models_simmim.py

import math
from functools import partial

import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import DropPath, to_2tuple, trunc_normal_


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        # x = self.drop(x)
        # comment out this for the orignal BERT implement
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Attention(nn.Module):
    def __init__(
            self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
            proj_drop=0., window_size=None, attn_head_dim=None):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        if attn_head_dim is not None:
            head_dim = attn_head_dim
        all_head_dim = head_dim * self.num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
        if qkv_bias:
            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
        else:
            self.q_bias = None
            self.v_bias = None

        if window_size:
            self.window_size = window_size
            # cls to token & token to cls & cls to cls
            self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
            self.relative_position_bias_table = nn.Parameter(
                torch.zeros(self.num_relative_distance, num_heads))  # 2*Wh-1 * 2*Ww-1, nH

            # get pair-wise relative position index for each token inside the window
            coords_h = torch.arange(window_size[0])
            coords_w = torch.arange(window_size[1])
            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
            coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
            relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
            relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
            relative_coords[:, :, 1] += window_size[1] - 1
            relative_coords[:, :, 0] *= 2 * window_size[1] - 1
            relative_position_index = \
                torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype)
            relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
            relative_position_index[0, 0:] = self.num_relative_distance - 3
            relative_position_index[0:, 0] = self.num_relative_distance - 2
            relative_position_index[0, 0] = self.num_relative_distance - 1

            self.register_buffer("relative_position_index", relative_position_index)
        else:
            self.window_size = None
            self.relative_position_bias_table = None
            self.relative_position_index = None

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(all_head_dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x, rel_pos_bias=None):
        B, N, C = x.shape
        qkv_bias = None
        if self.q_bias is not None:
            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))

        if self.relative_position_bias_table is not None:
            relative_position_bias = \
                self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
                    self.window_size[0] * self.window_size[1] + 1,
                    self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
            relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
            attn = attn + relative_position_bias.unsqueeze(0)

        if rel_pos_bias is not None:
            attn = attn + rel_pos_bias

        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x, attn


class Block(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
                 window_size=None, attn_head_dim=None):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
            attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        if init_values is not None:
            self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
            self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
        else:
            self.gamma_1, self.gamma_2 = None, None


    def forward(self, x: torch.Tensor, rel_pos_bias=None, return_attention=False) -> torch.Tensor:
        y, attention = self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias)

        x_norm = torch.linalg.vector_norm(x, dim=2)
        y_norm = torch.linalg.vector_norm(y, dim=2)

        magnitudes = torch.stack((x_norm, y_norm), dim=0)

        x = x + self.drop_path(self.gamma_1 * y)
        x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))

        if return_attention:
            return x, attention, magnitudes

        return x


class PatchEmbed(nn.Module):
    """ Image to Patch Embedding
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
        self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x, **kwargs):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


class RelativePositionBias(nn.Module):

    def __init__(self, window_size, num_heads):
        super().__init__()
        self.window_size = window_size
        self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros(self.num_relative_distance, num_heads))  # 2*Wh-1 * 2*Ww-1, nH
        # cls to token & token 2 cls & cls to cls

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(window_size[0])
        coords_w = torch.arange(window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * window_size[1] - 1
        relative_position_index = \
            torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
        relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        relative_position_index[0, 0:] = self.num_relative_distance - 3
        relative_position_index[0:, 0] = self.num_relative_distance - 2
        relative_position_index[0, 0] = self.num_relative_distance - 1

        self.register_buffer("relative_position_index", relative_position_index)

    def forward(self):
        relative_position_bias = \
            self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
                self.window_size[0] * self.window_size[1] + 1,
                self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
        return relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww


class VisionTransformerSimMIM(nn.Module):
    """ Vision Transformer with support for patch or hybrid CNN input stage
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
                 drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None,
                 use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False,
                 use_mean_pooling=True, init_scale=0.001):
        super().__init__()
        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim
        self.patch_size = patch_size
        self.in_chans = in_chans

        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        if use_abs_pos_emb:
            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
        else:
            self.pos_embed = None
        self.pos_drop = nn.Dropout(p=drop_rate)

        if use_shared_rel_pos_bias:
            self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads)
        else:
            self.rel_pos_bias = None

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        self.use_rel_pos_bias = use_rel_pos_bias
        self.blocks = nn.ModuleList([
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None)
            for i in range(depth)])
        self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
        self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()

        if self.pos_embed is not None:
            self._trunc_normal_(self.pos_embed, std=.02)
        self._trunc_normal_(self.cls_token, std=.02)
        if num_classes > 0:
            self._trunc_normal_(self.head.weight, std=.02)
        self.apply(self._init_weights)
        self.fix_init_weight()

        if num_classes > 0:
            self.head.weight.data.mul_(init_scale)
            self.head.bias.data.mul_(init_scale)

    def _trunc_normal_(self, tensor, mean=0., std=1.):
        trunc_normal_(tensor, mean=mean, std=std)

    def fix_init_weight(self):
        def rescale(param, layer_id):
            param.div_(math.sqrt(2.0 * layer_id))

        for layer_id, layer in enumerate(self.blocks):
            rescale(layer.attn.proj.weight.data, layer_id + 1)
            rescale(layer.mlp.fc2.weight.data, layer_id + 1)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            self._trunc_normal_(m.weight, std=.02)
            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)
        elif isinstance(m, nn.Conv2d):
            self._trunc_normal_(m.weight, std=.02)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)

    def get_num_layers(self):
        return len(self.blocks)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed', 'cls_token'}

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x, return_features: str = "cls", shuffle_subsets=None, return_block=12):
        x = self.patch_embed(x)
        batch_size, seq_len, _ = x.size()

        cls_tokens = self.cls_token.expand(batch_size, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
        x = torch.cat((cls_tokens, x), dim=1)
        if self.pos_embed is not None:
            x = x + self.pos_embed
        x = self.pos_drop(x)

        rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None

        attentions = [] # !
        magnitudes = []  # !
        to_return = None
        for i, blk in enumerate(self.blocks):
            x, attn, magn = blk(x, rel_pos_bias=rel_pos_bias, return_attention=True)  # !
            calculate_attn_stuff(attn, magn, attentions, magnitudes) # !
            if i == return_block:
                to_return = x

        if return_block is None or return_block >= len(self.blocks):
            to_return = x

        x_cls = to_return[:, 0]
        x_pos = to_return[:, 1:].mean(dim=1)
        if return_features == "cls":
            ret = x_cls
        elif return_features == "pos":
            ret = x_pos
        elif return_features == "raw":
            ret = to_return
        elif return_features == "both":
            ret = torch.concat([x_cls, x_pos], dim=2)
        else:
            raise NotImplementedError(return_features)

        attentions = torch.cat(attentions, dim=2) # kind, batch, blocks, heads, tokens
        magnitudes = torch.cat(magnitudes, dim=2) # kind, batch, blocks, tokens

        return ret, attentions, magnitudes

    def forward(self, x: torch.Tensor, return_features: str = "cls", return_block=12) -> torch.Tensor:
        if return_features.startswith("abmilp"):
            return_features = "raw"

        x, attn, magnitudes = self.forward_features(x, return_features=return_features, return_block=return_block)
        x = self.head(x)
        return x
            

def calculate_attn_stuff(attn, magn, attentions, magnitudes):
    B, H, T, T = attn.shape
    attn_range = torch.arange(T)
    attn_diag = attn[:, :, attn_range, attn_range] # attention of tokens w.r.t. themselves
    cls_all_attn = attn[:, :, 0, ]  # attention of cls token to all tokens
    all_cls_attn = attn[:, :, :, 0] # attention of all tokens to cls token

    attn_wo_cls = attn[:, :, :, 1:]
    attn_wo_cls_denom = attn_wo_cls.sum(dim=3, keepdim=True)

    # print(attn_wo_cls_denom[0,0])

    attn_wo_cls = attn_wo_cls / (attn_wo_cls_denom + 1e-6)


    all_pos_attn_entropy = -(attn_wo_cls * (attn_wo_cls + 1e-6).log()).sum(dim=3)

    attn_adj_for_cls = attn / (attn_wo_cls_denom + 1e-6)

    attn_diag_adj_for_cls = attn_adj_for_cls[:, :, attn_range, attn_range]

    attn_stats = torch.stack([attn_diag, attn_diag_adj_for_cls, cls_all_attn, all_cls_attn, all_pos_attn_entropy])

    attn_stats = attn_stats.unsqueeze(2)

    # assert False, attn_stats.shape
    attentions.append(attn_stats.detach())
    magnitudes.append(magn.unsqueeze(2).detach())


def build_vit(config):
    model = VisionTransformerSimMIM(
        img_size=config.DATA.IMG_SIZE,
        patch_size=config.MODEL.VIT.PATCH_SIZE,
        in_chans=config.MODEL.VIT.IN_CHANS,
        num_classes=config.MODEL.NUM_CLASSES,
        embed_dim=config.MODEL.VIT.EMBED_DIM,
        depth=config.MODEL.VIT.DEPTH,
        num_heads=config.MODEL.VIT.NUM_HEADS,
        mlp_ratio=config.MODEL.VIT.MLP_RATIO,
        qkv_bias=config.MODEL.VIT.QKV_BIAS,
        drop_rate=config.MODEL.DROP_RATE,
        drop_path_rate=config.MODEL.DROP_PATH_RATE,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        init_values=config.MODEL.VIT.INIT_VALUES,
        use_abs_pos_emb=config.MODEL.VIT.USE_APE,
        use_rel_pos_bias=config.MODEL.VIT.USE_RPB,
        use_shared_rel_pos_bias=config.MODEL.VIT.USE_SHARED_RPB,
        use_mean_pooling=config.MODEL.VIT.USE_MEAN_POOLING)

    return model


def vit_base_pretrained(
        checkpoint_path="simmim_pretrain__vit_base__img224__800ep.pth"
):
    kwargs = {
        'img_size': 224,
        'patch_size': 16,
        'in_chans': 3,
        'num_classes': 1000,
        'embed_dim': 768,
        'depth': 12,
        'num_heads': 12,
        'mlp_ratio': 4.0,
        'qkv_bias': True,
        'drop_rate': 0.0,
        'drop_path_rate': 0.1,
        'norm_layer': partial(nn.LayerNorm, eps=1e-6),
        'init_values': 0.1,
        'use_abs_pos_emb': False,
        'use_rel_pos_bias': False,
        'use_shared_rel_pos_bias': True,
        'use_mean_pooling': False
    }

    model = VisionTransformerSimMIM(**kwargs)

    state_dict = torch.load(checkpoint_path)["model"]
    state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items()}

    try:
        del state_dict["mask_token"]
        del state_dict["decoder.0.bias"]
        del state_dict["decoder.0.weight"]
    except Exception as e:
        print(e)
    
    res = model.load_state_dict(state_dict, strict=False)
    print(res)
    
    return model


def vit_base_finetuned():
    kwargs = {
        'img_size': 224,
        'patch_size': 16,
        'in_chans': 3,
        'num_classes': 1000,
        'embed_dim': 768,
        'depth': 12,
        'num_heads': 12,
        'mlp_ratio': 4.0,
        'qkv_bias': True,
        'drop_rate': 0.0,
        'drop_path_rate': 0.1,
        'norm_layer': partial(nn.LayerNorm, eps=1e-6),
        'init_values': 0.1,
        'use_abs_pos_emb': False,
        'use_rel_pos_bias': True,
        'use_shared_rel_pos_bias': False,
        'use_mean_pooling': True
    }

    model = VisionTransformerSimMIM(**kwargs)

    state_dict = torch.load("simmim_finetune__vit_base__img224__800ep.pth")["model"]
    state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items()}

    res = model.load_state_dict(state_dict)
    print(res)

    return model
        
        
    
    
vit_base_patch16 = vit_base_pretrained