What is the difference between the ReinMask2FormerHead and original Mask2FormerHead?

[hongge831]

Nice work. I noticed that you reweite the Mask2FormerHead as ReinMask2FormerHead, I wonder that what is the difference between them? By when you use the VPT to comprare your Rein, What are the detailed settings of VPT? For example, how many tokens are used, and are there any differences in hyperparameter settings?

looking for your reply

[w1oves]

For VPT, it has 150 tokens, and is deep.

For ReinMask2Former, we replace the query embed of this by Rein's tokens' transformation.

---- Replied Message ---- | From | @.> | | Date | 03/16/2024 22:31 | | To | @.> | | Cc | @.***> | | Subject | [w1oves/Rein] What is the difference between the ReinMask2FormerHead and original Mask2FormerHead? (Issue #12) |

Nice work. I noticed that you reweite the Mask2FormerHead as ReinMask2FormerHead, I wonder that what is the difference between them? By when you use the VPT to comprare your Rein, What are the detailed settings of VPT? For example, how many tokens are used, and are there any differences in hyperparameter settings?

looking for your reply

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[hongge831]

Thanks for your reply, would mind uploading this part of code for us as a reference? I would be grateful if you could do this

[w1oves]

ReinMask2Former

You can find details of ReinMask2Former at rein/models/heads/rein_mask2former.py.

Implemented VPT Version:

class SimpleVPT(nn.Module):
    def __init__(self, embed_dim, depth, num_tokens) -> None:
        super().__init__()
        self.num_tokens = num_tokens
        self.embed_dim = embed_dim
        # Initialize prompt embeddings
        self.prompt_embeddings = nn.Parameter(torch.zeros(depth, num_tokens, embed_dim))
        # Uniform initialization of embeddings
        val = math.sqrt(6.0 / float(3 * reduce(mul, (16, 16), 1) + embed_dim))
        nn.init.uniform_(self.prompt_embeddings.data, -val, val)

    def forward(self, idx, layer, x: Tensor, batch_first=True, cls_token=True):
        if not batch_first:
            x = x.permute(1, 0, 2)  # Adjust for batch first convention
        B, _, _ = x.shape
        # Concatenate prompt embeddings
        x = torch.cat([self.prompt_embeddings[idx].expand(B, -1, -1), x], dim=1)
        x = layer(x)
        # Split and permute if necessary
        _, x = torch.tensor_split(x, [self.num_tokens], dim=1)
        if not batch_first:
            x = x.permute(1, 0, 2)
        return x

DINOv2 + VPT Integration:

class VPTDinoVisionTransformer(DinoVisionTransformer):
    def __init__(
        self,
        img_size=224,
        patch_size=16,
        in_chans=3,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4.0,
        qkv_bias=True,
        ffn_bias=True,
        proj_bias=True,
        drop_path_rate=0.0,
        drop_path_uniform=False,
        init_values=None,  # for layerscale: None or 0 => no layerscale
        embed_layer=PatchEmbed,
        act_layer=nn.GELU,
        block_fn=partial(Block, attn_class=MemEffAttention),
        ffn_layer="mlp",
        block_chunks=1,
        out_indices=[7, 11, 15, 23],
        upscale_feats=False,
        init_cfg=None,
    ):
        super().__init__(
            img_size,
            patch_size,
            in_chans,
            embed_dim,
            depth,
            num_heads,
            mlp_ratio,
            qkv_bias,
            ffn_bias,
            proj_bias,
            drop_path_rate,
            drop_path_uniform,
            init_values,
            embed_layer,
            act_layer,
            block_fn,
            ffn_layer,
            block_chunks,
            out_indices,
            upscale_feats,
            init_cfg,
        )
        # Initialize VPT
        self.vpt = SimpleVPT(embed_dim=embed_dim, depth=depth, num_tokens=150)

    def forward_features(self, x, masks=None):
        B, _, h, w = x.shape
        H, W = h // self.patch_size, w // self.patch_size

        if isinstance(x, list):
            return self.forward_features_list(x, masks)

        x = self.prepare_tokens_with_masks(x, masks)
        outs = []
        for idx, blk in enumerate(self.blocks):
            x = self.vpt.forward(idx, blk, x, batch_first=True, cls_token=True)
            if idx in self.out_indices:
                outs.append(x[:, 1:, :].permute(0, 2, 1).reshape(B, -1, H, W).contiguous())
        return outs

    def train(self, mode: bool = True):
        if not mode:
            return super().train(mode)
        set_requires_grad(self, ["vpt"])
        set_train(self, ["vpt"])