1013 lines
40 KiB
Python
1013 lines
40 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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# --------------------------------------------------------
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# TinyViT Model Architecture
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# Copyright (c) 2022 Microsoft
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# Adapted from LeViT and Swin Transformer
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# LeViT: (https://github.com/facebookresearch/levit)
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# Swin: (https://github.com/microsoft/swin-transformer)
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# Build the TinyViT Model
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# --------------------------------------------------------
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import itertools
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from typing import Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint as checkpoint
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from ultralytics.nn.modules import LayerNorm2d
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from ultralytics.utils.instance import to_2tuple
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class Conv2d_BN(torch.nn.Sequential):
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"""
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A sequential container that performs 2D convolution followed by batch normalization.
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Attributes:
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c (torch.nn.Conv2d): 2D convolution layer.
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1 (torch.nn.BatchNorm2d): Batch normalization layer.
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Methods:
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__init__: Initializes the Conv2d_BN with specified parameters.
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Args:
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a (int): Number of input channels.
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b (int): Number of output channels.
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ks (int): Kernel size for the convolution. Defaults to 1.
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stride (int): Stride for the convolution. Defaults to 1.
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pad (int): Padding for the convolution. Defaults to 0.
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dilation (int): Dilation factor for the convolution. Defaults to 1.
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groups (int): Number of groups for the convolution. Defaults to 1.
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bn_weight_init (float): Initial value for batch normalization weight. Defaults to 1.
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Examples:
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>>> conv_bn = Conv2d_BN(3, 64, ks=3, stride=1, pad=1)
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>>> input_tensor = torch.randn(1, 3, 224, 224)
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>>> output = conv_bn(input_tensor)
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>>> print(output.shape)
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"""
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def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1):
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"""Initializes a sequential container with 2D convolution followed by batch normalization."""
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super().__init__()
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self.add_module("c", torch.nn.Conv2d(a, b, ks, stride, pad, dilation, groups, bias=False))
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bn = torch.nn.BatchNorm2d(b)
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torch.nn.init.constant_(bn.weight, bn_weight_init)
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torch.nn.init.constant_(bn.bias, 0)
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self.add_module("bn", bn)
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class PatchEmbed(nn.Module):
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"""
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Embeds images into patches and projects them into a specified embedding dimension.
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Attributes:
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patches_resolution (Tuple[int, int]): Resolution of the patches after embedding.
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num_patches (int): Total number of patches.
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in_chans (int): Number of input channels.
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embed_dim (int): Dimension of the embedding.
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seq (nn.Sequential): Sequence of convolutional and activation layers for patch embedding.
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Methods:
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forward: Processes the input tensor through the patch embedding sequence.
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Examples:
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>>> import torch
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>>> patch_embed = PatchEmbed(in_chans=3, embed_dim=96, resolution=224, activation=nn.GELU)
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>>> x = torch.randn(1, 3, 224, 224)
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>>> output = patch_embed(x)
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>>> print(output.shape)
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"""
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def __init__(self, in_chans, embed_dim, resolution, activation):
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"""Initializes patch embedding with convolutional layers for image-to-patch conversion and projection."""
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super().__init__()
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img_size: Tuple[int, int] = to_2tuple(resolution)
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self.patches_resolution = (img_size[0] // 4, img_size[1] // 4)
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self.num_patches = self.patches_resolution[0] * self.patches_resolution[1]
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self.in_chans = in_chans
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self.embed_dim = embed_dim
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n = embed_dim
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self.seq = nn.Sequential(
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Conv2d_BN(in_chans, n // 2, 3, 2, 1),
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activation(),
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Conv2d_BN(n // 2, n, 3, 2, 1),
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)
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def forward(self, x):
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"""Processes input tensor through patch embedding sequence, converting images to patch embeddings."""
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return self.seq(x)
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class MBConv(nn.Module):
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"""
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Mobile Inverted Bottleneck Conv (MBConv) layer, part of the EfficientNet architecture.
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Attributes:
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in_chans (int): Number of input channels.
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hidden_chans (int): Number of hidden channels.
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out_chans (int): Number of output channels.
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conv1 (Conv2d_BN): First convolutional layer.
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act1 (nn.Module): First activation function.
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conv2 (Conv2d_BN): Depthwise convolutional layer.
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act2 (nn.Module): Second activation function.
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conv3 (Conv2d_BN): Final convolutional layer.
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act3 (nn.Module): Third activation function.
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drop_path (nn.Module): Drop path layer (Identity for inference).
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Methods:
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forward: Performs the forward pass through the MBConv layer.
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Examples:
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>>> in_chans, out_chans = 32, 64
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>>> mbconv = MBConv(in_chans, out_chans, expand_ratio=4, activation=nn.ReLU, drop_path=0.1)
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>>> x = torch.randn(1, in_chans, 56, 56)
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>>> output = mbconv(x)
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>>> print(output.shape)
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torch.Size([1, 64, 56, 56])
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"""
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def __init__(self, in_chans, out_chans, expand_ratio, activation, drop_path):
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"""Initializes the MBConv layer with specified input/output channels, expansion ratio, and activation."""
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super().__init__()
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self.in_chans = in_chans
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self.hidden_chans = int(in_chans * expand_ratio)
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self.out_chans = out_chans
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self.conv1 = Conv2d_BN(in_chans, self.hidden_chans, ks=1)
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self.act1 = activation()
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self.conv2 = Conv2d_BN(self.hidden_chans, self.hidden_chans, ks=3, stride=1, pad=1, groups=self.hidden_chans)
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self.act2 = activation()
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self.conv3 = Conv2d_BN(self.hidden_chans, out_chans, ks=1, bn_weight_init=0.0)
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self.act3 = activation()
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# NOTE: `DropPath` is needed only for training.
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# self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.drop_path = nn.Identity()
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def forward(self, x):
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"""Implements the forward pass of MBConv, applying convolutions and skip connection."""
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shortcut = x
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x = self.conv1(x)
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x = self.act1(x)
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x = self.conv2(x)
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x = self.act2(x)
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x = self.conv3(x)
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x = self.drop_path(x)
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x += shortcut
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return self.act3(x)
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class PatchMerging(nn.Module):
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"""
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Merges neighboring patches in the feature map and projects to a new dimension.
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This class implements a patch merging operation that combines spatial information and adjusts the feature
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dimension. It uses a series of convolutional layers with batch normalization to achieve this.
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Attributes:
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input_resolution (Tuple[int, int]): The input resolution (height, width) of the feature map.
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dim (int): The input dimension of the feature map.
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out_dim (int): The output dimension after merging and projection.
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act (nn.Module): The activation function used between convolutions.
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conv1 (Conv2d_BN): The first convolutional layer for dimension projection.
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conv2 (Conv2d_BN): The second convolutional layer for spatial merging.
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conv3 (Conv2d_BN): The third convolutional layer for final projection.
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Methods:
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forward: Applies the patch merging operation to the input tensor.
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Examples:
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>>> input_resolution = (56, 56)
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>>> patch_merging = PatchMerging(input_resolution, dim=64, out_dim=128, activation=nn.ReLU)
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>>> x = torch.randn(4, 64, 56, 56)
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>>> output = patch_merging(x)
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>>> print(output.shape)
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"""
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def __init__(self, input_resolution, dim, out_dim, activation):
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"""Initializes the PatchMerging module for merging and projecting neighboring patches in feature maps."""
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super().__init__()
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self.input_resolution = input_resolution
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self.dim = dim
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self.out_dim = out_dim
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self.act = activation()
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self.conv1 = Conv2d_BN(dim, out_dim, 1, 1, 0)
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stride_c = 1 if out_dim in {320, 448, 576} else 2
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self.conv2 = Conv2d_BN(out_dim, out_dim, 3, stride_c, 1, groups=out_dim)
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self.conv3 = Conv2d_BN(out_dim, out_dim, 1, 1, 0)
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def forward(self, x):
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"""Applies patch merging and dimension projection to the input feature map."""
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if x.ndim == 3:
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H, W = self.input_resolution
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B = len(x)
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# (B, C, H, W)
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x = x.view(B, H, W, -1).permute(0, 3, 1, 2)
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x = self.conv1(x)
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x = self.act(x)
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x = self.conv2(x)
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x = self.act(x)
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x = self.conv3(x)
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return x.flatten(2).transpose(1, 2)
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class ConvLayer(nn.Module):
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"""
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Convolutional Layer featuring multiple MobileNetV3-style inverted bottleneck convolutions (MBConv).
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This layer optionally applies downsample operations to the output and supports gradient checkpointing.
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Attributes:
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dim (int): Dimensionality of the input and output.
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input_resolution (Tuple[int, int]): Resolution of the input image.
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depth (int): Number of MBConv layers in the block.
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use_checkpoint (bool): Whether to use gradient checkpointing to save memory.
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blocks (nn.ModuleList): List of MBConv layers.
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downsample (Optional[Callable]): Function for downsampling the output.
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Methods:
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forward: Processes the input through the convolutional layers.
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Examples:
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>>> input_tensor = torch.randn(1, 64, 56, 56)
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>>> conv_layer = ConvLayer(64, (56, 56), depth=3, activation=nn.ReLU)
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>>> output = conv_layer(input_tensor)
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>>> print(output.shape)
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"""
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def __init__(
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self,
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dim,
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input_resolution,
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depth,
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activation,
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drop_path=0.0,
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downsample=None,
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use_checkpoint=False,
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out_dim=None,
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conv_expand_ratio=4.0,
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):
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"""
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Initializes the ConvLayer with the given dimensions and settings.
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This layer consists of multiple MobileNetV3-style inverted bottleneck convolutions (MBConv) and
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optionally applies downsampling to the output.
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Args:
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dim (int): The dimensionality of the input and output.
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input_resolution (Tuple[int, int]): The resolution of the input image.
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depth (int): The number of MBConv layers in the block.
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activation (Callable): Activation function applied after each convolution.
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drop_path (float | List[float]): Drop path rate. Single float or a list of floats for each MBConv.
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downsample (Optional[Callable]): Function for downsampling the output. None to skip downsampling.
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use_checkpoint (bool): Whether to use gradient checkpointing to save memory.
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out_dim (Optional[int]): The dimensionality of the output. None means it will be the same as `dim`.
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conv_expand_ratio (float): Expansion ratio for the MBConv layers.
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Examples:
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>>> input_tensor = torch.randn(1, 64, 56, 56)
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>>> conv_layer = ConvLayer(64, (56, 56), depth=3, activation=nn.ReLU)
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>>> output = conv_layer(input_tensor)
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>>> print(output.shape)
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"""
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super().__init__()
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self.dim = dim
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self.input_resolution = input_resolution
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self.depth = depth
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self.use_checkpoint = use_checkpoint
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# Build blocks
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self.blocks = nn.ModuleList(
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[
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MBConv(
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dim,
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dim,
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conv_expand_ratio,
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activation,
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drop_path[i] if isinstance(drop_path, list) else drop_path,
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)
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for i in range(depth)
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]
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)
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# Patch merging layer
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self.downsample = (
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None
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if downsample is None
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else downsample(input_resolution, dim=dim, out_dim=out_dim, activation=activation)
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)
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def forward(self, x):
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"""Processes input through convolutional layers, applying MBConv blocks and optional downsampling."""
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for blk in self.blocks:
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x = checkpoint.checkpoint(blk, x) if self.use_checkpoint else blk(x)
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return x if self.downsample is None else self.downsample(x)
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class Mlp(nn.Module):
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"""
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Multi-layer Perceptron (MLP) module for transformer architectures.
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This module applies layer normalization, two fully-connected layers with an activation function in between,
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and dropout. It is commonly used in transformer-based architectures.
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Attributes:
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norm (nn.LayerNorm): Layer normalization applied to the input.
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fc1 (nn.Linear): First fully-connected layer.
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fc2 (nn.Linear): Second fully-connected layer.
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act (nn.Module): Activation function applied after the first fully-connected layer.
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drop (nn.Dropout): Dropout layer applied after the activation function.
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Methods:
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forward: Applies the MLP operations on the input tensor.
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Examples:
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>>> import torch
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>>> from torch import nn
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>>> mlp = Mlp(in_features=256, hidden_features=512, out_features=256, act_layer=nn.GELU, drop=0.1)
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>>> x = torch.randn(32, 100, 256)
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>>> output = mlp(x)
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>>> print(output.shape)
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torch.Size([32, 100, 256])
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"""
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0):
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"""Initializes a multi-layer perceptron with configurable input, hidden, and output dimensions."""
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.norm = nn.LayerNorm(in_features)
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.act = act_layer()
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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"""Applies MLP operations: layer norm, FC layers, activation, and dropout to the input tensor."""
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x = self.norm(x)
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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return self.drop(x)
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class Attention(torch.nn.Module):
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"""
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Multi-head attention module with spatial awareness and trainable attention biases.
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This module implements a multi-head attention mechanism with support for spatial awareness, applying
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attention biases based on spatial resolution. It includes trainable attention biases for each unique
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offset between spatial positions in the resolution grid.
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Attributes:
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num_heads (int): Number of attention heads.
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scale (float): Scaling factor for attention scores.
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key_dim (int): Dimensionality of the keys and queries.
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nh_kd (int): Product of num_heads and key_dim.
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d (int): Dimensionality of the value vectors.
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dh (int): Product of d and num_heads.
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attn_ratio (float): Attention ratio affecting the dimensions of the value vectors.
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norm (nn.LayerNorm): Layer normalization applied to input.
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qkv (nn.Linear): Linear layer for computing query, key, and value projections.
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proj (nn.Linear): Linear layer for final projection.
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attention_biases (nn.Parameter): Learnable attention biases.
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attention_bias_idxs (Tensor): Indices for attention biases.
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ab (Tensor): Cached attention biases for inference, deleted during training.
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Methods:
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train: Sets the module in training mode and handles the 'ab' attribute.
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forward: Performs the forward pass of the attention mechanism.
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Examples:
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>>> attn = Attention(dim=256, key_dim=64, num_heads=8, resolution=(14, 14))
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>>> x = torch.randn(1, 196, 256)
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>>> output = attn(x)
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>>> print(output.shape)
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torch.Size([1, 196, 256])
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"""
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def __init__(
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self,
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dim,
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key_dim,
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num_heads=8,
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attn_ratio=4,
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resolution=(14, 14),
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):
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"""
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Initializes the Attention module for multi-head attention with spatial awareness.
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This module implements a multi-head attention mechanism with support for spatial awareness, applying
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attention biases based on spatial resolution. It includes trainable attention biases for each unique
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offset between spatial positions in the resolution grid.
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Args:
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dim (int): The dimensionality of the input and output.
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key_dim (int): The dimensionality of the keys and queries.
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num_heads (int): Number of attention heads. Default is 8.
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attn_ratio (float): Attention ratio, affecting the dimensions of the value vectors. Default is 4.
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resolution (Tuple[int, int]): Spatial resolution of the input feature map. Default is (14, 14).
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Raises:
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AssertionError: If 'resolution' is not a tuple of length 2.
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Examples:
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>>> attn = Attention(dim=256, key_dim=64, num_heads=8, resolution=(14, 14))
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>>> x = torch.randn(1, 196, 256)
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>>> output = attn(x)
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>>> print(output.shape)
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torch.Size([1, 196, 256])
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"""
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super().__init__()
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assert isinstance(resolution, tuple) and len(resolution) == 2, "'resolution' argument not tuple of length 2"
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self.num_heads = num_heads
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self.scale = key_dim**-0.5
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self.key_dim = key_dim
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self.nh_kd = nh_kd = key_dim * num_heads
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self.d = int(attn_ratio * key_dim)
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self.dh = int(attn_ratio * key_dim) * num_heads
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self.attn_ratio = attn_ratio
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h = self.dh + nh_kd * 2
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self.norm = nn.LayerNorm(dim)
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self.qkv = nn.Linear(dim, h)
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self.proj = nn.Linear(self.dh, dim)
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points = list(itertools.product(range(resolution[0]), range(resolution[1])))
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N = len(points)
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attention_offsets = {}
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idxs = []
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for p1 in points:
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for p2 in points:
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offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
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if offset not in attention_offsets:
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attention_offsets[offset] = len(attention_offsets)
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idxs.append(attention_offsets[offset])
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self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))
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self.register_buffer("attention_bias_idxs", torch.LongTensor(idxs).view(N, N), persistent=False)
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@torch.no_grad()
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def train(self, mode=True):
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"""Performs multi-head attention with spatial awareness and trainable attention biases."""
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super().train(mode)
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if mode and hasattr(self, "ab"):
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del self.ab
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else:
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self.ab = self.attention_biases[:, self.attention_bias_idxs]
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def forward(self, x): # x
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"""Applies multi-head attention with spatial awareness and trainable attention biases."""
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B, N, _ = x.shape # B, N, C
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# Normalization
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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)
|
|
|
|
|
|
class TinyViTBlock(nn.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:
|
|
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,
|
|
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)
|
|
|
|
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))
|
|
|
|
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}"
|
|
)
|
|
|
|
|
|
class BasicLayer(nn.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:
|
|
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,
|
|
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)
|
|
)
|
|
|
|
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)
|
|
|
|
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}"
|
|
|
|
|
|
class TinyViT(nn.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:
|
|
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 (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.
|
|
|
|
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__(
|
|
self,
|
|
img_size=224,
|
|
in_chans=3,
|
|
num_classes=1000,
|
|
embed_dims=(96, 192, 384, 768),
|
|
depths=(2, 2, 6, 2),
|
|
num_heads=(3, 6, 12, 24),
|
|
window_sizes=(7, 7, 14, 7),
|
|
mlp_ratio=4.0,
|
|
drop_rate=0.0,
|
|
drop_path_rate=0.1,
|
|
use_checkpoint=False,
|
|
mbconv_expand_ratio=4.0,
|
|
local_conv_size=3,
|
|
layer_lr_decay=1.0,
|
|
):
|
|
"""
|
|
Initializes the TinyViT model.
|
|
|
|
This constructor sets up the TinyViT architecture, including patch embedding, multiple layers of
|
|
attention and convolution blocks, and a classification head.
|
|
|
|
Args:
|
|
img_size (int): Size of the input image. Default is 224.
|
|
in_chans (int): Number of input channels. Default is 3.
|
|
num_classes (int): Number of classes for classification. Default is 1000.
|
|
embed_dims (Tuple[int, int, int, int]): Embedding dimensions for each stage.
|
|
Default is (96, 192, 384, 768).
|
|
depths (Tuple[int, int, int, int]): Number of blocks in each stage. Default is (2, 2, 6, 2).
|
|
num_heads (Tuple[int, int, int, int]): Number of attention heads in each stage.
|
|
Default is (3, 6, 12, 24).
|
|
window_sizes (Tuple[int, int, int, int]): Window sizes for each stage. Default is (7, 7, 14, 7).
|
|
mlp_ratio (float): Ratio of MLP hidden dim to embedding dim. Default is 4.0.
|
|
drop_rate (float): Dropout rate. Default is 0.0.
|
|
drop_path_rate (float): Stochastic depth rate. Default is 0.1.
|
|
use_checkpoint (bool): Whether to use checkpointing to save memory. Default is False.
|
|
mbconv_expand_ratio (float): Expansion ratio for MBConv layer. Default is 4.0.
|
|
local_conv_size (int): Kernel size for local convolutions. Default is 3.
|
|
layer_lr_decay (float): Layer-wise learning rate decay factor. Default is 1.0.
|
|
|
|
Examples:
|
|
>>> model = TinyViT(img_size=224, num_classes=1000)
|
|
>>> x = torch.randn(1, 3, 224, 224)
|
|
>>> output = model(x)
|
|
>>> print(output.shape)
|
|
torch.Size([1, 1000])
|
|
"""
|
|
super().__init__()
|
|
self.img_size = img_size
|
|
self.num_classes = num_classes
|
|
self.depths = depths
|
|
self.num_layers = len(depths)
|
|
self.mlp_ratio = mlp_ratio
|
|
|
|
activation = nn.GELU
|
|
|
|
self.patch_embed = PatchEmbed(
|
|
in_chans=in_chans, embed_dim=embed_dims[0], resolution=img_size, activation=activation
|
|
)
|
|
|
|
patches_resolution = self.patch_embed.patches_resolution
|
|
self.patches_resolution = patches_resolution
|
|
|
|
# Stochastic depth
|
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
|
|
|
# Build layers
|
|
self.layers = nn.ModuleList()
|
|
for i_layer in range(self.num_layers):
|
|
kwargs = dict(
|
|
dim=embed_dims[i_layer],
|
|
input_resolution=(
|
|
patches_resolution[0] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)),
|
|
patches_resolution[1] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)),
|
|
),
|
|
# input_resolution=(patches_resolution[0] // (2 ** i_layer),
|
|
# patches_resolution[1] // (2 ** i_layer)),
|
|
depth=depths[i_layer],
|
|
drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
|
|
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
|
|
use_checkpoint=use_checkpoint,
|
|
out_dim=embed_dims[min(i_layer + 1, len(embed_dims) - 1)],
|
|
activation=activation,
|
|
)
|
|
if i_layer == 0:
|
|
layer = ConvLayer(conv_expand_ratio=mbconv_expand_ratio, **kwargs)
|
|
else:
|
|
layer = BasicLayer(
|
|
num_heads=num_heads[i_layer],
|
|
window_size=window_sizes[i_layer],
|
|
mlp_ratio=self.mlp_ratio,
|
|
drop=drop_rate,
|
|
local_conv_size=local_conv_size,
|
|
**kwargs,
|
|
)
|
|
self.layers.append(layer)
|
|
|
|
# Classifier head
|
|
self.norm_head = nn.LayerNorm(embed_dims[-1])
|
|
self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else torch.nn.Identity()
|
|
|
|
# Init weights
|
|
self.apply(self._init_weights)
|
|
self.set_layer_lr_decay(layer_lr_decay)
|
|
self.neck = nn.Sequential(
|
|
nn.Conv2d(
|
|
embed_dims[-1],
|
|
256,
|
|
kernel_size=1,
|
|
bias=False,
|
|
),
|
|
LayerNorm2d(256),
|
|
nn.Conv2d(
|
|
256,
|
|
256,
|
|
kernel_size=3,
|
|
padding=1,
|
|
bias=False,
|
|
),
|
|
LayerNorm2d(256),
|
|
)
|
|
|
|
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)
|
|
|
|
def _init_weights(self, m):
|
|
"""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)
|
|
if m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.LayerNorm):
|
|
nn.init.constant_(m.bias, 0)
|
|
nn.init.constant_(m.weight, 1.0)
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay_keywords(self):
|
|
"""Returns a set of keywords for parameters that should not use weight decay."""
|
|
return {"attention_biases"}
|
|
|
|
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)
|
|
|
|
def forward(self, x):
|
|
"""Performs the forward pass through the TinyViT model, extracting features from the input image."""
|
|
return self.forward_features(x)
|
|
|
|
def set_imgsz(self, imgsz=[1024, 1024]):
|
|
"""
|
|
Set image size to make model compatible with different image sizes.
|
|
|
|
Args:
|
|
imgsz (Tuple[int, int]): The size of the input image.
|
|
"""
|
|
imgsz = [s // 4 for s in imgsz]
|
|
self.patches_resolution = imgsz
|
|
for i, layer in enumerate(self.layers):
|
|
input_resolution = (
|
|
imgsz[0] // (2 ** (i - 1 if i == 3 else i)),
|
|
imgsz[1] // (2 ** (i - 1 if i == 3 else i)),
|
|
)
|
|
layer.input_resolution = input_resolution
|
|
if layer.downsample is not None:
|
|
layer.downsample.input_resolution = input_resolution
|
|
if isinstance(layer, BasicLayer):
|
|
for b in layer.blocks:
|
|
b.input_resolution = input_resolution
|