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参考资料 ultralytics/models/sam/modules/tiny_encoder.py

备注

该文件可在https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/ sam/modules/tiny_encoder .py 下找到。如果您发现问题,请通过提交 Pull Request🛠️ 帮助修复。谢谢🙏!



ultralytics.models.sam.modules.tiny_encoder.Conv2d_BN

垒球 Sequential

这是一个顺序容器,先进行二维卷积,然后批量归一化。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
class Conv2d_BN(torch.nn.Sequential):
    """A sequential container that performs 2D convolution followed by batch normalization."""

    def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1):
        """Initializes the MBConv model with given input channels, output channels, expansion ratio, activation, and
        drop path.
        """
        super().__init__()
        self.add_module("c", torch.nn.Conv2d(a, b, ks, stride, pad, dilation, groups, bias=False))
        bn = torch.nn.BatchNorm2d(b)
        torch.nn.init.constant_(bn.weight, bn_weight_init)
        torch.nn.init.constant_(bn.bias, 0)
        self.add_module("bn", bn)

__init__(a, b, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1)

用给定的输入通道、输出通道、扩展比、激活和 下降路径。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1):
    """Initializes the MBConv model with given input channels, output channels, expansion ratio, activation, and
    drop path.
    """
    super().__init__()
    self.add_module("c", torch.nn.Conv2d(a, b, ks, stride, pad, dilation, groups, bias=False))
    bn = torch.nn.BatchNorm2d(b)
    torch.nn.init.constant_(bn.weight, bn_weight_init)
    torch.nn.init.constant_(bn.bias, 0)
    self.add_module("bn", bn)



ultralytics.models.sam.modules.tiny_encoder.PatchEmbed

垒球 Module

将图像嵌入补丁,并投影到指定的嵌入维度。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
class PatchEmbed(nn.Module):
    """Embeds images into patches and projects them into a specified embedding dimension."""

    def __init__(self, in_chans, embed_dim, resolution, activation):
        """Initialize the PatchMerging class with specified input, output dimensions, resolution and activation
        function.
        """
        super().__init__()
        img_size: Tuple[int, int] = to_2tuple(resolution)
        self.patches_resolution = (img_size[0] // 4, img_size[1] // 4)
        self.num_patches = self.patches_resolution[0] * self.patches_resolution[1]
        self.in_chans = in_chans
        self.embed_dim = embed_dim
        n = embed_dim
        self.seq = nn.Sequential(
            Conv2d_BN(in_chans, n // 2, 3, 2, 1),
            activation(),
            Conv2d_BN(n // 2, n, 3, 2, 1),
        )

    def forward(self, x):
        """Runs input tensor 'x' through the PatchMerging model's sequence of operations."""
        return self.seq(x)

__init__(in_chans, embed_dim, resolution, activation)

用指定的输入、输出尺寸、分辨率和激活函数初始化 PatchMerging 类。 功能。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def __init__(self, in_chans, embed_dim, resolution, activation):
    """Initialize the PatchMerging class with specified input, output dimensions, resolution and activation
    function.
    """
    super().__init__()
    img_size: Tuple[int, int] = to_2tuple(resolution)
    self.patches_resolution = (img_size[0] // 4, img_size[1] // 4)
    self.num_patches = self.patches_resolution[0] * self.patches_resolution[1]
    self.in_chans = in_chans
    self.embed_dim = embed_dim
    n = embed_dim
    self.seq = nn.Sequential(
        Conv2d_BN(in_chans, n // 2, 3, 2, 1),
        activation(),
        Conv2d_BN(n // 2, n, 3, 2, 1),
    )

forward(x)

通过 PatchMerging 模型的操作序列运行输入tensor 'x'。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Runs input tensor 'x' through the PatchMerging model's sequence of operations."""
    return self.seq(x)



ultralytics.models.sam.modules.tiny_encoder.MBConv

垒球 Module

移动反向瓶颈转换层(MBConv),EfficientNet 架构的一部分。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
class MBConv(nn.Module):
    """Mobile Inverted Bottleneck Conv (MBConv) layer, part of the EfficientNet architecture."""

    def __init__(self, in_chans, out_chans, expand_ratio, activation, drop_path):
        """Initializes a convolutional layer with specified dimensions, input resolution, depth, and activation
        function.
        """
        super().__init__()
        self.in_chans = in_chans
        self.hidden_chans = int(in_chans * expand_ratio)
        self.out_chans = out_chans

        self.conv1 = Conv2d_BN(in_chans, self.hidden_chans, ks=1)
        self.act1 = activation()

        self.conv2 = Conv2d_BN(self.hidden_chans, self.hidden_chans, ks=3, stride=1, pad=1, groups=self.hidden_chans)
        self.act2 = activation()

        self.conv3 = Conv2d_BN(self.hidden_chans, out_chans, ks=1, bn_weight_init=0.0)
        self.act3 = activation()

        # NOTE: `DropPath` is needed only for training.
        # self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.drop_path = nn.Identity()

    def forward(self, x):
        """Implements the forward pass for the model architecture."""
        shortcut = x
        x = self.conv1(x)
        x = self.act1(x)
        x = self.conv2(x)
        x = self.act2(x)
        x = self.conv3(x)
        x = self.drop_path(x)
        x += shortcut
        return self.act3(x)

__init__(in_chans, out_chans, expand_ratio, activation, drop_path)

以指定的维数、输入分辨率、深度和激活函数初始化卷积层。 功能。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def __init__(self, in_chans, out_chans, expand_ratio, activation, drop_path):
    """Initializes a convolutional layer with specified dimensions, input resolution, depth, and activation
    function.
    """
    super().__init__()
    self.in_chans = in_chans
    self.hidden_chans = int(in_chans * expand_ratio)
    self.out_chans = out_chans

    self.conv1 = Conv2d_BN(in_chans, self.hidden_chans, ks=1)
    self.act1 = activation()

    self.conv2 = Conv2d_BN(self.hidden_chans, self.hidden_chans, ks=3, stride=1, pad=1, groups=self.hidden_chans)
    self.act2 = activation()

    self.conv3 = Conv2d_BN(self.hidden_chans, out_chans, ks=1, bn_weight_init=0.0)
    self.act3 = activation()

    # NOTE: `DropPath` is needed only for training.
    # self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
    self.drop_path = nn.Identity()

forward(x)

为模型结构执行前向传递。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Implements the forward pass for the model architecture."""
    shortcut = x
    x = self.conv1(x)
    x = self.act1(x)
    x = self.conv2(x)
    x = self.act2(x)
    x = self.conv3(x)
    x = self.drop_path(x)
    x += shortcut
    return self.act3(x)



ultralytics.models.sam.modules.tiny_encoder.PatchMerging

垒球 Module

合并地物图中的相邻补丁,并投影到新的维度。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
class PatchMerging(nn.Module):
    """Merges neighboring patches in the feature map and projects to a new dimension."""

    def __init__(self, input_resolution, dim, out_dim, activation):
        """Initializes the ConvLayer with specific dimension, input resolution, depth, activation, drop path, and other
        optional parameters.
        """
        super().__init__()

        self.input_resolution = input_resolution
        self.dim = dim
        self.out_dim = out_dim
        self.act = activation()
        self.conv1 = Conv2d_BN(dim, out_dim, 1, 1, 0)
        stride_c = 1 if out_dim in [320, 448, 576] else 2
        self.conv2 = Conv2d_BN(out_dim, out_dim, 3, stride_c, 1, groups=out_dim)
        self.conv3 = Conv2d_BN(out_dim, out_dim, 1, 1, 0)

    def forward(self, x):
        """Applies forward pass on the input utilizing convolution and activation layers, and returns the result."""
        if x.ndim == 3:
            H, W = self.input_resolution
            B = len(x)
            # (B, C, H, W)
            x = x.view(B, H, W, -1).permute(0, 3, 1, 2)

        x = self.conv1(x)
        x = self.act(x)

        x = self.conv2(x)
        x = self.act(x)
        x = self.conv3(x)
        return x.flatten(2).transpose(1, 2)

__init__(input_resolution, dim, out_dim, activation)

用特定维度、输入分辨率、深度、激活、下降路径和其他可选参数初始化 ConvLayer。 可选参数。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def __init__(self, input_resolution, dim, out_dim, activation):
    """Initializes the ConvLayer with specific dimension, input resolution, depth, activation, drop path, and other
    optional parameters.
    """
    super().__init__()

    self.input_resolution = input_resolution
    self.dim = dim
    self.out_dim = out_dim
    self.act = activation()
    self.conv1 = Conv2d_BN(dim, out_dim, 1, 1, 0)
    stride_c = 1 if out_dim in [320, 448, 576] else 2
    self.conv2 = Conv2d_BN(out_dim, out_dim, 3, stride_c, 1, groups=out_dim)
    self.conv3 = Conv2d_BN(out_dim, out_dim, 1, 1, 0)

forward(x)

利用卷积层和激活层对输入进行前向传递,并返回结果。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Applies forward pass on the input utilizing convolution and activation layers, and returns the result."""
    if x.ndim == 3:
        H, W = self.input_resolution
        B = len(x)
        # (B, C, H, W)
        x = x.view(B, H, W, -1).permute(0, 3, 1, 2)

    x = self.conv1(x)
    x = self.act(x)

    x = self.conv2(x)
    x = self.act(x)
    x = self.conv3(x)
    return x.flatten(2).transpose(1, 2)



ultralytics.models.sam.modules.tiny_encoder.ConvLayer

垒球 Module

卷积层具有多个 MobileNetV3 风格的倒置瓶颈卷积 (MBConv)。

可选择对输出进行下采样操作,并提供梯度检查点支持。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
class ConvLayer(nn.Module):
    """
    Convolutional Layer featuring multiple MobileNetV3-style inverted bottleneck convolutions (MBConv).

    Optionally applies downsample operations to the output, and provides support for gradient checkpointing.
    """

    def __init__(
        self,
        dim,
        input_resolution,
        depth,
        activation,
        drop_path=0.0,
        downsample=None,
        use_checkpoint=False,
        out_dim=None,
        conv_expand_ratio=4.0,
    ):
        """
        Initializes the ConvLayer with the given dimensions and settings.

        Args:
            dim (int): The dimensionality of the input and output.
            input_resolution (Tuple[int, int]): The resolution of the input image.
            depth (int): The number of MBConv layers in the block.
            activation (Callable): Activation function applied after each convolution.
            drop_path (Union[float, List[float]]): Drop path rate. Single float or a list of floats for each MBConv.
            downsample (Optional[Callable]): Function for downsampling the output. None to skip downsampling.
            use_checkpoint (bool): Whether to use gradient checkpointing to save memory.
            out_dim (Optional[int]): The dimensionality of the output. None means it will be the same as `dim`.
            conv_expand_ratio (float): Expansion ratio for the MBConv layers.
        """
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # Build blocks
        self.blocks = nn.ModuleList(
            [
                MBConv(
                    dim,
                    dim,
                    conv_expand_ratio,
                    activation,
                    drop_path[i] if isinstance(drop_path, list) else drop_path,
                )
                for i in range(depth)
            ]
        )

        # Patch merging layer
        self.downsample = (
            None
            if downsample is None
            else downsample(input_resolution, dim=dim, out_dim=out_dim, activation=activation)
        )

    def forward(self, x):
        """Processes the input through a series of convolutional layers and returns the activated output."""
        for blk in self.blocks:
            x = checkpoint.checkpoint(blk, x) if self.use_checkpoint else blk(x)
        return x if self.downsample is None else self.downsample(x)

__init__(dim, input_resolution, depth, activation, drop_path=0.0, downsample=None, use_checkpoint=False, out_dim=None, conv_expand_ratio=4.0)

使用给定的尺寸和设置初始化 ConvLayer。

参数

名称 类型 说明 默认值
dim int

输入和输出的维度。

所需
input_resolution Tuple[int, int]

输入图像的分辨率。

所需
depth int

区块中的 MBConv 层数。

所需
activation Callable

每次卷积后应用的激活函数

所需
drop_path Union[float, List[float]]

下降路径速率。每个 MBConv 的单个浮点或浮点列表。

0.0
downsample Optional[Callable]

对输出进行降采样的函数。无表示跳过降采样。

None
use_checkpoint bool

是否使用梯度检查点来节省内存。

False
out_dim Optional[int]

输出的维度。无表示与 dim.

None
conv_expand_ratio float

MBConv 图层的膨胀率。

4.0
源代码 ultralytics/models/sam/modules/tiny_encoder.py
def __init__(
    self,
    dim,
    input_resolution,
    depth,
    activation,
    drop_path=0.0,
    downsample=None,
    use_checkpoint=False,
    out_dim=None,
    conv_expand_ratio=4.0,
):
    """
    Initializes the ConvLayer with the given dimensions and settings.

    Args:
        dim (int): The dimensionality of the input and output.
        input_resolution (Tuple[int, int]): The resolution of the input image.
        depth (int): The number of MBConv layers in the block.
        activation (Callable): Activation function applied after each convolution.
        drop_path (Union[float, List[float]]): Drop path rate. Single float or a list of floats for each MBConv.
        downsample (Optional[Callable]): Function for downsampling the output. None to skip downsampling.
        use_checkpoint (bool): Whether to use gradient checkpointing to save memory.
        out_dim (Optional[int]): The dimensionality of the output. None means it will be the same as `dim`.
        conv_expand_ratio (float): Expansion ratio for the MBConv layers.
    """
    super().__init__()
    self.dim = dim
    self.input_resolution = input_resolution
    self.depth = depth
    self.use_checkpoint = use_checkpoint

    # Build blocks
    self.blocks = nn.ModuleList(
        [
            MBConv(
                dim,
                dim,
                conv_expand_ratio,
                activation,
                drop_path[i] if isinstance(drop_path, list) else drop_path,
            )
            for i in range(depth)
        ]
    )

    # Patch merging layer
    self.downsample = (
        None
        if downsample is None
        else downsample(input_resolution, dim=dim, out_dim=out_dim, activation=activation)
    )

forward(x)

通过一系列卷积层处理输入,并返回激活的输出。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Processes the input through a series of convolutional layers and returns the activated output."""
    for blk in self.blocks:
        x = checkpoint.checkpoint(blk, x) if self.use_checkpoint else blk(x)
    return x if self.downsample is None else self.downsample(x)



ultralytics.models.sam.modules.tiny_encoder.Mlp

垒球 Module

用于变压器架构的多层感知器(MLP)。

该层接收包含 in_features 的输入,应用层归一化和两个全连接层。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
class Mlp(nn.Module):
    """
    Multi-layer Perceptron (MLP) for transformer architectures.

    This layer takes an input with in_features, applies layer normalization and two fully-connected layers.
    """

    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0):
        """Initializes Attention module with the given parameters including dimension, key_dim, number of heads, etc."""
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.norm = nn.LayerNorm(in_features)
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.act = act_layer()
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        """Applies operations on input x and returns modified x, runs downsample if not None."""
        x = self.norm(x)
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        return self.drop(x)

__init__(in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0)

使用给定的参数(包括 dimension、key_dim、磁头数量等)初始化 Attention 模块。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0):
    """Initializes Attention module with the given parameters including dimension, key_dim, number of heads, etc."""
    super().__init__()
    out_features = out_features or in_features
    hidden_features = hidden_features or in_features
    self.norm = nn.LayerNorm(in_features)
    self.fc1 = nn.Linear(in_features, hidden_features)
    self.fc2 = nn.Linear(hidden_features, out_features)
    self.act = act_layer()
    self.drop = nn.Dropout(drop)

forward(x)

对输入 x 进行运算并返回修改后的 x,如果不是 None,则运行下采样。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Applies operations on input x and returns modified x, runs downsample if not None."""
    x = self.norm(x)
    x = self.fc1(x)
    x = self.act(x)
    x = self.drop(x)
    x = self.fc2(x)
    return self.drop(x)



ultralytics.models.sam.modules.tiny_encoder.Attention

垒球 Module

多头注意力模块,支持空间感知,根据空间分辨率应用注意力偏差。 分辨率。针对分辨率网格中空间位置之间的每个独特偏移,实施可训练的注意力偏差 网格。

属性

名称 类型 说明
ab Tensor

用于推理的缓存注意力偏差,在训练过程中被删除。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
class Attention(torch.nn.Module):
    """
    Multi-head attention module with support for spatial awareness, applying attention biases based on spatial
    resolution. Implements trainable attention biases for each unique offset between spatial positions in the resolution
    grid.

    Attributes:
        ab (Tensor, optional): Cached attention biases for inference, deleted during training.
    """

    def __init__(
        self,
        dim,
        key_dim,
        num_heads=8,
        attn_ratio=4,
        resolution=(14, 14),
    ):
        """
        Initializes the Attention module.

        Args:
            dim (int): The dimensionality of the input and output.
            key_dim (int): The dimensionality of the keys and queries.
            num_heads (int, optional): Number of attention heads. Default is 8.
            attn_ratio (float, optional): Attention ratio, affecting the dimensions of the value vectors. Default is 4.
            resolution (Tuple[int, int], optional): Spatial resolution of the input feature map. Default is (14, 14).

        Raises:
            AssertionError: If `resolution` is not a tuple of length 2.
        """
        super().__init__()

        assert isinstance(resolution, tuple) and len(resolution) == 2
        self.num_heads = num_heads
        self.scale = key_dim**-0.5
        self.key_dim = key_dim
        self.nh_kd = nh_kd = key_dim * num_heads
        self.d = int(attn_ratio * key_dim)
        self.dh = int(attn_ratio * key_dim) * num_heads
        self.attn_ratio = attn_ratio
        h = self.dh + nh_kd * 2

        self.norm = nn.LayerNorm(dim)
        self.qkv = nn.Linear(dim, h)
        self.proj = nn.Linear(self.dh, dim)

        points = list(itertools.product(range(resolution[0]), range(resolution[1])))
        N = len(points)
        attention_offsets = {}
        idxs = []
        for p1 in points:
            for p2 in points:
                offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
                if offset not in attention_offsets:
                    attention_offsets[offset] = len(attention_offsets)
                idxs.append(attention_offsets[offset])
        self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))
        self.register_buffer("attention_bias_idxs", torch.LongTensor(idxs).view(N, N), persistent=False)

    @torch.no_grad()
    def train(self, mode=True):
        """Sets the module in training mode and handles attribute 'ab' based on the mode."""
        super().train(mode)
        if mode and hasattr(self, "ab"):
            del self.ab
        else:
            self.ab = self.attention_biases[:, self.attention_bias_idxs]

    def forward(self, x):  # x
        """Performs forward pass over the input tensor 'x' by applying normalization and querying keys/values."""
        B, N, _ = x.shape  # B, N, C

        # Normalization
        x = self.norm(x)

        qkv = self.qkv(x)
        # (B, N, num_heads, d)
        q, k, v = qkv.view(B, N, self.num_heads, -1).split([self.key_dim, self.key_dim, self.d], dim=3)
        # (B, num_heads, N, d)
        q = q.permute(0, 2, 1, 3)
        k = k.permute(0, 2, 1, 3)
        v = v.permute(0, 2, 1, 3)
        self.ab = self.ab.to(self.attention_biases.device)

        attn = (q @ k.transpose(-2, -1)) * self.scale + (
            self.attention_biases[:, self.attention_bias_idxs] if self.training else self.ab
        )
        attn = attn.softmax(dim=-1)
        x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)
        return self.proj(x)

__init__(dim, key_dim, num_heads=8, attn_ratio=4, resolution=(14, 14))

初始化注意力模块。

参数

名称 类型 说明 默认值
dim int

输入和输出的维度。

所需
key_dim int

键和查询的维度。

所需
num_heads int

注意头的数量。默认为 8。

8
attn_ratio float

注意比率,影响值向量的尺寸。默认值为 4。

4
resolution Tuple[int, int]

输入特征地图的空间分辨率。默认为(14, 14)。

(14, 14)

加薪:

类型 说明
AssertionError

如果 resolution 不是长度为 2 的元组。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def __init__(
    self,
    dim,
    key_dim,
    num_heads=8,
    attn_ratio=4,
    resolution=(14, 14),
):
    """
    Initializes the Attention module.

    Args:
        dim (int): The dimensionality of the input and output.
        key_dim (int): The dimensionality of the keys and queries.
        num_heads (int, optional): Number of attention heads. Default is 8.
        attn_ratio (float, optional): Attention ratio, affecting the dimensions of the value vectors. Default is 4.
        resolution (Tuple[int, int], optional): Spatial resolution of the input feature map. Default is (14, 14).

    Raises:
        AssertionError: If `resolution` is not a tuple of length 2.
    """
    super().__init__()

    assert isinstance(resolution, tuple) and len(resolution) == 2
    self.num_heads = num_heads
    self.scale = key_dim**-0.5
    self.key_dim = key_dim
    self.nh_kd = nh_kd = key_dim * num_heads
    self.d = int(attn_ratio * key_dim)
    self.dh = int(attn_ratio * key_dim) * num_heads
    self.attn_ratio = attn_ratio
    h = self.dh + nh_kd * 2

    self.norm = nn.LayerNorm(dim)
    self.qkv = nn.Linear(dim, h)
    self.proj = nn.Linear(self.dh, dim)

    points = list(itertools.product(range(resolution[0]), range(resolution[1])))
    N = len(points)
    attention_offsets = {}
    idxs = []
    for p1 in points:
        for p2 in points:
            offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
            if offset not in attention_offsets:
                attention_offsets[offset] = len(attention_offsets)
            idxs.append(attention_offsets[offset])
    self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))
    self.register_buffer("attention_bias_idxs", torch.LongTensor(idxs).view(N, N), persistent=False)

forward(x)

通过应用规范化和查询键/值,对输入tensor 'x' 执行前向传递。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):  # x
    """Performs forward pass over the input tensor 'x' by applying normalization and querying keys/values."""
    B, N, _ = x.shape  # B, N, C

    # Normalization
    x = self.norm(x)

    qkv = self.qkv(x)
    # (B, N, num_heads, d)
    q, k, v = qkv.view(B, N, self.num_heads, -1).split([self.key_dim, self.key_dim, self.d], dim=3)
    # (B, num_heads, N, d)
    q = q.permute(0, 2, 1, 3)
    k = k.permute(0, 2, 1, 3)
    v = v.permute(0, 2, 1, 3)
    self.ab = self.ab.to(self.attention_biases.device)

    attn = (q @ k.transpose(-2, -1)) * self.scale + (
        self.attention_biases[:, self.attention_bias_idxs] if self.training else self.ab
    )
    attn = attn.softmax(dim=-1)
    x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)
    return self.proj(x)

train(mode=True)

将模块设置为训练模式,并根据模式处理属性 "ab"。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
@torch.no_grad()
def train(self, mode=True):
    """Sets the module in training mode and handles attribute 'ab' based on the mode."""
    super().train(mode)
    if mode and hasattr(self, "ab"):
        del self.ab
    else:
        self.ab = self.attention_biases[:, self.attention_bias_idxs]



ultralytics.models.sam.modules.tiny_encoder.TinyViTBlock

垒球 Module

TinyViT 块,对输入进行自我关注和局部卷积。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
class TinyViTBlock(nn.Module):
    """TinyViT Block that applies self-attention and a local convolution to the input."""

    def __init__(
        self,
        dim,
        input_resolution,
        num_heads,
        window_size=7,
        mlp_ratio=4.0,
        drop=0.0,
        drop_path=0.0,
        local_conv_size=3,
        activation=nn.GELU,
    ):
        """
        Initializes the TinyViTBlock.

        Args:
            dim (int): The dimensionality of the input and output.
            input_resolution (Tuple[int, int]): Spatial resolution of the input feature map.
            num_heads (int): Number of attention heads.
            window_size (int, optional): Window size for attention. Default is 7.
            mlp_ratio (float, optional): Ratio of mlp hidden dim to embedding dim. Default is 4.
            drop (float, optional): Dropout rate. Default is 0.
            drop_path (float, optional): Stochastic depth rate. Default is 0.
            local_conv_size (int, optional): The kernel size of the local convolution. Default is 3.
            activation (torch.nn, optional): Activation function for MLP. Default is nn.GELU.

        Raises:
            AssertionError: If `window_size` is not greater than 0.
            AssertionError: If `dim` is not divisible by `num_heads`.
        """
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        assert window_size > 0, "window_size must be greater than 0"
        self.window_size = window_size
        self.mlp_ratio = mlp_ratio

        # NOTE: `DropPath` is needed only for training.
        # self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.drop_path = nn.Identity()

        assert dim % num_heads == 0, "dim must be divisible by num_heads"
        head_dim = dim // num_heads

        window_resolution = (window_size, window_size)
        self.attn = Attention(dim, head_dim, num_heads, attn_ratio=1, resolution=window_resolution)

        mlp_hidden_dim = int(dim * mlp_ratio)
        mlp_activation = activation
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=mlp_activation, drop=drop)

        pad = local_conv_size // 2
        self.local_conv = Conv2d_BN(dim, dim, ks=local_conv_size, stride=1, pad=pad, groups=dim)

    def forward(self, x):
        """Applies attention-based transformation or padding to input 'x' before passing it through a local
        convolution.
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        res_x = x
        if H == self.window_size and W == self.window_size:
            x = self.attn(x)
        else:
            x = x.view(B, H, W, C)
            pad_b = (self.window_size - H % self.window_size) % self.window_size
            pad_r = (self.window_size - W % self.window_size) % self.window_size
            padding = pad_b > 0 or pad_r > 0

            if padding:
                x = F.pad(x, (0, 0, 0, pad_r, 0, pad_b))

            pH, pW = H + pad_b, W + pad_r
            nH = pH // self.window_size
            nW = pW // self.window_size
            # Window partition
            x = (
                x.view(B, nH, self.window_size, nW, self.window_size, C)
                .transpose(2, 3)
                .reshape(B * nH * nW, self.window_size * self.window_size, C)
            )
            x = self.attn(x)
            # Window reverse
            x = x.view(B, nH, nW, self.window_size, self.window_size, C).transpose(2, 3).reshape(B, pH, pW, C)

            if padding:
                x = x[:, :H, :W].contiguous()

            x = x.view(B, L, C)

        x = res_x + self.drop_path(x)

        x = x.transpose(1, 2).reshape(B, C, H, W)
        x = self.local_conv(x)
        x = x.view(B, C, L).transpose(1, 2)

        return x + self.drop_path(self.mlp(x))

    def extra_repr(self) -> str:
        """Returns a formatted string representing the TinyViTBlock's parameters: dimension, input resolution, number of
        attentions heads, window size, and MLP ratio.
        """
        return (
            f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
            f"window_size={self.window_size}, mlp_ratio={self.mlp_ratio}"
        )

__init__(dim, input_resolution, num_heads, window_size=7, mlp_ratio=4.0, drop=0.0, drop_path=0.0, local_conv_size=3, activation=nn.GELU)

初始化 TinyViTBlock。

参数

名称 类型 说明 默认值
dim int

输入和输出的维度。

所需
input_resolution Tuple[int, int]

输入特征图的空间分辨率。

所需
num_heads int

注意头数量。

所需
window_size int

关注窗口大小。默认为 7。

7
mlp_ratio float

mlp 隐藏暗区与嵌入暗区的比率。默认值为 4。

4.0
drop float

辍学率。默认为 0。

0.0
drop_path float

随机深度速率。默认为 0。

0.0
local_conv_size int

局部卷积的内核大小。默认为 3。

3
activation nn

MLP 的激活函数。默认为 nn.GELU。

GELU

加薪:

类型 说明
AssertionError

如果 window_size 不大于 0。

AssertionError

如果 dim 不能被 num_heads.

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def __init__(
    self,
    dim,
    input_resolution,
    num_heads,
    window_size=7,
    mlp_ratio=4.0,
    drop=0.0,
    drop_path=0.0,
    local_conv_size=3,
    activation=nn.GELU,
):
    """
    Initializes the TinyViTBlock.

    Args:
        dim (int): The dimensionality of the input and output.
        input_resolution (Tuple[int, int]): Spatial resolution of the input feature map.
        num_heads (int): Number of attention heads.
        window_size (int, optional): Window size for attention. Default is 7.
        mlp_ratio (float, optional): Ratio of mlp hidden dim to embedding dim. Default is 4.
        drop (float, optional): Dropout rate. Default is 0.
        drop_path (float, optional): Stochastic depth rate. Default is 0.
        local_conv_size (int, optional): The kernel size of the local convolution. Default is 3.
        activation (torch.nn, optional): Activation function for MLP. Default is nn.GELU.

    Raises:
        AssertionError: If `window_size` is not greater than 0.
        AssertionError: If `dim` is not divisible by `num_heads`.
    """
    super().__init__()
    self.dim = dim
    self.input_resolution = input_resolution
    self.num_heads = num_heads
    assert window_size > 0, "window_size must be greater than 0"
    self.window_size = window_size
    self.mlp_ratio = mlp_ratio

    # NOTE: `DropPath` is needed only for training.
    # self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
    self.drop_path = nn.Identity()

    assert dim % num_heads == 0, "dim must be divisible by num_heads"
    head_dim = dim // num_heads

    window_resolution = (window_size, window_size)
    self.attn = Attention(dim, head_dim, num_heads, attn_ratio=1, resolution=window_resolution)

    mlp_hidden_dim = int(dim * mlp_ratio)
    mlp_activation = activation
    self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=mlp_activation, drop=drop)

    pad = local_conv_size // 2
    self.local_conv = Conv2d_BN(dim, dim, ks=local_conv_size, stride=1, pad=pad, groups=dim)

extra_repr()

返回代表 TinyViTBlock 参数的格式化字符串:维度、输入分辨率、注意力头数、窗口大小和 MLP 比率。 注意头数、窗口大小和 MLP 比率。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def extra_repr(self) -> str:
    """Returns a formatted string representing the TinyViTBlock's parameters: dimension, input resolution, number of
    attentions heads, window size, and MLP ratio.
    """
    return (
        f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
        f"window_size={self.window_size}, mlp_ratio={self.mlp_ratio}"
    )

forward(x)

在输入 "x "经过局部卷积之前,对其进行基于注意力的变换或填充。 卷积。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Applies attention-based transformation or padding to input 'x' before passing it through a local
    convolution.
    """
    H, W = self.input_resolution
    B, L, C = x.shape
    assert L == H * W, "input feature has wrong size"
    res_x = x
    if H == self.window_size and W == self.window_size:
        x = self.attn(x)
    else:
        x = x.view(B, H, W, C)
        pad_b = (self.window_size - H % self.window_size) % self.window_size
        pad_r = (self.window_size - W % self.window_size) % self.window_size
        padding = pad_b > 0 or pad_r > 0

        if padding:
            x = F.pad(x, (0, 0, 0, pad_r, 0, pad_b))

        pH, pW = H + pad_b, W + pad_r
        nH = pH // self.window_size
        nW = pW // self.window_size
        # Window partition
        x = (
            x.view(B, nH, self.window_size, nW, self.window_size, C)
            .transpose(2, 3)
            .reshape(B * nH * nW, self.window_size * self.window_size, C)
        )
        x = self.attn(x)
        # Window reverse
        x = x.view(B, nH, nW, self.window_size, self.window_size, C).transpose(2, 3).reshape(B, pH, pW, C)

        if padding:
            x = x[:, :H, :W].contiguous()

        x = x.view(B, L, C)

    x = res_x + self.drop_path(x)

    x = x.transpose(1, 2).reshape(B, C, H, W)
    x = self.local_conv(x)
    x = x.view(B, C, L).transpose(1, 2)

    return x + self.drop_path(self.mlp(x))



ultralytics.models.sam.modules.tiny_encoder.BasicLayer

垒球 Module

TinyViT 架构中一个阶段的基本 TinyViT 层。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
class BasicLayer(nn.Module):
    """A basic TinyViT layer for one stage in a TinyViT architecture."""

    def __init__(
        self,
        dim,
        input_resolution,
        depth,
        num_heads,
        window_size,
        mlp_ratio=4.0,
        drop=0.0,
        drop_path=0.0,
        downsample=None,
        use_checkpoint=False,
        local_conv_size=3,
        activation=nn.GELU,
        out_dim=None,
    ):
        """
        Initializes the BasicLayer.

        Args:
            dim (int): The dimensionality of the input and output.
            input_resolution (Tuple[int, int]): Spatial resolution of the input feature map.
            depth (int): Number of TinyViT blocks.
            num_heads (int): Number of attention heads.
            window_size (int): Local window size.
            mlp_ratio (float, optional): Ratio of mlp hidden dim to embedding dim. Default is 4.
            drop (float, optional): Dropout rate. Default is 0.
            drop_path (float | tuple[float], optional): Stochastic depth rate. Default is 0.
            downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default is None.
            use_checkpoint (bool, optional): Whether to use checkpointing to save memory. Default is False.
            local_conv_size (int, optional): Kernel size of the local convolution. Default is 3.
            activation (torch.nn, optional): Activation function for MLP. Default is nn.GELU.
            out_dim (int | None, optional): The output dimension of the layer. Default is None.

        Raises:
            ValueError: If `drop_path` is a list of float but its length doesn't match `depth`.
        """
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # Build blocks
        self.blocks = nn.ModuleList(
            [
                TinyViTBlock(
                    dim=dim,
                    input_resolution=input_resolution,
                    num_heads=num_heads,
                    window_size=window_size,
                    mlp_ratio=mlp_ratio,
                    drop=drop,
                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                    local_conv_size=local_conv_size,
                    activation=activation,
                )
                for i in range(depth)
            ]
        )

        # Patch merging layer
        self.downsample = (
            None
            if downsample is None
            else downsample(input_resolution, dim=dim, out_dim=out_dim, activation=activation)
        )

    def forward(self, x):
        """Performs forward propagation on the input tensor and returns a normalized tensor."""
        for blk in self.blocks:
            x = checkpoint.checkpoint(blk, x) if self.use_checkpoint else blk(x)
        return x if self.downsample is None else self.downsample(x)

    def extra_repr(self) -> str:
        """Returns a string representation of the extra_repr function with the layer's parameters."""
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

__init__(dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4.0, drop=0.0, drop_path=0.0, downsample=None, use_checkpoint=False, local_conv_size=3, activation=nn.GELU, out_dim=None)

初始化基本图层。

参数

名称 类型 说明 默认值
dim int

输入和输出的维度。

所需
input_resolution Tuple[int, int]

输入特征图的空间分辨率。

所需
depth int

TinyViT 块的数量。

所需
num_heads int

注意头数量。

所需
window_size int

本地窗口大小。

所需
mlp_ratio float

mlp 隐藏暗区与嵌入暗区的比率。默认值为 4。

4.0
drop float

辍学率。默认为 0。

0.0
drop_path float | tuple[float]

随机深度速率。默认为 0。

0.0
downsample Module | None

在图层末尾对图层进行下采样。默认为无。

None
use_checkpoint bool

是否使用检查点来节省内存。默认为 "假"。

False
local_conv_size int

局部卷积的内核大小。默认为 3。

3
activation nn

MLP 的激活函数。默认为 nn.GELU。

GELU
out_dim int | None

层的输出维度。默认为 "无"。

None

加薪:

类型 说明
ValueError

如果 drop_path 是一个浮点数列表,但其长度与 depth.

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def __init__(
    self,
    dim,
    input_resolution,
    depth,
    num_heads,
    window_size,
    mlp_ratio=4.0,
    drop=0.0,
    drop_path=0.0,
    downsample=None,
    use_checkpoint=False,
    local_conv_size=3,
    activation=nn.GELU,
    out_dim=None,
):
    """
    Initializes the BasicLayer.

    Args:
        dim (int): The dimensionality of the input and output.
        input_resolution (Tuple[int, int]): Spatial resolution of the input feature map.
        depth (int): Number of TinyViT blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float, optional): Ratio of mlp hidden dim to embedding dim. Default is 4.
        drop (float, optional): Dropout rate. Default is 0.
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default is 0.
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default is None.
        use_checkpoint (bool, optional): Whether to use checkpointing to save memory. Default is False.
        local_conv_size (int, optional): Kernel size of the local convolution. Default is 3.
        activation (torch.nn, optional): Activation function for MLP. Default is nn.GELU.
        out_dim (int | None, optional): The output dimension of the layer. Default is None.

    Raises:
        ValueError: If `drop_path` is a list of float but its length doesn't match `depth`.
    """
    super().__init__()
    self.dim = dim
    self.input_resolution = input_resolution
    self.depth = depth
    self.use_checkpoint = use_checkpoint

    # Build blocks
    self.blocks = nn.ModuleList(
        [
            TinyViTBlock(
                dim=dim,
                input_resolution=input_resolution,
                num_heads=num_heads,
                window_size=window_size,
                mlp_ratio=mlp_ratio,
                drop=drop,
                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                local_conv_size=local_conv_size,
                activation=activation,
            )
            for i in range(depth)
        ]
    )

    # Patch merging layer
    self.downsample = (
        None
        if downsample is None
        else downsample(input_resolution, dim=dim, out_dim=out_dim, activation=activation)
    )

extra_repr()

返回带有图层参数的 extra_repr 函数的字符串表示。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def extra_repr(self) -> str:
    """Returns a string representation of the extra_repr function with the layer's parameters."""
    return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

forward(x)

对输入tensor 执行前向传播,并返回归一化后的tensor 。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Performs forward propagation on the input tensor and returns a normalized tensor."""
    for blk in self.blocks:
        x = checkpoint.checkpoint(blk, x) if self.use_checkpoint else blk(x)
    return x if self.downsample is None else self.downsample(x)



ultralytics.models.sam.modules.tiny_encoder.LayerNorm2d

垒球 Module

PyTorch 实现二维图层归一化。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
class LayerNorm2d(nn.Module):
    """A PyTorch implementation of Layer Normalization in 2D."""

    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
        """Initialize LayerNorm2d with the number of channels and an optional epsilon."""
        super().__init__()
        self.weight = nn.Parameter(torch.ones(num_channels))
        self.bias = nn.Parameter(torch.zeros(num_channels))
        self.eps = eps

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Perform a forward pass, normalizing the input tensor."""
        u = x.mean(1, keepdim=True)
        s = (x - u).pow(2).mean(1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.eps)
        return self.weight[:, None, None] * x + self.bias[:, None, None]

__init__(num_channels, eps=1e-06)

使用通道数和可选的ε初始化 LayerNorm2d。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
    """Initialize LayerNorm2d with the number of channels and an optional epsilon."""
    super().__init__()
    self.weight = nn.Parameter(torch.ones(num_channels))
    self.bias = nn.Parameter(torch.zeros(num_channels))
    self.eps = eps

forward(x)

执行前向传递,对输入tensor 进行归一化处理。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x: torch.Tensor) -> torch.Tensor:
    """Perform a forward pass, normalizing the input tensor."""
    u = x.mean(1, keepdim=True)
    s = (x - u).pow(2).mean(1, keepdim=True)
    x = (x - u) / torch.sqrt(s + self.eps)
    return self.weight[:, None, None] * x + self.bias[:, None, None]



ultralytics.models.sam.modules.tiny_encoder.TinyViT

垒球 Module

用于视觉任务的 TinyViT 架构。

属性

名称 类型 说明
img_size int

输入图像大小。

in_chans int

输入通道数

num_classes int

分类等级数量。

embed_dims List[int]

各层的嵌入尺寸列表。

depths List[int]

各层深度列表。

num_heads List[int]

每个层的注意头数量列表。

window_sizes List[int]

各层的窗口尺寸列表。

mlp_ratio float

MLP 隐藏维度与嵌入维度之比。

drop_rate float

落层的辍学率。

drop_path_rate float

随机深度的下降路径率

use_checkpoint bool

使用检查点功能,提高内存使用效率。

mbconv_expand_ratio float

MBConv 层的膨胀率。

local_conv_size int

局部卷积核大小

layer_lr_decay float

分层学习率衰减

备注

这种实现方式可以接受深度、注意头、嵌入尺寸和窗口大小的列表、 嵌入尺寸和窗口大小的列表,这样就可以创建一个 "堆叠 "不同配置的 TinyViT 模型。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
class TinyViT(nn.Module):
    """
    The TinyViT architecture for vision tasks.

    Attributes:
        img_size (int): Input image size.
        in_chans (int): Number of input channels.
        num_classes (int): Number of classification classes.
        embed_dims (List[int]): List of embedding dimensions for each layer.
        depths (List[int]): List of depths for each layer.
        num_heads (List[int]): List of number of attention heads for each layer.
        window_sizes (List[int]): List of window sizes for each layer.
        mlp_ratio (float): Ratio of MLP hidden dimension to embedding dimension.
        drop_rate (float): Dropout rate for drop layers.
        drop_path_rate (float): Drop path rate for stochastic depth.
        use_checkpoint (bool): Use checkpointing for efficient memory usage.
        mbconv_expand_ratio (float): Expansion ratio for MBConv layer.
        local_conv_size (int): Local convolution kernel size.
        layer_lr_decay (float): Layer-wise learning rate decay.

    Note:
        This implementation is generalized to accept a list of depths, attention heads,
        embedding dimensions and window sizes, which allows you to create a
        "stack" of TinyViT models of varying configurations.
    """

    def __init__(
        self,
        img_size=224,
        in_chans=3,
        num_classes=1000,
        embed_dims=[96, 192, 384, 768],
        depths=[2, 2, 6, 2],
        num_heads=[3, 6, 12, 24],
        window_sizes=[7, 7, 14, 7],
        mlp_ratio=4.0,
        drop_rate=0.0,
        drop_path_rate=0.1,
        use_checkpoint=False,
        mbconv_expand_ratio=4.0,
        local_conv_size=3,
        layer_lr_decay=1.0,
    ):
        """
        Initializes the TinyViT model.

        Args:
            img_size (int, optional): The input image size. Defaults to 224.
            in_chans (int, optional): Number of input channels. Defaults to 3.
            num_classes (int, optional): Number of classification classes. Defaults to 1000.
            embed_dims (List[int], optional): List of embedding dimensions for each layer. Defaults to [96, 192, 384, 768].
            depths (List[int], optional): List of depths for each layer. Defaults to [2, 2, 6, 2].
            num_heads (List[int], optional): List of number of attention heads for each layer. Defaults to [3, 6, 12, 24].
            window_sizes (List[int], optional): List of window sizes for each layer. Defaults to [7, 7, 14, 7].
            mlp_ratio (float, optional): Ratio of MLP hidden dimension to embedding dimension. Defaults to 4.
            drop_rate (float, optional): Dropout rate. Defaults to 0.
            drop_path_rate (float, optional): Drop path rate for stochastic depth. Defaults to 0.1.
            use_checkpoint (bool, optional): Whether to use checkpointing for efficient memory usage. Defaults to False.
            mbconv_expand_ratio (float, optional): Expansion ratio for MBConv layer. Defaults to 4.0.
            local_conv_size (int, optional): Local convolution kernel size. Defaults to 3.
            layer_lr_decay (float, optional): Layer-wise learning rate decay. Defaults to 1.0.
        """
        super().__init__()
        self.img_size = img_size
        self.num_classes = num_classes
        self.depths = depths
        self.num_layers = len(depths)
        self.mlp_ratio = mlp_ratio

        activation = nn.GELU

        self.patch_embed = PatchEmbed(
            in_chans=in_chans, embed_dim=embed_dims[0], resolution=img_size, activation=activation
        )

        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution

        # Stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        # Build layers
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            kwargs = dict(
                dim=embed_dims[i_layer],
                input_resolution=(
                    patches_resolution[0] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)),
                    patches_resolution[1] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)),
                ),
                #   input_resolution=(patches_resolution[0] // (2 ** i_layer),
                #                     patches_resolution[1] // (2 ** i_layer)),
                depth=depths[i_layer],
                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                use_checkpoint=use_checkpoint,
                out_dim=embed_dims[min(i_layer + 1, len(embed_dims) - 1)],
                activation=activation,
            )
            if i_layer == 0:
                layer = ConvLayer(conv_expand_ratio=mbconv_expand_ratio, **kwargs)
            else:
                layer = BasicLayer(
                    num_heads=num_heads[i_layer],
                    window_size=window_sizes[i_layer],
                    mlp_ratio=self.mlp_ratio,
                    drop=drop_rate,
                    local_conv_size=local_conv_size,
                    **kwargs,
                )
            self.layers.append(layer)

        # Classifier head
        self.norm_head = nn.LayerNorm(embed_dims[-1])
        self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else torch.nn.Identity()

        # Init weights
        self.apply(self._init_weights)
        self.set_layer_lr_decay(layer_lr_decay)
        self.neck = nn.Sequential(
            nn.Conv2d(
                embed_dims[-1],
                256,
                kernel_size=1,
                bias=False,
            ),
            LayerNorm2d(256),
            nn.Conv2d(
                256,
                256,
                kernel_size=3,
                padding=1,
                bias=False,
            ),
            LayerNorm2d(256),
        )

    def set_layer_lr_decay(self, layer_lr_decay):
        """Sets the learning rate decay for each layer in the TinyViT model."""
        decay_rate = layer_lr_decay

        # Layers -> blocks (depth)
        depth = sum(self.depths)
        lr_scales = [decay_rate ** (depth - i - 1) for i in range(depth)]

        def _set_lr_scale(m, scale):
            """Sets the learning rate scale for each layer in the model based on the layer's depth."""
            for p in m.parameters():
                p.lr_scale = scale

        self.patch_embed.apply(lambda x: _set_lr_scale(x, lr_scales[0]))
        i = 0
        for layer in self.layers:
            for block in layer.blocks:
                block.apply(lambda x: _set_lr_scale(x, lr_scales[i]))
                i += 1
            if layer.downsample is not None:
                layer.downsample.apply(lambda x: _set_lr_scale(x, lr_scales[i - 1]))
        assert i == depth
        for m in [self.norm_head, self.head]:
            m.apply(lambda x: _set_lr_scale(x, lr_scales[-1]))

        for k, p in self.named_parameters():
            p.param_name = k

        def _check_lr_scale(m):
            """Checks if the learning rate scale attribute is present in module's parameters."""
            for p in m.parameters():
                assert hasattr(p, "lr_scale"), p.param_name

        self.apply(_check_lr_scale)

    def _init_weights(self, m):
        """Initializes weights for linear layers and layer normalization in the given module."""
        if isinstance(m, nn.Linear):
            # NOTE: This initialization is needed only for training.
            # trunc_normal_(m.weight, std=.02)
            if 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_keywords(self):
        """Returns a dictionary of parameter names where weight decay should not be applied."""
        return {"attention_biases"}

    def forward_features(self, x):
        """Runs the input through the model layers and returns the transformed output."""
        x = self.patch_embed(x)  # x input is (N, C, H, W)

        x = self.layers[0](x)
        start_i = 1

        for i in range(start_i, len(self.layers)):
            layer = self.layers[i]
            x = layer(x)
        B, _, C = x.shape
        x = x.view(B, 64, 64, C)
        x = x.permute(0, 3, 1, 2)
        return self.neck(x)

    def forward(self, x):
        """Executes a forward pass on the input tensor through the constructed model layers."""
        return self.forward_features(x)

__init__(img_size=224, in_chans=3, num_classes=1000, embed_dims=[96, 192, 384, 768], depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_sizes=[7, 7, 14, 7], mlp_ratio=4.0, drop_rate=0.0, drop_path_rate=0.1, use_checkpoint=False, mbconv_expand_ratio=4.0, local_conv_size=3, layer_lr_decay=1.0)

初始化 TinyViT 模型。

参数

名称 类型 说明 默认值
img_size int

输入图像的大小。默认为 224。

224
in_chans int

输入通道数。默认为 3。

3
num_classes int

分类级别数。默认为 1000。

1000
embed_dims List[int]

每个层的嵌入尺寸列表。默认为 [96、192、384、768]。

[96, 192, 384, 768]
depths List[int]

每个图层的深度列表。默认为 [2、2、6、2]。

[2, 2, 6, 2]
num_heads List[int]

每层的注意头数量列表。默认为 [3、6、12、24]。

[3, 6, 12, 24]
window_sizes List[int]

各层的窗口大小列表。默认为 [7、7、14、7]。

[7, 7, 14, 7]
mlp_ratio float

MLP 隐藏维度与嵌入维度之比。默认为 4。

4.0
drop_rate float

辍学率。默认为 0。

0.0
drop_path_rate float

随机深度的下降路径速率。默认为 0.1。

0.1
use_checkpoint bool

是否使用检查点以提高内存使用效率。默认为 "假"。

False
mbconv_expand_ratio float

MBConv 层的扩展比率。默认为 4.0。

4.0
local_conv_size int

本地卷积核大小。默认为 3。

3
layer_lr_decay float

分层学习率衰减。默认为 1.0。

1.0
源代码 ultralytics/models/sam/modules/tiny_encoder.py
def __init__(
    self,
    img_size=224,
    in_chans=3,
    num_classes=1000,
    embed_dims=[96, 192, 384, 768],
    depths=[2, 2, 6, 2],
    num_heads=[3, 6, 12, 24],
    window_sizes=[7, 7, 14, 7],
    mlp_ratio=4.0,
    drop_rate=0.0,
    drop_path_rate=0.1,
    use_checkpoint=False,
    mbconv_expand_ratio=4.0,
    local_conv_size=3,
    layer_lr_decay=1.0,
):
    """
    Initializes the TinyViT model.

    Args:
        img_size (int, optional): The input image size. Defaults to 224.
        in_chans (int, optional): Number of input channels. Defaults to 3.
        num_classes (int, optional): Number of classification classes. Defaults to 1000.
        embed_dims (List[int], optional): List of embedding dimensions for each layer. Defaults to [96, 192, 384, 768].
        depths (List[int], optional): List of depths for each layer. Defaults to [2, 2, 6, 2].
        num_heads (List[int], optional): List of number of attention heads for each layer. Defaults to [3, 6, 12, 24].
        window_sizes (List[int], optional): List of window sizes for each layer. Defaults to [7, 7, 14, 7].
        mlp_ratio (float, optional): Ratio of MLP hidden dimension to embedding dimension. Defaults to 4.
        drop_rate (float, optional): Dropout rate. Defaults to 0.
        drop_path_rate (float, optional): Drop path rate for stochastic depth. Defaults to 0.1.
        use_checkpoint (bool, optional): Whether to use checkpointing for efficient memory usage. Defaults to False.
        mbconv_expand_ratio (float, optional): Expansion ratio for MBConv layer. Defaults to 4.0.
        local_conv_size (int, optional): Local convolution kernel size. Defaults to 3.
        layer_lr_decay (float, optional): Layer-wise learning rate decay. Defaults to 1.0.
    """
    super().__init__()
    self.img_size = img_size
    self.num_classes = num_classes
    self.depths = depths
    self.num_layers = len(depths)
    self.mlp_ratio = mlp_ratio

    activation = nn.GELU

    self.patch_embed = PatchEmbed(
        in_chans=in_chans, embed_dim=embed_dims[0], resolution=img_size, activation=activation
    )

    patches_resolution = self.patch_embed.patches_resolution
    self.patches_resolution = patches_resolution

    # Stochastic depth
    dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

    # Build layers
    self.layers = nn.ModuleList()
    for i_layer in range(self.num_layers):
        kwargs = dict(
            dim=embed_dims[i_layer],
            input_resolution=(
                patches_resolution[0] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)),
                patches_resolution[1] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)),
            ),
            #   input_resolution=(patches_resolution[0] // (2 ** i_layer),
            #                     patches_resolution[1] // (2 ** i_layer)),
            depth=depths[i_layer],
            drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
            downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
            use_checkpoint=use_checkpoint,
            out_dim=embed_dims[min(i_layer + 1, len(embed_dims) - 1)],
            activation=activation,
        )
        if i_layer == 0:
            layer = ConvLayer(conv_expand_ratio=mbconv_expand_ratio, **kwargs)
        else:
            layer = BasicLayer(
                num_heads=num_heads[i_layer],
                window_size=window_sizes[i_layer],
                mlp_ratio=self.mlp_ratio,
                drop=drop_rate,
                local_conv_size=local_conv_size,
                **kwargs,
            )
        self.layers.append(layer)

    # Classifier head
    self.norm_head = nn.LayerNorm(embed_dims[-1])
    self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else torch.nn.Identity()

    # Init weights
    self.apply(self._init_weights)
    self.set_layer_lr_decay(layer_lr_decay)
    self.neck = nn.Sequential(
        nn.Conv2d(
            embed_dims[-1],
            256,
            kernel_size=1,
            bias=False,
        ),
        LayerNorm2d(256),
        nn.Conv2d(
            256,
            256,
            kernel_size=3,
            padding=1,
            bias=False,
        ),
        LayerNorm2d(256),
    )

forward(x)

通过构建的模型层对输入tensor 执行前向传递。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Executes a forward pass on the input tensor through the constructed model layers."""
    return self.forward_features(x)

forward_features(x)

通过模型层运行输入,并返回转换后的输出。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def forward_features(self, x):
    """Runs the input through the model layers and returns the transformed output."""
    x = self.patch_embed(x)  # x input is (N, C, H, W)

    x = self.layers[0](x)
    start_i = 1

    for i in range(start_i, len(self.layers)):
        layer = self.layers[i]
        x = layer(x)
    B, _, C = x.shape
    x = x.view(B, 64, 64, C)
    x = x.permute(0, 3, 1, 2)
    return self.neck(x)

no_weight_decay_keywords()

返回不应用权重衰减的参数名称字典。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
@torch.jit.ignore
def no_weight_decay_keywords(self):
    """Returns a dictionary of parameter names where weight decay should not be applied."""
    return {"attention_biases"}

set_layer_lr_decay(layer_lr_decay)

设置 TinyViT 模型各层的学习率衰减。

源代码 ultralytics/models/sam/modules/tiny_encoder.py
def set_layer_lr_decay(self, layer_lr_decay):
    """Sets the learning rate decay for each layer in the TinyViT model."""
    decay_rate = layer_lr_decay

    # Layers -> blocks (depth)
    depth = sum(self.depths)
    lr_scales = [decay_rate ** (depth - i - 1) for i in range(depth)]

    def _set_lr_scale(m, scale):
        """Sets the learning rate scale for each layer in the model based on the layer's depth."""
        for p in m.parameters():
            p.lr_scale = scale

    self.patch_embed.apply(lambda x: _set_lr_scale(x, lr_scales[0]))
    i = 0
    for layer in self.layers:
        for block in layer.blocks:
            block.apply(lambda x: _set_lr_scale(x, lr_scales[i]))
            i += 1
        if layer.downsample is not None:
            layer.downsample.apply(lambda x: _set_lr_scale(x, lr_scales[i - 1]))
    assert i == depth
    for m in [self.norm_head, self.head]:
        m.apply(lambda x: _set_lr_scale(x, lr_scales[-1]))

    for k, p in self.named_parameters():
        p.param_name = k

    def _check_lr_scale(m):
        """Checks if the learning rate scale attribute is present in module's parameters."""
        for p in m.parameters():
            assert hasattr(p, "lr_scale"), p.param_name

    self.apply(_check_lr_scale)





创建于 2023-11-12,更新于 2023-11-25
作者:glenn-jocher(3),Laughing-q(1)