mae_vit.py

import torch
from mmcls.models import VisionTransformer
from torch import nn
from torch.utils.checkpoint import checkpoint
import copy

def build_2d_sincos_position_embedding(patches_resolution,
                                       embed_dims,
                                       temperature=10000.,
                                       cls_token=False):
    """The function is to build position embedding for model to obtain the
    position information of the image patches."""

    if isinstance(patches_resolution, int):
        patches_resolution = (patches_resolution, patches_resolution)

    h, w = patches_resolution
    grid_w = torch.arange(w, dtype=torch.float32)
    grid_h = torch.arange(h, dtype=torch.float32)
    grid_w, grid_h = torch.meshgrid(grid_w, grid_h)
    assert embed_dims % 4 == 0, \
        'Embed dimension must be divisible by 4.'
    pos_dim = embed_dims // 4

    omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim
    omega = 1. / (temperature**omega)
    out_w = torch.einsum('m,d->md', [grid_w.flatten(), omega])
    out_h = torch.einsum('m,d->md', [grid_h.flatten(), omega])

    pos_emb = torch.cat(
        [
            torch.sin(out_w),
            torch.cos(out_w),
            torch.sin(out_h),
            torch.cos(out_h)
        ],
        dim=1,
    )[None, :, :]

    if cls_token:
        cls_token_pe = torch.zeros([1, 1, embed_dims], dtype=torch.float32)
        pos_emb = torch.cat([cls_token_pe, pos_emb], dim=1)

    return pos_emb



class MAEViT(VisionTransformer):
    """Vision Transformer for MAE pre-training.

    A PyTorch implement of: `An Image is Worth 16x16 Words: Transformers
    for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`_

    Args:
        arch (str | dict): Vision Transformer architecture
            Default: 'b'
        img_size (int | tuple): Input image size
        patch_size (int | tuple): The patch size
        out_indices (Sequence | int): Output from which stages.
            Defaults to -1, means the last stage.
        drop_rate (float): Probability of an element to be zeroed.
            Defaults to 0.
        drop_path_rate (float): stochastic depth rate. Defaults to 0.
        norm_cfg (dict): Config dict for normalization layer.
            Defaults to ``dict(type='LN')``.
        final_norm (bool): Whether to add a additional layer to normalize
            final feature map. Defaults to True.
        output_cls_token (bool): Whether output the cls_token. If set True,
            `with_cls_token` must be True. Defaults to True.
        interpolate_mode (str): Select the interpolate mode for position
            embeding vector resize. Defaults to "bicubic".
        patch_cfg (dict): Configs of patch embeding. Defaults to an empty dict.
        layer_cfgs (Sequence | dict): Configs of each transformer layer in
            encoder. Defaults to an empty dict.
        mask_ratio (bool): The ratio of total number of patches to be masked.
            Defaults to 0.75.
        init_cfg (dict, optional): Initialization config dict.
            Defaults to None.
    """

    arch_zoo = {
        **dict.fromkeys(
            ['mocov3-s', 'mocov3-small'], {
                'embed_dims': 384,
                'num_layers': 12,
                'num_heads': 12,
                'feedforward_channels': 1536,
            }),
        **dict.fromkeys(
            ['b', 'base'], {
                'embed_dims': 768,
                'num_layers': 12,
                'num_heads': 12,
                'feedforward_channels': 3072
            }),
    }



    def __init__(self,
                 arch='b',
                 img_size=224,
                 patch_size=16,
                 out_indices=-1,
                 drop_rate=0,
                 drop_path_rate=0,
                 norm_cfg=dict(type='LN', eps=1e-6),
                 final_norm=True,
                 output_cls_token=False,
                 interpolate_mode='bicubic',
                 patch_cfg=dict(),
                 layer_cfgs=dict(),
                 gradientCKPT=False,
                 mask_ratio=0.75,
                 init_cfg=None):
        super().__init__(
            arch=arch,
            img_size=img_size,
            patch_size=patch_size,
            out_indices=out_indices,
            drop_rate=drop_rate,
            drop_path_rate=drop_path_rate,
            norm_cfg=norm_cfg,
            final_norm=final_norm,
            output_cls_token=output_cls_token,
            interpolate_mode=interpolate_mode,
            patch_cfg=patch_cfg,
            layer_cfgs=layer_cfgs,
            init_cfg=init_cfg)
        self.gradientCKPT = gradientCKPT
        self.pos_embed.requires_grad = False
        self.mask_ratio = mask_ratio
        self.num_patches = self.patch_resolution[0] * self.patch_resolution[1]
        # self.mask_embedding = copy.deepcopy(self.patch_embed)
        # self.mask_embedding.norm = None

    def init_weights(self):
        super(MAEViT, self).init_weights()
        if not (isinstance(self.init_cfg, dict)
                and self.init_cfg['type'] == 'Pretrained'):
            # initialize position  embedding in backbone
            pos_embed = build_2d_sincos_position_embedding(
                self.patch_resolution,
                self.pos_embed.shape[-1],
                cls_token=True)
            self.pos_embed.data.copy_(pos_embed.float())

            w = self.patch_embed.projection.weight.data
            torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))

            torch.nn.init.normal_(self.cls_token, std=.02)

            self.apply(self._init_weights)

        # mask_embedding transfers pixel level mask to token level
        # self.mask_embedding.apply(self._init_mask_embedding)
        # for para in self.mask_embedding.parameters():
        #     para.requires_grad = False

    def _init_mask_embedding(self,m):
        if hasattr(m,'weight'):
            nn.init.constant_(m.weight,1.0)
        if hasattr(m, 'bias'):
            nn.init.constant_(m.bias,0)

    def _init_weights(self, m):

        if isinstance(m, nn.Linear):
            torch.nn.init.xavier_uniform_(m.weight)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def random_masking(self, x, mask_ratio=0.75, attn_mask=None):
        """Generate the mask for MAE Pre-training.

        Args:
            x (torch.tensor): Image with data augmentation applied.
            mask_ratio (float): The mask ratio of total patches.
                Defaults to 0.75.

        Returns:
            tuple[Tensor, Tensor, Tensor]: masked image, mask and the ids
                to restore original image.

            - x_masked (Tensor): masked image.
            - mask (Tensor): mask used to mask image.
            - ids_restore (Tensor): ids to restore original image.
        """
        N, L, D = x.shape  # batch, length, dim
        len_keep = int(L * (1 - mask_ratio))

        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]

        # sort noise for each sample
        ids_shuffle = torch.argsort(
            noise, dim=1)  # ascend: small is keep, large is remove
        ids_restore = torch.argsort(ids_shuffle, dim=1)

        # keep the first subset
        ids_keep = ids_shuffle[:, :len_keep]
        x_masked = torch.gather(
            x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
        # modified_attn_mask = None if attn_mask is None else torch.gather(attn_mask,dim=1, index=ids_keep)

        # generate the binary mask: 0 is keep, 1 is remove
        mask = torch.ones([N, L], device=x.device)
        mask[:, :len_keep] = 0
        # unshuffle to get the binary mask
        mask = torch.gather(mask, dim=1, index=ids_restore)

        return x_masked, mask, ids_restore #, modified_attn_mask

    def generate_mask(self, pixel_level_attn_mask):
        '''
        pixel_level_attn_mask: (0,1) attn mask with the same shape as img
        '''
        if pixel_level_attn_mask is None: return None
        # H, W = patch_resolution
        # B, C = pixel_level_attn_mask.shape[:2]
        # attn_mask = torch.ones((B,C,H,W),device=pixel_level_attn_mask) 
        # H_splited = torch.chunk(pixel_level_attn_mask, H, -2)
        # HW_splited_mask = (torch.chunk(Hs, W, -1) for Hs in H_splited)

        #         if HW_splited_mask[:,:,hi,wi].sum().item() == 0:
        #             attn_mask[:,:,hi,wi] = 0

        # mask_patches = self.mask_embedding(pixel_level_attn_mask)[0]
        # attn_mask = mask_patches.sum(-1) != 0

        # return attn_mask

    def extract_feat(self, img ,attn_mask=None):
        x, *_ = self.forward(img,attn_mask)
        if self.output_cls_token:
            return x[:,0,:]
        else:
            return torch.mean(x,dim=1)

    def forward(self, x, attn_mask=None):
        if attn_mask is not None: assert self.output_cls_token
        
        B = x.shape[0]
        x = self.patch_embed(x)[0]
        # add pos embed w/o cls token
        x = x + self.pos_embed[:, 1:1+x.shape[1], :]
        # masking: length -> length * mask_ratio
        if True:
            assert self.mask_ratio == 0.
        else:
            x, mask, ids_restore = self.random_masking(x, self.mask_ratio)

        # append cls token
        cls_token = self.cls_token + self.pos_embed[:, :1, :]
        cls_tokens = cls_token.expand(B, -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        x = self.drop_after_pos(x)
        # if attn_mask is not None: 
        #     attn_mask = torch.concat((torch.ones((B,1),device=attn_mask.device) , attn_mask),dim=1)

        for i, layer in enumerate(self.layers):
            if self.gradientCKPT:
                x = checkpoint(layer,x) # ,attn_mask
            else:
                x = layer(x) # ,attn_mask
            if i == len(self.layers) - 1 and self.final_norm:
                x = self.norm1(x)
        if True: 
            return x
        else:
            return (x, mask, ids_restore)

    def forward_generator(self, x, attn_mask=None):
        if attn_mask is not None: assert self.output_cls_token
        
        B = x.shape[0]
        x = self.patch_embed(x)[0]
        # add pos embed w/o cls token
        x = x + self.pos_embed[:, 1:1+x.shape[1], :]

        # append cls token
        cls_token = self.cls_token + self.pos_embed[:, :1, :]
        cls_tokens = cls_token.expand(B, -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        x = self.drop_after_pos(x)

        for i, layer in enumerate(self.layers):
            if self.gradientCKPT:
                x = checkpoint(layer,x) # ,attn_mask
            else:
                x = layer(x) # ,attn_mask

            if i == len(self.layers) - 1 and self.final_norm:
                x = self.norm1(x)
         
            x = x if (new_x:=(yield x)) is None else new_x 

            debug = False
            if debug:
                print(f'layer {i}-th forwarded')