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Reference for ultralytics/models/sam/modules/tiny_encoder.py

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ultralytics.models.sam.modules.tiny_encoder.Conv2d_BN

Conv2d_BN(a, b, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1)

Bases: Sequential

A sequential container that performs 2D convolution followed by batch normalization.

Attributes:

NameTypeDescription
cConv2d

2D convolution layer.

1BatchNorm2d

Batch normalization layer.

Methods:

NameDescription

Parameters:

NameTypeDescriptionDefault
aint

Number of input channels.

required
bint

Number of output channels.

required
ksint

Kernel size for the convolution. Defaults to 1.

1
strideint

Stride for the convolution. Defaults to 1.

1
padint

Padding for the convolution. Defaults to 0.

0
dilationint

Dilation factor for the convolution. Defaults to 1.

1
groupsint

Number of groups for the convolution. Defaults to 1.

1
bn_weight_initfloat

Initial value for batch normalization weight. Defaults to 1.

1

Examples:

>>> conv_bn = Conv2d_BN(3, 64, ks=3, stride=1, pad=1)
>>> input_tensor = torch.randn(1, 3, 224, 224)
>>> output = conv_bn(input_tensor)
>>> print(output.shape)
Source code in 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 a sequential container with 2D convolution followed by batch normalization."""
    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

PatchEmbed(in_chans, embed_dim, resolution, activation)

Bases: Module

Embeds images into patches and projects them into a specified embedding dimension.

Attributes:

NameTypeDescription
patches_resolutionTuple[int, int]

Resolution of the patches after embedding.

num_patchesint

Total number of patches.

in_chansint

Number of input channels.

embed_dimint

Dimension of the embedding.

seqSequential

Sequence of convolutional and activation layers for patch embedding.

Methods:

NameDescription
forward

Processes the input tensor through the patch embedding sequence.

Examples:

>>> import torch
>>> patch_embed = PatchEmbed(in_chans=3, embed_dim=96, resolution=224, activation=nn.GELU)
>>> x = torch.randn(1, 3, 224, 224)
>>> output = patch_embed(x)
>>> print(output.shape)
Source code in ultralytics/models/sam/modules/tiny_encoder.py
def __init__(self, in_chans, embed_dim, resolution, activation):
    """Initializes patch embedding with convolutional layers for image-to-patch conversion and projection."""
    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

forward(x)

Processes input tensor through patch embedding sequence, converting images to patch embeddings.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Processes input tensor through patch embedding sequence, converting images to patch embeddings."""
    return self.seq(x)





ultralytics.models.sam.modules.tiny_encoder.MBConv

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

Bases: Module

Mobile Inverted Bottleneck Conv (MBConv) layer, part of the EfficientNet architecture.

Attributes:

NameTypeDescription
in_chansint

Number of input channels.

hidden_chansint

Number of hidden channels.

out_chansint

Number of output channels.

conv1Conv2d_BN

First convolutional layer.

act1Module

First activation function.

conv2Conv2d_BN

Depthwise convolutional layer.

act2Module

Second activation function.

conv3Conv2d_BN

Final convolutional layer.

act3Module

Third activation function.

drop_pathModule

Drop path layer (Identity for inference).

Methods:

NameDescription
forward

Performs the forward pass through the MBConv layer.

Examples:

>>> in_chans, out_chans = 32, 64
>>> mbconv = MBConv(in_chans, out_chans, expand_ratio=4, activation=nn.ReLU, drop_path=0.1)
>>> x = torch.randn(1, in_chans, 56, 56)
>>> output = mbconv(x)
>>> print(output.shape)
torch.Size([1, 64, 56, 56])
Source code in ultralytics/models/sam/modules/tiny_encoder.py
def __init__(self, in_chans, out_chans, expand_ratio, activation, drop_path):
    """Initializes the MBConv layer with specified input/output channels, expansion ratio, and activation."""
    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

forward(x)

Implements the forward pass of MBConv, applying convolutions and skip connection.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Implements the forward pass of MBConv, applying convolutions and skip connection."""
    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

PatchMerging(input_resolution, dim, out_dim, activation)

Bases: Module

Merges neighboring patches in the feature map and projects to a new dimension.

This class implements a patch merging operation that combines spatial information and adjusts the feature dimension. It uses a series of convolutional layers with batch normalization to achieve this.

Attributes:

NameTypeDescription
input_resolutionTuple[int, int]

The input resolution (height, width) of the feature map.

dimint

The input dimension of the feature map.

out_dimint

The output dimension after merging and projection.

actModule

The activation function used between convolutions.

conv1Conv2d_BN

The first convolutional layer for dimension projection.

conv2Conv2d_BN

The second convolutional layer for spatial merging.

conv3Conv2d_BN

The third convolutional layer for final projection.

Methods:

NameDescription
forward

Applies the patch merging operation to the input tensor.

Examples:

>>> input_resolution = (56, 56)
>>> patch_merging = PatchMerging(input_resolution, dim=64, out_dim=128, activation=nn.ReLU)
>>> x = torch.randn(4, 64, 56, 56)
>>> output = patch_merging(x)
>>> print(output.shape)
Source code in ultralytics/models/sam/modules/tiny_encoder.py
def __init__(self, input_resolution, dim, out_dim, activation):
    """Initializes the PatchMerging module for merging and projecting neighboring patches in feature maps."""
    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

forward(x)

Applies patch merging and dimension projection to the input feature map.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Applies patch merging and dimension projection to the input feature map."""
    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

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

Bases: Module

Convolutional Layer featuring multiple MobileNetV3-style inverted bottleneck convolutions (MBConv).

This layer optionally applies downsample operations to the output and supports gradient checkpointing.

Attributes:

NameTypeDescription
dimint

Dimensionality of the input and output.

input_resolutionTuple[int, int]

Resolution of the input image.

depthint

Number of MBConv layers in the block.

use_checkpointbool

Whether to use gradient checkpointing to save memory.

blocksModuleList

List of MBConv layers.

downsampleOptional[Callable]

Function for downsampling the output.

Methods:

NameDescription
forward

Processes the input through the convolutional layers.

Examples:

>>> input_tensor = torch.randn(1, 64, 56, 56)
>>> conv_layer = ConvLayer(64, (56, 56), depth=3, activation=nn.ReLU)
>>> output = conv_layer(input_tensor)
>>> print(output.shape)

This layer consists of multiple MobileNetV3-style inverted bottleneck convolutions (MBConv) and optionally applies downsampling to the output.

Parameters:

NameTypeDescriptionDefault
dimint

The dimensionality of the input and output.

required
input_resolutionTuple[int, int]

The resolution of the input image.

required
depthint

The number of MBConv layers in the block.

required
activationCallable

Activation function applied after each convolution.

required
drop_pathfloat | List[float]

Drop path rate. Single float or a list of floats for each MBConv.

0.0
downsampleOptional[Callable]

Function for downsampling the output. None to skip downsampling.

None
use_checkpointbool

Whether to use gradient checkpointing to save memory.

False
out_dimOptional[int]

The dimensionality of the output. None means it will be the same as dim.

None
conv_expand_ratiofloat

Expansion ratio for the MBConv layers.

4.0

Examples:

>>> input_tensor = torch.randn(1, 64, 56, 56)
>>> conv_layer = ConvLayer(64, (56, 56), depth=3, activation=nn.ReLU)
>>> output = conv_layer(input_tensor)
>>> print(output.shape)
Source code in 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.

    This layer consists of multiple MobileNetV3-style inverted bottleneck convolutions (MBConv) and
    optionally applies downsampling to the output.

    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 (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.

    Examples:
        >>> input_tensor = torch.randn(1, 64, 56, 56)
        >>> conv_layer = ConvLayer(64, (56, 56), depth=3, activation=nn.ReLU)
        >>> output = conv_layer(input_tensor)
        >>> print(output.shape)
    """
    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

forward(x)

Processes input through convolutional layers, applying MBConv blocks and optional downsampling.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Processes input through convolutional layers, applying MBConv blocks and optional downsampling."""
    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

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

Bases: Module

Multi-layer Perceptron (MLP) module for transformer architectures.

This module applies layer normalization, two fully-connected layers with an activation function in between, and dropout. It is commonly used in transformer-based architectures.

Attributes:

NameTypeDescription
normLayerNorm

Layer normalization applied to the input.

fc1Linear

First fully-connected layer.

fc2Linear

Second fully-connected layer.

actModule

Activation function applied after the first fully-connected layer.

dropDropout

Dropout layer applied after the activation function.

Methods:

NameDescription
forward

Applies the MLP operations on the input tensor.

Examples:

>>> import torch
>>> from torch import nn
>>> mlp = Mlp(in_features=256, hidden_features=512, out_features=256, act_layer=nn.GELU, drop=0.1)
>>> x = torch.randn(32, 100, 256)
>>> output = mlp(x)
>>> print(output.shape)
torch.Size([32, 100, 256])
Source code in 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 a multi-layer perceptron with configurable input, hidden, and output dimensions."""
    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

forward(x)

Applies MLP operations: layer norm, FC layers, activation, and dropout to the input tensor.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Applies MLP operations: layer norm, FC layers, activation, and dropout to the input tensor."""
    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

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

Bases: Module

Multi-head attention module with spatial awareness and trainable attention biases.

This module implements a multi-head attention mechanism with support for spatial awareness, applying attention biases based on spatial resolution. It includes trainable attention biases for each unique offset between spatial positions in the resolution grid.

Attributes:

NameTypeDescription
num_headsint

Number of attention heads.

scalefloat

Scaling factor for attention scores.

key_dimint

Dimensionality of the keys and queries.

nh_kdint

Product of num_heads and key_dim.

dint

Dimensionality of the value vectors.

dhint

Product of d and num_heads.

attn_ratiofloat

Attention ratio affecting the dimensions of the value vectors.

normLayerNorm

Layer normalization applied to input.

qkvLinear

Linear layer for computing query, key, and value projections.

projLinear

Linear layer for final projection.

attention_biasesParameter

Learnable attention biases.

attention_bias_idxsTensor

Indices for attention biases.

abTensor

Cached attention biases for inference, deleted during training.

Methods:

NameDescription
train

Sets the module in training mode and handles the 'ab' attribute.

forward

Performs the forward pass of the attention mechanism.

Examples:

>>> attn = Attention(dim=256, key_dim=64, num_heads=8, resolution=(14, 14))
>>> x = torch.randn(1, 196, 256)
>>> output = attn(x)
>>> print(output.shape)
torch.Size([1, 196, 256])

This module implements a multi-head attention mechanism with support for spatial awareness, applying attention biases based on spatial resolution. It includes trainable attention biases for each unique offset between spatial positions in the resolution grid.

Parameters:

NameTypeDescriptionDefault
dimint

The dimensionality of the input and output.

required
key_dimint

The dimensionality of the keys and queries.

required
num_headsint

Number of attention heads. Default is 8.

8
attn_ratiofloat

Attention ratio, affecting the dimensions of the value vectors. Default is 4.

4
resolutionTuple[int, int]

Spatial resolution of the input feature map. Default is (14, 14).

(14, 14)

Raises:

TypeDescription
AssertionError

If 'resolution' is not a tuple of length 2.

Examples:

>>> attn = Attention(dim=256, key_dim=64, num_heads=8, resolution=(14, 14))
>>> x = torch.randn(1, 196, 256)
>>> output = attn(x)
>>> print(output.shape)
torch.Size([1, 196, 256])
Source code in 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 for multi-head attention with spatial awareness.

    This module implements a multi-head attention mechanism with support for spatial awareness, applying
    attention biases based on spatial resolution. It includes trainable attention biases for each unique
    offset between spatial positions in the resolution grid.

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

    Raises:
        AssertionError: If 'resolution' is not a tuple of length 2.

    Examples:
        >>> attn = Attention(dim=256, key_dim=64, num_heads=8, resolution=(14, 14))
        >>> x = torch.randn(1, 196, 256)
        >>> output = attn(x)
        >>> print(output.shape)
        torch.Size([1, 196, 256])
    """
    super().__init__()

    assert isinstance(resolution, tuple) and len(resolution) == 2, "'resolution' argument not tuple of length 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

forward(x)

Applies multi-head attention with spatial awareness and trainable attention biases.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):  # x
    """Applies multi-head attention with spatial awareness and trainable attention biases."""
    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

train(mode=True)

Performs multi-head attention with spatial awareness and trainable attention biases.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
@torch.no_grad()
def train(self, mode=True):
    """Performs multi-head attention with spatial awareness and trainable attention biases."""
    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

TinyViTBlock(
    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,
)

Bases: Module

TinyViT Block that applies self-attention and a local convolution to the input.

This block is a key component of the TinyViT architecture, combining self-attention mechanisms with local convolutions to process input features efficiently.

Attributes:

NameTypeDescription
dimint

The dimensionality of the input and output.

input_resolutionTuple[int, int]

Spatial resolution of the input feature map.

num_headsint

Number of attention heads.

window_sizeint

Size of the attention window.

mlp_ratiofloat

Ratio of MLP hidden dimension to embedding dimension.

drop_pathModule

Stochastic depth layer, identity function during inference.

attnAttention

Self-attention module.

mlpMlp

Multi-layer perceptron module.

local_convConv2d_BN

Depth-wise local convolution layer.

Methods:

NameDescription
forward

Processes the input through the TinyViT block.

extra_repr

Returns a string with extra information about the block's parameters.

Examples:

>>> input_tensor = torch.randn(1, 196, 192)
>>> block = TinyViTBlock(dim=192, input_resolution=(14, 14), num_heads=3)
>>> output = block(input_tensor)
>>> print(output.shape)
torch.Size([1, 196, 192])

This block is a key component of the TinyViT architecture, combining self-attention mechanisms with local convolutions to process input features efficiently.

Parameters:

NameTypeDescriptionDefault
dimint

Dimensionality of the input and output features.

required
input_resolutionTuple[int, int]

Spatial resolution of the input feature map (height, width).

required
num_headsint

Number of attention heads.

required
window_sizeint

Size of the attention window. Must be greater than 0.

7
mlp_ratiofloat

Ratio of MLP hidden dimension to embedding dimension.

4.0
dropfloat

Dropout rate.

0.0
drop_pathfloat

Stochastic depth rate.

0.0
local_conv_sizeint

Kernel size of the local convolution.

3
activationModule

Activation function for MLP.

GELU

Raises:

TypeDescription
AssertionError

If window_size is not greater than 0.

AssertionError

If dim is not divisible by num_heads.

Examples:

>>> block = TinyViTBlock(dim=192, input_resolution=(14, 14), num_heads=3)
>>> input_tensor = torch.randn(1, 196, 192)
>>> output = block(input_tensor)
>>> print(output.shape)
torch.Size([1, 196, 192])
Source code in 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 a TinyViT block with self-attention and local convolution.

    This block is a key component of the TinyViT architecture, combining self-attention mechanisms with
    local convolutions to process input features efficiently.

    Args:
        dim (int): Dimensionality of the input and output features.
        input_resolution (Tuple[int, int]): Spatial resolution of the input feature map (height, width).
        num_heads (int): Number of attention heads.
        window_size (int): Size of the attention window. Must be greater than 0.
        mlp_ratio (float): Ratio of MLP hidden dimension to embedding dimension.
        drop (float): Dropout rate.
        drop_path (float): Stochastic depth rate.
        local_conv_size (int): Kernel size of the local convolution.
        activation (torch.nn.Module): Activation function for MLP.

    Raises:
        AssertionError: If window_size is not greater than 0.
        AssertionError: If dim is not divisible by num_heads.

    Examples:
        >>> block = TinyViTBlock(dim=192, input_resolution=(14, 14), num_heads=3)
        >>> input_tensor = torch.randn(1, 196, 192)
        >>> output = block(input_tensor)
        >>> print(output.shape)
        torch.Size([1, 196, 192])
    """
    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

extra_repr() -> str

Returns a string representation of the TinyViTBlock's parameters.

This method provides a formatted string containing key information about the TinyViTBlock, including its dimension, input resolution, number of attention heads, window size, and MLP ratio.

Returns:

TypeDescription
str

A formatted string containing the block's parameters.

Examples:

>>> block = TinyViTBlock(dim=192, input_resolution=(14, 14), num_heads=3, window_size=7, mlp_ratio=4.0)
>>> print(block.extra_repr())
dim=192, input_resolution=(14, 14), num_heads=3, window_size=7, mlp_ratio=4.0
Source code in ultralytics/models/sam/modules/tiny_encoder.py
def extra_repr(self) -> str:
    """
    Returns a string representation of the TinyViTBlock's parameters.

    This method provides a formatted string containing key information about the TinyViTBlock, including its
    dimension, input resolution, number of attention heads, window size, and MLP ratio.

    Returns:
        (str): A formatted string containing the block's parameters.

    Examples:
        >>> block = TinyViTBlock(dim=192, input_resolution=(14, 14), num_heads=3, window_size=7, mlp_ratio=4.0)
        >>> print(block.extra_repr())
        dim=192, input_resolution=(14, 14), num_heads=3, window_size=7, mlp_ratio=4.0
    """
    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

forward(x)

Applies self-attention, local convolution, and MLP operations to the input tensor.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Applies self-attention, local convolution, and MLP operations to the input tensor."""
    h, w = self.input_resolution
    b, hw, c = x.shape  # batch, height*width, channels
    assert hw == 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, hw, 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, hw).transpose(1, 2)

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





ultralytics.models.sam.modules.tiny_encoder.BasicLayer

BasicLayer(
    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,
)

Bases: Module

A basic TinyViT layer for one stage in a TinyViT architecture.

This class represents a single layer in the TinyViT model, consisting of multiple TinyViT blocks and an optional downsampling operation.

Attributes:

NameTypeDescription
dimint

The dimensionality of the input and output features.

input_resolutionTuple[int, int]

Spatial resolution of the input feature map.

depthint

Number of TinyViT blocks in this layer.

use_checkpointbool

Whether to use gradient checkpointing to save memory.

blocksModuleList

List of TinyViT blocks that make up this layer.

downsampleModule | None

Downsample layer at the end of the layer, if specified.

Methods:

NameDescription
forward

Processes the input through the layer's blocks and optional downsampling.

extra_repr

Returns a string with the layer's parameters for printing.

Examples:

>>> input_tensor = torch.randn(1, 3136, 192)
>>> layer = BasicLayer(dim=192, input_resolution=(56, 56), depth=2, num_heads=3, window_size=7)
>>> output = layer(input_tensor)
>>> print(output.shape)
torch.Size([1, 784, 384])

This layer consists of multiple TinyViT blocks and an optional downsampling operation. It is designed to process feature maps at a specific resolution and dimensionality within the TinyViT model.

Parameters:

NameTypeDescriptionDefault
dimint

Dimensionality of the input and output features.

required
input_resolutionTuple[int, int]

Spatial resolution of the input feature map (height, width).

required
depthint

Number of TinyViT blocks in this layer.

required
num_headsint

Number of attention heads in each TinyViT block.

required
window_sizeint

Size of the local window for attention computation.

required
mlp_ratiofloat

Ratio of MLP hidden dimension to embedding dimension.

4.0
dropfloat

Dropout rate.

0.0
drop_pathfloat | List[float]

Stochastic depth rate. Can be a float or a list of floats for each block.

0.0
downsampleModule | None

Downsampling layer at the end of the layer. None to skip downsampling.

None
use_checkpointbool

Whether to use gradient checkpointing to save memory.

False
local_conv_sizeint

Kernel size for the local convolution in each TinyViT block.

3
activationModule

Activation function used in the MLP.

GELU
out_dimint | None

Output dimension after downsampling. None means it will be the same as dim.

None

Raises:

TypeDescription
ValueError

If drop_path is a list and its length doesn't match depth.

Examples:

>>> layer = BasicLayer(dim=96, input_resolution=(56, 56), depth=2, num_heads=3, window_size=7)
>>> x = torch.randn(1, 56 * 56, 96)
>>> output = layer(x)
>>> print(output.shape)
Source code in 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 a BasicLayer in the TinyViT architecture.

    This layer consists of multiple TinyViT blocks and an optional downsampling operation. It is designed to
    process feature maps at a specific resolution and dimensionality within the TinyViT model.

    Args:
        dim (int): Dimensionality of the input and output features.
        input_resolution (Tuple[int, int]): Spatial resolution of the input feature map (height, width).
        depth (int): Number of TinyViT blocks in this layer.
        num_heads (int): Number of attention heads in each TinyViT block.
        window_size (int): Size of the local window for attention computation.
        mlp_ratio (float): Ratio of MLP hidden dimension to embedding dimension.
        drop (float): Dropout rate.
        drop_path (float | List[float]): Stochastic depth rate. Can be a float or a list of floats for each block.
        downsample (nn.Module | None): Downsampling layer at the end of the layer. None to skip downsampling.
        use_checkpoint (bool): Whether to use gradient checkpointing to save memory.
        local_conv_size (int): Kernel size for the local convolution in each TinyViT block.
        activation (nn.Module): Activation function used in the MLP.
        out_dim (int | None): Output dimension after downsampling. None means it will be the same as `dim`.

    Raises:
        ValueError: If `drop_path` is a list and its length doesn't match `depth`.

    Examples:
        >>> layer = BasicLayer(dim=96, input_resolution=(56, 56), depth=2, num_heads=3, window_size=7)
        >>> x = torch.randn(1, 56 * 56, 96)
        >>> output = layer(x)
        >>> print(output.shape)
    """
    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

extra_repr() -> str

Returns a string with the layer's parameters for printing.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
def extra_repr(self) -> str:
    """Returns a string with the layer's parameters for printing."""
    return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

forward

forward(x)

Processes input through TinyViT blocks and optional downsampling.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Processes input through TinyViT blocks and optional downsampling."""
    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.TinyViT

TinyViT(
    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,
)

Bases: Module

TinyViT: A compact vision transformer architecture for efficient image classification and feature extraction.

This class implements the TinyViT model, which combines elements of vision transformers and convolutional neural networks for improved efficiency and performance on vision tasks.

Attributes:

NameTypeDescription
img_sizeint

Input image size.

num_classesint

Number of classification classes.

depthsList[int]

Number of blocks in each stage.

num_layersint

Total number of layers in the network.

mlp_ratiofloat

Ratio of MLP hidden dimension to embedding dimension.

patch_embedPatchEmbed

Module for patch embedding.

patches_resolutionTuple[int, int]

Resolution of embedded patches.

layersModuleList

List of network layers.

norm_headLayerNorm

Layer normalization for the classifier head.

headLinear

Linear layer for final classification.

neckSequential

Neck module for feature refinement.

Methods:

NameDescription
set_layer_lr_decay

Sets layer-wise learning rate decay.

_init_weights

Initializes weights for linear and normalization layers.

no_weight_decay_keywords

Returns keywords for parameters that should not use weight decay.

forward_features

Processes input through the feature extraction layers.

forward

Performs a forward pass through the entire network.

Examples:

>>> model = TinyViT(img_size=224, num_classes=1000)
>>> x = torch.randn(1, 3, 224, 224)
>>> features = model.forward_features(x)
>>> print(features.shape)
torch.Size([1, 256, 64, 64])

This constructor sets up the TinyViT architecture, including patch embedding, multiple layers of attention and convolution blocks, and a classification head.

Parameters:

NameTypeDescriptionDefault
img_sizeint

Size of the input image. Default is 224.

224
in_chansint

Number of input channels. Default is 3.

3
num_classesint

Number of classes for classification. Default is 1000.

1000
embed_dimsTuple[int, int, int, int]

Embedding dimensions for each stage. Default is (96, 192, 384, 768).

(96, 192, 384, 768)
depthsTuple[int, int, int, int]

Number of blocks in each stage. Default is (2, 2, 6, 2).

(2, 2, 6, 2)
num_headsTuple[int, int, int, int]

Number of attention heads in each stage. Default is (3, 6, 12, 24).

(3, 6, 12, 24)
window_sizesTuple[int, int, int, int]

Window sizes for each stage. Default is (7, 7, 14, 7).

(7, 7, 14, 7)
mlp_ratiofloat

Ratio of MLP hidden dim to embedding dim. Default is 4.0.

4.0
drop_ratefloat

Dropout rate. Default is 0.0.

0.0
drop_path_ratefloat

Stochastic depth rate. Default is 0.1.

0.1
use_checkpointbool

Whether to use checkpointing to save memory. Default is False.

False
mbconv_expand_ratiofloat

Expansion ratio for MBConv layer. Default is 4.0.

4.0
local_conv_sizeint

Kernel size for local convolutions. Default is 3.

3
layer_lr_decayfloat

Layer-wise learning rate decay factor. Default is 1.0.

1.0

Examples:

>>> model = TinyViT(img_size=224, num_classes=1000)
>>> x = torch.randn(1, 3, 224, 224)
>>> output = model(x)
>>> print(output.shape)
torch.Size([1, 1000])
Source code in 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.

    This constructor sets up the TinyViT architecture, including patch embedding, multiple layers of
    attention and convolution blocks, and a classification head.

    Args:
        img_size (int): Size of the input image. Default is 224.
        in_chans (int): Number of input channels. Default is 3.
        num_classes (int): Number of classes for classification. Default is 1000.
        embed_dims (Tuple[int, int, int, int]): Embedding dimensions for each stage.
            Default is (96, 192, 384, 768).
        depths (Tuple[int, int, int, int]): Number of blocks in each stage. Default is (2, 2, 6, 2).
        num_heads (Tuple[int, int, int, int]): Number of attention heads in each stage.
            Default is (3, 6, 12, 24).
        window_sizes (Tuple[int, int, int, int]): Window sizes for each stage. Default is (7, 7, 14, 7).
        mlp_ratio (float): Ratio of MLP hidden dim to embedding dim. Default is 4.0.
        drop_rate (float): Dropout rate. Default is 0.0.
        drop_path_rate (float): Stochastic depth rate. Default is 0.1.
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default is False.
        mbconv_expand_ratio (float): Expansion ratio for MBConv layer. Default is 4.0.
        local_conv_size (int): Kernel size for local convolutions. Default is 3.
        layer_lr_decay (float): Layer-wise learning rate decay factor. Default is 1.0.

    Examples:
        >>> model = TinyViT(img_size=224, num_classes=1000)
        >>> x = torch.randn(1, 3, 224, 224)
        >>> output = model(x)
        >>> print(output.shape)
        torch.Size([1, 1000])
    """
    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

forward(x)

Performs the forward pass through the TinyViT model, extracting features from the input image.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
def forward(self, x):
    """Performs the forward pass through the TinyViT model, extracting features from the input image."""
    return self.forward_features(x)

forward_features

forward_features(x)

Processes input through feature extraction layers, returning spatial features.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
def forward_features(self, x):
    """Processes input through feature extraction layers, returning spatial features."""
    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)
    batch, _, channel = x.shape
    x = x.view(batch, self.patches_resolution[0] // 4, self.patches_resolution[1] // 4, channel)
    x = x.permute(0, 3, 1, 2)
    return self.neck(x)

no_weight_decay_keywords

no_weight_decay_keywords()

Returns a set of keywords for parameters that should not use weight decay.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
@torch.jit.ignore
def no_weight_decay_keywords(self):
    """Returns a set of keywords for parameters that should not use weight decay."""
    return {"attention_biases"}

set_imgsz

set_imgsz(imgsz=[1024, 1024])

Set image size to make model compatible with different image sizes.

Parameters:

NameTypeDescriptionDefault
imgszTuple[int, int]

The size of the input image.

[1024, 1024]
Source code in ultralytics/models/sam/modules/tiny_encoder.py
def set_imgsz(self, imgsz=[1024, 1024]):
    """
    Set image size to make model compatible with different image sizes.

    Args:
        imgsz (Tuple[int, int]): The size of the input image.
    """
    imgsz = [s // 4 for s in imgsz]
    self.patches_resolution = imgsz
    for i, layer in enumerate(self.layers):
        input_resolution = (
            imgsz[0] // (2 ** (i - 1 if i == 3 else i)),
            imgsz[1] // (2 ** (i - 1 if i == 3 else i)),
        )
        layer.input_resolution = input_resolution
        if layer.downsample is not None:
            layer.downsample.input_resolution = input_resolution
        if isinstance(layer, BasicLayer):
            for b in layer.blocks:
                b.input_resolution = input_resolution

set_layer_lr_decay

set_layer_lr_decay(layer_lr_decay)

Sets layer-wise learning rate decay for the TinyViT model based on depth.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
def set_layer_lr_decay(self, layer_lr_decay):
    """Sets layer-wise learning rate decay for the TinyViT model based on depth."""
    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)



📅 Created 11 months ago ✏️ Updated 1 month ago