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# Copyright (R) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------
import logging
import warnings
import torch
from image_encoder import SAM2ImageEncoder, random_sam2_input_image
from prompt_encoder import SAM2PromptEncoder
from sam2.modeling.sam2_base import SAM2Base
from torch import nn
logger = logging.getLogger(__name__)
class SAM2MaskDecoder(nn.Module):
def __init__(
self,
sam_model: SAM2Base,
multimask_output: bool,
dynamic_multimask_via_stability: bool = True,
) -> None:
super().__init__()
self.mask_decoder = sam_model.sam_mask_decoder
self.prompt_encoder = sam_model.sam_prompt_encoder
self.model = sam_model
self.multimask_output = multimask_output
self.dynamic_multimask_via_stability = dynamic_multimask_via_stability
@torch.no_grad()
def forward(
self,
image_features_0: torch.Tensor,
image_features_1: torch.Tensor,
image_embeddings: torch.Tensor,
image_pe: torch.Tensor,
sparse_embeddings: torch.Tensor,
dense_embeddings: torch.Tensor,
):
"""
Decode masks from image and prompt embeddings. Only support H=W=1024.
Args:
image_features_0 (torch.Tensor): [1, 32, H/4, W/4]. high resolution features of level 0 from image encoder.
image_features_1 (torch.Tensor): [1, 64, H/8, W/8]. high resolution features of level 1 from image encoder.
image_embeddings (torch.Tensor): [1, 256, H/16, W/16]. image embedding from image encoder.
image_pe (torch.Tensor): [1, 256, H/16, W/16]. image positional encoding.
sparse_embeddings (torch.Tensor): [L, P+1, 256], embedding for points and boxes.
dense_embeddings (torch.Tensor): [L, 256, H/16, W/16]. embedding for input masks.
Returns:
low_res_masks (torch.Tensor, optional): [1, M, H/4, W/4]. low resolution masks.
iou_predictions (torch.Tensor): [1, M]. scores for M masks.
"""
low_res_masks, iou_predictions, _, _ = self.mask_decoder.predict_masks(
image_embeddings=image_embeddings,
image_pe=image_pe,
sparse_prompt_embeddings=sparse_embeddings,
dense_prompt_embeddings=dense_embeddings,
repeat_image=sparse_embeddings.shape[0] > 1, # batch mode
high_res_features=[image_features_0, image_features_1],
)
if self.multimask_output:
low_res_masks = low_res_masks[:, 1:, :, :]
iou_predictions = iou_predictions[:, 1:]
elif self.dynamic_multimask_via_stability:
# When outputting a single mask, if the stability score from the current single-mask
# output (based on output token 0) falls below a threshold, we instead select from
# multi-mask outputs (based on output token 1~3) the mask with the highest predicted IoU score.
low_res_masks, iou_predictions = self.mask_decoder._dynamic_multimask_via_stability(
low_res_masks, iou_predictions
)
else:
low_res_masks = low_res_masks[:, 0:1, :, :]
iou_predictions = iou_predictions[:, 0:1]
return low_res_masks, iou_predictions
def export_mask_decoder_onnx(
sam2_model: SAM2Base,
onnx_model_path: str,
multimask_output: bool,
dynamic_multimask_via_stability: bool = True,
verbose=False,
):
sam2_prompt_encoder = SAM2PromptEncoder(sam2_model).cpu()
image = random_sam2_input_image()
sam2_encoder = SAM2ImageEncoder(sam2_model).cpu()
image_features_0, image_features_1, image_embeddings = sam2_encoder(image)
logger.info("image_features_0.shape: %s", image_features_0.shape)
logger.info("image_features_1.shape: %s", image_features_1.shape)
logger.info("image_embeddings.shape: %s", image_embeddings.shape)
# encode an random prompt
num_labels = 2
num_points = 3
point_coords = torch.randint(low=0, high=1024, size=(num_labels, num_points, 2), dtype=torch.float)
point_labels = torch.randint(low=0, high=1, size=(num_labels, num_points), dtype=torch.float)
input_masks = torch.zeros(num_labels, 1, 256, 256, dtype=torch.float)
has_input_masks = torch.ones(1, dtype=torch.float)
sparse_embeddings, dense_embeddings, image_pe = sam2_prompt_encoder(
point_coords, point_labels, input_masks, has_input_masks
)
logger.info("sparse_embeddings.shape: %s", sparse_embeddings.shape)
logger.info("dense_embeddings.shape: %s", dense_embeddings.shape)
logger.info("image_pe.shape: %s", image_pe.shape)
sam2_mask_decoder = SAM2MaskDecoder(sam2_model, multimask_output, dynamic_multimask_via_stability)
inputs = (image_features_0, image_features_1, image_embeddings, image_pe, sparse_embeddings, dense_embeddings)
low_res_masks, iou_predictions = sam2_mask_decoder(*inputs)
logger.info("low_res_masks.shape: %s", low_res_masks.shape)
logger.info("iou_predictions.shape: %s", iou_predictions.shape)
with warnings.catch_warnings():
if not verbose:
warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
warnings.filterwarnings("ignore", category=UserWarning)
torch.onnx.export(
sam2_mask_decoder,
inputs,
onnx_model_path,
export_params=True,
opset_version=18,
do_constant_folding=True,
input_names=[
"image_features_0",
"image_features_1",
"image_embeddings",
"image_pe",
"sparse_embeddings",
"dense_embeddings",
],
output_names=["low_res_masks", "iou_predictions"],
dynamic_axes={
"sparse_embeddings": {0: "num_labels", 1: "num_points+1"},
"dense_embeddings": {0: "num_labels"},
"low_res_masks": {0: "num_labels"},
"iou_predictions": {0: "num_labels"},
},
)
print("mask decoder onnx model saved to", onnx_model_path)
def test_mask_decoder_onnx(
sam2_model: SAM2Base,
onnx_model_path: str,
multimask_output: bool,
dynamic_multimask_via_stability: bool,
):
sam2_prompt_encoder = SAM2PromptEncoder(sam2_model).cpu()
image = random_sam2_input_image()
sam2_encoder = SAM2ImageEncoder(sam2_model).cpu()
image_features_0, image_features_1, image_embeddings = sam2_encoder(image)
num_labels = 1
num_points = 5
point_coords = torch.randint(low=0, high=1024, size=(num_labels, num_points, 2), dtype=torch.float)
point_labels = torch.randint(low=0, high=1, size=(num_labels, num_points), dtype=torch.float)
input_masks = torch.rand(num_labels, 1, 256, 256, dtype=torch.float)
has_input_masks = torch.ones(1, dtype=torch.float)
sparse_embeddings, dense_embeddings, image_pe = sam2_prompt_encoder(
point_coords, point_labels, input_masks, has_input_masks
)
sam2_mask_decoder = SAM2MaskDecoder(sam2_model, multimask_output, dynamic_multimask_via_stability)
inputs = (image_features_0, image_features_1, image_embeddings, image_pe, sparse_embeddings, dense_embeddings)
low_res_masks, iou_predictions = sam2_mask_decoder(*inputs)
import onnxruntime # noqa: PLC0415
ort_session = onnxruntime.InferenceSession(onnx_model_path, providers=["CPUExecutionProvider"])
model_inputs = ort_session.get_inputs()
input_names = [model_inputs[i].name for i in range(len(model_inputs))]
logger.info("input_names: %s", input_names)
model_outputs = ort_session.get_outputs()
output_names = [model_outputs[i].name for i in range(len(model_outputs))]
logger.info("output_names: %s", output_names)
outputs = ort_session.run(
output_names,
{
"image_features_0": image_features_0.numpy(),
"image_features_1": image_features_1.numpy(),
"image_embeddings": image_embeddings.numpy(),
"image_pe": image_pe.numpy(),
"sparse_embeddings": sparse_embeddings.numpy(),
"dense_embeddings": dense_embeddings.numpy(),
},
)
for i, output_name in enumerate(output_names):
logger.info("output %s shape: %s", output_name, outputs[i].shape)
ort_low_res_masks, ort_iou_predictions = outputs
torch.testing.assert_close(low_res_masks, torch.tensor(ort_low_res_masks), atol=5e-3, rtol=1e-4)
torch.testing.assert_close(iou_predictions, torch.tensor(ort_iou_predictions), atol=5e-3, rtol=1e-4)
print(f"onnx model has been verified: {onnx_model_path}")
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