| """Export the HyperKD-RS fine-tuned SAM 2.1 decoder to ONNX int8, drop-in |
| compatible with the Space's existing sam2bp_decoder_int8.onnx interface: |
| in : image_embed, high_res_feats_0/1, point_coords, point_labels, |
| mask_input, has_mask_input |
| out: masks (N,3,256,256) clamped, iou_predictions (N,3) |
| Quantization: dynamic int8 on MatMul only (matches the shipped encoder/decoder; |
| ort-web WASM has no ConvInteger kernel). |
| |
| Usage: python export_decoder.py (on the GPU box, from ~/run) |
| """ |
| import os |
| import torch |
| import torch.nn as nn |
|
|
| from sam2.build_sam import build_sam2 |
|
|
| sam = build_sam2("configs/sam2.1/sam2.1_hiera_b+.yaml", |
| os.path.expanduser("~/sam2/checkpoints/sam2.1_hiera_base_plus.pt"), |
| device="cpu") |
| ck = torch.load(os.path.expanduser("~/sam21_hyperkd_decoder.pt"), map_location="cpu") |
| sam.sam_mask_decoder.load_state_dict(ck["decoder"]) |
| sam.sam_prompt_encoder.load_state_dict(ck["prompt"]) |
| sam.eval() |
|
|
| class Decoder(nn.Module): |
| def __init__(self, model): |
| super().__init__() |
| self.pe = model.sam_prompt_encoder |
| self.md = model.sam_mask_decoder |
| self.img_size = model.image_size |
|
|
| def forward(self, image_embed, high_res_feats_0, high_res_feats_1, |
| point_coords, point_labels, mask_input, has_mask_input): |
| |
| pc = point_coords + 0.5 |
| pc = pc / self.img_size |
| pe = self.pe.pe_layer._pe_encoding(pc) |
| pl = point_labels.unsqueeze(-1) |
| pe = pe * (pl != -1) |
| pe = pe + self.pe.not_a_point_embed.weight * (pl == -1) |
| for i in range(self.pe.num_point_embeddings): |
| pe = pe + self.pe.point_embeddings[i].weight * (pl == i) |
| sparse = pe |
| dense = self.pe.mask_downscaling(mask_input) * has_mask_input.reshape(-1, 1, 1, 1) + \ |
| self.pe.no_mask_embed.weight.reshape(1, -1, 1, 1) * (1 - has_mask_input.reshape(-1, 1, 1, 1)) |
| masks, ious, _, _ = self.md( |
| image_embeddings=image_embed, |
| image_pe=self.pe.get_dense_pe(), |
| sparse_prompt_embeddings=sparse, |
| dense_prompt_embeddings=dense, |
| multimask_output=True, |
| repeat_image=point_coords.shape[0] > 1, |
| high_res_features=[high_res_feats_0, high_res_feats_1]) |
| return torch.clamp(masks, -32.0, 32.0), ious |
|
|
| dec = Decoder(sam) |
| args = (torch.randn(1, 256, 64, 64), torch.randn(1, 32, 256, 256), |
| torch.randn(1, 64, 128, 128), torch.randn(1, 1, 2), |
| torch.ones(1, 1), torch.zeros(1, 1, 256, 256), torch.zeros(1)) |
| torch.onnx.export(dec, args, os.path.expanduser("~/sam2bp_decoder_ft.onnx"), |
| input_names=["image_embed", "high_res_feats_0", "high_res_feats_1", |
| "point_coords", "point_labels", "mask_input", "has_mask_input"], |
| output_names=["masks", "iou_predictions"], |
| dynamic_axes={"point_coords": {0: "num_labels", 1: "num_points"}, |
| "point_labels": {0: "num_labels", 1: "num_points"}, |
| "mask_input": {0: "num_labels"}, |
| "has_mask_input": {0: "num_labels"}}, |
| opset_version=17) |
|
|
| from onnxruntime.quantization import quantize_dynamic, QuantType |
| quantize_dynamic(os.path.expanduser("~/sam2bp_decoder_ft.onnx"), |
| os.path.expanduser("~/sam2bp_decoder_ft_int8.onnx"), |
| weight_type=QuantType.QInt8, op_types_to_quantize=["MatMul"]) |
| import pathlib |
| for f in ("sam2bp_decoder_ft.onnx", "sam2bp_decoder_ft_int8.onnx"): |
| p = pathlib.Path.home() / f |
| print(f, f"{p.stat().st_size/1e6:.1f} MB") |
| print("EXPORT_DONE") |
|
|
