build_sam.py

# 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
from torch.nn import functional as F

def build_sam_vit_h(args):
    return _build_sam(
        encoder_embed_dim=1280,
        encoder_depth=32,
        encoder_num_heads=16,
        encoder_global_attn_indexes=[7, 15, 23, 31],
        image_size=args.image_size,
        checkpoint=args.sam_checkpoint,
    )


build_sam = build_sam_vit_h


def build_sam_vit_l(args):
    return _build_sam(
        encoder_embed_dim=1024,
        encoder_depth=24,
        encoder_num_heads=16,
        encoder_global_attn_indexes=[5, 11, 17, 23],
        image_size=args.image_size,
        checkpoint=args.sam_checkpoint,
    )


def build_sam_vit_b(args):
    return _build_sam(
        encoder_embed_dim=768,
        encoder_depth=12,
        encoder_num_heads=12,
        encoder_global_attn_indexes=[2, 5, 8, 11],
        image_size=args.image_size,
        checkpoint=args.sam_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,
    image_size,
    checkpoint,
):
    prompt_embed_dim = 256
    image_size = image_size
    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.train()
    if checkpoint is not None:
        with open(checkpoint, "rb") as f:
            state_dict = torch.load(f)
        try:
            if 'model' in state_dict.keys():
                sam.load_state_dict(state_dict['model'])
            else:
                sam.load_state_dict(state_dict)
        except:
            print('*******interpolate')
            new_state_dict = load_from(sam, state_dict, image_size, vit_patch_size)   
            sam.load_state_dict(new_state_dict)
        print(f"*******load {checkpoint}")
        
    return sam


def load_from(sam, state_dicts, image_size, vit_patch_size):
    sam_dict = sam.state_dict()
    except_keys = ['mask_tokens', 'output_hypernetworks_mlps', 'iou_prediction_head']
    new_state_dict = {k: v for k, v in state_dicts.items() if
                      k in sam_dict.keys() and except_keys[0] not in k and except_keys[1] not in k and except_keys[2] not in k}
    pos_embed = new_state_dict['image_encoder.pos_embed']
    token_size = int(image_size // vit_patch_size)
    if pos_embed.shape[1] != token_size:
        # resize pos embedding, which may sacrifice the performance, but I have no better idea
        pos_embed = pos_embed.permute(0, 3, 1, 2)  # [b, c, h, w]
        pos_embed = F.interpolate(pos_embed, (token_size, token_size), mode='bilinear', align_corners=False)
        pos_embed = pos_embed.permute(0, 2, 3, 1)  # [b, h, w, c]
        new_state_dict['image_encoder.pos_embed'] = pos_embed
        rel_pos_keys = [k for k in sam_dict.keys() if 'rel_pos' in k]

        global_rel_pos_keys = [k for k in rel_pos_keys if 
                                                        '2' in k or 
                                                        '5' in k or 
                                                        '7' in k or 
                                                        '8' in k or 
                                                        '11' in k or 
                                                        '13' in k or
                                                        '15' in k or 
                                                        '23' in k or 
                                                        '31' in k] 
        # print(sam_dict)
        for k in global_rel_pos_keys:
            h_check, w_check = sam_dict[k].shape
            rel_pos_params = new_state_dict[k]
            h, w = rel_pos_params.shape
            rel_pos_params = rel_pos_params.unsqueeze(0).unsqueeze(0)
            if h != h_check or w != w_check:
                rel_pos_params = F.interpolate(rel_pos_params, (h_check, w_check), mode='bilinear', align_corners=False)

            new_state_dict[k] = rel_pos_params[0, 0, ...]

    sam_dict.update(new_state_dict)
    return sam_dict