add Segment Anything (SAM) to foundational models

Note: dense prompts (i.e. masks) support is still partial (see MaskEncoder)

Co-authored-by: Cédric Deltheil <cedric@deltheil.me>

poetry.lock

@@ -651,21 +651,19 @@ tests = ["asttokens", "littleutils", "pytest", "rich"]
 
 [[package]]
 name = "filelock"
-version = "3.12.3"
+version = "3.12.4"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.12.3-py3-none-any.whl", hash = "sha256:f067e40ccc40f2b48395a80fcbd4728262fab54e232e090a4063ab804179efeb"},
-    {file = "filelock-3.12.3.tar.gz", hash = "sha256:0ecc1dd2ec4672a10c8550a8182f1bd0c0a5088470ecd5a125e45f49472fac3d"},
+    {file = "filelock-3.12.4-py3-none-any.whl", hash = "sha256:08c21d87ded6e2b9da6728c3dff51baf1dcecf973b768ef35bcbc3447edb9ad4"},
+    {file = "filelock-3.12.4.tar.gz", hash = "sha256:2e6f249f1f3654291606e046b09f1fd5eac39b360664c27f5aad072012f8bcbd"},
 ]
 
-[package.dependencies]
-typing-extensions = {version = ">=4.7.1", markers = "python_version < \"3.11\""}
-
 [package.extras]
 docs = ["furo (>=2023.7.26)", "sphinx (>=7.1.2)", "sphinx-autodoc-typehints (>=1.24)"]
 testing = ["covdefaults (>=2.3)", "coverage (>=7.3)", "diff-cover (>=7.7)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)", "pytest-timeout (>=2.1)"]
+typing = ["typing-extensions (>=4.7.1)"]
 
 [[package]]
 name = "frozenlist"
@@ -2507,6 +2505,25 @@ dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyl
 doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
 test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
+[[package]]
+name = "segment_anything"
+version = "1.0"
+description = ""
+optional = true
+python-versions = "*"
+files = []
+develop = false
+
+[package.extras]
+all = ["matplotlib", "onnx", "onnxruntime", "opencv-python", "pycocotools"]
+dev = ["black", "flake8", "isort", "mypy"]
+
+[package.source]
+type = "git"
+url = "https://github.com/facebookresearch/segment-anything"
+reference = "HEAD"
+resolved_reference = "6fdee8f2727f4506cfbbe553e23b895e27956588"
+
 [[package]]
 name = "sentry-sdk"
 version = "1.31.0"
@@ -3289,11 +3306,11 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link
 testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
 
 [extras]
-conversion = ["diffusers", "transformers"]
-test = ["diffusers", "invisible-watermark", "piq", "transformers"]
+conversion = ["diffusers", "segment-anything", "transformers"]
+test = ["diffusers", "invisible-watermark", "piq", "segment-anything", "transformers"]
 training = ["bitsandbytes", "datasets", "loguru", "prodigyopt", "pydantic", "scipy", "tomli", "torchvision", "wandb"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.12"
-content-hash = "b2646e04015b05eba32e6556b4aa37d3c5c31218c09fd0ea53d7540af558b7de"
+content-hash = "6e35f01f2fc8611203da972939170d18371182443e4c6f45b4cfc40b4e785dff"

pyproject.toml

@@ -29,11 +29,12 @@ diffusers = {version = "^0.18.0", optional = true}
 transformers = {version = "^4.27.4", optional = true}
 piq = {version = "^0.7.1", optional = true}
 invisible-watermark = {version = "^0.2.0", optional = true}
+segment-anything = {git = "https://github.com/facebookresearch/segment-anything", optional = true}
 
 [tool.poetry.extras]
 training = ["datasets", "tomli", "wandb", "loguru", "bitsandbytes", "prodigyopt", "pydantic", "scipy", "torchvision"]
-conversion = ["diffusers", "transformers"]
-test = ["diffusers", "transformers", "piq", "invisible-watermark"]
+conversion = ["diffusers", "transformers", "segment-anything"]
+test = ["diffusers", "transformers", "piq", "invisible-watermark", "segment-anything"]
 
 [tool.poetry.group.dev.dependencies]
 black = "^23.1.0"

scripts/conversion/convert_segment_anything.py

@@ -0,0 +1,236 @@
+import argparse
+import types
+from typing import Any, Callable, cast
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+import refiners.fluxion.layers as fl
+from refiners.fluxion.model_converter import ModelConverter
+from refiners.fluxion.utils import manual_seed, save_to_safetensors
+from refiners.foundationals.segment_anything.image_encoder import SAMViTH
+from refiners.foundationals.segment_anything.prompt_encoder import PointEncoder, MaskEncoder
+
+from segment_anything import build_sam_vit_h  # type: ignore
+from segment_anything.modeling.common import LayerNorm2d  # type: ignore
+
+from refiners.foundationals.segment_anything.mask_decoder import MaskDecoder
+
+
+class FacebookSAM(nn.Module):
+    image_encoder: nn.Module
+    prompt_encoder: nn.Module
+    mask_decoder: nn.Module
+
+
+build_sam_vit_h = cast(Callable[[], FacebookSAM], build_sam_vit_h)
+
+
+assert issubclass(LayerNorm2d, nn.Module)
+custom_layers = {LayerNorm2d: fl.LayerNorm2d}
+
+
+class Args(argparse.Namespace):
+    source_path: str
+    output_path: str
+    half: bool
+    verbose: bool
+
+
+def convert_mask_encoder(prompt_encoder: nn.Module) -> dict[str, Tensor]:
+    state_dict: dict[str, Tensor] = {
+        "no_mask_embedding": nn.Parameter(data=prompt_encoder.no_mask_embed.weight.clone()),  # type: ignore
+    }
+
+    refiners_mask_encoder = MaskEncoder()
+    # TODO: handle other weights
+    refiners_mask_encoder.load_state_dict(state_dict=state_dict, strict=False)
+
+    return state_dict
+
+
+def convert_point_encoder(prompt_encoder: nn.Module) -> dict[str, Tensor]:
+    manual_seed(seed=0)
+    point_embeddings: list[Tensor] = [pe.weight for pe in prompt_encoder.point_embeddings] + [prompt_encoder.not_a_point_embed.weight]  # type: ignore
+    pe = prompt_encoder.pe_layer.positional_encoding_gaussian_matrix  # type: ignore
+    assert isinstance(pe, Tensor)
+    state_dict: dict[str, Tensor] = {
+        "Residual.Chain.PointTypeEmbedding.weight": nn.Parameter(data=torch.cat(tensors=point_embeddings, dim=0)),
+        "CoordinateEncoder.Linear.weight": nn.Parameter(data=pe.T.contiguous()),
+    }
+
+    refiners_prompt_encoder = PointEncoder()
+    refiners_prompt_encoder.load_state_dict(state_dict=state_dict)
+
+    return state_dict
+
+
+def convert_vit(vit: nn.Module) -> dict[str, Tensor]:
+    manual_seed(seed=0)
+    refiners_sam_vit_h = SAMViTH()
+
+    converter = ModelConverter(
+        source_model=vit,
+        target_model=refiners_sam_vit_h,
+        custom_layer_mapping=custom_layers,  # type: ignore
+    )
+    converter.skip_init_check = True
+
+    x = torch.randn(1, 3, 1024, 1024)
+    mapping = converter.map_state_dicts(source_args=(x,))
+    assert mapping
+
+    mapping["PositionalEncoder.Chain.Parameter.parameter"] = "pos_embed"
+
+    target_state_dict = refiners_sam_vit_h.state_dict()
+    del target_state_dict["PositionalEncoder.Chain.Parameter.parameter"]
+
+    source_state_dict = vit.state_dict()
+    pos_embed = source_state_dict["pos_embed"]
+    del source_state_dict["pos_embed"]
+
+    target_rel_keys = [
+        (
+            f"Transformer.TransformerLayer_{i}.Residual_1.Chain.FusedSelfAttention.RelativePositionAttention.horizontal_embedding",
+            f"Transformer.TransformerLayer_{i}.Residual_1.Chain.FusedSelfAttention.RelativePositionAttention.vertical_embedding",
+        )
+        for i in range(1, 33)
+    ]
+    source_rel_keys = [(f"blocks.{i}.attn.rel_pos_w", f"blocks.{i}.attn.rel_pos_h") for i in range(32)]
+
+    rel_items: dict[str, Tensor] = {}
+
+    for (key_w, key_h), (target_key_w, target_key_h) in zip(source_rel_keys, target_rel_keys):
+        rel_items[target_key_w] = source_state_dict[key_w]
+        rel_items[target_key_h] = source_state_dict[key_h]
+        del source_state_dict[key_w]
+        del source_state_dict[key_h]
+        del target_state_dict[target_key_w]
+        del target_state_dict[target_key_h]
+
+    converted_source = converter._convert_state_dict(  # pyright: ignore[reportPrivateUsage]
+        source_state_dict=source_state_dict, target_state_dict=target_state_dict, state_dict_mapping=mapping
+    )
+
+    converted_source["PositionalEncoder.Chain.Parameter.parameter"] = pos_embed  # type: ignore
+    converted_source.update(rel_items)
+
+    refiners_sam_vit_h.load_state_dict(state_dict=converted_source)
+    assert converter.compare_models((x,), threshold=1e-3)
+
+    return converted_source
+
+
+def convert_mask_decoder(mask_decoder: nn.Module) -> dict[str, Tensor]:
+    manual_seed(seed=0)
+
+    refiners_mask_decoder = MaskDecoder()
+
+    image_embedding = torch.randn(1, 256, 64, 64)
+    dense_positional_embedding = torch.randn(1, 256, 64, 64)
+    point_embedding = torch.randn(1, 3, 256)
+    mask_embedding = torch.randn(1, 256, 64, 64)
+
+    import refiners.fluxion.layers as fl
+    from segment_anything.modeling.common import LayerNorm2d  # type: ignore
+
+    assert issubclass(LayerNorm2d, nn.Module)
+    custom_layers = {LayerNorm2d: fl.LayerNorm2d}
+
+    converter = ModelConverter(
+        source_model=mask_decoder,
+        target_model=refiners_mask_decoder,
+        custom_layer_mapping=custom_layers,  # type: ignore
+    )
+
+    inputs = {
+        "image_embeddings": image_embedding,
+        "image_pe": dense_positional_embedding,
+        "sparse_prompt_embeddings": point_embedding,
+        "dense_prompt_embeddings": mask_embedding,
+        "multimask_output": True,
+    }
+
+    refiners_mask_decoder.set_image_embedding(image_embedding)
+    refiners_mask_decoder.set_point_embedding(point_embedding)
+    refiners_mask_decoder.set_mask_embedding(mask_embedding)
+    refiners_mask_decoder.set_dense_positional_embedding(dense_positional_embedding)
+
+    mapping = converter.map_state_dicts(source_args=inputs, target_args={})
+    assert mapping is not None
+    mapping["IOUMaskEncoder"] = "iou_token"
+
+    state_dict = converter._convert_state_dict(source_state_dict=mask_decoder.state_dict(), target_state_dict=refiners_mask_decoder.state_dict(), state_dict_mapping=mapping)  # type: ignore
+    state_dict["IOUMaskEncoder.weight"] = torch.cat(tensors=[mask_decoder.iou_token.weight, mask_decoder.mask_tokens.weight], dim=0)  # type: ignore
+
+    refiners_mask_decoder.load_state_dict(state_dict=state_dict)
+
+    refiners_mask_decoder.set_image_embedding(image_embedding)
+    refiners_mask_decoder.set_point_embedding(point_embedding)
+    refiners_mask_decoder.set_mask_embedding(mask_embedding)
+    refiners_mask_decoder.set_dense_positional_embedding(dense_positional_embedding)
+
+    # Perform (1) upscaling then (2) mask prediction in this order (= like in the official implementation) to make
+    # `compare_models` happy (MaskPrediction's Matmul runs those in the reverse order by default)
+    matmul = refiners_mask_decoder.ensure_find(fl.Matmul)
+
+    def forward_swapped_order(self: Any, *args: Any) -> Any:
+        y = self[1](*args)  # (1)
+        x = self[0](*args)  # (2)
+        return torch.matmul(input=x, other=y)
+
+    matmul.forward = types.MethodType(forward_swapped_order, matmul)
+
+    assert converter.compare_models(source_args=inputs, target_args={}, threshold=1e-3)
+
+    return state_dict
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Converts a Segment Anything ViT model to a Refiners SAMViTH model")
+    parser.add_argument(
+        "--from",
+        type=str,
+        dest="source_path",
+        default="sam_vit_h_4b8939.pth",
+        # required=True,
+        help="Path to the Segment Anything model weights",
+    )
+    parser.add_argument(
+        "--to",
+        type=str,
+        dest="output_path",
+        default="segment-anything-h.safetensors",
+        help="Output path for converted model (as safetensors).",
+    )
+    parser.add_argument("--half", action="store_true", default=False, help="Convert to half precision. Default: False")
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        default=False,
+        help="Prints additional information during conversion. Default: False",
+    )
+    args = parser.parse_args(namespace=Args())
+
+    sam_h = build_sam_vit_h()  # type: ignore
+    sam_h.load_state_dict(state_dict=torch.load(f=args.source_path))  # type: ignore
+
+    vit_state_dict = convert_vit(vit=sam_h.image_encoder)
+    mask_decoder_state_dict = convert_mask_decoder(mask_decoder=sam_h.mask_decoder)
+    point_encoder_state_dict = convert_point_encoder(prompt_encoder=sam_h.prompt_encoder)
+    mask_encoder_state_dict = convert_mask_encoder(prompt_encoder=sam_h.prompt_encoder)
+
+    output_state_dict = {
+        **{".".join(("image_encoder", key)): value for key, value in vit_state_dict.items()},
+        **{".".join(("mask_decoder", key)): value for key, value in mask_decoder_state_dict.items()},
+        **{".".join(("point_encoder", key)): value for key, value in point_encoder_state_dict.items()},
+        **{".".join(("mask_encoder", key)): value for key, value in mask_encoder_state_dict.items()},
+    }
+    if args.half:
+        output_state_dict = {key: value.half() for key, value in output_state_dict.items()}
+
+    save_to_safetensors(path=args.output_path, tensors=output_state_dict)
+
+
+if __name__ == "__main__":
+    main()

src/refiners/foundationals/segment_anything/image_encoder.py

@@ -0,0 +1,369 @@
+from torch import device as Device, dtype as DType, Tensor
+from refiners.fluxion.context import Contexts
+import refiners.fluxion.layers as fl
+from refiners.fluxion.utils import pad
+from torch import nn
+import torch
+
+
+class PatchEncoder(fl.Chain):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        patch_size: int = 16,
+        use_bias: bool = True,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.patch_size = patch_size
+        self.use_bias = use_bias
+        super().__init__(
+            fl.Conv2d(
+                in_channels=self.in_channels,
+                out_channels=self.out_channels,
+                kernel_size=(self.patch_size, self.patch_size),
+                stride=(self.patch_size, self.patch_size),
+                use_bias=self.use_bias,
+                device=device,
+                dtype=dtype,
+            ),
+            fl.Permute(0, 2, 3, 1),
+        )
+
+
+class PositionalEncoder(fl.Residual):
+    def __init__(
+        self,
+        embedding_dim: int,
+        image_embedding_size: tuple[int, int],
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        self.embedding_dim = embedding_dim
+        self.image_embedding_size = image_embedding_size
+        super().__init__(
+            fl.Parameter(
+                1,
+                image_embedding_size[0],
+                image_embedding_size[1],
+                embedding_dim,
+                device=device,
+                dtype=dtype,
+            ),
+        )
+
+
+class RelativePositionAttention(fl.WeightedModule):
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        spatial_size: tuple[int, int],
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.num_heads = num_heads
+        self.head_dim = embedding_dim // num_heads
+        self.spatial_size = spatial_size
+        self.horizontal_embedding = nn.Parameter(
+            data=torch.zeros(2 * spatial_size[0] - 1, self.head_dim, device=device, dtype=dtype)
+        )
+        self.vertical_embedding = nn.Parameter(
+            data=torch.zeros(2 * spatial_size[1] - 1, self.head_dim, device=device, dtype=dtype)
+        )
+
+    @property
+    def device(self) -> Device:
+        return self.horizontal_embedding.device
+
+    @property
+    def dtype(self) -> DType:
+        return self.horizontal_embedding.dtype
+
+    def forward(self, x: Tensor) -> Tensor:
+        batch, height, width, _ = x.shape
+        x = (
+            x.reshape(batch, width * height, 3, self.num_heads, -1)
+            .permute(2, 0, 3, 1, 4)
+            .reshape(3, batch * self.num_heads, width * height, -1)
+        )
+        query, key, value = x.unbind(dim=0)
+        horizontal_relative_embedding, vertical_relative_embedding = self.compute_relative_embedding(x=query)
+        attention = (query * self.head_dim**-0.5) @ key.transpose(dim0=-2, dim1=-1)
+        # Order of operations is important here
+        attention = (
+            (attention.reshape(-1, height, width, height, width) + vertical_relative_embedding)
+            + horizontal_relative_embedding
+        ).reshape(attention.shape)
+        attention = attention.softmax(dim=-1)
+        attention = attention @ value
+        attention = (
+            attention.reshape(batch, self.num_heads, height, width, -1)
+            .permute(0, 2, 3, 1, 4)
+            .reshape(batch, height, width, -1)
+        )
+        return attention
+
+    def compute_relative_coords(self, size: int) -> Tensor:
+        x, y = torch.meshgrid(torch.arange(end=size), torch.arange(end=size), indexing="ij")
+        return x - y + size - 1
+
+    def compute_relative_embedding(self, x: Tensor) -> tuple[Tensor, Tensor]:
+        width, height = self.spatial_size
+        horizontal_coords = self.compute_relative_coords(size=width)
+        vertical_coords = self.compute_relative_coords(size=height)
+        horizontal_positional_embedding = self.horizontal_embedding[horizontal_coords]
+        vertical_positional_embedding = self.vertical_embedding[vertical_coords]
+        x = x.reshape(x.shape[0], width, height, -1)
+        horizontal_relative_embedding = torch.einsum("bhwc,wkc->bhwk", x, horizontal_positional_embedding).unsqueeze(
+            dim=-2
+        )
+        vertical_relative_embedding = torch.einsum("bhwc,hkc->bhwk", x, vertical_positional_embedding).unsqueeze(dim=-1)
+
+        return horizontal_relative_embedding, vertical_relative_embedding
+
+
+class FusedSelfAttention(fl.Chain):
+    def __init__(
+        self,
+        embedding_dim: int = 768,
+        spatial_size: tuple[int, int] = (64, 64),
+        num_heads: int = 1,
+        use_bias: bool = True,
+        is_causal: bool = False,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        assert (
+            embedding_dim % num_heads == 0
+        ), f"Embedding dim (embedding_dim={embedding_dim}) must be divisible by num heads (num_heads={num_heads})"
+        self.embedding_dim = embedding_dim
+        self.num_heads = num_heads
+        self.use_bias = use_bias
+        self.is_causal = is_causal
+        super().__init__(
+            fl.Linear(
+                in_features=self.embedding_dim,
+                out_features=3 * self.embedding_dim,
+                bias=self.use_bias,
+                device=device,
+                dtype=dtype,
+            ),
+            RelativePositionAttention(
+                embedding_dim=self.embedding_dim,
+                num_heads=self.num_heads,
+                spatial_size=spatial_size,
+                device=device,
+                dtype=dtype,
+            ),
+            fl.Linear(
+                in_features=self.embedding_dim,
+                out_features=self.embedding_dim,
+                bias=True,
+                device=device,
+                dtype=dtype,
+            ),
+        )
+
+
+class FeedForward(fl.Chain):
+    def __init__(
+        self,
+        embedding_dim: int,
+        feedforward_dim: int,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        self.embedding_dim = embedding_dim
+        self.feedforward_dim = feedforward_dim
+        super().__init__(
+            fl.Linear(
+                in_features=self.embedding_dim,
+                out_features=self.feedforward_dim,
+                bias=True,
+                device=device,
+                dtype=dtype,
+            ),
+            fl.GeLU(),
+            fl.Linear(
+                in_features=self.feedforward_dim,
+                out_features=self.embedding_dim,
+                bias=True,
+                device=device,
+                dtype=dtype,
+            ),
+        )
+
+
+class WindowPartition(fl.ContextModule):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def forward(self, x: Tensor) -> Tensor:
+        batch, height, width, channels = x.shape
+        context = self.use_context(context_name="window_partition")
+        context.update({"original_height": height, "original_width": width})
+        window_size = context["window_size"]
+        padding_height = (window_size - height % window_size) % window_size
+        padding_width = (window_size - width % window_size) % window_size
+        if padding_height > 0 or padding_width > 0:
+            x = pad(x=x, pad=(0, 0, 0, padding_width, 0, padding_height))
+        padded_height, padded_width = height + padding_height, width + padding_width
+        context.update({"padded_height": padded_height, "padded_width": padded_width})
+        x = x.view(batch, padded_height // window_size, window_size, padded_width // window_size, window_size, channels)
+        windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, channels)
+        return windows
+
+
+class WindowMerge(fl.ContextModule):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def forward(self, x: Tensor) -> Tensor:
+        context = self.use_context(context_name="window_partition")
+        window_size = context["window_size"]
+        padded_height, padded_width = context["padded_height"], context["padded_width"]
+        original_height, original_width = context["original_height"], context["original_width"]
+        batch_size = x.shape[0] // (padded_height * padded_width // window_size // window_size)
+        x = x.view(batch_size, padded_height // window_size, padded_width // window_size, window_size, window_size, -1)
+        x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(batch_size, padded_height, padded_width, -1)
+        if padded_height > original_height or padded_width > original_width:
+            x = x[:, :original_height, :original_width, :].contiguous()
+        return x
+
+
+class TransformerLayer(fl.Chain):
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        feedforward_dim: int,
+        image_embedding_size: tuple[int, int],
+        window_size: int | None = None,
+        layer_norm_eps: float = 1e-6,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        self.embedding_dim = embedding_dim
+        self.num_heads = num_heads
+        self.feedforward_dim = feedforward_dim
+        self.window_size = window_size
+        self.layer_norm_eps = layer_norm_eps
+        self.image_embedding_size = image_embedding_size
+        attention_spatial_size = (window_size, window_size) if window_size is not None else image_embedding_size
+        reshape_or_merge = (
+            WindowMerge()
+            if self.window_size is not None
+            else fl.Reshape(self.image_embedding_size[0], self.image_embedding_size[1], embedding_dim)
+        )
+        super().__init__(
+            fl.Residual(
+                fl.LayerNorm(normalized_shape=embedding_dim, eps=self.layer_norm_eps, device=device, dtype=dtype),
+                WindowPartition() if self.window_size is not None else fl.Identity(),
+                FusedSelfAttention(
+                    embedding_dim=embedding_dim,
+                    num_heads=num_heads,
+                    spatial_size=attention_spatial_size,
+                    device=device,
+                    dtype=dtype,
+                ),
+                reshape_or_merge,
+            ),
+            fl.Residual(
+                fl.LayerNorm(normalized_shape=embedding_dim, eps=self.layer_norm_eps, device=device, dtype=dtype),
+                FeedForward(embedding_dim=embedding_dim, feedforward_dim=feedforward_dim, device=device, dtype=dtype),
+            ),
+        )
+
+    def init_context(self) -> Contexts:
+        return {"window_partition": {"window_size": self.window_size}}
+
+
+class Neck(fl.Chain):
+    def __init__(self, in_channels: int = 768, device: Device | str | None = None, dtype: DType | None = None) -> None:
+        self.in_channels = in_channels
+        super().__init__(
+            fl.Permute(0, 3, 1, 2),
+            fl.Conv2d(
+                in_channels=self.in_channels,
+                out_channels=256,
+                kernel_size=1,
+                use_bias=False,
+                device=device,
+                dtype=dtype,
+            ),
+            fl.LayerNorm2d(channels=256, device=device, dtype=dtype),
+            fl.Conv2d(
+                in_channels=256,
+                out_channels=256,
+                kernel_size=3,
+                padding=1,
+                use_bias=False,
+                device=device,
+                dtype=dtype,
+            ),
+            fl.LayerNorm2d(channels=256, device=device, dtype=dtype),
+        )
+
+
+class Transformer(fl.Chain):
+    pass
+
+
+class SAMViT(fl.Chain):
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_layers: int,
+        num_heads: int,
+        global_attention_indices: tuple[int, ...] | None = None,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        self.embedding_dim = embedding_dim
+        self.num_layers = num_layers
+        self.num_heads = num_heads
+        self.image_size = (1024, 1024)
+        self.patch_size = 16
+        self.window_size = 14
+        self.image_embedding_size = (self.image_size[0] // self.patch_size, self.image_size[1] // self.patch_size)
+        self.feed_forward_dim = 4 * self.embedding_dim
+        self.global_attention_indices = global_attention_indices or tuple()
+        super().__init__(
+            PatchEncoder(
+                in_channels=3, out_channels=embedding_dim, patch_size=self.patch_size, device=device, dtype=dtype
+            ),
+            PositionalEncoder(
+                embedding_dim=embedding_dim, image_embedding_size=self.image_embedding_size, device=device, dtype=dtype
+            ),
+            Transformer(
+                TransformerLayer(
+                    embedding_dim=embedding_dim,
+                    num_heads=num_heads,
+                    feedforward_dim=self.feed_forward_dim,
+                    window_size=self.window_size if i not in self.global_attention_indices else None,
+                    image_embedding_size=self.image_embedding_size,
+                    device=device,
+                    dtype=dtype,
+                )
+                for i in range(num_layers)
+            ),
+            Neck(in_channels=embedding_dim, device=device, dtype=dtype),
+        )
+
+
+class SAMViTH(SAMViT):
+    def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None:
+        super().__init__(
+            embedding_dim=1280,
+            num_layers=32,
+            num_heads=16,
+            global_attention_indices=(7, 15, 23, 31),
+            device=device,
+            dtype=dtype,
+        )

src/refiners/foundationals/segment_anything/mask_decoder.py

@@ -0,0 +1,264 @@
+import refiners.fluxion.layers as fl
+from torch import device as Device, dtype as DType, Tensor, nn
+import torch
+
+from refiners.foundationals.segment_anything.transformer import (
+    SparseCrossDenseAttention,
+    TwoWayTranformerLayer,
+)
+from refiners.fluxion.context import Contexts
+
+
+class EmbeddingsAggregator(fl.ContextModule):
+    def __init__(self, num_output_mask: int = 3) -> None:
+        super().__init__()
+        self.num_mask_tokens = num_output_mask
+
+    def forward(self, iou_mask_tokens: Tensor) -> Tensor:
+        mask_decoder = self.ensure_parent
+        mask_decoder_context = mask_decoder.use_context(context_name="mask_decoder")
+        image_embedding = mask_decoder_context["image_embedding"]
+        point_embedding = mask_decoder_context["point_embedding"]
+        mask_embedding = mask_decoder_context["mask_embedding"]
+        dense_positional_embedding = mask_decoder_context["dense_positional_embedding"]
+
+        sparse_embedding = torch.cat(tensors=(iou_mask_tokens, point_embedding), dim=1)
+        dense_embedding = (image_embedding + mask_embedding).flatten(start_dim=2).transpose(1, 2)
+        if dense_positional_embedding.shape != dense_embedding.shape:
+            dense_positional_embedding = dense_positional_embedding.flatten(start_dim=2).transpose(1, 2)
+
+        mask_decoder_context.update(
+            {
+                "dense_embedding": dense_embedding,
+                "dense_positional_embedding": dense_positional_embedding,
+                "sparse_embedding": sparse_embedding,
+            }
+        )
+        mask_decoder.set_context(context="mask_decoder", value=mask_decoder_context)
+
+        return sparse_embedding
+
+
+class Transformer(fl.Chain):
+    pass
+
+
+class Hypernetworks(fl.Concatenate):
+    def __init__(
+        self,
+        embedding_dim: int = 256,
+        num_layers: int = 3,
+        num_mask_tokens: int = 3,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.num_layers = num_layers
+        self.num_mask_tokens = num_mask_tokens
+
+        super().__init__(
+            *[
+                fl.Chain(
+                    fl.Slicing(dim=1, start=i + 1, length=1),
+                    fl.MultiLinear(
+                        input_dim=embedding_dim,
+                        output_dim=embedding_dim // 8,
+                        inner_dim=embedding_dim,
+                        num_layers=num_layers,
+                        device=device,
+                        dtype=dtype,
+                    ),
+                )
+                for i in range(num_mask_tokens + 1)
+            ],
+            dim=1,
+        )
+
+
+class DenseEmbeddingUpscaling(fl.Chain):
+    def __init__(
+        self,
+        embedding_dim: int = 256,
+        dense_embedding_side_dim: int = 64,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.dense_embedding_side_dim = dense_embedding_side_dim
+
+        super().__init__(
+            fl.UseContext(context="mask_decoder", key="dense_embedding"),
+            fl.Transpose(dim0=1, dim1=2),
+            fl.Reshape(embedding_dim, dense_embedding_side_dim, dense_embedding_side_dim),
+            fl.ConvTranspose2d(
+                in_channels=embedding_dim,
+                out_channels=embedding_dim // 4,
+                kernel_size=2,
+                stride=2,
+                device=device,
+                dtype=dtype,
+            ),
+            fl.LayerNorm2d(channels=embedding_dim // 4, device=device, dtype=dtype),
+            fl.GeLU(),
+            fl.ConvTranspose2d(
+                in_channels=embedding_dim // 4,
+                out_channels=embedding_dim // 8,
+                kernel_size=2,
+                stride=2,
+                device=device,
+                dtype=dtype,
+            ),
+            fl.GeLU(),
+            fl.Flatten(start_dim=2),
+        )
+
+
+class IOUMaskEncoder(fl.WeightedModule):
+    def __init__(
+        self,
+        embedding_dim: int = 256,
+        num_mask_tokens: int = 4,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.num_mask_tokens = num_mask_tokens
+        # aka prompt tokens + output token (for IoU scores prediction)
+        self.weight = nn.Parameter(data=torch.randn(num_mask_tokens + 1, embedding_dim, device=device, dtype=dtype))
+
+    def forward(self) -> Tensor:
+        return self.weight.unsqueeze(dim=0)
+
+
+class MaskPrediction(fl.Chain):
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_mask_tokens: int,
+        num_layers: int = 3,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        self.embedding_dim = embedding_dim
+        self.num_mask_tokens = num_mask_tokens
+        self.num_layers = num_layers
+        super().__init__(
+            fl.Matmul(
+                input=Hypernetworks(
+                    embedding_dim=embedding_dim,
+                    num_layers=num_layers,
+                    num_mask_tokens=num_mask_tokens,
+                    device=device,
+                    dtype=dtype,
+                ),
+                other=DenseEmbeddingUpscaling(embedding_dim=embedding_dim, device=device, dtype=dtype),
+            ),
+            fl.Slicing(dim=1, start=1, length=num_mask_tokens),
+            fl.Reshape(num_mask_tokens, embedding_dim, embedding_dim),
+        )
+
+
+class IOUPrediction(fl.Chain):
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_layers: int,
+        num_mask_tokens: int,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        self.embedding_dim = embedding_dim
+        self.num_layers = num_layers
+        super().__init__(
+            fl.Slicing(dim=1, start=0, length=1),
+            fl.Squeeze(dim=0),
+            fl.MultiLinear(
+                input_dim=embedding_dim,
+                output_dim=num_mask_tokens + 1,
+                inner_dim=embedding_dim,
+                num_layers=num_layers,
+                device=device,
+                dtype=dtype,
+            ),
+            fl.Slicing(dim=-1, start=1, length=num_mask_tokens),
+        )
+
+
+class MaskDecoder(fl.Chain):
+    def __init__(
+        self,
+        embedding_dim: int = 256,
+        feed_forward_dim: int = 2048,
+        num_layers: int = 2,
+        num_output_mask: int = 3,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.num_mask_tokens = num_output_mask
+        self.feed_forward_dim = feed_forward_dim
+        self.num_layers = num_layers
+
+        super().__init__(
+            IOUMaskEncoder(
+                embedding_dim=embedding_dim, num_mask_tokens=num_output_mask + 1, device=device, dtype=dtype
+            ),
+            EmbeddingsAggregator(num_output_mask=num_output_mask),
+            Transformer(
+                *(
+                    TwoWayTranformerLayer(
+                        embedding_dim=embedding_dim,
+                        num_heads=8,
+                        feed_forward_dim=feed_forward_dim,
+                        use_residual_self_attention=i > 0,
+                        device=device,
+                        dtype=dtype,
+                    )
+                    for i in range(num_layers)
+                ),
+                SparseCrossDenseAttention(embedding_dim=embedding_dim, device=device, dtype=dtype),
+                fl.LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
+            ),
+            fl.Parallel(
+                MaskPrediction(
+                    embedding_dim=embedding_dim, num_mask_tokens=num_output_mask, device=device, dtype=dtype
+                ),
+                IOUPrediction(
+                    embedding_dim=embedding_dim,
+                    num_layers=3,
+                    num_mask_tokens=num_output_mask,
+                    device=device,
+                    dtype=dtype,
+                ),
+            ),
+        )
+
+    def init_context(self) -> Contexts:
+        return {
+            "mask_decoder": {
+                "image_embedding": None,
+                "point_embedding": None,
+                "mask_embedding": None,
+                "dense_positional_embedding": None,
+            }
+        }
+
+    def set_image_embedding(self, image_embedding: Tensor) -> None:
+        mask_decoder_context = self.use_context(context_name="mask_decoder")
+        mask_decoder_context["image_embedding"] = image_embedding
+
+    def set_point_embedding(self, point_embedding: Tensor) -> None:
+        mask_decoder_context = self.use_context(context_name="mask_decoder")
+        mask_decoder_context["point_embedding"] = point_embedding
+
+    def set_mask_embedding(self, mask_embedding: Tensor) -> None:
+        mask_decoder_context = self.use_context(context_name="mask_decoder")
+        mask_decoder_context["mask_embedding"] = mask_embedding
+
+    def set_dense_positional_embedding(self, dense_positional_embedding: Tensor) -> None:
+        mask_decoder_context = self.use_context(context_name="mask_decoder")
+        mask_decoder_context["dense_positional_embedding"] = dense_positional_embedding

src/refiners/foundationals/segment_anything/model.py

@@ -0,0 +1,166 @@
+from dataclasses import dataclass
+from typing import Sequence
+from PIL import Image
+from torch import device as Device, dtype as DType, Tensor
+import numpy as np
+import torch
+import refiners.fluxion.layers as fl
+from refiners.fluxion.utils import image_to_tensor, normalize, pad, interpolate
+from refiners.foundationals.segment_anything.image_encoder import SAMViT, SAMViTH
+from refiners.foundationals.segment_anything.mask_decoder import MaskDecoder
+from refiners.foundationals.segment_anything.prompt_encoder import MaskEncoder, PointEncoder
+
+
+@dataclass
+class ImageEmbedding:
+    features: Tensor
+    original_image_size: tuple[int, int]  # (height, width)
+
+
+class SegmentAnything(fl.Module):
+    mask_threshold: float = 0.0
+
+    def __init__(
+        self,
+        image_encoder: SAMViT,
+        point_encoder: PointEncoder,
+        mask_encoder: MaskEncoder,
+        mask_decoder: MaskDecoder,
+        device: Device | str = "cpu",
+        dtype: DType = torch.float32,
+    ) -> None:
+        super().__init__()
+        self.device: Device = device if isinstance(device, Device) else Device(device=device)
+        self.dtype = dtype
+        self.image_encoder = image_encoder.to(device=self.device, dtype=self.dtype)
+        self.point_encoder = point_encoder.to(device=self.device, dtype=self.dtype)
+        self.mask_encoder = mask_encoder.to(device=self.device, dtype=self.dtype)
+        self.mask_decoder = mask_decoder.to(device=self.device, dtype=self.dtype)
+
+    def compute_image_embedding(self, image: Image.Image) -> ImageEmbedding:
+        original_size = (image.height, image.width)
+        target_size = self.compute_target_size(original_size)
+        return ImageEmbedding(
+            features=self.image_encoder(self.preprocess_image(image=image, target_size=target_size)),
+            original_image_size=original_size,
+        )
+
+    def predict(
+        self,
+        input: Image.Image | ImageEmbedding,
+        foreground_points: Sequence[tuple[float, float]] | None = None,
+        background_points: Sequence[tuple[float, float]] | None = None,
+        box_points: Sequence[Sequence[tuple[float, float]]] | None = None,
+        masks: Sequence[Image.Image] | None = None,
+        binarize: bool = True,
+    ) -> tuple[Tensor, Tensor, Tensor]:
+        if isinstance(input, ImageEmbedding):
+            original_size = input.original_image_size
+            target_size = self.compute_target_size(original_size)
+            image_embedding = input.features
+        else:
+            original_size = (input.height, input.width)
+            target_size = self.compute_target_size(original_size)
+            image_embedding = self.image_encoder(self.preprocess_image(image=input, target_size=target_size))
+
+        coordinates, type_mask = self.point_encoder.points_to_tensor(
+            foreground_points=foreground_points,
+            background_points=background_points,
+            box_points=box_points,
+        )
+        self.point_encoder.set_type_mask(type_mask=type_mask)
+
+        if masks is not None:
+            mask_tensor = torch.stack(
+                tensors=[image_to_tensor(image=mask, device=self.device, dtype=self.dtype) for mask in masks]
+            )
+            mask_embedding = self.mask_encoder(mask_tensor)
+        else:
+            mask_embedding = self.mask_encoder.get_no_mask_dense_embedding(
+                image_embedding_size=self.image_encoder.image_embedding_size
+            )
+        point_embedding = self.point_encoder(
+            self.normalize(coordinates, target_size=target_size, original_size=original_size)
+        )
+        dense_positional_embedding = self.point_encoder.get_dense_positional_embedding(
+            image_embedding_size=self.image_encoder.image_embedding_size
+        )
+
+        self.mask_decoder.set_image_embedding(image_embedding=image_embedding)
+        self.mask_decoder.set_mask_embedding(mask_embedding=mask_embedding)
+        self.mask_decoder.set_point_embedding(point_embedding=point_embedding)
+        self.mask_decoder.set_dense_positional_embedding(dense_positional_embedding=dense_positional_embedding)
+
+        low_res_masks, iou_predictions = self.mask_decoder()
+
+        high_res_masks = self.postprocess_masks(
+            masks=low_res_masks, target_size=target_size, original_size=original_size
+        )
+
+        if binarize:
+            high_res_masks = high_res_masks > self.mask_threshold
+
+        return high_res_masks, iou_predictions, low_res_masks
+
+    @property
+    def image_size(self) -> int:
+        w, h = self.image_encoder.image_size
+        assert w == h
+        return w
+
+    def compute_target_size(self, size: tuple[int, int]) -> tuple[int, int]:
+        oldh, oldw = size
+        scale = self.image_size * 1.0 / max(oldh, oldw)
+        newh, neww = oldh * scale, oldw * scale
+        neww = int(neww + 0.5)
+        newh = int(newh + 0.5)
+        return (newh, neww)
+
+    def preprocess_image(self, image: Image.Image, target_size: tuple[int, int]) -> Tensor:
+        h, w = target_size
+        padh = self.image_size - h
+        padw = self.image_size - w
+        image_tensor = torch.tensor(
+            np.array(image.resize((w, h), resample=Image.Resampling.BILINEAR)).astype(np.float32).transpose(2, 0, 1),
+            device=self.device,
+            dtype=self.dtype,
+        ).unsqueeze(0)
+        return pad(
+            normalize(image_tensor, mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]), (0, padw, 0, padh)
+        )
+
+    def normalize(self, coordinates: Tensor, target_size: tuple[int, int], original_size: tuple[int, int]) -> Tensor:
+        coordinates[:, :, 0] = ((coordinates[:, :, 0] * (target_size[1] / original_size[1])) + 0.5) / self.image_size
+        coordinates[:, :, 1] = ((coordinates[:, :, 1] * (target_size[0] / original_size[0])) + 0.5) / self.image_size
+        return coordinates
+
+    def postprocess_masks(self, masks: Tensor, target_size: tuple[int, int], original_size: tuple[int, int]) -> Tensor:
+        masks = interpolate(masks, factor=torch.Size((self.image_size, self.image_size)), mode="bilinear")
+        masks = masks[..., : target_size[0], : target_size[1]]  # remove padding added at `preprocess_image` time
+        masks = interpolate(masks, factor=torch.Size(original_size), mode="bilinear")
+        return masks
+
+
+class SegmentAnythingH(SegmentAnything):
+    def __init__(
+        self,
+        image_encoder: SAMViTH | None = None,
+        point_encoder: PointEncoder | None = None,
+        mask_encoder: MaskEncoder | None = None,
+        mask_decoder: MaskDecoder | None = None,
+        device: Device | str = "cpu",
+        dtype: DType = torch.float32,
+    ) -> None:
+        image_encoder = image_encoder or SAMViTH()
+        point_encoder = point_encoder or PointEncoder()
+        mask_encoder = mask_encoder or MaskEncoder()
+        mask_decoder = mask_decoder or MaskDecoder()
+
+        super().__init__(
+            image_encoder=image_encoder,
+            point_encoder=point_encoder,
+            mask_encoder=mask_encoder,
+            mask_decoder=mask_decoder,
+            device=device,
+            dtype=dtype,
+        )

src/refiners/foundationals/segment_anything/prompt_encoder.py

@@ -0,0 +1,190 @@
+from enum import Enum, auto
+from collections.abc import Sequence
+from torch import device as Device, dtype as DType, Tensor, nn
+import torch
+from jaxtyping import Float, Int
+import refiners.fluxion.layers as fl
+from refiners.fluxion.context import Contexts
+
+
+class CoordinateEncoder(fl.Chain):
+    def __init__(
+        self,
+        num_positional_features: int = 64,
+        scale: float = 1,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        self.num_positional_features = num_positional_features
+        self.scale = scale
+
+        super().__init__(
+            fl.Multiply(scale=2, bias=-1),
+            fl.Linear(in_features=2, out_features=num_positional_features, bias=False, device=device, dtype=dtype),
+            fl.Multiply(scale=2 * torch.pi * self.scale),
+            fl.Concatenate(fl.Sin(), fl.Cos(), dim=-1),
+        )
+
+
+class PointType(Enum):
+    BACKGROUND = auto()
+    FOREGROUND = auto()
+    BOX_TOP_LEFT = auto()
+    BOX_BOTTOM_RIGHT = auto()
+    NOT_A_POINT = auto()
+
+
+class PointTypeEmbedding(fl.WeightedModule, fl.ContextModule):
+    def __init__(self, embedding_dim: int, device: Device | str | None = None, dtype: DType | None = None) -> None:
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.weight = nn.Parameter(data=torch.randn(len(PointType), self.embedding_dim, device=device, dtype=dtype))
+
+    def forward(self, type_mask: Int[Tensor, "1 num_points"]) -> Float[Tensor, "1 num_points embedding_dim"]:
+        assert isinstance(type_mask, Tensor), "type_mask must be a Tensor."
+
+        embeddings = torch.zeros(*type_mask.shape, self.embedding_dim).to(device=type_mask.device)
+        for type_id in PointType:
+            mask = type_mask == type_id.value
+            embeddings[mask] = self.weight[type_id.value - 1]
+
+        return embeddings
+
+
+class PointEncoder(fl.Chain):
+    def __init__(
+        self, embedding_dim: int = 256, scale: float = 1, device: Device | str | None = None, dtype: DType | None = None
+    ) -> None:
+        assert embedding_dim % 2 == 0, "embedding_dim must be divisible by 2."
+        self.embedding_dim = embedding_dim
+        self.scale = scale
+
+        super().__init__(
+            CoordinateEncoder(num_positional_features=embedding_dim // 2, scale=scale, device=device, dtype=dtype),
+            fl.Lambda(func=self.pad),
+            fl.Residual(
+                fl.UseContext(context="point_encoder", key="type_mask"),
+                PointTypeEmbedding(embedding_dim=embedding_dim, device=device, dtype=dtype),
+            ),
+        )
+
+    def pad(self, x: Tensor) -> Tensor:
+        type_mask: Tensor = self.use_context("point_encoder")["type_mask"]
+        if torch.any((type_mask == PointType.BOX_TOP_LEFT.value) | (type_mask == PointType.BOX_BOTTOM_RIGHT.value)):
+            # Some boxes have been passed: no need to pad in this case
+            return x
+        type_mask = torch.cat(
+            [type_mask, torch.full((type_mask.shape[0], 1), PointType.NOT_A_POINT.value, device=type_mask.device)],
+            dim=1,
+        )
+        self.set_context(context="point_encoder", value={"type_mask": type_mask})
+        return torch.cat([x, torch.zeros((x.shape[0], 1, x.shape[-1]), device=x.device)], dim=1)
+
+    def init_context(self) -> Contexts:
+        return {
+            "point_encoder": {
+                "type_mask": None,
+            }
+        }
+
+    def set_type_mask(self, type_mask: Int[Tensor, "1 num_points"]) -> None:
+        self.set_context(context="point_encoder", value={"type_mask": type_mask})
+
+    def get_dense_positional_embedding(
+        self, image_embedding_size: tuple[int, int]
+    ) -> Float[Tensor, "num_positional_features height width"]:
+        coordinate_encoder = self.ensure_find(layer_type=CoordinateEncoder)
+        height, width = image_embedding_size
+        grid = torch.ones((height, width), device=self.device, dtype=torch.float32)
+        y_embedding = grid.cumsum(dim=0) - 0.5
+        x_embedding = grid.cumsum(dim=1) - 0.5
+        y_embedding = y_embedding / height
+        x_embedding = x_embedding / width
+        positional_embedding = (
+            coordinate_encoder(torch.stack(tensors=[x_embedding, y_embedding], dim=-1))
+            .permute(2, 0, 1)
+            .unsqueeze(dim=0)
+        )
+        return positional_embedding
+
+    def points_to_tensor(
+        self,
+        foreground_points: Sequence[tuple[float, float]] | None = None,
+        background_points: Sequence[tuple[float, float]] | None = None,
+        not_a_points: Sequence[tuple[float, float]] | None = None,
+        box_points: Sequence[Sequence[tuple[float, float]]] | None = None,
+    ) -> tuple[Float[Tensor, "1 num_points 2"], Int[Tensor, "1 num_points"]]:
+        foreground_points = foreground_points or []
+        background_points = background_points or []
+        not_a_points = not_a_points or []
+        box_points = box_points or []
+        top_left_points = [box[0] for box in box_points]
+        bottom_right_points = [box[1] for box in box_points]
+        coordinates: list[Tensor] = []
+        type_ids: list[Tensor] = []
+
+        # Must be in sync with PointType enum
+        for type_id, coords_seq in zip(
+            PointType, [background_points, foreground_points, top_left_points, bottom_right_points, not_a_points]
+        ):
+            if len(coords_seq) > 0:
+                coords_tensor = torch.tensor(data=list(coords_seq), dtype=torch.float, device=self.device)
+                coordinates.append(coords_tensor)
+                point_ids = torch.tensor(data=[type_id.value] * len(coords_seq), dtype=torch.int, device=self.device)
+                type_ids.append(point_ids)
+
+        all_coordinates = torch.cat(tensors=coordinates, dim=0).unsqueeze(dim=0)
+        type_mask = torch.cat(tensors=type_ids, dim=0).unsqueeze(dim=0)
+
+        return all_coordinates, type_mask
+
+
+class MaskEncoder(fl.Chain):
+    def __init__(
+        self,
+        embedding_dim: int = 256,
+        intermediate_channels: int = 16,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        self.embedding_dim = embedding_dim
+        self.intermediate_channels = intermediate_channels
+        super().__init__(
+            fl.Conv2d(
+                in_channels=1,
+                out_channels=self.intermediate_channels // 4,
+                kernel_size=2,
+                stride=2,
+                device=device,
+                dtype=dtype,
+            ),
+            fl.LayerNorm2d(channels=self.intermediate_channels // 4, device=device, dtype=dtype),
+            fl.GeLU(),
+            fl.Conv2d(
+                in_channels=self.intermediate_channels // 4,
+                out_channels=self.intermediate_channels,
+                kernel_size=2,
+                stride=2,
+                device=device,
+                dtype=dtype,
+            ),
+            fl.LayerNorm2d(channels=self.intermediate_channels, device=device, dtype=dtype),
+            fl.GeLU(),
+            fl.Conv2d(
+                in_channels=self.intermediate_channels,
+                out_channels=self.embedding_dim,
+                kernel_size=1,
+                device=device,
+                dtype=dtype,
+            ),
+        )
+        self.register_parameter(
+            "no_mask_embedding", nn.Parameter(torch.randn(1, embedding_dim, device=device, dtype=dtype))
+        )
+
+    def get_no_mask_dense_embedding(
+        self, image_embedding_size: tuple[int, int], batch_size: int = 1
+    ) -> Float[Tensor, "batch embedding_dim image_embedding_height image_embedding_width"]:
+        return self.no_mask_embedding.reshape(1, -1, 1, 1).expand(
+            batch_size, -1, image_embedding_size[0], image_embedding_size[1]
+        )

src/refiners/foundationals/segment_anything/transformer.py

@@ -0,0 +1,157 @@
+from torch import dtype as DType, device as Device
+import refiners.fluxion.layers as fl
+
+
+class CrossAttention(fl.Attention):
+    def __init__(
+        self,
+        embedding_dim: int,
+        cross_embedding_dim: int | None = None,
+        num_heads: int = 1,
+        inner_dim: int | None = None,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        super().__init__(
+            embedding_dim=embedding_dim,
+            key_embedding_dim=cross_embedding_dim,
+            num_heads=num_heads,
+            inner_dim=inner_dim,
+            is_optimized=False,
+            device=device,
+            dtype=dtype,
+        )
+        self.cross_embedding_dim = cross_embedding_dim or embedding_dim
+        self.insert(index=0, module=fl.Parallel(fl.GetArg(index=0), fl.GetArg(index=1), fl.GetArg(index=1)))
+
+
+class FeedForward(fl.Residual):
+    def __init__(
+        self, embedding_dim: int, feed_forward_dim: int, device: Device | str | None = None, dtype: DType | None = None
+    ) -> None:
+        self.embedding_dim = embedding_dim
+        self.feed_forward_dim = feed_forward_dim
+        super().__init__(
+            fl.Linear(in_features=embedding_dim, out_features=feed_forward_dim, device=device, dtype=dtype),
+            fl.ReLU(),
+            fl.Linear(in_features=feed_forward_dim, out_features=embedding_dim, device=device, dtype=dtype),
+        )
+
+
+class SparseSelfAttention(fl.Residual):
+    def __init__(
+        self,
+        embedding_dim: int,
+        inner_dim: int | None = None,
+        num_heads: int = 1,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        add_sparse_embedding = fl.Residual(fl.UseContext(context="mask_decoder", key="sparse_embedding"))
+        super().__init__(
+            fl.Parallel(add_sparse_embedding, add_sparse_embedding, fl.Identity()),
+            fl.Attention(
+                embedding_dim=embedding_dim,
+                inner_dim=inner_dim,
+                num_heads=num_heads,
+                is_optimized=False,
+                device=device,
+                dtype=dtype,
+            ),
+        )
+
+
+class SparseCrossDenseAttention(fl.Residual):
+    def __init__(
+        self, embedding_dim: int, num_heads: int = 8, device: Device | str | None = None, dtype: DType | None = None
+    ) -> None:
+        self.embedding_dim = embedding_dim
+        self.num_heads = num_heads
+        super().__init__(
+            fl.Parallel(
+                fl.Residual(
+                    fl.UseContext(context="mask_decoder", key="sparse_embedding"),
+                ),
+                fl.Sum(
+                    fl.UseContext(context="mask_decoder", key="dense_embedding"),
+                    fl.UseContext(context="mask_decoder", key="dense_positional_embedding"),
+                ),
+                fl.UseContext(context="mask_decoder", key="dense_embedding"),
+            ),
+            fl.Attention(
+                embedding_dim=embedding_dim,
+                inner_dim=embedding_dim // 2,
+                num_heads=num_heads,
+                is_optimized=False,
+                device=device,
+                dtype=dtype,
+            ),
+        )
+
+
+class DenseCrossSparseAttention(fl.Chain):
+    def __init__(
+        self, embedding_dim: int, num_heads: int = 8, device: Device | str | None = None, dtype: DType | None = None
+    ) -> None:
+        super().__init__(
+            fl.Parallel(
+                fl.Sum(
+                    fl.UseContext(context="mask_decoder", key="dense_embedding"),
+                    fl.UseContext(context="mask_decoder", key="dense_positional_embedding"),
+                ),
+                fl.Residual(
+                    fl.UseContext(context="mask_decoder", key="sparse_embedding"),
+                ),
+                fl.Identity(),
+            ),
+            fl.Attention(
+                embedding_dim=embedding_dim,
+                inner_dim=embedding_dim // 2,
+                num_heads=num_heads,
+                is_optimized=False,
+                device=device,
+                dtype=dtype,
+            ),
+        )
+
+
+class TwoWayTranformerLayer(fl.Chain):
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int = 8,
+        feed_forward_dim: int = 2048,
+        use_residual_self_attention: bool = True,
+        device: Device | str | None = None,
+        dtype: DType | None = None,
+    ) -> None:
+        self.embedding_dim = embedding_dim
+        self.num_heads = num_heads
+        self.feed_forward_dim = feed_forward_dim
+
+        self_attention = (
+            SparseSelfAttention(embedding_dim=embedding_dim, num_heads=num_heads, device=device, dtype=dtype)
+            if use_residual_self_attention
+            else fl.SelfAttention(
+                embedding_dim=embedding_dim, num_heads=num_heads, is_optimized=False, device=device, dtype=dtype
+            )
+        )
+
+        super().__init__(
+            self_attention,
+            fl.LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
+            SparseCrossDenseAttention(embedding_dim=embedding_dim, num_heads=num_heads, device=device, dtype=dtype),
+            fl.LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
+            FeedForward(embedding_dim=embedding_dim, feed_forward_dim=feed_forward_dim, device=device, dtype=dtype),
+            fl.LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
+            fl.Passthrough(
+                fl.Sum(
+                    fl.UseContext(context="mask_decoder", key="dense_embedding"),
+                    DenseCrossSparseAttention(
+                        embedding_dim=embedding_dim, num_heads=num_heads, device=device, dtype=dtype
+                    ),
+                ),
+                fl.LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
+                fl.SetContext(context="mask_decoder", key="dense_embedding"),
+            ),
+        )

tests/conftest.py

@@ -28,3 +28,8 @@ def test_e2e_path() -> Path:
 @fixture(scope="session")
 def test_textual_inversion_path() -> Path:
     return PARENT_PATH / "foundationals" / "clip" / "test_concepts_ref"
+
+
+@fixture(scope="session")
+def test_sam_path() -> Path:
+    return PARENT_PATH / "foundationals" / "segment_anything"

tests/foundationals/segment_anything/test_sam.py

@@ -0,0 +1,317 @@
+from math import isclose
+from pathlib import Path
+from typing import cast
+from warnings import warn
+
+import pytest
+import torch
+import torch.nn as nn
+import numpy as np
+
+from PIL import Image
+from torch import Tensor
+from refiners.fluxion import manual_seed
+from refiners.fluxion.model_converter import ModelConverter
+
+from refiners.fluxion.utils import image_to_tensor
+from refiners.foundationals.segment_anything.image_encoder import FusedSelfAttention
+from refiners.foundationals.segment_anything.model import SegmentAnythingH
+from refiners.foundationals.segment_anything.transformer import TwoWayTranformerLayer
+from tests.foundationals.segment_anything.utils import (
+    FacebookSAM,
+    FacebookSAMPredictor,
+    SAMPrompt,
+    intersection_over_union,
+)
+
+# See predictor_example.ipynb official notebook (note: mask_input is not yet properly supported)
+PROMPTS: list[SAMPrompt] = [
+    SAMPrompt(foreground_points=((500, 375),)),
+    SAMPrompt(background_points=((500, 375),)),
+    SAMPrompt(foreground_points=((500, 375), (1125, 625))),
+    SAMPrompt(foreground_points=((500, 375),), background_points=((1125, 625),)),
+    SAMPrompt(box_points=[[(425, 600), (700, 875)]]),
+    SAMPrompt(box_points=[[(425, 600), (700, 875)]], background_points=((575, 750),)),
+]
+
+
+@pytest.fixture(params=PROMPTS)
+def prompt(request: pytest.FixtureRequest) -> SAMPrompt:
+    return request.param
+
+
+@pytest.fixture
+def one_prompt() -> SAMPrompt:
+    return PROMPTS[0]
+
+
+@pytest.fixture(scope="module")
+def facebook_sam_h_weights(test_weights_path: Path) -> Path:
+    sam_h_weights = test_weights_path / "sam_vit_h_4b8939.pth"
+    if not sam_h_weights.is_file():
+        warn(f"could not find weights at {sam_h_weights}, skipping")
+        pytest.skip(allow_module_level=True)
+    return sam_h_weights
+
+
+@pytest.fixture(scope="module")
+def sam_h_weights(test_weights_path: Path) -> Path:
+    sam_h_weights = test_weights_path / "segment-anything-h.safetensors"
+    if not sam_h_weights.is_file():
+        warn(f"could not find weights at {sam_h_weights}, skipping")
+        pytest.skip(allow_module_level=True)
+    return sam_h_weights
+
+
+@pytest.fixture(scope="module")
+def facebook_sam_h(facebook_sam_h_weights: Path, test_device: torch.device) -> FacebookSAM:
+    from segment_anything import build_sam_vit_h  # type: ignore
+
+    sam_h = cast(FacebookSAM, build_sam_vit_h())
+    sam_h.load_state_dict(state_dict=torch.load(f=facebook_sam_h_weights))  # type: ignore
+    return sam_h.to(device=test_device)
+
+
+@pytest.fixture(scope="module")
+def facebook_sam_h_predictor(facebook_sam_h: FacebookSAM) -> FacebookSAMPredictor:
+    from segment_anything import SamPredictor  # type: ignore
+    from segment_anything.modeling import Sam  # type: ignore
+
+    predictor = SamPredictor(cast(Sam, facebook_sam_h))
+    return cast(FacebookSAMPredictor, predictor)
+
+
+@pytest.fixture(scope="module")
+def sam_h(sam_h_weights: Path, test_device: torch.device) -> SegmentAnythingH:
+    sam_h = SegmentAnythingH(device=test_device)
+    # TODO: make strict=True when the MasKEncoder conversion is done
+    sam_h.load_from_safetensors(tensors_path=sam_h_weights, strict=False)
+    return sam_h
+
+
+@pytest.fixture(scope="module")
+def ref_path(test_sam_path: Path) -> Path:
+    return test_sam_path / "test_sam_ref"
+
+
+@pytest.fixture
+def truck(ref_path: Path) -> Image.Image:
+    return Image.open(ref_path / "truck.jpg").convert("RGB")
+
+
+@torch.no_grad()
+def test_fused_self_attention(facebook_sam_h: FacebookSAM) -> None:
+    manual_seed(seed=0)
+    x = torch.randn(25, 14, 14, 1280, device=facebook_sam_h.device)
+
+    attention = cast(nn.Module, facebook_sam_h.image_encoder.blocks[0].attn)  # type: ignore
+
+    refiners_attention = FusedSelfAttention(
+        embedding_dim=1280, num_heads=16, spatial_size=(14, 14), device=facebook_sam_h.device
+    )
+    refiners_attention.Linear_1.weight = attention.qkv.weight  # type: ignore
+    refiners_attention.Linear_1.bias = attention.qkv.bias  # type: ignore
+    refiners_attention.Linear_2.weight = attention.proj.weight  # type: ignore
+    refiners_attention.Linear_2.bias = attention.proj.bias  # type: ignore
+    refiners_attention.RelativePositionAttention.horizontal_embedding = attention.rel_pos_w
+    refiners_attention.RelativePositionAttention.vertical_embedding = attention.rel_pos_h
+
+    y_1 = attention(x)
+    assert y_1.shape == x.shape
+
+    y_2 = refiners_attention(x)
+    assert y_2.shape == x.shape
+
+    assert torch.equal(input=y_1, other=y_2)
+
+
+@torch.no_grad()
+def test_image_encoder(sam_h: SegmentAnythingH, facebook_sam_h: FacebookSAM, truck: Image.Image) -> None:
+    image_tensor = image_to_tensor(image=truck.resize(size=(1024, 1024)), device=facebook_sam_h.device)
+    y_1 = facebook_sam_h.image_encoder(image_tensor)
+    y_2 = sam_h.image_encoder(image_tensor)
+
+    assert torch.equal(input=y_1, other=y_2)
+
+
+@torch.no_grad()
+def test_prompt_encoder_dense_positional_embedding(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH) -> None:
+    facebook_prompt_encoder = facebook_sam_h.prompt_encoder
+    refiners_prompt_encoder = sam_h.point_encoder
+
+    facebook_dense_pe: Tensor = cast(Tensor, facebook_prompt_encoder.get_dense_pe())  # type: ignore
+    refiners_dense_pe = refiners_prompt_encoder.get_dense_positional_embedding(image_embedding_size=(64, 64))
+
+    assert torch.equal(input=refiners_dense_pe, other=facebook_dense_pe)
+
+
+@torch.no_grad()
+def test_prompt_encoder_no_mask_dense_embedding(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH) -> None:
+    facebook_prompt_encoder = facebook_sam_h.prompt_encoder
+    refiners_prompt_encoder = sam_h.mask_encoder
+
+    _, facebook_dense_pe = facebook_prompt_encoder(points=None, boxes=None, masks=None)
+    refiners_dense_pe = refiners_prompt_encoder.get_no_mask_dense_embedding(image_embedding_size=(64, 64))
+
+    assert torch.equal(input=refiners_dense_pe, other=facebook_dense_pe)
+
+
+@torch.no_grad()
+def test_point_encoder(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH, prompt: SAMPrompt) -> None:
+    facebook_prompt_encoder = facebook_sam_h.prompt_encoder
+    refiners_prompt_encoder = sam_h.point_encoder
+
+    facebook_sparse_pe, _ = facebook_prompt_encoder(
+        **prompt.facebook_prompt_encoder_kwargs(device=facebook_sam_h.device)
+    )
+
+    coordinates, type_mask = refiners_prompt_encoder.points_to_tensor(**prompt.__dict__)
+    # Shift to center of pixel + normalize in [0, 1] (see `_embed_points` in segment-anything official repo)
+    coordinates[:, :, 0] = (coordinates[:, :, 0] + 0.5) / 1024.0
+    coordinates[:, :, 1] = (coordinates[:, :, 1] + 0.5) / 1024.0
+    refiners_prompt_encoder.set_type_mask(type_mask=type_mask)
+    refiners_sparse_pe = refiners_prompt_encoder(coordinates)
+
+    assert torch.equal(input=refiners_sparse_pe, other=facebook_sparse_pe)
+
+
+@torch.no_grad()
+def test_two_way_transformer(facebook_sam_h: FacebookSAM) -> None:
+    dense_embedding = torch.randn(1, 64 * 64, 256, device=facebook_sam_h.device)
+    dense_positional_embedding = torch.randn(1, 64 * 64, 256, device=facebook_sam_h.device)
+    sparse_embedding = torch.randn(1, 3, 256, device=facebook_sam_h.device)
+
+    refiners_layer = TwoWayTranformerLayer(
+        embedding_dim=256, feed_forward_dim=2048, num_heads=8, device=facebook_sam_h.device
+    )
+    facebook_layer = facebook_sam_h.mask_decoder.transformer.layers[1]  # type: ignore
+    assert isinstance(facebook_layer, nn.Module)
+
+    refiners_layer.set_context(
+        context="mask_decoder",
+        value={
+            "dense_embedding": dense_embedding,
+            "dense_positional_embedding": dense_positional_embedding,
+            "sparse_embedding": sparse_embedding,
+        },
+    )
+    facebook_inputs = {
+        "queries": sparse_embedding,
+        "keys": dense_embedding,
+        "query_pe": sparse_embedding,
+        "key_pe": dense_positional_embedding,
+    }
+
+    converter = ModelConverter(
+        source_model=facebook_layer,
+        target_model=refiners_layer,
+        skip_output_check=True,  # done below, manually
+    )
+
+    assert converter.run(source_args=facebook_inputs, target_args=(sparse_embedding,))
+
+    refiners_layer.set_context(
+        context="mask_decoder",
+        value={
+            "dense_embedding": dense_embedding,
+            "dense_positional_embedding": dense_positional_embedding,
+            "sparse_embedding": sparse_embedding,
+        },
+    )
+    y_1 = facebook_layer(**facebook_inputs)[0]
+    y_2 = refiners_layer(sparse_embedding)[0]
+
+    assert torch.equal(input=y_1, other=y_2)
+
+
+@torch.no_grad()
+def test_mask_decoder(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH) -> None:
+    manual_seed(seed=0)
+    facebook_mask_decoder = facebook_sam_h.mask_decoder
+    refiners_mask_decoder = sam_h.mask_decoder
+
+    image_embedding = torch.randn(1, 256, 64, 64, device=facebook_sam_h.device)
+    dense_positional_embedding = torch.randn(1, 256, 64, 64, device=facebook_sam_h.device)
+    point_embedding = torch.randn(1, 3, 256, device=facebook_sam_h.device)
+    mask_embedding = torch.randn(1, 256, 64, 64, device=facebook_sam_h.device)
+
+    import refiners.fluxion.layers as fl
+    from segment_anything.modeling.common import LayerNorm2d  # type: ignore
+
+    assert issubclass(LayerNorm2d, nn.Module)
+    custom_layers = {LayerNorm2d: fl.LayerNorm2d}
+
+    converter = ModelConverter(
+        source_model=facebook_mask_decoder,
+        target_model=refiners_mask_decoder,
+        custom_layer_mapping=custom_layers,  # type: ignore
+    )
+
+    inputs = {
+        "image_embeddings": image_embedding,
+        "image_pe": dense_positional_embedding,
+        "sparse_prompt_embeddings": point_embedding,
+        "dense_prompt_embeddings": mask_embedding,
+        "multimask_output": True,
+    }
+
+    refiners_mask_decoder.set_image_embedding(image_embedding)
+    refiners_mask_decoder.set_point_embedding(point_embedding)
+    refiners_mask_decoder.set_mask_embedding(mask_embedding)
+    refiners_mask_decoder.set_dense_positional_embedding(dense_positional_embedding)
+
+    mapping = converter.map_state_dicts(source_args=inputs, target_args={})
+    assert mapping is not None
+    mapping["IOUMaskEncoder"] = "iou_token"
+
+    state_dict = converter._convert_state_dict(source_state_dict=facebook_mask_decoder.state_dict(), target_state_dict=refiners_mask_decoder.state_dict(), state_dict_mapping=mapping)  # type: ignore
+    state_dict["IOUMaskEncoder.weight"] = torch.cat([facebook_mask_decoder.iou_token.weight, facebook_mask_decoder.mask_tokens.weight], dim=0)  # type: ignore
+    refiners_mask_decoder.load_state_dict(state_dict=state_dict)
+
+    facebook_output = facebook_mask_decoder(**inputs)
+
+    refiners_mask_decoder.set_image_embedding(image_embedding)
+    refiners_mask_decoder.set_point_embedding(point_embedding)
+    refiners_mask_decoder.set_mask_embedding(mask_embedding)
+    refiners_mask_decoder.set_dense_positional_embedding(dense_positional_embedding)
+    mask_prediction, iou_prediction = refiners_mask_decoder()
+
+    facebook_masks = facebook_output[0]
+    facebook_prediction = facebook_output[1]
+
+    assert torch.equal(input=mask_prediction, other=facebook_masks)
+    assert torch.equal(input=iou_prediction, other=facebook_prediction)
+
+
+@torch.no_grad()
+def test_predictor(
+    facebook_sam_h_predictor: FacebookSAMPredictor, sam_h: SegmentAnythingH, truck: Image.Image, prompt: SAMPrompt
+) -> None:
+    predictor = facebook_sam_h_predictor
+    predictor.set_image(np.array(truck))
+    facebook_masks, facebook_scores, _ = predictor.predict(**prompt.facebook_predict_kwargs())  # type: ignore
+
+    assert len(facebook_masks) == 3
+
+    masks, scores, _ = sam_h.predict(truck, **prompt.__dict__)
+    masks = masks.squeeze(0)
+    scores = scores.squeeze(0)
+
+    assert len(masks) == 3
+
+    for i in range(3):
+        mask_prediction = masks[i].cpu()
+        facebook_mask = torch.as_tensor(facebook_masks[i])
+        assert isclose(intersection_over_union(mask_prediction, facebook_mask), 1.0, rel_tol=5e-05)
+        assert isclose(scores[i].item(), facebook_scores[i].item(), rel_tol=1e-05)
+
+
+@torch.no_grad()
+def test_predictor_image_embedding(sam_h: SegmentAnythingH, truck: Image.Image, one_prompt: SAMPrompt) -> None:
+    masks_ref, scores_ref, _ = sam_h.predict(truck, **one_prompt.__dict__)
+
+    image_embedding = sam_h.compute_image_embedding(truck)
+    masks, scores, _ = sam_h.predict(image_embedding, **one_prompt.__dict__)
+
+    assert torch.equal(masks, masks_ref)
+    assert torch.equal(scores_ref, scores)

tests/foundationals/segment_anything/test_sam_ref/README.md

@@ -0,0 +1,3 @@
+# Note about this data
+
+`truck.jpg` is one of the [images](https://github.com/facebookresearch/segment-anything/tree/main/notebooks/images) used in the official [segment-anything notebooks](https://github.com/facebookresearch/segment-anything/tree/main/notebooks).

tests/foundationals/segment_anything/utils.py

@@ -0,0 +1,107 @@
+from collections.abc import Sequence
+from dataclasses import dataclass
+from typing import Any, TypedDict
+
+from jaxtyping import Bool
+from torch import Tensor, nn
+import numpy as np
+import numpy.typing as npt
+import torch
+
+NDArrayUInt8 = npt.NDArray[np.uint8]
+NDArray = npt.NDArray[Any]
+
+
+class SAMInput(TypedDict):
+    image: Tensor
+    original_size: tuple[int, int]
+    point_coords: Tensor | None
+    point_labels: Tensor | None
+    boxes: Tensor | None
+    mask_inputs: Tensor | None
+
+
+class SAMOutput(TypedDict):
+    masks: Tensor
+    iou_predictions: Tensor
+    low_res_logits: Tensor
+
+
+class FacebookSAM(nn.Module):
+    image_encoder: nn.Module
+    prompt_encoder: nn.Module
+    mask_decoder: nn.Module
+
+    def __call__(self, batched_input: list[SAMInput], multimask_output: bool) -> list[SAMOutput]: ...
+
+    @property
+    def device(self) -> Any: ...
+
+
+class FacebookSAMPredictor:
+    model: FacebookSAM
+
+    def set_image(self, image: NDArrayUInt8, image_format: str = "RGB") -> None: ...
+
+    def predict(
+        self,
+        point_coords: NDArray | None = None,
+        point_labels: NDArray | None = None,
+        box: NDArray | None = None,
+        mask_input: NDArray | None = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+    ) -> tuple[NDArray, NDArray, NDArray]: ...
+
+
+@dataclass
+class SAMPrompt:
+    foreground_points: Sequence[tuple[float, float]] | None = None
+    background_points: Sequence[tuple[float, float]] | None = None
+    box_points: Sequence[Sequence[tuple[float, float]]] | None = None
+    # TODO: support masks
+    # masks: Sequence[Image.Image] | None = None
+
+    def facebook_predict_kwargs(self) -> dict[str, NDArray]:
+        prompt: dict[str, NDArray] = {}
+        # Note: the order matters since `points_to_tensor` processes points that way (background -> foreground -> etc)
+        if self.background_points:
+            prompt["point_coords"] = np.array(self.background_points)
+            prompt["point_labels"] = np.array([0] * len(self.background_points))
+        if self.foreground_points:
+            coords = np.array(self.foreground_points)
+            prompt["point_coords"] = (
+                coords if "point_coords" not in prompt else np.concatenate((prompt["point_coords"], coords))
+            )
+            labels = np.array([1] * len(self.foreground_points))
+            prompt["point_labels"] = (
+                labels if "point_labels" not in prompt else np.concatenate((prompt["point_labels"], labels))
+            )
+        if self.box_points:
+            prompt["box"] = np.array([coord for batch in self.box_points for xy in batch for coord in xy]).reshape(
+                len(self.box_points), 4
+            )
+        return prompt
+
+    def facebook_prompt_encoder_kwargs(self, device: torch.device | None = None):
+        prompt = self.facebook_predict_kwargs()
+        coords: Tensor | None = None
+        labels: Tensor | None = None
+        boxes: Tensor | None = None
+        if "point_coords" in prompt:
+            coords = torch.as_tensor(prompt["point_coords"], dtype=torch.float, device=device).unsqueeze(0)
+        if "point_labels" in prompt:
+            labels = torch.as_tensor(prompt["point_labels"], dtype=torch.int, device=device).unsqueeze(0)
+        if "box" in prompt:
+            boxes = torch.as_tensor(prompt["box"], dtype=torch.float, device=device).unsqueeze(0)
+        points = (coords, labels) if coords is not None else None
+        # TODO: support masks
+        return {"points": points, "boxes": boxes, "masks": None}
+
+
+def intersection_over_union(
+    input_mask: Bool[Tensor, "height width"], other_mask: Bool[Tensor, "height width"]
+) -> float:
+    inter = (input_mask & other_mask).sum(dtype=torch.float32).item()
+    union = (input_mask | other_mask).sum(dtype=torch.float32).item()
+    return inter / union if union > 0 else 1.0