convert_onnx.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import os
import urllib
import warnings
from typing import Tuple

import onnx
import torch
import torch.nn as nn
from onnxruntime.quantization import QuantType
from onnxruntime.quantization.quantize import quantize_dynamic
from segment_anything import sam_model_registry
from segment_anything.modeling import Sam
from segment_anything.utils.amg import calculate_stability_score
from torch.nn import functional as F

CHECKPOINT_PATH = os.path.join(os.path.expanduser("~"), ".cache", "SAM")
CHECKPOINT_NAME = "sam_vit_h_4b8939.pth"
CHECKPOINT_URL = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
MODEL_TYPE = "default"


class SamOnnxModel(nn.Module):
    """
    This model should not be called directly, but is used in ONNX export.
    It combines the prompt encoder, mask decoder, and mask postprocessing of Sam,
    with some functions modified to enable model tracing. Also supports extra
    options controlling what information. See the ONNX export script for details.
    """

    def __init__(
        self,
        model: Sam,
        return_single_mask: bool,
        use_stability_score: bool = False,
        return_extra_metrics: bool = False,
    ) -> None:
        super().__init__()
        self.mask_decoder = model.mask_decoder
        self.model = model
        self.img_size = model.image_encoder.img_size
        self.return_single_mask = return_single_mask
        self.use_stability_score = use_stability_score
        self.stability_score_offset = 1.0
        self.return_extra_metrics = return_extra_metrics

    @staticmethod
    def resize_longest_image_size(
        input_image_size: torch.Tensor, longest_side: int
    ) -> torch.Tensor:
        input_image_size = input_image_size.to(torch.float32)
        scale = longest_side / torch.max(input_image_size)
        transformed_size = scale * input_image_size
        transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64)
        return transformed_size

    def _embed_points(
        self, point_coords: torch.Tensor, point_labels: torch.Tensor
    ) -> torch.Tensor:
        point_coords = point_coords + 0.5
        point_coords = point_coords / self.img_size
        point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)
        point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)

        point_embedding = point_embedding * (point_labels != -1)
        point_embedding = (
            point_embedding
            + self.model.prompt_encoder.not_a_point_embed.weight * (point_labels == -1)
        )

        for i in range(self.model.prompt_encoder.num_point_embeddings):
            point_embedding = (
                point_embedding
                + self.model.prompt_encoder.point_embeddings[i].weight
                * (point_labels == i)
            )

        return point_embedding

    def _embed_masks(
        self, input_mask: torch.Tensor, has_mask_input: torch.Tensor
    ) -> torch.Tensor:
        mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(
            input_mask
        )
        mask_embedding = mask_embedding + (
            1 - has_mask_input
        ) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1)
        return mask_embedding

    def mask_postprocessing(
        self, masks: torch.Tensor, orig_im_size: torch.Tensor
    ) -> torch.Tensor:
        masks = F.interpolate(
            masks,
            size=(self.img_size, self.img_size),
            mode="bilinear",
            align_corners=False,
        )

        prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size).to(
            torch.int64
        )
        masks = masks[..., : prepadded_size[0], : prepadded_size[1]]  # type: ignore

        orig_im_size = orig_im_size.to(torch.int64)
        h, w = orig_im_size[0], orig_im_size[1]
        masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False)
        return masks

    def select_masks(
        self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # Determine if we should return the multiclick mask
        # or not from the number of points.
        # The reweighting is used to avoid control flow.
        score_reweight = torch.tensor(
            [[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]
        ).to(iou_preds.device)
        score = iou_preds + (num_points - 2.5) * score_reweight
        best_idx = torch.argmax(score, dim=1)
        masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)
        iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1)

        return masks, iou_preds

    @torch.no_grad()
    def forward(
        self,
        image_embeddings: torch.Tensor,
        point_coords: torch.Tensor,
        point_labels: torch.Tensor,
        mask_input: torch.Tensor,
        has_mask_input: torch.Tensor,
        orig_im_size: torch.Tensor,
    ):
        sparse_embedding = self._embed_points(point_coords, point_labels)
        dense_embedding = self._embed_masks(mask_input, has_mask_input)

        masks, scores = self.model.mask_decoder.predict_masks(
            image_embeddings=image_embeddings,
            image_pe=self.model.prompt_encoder.get_dense_pe(),
            sparse_prompt_embeddings=sparse_embedding,
            dense_prompt_embeddings=dense_embedding,
        )

        if self.use_stability_score:
            scores = calculate_stability_score(
                masks, self.model.mask_threshold, self.stability_score_offset
            )

        if self.return_single_mask:
            masks, scores = self.select_masks(masks, scores, point_coords.shape[1])

        upscaled_masks = self.mask_postprocessing(masks, orig_im_size)

        if self.return_extra_metrics:
            stability_scores = calculate_stability_score(
                upscaled_masks, self.model.mask_threshold, self.stability_score_offset
            )
            areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)
            return upscaled_masks, scores, stability_scores, areas, masks

        return upscaled_masks, scores, masks


def load_model(
    checkpoint_path: str = CHECKPOINT_PATH,
    checkpoint_name: str = CHECKPOINT_NAME,
    checkpoint_url: str = CHECKPOINT_URL,
    model_type: str = MODEL_TYPE,
) -> Sam:
    if not os.path.exists(checkpoint_path):
        os.makedirs(checkpoint_path)
    checkpoint = os.path.join(checkpoint_path, checkpoint_name)
    if not os.path.exists(checkpoint):
        print("Downloading the model weights...")
        urllib.request.urlretrieve(checkpoint_url, checkpoint)
        print(f"The model weights saved as {checkpoint}")
    print(f"Load the model weights from {checkpoint}")
    return sam_model_registry[model_type](checkpoint=checkpoint)


if __name__ == "__main__":
    sam = load_model()
    onnx_model = SamOnnxModel(sam, return_single_mask=True)

    dynamic_axes = {
        "point_coords": {1: "num_points"},
        "point_labels": {1: "num_points"},
    }

    embed_dim = sam.prompt_encoder.embed_dim
    embed_size = sam.prompt_encoder.image_embedding_size
    mask_input_size = [4 * x for x in embed_size]
    dummy_inputs = {
        "image_embeddings": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),
        "point_coords": torch.randint(
            low=0, high=1024, size=(1, 5, 2), dtype=torch.float
        ),
        "point_labels": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),
        "mask_input": torch.randn(1, 1, *mask_input_size, dtype=torch.float),
        "has_mask_input": torch.tensor([1], dtype=torch.float),
        "orig_im_size": torch.tensor([1500, 2250], dtype=torch.float),
    }
    output_names = ["masks", "iou_predictions", "low_res_masks"]

    with warnings.catch_warnings():
        warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
        warnings.filterwarnings("ignore", category=UserWarning)
        torch.onnx.export(
            onnx_model,
            tuple(dummy_inputs.values()),
            "sam_decoder.onnx",
            export_params=True,
            opset_version=17,
            do_constant_folding=True,
            input_names=list(dummy_inputs.keys()),
            output_names=output_names,
            dynamic_axes=dynamic_axes,
        )

    quantize_dynamic(
        model_input="sam_decoder.onnx",
        model_output="sam_decoder_uint8.onnx",
        optimize_model=True,
        per_channel=False,
        reduce_range=False,
        weight_type=QuantType.QUInt8,
    )

    # Validate
    onnx.checker.check_model("sam_decoder_uint8.onnx")