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

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This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/tiny_encoder.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


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:

Name Type Description
c Conv2d

2D convolution layer.

bn BatchNorm2d

Batch normalization layer.

Methods:

Name Description

Parameters:

Name Type Description Default
a int

Number of input channels.

required
b int

Number of output channels.

required
ks int

Kernel size for the convolution. Defaults to 1.

1
stride int

Stride for the convolution. Defaults to 1.

1
pad int

Padding for the convolution. Defaults to 0.

0
dilation int

Dilation factor for the convolution. Defaults to 1.

1
groups int

Number of groups for the convolution. Defaults to 1.

1
bn_weight_init float

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
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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:

Name Type Description
patches_resolution Tuple[int, int]

Resolution of the patches after embedding.

num_patches int

Total number of patches.

in_chans int

Number of input channels.

embed_dim int

Dimension of the embedding.

seq Sequential

Sequence of convolutional and activation layers for patch embedding.

Methods:

Name Description
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
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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
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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:

Name Type Description
in_chans int

Number of input channels.

hidden_chans int

Number of hidden channels.

out_chans int

Number of output channels.

conv1 Conv2d_BN

First convolutional layer.

act1 Module

First activation function.

conv2 Conv2d_BN

Depthwise convolutional layer.

act2 Module

Second activation function.

conv3 Conv2d_BN

Final convolutional layer.

act3 Module

Third activation function.

drop_path Module

Drop path layer (Identity for inference).

Methods:

Name Description
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
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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
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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:

Name Type Description
input_resolution Tuple[int, int]

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

dim int

The input dimension of the feature map.

out_dim int

The output dimension after merging and projection.

act Module

The activation function used between convolutions.

conv1 Conv2d_BN

The first convolutional layer for dimension projection.

conv2 Conv2d_BN

The second convolutional layer for spatial merging.

conv3 Conv2d_BN

The third convolutional layer for final projection.

Methods:

Name Description
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
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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
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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:

Name Type Description
dim int

Dimensionality of the input and output.

input_resolution Tuple[int, int]

Resolution of the input image.

depth int

Number of MBConv layers in the block.

use_checkpoint bool

Whether to use gradient checkpointing to save memory.

blocks ModuleList

List of MBConv layers.

downsample Optional[Callable]

Function for downsampling the output.

Methods:

Name Description
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:

Name Type Description Default
dim int

The dimensionality of the input and output.

required
input_resolution Tuple[int, int]

The resolution of the input image.

required
depth int

The number of MBConv layers in the block.

required
activation Module

Activation function applied after each convolution.

required
drop_path float | List[float]

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

0.0
downsample Optional[Module]

Function for downsampling the output. None to skip downsampling.

None
use_checkpoint bool

Whether to use gradient checkpointing to save memory.

False
out_dim Optional[int]

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

None
conv_expand_ratio float

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
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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 (nn.Module): 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[nn.Module]): 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
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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:

Name Type Description
norm LayerNorm

Layer normalization applied to the input.

fc1 Linear

First fully-connected layer.

fc2 Linear

Second fully-connected layer.

act Module

Activation function applied after the first fully-connected layer.

drop Dropout

Dropout layer applied after the activation function.

Methods:

Name Description
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
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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
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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:

Name Type Description
num_heads int

Number of attention heads.

scale float

Scaling factor for attention scores.

key_dim int

Dimensionality of the keys and queries.

nh_kd int

Product of num_heads and key_dim.

d int

Dimensionality of the value vectors.

dh int

Product of d and num_heads.

attn_ratio float

Attention ratio affecting the dimensions of the value vectors.

norm LayerNorm

Layer normalization applied to input.

qkv Linear

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

proj Linear

Linear layer for final projection.

attention_biases Parameter

Learnable attention biases.

attention_bias_idxs Tensor

Indices for attention biases.

ab Tensor

Cached attention biases for inference, deleted during training.

Methods:

Name Description
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:

Name Type Description Default
dim int

The dimensionality of the input and output.

required
key_dim int

The dimensionality of the keys and queries.

required
num_heads int

Number of attention heads.

8
attn_ratio float

Attention ratio, affecting the dimensions of the value vectors.

4
resolution Tuple[int, int]

Spatial resolution of the input feature map.

(14, 14)

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
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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.
        attn_ratio (float): Attention ratio, affecting the dimensions of the value vectors.
        resolution (Tuple[int, int]): Spatial resolution of the input feature map.

    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
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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
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@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:

Name Type Description
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

Size of the attention window.

mlp_ratio float

Ratio of MLP hidden dimension to embedding dimension.

drop_path Module

Stochastic depth layer, identity function during inference.

attn Attention

Self-attention module.

mlp Mlp

Multi-layer perceptron module.

local_conv Conv2d_BN

Depth-wise local convolution layer.

Methods:

Name Description
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:

Name Type Description Default
dim int

Dimensionality of the input and output features.

required
input_resolution Tuple[int, int]

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

required
num_heads int

Number of attention heads.

required
window_size int

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

7
mlp_ratio float

Ratio of MLP hidden dimension to embedding dimension.

4.0
drop float

Dropout rate.

0.0
drop_path float

Stochastic depth rate.

0.0
local_conv_size int

Kernel size of the local convolution.

3
activation Module

Activation function for MLP.

GELU

Raises:

Type Description
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
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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:

Type Description
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
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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
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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:

Name Type Description
dim int

The dimensionality of the input and output features.

input_resolution Tuple[int, int]

Spatial resolution of the input feature map.

depth int

Number of TinyViT blocks in this layer.

use_checkpoint bool

Whether to use gradient checkpointing to save memory.

blocks ModuleList

List of TinyViT blocks that make up this layer.

downsample Module | None

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

Methods:

Name Description
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:

Name Type Description Default
dim int

Dimensionality of the input and output features.

required
input_resolution Tuple[int, int]

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

required
depth int

Number of TinyViT blocks in this layer.

required
num_heads int

Number of attention heads in each TinyViT block.

required
window_size int

Size of the local window for attention computation.

required
mlp_ratio float

Ratio of MLP hidden dimension to embedding dimension.

4.0
drop float

Dropout rate.

0.0
drop_path float | List[float]

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

0.0
downsample Module | None

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

None
use_checkpoint bool

Whether to use gradient checkpointing to save memory.

False
local_conv_size int

Kernel size for the local convolution in each TinyViT block.

3
activation Module

Activation function used in the MLP.

GELU
out_dim int | None

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

None

Raises:

Type Description
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
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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
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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
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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:

Name Type Description
img_size int

Input image size.

num_classes int

Number of classification classes.

depths List[int]

Number of blocks in each stage.

num_layers int

Total number of layers in the network.

mlp_ratio float

Ratio of MLP hidden dimension to embedding dimension.

patch_embed PatchEmbed

Module for patch embedding.

patches_resolution Tuple[int, int]

Resolution of embedded patches.

layers ModuleList

List of network layers.

norm_head LayerNorm

Layer normalization for the classifier head.

head Linear

Linear layer for final classification.

neck Sequential

Neck module for feature refinement.

Methods:

Name Description
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:

Name Type Description Default
img_size int

Size of the input image.

224
in_chans int

Number of input channels.

3
num_classes int

Number of classes for classification.

1000
embed_dims Tuple[int, int, int, int]

Embedding dimensions for each stage.

(96, 192, 384, 768)
depths Tuple[int, int, int, int]

Number of blocks in each stage.

(2, 2, 6, 2)
num_heads Tuple[int, int, int, int]

Number of attention heads in each stage.

(3, 6, 12, 24)
window_sizes Tuple[int, int, int, int]

Window sizes for each stage.

(7, 7, 14, 7)
mlp_ratio float

Ratio of MLP hidden dim to embedding dim.

4.0
drop_rate float

Dropout rate.

0.0
drop_path_rate float

Stochastic depth rate.

0.1
use_checkpoint bool

Whether to use checkpointing to save memory.

False
mbconv_expand_ratio float

Expansion ratio for MBConv layer.

4.0
local_conv_size int

Kernel size for local convolutions.

3
layer_lr_decay float

Layer-wise learning rate decay factor.

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
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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.
        in_chans (int): Number of input channels.
        num_classes (int): Number of classes for classification.
        embed_dims (Tuple[int, int, int, int]): Embedding dimensions for each stage.
        depths (Tuple[int, int, int, int]): Number of blocks in each stage.
        num_heads (Tuple[int, int, int, int]): Number of attention heads in each stage.
        window_sizes (Tuple[int, int, int, int]): Window sizes for each stage.
        mlp_ratio (float): Ratio of MLP hidden dim to embedding dim.
        drop_rate (float): Dropout rate.
        drop_path_rate (float): Stochastic depth rate.
        use_checkpoint (bool): Whether to use checkpointing to save memory.
        mbconv_expand_ratio (float): Expansion ratio for MBConv layer.
        local_conv_size (int): Kernel size for local convolutions.
        layer_lr_decay (float): Layer-wise learning rate decay factor.

    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
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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
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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
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@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.

Source code in ultralytics/models/sam/modules/tiny_encoder.py
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def set_imgsz(self, imgsz=[1024, 1024]):
    """Set image size to make model compatible with different image sizes."""
    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
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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 1 year ago ✏️ Updated 7 months ago