ceit_model.py

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial

from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.vision_transformer import default_cfgs, _cfg


__all__ = [
    'ceit_tiny_patch16_224', 'ceit_small_patch16_224', 'ceit_base_patch16_224',
    'ceit_tiny_patch16_384', 'ceit_small_patch16_384',
]


class Image2Tokens(nn.Module):
    def __init__(self, in_chans=3, out_chans=64, kernel_size=7, stride=2):
        super(Image2Tokens, self).__init__()
        self.conv = nn.Conv2d(in_chans, out_chans, kernel_size=kernel_size, stride=stride,
                              padding=kernel_size // 2, bias=False)
        self.bn = nn.BatchNorm2d(out_chans)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.maxpool(x)
        return x


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)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class LocallyEnhancedFeedForward(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.,
                 kernel_size=3, with_bn=True):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        # pointwise
        self.conv1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, stride=1, padding=0)
        # depthwise
        self.conv2 = nn.Conv2d(
            hidden_features, hidden_features, kernel_size=kernel_size, stride=1,
            padding=(kernel_size - 1) // 2, groups=hidden_features
        )
        # pointwise
        self.conv3 = nn.Conv2d(hidden_features, out_features, kernel_size=1, stride=1, padding=0)
        self.act = act_layer()
        # self.drop = nn.Dropout(drop)

        self.with_bn = with_bn
        if self.with_bn:
            self.bn1 = nn.BatchNorm2d(hidden_features)
            self.bn2 = nn.BatchNorm2d(hidden_features)
            self.bn3 = nn.BatchNorm2d(out_features)

    def forward(self, x):
        b, n, k = x.size()
        cls_token, tokens = torch.split(x, [1, n - 1], dim=1)
        x = tokens.reshape(b, int(math.sqrt(n - 1)), int(math.sqrt(n - 1)), k).permute(0, 3, 1, 2)
        if self.with_bn:
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.act(x)
            x = self.conv2(x)
            x = self.bn2(x)
            x = self.act(x)
            x = self.conv3(x)
            x = self.bn3(x)
        else:
            x = self.conv1(x)
            x = self.act(x)
            x = self.conv2(x)
            x = self.act(x)
            x = self.conv3(x)

        tokens = x.flatten(2).permute(0, 2, 1)
        out = torch.cat((cls_token, tokens), dim=1)
        return out


class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.attention_map = None

    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        # self.attention_map = attn
        attn = self.attn_drop(attn)

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


class AttentionLCA(Attention):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
        super(AttentionLCA, self).__init__(dim, num_heads, qkv_bias, qk_scale, attn_drop, proj_drop)
        self.dim = dim
        self.qkv_bias = qkv_bias
        
    def forward(self, x):

        q_weight = self.qkv.weight[:self.dim, :]
        q_bias = None if not self.qkv_bias else self.qkv.bias[:self.dim]
        kv_weight = self.qkv.weight[self.dim:, :]
        kv_bias = None if not self.qkv_bias else self.qkv.bias[self.dim:]
        
        B, N, C = x.shape
        _, last_token = torch.split(x, [N-1, 1], dim=1)
        
        q = F.linear(last_token, q_weight, q_bias)\
             .reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
        kv = F.linear(x, kv_weight, kv_bias)\
              .reshape(B, N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        k, v = kv[0], kv[1]

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        # self.attention_map = attn
        attn = self.attn_drop(attn)

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


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., act_layer=nn.GELU, norm_layer=nn.LayerNorm, kernel_size=3, with_bn=True, 
                 feedforward_type='leff'):
        super().__init__()
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        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.norm1 = norm_layer(dim)
        self.feedforward_type = feedforward_type

        if feedforward_type == 'leff':
            self.attn = Attention(
                dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
            self.leff = LocallyEnhancedFeedForward(
                in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop,
                kernel_size=kernel_size, with_bn=with_bn,
            )
        else:  # LCA
            self.attn = AttentionLCA(
                dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
            self.feedforward = Mlp(
                in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop
            )

    def forward(self, x):
        if self.feedforward_type == 'leff':
            x = x + self.drop_path(self.attn(self.norm1(x)))
            x = x + self.drop_path(self.leff(self.norm2(x)))
            return x, x[:, 0]
        else:  # LCA
            _, last_token = torch.split(x, [x.size(1)-1, 1], dim=1)
            x = last_token + self.drop_path(self.attn(self.norm1(x)))
            x = x + self.drop_path(self.feedforward(self.norm2(x)))
            return x


class HybridEmbed(nn.Module):
    """ CNN Feature Map Embedding
    Extract feature map from CNN, flatten, project to embedding dim.
    """
    def __init__(self, backbone, img_size=224, patch_size=16, feature_size=None, in_chans=3, embed_dim=768):
        super().__init__()
        assert isinstance(backbone, nn.Module)
        img_size = to_2tuple(img_size)
        self.img_size = img_size
        self.backbone = backbone
        if feature_size is None:
            with torch.no_grad():
                # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
                # map for all networks, the feature metadata has reliable channel and stride info, but using
                # stride to calc feature dim requires info about padding of each stage that isn't captured.
                training = backbone.training
                if training:
                    backbone.eval()
                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))
                if isinstance(o, (list, tuple)):
                    o = o[-1]  # last feature if backbone outputs list/tuple of features
                feature_size = o.shape[-2:]
                feature_dim = o.shape[1]
                backbone.train(training)
        else:
            feature_size = to_2tuple(feature_size)
            feature_dim = self.backbone.feature_info.channels()[-1]
        print('feature_size is {}, feature_dim is {}, patch_size is {}'.format(
            feature_size, feature_dim, patch_size
        ))
        self.num_patches = (feature_size[0] // patch_size) * (feature_size[1] // patch_size)
        self.proj = nn.Conv2d(feature_dim, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        x = self.backbone(x)
        if isinstance(x, (list, tuple)):
            x = x[-1]  # last feature if backbone outputs list/tuple of features
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


class CeIT(nn.Module):
    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.,
                 hybrid_backbone=None,
                 norm_layer=nn.LayerNorm,
                 leff_local_size=3,
                 leff_with_bn=True):
        """
        args:
            - img_size (:obj:`int`): input image size
            - patch_size (:obj:`int`): patch size
            - in_chans (:obj:`int`): input channels
            - num_classes (:obj:`int`): number of classes
            - embed_dim (:obj:`int`): embedding dimensions for tokens
            - depth (:obj:`int`): depth of encoder
            - num_heads (:obj:`int`): number of heads in multi-head self-attention
            - mlp_ratio (:obj:`float`): expand ratio in feedforward
            - qkv_bias (:obj:`bool`): whether to add bias for mlp of qkv
            - qk_scale (:obj:`float`): scale ratio for qk, default is head_dim ** -0.5
            - drop_rate (:obj:`float`): dropout rate in feedforward module after linear operation
                and projection drop rate in attention
            - attn_drop_rate (:obj:`float`): dropout rate for attention
            - drop_path_rate (:obj:`float`): drop_path rate after attention
            - hybrid_backbone (:obj:`nn.Module`): backbone e.g. resnet
            - norm_layer (:obj:`nn.Module`): normalization type
            - leff_local_size (:obj:`int`): kernel size in LocallyEnhancedFeedForward
            - leff_with_bn (:obj:`bool`): whether add bn in LocallyEnhancedFeedForward
        """
        super().__init__()
        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models

        self.i2t = HybridEmbed(
            hybrid_backbone, img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
        num_patches = self.i2t.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        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,
                kernel_size=leff_local_size, with_bn=leff_with_bn)
            for i in range(depth)])

        # without droppath
        self.lca = 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=0., norm_layer=norm_layer,
            feedforward_type = 'lca'
        )
        self.pos_layer_embed = nn.Parameter(torch.zeros(1, depth, embed_dim))

        self.norm = norm_layer(embed_dim)

        # NOTE as per official impl, we could have a pre-logits representation dense layer + tanh here
        # self.repr = nn.Linear(embed_dim, representation_size)
        # self.repr_act = nn.Tanh()

        # Classifier head
        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()

        trunc_normal_(self.pos_embed, std=.02)
        trunc_normal_(self.cls_token, std=.02)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            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)

    @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):
        B = x.shape[0]
        x = self.i2t(x)

        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
        x = torch.cat((cls_tokens, x), dim=1)
        x = x + self.pos_embed
        x = self.pos_drop(x)

        cls_token_list = []
        for blk in self.blocks:
            x, curr_cls_token = blk(x)
            cls_token_list.append(curr_cls_token)

        all_cls_token = torch.stack(cls_token_list, dim=1)  # B*D*K
        all_cls_token = all_cls_token + self.pos_layer_embed
        # attention over cls tokens
        last_cls_token = self.lca(all_cls_token)
        last_cls_token = self.norm(last_cls_token)

        return last_cls_token.view(B, -1)

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x


@register_model
def ceit_tiny_patch16_224(pretrained=False, **kwargs):
    """
    convolutional + pooling stem
    local enhanced feedforward
    attention over cls_tokens
    """
    i2t = Image2Tokens()
    model = CeIT(
        hybrid_backbone=i2t,
        patch_size=4, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    return model


@register_model
def ceit_small_patch16_224(pretrained=False, **kwargs):
    """
    convolutional + pooling stem
    local enhanced feedforward
    attention over cls_tokens
    """
    i2t = Image2Tokens()
    model = CeIT(
        hybrid_backbone=i2t,
        patch_size=4, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    return model


@register_model
def ceit_base_patch16_224(pretrained=False, **kwargs):
    """
    convolutional + pooling stem
    local enhanced feedforward
    attention over cls_tokens
    """
    i2t = Image2Tokens()
    model = CeIT(
        hybrid_backbone=i2t,
        patch_size=4, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    return model


@register_model
def ceit_tiny_patch16_384(pretrained=False, **kwargs):
    """
    convolutional + pooling stem
    local enhanced feedforward
    attention over cls_tokens
    """
    i2t = Image2Tokens()
    model = CeIT(
        hybrid_backbone=i2t, img_size=384,
        patch_size=4, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    return model


@register_model
def ceit_small_patch16_384(pretrained=False, **kwargs):
    """
    convolutional + pooling stem
    local enhanced feedforward
    attention over cls_tokens
    """
    i2t = Image2Tokens()
    model = CeIT(
        hybrid_backbone=i2t, img_size=384,
        patch_size=4, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    return model