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ultralytics 8.2.73 Meta SAM2 Refactor (#14867)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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ultralytics/models/sam/modules/tiny_encoder.py
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@ -17,16 +17,40 @@ import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from ultralytics.nn.modules import LayerNorm2d
from ultralytics.utils.instance import to_2tuple
class Conv2d_BN(torch.nn.Sequential):
"""A sequential container that performs 2D convolution followed by batch normalization."""
"""
A sequential container that performs 2D convolution followed by batch normalization.
Attributes:
c (torch.nn.Conv2d): 2D convolution layer.
1 (torch.nn.BatchNorm2d): Batch normalization layer.
Methods:
__init__: Initializes the Conv2d_BN with specified parameters.
Args:
a (int): Number of input channels.
b (int): Number of output channels.
ks (int): Kernel size for the convolution. Defaults to 1.
stride (int): Stride for the convolution. Defaults to 1.
pad (int): Padding for the convolution. Defaults to 0.
dilation (int): Dilation factor for the convolution. Defaults to 1.
groups (int): Number of groups for the convolution. Defaults to 1.
bn_weight_init (float): Initial value for batch normalization weight. Defaults to 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)
"""
def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1):
"""Initializes the MBConv model with given input channels, output channels, expansion ratio, activation, and
drop path.
"""
"""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)
@ -36,12 +60,29 @@ class Conv2d_BN(torch.nn.Sequential):
class PatchEmbed(nn.Module):
"""Embeds images into patches and projects them into a specified embedding dimension."""
"""
Embeds images into patches and projects them into a specified embedding dimension.
Attributes:
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 (nn.Sequential): Sequence of convolutional and activation layers for patch embedding.
Methods:
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)
"""
def __init__(self, in_chans, embed_dim, resolution, activation):
"""Initialize the PatchMerging class with specified input, output dimensions, resolution and activation
function.
"""
"""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)
@ -56,17 +97,40 @@ class PatchEmbed(nn.Module):
)
def forward(self, x):
"""Runs input tensor 'x' through the PatchMerging model's sequence of operations."""
"""Processes input tensor through patch embedding sequence, converting images to patch embeddings."""
return self.seq(x)
class MBConv(nn.Module):
"""Mobile Inverted Bottleneck Conv (MBConv) layer, part of the EfficientNet architecture."""
"""
Mobile Inverted Bottleneck Conv (MBConv) layer, part of the EfficientNet architecture.
Attributes:
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 (nn.Module): First activation function.
conv2 (Conv2d_BN): Depthwise convolutional layer.
act2 (nn.Module): Second activation function.
conv3 (Conv2d_BN): Final convolutional layer.
act3 (nn.Module): Third activation function.
drop_path (nn.Module): Drop path layer (Identity for inference).
Methods:
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])
"""
def __init__(self, in_chans, out_chans, expand_ratio, activation, drop_path):
"""Initializes a convolutional layer with specified dimensions, input resolution, depth, and activation
function.
"""
"""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)
@ -86,7 +150,7 @@ class MBConv(nn.Module):
self.drop_path = nn.Identity()
def forward(self, x):
"""Implements the forward pass for the model architecture."""
"""Implements the forward pass of MBConv, applying convolutions and skip connection."""
shortcut = x
x = self.conv1(x)
x = self.act1(x)
@ -99,12 +163,34 @@ class MBConv(nn.Module):
class PatchMerging(nn.Module):
"""Merges neighboring patches in the feature map and projects to a new dimension."""
"""
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:
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 (nn.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:
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)
"""
def __init__(self, input_resolution, dim, out_dim, activation):
"""Initializes the ConvLayer with specific dimension, input resolution, depth, activation, drop path, and other
optional parameters.
"""
"""Initializes the PatchMerging module for merging and projecting neighboring patches in feature maps."""
super().__init__()
self.input_resolution = input_resolution
@ -117,7 +203,7 @@ class PatchMerging(nn.Module):
self.conv3 = Conv2d_BN(out_dim, out_dim, 1, 1, 0)
def forward(self, x):
"""Applies forward pass on the input utilizing convolution and activation layers, and returns the result."""
"""Applies patch merging and dimension projection to the input feature map."""
if x.ndim == 3:
H, W = self.input_resolution
B = len(x)
@ -137,7 +223,24 @@ class ConvLayer(nn.Module):
"""
Convolutional Layer featuring multiple MobileNetV3-style inverted bottleneck convolutions (MBConv).
Optionally applies downsample operations to the output, and provides support for gradient checkpointing.
This layer optionally applies downsample operations to the output and supports gradient checkpointing.
Attributes:
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 (nn.ModuleList): List of MBConv layers.
downsample (Optional[Callable]): Function for downsampling the output.
Methods:
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)
"""
def __init__(
@ -155,16 +258,25 @@ class ConvLayer(nn.Module):
"""
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 (Union[float, List[float]]): Drop path rate. Single float or a list of floats for each MBConv.
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
@ -194,7 +306,7 @@ class ConvLayer(nn.Module):
)
def forward(self, x):
"""Processes the input through a series of convolutional layers and returns the activated output."""
"""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)
@ -202,13 +314,33 @@ class ConvLayer(nn.Module):
class Mlp(nn.Module):
"""
Multi-layer Perceptron (MLP) for transformer architectures.
Multi-layer Perceptron (MLP) module for transformer architectures.
This layer takes an input with in_features, applies layer normalization and two fully-connected layers.
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:
norm (nn.LayerNorm): Layer normalization applied to the input.
fc1 (nn.Linear): First fully-connected layer.
fc2 (nn.Linear): Second fully-connected layer.
act (nn.Module): Activation function applied after the first fully-connected layer.
drop (nn.Dropout): Dropout layer applied after the activation function.
Methods:
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])
"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0):
"""Initializes Attention module with the given parameters including dimension, key_dim, number of heads, etc."""
"""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
@ -219,7 +351,7 @@ class Mlp(nn.Module):
self.drop = nn.Dropout(drop)
def forward(self, x):
"""Applies operations on input x and returns modified x, runs downsample if not None."""
"""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)
@ -230,12 +362,37 @@ class Mlp(nn.Module):
class Attention(torch.nn.Module):
"""
Multi-head attention module with support for spatial awareness, applying attention biases based on spatial
resolution. Implements trainable attention biases for each unique offset between spatial positions in the resolution
grid.
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:
ab (Tensor, optional): Cached attention biases for inference, deleted during training.
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 (nn.LayerNorm): Layer normalization applied to input.
qkv (nn.Linear): Linear layer for computing query, key, and value projections.
proj (nn.Linear): Linear layer for final projection.
attention_biases (nn.Parameter): Learnable attention biases.
attention_bias_idxs (Tensor): Indices for attention biases.
ab (Tensor): Cached attention biases for inference, deleted during training.
Methods:
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])
"""
def __init__(
@ -247,17 +404,28 @@ class Attention(torch.nn.Module):
resolution=(14, 14),
):
"""
Initializes the Attention module.
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, optional): Number of attention heads. Default is 8.
attn_ratio (float, optional): Attention ratio, affecting the dimensions of the value vectors. Default is 4.
resolution (Tuple[int, int], optional): Spatial resolution of the input feature map. Default is (14, 14).
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.
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__()
@ -290,7 +458,7 @@ class Attention(torch.nn.Module):
@torch.no_grad()
def train(self, mode=True):
"""Sets the module in training mode and handles attribute 'ab' based on the mode."""
"""Performs multi-head attention with spatial awareness and trainable attention biases."""
super().train(mode)
if mode and hasattr(self, "ab"):
del self.ab
@ -298,7 +466,7 @@ class Attention(torch.nn.Module):
self.ab = self.attention_biases[:, self.attention_bias_idxs]
def forward(self, x): # x
"""Performs forward pass over the input tensor 'x' by applying normalization and querying keys/values."""
"""Applies multi-head attention with spatial awareness and trainable attention biases."""
B, N, _ = x.shape # B, N, C
# Normalization
@ -322,7 +490,34 @@ class Attention(torch.nn.Module):
class TinyViTBlock(nn.Module):
"""TinyViT Block that applies self-attention and a local convolution to the input."""
"""
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:
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 (nn.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:
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])
"""
def __init__(
self,
@ -337,22 +532,32 @@ class TinyViTBlock(nn.Module):
activation=nn.GELU,
):
"""
Initializes the TinyViTBlock.
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): The dimensionality of the input and output.
input_resolution (Tuple[int, int]): Spatial resolution of the input feature map.
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, optional): Window size for attention. Default is 7.
mlp_ratio (float, optional): Ratio of mlp hidden dim to embedding dim. Default is 4.
drop (float, optional): Dropout rate. Default is 0.
drop_path (float, optional): Stochastic depth rate. Default is 0.
local_conv_size (int, optional): The kernel size of the local convolution. Default is 3.
activation (torch.nn, optional): Activation function for MLP. Default is nn.GELU.
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`.
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
@ -380,9 +585,7 @@ class TinyViTBlock(nn.Module):
self.local_conv = Conv2d_BN(dim, dim, ks=local_conv_size, stride=1, pad=pad, groups=dim)
def forward(self, x):
"""Applies attention-based transformation or padding to input 'x' before passing it through a local
convolution.
"""
"""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"
@ -424,8 +627,19 @@ class TinyViTBlock(nn.Module):
return x + self.drop_path(self.mlp(x))
def extra_repr(self) -> str:
"""Returns a formatted string representing the TinyViTBlock's parameters: dimension, input resolution, number of
attentions heads, window size, and MLP ratio.
"""
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}, "
@ -434,7 +648,31 @@ class TinyViTBlock(nn.Module):
class BasicLayer(nn.Module):
"""A basic TinyViT layer for one stage in a TinyViT architecture."""
"""
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:
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 (nn.ModuleList): List of TinyViT blocks that make up this layer.
downsample (nn.Module | None): Downsample layer at the end of the layer, if specified.
Methods:
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])
"""
def __init__(
self,
@ -453,25 +691,34 @@ class BasicLayer(nn.Module):
out_dim=None,
):
"""
Initializes the BasicLayer.
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): The dimensionality of the input and output.
input_resolution (Tuple[int, int]): Spatial resolution of the input feature map.
depth (int): Number of TinyViT blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
mlp_ratio (float, optional): Ratio of mlp hidden dim to embedding dim. Default is 4.
drop (float, optional): Dropout rate. Default is 0.
drop_path (float | tuple[float], optional): Stochastic depth rate. Default is 0.
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default is None.
use_checkpoint (bool, optional): Whether to use checkpointing to save memory. Default is False.
local_conv_size (int, optional): Kernel size of the local convolution. Default is 3.
activation (torch.nn, optional): Activation function for MLP. Default is nn.GELU.
out_dim (int | None, optional): The output dimension of the layer. Default is None.
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 of float but its length doesn't match `depth`.
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
@ -505,58 +752,49 @@ class BasicLayer(nn.Module):
)
def forward(self, x):
"""Performs forward propagation on the input tensor and returns a normalized tensor."""
"""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)
def extra_repr(self) -> str:
"""Returns a string representation of the extra_repr function with the layer's parameters."""
"""Returns a string with the layer's parameters for printing."""
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
class LayerNorm2d(nn.Module):
"""A PyTorch implementation of Layer Normalization in 2D."""
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
"""Initialize LayerNorm2d with the number of channels and an optional epsilon."""
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Perform a forward pass, normalizing the input tensor."""
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
return self.weight[:, None, None] * x + self.bias[:, None, None]
class TinyViT(nn.Module):
"""
The TinyViT architecture for vision tasks.
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:
img_size (int): Input image size.
in_chans (int): Number of input channels.
num_classes (int): Number of classification classes.
embed_dims (List[int]): List of embedding dimensions for each layer.
depths (List[int]): List of depths for each layer.
num_heads (List[int]): List of number of attention heads for each layer.
window_sizes (List[int]): List of window sizes for each layer.
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.
drop_rate (float): Dropout rate for drop layers.
drop_path_rate (float): Drop path rate for stochastic depth.
use_checkpoint (bool): Use checkpointing for efficient memory usage.
mbconv_expand_ratio (float): Expansion ratio for MBConv layer.
local_conv_size (int): Local convolution kernel size.
layer_lr_decay (float): Layer-wise learning rate decay.
patch_embed (PatchEmbed): Module for patch embedding.
patches_resolution (Tuple[int, int]): Resolution of embedded patches.
layers (nn.ModuleList): List of network layers.
norm_head (nn.LayerNorm): Layer normalization for the classifier head.
head (nn.Linear): Linear layer for final classification.
neck (nn.Sequential): Neck module for feature refinement.
Note:
This implementation is generalized to accept a list of depths, attention heads,
embedding dimensions and window sizes, which allows you to create a
"stack" of TinyViT models of varying configurations.
Methods:
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])
"""
def __init__(
@ -579,21 +817,33 @@ class TinyViT(nn.Module):
"""
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, optional): The input image size. Defaults to 224.
in_chans (int, optional): Number of input channels. Defaults to 3.
num_classes (int, optional): Number of classification classes. Defaults to 1000.
embed_dims (List[int], optional): List of embedding dimensions per layer. Defaults to [96, 192, 384, 768].
depths (List[int], optional): List of depths for each layer. Defaults to [2, 2, 6, 2].
num_heads (List[int], optional): List of number of attention heads per layer. Defaults to [3, 6, 12, 24].
window_sizes (List[int], optional): List of window sizes for each layer. Defaults to [7, 7, 14, 7].
mlp_ratio (float, optional): Ratio of MLP hidden dimension to embedding dimension. Defaults to 4.
drop_rate (float, optional): Dropout rate. Defaults to 0.
drop_path_rate (float, optional): Drop path rate for stochastic depth. Defaults to 0.1.
use_checkpoint (bool, optional): Whether to use checkpointing for efficient memory usage. Defaults to False.
mbconv_expand_ratio (float, optional): Expansion ratio for MBConv layer. Defaults to 4.0.
local_conv_size (int, optional): Local convolution kernel size. Defaults to 3.
layer_lr_decay (float, optional): Layer-wise learning rate decay. Defaults to 1.0.
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
@ -671,7 +921,7 @@ class TinyViT(nn.Module):
)
def set_layer_lr_decay(self, layer_lr_decay):
"""Sets the learning rate decay for each layer in the TinyViT model."""
"""Sets layer-wise learning rate decay for the TinyViT model based on depth."""
decay_rate = layer_lr_decay
# Layers -> blocks (depth)
@ -706,7 +956,7 @@ class TinyViT(nn.Module):
self.apply(_check_lr_scale)
def _init_weights(self, m):
"""Initializes weights for linear layers and layer normalization in the given module."""
"""Initializes weights for linear and normalization layers in the TinyViT model."""
if isinstance(m, nn.Linear):
# NOTE: This initialization is needed only for training.
# trunc_normal_(m.weight, std=.02)
@ -718,11 +968,11 @@ class TinyViT(nn.Module):
@torch.jit.ignore
def no_weight_decay_keywords(self):
"""Returns a dictionary of parameter names where weight decay should not be applied."""
"""Returns a set of keywords for parameters that should not use weight decay."""
return {"attention_biases"}
def forward_features(self, x):
"""Runs the input through the model layers and returns the transformed output."""
"""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)
@ -737,5 +987,5 @@ class TinyViT(nn.Module):
return self.neck(x)
def forward(self, x):
"""Executes a forward pass on the input tensor through the constructed model layers."""
"""Performs the forward pass through the TinyViT model, extracting features from the input image."""
return self.forward_features(x)