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# 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")
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