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Reference for ultralytics/nn/modules/transformer.py

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


ultralytics.nn.modules.transformer.TransformerEncoderLayer

TransformerEncoderLayer(
    c1, cm=2048, num_heads=8, dropout=0.0, act=nn.GELU(), normalize_before=False
)

Bases: Module

Defines a single layer of the transformer encoder.

Attributes:

Name Type Description
ma MultiheadAttention

Multi-head attention module.

fc1 Linear

First linear layer in the feedforward network.

fc2 Linear

Second linear layer in the feedforward network.

norm1 LayerNorm

Layer normalization after attention.

norm2 LayerNorm

Layer normalization after feedforward network.

dropout Dropout

Dropout layer for the feedforward network.

dropout1 Dropout

Dropout layer after attention.

dropout2 Dropout

Dropout layer after feedforward network.

act Module

Activation function.

normalize_before bool

Whether to apply normalization before attention and feedforward.

Parameters:

Name Type Description Default
c1 int

Input dimension.

required
cm int

Hidden dimension in the feedforward network.

2048
num_heads int

Number of attention heads.

8
dropout float

Dropout probability.

0.0
act Module

Activation function.

GELU()
normalize_before bool

Whether to apply normalization before attention and feedforward.

False
Source code in ultralytics/nn/modules/transformer.py
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def __init__(self, c1, cm=2048, num_heads=8, dropout=0.0, act=nn.GELU(), normalize_before=False):
    """
    Initialize the TransformerEncoderLayer with specified parameters.

    Args:
        c1 (int): Input dimension.
        cm (int): Hidden dimension in the feedforward network.
        num_heads (int): Number of attention heads.
        dropout (float): Dropout probability.
        act (nn.Module): Activation function.
        normalize_before (bool): Whether to apply normalization before attention and feedforward.
    """
    super().__init__()
    from ...utils.torch_utils import TORCH_1_9

    if not TORCH_1_9:
        raise ModuleNotFoundError(
            "TransformerEncoderLayer() requires torch>=1.9 to use nn.MultiheadAttention(batch_first=True)."
        )
    self.ma = nn.MultiheadAttention(c1, num_heads, dropout=dropout, batch_first=True)
    # Implementation of Feedforward model
    self.fc1 = nn.Linear(c1, cm)
    self.fc2 = nn.Linear(cm, c1)

    self.norm1 = nn.LayerNorm(c1)
    self.norm2 = nn.LayerNorm(c1)
    self.dropout = nn.Dropout(dropout)
    self.dropout1 = nn.Dropout(dropout)
    self.dropout2 = nn.Dropout(dropout)

    self.act = act
    self.normalize_before = normalize_before

forward

forward(src, src_mask=None, src_key_padding_mask=None, pos=None)

Forward propagates the input through the encoder module.

Parameters:

Name Type Description Default
src Tensor

Input tensor.

required
src_mask Tensor

Mask for the src sequence.

None
src_key_padding_mask Tensor

Mask for the src keys per batch.

None
pos Tensor

Positional encoding.

None

Returns:

Type Description
Tensor

Output tensor after transformer encoder layer.

Source code in ultralytics/nn/modules/transformer.py
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def forward(self, src, src_mask=None, src_key_padding_mask=None, pos=None):
    """
    Forward propagates the input through the encoder module.

    Args:
        src (torch.Tensor): Input tensor.
        src_mask (torch.Tensor, optional): Mask for the src sequence.
        src_key_padding_mask (torch.Tensor, optional): Mask for the src keys per batch.
        pos (torch.Tensor, optional): Positional encoding.

    Returns:
        (torch.Tensor): Output tensor after transformer encoder layer.
    """
    if self.normalize_before:
        return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
    return self.forward_post(src, src_mask, src_key_padding_mask, pos)

forward_post

forward_post(src, src_mask=None, src_key_padding_mask=None, pos=None)

Perform forward pass with post-normalization.

Parameters:

Name Type Description Default
src Tensor

Input tensor.

required
src_mask Tensor

Mask for the src sequence.

None
src_key_padding_mask Tensor

Mask for the src keys per batch.

None
pos Tensor

Positional encoding.

None

Returns:

Type Description
Tensor

Output tensor after attention and feedforward.

Source code in ultralytics/nn/modules/transformer.py
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def forward_post(self, src, src_mask=None, src_key_padding_mask=None, pos=None):
    """
    Perform forward pass with post-normalization.

    Args:
        src (torch.Tensor): Input tensor.
        src_mask (torch.Tensor, optional): Mask for the src sequence.
        src_key_padding_mask (torch.Tensor, optional): Mask for the src keys per batch.
        pos (torch.Tensor, optional): Positional encoding.

    Returns:
        (torch.Tensor): Output tensor after attention and feedforward.
    """
    q = k = self.with_pos_embed(src, pos)
    src2 = self.ma(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
    src = src + self.dropout1(src2)
    src = self.norm1(src)
    src2 = self.fc2(self.dropout(self.act(self.fc1(src))))
    src = src + self.dropout2(src2)
    return self.norm2(src)

forward_pre

forward_pre(src, src_mask=None, src_key_padding_mask=None, pos=None)

Perform forward pass with pre-normalization.

Parameters:

Name Type Description Default
src Tensor

Input tensor.

required
src_mask Tensor

Mask for the src sequence.

None
src_key_padding_mask Tensor

Mask for the src keys per batch.

None
pos Tensor

Positional encoding.

None

Returns:

Type Description
Tensor

Output tensor after attention and feedforward.

Source code in ultralytics/nn/modules/transformer.py
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def forward_pre(self, src, src_mask=None, src_key_padding_mask=None, pos=None):
    """
    Perform forward pass with pre-normalization.

    Args:
        src (torch.Tensor): Input tensor.
        src_mask (torch.Tensor, optional): Mask for the src sequence.
        src_key_padding_mask (torch.Tensor, optional): Mask for the src keys per batch.
        pos (torch.Tensor, optional): Positional encoding.

    Returns:
        (torch.Tensor): Output tensor after attention and feedforward.
    """
    src2 = self.norm1(src)
    q = k = self.with_pos_embed(src2, pos)
    src2 = self.ma(q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
    src = src + self.dropout1(src2)
    src2 = self.norm2(src)
    src2 = self.fc2(self.dropout(self.act(self.fc1(src2))))
    return src + self.dropout2(src2)

with_pos_embed staticmethod

with_pos_embed(tensor, pos=None)

Add position embeddings to the tensor if provided.

Source code in ultralytics/nn/modules/transformer.py
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@staticmethod
def with_pos_embed(tensor, pos=None):
    """Add position embeddings to the tensor if provided."""
    return tensor if pos is None else tensor + pos





ultralytics.nn.modules.transformer.AIFI

AIFI(
    c1, cm=2048, num_heads=8, dropout=0, act=nn.GELU(), normalize_before=False
)

Bases: TransformerEncoderLayer

Defines the AIFI transformer layer.

This class extends TransformerEncoderLayer to work with 2D data by adding positional embeddings.

Parameters:

Name Type Description Default
c1 int

Input dimension.

required
cm int

Hidden dimension in the feedforward network.

2048
num_heads int

Number of attention heads.

8
dropout float

Dropout probability.

0
act Module

Activation function.

GELU()
normalize_before bool

Whether to apply normalization before attention and feedforward.

False
Source code in ultralytics/nn/modules/transformer.py
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def __init__(self, c1, cm=2048, num_heads=8, dropout=0, act=nn.GELU(), normalize_before=False):
    """
    Initialize the AIFI instance with specified parameters.

    Args:
        c1 (int): Input dimension.
        cm (int): Hidden dimension in the feedforward network.
        num_heads (int): Number of attention heads.
        dropout (float): Dropout probability.
        act (nn.Module): Activation function.
        normalize_before (bool): Whether to apply normalization before attention and feedforward.
    """
    super().__init__(c1, cm, num_heads, dropout, act, normalize_before)

build_2d_sincos_position_embedding staticmethod

build_2d_sincos_position_embedding(w, h, embed_dim=256, temperature=10000.0)

Build 2D sine-cosine position embedding.

Parameters:

Name Type Description Default
w int

Width of the feature map.

required
h int

Height of the feature map.

required
embed_dim int

Embedding dimension.

256
temperature float

Temperature for the sine/cosine functions.

10000.0

Returns:

Type Description
Tensor

Position embedding with shape [1, embed_dim, h*w].

Source code in ultralytics/nn/modules/transformer.py
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@staticmethod
def build_2d_sincos_position_embedding(w, h, embed_dim=256, temperature=10000.0):
    """
    Build 2D sine-cosine position embedding.

    Args:
        w (int): Width of the feature map.
        h (int): Height of the feature map.
        embed_dim (int): Embedding dimension.
        temperature (float): Temperature for the sine/cosine functions.

    Returns:
        (torch.Tensor): Position embedding with shape [1, embed_dim, h*w].
    """
    assert embed_dim % 4 == 0, "Embed dimension must be divisible by 4 for 2D sin-cos position embedding"
    grid_w = torch.arange(w, dtype=torch.float32)
    grid_h = torch.arange(h, dtype=torch.float32)
    grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="ij")
    pos_dim = embed_dim // 4
    omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim
    omega = 1.0 / (temperature**omega)

    out_w = grid_w.flatten()[..., None] @ omega[None]
    out_h = grid_h.flatten()[..., None] @ omega[None]

    return torch.cat([torch.sin(out_w), torch.cos(out_w), torch.sin(out_h), torch.cos(out_h)], 1)[None]

forward

forward(x)

Forward pass for the AIFI transformer layer.

Parameters:

Name Type Description Default
x Tensor

Input tensor with shape [B, C, H, W].

required

Returns:

Type Description
Tensor

Output tensor with shape [B, C, H, W].

Source code in ultralytics/nn/modules/transformer.py
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def forward(self, x):
    """
    Forward pass for the AIFI transformer layer.

    Args:
        x (torch.Tensor): Input tensor with shape [B, C, H, W].

    Returns:
        (torch.Tensor): Output tensor with shape [B, C, H, W].
    """
    c, h, w = x.shape[1:]
    pos_embed = self.build_2d_sincos_position_embedding(w, h, c)
    # Flatten [B, C, H, W] to [B, HxW, C]
    x = super().forward(x.flatten(2).permute(0, 2, 1), pos=pos_embed.to(device=x.device, dtype=x.dtype))
    return x.permute(0, 2, 1).view([-1, c, h, w]).contiguous()





ultralytics.nn.modules.transformer.TransformerLayer

TransformerLayer(c, num_heads)

Bases: Module

Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance).

Parameters:

Name Type Description Default
c int

Input and output channel dimension.

required
num_heads int

Number of attention heads.

required
Source code in ultralytics/nn/modules/transformer.py
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def __init__(self, c, num_heads):
    """
    Initialize a self-attention mechanism using linear transformations and multi-head attention.

    Args:
        c (int): Input and output channel dimension.
        num_heads (int): Number of attention heads.
    """
    super().__init__()
    self.q = nn.Linear(c, c, bias=False)
    self.k = nn.Linear(c, c, bias=False)
    self.v = nn.Linear(c, c, bias=False)
    self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
    self.fc1 = nn.Linear(c, c, bias=False)
    self.fc2 = nn.Linear(c, c, bias=False)

forward

forward(x)

Apply a transformer block to the input x and return the output.

Parameters:

Name Type Description Default
x Tensor

Input tensor.

required

Returns:

Type Description
Tensor

Output tensor after transformer layer.

Source code in ultralytics/nn/modules/transformer.py
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def forward(self, x):
    """
    Apply a transformer block to the input x and return the output.

    Args:
        x (torch.Tensor): Input tensor.

    Returns:
        (torch.Tensor): Output tensor after transformer layer.
    """
    x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
    return self.fc2(self.fc1(x)) + x





ultralytics.nn.modules.transformer.TransformerBlock

TransformerBlock(c1, c2, num_heads, num_layers)

Bases: Module

Vision Transformer https://arxiv.org/abs/2010.11929.

Attributes:

Name Type Description
conv Conv

Convolution layer if input and output channels differ.

linear Linear

Learnable position embedding.

tr Sequential

Sequential container of transformer layers.

c2 int

Output channel dimension.

Parameters:

Name Type Description Default
c1 int

Input channel dimension.

required
c2 int

Output channel dimension.

required
num_heads int

Number of attention heads.

required
num_layers int

Number of transformer layers.

required
Source code in ultralytics/nn/modules/transformer.py
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def __init__(self, c1, c2, num_heads, num_layers):
    """
    Initialize a Transformer module with position embedding and specified number of heads and layers.

    Args:
        c1 (int): Input channel dimension.
        c2 (int): Output channel dimension.
        num_heads (int): Number of attention heads.
        num_layers (int): Number of transformer layers.
    """
    super().__init__()
    self.conv = None
    if c1 != c2:
        self.conv = Conv(c1, c2)
    self.linear = nn.Linear(c2, c2)  # learnable position embedding
    self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))
    self.c2 = c2

forward

forward(x)

Forward propagates the input through the bottleneck module.

Parameters:

Name Type Description Default
x Tensor

Input tensor with shape [b, c1, w, h].

required

Returns:

Type Description
Tensor

Output tensor with shape [b, c2, w, h].

Source code in ultralytics/nn/modules/transformer.py
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def forward(self, x):
    """
    Forward propagates the input through the bottleneck module.

    Args:
        x (torch.Tensor): Input tensor with shape [b, c1, w, h].

    Returns:
        (torch.Tensor): Output tensor with shape [b, c2, w, h].
    """
    if self.conv is not None:
        x = self.conv(x)
    b, _, w, h = x.shape
    p = x.flatten(2).permute(2, 0, 1)
    return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)





ultralytics.nn.modules.transformer.MLPBlock

MLPBlock(embedding_dim, mlp_dim, act=nn.GELU)

Bases: Module

Implements a single block of a multi-layer perceptron.

Parameters:

Name Type Description Default
embedding_dim int

Input and output dimension.

required
mlp_dim int

Hidden dimension.

required
act Module

Activation function.

GELU
Source code in ultralytics/nn/modules/transformer.py
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def __init__(self, embedding_dim, mlp_dim, act=nn.GELU):
    """
    Initialize the MLPBlock with specified embedding dimension, MLP dimension, and activation function.

    Args:
        embedding_dim (int): Input and output dimension.
        mlp_dim (int): Hidden dimension.
        act (nn.Module): Activation function.
    """
    super().__init__()
    self.lin1 = nn.Linear(embedding_dim, mlp_dim)
    self.lin2 = nn.Linear(mlp_dim, embedding_dim)
    self.act = act()

forward

forward(x: Tensor) -> torch.Tensor

Forward pass for the MLPBlock.

Parameters:

Name Type Description Default
x Tensor

Input tensor.

required

Returns:

Type Description
Tensor

Output tensor after MLP block.

Source code in ultralytics/nn/modules/transformer.py
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def forward(self, x: torch.Tensor) -> torch.Tensor:
    """
    Forward pass for the MLPBlock.

    Args:
        x (torch.Tensor): Input tensor.

    Returns:
        (torch.Tensor): Output tensor after MLP block.
    """
    return self.lin2(self.act(self.lin1(x)))





ultralytics.nn.modules.transformer.MLP

MLP(input_dim, hidden_dim, output_dim, num_layers, act=nn.ReLU, sigmoid=False)

Bases: Module

Implements a simple multi-layer perceptron (also called FFN).

Attributes:

Name Type Description
num_layers int

Number of layers in the MLP.

layers ModuleList

List of linear layers.

sigmoid bool

Whether to apply sigmoid to the output.

act Module

Activation function.

Parameters:

Name Type Description Default
input_dim int

Input dimension.

required
hidden_dim int

Hidden dimension.

required
output_dim int

Output dimension.

required
num_layers int

Number of layers.

required
act Module

Activation function.

ReLU
sigmoid bool

Whether to apply sigmoid to the output.

False
Source code in ultralytics/nn/modules/transformer.py
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def __init__(self, input_dim, hidden_dim, output_dim, num_layers, act=nn.ReLU, sigmoid=False):
    """
    Initialize the MLP with specified input, hidden, output dimensions and number of layers.

    Args:
        input_dim (int): Input dimension.
        hidden_dim (int): Hidden dimension.
        output_dim (int): Output dimension.
        num_layers (int): Number of layers.
        act (nn.Module): Activation function.
        sigmoid (bool): Whether to apply sigmoid to the output.
    """
    super().__init__()
    self.num_layers = num_layers
    h = [hidden_dim] * (num_layers - 1)
    self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
    self.sigmoid = sigmoid
    self.act = act()

forward

forward(x)

Forward pass for the entire MLP.

Parameters:

Name Type Description Default
x Tensor

Input tensor.

required

Returns:

Type Description
Tensor

Output tensor after MLP.

Source code in ultralytics/nn/modules/transformer.py
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def forward(self, x):
    """
    Forward pass for the entire MLP.

    Args:
        x (torch.Tensor): Input tensor.

    Returns:
        (torch.Tensor): Output tensor after MLP.
    """
    for i, layer in enumerate(self.layers):
        x = getattr(self, "act", nn.ReLU())(layer(x)) if i < self.num_layers - 1 else layer(x)
    return x.sigmoid() if getattr(self, "sigmoid", False) else x





ultralytics.nn.modules.transformer.LayerNorm2d

LayerNorm2d(num_channels, eps=1e-06)

Bases: Module

2D Layer Normalization module inspired by Detectron2 and ConvNeXt implementations.

Original implementations in https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py and https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py.

Attributes:

Name Type Description
weight Parameter

Learnable scale parameter.

bias Parameter

Learnable bias parameter.

eps float

Small constant for numerical stability.

Parameters:

Name Type Description Default
num_channels int

Number of channels in the input.

required
eps float

Small constant for numerical stability.

1e-06
Source code in ultralytics/nn/modules/transformer.py
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def __init__(self, num_channels, eps=1e-6):
    """
    Initialize LayerNorm2d with the given parameters.

    Args:
        num_channels (int): Number of channels in the input.
        eps (float): Small constant for numerical stability.
    """
    super().__init__()
    self.weight = nn.Parameter(torch.ones(num_channels))
    self.bias = nn.Parameter(torch.zeros(num_channels))
    self.eps = eps

forward

forward(x)

Perform forward pass for 2D layer normalization.

Parameters:

Name Type Description Default
x Tensor

Input tensor.

required

Returns:

Type Description
Tensor

Normalized output tensor.

Source code in ultralytics/nn/modules/transformer.py
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def forward(self, x):
    """
    Perform forward pass for 2D layer normalization.

    Args:
        x (torch.Tensor): Input tensor.

    Returns:
        (torch.Tensor): Normalized output tensor.
    """
    u = x.mean(1, keepdim=True)
    s = (x - u).pow(2).mean(1, keepdim=True)
    x = (x - u) / torch.sqrt(s + self.eps)
    return self.weight[:, None, None] * x + self.bias[:, None, None]





ultralytics.nn.modules.transformer.MSDeformAttn

MSDeformAttn(d_model=256, n_levels=4, n_heads=8, n_points=4)

Bases: Module

Multiscale Deformable Attention Module based on Deformable-DETR and PaddleDetection implementations.

https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py

Attributes:

Name Type Description
im2col_step int

Step size for im2col operations.

d_model int

Model dimension.

n_levels int

Number of feature levels.

n_heads int

Number of attention heads.

n_points int

Number of sampling points per attention head per feature level.

sampling_offsets Linear

Linear layer for generating sampling offsets.

attention_weights Linear

Linear layer for generating attention weights.

value_proj Linear

Linear layer for projecting values.

output_proj Linear

Linear layer for projecting output.

Parameters:

Name Type Description Default
d_model int

Model dimension.

256
n_levels int

Number of feature levels.

4
n_heads int

Number of attention heads.

8
n_points int

Number of sampling points per attention head per feature level.

4
Source code in ultralytics/nn/modules/transformer.py
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def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
    """
    Initialize MSDeformAttn with the given parameters.

    Args:
        d_model (int): Model dimension.
        n_levels (int): Number of feature levels.
        n_heads (int): Number of attention heads.
        n_points (int): Number of sampling points per attention head per feature level.
    """
    super().__init__()
    if d_model % n_heads != 0:
        raise ValueError(f"d_model must be divisible by n_heads, but got {d_model} and {n_heads}")
    _d_per_head = d_model // n_heads
    # Better to set _d_per_head to a power of 2 which is more efficient in a CUDA implementation
    assert _d_per_head * n_heads == d_model, "`d_model` must be divisible by `n_heads`"

    self.im2col_step = 64

    self.d_model = d_model
    self.n_levels = n_levels
    self.n_heads = n_heads
    self.n_points = n_points

    self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)
    self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)
    self.value_proj = nn.Linear(d_model, d_model)
    self.output_proj = nn.Linear(d_model, d_model)

    self._reset_parameters()

forward

forward(query, refer_bbox, value, value_shapes, value_mask=None)

Perform forward pass for multiscale deformable attention.

https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/transformers/deformable_transformer.py

Parameters:

Name Type Description Default
query Tensor

Tensor with shape [bs, query_length, C].

required
refer_bbox Tensor

Tensor with shape [bs, query_length, n_levels, 2], range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area.

required
value Tensor

Tensor with shape [bs, value_length, C].

required
value_shapes list

List with shape [n_levels, 2], [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})].

required
value_mask Tensor

Tensor with shape [bs, value_length], True for non-padding elements, False for padding elements.

None

Returns:

Type Description
Tensor

Output tensor with shape [bs, Length_{query}, C].

Source code in ultralytics/nn/modules/transformer.py
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def forward(self, query, refer_bbox, value, value_shapes, value_mask=None):
    """
    Perform forward pass for multiscale deformable attention.

    https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/transformers/deformable_transformer.py

    Args:
        query (torch.Tensor): Tensor with shape [bs, query_length, C].
        refer_bbox (torch.Tensor): Tensor with shape [bs, query_length, n_levels, 2], range in [0, 1],
            top-left (0,0), bottom-right (1, 1), including padding area.
        value (torch.Tensor): Tensor with shape [bs, value_length, C].
        value_shapes (list): List with shape [n_levels, 2], [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})].
        value_mask (torch.Tensor, optional): Tensor with shape [bs, value_length], True for non-padding elements,
            False for padding elements.

    Returns:
        (torch.Tensor): Output tensor with shape [bs, Length_{query}, C].
    """
    bs, len_q = query.shape[:2]
    len_v = value.shape[1]
    assert sum(s[0] * s[1] for s in value_shapes) == len_v

    value = self.value_proj(value)
    if value_mask is not None:
        value = value.masked_fill(value_mask[..., None], float(0))
    value = value.view(bs, len_v, self.n_heads, self.d_model // self.n_heads)
    sampling_offsets = self.sampling_offsets(query).view(bs, len_q, self.n_heads, self.n_levels, self.n_points, 2)
    attention_weights = self.attention_weights(query).view(bs, len_q, self.n_heads, self.n_levels * self.n_points)
    attention_weights = F.softmax(attention_weights, -1).view(bs, len_q, self.n_heads, self.n_levels, self.n_points)
    # N, Len_q, n_heads, n_levels, n_points, 2
    num_points = refer_bbox.shape[-1]
    if num_points == 2:
        offset_normalizer = torch.as_tensor(value_shapes, dtype=query.dtype, device=query.device).flip(-1)
        add = sampling_offsets / offset_normalizer[None, None, None, :, None, :]
        sampling_locations = refer_bbox[:, :, None, :, None, :] + add
    elif num_points == 4:
        add = sampling_offsets / self.n_points * refer_bbox[:, :, None, :, None, 2:] * 0.5
        sampling_locations = refer_bbox[:, :, None, :, None, :2] + add
    else:
        raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {num_points}.")
    output = multi_scale_deformable_attn_pytorch(value, value_shapes, sampling_locations, attention_weights)
    return self.output_proj(output)





ultralytics.nn.modules.transformer.DeformableTransformerDecoderLayer

DeformableTransformerDecoderLayer(
    d_model=256,
    n_heads=8,
    d_ffn=1024,
    dropout=0.0,
    act=nn.ReLU(),
    n_levels=4,
    n_points=4,
)

Bases: Module

Deformable Transformer Decoder Layer inspired by PaddleDetection and Deformable-DETR implementations.

https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/transformers/deformable_transformer.py https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/deformable_transformer.py

Attributes:

Name Type Description
self_attn MultiheadAttention

Self-attention module.

dropout1 Dropout

Dropout after self-attention.

norm1 LayerNorm

Layer normalization after self-attention.

cross_attn MSDeformAttn

Cross-attention module.

dropout2 Dropout

Dropout after cross-attention.

norm2 LayerNorm

Layer normalization after cross-attention.

linear1 Linear

First linear layer in the feedforward network.

act Module

Activation function.

dropout3 Dropout

Dropout in the feedforward network.

linear2 Linear

Second linear layer in the feedforward network.

dropout4 Dropout

Dropout after the feedforward network.

norm3 LayerNorm

Layer normalization after the feedforward network.

Parameters:

Name Type Description Default
d_model int

Model dimension.

256
n_heads int

Number of attention heads.

8
d_ffn int

Dimension of the feedforward network.

1024
dropout float

Dropout probability.

0.0
act Module

Activation function.

ReLU()
n_levels int

Number of feature levels.

4
n_points int

Number of sampling points.

4
Source code in ultralytics/nn/modules/transformer.py
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def __init__(self, d_model=256, n_heads=8, d_ffn=1024, dropout=0.0, act=nn.ReLU(), n_levels=4, n_points=4):
    """
    Initialize the DeformableTransformerDecoderLayer with the given parameters.

    Args:
        d_model (int): Model dimension.
        n_heads (int): Number of attention heads.
        d_ffn (int): Dimension of the feedforward network.
        dropout (float): Dropout probability.
        act (nn.Module): Activation function.
        n_levels (int): Number of feature levels.
        n_points (int): Number of sampling points.
    """
    super().__init__()

    # Self attention
    self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
    self.dropout1 = nn.Dropout(dropout)
    self.norm1 = nn.LayerNorm(d_model)

    # Cross attention
    self.cross_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
    self.dropout2 = nn.Dropout(dropout)
    self.norm2 = nn.LayerNorm(d_model)

    # FFN
    self.linear1 = nn.Linear(d_model, d_ffn)
    self.act = act
    self.dropout3 = nn.Dropout(dropout)
    self.linear2 = nn.Linear(d_ffn, d_model)
    self.dropout4 = nn.Dropout(dropout)
    self.norm3 = nn.LayerNorm(d_model)

forward

forward(
    embed,
    refer_bbox,
    feats,
    shapes,
    padding_mask=None,
    attn_mask=None,
    query_pos=None,
)

Perform the forward pass through the entire decoder layer.

Parameters:

Name Type Description Default
embed Tensor

Input embeddings.

required
refer_bbox Tensor

Reference bounding boxes.

required
feats Tensor

Feature maps.

required
shapes list

Feature shapes.

required
padding_mask Tensor

Padding mask.

None
attn_mask Tensor

Attention mask.

None
query_pos Tensor

Query position embeddings.

None

Returns:

Type Description
Tensor

Output tensor after decoder layer.

Source code in ultralytics/nn/modules/transformer.py
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def forward(self, embed, refer_bbox, feats, shapes, padding_mask=None, attn_mask=None, query_pos=None):
    """
    Perform the forward pass through the entire decoder layer.

    Args:
        embed (torch.Tensor): Input embeddings.
        refer_bbox (torch.Tensor): Reference bounding boxes.
        feats (torch.Tensor): Feature maps.
        shapes (list): Feature shapes.
        padding_mask (torch.Tensor, optional): Padding mask.
        attn_mask (torch.Tensor, optional): Attention mask.
        query_pos (torch.Tensor, optional): Query position embeddings.

    Returns:
        (torch.Tensor): Output tensor after decoder layer.
    """
    # Self attention
    q = k = self.with_pos_embed(embed, query_pos)
    tgt = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), embed.transpose(0, 1), attn_mask=attn_mask)[
        0
    ].transpose(0, 1)
    embed = embed + self.dropout1(tgt)
    embed = self.norm1(embed)

    # Cross attention
    tgt = self.cross_attn(
        self.with_pos_embed(embed, query_pos), refer_bbox.unsqueeze(2), feats, shapes, padding_mask
    )
    embed = embed + self.dropout2(tgt)
    embed = self.norm2(embed)

    # FFN
    return self.forward_ffn(embed)

forward_ffn

forward_ffn(tgt)

Perform forward pass through the Feed-Forward Network part of the layer.

Parameters:

Name Type Description Default
tgt Tensor

Input tensor.

required

Returns:

Type Description
Tensor

Output tensor after FFN.

Source code in ultralytics/nn/modules/transformer.py
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def forward_ffn(self, tgt):
    """
    Perform forward pass through the Feed-Forward Network part of the layer.

    Args:
        tgt (torch.Tensor): Input tensor.

    Returns:
        (torch.Tensor): Output tensor after FFN.
    """
    tgt2 = self.linear2(self.dropout3(self.act(self.linear1(tgt))))
    tgt = tgt + self.dropout4(tgt2)
    return self.norm3(tgt)

with_pos_embed staticmethod

with_pos_embed(tensor, pos)

Add positional embeddings to the input tensor, if provided.

Source code in ultralytics/nn/modules/transformer.py
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@staticmethod
def with_pos_embed(tensor, pos):
    """Add positional embeddings to the input tensor, if provided."""
    return tensor if pos is None else tensor + pos





ultralytics.nn.modules.transformer.DeformableTransformerDecoder

DeformableTransformerDecoder(
    hidden_dim, decoder_layer, num_layers, eval_idx=-1
)

Bases: Module

Implementation of Deformable Transformer Decoder based on PaddleDetection.

https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/transformers/deformable_transformer.py

Attributes:

Name Type Description
layers ModuleList

List of decoder layers.

num_layers int

Number of decoder layers.

hidden_dim int

Hidden dimension.

eval_idx int

Index of the layer to use during evaluation.

Parameters:

Name Type Description Default
hidden_dim int

Hidden dimension.

required
decoder_layer Module

Decoder layer module.

required
num_layers int

Number of decoder layers.

required
eval_idx int

Index of the layer to use during evaluation.

-1
Source code in ultralytics/nn/modules/transformer.py
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def __init__(self, hidden_dim, decoder_layer, num_layers, eval_idx=-1):
    """
    Initialize the DeformableTransformerDecoder with the given parameters.

    Args:
        hidden_dim (int): Hidden dimension.
        decoder_layer (nn.Module): Decoder layer module.
        num_layers (int): Number of decoder layers.
        eval_idx (int): Index of the layer to use during evaluation.
    """
    super().__init__()
    self.layers = _get_clones(decoder_layer, num_layers)
    self.num_layers = num_layers
    self.hidden_dim = hidden_dim
    self.eval_idx = eval_idx if eval_idx >= 0 else num_layers + eval_idx

forward

forward(
    embed,
    refer_bbox,
    feats,
    shapes,
    bbox_head,
    score_head,
    pos_mlp,
    attn_mask=None,
    padding_mask=None,
)

Perform the forward pass through the entire decoder.

Parameters:

Name Type Description Default
embed Tensor

Decoder embeddings.

required
refer_bbox Tensor

Reference bounding boxes.

required
feats Tensor

Image features.

required
shapes list

Feature shapes.

required
bbox_head Module

Bounding box prediction head.

required
score_head Module

Score prediction head.

required
pos_mlp Module

Position MLP.

required
attn_mask Tensor

Attention mask.

None
padding_mask Tensor

Padding mask.

None

Returns:

Name Type Description
dec_bboxes Tensor

Decoded bounding boxes.

dec_cls Tensor

Decoded classification scores.

Source code in ultralytics/nn/modules/transformer.py
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def forward(
    self,
    embed,  # decoder embeddings
    refer_bbox,  # anchor
    feats,  # image features
    shapes,  # feature shapes
    bbox_head,
    score_head,
    pos_mlp,
    attn_mask=None,
    padding_mask=None,
):
    """
    Perform the forward pass through the entire decoder.

    Args:
        embed (torch.Tensor): Decoder embeddings.
        refer_bbox (torch.Tensor): Reference bounding boxes.
        feats (torch.Tensor): Image features.
        shapes (list): Feature shapes.
        bbox_head (nn.Module): Bounding box prediction head.
        score_head (nn.Module): Score prediction head.
        pos_mlp (nn.Module): Position MLP.
        attn_mask (torch.Tensor, optional): Attention mask.
        padding_mask (torch.Tensor, optional): Padding mask.

    Returns:
        dec_bboxes (torch.Tensor): Decoded bounding boxes.
        dec_cls (torch.Tensor): Decoded classification scores.
    """
    output = embed
    dec_bboxes = []
    dec_cls = []
    last_refined_bbox = None
    refer_bbox = refer_bbox.sigmoid()
    for i, layer in enumerate(self.layers):
        output = layer(output, refer_bbox, feats, shapes, padding_mask, attn_mask, pos_mlp(refer_bbox))

        bbox = bbox_head[i](output)
        refined_bbox = torch.sigmoid(bbox + inverse_sigmoid(refer_bbox))

        if self.training:
            dec_cls.append(score_head[i](output))
            if i == 0:
                dec_bboxes.append(refined_bbox)
            else:
                dec_bboxes.append(torch.sigmoid(bbox + inverse_sigmoid(last_refined_bbox)))
        elif i == self.eval_idx:
            dec_cls.append(score_head[i](output))
            dec_bboxes.append(refined_bbox)
            break

        last_refined_bbox = refined_bbox
        refer_bbox = refined_bbox.detach() if self.training else refined_bbox

    return torch.stack(dec_bboxes), torch.stack(dec_cls)





📅 Created 1 year ago ✏️ Updated 7 months ago