ConViT：引入归纳偏置的ViT

import paddleimport paddle.nn as nnimport paddle.nn.functional as Ffrom functools import partialimport numpy as np

zeros_ = nn.initializer.Constant(value=0.)
ones_ = nn.initializer.Constant(value=1.)
trunc_normal_ = nn.initializer.TruncatedNormal(std=.02)def to_2tuple(x):
    return tuple([x] * 2)def drop_path(x, drop_prob = 0., training = False):
    if drop_prob == 0. or not training:        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  
    random_tensor = paddle.to_tensor(keep_prob) + paddle.rand(shape)
    random_tensor = paddle.floor(random_tensor) 
    output = x.divide(keep_prob) * random_tensor    return outputclass DropPath(nn.Layer):
    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)class Identity(nn.Layer):                      

    def __init__(self, *args, **kwargs):
        super(Identity, self).__init__() 
    def forward(self, input):
        return inputclass Mlp(nn.Layer):
    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 xclass PatchEmbed(nn.Layer):
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1]
        self.flatten = flatten

        self.proj = nn.Conv2D(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        self.norm = norm_layer(embed_dim) if norm_layer else Identity()    def forward(self, x):
        B, C, H, W = x.shape        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)        if self.flatten:
            x = x.flatten(2).transpose((0, 2, 1))  # BCHW -> BNC
        x = self.norm(x)        return xclass HybridEmbed(nn.Layer):

    def __init__(self, backbone, img_size=224, patch_size=1, feature_size=None, in_chans=3, embed_dim=768):
        super().__init__()        assert isinstance(backbone, nn.Module)
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.backbone = backbone        if feature_size is None:            with paddle.no_grad():
               
                training = backbone.training                if training:
                    backbone.eval()
                o = self.backbone(paddle.zeros([1, in_chans, img_size[0], img_size[1]]))                if isinstance(o, (list, tuple)):
                    o = o[-1]  
                feature_dim = o.shape[1]
                backbone.train(training)        else:
            feature_size = to_2tuple(feature_size)            if hasattr(self.backbone, 'feature_info'):
                feature_dim = self.backbone.feature_info.channels()[-1]            else:
                feature_dim = self.backbone.num_features        assert feature_size[0] % patch_size[0] == 0 and feature_size[1] % patch_size[1] == 0
        self.num_patches = feature_size[0] // patch_size[0] * feature_size[1] // patch_size[1]
        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]  
        x = self.proj(x).flatten(2).transpose([0, 2, 1])        return xdef repeat(x, rep):
    return paddle.to_tensor(np.tile(x.numpy(), rep))def repeat_interleave(x, rep, axis):
    return paddle.to_tensor(np.repeat(x.numpy(), rep, axis=axis))def einsum(str, distances, attn_map):
    d = distances.numpy()
    a = attn_map.numpy()
    out = np.einsum(str, (d, a))    
    return paddle.to_tensor(out)

class GPSA(nn.Layer):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.,
                 locality_strength=1., use_local_init=True):
        super().__init__()
        self.num_heads = num_heads
        self.dim = dim
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.qk = nn.Linear(dim, dim * 2, bias_attr=qkv_bias)       
        self.v = nn.Linear(dim, dim, bias_attr=qkv_bias)       
        
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.pos_proj = nn.Linear(3, num_heads)
        self.proj_drop = nn.Dropout(proj_drop)
        self.locality_strength = locality_strength

        self.gating_param = self.create_parameter(shape=[self.num_heads], default_initializer=ones_)
        self.add_parameter("gating_param", self.gating_param)        

    def forward(self, x):
        B, N, C = x.shape        if not hasattr(self, 'rel_indices') or self.rel_indices.shape[1]!=N:
            self.get_rel_indices(N)

        attn = self.get_attention(x)
        v = self.v(x).reshape([B, N, self.num_heads, C // self.num_heads]).transpose([0, 2, 1, 3])
        x = (attn @ v).transpose([0, 2, 1, 3])
        x = x.reshape([B, N, C])
        x = self.proj(x)
        x = self.proj_drop(x)        return x    def get_attention(self, x):
        B, N, C = x.shape        
        qk = self.qk(x).reshape([B, N, 2, self.num_heads, C // self.num_heads]).transpose([2, 0, 3, 1, 4])
        q, k = qk[0], qk[1]
        pos_score = self.rel_indices.expand([B, -1, -1,-1])
        pos_score = self.pos_proj(pos_score).transpose([0,3,1,2]) 
        patch_score = (q @ k.transpose([0, 1, 3, 2])) * self.scale
        patch_score = F.softmax(patch_score, axis=-1)
        pos_score = F.softmax(pos_score, axis=-1)

        gating = self.gating_param.reshape([1, -1, 1, 1])
        attn = (1. - F.sigmoid(gating)) * patch_score + F.sigmoid(gating) * pos_score
        attn /= attn.sum(axis=-1).unsqueeze(-1)
        attn = self.attn_drop(attn)        return attn    def get_attention_map(self, x, return_map = False):

        attn_map = self.get_attention(x).mean(0) 
        distances = self.rel_indices.squeeze()[:,:,-1]**.5
        dist = einsum('nm,hnm->h', distances, attn_map)      # einsum
        dist /= distances.shape[0]        if return_map:            return dist, attn_map        else:            return dist    def get_rel_indices(self, num_patches):
        img_size = int(num_patches**.5)
        rel_indices = paddle.zeros([1, num_patches, num_patches, 3])
        ind = paddle.arange(img_size).reshape([1,-1]) - paddle.arange(img_size).reshape([-1, 1])
        indx = repeat(ind, [img_size, img_size])
        indy = repeat_interleave(ind, img_size, axis=0)
        indy = repeat_interleave(indy, img_size, axis=1)
        indd = indx**2 + indy**2
        rel_indices[:,:,:,2] = indd.unsqueeze(0)
        rel_indices[:,:,:,1] = indy.unsqueeze(0)
        rel_indices[:,:,:,0] = indx.unsqueeze(0)
        self.rel_indices = rel_indices    def local_init(self):
        self.v.weight.set_value(paddle.eye(self.dim))
        locality_distance = 1  # max(1,1/locality_strength**.5)

        kernel_size = int(self.num_heads ** .5)
        center = (kernel_size - 1) / 2 if kernel_size % 2 == 0 else kernel_size // 2
        for h1 in range(kernel_size):            for h2 in range(kernel_size):
                position = h1 + kernel_size * h2
                self.pos_proj.weight[2, position] = -1
                self.pos_proj.weight[1, position] = 2 * (h1 - center) * locality_distance
                self.pos_proj.weight[0, position] = 2 * (h2 - center) * locality_distance
        
        self.pos_proj.weight.set_value(self.pos_proj.weight * self.locality_strength)class MHSA(nn.Layer):
    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
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias_attr=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)    def get_attention_map(self, x, return_map = False):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape([B, N, 3, self.num_heads, C // self.num_heads]).transpose([2, 0, 3, 1, 4])
        q, k, v = qkv[0], qkv[1], qkv[2]
        attn_map = (q @ k.transpose([0, 1, 3, 2])) * self.scale
        attn_map = F.softmax(attn_map, axis=-1).mean(0)

        img_size = int(N**.5)
        ind = paddle.arange(img_size).reshape([1,-1]) - paddle.arange(img_size).reshape([-1, 1])
        indx = repeat(ind, [img_size, img_size])
        indy = repeat_interleave(ind, img_size, axis=0)
        indy = repeat_interleave(indy, img_size, axis=1)
        indd = indx**2 + indy**2
        distances = indd**.5
        
        
        dist = einsum('nm,hnm->h', distances, attn_map)   # einsum
        dist /= N        
        if return_map:            return dist, attn_map        else:            return dist            
    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape([B, N, 3, self.num_heads, C // self.num_heads]).transpose([2, 0, 3, 1, 4])
        q, k, v = qkv[0], qkv[1], qkv[2]

        attn = (q @ k.transpose([0, 1, 3, 2])) * self.scale
        attn = F.softmax(attn, axis=-1)
        attn = self.attn_drop(attn)
        x = (attn @ v).transpose([0,2,1,3]).reshape([B, N, C])
        x = self.proj(x)
        x = self.proj_drop(x)        return x    
class Block(nn.Layer):

    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, use_gpsa=True, **kwargs):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.use_gpsa = use_gpsa        if self.use_gpsa:
            self.attn = GPSA(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, **kwargs)        else:
            self.attn = MHSA(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, **kwargs)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else 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)    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))        return x    


class VisionTransformer(nn.Layer):
    """ 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=48, 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, global_pool=None,
                 local_up_to_layer=10, locality_strength=1., use_pos_embed=True):
        super().__init__()
        embed_dim *= num_heads
        self.num_classes = num_classes
        self.local_up_to_layer = local_up_to_layer
        self.num_features = self.embed_dim = embed_dim  
        self.use_pos_embed = use_pos_embed        if hybrid_backbone is not None:
            self.patch_embed = HybridEmbed(
                hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)        else:
            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.num_patches = num_patches

        self.cls_token = self.create_parameter(shape=[1, 1, embed_dim], default_initializer=nn.initializer.TruncatedNormal(mean=0.0, std=.02))
        self.add_parameter("cls_token", self.cls_token)

        self.pos_drop = nn.Dropout(p=drop_rate)        if self.use_pos_embed:

            self.pos_embed = self.create_parameter(shape=[1, num_patches, embed_dim], default_initializer=nn.initializer.TruncatedNormal(mean=0.0, std=.02))
            self.add_parameter("pos_embed", self.pos_embed)


        dpr = [x for x in paddle.linspace(0, drop_path_rate, depth)]  
        self.blocks = nn.LayerList([
            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,
                use_gpsa=True,
                locality_strength=locality_strength)            if i<local_up_to_layer else
            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,
                use_gpsa=False)            for i in range(depth)])
        self.norm = norm_layer(embed_dim)

        self.feature_info = [dict(num_chs=embed_dim, reduction=0, module='head')]
        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else Identity()

        self.apply(self._init_weights)        for n, m in self.named_sublayers():            if hasattr(m, 'local_init'):
                m.local_init()    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight)            if isinstance(m, nn.Linear) and m.bias is not None:
                zeros_(m.bias)        elif isinstance(m, nn.LayerNorm):
            zeros_(m.bias)
            ones_(m.weight)    def forward_features(self, x):
        B = x.shape[0]
        x = self.patch_embed(x)

        cls_tokens = self.cls_token.expand([B, -1, -1])        if self.use_pos_embed:
            x = x + self.pos_embed
        x = self.pos_drop(x)        for u,blk in enumerate(self.blocks):            if u == self.local_up_to_layer :
                x = paddle.concat((cls_tokens, x), axis=1)
            x = blk(x)

        x = self.norm(x)        return x[:, 0]    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)        return x

def convit_tiny(**kwargs):
    model = VisionTransformer(
        num_heads=4,
        norm_layer=partial(nn.LayerNorm, epsilon=1e-6), **kwargs)    return modeldef convit_small(**kwargs):
    model = VisionTransformer(
        num_heads=9,
        norm_layer=partial(nn.LayerNorm, epsilon=1e-6), **kwargs)    return modeldef convit_base(**kwargs):
    model = VisionTransformer(
        num_heads=16,
        norm_layer=partial(nn.LayerNorm, epsilon=1e-6), **kwargs)    return model

paddle.Model(convit_base()).summary((1, 3, 224, 224))

import paddle.vision.transforms as Tfrom paddle.vision.datasets import Cifar10

paddle.set_device('gpu')#数据准备transform = T.Compose([
    T.Resize(size=(224,224)),
    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225],data_format='HWC'),
    T.ToTensor()
])

train_dataset = Cifar10(mode='train', transform=transform)
val_dataset = Cifar10(mode='test',  transform=transform)

model=paddle.Model(convit_small(num_classes=10))

model.prepare(optimizer=paddle.optimizer.Adam(learning_rate=0.001,parameters=model.parameters()),
              loss=paddle.nn.CrossEntropyLoss(),
              metrics=paddle.metric.Accuracy())

visualdl=paddle.callbacks.VisualDL(log_dir='visual_log') # 开启训练可视化model.fit(
    train_data=train_dataset, 
    eval_data=val_dataset, 
    batch_size=64, 
    epochs=6, 
    verbose=1,
    callbacks=[visualdl] 
)

# convit tiny model = convit_tiny()
model.set_state_dict(paddle.load('data/data93780/convit_tiny.pdparams'))# convit small model = convit_small()
model.set_state_dict(paddle.load('data/data93780/convit_small.pdparams'))# convit basemodel = convit_base()
model.set_state_dict(paddle.load('data/data93780/convit_base.pdparams'))