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import torch
from functools import partial
from . import image_encoder, prompt_encoder, mask_decoder, sam3D, segmamba_encoder
def build_sam3D_vit_b_ori(args=None, checkpoint=None):
return _build_sam3D_ori(
encoder_embed_dim=768,
encoder_depth=12,
encoder_num_heads=12,
encoder_global_attn_indexes=[2, 5, 8, 11],
checkpoint=checkpoint,
args=args,
)
def build_sam3D_segmamba(args=None, checkpoint=None):
return _build_sam3D_segmamba(
checkpoint=checkpoint,
args=args,
)
sam_model_registry3D = {
"vit_b_ori": build_sam3D_vit_b_ori,
"segmamba": build_sam3D_segmamba,
}
def _build_sam3D_ori(
encoder_embed_dim,
encoder_depth,
encoder_num_heads,
encoder_global_attn_indexes,
checkpoint=None,
args=None,
):
prompt_embed_dim = 384
image_size = args.image_size
vit_patch_size = 16
image_embedding_size = image_size // vit_patch_size
sam = sam3D.Sam3D(
image_encoder=image_encoder.ImageEncoderViT(
args,
depth=encoder_depth,
embed_dim=encoder_embed_dim,
img_size=image_size,
mlp_ratio=4,
norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
num_heads=encoder_num_heads,
patch_size=vit_patch_size,
qkv_bias=True,
use_rel_pos=True,
global_attn_indexes=encoder_global_attn_indexes,
window_size=14,
out_chans=prompt_embed_dim,
),
prompt_encoder=prompt_encoder.PromptEncoder3D(
embed_dim=prompt_embed_dim,
image_embedding_size=(image_embedding_size, image_embedding_size, image_embedding_size),
input_image_size=(image_size, image_size, image_size),
mask_in_chans=16,
num_multiple_outputs=args.num_multiple_outputs,
multiple_outputs=args.multiple_outputs,
),
mask_decoder=mask_decoder.MaskDecoder3D(
args,
transformer_dim=prompt_embed_dim,
num_multiple_outputs=args.num_multiple_outputs,
multiple_outputs=args.multiple_outputs,
),
)
sam.eval()
if checkpoint is not None:
with open(checkpoint, "rb") as f:
state_dict = torch.load(f, map_location=args.device)
if args.use_sam3d_turbo and args.split == 'train':
# Initialize a new state dictionary for the image_encoder
encoder_state_dict = {}
for key in state_dict['model_state_dict']:
if key.startswith(
'image_encoder.'): # Adjust 'image_encoder.' based on how the keys are named in your state_dict
# Remove the 'image_encoder.' prefix and save the modified key
new_key = key[len('image_encoder.'):]
encoder_state_dict[new_key] = state_dict['model_state_dict'][key]
# Now load the adjusted state dict into the image_encoder part of your model
sam.image_encoder.load_state_dict(encoder_state_dict, strict=False)
else:
sam.load_state_dict(state_dict['model_state_dict'])
return sam
def _build_sam3D_segmamba(
checkpoint=None,
args=None,
):
prompt_embed_dim = 384
image_size = args.image_size
image_embedding_size = image_size // 16
sam = sam3D.Sam3D(
image_encoder=segmamba_encoder.ImageEncoderSegMamba(
args,
img_size=image_size,
in_chans=1,
embed_dim=prompt_embed_dim,
),
prompt_encoder=prompt_encoder.PromptEncoder3D(
embed_dim=prompt_embed_dim,
image_embedding_size=(image_embedding_size, image_embedding_size, image_embedding_size),
input_image_size=(image_size, image_size, image_size),
mask_in_chans=16,
num_multiple_outputs=args.num_multiple_outputs,
multiple_outputs=args.multiple_outputs,
),
mask_decoder=mask_decoder.MaskDecoder3D(
args,
transformer_dim=prompt_embed_dim,
num_multiple_outputs=args.num_multiple_outputs,
multiple_outputs=args.multiple_outputs,
),
)
sam.eval()
if checkpoint is not None:
with open(checkpoint, "rb") as f:
state_dict = torch.load(f, map_location=args.device)
sam.load_state_dict(state_dict["model_state_dict"], strict=False)
return sam
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