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pt2onnx.py
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# 将pt转为onnx
from typing import Optional, Tuple, Any
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.init import trunc_normal_
from sam2.modeling.sam2_base import SAM2Base
class SAM2ImageEncoder(nn.Module):
def __init__(self, sam_model: SAM2Base) -> None:
super().__init__()
self.model = sam_model
self.image_encoder = sam_model.image_encoder
self.no_mem_embed = sam_model.no_mem_embed
def forward(self, x: torch.Tensor) -> tuple[Any, Any, Any]:
backbone_out=self.image_encoder(x)
backbone_out["backbone_fpn"][0] = self.model.sam_mask_decoder.conv_s0(
backbone_out["backbone_fpn"][0]
)
backbone_out["backbone_fpn"][1] = self.model.sam_mask_decoder.conv_s1(
backbone_out["backbone_fpn"][1]
)
feature_maps = backbone_out["backbone_fpn"][-self.model.num_feature_levels :]
vision_pos_embeds = backbone_out["vision_pos_enc"][-self.model.num_feature_levels :]
feat_sizes = [(x.shape[-2], x.shape[-1]) for x in vision_pos_embeds]
# flatten NxCxHxW to HWxNxC
vision_feats = [x.flatten(2).permute(2, 0, 1) for x in feature_maps]
vision_feats[-1] = vision_feats[-1] + self.no_mem_embed
feats = [feat.permute(1, 2, 0).reshape(1, -1, *feat_size)
for feat, feat_size in zip(vision_feats[::-1], feat_sizes[::-1])][::-1]
return feats[0],feats[1],feats[2]
class SAM2ImageDecoder(nn.Module):
def __init__(
self,
sam_model: SAM2Base,
multimask_output: bool
) -> None:
super().__init__()
self.mask_decoder = sam_model.sam_mask_decoder
self.prompt_encoder = sam_model.sam_prompt_encoder
self.model = sam_model
self.img_size = sam_model.image_size
self.multimask_output = multimask_output
@torch.no_grad()
def forward(
self,
image_embed: torch.Tensor,
high_res_feats_0: torch.Tensor,
high_res_feats_1: torch.Tensor,
point_coords: torch.Tensor,
point_labels: torch.Tensor,
mask_input: torch.Tensor,
has_mask_input: torch.Tensor,
):
sparse_embedding = self._embed_points(point_coords, point_labels)
self.sparse_embedding = sparse_embedding
dense_embedding = self._embed_masks(mask_input, has_mask_input)
high_res_feats = [high_res_feats_0, high_res_feats_1]
image_embed = image_embed
masks, iou_predictions, _, _ = self.mask_decoder.predict_masks(
image_embeddings=image_embed,
image_pe=self.prompt_encoder.get_dense_pe(),
sparse_prompt_embeddings=sparse_embedding,
dense_prompt_embeddings=dense_embedding,
repeat_image=False,
high_res_features=high_res_feats,
)
if self.multimask_output:
masks = masks[:, 1:, :, :]
iou_predictions = iou_predictions[:, 1:]
else:
masks, iou_pred = self.mask_decoder._dynamic_multimask_via_stability(masks, iou_predictions)
masks = torch.clamp(masks, -32.0, 32.0)
return masks, iou_predictions
def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:
point_coords = point_coords + 0.5
padding_point = torch.zeros((point_coords.shape[0], 1, 2), device=point_coords.device)
padding_label = -torch.ones((point_labels.shape[0], 1), device=point_labels.device)
point_coords = torch.cat([point_coords, padding_point], dim=1)
point_labels = torch.cat([point_labels, padding_label], dim=1)
point_coords[:, :, 0] = point_coords[:, :, 0] / self.model.image_size
point_coords[:, :, 1] = point_coords[:, :, 1] / self.model.image_size
point_embedding = self.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.prompt_encoder.not_a_point_embed.weight * (
point_labels == -1
)
for i in range(self.prompt_encoder.num_point_embeddings):
point_embedding = point_embedding + self.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.prompt_encoder.mask_downscaling(input_mask)
mask_embedding = mask_embedding + (
1 - has_mask_input
) * self.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1)
return mask_embedding
from sam2.build_sam import build_sam2
input_size = 512
model_cfg = "sam2_hiera_l.yaml"
sam2_checkpoint = r"checkpoints//sam2_hiera_large.pt"
sam2_model = build_sam2(model_cfg, sam2_checkpoint, device="cpu")
img=torch.randn(1, 3, input_size, input_size).cpu()
sam2_encoder = SAM2ImageEncoder(sam2_model).cpu()
high_res_feats_0, high_res_feats_1, image_embed = sam2_encoder(img)
print(high_res_feats_0.shape)
print(high_res_feats_1.shape)
print(image_embed.shape)
torch.onnx.export(sam2_encoder,
img,
"sam2_encoder.onnx",
export_params=True,
opset_version=17,
do_constant_folding=True,
input_names = ['image'],
output_names = ['high_res_feats_0', 'high_res_feats_1', 'image_embed']
)