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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 torch | |
from functools import partial | |
from .image_encoder import ImageEncoderViT | |
from .mask_decoder_hq import MaskDecoderHQ | |
from .prompt_encoder import PromptEncoder | |
from .transformer import TwoWayTransformer | |
from .sam import Sam | |
def build_sam_vit_h(checkpoint=None): | |
return _build_sam( | |
encoder_embed_dim=1280, | |
encoder_depth=32, | |
encoder_num_heads=16, | |
encoder_global_attn_indexes=[7, 15, 23, 31], | |
checkpoint=checkpoint, | |
) | |
build_sam = build_sam_vit_h | |
def build_sam_vit_l(checkpoint=None): | |
return _build_sam( | |
encoder_embed_dim=1024, | |
encoder_depth=24, | |
encoder_num_heads=16, | |
encoder_global_attn_indexes=[5, 11, 17, 23], | |
checkpoint=checkpoint, | |
) | |
def build_sam_vit_b(checkpoint=None): | |
return _build_sam( | |
encoder_embed_dim=768, | |
encoder_depth=12, | |
encoder_num_heads=12, | |
encoder_global_attn_indexes=[2, 5, 8, 11], | |
checkpoint=checkpoint, | |
) | |
sam_model_registry = { | |
"default": build_sam_vit_h, | |
"vit_h": build_sam_vit_h, | |
"vit_l": build_sam_vit_l, | |
"vit_b": build_sam_vit_b, | |
} | |
def _build_sam( | |
encoder_embed_dim, | |
encoder_depth, | |
encoder_num_heads, | |
encoder_global_attn_indexes, | |
checkpoint=None, | |
): | |
prompt_embed_dim = 256 | |
image_size = 1024 | |
vit_patch_size = 16 | |
image_embedding_size = image_size // vit_patch_size | |
sam = Sam( | |
image_encoder=ImageEncoderViT( | |
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=PromptEncoder( | |
embed_dim=prompt_embed_dim, | |
image_embedding_size=(image_embedding_size, image_embedding_size), | |
input_image_size=(image_size, image_size), | |
mask_in_chans=16, | |
), | |
mask_decoder=MaskDecoderHQ( | |
num_multimask_outputs=3, | |
transformer=TwoWayTransformer( | |
depth=2, | |
embedding_dim=prompt_embed_dim, | |
mlp_dim=2048, | |
num_heads=8, | |
), | |
transformer_dim=prompt_embed_dim, | |
iou_head_depth=3, | |
iou_head_hidden_dim=256, | |
vit_dim=encoder_embed_dim, | |
), | |
pixel_mean=[123.675, 116.28, 103.53], | |
pixel_std=[58.395, 57.12, 57.375], | |
) | |
# sam.eval() | |
if checkpoint is not None: | |
with open(checkpoint, "rb") as f: | |
state_dict = torch.load(f, map_location=torch.device("cpu")) | |
info = sam.load_state_dict(state_dict, strict=False) | |
print(info) | |
for n, p in sam.named_parameters(): | |
if 'hf_token' not in n and 'hf_mlp' not in n and 'compress_vit_feat' not in n and 'embedding_encoder' not in n and 'embedding_maskfeature' not in n: | |
p.requires_grad = False | |
return sam | |