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import torch |
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from functools import partial |
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from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer |
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def build_sam_vit_h(checkpoint=None): |
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return _build_sam( |
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encoder_embed_dim=1280, |
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encoder_depth=32, |
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encoder_num_heads=16, |
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encoder_global_attn_indexes=[7, 15, 23, 31], |
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checkpoint=checkpoint, |
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) |
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build_sam = build_sam_vit_h |
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def build_sam_vit_l(checkpoint=None): |
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return _build_sam( |
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encoder_embed_dim=1024, |
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encoder_depth=24, |
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encoder_num_heads=16, |
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encoder_global_attn_indexes=[5, 11, 17, 23], |
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checkpoint=checkpoint, |
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) |
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def build_sam_vit_b(checkpoint=None): |
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return _build_sam( |
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encoder_embed_dim=768, |
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encoder_depth=12, |
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encoder_num_heads=12, |
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encoder_global_attn_indexes=[2, 5, 8, 11], |
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checkpoint=checkpoint, |
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) |
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sam_model_registry = { |
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"default": build_sam_vit_h, |
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"vit_h": build_sam_vit_h, |
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"vit_l": build_sam_vit_l, |
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"vit_b": build_sam_vit_b, |
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} |
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def _build_sam( |
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encoder_embed_dim, |
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encoder_depth, |
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encoder_num_heads, |
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encoder_global_attn_indexes, |
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checkpoint=None, |
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): |
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prompt_embed_dim = 256 |
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image_size = 1024 |
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vit_patch_size = 16 |
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image_embedding_size = image_size // vit_patch_size |
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sam = Sam( |
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image_encoder=ImageEncoderViT( |
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depth=encoder_depth, |
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embed_dim=encoder_embed_dim, |
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img_size=image_size, |
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mlp_ratio=4, |
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norm_layer=partial(torch.nn.LayerNorm, eps=1e-6), |
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num_heads=encoder_num_heads, |
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patch_size=vit_patch_size, |
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qkv_bias=True, |
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use_rel_pos=True, |
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global_attn_indexes=encoder_global_attn_indexes, |
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window_size=14, |
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out_chans=prompt_embed_dim, |
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), |
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prompt_encoder=PromptEncoder( |
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embed_dim=prompt_embed_dim, |
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image_embedding_size=(image_embedding_size, image_embedding_size), |
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input_image_size=(image_size, image_size), |
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mask_in_chans=16, |
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), |
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mask_decoder=MaskDecoder( |
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num_multimask_outputs=3, |
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transformer=TwoWayTransformer( |
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depth=2, |
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embedding_dim=prompt_embed_dim, |
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mlp_dim=2048, |
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num_heads=8, |
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), |
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transformer_dim=prompt_embed_dim, |
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iou_head_depth=3, |
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iou_head_hidden_dim=256, |
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), |
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pixel_mean=[123.675, 116.28, 103.53], |
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pixel_std=[58.395, 57.12, 57.375], |
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) |
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sam.eval() |
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if checkpoint is not None: |
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with open(checkpoint, "rb") as f: |
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state_dict = torch.load(f) |
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sam.load_state_dict(state_dict) |
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return sam |
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