浅析 Swin Transformer

import paddleimport paddle.nn as nnfrom itertools import repeatdef masked_fill(tensor, mask, value):
    cover = paddle.full_like(tensor, value)
    out = paddle.where(mask, tensor, cover)    return outdef swapdim(x,num1,num2):
    a=list(range(len(x.shape)))
    a[num1], a[num2] = a[num2], a[num1]    return x.transpose(a)def to_2tuple(x):
    return tuple(repeat(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 input

class 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 x

def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.reshape([B, H // window_size, window_size, W // window_size, window_size, C])
    windows = x.transpose([0, 1, 3, 2, 4, 5]).reshape([-1, window_size, window_size, C])    return windowsdef window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image

    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.reshape([B, H // window_size, W // window_size, window_size, window_size, -1])
    x = x.transpose([0, 1, 3, 2, 4, 5]).reshape([B, H, W, -1])    return x

class WindowAttention(nn.Layer):
    """ Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        # define a parameter table of relative position bias
        relative_position_bias_table = self.create_parameter(
            shape=((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads), default_initializer=nn.initializer.Constant(value=0))  # 2*Wh-1 * 2*Ww-1, nH
        self.add_parameter("relative_position_bias_table", relative_position_bias_table)        # get pair-wise relative position index for each token inside the window
        coords_h = paddle.arange(self.window_size[0])
        coords_w = paddle.arange(self.window_size[1])
        coords = paddle.stack(paddle.meshgrid([coords_h, coords_w]))                   # 2, Wh, Ww
        coords_flatten = paddle.flatten(coords, 1)                                     # 2, Wh*Ww
        relative_coords = coords_flatten.unsqueeze(-1) - coords_flatten.unsqueeze(1)   # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.transpose([1, 2, 0])                         # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1                            # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        self.relative_position_index = relative_coords.sum(-1)                         # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index", self.relative_position_index)

        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)

        self.softmax = nn.Softmax(axis=-1)    def forward(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        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]  # make torchscript happy (cannot use tensor as tuple)

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

        relative_position_bias = paddle.index_select(self.relative_position_bias_table,
                                                     self.relative_position_index.reshape((-1,)),axis=0).reshape((self.window_size[0] * self.window_size[1],self.window_size[0] * self.window_size[1], -1))

        relative_position_bias = relative_position_bias.transpose([2, 0, 1])  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)        if mask is not None:
            nW = mask.shape[0]
            attn = attn.reshape([B_ // nW, nW, self.num_heads, N, N]) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.reshape([-1, self.num_heads, N, N])
            attn = self.softmax(attn)        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = swapdim((attn @ v),1, 2).reshape([B_, N, C])
        x = self.proj(x)
        x = self.proj_drop(x)        return x

class SwinTransformerBlock(nn.Layer):
    """ Swin Transformer Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio        if min(self.input_resolution) <= self.window_size:            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        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)        if self.shift_size > 0:            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = paddle.zeros((1, H, W, 1))  # 1 H W 1

            h_slices = (slice(0, -self.window_size),                        slice(-self.window_size, -self.shift_size),                        slice(-self.shift_size, None))
            w_slices = (slice(0, -self.window_size),                        slice(-self.window_size, -self.shift_size),                        slice(-self.shift_size, None))
            cnt = 0
            for h in h_slices:                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
            mask_windows = mask_windows.reshape([-1, self.window_size * self.window_size])
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)

            attn_mask = masked_fill(attn_mask, attn_mask == 0, float(-100.0))
            attn_mask = masked_fill(attn_mask, attn_mask != 0, float(0.0))        else:
            attn_mask = None

        self.register_buffer("attn_mask", attn_mask)    def forward(self, x):
        H, W = self.input_resolution
        B, L, C = x.shape        assert L == H * W, "input feature has wrong size"

        shortcut = x
        x = self.norm1(x)
        x = x.reshape([B, H, W, C])        # cyclic shift
        if self.shift_size > 0:
            shifted_x = paddle.roll(x, shifts=(-self.shift_size, -self.shift_size), axis=(1, 2))        else:
            shifted_x = x        # partition windows
        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
        x_windows = x_windows.reshape([-1, self.window_size * self.window_size, C])  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C

        # merge windows
        attn_windows = attn_windows.reshape([-1, self.window_size, self.window_size, C])
        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C

        # reverse cyclic shift
        if self.shift_size > 0:
            x = paddle.roll(shifted_x, shifts=(self.shift_size, self.shift_size), axis=(1, 2))        else:
            x = shifted_x
        x = x.reshape([B, H * W, C])        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))        return xclass PatchMerging(nn.Layer):
    """ Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias_attr=False)
        self.norm = norm_layer(4 * dim)    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        x = x.reshape([B, H, W, C])

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = paddle.concat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.reshape([B, -1, 4 * C])  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)        return x

class BasicLayer(nn.Layer):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth        

        # build blocks
        self.blocks = nn.LayerList([
            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                 norm_layer=norm_layer) 
                                 for i in range(depth)])        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)        else:
            self.downsample = None

    def forward(self, x):
        for blk in self.blocks:
                x = blk(x)        if self.downsample is not None:
            x = self.downsample(x)        return xclass PatchEmbed(nn.Layer):
    """ Image to Patch Embedding

    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2D(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)        else:
            self.norm = None

    def forward(self, x):
        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 = swapdim(self.proj(x).flatten(2), 1, 2)  # B Ph*Pw C
        if self.norm is not None:
            x = self.norm(x)        return x

class SwinTransformer(nn.Layer):
    """ Swin Transformer
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030

    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
                 **kwargs):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = self.create_parameter(shape=(1, num_patches, embed_dim),default_initializer=nn.initializer.Constant(value=0))

            self.add_parameter("absolute_pos_embed", self.absolute_pos_embed)

        self.pos_drop = nn.Dropout(p=drop_rate)        # stochastic depth
        dpr = [x for x in paddle.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        # build layers
        self.layers = nn.LayerList()        for i_layer in range(self.num_layers):
            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
                                                 patches_resolution[1] // (2 ** i_layer)),
                               depth=depths[i_layer],
                               num_heads=num_heads[i_layer],
                               window_size=window_size,
                               mlp_ratio=self.mlp_ratio,
                               qkv_bias=qkv_bias, qk_scale=qk_scale,
                               drop=drop_rate, attn_drop=attn_drop_rate,
                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                               norm_layer=norm_layer,
                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None
                               )
            self.layers.append(layer)

        self.norm = norm_layer(self.num_features)
        self.avgpool = nn.AdaptiveAvgPool1D(1)
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else Identity()    def forward_features(self, x):
        x = self.patch_embed(x)        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)        for layer in self.layers:
            x = layer(x)

        x = self.norm(x)  # B L C
        x = self.avgpool(swapdim(x,1, 2))  # B C 1
        x = paddle.flatten(x, 1)        return x    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)        return x

def swin_tiny_window7_224(**kwargs):
    model = SwinTransformer(img_size = 224,
                            embed_dim = 96,
                            depths = [ 2, 2, 6, 2 ],
                            num_heads = [ 3, 6, 12, 24 ],
                            window_size = 7,
                            drop_path_rate=0.2,
                            **kwargs)    return modeldef swin_small_window7_224(**kwargs):
    model = SwinTransformer(img_size = 224,
                            embed_dim = 96,
                            depths = [ 2, 2, 18, 2 ],
                            num_heads = [ 3, 6, 12, 24 ],
                            window_size = 7,
                            drop_path_rate=0.3,
                            **kwargs)    return modeldef swin_base_window7_224(**kwargs):
    model = SwinTransformer(img_size = 224,
                            embed_dim = 128,
                            depths = [ 2, 2, 18, 2 ],
                            num_heads = [ 4, 8, 16, 32 ],
                            window_size = 7,
                            drop_path_rate=0.5,
                            **kwargs)    return modeldef swin_large_window7_224(**kwargs):
    model = SwinTransformer(img_size = 224,
                            embed_dim = 192,
                            depths = [ 2, 2, 18, 2 ],
                            num_heads = [ 6, 12, 24, 48 ],
                            window_size = 7,
                            **kwargs)    return modeldef swin_base_window12_384(**kwargs):
    model = SwinTransformer(img_size = 384,
                            embed_dim = 128,
                            depths = [ 2, 2, 18, 2 ],
                            num_heads = [ 4, 8, 16, 32 ],
                            window_size = 12,
                            **kwargs)    return modeldef swin_large_window12_384(**kwargs):
    model = SwinTransformer(img_size = 384,
                            embed_dim = 192,
                            depths = [ 2, 2, 18, 2 ],
                            num_heads = [ 6, 12, 24, 48 ],
                            window_size = 12,
                            **kwargs)    return model

model = swin_tiny_window7_224(num_classes = 10)
model = paddle.Model(model)
model.summary((1,3,224,224))

import paddle.vision.transforms as Tfrom paddle.vision.datasets import Cifar10#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.prepare(optimizer=paddle.optimizer.SGD(learning_rate=0.001,parameters=model.parameters()),
              loss=paddle.nn.CrossEntropyLoss(),
              metrics=paddle.metric.Accuracy())

vdl_callback = paddle.callbacks.VisualDL(log_dir='log') # 训练可视化model.fit(
    train_data=train_dataset, 
    eval_data=val_dataset, 
    batch_size=8, 
    epochs=10, 
    verbose=1, 
    callbacks=vdl_callback # 训练可视化)