feat: initial HSAP platform

Huaxu Sentinel Active Safety Platform with embedded algorithm code,
Docker Compose setup, and vendored dataset scaffolds for clone-and-run.

Co-authored-by: Cursor <cursoragent@cursor.com>

algorithms/dms_yolo/code/ultralytics/models/sam/__init__.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .model import SAM
+from .predict import (
+    Predictor,
+    SAM2DynamicInteractivePredictor,
+    SAM2Predictor,
+    SAM2VideoPredictor,
+    SAM3Predictor,
+    SAM3SemanticPredictor,
+    SAM3VideoPredictor,
+    SAM3VideoSemanticPredictor,
+)
+
+__all__ = (
+    "SAM",
+    "Predictor",
+    "SAM2DynamicInteractivePredictor",
+    "SAM2Predictor",
+    "SAM2VideoPredictor",
+    "SAM3Predictor",
+    "SAM3SemanticPredictor",
+    "SAM3VideoPredictor",
+    "SAM3VideoSemanticPredictor",
+)  # tuple or list of exportable items

algorithms/dms_yolo/code/ultralytics/models/sam/amg.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import math
+from collections.abc import Generator
+from itertools import product
+from typing import Any
+
+import numpy as np
+import torch
+
+
+def is_box_near_crop_edge(
+    boxes: torch.Tensor, crop_box: list[int], orig_box: list[int], atol: float = 20.0
+) -> torch.Tensor:
+    """Determine if bounding boxes are near the edge of a cropped image region using a specified tolerance.
+
+    Args:
+        boxes (torch.Tensor): Bounding boxes in XYXY format.
+        crop_box (list[int]): Crop box coordinates in [x0, y0, x1, y1] format.
+        orig_box (list[int]): Original image box coordinates in [x0, y0, x1, y1] format.
+        atol (float, optional): Absolute tolerance for edge proximity detection.
+
+    Returns:
+        (torch.Tensor): Boolean tensor indicating which boxes are near crop edges.
+
+    Examples:
+        >>> boxes = torch.tensor([[10, 10, 50, 50], [100, 100, 150, 150]])
+        >>> crop_box = [0, 0, 200, 200]
+        >>> orig_box = [0, 0, 300, 300]
+        >>> near_edge = is_box_near_crop_edge(boxes, crop_box, orig_box, atol=20.0)
+    """
+    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 batch_iterator(batch_size: int, *args) -> Generator[list[Any]]:
+    """Yield batches of data from input arguments with specified batch size for efficient processing.
+
+    This function takes a batch size and any number of iterables, then yields batches of elements from those
+    iterables. All input iterables must have the same length.
+
+    Args:
+        batch_size (int): Size of each batch to yield.
+        *args (Any): Variable length input iterables to batch. All iterables must have the same length.
+
+    Yields:
+        (list[Any]): A list of batched elements from each input iterable.
+
+    Examples:
+        >>> data = [1, 2, 3, 4, 5]
+        >>> labels = ["a", "b", "c", "d", "e"]
+        >>> for batch in batch_iterator(2, data, labels):
+        ...     print(batch)
+        [[1, 2], ['a', 'b']]
+        [[3, 4], ['c', 'd']]
+        [[5], ['e']]
+    """
+    assert args and all(len(a) == len(args[0]) for a in args), "Batched iteration must have same-size inputs."
+    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 calculate_stability_score(masks: torch.Tensor, mask_threshold: float, threshold_offset: float) -> torch.Tensor:
+    """Compute the stability score for a batch of masks.
+
+    The stability score is the IoU between binary masks obtained by thresholding the predicted mask logits at high and
+    low values.
+
+    Args:
+        masks (torch.Tensor): Batch of predicted mask logits.
+        mask_threshold (float): Threshold value for creating binary masks.
+        threshold_offset (float): Offset applied to the threshold for creating high and low binary masks.
+
+    Returns:
+        (torch.Tensor): Stability scores for each mask in the batch.
+
+    Examples:
+        >>> masks = torch.rand(10, 256, 256)  # Batch of 10 masks
+        >>> mask_threshold = 0.5
+        >>> threshold_offset = 0.1
+        >>> stability_scores = calculate_stability_score(masks, mask_threshold, threshold_offset)
+
+    Notes:
+        - One mask is always contained inside the other.
+        - Memory is saved 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:
+    """Generate a 2D grid of evenly spaced points in the range [0,1]x[0,1] for image segmentation tasks."""
+    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))
+    return np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
+
+
+def build_all_layer_point_grids(n_per_side: int, n_layers: int, scale_per_layer: int) -> list[np.ndarray]:
+    """Generate point grids for multiple crop layers with varying scales and densities."""
+    return [build_point_grid(int(n_per_side / (scale_per_layer**i))) for i in range(n_layers + 1)]
+
+
+def generate_crop_boxes(
+    im_size: tuple[int, ...], n_layers: int, overlap_ratio: float
+) -> tuple[list[list[int]], list[int]]:
+    """Generate crop boxes of varying sizes for multiscale image processing, with layered overlapping regions.
+
+    Args:
+        im_size (tuple[int, ...]): Height and width of the input image.
+        n_layers (int): Number of layers to generate crop boxes for.
+        overlap_ratio (float): Ratio of overlap between adjacent crop boxes.
+
+    Returns:
+        crop_boxes (list[list[int]]): List of crop boxes in [x0, y0, x1, y1] format.
+        layer_idxs (list[int]): List of layer indices corresponding to each crop box.
+
+    Examples:
+        >>> im_size = (800, 1200)  # Height, width
+        >>> n_layers = 3
+        >>> overlap_ratio = 0.25
+        >>> crop_boxes, layer_idxs = generate_crop_boxes(im_size, n_layers, overlap_ratio)
+    """
+    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):
+        """Calculate the length of each crop given the original length, number of crops, and overlap."""
+        return 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:
+    """Uncrop bounding boxes by adding the crop box offset to their coordinates."""
+    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:
+    """Uncrop points by adding the crop box offset to their coordinates."""
+    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:
+    """Uncrop masks by padding them to the original image size, handling coordinate transformations."""
+    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]:
+    """Remove small disconnected regions or holes in a mask based on area threshold and mode.
+
+    Args:
+        mask (np.ndarray): Binary mask to process.
+        area_thresh (float): Area threshold below which regions will be removed.
+        mode (str): Processing mode, either 'holes' to fill small holes or 'islands' to remove small disconnected
+            regions.
+
+    Returns:
+        processed_mask (np.ndarray): Processed binary mask with small regions removed.
+        modified (bool): Whether any regions were modified.
+
+    Examples:
+        >>> mask = np.zeros((100, 100), dtype=np.bool_)
+        >>> mask[40:60, 40:60] = True  # Create a square
+        >>> mask[45:55, 45:55] = False  # Create a hole
+        >>> processed_mask, modified = remove_small_regions(mask, 50, "holes")
+    """
+    import cv2  # type: ignore
+
+    assert mode in {"holes", "islands"}, f"Provided mode {mode} is invalid"
+    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 not small_regions:
+        return mask, False
+    fill_labels = [0, *small_regions]
+    if not correct_holes:
+        # If every region is below threshold, keep largest
+        fill_labels = [i for i in range(n_labels) if i not in fill_labels] or [int(np.argmax(sizes)) + 1]
+    mask = np.isin(regions, fill_labels)
+    return mask, True
+
+
+def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
+    """Calculate bounding boxes in XYXY format around binary masks.
+
+    Args:
+        masks (torch.Tensor): Binary masks with shape (B, H, W) or (B, C, H, W).
+
+    Returns:
+        (torch.Tensor): Bounding boxes in XYXY format with shape (B, 4) or (B, C, 4).
+
+    Notes:
+        - Handles empty masks by returning zero boxes.
+        - Preserves input tensor dimensions in the output.
+    """
+    # 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:]
+    masks = masks.flatten(0, -3) if len(shape) > 2 else 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
+    return out.reshape(*shape[:-2], 4) if len(shape) > 2 else out[0]

algorithms/dms_yolo/code/ultralytics/models/sam/build.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# 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 functools import partial
+
+import torch
+
+from ultralytics.utils.downloads import attempt_download_asset
+from ultralytics.utils.patches import torch_load
+
+from .modules.decoders import MaskDecoder
+from .modules.encoders import FpnNeck, Hiera, ImageEncoder, ImageEncoderViT, MemoryEncoder, PromptEncoder
+from .modules.memory_attention import MemoryAttention, MemoryAttentionLayer
+from .modules.sam import SAM2Model, SAMModel
+from .modules.tiny_encoder import TinyViT
+from .modules.transformer import TwoWayTransformer
+
+
+def _load_checkpoint(model, checkpoint):
+    """Load checkpoint into model from file path."""
+    if checkpoint is None:
+        return model
+
+    checkpoint = attempt_download_asset(checkpoint)
+    with open(checkpoint, "rb") as f:
+        state_dict = torch_load(f)
+    # Handle nested "model" key
+    if "model" in state_dict and isinstance(state_dict["model"], dict):
+        state_dict = state_dict["model"]
+    model.load_state_dict(state_dict)
+    return model
+
+
+def build_sam_vit_h(checkpoint=None):
+    """Build and return a Segment Anything Model (SAM) h-size model with specified encoder parameters."""
+    return _build_sam(
+        encoder_embed_dim=1280,
+        encoder_depth=32,
+        encoder_num_heads=16,
+        encoder_global_attn_indexes=[7, 15, 23, 31],
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam_vit_l(checkpoint=None):
+    """Build and return a Segment Anything Model (SAM) l-size model with specified encoder parameters."""
+    return _build_sam(
+        encoder_embed_dim=1024,
+        encoder_depth=24,
+        encoder_num_heads=16,
+        encoder_global_attn_indexes=[5, 11, 17, 23],
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam_vit_b(checkpoint=None):
+    """Build and return a Segment Anything Model (SAM) b-size model with specified encoder parameters."""
+    return _build_sam(
+        encoder_embed_dim=768,
+        encoder_depth=12,
+        encoder_num_heads=12,
+        encoder_global_attn_indexes=[2, 5, 8, 11],
+        checkpoint=checkpoint,
+    )
+
+
+def build_mobile_sam(checkpoint=None):
+    """Build and return a Mobile Segment Anything Model (Mobile-SAM) for efficient image segmentation."""
+    return _build_sam(
+        encoder_embed_dim=[64, 128, 160, 320],
+        encoder_depth=[2, 2, 6, 2],
+        encoder_num_heads=[2, 4, 5, 10],
+        encoder_global_attn_indexes=None,
+        mobile_sam=True,
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam2_t(checkpoint=None):
+    """Build and return a Segment Anything Model 2 (SAM2) tiny-size model with specified architecture parameters."""
+    return _build_sam2(
+        encoder_embed_dim=96,
+        encoder_stages=[1, 2, 7, 2],
+        encoder_num_heads=1,
+        encoder_global_att_blocks=[5, 7, 9],
+        encoder_window_spec=[8, 4, 14, 7],
+        encoder_backbone_channel_list=[768, 384, 192, 96],
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam2_s(checkpoint=None):
+    """Build and return a small-size Segment Anything Model 2 (SAM2) with specified architecture parameters."""
+    return _build_sam2(
+        encoder_embed_dim=96,
+        encoder_stages=[1, 2, 11, 2],
+        encoder_num_heads=1,
+        encoder_global_att_blocks=[7, 10, 13],
+        encoder_window_spec=[8, 4, 14, 7],
+        encoder_backbone_channel_list=[768, 384, 192, 96],
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam2_b(checkpoint=None):
+    """Build and return a Segment Anything Model 2 (SAM2) base-size model with specified architecture parameters."""
+    return _build_sam2(
+        encoder_embed_dim=112,
+        encoder_stages=[2, 3, 16, 3],
+        encoder_num_heads=2,
+        encoder_global_att_blocks=[12, 16, 20],
+        encoder_window_spec=[8, 4, 14, 7],
+        encoder_window_spatial_size=[14, 14],
+        encoder_backbone_channel_list=[896, 448, 224, 112],
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam2_l(checkpoint=None):
+    """Build and return a large-size Segment Anything Model 2 (SAM2) with specified architecture parameters."""
+    return _build_sam2(
+        encoder_embed_dim=144,
+        encoder_stages=[2, 6, 36, 4],
+        encoder_num_heads=2,
+        encoder_global_att_blocks=[23, 33, 43],
+        encoder_window_spec=[8, 4, 16, 8],
+        encoder_backbone_channel_list=[1152, 576, 288, 144],
+        checkpoint=checkpoint,
+    )
+
+
+def _build_sam(
+    encoder_embed_dim,
+    encoder_depth,
+    encoder_num_heads,
+    encoder_global_attn_indexes,
+    checkpoint=None,
+    mobile_sam=False,
+):
+    """Build a Segment Anything Model (SAM) with specified encoder parameters.
+
+    Args:
+        encoder_embed_dim (int | list[int]): Embedding dimension for the encoder.
+        encoder_depth (int | list[int]): Depth of the encoder.
+        encoder_num_heads (int | list[int]): Number of attention heads in the encoder.
+        encoder_global_attn_indexes (list[int] | None): Indexes for global attention in the encoder.
+        checkpoint (str | None, optional): Path to the model checkpoint file.
+        mobile_sam (bool, optional): Whether to build a Mobile-SAM model.
+
+    Returns:
+        (SAMModel): A Segment Anything Model instance with the specified architecture.
+
+    Examples:
+        >>> sam = _build_sam(768, 12, 12, [2, 5, 8, 11])
+        >>> sam = _build_sam([64, 128, 160, 320], [2, 2, 6, 2], [2, 4, 5, 10], None, mobile_sam=True)
+    """
+    prompt_embed_dim = 256
+    image_size = 1024
+    vit_patch_size = 16
+    image_embedding_size = image_size // vit_patch_size
+    image_encoder = (
+        TinyViT(
+            img_size=1024,
+            in_chans=3,
+            num_classes=1000,
+            embed_dims=encoder_embed_dim,
+            depths=encoder_depth,
+            num_heads=encoder_num_heads,
+            window_sizes=[7, 7, 14, 7],
+            mlp_ratio=4.0,
+            drop_rate=0.0,
+            drop_path_rate=0.0,
+            use_checkpoint=False,
+            mbconv_expand_ratio=4.0,
+            local_conv_size=3,
+            layer_lr_decay=0.8,
+        )
+        if mobile_sam
+        else ImageEncoderViT(
+            depth=encoder_depth,
+            embed_dim=encoder_embed_dim,
+            img_size=image_size,
+            mlp_ratio=4,
+            norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
+            num_heads=encoder_num_heads,
+            patch_size=vit_patch_size,
+            qkv_bias=True,
+            use_rel_pos=True,
+            global_attn_indexes=encoder_global_attn_indexes,
+            window_size=14,
+            out_chans=prompt_embed_dim,
+        )
+    )
+    sam = SAMModel(
+        image_encoder=image_encoder,
+        prompt_encoder=PromptEncoder(
+            embed_dim=prompt_embed_dim,
+            image_embedding_size=(image_embedding_size, image_embedding_size),
+            input_image_size=(image_size, image_size),
+            mask_in_chans=16,
+        ),
+        mask_decoder=MaskDecoder(
+            num_multimask_outputs=3,
+            transformer=TwoWayTransformer(
+                depth=2,
+                embedding_dim=prompt_embed_dim,
+                mlp_dim=2048,
+                num_heads=8,
+            ),
+            transformer_dim=prompt_embed_dim,
+            iou_head_depth=3,
+            iou_head_hidden_dim=256,
+        ),
+        pixel_mean=[123.675, 116.28, 103.53],
+        pixel_std=[58.395, 57.12, 57.375],
+    )
+    if checkpoint is not None:
+        sam = _load_checkpoint(sam, checkpoint)
+    sam.eval()
+    return sam
+
+
+def _build_sam2(
+    encoder_embed_dim=1280,
+    encoder_stages=(2, 6, 36, 4),
+    encoder_num_heads=2,
+    encoder_global_att_blocks=(7, 15, 23, 31),
+    encoder_backbone_channel_list=(1152, 576, 288, 144),
+    encoder_window_spatial_size=(7, 7),
+    encoder_window_spec=(8, 4, 16, 8),
+    checkpoint=None,
+):
+    """Build and return a Segment Anything Model 2 (SAM2) with specified architecture parameters.
+
+    Args:
+        encoder_embed_dim (int, optional): Embedding dimension for the encoder.
+        encoder_stages (list[int], optional): Number of blocks in each stage of the encoder.
+        encoder_num_heads (int, optional): Number of attention heads in the encoder.
+        encoder_global_att_blocks (list[int], optional): Indices of global attention blocks in the encoder.
+        encoder_backbone_channel_list (list[int], optional): Channel dimensions for each level of the encoder backbone.
+        encoder_window_spatial_size (list[int], optional): Spatial size of the window for position embeddings.
+        encoder_window_spec (list[int], optional): Window specifications for each stage of the encoder.
+        checkpoint (str | None, optional): Path to the checkpoint file for loading pre-trained weights.
+
+    Returns:
+        (SAM2Model): A configured and initialized SAM2 model.
+
+    Examples:
+        >>> sam2_model = _build_sam2(encoder_embed_dim=96, encoder_stages=[1, 2, 7, 2])
+        >>> sam2_model.eval()
+    """
+    image_encoder = ImageEncoder(
+        trunk=Hiera(
+            embed_dim=encoder_embed_dim,
+            num_heads=encoder_num_heads,
+            stages=encoder_stages,
+            global_att_blocks=encoder_global_att_blocks,
+            window_pos_embed_bkg_spatial_size=encoder_window_spatial_size,
+            window_spec=encoder_window_spec,
+        ),
+        neck=FpnNeck(
+            d_model=256,
+            backbone_channel_list=encoder_backbone_channel_list,
+            fpn_top_down_levels=[2, 3],
+            fpn_interp_model="nearest",
+        ),
+        scalp=1,
+    )
+    memory_attention = MemoryAttention(d_model=256, pos_enc_at_input=True, num_layers=4, layer=MemoryAttentionLayer())
+    memory_encoder = MemoryEncoder(out_dim=64)
+
+    is_sam2_1 = checkpoint is not None and "sam2.1" in checkpoint
+    sam2 = SAM2Model(
+        image_encoder=image_encoder,
+        memory_attention=memory_attention,
+        memory_encoder=memory_encoder,
+        num_maskmem=7,
+        image_size=1024,
+        sigmoid_scale_for_mem_enc=20.0,
+        sigmoid_bias_for_mem_enc=-10.0,
+        use_mask_input_as_output_without_sam=True,
+        directly_add_no_mem_embed=True,
+        use_high_res_features_in_sam=True,
+        multimask_output_in_sam=True,
+        iou_prediction_use_sigmoid=True,
+        use_obj_ptrs_in_encoder=True,
+        add_tpos_enc_to_obj_ptrs=True,
+        only_obj_ptrs_in_the_past_for_eval=True,
+        pred_obj_scores=True,
+        pred_obj_scores_mlp=True,
+        fixed_no_obj_ptr=True,
+        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,
+        compile_image_encoder=False,
+        no_obj_embed_spatial=is_sam2_1,
+        proj_tpos_enc_in_obj_ptrs=is_sam2_1,
+        use_signed_tpos_enc_to_obj_ptrs=is_sam2_1,
+        sam_mask_decoder_extra_args=dict(
+            dynamic_multimask_via_stability=True,
+            dynamic_multimask_stability_delta=0.05,
+            dynamic_multimask_stability_thresh=0.98,
+        ),
+    )
+
+    if checkpoint is not None:
+        sam2 = _load_checkpoint(sam2, checkpoint)
+    sam2.eval()
+    return sam2
+
+
+sam_model_map = {
+    "sam_h.pt": build_sam_vit_h,
+    "sam_l.pt": build_sam_vit_l,
+    "sam_b.pt": build_sam_vit_b,
+    "mobile_sam.pt": build_mobile_sam,
+    "sam2_t.pt": build_sam2_t,
+    "sam2_s.pt": build_sam2_s,
+    "sam2_b.pt": build_sam2_b,
+    "sam2_l.pt": build_sam2_l,
+    "sam2.1_t.pt": build_sam2_t,
+    "sam2.1_s.pt": build_sam2_s,
+    "sam2.1_b.pt": build_sam2_b,
+    "sam2.1_l.pt": build_sam2_l,
+}
+
+
+def build_sam(ckpt="sam_b.pt"):
+    """Build and return a Segment Anything Model (SAM) based on the provided checkpoint.
+
+    Args:
+        ckpt (str | Path, optional): Path to the checkpoint file or name of a pre-defined SAM model.
+
+    Returns:
+        (SAMModel | SAM2Model): A configured and initialized SAM or SAM2 model instance.
+
+    Raises:
+        FileNotFoundError: If the provided checkpoint is not a supported SAM model.
+
+    Examples:
+        >>> sam_model = build_sam("sam_b.pt")
+        >>> sam_model = build_sam("path/to/custom_checkpoint.pt")
+
+    Notes:
+        Supported pre-defined models include:
+        - SAM: 'sam_h.pt', 'sam_l.pt', 'sam_b.pt', 'mobile_sam.pt'
+        - SAM2: 'sam2_t.pt', 'sam2_s.pt', 'sam2_b.pt', 'sam2_l.pt'
+    """
+    model_builder = None
+    ckpt = str(ckpt)  # to allow Path ckpt types
+    for k in sam_model_map.keys():
+        if ckpt.endswith(k):
+            model_builder = sam_model_map.get(k)
+
+    if not model_builder:
+        raise FileNotFoundError(f"{ckpt} is not a supported SAM model. Available models are: \n {sam_model_map.keys()}")
+
+    return model_builder(ckpt)

algorithms/dms_yolo/code/ultralytics/models/sam/build_sam3.py

@@ -0,0 +1,377 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+import torch.nn as nn
+
+from ultralytics.nn.modules.transformer import MLP
+from ultralytics.utils.patches import torch_load
+
+from .modules.blocks import PositionEmbeddingSine, RoPEAttention
+from .modules.encoders import MemoryEncoder
+from .modules.memory_attention import MemoryAttention, MemoryAttentionLayer
+from .modules.sam import SAM3Model
+from .sam3.decoder import TransformerDecoder, TransformerDecoderLayer
+from .sam3.encoder import TransformerEncoderFusion, TransformerEncoderLayer
+from .sam3.geometry_encoders import SequenceGeometryEncoder
+from .sam3.maskformer_segmentation import PixelDecoder, UniversalSegmentationHead
+from .sam3.model_misc import DotProductScoring, TransformerWrapper
+from .sam3.necks import Sam3DualViTDetNeck
+from .sam3.sam3_image import SAM3SemanticModel
+from .sam3.text_encoder_ve import VETextEncoder
+from .sam3.vitdet import ViT
+from .sam3.vl_combiner import SAM3VLBackbone
+
+
+def _create_vision_backbone(compile_mode=None, enable_inst_interactivity=True) -> Sam3DualViTDetNeck:
+    """Create SAM3 visual backbone with ViT and neck."""
+    # Position encoding
+    position_encoding = PositionEmbeddingSine(
+        num_pos_feats=256,
+        normalize=True,
+        scale=None,
+        temperature=10000,
+    )
+
+    # ViT backbone
+    vit_backbone = ViT(
+        img_size=1008,
+        pretrain_img_size=336,
+        patch_size=14,
+        embed_dim=1024,
+        depth=32,
+        num_heads=16,
+        mlp_ratio=4.625,
+        norm_layer="LayerNorm",
+        drop_path_rate=0.1,
+        qkv_bias=True,
+        use_abs_pos=True,
+        tile_abs_pos=True,
+        global_att_blocks=(7, 15, 23, 31),
+        rel_pos_blocks=(),
+        use_rope=True,
+        use_interp_rope=True,
+        window_size=24,
+        pretrain_use_cls_token=True,
+        retain_cls_token=False,
+        ln_pre=True,
+        ln_post=False,
+        return_interm_layers=False,
+        bias_patch_embed=False,
+        compile_mode=compile_mode,
+    )
+    return Sam3DualViTDetNeck(
+        position_encoding=position_encoding,
+        d_model=256,
+        scale_factors=[4.0, 2.0, 1.0, 0.5],
+        trunk=vit_backbone,
+        add_sam2_neck=enable_inst_interactivity,
+    )
+
+
+def _create_sam3_transformer() -> TransformerWrapper:
+    """Create SAM3 detector encoder and decoder."""
+    encoder: TransformerEncoderFusion = TransformerEncoderFusion(
+        layer=TransformerEncoderLayer(
+            d_model=256,
+            dim_feedforward=2048,
+            dropout=0.1,
+            pos_enc_at_attn=True,
+            pos_enc_at_cross_attn_keys=False,
+            pos_enc_at_cross_attn_queries=False,
+            pre_norm=True,
+            self_attention=nn.MultiheadAttention(
+                num_heads=8,
+                dropout=0.1,
+                embed_dim=256,
+                batch_first=True,
+            ),
+            cross_attention=nn.MultiheadAttention(
+                num_heads=8,
+                dropout=0.1,
+                embed_dim=256,
+                batch_first=True,
+            ),
+        ),
+        num_layers=6,
+        d_model=256,
+        num_feature_levels=1,
+        frozen=False,
+        use_act_checkpoint=True,
+        add_pooled_text_to_img_feat=False,
+        pool_text_with_mask=True,
+    )
+    decoder: TransformerDecoder = TransformerDecoder(
+        layer=TransformerDecoderLayer(
+            d_model=256,
+            dim_feedforward=2048,
+            dropout=0.1,
+            cross_attention=nn.MultiheadAttention(
+                num_heads=8,
+                dropout=0.1,
+                embed_dim=256,
+            ),
+            n_heads=8,
+            use_text_cross_attention=True,
+        ),
+        num_layers=6,
+        num_queries=200,
+        return_intermediate=True,
+        box_refine=True,
+        num_o2m_queries=0,
+        dac=True,
+        boxRPB="log",
+        d_model=256,
+        frozen=False,
+        interaction_layer=None,
+        dac_use_selfatt_ln=True,
+        use_act_checkpoint=True,
+        presence_token=True,
+    )
+
+    return TransformerWrapper(encoder=encoder, decoder=decoder, d_model=256)
+
+
+def build_sam3_image_model(checkpoint_path: str, enable_segmentation: bool = True, compile: bool = False):
+    """Build SAM3 image model.
+
+    Args:
+        checkpoint_path: Optional path to model checkpoint
+        enable_segmentation: Whether to enable segmentation head
+        compile: To enable compilation, set to "default"
+
+    Returns:
+        A SAM3 image model
+    """
+    try:
+        import clip
+    except ImportError:
+        from ultralytics.utils.checks import check_requirements
+
+        check_requirements("git+https://github.com/ultralytics/CLIP.git")
+        import clip
+    # Create visual components
+    compile_mode = "default" if compile else None
+    vision_encoder = _create_vision_backbone(compile_mode=compile_mode, enable_inst_interactivity=True)
+
+    # Create text components
+    text_encoder = VETextEncoder(
+        tokenizer=clip.simple_tokenizer.SimpleTokenizer(),
+        d_model=256,
+        width=1024,
+        heads=16,
+        layers=24,
+    )
+
+    # Create visual-language backbone
+    backbone = SAM3VLBackbone(visual=vision_encoder, text=text_encoder, scalp=1)
+
+    # Create transformer components
+    transformer = _create_sam3_transformer()
+
+    # Create dot product scoring
+    dot_prod_scoring = DotProductScoring(
+        d_model=256,
+        d_proj=256,
+        prompt_mlp=MLP(
+            input_dim=256,
+            hidden_dim=2048,
+            output_dim=256,
+            num_layers=2,
+            residual=True,
+            out_norm=nn.LayerNorm(256),
+        ),
+    )
+
+    # Create segmentation head if enabled
+    segmentation_head = (
+        UniversalSegmentationHead(
+            hidden_dim=256,
+            upsampling_stages=3,
+            aux_masks=False,
+            presence_head=False,
+            dot_product_scorer=None,
+            act_ckpt=True,
+            cross_attend_prompt=nn.MultiheadAttention(
+                num_heads=8,
+                dropout=0,
+                embed_dim=256,
+            ),
+            pixel_decoder=PixelDecoder(
+                num_upsampling_stages=3,
+                interpolation_mode="nearest",
+                hidden_dim=256,
+                compile_mode=compile_mode,
+            ),
+        )
+        if enable_segmentation
+        else None
+    )
+
+    # Create geometry encoder
+    input_geometry_encoder = SequenceGeometryEncoder(
+        pos_enc=PositionEmbeddingSine(
+            num_pos_feats=256,
+            normalize=True,
+            scale=None,
+            temperature=10000,
+        ),
+        encode_boxes_as_points=False,
+        boxes_direct_project=True,
+        boxes_pool=True,
+        boxes_pos_enc=True,
+        d_model=256,
+        num_layers=3,
+        layer=TransformerEncoderLayer(
+            d_model=256,
+            dim_feedforward=2048,
+            dropout=0.1,
+            pos_enc_at_attn=False,
+            pre_norm=True,
+            pos_enc_at_cross_attn_queries=False,
+            pos_enc_at_cross_attn_keys=True,
+        ),
+        use_act_ckpt=True,
+        add_cls=True,
+        add_post_encode_proj=True,
+    )
+
+    # Create the SAM3SemanticModel model
+    model = SAM3SemanticModel(
+        backbone=backbone,
+        transformer=transformer,
+        input_geometry_encoder=input_geometry_encoder,
+        segmentation_head=segmentation_head,
+        num_feature_levels=1,
+        o2m_mask_predict=True,
+        dot_prod_scoring=dot_prod_scoring,
+        use_instance_query=False,
+        multimask_output=True,
+    )
+
+    # Load checkpoint
+    model = _load_checkpoint(model, checkpoint_path)
+    model.eval()
+    return model
+
+
+def build_interactive_sam3(checkpoint_path: str, compile=None, with_backbone=True) -> SAM3Model:
+    """Build the SAM3 Tracker module for video tracking.
+
+    Returns:
+        Sam3TrackerPredictor: Wrapped SAM3 Tracker module
+    """
+    # Create model components
+    memory_encoder = MemoryEncoder(out_dim=64, interpol_size=[1152, 1152])
+    memory_attention = MemoryAttention(
+        batch_first=True,
+        d_model=256,
+        pos_enc_at_input=True,
+        layer=MemoryAttentionLayer(
+            dim_feedforward=2048,
+            dropout=0.1,
+            pos_enc_at_attn=False,
+            pos_enc_at_cross_attn_keys=True,
+            pos_enc_at_cross_attn_queries=False,
+            self_attn=RoPEAttention(
+                embedding_dim=256,
+                num_heads=1,
+                downsample_rate=1,
+                rope_theta=10000.0,
+                feat_sizes=[72, 72],
+            ),
+            d_model=256,
+            cross_attn=RoPEAttention(
+                embedding_dim=256,
+                num_heads=1,
+                downsample_rate=1,
+                kv_in_dim=64,
+                rope_theta=10000.0,
+                feat_sizes=[72, 72],
+                rope_k_repeat=True,
+            ),
+        ),
+        num_layers=4,
+    )
+
+    backbone = (
+        SAM3VLBackbone(scalp=1, visual=_create_vision_backbone(compile_mode=compile), text=None)
+        if with_backbone
+        else None
+    )
+    model = SAM3Model(
+        image_size=1008,
+        image_encoder=backbone,
+        memory_attention=memory_attention,
+        memory_encoder=memory_encoder,
+        backbone_stride=14,
+        num_maskmem=7,
+        sigmoid_scale_for_mem_enc=20.0,
+        sigmoid_bias_for_mem_enc=-10.0,
+        use_mask_input_as_output_without_sam=True,
+        directly_add_no_mem_embed=True,
+        use_high_res_features_in_sam=True,
+        multimask_output_in_sam=True,
+        iou_prediction_use_sigmoid=True,
+        use_obj_ptrs_in_encoder=True,
+        add_tpos_enc_to_obj_ptrs=True,
+        only_obj_ptrs_in_the_past_for_eval=True,
+        pred_obj_scores=True,
+        pred_obj_scores_mlp=True,
+        fixed_no_obj_ptr=True,
+        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,
+        compile_image_encoder=False,
+        no_obj_embed_spatial=True,
+        proj_tpos_enc_in_obj_ptrs=True,
+        use_signed_tpos_enc_to_obj_ptrs=True,
+        sam_mask_decoder_extra_args=dict(
+            dynamic_multimask_via_stability=True,
+            dynamic_multimask_stability_delta=0.05,
+            dynamic_multimask_stability_thresh=0.98,
+        ),
+    )
+
+    # Load checkpoint if provided
+    model = _load_checkpoint(model, checkpoint_path, interactive=True)
+
+    # Setup device and mode
+    model.eval()
+    return model
+
+
+def _load_checkpoint(model, checkpoint, interactive=False):
+    """Load SAM3 model checkpoint from file."""
+    with open(checkpoint, "rb") as f:
+        ckpt = torch_load(f)
+    if "model" in ckpt and isinstance(ckpt["model"], dict):
+        ckpt = ckpt["model"]
+    sam3_image_ckpt = {k.replace("detector.", ""): v for k, v in ckpt.items() if "detector" in k}
+    if interactive:
+        sam3_image_ckpt.update(
+            {
+                k.replace("backbone.vision_backbone", "image_encoder.vision_backbone"): v
+                for k, v in sam3_image_ckpt.items()
+                if "backbone.vision_backbone" in k
+            }
+        )
+        sam3_image_ckpt.update(
+            {
+                k.replace("tracker.transformer.encoder", "memory_attention"): v
+                for k, v in ckpt.items()
+                if "tracker.transformer" in k
+            }
+        )
+        sam3_image_ckpt.update(
+            {
+                k.replace("tracker.maskmem_backbone", "memory_encoder"): v
+                for k, v in ckpt.items()
+                if "tracker.maskmem_backbone" in k
+            }
+        )
+        sam3_image_ckpt.update({k.replace("tracker.", ""): v for k, v in ckpt.items() if "tracker." in k})
+    model.load_state_dict(sam3_image_ckpt, strict=False)
+    return model

algorithms/dms_yolo/code/ultralytics/models/sam/model.py

@@ -0,0 +1,169 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+"""
+SAM model interface.
+
+This module provides an interface to the Segment Anything Model (SAM) from Ultralytics, designed for real-time image
+segmentation tasks. The SAM model allows for promptable segmentation with unparalleled versatility in image analysis,
+and has been trained on the SA-1B dataset. It features zero-shot performance capabilities, enabling it to adapt to new
+image distributions and tasks without prior knowledge.
+
+Key Features:
+    - Promptable segmentation
+    - Real-time performance
+    - Zero-shot transfer capabilities
+    - Trained on SA-1B dataset
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from ultralytics.engine.model import Model
+from ultralytics.utils.torch_utils import model_info
+
+from .predict import Predictor, SAM2Predictor, SAM3Predictor
+
+
+class SAM(Model):
+    """SAM (Segment Anything Model) interface class for real-time image segmentation tasks.
+
+    This class provides an interface to the Segment Anything Model (SAM) from Ultralytics, designed for promptable
+    segmentation with versatility in image analysis. It supports various prompts such as bounding boxes, points, or
+    labels, and features zero-shot performance capabilities.
+
+    Attributes:
+        model (torch.nn.Module): The loaded SAM model.
+        is_sam2 (bool): Indicates whether the model is SAM2 variant.
+        task (str): The task type, set to "segment" for SAM models.
+
+    Methods:
+        predict: Perform segmentation prediction on the given image or video source.
+        info: Log information about the SAM model.
+
+    Examples:
+        >>> sam = SAM("sam_b.pt")
+        >>> results = sam.predict("image.jpg", points=[[500, 375]])
+        >>> for r in results:
+        ...     print(f"Detected {len(r.masks)} masks")
+    """
+
+    def __init__(self, model: str = "sam_b.pt") -> None:
+        """Initialize the SAM (Segment Anything Model) instance.
+
+        Args:
+            model (str): Path to the pre-trained SAM model file. File should have a .pt or .pth extension.
+
+        Raises:
+            NotImplementedError: If the model file extension is not .pt or .pth.
+        """
+        if model and Path(model).suffix not in {".pt", ".pth"}:
+            raise NotImplementedError("SAM prediction requires pre-trained *.pt or *.pth model.")
+        self.is_sam2 = "sam2" in Path(model).stem
+        self.is_sam3 = "sam3" in Path(model).stem
+        super().__init__(model=model, task="segment")
+
+    def _load(self, weights: str, task=None):
+        """Load the specified weights into the SAM model.
+
+        Args:
+            weights (str): Path to the weights file. Should be a .pt or .pth file containing the model parameters.
+            task (str | None): Task name. If provided, it specifies the particular task the model is being loaded for.
+
+        Examples:
+            >>> sam = SAM("sam_b.pt")
+            >>> sam._load("path/to/custom_weights.pt")
+        """
+        if self.is_sam3:
+            from .build_sam3 import build_interactive_sam3
+
+            self.model = build_interactive_sam3(weights)
+        else:
+            from .build import build_sam  # slow import
+
+            self.model = build_sam(weights)
+
+    def predict(self, source, stream: bool = False, bboxes=None, points=None, labels=None, **kwargs):
+        """Perform segmentation prediction on the given image or video source.
+
+        Args:
+            source (str | PIL.Image | np.ndarray): Path to the image or video file, or a PIL.Image object, or a
+                np.ndarray object.
+            stream (bool): If True, enables real-time streaming.
+            bboxes (list[list[float]] | None): List of bounding box coordinates for prompted segmentation.
+            points (list[list[float]] | None): List of points for prompted segmentation.
+            labels (list[int] | None): List of labels for prompted segmentation.
+            **kwargs (Any): Additional keyword arguments for prediction.
+
+        Returns:
+            (list): The model predictions.
+
+        Examples:
+            >>> sam = SAM("sam_b.pt")
+            >>> results = sam.predict("image.jpg", points=[[500, 375]])
+            >>> for r in results:
+            ...     print(f"Detected {len(r.masks)} masks")
+        """
+        overrides = dict(conf=0.25, task="segment", mode="predict", imgsz=1024)
+        kwargs = {**overrides, **kwargs}
+        prompts = dict(bboxes=bboxes, points=points, labels=labels)
+        return super().predict(source, stream, prompts=prompts, **kwargs)
+
+    def __call__(self, source=None, stream: bool = False, bboxes=None, points=None, labels=None, **kwargs):
+        """Perform segmentation prediction on the given image or video source.
+
+        This method is an alias for the 'predict' method, providing a convenient way to call the SAM model for
+        segmentation tasks.
+
+        Args:
+            source (str | PIL.Image | np.ndarray | None): Path to the image or video file, or a PIL.Image object, or a
+                np.ndarray object.
+            stream (bool): If True, enables real-time streaming.
+            bboxes (list[list[float]] | None): List of bounding box coordinates for prompted segmentation.
+            points (list[list[float]] | None): List of points for prompted segmentation.
+            labels (list[int] | None): List of labels for prompted segmentation.
+            **kwargs (Any): Additional keyword arguments to be passed to the predict method.
+
+        Returns:
+            (list): The model predictions, typically containing segmentation masks and other relevant information.
+
+        Examples:
+            >>> sam = SAM("sam_b.pt")
+            >>> results = sam("image.jpg", points=[[500, 375]])
+            >>> print(f"Detected {len(results[0].masks)} masks")
+        """
+        return self.predict(source, stream, bboxes, points, labels, **kwargs)
+
+    def info(self, detailed: bool = False, verbose: bool = True):
+        """Log information about the SAM model.
+
+        Args:
+            detailed (bool): If True, displays detailed information about the model layers and operations.
+            verbose (bool): If True, prints the information to the console.
+
+        Returns:
+            (tuple): A tuple containing the model's information (string representations of the model).
+
+        Examples:
+            >>> sam = SAM("sam_b.pt")
+            >>> info = sam.info()
+            >>> print(info[0])  # Print summary information
+        """
+        return model_info(self.model, detailed=detailed, verbose=verbose)
+
+    @property
+    def task_map(self) -> dict[str, dict[str, type[Predictor]]]:
+        """Provide a mapping from the 'segment' task to its corresponding 'Predictor'.
+
+        Returns:
+            (dict[str, dict[str, Type[Predictor]]]): A dictionary mapping the 'segment' task to its corresponding
+                Predictor class. For SAM2 models, it maps to SAM2Predictor, otherwise to the standard Predictor.
+
+        Examples:
+            >>> sam = SAM("sam_b.pt")
+            >>> task_map = sam.task_map
+            >>> print(task_map)
+            {'segment': {'predictor': <class 'ultralytics.models.sam.predict.Predictor'>}}
+        """
+        return {
+            "segment": {"predictor": SAM2Predictor if self.is_sam2 else SAM3Predictor if self.is_sam3 else Predictor}
+        }

algorithms/dms_yolo/code/ultralytics/models/sam/modules/__init__.py

@@ -0,0 +1 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

algorithms/dms_yolo/code/ultralytics/models/sam/modules/decoders.py

@@ -0,0 +1,495 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import torch
+from torch import nn
+
+from ultralytics.nn.modules import MLP, LayerNorm2d
+
+
+class MaskDecoder(nn.Module):
+    """Decoder module for generating masks and their associated quality scores using a transformer architecture.
+
+    This class predicts masks given image and prompt embeddings, utilizing a transformer to process the inputs and
+    generate mask predictions along with their quality scores.
+
+    Attributes:
+        transformer_dim (int): Channel dimension for the transformer module.
+        transformer (nn.Module): Transformer module used for mask prediction.
+        num_multimask_outputs (int): Number of masks to predict for disambiguating masks.
+        iou_token (nn.Embedding): Embedding for the IoU token.
+        num_mask_tokens (int): Number of mask tokens.
+        mask_tokens (nn.Embedding): Embedding for the mask tokens.
+        output_upscaling (nn.Sequential): Neural network sequence for upscaling the output.
+        output_hypernetworks_mlps (nn.ModuleList): Hypernetwork MLPs for generating masks.
+        iou_prediction_head (nn.Module): MLP for predicting mask quality.
+
+    Methods:
+        forward: Predict masks given image and prompt embeddings.
+        predict_masks: Internal method for mask prediction.
+
+    Examples:
+        >>> decoder = MaskDecoder(transformer_dim=256, transformer=transformer_module)
+        >>> masks, iou_pred = decoder(
+        ...     image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, multimask_output=True
+        ... )
+        >>> print(f"Predicted masks shape: {masks.shape}, IoU predictions shape: {iou_pred.shape}")
+    """
+
+    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,
+    ) -> None:
+        """Initialize the MaskDecoder module for generating masks and their associated quality scores.
+
+        Args:
+            transformer_dim (int): Channel dimension for the transformer module.
+            transformer (nn.Module): Transformer module used for mask prediction.
+            num_multimask_outputs (int): Number of masks to predict for disambiguating masks.
+            activation (Type[nn.Module]): Type of activation to use when upscaling masks.
+            iou_head_depth (int): Depth of the MLP used to predict mask quality.
+            iou_head_hidden_dim (int): 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.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.output_hypernetworks_mlps = nn.ModuleList(
+            [MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) for _ in range(self.num_mask_tokens)]
+        )
+
+        self.iou_prediction_head = MLP(transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth)
+
+    def forward(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+        multimask_output: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Predict masks given image and prompt embeddings.
+
+        Args:
+            image_embeddings (torch.Tensor): Embeddings from the image encoder.
+            image_pe (torch.Tensor): Positional encoding with the shape of image_embeddings.
+            sparse_prompt_embeddings (torch.Tensor): Embeddings of the points and boxes.
+            dense_prompt_embeddings (torch.Tensor): Embeddings of the mask inputs.
+            multimask_output (bool): Whether to return multiple masks or a single mask.
+
+        Returns:
+            masks (torch.Tensor): Batched predicted masks.
+            iou_pred (torch.Tensor): Batched predictions of mask quality.
+
+        Examples:
+            >>> decoder = MaskDecoder(transformer_dim=256, transformer=transformer_module)
+            >>> image_emb = torch.rand(1, 256, 64, 64)
+            >>> image_pe = torch.rand(1, 256, 64, 64)
+            >>> sparse_emb = torch.rand(1, 2, 256)
+            >>> dense_emb = torch.rand(1, 256, 64, 64)
+            >>> masks, iou_pred = decoder(image_emb, image_pe, sparse_emb, dense_emb, multimask_output=True)
+            >>> print(f"Masks shape: {masks.shape}, IoU predictions shape: {iou_pred.shape}")
+        """
+        masks, iou_pred = self.predict_masks(
+            image_embeddings=image_embeddings,
+            image_pe=image_pe,
+            sparse_prompt_embeddings=sparse_prompt_embeddings,
+            dense_prompt_embeddings=dense_prompt_embeddings,
+        )
+
+        # Select the correct mask or masks for output
+        mask_slice = slice(1, None) if multimask_output else slice(0, 1)
+        masks = masks[:, mask_slice, :, :]
+        iou_pred = iou_pred[:, mask_slice]
+
+        return masks, iou_pred
+
+    def predict_masks(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Predict masks and quality scores using image and prompt embeddings via transformer architecture."""
+        # Concatenate output tokens
+        output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
+        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.shape[0], -1, -1)
+        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
+
+        # Expand per-image data in batch direction to be per-mask
+        src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
+        src = src + dense_prompt_embeddings
+        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[:, 0, :]
+        mask_tokens_out = hs[:, 1 : (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)
+        upscaled_embedding = self.output_upscaling(src)
+        hyper_in_list: list[torch.Tensor] = [
+            self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]) for i in range(self.num_mask_tokens)
+        ]
+        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)
+
+        return masks, iou_pred
+
+
+class SAM2MaskDecoder(nn.Module):
+    """Transformer-based decoder for predicting instance segmentation masks from image and prompt embeddings.
+
+    This class extends the functionality of the MaskDecoder, incorporating additional features such as high-resolution
+    feature processing, dynamic multimask output, and object score prediction.
+
+    Attributes:
+        transformer_dim (int): Channel dimension of the transformer.
+        transformer (nn.Module): Transformer used to predict masks.
+        num_multimask_outputs (int): Number of masks to predict when disambiguating masks.
+        iou_token (nn.Embedding): Embedding for IOU token.
+        num_mask_tokens (int): Total number of mask tokens.
+        mask_tokens (nn.Embedding): Embedding for mask tokens.
+        pred_obj_scores (bool): Whether to predict object scores.
+        obj_score_token (nn.Embedding): Embedding for object score token.
+        use_multimask_token_for_obj_ptr (bool): Whether to use multimask token for object pointer.
+        output_upscaling (nn.Sequential): Upscaling layers for output.
+        use_high_res_features (bool): Whether to use high-resolution features.
+        conv_s0 (nn.Conv2d): Convolutional layer for high-resolution features (s0).
+        conv_s1 (nn.Conv2d): Convolutional layer for high-resolution features (s1).
+        output_hypernetworks_mlps (nn.ModuleList): List of MLPs for output hypernetworks.
+        iou_prediction_head (MLP): MLP for IOU prediction.
+        pred_obj_score_head (nn.Linear | MLP): Linear layer or MLP for object score prediction.
+        dynamic_multimask_via_stability (bool): Whether to use dynamic multimask via stability.
+        dynamic_multimask_stability_delta (float): Delta value for dynamic multimask stability.
+        dynamic_multimask_stability_thresh (float): Threshold for dynamic multimask stability.
+
+    Methods:
+        forward: Predict masks given image and prompt embeddings.
+        predict_masks: Predict instance segmentation masks from image and prompt embeddings.
+        _get_stability_scores: Compute mask stability scores based on IoU between thresholds.
+        _dynamic_multimask_via_stability: Dynamically select the most stable mask output.
+
+    Examples:
+        >>> image_embeddings = torch.rand(1, 256, 64, 64)
+        >>> image_pe = torch.rand(1, 256, 64, 64)
+        >>> sparse_prompt_embeddings = torch.rand(1, 2, 256)
+        >>> dense_prompt_embeddings = torch.rand(1, 256, 64, 64)
+        >>> decoder = SAM2MaskDecoder(256, transformer)
+        >>> masks, iou_pred, sam_tokens_out, obj_score_logits = decoder.forward(
+        ...     image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, True, False
+        ... )
+    """
+
+    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:
+        """Initialize the SAM2MaskDecoder module for predicting instance segmentation masks.
+
+        This decoder extends the functionality of MaskDecoder, incorporating additional features such as high-resolution
+        feature processing, dynamic multimask output, and object score prediction.
+
+        Args:
+            transformer_dim (int): Channel dimension of the transformer.
+            transformer (nn.Module): Transformer used to predict masks.
+            num_multimask_outputs (int): Number of masks to predict when disambiguating masks.
+            activation (Type[nn.Module]): Type of activation to use when upscaling masks.
+            iou_head_depth (int): Depth of the MLP used to predict mask quality.
+            iou_head_hidden_dim (int): Hidden dimension of the MLP used to predict mask quality.
+            use_high_res_features (bool): Whether to use high-resolution features.
+            iou_prediction_use_sigmoid (bool): Whether to use sigmoid for IOU prediction.
+            dynamic_multimask_via_stability (bool): Whether to use dynamic multimask via stability.
+            dynamic_multimask_stability_delta (float): Delta value for dynamic multimask stability.
+            dynamic_multimask_stability_thresh (float): Threshold for dynamic multimask stability.
+            pred_obj_scores (bool): Whether to predict object scores.
+            pred_obj_scores_mlp (bool): Whether to use MLP for object score prediction.
+            use_multimask_token_for_obj_ptr (bool): Whether to use multimask token for object pointer.
+        """
+        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 _ 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=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: list[torch.Tensor] | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Predict masks given image and prompt embeddings.
+
+        Args:
+            image_embeddings (torch.Tensor): Embeddings from the image encoder with shape (B, C, H, W).
+            image_pe (torch.Tensor): Positional encoding with the shape of image_embeddings (B, C, H, W).
+            sparse_prompt_embeddings (torch.Tensor): Embeddings of the points and boxes with shape (B, N, C).
+            dense_prompt_embeddings (torch.Tensor): Embeddings of the mask inputs with shape (B, C, H, W).
+            multimask_output (bool): Whether to return multiple masks or a single mask.
+            repeat_image (bool): Flag to repeat the image embeddings.
+            high_res_features (list[torch.Tensor] | None, optional): Optional high-resolution features.
+
+        Returns:
+            masks (torch.Tensor): Batched predicted masks with shape (B, N, H, W).
+            iou_pred (torch.Tensor): Batched predictions of mask quality with shape (B, N).
+            sam_tokens_out (torch.Tensor): Batched SAM token for mask output with shape (B, N, C).
+            object_score_logits (torch.Tensor): Batched object score logits with shape (B, 1).
+
+        Examples:
+            >>> image_embeddings = torch.rand(1, 256, 64, 64)
+            >>> image_pe = torch.rand(1, 256, 64, 64)
+            >>> sparse_prompt_embeddings = torch.rand(1, 2, 256)
+            >>> dense_prompt_embeddings = torch.rand(1, 256, 64, 64)
+            >>> decoder = SAM2MaskDecoder(256, transformer)
+            >>> masks, iou_pred, sam_tokens_out, obj_score_logits = decoder.forward(
+            ...     image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, True, False
+            ... )
+        """
+        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
+
+        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: list[torch.Tensor] | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Predict instance segmentation masks from image and prompt embeddings using a transformer."""
+        # 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.shape[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.shape[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 or high_res_features is None:
+            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] = [
+            self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]) for i in range(self.num_mask_tokens)
+        ]
+        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 mask stability scores based on IoU between upper and lower thresholds."""
+        mask_logits = mask_logits.flatten(-2)
+        area_i = torch.sum(mask_logits > self.dynamic_multimask_stability_delta, dim=-1).float()
+        area_u = torch.sum(mask_logits > -self.dynamic_multimask_stability_delta, dim=-1).float()
+        return torch.where(area_u > 0, area_i / area_u, 1.0)
+
+    def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores):
+        """Dynamically select the most stable mask output based on stability scores and IoU predictions.
+
+        This method is used when outputting a single mask. If the stability score from the current single-mask output
+        (based on output token 0) falls below a threshold, it instead selects from multi-mask outputs (based on output
+        tokens 1-3) the mask with the highest predicted IoU score. This ensures a valid mask for both clicking and
+        tracking scenarios.
+
+        Args:
+            all_mask_logits (torch.Tensor): Logits for all predicted masks, shape (B, N, H, W) where B is batch size, N
+                is number of masks (typically 4), and H, W are mask dimensions.
+            all_iou_scores (torch.Tensor): Predicted IoU scores for all masks, shape (B, N).
+
+        Returns:
+            mask_logits_out (torch.Tensor): Selected mask logits, shape (B, 1, H, W).
+            iou_scores_out (torch.Tensor): Selected IoU scores, shape (B, 1).
+
+        Examples:
+            >>> decoder = SAM2MaskDecoder(...)
+            >>> all_mask_logits = torch.rand(2, 4, 256, 256)  # 2 images, 4 masks each
+            >>> all_iou_scores = torch.rand(2, 4)
+            >>> mask_logits, iou_scores = decoder._dynamic_multimask_via_stability(all_mask_logits, all_iou_scores)
+            >>> print(mask_logits.shape, iou_scores.shape)
+            torch.Size([2, 1, 256, 256]) torch.Size([2, 1])
+        """
+        # 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.shape[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

algorithms/dms_yolo/code/ultralytics/models/sam/modules/encoders.py

@@ -0,0 +1,794 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ultralytics.nn.modules import LayerNorm2d
+
+from .blocks import (
+    Block,
+    CXBlock,
+    Fuser,
+    MaskDownSampler,
+    MultiScaleBlock,
+    PatchEmbed,
+    PositionEmbeddingRandom,
+    PositionEmbeddingSine,
+)
+
+
+class ImageEncoderViT(nn.Module):
+    """An image encoder using Vision Transformer (ViT) architecture for encoding images into a compact latent space.
+
+    This class processes images by splitting them into patches, applying transformer blocks, and generating a final
+    encoded representation through a neck module.
+
+    Attributes:
+        img_size (int): Dimension of input images, assumed to be square.
+        patch_embed (PatchEmbed): Module for patch embedding.
+        pos_embed (nn.Parameter | None): Absolute positional embedding for patches.
+        blocks (nn.ModuleList): List of transformer blocks for processing patch embeddings.
+        neck (nn.Sequential): Neck module to further process the output.
+
+    Methods:
+        forward: Process input through patch embedding, positional embedding, blocks, and neck.
+
+    Examples:
+        >>> import torch
+        >>> encoder = ImageEncoderViT(img_size=224, patch_size=16, embed_dim=768, depth=12, num_heads=12)
+        >>> input_image = torch.randn(1, 3, 224, 224)
+        >>> output = encoder(input_image)
+        >>> print(output.shape)
+    """
+
+    def __init__(
+        self,
+        img_size: int = 1024,
+        patch_size: int = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        out_chans: int = 256,
+        qkv_bias: bool = True,
+        norm_layer: type[nn.Module] = nn.LayerNorm,
+        act_layer: type[nn.Module] = nn.GELU,
+        use_abs_pos: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        global_attn_indexes: tuple[int, ...] = (),
+    ) -> None:
+        """Initialize an ImageEncoderViT instance for encoding images using Vision Transformer architecture.
+
+        Args:
+            img_size (int): Input image size, assumed to be square.
+            patch_size (int): Size of image patches.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Dimension of patch embeddings.
+            depth (int): Number of transformer blocks.
+            num_heads (int): Number of attention heads in each block.
+            mlp_ratio (float): Ratio of MLP hidden dimension to embedding dimension.
+            out_chans (int): Number of output channels from the neck module.
+            qkv_bias (bool): If True, adds learnable bias to query, key, value projections.
+            norm_layer (Type[nn.Module]): Type of normalization layer to use.
+            act_layer (Type[nn.Module]): Type of activation layer to use.
+            use_abs_pos (bool): If True, uses absolute positional embeddings.
+            use_rel_pos (bool): If True, adds relative positional embeddings to attention maps.
+            rel_pos_zero_init (bool): If True, initializes relative positional parameters to zero.
+            window_size (int): Size of attention window for windowed attention blocks.
+            global_attn_indexes (tuple[int, ...]): Indices of blocks that use global attention.
+        """
+        super().__init__()
+        self.img_size = img_size
+
+        self.patch_embed = PatchEmbed(
+            kernel_size=(patch_size, patch_size),
+            stride=(patch_size, patch_size),
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
+
+        self.pos_embed: nn.Parameter | None = None
+        if use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size
+            self.pos_embed = nn.Parameter(torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim))
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            block = Block(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                use_rel_pos=use_rel_pos,
+                rel_pos_zero_init=rel_pos_zero_init,
+                window_size=window_size if i not in global_attn_indexes else 0,
+                input_size=(img_size // patch_size, img_size // patch_size),
+            )
+            self.blocks.append(block)
+
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dim,
+                out_chans,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+            nn.Conv2d(
+                out_chans,
+                out_chans,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Process input through patch embedding, positional embedding, transformer blocks, and neck module."""
+        x = self.patch_embed(x)
+        if self.pos_embed is not None:
+            pos_embed = (
+                F.interpolate(self.pos_embed.permute(0, 3, 1, 2), scale_factor=self.img_size / 1024).permute(0, 2, 3, 1)
+                if self.img_size != 1024
+                else self.pos_embed
+            )
+            x = x + pos_embed
+        for blk in self.blocks:
+            x = blk(x)
+        return self.neck(x.permute(0, 3, 1, 2))
+
+
+class PromptEncoder(nn.Module):
+    """Encode different types of prompts for input to SAM's mask decoder, producing sparse and dense embeddings.
+
+    Attributes:
+        embed_dim (int): Dimension of the embeddings.
+        input_image_size (tuple[int, int]): Size of the input image as (H, W).
+        image_embedding_size (tuple[int, int]): Spatial size of the image embedding as (H, W).
+        pe_layer (PositionEmbeddingRandom): Module for random position embedding.
+        num_point_embeddings (int): Number of point embeddings for different types of points.
+        point_embeddings (nn.ModuleList): List of point embeddings.
+        not_a_point_embed (nn.Embedding): Embedding for points that are not part of any label.
+        mask_input_size (tuple[int, int]): Size of the input mask.
+        mask_downscaling (nn.Sequential): Neural network for downscaling the mask.
+        no_mask_embed (nn.Embedding): Embedding for cases where no mask is provided.
+
+    Methods:
+        get_dense_pe: Return the positional encoding used to encode point prompts.
+        forward: Embed different types of prompts, returning both sparse and dense embeddings.
+
+    Examples:
+        >>> prompt_encoder = PromptEncoder(256, (64, 64), (1024, 1024), 16)
+        >>> points = (torch.rand(1, 5, 2), torch.randint(0, 4, (1, 5)))
+        >>> boxes = torch.rand(1, 2, 2)
+        >>> masks = torch.rand(1, 1, 256, 256)
+        >>> sparse_embeddings, dense_embeddings = prompt_encoder(points, boxes, masks)
+        >>> print(sparse_embeddings.shape, dense_embeddings.shape)
+        torch.Size([1, 7, 256]) torch.Size([1, 256, 64, 64])
+    """
+
+    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:
+        """Initialize the PromptEncoder module for encoding various types of prompts.
+
+        Args:
+            embed_dim (int): The dimension of the embeddings.
+            image_embedding_size (tuple[int, int]): The spatial size of the image embedding as (H, W).
+            input_image_size (tuple[int, int]): The padded size of the input image as (H, W).
+            mask_in_chans (int): The number of hidden channels used for encoding input masks.
+            activation (Type[nn.Module]): The activation function 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 _ 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:
+        """Return the dense positional encoding used for encoding point prompts.
+
+        Generate a positional encoding for a dense set of points matching the shape of the image
+        encoding. The encoding is used to provide spatial information to the model when processing point prompts.
+
+        Returns:
+            (torch.Tensor): Positional encoding tensor with shape (1, embed_dim, H, W), where H and W are the height and
+                width of the image embedding size, respectively.
+
+        Examples:
+            >>> prompt_encoder = PromptEncoder(256, (64, 64), (1024, 1024), 16)
+            >>> dense_pe = prompt_encoder.get_dense_pe()
+            >>> print(dense_pe.shape)
+            torch.Size([1, 256, 64, 64])
+        """
+        return self.pe_layer(self.image_embedding_size).unsqueeze(0)
+
+    def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pad: bool) -> torch.Tensor:
+        """Embed point prompts by applying positional encoding and label-specific embeddings."""
+        points = points + 0.5  # Shift to center of pixel
+        if pad:
+            padding_point = torch.zeros((points.shape[0], 1, 2), dtype=points.dtype, device=points.device)
+            padding_label = -torch.ones((labels.shape[0], 1), dtype=labels.dtype, 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:
+        """Embed box prompts by applying positional encoding and adding corner embeddings."""
+        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:
+        """Embed mask inputs by downscaling and processing through convolutional layers."""
+        return self.mask_downscaling(masks)
+
+    @staticmethod
+    def _get_batch_size(
+        points: tuple[torch.Tensor, torch.Tensor] | None,
+        boxes: torch.Tensor | None,
+        masks: torch.Tensor | None,
+    ) -> int:
+        """Get 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 forward(
+        self,
+        points: tuple[torch.Tensor, torch.Tensor] | None,
+        boxes: torch.Tensor | None,
+        masks: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Embed different types of prompts, returning both sparse and dense embeddings.
+
+        Args:
+            points (tuple[torch.Tensor, torch.Tensor] | None): Point coordinates and labels to embed. The first tensor
+                contains coordinates of shape (B, N, 2), and the second tensor contains labels of shape (B, N).
+            boxes (torch.Tensor | None): Boxes to embed with shape (B, M, 2, 2), where M is the number of boxes.
+            masks (torch.Tensor | None): Masks to embed with shape (B, 1, H, W).
+
+        Returns:
+            sparse_embeddings (torch.Tensor): Sparse embeddings for points and boxes with shape (B, N, embed_dim).
+            dense_embeddings (torch.Tensor): Dense embeddings for masks of shape (B, embed_dim, embed_H, embed_W).
+
+        Examples:
+            >>> encoder = PromptEncoder(256, (64, 64), (1024, 1024), 16)
+            >>> points = (torch.rand(1, 5, 2), torch.randint(0, 4, (1, 5)))
+            >>> boxes = torch.rand(1, 2, 2, 2)
+            >>> masks = torch.rand(1, 1, 256, 256)
+            >>> sparse_emb, dense_emb = encoder(points, boxes, masks)
+            >>> print(sparse_emb.shape, dense_emb.shape)
+            torch.Size([1, 7, 256]) torch.Size([1, 256, 64, 64])
+        """
+        bs = self._get_batch_size(points, boxes, masks)
+        sparse_embeddings = torch.empty(
+            (bs, 0, self.embed_dim),
+            dtype=self.point_embeddings[0].weight.dtype,
+            device=self.point_embeddings[0].weight.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
+
+
+class MemoryEncoder(nn.Module):
+    """Encode pixel features and masks into a memory representation for efficient image segmentation.
+
+    This class processes pixel-level features and masks, fusing them to generate encoded memory representations suitable
+    for downstream tasks in image segmentation models like SAM (Segment Anything Model).
+
+    Attributes:
+        mask_downsampler (MaskDownSampler): Module for downsampling input masks.
+        pix_feat_proj (nn.Conv2d): Convolutional layer for projecting pixel features.
+        fuser (Fuser): Module for fusing pixel features and masks.
+        position_encoding (PositionEmbeddingSine): Module for adding positional encoding to features.
+        out_proj (nn.Module): Output projection layer, either nn.Identity or nn.Conv2d.
+
+    Methods:
+        forward: Process input pixel features and masks to generate encoded memory representations.
+
+    Examples:
+        >>> import torch
+        >>> encoder = MemoryEncoder(out_dim=256, in_dim=256)
+        >>> pix_feat = torch.randn(1, 256, 64, 64)
+        >>> masks = torch.randn(1, 1, 64, 64)
+        >>> encoded_feat, pos = encoder(pix_feat, masks)
+        >>> print(encoded_feat.shape, pos.shape)
+        torch.Size([1, 256, 64, 64]) torch.Size([1, 128, 64, 64])
+    """
+
+    def __init__(
+        self,
+        out_dim,
+        in_dim=256,  # in_dim of pix_feats
+        interpol_size: tuple[int, int] | None = None,
+    ):
+        """Initialize the MemoryEncoder for encoding pixel features and masks into memory representations.
+
+        This encoder processes pixel-level features and masks, fusing them to generate encoded memory representations
+        suitable for downstream tasks in image segmentation models like SAM (Segment Anything Model).
+
+        Args:
+            out_dim (int): Output dimension of the encoded features.
+            in_dim (int): Input dimension of the pixel features.
+            interpol_size (tuple[int, int] | None): Size to interpolate masks to. If None, uses the size of pixel
+                features.
+        """
+        super().__init__()
+
+        self.mask_downsampler = MaskDownSampler(kernel_size=3, stride=2, padding=1, interpol_size=interpol_size)
+
+        self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1)
+        self.fuser = Fuser(CXBlock(dim=256), num_layers=2)
+        self.position_encoding = PositionEmbeddingSine(num_pos_feats=64)
+        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,
+    ) -> dict:
+        """Process pixel features and masks to generate encoded memory representations for segmentation."""
+        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]}
+
+
+class ImageEncoder(nn.Module):
+    """Encode images using a trunk-neck architecture, producing multiscale features and positional encodings.
+
+    This class combines a trunk network for feature extraction with a neck network for feature refinement and positional
+    encoding generation. It can optionally discard the lowest resolution features.
+
+    Attributes:
+        trunk (nn.Module): The trunk network for initial feature extraction.
+        neck (nn.Module): The neck network for feature refinement and positional encoding generation.
+        scalp (int): Number of lowest resolution feature levels to discard.
+
+    Methods:
+        forward: Process the input image through the trunk and neck networks.
+
+    Examples:
+        >>> trunk = SomeTrunkNetwork()
+        >>> neck = SomeNeckNetwork()
+        >>> encoder = ImageEncoder(trunk, neck, scalp=1)
+        >>> image = torch.randn(1, 3, 224, 224)
+        >>> output = encoder(image)
+        >>> print(output.keys())
+        dict_keys(['vision_features', 'vision_pos_enc', 'backbone_fpn'])
+    """
+
+    def __init__(
+        self,
+        trunk: nn.Module,
+        neck: nn.Module,
+        scalp: int = 0,
+    ):
+        """Initialize the ImageEncoder with trunk and neck networks for feature extraction and refinement.
+
+        This encoder combines a trunk network for feature extraction with a neck network for feature refinement and
+        positional encoding generation. It can optionally discard the lowest resolution features.
+
+        Args:
+            trunk (nn.Module): The trunk network for initial feature extraction.
+            neck (nn.Module): The neck network for feature refinement and positional encoding generation.
+            scalp (int): Number of lowest resolution feature levels to discard.
+        """
+        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 {self.trunk.channel_list} and neck {self.neck.backbone_channel_list} do not match."
+        )
+
+    def forward(self, sample: torch.Tensor):
+        """Encode input through trunk and neck networks, returning multiscale features and positional encodings."""
+        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]
+        return {
+            "vision_features": src,
+            "vision_pos_enc": pos,
+            "backbone_fpn": features,
+        }
+
+
+class FpnNeck(nn.Module):
+    """A Feature Pyramid Network (FPN) neck variant for multiscale feature fusion in object detection models.
+
+    This FPN variant removes the output convolution and uses bicubic interpolation for feature resizing, similar to ViT
+    positional embedding interpolation.
+
+    Attributes:
+        position_encoding (PositionEmbeddingSine): Sinusoidal positional encoding module.
+        convs (nn.ModuleList): List of convolutional layers for each backbone level.
+        backbone_channel_list (list[int]): List of channel dimensions from the backbone.
+        fpn_interp_model (str): Interpolation mode for FPN feature resizing.
+        fuse_type (str): Type of feature fusion, either 'sum' or 'avg'.
+        fpn_top_down_levels (list[int]): Levels to have top-down features in outputs.
+
+    Methods:
+        forward: Perform forward pass through the FPN neck.
+
+    Examples:
+        >>> backbone_channels = [64, 128, 256, 512]
+        >>> fpn_neck = FpnNeck(256, backbone_channels)
+        >>> inputs = [torch.rand(1, c, 32, 32) for c in backbone_channels]
+        >>> outputs, positions = fpn_neck(inputs)
+        >>> print(len(outputs), len(positions))
+        4 4
+    """
+
+    def __init__(
+        self,
+        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: list[int] | None = None,
+    ):
+        """Initialize a modified Feature Pyramid Network (FPN) neck.
+
+        This FPN variant removes the output convolution and uses bicubic interpolation for feature resizing, similar to
+        ViT positional embedding interpolation.
+
+        Args:
+            d_model (int): Dimension of the model.
+            backbone_channel_list (list[int]): List of channel dimensions from the backbone.
+            kernel_size (int): Kernel size for the convolutional layers.
+            stride (int): Stride for the convolutional layers.
+            padding (int): Padding for the convolutional layers.
+            fpn_interp_model (str): Interpolation mode for FPN feature resizing.
+            fuse_type (str): Type of feature fusion, either 'sum' or 'avg'.
+            fpn_top_down_levels (Optional[list[int]]): Levels to have top-down features in outputs.
+        """
+        super().__init__()
+        self.position_encoding = PositionEmbeddingSine(num_pos_feats=256)
+        self.convs = nn.ModuleList()
+        self.backbone_channel_list = backbone_channel_list
+        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]):
+        """Perform forward pass through the Feature Pyramid Network (FPN) neck.
+
+        This method processes a list of input tensors from the backbone through the FPN, applying lateral connections
+        and top-down feature fusion. It generates output feature maps and corresponding positional encodings.
+
+        Args:
+            xs (list[torch.Tensor]): List of input tensors from the backbone, each with shape (B, C, H, W).
+
+        Returns:
+            out (list[torch.Tensor]): List of output feature maps after FPN processing, each with shape (B, d_model, H,
+                W).
+            pos (list[torch.Tensor]): List of positional encodings corresponding to each output feature map.
+
+        Examples:
+            >>> fpn_neck = FpnNeck(d_model=256, backbone_channel_list=[64, 128, 256, 512])
+            >>> inputs = [torch.rand(1, c, 32, 32) for c in [64, 128, 256, 512]]
+            >>> outputs, positions = fpn_neck(inputs)
+            >>> print(len(outputs), len(positions))
+            4 4
+        """
+        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=x.dtype),
+                    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
+
+
+class Hiera(nn.Module):
+    """Hierarchical vision transformer for efficient multiscale feature extraction in image processing tasks.
+
+    This class implements a Hiera model, which is a hierarchical vision transformer architecture designed for efficient
+    multiscale feature extraction. It uses a series of transformer blocks organized into stages, with optional pooling
+    and global attention mechanisms.
+
+    Attributes:
+        window_spec (tuple[int, ...]): Window sizes for each stage.
+        q_stride (tuple[int, int]): Downsampling stride between stages.
+        stage_ends (list[int]): Indices of the last block in each stage.
+        q_pool_blocks (list[int]): Indices of blocks where pooling is applied.
+        return_interm_layers (bool): Whether to return intermediate layer outputs.
+        patch_embed (PatchEmbed): Module for patch embedding.
+        global_att_blocks (tuple[int, ...]): Indices of blocks with global attention.
+        window_pos_embed_bkg_spatial_size (tuple[int, int]): Spatial size for window positional embedding background.
+        pos_embed (nn.Parameter): Positional embedding for the background.
+        pos_embed_window (nn.Parameter): Positional embedding for the window.
+        blocks (nn.ModuleList): List of MultiScaleBlock modules.
+        channel_list (list[int]): List of output channel dimensions for each stage.
+
+    Methods:
+        _get_pos_embed: Generate positional embeddings by interpolating and combining window and background embeddings.
+        forward: Perform the forward pass through the Hiera model.
+
+    Examples:
+        >>> model = Hiera(embed_dim=96, num_heads=1, stages=(2, 3, 16, 3))
+        >>> input_tensor = torch.randn(1, 3, 224, 224)
+        >>> output_features = model(input_tensor)
+        >>> for feat in output_features:
+        ...     print(feat.shape)
+    """
+
+    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,
+        ),
+        return_interm_layers=True,  # return feats from every stage
+    ):
+        """Initialize a Hiera model, a hierarchical vision transformer for efficient multiscale feature extraction.
+
+        Hiera is a hierarchical vision transformer architecture designed for efficient multiscale feature extraction in
+        image processing tasks. It uses a series of transformer blocks organized into stages, with optional pooling and
+        global attention mechanisms.
+
+        Args:
+            embed_dim (int): Initial embedding dimension for the model.
+            num_heads (int): Initial number of attention heads.
+            drop_path_rate (float): Stochastic depth rate.
+            q_pool (int): Number of query pooling stages.
+            q_stride (tuple[int, int]): Downsampling stride between stages.
+            stages (tuple[int, ...]): Number of blocks per stage.
+            dim_mul (float): Dimension multiplier factor at stage transitions.
+            head_mul (float): Head multiplier factor at stage transitions.
+            window_pos_embed_bkg_spatial_size (tuple[int, int]): Spatial size for window positional embedding
+                background.
+            window_spec (tuple[int, ...]): Window sizes for each stage when not using global attention.
+            global_att_blocks (tuple[int, ...]): Indices of blocks that use global attention.
+            return_interm_layers (bool): Whether to return intermediate layer outputs.
+        """
+        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,
+            kernel_size=(7, 7),
+            stride=(4, 4),
+            padding=(3, 3),
+        )
+        # Which blocks have global attention?
+        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]
+        )
+
+    def _get_pos_embed(self, hw: tuple[int, int]) -> torch.Tensor:
+        """Generate positional embeddings by interpolating and combining window and background embeddings."""
+        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]:
+        """Perform forward pass through Hiera model, extracting multiscale features from input images.
+
+        Args:
+            x (torch.Tensor): Input tensor with shape (B, C, H, W) representing a batch of images.
+
+        Returns:
+            (list[torch.Tensor]): List of feature maps at different scales, each with shape (B, C_i, H_i, W_i), where
+                C_i is the channel dimension and H_i, W_i are the spatial dimensions at scale i. The list is ordered
+                from highest resolution (fine features) to lowest resolution (coarse features) if return_interm_layers
+                is True, otherwise contains only the final output.
+
+        Examples:
+            >>> model = Hiera(embed_dim=96, num_heads=1, stages=(2, 3, 16, 3))
+            >>> input_tensor = torch.randn(1, 3, 224, 224)
+            >>> output_features = model(input_tensor)
+            >>> for feat in output_features:
+            ...     print(feat.shape)
+        """
+        x = self.patch_embed(x)
+        # x: (B, H, W, C)
+
+        # Add positional embedding
+        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

algorithms/dms_yolo/code/ultralytics/models/sam/modules/memory_attention.py

@@ -0,0 +1,298 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import copy
+
+import torch
+from torch import nn
+
+from .blocks import RoPEAttention
+
+
+class MemoryAttentionLayer(nn.Module):
+    """Implements a memory attention layer with self-attention and cross-attention mechanisms for neural networks.
+
+    This class combines self-attention, cross-attention, and feedforward components to process input tensors and
+    generate memory-based attention outputs.
+
+    Attributes:
+        d_model (int): Dimensionality of the model.
+        dim_feedforward (int): Dimensionality of the feedforward network.
+        dropout_value (float): Dropout rate for regularization.
+        self_attn (RoPEAttention): Self-attention mechanism using RoPE (Rotary Position Embedding).
+        cross_attn_image (RoPEAttention): Cross-attention mechanism for image processing.
+        linear1 (nn.Linear): First linear layer of the feedforward network.
+        linear2 (nn.Linear): Second linear layer of the feedforward network.
+        norm1 (nn.LayerNorm): Layer normalization for self-attention output.
+        norm2 (nn.LayerNorm): Layer normalization for cross-attention output.
+        norm3 (nn.LayerNorm): Layer normalization for feedforward network output.
+        dropout1 (nn.Dropout): Dropout layer after self-attention.
+        dropout2 (nn.Dropout): Dropout layer after cross-attention.
+        dropout3 (nn.Dropout): Dropout layer after feedforward network.
+        activation (nn.ReLU): Activation function for the feedforward network.
+        pos_enc_at_attn (bool): Flag to add positional encoding at attention.
+        pos_enc_at_cross_attn_queries (bool): Flag to add positional encoding to cross-attention queries.
+        pos_enc_at_cross_attn_keys (bool): Flag to add positional encoding to cross-attention keys.
+
+    Methods:
+        forward: Performs the full memory attention operation on input tensors.
+        _forward_sa: Performs self-attention on input tensor.
+        _forward_ca: Performs cross-attention between target and memory tensors.
+
+    Examples:
+        >>> layer = MemoryAttentionLayer(d_model=256, dim_feedforward=2048, dropout=0.1)
+        >>> tgt = torch.randn(1, 100, 256)
+        >>> memory = torch.randn(1, 100, 64)
+        >>> pos = torch.randn(1, 100, 256)
+        >>> query_pos = torch.randn(1, 100, 256)
+        >>> output = layer(tgt, memory, pos, query_pos)
+        >>> print(output.shape)
+        torch.Size([1, 100, 256])
+    """
+
+    def __init__(
+        self,
+        d_model: int = 256,
+        dim_feedforward: int = 2048,
+        dropout: float = 0.1,
+        pos_enc_at_attn: bool = False,
+        pos_enc_at_cross_attn_keys: bool = True,
+        pos_enc_at_cross_attn_queries: bool = False,
+        self_attn: nn.Module | None = None,
+        cross_attn: nn.Module | None = None,
+    ):
+        """Initialize a memory attention layer with self-attention, cross-attention, and feedforward components.
+
+        Args:
+            d_model (int): Dimensionality of the model.
+            dim_feedforward (int): Dimensionality of the feedforward network.
+            dropout (float): Dropout rate for regularization.
+            pos_enc_at_attn (bool): Whether to add positional encoding at attention.
+            pos_enc_at_cross_attn_keys (bool): Whether to add positional encoding to cross-attention keys.
+            pos_enc_at_cross_attn_queries (bool): Whether to add positional encoding to cross-attention queries.
+            self_attn (nn.Module | None): Custom self-attention module. If None, a default RoPEAttention is used.
+            cross_attn (nn.Module | None): Custom cross-attention module. If None, a default RoPEAttention is used.
+        """
+        super().__init__()
+        self.d_model = d_model
+        self.dim_feedforward = dim_feedforward
+        self.dropout_value = dropout
+        self.self_attn = self_attn or RoPEAttention(embedding_dim=256, num_heads=1, downsample_rate=1)
+        self.cross_attn_image = cross_attn or RoPEAttention(
+            rope_k_repeat=True,
+            embedding_dim=256,
+            num_heads=1,
+            downsample_rate=1,
+            kv_in_dim=64,
+        )
+
+        # 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 = nn.ReLU()
+
+        # 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: torch.Tensor, query_pos: torch.Tensor | None) -> torch.Tensor:
+        """Perform self-attention on input tensor using positional encoding and RoPE attention mechanism."""
+        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: torch.Tensor,
+        memory: torch.Tensor,
+        query_pos: torch.Tensor | None,
+        pos: torch.Tensor | None,
+        num_k_exclude_rope: int = 0,
+    ) -> torch.Tensor:
+        """Perform cross-attention between target and memory tensors using RoPEAttention mechanism."""
+        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)
+        tgt2 = self.cross_attn_image(
+            q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,
+            k=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
+            v=memory,
+            **kwds,
+        )
+        tgt = tgt + self.dropout2(tgt2)
+        return tgt
+
+    def forward(
+        self,
+        tgt: torch.Tensor,
+        memory: torch.Tensor,
+        pos: torch.Tensor | None = None,
+        query_pos: torch.Tensor | None = None,
+        num_k_exclude_rope: int = 0,
+    ) -> torch.Tensor:
+        """Process input tensors through self-attention, cross-attention, and feedforward network layers.
+
+        Args:
+            tgt (torch.Tensor): Target tensor for self-attention with shape (N, L, D).
+            memory (torch.Tensor): Memory tensor for cross-attention with shape (N, S, D).
+            pos (Optional[torch.Tensor]): Positional encoding for memory tensor.
+            query_pos (Optional[torch.Tensor]): Positional encoding for target tensor.
+            num_k_exclude_rope (int): Number of keys to exclude from rotary position embedding.
+
+        Returns:
+            (torch.Tensor): Processed tensor after attention and feedforward layers with shape (N, L, D).
+        """
+        tgt = self._forward_sa(tgt, query_pos)
+        tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope)
+        # 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):
+    """Memory attention module for processing sequential data with self and cross-attention mechanisms.
+
+    This class implements a multi-layer attention mechanism that combines self-attention and cross-attention for
+    processing sequential data, particularly useful in transformer-like architectures.
+
+    Attributes:
+        d_model (int): The dimension of the model's hidden state.
+        layers (nn.ModuleList): A list of MemoryAttentionLayer modules.
+        num_layers (int): The number of attention layers.
+        norm (nn.LayerNorm): Layer normalization applied to the output.
+        pos_enc_at_input (bool): Whether to apply positional encoding at the input.
+        batch_first (bool): Whether the input tensors are in batch-first format.
+
+    Methods:
+        forward: Processes input tensors through the attention layers.
+
+    Examples:
+        >>> d_model = 256
+        >>> layer = MemoryAttentionLayer(d_model)
+        >>> attention = MemoryAttention(d_model, pos_enc_at_input=True, layer=layer, num_layers=3)
+        >>> curr = torch.randn(10, 32, d_model)  # (seq_len, batch_size, d_model)
+        >>> memory = torch.randn(20, 32, d_model)  # (mem_len, batch_size, d_model)
+        >>> curr_pos = torch.randn(10, 32, d_model)
+        >>> memory_pos = torch.randn(20, 32, d_model)
+        >>> output = attention(curr, memory, curr_pos, memory_pos)
+        >>> print(output.shape)
+        torch.Size([10, 32, 256])
+    """
+
+    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?
+    ):
+        """Initialize MemoryAttention with specified layers and normalization for sequential data processing.
+
+        This class implements a multi-layer attention mechanism that combines self-attention and cross-attention for
+        processing sequential data, particularly useful in transformer-like architectures.
+
+        Args:
+            d_model (int): The dimension of the model's hidden state.
+            pos_enc_at_input (bool): Whether to apply positional encoding at the input.
+            layer (nn.Module): The attention layer to be used in the module.
+            num_layers (int): The number of attention layers.
+            batch_first (bool): Whether the input tensors are in batch-first format.
+        """
+        super().__init__()
+        self.d_model = d_model
+        self.layers = nn.ModuleList([copy.deepcopy(layer) for _ in range(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: torch.Tensor | None = None,  # pos_enc for self-attention inputs
+        memory_pos: torch.Tensor | None = None,  # pos_enc for cross-attention inputs
+        num_obj_ptr_tokens: int = 0,  # number of object pointer *tokens*
+    ) -> torch.Tensor:
+        """Process inputs through attention layers, applying self and cross-attention with positional encoding.
+
+        Args:
+            curr (torch.Tensor): Self-attention input tensor, representing the current state.
+            memory (torch.Tensor): Cross-attention input tensor, representing memory information.
+            curr_pos (Optional[torch.Tensor]): Positional encoding for self-attention inputs.
+            memory_pos (Optional[torch.Tensor]): Positional encoding for cross-attention inputs.
+            num_obj_ptr_tokens (int): Number of object pointer tokens to exclude from rotary position embedding.
+
+        Returns:
+            (torch.Tensor): Processed output tensor after applying attention layers and normalization.
+
+        Examples:
+            >>> d_model = 256
+            >>> layer = MemoryAttentionLayer(d_model)
+            >>> attention = MemoryAttention(d_model, pos_enc_at_input=True, layer=layer, num_layers=3)
+            >>> curr = torch.randn(10, 32, d_model)  # (seq_len, batch_size, d_model)
+            >>> memory = torch.randn(20, 32, d_model)  # (mem_len, batch_size, d_model)
+            >>> curr_pos = torch.randn(10, 32, d_model)
+            >>> memory_pos = torch.randn(20, 32, d_model)
+            >>> output = attention(curr, memory, curr_pos, memory_pos)
+            >>> print(output.shape)
+            torch.Size([10, 32, 256])
+        """
+        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}
+
+            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

algorithms/dms_yolo/code/ultralytics/models/sam/modules/tiny_encoder.py

@@ -0,0 +1,979 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# --------------------------------------------------------
+# TinyViT Model Architecture
+# Copyright (c) 2022 Microsoft
+# Adapted from LeViT and Swin Transformer
+#   LeViT: (https://github.com/facebookresearch/levit)
+#   Swin: (https://github.com/microsoft/swin-transformer)
+# Build the TinyViT Model
+# --------------------------------------------------------
+
+from __future__ import annotations
+
+import itertools
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ultralytics.nn.modules import LayerNorm2d
+from ultralytics.utils.instance import to_2tuple
+
+
+class Conv2d_BN(torch.nn.Sequential):
+    """A sequential container that performs 2D convolution followed by batch normalization.
+
+    This module combines a 2D convolution layer with batch normalization, providing a common building block for
+    convolutional neural networks. The batch normalization weights and biases are initialized to specific values for
+    optimal training performance.
+
+    Attributes:
+        c (torch.nn.Conv2d): 2D convolution layer.
+        bn (torch.nn.BatchNorm2d): Batch normalization layer.
+
+    Examples:
+        >>> conv_bn = Conv2d_BN(3, 64, ks=3, stride=1, pad=1)
+        >>> input_tensor = torch.randn(1, 3, 224, 224)
+        >>> output = conv_bn(input_tensor)
+        >>> print(output.shape)
+        torch.Size([1, 64, 224, 224])
+    """
+
+    def __init__(
+        self,
+        a: int,
+        b: int,
+        ks: int = 1,
+        stride: int = 1,
+        pad: int = 0,
+        dilation: int = 1,
+        groups: int = 1,
+        bn_weight_init: float = 1,
+    ):
+        """Initialize a sequential container with 2D convolution followed by batch normalization.
+
+        Args:
+            a (int): Number of input channels.
+            b (int): Number of output channels.
+            ks (int, optional): Kernel size for the convolution.
+            stride (int, optional): Stride for the convolution.
+            pad (int, optional): Padding for the convolution.
+            dilation (int, optional): Dilation factor for the convolution.
+            groups (int, optional): Number of groups for the convolution.
+            bn_weight_init (float, optional): Initial value for batch normalization weight.
+        """
+        super().__init__()
+        self.add_module("c", torch.nn.Conv2d(a, b, ks, stride, pad, dilation, groups, bias=False))
+        bn = torch.nn.BatchNorm2d(b)
+        torch.nn.init.constant_(bn.weight, bn_weight_init)
+        torch.nn.init.constant_(bn.bias, 0)
+        self.add_module("bn", bn)
+
+
+class PatchEmbed(nn.Module):
+    """Embed images into patches and project them into a specified embedding dimension.
+
+    This module converts input images into patch embeddings using a sequence of convolutional layers, effectively
+    downsampling the spatial dimensions while increasing the channel dimension.
+
+    Attributes:
+        patches_resolution (tuple[int, int]): Resolution of the patches after embedding.
+        num_patches (int): Total number of patches.
+        in_chans (int): Number of input channels.
+        embed_dim (int): Dimension of the embedding.
+        seq (nn.Sequential): Sequence of convolutional and activation layers for patch embedding.
+
+    Examples:
+        >>> import torch
+        >>> patch_embed = PatchEmbed(in_chans=3, embed_dim=96, resolution=224, activation=nn.GELU)
+        >>> x = torch.randn(1, 3, 224, 224)
+        >>> output = patch_embed(x)
+        >>> print(output.shape)
+        torch.Size([1, 96, 56, 56])
+    """
+
+    def __init__(self, in_chans: int, embed_dim: int, resolution: int, activation):
+        """Initialize patch embedding with convolutional layers for image-to-patch conversion and projection.
+
+        Args:
+            in_chans (int): Number of input channels.
+            embed_dim (int): Dimension of the embedding.
+            resolution (int): Input image resolution.
+            activation (nn.Module): Activation function to use between convolutions.
+        """
+        super().__init__()
+        img_size: tuple[int, int] = to_2tuple(resolution)
+        self.patches_resolution = (img_size[0] // 4, img_size[1] // 4)
+        self.num_patches = self.patches_resolution[0] * self.patches_resolution[1]
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+        n = embed_dim
+        self.seq = nn.Sequential(
+            Conv2d_BN(in_chans, n // 2, 3, 2, 1),
+            activation(),
+            Conv2d_BN(n // 2, n, 3, 2, 1),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Process input tensor through patch embedding sequence, converting images to patch embeddings."""
+        return self.seq(x)
+
+
+class MBConv(nn.Module):
+    """Mobile Inverted Bottleneck Conv (MBConv) layer, part of the EfficientNet architecture.
+
+    This module implements the mobile inverted bottleneck convolution with expansion, depthwise convolution, and
+    projection phases, along with residual connections for improved gradient flow.
+
+    Attributes:
+        in_chans (int): Number of input channels.
+        hidden_chans (int): Number of hidden channels after expansion.
+        out_chans (int): Number of output channels.
+        conv1 (Conv2d_BN): First convolutional layer for channel expansion.
+        act1 (nn.Module): First activation function.
+        conv2 (Conv2d_BN): Depthwise convolutional layer.
+        act2 (nn.Module): Second activation function.
+        conv3 (Conv2d_BN): Final convolutional layer for projection.
+        act3 (nn.Module): Third activation function.
+        drop_path (nn.Module): Drop path layer (Identity for inference).
+
+    Examples:
+        >>> in_chans, out_chans = 32, 64
+        >>> mbconv = MBConv(in_chans, out_chans, expand_ratio=4, activation=nn.ReLU, drop_path=0.1)
+        >>> x = torch.randn(1, in_chans, 56, 56)
+        >>> output = mbconv(x)
+        >>> print(output.shape)
+        torch.Size([1, 64, 56, 56])
+    """
+
+    def __init__(self, in_chans: int, out_chans: int, expand_ratio: float, activation, drop_path: float):
+        """Initialize the MBConv layer with specified input/output channels, expansion ratio, and activation.
+
+        Args:
+            in_chans (int): Number of input channels.
+            out_chans (int): Number of output channels.
+            expand_ratio (float): Channel expansion ratio for the hidden layer.
+            activation (nn.Module): Activation function to use.
+            drop_path (float): Drop path rate for stochastic depth.
+        """
+        super().__init__()
+        self.in_chans = in_chans
+        self.hidden_chans = int(in_chans * expand_ratio)
+        self.out_chans = out_chans
+
+        self.conv1 = Conv2d_BN(in_chans, self.hidden_chans, ks=1)
+        self.act1 = activation()
+
+        self.conv2 = Conv2d_BN(self.hidden_chans, self.hidden_chans, ks=3, stride=1, pad=1, groups=self.hidden_chans)
+        self.act2 = activation()
+
+        self.conv3 = Conv2d_BN(self.hidden_chans, out_chans, ks=1, bn_weight_init=0.0)
+        self.act3 = activation()
+
+        # NOTE: `DropPath` is needed only for training.
+        # self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.drop_path = nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Implement the forward pass of MBConv, applying convolutions and skip connection."""
+        shortcut = x
+        x = self.conv1(x)
+        x = self.act1(x)
+        x = self.conv2(x)
+        x = self.act2(x)
+        x = self.conv3(x)
+        x = self.drop_path(x)
+        x += shortcut
+        return self.act3(x)
+
+
+class PatchMerging(nn.Module):
+    """Merge neighboring patches in the feature map and project to a new dimension.
+
+    This class implements a patch merging operation that combines spatial information and adjusts the feature dimension
+    using a series of convolutional layers with batch normalization. It effectively reduces spatial resolution while
+    potentially increasing channel dimensions.
+
+    Attributes:
+        input_resolution (tuple[int, int]): The input resolution (height, width) of the feature map.
+        dim (int): The input dimension of the feature map.
+        out_dim (int): The output dimension after merging and projection.
+        act (nn.Module): The activation function used between convolutions.
+        conv1 (Conv2d_BN): The first convolutional layer for dimension projection.
+        conv2 (Conv2d_BN): The second convolutional layer for spatial merging.
+        conv3 (Conv2d_BN): The third convolutional layer for final projection.
+
+    Examples:
+        >>> input_resolution = (56, 56)
+        >>> patch_merging = PatchMerging(input_resolution, dim=64, out_dim=128, activation=nn.ReLU)
+        >>> x = torch.randn(4, 64, 56, 56)
+        >>> output = patch_merging(x)
+        >>> print(output.shape)
+        torch.Size([4, 3136, 128])
+    """
+
+    def __init__(self, input_resolution: tuple[int, int], dim: int, out_dim: int, activation):
+        """Initialize the PatchMerging module for merging and projecting neighboring patches in feature maps.
+
+        Args:
+            input_resolution (tuple[int, int]): The input resolution (height, width) of the feature map.
+            dim (int): The input dimension of the feature map.
+            out_dim (int): The output dimension after merging and projection.
+            activation (nn.Module): The activation function used between convolutions.
+        """
+        super().__init__()
+
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.out_dim = out_dim
+        self.act = activation()
+        self.conv1 = Conv2d_BN(dim, out_dim, 1, 1, 0)
+        stride_c = 1 if out_dim in {320, 448, 576} else 2
+        self.conv2 = Conv2d_BN(out_dim, out_dim, 3, stride_c, 1, groups=out_dim)
+        self.conv3 = Conv2d_BN(out_dim, out_dim, 1, 1, 0)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply patch merging and dimension projection to the input feature map."""
+        if x.ndim == 3:
+            H, W = self.input_resolution
+            B = len(x)
+            # (B, C, H, W)
+            x = x.view(B, H, W, -1).permute(0, 3, 1, 2)
+
+        x = self.conv1(x)
+        x = self.act(x)
+
+        x = self.conv2(x)
+        x = self.act(x)
+        x = self.conv3(x)
+        return x.flatten(2).transpose(1, 2)
+
+
+class ConvLayer(nn.Module):
+    """Convolutional Layer featuring multiple MobileNetV3-style inverted bottleneck convolutions (MBConv).
+
+    This layer optionally applies downsample operations to the output and supports gradient checkpointing for memory
+    efficiency during training.
+
+    Attributes:
+        dim (int): Dimensionality of the input and output.
+        input_resolution (tuple[int, int]): Resolution of the input image.
+        depth (int): Number of MBConv layers in the block.
+        use_checkpoint (bool): Whether to use gradient checkpointing to save memory.
+        blocks (nn.ModuleList): List of MBConv layers.
+        downsample (Optional[nn.Module]): Function for downsampling the output.
+
+    Examples:
+        >>> input_tensor = torch.randn(1, 64, 56, 56)
+        >>> conv_layer = ConvLayer(64, (56, 56), depth=3, activation=nn.ReLU)
+        >>> output = conv_layer(input_tensor)
+        >>> print(output.shape)
+        torch.Size([1, 3136, 128])
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        input_resolution: tuple[int, int],
+        depth: int,
+        activation,
+        drop_path: float | list[float] = 0.0,
+        downsample: nn.Module | None = None,
+        use_checkpoint: bool = False,
+        out_dim: int | None = None,
+        conv_expand_ratio: float = 4.0,
+    ):
+        """Initialize the ConvLayer with the given dimensions and settings.
+
+        This layer consists of multiple MobileNetV3-style inverted bottleneck convolutions (MBConv) and optionally
+        applies downsampling to the output.
+
+        Args:
+            dim (int): The dimensionality of the input and output.
+            input_resolution (tuple[int, int]): The resolution of the input image.
+            depth (int): The number of MBConv layers in the block.
+            activation (nn.Module): Activation function applied after each convolution.
+            drop_path (float | list[float], optional): Drop path rate. Single float or a list of floats for each MBConv.
+            downsample (Optional[nn.Module], optional): Function for downsampling the output. None to skip downsampling.
+            use_checkpoint (bool, optional): Whether to use gradient checkpointing to save memory.
+            out_dim (Optional[int], optional): Output dimensions. None means it will be the same as `dim`.
+            conv_expand_ratio (float, optional): Expansion ratio for the MBConv layers.
+        """
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # Build blocks
+        self.blocks = nn.ModuleList(
+            [
+                MBConv(
+                    dim,
+                    dim,
+                    conv_expand_ratio,
+                    activation,
+                    drop_path[i] if isinstance(drop_path, list) else drop_path,
+                )
+                for i in range(depth)
+            ]
+        )
+
+        # Patch merging layer
+        self.downsample = (
+            None
+            if downsample is None
+            else downsample(input_resolution, dim=dim, out_dim=out_dim, activation=activation)
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Process input through convolutional layers, applying MBConv blocks and optional downsampling."""
+        for blk in self.blocks:
+            x = torch.utils.checkpoint(blk, x) if self.use_checkpoint else blk(x)  # warn: checkpoint is slow import
+        return x if self.downsample is None else self.downsample(x)
+
+
+class MLP(nn.Module):
+    """Multi-layer Perceptron (MLP) module for transformer architectures.
+
+    This module applies layer normalization, two fully-connected layers with an activation function in between, and
+    dropout. It is commonly used in transformer-based architectures for processing token embeddings.
+
+    Attributes:
+        norm (nn.LayerNorm): Layer normalization applied to the input.
+        fc1 (nn.Linear): First fully-connected layer.
+        fc2 (nn.Linear): Second fully-connected layer.
+        act (nn.Module): Activation function applied after the first fully-connected layer.
+        drop (nn.Dropout): Dropout layer applied after the activation function.
+
+    Examples:
+        >>> import torch
+        >>> from torch import nn
+        >>> mlp = MLP(in_features=256, hidden_features=512, out_features=256, activation=nn.GELU, drop=0.1)
+        >>> x = torch.randn(32, 100, 256)
+        >>> output = mlp(x)
+        >>> print(output.shape)
+        torch.Size([32, 100, 256])
+    """
+
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int | None = None,
+        out_features: int | None = None,
+        activation=nn.GELU,
+        drop: float = 0.0,
+    ):
+        """Initialize a multi-layer perceptron with configurable input, hidden, and output dimensions.
+
+        Args:
+            in_features (int): Number of input features.
+            hidden_features (Optional[int], optional): Number of hidden features.
+            out_features (Optional[int], optional): Number of output features.
+            activation (nn.Module): Activation function applied after the first fully-connected layer.
+            drop (float, optional): Dropout probability.
+        """
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.norm = nn.LayerNorm(in_features)
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.act = activation()
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply MLP operations: layer norm, FC layers, activation, and dropout to the input tensor."""
+        x = self.norm(x)
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        return self.drop(x)
+
+
+class Attention(torch.nn.Module):
+    """Multi-head attention module with spatial awareness and trainable attention biases.
+
+    This module implements a multi-head attention mechanism with support for spatial awareness, applying attention
+    biases based on spatial resolution. It includes trainable attention biases for each unique offset between spatial
+    positions in the resolution grid.
+
+    Attributes:
+        num_heads (int): Number of attention heads.
+        scale (float): Scaling factor for attention scores.
+        key_dim (int): Dimensionality of the keys and queries.
+        nh_kd (int): Product of num_heads and key_dim.
+        d (int): Dimensionality of the value vectors.
+        dh (int): Product of d and num_heads.
+        attn_ratio (float): Attention ratio affecting the dimensions of the value vectors.
+        norm (nn.LayerNorm): Layer normalization applied to input.
+        qkv (nn.Linear): Linear layer for computing query, key, and value projections.
+        proj (nn.Linear): Linear layer for final projection.
+        attention_biases (nn.Parameter): Learnable attention biases.
+        attention_bias_idxs (torch.Tensor): Indices for attention biases.
+        ab (torch.Tensor): Cached attention biases for inference, deleted during training.
+
+    Examples:
+        >>> attn = Attention(dim=256, key_dim=64, num_heads=8, resolution=(14, 14))
+        >>> x = torch.randn(1, 196, 256)
+        >>> output = attn(x)
+        >>> print(output.shape)
+        torch.Size([1, 196, 256])
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        key_dim: int,
+        num_heads: int = 8,
+        attn_ratio: float = 4,
+        resolution: tuple[int, int] = (14, 14),
+    ):
+        """Initialize the Attention module for multi-head attention with spatial awareness.
+
+        This module implements a multi-head attention mechanism with support for spatial awareness, applying attention
+        biases based on spatial resolution. It includes trainable attention biases for each unique offset between
+        spatial positions in the resolution grid.
+
+        Args:
+            dim (int): The dimensionality of the input and output.
+            key_dim (int): The dimensionality of the keys and queries.
+            num_heads (int, optional): Number of attention heads.
+            attn_ratio (float, optional): Attention ratio, affecting the dimensions of the value vectors.
+            resolution (tuple[int, int], optional): Spatial resolution of the input feature map.
+        """
+        super().__init__()
+
+        assert isinstance(resolution, tuple) and len(resolution) == 2, "'resolution' argument not tuple of length 2"
+        self.num_heads = num_heads
+        self.scale = key_dim**-0.5
+        self.key_dim = key_dim
+        self.nh_kd = nh_kd = key_dim * num_heads
+        self.d = int(attn_ratio * key_dim)
+        self.dh = int(attn_ratio * key_dim) * num_heads
+        self.attn_ratio = attn_ratio
+        h = self.dh + nh_kd * 2
+
+        self.norm = nn.LayerNorm(dim)
+        self.qkv = nn.Linear(dim, h)
+        self.proj = nn.Linear(self.dh, dim)
+
+        points = list(itertools.product(range(resolution[0]), range(resolution[1])))
+        N = len(points)
+        attention_offsets = {}
+        idxs = []
+        for p1 in points:
+            for p2 in points:
+                offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
+                if offset not in attention_offsets:
+                    attention_offsets[offset] = len(attention_offsets)
+                idxs.append(attention_offsets[offset])
+        self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))
+        self.register_buffer("attention_bias_idxs", torch.LongTensor(idxs).view(N, N), persistent=False)
+
+    @torch.no_grad()
+    def train(self, mode: bool = True):
+        """Set the module in training mode and handle the 'ab' attribute for cached attention biases."""
+        super().train(mode)
+        if mode and hasattr(self, "ab"):
+            del self.ab
+        else:
+            self.ab = self.attention_biases[:, self.attention_bias_idxs]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply multi-head attention with spatial awareness and trainable attention biases."""
+        B, N, _ = x.shape  # B, N, C
+
+        # Normalization
+        x = self.norm(x)
+
+        qkv = self.qkv(x)
+        # (B, N, num_heads, d)
+        q, k, v = qkv.view(B, N, self.num_heads, -1).split([self.key_dim, self.key_dim, self.d], dim=3)
+        # (B, num_heads, N, d)
+        q = q.permute(0, 2, 1, 3)
+        k = k.permute(0, 2, 1, 3)
+        v = v.permute(0, 2, 1, 3)
+        self.ab = self.ab.to(self.attention_biases.device)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale + (
+            self.attention_biases[:, self.attention_bias_idxs] if self.training else self.ab
+        )
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)
+        return self.proj(x)
+
+
+class TinyViTBlock(nn.Module):
+    """TinyViT Block that applies self-attention and a local convolution to the input.
+
+    This block is a key component of the TinyViT architecture, combining self-attention mechanisms with local
+    convolutions to process input features efficiently. It supports windowed attention for computational efficiency and
+    includes residual connections.
+
+    Attributes:
+        dim (int): The dimensionality of the input and output.
+        input_resolution (tuple[int, int]): Spatial resolution of the input feature map.
+        num_heads (int): Number of attention heads.
+        window_size (int): Size of the attention window.
+        mlp_ratio (float): Ratio of MLP hidden dimension to embedding dimension.
+        drop_path (nn.Module): Stochastic depth layer, identity function during inference.
+        attn (Attention): Self-attention module.
+        mlp (MLP): Multi-layer perceptron module.
+        local_conv (Conv2d_BN): Depth-wise local convolution layer.
+
+    Examples:
+        >>> input_tensor = torch.randn(1, 196, 192)
+        >>> block = TinyViTBlock(dim=192, input_resolution=(14, 14), num_heads=3)
+        >>> output = block(input_tensor)
+        >>> print(output.shape)
+        torch.Size([1, 196, 192])
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        input_resolution: tuple[int, int],
+        num_heads: int,
+        window_size: int = 7,
+        mlp_ratio: float = 4.0,
+        drop: float = 0.0,
+        drop_path: float = 0.0,
+        local_conv_size: int = 3,
+        activation=nn.GELU,
+    ):
+        """Initialize a TinyViT block with self-attention and local convolution.
+
+        This block is a key component of the TinyViT architecture, combining self-attention mechanisms with local
+        convolutions to process input features efficiently.
+
+        Args:
+            dim (int): Dimensionality of the input and output features.
+            input_resolution (tuple[int, int]): Spatial resolution of the input feature map (height, width).
+            num_heads (int): Number of attention heads.
+            window_size (int, optional): Size of the attention window. Must be greater than 0.
+            mlp_ratio (float, optional): Ratio of MLP hidden dimension to embedding dimension.
+            drop (float, optional): Dropout rate.
+            drop_path (float, optional): Stochastic depth rate.
+            local_conv_size (int, optional): Kernel size of the local convolution.
+            activation (nn.Module): Activation function for MLP.
+        """
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        assert window_size > 0, "window_size must be greater than 0"
+        self.window_size = window_size
+        self.mlp_ratio = mlp_ratio
+
+        # NOTE: `DropPath` is needed only for training.
+        # self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.drop_path = nn.Identity()
+
+        assert dim % num_heads == 0, "dim must be divisible by num_heads"
+        head_dim = dim // num_heads
+
+        window_resolution = (window_size, window_size)
+        self.attn = Attention(dim, head_dim, num_heads, attn_ratio=1, resolution=window_resolution)
+
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        mlp_activation = activation
+        self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, activation=mlp_activation, drop=drop)
+
+        pad = local_conv_size // 2
+        self.local_conv = Conv2d_BN(dim, dim, ks=local_conv_size, stride=1, pad=pad, groups=dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply self-attention, local convolution, and MLP operations to the input tensor."""
+        h, w = self.input_resolution
+        b, hw, c = x.shape  # batch, height*width, channels
+        assert hw == h * w, "input feature has wrong size"
+        res_x = x
+        if h == self.window_size and w == self.window_size:
+            x = self.attn(x)
+        else:
+            x = x.view(b, h, w, c)
+            pad_b = (self.window_size - h % self.window_size) % self.window_size
+            pad_r = (self.window_size - w % self.window_size) % self.window_size
+            padding = pad_b > 0 or pad_r > 0
+            if padding:
+                x = F.pad(x, (0, 0, 0, pad_r, 0, pad_b))
+
+            pH, pW = h + pad_b, w + pad_r
+            nH = pH // self.window_size
+            nW = pW // self.window_size
+
+            # Window partition
+            x = (
+                x.view(b, nH, self.window_size, nW, self.window_size, c)
+                .transpose(2, 3)
+                .reshape(b * nH * nW, self.window_size * self.window_size, c)
+            )
+            x = self.attn(x)
+
+            # Window reverse
+            x = x.view(b, nH, nW, self.window_size, self.window_size, c).transpose(2, 3).reshape(b, pH, pW, c)
+            if padding:
+                x = x[:, :h, :w].contiguous()
+
+            x = x.view(b, hw, c)
+
+        x = res_x + self.drop_path(x)
+        x = x.transpose(1, 2).reshape(b, c, h, w)
+        x = self.local_conv(x)
+        x = x.view(b, c, hw).transpose(1, 2)
+
+        return x + self.drop_path(self.mlp(x))
+
+    def extra_repr(self) -> str:
+        """Return a string representation of the TinyViTBlock's parameters.
+
+        This method provides a formatted string containing key information about the TinyViTBlock, including its
+        dimension, input resolution, number of attention heads, window size, and MLP ratio.
+
+        Returns:
+            (str): A formatted string containing the block's parameters.
+
+        Examples:
+            >>> block = TinyViTBlock(dim=192, input_resolution=(14, 14), num_heads=3, window_size=7, mlp_ratio=4.0)
+            >>> print(block.extra_repr())
+            dim=192, input_resolution=(14, 14), num_heads=3, window_size=7, mlp_ratio=4.0
+        """
+        return (
+            f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
+            f"window_size={self.window_size}, mlp_ratio={self.mlp_ratio}"
+        )
+
+
+class BasicLayer(nn.Module):
+    """A basic TinyViT layer for one stage in a TinyViT architecture.
+
+    This class represents a single layer in the TinyViT model, consisting of multiple TinyViT blocks and an optional
+    downsampling operation. It processes features at a specific resolution and dimensionality within the overall
+    architecture.
+
+    Attributes:
+        dim (int): The dimensionality of the input and output features.
+        input_resolution (tuple[int, int]): Spatial resolution of the input feature map.
+        depth (int): Number of TinyViT blocks in this layer.
+        use_checkpoint (bool): Whether to use gradient checkpointing to save memory.
+        blocks (nn.ModuleList): List of TinyViT blocks that make up this layer.
+        downsample (nn.Module | None): Downsample layer at the end of the layer, if specified.
+
+    Examples:
+        >>> input_tensor = torch.randn(1, 3136, 192)
+        >>> layer = BasicLayer(dim=192, input_resolution=(56, 56), depth=2, num_heads=3, window_size=7)
+        >>> output = layer(input_tensor)
+        >>> print(output.shape)
+        torch.Size([1, 784, 384])
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        input_resolution: tuple[int, int],
+        depth: int,
+        num_heads: int,
+        window_size: int,
+        mlp_ratio: float = 4.0,
+        drop: float = 0.0,
+        drop_path: float | list[float] = 0.0,
+        downsample: nn.Module | None = None,
+        use_checkpoint: bool = False,
+        local_conv_size: int = 3,
+        activation=nn.GELU,
+        out_dim: int | None = None,
+    ):
+        """Initialize a BasicLayer in the TinyViT architecture.
+
+        This layer consists of multiple TinyViT blocks and an optional downsampling operation. It is designed to process
+        feature maps at a specific resolution and dimensionality within the TinyViT model.
+
+        Args:
+            dim (int): Dimensionality of the input and output features.
+            input_resolution (tuple[int, int]): Spatial resolution of the input feature map (height, width).
+            depth (int): Number of TinyViT blocks in this layer.
+            num_heads (int): Number of attention heads in each TinyViT block.
+            window_size (int): Size of the local window for attention computation.
+            mlp_ratio (float, optional): Ratio of MLP hidden dimension to embedding dimension.
+            drop (float, optional): Dropout rate.
+            drop_path (float | list[float], optional): Stochastic depth rate. Can be a float or a list of floats for
+                each block.
+            downsample (nn.Module | None, optional): Downsampling layer at the end of the layer. None to skip
+                downsampling.
+            use_checkpoint (bool, optional): Whether to use gradient checkpointing to save memory.
+            local_conv_size (int, optional): Kernel size for the local convolution in each TinyViT block.
+            activation (nn.Module): Activation function used in the MLP.
+            out_dim (int | None, optional): Output dimension after downsampling. None means it will be the same as dim.
+        """
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # Build blocks
+        self.blocks = nn.ModuleList(
+            [
+                TinyViTBlock(
+                    dim=dim,
+                    input_resolution=input_resolution,
+                    num_heads=num_heads,
+                    window_size=window_size,
+                    mlp_ratio=mlp_ratio,
+                    drop=drop,
+                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                    local_conv_size=local_conv_size,
+                    activation=activation,
+                )
+                for i in range(depth)
+            ]
+        )
+
+        # Patch merging layer
+        self.downsample = (
+            None
+            if downsample is None
+            else downsample(input_resolution, dim=dim, out_dim=out_dim, activation=activation)
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Process input through TinyViT blocks and optional downsampling."""
+        for blk in self.blocks:
+            x = torch.utils.checkpoint(blk, x) if self.use_checkpoint else blk(x)  # warn: checkpoint is slow import
+        return x if self.downsample is None else self.downsample(x)
+
+    def extra_repr(self) -> str:
+        """Return a string with the layer's parameters for printing."""
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+
+class TinyViT(nn.Module):
+    """TinyViT: A compact vision transformer architecture for efficient image classification and feature extraction.
+
+    This class implements the TinyViT model, which combines elements of vision transformers and convolutional neural
+    networks for improved efficiency and performance on vision tasks. It features hierarchical processing with patch
+    embedding, multiple stages of attention and convolution blocks, and a feature refinement neck.
+
+    Attributes:
+        img_size (int): Input image size.
+        num_classes (int): Number of classification classes.
+        depths (tuple[int, int, int, int]): Number of blocks in each stage.
+        num_layers (int): Total number of layers in the network.
+        mlp_ratio (float): Ratio of MLP hidden dimension to embedding dimension.
+        patch_embed (PatchEmbed): Module for patch embedding.
+        patches_resolution (tuple[int, int]): Resolution of embedded patches.
+        layers (nn.ModuleList): List of network layers.
+        norm_head (nn.LayerNorm): Layer normalization for the classifier head.
+        head (nn.Linear): Linear layer for final classification.
+        neck (nn.Sequential): Neck module for feature refinement.
+
+    Examples:
+        >>> model = TinyViT(img_size=224, num_classes=1000)
+        >>> x = torch.randn(1, 3, 224, 224)
+        >>> features = model.forward_features(x)
+        >>> print(features.shape)
+        torch.Size([1, 256, 56, 56])
+    """
+
+    def __init__(
+        self,
+        img_size: int = 224,
+        in_chans: int = 3,
+        num_classes: int = 1000,
+        embed_dims: tuple[int, int, int, int] = (96, 192, 384, 768),
+        depths: tuple[int, int, int, int] = (2, 2, 6, 2),
+        num_heads: tuple[int, int, int, int] = (3, 6, 12, 24),
+        window_sizes: tuple[int, int, int, int] = (7, 7, 14, 7),
+        mlp_ratio: float = 4.0,
+        drop_rate: float = 0.0,
+        drop_path_rate: float = 0.1,
+        use_checkpoint: bool = False,
+        mbconv_expand_ratio: float = 4.0,
+        local_conv_size: int = 3,
+        layer_lr_decay: float = 1.0,
+    ):
+        """Initialize the TinyViT model.
+
+        This constructor sets up the TinyViT architecture, including patch embedding, multiple layers of attention and
+        convolution blocks, and a classification head.
+
+        Args:
+            img_size (int, optional): Size of the input image.
+            in_chans (int, optional): Number of input channels.
+            num_classes (int, optional): Number of classes for classification.
+            embed_dims (tuple[int, int, int, int], optional): Embedding dimensions for each stage.
+            depths (tuple[int, int, int, int], optional): Number of blocks in each stage.
+            num_heads (tuple[int, int, int, int], optional): Number of attention heads in each stage.
+            window_sizes (tuple[int, int, int, int], optional): Window sizes for each stage.
+            mlp_ratio (float, optional): Ratio of MLP hidden dim to embedding dim.
+            drop_rate (float, optional): Dropout rate.
+            drop_path_rate (float, optional): Stochastic depth rate.
+            use_checkpoint (bool, optional): Whether to use checkpointing to save memory.
+            mbconv_expand_ratio (float, optional): Expansion ratio for MBConv layer.
+            local_conv_size (int, optional): Kernel size for local convolutions.
+            layer_lr_decay (float, optional): Layer-wise learning rate decay factor.
+        """
+        super().__init__()
+        self.img_size = img_size
+        self.num_classes = num_classes
+        self.depths = depths
+        self.num_layers = len(depths)
+        self.mlp_ratio = mlp_ratio
+
+        activation = nn.GELU
+
+        self.patch_embed = PatchEmbed(
+            in_chans=in_chans, embed_dim=embed_dims[0], resolution=img_size, activation=activation
+        )
+
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # Stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # Build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            kwargs = dict(
+                dim=embed_dims[i_layer],
+                input_resolution=(
+                    patches_resolution[0] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)),
+                    patches_resolution[1] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)),
+                ),
+                #   input_resolution=(patches_resolution[0] // (2 ** i_layer),
+                #                     patches_resolution[1] // (2 ** i_layer)),
+                depth=depths[i_layer],
+                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
+                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                use_checkpoint=use_checkpoint,
+                out_dim=embed_dims[min(i_layer + 1, len(embed_dims) - 1)],
+                activation=activation,
+            )
+            if i_layer == 0:
+                layer = ConvLayer(conv_expand_ratio=mbconv_expand_ratio, **kwargs)
+            else:
+                layer = BasicLayer(
+                    num_heads=num_heads[i_layer],
+                    window_size=window_sizes[i_layer],
+                    mlp_ratio=self.mlp_ratio,
+                    drop=drop_rate,
+                    local_conv_size=local_conv_size,
+                    **kwargs,
+                )
+            self.layers.append(layer)
+
+        # Classifier head
+        self.norm_head = nn.LayerNorm(embed_dims[-1])
+        self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else torch.nn.Identity()
+
+        # Init weights
+        self.apply(self._init_weights)
+        self.set_layer_lr_decay(layer_lr_decay)
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dims[-1],
+                256,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(256),
+            nn.Conv2d(
+                256,
+                256,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(256),
+        )
+
+    def set_layer_lr_decay(self, layer_lr_decay: float):
+        """Set layer-wise learning rate decay for the TinyViT model based on depth."""
+        decay_rate = layer_lr_decay
+
+        # Layers -> blocks (depth)
+        depth = sum(self.depths)
+        lr_scales = [decay_rate ** (depth - i - 1) for i in range(depth)]
+
+        def _set_lr_scale(m, scale):
+            """Set the learning rate scale for each layer in the model based on the layer's depth."""
+            for p in m.parameters():
+                p.lr_scale = scale
+
+        self.patch_embed.apply(lambda x: _set_lr_scale(x, lr_scales[0]))
+        i = 0
+        for layer in self.layers:
+            for block in layer.blocks:
+                block.apply(lambda x: _set_lr_scale(x, lr_scales[i]))
+                i += 1
+            if layer.downsample is not None:
+                layer.downsample.apply(lambda x: _set_lr_scale(x, lr_scales[i - 1]))
+        assert i == depth
+        for m in {self.norm_head, self.head}:
+            m.apply(lambda x: _set_lr_scale(x, lr_scales[-1]))
+
+        for k, p in self.named_parameters():
+            p.param_name = k
+
+        def _check_lr_scale(m):
+            """Check if the learning rate scale attribute is present in module's parameters."""
+            for p in m.parameters():
+                assert hasattr(p, "lr_scale"), p.param_name
+
+        self.apply(_check_lr_scale)
+
+    @staticmethod
+    def _init_weights(m):
+        """Initialize weights for linear and normalization layers in the TinyViT model."""
+        if isinstance(m, nn.Linear):
+            # NOTE: This initialization is needed only for training.
+            # trunc_normal_(m.weight, std=.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        """Return a set of keywords for parameters that should not use weight decay."""
+        return {"attention_biases"}
+
+    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
+        """Process input through feature extraction layers, returning spatial features."""
+        x = self.patch_embed(x)  # x input is (N, C, H, W)
+
+        x = self.layers[0](x)
+        start_i = 1
+
+        for i in range(start_i, len(self.layers)):
+            layer = self.layers[i]
+            x = layer(x)
+        batch, _, channel = x.shape
+        x = x.view(batch, self.patches_resolution[0] // 4, self.patches_resolution[1] // 4, channel)
+        x = x.permute(0, 3, 1, 2)
+        return self.neck(x)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Perform the forward pass through the TinyViT model, extracting features from the input image."""
+        return self.forward_features(x)
+
+    def set_imgsz(self, imgsz: list[int] = [1024, 1024]):
+        """Set image size to make model compatible with different image sizes."""
+        imgsz = [s // 4 for s in imgsz]
+        self.patches_resolution = imgsz
+        for i, layer in enumerate(self.layers):
+            input_resolution = (
+                imgsz[0] // (2 ** (i - 1 if i == 3 else i)),
+                imgsz[1] // (2 ** (i - 1 if i == 3 else i)),
+            )
+            layer.input_resolution = input_resolution
+            if layer.downsample is not None:
+                layer.downsample.input_resolution = input_resolution
+            if isinstance(layer, BasicLayer):
+                for b in layer.blocks:
+                    b.input_resolution = input_resolution

algorithms/dms_yolo/code/ultralytics/models/sam/modules/transformer.py

@@ -0,0 +1,344 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import math
+
+import torch
+from torch import Tensor, nn
+
+from ultralytics.nn.modules import MLPBlock
+
+
+class TwoWayTransformer(nn.Module):
+    """A Two-Way Transformer module for simultaneous attention to image and query points.
+
+    This class implements a specialized transformer decoder that attends to an input image using queries with supplied
+    positional embeddings. It's useful for tasks like object detection, image segmentation, and point cloud processing.
+
+    Attributes:
+        depth (int): Number of layers in the transformer.
+        embedding_dim (int): Channel dimension for input embeddings.
+        num_heads (int): Number of heads for multihead attention.
+        mlp_dim (int): Internal channel dimension for the MLP block.
+        layers (nn.ModuleList): List of TwoWayAttentionBlock layers composing the transformer.
+        final_attn_token_to_image (Attention): Final attention layer from queries to image.
+        norm_final_attn (nn.LayerNorm): Layer normalization applied to final queries.
+
+    Methods:
+        forward: Process image and point embeddings through the transformer.
+
+    Examples:
+        >>> transformer = TwoWayTransformer(depth=6, embedding_dim=256, num_heads=8, mlp_dim=2048)
+        >>> image_embedding = torch.randn(1, 256, 32, 32)
+        >>> image_pe = torch.randn(1, 256, 32, 32)
+        >>> point_embedding = torch.randn(1, 100, 256)
+        >>> output_queries, output_image = transformer(image_embedding, image_pe, point_embedding)
+        >>> print(output_queries.shape, output_image.shape)
+    """
+
+    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:
+        """Initialize a Two-Way Transformer for simultaneous attention to image and query points.
+
+        Args:
+            depth (int): Number of layers in the transformer.
+            embedding_dim (int): Channel dimension for input embeddings.
+            num_heads (int): Number of heads for multihead attention. Must divide embedding_dim.
+            mlp_dim (int): Internal channel dimension for the MLP block.
+            activation (Type[nn.Module], optional): Activation function to use in the MLP block.
+            attention_downsample_rate (int, optional): Downsampling rate for attention mechanism.
+        """
+        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: torch.Tensor,
+        image_pe: torch.Tensor,
+        point_embedding: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Process image and point embeddings through the Two-Way Transformer.
+
+        Args:
+            image_embedding (torch.Tensor): Image to attend to, with shape (B, embedding_dim, H, W).
+            image_pe (torch.Tensor): Positional encoding to add to the image, with same shape as image_embedding.
+            point_embedding (torch.Tensor): Embedding to add to query points, with shape (B, N_points, embedding_dim).
+
+        Returns:
+            queries (torch.Tensor): Processed point embeddings with shape (B, N_points, embedding_dim).
+            keys (torch.Tensor): Processed image embeddings with shape (B, H*W, embedding_dim).
+        """
+        # BxCxHxW -> BxHWxC == B x N_image_tokens x C
+        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):
+    """A two-way attention block for simultaneous attention to image and query points.
+
+    This class implements a specialized transformer block with four main layers: self-attention on sparse inputs,
+    cross-attention of sparse inputs to dense inputs, MLP block on sparse inputs, and cross-attention of dense inputs to
+    sparse inputs.
+
+    Attributes:
+        self_attn (Attention): Self-attention layer for queries.
+        norm1 (nn.LayerNorm): Layer normalization after self-attention.
+        cross_attn_token_to_image (Attention): Cross-attention layer from queries to keys.
+        norm2 (nn.LayerNorm): Layer normalization after token-to-image attention.
+        mlp (MLPBlock): MLP block for transforming query embeddings.
+        norm3 (nn.LayerNorm): Layer normalization after MLP block.
+        norm4 (nn.LayerNorm): Layer normalization after image-to-token attention.
+        cross_attn_image_to_token (Attention): Cross-attention layer from keys to queries.
+        skip_first_layer_pe (bool): Whether to skip positional encoding in the first layer.
+
+    Methods:
+        forward: Apply self-attention and cross-attention to queries and keys.
+
+    Examples:
+        >>> embedding_dim, num_heads = 256, 8
+        >>> block = TwoWayAttentionBlock(embedding_dim, num_heads)
+        >>> queries = torch.randn(1, 100, embedding_dim)
+        >>> keys = torch.randn(1, 1000, embedding_dim)
+        >>> query_pe = torch.randn(1, 100, embedding_dim)
+        >>> key_pe = torch.randn(1, 1000, embedding_dim)
+        >>> processed_queries, processed_keys = block(queries, keys, query_pe, key_pe)
+    """
+
+    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:
+        """Initialize a TwoWayAttentionBlock for simultaneous attention to image and query points.
+
+        This block implements a specialized transformer layer with four main components: self-attention on sparse
+        inputs, cross-attention of sparse inputs to dense inputs, MLP block on sparse inputs, and cross-attention of
+        dense inputs to sparse inputs.
+
+        Args:
+            embedding_dim (int): Channel dimension of the embeddings.
+            num_heads (int): Number of attention heads in the attention layers.
+            mlp_dim (int, optional): Hidden dimension of the MLP block.
+            activation (Type[nn.Module], optional): Activation function for the MLP block.
+            attention_downsample_rate (int, optional): Downsampling rate for the attention mechanism.
+            skip_first_layer_pe (bool, optional): Whether to skip positional encoding in 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 = MLPBlock(embedding_dim, mlp_dim, 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: torch.Tensor, keys: torch.Tensor, query_pe: torch.Tensor, key_pe: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Apply two-way attention to process query and key embeddings in a transformer block.
+
+        Args:
+            queries (torch.Tensor): Query embeddings with shape (B, N_queries, embedding_dim).
+            keys (torch.Tensor): Key embeddings with shape (B, N_keys, embedding_dim).
+            query_pe (torch.Tensor): Positional encodings for queries with same shape as queries.
+            key_pe (torch.Tensor): Positional encodings for keys with same shape as keys.
+
+        Returns:
+            queries (torch.Tensor): Processed query embeddings with shape (B, N_queries, embedding_dim).
+            keys (torch.Tensor): Processed key embeddings with shape (B, N_keys, embedding_dim).
+        """
+        # 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 with downscaling capability for embedding size after projection.
+
+    This class implements a multi-head attention mechanism with the option to downsample the internal dimension of
+    queries, keys, and values.
+
+    Attributes:
+        embedding_dim (int): Dimensionality of input embeddings.
+        kv_in_dim (int): Dimensionality of key and value inputs.
+        internal_dim (int): Internal dimension after downsampling.
+        num_heads (int): Number of attention heads.
+        q_proj (nn.Linear): Linear projection for queries.
+        k_proj (nn.Linear): Linear projection for keys.
+        v_proj (nn.Linear): Linear projection for values.
+        out_proj (nn.Linear): Linear projection for output.
+
+    Methods:
+        _separate_heads: Separate input tensor into attention heads.
+        _recombine_heads: Recombine separated attention heads.
+        forward: Compute attention output for given query, key, and value tensors.
+
+    Examples:
+        >>> attn = Attention(embedding_dim=256, num_heads=8, downsample_rate=2)
+        >>> q = torch.randn(1, 100, 256)
+        >>> k = v = torch.randn(1, 50, 256)
+        >>> output = attn(q, k, v)
+        >>> print(output.shape)
+        torch.Size([1, 100, 256])
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        downsample_rate: int = 1,
+        kv_in_dim: int | None = None,
+    ) -> None:
+        """Initialize the Attention module with specified dimensions and settings.
+
+        Args:
+            embedding_dim (int): Dimensionality of input embeddings.
+            num_heads (int): Number of attention heads.
+            downsample_rate (int, optional): Factor by which internal dimensions are downsampled.
+            kv_in_dim (int | None, optional): Dimensionality of key and value inputs. If None, uses embedding_dim.
+
+        Raises:
+            AssertionError: If num_heads does not evenly divide the internal dim (embedding_dim / downsample_rate).
+        """
+        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)
+
+    @staticmethod
+    def _separate_heads(x: torch.Tensor, num_heads: int) -> torch.Tensor:
+        """Separate the input tensor into the specified number of attention heads."""
+        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
+
+    @staticmethod
+    def _recombine_heads(x: Tensor) -> Tensor:
+        """Recombine separated attention heads into a single 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: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
+        """Apply multi-head attention to query, key, and value tensors with optional downsampling.
+
+        Args:
+            q (torch.Tensor): Query tensor with shape (B, N_q, embedding_dim).
+            k (torch.Tensor): Key tensor with shape (B, N_k, embedding_dim).
+            v (torch.Tensor): Value tensor with shape (B, N_k, embedding_dim).
+
+        Returns:
+            (torch.Tensor): Output tensor after attention with shape (B, N_q, embedding_dim).
+        """
+        # 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)
+
+        # Attention
+        _, _, _, c_per_head = q.shape
+        attn = q @ k.permute(0, 1, 3, 2)  # B x N_heads x N_tokens x N_tokens
+        attn = attn / math.sqrt(c_per_head)
+        attn = torch.softmax(attn, dim=-1)
+
+        # Get output
+        out = attn @ v
+        out = self._recombine_heads(out)
+        return self.out_proj(out)

algorithms/dms_yolo/code/ultralytics/models/sam/modules/utils.py

@@ -0,0 +1,512 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+import torch
+import torch.nn.functional as F
+
+
+def select_closest_cond_frames(frame_idx: int, cond_frame_outputs: dict[int, Any], max_cond_frame_num: int):
+    """Select the closest conditioning frames to a given frame index.
+
+    Args:
+        frame_idx (int): Current frame index.
+        cond_frame_outputs (dict[int, Any]): Dictionary of conditioning frame outputs keyed by frame indices.
+        max_cond_frame_num (int): Maximum number of conditioning frames to select.
+
+    Returns:
+        selected_outputs (dict[int, Any]): Selected items from cond_frame_outputs.
+        unselected_outputs (dict[int, Any]): Items not selected from cond_frame_outputs.
+
+    Examples:
+        >>> frame_idx = 5
+        >>> cond_frame_outputs = {1: "a", 3: "b", 7: "c", 9: "d"}
+        >>> max_cond_frame_num = 2
+        >>> selected, unselected = select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num)
+        >>> print(selected)
+        {3: 'b', 7: 'c'}
+        >>> print(unselected)
+        {1: 'a', 9: 'd'}
+    """
+    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: torch.Tensor, dim: int, temperature: float = 10000):
+    """Generate 1D sinusoidal positional embeddings for given positions and dimensions.
+
+    Args:
+        pos_inds (torch.Tensor): Position indices for which to generate embeddings.
+        dim (int): Dimension of the positional embeddings. Should be an even number.
+        temperature (float, optional): Scaling factor for the frequency of the sinusoidal functions.
+
+    Returns:
+        (torch.Tensor): Sinusoidal positional embeddings with shape (pos_inds.shape, dim).
+
+    Examples:
+        >>> pos = torch.tensor([0, 1, 2, 3])
+        >>> embeddings = get_1d_sine_pe(pos, 128)
+        >>> embeddings.shape
+        torch.Size([4, 128])
+    """
+    pe_dim = dim // 2
+    dim_t = torch.arange(pe_dim, dtype=pos_inds.dtype, 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 init_t_xy(end_x: int, end_y: int, scale: float = 1.0, offset: int = 0):
+    """Initialize 1D and 2D coordinate tensors for a grid of specified dimensions.
+
+    This function creates coordinate tensors for a grid with dimensions end_x × end_y. It generates a linear index
+    tensor and corresponding x and y coordinate tensors.
+
+    Args:
+        end_x (int): Width of the grid (number of columns).
+        end_y (int): Height of the grid (number of rows).
+        scale (float): Scaling factor to apply to the coordinates.
+        offset (int): Offset to add to the coordinates.
+
+    Returns:
+        t_x (torch.Tensor): X-coordinates for each position, with shape (end_x * end_y).
+        t_y (torch.Tensor): Y-coordinates for each position, with shape (end_x * end_y).
+
+    Examples:
+        >>> t_x, t_y = init_t_xy(3, 2)
+        >>> print(t_x)
+        tensor([0., 1., 2., 0., 1., 2.])
+        >>> print(t_y)
+        tensor([0., 0., 0., 1., 1., 1.])
+    """
+    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 * scale + offset, t_y * scale + offset
+
+
+def compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float = 10000.0, scale_pos: float = 1.0):
+    """Compute axial complex exponential positional encodings for 2D spatial positions in a grid.
+
+    This function generates complex exponential positional encodings for a 2D grid of spatial positions, using separate
+    frequency components for the x and y dimensions.
+
+    Args:
+        dim (int): Dimension of the positional encoding.
+        end_x (int): Width of the 2D grid.
+        end_y (int): Height of the 2D grid.
+        theta (float, optional): Scaling factor for frequency computation.
+        scale_pos (float, optional): Scaling factor for position coordinates.
+
+    Returns:
+        (torch.Tensor): Complex exponential positional encodings with shape (end_x*end_y, dim//2).
+
+    Examples:
+        >>> dim, end_x, end_y = 128, 8, 8
+        >>> freqs_cis = compute_axial_cis(dim, end_x, end_y)
+        >>> freqs_cis.shape
+        torch.Size([64, 64])
+    """
+    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, scale=scale_pos)
+    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):
+    """Reshape frequency tensor for broadcasting with input tensor.
+
+    Reshapes a frequency tensor to ensure dimensional compatibility for broadcasting with an input tensor. This function
+    is typically used in positional encoding operations.
+
+    Args:
+        freqs_cis (torch.Tensor): Frequency tensor with shape matching the last two dimensions of x.
+        x (torch.Tensor): Input tensor to broadcast with.
+
+    Returns:
+        (torch.Tensor): Reshaped frequency tensor ready for broadcasting with the input tensor.
+
+    Raises:
+        AssertionError: If the shape of freqs_cis doesn't match the last two dimensions of x.
+    """
+    ndim = x.ndim
+    assert ndim >= 2
+    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,
+):
+    """Apply rotary positional encoding to query and key tensors.
+
+    This function applies rotary positional encoding (RoPE) to query and key tensors using complex-valued frequency
+    components. RoPE is a technique that injects relative position information into self-attention mechanisms.
+
+    Args:
+        xq (torch.Tensor): Query tensor to encode with positional information.
+        xk (torch.Tensor): Key tensor to encode with positional information.
+        freqs_cis (torch.Tensor): Complex-valued frequency components for rotary encoding with shape matching the last
+            two dimensions of xq.
+        repeat_freqs_k (bool, optional): Whether to repeat frequency components along sequence length dimension to match
+            key sequence length.
+
+    Returns:
+        xq_out (torch.Tensor): Query tensor with rotary positional encoding applied.
+        xk_out (torch.Tensor): Key tensor with rotary positional encoding applied, or original xk if xk is empty.
+
+    Examples:
+        >>> import torch
+        >>> xq = torch.randn(2, 8, 16, 64)  # [batch, heads, seq_len, dim]
+        >>> xk = torch.randn(2, 8, 16, 64)
+        >>> freqs_cis = compute_axial_cis(64, 4, 4)  # For a 4x4 spatial grid with dim=64
+        >>> q_encoded, k_encoded = apply_rotary_enc(xq, xk, freqs_cis)
+    """
+    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 and (r := xk_.shape[-2] // xq_.shape[-2]) > 1:
+        # MPS doesn't support repeat on complex tensors, decompose to real representation
+        if freqs_cis.device.type == "mps":
+            freqs_cis = torch.view_as_real(freqs_cis)
+            freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 3)), r, 1, 1)
+            freqs_cis = torch.view_as_complex(freqs_cis.contiguous())
+        else:
+            freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1)
+    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)
+
+
+def window_partition(x: torch.Tensor, window_size: int):
+    """Partition input tensor into non-overlapping windows with padding if needed.
+
+    Args:
+        x (torch.Tensor): Input tensor with shape (B, H, W, C).
+        window_size (int): Size of each window.
+
+    Returns:
+        windows (torch.Tensor): Partitioned windows with shape (B * num_windows, window_size, window_size, C).
+        padded_h_w (tuple[int, int]): Padded height and width before partition.
+
+    Examples:
+        >>> x = torch.randn(1, 16, 16, 3)
+        >>> windows, (Hp, Wp) = window_partition(x, window_size=4)
+        >>> print(windows.shape, Hp, Wp)
+        torch.Size([16, 4, 4, 3]) 16 16
+    """
+    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: torch.Tensor, window_size: int, pad_hw: tuple[int, int], hw: tuple[int, int]):
+    """Unpartition windowed sequences into original sequences and remove padding.
+
+    This function reverses the windowing process, reconstructing the original input from windowed segments and removing
+    any padding that was added during the windowing process.
+
+    Args:
+        windows (torch.Tensor): Input tensor of windowed sequences with shape (B * num_windows, window_size,
+            window_size, C), where B is the batch size, num_windows is the number of windows, window_size is the size of
+            each window, and C is the number of channels.
+        window_size (int): Size of each window.
+        pad_hw (tuple[int, int]): Padded height and width (Hp, Wp) of the input before windowing.
+        hw (tuple[int, int]): Original height and width (H, W) of the input before padding and windowing.
+
+    Returns:
+        (torch.Tensor): Unpartitioned sequences with shape (B, H, W, C), where B is the batch size, H and W are the
+            original height and width, and C is the number of channels.
+
+    Examples:
+        >>> windows = torch.rand(32, 8, 8, 64)  # 32 windows of size 8x8 with 64 channels
+        >>> pad_hw = (16, 16)  # Padded height and width
+        >>> hw = (15, 14)  # Original height and width
+        >>> x = window_unpartition(windows, window_size=8, pad_hw=pad_hw, hw=hw)
+        >>> print(x.shape)
+        torch.Size([1, 15, 14, 64])
+    """
+    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
+
+
+def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
+    """Extract relative positional embeddings based on query and key sizes.
+
+    Args:
+        q_size (int): Size of the query.
+        k_size (int): Size of the key.
+        rel_pos (torch.Tensor): Relative position embeddings with shape (L, C), where L is the maximum relative distance
+            and C is the embedding dimension.
+
+    Returns:
+        (torch.Tensor): Extracted positional embeddings according to relative positions, with shape (q_size, k_size, C).
+
+    Examples:
+        >>> q_size, k_size = 8, 16
+        >>> rel_pos = torch.randn(31, 64)  # 31 = 2 * max(8, 16) - 1
+        >>> extracted_pos = get_rel_pos(q_size, k_size, rel_pos)
+        >>> print(extracted_pos.shape)
+        torch.Size([8, 16, 64])
+    """
+    max_rel_dist = int(2 * max(q_size, k_size) - 1)
+    # Interpolate rel pos if needed.
+    if rel_pos.shape[0] != max_rel_dist:
+        # Interpolate rel pos.
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode="linear",
+        )
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+    else:
+        rel_pos_resized = rel_pos
+
+    # Scale the coords with short length if shapes for q and k are different.
+    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
+    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
+    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+    return rel_pos_resized[relative_coords.long()]
+
+
+def add_decomposed_rel_pos(
+    attn: torch.Tensor,
+    q: torch.Tensor,
+    rel_pos_h: torch.Tensor,
+    rel_pos_w: torch.Tensor,
+    q_size: tuple[int, int],
+    k_size: tuple[int, int],
+) -> torch.Tensor:
+    """Add decomposed Relative Positional Embeddings to the attention map.
+
+    This function calculates and applies decomposed Relative Positional Embeddings as described in the MVITv2
+    paper. It enhances the attention mechanism by incorporating spatial relationships between query and key
+    positions.
+
+    Args:
+        attn (torch.Tensor): Attention map with shape (B, q_h * q_w, k_h * k_w).
+        q (torch.Tensor): Query tensor in the attention layer with shape (B, q_h * q_w, C).
+        rel_pos_h (torch.Tensor): Relative position embeddings for height axis with shape (Lh, C).
+        rel_pos_w (torch.Tensor): Relative position embeddings for width axis with shape (Lw, C).
+        q_size (tuple[int, int]): Spatial sequence size of query q as (q_h, q_w).
+        k_size (tuple[int, int]): Spatial sequence size of key k as (k_h, k_w).
+
+    Returns:
+        (torch.Tensor): Updated attention map with added relative positional embeddings, shape (B, q_h * q_w, k_h *
+            k_w).
+
+    Examples:
+        >>> B, C, q_h, q_w, k_h, k_w = 1, 64, 8, 8, 8, 8
+        >>> attn = torch.rand(B, q_h * q_w, k_h * k_w)
+        >>> q = torch.rand(B, q_h * q_w, C)
+        >>> rel_pos_h = torch.rand(2 * max(q_h, k_h) - 1, C)
+        >>> rel_pos_w = torch.rand(2 * max(q_w, k_w) - 1, C)
+        >>> q_size, k_size = (q_h, q_w), (k_h, k_w)
+        >>> updated_attn = add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size)
+        >>> print(updated_attn.shape)
+        torch.Size([1, 64, 64])
+
+    References:
+        https://github.com/facebookresearch/mvit/blob/main/mvit/models/attention.py
+    """
+    q_h, q_w = q_size
+    k_h, k_w = k_size
+    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
+    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
+
+    attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]).view(
+        B, q_h * q_w, k_h * k_w
+    )
+
+    return attn
+
+
+def get_abs_pos(
+    abs_pos: torch.Tensor,
+    has_cls_token: bool,
+    hw: tuple[int, int],
+    retain_cls_token: bool = False,
+    tiling: bool = False,
+) -> torch.Tensor:
+    """Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token dimension for the
+    original embeddings.
+
+    Args:
+        abs_pos (Tensor): absolute positional embeddings with (1, num_position, C).
+        has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token.
+        hw (Tuple): size of input image tokens.
+        retain_cls_token: whether to retain the cls_token
+        tiling: whether to tile the embeddings, *instead* of interpolation (a la abs_win)
+
+    Returns:
+        Absolute positional embeddings after processing with shape (1, H, W, C),: if retain_cls_token is False,
+            otherwise (1, 1+H*W, C).
+    """
+    if retain_cls_token:
+        assert has_cls_token
+
+    h, w = hw
+    if has_cls_token:
+        cls_pos = abs_pos[:, :1]
+        abs_pos = abs_pos[:, 1:]
+
+    xy_num = abs_pos.shape[1]
+    size = int(math.sqrt(xy_num))
+    assert size * size == xy_num
+
+    if size != h or size != w:
+        new_abs_pos = abs_pos.reshape(1, size, size, -1).permute(0, 3, 1, 2)
+        if tiling:
+            new_abs_pos = new_abs_pos.tile([1, 1] + [x // y + 1 for x, y in zip((h, w), new_abs_pos.shape[2:])])[
+                :, :, :h, :w
+            ]
+        else:
+            new_abs_pos = F.interpolate(
+                new_abs_pos,
+                size=(h, w),
+                mode="bicubic",
+                align_corners=False,
+            )
+
+        if not retain_cls_token:
+            return new_abs_pos.permute(0, 2, 3, 1)
+        else:
+            # add cls_token back, flatten spatial dims
+            assert has_cls_token
+            return torch.cat(
+                [cls_pos, new_abs_pos.permute(0, 2, 3, 1).reshape(1, h * w, -1)],
+                dim=1,
+            )
+
+    else:
+        if not retain_cls_token:
+            return abs_pos.reshape(1, h, w, -1)
+        else:
+            assert has_cls_token
+            return torch.cat([cls_pos, abs_pos], dim=1)
+
+
+def concat_rel_pos(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    q_hw: tuple[int, int],
+    k_hw: tuple[int, int],
+    rel_pos_h: torch.Tensor,
+    rel_pos_w: torch.Tensor,
+    rescale: bool = False,
+    relative_coords: torch.Tensor = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Concatenate rel pos coeffs to the q & k tensors, so that qk^T is now effectively including rel pos biases.
+
+    Args:
+        q (torch.Tensor): q tensor with shape (B, L_q, C).
+        k (torch.Tensor): k tensor with shape (B, L_k, C).
+        q_hw: These are spatial size of q tensors.
+        k_hw: These are spatial size of k tensors.
+        rel_pos_h: These are relative pos embeddings/params of height.
+        rel_pos_w: These are relative pos embeddings/params of width.
+        rescale (bool): whether to rescale. e.g. for use when using sdpa, pytorch will scale by the wrong factor due to
+            the concat.
+        relative_coords (torch.Tensor, optional): Precomputed relative coords index tensor.
+
+    Returns:
+        q, k: But, padded so that qk^T accounts for rel pos biases.
+    """
+    q_h, q_w = q_hw
+    k_h, k_w = k_hw
+
+    assert (q_h == q_w) and (k_h == k_w), "only square inputs supported"
+
+    if relative_coords is not None:
+        Rh = rel_pos_h[relative_coords]
+        Rw = rel_pos_w[relative_coords]
+    else:
+        Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+        Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+
+    old_scale = dim**0.5
+    new_scale = (dim + k_h + k_w) ** 0.5 if rescale else old_scale  # for sdpa
+    # attn will be divided by new_scale, but we want to divide q by old_scale
+    scale_ratio = new_scale / old_scale
+
+    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) * new_scale  # (B, q_h, q_w, k_h)
+    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) * new_scale  # (B, q_h, q_w, k_w)
+
+    eye_h = torch.eye(k_h, dtype=q.dtype, device=q.device)
+    eye_w = torch.eye(k_w, dtype=q.dtype, device=q.device)
+
+    eye_h = eye_h.view(1, k_h, 1, k_h).expand([B, k_h, k_w, k_h])
+    eye_w = eye_w.view(1, 1, k_w, k_w).expand([B, k_h, k_w, k_w])
+
+    q = torch.cat([r_q * scale_ratio, rel_h, rel_w], dim=-1).view(B, q_h * q_w, -1)
+    k = torch.cat([k.view(B, k_h, k_w, -1), eye_h, eye_w], dim=-1).view(B, k_h * k_w, -1)
+
+    return q, k

algorithms/dms_yolo/code/ultralytics/models/sam/sam3/__init__.py

@@ -0,0 +1,3 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved

algorithms/dms_yolo/code/ultralytics/models/sam/sam3/decoder.py

@@ -0,0 +1,546 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+"""
+Transformer decoder.
+Inspired from Pytorch's version, adds the pre-norm variant.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import torch
+from torch import nn
+from torchvision.ops.roi_align import RoIAlign
+
+from ultralytics.nn.modules.transformer import MLP
+from ultralytics.nn.modules.utils import _get_clones, inverse_sigmoid
+from ultralytics.utils.ops import xywh2xyxy
+
+from .model_misc import gen_sineembed_for_position
+
+
+class TransformerDecoderLayer(nn.Module):
+    """TransformerDecoderLayer is made up of self-attn, cross-attn, and feedforward network (FFN)."""
+
+    def __init__(
+        self,
+        d_model: int,
+        dim_feedforward: int,
+        dropout: float,
+        cross_attention: nn.Module,
+        n_heads: int,
+        use_text_cross_attention: bool = False,
+    ):
+        """Initialize the TransformerDecoderLayer."""
+        super().__init__()
+        # cross attention
+        self.cross_attn = cross_attention
+        self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.norm1 = nn.LayerNorm(d_model)
+
+        # cross attention text
+        self.use_text_cross_attention = use_text_cross_attention
+        if use_text_cross_attention:
+            self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
+            self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+            self.catext_norm = nn.LayerNorm(d_model)
+
+        # self attention
+        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
+        self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.norm2 = nn.LayerNorm(d_model)
+
+        # ffn
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.activation = nn.ReLU()
+        self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+        self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.norm3 = nn.LayerNorm(d_model)
+
+    @staticmethod
+    def with_pos_embed(tensor, pos):
+        """Add positional embedding to the tensor."""
+        return tensor if pos is None else tensor + pos
+
+    def forward_ffn(self, tgt):
+        """Feedforward network forward pass."""
+        tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout4(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+    def forward(
+        self,
+        # for tgt
+        tgt: torch.Tensor,  # nq, bs, d_model
+        tgt_query_pos: torch.Tensor = None,  # pos for query. MLP(Sine(pos))
+        memory_text: torch.Tensor = None,  # num_token, bs, d_model
+        text_attention_mask: torch.Tensor = None,  # bs, num_token
+        # for memory
+        memory: torch.Tensor = None,  # hw, bs, d_model
+        memory_key_padding_mask: torch.Tensor = None,
+        memory_pos: torch.Tensor = None,  # pos for memory
+        # sa
+        self_attn_mask: torch.Tensor = None,  # mask used for self-attention
+        cross_attn_mask: torch.Tensor = None,  # mask used for cross-attention
+        # dac
+        dac=False,
+        dac_use_selfatt_ln=True,
+        presence_token=None,
+        # skip inside deformable attn
+        **kwargs,  # additional kwargs for compatibility
+    ):
+        """Input: - tgt/tgt_query_pos: nq, bs, d_model. -."""
+        # self attention
+        tgt, tgt_query_pos = self._apply_self_attention(
+            tgt, tgt_query_pos, dac, dac_use_selfatt_ln, presence_token, self_attn_mask
+        )
+
+        if self.use_text_cross_attention:
+            tgt2 = self.ca_text(
+                self.with_pos_embed(tgt, tgt_query_pos),
+                memory_text.to(tgt.dtype),
+                memory_text.to(tgt.dtype),
+                key_padding_mask=text_attention_mask,
+            )[0]
+            tgt = tgt + self.catext_dropout(tgt2)
+            tgt = self.catext_norm(tgt)
+
+        if presence_token is not None:
+            presence_token_mask = torch.zeros_like(cross_attn_mask[:, :1, :])
+            cross_attn_mask = torch.cat([presence_token_mask, cross_attn_mask], dim=1)  # (bs*nheads, 1+nq, hw)
+
+        # Cross attention to image
+        tgt2 = self.cross_attn(
+            query=self.with_pos_embed(tgt, tgt_query_pos),
+            key=self.with_pos_embed(memory, memory_pos),
+            value=memory,
+            attn_mask=cross_attn_mask,
+            key_padding_mask=(memory_key_padding_mask.transpose(0, 1) if memory_key_padding_mask is not None else None),
+            need_weights=False,
+        )[0]
+
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+
+        # ffn
+        tgt = self.forward_ffn(tgt.to(memory.dtype))
+
+        presence_token_out = None
+        if presence_token is not None:
+            presence_token_out = tgt[:1]
+            tgt = tgt[1:]
+
+        return tgt, presence_token_out
+
+    def _apply_self_attention(self, tgt, tgt_query_pos, dac, dac_use_selfatt_ln, presence_token, self_attn_mask):
+        """Apply self-attention with optional DAC splitting."""
+        if self.self_attn is None:
+            return tgt
+
+        if dac:
+            # Split queries for DAC (detect-and-classify)
+            assert tgt.shape[0] % 2 == 0, "DAC requires even number of queries"
+            num_o2o_queries = tgt.shape[0] // 2
+            tgt_o2o = tgt[:num_o2o_queries]
+            tgt_query_pos_o2o = tgt_query_pos[:num_o2o_queries]
+            tgt_o2m = tgt[num_o2o_queries:]
+        else:
+            tgt_o2o = tgt
+            tgt_query_pos_o2o = tgt_query_pos
+
+        # Handle presence token
+        if presence_token is not None:
+            tgt_o2o = torch.cat([presence_token, tgt_o2o], dim=0)
+            tgt_query_pos_o2o = torch.cat([torch.zeros_like(presence_token), tgt_query_pos_o2o], dim=0).to(
+                tgt_o2o.dtype
+            )
+            tgt_query_pos = torch.cat([torch.zeros_like(presence_token), tgt_query_pos], dim=0)
+
+        # Self-attention
+        q = k = self.with_pos_embed(tgt_o2o, tgt_query_pos_o2o)
+        tgt2 = self.self_attn(q, k, tgt_o2o, attn_mask=self_attn_mask)[0].to(tgt.dtype)
+        tgt_o2o = tgt_o2o + self.dropout2(tgt2)
+
+        # Recombine and normalize
+        if dac:
+            if not dac_use_selfatt_ln:
+                tgt_o2o = self.norm2(tgt_o2o)
+            tgt = torch.cat((tgt_o2o, tgt_o2m), dim=0)
+            if dac_use_selfatt_ln:
+                tgt = self.norm2(tgt)
+        else:
+            tgt = tgt_o2o
+            tgt = self.norm2(tgt)
+
+        return tgt, tgt_query_pos
+
+
+class TransformerDecoder(nn.Module):
+    """Transformer Decoder consisting of multiple layers."""
+
+    def __init__(
+        self,
+        d_model: int,
+        frozen: bool,
+        interaction_layer,
+        layer,
+        num_layers: int,
+        num_queries: int,
+        return_intermediate: bool,
+        box_refine: bool = False,
+        num_o2m_queries: int = 0,
+        dac: bool = False,
+        boxRPB: str = "none",
+        # Experimental: An object query for SAM 2 tasks
+        instance_query: bool = False,
+        # Defines the number of additional instance queries,
+        # 1 or 4 are the most likely for single vs multi mask support
+        num_instances: int = 1,  # Irrelevant if instance_query is False
+        dac_use_selfatt_ln: bool = True,
+        use_act_checkpoint: bool = False,
+        compile_mode=None,
+        presence_token: bool = False,
+        clamp_presence_logits: bool = True,
+        clamp_presence_logit_max_val: float = 10.0,
+        use_normed_output_consistently: bool = True,
+        separate_box_head_instance: bool = False,
+        separate_norm_instance: bool = False,
+    ):
+        """Initialize the TransformerDecoder."""
+        super().__init__()
+        self.d_model = d_model
+        self.layers = _get_clones(layer, num_layers)
+        self.fine_layers = (
+            _get_clones(interaction_layer, num_layers) if interaction_layer is not None else [None] * num_layers
+        )
+        self.num_layers = num_layers
+        self.num_queries = num_queries
+        self.dac = dac
+        if dac:
+            self.num_o2m_queries = num_queries
+            tot_num_queries = num_queries
+        else:
+            self.num_o2m_queries = num_o2m_queries
+            tot_num_queries = num_queries + num_o2m_queries
+        self.norm = nn.LayerNorm(d_model)
+        self.return_intermediate = return_intermediate
+        self.bbox_embed = MLP(d_model, d_model, 4, 3)
+        self.query_embed = nn.Embedding(tot_num_queries, d_model)
+        self.instance_query_embed = None
+        self.instance_query_reference_points = None
+        self.use_instance_query = instance_query
+        self.num_instances = num_instances
+        self.use_normed_output_consistently = use_normed_output_consistently
+
+        self.instance_norm = nn.LayerNorm(d_model) if separate_norm_instance else None
+        self.instance_bbox_embed = None
+        if separate_box_head_instance:
+            self.instance_bbox_embed = MLP(d_model, d_model, 4, 3)
+        if instance_query:
+            self.instance_query_embed = nn.Embedding(num_instances, d_model)
+        self.box_refine = box_refine
+        if box_refine:
+            nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0)
+            nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0)
+
+            self.reference_points = nn.Embedding(num_queries, 4)
+            if instance_query:
+                self.instance_reference_points = nn.Embedding(num_instances, 4)
+
+        assert boxRPB in ["none", "log", "linear", "both"]
+        self.boxRPB = boxRPB
+        if boxRPB != "none":
+            try:
+                nheads = self.layers[0].cross_attn_image.num_heads
+            except AttributeError:
+                nheads = self.layers[0].cross_attn.num_heads
+
+            n_input = 4 if boxRPB == "both" else 2
+            self.boxRPB_embed_x = MLP(n_input, d_model, nheads, 2)
+            self.boxRPB_embed_y = MLP(n_input, d_model, nheads, 2)
+            self.compilable_cord_cache = None
+            self.compilable_stored_size = None
+            self.coord_cache = {}
+
+        self.roi_pooler = (
+            RoIAlign(output_size=7, spatial_scale=1, sampling_ratio=-1, aligned=True)
+            if interaction_layer is not None
+            else None
+        )
+        if frozen:
+            for p in self.parameters():
+                p.requires_grad_(False)
+
+        self.presence_token = None
+        self.clamp_presence_logits = clamp_presence_logits
+        self.clamp_presence_logit_max_val = clamp_presence_logit_max_val
+        if presence_token:
+            self.presence_token = nn.Embedding(1, d_model)
+            self.presence_token_head = MLP(d_model, d_model, 1, 3)
+            self.presence_token_out_norm = nn.LayerNorm(d_model)
+
+        self.ref_point_head = MLP(2 * self.d_model, self.d_model, self.d_model, 2)
+        self.dac_use_selfatt_ln = dac_use_selfatt_ln
+        self.use_act_checkpoint = use_act_checkpoint
+
+        nn.init.normal_(self.query_embed.weight.data)
+        if self.instance_query_embed is not None:
+            nn.init.normal_(self.instance_query_embed.weight.data)
+
+        assert self.roi_pooler is None
+        assert self.return_intermediate, "support return_intermediate only"
+        assert self.box_refine, "support box refine only"
+
+        self.compile_mode = compile_mode
+        self.compiled = False
+        # We defer compilation till after the first forward, to first warm-up the boxRPB cache
+
+        # assign layer index to each layer so that some layers can decide what to do
+        # based on which layer index they are (e.g. cross attention to memory bank only
+        # in selected layers)
+        for layer_idx, layer in enumerate(self.layers):
+            layer.layer_idx = layer_idx
+
+    @staticmethod
+    def _get_coords(H, W, device, dtype):
+        """Get normalized coordinates for height and width."""
+        coords_h = torch.arange(0, H, dtype=dtype, device=device) / H
+        coords_w = torch.arange(0, W, dtype=dtype, device=device) / W
+        return coords_h, coords_w
+
+    def _get_rpb_matrix(self, reference_boxes, feat_size):
+        """Get the relative position bias (RPB) matrix for box-relative position bias."""
+        H, W = feat_size
+        boxes_xyxy = xywh2xyxy(reference_boxes).transpose(0, 1)
+        bs, num_queries, _ = boxes_xyxy.shape
+        if self.compilable_cord_cache is None:
+            self.compilable_cord_cache = self._get_coords(H, W, reference_boxes.device, reference_boxes.dtype)
+            self.compilable_stored_size = (H, W)
+
+        if torch.compiler.is_dynamo_compiling() or self.compilable_stored_size == (
+            H,
+            W,
+        ):
+            # good, hitting the cache, will be compilable
+            coords_h, coords_w = self.compilable_cord_cache
+        else:
+            # cache miss, will create compilation issue
+            # In case we're not compiling, we'll still rely on the dict-based cache
+            if feat_size not in self.coord_cache:
+                self.coord_cache[feat_size] = self._get_coords(H, W, reference_boxes.device)
+            coords_h, coords_w = self.coord_cache[feat_size]
+
+            assert coords_h.shape == (H,)
+            assert coords_w.shape == (W,)
+
+        deltas_y = coords_h.view(1, -1, 1) - boxes_xyxy.reshape(-1, 1, 4)[:, :, 1:4:2]
+        deltas_y = deltas_y.view(bs, num_queries, -1, 2)
+        deltas_x = coords_w.view(1, -1, 1) - boxes_xyxy.reshape(-1, 1, 4)[:, :, 0:3:2]
+        deltas_x = deltas_x.view(bs, num_queries, -1, 2)
+
+        if self.boxRPB in ["log", "both"]:
+            deltas_x_log = deltas_x * 8  # normalize to -8, 8
+            deltas_x_log = torch.sign(deltas_x_log) * torch.log2(torch.abs(deltas_x_log) + 1.0) / np.log2(8)
+
+            deltas_y_log = deltas_y * 8  # normalize to -8, 8
+            deltas_y_log = torch.sign(deltas_y_log) * torch.log2(torch.abs(deltas_y_log) + 1.0) / np.log2(8)
+            if self.boxRPB == "log":
+                deltas_x = deltas_x_log
+                deltas_y = deltas_y_log
+            else:
+                deltas_x = torch.cat([deltas_x, deltas_x_log], dim=-1)
+                deltas_y = torch.cat([deltas_y, deltas_y_log], dim=-1)
+
+        if self.training:
+            assert self.use_act_checkpoint, "activation ckpt not enabled in decoder"
+        deltas_x = self.boxRPB_embed_x(x=deltas_x)  # bs, num_queries, W, n_heads
+        deltas_y = self.boxRPB_embed_y(x=deltas_y)  # bs, num_queries, H, n_heads
+
+        if not torch.compiler.is_dynamo_compiling():
+            assert deltas_x.shape[:3] == (bs, num_queries, W)
+            assert deltas_y.shape[:3] == (bs, num_queries, H)
+
+        B = deltas_y.unsqueeze(3) + deltas_x.unsqueeze(2)  # bs, num_queries, H, W, n_heads
+        if not torch.compiler.is_dynamo_compiling():
+            assert B.shape[:4] == (bs, num_queries, H, W)
+        B = B.flatten(2, 3)  # bs, num_queries, H*W, n_heads
+        B = B.permute(0, 3, 1, 2)  # bs, n_heads, num_queries, H*W
+        B = B.contiguous()  # memeff attn likes ordered strides
+        if not torch.compiler.is_dynamo_compiling():
+            assert B.shape[2:] == (num_queries, H * W)
+        return B
+
+    def forward(
+        self,
+        tgt,
+        memory,
+        tgt_mask: torch.Tensor = None,
+        memory_mask: torch.Tensor = None,
+        memory_key_padding_mask: torch.Tensor = None,
+        pos: torch.Tensor = None,
+        reference_boxes: torch.Tensor = None,  # num_queries, bs, 4
+        # for memory
+        spatial_shapes: torch.Tensor = None,  # bs, num_levels, 2
+        valid_ratios: torch.Tensor = None,
+        # for text
+        memory_text: torch.Tensor = None,
+        text_attention_mask: torch.Tensor = None,
+        # if `apply_dac` is None, it will default to `self.dac`
+        apply_dac: bool | None = None,
+        is_instance_prompt=False,
+        decoder_extra_kwargs: dict | None = None,
+        # ROI memory bank
+        obj_roi_memory_feat=None,
+        obj_roi_memory_mask=None,
+        box_head_trk=None,
+    ):
+        """Forward pass of the TransformerDecoder."""
+        if memory_mask is not None:
+            assert self.boxRPB == "none", (
+                "inputting a memory_mask in the presence of boxRPB is unexpected/not implemented"
+            )
+
+        apply_dac = apply_dac if apply_dac is not None else self.dac
+        if apply_dac:
+            assert (tgt.shape[0] == self.num_queries) or (
+                self.use_instance_query and (tgt.shape[0] == self.instance_query_embed.num_embeddings)
+            )
+
+            tgt = tgt.repeat(2, 1, 1)
+            # note that we don't tile tgt_mask, since DAC doesn't
+            # use self-attention in o2m queries
+            if reference_boxes is not None:
+                assert (reference_boxes.shape[0] == self.num_queries) or (
+                    self.use_instance_query and (reference_boxes.shape[0] == self.instance_query_embed.num_embeddings)
+                )
+                reference_boxes = reference_boxes.repeat(2, 1, 1)
+
+        bs = tgt.shape[1]
+        intermediate = []
+        intermediate_presence_logits = []
+        presence_feats = None
+
+        if self.box_refine:
+            if reference_boxes is None:
+                # In this case, we're in a one-stage model, so we generate the reference boxes
+                reference_boxes = self.reference_points.weight.unsqueeze(1)
+                reference_boxes = reference_boxes.repeat(2, bs, 1) if apply_dac else reference_boxes.repeat(1, bs, 1)
+                reference_boxes = reference_boxes.sigmoid()
+            intermediate_ref_boxes = [reference_boxes]
+        else:
+            reference_boxes = None
+            intermediate_ref_boxes = None
+
+        output = tgt
+        presence_out = None
+        if self.presence_token is not None and is_instance_prompt is False:
+            # expand to batch dim
+            presence_out = self.presence_token.weight[None].expand(1, bs, -1)
+
+        box_head = self.bbox_embed
+        if is_instance_prompt and self.instance_bbox_embed is not None:
+            box_head = self.instance_bbox_embed
+
+        out_norm = self.norm
+        if is_instance_prompt and self.instance_norm is not None:
+            out_norm = self.instance_norm
+
+        for layer_idx, layer in enumerate(self.layers):
+            reference_points_input = (
+                reference_boxes[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[None, :]
+            )  # nq, bs, nlevel, 4
+
+            query_sine_embed = gen_sineembed_for_position(
+                reference_points_input[:, :, 0, :], self.d_model
+            )  # nq, bs, d_model*2
+
+            # conditional query
+            query_pos = self.ref_point_head(query_sine_embed)  # nq, bs, d_model
+
+            if self.boxRPB != "none" and reference_boxes is not None:
+                assert spatial_shapes.shape[0] == 1, "only single scale support implemented"
+                memory_mask = self._get_rpb_matrix(
+                    reference_boxes,
+                    (spatial_shapes[0, 0], spatial_shapes[0, 1]),
+                )
+                memory_mask = memory_mask.flatten(0, 1)  # (bs*n_heads, nq, H*W)
+            if self.training:
+                assert self.use_act_checkpoint, "Activation checkpointing not enabled in the decoder"
+            output, presence_out = layer(
+                tgt=output,
+                tgt_query_pos=query_pos,
+                memory_text=memory_text,
+                text_attention_mask=text_attention_mask,
+                memory=memory,
+                memory_key_padding_mask=memory_key_padding_mask,
+                memory_pos=pos,
+                self_attn_mask=tgt_mask,
+                cross_attn_mask=memory_mask,
+                dac=apply_dac,
+                dac_use_selfatt_ln=self.dac_use_selfatt_ln,
+                presence_token=presence_out,
+                **(decoder_extra_kwargs or {}),
+                # ROI memory bank
+                obj_roi_memory_feat=obj_roi_memory_feat,
+                obj_roi_memory_mask=obj_roi_memory_mask,
+            )
+
+            # iter update
+            if self.box_refine:
+                reference_before_sigmoid = inverse_sigmoid(reference_boxes)
+                if box_head_trk is None:
+                    # delta_unsig = self.bbox_embed(output)
+                    if not self.use_normed_output_consistently:
+                        delta_unsig = box_head(output)
+                    else:
+                        delta_unsig = box_head(out_norm(output))
+                else:
+                    # box_head_trk use a separate box head for tracking queries
+                    Q_det = decoder_extra_kwargs["Q_det"]
+                    assert output.size(0) >= Q_det
+                    delta_unsig_det = self.bbox_embed(output[:Q_det])
+                    delta_unsig_trk = box_head_trk(output[Q_det:])
+                    delta_unsig = torch.cat([delta_unsig_det, delta_unsig_trk], dim=0)
+                outputs_unsig = delta_unsig + reference_before_sigmoid
+                new_reference_points = outputs_unsig.sigmoid()
+
+                reference_boxes = new_reference_points.detach()
+                if layer_idx != self.num_layers - 1:
+                    intermediate_ref_boxes.append(new_reference_points)
+            else:
+                raise NotImplementedError("not implemented yet")
+
+            intermediate.append(out_norm(output))
+            if self.presence_token is not None and is_instance_prompt is False:
+                # norm, mlp head
+                intermediate_layer_presence_logits = self.presence_token_head(
+                    self.presence_token_out_norm(presence_out)
+                ).squeeze(-1)
+
+                # clamp to mitigate numerical issues
+                if self.clamp_presence_logits:
+                    intermediate_layer_presence_logits.clamp(
+                        min=-self.clamp_presence_logit_max_val,
+                        max=self.clamp_presence_logit_max_val,
+                    )
+
+                intermediate_presence_logits.append(intermediate_layer_presence_logits)
+                presence_feats = presence_out.clone()
+
+        if not self.compiled and self.compile_mode is not None:
+            self.forward = torch.compile(self.forward, mode=self.compile_mode, fullgraph=True)
+            self.compiled = True
+
+        return (
+            torch.stack(intermediate),
+            torch.stack(intermediate_ref_boxes),
+            (
+                torch.stack(intermediate_presence_logits)
+                if self.presence_token is not None and is_instance_prompt is False
+                else None
+            ),
+            presence_feats,
+        )

algorithms/dms_yolo/code/ultralytics/models/sam/sam3/encoder.py

@@ -0,0 +1,529 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+# Based on https://github.com/IDEA-Research/GroundingDINO
+from __future__ import annotations
+
+import torch
+from torch import nn
+
+from ultralytics.nn.modules.utils import _get_clones
+
+from .model_misc import get_valid_ratio
+
+
+class TransformerEncoderLayer(nn.Module):
+    """Transformer encoder layer that performs self-attention followed by cross-attention.
+
+    This layer was previously called TransformerDecoderLayer but was renamed to better reflect its role in the
+    architecture. It processes input sequences through self-attention and then cross-attention with another input
+    (typically image features).
+
+    The layer supports both pre-norm and post-norm configurations, as well as positional encoding at different stages of
+    the attention mechanism.
+    """
+
+    def __init__(
+        self,
+        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,
+        pre_norm: bool,
+        self_attention: nn.Module = None,
+        cross_attention: nn.Module = None,
+    ):
+        """Initialize a transformer encoder layer.
+
+        Args:
+            cross_attention: Cross-attention module for attending to image features
+            d_model: Model dimension/hidden size
+            dim_feedforward: Dimension of the feedforward network
+            dropout: Dropout probability
+            pos_enc_at_attn: Whether to add positional encodings at self-attention
+            pos_enc_at_cross_attn_keys: Whether to add positional encodings to keys in cross-attention
+            pos_enc_at_cross_attn_queries: Whether to add positional encodings to queries in cross-attention
+            pre_norm: Whether to use pre-norm (True) or post-norm (False) architecture
+            self_attention: Self-attention module
+        """
+        super().__init__()
+        self.d_model = d_model
+        self.dim_feedforward = dim_feedforward
+        self.dropout_value = dropout
+        self.self_attn = self_attention or nn.MultiheadAttention(num_heads=8, dropout=0.1, embed_dim=256)
+        self.cross_attn_image = cross_attention or nn.MultiheadAttention(num_heads=8, dropout=0.1, embed_dim=256)
+
+        # 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 = nn.ReLU()
+        self.pre_norm = pre_norm
+
+        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
+
+        self.layer_idx = None
+
+    def forward_post(
+        self,
+        tgt: torch.Tensor,
+        memory: torch.Tensor,
+        tgt_mask: torch.Tensor = None,
+        memory_mask: torch.Tensor = None,
+        tgt_key_padding_mask: torch.Tensor = None,
+        memory_key_padding_mask: torch.Tensor = None,
+        pos: torch.Tensor = None,
+        query_pos: torch.Tensor = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """Forward pass for post-norm architecture.
+
+        In post-norm architecture, normalization is applied after attention and feedforward operations.
+
+        Args:
+            tgt: Input tensor to be processed
+            memory: Memory tensor for cross-attention
+            tgt_mask: Mask for self-attention
+            memory_mask: Mask for cross-attention
+            tgt_key_padding_mask: Key padding mask for self-attention
+            memory_key_padding_mask: Key padding mask for cross-attention
+            pos: Positional encoding for memory
+            query_pos: Positional encoding for query
+            **kwargs: Additional keyword arguments
+
+        Returns:
+            Processed tensor
+        """
+        q = k = tgt + query_pos if self.pos_enc_at_attn else tgt
+
+        # Self attention
+        tgt2 = self.self_attn(
+            q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask, need_weights=False
+        )[0]
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+
+        # Cross attention to image
+        tgt2 = self.cross_attn_image(
+            query=tgt + query_pos if self.pos_enc_at_cross_attn_queries else tgt,
+            key=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
+            value=memory,
+            attn_mask=memory_mask,
+            key_padding_mask=memory_key_padding_mask,
+            need_weights=False,
+        )[0]
+        tgt = tgt + self.dropout2(tgt2)
+        tgt = self.norm2(tgt)
+
+        # FFN
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout3(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+    def forward_pre(
+        self,
+        tgt: torch.Tensor,
+        memory: torch.Tensor,
+        dac: bool = False,
+        tgt_mask: torch.Tensor = None,
+        memory_mask: torch.Tensor = None,
+        tgt_key_padding_mask: torch.Tensor = None,
+        memory_key_padding_mask: torch.Tensor = None,
+        pos: torch.Tensor = None,
+        query_pos: torch.Tensor = None,
+    ) -> torch.Tensor:
+        """Forward pass for pre-norm architecture.
+
+        In pre-norm architecture, normalization is applied before attention and feedforward operations.
+
+        Args:
+            tgt: Input tensor to be processed
+            memory: Memory tensor for cross-attention
+            dac: Whether to use Divide-and-Conquer attention
+            tgt_mask: Mask for self-attention
+            memory_mask: Mask for cross-attention
+            tgt_key_padding_mask: Key padding mask for self-attention
+            memory_key_padding_mask: Key padding mask for cross-attention
+            pos: Positional encoding for memory
+            query_pos: Positional encoding for query
+
+        Returns:
+            Processed tensor
+        """
+        if dac:
+            # we only apply self attention to the first half of the queries
+            assert tgt.shape[0] % 2 == 0
+            other_tgt = tgt[tgt.shape[0] // 2 :]
+            tgt = tgt[: tgt.shape[0] // 2]
+        tgt2 = self.norm1(tgt).contiguous()
+        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2
+        tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0]
+        tgt = tgt + self.dropout1(tgt2)
+        if dac:
+            # Recombine
+            tgt = torch.cat((tgt, other_tgt), dim=0)
+        tgt2 = self.norm2(tgt)
+        memory = memory.to(tgt2.dtype).contiguous()
+        tgt2 = self.cross_attn_image(
+            query=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,
+            key=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
+            value=memory,
+            attn_mask=memory_mask,
+            key_padding_mask=memory_key_padding_mask,
+        )[0]
+        tgt = tgt + self.dropout2(tgt2)
+        tgt2 = self.norm3(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+        tgt = tgt + self.dropout3(tgt2)
+        return tgt
+
+    def forward(
+        self,
+        tgt: torch.Tensor,
+        memory: torch.Tensor,
+        dac: bool = False,
+        tgt_mask: torch.Tensor = None,
+        memory_mask: torch.Tensor = None,
+        tgt_key_padding_mask: torch.Tensor = None,
+        memory_key_padding_mask: torch.Tensor = None,
+        pos: torch.Tensor = None,
+        query_pos: torch.Tensor = None,
+    ) -> torch.Tensor:
+        """Forward pass for the transformer encoder layer.
+
+        Args:
+            tgt: Input tensor to be processed
+            memory: Memory tensor (e.g., image features) for cross-attention
+            dac: Whether to use Divide-and-Conquer attention (only apply self-attention to first half)
+            tgt_mask: Mask for self-attention
+            memory_mask: Mask for cross-attention
+            tgt_key_padding_mask: Key padding mask for self-attention
+            memory_key_padding_mask: Key padding mask for cross-attention
+            pos: Positional encoding for memory
+            query_pos: Positional encoding for query
+
+        Returns:
+            Processed tensor after self-attention, cross-attention, and feedforward network
+        """
+        fwd_fn = self.forward_pre if self.pre_norm else self.forward_post
+        return fwd_fn(
+            tgt,
+            memory,
+            dac=dac,
+            tgt_mask=tgt_mask,
+            memory_mask=memory_mask,
+            tgt_key_padding_mask=tgt_key_padding_mask,
+            memory_key_padding_mask=memory_key_padding_mask,
+            pos=pos,
+            query_pos=query_pos,
+            # attn_bias=attn_bias,
+            # **kwds,
+        )
+
+
+class TransformerEncoder(nn.Module):
+    """Transformer encoder that processes multi-level features.
+
+    This encoder takes multi-level features (e.g., from a backbone network) and processes them through a stack of
+    transformer encoder layers. It supports features from multiple levels (e.g., different resolutions) and can apply
+    activation checkpointing for memory efficiency during training.
+
+    Args:
+        layer: The encoder layer to be stacked multiple times
+        num_layers: Number of encoder layers to stack
+        d_model: Model dimension/hidden size
+        num_feature_levels: Number of feature levels to process
+        frozen: Whether to freeze the parameters of this module
+        use_act_checkpoint: Whether to use activation checkpointing during training
+    """
+
+    def __init__(
+        self,
+        layer: nn.Module,
+        num_layers: int,
+        d_model: int,
+        num_feature_levels: int,
+        frozen: bool = False,
+        use_act_checkpoint: bool = False,
+    ):
+        """Initialize the transformer encoder."""
+        super().__init__()
+        self.layers = _get_clones(layer, num_layers)
+        self.num_layers = num_layers
+
+        self.num_feature_levels = num_feature_levels
+        self.level_embed = None
+        if num_feature_levels > 1:
+            self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
+
+        if frozen:
+            for p in self.parameters():
+                p.requires_grad_(False)
+
+        self.use_act_checkpoint = use_act_checkpoint
+
+        # assign layer index to each layer so that some layers can decide what to do
+        # based on which layer index they are (e.g. cross attention to memory bank only
+        # in selected layers)
+        for layer_idx, layer in enumerate(self.layers):
+            layer.layer_idx = layer_idx
+
+    def _prepare_multilevel_features(self, srcs, masks, pos_embeds):
+        """Prepare multi-level features for transformer encoder."""
+        assert len(srcs) == self.num_feature_levels, "mismatch between expected and received # of feature levels"
+
+        src_flatten = []
+        mask_flatten = []
+        lvl_pos_embed_flatten = []
+        spatial_shapes = []
+        has_mask = masks is not None and masks[0] is not None
+        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
+            _, _, h, w = src.shape
+            spatial_shape = (h, w)
+            spatial_shapes.append(spatial_shape)
+
+            src = src.flatten(2).transpose(1, 2)  # bs, hw, c
+            if has_mask:
+                mask = mask.flatten(1)
+            pos_embed = pos_embed.flatten(2).transpose(1, 2)  # bs, hw, c
+            if self.level_embed is not None:
+                lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
+            else:
+                lvl_pos_embed = pos_embed
+            lvl_pos_embed_flatten.append(lvl_pos_embed)
+            src_flatten.append(src)
+            if has_mask:
+                mask_flatten.append(mask)
+        src_flatten = torch.cat(src_flatten, 1)  # bs, \sum{hxw}, c
+        mask_flatten = torch.cat(mask_flatten, 1) if has_mask else None  # bs, \sum{hxw}
+        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)  # bs, \sum{hxw}, c
+        spatial_shapes = torch.tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)
+        level_start_index = torch.cat(
+            (
+                spatial_shapes.new_zeros((1,)),
+                spatial_shapes.prod(1).cumsum(0)[:-1],
+            )
+        )
+        if has_mask:
+            valid_ratios = torch.stack([get_valid_ratio(m) for m in masks], 1)
+        else:
+            valid_ratios = torch.ones(
+                (src_flatten.shape[0], self.num_feature_levels, 2),
+                device=src_flatten.device,
+                dtype=src_flatten.dtype,
+            )
+
+        return (
+            src_flatten,
+            mask_flatten,
+            lvl_pos_embed_flatten,
+            level_start_index,
+            valid_ratios,
+            spatial_shapes,
+        )
+
+    def forward(
+        self,
+        src: list[torch.Tensor],
+        src_key_padding_masks: list[torch.Tensor] | None = None,
+        pos: list[torch.Tensor] | None = None,
+        prompt: torch.Tensor = None,
+        prompt_key_padding_mask: torch.Tensor = None,
+        encoder_extra_kwargs: dict | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Process multi-level features through the transformer encoder.
+
+        Args:
+            src: List of multi-level features, each with shape (batch_size, channels, height, width)
+            src_key_padding_masks: List of padding masks for each feature level, each with shape (batch_size, height,
+                width)
+            pos: List of positional embeddings for each feature level, each with shape (batch_size, channels, height,
+                width)
+            prompt: Optional text/prompt features to attend to, with shape (seq_len, batch_size, d_model)
+            prompt_key_padding_mask: Optional padding mask for prompt, with shape (batch_size, seq_len)
+            encoder_extra_kwargs: Optional additional arguments to pass to each encoder layer
+
+        Returns:
+            A tuple containing:
+            - output: Processed features with shape (seq_len, batch_size, d_model)
+            - key_padding_masks_flatten: Flattened padding masks
+            - lvl_pos_embed_flatten: Flattened positional embeddings
+            - level_start_index: Starting indices for each feature level
+            - spatial_shapes: Spatial dimensions of each feature level
+            - valid_ratios: Valid ratios for each feature level
+        """
+        assert len(src) == self.num_feature_levels, "must be equal to num_feature_levels"
+        if src_key_padding_masks is not None:
+            assert len(src_key_padding_masks) == self.num_feature_levels
+        if pos is not None:
+            assert len(pos) == self.num_feature_levels
+        # Flatten multilevel feats and add level pos embeds
+        (
+            src_flatten,
+            key_padding_masks_flatten,
+            lvl_pos_embed_flatten,
+            level_start_index,
+            valid_ratios,
+            spatial_shapes,
+        ) = self._prepare_multilevel_features(src, src_key_padding_masks, pos)
+
+        output = src_flatten
+        for layer in self.layers:
+            layer_kwargs = {}
+
+            assert isinstance(layer, TransformerEncoderLayer)
+            layer_kwargs["memory"] = prompt
+            layer_kwargs["memory_key_padding_mask"] = prompt_key_padding_mask
+            layer_kwargs["query_pos"] = lvl_pos_embed_flatten
+            layer_kwargs["tgt"] = output
+            layer_kwargs["tgt_key_padding_mask"] = key_padding_masks_flatten
+
+            if self.training:
+                assert self.use_act_checkpoint, "activation ckpt not enabled in encoder"
+            if encoder_extra_kwargs is not None:
+                layer_kwargs.update(encoder_extra_kwargs)
+            output = layer(**layer_kwargs)
+        # return as seq first
+        return (
+            output.transpose(0, 1),
+            (key_padding_masks_flatten.transpose(0, 1) if key_padding_masks_flatten is not None else None),
+            lvl_pos_embed_flatten.transpose(0, 1),
+            level_start_index,
+            spatial_shapes,
+            valid_ratios,
+        )
+
+
+class TransformerEncoderFusion(TransformerEncoder):
+    """Transformer encoder that fuses text and image features.
+
+    This encoder extends TransformerEncoder to handle both text and image features, with the ability to add pooled text
+    features to image features for better cross-modal fusion. It supports torch.compile for performance optimization.
+
+    Args:
+        layer: The encoder layer to be stacked multiple times
+        num_layers: Number of encoder layers to stack
+        d_model: Model dimension/hidden size
+        num_feature_levels: Number of feature levels to process
+        add_pooled_text_to_img_feat: Whether to add pooled text features to image features
+        pool_text_with_mask: Whether to use the mask when pooling text features
+        compile_mode: Mode for torch.compile, or None to disable compilation
+        **kwargs: Additional arguments to pass to the parent class
+    """
+
+    def __init__(
+        self,
+        layer: nn.Module,
+        num_layers: int,
+        d_model: int,
+        num_feature_levels: int,
+        add_pooled_text_to_img_feat: bool = True,
+        pool_text_with_mask: bool = False,
+        compile_mode: str | None = None,
+        **kwargs,
+    ):
+        """Initialize the transformer encoder with text-image fusion."""
+        super().__init__(
+            layer,
+            num_layers,
+            d_model,
+            num_feature_levels,
+            **kwargs,
+        )
+        self.add_pooled_text_to_img_feat = add_pooled_text_to_img_feat
+        if self.add_pooled_text_to_img_feat:
+            self.text_pooling_proj = nn.Linear(d_model, d_model)
+        self.pool_text_with_mask = pool_text_with_mask
+        if compile_mode is not None:
+            self.forward = torch.compile(self.forward, mode=compile_mode, fullgraph=True)
+
+    def forward(
+        self,
+        src: list[torch.Tensor],
+        prompt: torch.Tensor,
+        src_key_padding_mask: list[torch.Tensor] | None = None,
+        src_pos: list[torch.Tensor] | None = None,
+        prompt_key_padding_mask: torch.Tensor = None,
+        feat_sizes: list[int] | None = None,
+        encoder_extra_kwargs: dict | None = None,
+    ):
+        """Forward pass for the transformer encoder with text-image fusion."""
+        # Restore spatial shapes of vision
+        bs = src[0].shape[1]  # seq first
+        if feat_sizes is not None:
+            assert len(feat_sizes) == len(src)
+            if src_key_padding_mask is None:
+                src_key_padding_mask = [None] * len(src)
+            for i, (h, w) in enumerate(feat_sizes):
+                src[i] = src[i].reshape(h, w, bs, -1).permute(2, 3, 0, 1)
+                src_pos[i] = src_pos[i].reshape(h, w, bs, -1).permute(2, 3, 0, 1)
+                src_key_padding_mask[i] = (
+                    src_key_padding_mask[i].reshape(h, w, bs).permute(2, 0, 1)
+                    if src_key_padding_mask[i] is not None
+                    else None
+                )
+        else:
+            assert all(x.dim == 4 for x in src), "expected list of (bs, c, h, w) tensors"
+
+        if self.add_pooled_text_to_img_feat:
+            # Fusion: Add mean pooled text to image features
+            pooled_text = pool_text_feat(prompt, prompt_key_padding_mask, self.pool_text_with_mask)
+            pooled_text = self.text_pooling_proj(pooled_text)[..., None, None]  # prompt is seq first
+            src = [x.add_(pooled_text) for x in src]
+
+        (
+            out,
+            key_padding_masks_flatten,
+            lvl_pos_embed_flatten,
+            level_start_index,
+            spatial_shapes,
+            valid_ratios,
+        ) = super().forward(
+            src,
+            src_key_padding_masks=src_key_padding_mask,
+            pos=src_pos,
+            prompt=prompt.transpose(0, 1),
+            prompt_key_padding_mask=prompt_key_padding_mask,
+            encoder_extra_kwargs=encoder_extra_kwargs,
+        )
+
+        return {
+            "memory": out,
+            "padding_mask": key_padding_masks_flatten,
+            "pos_embed": lvl_pos_embed_flatten,
+            "memory_text": prompt,
+            "level_start_index": level_start_index,
+            "spatial_shapes": spatial_shapes,
+            "valid_ratios": valid_ratios,
+        }
+
+
+def pool_text_feat(prompt, prompt_mask, pool_with_mask):
+    """Mean-pool the prompt embeddings over the valid tokens only."""
+    # prompt has shape (seq, bs, dim)
+    if not pool_with_mask:
+        return prompt.mean(dim=0)
+
+    # prompt_mask has shape (bs, seq), where False is valid and True is padding
+    assert prompt_mask.dim() == 2
+    # is_valid has shape (seq, bs, 1), where 1 is valid and 0 is padding
+    is_valid = (~prompt_mask).float().permute(1, 0)[..., None]
+    # num_valid has shape (bs, 1)
+    num_valid = torch.clamp(torch.sum(is_valid, dim=0), min=1.0)
+
+    # mean pool over all the valid tokens
+    pooled_text = (prompt * is_valid).sum(dim=0) / num_valid
+    return pooled_text

algorithms/dms_yolo/code/ultralytics/models/sam/sam3/geometry_encoders.py

@@ -0,0 +1,415 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+import torch
+import torch.nn as nn
+import torchvision
+
+from ultralytics.nn.modules.utils import _get_clones
+from ultralytics.utils.ops import xywh2xyxy
+
+
+def is_right_padded(mask: torch.Tensor):
+    """Given a padding mask (following pytorch convention, 1s for padded values), returns whether the padding is on the
+    right or not.
+    """
+    return (mask.long() == torch.sort(mask.long(), dim=-1)[0]).all()
+
+
+def concat_padded_sequences(seq1, mask1, seq2, mask2, return_index: bool = False):
+    """
+    Concatenates two right-padded sequences, such that the resulting sequence
+    is contiguous and also right-padded.
+
+    Following pytorch's convention, tensors are sequence first, and the mask are
+    batch first, with 1s for padded values.
+
+    :param seq1: A tensor of shape (seq1_length, batch_size, hidden_size).
+    :param mask1: A tensor of shape (batch_size, seq1_length).
+    :param seq2: A tensor of shape (seq2_length, batch_size,  hidden_size).
+    :param mask2: A tensor of shape (batch_size, seq2_length).
+    :param return_index: If True, also returns the index of the ids of the element of seq2
+        in the concatenated sequence. This can be used to retrieve the elements of seq2
+    :return: A tuple (concatenated_sequence, concatenated_mask) if return_index is False,
+        otherwise (concatenated_sequence, concatenated_mask, index).
+    """
+    seq1_length, batch_size, hidden_size = seq1.shape
+    seq2_length, batch_size, hidden_size = seq2.shape
+
+    assert batch_size == seq1.size(1) == seq2.size(1) == mask1.size(0) == mask2.size(0)
+    assert hidden_size == seq1.size(2) == seq2.size(2)
+    assert seq1_length == mask1.size(1)
+    assert seq2_length == mask2.size(1)
+
+    torch._assert(is_right_padded(mask1), "Mask is not right padded")
+    torch._assert(is_right_padded(mask2), "Mask is not right padded")
+
+    actual_seq1_lengths = (~mask1).sum(dim=-1)
+    actual_seq2_lengths = (~mask2).sum(dim=-1)
+
+    final_lengths = actual_seq1_lengths + actual_seq2_lengths
+    max_length = seq1_length + seq2_length
+    concatenated_mask = (
+        torch.arange(max_length, device=seq2.device)[None].repeat(batch_size, 1) >= final_lengths[:, None]
+    )
+
+    # (max_len, batch_size, hidden_size)
+    concatenated_sequence = torch.zeros((max_length, batch_size, hidden_size), device=seq2.device, dtype=seq2.dtype)
+    concatenated_sequence[:seq1_length, :, :] = seq1
+
+    # At this point, the element of seq1 are in the right place
+    # We just need to shift the elements of seq2
+
+    index = torch.arange(seq2_length, device=seq2.device)[:, None].repeat(1, batch_size)
+    index = index + actual_seq1_lengths[None]
+
+    concatenated_sequence = concatenated_sequence.scatter(0, index[:, :, None].expand(-1, -1, hidden_size), seq2)
+
+    if return_index:
+        return concatenated_sequence, concatenated_mask, index
+
+    return concatenated_sequence, concatenated_mask
+
+
+class Prompt:
+    """Utility class to manipulate geometric prompts.
+
+    We expect the sequences in pytorch convention, that is sequence first, batch second The dimensions are expected as
+    follows: box_embeddings shape: N_boxes x B x C_box box_mask shape: B x N_boxes. Can be None if nothing is masked out
+    point_embeddings shape: N_points x B x C_point point_mask shape: B x N_points. Can be None if nothing is masked out
+    mask_embeddings shape: N_masks x B x 1 x H_mask x W_mask mask_mask shape: B x N_masks. Can be None if nothing is
+    masked out
+
+    We also store positive/negative labels. These tensors are also stored batch-first If they are None, we'll assume
+    positive labels everywhere box_labels: long tensor of shape N_boxes x B point_labels: long tensor of shape N_points
+    x B mask_labels: long tensor of shape N_masks x B
+    """
+
+    def __init__(self, box_embeddings=None, box_mask=None, box_labels=None):
+        """Initialize the Prompt object."""
+        # Check for null prompt
+        # Check for null prompt
+        if box_embeddings is None:
+            self.box_embeddings = None
+            self.box_labels = None
+            self.box_mask = None
+            return
+
+        # Get sequence length, batch size, and device
+        box_seq_len = box_embeddings.shape[0]
+        bs = box_embeddings.shape[1]
+        device = box_embeddings.device
+
+        # Initialize labels and attention mask if not provided
+        if box_labels is None:
+            box_labels = torch.ones(box_seq_len, bs, device=device, dtype=torch.long)
+        if box_mask is None:
+            box_mask = torch.zeros(bs, box_seq_len, device=device, dtype=torch.bool)
+
+        # Dimension checks
+        assert list(box_embeddings.shape[:2]) == [box_seq_len, bs], (
+            f"Wrong dimension for box embeddings. Expected [{box_seq_len}, {bs}, *] got {box_embeddings.shape}"
+        )
+        assert box_embeddings.shape[-1] == 4, (
+            f"Expected box embeddings to have 4 coordinates, got {box_embeddings.shape[-1]}"
+        )
+        assert list(box_mask.shape) == [bs, box_seq_len], (
+            f"Wrong dimension for box mask. Expected [{bs}, {box_seq_len}] got {box_mask.shape}"
+        )
+        assert list(box_labels.shape) == [box_seq_len, bs], (
+            f"Wrong dimension for box labels. Expected [{box_seq_len}, {bs}] got {box_labels.shape}"
+        )
+
+        # Device checks
+        assert box_embeddings.device == device, (
+            f"Expected box embeddings to be on device {device}, got {box_embeddings.device}"
+        )
+        assert box_mask.device == device, f"Expected box mask to be on device {device}, got {box_mask.device}"
+        assert box_labels.device == device, f"Expected box labels to be on device {device}, got {box_labels.device}"
+
+        self.box_embeddings = box_embeddings
+        self.box_mask = box_mask
+        self.box_labels = box_labels
+
+    def append_boxes(self, boxes, labels=None, mask=None):
+        """Append box prompts to existing prompts.
+
+        Args:
+            boxes: Tensor of shape (N_new_boxes, B, 4) with normalized box coordinates
+            labels: Optional tensor of shape (N_new_boxes, B) with positive/negative labels
+            mask: Optional tensor of shape (B, N_new_boxes) for attention mask
+        """
+        if self.box_embeddings is None:
+            # First boxes - initialize
+            self.box_embeddings = boxes
+            bs = boxes.shape[1]
+            box_seq_len = boxes.shape[0]
+
+            if labels is None:
+                labels = torch.ones(box_seq_len, bs, device=boxes.device, dtype=torch.long)
+            if mask is None:
+                mask = torch.zeros(bs, box_seq_len, device=boxes.device, dtype=torch.bool)
+
+            self.box_labels = labels
+            self.box_mask = mask
+            return
+
+        # Append to existing boxes
+        bs = self.box_embeddings.shape[1]
+        assert boxes.shape[1] == bs, f"Batch size mismatch: expected {bs}, got {boxes.shape[1]}"
+
+        if labels is None:
+            labels = torch.ones(boxes.shape[0], bs, device=boxes.device, dtype=torch.long)
+        if mask is None:
+            mask = torch.zeros(bs, boxes.shape[0], dtype=torch.bool, device=boxes.device)
+
+        assert list(boxes.shape[:2]) == list(labels.shape[:2]), (
+            f"Shape mismatch between boxes {boxes.shape} and labels {labels.shape}"
+        )
+
+        # Concatenate using the helper function
+        self.box_labels, _ = concat_padded_sequences(
+            self.box_labels.unsqueeze(-1), self.box_mask, labels.unsqueeze(-1), mask
+        )
+        self.box_labels = self.box_labels.squeeze(-1)
+        self.box_embeddings, self.box_mask = concat_padded_sequences(self.box_embeddings, self.box_mask, boxes, mask)
+
+
+class SequenceGeometryEncoder(nn.Module):
+    """Encoder for geometric box prompts. Assumes boxes are passed in the "normalized CxCyWH" format.
+
+    Boxes can be encoded with any of the three possibilities:
+    - direct projection: linear projection from coordinate space to d_model
+    - pooling: RoI align features from the backbone
+    - pos encoder: position encoding of the box center
+
+    These three options are mutually compatible and will be summed if multiple are selected.
+
+    As an alternative, boxes can be encoded as two corner points (top-left and bottom-right).
+
+    The encoded sequence can be further processed with a transformer.
+    """
+
+    def __init__(
+        self,
+        encode_boxes_as_points: bool,
+        boxes_direct_project: bool,
+        boxes_pool: bool,
+        boxes_pos_enc: bool,
+        d_model: int,
+        pos_enc,
+        num_layers: int,
+        layer: nn.Module,
+        roi_size: int = 7,
+        add_cls: bool = True,
+        add_post_encode_proj: bool = True,
+        use_act_ckpt: bool = False,
+    ):
+        """Initialize the SequenceGeometryEncoder."""
+        super().__init__()
+
+        self.d_model = d_model
+        self.pos_enc = pos_enc
+        self.encode_boxes_as_points = encode_boxes_as_points
+        self.roi_size = roi_size
+
+        # Label embeddings: 2 labels if encoding as boxes (pos/neg)
+        # 6 labels if encoding as points (regular pos/neg, top-left pos/neg, bottom-right pos/neg)
+        num_labels = 6 if self.encode_boxes_as_points else 2
+        self.label_embed = torch.nn.Embedding(num_labels, self.d_model)
+
+        # CLS token for pooling
+        self.cls_embed = None
+        if add_cls:
+            self.cls_embed = torch.nn.Embedding(1, self.d_model)
+
+        # Point encoding (used when encode_boxes_as_points is True)
+        if encode_boxes_as_points:
+            self.points_direct_project = nn.Linear(2, self.d_model)
+            self.points_pool_project = None
+            self.points_pos_enc_project = None
+        else:
+            # Box encoding modules
+            assert boxes_direct_project or boxes_pos_enc or boxes_pool, "Error: need at least one way to encode boxes"
+            self.points_direct_project = None
+            self.points_pool_project = None
+            self.points_pos_enc_project = None
+
+            self.boxes_direct_project = None
+            self.boxes_pool_project = None
+            self.boxes_pos_enc_project = None
+
+            if boxes_direct_project:
+                self.boxes_direct_project = nn.Linear(4, self.d_model)
+            if boxes_pool:
+                self.boxes_pool_project = nn.Conv2d(self.d_model, self.d_model, self.roi_size)
+            if boxes_pos_enc:
+                self.boxes_pos_enc_project = nn.Linear(self.d_model + 2, self.d_model)
+
+        self.final_proj = None
+        if add_post_encode_proj:
+            self.final_proj = nn.Linear(self.d_model, self.d_model)
+            self.norm = nn.LayerNorm(self.d_model)
+
+        self.img_pre_norm = nn.Identity()
+        if self.points_pool_project is not None or self.boxes_pool_project is not None:
+            self.img_pre_norm = nn.LayerNorm(self.d_model)
+
+        self.encode = None
+        if num_layers > 0:
+            assert add_cls, "It's currently highly recommended to add a CLS when using a transformer"
+            self.encode = _get_clones(layer, num_layers)
+            self.encode_norm = nn.LayerNorm(self.d_model)
+
+        self.use_act_ckpt = use_act_ckpt
+
+    def _encode_points(self, points, points_mask, points_labels, img_feats):
+        """Encode points (used when boxes are converted to corner points)."""
+        # Direct projection of coordinates
+        points_embed = self.points_direct_project(points.to(img_feats.dtype))
+
+        # Add label embeddings
+        type_embed = self.label_embed(points_labels.long())
+        return type_embed + points_embed, points_mask
+
+    def _encode_boxes(self, boxes, boxes_mask, boxes_labels, img_feats: torch.Tensor):
+        """Encode boxes using configured encoding methods."""
+        boxes_embed = None
+        n_boxes, bs = boxes.shape[:2]
+
+        if self.boxes_direct_project is not None:
+            proj = self.boxes_direct_project(boxes.to(img_feats.dtype))
+            boxes_embed = proj
+
+        if self.boxes_pool_project is not None:
+            H, W = img_feats.shape[-2:]
+
+            # Convert boxes to xyxy format and denormalize
+            boxes_xyxy = xywh2xyxy(boxes.to(img_feats.dtype))
+            scale = torch.tensor([W, H, W, H], dtype=boxes_xyxy.dtype)
+            scale = scale.to(device=boxes_xyxy.device, non_blocking=True)
+            scale = scale.view(1, 1, 4)
+            boxes_xyxy = boxes_xyxy * scale
+
+            # RoI align
+            sampled = torchvision.ops.roi_align(img_feats, boxes_xyxy.transpose(0, 1).unbind(0), self.roi_size)
+            assert list(sampled.shape) == [
+                bs * n_boxes,
+                self.d_model,
+                self.roi_size,
+                self.roi_size,
+            ]
+            proj = self.boxes_pool_project(sampled)
+            proj = proj.view(bs, n_boxes, self.d_model).transpose(0, 1)
+
+            if boxes_embed is None:
+                boxes_embed = proj
+            else:
+                boxes_embed = boxes_embed + proj
+
+        if self.boxes_pos_enc_project is not None:
+            cx, cy, w, h = boxes.unbind(-1)
+            enc = self.pos_enc.encode_boxes(cx.flatten(), cy.flatten(), w.flatten(), h.flatten())
+            enc = enc.view(boxes.shape[0], boxes.shape[1], enc.shape[-1])
+
+            proj = self.boxes_pos_enc_project(enc.to(img_feats.dtype))
+            if boxes_embed is None:
+                boxes_embed = proj
+            else:
+                boxes_embed = boxes_embed + proj
+
+        # Add label embeddings
+        type_embed = self.label_embed(boxes_labels.long())
+        return type_embed + boxes_embed, boxes_mask
+
+    def forward(self, geo_prompt: Prompt, img_feats, img_sizes, img_pos_embeds=None):
+        """Encode geometric box prompts.
+
+        Args:
+            geo_prompt: Prompt object containing box embeddings, masks, and labels
+            img_feats: List of image features from backbone
+            img_sizes: List of (H, W) tuples for each feature level
+            img_pos_embeds: Optional position embeddings for image features
+
+        Returns:
+            Tuple of (encoded_embeddings, attention_mask)
+        """
+        boxes = geo_prompt.box_embeddings
+        boxes_mask = geo_prompt.box_mask
+        boxes_labels = geo_prompt.box_labels
+
+        seq_first_img_feats = img_feats[-1]  # [H*W, B, C]
+        seq_first_img_pos_embeds = (
+            img_pos_embeds[-1] if img_pos_embeds is not None else torch.zeros_like(seq_first_img_feats)
+        )
+
+        # Prepare image features for pooling if needed
+        if self.points_pool_project or self.boxes_pool_project:
+            assert len(img_feats) == len(img_sizes)
+            cur_img_feat = img_feats[-1]
+            cur_img_feat = self.img_pre_norm(cur_img_feat)
+            H, W = img_sizes[-1]
+            assert cur_img_feat.shape[0] == H * W
+            N, C = cur_img_feat.shape[-2:]
+            # Reshape to NxCxHxW
+            cur_img_feat = cur_img_feat.permute(1, 2, 0)
+            cur_img_feat = cur_img_feat.view(N, C, H, W)
+            img_feats = cur_img_feat
+
+        if self.encode_boxes_as_points:
+            # Convert boxes to corner points
+            assert boxes is not None and boxes.shape[-1] == 4
+
+            boxes_xyxy = xywh2xyxy(boxes)
+            top_left, bottom_right = boxes_xyxy.split(split_size=2, dim=-1)
+
+            # Adjust labels for corner points (offset by 2 and 4)
+            labels_tl = boxes_labels + 2
+            labels_br = boxes_labels + 4
+
+            # Concatenate top-left and bottom-right points
+            points = torch.cat([top_left, bottom_right], dim=0)
+            points_labels = torch.cat([labels_tl, labels_br], dim=0)
+            points_mask = torch.cat([boxes_mask, boxes_mask], dim=1)
+
+            final_embeds, final_mask = self._encode_points(
+                points=points,
+                points_mask=points_mask,
+                points_labels=points_labels,
+                img_feats=img_feats,
+            )
+        else:
+            # Encode boxes directly
+            final_embeds, final_mask = self._encode_boxes(
+                boxes=boxes,
+                boxes_mask=boxes_mask,
+                boxes_labels=boxes_labels,
+                img_feats=img_feats,
+            )
+
+        bs = final_embeds.shape[1]
+        assert final_mask.shape[0] == bs
+
+        # Add CLS token if configured
+        if self.cls_embed is not None:
+            cls = self.cls_embed.weight.view(1, 1, self.d_model).repeat(1, bs, 1)
+            cls_mask = torch.zeros(bs, 1, dtype=final_mask.dtype, device=final_mask.device)
+            final_embeds, final_mask = concat_padded_sequences(final_embeds, final_mask, cls, cls_mask)
+
+        # Final projection
+        if self.final_proj is not None:
+            final_embeds = self.norm(self.final_proj(final_embeds))
+
+        # Transformer encoding layers
+        if self.encode is not None:
+            for lay in self.encode:
+                final_embeds = lay(
+                    tgt=final_embeds,
+                    memory=seq_first_img_feats,
+                    tgt_key_padding_mask=final_mask,
+                    pos=seq_first_img_pos_embeds,
+                )
+            final_embeds = self.encode_norm(final_embeds)
+
+        return final_embeds, final_mask

algorithms/dms_yolo/code/ultralytics/models/sam/sam3/maskformer_segmentation.py

@@ -0,0 +1,286 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+from __future__ import annotations
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+
+from ultralytics.nn.modules.transformer import MLP
+
+
+class LinearPresenceHead(nn.Sequential):
+    """Linear presence head for predicting the presence of classes in an image."""
+
+    def __init__(self, d_model):
+        """Initializes the LinearPresenceHead."""
+        # a hack to make `LinearPresenceHead` compatible with old checkpoints
+        super().__init__(nn.Identity(), nn.Identity(), nn.Linear(d_model, 1))
+
+    def forward(self, hs, prompt, prompt_mask):
+        """Forward pass of the presence head."""
+        return super().forward(hs)
+
+
+class MaskPredictor(nn.Module):
+    """Predicts masks from object queries and pixel embeddings."""
+
+    def __init__(self, hidden_dim, mask_dim):
+        """Initializes the MaskPredictor."""
+        super().__init__()
+        self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)
+
+    def forward(self, obj_queries, pixel_embed):
+        """Predicts masks from object queries and pixel embeddings."""
+        if len(obj_queries.shape) == 3:
+            if pixel_embed.ndim == 3:
+                # batch size was omitted
+                mask_preds = torch.einsum("bqc,chw->bqhw", self.mask_embed(obj_queries), pixel_embed)
+            else:
+                mask_preds = torch.einsum("bqc,bchw->bqhw", self.mask_embed(obj_queries), pixel_embed)
+        else:
+            # Assumed to have aux masks
+            if pixel_embed.ndim == 3:
+                # batch size was omitted
+                mask_preds = torch.einsum("lbqc,chw->lbqhw", self.mask_embed(obj_queries), pixel_embed)
+            else:
+                mask_preds = torch.einsum("lbqc,bchw->lbqhw", self.mask_embed(obj_queries), pixel_embed)
+
+        return mask_preds
+
+
+class SegmentationHead(nn.Module):
+    """Segmentation head that predicts masks from backbone features and object queries."""
+
+    def __init__(
+        self,
+        hidden_dim,
+        upsampling_stages,
+        use_encoder_inputs=False,
+        aux_masks=False,
+        no_dec=False,
+        pixel_decoder=None,
+        act_ckpt=False,
+        shared_conv=False,
+        compile_mode_pixel_decoder=None,
+    ):
+        """Initializes the SegmentationHead."""
+        super().__init__()
+        self.use_encoder_inputs = use_encoder_inputs
+        self.aux_masks = aux_masks
+        if pixel_decoder is not None:
+            self.pixel_decoder = pixel_decoder
+        else:
+            self.pixel_decoder = PixelDecoder(
+                hidden_dim,
+                upsampling_stages,
+                shared_conv=shared_conv,
+                compile_mode=compile_mode_pixel_decoder,
+            )
+        self.no_dec = no_dec
+        if no_dec:
+            self.mask_predictor = nn.Conv2d(hidden_dim, 1, kernel_size=3, stride=1, padding=1)
+        else:
+            self.mask_predictor = MaskPredictor(hidden_dim, mask_dim=hidden_dim)
+
+        self.act_ckpt = act_ckpt
+
+        # used to update the output dictionary
+        self.instance_keys = ["pred_masks"]
+
+    def _embed_pixels(self, backbone_feats: list[torch.Tensor], encoder_hidden_states) -> torch.Tensor:
+        """Embeds pixels using the pixel decoder."""
+        if self.use_encoder_inputs:
+            backbone_visual_feats = [bb_feat.clone() for bb_feat in backbone_feats]
+            # Extract visual embeddings
+            encoder_hidden_states = encoder_hidden_states.permute(1, 2, 0)
+            spatial_dim = math.prod(backbone_feats[-1].shape[-2:])
+            encoder_visual_embed = encoder_hidden_states[..., :spatial_dim].reshape(-1, *backbone_feats[-1].shape[1:])
+
+            backbone_visual_feats[-1] = encoder_visual_embed
+            if self.act_ckpt:
+                pixel_embed = checkpoint.checkpoint(self.pixel_decoder, backbone_visual_feats, use_reentrant=False)
+            else:
+                pixel_embed = self.pixel_decoder(backbone_visual_feats)
+        else:
+            backbone_feats = [x for x in backbone_feats]
+            pixel_embed = self.pixel_decoder(backbone_feats)
+            if pixel_embed.shape[0] == 1:
+                # For batch_size=1 training, we can avoid the indexing to save memory
+                pixel_embed = pixel_embed.squeeze(0)
+            else:
+                pixel_embed = pixel_embed[[0], ...]
+        return pixel_embed
+
+    def forward(
+        self,
+        backbone_feats: list[torch.Tensor],
+        obj_queries: torch.Tensor,
+        encoder_hidden_states: torch.Tensor = None,
+        **kwargs,
+    ) -> dict[str, torch.Tensor]:
+        """Forward pass of the SegmentationHead."""
+        if self.use_encoder_inputs:
+            assert encoder_hidden_states is not None
+
+        pixel_embed = self._embed_pixels(backbone_feats=backbone_feats, encoder_hidden_states=encoder_hidden_states)
+
+        if self.no_dec:
+            mask_pred = self.mask_predictor(pixel_embed)
+        elif self.aux_masks:
+            mask_pred = self.mask_predictor(obj_queries, pixel_embed)
+        else:
+            mask_pred = self.mask_predictor(obj_queries[-1], pixel_embed)
+
+        return {"pred_masks": mask_pred}
+
+
+class PixelDecoder(nn.Module):
+    """Pixel decoder module that upsamples backbone features."""
+
+    def __init__(
+        self,
+        hidden_dim,
+        num_upsampling_stages,
+        interpolation_mode="nearest",
+        shared_conv=False,
+        compile_mode=None,
+    ):
+        """Initializes the PixelDecoder."""
+        super().__init__()
+        self.hidden_dim = hidden_dim
+        self.num_upsampling_stages = num_upsampling_stages
+        self.interpolation_mode = interpolation_mode
+        conv_layers = []
+        norms = []
+        num_convs = 1 if shared_conv else num_upsampling_stages
+        for _ in range(num_convs):
+            conv_layers.append(nn.Conv2d(self.hidden_dim, self.hidden_dim, 3, 1, 1))
+            norms.append(nn.GroupNorm(8, self.hidden_dim))
+
+        self.conv_layers = nn.ModuleList(conv_layers)
+        self.norms = nn.ModuleList(norms)
+        self.shared_conv = shared_conv
+        self.out_dim = self.conv_layers[-1].out_channels
+        if compile_mode is not None:
+            self.forward = torch.compile(self.forward, mode=compile_mode, dynamic=True, fullgraph=True)
+            # Needed to make checkpointing happy. But we don't know if the module is checkpointed, so we disable it by default.
+            torch._dynamo.config.optimize_ddp = False
+
+    def forward(self, backbone_feats: list[torch.Tensor]):
+        """Forward pass of the PixelDecoder."""
+        prev_fpn = backbone_feats[-1]
+        fpn_feats = backbone_feats[:-1]
+        for layer_idx, bb_feat in enumerate(fpn_feats[::-1]):
+            curr_fpn = bb_feat
+            prev_fpn = curr_fpn + F.interpolate(prev_fpn, size=curr_fpn.shape[-2:], mode=self.interpolation_mode)
+            if self.shared_conv:
+                # only one conv layer
+                layer_idx = 0
+            prev_fpn = self.conv_layers[layer_idx](prev_fpn)
+            prev_fpn = F.relu(self.norms[layer_idx](prev_fpn))
+
+        return prev_fpn
+
+
+class UniversalSegmentationHead(SegmentationHead):
+    """This module handles semantic+instance segmentation."""
+
+    def __init__(
+        self,
+        hidden_dim,
+        upsampling_stages,
+        pixel_decoder,
+        aux_masks=False,
+        no_dec=False,
+        act_ckpt=False,
+        presence_head: bool = False,
+        dot_product_scorer=None,
+        cross_attend_prompt=None,
+    ):
+        """Initializes the UniversalSegmentationHead."""
+        super().__init__(
+            hidden_dim=hidden_dim,
+            upsampling_stages=upsampling_stages,
+            use_encoder_inputs=True,
+            aux_masks=aux_masks,
+            no_dec=no_dec,
+            pixel_decoder=pixel_decoder,
+            act_ckpt=act_ckpt,
+        )
+        self.d_model = hidden_dim
+
+        if dot_product_scorer is not None:
+            assert presence_head, "Specifying a dot product scorer without a presence head is likely a mistake"
+
+        self.presence_head = None
+        if presence_head:
+            self.presence_head = (
+                dot_product_scorer if dot_product_scorer is not None else LinearPresenceHead(self.d_model)
+            )
+
+        self.cross_attend_prompt = cross_attend_prompt
+        if self.cross_attend_prompt is not None:
+            self.cross_attn_norm = nn.LayerNorm(self.d_model)
+
+        self.semantic_seg_head = nn.Conv2d(self.pixel_decoder.out_dim, 1, kernel_size=1)
+        self.instance_seg_head = nn.Conv2d(self.pixel_decoder.out_dim, self.d_model, kernel_size=1)
+
+    def forward(
+        self,
+        backbone_feats: list[torch.Tensor],
+        obj_queries: torch.Tensor,
+        encoder_hidden_states: torch.Tensor = None,
+        prompt: torch.Tensor = None,
+        prompt_mask: torch.Tensor = None,
+        **kwargs,
+    ) -> dict[str, torch.Tensor]:
+        """Forward pass of the UniversalSegmentationHead."""
+        assert encoder_hidden_states is not None
+        bs = encoder_hidden_states.shape[1]
+
+        if self.cross_attend_prompt is not None:
+            tgt2 = self.cross_attn_norm(encoder_hidden_states)
+            tgt2 = self.cross_attend_prompt(
+                query=tgt2,
+                key=prompt.to(tgt2.dtype),
+                value=prompt.to(tgt2.dtype),
+                key_padding_mask=prompt_mask,
+                need_weights=False,
+            )[0]
+            encoder_hidden_states = tgt2 + encoder_hidden_states
+
+        presence_logit = None
+        if self.presence_head is not None:
+            pooled_enc = encoder_hidden_states.mean(0)
+            presence_logit = (
+                self.presence_head(
+                    pooled_enc.view(1, bs, 1, self.d_model),
+                    prompt=prompt,
+                    prompt_mask=prompt_mask,
+                )
+                .squeeze(0)
+                .squeeze(1)
+            )
+
+        pixel_embed = self._embed_pixels(backbone_feats=backbone_feats, encoder_hidden_states=encoder_hidden_states)
+
+        instance_embeds = self.instance_seg_head(pixel_embed)
+
+        if self.no_dec:
+            mask_pred = self.mask_predictor(instance_embeds)
+        elif self.aux_masks:
+            mask_pred = self.mask_predictor(obj_queries, instance_embeds)
+        else:
+            mask_pred = self.mask_predictor(obj_queries[-1], instance_embeds)
+
+        return {
+            "pred_masks": mask_pred,
+            "semantic_seg": self.semantic_seg_head(pixel_embed),
+            "presence_logit": presence_logit,
+        }

algorithms/dms_yolo/code/ultralytics/models/sam/sam3/model_misc.py

@@ -0,0 +1,199 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+"""Various utility models."""
+
+from __future__ import annotations
+
+import math
+
+import numpy as np
+import torch
+from torch import Tensor, nn
+
+
+class DotProductScoring(torch.nn.Module):
+    """A module that computes dot-product scores between a set of query features and a."""
+
+    def __init__(
+        self,
+        d_model,
+        d_proj,
+        prompt_mlp=None,
+        clamp_logits=True,
+        clamp_max_val=12.0,
+    ):
+        """Initialize the DotProductScoring module."""
+        super().__init__()
+        self.d_proj = d_proj
+        assert isinstance(prompt_mlp, torch.nn.Module) or prompt_mlp is None
+        self.prompt_mlp = prompt_mlp  # an optional MLP projection for prompt
+        self.prompt_proj = torch.nn.Linear(d_model, d_proj)
+        self.hs_proj = torch.nn.Linear(d_model, d_proj)
+        self.scale = float(1.0 / np.sqrt(d_proj))
+        self.clamp_logits = clamp_logits
+        if self.clamp_logits:
+            self.clamp_max_val = clamp_max_val
+
+    @staticmethod
+    def mean_pool_text(prompt, prompt_mask):
+        """Mean-pool the prompt embeddings over the valid tokens only."""
+        # is_valid has shape (seq, bs, 1), where 1 is valid and 0 is padding
+        is_valid = (~prompt_mask).to(prompt.dtype).permute(1, 0)[..., None]
+        # num_valid has shape (bs, 1)
+        num_valid = torch.clamp(torch.sum(is_valid, dim=0), min=1.0)
+        # mean pool over all the valid tokens -- pooled_prompt has shape (bs, proj_dim)
+        pooled_prompt = (prompt * is_valid).sum(dim=0) / num_valid
+        return pooled_prompt
+
+    def forward(self, hs, prompt, prompt_mask):
+        """Compute dot-product scores between hs and prompt."""
+        # hs has shape (num_layer, bs, num_query, d_model)
+        # prompt has shape (seq, bs, d_model)
+        # prompt_mask has shape (bs, seq), where 1 is valid and 0 is padding
+        assert hs.dim() == 4 and prompt.dim() == 3 and prompt_mask.dim() == 2
+
+        # apply MLP on prompt if specified
+        if self.prompt_mlp is not None:
+            prompt = self.prompt_mlp(prompt.to(hs.dtype))
+
+        # first, get the mean-pooled version of the prompt
+        pooled_prompt = self.mean_pool_text(prompt, prompt_mask)
+
+        # then, project pooled_prompt and hs to d_proj dimensions
+        proj_pooled_prompt = self.prompt_proj(pooled_prompt)  # (bs, d_proj)
+        proj_hs = self.hs_proj(hs)  # (num_layer, bs, num_query, d_proj)
+
+        # finally, get dot-product scores of shape (num_layer, bs, num_query, 1)
+        scores = torch.matmul(proj_hs, proj_pooled_prompt.unsqueeze(-1))
+        scores *= self.scale
+
+        # clamp scores to a max value to avoid numerical issues in loss or matcher
+        if self.clamp_logits:
+            scores.clamp_(min=-self.clamp_max_val, max=self.clamp_max_val)
+
+        return scores
+
+
+class LayerScale(nn.Module):
+    """LayerScale module as introduced in "Meta Pseudo Labels" and used in."""
+
+    def __init__(
+        self,
+        dim: int,
+        init_values: float | Tensor = 1e-5,
+        inplace: bool = False,
+    ) -> None:
+        """Initialize the LayerScale module."""
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Apply LayerScale to the input tensor."""
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
+
+
+class TransformerWrapper(nn.Module):
+    """A wrapper for the transformer consisting of an encoder and a decoder."""
+
+    def __init__(
+        self,
+        encoder,
+        decoder,
+        d_model: int,
+        two_stage_type="none",  # ["none"] only for now
+        pos_enc_at_input_dec=True,
+    ):
+        """Initialize the TransformerWrapper."""
+        super().__init__()
+        self.encoder = encoder
+        self.decoder = decoder
+        self.num_queries = decoder.num_queries if decoder is not None else None
+        self.pos_enc_at_input_dec = pos_enc_at_input_dec
+
+        # for two stage
+        assert two_stage_type in ["none"], f"unknown param {two_stage_type} of two_stage_type"
+        self.two_stage_type = two_stage_type
+
+        self._reset_parameters()
+        self.d_model = d_model
+
+    def _reset_parameters(self):
+        """Initialize the parameters of the model."""
+        for n, p in self.named_parameters():
+            if p.dim() > 1:
+                if "box_embed" not in n and "query_embed" not in n and "reference_points" not in n:
+                    nn.init.xavier_uniform_(p)
+
+
+def get_valid_ratio(mask):
+    """Compute the valid ratio of height and width from the mask."""
+    _, H, W = mask.shape
+    valid_H = torch.sum(~mask[:, :, 0], 1)
+    valid_W = torch.sum(~mask[:, 0, :], 1)
+    valid_ratio_h = valid_H.float() / H
+    valid_ratio_w = valid_W.float() / W
+    valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
+    return valid_ratio
+
+
+def gen_sineembed_for_position(pos_tensor: torch.Tensor, num_feats: int = 256):
+    """Generate sinusoidal position embeddings for 2D or 4D coordinate tensors.
+
+    This function creates sinusoidal embeddings using sine and cosine functions at different frequencies, similar to the
+    positional encoding used in Transformer models. It supports both 2D position tensors (x, y) and 4D tensors (x, y, w,
+    h) for bounding box coordinates.
+
+    Args:
+        pos_tensor (torch.Tensor): Input position tensor of shape (n_query, bs, 2) for 2D coordinates or (n_query, bs,
+            4) for 4D coordinates (bounding boxes).
+        num_feats (int): Number of feature dimensions for the output embedding. Must be even. Defaults to 256.
+
+    Returns:
+        (torch.Tensor): Sinusoidal position embeddings of shape (n_query, bs, num_feats) for 2D input or (n_query, bs,
+            num_feats * 2) for 4D input.
+
+    Raises:
+        AssertionError: If num_feats is not even.
+        ValueError: If pos_tensor.size(-1) is not 2 or 4.
+
+    Examples:
+        >>> pos_2d = torch.rand(100, 8, 2)  # 100 queries, batch size 8, 2D coordinates
+        >>> embeddings_2d = gen_sineembed_for_position(pos_2d, num_feats=256)
+        >>> embeddings_2d.shape
+        torch.Size([100, 8, 256])
+        >>> pos_4d = torch.rand(50, 4, 4)  # 50 queries, batch size 4, 4D coordinates
+        >>> embeddings_4d = gen_sineembed_for_position(pos_4d, num_feats=128)
+        >>> embeddings_4d.shape
+        torch.Size([50, 4, 256])
+    """
+    assert num_feats % 2 == 0
+    num_feats = num_feats // 2
+    # n_query, bs, _ = pos_tensor.size()
+    # sineembed_tensor = torch.zeros(n_query, bs, 256)
+    scale = 2 * math.pi
+    dim_t = torch.arange(num_feats, dtype=pos_tensor.dtype, device=pos_tensor.device)
+    dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode="floor")) / num_feats)
+    x_embed = pos_tensor[:, :, 0] * scale
+    y_embed = pos_tensor[:, :, 1] * scale
+    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=3).flatten(2)
+    pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
+    if pos_tensor.size(-1) == 2:
+        pos = torch.cat((pos_y, pos_x), dim=2)
+    elif pos_tensor.size(-1) == 4:
+        w_embed = pos_tensor[:, :, 2] * scale
+        pos_w = w_embed[:, :, None] / dim_t
+        pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)
+
+        h_embed = pos_tensor[:, :, 3] * scale
+        pos_h = h_embed[:, :, None] / dim_t
+        pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)
+
+        pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
+    else:
+        raise ValueError(f"Unknown pos_tensor shape(-1):{pos_tensor.size(-1)}")
+    return pos

algorithms/dms_yolo/code/ultralytics/models/sam/sam3/necks.py

@@ -0,0 +1,129 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+"""Necks are the interface between a vision backbone and the rest of the detection model."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+
+import torch
+import torch.nn as nn
+
+
+class Sam3DualViTDetNeck(nn.Module):
+    """A neck that implements a simple FPN as in ViTDet, with support for dual necks (for SAM3 and SAM2)."""
+
+    def __init__(
+        self,
+        trunk: nn.Module,
+        position_encoding: nn.Module,
+        d_model: int,
+        scale_factors=(4.0, 2.0, 1.0, 0.5),
+        add_sam2_neck: bool = False,
+    ):
+        """
+        SimpleFPN neck a la ViTDet
+        (From detectron2, very lightly adapted)
+        It supports a "dual neck" setting, where we have two identical necks (for SAM3 and SAM2), with different weights.
+
+        :param trunk: the backbone
+        :param position_encoding: the positional encoding to use
+        :param d_model: the dimension of the model
+        """
+        super().__init__()
+        self.trunk = trunk
+        self.position_encoding = position_encoding
+        self.convs = nn.ModuleList()
+
+        self.scale_factors = scale_factors
+        use_bias = True
+        dim: int = self.trunk.channel_list[-1]
+
+        for _, scale in enumerate(scale_factors):
+            current = nn.Sequential()
+
+            if scale == 4.0:
+                current.add_module(
+                    "dconv_2x2_0",
+                    nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2),
+                )
+                current.add_module(
+                    "gelu",
+                    nn.GELU(),
+                )
+                current.add_module(
+                    "dconv_2x2_1",
+                    nn.ConvTranspose2d(dim // 2, dim // 4, kernel_size=2, stride=2),
+                )
+                out_dim = dim // 4
+            elif scale == 2.0:
+                current.add_module(
+                    "dconv_2x2",
+                    nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2),
+                )
+                out_dim = dim // 2
+            elif scale == 1.0:
+                out_dim = dim
+            elif scale == 0.5:
+                current.add_module(
+                    "maxpool_2x2",
+                    nn.MaxPool2d(kernel_size=2, stride=2),
+                )
+                out_dim = dim
+            else:
+                raise NotImplementedError(f"scale_factor={scale} is not supported yet.")
+
+            current.add_module(
+                "conv_1x1",
+                nn.Conv2d(
+                    in_channels=out_dim,
+                    out_channels=d_model,
+                    kernel_size=1,
+                    bias=use_bias,
+                ),
+            )
+            current.add_module(
+                "conv_3x3",
+                nn.Conv2d(
+                    in_channels=d_model,
+                    out_channels=d_model,
+                    kernel_size=3,
+                    padding=1,
+                    bias=use_bias,
+                ),
+            )
+            self.convs.append(current)
+
+        self.sam2_convs = None
+        if add_sam2_neck:
+            # Assumes sam2 neck is just a clone of the original neck
+            self.sam2_convs = deepcopy(self.convs)
+
+    def forward(
+        self, tensor_list: list[torch.Tensor]
+    ) -> tuple[list[torch.Tensor], list[torch.Tensor], list[torch.Tensor] | None, list[torch.Tensor] | None]:
+        """Get feature maps and positional encodings from the neck."""
+        xs = self.trunk(tensor_list)
+        x = xs[-1]  # simpleFPN
+        sam3_out, sam3_pos = self.sam_forward_feature_levels(x, self.convs)
+        if self.sam2_convs is None:
+            return sam3_out, sam3_pos, None, None
+        sam2_out, sam2_pos = self.sam_forward_feature_levels(x, self.sam2_convs)
+        return sam3_out, sam3_pos, sam2_out, sam2_pos
+
+    def sam_forward_feature_levels(
+        self, x: torch.Tensor, convs: nn.ModuleList
+    ) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
+        """Run neck convolutions and compute positional encodings for each feature level."""
+        outs, poss = [], []
+        for conv in convs:
+            feat = conv(x)
+            outs.append(feat)
+            poss.append(self.position_encoding(feat).to(feat.dtype))
+        return outs, poss
+
+    def set_imgsz(self, imgsz: list[int] = [1008, 1008]):
+        """Set the image size for the trunk backbone."""
+        self.trunk.set_imgsz(imgsz)

algorithms/dms_yolo/code/ultralytics/models/sam/sam3/sam3_image.py

@@ -0,0 +1,339 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+from __future__ import annotations
+
+from copy import deepcopy
+
+import torch
+
+from ultralytics.nn.modules.utils import inverse_sigmoid
+from ultralytics.utils.ops import xywh2xyxy
+
+from ..modules.sam import SAM2Model
+from .geometry_encoders import Prompt
+from .vl_combiner import SAM3VLBackbone
+
+
+def _update_out(out, out_name, out_value, auxiliary=True, update_aux=True):
+    """Helper function to update output dictionary with main and auxiliary outputs."""
+    out[out_name] = out_value[-1] if auxiliary else out_value
+    if auxiliary and update_aux:
+        if "aux_outputs" not in out:
+            out["aux_outputs"] = [{} for _ in range(len(out_value) - 1)]
+        assert len(out["aux_outputs"]) == len(out_value) - 1
+        for aux_output, aux_value in zip(out["aux_outputs"], out_value[:-1]):
+            aux_output[out_name] = aux_value
+
+
+class SAM3SemanticModel(torch.nn.Module):
+    """SAM3 model for semantic segmentation with vision-language backbone."""
+
+    def __init__(
+        self,
+        backbone: SAM3VLBackbone,
+        transformer,
+        input_geometry_encoder,
+        segmentation_head=None,
+        num_feature_levels=1,
+        o2m_mask_predict=True,
+        dot_prod_scoring=None,
+        use_instance_query: bool = True,
+        multimask_output: bool = True,
+        use_act_checkpoint_seg_head: bool = True,
+        matcher=None,
+        use_dot_prod_scoring=True,
+        supervise_joint_box_scores: bool = False,  # only relevant if using presence token/score
+        detach_presence_in_joint_score: bool = False,  # only relevant if using presence token/score
+        separate_scorer_for_instance: bool = False,
+        num_interactive_steps_val: int = 0,
+    ):
+        """Initialize the SAM3SemanticModel."""
+        super().__init__()
+        self.backbone = backbone
+        self.geometry_encoder = input_geometry_encoder
+        self.transformer = transformer
+        self.hidden_dim = transformer.d_model
+        self.num_feature_levels = num_feature_levels
+        self.segmentation_head = segmentation_head
+
+        self.o2m_mask_predict = o2m_mask_predict
+
+        self.dot_prod_scoring = dot_prod_scoring
+        self.use_act_checkpoint_seg_head = use_act_checkpoint_seg_head
+        self.matcher = matcher
+
+        self.num_interactive_steps_val = num_interactive_steps_val
+        self.use_dot_prod_scoring = use_dot_prod_scoring
+
+        if self.use_dot_prod_scoring:
+            assert dot_prod_scoring is not None
+            self.dot_prod_scoring = dot_prod_scoring
+            self.instance_dot_prod_scoring = None
+            if separate_scorer_for_instance:
+                self.instance_dot_prod_scoring = deepcopy(dot_prod_scoring)
+        else:
+            self.class_embed = torch.nn.Linear(self.hidden_dim, 1)
+            self.instance_class_embed = None
+            if separate_scorer_for_instance:
+                self.instance_class_embed = deepcopy(self.class_embed)
+
+        self.supervise_joint_box_scores = supervise_joint_box_scores
+        self.detach_presence_in_joint_score = detach_presence_in_joint_score
+
+        # verify the number of queries for O2O and O2M
+        num_o2o_static = self.transformer.decoder.num_queries
+        num_o2m_static = self.transformer.decoder.num_o2m_queries
+        assert num_o2m_static == (num_o2o_static if self.transformer.decoder.dac else 0)
+        self.dac = self.transformer.decoder.dac
+
+        self.use_instance_query = use_instance_query
+        self.multimask_output = multimask_output
+
+        self.text_embeddings = {}
+        self.names = []
+
+    def _encode_prompt(
+        self,
+        img_feats,
+        img_pos_embeds,
+        vis_feat_sizes,
+        geometric_prompt,
+        visual_prompt_embed=None,
+        visual_prompt_mask=None,
+        prev_mask_pred=None,
+    ):
+        """Encode the geometric and visual prompts."""
+        if prev_mask_pred is not None:
+            img_feats = [img_feats[-1] + prev_mask_pred]
+        # Encode geometry
+        geo_feats, geo_masks = self.geometry_encoder(
+            geo_prompt=geometric_prompt,
+            img_feats=img_feats,
+            img_sizes=vis_feat_sizes,
+            img_pos_embeds=img_pos_embeds,
+        )
+        if visual_prompt_embed is None:
+            visual_prompt_embed = torch.zeros((0, *geo_feats.shape[1:]), device=geo_feats.device)
+            visual_prompt_mask = torch.zeros(
+                (*geo_masks.shape[:-1], 0),
+                device=geo_masks.device,
+                dtype=geo_masks.dtype,
+            )
+        prompt = torch.cat([geo_feats, visual_prompt_embed], dim=0)
+        prompt_mask = torch.cat([geo_masks, visual_prompt_mask], dim=1)
+        return prompt, prompt_mask
+
+    def _run_encoder(
+        self,
+        img_feats,
+        img_pos_embeds,
+        vis_feat_sizes,
+        prompt,
+        prompt_mask,
+        encoder_extra_kwargs: dict | None = None,
+    ):
+        """Run the transformer encoder."""
+        # Run the encoder
+        # make a copy of the image feature lists since the encoder may modify these lists in-place
+        memory = self.transformer.encoder(
+            src=img_feats.copy(),
+            src_key_padding_mask=None,
+            src_pos=img_pos_embeds.copy(),
+            prompt=prompt,
+            prompt_key_padding_mask=prompt_mask,
+            feat_sizes=vis_feat_sizes,
+            encoder_extra_kwargs=encoder_extra_kwargs,
+        )
+        encoder_out = {
+            # encoded image features
+            "encoder_hidden_states": memory["memory"],
+            "pos_embed": memory["pos_embed"],
+            "padding_mask": memory["padding_mask"],
+            "spatial_shapes": memory["spatial_shapes"],
+            "valid_ratios": memory["valid_ratios"],
+            "vis_feat_sizes": vis_feat_sizes,
+            # encoded text features (or other prompts)
+            "prompt_before_enc": prompt,
+            "prompt_after_enc": memory.get("memory_text", prompt),
+            "prompt_mask": prompt_mask,
+        }
+        return encoder_out
+
+    def _run_decoder(
+        self,
+        pos_embed,
+        memory,
+        src_mask,
+        out,
+        prompt,
+        prompt_mask,
+        encoder_out,
+    ):
+        """Run the transformer decoder."""
+        bs = memory.shape[1]
+        query_embed = self.transformer.decoder.query_embed.weight
+        tgt = query_embed.unsqueeze(1).repeat(1, bs, 1)
+
+        hs, reference_boxes, dec_presence_out, _ = self.transformer.decoder(
+            tgt=tgt,
+            memory=memory,
+            memory_key_padding_mask=src_mask,
+            pos=pos_embed,
+            reference_boxes=None,
+            spatial_shapes=encoder_out["spatial_shapes"],
+            valid_ratios=encoder_out["valid_ratios"],
+            tgt_mask=None,
+            memory_text=prompt,
+            text_attention_mask=prompt_mask,
+            apply_dac=False,
+        )
+        hs = hs.transpose(1, 2)  # seq-first to batch-first
+        reference_boxes = reference_boxes.transpose(1, 2)  # seq-first to batch-first
+        if dec_presence_out is not None:
+            # seq-first to batch-first
+            dec_presence_out = dec_presence_out.transpose(1, 2)
+        self._update_scores_and_boxes(
+            out,
+            hs,
+            reference_boxes,
+            prompt,
+            prompt_mask,
+            dec_presence_out=dec_presence_out,
+        )
+        return out, hs
+
+    def _update_scores_and_boxes(
+        self,
+        out,
+        hs,
+        reference_boxes,
+        prompt,
+        prompt_mask,
+        dec_presence_out=None,
+        is_instance_prompt=False,
+    ):
+        """Update output dict with class scores and box predictions."""
+        num_o2o = hs.size(2)
+        # score prediction
+        if self.use_dot_prod_scoring:
+            dot_prod_scoring_head = self.dot_prod_scoring
+            if is_instance_prompt and self.instance_dot_prod_scoring is not None:
+                dot_prod_scoring_head = self.instance_dot_prod_scoring
+            outputs_class = dot_prod_scoring_head(hs, prompt, prompt_mask)
+        else:
+            class_embed_head = self.class_embed
+            if is_instance_prompt and self.instance_class_embed is not None:
+                class_embed_head = self.instance_class_embed
+            outputs_class = class_embed_head(hs)
+
+        # box prediction
+        box_head = self.transformer.decoder.bbox_embed
+        if is_instance_prompt and self.transformer.decoder.instance_bbox_embed is not None:
+            box_head = self.transformer.decoder.instance_bbox_embed
+        anchor_box_offsets = box_head(hs)
+        reference_boxes_inv_sig = inverse_sigmoid(reference_boxes)
+        outputs_coord = (reference_boxes_inv_sig + anchor_box_offsets).sigmoid()
+        outputs_boxes_xyxy = xywh2xyxy(outputs_coord)
+
+        if dec_presence_out is not None:
+            _update_out(out, "presence_logit_dec", dec_presence_out, update_aux=False)
+
+        if self.supervise_joint_box_scores:
+            assert dec_presence_out is not None
+            prob_dec_presence_out = dec_presence_out.clone().sigmoid()
+            if self.detach_presence_in_joint_score:
+                prob_dec_presence_out = prob_dec_presence_out.detach()
+
+            outputs_class = inverse_sigmoid(outputs_class.sigmoid() * prob_dec_presence_out.unsqueeze(2)).clamp(
+                min=-10.0, max=10.0
+            )
+
+        _update_out(out, "pred_logits", outputs_class[:, :, :num_o2o], update_aux=False)
+        _update_out(out, "pred_boxes", outputs_coord[:, :, :num_o2o], update_aux=False)
+        _update_out(out, "pred_boxes_xyxy", outputs_boxes_xyxy[:, :, :num_o2o], update_aux=False)
+
+    def _run_segmentation_heads(
+        self,
+        out,
+        backbone_out,
+        encoder_hidden_states,
+        prompt,
+        prompt_mask,
+        hs,
+    ):
+        """Run segmentation heads and get masks."""
+        if self.segmentation_head is not None:
+            num_o2o = hs.size(2)
+            obj_queries = hs if self.o2m_mask_predict else hs[:, :, :num_o2o]
+            seg_head_outputs = self.segmentation_head(
+                backbone_feats=backbone_out["backbone_fpn"],
+                obj_queries=obj_queries,
+                encoder_hidden_states=encoder_hidden_states,
+                prompt=prompt,
+                prompt_mask=prompt_mask,
+            )
+            for k, v in seg_head_outputs.items():
+                if k in self.segmentation_head.instance_keys:
+                    _update_out(out, k, v[:, :num_o2o], auxiliary=False)
+                else:
+                    out[k] = v
+        else:
+            backbone_out.pop("backbone_fpn", None)
+
+    def forward_grounding(
+        self, backbone_out: dict[str, torch.Tensor], text_ids: torch.Tensor, geometric_prompt: Prompt = None
+    ):
+        """Forward pass for grounding (detection + segmentation) given input images and text."""
+        backbone_out, img_feats, img_pos_embeds, vis_feat_sizes = SAM2Model._prepare_backbone_features(
+            self, backbone_out, batch=len(text_ids)
+        )
+        backbone_out.update({k: v for k, v in self.text_embeddings.items()})
+        with torch.profiler.record_function("SAM3Image._encode_prompt"):
+            prompt, prompt_mask = self._encode_prompt(img_feats, img_pos_embeds, vis_feat_sizes, geometric_prompt)
+        # index text features (note that regardless of early or late fusion, the batch size of
+        # `txt_feats` is always the number of *prompts* in the encoder)
+        txt_feats = backbone_out["language_features"][:, text_ids]
+        txt_masks = backbone_out["language_mask"][text_ids]
+        # encode text
+        prompt = torch.cat([txt_feats, prompt], dim=0)
+        prompt_mask = torch.cat([txt_masks, prompt_mask], dim=1)
+
+        # Run the encoder
+        with torch.profiler.record_function("SAM3Image._run_encoder"):
+            encoder_out = self._run_encoder(img_feats, img_pos_embeds, vis_feat_sizes, prompt, prompt_mask)
+        out = {"backbone_out": backbone_out}
+
+        # Run the decoder
+        with torch.profiler.record_function("SAM3Image._run_decoder"):
+            out, hs = self._run_decoder(
+                memory=encoder_out["encoder_hidden_states"],
+                pos_embed=encoder_out["pos_embed"],
+                src_mask=encoder_out["padding_mask"],
+                out=out,
+                prompt=prompt,
+                prompt_mask=prompt_mask,
+                encoder_out=encoder_out,
+            )
+
+        # Run segmentation heads
+        with torch.profiler.record_function("SAM3Image._run_segmentation_heads"):
+            self._run_segmentation_heads(
+                out=out,
+                backbone_out=backbone_out,
+                encoder_hidden_states=encoder_out["encoder_hidden_states"],
+                prompt=prompt,
+                prompt_mask=prompt_mask,
+                hs=hs,
+            )
+        return out
+
+    def set_classes(self, text: list[str]):
+        """Set the text embeddings for the given class names."""
+        self.text_embeddings = self.backbone.forward_text(text)
+        self.names = text
+
+    def set_imgsz(self, imgsz: tuple[int, int]):
+        """Set the image size for the model."""
+        self.backbone.set_imgsz(imgsz)

algorithms/dms_yolo/code/ultralytics/models/sam/sam3/text_encoder_ve.py

@@ -0,0 +1,307 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+from __future__ import annotations
+
+from collections import OrderedDict
+from typing import Callable
+
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+
+from .model_misc import LayerScale
+
+
+class ResidualAttentionBlock(nn.Module):
+    """Transformer block with multi-head attention, layer normalization, and MLP feed-forward network."""
+
+    def __init__(
+        self,
+        d_model: int,
+        n_head: int,
+        mlp_ratio: float = 4.0,
+        ls_init_value: float | None = None,
+        act_layer: Callable[[], nn.Module] = nn.GELU,
+        norm_layer: Callable[[int], nn.Module] = nn.LayerNorm,
+    ):
+        """Initialize residual attention block with configurable dimensions and normalization."""
+        super().__init__()
+        # Attention
+        self.attn = nn.MultiheadAttention(d_model, n_head, batch_first=True)
+
+        # LayerNorm, LayerScale
+        self.ln_1 = norm_layer(d_model)
+        self.ln_2 = norm_layer(d_model)
+
+        self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
+        self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
+
+        # MLP
+        mlp_width = int(d_model * mlp_ratio)
+        self.mlp = nn.Sequential(
+            OrderedDict(
+                [
+                    ("c_fc", nn.Linear(d_model, mlp_width)),
+                    ("gelu", act_layer()),
+                    ("c_proj", nn.Linear(mlp_width, d_model)),
+                ]
+            )
+        )
+
+    def attention(
+        self, q_x: torch.Tensor, k_x: torch.Tensor = None, v_x: torch.Tensor = None, attn_mask: torch.Tensor = None
+    ) -> torch.Tensor:
+        """Compute multi-head attention with optional cross-attention support and masking."""
+        k_x = k_x if k_x is not None else q_x
+        v_x = v_x if v_x is not None else q_x
+        if attn_mask is not None:
+            # Leave boolean masks as is
+            if not attn_mask.dtype == torch.bool:
+                attn_mask = attn_mask.to(q_x.dtype)
+
+        return self.attn(q_x, k_x, v_x, need_weights=False, attn_mask=attn_mask)[0]
+
+    def forward(
+        self, q_x: torch.Tensor, k_x: torch.Tensor = None, v_x: torch.Tensor = None, attn_mask: torch.Tensor = None
+    ) -> torch.Tensor:
+        """Apply residual attention with layer normalization and MLP, supporting optional cross-attention."""
+        k_x = self.ln_1_kv(k_x) if hasattr(self, "ln_1_kv") and k_x is not None else None
+        v_x = self.ln_1_kv(v_x) if hasattr(self, "ln_1_kv") and v_x is not None else None
+        x = q_x + self.ls_1(self.attention(q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask))
+        x = x + self.ls_2(self.mlp(self.ln_2(x)))
+        return x
+
+
+class Transformer(nn.Module):
+    """Stack of residual attention blocks forming a transformer encoder with optional gradient checkpointing."""
+
+    def __init__(
+        self,
+        width: int,
+        layers: int,
+        heads: int,
+        mlp_ratio: float = 4.0,
+        ls_init_value: float | None = None,
+        act_layer: Callable[[], nn.Module] = nn.GELU,
+        norm_layer: Callable[[int], nn.Module] = nn.LayerNorm,
+        compile_mode: str | None = None,
+        use_act_checkpoint: bool = False,
+    ):
+        """Initialize transformer with configurable depth, width, and optional compilation/checkpointing."""
+        super().__init__()
+        self.width = width
+        self.layers = layers
+        self.grad_checkpointing = use_act_checkpoint
+        self.resblocks = nn.ModuleList(
+            [
+                ResidualAttentionBlock(
+                    width,
+                    heads,
+                    mlp_ratio,
+                    ls_init_value=ls_init_value,
+                    act_layer=act_layer,
+                    norm_layer=norm_layer,
+                )
+                for _ in range(layers)
+            ]
+        )
+
+        if compile_mode is not None:
+            self.forward = torch.compile(self.forward, mode=compile_mode, fullgraph=True)
+            if self.grad_checkpointing:
+                torch._dynamo.config.optimize_ddp = False
+
+    def forward(self, x: torch.Tensor, attn_mask: torch.Tensor = None) -> torch.Tensor:
+        """Process input through all transformer blocks with optional gradient checkpointing during training."""
+        for _, r in enumerate(self.resblocks):
+            if self.grad_checkpointing and not torch.jit.is_scripting() and self.training:
+                x = checkpoint(r, x, None, None, attn_mask, use_reentrant=False)
+            else:
+                x = r(x, attn_mask=attn_mask)
+        return x
+
+
+def text_global_pool(
+    x: torch.Tensor, text: torch.Tensor = None, pool_type: str = "argmax"
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Extract pooled representation and tokens from text embeddings using specified pooling strategy
+    (first/last/argmax/none).
+    """
+    if pool_type == "first":
+        pooled, tokens = x[:, 0], x[:, 1:]
+    elif pool_type == "last":
+        pooled, tokens = x[:, -1], x[:, :-1]
+    elif pool_type == "argmax":
+        # take features from the eot embedding (eot_token is the highest number in each sequence)
+        assert text is not None
+        pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x
+    else:
+        pooled = tokens = x
+    return pooled, tokens
+
+
+class TextTransformer(nn.Module):
+    """Text transformer encoder with causal masking and flexible pooling strategies."""
+
+    def __init__(
+        self,
+        context_length: int = 77,
+        vocab_size: int = 49408,
+        width: int = 512,
+        heads: int = 8,
+        layers: int = 12,
+        mlp_ratio: float = 4.0,
+        ls_init_value: float | None = None,
+        output_dim: int = 512,
+        no_causal_mask: bool = False,
+        pool_type: str = "none",  # no pooling
+        proj_bias: bool = False,
+        act_layer: Callable = nn.GELU,
+        norm_layer: Callable = nn.LayerNorm,
+        output_tokens: bool = False,
+        use_ln_post: bool = True,
+        compile_mode: str | None = None,
+        use_act_checkpoint: bool = False,
+    ):
+        """Initialize text transformer with embedding layers, transformer blocks, and pooling options."""
+        super().__init__()
+        assert pool_type in ("first", "last", "argmax", "none")
+        self.output_tokens = output_tokens
+        self.num_pos = self.context_length = context_length
+        self.vocab_size = vocab_size
+        self.width = width
+        self.output_dim = output_dim
+        self.heads = heads
+        self.pool_type = pool_type
+
+        self.token_embedding = nn.Embedding(self.vocab_size, width)
+        self.positional_embedding = nn.Parameter(torch.empty(self.num_pos, width))
+        self.transformer = Transformer(
+            width=width,
+            layers=layers,
+            heads=heads,
+            mlp_ratio=mlp_ratio,
+            ls_init_value=ls_init_value,
+            act_layer=act_layer,
+            norm_layer=norm_layer,
+            compile_mode=compile_mode,
+            use_act_checkpoint=use_act_checkpoint,
+        )
+        self.ln_final = norm_layer(width) if use_ln_post else nn.Identity()
+        if no_causal_mask:
+            self.attn_mask = None
+        else:
+            self.register_buffer("attn_mask", self.build_causal_mask(), persistent=False)
+        if proj_bias:
+            self.text_projection = nn.Linear(width, output_dim)
+        else:
+            self.text_projection = nn.Parameter(torch.empty(width, output_dim))
+
+    def build_causal_mask(self) -> torch.Tensor:
+        """Create a causal attention mask to prevent attention to future tokens."""
+        # lazily create causal attention mask, with full attention between the tokens
+        # pytorch uses additive attention mask; fill with -inf
+        mask = torch.empty(self.num_pos, self.num_pos)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)  # zero out the lower diagonal
+        return mask
+
+    def forward(self, text: torch.Tensor) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        """Forward pass through the text transformer, returning pooled output and optionally token embeddings."""
+        seq_len = text.shape[1]
+        x = self.token_embedding(text)  # [batch_size, n_ctx, d_model]
+
+        attn_mask = self.attn_mask
+        if attn_mask is not None:
+            attn_mask = attn_mask[:seq_len, :seq_len]
+
+        x = x + self.positional_embedding[:seq_len]
+        x = self.transformer(x, attn_mask=attn_mask)
+
+        x = self.ln_final(x)
+        pooled, tokens = text_global_pool(x, text, pool_type=self.pool_type)
+        if self.text_projection is not None:
+            if isinstance(self.text_projection, nn.Linear):
+                pooled = self.text_projection(pooled)
+            else:
+                pooled = pooled @ self.text_projection
+        if self.output_tokens:
+            return pooled, tokens
+        return pooled
+
+
+class VETextEncoder(nn.Module):
+    """Text encoder for Vision Encoder (VE) models, combining a text transformer and a linear resizer."""
+
+    def __init__(
+        self,
+        d_model: int,
+        tokenizer: Callable,
+        width: int = 1024,
+        heads: int = 16,
+        layers: int = 24,
+        context_length: int = 32,
+        vocab_size: int = 49408,
+        use_ln_post: bool = True,
+        compile_mode: str | None = None,
+        use_act_checkpoint: bool = True,
+    ):
+        """Initialize VE text encoder with a text transformer and a linear resizer to match decoder dimensions."""
+        super().__init__()
+        self.context_length = context_length
+        self.use_ln_post = use_ln_post
+        self.tokenizer = tokenizer
+
+        self.encoder = TextTransformer(
+            context_length=self.context_length,
+            vocab_size=vocab_size,
+            width=width,
+            heads=heads,
+            layers=layers,
+            # we want the tokens, not just the pooled output
+            output_tokens=True,
+            use_ln_post=use_ln_post,
+            compile_mode=compile_mode,
+            use_act_checkpoint=use_act_checkpoint,
+        )
+        self.resizer = nn.Linear(self.encoder.width, d_model)
+
+    def forward(
+        self, text: list[str] | tuple[torch.Tensor, torch.Tensor, dict], input_boxes: list | None = None
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Encode text input, either raw strings or pre-encoded tensors, and resize to match decoder dimensions."""
+        if isinstance(text[0], str):
+            # no use case for this
+            assert input_boxes is None or len(input_boxes) == 0, "not supported"
+
+            # Encode the text
+            tokenized = self.tokenizer(text, context_length=self.context_length).to(
+                self.resizer.weight.device
+            )  # [b, seq_len]
+            text_attention_mask = (tokenized != 0).bool()
+
+            # manually embed the tokens
+            inputs_embeds = self.encoder.token_embedding(tokenized)  # [b, seq_len, d=1024]
+            _, text_memory = self.encoder(tokenized)  # [b, seq_len, d=1024]
+
+            assert text_memory.shape[1] == inputs_embeds.shape[1]
+            # Invert attention mask because its the opposite in pytorch transformer
+            text_attention_mask = text_attention_mask.ne(1)
+            # Transpose memory because pytorch's attention expects sequence first
+            text_memory = text_memory.transpose(0, 1)
+            # Resize the encoder hidden states to be of the same d_model as the decoder
+            text_memory_resized = self.resizer(text_memory)
+        else:
+            # The text is already encoded, use as is.
+            text_attention_mask, text_memory_resized, tokenized = text
+            inputs_embeds = tokenized["inputs_embeds"]
+            assert input_boxes is None or len(input_boxes) == 0, "Can't replace boxes in text if it's already encoded"
+
+        # Note that the input_embeds are returned in pytorch's convention (sequence first)
+        return (
+            text_attention_mask,
+            text_memory_resized,
+            inputs_embeds.transpose(0, 1),
+        )

algorithms/dms_yolo/code/ultralytics/models/sam/sam3/vitdet.py

@@ -0,0 +1,547 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+"""
+ViTDet backbone adapted from Detectron2.
+This module implements Vision Transformer (ViT) backbone for object detection.
+
+Rope embedding code adopted 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
+"""
+
+from __future__ import annotations
+
+import math
+from functools import partial
+from typing import Callable
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from torch import Tensor
+
+from ultralytics.models.sam.modules.blocks import PatchEmbed
+from ultralytics.models.sam.modules.utils import (
+    apply_rotary_enc,
+    compute_axial_cis,
+    concat_rel_pos,
+    get_abs_pos,
+    window_partition,
+    window_unpartition,
+)
+from ultralytics.utils.checks import check_requirements
+
+from .model_misc import LayerScale
+
+
+class Attention(nn.Module):
+    """Multi-head Attention block with relative position embeddings and 2d-rope."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        input_size: tuple[int, int] | None = None,
+        cls_token: bool = False,
+        use_rope: bool = False,
+        rope_theta: float = 10000.0,
+        rope_pt_size: tuple[int, int] | None = None,
+        rope_interp: bool = False,
+    ):
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            input_size (int or None): Input resolution for calculating the relative positional parameter size or rope
+                size.
+            attn_type: Type of attention operation, e.g. "vanilla", "vanilla-xformer".
+            cls_token: whether a cls_token is present.
+            use_rope: whether to use rope 2d (indep of use_rel_pos, as it can be used together)
+            use_rel_pos: whether to use relative positional embeddings
+            rope_theta: control frequencies of rope
+            rope_pt_size: size of rope in previous stage of training, needed for interpolation or tiling
+            rope_interp: whether to interpolate (or extrapolate) rope to match input size.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim**-0.5
+        self.cls_token = cls_token
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+        # rel_pos embeddings and rope
+        self.use_rel_pos = use_rel_pos
+        self.input_size = input_size
+
+        self.use_rope = use_rope
+        self.rope_theta = rope_theta
+        self.rope_pt_size = rope_pt_size
+        self.rope_interp = rope_interp
+
+        # init rel_pos embeddings and rope
+        self._setup_rel_pos(rel_pos_zero_init, input_size)
+        self._setup_rope_freqs(input_size)
+
+    def _setup_rel_pos(self, rel_pos_zero_init: bool = True, input_size: tuple[int, int] | None = None) -> None:
+        """Setup relative positional embeddings."""
+        if not self.use_rel_pos:
+            self.rel_pos_h = None
+            self.rel_pos_w = None
+            return
+
+        assert input_size is not None
+        assert self.cls_token is False, "not supported"
+        # initialize relative positional embeddings
+        self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, self.head_dim))
+        self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, self.head_dim))
+
+        if not rel_pos_zero_init:
+            nn.init.trunc_normal_(self.rel_pos_h, std=0.02)
+            nn.init.trunc_normal_(self.rel_pos_w, std=0.02)
+
+        # Precompute the relative coords
+        H, W = input_size
+        q_coords = torch.arange(H)[:, None]
+        k_coords = torch.arange(W)[None, :]
+        relative_coords = (q_coords - k_coords) + (H - 1)
+        self.relative_coords = relative_coords.long()
+
+    def _setup_rope_freqs(self, input_size: tuple[int, int] | None = None) -> None:
+        """Setup 2d-rope frequencies."""
+        if not self.use_rope:
+            self.freqs_cis = None
+            return
+
+        assert input_size is not None
+        # determine rope input size
+        if self.rope_pt_size is None:
+            self.rope_pt_size = input_size
+
+        # initialize 2d rope freqs
+        self.compute_cis = partial(
+            compute_axial_cis,
+            dim=self.head_dim,
+            theta=self.rope_theta,
+        )
+
+        # interpolate rope
+        scale_pos = 1.0
+        if self.rope_interp:
+            scale_pos = self.rope_pt_size[0] / input_size[0]
+        # get scaled freqs_cis
+        freqs_cis = self.compute_cis(
+            end_x=input_size[0],
+            end_y=input_size[1],
+            scale_pos=scale_pos,
+        )
+        if self.cls_token:
+            t = torch.zeros(
+                self.head_dim // 2,
+                dtype=torch.float32,
+                device=freqs_cis.device,
+            )
+            cls_freqs_cis = torch.polar(torch.ones_like(t), t)[None, :]
+            freqs_cis = torch.cat([cls_freqs_cis, freqs_cis], dim=0)
+
+        self.freqs_cis = freqs_cis
+
+    def _apply_rope(self, q, k) -> tuple[Tensor, Tensor]:
+        """Apply 2d-rope to q and k."""
+        if not self.use_rope:
+            return q, k
+
+        assert self.freqs_cis is not None
+        return apply_rotary_enc(q, k, freqs_cis=self.freqs_cis.to(q.device))
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Forward pass of attention block."""
+        s = 1 if self.cls_token else 0  # used to exclude cls_token
+        if x.ndim == 4:
+            B, H, W, _ = x.shape
+            assert s == 0  # no cls_token
+            L = H * W
+            ndim = 4
+        else:
+            assert x.ndim == 3
+            B, L, _ = x.shape
+            ndim = 3
+            H = W = math.sqrt(L - s)
+
+        # qkv with shape (3, B, nHead, L, C)
+        qkv = self.qkv(x).reshape(B, L, 3, self.num_heads, -1)
+        # q, k, v with shape (B, nHead, L, C)
+        q, k, v = qkv.permute(2, 0, 3, 1, 4).unbind(0)
+
+        # handle rope and rel pos embeddings
+        q, k = self._apply_rope(q, k)
+        if self.use_rel_pos:
+            q, k = concat_rel_pos(
+                q.flatten(0, 1),
+                k.flatten(0, 1),
+                (H, W),
+                x.shape[1:3],
+                self.rel_pos_h,
+                self.rel_pos_w,
+                rescale=True,
+                relative_coords=self.relative_coords,
+            )
+
+            # sdpa expects [B, nheads, H*W, C] so we transpose back
+            q = q.reshape(B, self.num_heads, H * W, -1)
+            k = k.reshape(B, self.num_heads, H * W, -1)
+
+        x = F.scaled_dot_product_attention(q, k, v)
+
+        if ndim == 4:
+            x = x.view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
+        else:
+            x = x.view(B, self.num_heads, L, -1).permute(0, 2, 1, 3).reshape(B, L, -1)
+
+        x = self.proj(x)
+
+        return x
+
+
+class Block(nn.Module):
+    """Transformer blocks with support of window attention."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        drop_path: float = 0.0,
+        norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        input_size: tuple[int, int] | None = None,
+        use_rope: bool = False,
+        rope_pt_size: tuple[int, int] | None = None,
+        rope_interp: bool = False,
+        cls_token: bool = False,
+        dropout: float = 0.0,
+        init_values: float | None = None,
+    ):
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            drop_path (float): Stochastic depth rate.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks. If it equals 0, then not use window attention.
+            input_size (int or None): Input resolution for calculating the relative positional parameter size.
+            dropout (float): Dropout rate.
+            cls_token: whether a cls_token is present.
+            use_rope: whether to use rope 2d (indep of use_rel_pos, as it can be used together)
+            rope_pt_size: size of rope in previous stage of training, needed for interpolation or tiling
+            rope_interp: whether to interpolate (or extrapolate) rope to match target input size, expected to specify
+                source size as rope_pt_size.
+            init_values: layer scale init, None for no layer scale.
+        """
+        super().__init__()
+
+        check_requirements("timm")
+        from timm.layers import DropPath, Mlp
+
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            use_rel_pos=use_rel_pos,
+            rel_pos_zero_init=rel_pos_zero_init,
+            input_size=input_size if window_size == 0 else (window_size, window_size),
+            use_rope=use_rope,
+            rope_pt_size=rope_pt_size,
+            rope_interp=rope_interp,
+            cls_token=cls_token,
+        )
+        self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = Mlp(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=(dropout, 0.0),
+        )
+        self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.dropout = nn.Dropout(dropout)
+        self.window_size = window_size
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Forward pass of the transformer block."""
+        shortcut = x
+        x = self.norm1(x)
+        # Window partition
+        if self.window_size > 0:
+            H, W = x.shape[1], x.shape[2]
+            x, pad_hw = window_partition(x, self.window_size)
+
+        x = self.ls1(self.attn(x))
+        # Reverse window partition
+        if self.window_size > 0:
+            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
+
+        x = shortcut + self.dropout(self.drop_path(x))
+        x = x + self.dropout(self.drop_path(self.ls2(self.mlp(self.norm2(x)))))
+
+        return x
+
+
+class ViT(nn.Module):
+    """This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`. "Exploring Plain Vision Transformer
+    Backbones for Object Detection", https://arxiv.org/abs/2203.16527.
+    """
+
+    def __init__(
+        self,
+        img_size: int = 1024,
+        patch_size: int = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        drop_path_rate: float = 0.0,
+        norm_layer: Callable[..., nn.Module] | str = "LayerNorm",
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        use_abs_pos: bool = True,
+        tile_abs_pos: bool = True,
+        rel_pos_blocks: tuple[int, ...] | bool = (2, 5, 8, 11),
+        rel_pos_zero_init: bool = True,
+        window_size: int = 14,
+        global_att_blocks: tuple[int, ...] = (2, 5, 8, 11),
+        use_rope: bool = False,
+        rope_pt_size: int | None = None,
+        use_interp_rope: bool = False,
+        pretrain_img_size: int = 224,
+        pretrain_use_cls_token: bool = True,
+        retain_cls_token: bool = True,
+        dropout: float = 0.0,
+        return_interm_layers: bool = False,
+        init_values: float | None = None,  # for layerscale
+        ln_pre: bool = False,
+        ln_post: bool = False,
+        bias_patch_embed: bool = True,
+        compile_mode: str | None = None,
+        use_act_checkpoint: bool = True,
+    ):
+        """
+        Args:
+            img_size (int): Input image size. Only relevant for rel pos or rope.
+            patch_size (int): Patch size.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+            depth (int): Depth of ViT.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            drop_path_rate (float): Stochastic depth rate.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_abs_pos (bool): If True, use absolute positional embeddings.
+            tile_abs_pos (bool): If True, tile absolute positional embeddings instead of interpolation.
+            rel_pos_blocks (list): Blocks which have rel pos embeddings.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks.
+            global_att_blocks (list): Indexes for blocks using global attention (other blocks use window attention).
+            use_rope (bool): whether to use rope 2d (indep of rel_pos_blocks, as it can be used together).
+            rope_pt_size (int): size of rope in previous stage of training, needed for interpolation or tiling.
+            use_interp_rope: whether to interpolate (or extrapolate) rope to match target input size, expected to
+                specify source size as rope_pt_size.
+            use_act_checkpoint (bool): If True, use activation checkpointing.
+            pretrain_img_size (int): input image size for pretraining models.
+            pretrain_use_cls_token (bool): If True, pretraining models use class token.
+            retain_cls_token: whether cls_token should be retained.
+            dropout (float): Dropout rate. Applied in residual blocks of attn, mlp and inside the mlp.
+            return_interm_layers (bool): Whether to return intermediate layers (all global attention blocks).
+            init_values: layer scale init, None for no layer scale.
+            ln_pre (bool): If True, apply layer norm before transformer blocks.
+            ln_post (bool): If True, apply layer norm after transformer blocks.
+            bias_patch_embed (bool): bias in conv for patch embed?
+            compile_mode (str): mode to compile the forward.
+        """
+        super().__init__()
+        self.pretrain_use_cls_token = pretrain_use_cls_token
+
+        window_block_indexes = [i for i in range(depth) if i not in global_att_blocks]
+        self.full_attn_ids = list(global_att_blocks)
+        self.rel_pos_blocks = [False] * depth
+        if isinstance(rel_pos_blocks, bool) and rel_pos_blocks:
+            self.rel_pos_blocks = [True] * depth
+        else:
+            for i in rel_pos_blocks:
+                self.rel_pos_blocks[i] = True
+
+        self.retain_cls_token = retain_cls_token
+        if self.retain_cls_token:
+            assert pretrain_use_cls_token
+            assert len(window_block_indexes) == 0, "windowing not supported with cls token"
+
+            assert sum(self.rel_pos_blocks) == 0, "rel pos not supported with cls token"
+
+            scale = embed_dim**-0.5
+            self.class_embedding = nn.Parameter(scale * torch.randn(1, 1, embed_dim))
+
+        if isinstance(norm_layer, str):
+            norm_layer = partial(getattr(nn, norm_layer), eps=1e-5)
+
+        self.patch_embed = PatchEmbed(
+            kernel_size=(patch_size, patch_size),
+            stride=(patch_size, patch_size),
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            bias=bias_patch_embed,
+        )
+
+        # Handle absolute positional embedding
+        self.tile_abs_pos = tile_abs_pos
+        self.use_abs_pos = use_abs_pos
+        if self.tile_abs_pos:
+            assert self.use_abs_pos
+
+        if self.use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size.
+            num_patches = (pretrain_img_size // patch_size) * (pretrain_img_size // patch_size)
+            num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches
+            self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, embed_dim))
+        else:
+            self.pos_embed = None
+
+        # stochastic depth decay rule
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
+
+        self.patch_size = patch_size
+        self.window_size = window_size
+        self.blocks = nn.ModuleList()
+        cur_stage = 1
+        for i in range(depth):
+            block = Block(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                drop_path=dpr[i],
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                use_rel_pos=self.rel_pos_blocks[i],
+                rel_pos_zero_init=rel_pos_zero_init,
+                window_size=window_size if i in window_block_indexes else 0,
+                input_size=(img_size // patch_size, img_size // patch_size),
+                use_rope=use_rope,
+                rope_pt_size=((window_size, window_size) if rope_pt_size is None else (rope_pt_size, rope_pt_size)),
+                rope_interp=use_interp_rope,
+                cls_token=self.retain_cls_token,
+                dropout=dropout,
+                init_values=init_values,
+            )
+
+            if i not in window_block_indexes:
+                cur_stage += 1
+
+            self.use_act_checkpoint = use_act_checkpoint
+
+            self.blocks.append(block)
+
+        self.return_interm_layers = return_interm_layers
+        self.channel_list = [embed_dim] * len(self.full_attn_ids) if return_interm_layers else [embed_dim]
+
+        if self.pos_embed is not None:
+            nn.init.trunc_normal_(self.pos_embed, std=0.02)
+
+        self.ln_pre = norm_layer(embed_dim) if ln_pre else nn.Identity()
+        self.ln_post = norm_layer(embed_dim) if ln_post else nn.Identity()
+
+        self.apply(self._init_weights)
+
+        if compile_mode is not None:
+            self.forward = torch.compile(self.forward, mode=compile_mode, fullgraph=True)
+            if self.use_act_checkpoint and self.training:
+                torch._dynamo.config.optimize_ddp = False
+
+    @staticmethod
+    def _init_weights(m: nn.Module) -> None:
+        """Initialize the weights."""
+        if isinstance(m, nn.Linear):
+            nn.init.trunc_normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
+        """Vit forward path and get feature maps."""
+        x = self.patch_embed(x)
+        h, w = x.shape[1], x.shape[2]
+
+        s = 0
+        if self.retain_cls_token:
+            # If cls_token is retained, we don't
+            # maintain spatial shape
+            x = torch.cat([self.class_embedding, x.flatten(1, 2)], dim=1)
+            s = 1
+
+        if self.pos_embed is not None:
+            x = x + get_abs_pos(
+                self.pos_embed,
+                self.pretrain_use_cls_token,
+                (h, w),
+                self.retain_cls_token,
+                tiling=self.tile_abs_pos,
+            )
+
+        x = self.ln_pre(x)
+
+        outputs = []
+        for i, blk in enumerate(self.blocks):
+            if self.use_act_checkpoint and self.training:
+                x = checkpoint.checkpoint(blk, x, use_reentrant=False)
+            else:
+                x = blk(x)
+            if (i == self.full_attn_ids[-1]) or (self.return_interm_layers and i in self.full_attn_ids):
+                if i == self.full_attn_ids[-1]:
+                    x = self.ln_post(x)
+
+                feats = x[:, s:]
+                if feats.ndim == 4:
+                    feats = feats.permute(0, 3, 1, 2)
+                else:
+                    assert feats.ndim == 3
+                    h = w = math.sqrt(feats.shape[1])
+                    feats = feats.reshape(feats.shape[0], h, w, feats.shape[-1]).permute(0, 3, 1, 2)
+
+                outputs.append(feats)
+
+        return outputs
+
+    def set_imgsz(self, imgsz: list[int] = [1008, 1008]):
+        """Setup rel pos embeddings and rope freqs for a new input image size."""
+        for block in self.blocks:
+            if block.window_size != 0:
+                continue
+            block.attn._setup_rel_pos(input_size=(imgsz[0] // self.patch_size, imgsz[1] // self.patch_size))
+            block.attn._setup_rope_freqs(input_size=(imgsz[0] // self.patch_size, imgsz[1] // self.patch_size))

algorithms/dms_yolo/code/ultralytics/models/sam/sam3/vl_combiner.py

@@ -0,0 +1,160 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+"""Provides utility to combine a vision backbone with a language backbone."""
+
+from __future__ import annotations
+
+from copy import copy
+
+import torch
+import torch.nn as nn
+from torch.nn.attention import SDPBackend, sdpa_kernel
+
+from .necks import Sam3DualViTDetNeck
+
+
+class SAM3VLBackbone(nn.Module):
+    """This backbone combines a vision backbone and a language backbone without fusion. As such it is more of a
+    convenience wrapper to handle the two backbones together.
+
+    It adds support for activation checkpointing and compilation.
+    """
+
+    def __init__(
+        self,
+        visual: Sam3DualViTDetNeck,
+        text,
+        compile_visual: bool = False,
+        act_ckpt_whole_vision_backbone: bool = False,
+        act_ckpt_whole_language_backbone: bool = False,
+        scalp=0,
+    ):
+        """Initialize the backbone combiner.
+
+        :param visual: The vision backbone to use
+        :param text: The text encoder to use
+        """
+        super().__init__()
+        self.vision_backbone: Sam3DualViTDetNeck = torch.compile(visual) if compile_visual else visual
+        self.language_backbone = text
+        self.scalp = scalp
+        # allow running activation checkpointing on the entire vision and language backbones
+        self.act_ckpt_whole_vision_backbone = act_ckpt_whole_vision_backbone
+        self.act_ckpt_whole_language_backbone = act_ckpt_whole_language_backbone
+
+    def forward(
+        self,
+        samples: torch.Tensor,
+        captions: list[str],
+        input_boxes: torch.Tensor = None,
+        additional_text: list[str] | None = None,
+    ):
+        """Forward pass of the backbone combiner.
+
+        :param samples: The input images
+        :param captions: The input captions
+        :param input_boxes: If the text contains place-holders for boxes, this
+            parameter contains the tensor containing their spatial features
+        :param additional_text: This can be used to encode some additional text
+            (different from the captions) in the same forward of the backbone
+        :return: Output dictionary with the following keys:
+            - vision_features: The output of the vision backbone
+            - language_features: The output of the language backbone
+            - language_mask: The attention mask of the language backbone
+            - vision_pos_enc: The positional encoding of the vision backbone
+            - (optional) additional_text_features: The output of the language
+                backbone for the additional text
+            - (optional) additional_text_mask: The attention mask of the
+                language backbone for the additional text
+        """
+        output = self.forward_image(samples)
+        output.update(self.forward_text(captions, input_boxes, additional_text))
+        return output
+
+    def forward_image(self, samples: torch.Tensor):
+        """Forward pass of the vision backbone and get both SAM3 and SAM2 features."""
+        # Forward through backbone
+        sam3_features, sam3_pos, sam2_features, sam2_pos = self.vision_backbone.forward(samples)
+        if self.scalp > 0:
+            # Discard the lowest resolution features
+            sam3_features, sam3_pos = (
+                sam3_features[: -self.scalp],
+                sam3_pos[: -self.scalp],
+            )
+            if sam2_features is not None and sam2_pos is not None:
+                sam2_features, sam2_pos = (
+                    sam2_features[: -self.scalp],
+                    sam2_pos[: -self.scalp],
+                )
+
+        sam2_output = None
+
+        if sam2_features is not None and sam2_pos is not None:
+            sam2_src = sam2_features[-1]
+            sam2_output = {
+                "vision_features": sam2_src,
+                "vision_pos_enc": sam2_pos,
+                "backbone_fpn": sam2_features,
+            }
+
+        sam3_src = sam3_features[-1]
+        return {
+            "vision_features": sam3_src,
+            "vision_pos_enc": sam3_pos,
+            "backbone_fpn": sam3_features,
+            "sam2_backbone_out": sam2_output,
+        }
+
+    def forward_image_sam2(self, samples: torch.Tensor):
+        """Forward pass of the vision backbone to get SAM2 features only."""
+        xs = self.vision_backbone.trunk(samples)
+        x = xs[-1]  # simpleFPN
+
+        assert self.vision_backbone.sam2_convs is not None, "SAM2 neck is not available."
+        sam2_features, sam2_pos = self.vision_backbone.sam_forward_feature_levels(x, self.vision_backbone.sam2_convs)
+
+        if self.scalp > 0:
+            # Discard the lowest resolution features
+            sam2_features, sam2_pos = (
+                sam2_features[: -self.scalp],
+                sam2_pos[: -self.scalp],
+            )
+
+        return {
+            "vision_features": sam2_features[-1],
+            "vision_pos_enc": sam2_pos,
+            "backbone_fpn": sam2_features,
+        }
+
+    def forward_text(self, captions, input_boxes=None, additional_text=None):
+        """Forward pass of the text encoder."""
+        output = {}
+
+        # Forward through text_encoder
+        text_to_encode = copy(captions)
+        if additional_text is not None:
+            # if there are additional_text, we piggy-back them into this forward.
+            # They'll be used later for output alignment
+            text_to_encode += additional_text
+
+        with sdpa_kernel([SDPBackend.MATH, SDPBackend.EFFICIENT_ATTENTION, SDPBackend.FLASH_ATTENTION]):
+            text_attention_mask, text_memory, text_embeds = self.language_backbone(text_to_encode, input_boxes)
+
+        if additional_text is not None:
+            output["additional_text_features"] = text_memory[:, -len(additional_text) :]
+            output["additional_text_mask"] = text_attention_mask[-len(additional_text) :]
+
+        text_memory = text_memory[:, : len(captions)]
+        text_attention_mask = text_attention_mask[: len(captions)]
+        text_embeds = text_embeds[:, : len(captions)]
+        output["language_features"] = text_memory
+        output["language_mask"] = text_attention_mask
+        output["language_embeds"] = text_embeds  # Text embeddings before forward to the encoder
+
+        return output
+
+    def set_imgsz(self, imgsz: list[int] = [1008, 1008]):
+        """Set the image size for the vision backbone."""
+        self.vision_backbone.set_imgsz(imgsz)