial memory to indicate that the frame
+ # is predicted to be occluded (i.e. no object is appearing in the frame)
+ if self.no_obj_embed_spatial is not None:
+ is_obj_appearing = (object_score_logits > 0).float()
+ maskmem_features += (
+ 1 - is_obj_appearing[..., None, None]
+ ) * self.no_obj_embed_spatial[..., None, None].expand(
+ *maskmem_features.shape
+ )
+
+ return maskmem_features, maskmem_pos_enc
+
+ def _track_step(
+ self,
+ frame_idx,
+ is_init_cond_frame,
+ current_vision_feats,
+ current_vision_pos_embeds,
+ feat_sizes,
+ point_inputs,
+ mask_inputs,
+ output_dict,
+ num_frames,
+ track_in_reverse,
+ prev_sam_mask_logits,
+ mem_pick_index=0,
+ start_frame_idx=0,
+ iou_thre=0.1,
+ ):
+ current_out = {"point_inputs": point_inputs, "mask_inputs": mask_inputs}
+ # High-resolution feature maps for the SAM head, reshape (HW)BC => BCHW
+ if len(current_vision_feats) > 1:
+ high_res_features = [
+ x.permute(1, 2, 0).view(x.size(1), x.size(2), *s)
+ for x, s in zip(current_vision_feats[:-1], feat_sizes[:-1])
+ ]
+ else:
+ high_res_features = None
+ if mask_inputs is not None and self.use_mask_input_as_output_without_sam:
+ # When use_mask_input_as_output_without_sam=True, we directly output the mask input
+ # (see it as a GT mask) without using a SAM prompt encoder + mask decoder.
+ pix_feat = current_vision_feats[-1].permute(1, 2, 0)
+ pix_feat = pix_feat.view(-1, self.hidden_dim, *feat_sizes[-1])
+ sam_outputs = self._use_mask_as_output(
+ pix_feat, high_res_features, mask_inputs
+ )
+ else:
+ # fused the visual feature with previous memory features in the memory bank
+ pix_feat = self._prepare_memory_conditioned_features(
+ frame_idx=frame_idx,
+ is_init_cond_frame=is_init_cond_frame,
+ current_vision_feats=current_vision_feats[-1:],
+ current_vision_pos_embeds=current_vision_pos_embeds[-1:],
+ feat_sizes=feat_sizes[-1:],
+ output_dict=output_dict,
+ num_frames=num_frames,
+ track_in_reverse=track_in_reverse,
+ mem_pick_index=mem_pick_index,
+ start_frame_idx=start_frame_idx,
+ iou_thre=iou_thre,
+ )
+ # apply SAM-style segmentation head
+ # here we might feed previously predicted low-res SAM mask logits into the SAM mask decoder,
+ # e.g. in demo where such logits come from earlier interaction instead of correction sampling
+ # (in this case, any `mask_inputs` shouldn't reach here as they are sent to _use_mask_as_output instead)
+ if prev_sam_mask_logits is not None:
+ assert point_inputs is not None and mask_inputs is None
+ mask_inputs = prev_sam_mask_logits
+ multimask_output = self._use_multimask(is_init_cond_frame, point_inputs)
+ sam_outputs = self._forward_sam_heads(
+ backbone_features=pix_feat,
+ point_inputs=point_inputs,
+ mask_inputs=mask_inputs,
+ high_res_features=high_res_features,
+ multimask_output=multimask_output,
+ )
+
+ return current_out, sam_outputs, high_res_features, pix_feat
+
+ def _encode_memory_in_output(
+ self,
+ current_vision_feats,
+ feat_sizes,
+ point_inputs,
+ run_mem_encoder,
+ high_res_masks,
+ object_score_logits,
+ current_out,
+ ):
+ if run_mem_encoder and self.num_maskmem > 0:
+ high_res_masks_for_mem_enc = high_res_masks
+ maskmem_features, maskmem_pos_enc = self._encode_new_memory(
+ current_vision_feats=current_vision_feats,
+ feat_sizes=feat_sizes,
+ pred_masks_high_res=high_res_masks_for_mem_enc,
+ object_score_logits=object_score_logits,
+ is_mask_from_pts=(point_inputs is not None),
+ )
+ current_out["maskmem_features"] = maskmem_features
+ current_out["maskmem_pos_enc"] = maskmem_pos_enc
+ else:
+ current_out["maskmem_features"] = None
+ current_out["maskmem_pos_enc"] = None
+
+ def track_step(
+ self,
+ frame_idx,
+ is_init_cond_frame,
+ current_vision_feats,
+ current_vision_pos_embeds,
+ feat_sizes,
+ point_inputs,
+ mask_inputs,
+ output_dict,
+ num_frames,
+ track_in_reverse=False, # tracking in reverse time order (for demo usage)
+ # Whether to run the memory encoder on the predicted masks. Sometimes we might want
+ # to skip the memory encoder with `run_mem_encoder=False`. For example,
+ # in demo we might call `track_step` multiple times for each user click,
+ # and only encode the memory when the user finalizes their clicks. And in ablation
+ # settings like SAM training on static images, we don't need the memory encoder.
+ run_mem_encoder=True,
+ # The previously predicted SAM mask logits (which can be fed together with new clicks in demo).
+ prev_sam_mask_logits=None,
+ mem_pick_index=0,
+ start_frame_idx=0,
+ iou_thre=0.1,
+ ):
+ current_out, sam_outputs, _, _ = self._track_step(
+ frame_idx,
+ is_init_cond_frame,
+ current_vision_feats,
+ current_vision_pos_embeds,
+ feat_sizes,
+ point_inputs,
+ mask_inputs,
+ output_dict,
+ num_frames,
+ track_in_reverse,
+ prev_sam_mask_logits,
+ mem_pick_index,
+ start_frame_idx,
+ iou_thre,
+ )
+
+ (
+ low_res_multimasks,
+ high_res_multimasks,
+ ious,
+ low_res_masks,
+ high_res_masks,
+ obj_ptr,
+ object_score_logits,
+ obj_ptrs,
+ ) = sam_outputs
+
+
+ if mem_pick_index == 0:
+ current_out["pred_masks"] = low_res_masks
+ current_out["ious"] = ious.max(-1)[0]
+ current_out["object_score"] = object_score_logits[:,0]
+ current_out["obj_ptr"] = obj_ptr
+ current_out["pred_masks_high_res"] = high_res_masks
+ else:
+ current_out["pred_masks"] = low_res_multimasks
+ current_out["ious"] = ious
+ current_out["object_score"] = object_score_logits[:,0]
+ current_out["obj_ptr"] = obj_ptrs
+ current_out["pred_masks_high_res"] = high_res_multimasks
+
+
+
+
+ if not self.training:
+ # Only add this in inference (to avoid unused param in activation checkpointing;
+ # it's mainly used in the demo to encode spatial memories w/ consolidated masks)
+ current_out["object_score_logits"] = object_score_logits
+
+
+ return current_out
+
+ def _use_multimask(self, is_init_cond_frame, point_inputs):
+ """Whether to use multimask output in the SAM head."""
+ num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1)
+ multimask_output = (
+ self.multimask_output_in_sam
+ and (is_init_cond_frame or self.multimask_output_for_tracking)
+ and (self.multimask_min_pt_num <= num_pts <= self.multimask_max_pt_num)
+ )
+ return multimask_output
+
+ def _apply_non_overlapping_constraints(self, pred_masks):
+ """
+ Apply non-overlapping constraints to the object scores in pred_masks. Here we
+ keep only the highest scoring object at each spatial location in pred_masks.
+ """
+ batch_size = pred_masks.size(0)
+ if batch_size == 1:
+ return pred_masks
+
+ device = pred_masks.device
+ # "max_obj_inds": object index of the object with the highest score at each location
+ max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True)
+ # "batch_obj_inds": object index of each object slice (along dim 0) in `pred_masks`
+ batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None]
+ keep = max_obj_inds == batch_obj_inds
+ # suppress overlapping regions' scores below -10.0 so that the foreground regions
+ # don't overlap (here sigmoid(-10.0)=4.5398e-05)
+ pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0))
+ return pred_masks
\ No newline at end of file
diff --git a/sam2/modeling/sam2_utils.py b/sam2/modeling/sam2_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e16caae3a9a49e451b2d03d1ee60c47f8e9ed23c
--- /dev/null
+++ b/sam2/modeling/sam2_utils.py
@@ -0,0 +1,323 @@
+# 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 copy
+from typing import Tuple
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from sam2.utils.misc import mask_to_box
+
+
+def select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num):
+ """
+ Select up to `max_cond_frame_num` conditioning frames from `cond_frame_outputs`
+ that are temporally closest to the current frame at `frame_idx`. Here, we take
+ - a) the closest conditioning frame before `frame_idx` (if any);
+ - b) the closest conditioning frame after `frame_idx` (if any);
+ - c) any other temporally closest conditioning frames until reaching a total
+ of `max_cond_frame_num` conditioning frames.
+
+ Outputs:
+ - selected_outputs: selected items (keys & values) from `cond_frame_outputs`.
+ - unselected_outputs: items (keys & values) not selected in `cond_frame_outputs`.
+ """
+ if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num:
+ selected_outputs = cond_frame_outputs
+ unselected_outputs = {}
+ else:
+ assert max_cond_frame_num >= 2, "we should allow using 2+ conditioning frames"
+ selected_outputs = {}
+
+ # the closest conditioning frame before `frame_idx` (if any)
+ idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None)
+ if idx_before is not None:
+ selected_outputs[idx_before] = cond_frame_outputs[idx_before]
+
+ # the closest conditioning frame after `frame_idx` (if any)
+ idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None)
+ if idx_after is not None:
+ selected_outputs[idx_after] = cond_frame_outputs[idx_after]
+
+ # add other temporally closest conditioning frames until reaching a total
+ # of `max_cond_frame_num` conditioning frames.
+ num_remain = max_cond_frame_num - len(selected_outputs)
+ inds_remain = sorted(
+ (t for t in cond_frame_outputs if t not in selected_outputs),
+ key=lambda x: abs(x - frame_idx),
+ )[:num_remain]
+ selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain)
+ unselected_outputs = {
+ t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs
+ }
+
+ return selected_outputs, unselected_outputs
+
+
+def get_1d_sine_pe(pos_inds, dim, temperature=10000):
+ """
+ Get 1D sine positional embedding as in the original Transformer paper.
+ """
+ pe_dim = dim // 2
+ dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device)
+ dim_t = temperature ** (2 * (dim_t // 2) / pe_dim)
+
+ pos_embed = pos_inds.unsqueeze(-1) / dim_t
+ pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1)
+ return pos_embed
+
+
+def get_activation_fn(activation):
+ """Return an activation function given a string"""
+ if activation == "relu":
+ return F.relu
+ if activation == "gelu":
+ return F.gelu
+ if activation == "glu":
+ return F.glu
+ raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
+
+
+def get_clones(module, N):
+ return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+class DropPath(nn.Module):
+ # adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py
+ def __init__(self, drop_prob=0.0, scale_by_keep=True):
+ super(DropPath, self).__init__()
+ self.drop_prob = drop_prob
+ self.scale_by_keep = scale_by_keep
+
+ def forward(self, x):
+ if self.drop_prob == 0.0 or not self.training:
+ return x
+ keep_prob = 1 - self.drop_prob
+ shape = (x.shape[0],) + (1,) * (x.ndim - 1)
+ random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+ if keep_prob > 0.0 and self.scale_by_keep:
+ random_tensor.div_(keep_prob)
+ return x * random_tensor
+
+
+# Lightly adapted from
+# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
+class MLP(nn.Module):
+ def __init__(
+ self,
+ input_dim: int,
+ hidden_dim: int,
+ output_dim: int,
+ num_layers: int,
+ activation: nn.Module = nn.ReLU,
+ sigmoid_output: bool = False,
+ ) -> None:
+ super().__init__()
+ self.num_layers = num_layers
+ h = [hidden_dim] * (num_layers - 1)
+ self.layers = nn.ModuleList(
+ nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
+ )
+ self.sigmoid_output = sigmoid_output
+ self.act = activation()
+
+ def forward(self, x):
+ for i, layer in enumerate(self.layers):
+ x = self.act(layer(x)) if i < self.num_layers - 1 else layer(x)
+ if self.sigmoid_output:
+ x = F.sigmoid(x)
+ return x
+
+
+# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
+# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
+class LayerNorm2d(nn.Module):
+ def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(num_channels))
+ self.bias = nn.Parameter(torch.zeros(num_channels))
+ self.eps = eps
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ u = x.mean(1, keepdim=True)
+ s = (x - u).pow(2).mean(1, keepdim=True)
+ x = (x - u) / torch.sqrt(s + self.eps)
+ x = self.weight[:, None, None] * x + self.bias[:, None, None]
+ return x
+
+
+def sample_box_points(
+ masks: torch.Tensor,
+ noise: float = 0.1, # SAM default
+ noise_bound: int = 20, # SAM default
+ top_left_label: int = 2,
+ bottom_right_label: int = 3,
+) -> Tuple[np.array, np.array]:
+ """
+ Sample a noised version of the top left and bottom right corners of a given `bbox`
+
+ Inputs:
+ - masks: [B, 1, H,W] boxes, dtype=torch.Tensor
+ - noise: noise as a fraction of box width and height, dtype=float
+ - noise_bound: maximum amount of noise (in pure pixesl), dtype=int
+
+ Returns:
+ - box_coords: [B, num_pt, 2], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.float
+ - box_labels: [B, num_pt], label 2 is reserverd for top left and 3 for bottom right corners, dtype=torch.int32
+ """
+ device = masks.device
+ box_coords = mask_to_box(masks)
+ B, _, H, W = masks.shape
+ box_labels = torch.tensor(
+ [top_left_label, bottom_right_label], dtype=torch.int, device=device
+ ).repeat(B)
+ if noise > 0.0:
+ if not isinstance(noise_bound, torch.Tensor):
+ noise_bound = torch.tensor(noise_bound, device=device)
+ bbox_w = box_coords[..., 2] - box_coords[..., 0]
+ bbox_h = box_coords[..., 3] - box_coords[..., 1]
+ max_dx = torch.min(bbox_w * noise, noise_bound)
+ max_dy = torch.min(bbox_h * noise, noise_bound)
+ box_noise = 2 * torch.rand(B, 1, 4, device=device) - 1
+ box_noise = box_noise * torch.stack((max_dx, max_dy, max_dx, max_dy), dim=-1)
+
+ box_coords = box_coords + box_noise
+ img_bounds = (
+ torch.tensor([W, H, W, H], device=device) - 1
+ ) # uncentered pixel coords
+ box_coords.clamp_(torch.zeros_like(img_bounds), img_bounds) # In place clamping
+
+ box_coords = box_coords.reshape(-1, 2, 2) # always 2 points
+ box_labels = box_labels.reshape(-1, 2)
+ return box_coords, box_labels
+
+
+def sample_random_points_from_errors(gt_masks, pred_masks, num_pt=1):
+ """
+ Sample `num_pt` random points (along with their labels) independently from the error regions.
+
+ Inputs:
+ - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool
+ - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None
+ - num_pt: int, number of points to sample independently for each of the B error maps
+
+ Outputs:
+ - points: [B, num_pt, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point
+ - labels: [B, num_pt], dtype=torch.int32, where 1 means positive clicks and 0 means
+ negative clicks
+ """
+ if pred_masks is None: # if pred_masks is not provided, treat it as empty
+ pred_masks = torch.zeros_like(gt_masks)
+ assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1
+ assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape
+ assert num_pt >= 0
+
+ B, _, H_im, W_im = gt_masks.shape
+ device = gt_masks.device
+
+ # false positive region, a new point sampled in this region should have
+ # negative label to correct the FP error
+ fp_masks = ~gt_masks & pred_masks
+ # false negative region, a new point sampled in this region should have
+ # positive label to correct the FN error
+ fn_masks = gt_masks & ~pred_masks
+ # whether the prediction completely match the ground-truth on each mask
+ all_correct = torch.all((gt_masks == pred_masks).flatten(2), dim=2)
+ all_correct = all_correct[..., None, None]
+
+ # channel 0 is FP map, while channel 1 is FN map
+ pts_noise = torch.rand(B, num_pt, H_im, W_im, 2, device=device)
+ # sample a negative new click from FP region or a positive new click
+ # from FN region, depend on where the maximum falls,
+ # and in case the predictions are all correct (no FP or FN), we just
+ # sample a negative click from the background region
+ pts_noise[..., 0] *= fp_masks | (all_correct & ~gt_masks)
+ pts_noise[..., 1] *= fn_masks
+ pts_idx = pts_noise.flatten(2).argmax(dim=2)
+ labels = (pts_idx % 2).to(torch.int32)
+ pts_idx = pts_idx // 2
+ pts_x = pts_idx % W_im
+ pts_y = pts_idx // W_im
+ points = torch.stack([pts_x, pts_y], dim=2).to(torch.float)
+ return points, labels
+
+
+def sample_one_point_from_error_center(gt_masks, pred_masks, padding=True):
+ """
+ Sample 1 random point (along with its label) from the center of each error region,
+ that is, the point with the largest distance to the boundary of each error region.
+ This is the RITM sampling method from https://github.com/saic-vul/ritm_interactive_segmentation/blob/master/isegm/inference/clicker.py
+
+ Inputs:
+ - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool
+ - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None
+ - padding: if True, pad with boundary of 1 px for distance transform
+
+ Outputs:
+ - points: [B, 1, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point
+ - labels: [B, 1], dtype=torch.int32, where 1 means positive clicks and 0 means negative clicks
+ """
+ import cv2
+
+ if pred_masks is None:
+ pred_masks = torch.zeros_like(gt_masks)
+ assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1
+ assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape
+
+ B, _, _, W_im = gt_masks.shape
+ device = gt_masks.device
+
+ # false positive region, a new point sampled in this region should have
+ # negative label to correct the FP error
+ fp_masks = ~gt_masks & pred_masks
+ # false negative region, a new point sampled in this region should have
+ # positive label to correct the FN error
+ fn_masks = gt_masks & ~pred_masks
+
+ fp_masks = fp_masks.cpu().numpy()
+ fn_masks = fn_masks.cpu().numpy()
+ points = torch.zeros(B, 1, 2, dtype=torch.float)
+ labels = torch.ones(B, 1, dtype=torch.int32)
+ for b in range(B):
+ fn_mask = fn_masks[b, 0]
+ fp_mask = fp_masks[b, 0]
+ if padding:
+ fn_mask = np.pad(fn_mask, ((1, 1), (1, 1)), "constant")
+ fp_mask = np.pad(fp_mask, ((1, 1), (1, 1)), "constant")
+ # compute the distance of each point in FN/FP region to its boundary
+ fn_mask_dt = cv2.distanceTransform(fn_mask.astype(np.uint8), cv2.DIST_L2, 0)
+ fp_mask_dt = cv2.distanceTransform(fp_mask.astype(np.uint8), cv2.DIST_L2, 0)
+ if padding:
+ fn_mask_dt = fn_mask_dt[1:-1, 1:-1]
+ fp_mask_dt = fp_mask_dt[1:-1, 1:-1]
+
+ # take the point in FN/FP region with the largest distance to its boundary
+ fn_mask_dt_flat = fn_mask_dt.reshape(-1)
+ fp_mask_dt_flat = fp_mask_dt.reshape(-1)
+ fn_argmax = np.argmax(fn_mask_dt_flat)
+ fp_argmax = np.argmax(fp_mask_dt_flat)
+ is_positive = fn_mask_dt_flat[fn_argmax] > fp_mask_dt_flat[fp_argmax]
+ pt_idx = fn_argmax if is_positive else fp_argmax
+ points[b, 0, 0] = pt_idx % W_im # x
+ points[b, 0, 1] = pt_idx // W_im # y
+ labels[b, 0] = int(is_positive)
+
+ points = points.to(device)
+ labels = labels.to(device)
+ return points, labels
+
+
+def get_next_point(gt_masks, pred_masks, method):
+ if method == "uniform":
+ return sample_random_points_from_errors(gt_masks, pred_masks)
+ elif method == "center":
+ return sample_one_point_from_error_center(gt_masks, pred_masks)
+ else:
+ raise ValueError(f"unknown sampling method {method}")
diff --git a/sam2/sam2_image_predictor.py b/sam2/sam2_image_predictor.py
new file mode 100644
index 0000000000000000000000000000000000000000..41ce53af5924504c07216df52b2d2eefaeec7ae9
--- /dev/null
+++ b/sam2/sam2_image_predictor.py
@@ -0,0 +1,466 @@
+# 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
+
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from PIL.Image import Image
+
+from sam2.modeling.sam2_base import SAM2Base
+
+from sam2.utils.transforms import SAM2Transforms
+
+
+class SAM2ImagePredictor:
+ def __init__(
+ self,
+ sam_model: SAM2Base,
+ mask_threshold=0.0,
+ max_hole_area=0.0,
+ max_sprinkle_area=0.0,
+ **kwargs,
+ ) -> None:
+ """
+ Uses SAM-2 to calculate the image embedding for an image, and then
+ allow repeated, efficient mask prediction given prompts.
+
+ Arguments:
+ sam_model (Sam-2): The model to use for mask prediction.
+ mask_threshold (float): The threshold to use when converting mask logits
+ to binary masks. Masks are thresholded at 0 by default.
+ max_hole_area (int): If max_hole_area > 0, we fill small holes in up to
+ the maximum area of max_hole_area in low_res_masks.
+ max_sprinkle_area (int): If max_sprinkle_area > 0, we remove small sprinkles up to
+ the maximum area of max_sprinkle_area in low_res_masks.
+ """
+ super().__init__()
+ self.model = sam_model
+ self._transforms = SAM2Transforms(
+ resolution=self.model.image_size,
+ mask_threshold=mask_threshold,
+ max_hole_area=max_hole_area,
+ max_sprinkle_area=max_sprinkle_area,
+ )
+
+ # Predictor state
+ self._is_image_set = False
+ self._features = None
+ self._orig_hw = None
+ # Whether the predictor is set for single image or a batch of images
+ self._is_batch = False
+
+ # Predictor config
+ self.mask_threshold = mask_threshold
+
+ # Spatial dim for backbone feature maps
+ self._bb_feat_sizes = [
+ (256, 256),
+ (128, 128),
+ (64, 64),
+ ]
+
+ @classmethod
+ def from_pretrained(cls, model_id: str, **kwargs) -> "SAM2ImagePredictor":
+ """
+ Load a pretrained model from the Hugging Face hub.
+
+ Arguments:
+ model_id (str): The Hugging Face repository ID.
+ **kwargs: Additional arguments to pass to the model constructor.
+
+ Returns:
+ (SAM2ImagePredictor): The loaded model.
+ """
+ from sam2.build_sam import build_sam2_hf
+
+ sam_model = build_sam2_hf(model_id, **kwargs)
+ return cls(sam_model, **kwargs)
+
+ @torch.no_grad()
+ def set_image(
+ self,
+ image: Union[np.ndarray, Image],
+ ) -> None:
+ """
+ Calculates the image embeddings for the provided image, allowing
+ masks to be predicted with the 'predict' method.
+
+ Arguments:
+ image (np.ndarray or PIL Image): The input image to embed in RGB format. The image should be in HWC format if np.ndarray, or WHC format if PIL Image
+ with pixel values in [0, 255].
+ image_format (str): The color format of the image, in ['RGB', 'BGR'].
+ """
+ self.reset_predictor()
+ # Transform the image to the form expected by the model
+ if isinstance(image, np.ndarray):
+ logging.info("For numpy array image, we assume (HxWxC) format")
+ self._orig_hw = [image.shape[:2]]
+ elif isinstance(image, Image):
+ w, h = image.size
+ self._orig_hw = [(h, w)]
+ else:
+ raise NotImplementedError("Image format not supported")
+
+ input_image = self._transforms(image)
+ input_image = input_image[None, ...].to(self.device)
+
+ assert (
+ len(input_image.shape) == 4 and input_image.shape[1] == 3
+ ), f"input_image must be of size 1x3xHxW, got {input_image.shape}"
+ logging.info("Computing image embeddings for the provided image...")
+ backbone_out = self.model.forward_image(input_image)
+ _, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out)
+ # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos
+ if self.model.directly_add_no_mem_embed:
+ vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed
+
+ feats = [
+ feat.permute(1, 2, 0).view(1, -1, *feat_size)
+ for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])
+ ][::-1]
+ self._features = {"image_embed": feats[-1], "high_res_feats": feats[:-1]}
+ self._is_image_set = True
+ logging.info("Image embeddings computed.")
+
+ @torch.no_grad()
+ def set_image_batch(
+ self,
+ image_list: List[Union[np.ndarray]],
+ ) -> None:
+ """
+ Calculates the image embeddings for the provided image batch, allowing
+ masks to be predicted with the 'predict_batch' method.
+
+ Arguments:
+ image_list (List[np.ndarray]): The input images to embed in RGB format. The image should be in HWC format if np.ndarray
+ with pixel values in [0, 255].
+ """
+ self.reset_predictor()
+ assert isinstance(image_list, list)
+ self._orig_hw = []
+ for image in image_list:
+ assert isinstance(
+ image, np.ndarray
+ ), "Images are expected to be an np.ndarray in RGB format, and of shape HWC"
+ self._orig_hw.append(image.shape[:2])
+ # Transform the image to the form expected by the model
+ img_batch = self._transforms.forward_batch(image_list)
+ img_batch = img_batch.to(self.device)
+ batch_size = img_batch.shape[0]
+ assert (
+ len(img_batch.shape) == 4 and img_batch.shape[1] == 3
+ ), f"img_batch must be of size Bx3xHxW, got {img_batch.shape}"
+ logging.info("Computing image embeddings for the provided images...")
+ backbone_out = self.model.forward_image(img_batch)
+ _, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out)
+ # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos
+ if self.model.directly_add_no_mem_embed:
+ vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed
+
+ feats = [
+ feat.permute(1, 2, 0).view(batch_size, -1, *feat_size)
+ for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])
+ ][::-1]
+ self._features = {"image_embed": feats[-1], "high_res_feats": feats[:-1]}
+ self._is_image_set = True
+ self._is_batch = True
+ logging.info("Image embeddings computed.")
+
+ def predict_batch(
+ self,
+ point_coords_batch: List[np.ndarray] = None,
+ point_labels_batch: List[np.ndarray] = None,
+ box_batch: List[np.ndarray] = None,
+ mask_input_batch: List[np.ndarray] = None,
+ multimask_output: bool = True,
+ return_logits: bool = False,
+ normalize_coords=True,
+ ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:
+ """This function is very similar to predict(...), however it is used for batched mode, when the model is expected to generate predictions on multiple images.
+ It returns a tuple of lists of masks, ious, and low_res_masks_logits.
+ """
+ assert self._is_batch, "This function should only be used when in batched mode"
+ if not self._is_image_set:
+ raise RuntimeError(
+ "An image must be set with .set_image_batch(...) before mask prediction."
+ )
+ num_images = len(self._features["image_embed"])
+ all_masks = []
+ all_ious = []
+ all_low_res_masks = []
+ for img_idx in range(num_images):
+ # Transform input prompts
+ point_coords = (
+ point_coords_batch[img_idx] if point_coords_batch is not None else None
+ )
+ point_labels = (
+ point_labels_batch[img_idx] if point_labels_batch is not None else None
+ )
+ box = box_batch[img_idx] if box_batch is not None else None
+ mask_input = (
+ mask_input_batch[img_idx] if mask_input_batch is not None else None
+ )
+ mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts(
+ point_coords,
+ point_labels,
+ box,
+ mask_input,
+ normalize_coords,
+ img_idx=img_idx,
+ )
+ masks, iou_predictions, low_res_masks = self._predict(
+ unnorm_coords,
+ labels,
+ unnorm_box,
+ mask_input,
+ multimask_output,
+ return_logits=return_logits,
+ img_idx=img_idx,
+ )
+ masks_np = masks.squeeze(0).float().detach().cpu().numpy()
+ iou_predictions_np = (
+ iou_predictions.squeeze(0).float().detach().cpu().numpy()
+ )
+ low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy()
+ all_masks.append(masks_np)
+ all_ious.append(iou_predictions_np)
+ all_low_res_masks.append(low_res_masks_np)
+
+ return all_masks, all_ious, all_low_res_masks
+
+ def predict(
+ self,
+ point_coords: Optional[np.ndarray] = None,
+ point_labels: Optional[np.ndarray] = None,
+ box: Optional[np.ndarray] = None,
+ mask_input: Optional[np.ndarray] = None,
+ multimask_output: bool = True,
+ return_logits: bool = False,
+ normalize_coords=True,
+ ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+ """
+ Predict masks for the given input prompts, using the currently set image.
+
+ Arguments:
+ point_coords (np.ndarray or None): A Nx2 array of point prompts to the
+ model. Each point is in (X,Y) in pixels.
+ point_labels (np.ndarray or None): A length N array of labels for the
+ point prompts. 1 indicates a foreground point and 0 indicates a
+ background point.
+ box (np.ndarray or None): A length 4 array given a box prompt to the
+ model, in XYXY format.
+ mask_input (np.ndarray): A low resolution mask input to the model, typically
+ coming from a previous prediction iteration. Has form 1xHxW, where
+ for SAM, H=W=256.
+ multimask_output (bool): If true, the model will return three masks.
+ For ambiguous input prompts (such as a single click), this will often
+ produce better masks than a single prediction. If only a single
+ mask is needed, the model's predicted quality score can be used
+ to select the best mask. For non-ambiguous prompts, such as multiple
+ input prompts, multimask_output=False can give better results.
+ return_logits (bool): If true, returns un-thresholded masks logits
+ instead of a binary mask.
+ normalize_coords (bool): If true, the point coordinates will be normalized to the range [0,1] and point_coords is expected to be wrt. image dimensions.
+
+ Returns:
+ (np.ndarray): The output masks in CxHxW format, where C is the
+ number of masks, and (H, W) is the original image size.
+ (np.ndarray): An array of length C containing the model's
+ predictions for the quality of each mask.
+ (np.ndarray): An array of shape CxHxW, where C is the number
+ of masks and H=W=256. These low resolution logits can be passed to
+ a subsequent iteration as mask input.
+ """
+ if not self._is_image_set:
+ raise RuntimeError(
+ "An image must be set with .set_image(...) before mask prediction."
+ )
+
+ # Transform input prompts
+
+ mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts(
+ point_coords, point_labels, box, mask_input, normalize_coords
+ )
+
+ masks, iou_predictions, low_res_masks = self._predict(
+ unnorm_coords,
+ labels,
+ unnorm_box,
+ mask_input,
+ multimask_output,
+ return_logits=return_logits,
+ )
+
+ masks_np = masks.squeeze(0).float().detach().cpu().numpy()
+ iou_predictions_np = iou_predictions.squeeze(0).float().detach().cpu().numpy()
+ low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy()
+ return masks_np, iou_predictions_np, low_res_masks_np
+
+ def _prep_prompts(
+ self, point_coords, point_labels, box, mask_logits, normalize_coords, img_idx=-1
+ ):
+
+ unnorm_coords, labels, unnorm_box, mask_input = None, None, None, None
+ if point_coords is not None:
+ assert (
+ point_labels is not None
+ ), "point_labels must be supplied if point_coords is supplied."
+ point_coords = torch.as_tensor(
+ point_coords, dtype=torch.float, device=self.device
+ )
+ unnorm_coords = self._transforms.transform_coords(
+ point_coords, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx]
+ )
+ labels = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
+ if len(unnorm_coords.shape) == 2:
+ unnorm_coords, labels = unnorm_coords[None, ...], labels[None, ...]
+ if box is not None:
+ box = torch.as_tensor(box, dtype=torch.float, device=self.device)
+ unnorm_box = self._transforms.transform_boxes(
+ box, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx]
+ ) # Bx2x2
+ if mask_logits is not None:
+ mask_input = torch.as_tensor(
+ mask_logits, dtype=torch.float, device=self.device
+ )
+ if len(mask_input.shape) == 3:
+ mask_input = mask_input[None, :, :, :]
+ return mask_input, unnorm_coords, labels, unnorm_box
+
+ @torch.no_grad()
+ def _predict(
+ self,
+ point_coords: Optional[torch.Tensor],
+ point_labels: Optional[torch.Tensor],
+ boxes: Optional[torch.Tensor] = None,
+ mask_input: Optional[torch.Tensor] = None,
+ multimask_output: bool = True,
+ return_logits: bool = False,
+ img_idx: int = -1,
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """
+ Predict masks for the given input prompts, using the currently set image.
+ Input prompts are batched torch tensors and are expected to already be
+ transformed to the input frame using SAM2Transforms.
+
+ Arguments:
+ point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
+ model. Each point is in (X,Y) in pixels.
+ point_labels (torch.Tensor or None): A BxN array of labels for the
+ point prompts. 1 indicates a foreground point and 0 indicates a
+ background point.
+ boxes (np.ndarray or None): A Bx4 array given a box prompt to the
+ model, in XYXY format.
+ mask_input (np.ndarray): A low resolution mask input to the model, typically
+ coming from a previous prediction iteration. Has form Bx1xHxW, where
+ for SAM, H=W=256. Masks returned by a previous iteration of the
+ predict method do not need further transformation.
+ multimask_output (bool): If true, the model will return three masks.
+ For ambiguous input prompts (such as a single click), this will often
+ produce better masks than a single prediction. If only a single
+ mask is needed, the model's predicted quality score can be used
+ to select the best mask. For non-ambiguous prompts, such as multiple
+ input prompts, multimask_output=False can give better results.
+ return_logits (bool): If true, returns un-thresholded masks logits
+ instead of a binary mask.
+
+ Returns:
+ (torch.Tensor): The output masks in BxCxHxW format, where C is the
+ number of masks, and (H, W) is the original image size.
+ (torch.Tensor): An array of shape BxC containing the model's
+ predictions for the quality of each mask.
+ (torch.Tensor): An array of shape BxCxHxW, where C is the number
+ of masks and H=W=256. These low res logits can be passed to
+ a subsequent iteration as mask input.
+ """
+ if not self._is_image_set:
+ raise RuntimeError(
+ "An image must be set with .set_image(...) before mask prediction."
+ )
+
+ if point_coords is not None:
+ concat_points = (point_coords, point_labels)
+ else:
+ concat_points = None
+
+ # Embed prompts
+ if boxes is not None:
+ box_coords = boxes.reshape(-1, 2, 2)
+ box_labels = torch.tensor([[2, 3]], dtype=torch.int, device=boxes.device)
+ box_labels = box_labels.repeat(boxes.size(0), 1)
+ # we merge "boxes" and "points" into a single "concat_points" input (where
+ # boxes are added at the beginning) to sam_prompt_encoder
+ if concat_points is not None:
+ concat_coords = torch.cat([box_coords, concat_points[0]], dim=1)
+ concat_labels = torch.cat([box_labels, concat_points[1]], dim=1)
+ concat_points = (concat_coords, concat_labels)
+ else:
+ concat_points = (box_coords, box_labels)
+
+ sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder(
+ points=concat_points,
+ boxes=None,
+ masks=mask_input,
+ )
+
+ # Predict masks
+ batched_mode = (
+ concat_points is not None and concat_points[0].shape[0] > 1
+ ) # multi object prediction
+ high_res_features = [
+ feat_level[img_idx].unsqueeze(0)
+ for feat_level in self._features["high_res_feats"]
+ ]
+ low_res_masks, iou_predictions, _, _ = self.model.sam_mask_decoder(
+ image_embeddings=self._features["image_embed"][img_idx].unsqueeze(0),
+ image_pe=self.model.sam_prompt_encoder.get_dense_pe(),
+ sparse_prompt_embeddings=sparse_embeddings,
+ dense_prompt_embeddings=dense_embeddings,
+ multimask_output=multimask_output,
+ repeat_image=batched_mode,
+ high_res_features=high_res_features,
+ )
+
+ # Upscale the masks to the original image resolution
+ masks = self._transforms.postprocess_masks(
+ low_res_masks, self._orig_hw[img_idx]
+ )
+ low_res_masks = torch.clamp(low_res_masks, -32.0, 32.0)
+ if not return_logits:
+ masks = masks > self.mask_threshold
+
+ return masks, iou_predictions, low_res_masks
+
+ def get_image_embedding(self) -> torch.Tensor:
+ """
+ Returns the image embeddings for the currently set image, with
+ shape 1xCxHxW, where C is the embedding dimension and (H,W) are
+ the embedding spatial dimension of SAM (typically C=256, H=W=64).
+ """
+ if not self._is_image_set:
+ raise RuntimeError(
+ "An image must be set with .set_image(...) to generate an embedding."
+ )
+ assert (
+ self._features is not None
+ ), "Features must exist if an image has been set."
+ return self._features["image_embed"]
+
+ @property
+ def device(self) -> torch.device:
+ return self.model.device
+
+ def reset_predictor(self) -> None:
+ """
+ Resets the image embeddings and other state variables.
+ """
+ self._is_image_set = False
+ self._features = None
+ self._orig_hw = None
+ self._is_batch = False
diff --git a/sam2/sam2_video_predictor.py b/sam2/sam2_video_predictor.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4aebb9b8073ce49b16ad18ebeebf4cf55641419
--- /dev/null
+++ b/sam2/sam2_video_predictor.py
@@ -0,0 +1,1312 @@
+# 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 warnings
+from collections import OrderedDict
+
+import torch
+
+from tqdm import tqdm
+
+from sam2.modeling.sam2_base import NO_OBJ_SCORE, SAM2Base
+from sam2.utils.misc import concat_points, fill_holes_in_mask_scores, load_video_frames
+import sys
+import pdb
+import numpy as np
+from copy import deepcopy
+
+class SAM2VideoPredictor(SAM2Base):
+ """The predictor class to handle user interactions and manage inference states."""
+
+ def __init__(
+ self,
+ fill_hole_area=0,
+ # whether to apply non-overlapping constraints on the output object masks
+ non_overlap_masks=False,
+ # whether to clear non-conditioning memory of the surrounding frames (which may contain outdated information) after adding correction clicks;
+ # note that this would only apply to *single-object tracking* unless `clear_non_cond_mem_for_multi_obj` is also set to True)
+ clear_non_cond_mem_around_input=False,
+ # whether to also clear non-conditioning memory of the surrounding frames (only effective when `clear_non_cond_mem_around_input` is True).
+ clear_non_cond_mem_for_multi_obj=False,
+ # if `add_all_frames_to_correct_as_cond` is True, we also append to the conditioning frame list any frame that receives a later correction click
+ # if `add_all_frames_to_correct_as_cond` is False, we conditioning frame list to only use those initial conditioning frames
+ add_all_frames_to_correct_as_cond=False,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.fill_hole_area = fill_hole_area
+ self.non_overlap_masks = non_overlap_masks
+ self.clear_non_cond_mem_around_input = clear_non_cond_mem_around_input
+ self.clear_non_cond_mem_for_multi_obj = clear_non_cond_mem_for_multi_obj
+ self.add_all_frames_to_correct_as_cond = add_all_frames_to_correct_as_cond
+
+ @torch.inference_mode()
+ def init_state(
+ self,
+ video_path,
+ offload_video_to_cpu=False,
+ offload_state_to_cpu=False,
+ async_loading_frames=False,
+ ):
+ """Initialize an inference state."""
+ compute_device = self.device # device of the model
+ images, video_height, video_width = load_video_frames(
+ video_path=video_path,
+ image_size=self.image_size,
+ offload_video_to_cpu=offload_video_to_cpu,
+ async_loading_frames=async_loading_frames,
+ compute_device=compute_device,
+ )
+ inference_state = {}
+ inference_state["images"] = images
+ inference_state["num_frames"] = len(images)
+ # whether to offload the video frames to CPU memory
+ # turning on this option saves the GPU memory with only a very small overhead
+ inference_state["offload_video_to_cpu"] = offload_video_to_cpu
+ # whether to offload the inference state to CPU memory
+ # turning on this option saves the GPU memory at the cost of a lower tracking fps
+ # (e.g. in a test case of 768x768 model, fps dropped from 27 to 24 when tracking one object
+ # and from 24 to 21 when tracking two objects)
+ # inference_state["offload_state_to_cpu"] = offload_state_to_cpu
+ inference_state["offload_state_to_cpu"] = True
+ # the original video height and width, used for resizing final output scores
+ inference_state["video_height"] = video_height
+ inference_state["video_width"] = video_width
+ inference_state["device"] = compute_device
+ if offload_state_to_cpu:
+ inference_state["storage_device"] = torch.device("cpu")
+ else:
+ inference_state["storage_device"] = compute_device
+ # inputs on each frame
+ inference_state["point_inputs_per_obj"] = {}
+ inference_state["mask_inputs_per_obj"] = {}
+ # visual features on a small number of recently visited frames for quick interactions
+ inference_state["cached_features"] = {}
+ # values that don't change across frames (so we only need to hold one copy of them)
+ inference_state["constants"] = {}
+ # mapping between client-side object id and model-side object index
+ inference_state["obj_id_to_idx"] = OrderedDict()
+ inference_state["obj_idx_to_id"] = OrderedDict()
+ inference_state["obj_ids"] = []
+ # A storage to hold the model's tracking results and states on each frame
+ inference_state["output_dict"] = {
+ "cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
+ "non_cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
+ }
+ # Slice (view) of each object tracking results, sharing the same memory with "output_dict"
+ inference_state["output_dict_per_obj"] = {}
+ # A temporary storage to hold new outputs when user interact with a frame
+ # to add clicks or mask (it's merged into "output_dict" before propagation starts)
+ inference_state["temp_output_dict_per_obj"] = {}
+ # Frames that already holds consolidated outputs from click or mask inputs
+ # (we directly use their consolidated outputs during tracking)
+ inference_state["consolidated_frame_inds"] = {
+ "cond_frame_outputs": set(), # set containing frame indices
+ "non_cond_frame_outputs": set(), # set containing frame indices
+ }
+ # metadata for each tracking frame (e.g. which direction it's tracked)
+ inference_state["tracking_has_started"] = False
+ inference_state["frames_already_tracked"] = {}
+ # Warm up the visual backbone and cache the image feature on frame 0
+ self._get_image_feature(inference_state, frame_idx=0, batch_size=1)
+ return inference_state
+
+ @classmethod
+ def from_pretrained(cls, model_id: str, **kwargs) -> "SAM2VideoPredictor":
+ """
+ Load a pretrained model from the Hugging Face hub.
+
+ Arguments:
+ model_id (str): The Hugging Face repository ID.
+ **kwargs: Additional arguments to pass to the model constructor.
+
+ Returns:
+ (SAM2VideoPredictor): The loaded model.
+ """
+ from sam2.build_sam import build_sam2_video_predictor_hf
+
+ sam_model = build_sam2_video_predictor_hf(model_id, **kwargs)
+ return sam_model
+
+ def _obj_id_to_idx(self, inference_state, obj_id):
+ """Map client-side object id to model-side object index."""
+ obj_idx = inference_state["obj_id_to_idx"].get(obj_id, None)
+ if obj_idx is not None:
+ return obj_idx
+
+ # This is a new object id not sent to the server before. We only allow adding
+ # new objects *before* the tracking starts.
+ allow_new_object = not inference_state["tracking_has_started"]
+ if allow_new_object:
+ # get the next object slot
+ obj_idx = len(inference_state["obj_id_to_idx"])
+ inference_state["obj_id_to_idx"][obj_id] = obj_idx
+ inference_state["obj_idx_to_id"][obj_idx] = obj_id
+ inference_state["obj_ids"] = list(inference_state["obj_id_to_idx"])
+ # set up input and output structures for this object
+ inference_state["point_inputs_per_obj"][obj_idx] = {}
+ inference_state["mask_inputs_per_obj"][obj_idx] = {}
+ inference_state["output_dict_per_obj"][obj_idx] = {
+ "cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
+ "non_cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
+ }
+ inference_state["temp_output_dict_per_obj"][obj_idx] = {
+ "cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
+ "non_cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
+ }
+ return obj_idx
+ else:
+ raise RuntimeError(
+ f"Cannot add new object id {obj_id} after tracking starts. "
+ f"All existing object ids: {inference_state['obj_ids']}. "
+ f"Please call 'reset_state' to restart from scratch."
+ )
+
+ def _obj_idx_to_id(self, inference_state, obj_idx):
+ """Map model-side object index to client-side object id."""
+ return inference_state["obj_idx_to_id"][obj_idx]
+
+ def _get_obj_num(self, inference_state):
+ """Get the total number of unique object ids received so far in this session."""
+ return len(inference_state["obj_idx_to_id"])
+
+ @torch.inference_mode()
+ def add_new_points_or_box(
+ self,
+ inference_state,
+ frame_idx,
+ obj_id,
+ points=None,
+ labels=None,
+ clear_old_points=True,
+ normalize_coords=True,
+ box=None,
+ ):
+ """Add new points to a frame."""
+ obj_idx = self._obj_id_to_idx(inference_state, obj_id)
+ point_inputs_per_frame = inference_state["point_inputs_per_obj"][obj_idx]
+ mask_inputs_per_frame = inference_state["mask_inputs_per_obj"][obj_idx]
+
+ if (points is not None) != (labels is not None):
+ raise ValueError("points and labels must be provided together")
+ if points is None and box is None:
+ raise ValueError("at least one of points or box must be provided as input")
+
+ if points is None:
+ points = torch.zeros(0, 2, dtype=torch.float32)
+ elif not isinstance(points, torch.Tensor):
+ points = torch.tensor(points, dtype=torch.float32)
+ if labels is None:
+ labels = torch.zeros(0, dtype=torch.int32)
+ elif not isinstance(labels, torch.Tensor):
+ labels = torch.tensor(labels, dtype=torch.int32)
+ if points.dim() == 2:
+ points = points.unsqueeze(0) # add batch dimension
+ if labels.dim() == 1:
+ labels = labels.unsqueeze(0) # add batch dimension
+
+ # If `box` is provided, we add it as the first two points with labels 2 and 3
+ # along with the user-provided points (consistent with how SAM 2 is trained).
+ if box is not None:
+ if not clear_old_points:
+ raise ValueError(
+ "cannot add box without clearing old points, since "
+ "box prompt must be provided before any point prompt "
+ "(please use clear_old_points=True instead)"
+ )
+ if inference_state["tracking_has_started"]:
+ warnings.warn(
+ "You are adding a box after tracking starts. SAM 2 may not always be "
+ "able to incorporate a box prompt for *refinement*. If you intend to "
+ "use box prompt as an *initial* input before tracking, please call "
+ "'reset_state' on the inference state to restart from scratch.",
+ category=UserWarning,
+ stacklevel=2,
+ )
+ if not isinstance(box, torch.Tensor):
+ box = torch.tensor(box, dtype=torch.float32, device=points.device)
+ box_coords = box.reshape(1, 2, 2)
+ box_labels = torch.tensor([2, 3], dtype=torch.int32, device=labels.device)
+ box_labels = box_labels.reshape(1, 2)
+ points = torch.cat([box_coords, points], dim=1)
+ labels = torch.cat([box_labels, labels], dim=1)
+
+ if normalize_coords:
+ video_H = inference_state["video_height"]
+ video_W = inference_state["video_width"]
+ points = points / torch.tensor([video_W, video_H]).to(points.device)
+ # scale the (normalized) coordinates by the model's internal image size
+ points = points * self.image_size
+ points = points.to(inference_state["device"])
+ labels = labels.to(inference_state["device"])
+
+ if not clear_old_points:
+ point_inputs = point_inputs_per_frame.get(frame_idx, None)
+ else:
+ point_inputs = None
+ point_inputs = concat_points(point_inputs, points, labels)
+
+ point_inputs_per_frame[frame_idx] = point_inputs
+ mask_inputs_per_frame.pop(frame_idx, None)
+ # If this frame hasn't been tracked before, we treat it as an initial conditioning
+ # frame, meaning that the inputs points are to generate segments on this frame without
+ # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),
+ # the input points will be used to correct the already tracked masks.
+ is_init_cond_frame = frame_idx not in inference_state["frames_already_tracked"]
+ # whether to track in reverse time order
+ if is_init_cond_frame:
+ reverse = False
+ else:
+ reverse = inference_state["frames_already_tracked"][frame_idx]["reverse"]
+ obj_output_dict = inference_state["output_dict_per_obj"][obj_idx]
+ obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx]
+ # Add a frame to conditioning output if it's an initial conditioning frame or
+ # if the model sees all frames receiving clicks/mask as conditioning frames.
+ is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond
+ storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
+
+ # Get any previously predicted mask logits on this object and feed it along with
+ # the new clicks into the SAM mask decoder.
+ prev_sam_mask_logits = None
+ # lookup temporary output dict first, which contains the most recent output
+ # (if not found, then lookup conditioning and non-conditioning frame output)
+ prev_out = obj_temp_output_dict[storage_key].get(frame_idx)
+ if prev_out is None:
+ prev_out = obj_output_dict["cond_frame_outputs"].get(frame_idx)
+ if prev_out is None:
+ prev_out = obj_output_dict["non_cond_frame_outputs"].get(frame_idx)
+
+ if prev_out is not None and prev_out["pred_masks"] is not None:
+ device = inference_state["device"]
+ prev_sam_mask_logits = prev_out["pred_masks"].to(device, non_blocking=True)
+ # Clamp the scale of prev_sam_mask_logits to avoid rare numerical issues.
+ prev_sam_mask_logits = torch.clamp(prev_sam_mask_logits, -32.0, 32.0)
+ current_out, _, _ = self._run_single_frame_inference(
+ inference_state=inference_state,
+ output_dict=obj_output_dict, # run on the slice of a single object
+ frame_idx=frame_idx,
+ batch_size=1, # run on the slice of a single object
+ is_init_cond_frame=is_init_cond_frame,
+ point_inputs=point_inputs,
+ mask_inputs=None,
+ reverse=reverse,
+ # Skip the memory encoder when adding clicks or mask. We execute the memory encoder
+ # at the beginning of `propagate_in_video` (after user finalize their clicks). This
+ # allows us to enforce non-overlapping constraints on all objects before encoding
+ # them into memory.
+ run_mem_encoder=False,
+ prev_sam_mask_logits=prev_sam_mask_logits,
+ start_frame_idx=frame_idx,
+ )
+ # Add the output to the output dict (to be used as future memory)
+ obj_temp_output_dict[storage_key][frame_idx] = current_out
+
+ # Resize the output mask to the original video resolution
+ obj_ids = inference_state["obj_ids"]
+ consolidated_out = self._consolidate_temp_output_across_obj(
+ inference_state,
+ frame_idx,
+ is_cond=is_cond,
+ run_mem_encoder=False,
+ consolidate_at_video_res=True,
+ )
+ _, video_res_masks = self._get_orig_video_res_output(
+ inference_state, consolidated_out["pred_masks_video_res"]
+ )
+ return frame_idx, obj_ids, video_res_masks
+
+ def add_new_points(self, *args, **kwargs):
+ """Deprecated method. Please use `add_new_points_or_box` instead."""
+ return self.add_new_points_or_box(*args, **kwargs)
+
+ @torch.inference_mode()
+ def add_new_mask(
+ self,
+ inference_state,
+ frame_idx,
+ obj_id,
+ mask,
+ ):
+ """Add new mask to a frame."""
+ obj_idx = self._obj_id_to_idx(inference_state, obj_id)
+ point_inputs_per_frame = inference_state["point_inputs_per_obj"][obj_idx]
+ mask_inputs_per_frame = inference_state["mask_inputs_per_obj"][obj_idx]
+
+ if not isinstance(mask, torch.Tensor):
+ mask = torch.tensor(mask, dtype=torch.bool)
+ assert mask.dim() == 2
+ mask_H, mask_W = mask.shape
+ mask_inputs_orig = mask[None, None] # add batch and channel dimension
+ mask_inputs_orig = mask_inputs_orig.float().to(inference_state["device"])
+
+ # resize the mask if it doesn't match the model's image size
+ if mask_H != self.image_size or mask_W != self.image_size:
+ mask_inputs = torch.nn.functional.interpolate(
+ mask_inputs_orig,
+ size=(self.image_size, self.image_size),
+ align_corners=False,
+ mode="bilinear",
+ antialias=True, # use antialias for downsampling
+ )
+ mask_inputs = (mask_inputs >= 0.5).float()
+ else:
+ mask_inputs = mask_inputs_orig
+
+ mask_inputs_per_frame[frame_idx] = mask_inputs
+ point_inputs_per_frame.pop(frame_idx, None)
+ # If this frame hasn't been tracked before, we treat it as an initial conditioning
+ # frame, meaning that the inputs points are to generate segments on this frame without
+ # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),
+ # the input points will be used to correct the already tracked masks.
+ is_init_cond_frame = frame_idx not in inference_state["frames_already_tracked"]
+ # whether to track in reverse time order
+ if is_init_cond_frame:
+ reverse = False
+ else:
+ reverse = inference_state["frames_already_tracked"][frame_idx]["reverse"]
+ obj_output_dict = inference_state["output_dict_per_obj"][obj_idx]
+ obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx]
+ # Add a frame to conditioning output if it's an initial conditioning frame or
+ # if the model sees all frames receiving clicks/mask as conditioning frames.
+ is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond
+ storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
+
+ current_out, _, _ = self._run_single_frame_inference(
+ inference_state=inference_state,
+ output_dict=obj_output_dict, # run on the slice of a single object
+ frame_idx=frame_idx,
+ batch_size=1, # run on the slice of a single object
+ is_init_cond_frame=is_init_cond_frame,
+ point_inputs=None,
+ mask_inputs=mask_inputs,
+ reverse=reverse,
+ # Skip the memory encoder when adding clicks or mask. We execute the memory encoder
+ # at the beginning of `propagate_in_video` (after user finalize their clicks). This
+ # allows us to enforce non-overlapping constraints on all objects before encoding
+ # them into memory.
+ run_mem_encoder=False,
+ start_frame_idx=frame_idx,
+ )
+ # Add the output to the output dict (to be used as future memory)
+ obj_temp_output_dict[storage_key][frame_idx] = current_out
+
+ # Resize the output mask to the original video resolution
+ obj_ids = inference_state["obj_ids"]
+ consolidated_out = self._consolidate_temp_output_across_obj(
+ inference_state,
+ frame_idx,
+ is_cond=is_cond,
+ run_mem_encoder=False,
+ consolidate_at_video_res=True,
+ )
+ _, video_res_masks = self._get_orig_video_res_output(
+ inference_state, consolidated_out["pred_masks_video_res"]
+ )
+ return frame_idx, obj_ids, video_res_masks
+
+ def _get_orig_video_res_output(self, inference_state, any_res_masks):
+ """
+ Resize the object scores to the original video resolution (video_res_masks)
+ and apply non-overlapping constraints for final output.
+ """
+ device = inference_state["device"]
+ video_H = inference_state["video_height"]
+ video_W = inference_state["video_width"]
+ any_res_masks = any_res_masks.to(device, non_blocking=True)
+ if any_res_masks.shape[-2:] == (video_H, video_W):
+ video_res_masks = any_res_masks
+ else:
+ video_res_masks = torch.nn.functional.interpolate(
+ any_res_masks,
+ size=(video_H, video_W),
+ mode="bilinear",
+ align_corners=False,
+ )
+ if self.non_overlap_masks:
+ video_res_masks = self._apply_non_overlapping_constraints(video_res_masks)
+ return any_res_masks, video_res_masks
+
+ def _consolidate_temp_output_across_obj(
+ self,
+ inference_state,
+ frame_idx,
+ is_cond,
+ run_mem_encoder,
+ consolidate_at_video_res=False,
+ ):
+ """
+ Consolidate the per-object temporary outputs in `temp_output_dict_per_obj` on
+ a frame into a single output for all objects, including
+ 1) fill any missing objects either from `output_dict_per_obj` (if they exist in
+ `output_dict_per_obj` for this frame) or leave them as placeholder values
+ (if they don't exist in `output_dict_per_obj` for this frame);
+ 2) if specified, rerun memory encoder after apply non-overlapping constraints
+ on the object scores.
+ """
+ batch_size = self._get_obj_num(inference_state)
+ storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
+ # Optionally, we allow consolidating the temporary outputs at the original
+ # video resolution (to provide a better editing experience for mask prompts).
+ if consolidate_at_video_res:
+ assert not run_mem_encoder, "memory encoder cannot run at video resolution"
+ consolidated_H = inference_state["video_height"]
+ consolidated_W = inference_state["video_width"]
+ consolidated_mask_key = "pred_masks_video_res"
+ else:
+ consolidated_H = consolidated_W = self.image_size // 4
+ consolidated_mask_key = "pred_masks"
+
+ # Initialize `consolidated_out`. Its "maskmem_features" and "maskmem_pos_enc"
+ # will be added when rerunning the memory encoder after applying non-overlapping
+ # constraints to object scores. Its "pred_masks" are prefilled with a large
+ # negative value (NO_OBJ_SCORE) to represent missing objects.
+ consolidated_out = {
+ "maskmem_features": None,
+ "maskmem_pos_enc": None,
+ consolidated_mask_key: torch.full(
+ size=(batch_size, 1, consolidated_H, consolidated_W),
+ fill_value=NO_OBJ_SCORE,
+ dtype=torch.float32,
+ device=inference_state["storage_device"],
+ ),
+ "obj_ptr": torch.full(
+ size=(batch_size, self.hidden_dim),
+ fill_value=NO_OBJ_SCORE,
+ dtype=torch.float32,
+ device=inference_state["device"],
+ ),
+ "object_score_logits": torch.full(
+ size=(batch_size, 1),
+ # default to 10.0 for object_score_logits, i.e. assuming the object is
+ # present as sigmoid(10)=1, same as in `predict_masks` of `MaskDecoder`
+ fill_value=10.0,
+ dtype=torch.float32,
+ device=inference_state["device"],
+ ),
+ }
+ empty_mask_ptr = None
+ for obj_idx in range(batch_size):
+ obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx]
+ obj_output_dict = inference_state["output_dict_per_obj"][obj_idx]
+ out = obj_temp_output_dict[storage_key].get(frame_idx, None)
+ # If the object doesn't appear in "temp_output_dict_per_obj" on this frame,
+ # we fall back and look up its previous output in "output_dict_per_obj".
+ # We look up both "cond_frame_outputs" and "non_cond_frame_outputs" in
+ # "output_dict_per_obj" to find a previous output for this object.
+ if out is None:
+ out = obj_output_dict["cond_frame_outputs"].get(frame_idx, None)
+ if out is None:
+ out = obj_output_dict["non_cond_frame_outputs"].get(frame_idx, None)
+ # If the object doesn't appear in "output_dict_per_obj" either, we skip it
+ # and leave its mask scores to the default scores (i.e. the NO_OBJ_SCORE
+ # placeholder above) and set its object pointer to be a dummy pointer.
+ if out is None:
+ # Fill in dummy object pointers for those objects without any inputs or
+ # tracking outcomes on this frame (only do it under `run_mem_encoder=True`,
+ # i.e. when we need to build the memory for tracking).
+ if run_mem_encoder:
+ if empty_mask_ptr is None:
+ empty_mask_ptr = self._get_empty_mask_ptr(
+ inference_state, frame_idx
+ )
+ # fill object pointer with a dummy pointer (based on an empty mask)
+ consolidated_out["obj_ptr"][obj_idx : obj_idx + 1] = empty_mask_ptr
+ continue
+ # Add the temporary object output mask to consolidated output mask
+ obj_mask = out["pred_masks"]
+ consolidated_pred_masks = consolidated_out[consolidated_mask_key]
+ if obj_mask.shape[-2:] == consolidated_pred_masks.shape[-2:]:
+ consolidated_pred_masks[obj_idx : obj_idx + 1] = obj_mask
+ else:
+ # Resize first if temporary object mask has a different resolution
+ resized_obj_mask = torch.nn.functional.interpolate(
+ obj_mask,
+ size=consolidated_pred_masks.shape[-2:],
+ mode="bilinear",
+ align_corners=False,
+ )
+ consolidated_pred_masks[obj_idx : obj_idx + 1] = resized_obj_mask
+ consolidated_out["obj_ptr"][obj_idx : obj_idx + 1] = out["obj_ptr"]
+ consolidated_out["object_score_logits"][obj_idx : obj_idx + 1] = out[
+ "object_score_logits"
+ ]
+
+ # Optionally, apply non-overlapping constraints on the consolidated scores
+ # and rerun the memory encoder
+ if run_mem_encoder:
+ device = inference_state["device"]
+ high_res_masks = torch.nn.functional.interpolate(
+ consolidated_out["pred_masks"].to(device, non_blocking=True),
+ size=(self.image_size, self.image_size),
+ mode="bilinear",
+ align_corners=False,
+ )
+ if self.non_overlap_masks_for_mem_enc:
+ high_res_masks = self._apply_non_overlapping_constraints(high_res_masks)
+ maskmem_features, maskmem_pos_enc = self._run_memory_encoder(
+ inference_state=inference_state,
+ frame_idx=frame_idx,
+ batch_size=batch_size,
+ high_res_masks=high_res_masks,
+ object_score_logits=consolidated_out["object_score_logits"],
+ is_mask_from_pts=True, # these frames are what the user interacted with
+ )
+ consolidated_out["maskmem_features"] = maskmem_features
+ consolidated_out["maskmem_pos_enc"] = maskmem_pos_enc
+
+ return consolidated_out
+
+ def _get_empty_mask_ptr(self, inference_state, frame_idx):
+ """Get a dummy object pointer based on an empty mask on the current frame."""
+ # A dummy (empty) mask with a single object
+ batch_size = 1
+ mask_inputs = torch.zeros(
+ (batch_size, 1, self.image_size, self.image_size),
+ dtype=torch.float32,
+ device=inference_state["device"],
+ )
+
+ # Retrieve correct image features
+ (
+ _,
+ _,
+ current_vision_feats,
+ current_vision_pos_embeds,
+ feat_sizes,
+ ) = self._get_image_feature(inference_state, frame_idx, batch_size)
+
+ # Feed the empty mask and image feature above to get a dummy object pointer
+ current_out = self.track_step(
+ frame_idx=frame_idx,
+ is_init_cond_frame=True,
+ current_vision_feats=current_vision_feats,
+ current_vision_pos_embeds=current_vision_pos_embeds,
+ feat_sizes=feat_sizes,
+ point_inputs=None,
+ mask_inputs=mask_inputs,
+ output_dict={},
+ num_frames=inference_state["num_frames"],
+ track_in_reverse=False,
+ run_mem_encoder=False,
+ prev_sam_mask_logits=None,
+ )
+ return current_out["obj_ptr"]
+
+ @torch.inference_mode()
+ def propagate_in_video_preflight(self, inference_state):
+ """Prepare inference_state and consolidate temporary outputs before tracking."""
+ # Tracking has started and we don't allow adding new objects until session is reset.
+ inference_state["tracking_has_started"] = True
+ batch_size = self._get_obj_num(inference_state)
+
+ # Consolidate per-object temporary outputs in "temp_output_dict_per_obj" and
+ # add them into "output_dict".
+ temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"]
+ output_dict = inference_state["output_dict"]
+ # "consolidated_frame_inds" contains indices of those frames where consolidated
+ # temporary outputs have been added (either in this call or any previous calls
+ # to `propagate_in_video_preflight`).
+ consolidated_frame_inds = inference_state["consolidated_frame_inds"]
+ for is_cond in [False, True]:
+ # Separately consolidate conditioning and non-conditioning temp outputs
+ storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
+ # Find all the frames that contain temporary outputs for any objects
+ # (these should be the frames that have just received clicks for mask inputs
+ # via `add_new_points_or_box` or `add_new_mask`)
+ temp_frame_inds = set()
+ for obj_temp_output_dict in temp_output_dict_per_obj.values():
+ temp_frame_inds.update(obj_temp_output_dict[storage_key].keys())
+ consolidated_frame_inds[storage_key].update(temp_frame_inds)
+ # consolidate the temporary output across all objects on this frame
+ for frame_idx in temp_frame_inds:
+ consolidated_out = self._consolidate_temp_output_across_obj(
+ inference_state, frame_idx, is_cond=is_cond, run_mem_encoder=True
+ )
+ # merge them into "output_dict" and also create per-object slices
+ output_dict[storage_key][frame_idx] = consolidated_out
+ self._add_output_per_object(
+ inference_state, frame_idx, consolidated_out, storage_key
+ )
+ clear_non_cond_mem = self.clear_non_cond_mem_around_input and (
+ self.clear_non_cond_mem_for_multi_obj or batch_size <= 1
+ )
+ if clear_non_cond_mem:
+ # clear non-conditioning memory of the surrounding frames
+ self._clear_non_cond_mem_around_input(inference_state, frame_idx)
+
+ # clear temporary outputs in `temp_output_dict_per_obj`
+ for obj_temp_output_dict in temp_output_dict_per_obj.values():
+ obj_temp_output_dict[storage_key].clear()
+
+ # edge case: if an output is added to "cond_frame_outputs", we remove any prior
+ # output on the same frame in "non_cond_frame_outputs"
+ for frame_idx in output_dict["cond_frame_outputs"]:
+ output_dict["non_cond_frame_outputs"].pop(frame_idx, None)
+ for obj_output_dict in inference_state["output_dict_per_obj"].values():
+ for frame_idx in obj_output_dict["cond_frame_outputs"]:
+ obj_output_dict["non_cond_frame_outputs"].pop(frame_idx, None)
+ for frame_idx in consolidated_frame_inds["cond_frame_outputs"]:
+ assert frame_idx in output_dict["cond_frame_outputs"]
+ consolidated_frame_inds["non_cond_frame_outputs"].discard(frame_idx)
+
+ # Make sure that the frame indices in "consolidated_frame_inds" are exactly those frames
+ # with either points or mask inputs (which should be true under a correct workflow).
+ all_consolidated_frame_inds = (
+ consolidated_frame_inds["cond_frame_outputs"]
+ | consolidated_frame_inds["non_cond_frame_outputs"]
+ )
+ input_frames_inds = set()
+ for point_inputs_per_frame in inference_state["point_inputs_per_obj"].values():
+ input_frames_inds.update(point_inputs_per_frame.keys())
+ for mask_inputs_per_frame in inference_state["mask_inputs_per_obj"].values():
+ input_frames_inds.update(mask_inputs_per_frame.keys())
+ assert all_consolidated_frame_inds == input_frames_inds
+
+ @torch.inference_mode()
+ def propagate_in_video(
+ self,
+ inference_state,
+ start_frame_idx=None,
+ max_frame_num_to_track=None,
+ reverse=False,
+ ):
+ """Propagate the input points across frames to track in the entire video."""
+ self.propagate_in_video_preflight(inference_state)
+ output_dict = inference_state["output_dict"]
+ consolidated_frame_inds = inference_state["consolidated_frame_inds"]
+ obj_ids = inference_state["obj_ids"]
+ num_frames = inference_state["num_frames"]
+ batch_size = self._get_obj_num(inference_state)
+ if len(output_dict["cond_frame_outputs"]) == 0:
+ raise RuntimeError("No points are provided; please add points first")
+ clear_non_cond_mem = self.clear_non_cond_mem_around_input and (
+ self.clear_non_cond_mem_for_multi_obj or batch_size <= 1
+ )
+
+ # set start index, end index, and processing order
+ if start_frame_idx is None:
+ # default: start from the earliest frame with input points
+ start_frame_idx = min(output_dict["cond_frame_outputs"])
+ if max_frame_num_to_track is None:
+ # default: track all the frames in the video
+ max_frame_num_to_track = num_frames
+ if reverse:
+ end_frame_idx = max(start_frame_idx - max_frame_num_to_track, 0)
+ if start_frame_idx > 0:
+ processing_order = range(start_frame_idx, end_frame_idx - 1, -1)
+ else:
+ processing_order = [] # skip reverse tracking if starting from frame 0
+ else:
+ end_frame_idx = min(
+ start_frame_idx + max_frame_num_to_track, num_frames - 1
+ )
+ processing_order = range(start_frame_idx, end_frame_idx + 1)
+
+ mem_pick_indexs = 0 ###initialize the memory index
+ for frame_idx in tqdm(processing_order, desc="propagate in video"):
+ # We skip those frames already in consolidated outputs (these are frames
+ # that received input clicks or mask). Note that we cannot directly run
+ # batched forward on them via `_run_single_frame_inference` because the
+ # number of clicks on each object might be different.
+ if frame_idx in consolidated_frame_inds["cond_frame_outputs"]:
+ storage_key = "cond_frame_outputs"
+ current_out = output_dict[storage_key][frame_idx]
+ pred_masks = current_out["pred_masks"]
+ if clear_non_cond_mem:
+ # clear non-conditioning memory of the surrounding frames
+ self._clear_non_cond_mem_around_input(inference_state, frame_idx)
+ elif frame_idx in consolidated_frame_inds["non_cond_frame_outputs"]:
+ storage_key = "non_cond_frame_outputs"
+ current_out = output_dict[storage_key][frame_idx]
+ pred_masks = current_out["pred_masks"]
+ else:
+ storage_key = "non_cond_frame_outputs"
+ current_out, _, mem_pick_indexs = self._run_single_frame_inference(
+ inference_state=inference_state,
+ output_dict=output_dict,
+ frame_idx=frame_idx,
+ batch_size=batch_size,
+ is_init_cond_frame=False,
+ point_inputs=None,
+ mask_inputs=None,
+ reverse=reverse,
+ run_mem_encoder=True,
+ mem_pick_indexs=mem_pick_indexs,
+ start_frame_idx=start_frame_idx,
+ )
+ output_dict[storage_key][frame_idx] = current_out
+ mask = [self._get_orig_video_res_output(inference_state, output_dict["cond_frame_outputs"][start_frame_idx]["pred_masks"])[1]]
+ for i in range(start_frame_idx+1, num_frames):
+ mask.append(
+ self._get_orig_video_res_output(
+ inference_state,
+ output_dict["non_cond_frame_outputs"][i]["pred_masks"][...,mem_pick_indexs[0][i]])[1]
+ )
+ return obj_ids, mask
+
+ # Create slices of per-object outputs for subsequent interaction with each
+ # individual object after tracking.
+ # self._add_output_per_object(
+ # inference_state, frame_idx, current_out, storage_key
+ # )
+ # inference_state["frames_already_tracked"][frame_idx] = {"reverse": reverse}
+
+ # # Resize the output mask to the original video resolution (we directly use
+ # # the mask scores on GPU for output to avoid any CPU conversion in between)
+ # _, video_res_masks = self._get_orig_video_res_output(
+ # inference_state, pred_masks
+ # )
+ # yield frame_idx, obj_ids, video_res_masks
+
+ def _add_output_per_object(
+ self, inference_state, frame_idx, current_out, storage_key
+ ):
+ """
+ Split a multi-object output into per-object output slices and add them into
+ `output_dict_per_obj`. The resulting slices share the same tensor storage.
+ """
+ maskmem_features = current_out["maskmem_features"]
+ assert maskmem_features is None or isinstance(maskmem_features, torch.Tensor)
+
+ maskmem_pos_enc = current_out["maskmem_pos_enc"]
+ assert maskmem_pos_enc is None or isinstance(maskmem_pos_enc, list)
+
+ output_dict_per_obj = inference_state["output_dict_per_obj"]
+ for obj_idx, obj_output_dict in output_dict_per_obj.items():
+ obj_slice = slice(obj_idx, obj_idx + 1)
+ obj_out = {
+ "maskmem_features": None,
+ "maskmem_pos_enc": None,
+ "pred_masks": current_out["pred_masks"][obj_slice],
+ "obj_ptr": current_out["obj_ptr"][obj_slice],
+ "object_score_logits": current_out["object_score_logits"][obj_slice],
+ }
+ if maskmem_features is not None:
+ obj_out["maskmem_features"] = maskmem_features[obj_slice]
+ if maskmem_pos_enc is not None:
+ obj_out["maskmem_pos_enc"] = [x[obj_slice] for x in maskmem_pos_enc]
+ obj_output_dict[storage_key][frame_idx] = obj_out
+
+ @torch.inference_mode()
+ def clear_all_prompts_in_frame(
+ self, inference_state, frame_idx, obj_id, need_output=True
+ ):
+ """Remove all input points or mask in a specific frame for a given object."""
+ obj_idx = self._obj_id_to_idx(inference_state, obj_id)
+
+ # Clear the conditioning information on the given frame
+ inference_state["point_inputs_per_obj"][obj_idx].pop(frame_idx, None)
+ inference_state["mask_inputs_per_obj"][obj_idx].pop(frame_idx, None)
+
+ temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"]
+ temp_output_dict_per_obj[obj_idx]["cond_frame_outputs"].pop(frame_idx, None)
+ temp_output_dict_per_obj[obj_idx]["non_cond_frame_outputs"].pop(frame_idx, None)
+
+ # Check and see if there are still any inputs left on this frame
+ batch_size = self._get_obj_num(inference_state)
+ frame_has_input = False
+ for obj_idx2 in range(batch_size):
+ if frame_idx in inference_state["point_inputs_per_obj"][obj_idx2]:
+ frame_has_input = True
+ break
+ if frame_idx in inference_state["mask_inputs_per_obj"][obj_idx2]:
+ frame_has_input = True
+ break
+
+ # If this frame has no remaining inputs for any objects, we further clear its
+ # conditioning frame status
+ if not frame_has_input:
+ output_dict = inference_state["output_dict"]
+ consolidated_frame_inds = inference_state["consolidated_frame_inds"]
+ consolidated_frame_inds["cond_frame_outputs"].discard(frame_idx)
+ consolidated_frame_inds["non_cond_frame_outputs"].discard(frame_idx)
+ # Remove the frame's conditioning output (possibly downgrading it to non-conditioning)
+ out = output_dict["cond_frame_outputs"].pop(frame_idx, None)
+ if out is not None:
+ # The frame is not a conditioning frame anymore since it's not receiving inputs,
+ # so we "downgrade" its output (if exists) to a non-conditioning frame output.
+ output_dict["non_cond_frame_outputs"][frame_idx] = out
+ inference_state["frames_already_tracked"].pop(frame_idx, None)
+ # Similarly, do it for the sliced output on each object.
+ for obj_idx2 in range(batch_size):
+ obj_output_dict = inference_state["output_dict_per_obj"][obj_idx2]
+ obj_out = obj_output_dict["cond_frame_outputs"].pop(frame_idx, None)
+ if obj_out is not None:
+ obj_output_dict["non_cond_frame_outputs"][frame_idx] = obj_out
+
+ # If all the conditioning frames have been removed, we also clear the tracking outputs
+ if len(output_dict["cond_frame_outputs"]) == 0:
+ self._reset_tracking_results(inference_state)
+
+ if not need_output:
+ return
+ # Finally, output updated masks per object (after removing the inputs above)
+ obj_ids = inference_state["obj_ids"]
+ is_cond = any(
+ frame_idx in obj_temp_output_dict["cond_frame_outputs"]
+ for obj_temp_output_dict in temp_output_dict_per_obj.values()
+ )
+ consolidated_out = self._consolidate_temp_output_across_obj(
+ inference_state,
+ frame_idx,
+ is_cond=is_cond,
+ run_mem_encoder=False,
+ consolidate_at_video_res=True,
+ )
+ _, video_res_masks = self._get_orig_video_res_output(
+ inference_state, consolidated_out["pred_masks_video_res"]
+ )
+ return frame_idx, obj_ids, video_res_masks
+
+ @torch.inference_mode()
+ def reset_state(self, inference_state):
+ """Remove all input points or mask in all frames throughout the video."""
+ self._reset_tracking_results(inference_state)
+ # Remove all object ids
+ inference_state["obj_id_to_idx"].clear()
+ inference_state["obj_idx_to_id"].clear()
+ inference_state["obj_ids"].clear()
+ inference_state["point_inputs_per_obj"].clear()
+ inference_state["mask_inputs_per_obj"].clear()
+ inference_state["output_dict_per_obj"].clear()
+ inference_state["temp_output_dict_per_obj"].clear()
+
+ def _reset_tracking_results(self, inference_state):
+ """Reset all tracking inputs and results across the videos."""
+ for v in inference_state["point_inputs_per_obj"].values():
+ v.clear()
+ for v in inference_state["mask_inputs_per_obj"].values():
+ v.clear()
+ for v in inference_state["output_dict_per_obj"].values():
+ v["cond_frame_outputs"].clear()
+ v["non_cond_frame_outputs"].clear()
+ for v in inference_state["temp_output_dict_per_obj"].values():
+ v["cond_frame_outputs"].clear()
+ v["non_cond_frame_outputs"].clear()
+ inference_state["output_dict"]["cond_frame_outputs"].clear()
+ inference_state["output_dict"]["non_cond_frame_outputs"].clear()
+ inference_state["consolidated_frame_inds"]["cond_frame_outputs"].clear()
+ inference_state["consolidated_frame_inds"]["non_cond_frame_outputs"].clear()
+ inference_state["tracking_has_started"] = False
+ inference_state["frames_already_tracked"].clear()
+
+ def _get_image_feature(self, inference_state, frame_idx, batch_size):
+ """Compute the image features on a given frame."""
+ # Look up in the cache first
+ image, backbone_out = inference_state["cached_features"].get(
+ frame_idx, (None, None)
+ )
+ if backbone_out is None:
+ # Cache miss -- we will run inference on a single image
+ device = inference_state["device"]
+ image = inference_state["images"][frame_idx].to(device).float().unsqueeze(0)
+ backbone_out = self.forward_image(image)
+ # Cache the most recent frame's feature (for repeated interactions with
+ # a frame; we can use an LRU cache for more frames in the future).
+ inference_state["cached_features"] = {frame_idx: (image, backbone_out)}
+
+ # expand the features to have the same dimension as the number of objects
+ expanded_image = image.expand(batch_size, -1, -1, -1)
+ expanded_backbone_out = {
+ "backbone_fpn": backbone_out["backbone_fpn"].copy(),
+ "vision_pos_enc": backbone_out["vision_pos_enc"].copy(),
+ }
+ for i, feat in enumerate(expanded_backbone_out["backbone_fpn"]):
+ expanded_backbone_out["backbone_fpn"][i] = feat.expand(
+ batch_size, -1, -1, -1
+ )
+ for i, pos in enumerate(expanded_backbone_out["vision_pos_enc"]):
+ pos = pos.expand(batch_size, -1, -1, -1)
+ expanded_backbone_out["vision_pos_enc"][i] = pos
+
+ features = self._prepare_backbone_features(expanded_backbone_out)
+ features = (expanded_image,) + features
+ return features
+
+ def _run_single_frame_inference(
+ self,
+ inference_state,
+ output_dict,
+ frame_idx,
+ batch_size,
+ is_init_cond_frame,
+ point_inputs,
+ mask_inputs,
+ reverse,
+ run_mem_encoder,
+ prev_sam_mask_logits=None,
+ mem_pick_indexs=0,
+ start_frame_idx=0,
+ ):
+ """Run tracking on a single frame based on current inputs and previous memory."""
+ # Retrieve correct image features
+ (
+ _,
+ _,
+ current_vision_feats,
+ current_vision_pos_embeds,
+ feat_sizes,
+ ) = self._get_image_feature(inference_state, frame_idx, batch_size)
+
+ # point and mask should not appear as input simultaneously on the same frame
+ assert point_inputs is None or mask_inputs is None
+ storage_device = inference_state["storage_device"]
+
+ current_outs = []
+ if frame_idx <= start_frame_idx+1:
+ current_out = self.track_step(
+ frame_idx=frame_idx,
+ is_init_cond_frame=is_init_cond_frame,
+ current_vision_feats=current_vision_feats,
+ current_vision_pos_embeds=current_vision_pos_embeds,
+ feat_sizes=feat_sizes,
+ point_inputs=point_inputs,
+ mask_inputs=mask_inputs,
+ output_dict=output_dict,
+ num_frames=inference_state["num_frames"],
+ track_in_reverse=reverse,
+ run_mem_encoder=run_mem_encoder,
+ prev_sam_mask_logits=prev_sam_mask_logits,
+ mem_pick_index=0, ###0 means no multiple pathway
+ start_frame_idx=start_frame_idx,
+ )
+ if run_mem_encoder:
+ maskmem_features, maskmem_pos_enc = self._encode_new_memory(
+ current_vision_feats=current_vision_feats,
+ feat_sizes=feat_sizes,
+ pred_masks_high_res=current_out["pred_masks_high_res"],
+ object_score_logits=current_out["object_score_logits"],
+ is_mask_from_pts=(point_inputs is not None),
+ )
+ current_out["maskmem_features"] = maskmem_features
+ current_out["maskmem_pos_enc"] = maskmem_pos_enc
+ else:
+ current_out["maskmem_features"] = None
+ current_out["maskmem_pos_enc"] = None
+ maskmem_features = current_out["maskmem_features"]
+
+ if maskmem_features is not None:
+ maskmem_features = maskmem_features.to(torch.bfloat16)
+ maskmem_features = maskmem_features.to(storage_device, non_blocking=True)
+
+ maskmem_pos_enc = self._get_maskmem_pos_enc(inference_state, current_out)
+ pred_masks_gpu = current_out["pred_masks"]
+ pred_masks = pred_masks_gpu.to(storage_device, non_blocking=True)
+
+
+ if frame_idx == start_frame_idx + 1: ###initialize
+ compact_current_out = {
+ "maskmem_features": maskmem_features[..., None], ### N, 64, 64, 64
+ "maskmem_pos_enc": maskmem_pos_enc,
+ "pred_masks": pred_masks[..., None], ### N, 1, 256, 256
+ "obj_ptr": current_out["obj_ptr"][..., None], ### N, 256
+ "object_score_logits": current_out["object_score_logits"][..., None],
+ "acc_score": [0 for _ in range(inference_state['num_pathway'])],
+ "ious": current_out["ious"][...,None],
+ }
+ else:
+ compact_current_out = {
+ "maskmem_features": maskmem_features,
+ "maskmem_pos_enc": maskmem_pos_enc,
+ "pred_masks": pred_masks,
+ "obj_ptr": current_out["obj_ptr"],
+ "object_score_logits": current_out["object_score_logits"],
+ }
+ mem_pick_indexs = [{i: 0 for i in range(start_frame_idx, frame_idx+1)} for _ in range(inference_state['num_pathway'])]
+ return compact_current_out, pred_masks_gpu, mem_pick_indexs
+ else:
+ run_time = inference_state['num_pathway']
+ for pathway_id in range(run_time):
+ ########if run_time greater than 1, load mulitple pathways in output dict with frame selection and attention modulation##########
+ current_out = self.track_step(
+ frame_idx=frame_idx,
+ is_init_cond_frame=is_init_cond_frame,
+ current_vision_feats=current_vision_feats,
+ current_vision_pos_embeds=current_vision_pos_embeds,
+ feat_sizes=feat_sizes,
+ point_inputs=point_inputs,
+ mask_inputs=mask_inputs,
+ output_dict=output_dict,
+ num_frames=inference_state["num_frames"],
+ track_in_reverse=reverse,
+ run_mem_encoder=run_mem_encoder,
+ prev_sam_mask_logits=prev_sam_mask_logits,
+ mem_pick_index=mem_pick_indexs[pathway_id], ###dict: frame_idx -> memory_pathway
+ start_frame_idx=start_frame_idx,
+ iou_thre=inference_state['iou_thre'],
+ )
+ current_outs.append((pathway_id, current_out))
+
+ all_scores = []
+ object_scores = []
+ for pathway_id, current_out in current_outs:
+ score_i = output_dict['non_cond_frame_outputs'][frame_idx-1]['acc_score'][pathway_id]
+ object_score = current_out['object_score_logits']
+ object_scores.append(object_score.abs().item())
+ ious = current_out['ious']
+ for j in range(3): # son branch
+ score_j = score_i + np.log(ious[0, j].item()+1e-5)
+ iou_with_object = ious[0, j].float() if object_score.item() > 0 else -ious[0, j].float()
+ all_scores.append((pathway_id, j, score_j, iou_with_object.item(), current_out))
+ topk_scores = []
+ sorted_scores = sorted(all_scores, key=lambda x: x[2], reverse=True)
+ if max(object_scores) > inference_state['uncertainty']:
+ for score in sorted_scores[:run_time]:
+ topk_scores.append(score)
+ else:
+ seen_values = set()
+ for score in sorted_scores:
+ rounded_value = round(score[3], 2)
+ if rounded_value not in seen_values:
+ topk_scores.append(score)
+ seen_values.add(rounded_value)
+ if len(topk_scores) == inference_state['num_pathway']:
+ break
+ ### corner case: most masks overlap, prioritize diverse memory branch selection
+ if len(topk_scores) < inference_state['num_pathway']:
+ memory = {score[0] for score in topk_scores}
+ for i in range(run_time):
+ if i not in memory:
+ for score in sorted_scores:
+ if score[0] == i:
+ topk_scores.append(score)
+ break
+ if len(topk_scores) == inference_state['num_pathway']:
+ break
+
+ temp_maskmem_feat = []
+ temp_pred_masks = []
+ temp_obj_ptr = []
+ temp_acc_score = []
+ temp_ious = []
+ temp_score_logit = []
+ mem_pick_indexs_new = [deepcopy(mem_pick_indexs[pathway_id]) for pathway_id, _, _, _, _ in topk_scores]
+ for ind, (pathway_id, j, score_j, _, current_out) in enumerate(topk_scores):
+ maskmem_features, maskmem_pos_enc = self._encode_new_memory(
+ current_vision_feats=current_vision_feats,
+ feat_sizes=feat_sizes,
+ pred_masks_high_res=current_out["pred_masks_high_res"][:,j:j+1],
+ object_score_logits=current_out["object_score_logits"],
+ is_mask_from_pts=False,
+ )
+ maskmem_features = maskmem_features.to(torch.bfloat16)
+ maskmem_features = maskmem_features.to(storage_device, non_blocking=True)
+ temp_maskmem_feat.append(maskmem_features)
+ temp_pred_masks.append(current_out["pred_masks"][:,j:j+1])
+ temp_obj_ptr.append(current_out["obj_ptr"][:,j])
+ temp_acc_score.append(score_j)
+ temp_ious.append(current_out["ious"][0,j])
+ temp_score_logit.append(current_out['object_score_logits'])
+ mem_pick_indexs_new[ind][frame_idx] = ind
+ mem_pick_indexs[ind] = mem_pick_indexs_new[ind]
+
+
+ current_out["maskmem_pos_enc"] = maskmem_pos_enc
+ maskmem_pos_enc = self._get_maskmem_pos_enc(inference_state, current_out)
+ compact_current_out = {
+ "maskmem_features": torch.stack(temp_maskmem_feat, -1), ### N, 64, 64, 64
+ "maskmem_pos_enc": maskmem_pos_enc,
+ "pred_masks": torch.stack(temp_pred_masks, -1).to(storage_device, non_blocking=True), ### N, 1, 256, 256
+ "obj_ptr": torch.stack(temp_obj_ptr, -1), ### N, 256
+ "object_score_logits": torch.stack(temp_score_logit, -1),
+ "acc_score": temp_acc_score,
+ "ious": torch.stack(temp_ious, -1),
+ }
+ return compact_current_out, None, mem_pick_indexs
+
+ def _run_memory_encoder(
+ self,
+ inference_state,
+ frame_idx,
+ batch_size,
+ high_res_masks,
+ object_score_logits,
+ is_mask_from_pts,
+ ):
+ """
+ Run the memory encoder on `high_res_masks`. This is usually after applying
+ non-overlapping constraints to object scores. Since their scores changed, their
+ memory also need to be computed again with the memory encoder.
+ """
+ # Retrieve correct image features
+ _, _, current_vision_feats, _, feat_sizes = self._get_image_feature(
+ inference_state, frame_idx, batch_size
+ )
+ maskmem_features, maskmem_pos_enc = self._encode_new_memory(
+ current_vision_feats=current_vision_feats,
+ feat_sizes=feat_sizes,
+ pred_masks_high_res=high_res_masks,
+ object_score_logits=object_score_logits,
+ is_mask_from_pts=is_mask_from_pts,
+ )
+
+ # optionally offload the output to CPU memory to save GPU space
+ storage_device = inference_state["storage_device"]
+ maskmem_features = maskmem_features.to(torch.bfloat16)
+ maskmem_features = maskmem_features.to(storage_device, non_blocking=True)
+ # "maskmem_pos_enc" is the same across frames, so we only need to store one copy of it
+ maskmem_pos_enc = self._get_maskmem_pos_enc(
+ inference_state, {"maskmem_pos_enc": maskmem_pos_enc}
+ )
+ return maskmem_features, maskmem_pos_enc
+
+ def _get_maskmem_pos_enc(self, inference_state, current_out):
+ """
+ `maskmem_pos_enc` is the same across frames and objects, so we cache it as
+ a constant in the inference session to reduce session storage size.
+ """
+ model_constants = inference_state["constants"]
+ # "out_maskmem_pos_enc" should be either a list of tensors or None
+ out_maskmem_pos_enc = current_out["maskmem_pos_enc"]
+ if out_maskmem_pos_enc is not None:
+ if "maskmem_pos_enc" not in model_constants:
+ assert isinstance(out_maskmem_pos_enc, list)
+ # only take the slice for one object, since it's same across objects
+ maskmem_pos_enc = [x[0:1].clone() for x in out_maskmem_pos_enc]
+ model_constants["maskmem_pos_enc"] = maskmem_pos_enc
+ else:
+ maskmem_pos_enc = model_constants["maskmem_pos_enc"]
+ # expand the cached maskmem_pos_enc to the actual batch size
+ batch_size = out_maskmem_pos_enc[0].size(0)
+ expanded_maskmem_pos_enc = [
+ x.expand(batch_size, -1, -1, -1) for x in maskmem_pos_enc
+ ]
+ else:
+ expanded_maskmem_pos_enc = None
+ return expanded_maskmem_pos_enc
+
+ @torch.inference_mode()
+ def remove_object(self, inference_state, obj_id, strict=False, need_output=True):
+ """
+ Remove an object id from the tracking state. If strict is True, we check whether
+ the object id actually exists and raise an error if it doesn't exist.
+ """
+ old_obj_idx_to_rm = inference_state["obj_id_to_idx"].get(obj_id, None)
+ updated_frames = []
+ # Check whether this object_id to remove actually exists and possibly raise an error.
+ if old_obj_idx_to_rm is None:
+ if not strict:
+ return inference_state["obj_ids"], updated_frames
+ raise RuntimeError(
+ f"Cannot remove object id {obj_id} as it doesn't exist. "
+ f"All existing object ids: {inference_state['obj_ids']}."
+ )
+
+ # If this is the only remaining object id, we simply reset the state.
+ if len(inference_state["obj_id_to_idx"]) == 1:
+ self.reset_state(inference_state)
+ return inference_state["obj_ids"], updated_frames
+
+ # There are still remaining objects after removing this object id. In this case,
+ # we need to delete the object storage from inference state tensors.
+ # Step 0: clear the input on those frames where this object id has point or mask input
+ # (note that this step is required as it might downgrade conditioning frames to
+ # non-conditioning ones)
+ obj_input_frames_inds = set()
+ obj_input_frames_inds.update(
+ inference_state["point_inputs_per_obj"][old_obj_idx_to_rm]
+ )
+ obj_input_frames_inds.update(
+ inference_state["mask_inputs_per_obj"][old_obj_idx_to_rm]
+ )
+ for frame_idx in obj_input_frames_inds:
+ self.clear_all_prompts_in_frame(
+ inference_state, frame_idx, obj_id, need_output=False
+ )
+
+ # Step 1: Update the object id mapping (note that it must be done after Step 0,
+ # since Step 0 still requires the old object id mappings in inference_state)
+ old_obj_ids = inference_state["obj_ids"]
+ old_obj_inds = list(range(len(old_obj_ids)))
+ remain_old_obj_inds = old_obj_inds.copy()
+ remain_old_obj_inds.remove(old_obj_idx_to_rm)
+ new_obj_ids = [old_obj_ids[old_idx] for old_idx in remain_old_obj_inds]
+ new_obj_inds = list(range(len(new_obj_ids)))
+ # build new mappings
+ old_idx_to_new_idx = dict(zip(remain_old_obj_inds, new_obj_inds))
+ inference_state["obj_id_to_idx"] = dict(zip(new_obj_ids, new_obj_inds))
+ inference_state["obj_idx_to_id"] = dict(zip(new_obj_inds, new_obj_ids))
+ inference_state["obj_ids"] = new_obj_ids
+
+ # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys.
+ # (note that "consolidated_frame_inds" doesn't need to be updated in this step as
+ # it's already handled in Step 0)
+ def _map_keys(container):
+ new_kvs = []
+ for k in old_obj_inds:
+ v = container.pop(k)
+ if k in old_idx_to_new_idx:
+ new_kvs.append((old_idx_to_new_idx[k], v))
+ container.update(new_kvs)
+
+ _map_keys(inference_state["point_inputs_per_obj"])
+ _map_keys(inference_state["mask_inputs_per_obj"])
+ _map_keys(inference_state["output_dict_per_obj"])
+ _map_keys(inference_state["temp_output_dict_per_obj"])
+
+ # Step 3: For packed tensor storage, we index the remaining ids and rebuild the per-object slices.
+ def _slice_state(output_dict, storage_key):
+ for frame_idx, out in output_dict[storage_key].items():
+ out["maskmem_features"] = out["maskmem_features"][remain_old_obj_inds]
+ out["maskmem_pos_enc"] = [
+ x[remain_old_obj_inds] for x in out["maskmem_pos_enc"]
+ ]
+ # "maskmem_pos_enc" is the same across frames, so we only need to store one copy of it
+ out["maskmem_pos_enc"] = self._get_maskmem_pos_enc(inference_state, out)
+ out["pred_masks"] = out["pred_masks"][remain_old_obj_inds]
+ out["obj_ptr"] = out["obj_ptr"][remain_old_obj_inds]
+ out["object_score_logits"] = out["object_score_logits"][
+ remain_old_obj_inds
+ ]
+ # also update the per-object slices
+ self._add_output_per_object(
+ inference_state, frame_idx, out, storage_key
+ )
+
+ _slice_state(inference_state["output_dict"], "cond_frame_outputs")
+ _slice_state(inference_state["output_dict"], "non_cond_frame_outputs")
+
+ # Step 4: Further collect the outputs on those frames in `obj_input_frames_inds`, which
+ # could show an updated mask for objects previously occluded by the object being removed
+ if need_output:
+ temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"]
+ for frame_idx in obj_input_frames_inds:
+ is_cond = any(
+ frame_idx in obj_temp_output_dict["cond_frame_outputs"]
+ for obj_temp_output_dict in temp_output_dict_per_obj.values()
+ )
+ consolidated_out = self._consolidate_temp_output_across_obj(
+ inference_state,
+ frame_idx,
+ is_cond=is_cond,
+ run_mem_encoder=False,
+ consolidate_at_video_res=True,
+ )
+ _, video_res_masks = self._get_orig_video_res_output(
+ inference_state, consolidated_out["pred_masks_video_res"]
+ )
+ updated_frames.append((frame_idx, video_res_masks))
+
+ return inference_state["obj_ids"], updated_frames
+
+ def _clear_non_cond_mem_around_input(self, inference_state, frame_idx):
+ """
+ Remove the non-conditioning memory around the input frame. When users provide
+ correction clicks, the surrounding frames' non-conditioning memories can still
+ contain outdated object appearance information and could confuse the model.
+
+ This method clears those non-conditioning memories surrounding the interacted
+ frame to avoid giving the model both old and new information about the object.
+ """
+ r = self.memory_temporal_stride_for_eval
+ frame_idx_begin = frame_idx - r * self.num_maskmem
+ frame_idx_end = frame_idx + r * self.num_maskmem
+ output_dict = inference_state["output_dict"]
+ non_cond_frame_outputs = output_dict["non_cond_frame_outputs"]
+ for t in range(frame_idx_begin, frame_idx_end + 1):
+ non_cond_frame_outputs.pop(t, None)
+ for obj_output_dict in inference_state["output_dict_per_obj"].values():
+ obj_output_dict["non_cond_frame_outputs"].pop(t, None)
\ No newline at end of file
diff --git a/sam2/utils/__init__.py b/sam2/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae
--- /dev/null
+++ b/sam2/utils/__init__.py
@@ -0,0 +1,5 @@
+# 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.
diff --git a/sam2/utils/__pycache__/__init__.cpython-310.pyc b/sam2/utils/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..37e6dce6d8830a63b3851f370b9906f5ca62a586
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diff --git a/sam2/utils/__pycache__/misc.cpython-310.pyc b/sam2/utils/__pycache__/misc.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..66890a1110bf0b6afa68420053e242455dacf057
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diff --git a/sam2/utils/__pycache__/transforms.cpython-310.pyc b/sam2/utils/__pycache__/transforms.cpython-310.pyc
new file mode 100644
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diff --git a/sam2/utils/amg.py b/sam2/utils/amg.py
new file mode 100644
index 0000000000000000000000000000000000000000..986842960cf5deca00614b7b1cde1ab77dad7e6e
--- /dev/null
+++ b/sam2/utils/amg.py
@@ -0,0 +1,348 @@
+# 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 math
+from copy import deepcopy
+from itertools import product
+from typing import Any, Dict, Generator, ItemsView, List, Tuple
+
+import numpy as np
+import torch
+
+# Very lightly adapted from https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/utils/amg.py
+
+
+class MaskData:
+ """
+ A structure for storing masks and their related data in batched format.
+ Implements basic filtering and concatenation.
+ """
+
+ def __init__(self, **kwargs) -> None:
+ for v in kwargs.values():
+ assert isinstance(
+ v, (list, np.ndarray, torch.Tensor)
+ ), "MaskData only supports list, numpy arrays, and torch tensors."
+ self._stats = dict(**kwargs)
+
+ def __setitem__(self, key: str, item: Any) -> None:
+ assert isinstance(
+ item, (list, np.ndarray, torch.Tensor)
+ ), "MaskData only supports list, numpy arrays, and torch tensors."
+ self._stats[key] = item
+
+ def __delitem__(self, key: str) -> None:
+ del self._stats[key]
+
+ def __getitem__(self, key: str) -> Any:
+ return self._stats[key]
+
+ def items(self) -> ItemsView[str, Any]:
+ return self._stats.items()
+
+ def filter(self, keep: torch.Tensor) -> None:
+ for k, v in self._stats.items():
+ if v is None:
+ self._stats[k] = None
+ elif isinstance(v, torch.Tensor):
+ self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
+ elif isinstance(v, np.ndarray):
+ self._stats[k] = v[keep.detach().cpu().numpy()]
+ elif isinstance(v, list) and keep.dtype == torch.bool:
+ self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
+ elif isinstance(v, list):
+ self._stats[k] = [v[i] for i in keep]
+ else:
+ raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
+
+ def cat(self, new_stats: "MaskData") -> None:
+ for k, v in new_stats.items():
+ if k not in self._stats or self._stats[k] is None:
+ self._stats[k] = deepcopy(v)
+ elif isinstance(v, torch.Tensor):
+ self._stats[k] = torch.cat([self._stats[k], v], dim=0)
+ elif isinstance(v, np.ndarray):
+ self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
+ elif isinstance(v, list):
+ self._stats[k] = self._stats[k] + deepcopy(v)
+ else:
+ raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
+
+ def to_numpy(self) -> None:
+ for k, v in self._stats.items():
+ if isinstance(v, torch.Tensor):
+ self._stats[k] = v.float().detach().cpu().numpy()
+
+
+def is_box_near_crop_edge(
+ boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0
+) -> torch.Tensor:
+ """Filter masks at the edge of a crop, but not at the edge of the original image."""
+ crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
+ orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
+ boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
+ near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
+ near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
+ near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
+ return torch.any(near_crop_edge, dim=1)
+
+
+def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
+ box_xywh = deepcopy(box_xyxy)
+ box_xywh[2] = box_xywh[2] - box_xywh[0]
+ box_xywh[3] = box_xywh[3] - box_xywh[1]
+ return box_xywh
+
+
+def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
+ assert len(args) > 0 and all(
+ len(a) == len(args[0]) for a in args
+ ), "Batched iteration must have inputs of all the same size."
+ n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
+ for b in range(n_batches):
+ yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
+
+
+def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
+ """
+ Encodes masks to an uncompressed RLE, in the format expected by
+ pycoco tools.
+ """
+ # Put in fortran order and flatten h,w
+ b, h, w = tensor.shape
+ tensor = tensor.permute(0, 2, 1).flatten(1)
+
+ # Compute change indices
+ diff = tensor[:, 1:] ^ tensor[:, :-1]
+ change_indices = diff.nonzero()
+
+ # Encode run length
+ out = []
+ for i in range(b):
+ cur_idxs = change_indices[change_indices[:, 0] == i, 1]
+ cur_idxs = torch.cat(
+ [
+ torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
+ cur_idxs + 1,
+ torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),
+ ]
+ )
+ btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
+ counts = [] if tensor[i, 0] == 0 else [0]
+ counts.extend(btw_idxs.detach().cpu().tolist())
+ out.append({"size": [h, w], "counts": counts})
+ return out
+
+
+def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
+ """Compute a binary mask from an uncompressed RLE."""
+ h, w = rle["size"]
+ mask = np.empty(h * w, dtype=bool)
+ idx = 0
+ parity = False
+ for count in rle["counts"]:
+ mask[idx : idx + count] = parity
+ idx += count
+ parity ^= True
+ mask = mask.reshape(w, h)
+ return mask.transpose() # Put in C order
+
+
+def area_from_rle(rle: Dict[str, Any]) -> int:
+ return sum(rle["counts"][1::2])
+
+
+def calculate_stability_score(
+ masks: torch.Tensor, mask_threshold: float, threshold_offset: float
+) -> torch.Tensor:
+ """
+ Computes the stability score for a batch of masks. The stability
+ score is the IoU between the binary masks obtained by thresholding
+ the predicted mask logits at high and low values.
+ """
+ # One mask is always contained inside the other.
+ # Save memory by preventing unnecessary cast to torch.int64
+ intersections = (
+ (masks > (mask_threshold + threshold_offset))
+ .sum(-1, dtype=torch.int16)
+ .sum(-1, dtype=torch.int32)
+ )
+ unions = (
+ (masks > (mask_threshold - threshold_offset))
+ .sum(-1, dtype=torch.int16)
+ .sum(-1, dtype=torch.int32)
+ )
+ return intersections / unions
+
+
+def build_point_grid(n_per_side: int) -> np.ndarray:
+ """Generates a 2D grid of points evenly spaced in [0,1]x[0,1]."""
+ offset = 1 / (2 * n_per_side)
+ points_one_side = np.linspace(offset, 1 - offset, n_per_side)
+ points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
+ points_y = np.tile(points_one_side[:, None], (1, n_per_side))
+ points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
+ return points
+
+
+def build_all_layer_point_grids(
+ n_per_side: int, n_layers: int, scale_per_layer: int
+) -> List[np.ndarray]:
+ """Generates point grids for all crop layers."""
+ points_by_layer = []
+ for i in range(n_layers + 1):
+ n_points = int(n_per_side / (scale_per_layer**i))
+ points_by_layer.append(build_point_grid(n_points))
+ return points_by_layer
+
+
+def generate_crop_boxes(
+ im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float
+) -> Tuple[List[List[int]], List[int]]:
+ """
+ Generates a list of crop boxes of different sizes. Each layer
+ has (2**i)**2 boxes for the ith layer.
+ """
+ crop_boxes, layer_idxs = [], []
+ im_h, im_w = im_size
+ short_side = min(im_h, im_w)
+
+ # Original image
+ crop_boxes.append([0, 0, im_w, im_h])
+ layer_idxs.append(0)
+
+ def crop_len(orig_len, n_crops, overlap):
+ return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
+
+ for i_layer in range(n_layers):
+ n_crops_per_side = 2 ** (i_layer + 1)
+ overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
+
+ crop_w = crop_len(im_w, n_crops_per_side, overlap)
+ crop_h = crop_len(im_h, n_crops_per_side, overlap)
+
+ crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
+ crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
+
+ # Crops in XYWH format
+ for x0, y0 in product(crop_box_x0, crop_box_y0):
+ box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
+ crop_boxes.append(box)
+ layer_idxs.append(i_layer + 1)
+
+ return crop_boxes, layer_idxs
+
+
+def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
+ x0, y0, _, _ = crop_box
+ offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
+ # Check if boxes has a channel dimension
+ if len(boxes.shape) == 3:
+ offset = offset.unsqueeze(1)
+ return boxes + offset
+
+
+def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
+ x0, y0, _, _ = crop_box
+ offset = torch.tensor([[x0, y0]], device=points.device)
+ # Check if points has a channel dimension
+ if len(points.shape) == 3:
+ offset = offset.unsqueeze(1)
+ return points + offset
+
+
+def uncrop_masks(
+ masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int
+) -> torch.Tensor:
+ x0, y0, x1, y1 = crop_box
+ if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
+ return masks
+ # Coordinate transform masks
+ pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
+ pad = (x0, pad_x - x0, y0, pad_y - y0)
+ return torch.nn.functional.pad(masks, pad, value=0)
+
+
+def remove_small_regions(
+ mask: np.ndarray, area_thresh: float, mode: str
+) -> Tuple[np.ndarray, bool]:
+ """
+ Removes small disconnected regions and holes in a mask. Returns the
+ mask and an indicator of if the mask has been modified.
+ """
+ import cv2 # type: ignore
+
+ assert mode in ["holes", "islands"]
+ correct_holes = mode == "holes"
+ working_mask = (correct_holes ^ mask).astype(np.uint8)
+ n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
+ sizes = stats[:, -1][1:] # Row 0 is background label
+ small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
+ if len(small_regions) == 0:
+ return mask, False
+ fill_labels = [0] + small_regions
+ if not correct_holes:
+ fill_labels = [i for i in range(n_labels) if i not in fill_labels]
+ # If every region is below threshold, keep largest
+ if len(fill_labels) == 0:
+ fill_labels = [int(np.argmax(sizes)) + 1]
+ mask = np.isin(regions, fill_labels)
+ return mask, True
+
+
+def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
+ from pycocotools import mask as mask_utils # type: ignore
+
+ h, w = uncompressed_rle["size"]
+ rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
+ rle["counts"] = rle["counts"].decode("utf-8") # Necessary to serialize with json
+ return rle
+
+
+def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
+ """
+ Calculates boxes in XYXY format around masks. Return [0,0,0,0] for
+ an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.
+ """
+ # torch.max below raises an error on empty inputs, just skip in this case
+ if torch.numel(masks) == 0:
+ return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
+
+ # Normalize shape to CxHxW
+ shape = masks.shape
+ h, w = shape[-2:]
+ if len(shape) > 2:
+ masks = masks.flatten(0, -3)
+ else:
+ masks = masks.unsqueeze(0)
+
+ # Get top and bottom edges
+ in_height, _ = torch.max(masks, dim=-1)
+ in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
+ bottom_edges, _ = torch.max(in_height_coords, dim=-1)
+ in_height_coords = in_height_coords + h * (~in_height)
+ top_edges, _ = torch.min(in_height_coords, dim=-1)
+
+ # Get left and right edges
+ in_width, _ = torch.max(masks, dim=-2)
+ in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
+ right_edges, _ = torch.max(in_width_coords, dim=-1)
+ in_width_coords = in_width_coords + w * (~in_width)
+ left_edges, _ = torch.min(in_width_coords, dim=-1)
+
+ # If the mask is empty the right edge will be to the left of the left edge.
+ # Replace these boxes with [0, 0, 0, 0]
+ empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
+ out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
+ out = out * (~empty_filter).unsqueeze(-1)
+
+ # Return to original shape
+ if len(shape) > 2:
+ out = out.reshape(*shape[:-2], 4)
+ else:
+ out = out[0]
+
+ return out
diff --git a/sam2/utils/misc.py b/sam2/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..6de8995a5bf751b61f07e0e9b5e1b76667bad831
--- /dev/null
+++ b/sam2/utils/misc.py
@@ -0,0 +1,350 @@
+# 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 os
+import warnings
+from threading import Thread
+
+import numpy as np
+import torch
+from PIL import Image
+from tqdm import tqdm
+
+
+def get_sdpa_settings():
+ if torch.cuda.is_available():
+ old_gpu = torch.cuda.get_device_properties(0).major < 7
+ # only use Flash Attention on Ampere (8.0) or newer GPUs
+ use_flash_attn = torch.cuda.get_device_properties(0).major >= 8
+ if not use_flash_attn:
+ warnings.warn(
+ "Flash Attention is disabled as it requires a GPU with Ampere (8.0) CUDA capability.",
+ category=UserWarning,
+ stacklevel=2,
+ )
+ # keep math kernel for PyTorch versions before 2.2 (Flash Attention v2 is only
+ # available on PyTorch 2.2+, while Flash Attention v1 cannot handle all cases)
+ pytorch_version = tuple(int(v) for v in torch.__version__.split(".")[:2])
+ if pytorch_version < (2, 2):
+ warnings.warn(
+ f"You are using PyTorch {torch.__version__} without Flash Attention v2 support. "
+ "Consider upgrading to PyTorch 2.2+ for Flash Attention v2 (which could be faster).",
+ category=UserWarning,
+ stacklevel=2,
+ )
+ math_kernel_on = pytorch_version < (2, 2) or not use_flash_attn
+ else:
+ old_gpu = True
+ use_flash_attn = False
+ math_kernel_on = True
+
+ return old_gpu, use_flash_attn, math_kernel_on
+
+
+def get_connected_components(mask):
+ """
+ Get the connected components (8-connectivity) of binary masks of shape (N, 1, H, W).
+
+ Inputs:
+ - mask: A binary mask tensor of shape (N, 1, H, W), where 1 is foreground and 0 is
+ background.
+
+ Outputs:
+ - labels: A tensor of shape (N, 1, H, W) containing the connected component labels
+ for foreground pixels and 0 for background pixels.
+ - counts: A tensor of shape (N, 1, H, W) containing the area of the connected
+ components for foreground pixels and 0 for background pixels.
+ """
+ from sam2 import _C
+
+ return _C.get_connected_componnets(mask.to(torch.uint8).contiguous())
+
+
+def mask_to_box(masks: torch.Tensor):
+ """
+ compute bounding box given an input mask
+
+ Inputs:
+ - masks: [B, 1, H, W] masks, dtype=torch.Tensor
+
+ Returns:
+ - box_coords: [B, 1, 4], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.Tensor
+ """
+ B, _, h, w = masks.shape
+ device = masks.device
+ xs = torch.arange(w, device=device, dtype=torch.int32)
+ ys = torch.arange(h, device=device, dtype=torch.int32)
+ grid_xs, grid_ys = torch.meshgrid(xs, ys, indexing="xy")
+ grid_xs = grid_xs[None, None, ...].expand(B, 1, h, w)
+ grid_ys = grid_ys[None, None, ...].expand(B, 1, h, w)
+ min_xs, _ = torch.min(torch.where(masks, grid_xs, w).flatten(-2), dim=-1)
+ max_xs, _ = torch.max(torch.where(masks, grid_xs, -1).flatten(-2), dim=-1)
+ min_ys, _ = torch.min(torch.where(masks, grid_ys, h).flatten(-2), dim=-1)
+ max_ys, _ = torch.max(torch.where(masks, grid_ys, -1).flatten(-2), dim=-1)
+ bbox_coords = torch.stack((min_xs, min_ys, max_xs, max_ys), dim=-1)
+
+ return bbox_coords
+
+
+def _load_img_as_tensor(img_path, image_size):
+ img_pil = Image.open(img_path)
+ img_np = np.array(img_pil.convert("RGB").resize((image_size, image_size)))
+ if img_np.dtype == np.uint8: # np.uint8 is expected for JPEG images
+ img_np = img_np / 255.0
+ else:
+ raise RuntimeError(f"Unknown image dtype: {img_np.dtype} on {img_path}")
+ img = torch.from_numpy(img_np).permute(2, 0, 1)
+ video_width, video_height = img_pil.size # the original video size
+ return img, video_height, video_width
+
+
+class AsyncVideoFrameLoader:
+ """
+ A list of video frames to be load asynchronously without blocking session start.
+ """
+
+ def __init__(
+ self,
+ img_paths,
+ image_size,
+ offload_video_to_cpu,
+ img_mean,
+ img_std,
+ compute_device,
+ ):
+ self.img_paths = img_paths
+ self.image_size = image_size
+ self.offload_video_to_cpu = offload_video_to_cpu
+ self.img_mean = img_mean
+ self.img_std = img_std
+ # items in `self.images` will be loaded asynchronously
+ self.images = [None] * len(img_paths)
+ # catch and raise any exceptions in the async loading thread
+ self.exception = None
+ # video_height and video_width be filled when loading the first image
+ self.video_height = None
+ self.video_width = None
+ self.compute_device = compute_device
+
+ # load the first frame to fill video_height and video_width and also
+ # to cache it (since it's most likely where the user will click)
+ self.__getitem__(0)
+
+ # load the rest of frames asynchronously without blocking the session start
+ def _load_frames():
+ try:
+ for n in tqdm(range(len(self.images)), desc="frame loading (JPEG)"):
+ self.__getitem__(n)
+ except Exception as e:
+ self.exception = e
+
+ self.thread = Thread(target=_load_frames, daemon=True)
+ self.thread.start()
+
+ def __getitem__(self, index):
+ if self.exception is not None:
+ raise RuntimeError("Failure in frame loading thread") from self.exception
+
+ img = self.images[index]
+ if img is not None:
+ return img
+
+ img, video_height, video_width = _load_img_as_tensor(
+ self.img_paths[index], self.image_size
+ )
+ self.video_height = video_height
+ self.video_width = video_width
+ # normalize by mean and std
+ img -= self.img_mean
+ img /= self.img_std
+ if not self.offload_video_to_cpu:
+ img = img.to(self.compute_device, non_blocking=True)
+ self.images[index] = img
+ return img
+
+ def __len__(self):
+ return len(self.images)
+
+
+def load_video_frames(
+ video_path,
+ image_size,
+ offload_video_to_cpu,
+ img_mean=(0.485, 0.456, 0.406),
+ img_std=(0.229, 0.224, 0.225),
+ async_loading_frames=False,
+ compute_device=torch.device("cuda"),
+):
+ """
+ Load the video frames from video_path. The frames are resized to image_size as in
+ the model and are loaded to GPU if offload_video_to_cpu=False. This is used by the demo.
+ """
+ is_bytes = isinstance(video_path, bytes)
+ is_str = isinstance(video_path, str)
+ is_mp4_path = is_str and os.path.splitext(video_path)[-1] in [".mp4", ".MP4"]
+ if is_bytes or is_mp4_path:
+ return load_video_frames_from_video_file(
+ video_path=video_path,
+ image_size=image_size,
+ offload_video_to_cpu=offload_video_to_cpu,
+ img_mean=img_mean,
+ img_std=img_std,
+ compute_device=compute_device,
+ )
+ elif is_str and os.path.isdir(video_path):
+ return load_video_frames_from_jpg_images(
+ video_path=video_path,
+ image_size=image_size,
+ offload_video_to_cpu=offload_video_to_cpu,
+ img_mean=img_mean,
+ img_std=img_std,
+ async_loading_frames=async_loading_frames,
+ compute_device=compute_device,
+ )
+ else:
+ raise NotImplementedError(
+ "Only MP4 video and JPEG folder are supported at this moment"
+ )
+
+
+def load_video_frames_from_jpg_images(
+ video_path,
+ image_size,
+ offload_video_to_cpu,
+ img_mean=(0.485, 0.456, 0.406),
+ img_std=(0.229, 0.224, 0.225),
+ async_loading_frames=False,
+ compute_device=torch.device("cuda"),
+):
+ """
+ Load the video frames from a directory of JPEG files ("<frame_index>.jpg" format).
+
+ The frames are resized to image_size x image_size and are loaded to GPU if
+ `offload_video_to_cpu` is `False` and to CPU if `offload_video_to_cpu` is `True`.
+
+ You can load a frame asynchronously by setting `async_loading_frames` to `True`.
+ """
+ if isinstance(video_path, str) and os.path.isdir(video_path):
+ jpg_folder = video_path
+ else:
+ raise NotImplementedError(
+ "Only JPEG frames are supported at this moment. For video files, you may use "
+ "ffmpeg (https://ffmpeg.org/) to extract frames into a folder of JPEG files, such as \n"
+ "```\n"
+ "ffmpeg -i <your_video>.mp4 -q:v 2 -start_number 0 <output_dir>/'%05d.jpg'\n"
+ "```\n"
+ "where `-q:v` generates high-quality JPEG frames and `-start_number 0` asks "
+ "ffmpeg to start the JPEG file from 00000.jpg."
+ )
+
+ frame_names = [
+ p
+ for p in os.listdir(jpg_folder)
+ if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"]
+ ]
+ frame_names.sort(key=lambda p: int(os.path.splitext(p)[0].split('frame_')[-1]))
+ # frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))
+ num_frames = len(frame_names)
+ if num_frames == 0:
+ raise RuntimeError(f"no images found in {jpg_folder}")
+ img_paths = [os.path.join(jpg_folder, frame_name) for frame_name in frame_names]
+ img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None]
+ img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None]
+
+ if async_loading_frames:
+ lazy_images = AsyncVideoFrameLoader(
+ img_paths,
+ image_size,
+ offload_video_to_cpu,
+ img_mean,
+ img_std,
+ compute_device,
+ )
+ return lazy_images, lazy_images.video_height, lazy_images.video_width
+
+ images = torch.zeros(num_frames, 3, image_size, image_size, dtype=torch.float32)
+ for n, img_path in enumerate(tqdm(img_paths, desc="frame loading (JPEG)")):
+ images[n], video_height, video_width = _load_img_as_tensor(img_path, image_size)
+ if not offload_video_to_cpu:
+ images = images.to(compute_device)
+ img_mean = img_mean.to(compute_device)
+ img_std = img_std.to(compute_device)
+ # normalize by mean and std
+ images -= img_mean
+ images /= img_std
+ return images, video_height, video_width
+
+
+def load_video_frames_from_video_file(
+ video_path,
+ image_size,
+ offload_video_to_cpu,
+ img_mean=(0.485, 0.456, 0.406),
+ img_std=(0.229, 0.224, 0.225),
+ compute_device=torch.device("cuda"),
+):
+ """Load the video frames from a video file."""
+ import decord
+
+ img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None]
+ img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None]
+ # Get the original video height and width
+ decord.bridge.set_bridge("torch")
+ video_height, video_width, _ = decord.VideoReader(video_path).next().shape
+ # Iterate over all frames in the video
+ images = []
+ for frame in decord.VideoReader(video_path, width=image_size, height=image_size):
+ images.append(frame.permute(2, 0, 1))
+
+ images = torch.stack(images, dim=0).float() / 255.0
+ if not offload_video_to_cpu:
+ images = images.to(compute_device)
+ img_mean = img_mean.to(compute_device)
+ img_std = img_std.to(compute_device)
+ # normalize by mean and std
+ images -= img_mean
+ images /= img_std
+ return images, video_height, video_width
+
+
+def fill_holes_in_mask_scores(mask, max_area):
+ """
+ A post processor to fill small holes in mask scores with area under `max_area`.
+ """
+ # Holes are those connected components in background with area <= self.max_area
+ # (background regions are those with mask scores <= 0)
+ assert max_area > 0, "max_area must be positive"
+
+ input_mask = mask
+ try:
+ labels, areas = get_connected_components(mask <= 0)
+ is_hole = (labels > 0) & (areas <= max_area)
+ # We fill holes with a small positive mask score (0.1) to change them to foreground.
+ mask = torch.where(is_hole, 0.1, mask)
+ except Exception as e:
+ # Skip the post-processing step on removing small holes if the CUDA kernel fails
+ warnings.warn(
+ f"{e}\n\nSkipping the post-processing step due to the error above. You can "
+ "still use SAM 2 and it's OK to ignore the error above, although some post-processing "
+ "functionality may be limited (which doesn't affect the results in most cases; see "
+ "https://github.com/facebookresearch/sam2/blob/main/INSTALL.md).",
+ category=UserWarning,
+ stacklevel=2,
+ )
+ mask = input_mask
+
+ return mask
+
+
+def concat_points(old_point_inputs, new_points, new_labels):
+ """Add new points and labels to previous point inputs (add at the end)."""
+ if old_point_inputs is None:
+ points, labels = new_points, new_labels
+ else:
+ points = torch.cat([old_point_inputs["point_coords"], new_points], dim=1)
+ labels = torch.cat([old_point_inputs["point_labels"], new_labels], dim=1)
+
+ return {"point_coords": points, "point_labels": labels}
diff --git a/sam2/utils/transforms.py b/sam2/utils/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc17bebfab104b659c5469e8434cf357ae7e24b6
--- /dev/null
+++ b/sam2/utils/transforms.py
@@ -0,0 +1,118 @@
+# 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 warnings
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision.transforms import Normalize, Resize, ToTensor
+
+
+class SAM2Transforms(nn.Module):
+ def __init__(
+ self, resolution, mask_threshold, max_hole_area=0.0, max_sprinkle_area=0.0
+ ):
+ """
+ Transforms for SAM2.
+ """
+ super().__init__()
+ self.resolution = resolution
+ self.mask_threshold = mask_threshold
+ self.max_hole_area = max_hole_area
+ self.max_sprinkle_area = max_sprinkle_area
+ self.mean = [0.485, 0.456, 0.406]
+ self.std = [0.229, 0.224, 0.225]
+ self.to_tensor = ToTensor()
+ self.transforms = torch.jit.script(
+ nn.Sequential(
+ Resize((self.resolution, self.resolution)),
+ Normalize(self.mean, self.std),
+ )
+ )
+
+ def __call__(self, x):
+ x = self.to_tensor(x)
+ return self.transforms(x)
+
+ def forward_batch(self, img_list):
+ img_batch = [self.transforms(self.to_tensor(img)) for img in img_list]
+ img_batch = torch.stack(img_batch, dim=0)
+ return img_batch
+
+ def transform_coords(
+ self, coords: torch.Tensor, normalize=False, orig_hw=None
+ ) -> torch.Tensor:
+ """
+ Expects a torch tensor with length 2 in the last dimension. The coordinates can be in absolute image or normalized coordinates,
+ If the coords are in absolute image coordinates, normalize should be set to True and original image size is required.
+
+ Returns
+ Un-normalized coordinates in the range of [0, 1] which is expected by the SAM2 model.
+ """
+ if normalize:
+ assert orig_hw is not None
+ h, w = orig_hw
+ coords = coords.clone()
+ coords[..., 0] = coords[..., 0] / w
+ coords[..., 1] = coords[..., 1] / h
+
+ coords = coords * self.resolution # unnormalize coords
+ return coords
+
+ def transform_boxes(
+ self, boxes: torch.Tensor, normalize=False, orig_hw=None
+ ) -> torch.Tensor:
+ """
+ Expects a tensor of shape Bx4. The coordinates can be in absolute image or normalized coordinates,
+ if the coords are in absolute image coordinates, normalize should be set to True and original image size is required.
+ """
+ boxes = self.transform_coords(boxes.reshape(-1, 2, 2), normalize, orig_hw)
+ return boxes
+
+ def postprocess_masks(self, masks: torch.Tensor, orig_hw) -> torch.Tensor:
+ """
+ Perform PostProcessing on output masks.
+ """
+ from sam2.utils.misc import get_connected_components
+
+ masks = masks.float()
+ input_masks = masks
+ mask_flat = masks.flatten(0, 1).unsqueeze(1) # flatten as 1-channel image
+ try:
+ if self.max_hole_area > 0:
+ # Holes are those connected components in background with area <= self.fill_hole_area
+ # (background regions are those with mask scores <= self.mask_threshold)
+ labels, areas = get_connected_components(
+ mask_flat <= self.mask_threshold
+ )
+ is_hole = (labels > 0) & (areas <= self.max_hole_area)
+ is_hole = is_hole.reshape_as(masks)
+ # We fill holes with a small positive mask score (10.0) to change them to foreground.
+ masks = torch.where(is_hole, self.mask_threshold + 10.0, masks)
+
+ if self.max_sprinkle_area > 0:
+ labels, areas = get_connected_components(
+ mask_flat > self.mask_threshold
+ )
+ is_hole = (labels > 0) & (areas <= self.max_sprinkle_area)
+ is_hole = is_hole.reshape_as(masks)
+ # We fill holes with negative mask score (-10.0) to change them to background.
+ masks = torch.where(is_hole, self.mask_threshold - 10.0, masks)
+ except Exception as e:
+ # Skip the post-processing step if the CUDA kernel fails
+ warnings.warn(
+ f"{e}\n\nSkipping the post-processing step due to the error above. You can "
+ "still use SAM 2 and it's OK to ignore the error above, although some post-processing "
+ "functionality may be limited (which doesn't affect the results in most cases; see "
+ "https://github.com/facebookresearch/sam2/blob/main/INSTALL.md).",
+ category=UserWarning,
+ stacklevel=2,
+ )
+ masks = input_masks
+
+ masks = F.interpolate(masks, orig_hw, mode="bilinear", align_corners=False)
+ return masks
diff --git a/tools/README.md b/tools/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a8ad2d65483ed3afe6de85133f2cf3c8689a0048
--- /dev/null
+++ b/tools/README.md
@@ -0,0 +1,36 @@
+## SAM 2 toolkits
+
+This directory provides toolkits for additional SAM 2 use cases.
+
+### Semi-supervised VOS inference
+
+The `vos_inference.py` script can be used to generate predictions for semi-supervised video object segmentation (VOS) evaluation on datasets such as [DAVIS](https://davischallenge.org/index.html), [MOSE](https://henghuiding.github.io/MOSE/) or the SA-V dataset.
+
+After installing SAM 2 and its dependencies, it can be used as follows ([DAVIS 2017 dataset](https://davischallenge.org/davis2017/code.html) as an example). This script saves the prediction PNG files to the `--output_mask_dir`.
+```bash
+python ./tools/vos_inference.py \
+ --sam2_cfg sam2_hiera_b+.yaml \
+ --sam2_checkpoint ./checkpoints/sam2_hiera_base_plus.pt \
+ --base_video_dir /path-to-davis-2017/JPEGImages/480p \
+ --input_mask_dir /path-to-davis-2017/Annotations/480p \
+ --video_list_file /path-to-davis-2017/ImageSets/2017/val.txt \
+ --output_mask_dir ./outputs/davis_2017_pred_pngs
+```
+(replace `/path-to-davis-2017` with the path to DAVIS 2017 dataset)
+
+To evaluate on the SA-V dataset with per-object PNG files for the object masks, we need to **add the `--per_obj_png_file` flag** as follows (using SA-V val as an example). This script will also save per-object PNG files for the output masks under the `--per_obj_png_file` flag.
+```bash
+python ./tools/vos_inference.py \
+ --sam2_cfg sam2_hiera_b+.yaml \
+ --sam2_checkpoint ./checkpoints/sam2_hiera_base_plus.pt \
+ --base_video_dir /path-to-sav-val/JPEGImages_24fps \
+ --input_mask_dir /path-to-sav-val/Annotations_6fps \
+ --video_list_file /path-to-sav-val/sav_val.txt \
+ --per_obj_png_file \
+ --output_mask_dir ./outputs/sav_val_pred_pngs
+```
+(replace `/path-to-sav-val` with the path to SA-V val)
+
+Then, we can use the evaluation tools or servers for each dataset to get the performance of the prediction PNG files above.
+
+**Note: a limitation of the `vos_inference.py` script above is that currently it only supports VOS datasets where all objects to track already appear on frame 0 in each video** (and therefore it doesn't apply to some datasets such as [LVOS](https://lingyihongfd.github.io/lvos.github.io/) that have objects only appearing in the middle of a video).
diff --git a/tools/vos_inference.py b/tools/vos_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..6348f4bf0af9cb2f6cf8492376ad34c5ef392b8d
--- /dev/null
+++ b/tools/vos_inference.py
@@ -0,0 +1,320 @@
+# 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 argparse
+import os
+
+import numpy as np
+import torch
+from PIL import Image
+from sam2.build_sam import build_sam2_video_predictor
+
+
+# the PNG palette for DAVIS 2017 dataset
+DAVIS_PALETTE = b"\x00\x00\x00\x80\x00\x00\x00\x80\x00\x80\x80\x00\x00\x00\x80\x80\x00\x80\x00\x80\x80\x80\x80\x80@\x00\x00\xc0\x00\x00@\x80\x00\xc0\x80\x00@\x00\x80\xc0\x00\x80@\x80\x80\xc0\x80\x80\x00@\x00\x80@\x00\x00\xc0\x00\x80\xc0\x00\x00@\x80\x80@\x80\x00\xc0\x80\x80\xc0\x80@@\x00\xc0@\x00@\xc0\x00\xc0\xc0\x00@@\x80\xc0@\x80@\xc0\x80\xc0\xc0\x80\x00\x00@\x80\x00@\x00\x80@\x80\x80@\x00\x00\xc0\x80\x00\xc0\x00\x80\xc0\x80\x80\xc0@\x00@\xc0\x00@@\x80@\xc0\x80@@\x00\xc0\xc0\x00\xc0@\x80\xc0\xc0\x80\xc0\x00@@\x80@@\x00\xc0@\x80\xc0@\x00@\xc0\x80@\xc0\x00\xc0\xc0\x80\xc0\xc0@@@\xc0@@@\xc0@\xc0\xc0@@@\xc0\xc0@\xc0@\xc0\xc0\xc0\xc0\xc0 \x00\x00\xa0\x00\x00 \x80\x00\xa0\x80\x00 \x00\x80\xa0\x00\x80 \x80\x80\xa0\x80\x80`\x00\x00\xe0\x00\x00`\x80\x00\xe0\x80\x00`\x00\x80\xe0\x00\x80`\x80\x80\xe0\x80\x80 @\x00\xa0@\x00 \xc0\x00\xa0\xc0\x00 @\x80\xa0@\x80 \xc0\x80\xa0\xc0\x80`@\x00\xe0@\x00`\xc0\x00\xe0\xc0\x00`@\x80\xe0@\x80`\xc0\x80\xe0\xc0\x80 \x00@\xa0\x00@ \x80@\xa0\x80@ \x00\xc0\xa0\x00\xc0 \x80\xc0\xa0\x80\xc0`\x00@\xe0\x00@`\x80@\xe0\x80@`\x00\xc0\xe0\x00\xc0`\x80\xc0\xe0\x80\xc0 @@\xa0@@ \xc0@\xa0\xc0@ @\xc0\xa0@\xc0 \xc0\xc0\xa0\xc0\xc0`@@\xe0@@`\xc0@\xe0\xc0@`@\xc0\xe0@\xc0`\xc0\xc0\xe0\xc0\xc0\x00 \x00\x80 \x00\x00\xa0\x00\x80\xa0\x00\x00 \x80\x80 \x80\x00\xa0\x80\x80\xa0\x80@ \x00\xc0 \x00@\xa0\x00\xc0\xa0\x00@ \x80\xc0 \x80@\xa0\x80\xc0\xa0\x80\x00`\x00\x80`\x00\x00\xe0\x00\x80\xe0\x00\x00`\x80\x80`\x80\x00\xe0\x80\x80\xe0\x80@`\x00\xc0`\x00@\xe0\x00\xc0\xe0\x00@`\x80\xc0`\x80@\xe0\x80\xc0\xe0\x80\x00 @\x80 @\x00\xa0@\x80\xa0@\x00 \xc0\x80 \xc0\x00\xa0\xc0\x80\xa0\xc0@ @\xc0 @@\xa0@\xc0\xa0@@ \xc0\xc0 \xc0@\xa0\xc0\xc0\xa0\xc0\x00`@\x80`@\x00\xe0@\x80\xe0@\x00`\xc0\x80`\xc0\x00\xe0\xc0\x80\xe0\xc0@`@\xc0`@@\xe0@\xc0\xe0@@`\xc0\xc0`\xc0@\xe0\xc0\xc0\xe0\xc0 \x00\xa0 \x00 \xa0\x00\xa0\xa0\x00 \x80\xa0 \x80 \xa0\x80\xa0\xa0\x80` \x00\xe0 \x00`\xa0\x00\xe0\xa0\x00` \x80\xe0 \x80`\xa0\x80\xe0\xa0\x80 `\x00\xa0`\x00 \xe0\x00\xa0\xe0\x00 `\x80\xa0`\x80 \xe0\x80\xa0\xe0\x80``\x00\xe0`\x00`\xe0\x00\xe0\xe0\x00``\x80\xe0`\x80`\xe0\x80\xe0\xe0\x80 @\xa0 @ \xa0@\xa0\xa0@ \xc0\xa0 \xc0 \xa0\xc0\xa0\xa0\xc0` @\xe0 @`\xa0@\xe0\xa0@` \xc0\xe0 \xc0`\xa0\xc0\xe0\xa0\xc0 `@\xa0`@ \xe0@\xa0\xe0@ `\xc0\xa0`\xc0 \xe0\xc0\xa0\xe0\xc0``@\xe0`@`\xe0@\xe0\xe0@``\xc0\xe0`\xc0`\xe0\xc0\xe0\xe0\xc0"
+
+
+def load_ann_png(path):
+ """Load a PNG file as a mask and its palette."""
+ mask = Image.open(path)
+ palette = mask.getpalette()
+ mask = np.array(mask).astype(np.uint8)
+ return mask, palette
+
+
+def save_ann_png(path, mask, palette):
+ """Save a mask as a PNG file with the given palette."""
+ assert mask.dtype == np.uint8
+ assert mask.ndim == 2
+ output_mask = Image.fromarray(mask)
+ output_mask.putpalette(palette)
+ output_mask.save(path)
+
+
+def get_per_obj_mask(mask):
+ """Split a mask into per-object masks."""
+ object_ids = np.unique(mask)
+ object_ids = object_ids[object_ids > 0].tolist()
+ per_obj_mask = {object_id: (mask == object_id) for object_id in object_ids}
+ return per_obj_mask
+
+
+def put_per_obj_mask(per_obj_mask, height, width):
+ """Combine per-object masks into a single mask."""
+ mask = np.zeros((height, width), dtype=np.uint8)
+ object_ids = sorted(per_obj_mask)[::-1]
+ for object_id in object_ids:
+ object_mask = per_obj_mask[object_id]
+ object_mask = object_mask.reshape(height, width)
+ mask[object_mask] = object_id
+ return mask
+
+
+def load_masks_from_dir(input_mask_dir, video_name, frame_name, per_obj_png_file):
+ """Load masks from a directory as a dict of per-object masks."""
+ if not per_obj_png_file:
+ input_mask_path = os.path.join(input_mask_dir, video_name, f"{frame_name}.png")
+ input_mask, input_palette = load_ann_png(input_mask_path)
+ per_obj_input_mask = get_per_obj_mask(input_mask)
+ else:
+ per_obj_input_mask = {}
+ # each object is a directory in "{object_id:%03d}" format
+ for object_name in os.listdir(os.path.join(input_mask_dir, video_name)):
+ object_id = int(object_name)
+ input_mask_path = os.path.join(
+ input_mask_dir, video_name, object_name, f"{frame_name}.png"
+ )
+ input_mask, input_palette = load_ann_png(input_mask_path)
+ per_obj_input_mask[object_id] = input_mask > 0
+
+ return per_obj_input_mask, input_palette
+
+
+def save_masks_to_dir(
+ output_mask_dir,
+ video_name,
+ frame_name,
+ per_obj_output_mask,
+ height,
+ width,
+ per_obj_png_file,
+ output_palette,
+):
+ """Save masks to a directory as PNG files."""
+ os.makedirs(os.path.join(output_mask_dir, video_name), exist_ok=True)
+ if not per_obj_png_file:
+ output_mask = put_per_obj_mask(per_obj_output_mask, height, width)
+ output_mask_path = os.path.join(
+ output_mask_dir, video_name, f"{frame_name}.png"
+ )
+ save_ann_png(output_mask_path, output_mask, output_palette)
+ else:
+ for object_id, object_mask in per_obj_output_mask.items():
+ object_name = f"{object_id:03d}"
+ os.makedirs(
+ os.path.join(output_mask_dir, video_name, object_name),
+ exist_ok=True,
+ )
+ output_mask = object_mask.reshape(height, width).astype(np.uint8)
+ output_mask_path = os.path.join(
+ output_mask_dir, video_name, object_name, f"{frame_name}.png"
+ )
+ save_ann_png(output_mask_path, output_mask, output_palette)
+
+
+@torch.inference_mode()
+@torch.autocast(device_type="cuda", dtype=torch.bfloat16)
+def vos_inference(
+ predictor,
+ base_video_dir,
+ input_mask_dir,
+ output_mask_dir,
+ video_name,
+ score_thresh=0.0,
+ use_all_masks=False,
+ per_obj_png_file=False,
+):
+ """Run VOS inference on a single video with the given predictor."""
+ # load the video frames and initialize the inference state on this video
+ video_dir = os.path.join(base_video_dir, video_name)
+ frame_names = [
+ os.path.splitext(p)[0]
+ for p in os.listdir(video_dir)
+ if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"]
+ ]
+ frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))
+ inference_state = predictor.init_state(
+ video_path=video_dir, async_loading_frames=False
+ )
+ height = inference_state["video_height"]
+ width = inference_state["video_width"]
+ input_palette = None
+
+ # fetch mask inputs from input_mask_dir (either only mask for the first frame, or all available masks)
+ if not use_all_masks:
+ # use only the first video's ground-truth mask as the input mask
+ input_frame_inds = [0]
+ else:
+ # use all mask files available in the input_mask_dir as the input masks
+ if not per_obj_png_file:
+ input_frame_inds = [
+ idx
+ for idx, name in enumerate(frame_names)
+ if os.path.exists(
+ os.path.join(input_mask_dir, video_name, f"{name}.png")
+ )
+ ]
+ else:
+ input_frame_inds = [
+ idx
+ for object_name in os.listdir(os.path.join(input_mask_dir, video_name))
+ for idx, name in enumerate(frame_names)
+ if os.path.exists(
+ os.path.join(input_mask_dir, video_name, object_name, f"{name}.png")
+ )
+ ]
+ input_frame_inds = sorted(set(input_frame_inds))
+
+ # add those input masks to SAM 2 inference state before propagation
+ for input_frame_idx in input_frame_inds:
+ per_obj_input_mask, input_palette = load_masks_from_dir(
+ input_mask_dir=input_mask_dir,
+ video_name=video_name,
+ frame_name=frame_names[input_frame_idx],
+ per_obj_png_file=per_obj_png_file,
+ )
+ for object_id, object_mask in per_obj_input_mask.items():
+ predictor.add_new_mask(
+ inference_state=inference_state,
+ frame_idx=input_frame_idx,
+ obj_id=object_id,
+ mask=object_mask,
+ )
+
+ # run propagation throughout the video and collect the results in a dict
+ os.makedirs(os.path.join(output_mask_dir, video_name), exist_ok=True)
+ output_palette = input_palette or DAVIS_PALETTE
+ video_segments = {} # video_segments contains the per-frame segmentation results
+ for out_frame_idx, out_obj_ids, out_mask_logits in predictor.propagate_in_video(
+ inference_state
+ ):
+ per_obj_output_mask = {
+ out_obj_id: (out_mask_logits[i] > score_thresh).cpu().numpy()
+ for i, out_obj_id in enumerate(out_obj_ids)
+ }
+ video_segments[out_frame_idx] = per_obj_output_mask
+
+ # write the output masks as palette PNG files to output_mask_dir
+ for out_frame_idx, per_obj_output_mask in video_segments.items():
+ save_masks_to_dir(
+ output_mask_dir=output_mask_dir,
+ video_name=video_name,
+ frame_name=frame_names[out_frame_idx],
+ per_obj_output_mask=per_obj_output_mask,
+ height=height,
+ width=width,
+ per_obj_png_file=per_obj_png_file,
+ output_palette=output_palette,
+ )
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--sam2_cfg",
+ type=str,
+ default="sam2_hiera_b+.yaml",
+ help="SAM 2 model configuration file",
+ )
+ parser.add_argument(
+ "--sam2_checkpoint",
+ type=str,
+ default="./checkpoints/sam2_hiera_b+.pt",
+ help="path to the SAM 2 model checkpoint",
+ )
+ parser.add_argument(
+ "--base_video_dir",
+ type=str,
+ required=True,
+ help="directory containing videos (as JPEG files) to run VOS prediction on",
+ )
+ parser.add_argument(
+ "--input_mask_dir",
+ type=str,
+ required=True,
+ help="directory containing input masks (as PNG files) of each video",
+ )
+ parser.add_argument(
+ "--video_list_file",
+ type=str,
+ default=None,
+ help="text file containing the list of video names to run VOS prediction on",
+ )
+ parser.add_argument(
+ "--output_mask_dir",
+ type=str,
+ required=True,
+ help="directory to save the output masks (as PNG files)",
+ )
+ parser.add_argument(
+ "--score_thresh",
+ type=float,
+ default=0.0,
+ help="threshold for the output mask logits (default: 0.0)",
+ )
+ parser.add_argument(
+ "--use_all_masks",
+ action="store_true",
+ help="whether to use all available PNG files in input_mask_dir "
+ "(default without this flag: just the first PNG file as input to the SAM 2 model; "
+ "usually we don't need this flag, since semi-supervised VOS evaluation usually takes input from the first frame only)",
+ )
+ parser.add_argument(
+ "--per_obj_png_file",
+ action="store_true",
+ help="whether use separate per-object PNG files for input and output masks "
+ "(default without this flag: all object masks are packed into a single PNG file on each frame following DAVIS format; "
+ "note that the SA-V dataset stores each object mask as an individual PNG file and requires this flag)",
+ )
+ parser.add_argument(
+ "--apply_postprocessing",
+ action="store_true",
+ help="whether to apply postprocessing (e.g. hole-filling) to the output masks "
+ "(we don't apply such post-processing in the SAM 2 model evaluation)",
+ )
+ args = parser.parse_args()
+
+ # if we use per-object PNG files, they could possibly overlap in inputs and outputs
+ hydra_overrides_extra = [
+ "++model.non_overlap_masks=" + ("false" if args.per_obj_png_file else "true")
+ ]
+ predictor = build_sam2_video_predictor(
+ config_file=args.sam2_cfg,
+ ckpt_path=args.sam2_checkpoint,
+ apply_postprocessing=args.apply_postprocessing,
+ hydra_overrides_extra=hydra_overrides_extra,
+ )
+
+ if args.use_all_masks:
+ print("using all available masks in input_mask_dir as input to the SAM 2 model")
+ else:
+ print(
+ "using only the first frame's mask in input_mask_dir as input to the SAM 2 model"
+ )
+ # if a video list file is provided, read the video names from the file
+ # (otherwise, we use all subdirectories in base_video_dir)
+ if args.video_list_file is not None:
+ with open(args.video_list_file, "r") as f:
+ video_names = [v.strip() for v in f.readlines()]
+ else:
+ video_names = [
+ p
+ for p in os.listdir(args.base_video_dir)
+ if os.path.isdir(os.path.join(args.base_video_dir, p))
+ ]
+ print(f"running VOS prediction on {len(video_names)} videos:\n{video_names}")
+
+ for n_video, video_name in enumerate(video_names):
+ print(f"\n{n_video + 1}/{len(video_names)} - running on {video_name}")
+ vos_inference(
+ predictor=predictor,
+ base_video_dir=args.base_video_dir,
+ input_mask_dir=args.input_mask_dir,
+ output_mask_dir=args.output_mask_dir,
+ video_name=video_name,
+ score_thresh=args.score_thresh,
+ use_all_masks=args.use_all_masks,
+ per_obj_png_file=args.per_obj_png_file,
+ )
+
+ print(
+ f"completed VOS prediction on {len(video_names)} videos -- "
+ f"output masks saved to {args.output_mask_dir}"
+ )
+
+
+if __name__ == "__main__":
+ main()