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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 .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer | |
| 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=MaskDecoder( | |
| 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, | |
| ), | |
| 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) | |
| sam.load_state_dict(state_dict) | |
| print(f"Loaded SAM model from {checkpoint}") | |
| return sam | |