import torch
from segment_anything.mobile_encoder.tiny_vit_sam import TinyViT
from segment_anything.modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer
def setup_model():
    prompt_embed_dim = 256
    image_size = 1024
    vit_patch_size = 16
    image_embedding_size = image_size // vit_patch_size
    mobile_sam = Sam(
            image_encoder=TinyViT(img_size=1024, in_chans=3, num_classes=1000,
                embed_dims=[64, 128, 160, 320],
                depths=[2, 2, 6, 2],
                num_heads=[2, 4, 5, 10],
                window_sizes=[7, 7, 14, 7],
                mlp_ratio=4.,
                drop_rate=0.,
                drop_path_rate=0.0,
                use_checkpoint=False,
                mbconv_expand_ratio=4.0,
                local_conv_size=3,
                layer_lr_decay=0.8
            ),
            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],
        )
    return mobile_sam
def load_mobile_sam(mobile_sam_checkpoint_path,
                    device='cuda'):
    checkpoint = torch.load(mobile_sam_checkpoint_path)
    mobile_sam = setup_model()
    mobile_sam.load_state_dict(checkpoint, strict=True)
    mobile_sam.to(device=device)
    return mobile_sam