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720 lines
28 KiB
720 lines
28 KiB
# 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.utils.instance import to_2tuple |
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class Conv2d_BN(torch.nn.Sequential): |
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"""A sequential container that performs 2D convolution followed by batch normalization.""" |
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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 the MBConv model with given input channels, output channels, expansion ratio, activation, and |
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drop path. |
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""" |
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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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"""Embeds images into patches and projects them into a specified embedding dimension.""" |
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def __init__(self, in_chans, embed_dim, resolution, activation): |
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"""Initialize the PatchMerging class with specified input, output dimensions, resolution and activation |
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function. |
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""" |
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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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"""Runs input tensor 'x' through the PatchMerging model's sequence of operations.""" |
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return self.seq(x) |
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class MBConv(nn.Module): |
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"""Mobile Inverted Bottleneck Conv (MBConv) layer, part of the EfficientNet architecture.""" |
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def __init__(self, in_chans, out_chans, expand_ratio, activation, drop_path): |
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"""Initializes a convolutional layer with specified dimensions, input resolution, depth, and activation |
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function. |
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""" |
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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 for the model architecture.""" |
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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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"""Merges neighboring patches in the feature map and projects to a new dimension.""" |
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def __init__(self, input_resolution, dim, out_dim, activation): |
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"""Initializes the ConvLayer with specific dimension, input resolution, depth, activation, drop path, and other |
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optional parameters. |
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""" |
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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 forward pass on the input utilizing convolution and activation layers, and returns the result.""" |
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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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Optionally applies downsample operations to the output, and provides support for gradient checkpointing. |
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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., |
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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., |
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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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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 (Union[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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""" |
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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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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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) for i in range(depth)]) |
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# Patch merging layer |
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self.downsample = None if downsample is None else downsample( |
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input_resolution, dim=dim, out_dim=out_dim, activation=activation) |
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def forward(self, x): |
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"""Processes the input through a series of convolutional layers and returns the activated output.""" |
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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) for transformer architectures. |
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This layer takes an input with in_features, applies layer normalization and two fully-connected layers. |
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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.): |
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"""Initializes Attention module with the given parameters including dimension, key_dim, number of heads, etc.""" |
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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 operations on input x and returns modified x, runs downsample if not None.""" |
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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 support for spatial awareness, applying attention biases based on spatial |
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resolution. Implements trainable attention biases for each unique offset between spatial positions in the resolution |
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grid. |
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Attributes: |
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ab (Tensor, optional): Cached attention biases for inference, deleted during training. |
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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. |
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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, optional): Number of attention heads. Default is 8. |
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attn_ratio (float, optional): Attention ratio, affecting the dimensions of the value vectors. Default is 4. |
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resolution (Tuple[int, int], optional): 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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""" |
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super().__init__() |
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assert isinstance(resolution, tuple) and len(resolution) == 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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"""Sets the module in training mode and handles attribute 'ab' based on the mode.""" |
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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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"""Performs forward pass over the input tensor 'x' by applying normalization and querying keys/values.""" |
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B, N, _ = x.shape # B, N, C |
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|
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# Normalization |
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x = self.norm(x) |
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qkv = self.qkv(x) |
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# (B, N, num_heads, d) |
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q, k, v = qkv.view(B, N, self.num_heads, -1).split([self.key_dim, self.key_dim, self.d], dim=3) |
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# (B, num_heads, N, d) |
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q = q.permute(0, 2, 1, 3) |
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k = k.permute(0, 2, 1, 3) |
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v = v.permute(0, 2, 1, 3) |
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self.ab = self.ab.to(self.attention_biases.device) |
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attn = ((q @ k.transpose(-2, -1)) * self.scale + |
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(self.attention_biases[:, self.attention_bias_idxs] if self.training else self.ab)) |
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attn = attn.softmax(dim=-1) |
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x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh) |
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return self.proj(x) |
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class TinyViTBlock(nn.Module): |
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"""TinyViT Block that applies self-attention and a local convolution to the input.""" |
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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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num_heads, |
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window_size=7, |
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mlp_ratio=4., |
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drop=0., |
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drop_path=0., |
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local_conv_size=3, |
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activation=nn.GELU, |
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): |
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""" |
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Initializes the TinyViTBlock. |
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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]): Spatial resolution of the input feature map. |
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num_heads (int): Number of attention heads. |
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window_size (int, optional): Window size for attention. Default is 7. |
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mlp_ratio (float, optional): Ratio of mlp hidden dim to embedding dim. Default is 4. |
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drop (float, optional): Dropout rate. Default is 0. |
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drop_path (float, optional): Stochastic depth rate. Default is 0. |
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local_conv_size (int, optional): The kernel size of the local convolution. Default is 3. |
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activation (torch.nn, optional): Activation function for MLP. Default is nn.GELU. |
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Raises: |
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AssertionError: If `window_size` is not greater than 0. |
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AssertionError: If `dim` is not divisible by `num_heads`. |
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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.num_heads = num_heads |
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assert window_size > 0, 'window_size must be greater than 0' |
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self.window_size = window_size |
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self.mlp_ratio = mlp_ratio |
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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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assert dim % num_heads == 0, 'dim must be divisible by num_heads' |
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head_dim = dim // num_heads |
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window_resolution = (window_size, window_size) |
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self.attn = Attention(dim, head_dim, num_heads, attn_ratio=1, resolution=window_resolution) |
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mlp_hidden_dim = int(dim * mlp_ratio) |
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mlp_activation = activation |
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=mlp_activation, drop=drop) |
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pad = local_conv_size // 2 |
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self.local_conv = Conv2d_BN(dim, dim, ks=local_conv_size, stride=1, pad=pad, groups=dim) |
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def forward(self, x): |
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"""Applies attention-based transformation or padding to input 'x' before passing it through a local |
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convolution. |
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""" |
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H, W = self.input_resolution |
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B, L, C = x.shape |
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assert L == H * W, 'input feature has wrong size' |
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res_x = x |
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if H == self.window_size and W == self.window_size: |
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x = self.attn(x) |
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else: |
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x = x.view(B, H, W, C) |
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pad_b = (self.window_size - H % self.window_size) % self.window_size |
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pad_r = (self.window_size - W % self.window_size) % self.window_size |
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padding = pad_b > 0 or pad_r > 0 |
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if padding: |
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x = F.pad(x, (0, 0, 0, pad_r, 0, pad_b)) |
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pH, pW = H + pad_b, W + pad_r |
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nH = pH // self.window_size |
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nW = pW // self.window_size |
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# Window partition |
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x = x.view(B, nH, self.window_size, nW, self.window_size, |
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C).transpose(2, 3).reshape(B * nH * nW, self.window_size * self.window_size, C) |
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x = self.attn(x) |
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# Window reverse |
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x = x.view(B, nH, nW, self.window_size, self.window_size, C).transpose(2, 3).reshape(B, pH, pW, C) |
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|
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if padding: |
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x = x[:, :H, :W].contiguous() |
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x = x.view(B, L, C) |
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x = res_x + self.drop_path(x) |
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x = x.transpose(1, 2).reshape(B, C, H, W) |
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x = self.local_conv(x) |
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x = x.view(B, C, L).transpose(1, 2) |
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return x + self.drop_path(self.mlp(x)) |
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def extra_repr(self) -> str: |
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"""Returns a formatted string representing the TinyViTBlock's parameters: dimension, input resolution, number of |
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attentions heads, window size, and MLP ratio. |
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""" |
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return f'dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, ' \ |
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f'window_size={self.window_size}, mlp_ratio={self.mlp_ratio}' |
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|
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class BasicLayer(nn.Module): |
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"""A basic TinyViT layer for one stage in a TinyViT architecture.""" |
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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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num_heads, |
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window_size, |
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mlp_ratio=4., |
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drop=0., |
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drop_path=0., |
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downsample=None, |
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use_checkpoint=False, |
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local_conv_size=3, |
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activation=nn.GELU, |
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out_dim=None, |
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): |
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""" |
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Initializes the BasicLayer. |
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|
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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]): Spatial resolution of the input feature map. |
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depth (int): Number of TinyViT blocks. |
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num_heads (int): Number of attention heads. |
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window_size (int): Local window size. |
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mlp_ratio (float, optional): Ratio of mlp hidden dim to embedding dim. Default is 4. |
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drop (float, optional): Dropout rate. Default is 0. |
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drop_path (float | tuple[float], optional): Stochastic depth rate. Default is 0. |
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downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default is None. |
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use_checkpoint (bool, optional): Whether to use checkpointing to save memory. Default is False. |
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local_conv_size (int, optional): Kernel size of the local convolution. Default is 3. |
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activation (torch.nn, optional): Activation function for MLP. Default is nn.GELU. |
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out_dim (int | None, optional): The output dimension of the layer. Default is None. |
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|
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Raises: |
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ValueError: If `drop_path` is a list of float but its length doesn't match `depth`. |
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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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|
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# Build blocks |
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self.blocks = nn.ModuleList([ |
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TinyViTBlock( |
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dim=dim, |
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input_resolution=input_resolution, |
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num_heads=num_heads, |
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window_size=window_size, |
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mlp_ratio=mlp_ratio, |
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drop=drop, |
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drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, |
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local_conv_size=local_conv_size, |
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activation=activation, |
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) for i in range(depth)]) |
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|
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# Patch merging layer |
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self.downsample = None if downsample is None else downsample( |
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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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"""Performs forward propagation on the input tensor and returns a normalized tensor.""" |
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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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|
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def extra_repr(self) -> str: |
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"""Returns a string representation of the extra_repr function with the layer's parameters.""" |
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return f'dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}' |
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|
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|
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class LayerNorm2d(nn.Module): |
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"""A PyTorch implementation of Layer Normalization in 2D.""" |
|
|
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def __init__(self, num_channels: int, eps: float = 1e-6) -> None: |
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"""Initialize LayerNorm2d with the number of channels and an optional epsilon.""" |
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super().__init__() |
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self.weight = nn.Parameter(torch.ones(num_channels)) |
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self.bias = nn.Parameter(torch.zeros(num_channels)) |
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self.eps = eps |
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|
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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"""Perform a forward pass, normalizing the input tensor.""" |
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u = x.mean(1, keepdim=True) |
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s = (x - u).pow(2).mean(1, keepdim=True) |
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x = (x - u) / torch.sqrt(s + self.eps) |
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return self.weight[:, None, None] * x + self.bias[:, None, None] |
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|
|
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class TinyViT(nn.Module): |
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""" |
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The TinyViT architecture for vision tasks. |
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|
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Attributes: |
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img_size (int): Input image size. |
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in_chans (int): Number of input channels. |
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num_classes (int): Number of classification classes. |
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embed_dims (List[int]): List of embedding dimensions for each layer. |
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depths (List[int]): List of depths for each layer. |
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num_heads (List[int]): List of number of attention heads for each layer. |
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window_sizes (List[int]): List of window sizes for each layer. |
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mlp_ratio (float): Ratio of MLP hidden dimension to embedding dimension. |
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drop_rate (float): Dropout rate for drop layers. |
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drop_path_rate (float): Drop path rate for stochastic depth. |
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use_checkpoint (bool): Use checkpointing for efficient memory usage. |
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mbconv_expand_ratio (float): Expansion ratio for MBConv layer. |
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local_conv_size (int): Local convolution kernel size. |
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layer_lr_decay (float): Layer-wise learning rate decay. |
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|
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Note: |
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This implementation is generalized to accept a list of depths, attention heads, |
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embedding dimensions and window sizes, which allows you to create a |
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"stack" of TinyViT models of varying configurations. |
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""" |
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|
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def __init__( |
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self, |
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img_size=224, |
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in_chans=3, |
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num_classes=1000, |
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embed_dims=[96, 192, 384, 768], |
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depths=[2, 2, 6, 2], |
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num_heads=[3, 6, 12, 24], |
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window_sizes=[7, 7, 14, 7], |
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mlp_ratio=4., |
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drop_rate=0., |
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drop_path_rate=0.1, |
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use_checkpoint=False, |
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mbconv_expand_ratio=4.0, |
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local_conv_size=3, |
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layer_lr_decay=1.0, |
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): |
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""" |
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Initializes the TinyViT model. |
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|
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Args: |
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img_size (int, optional): The input image size. Defaults to 224. |
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in_chans (int, optional): Number of input channels. Defaults to 3. |
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num_classes (int, optional): Number of classification classes. Defaults to 1000. |
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embed_dims (List[int], optional): List of embedding dimensions for each layer. Defaults to [96, 192, 384, 768]. |
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depths (List[int], optional): List of depths for each layer. Defaults to [2, 2, 6, 2]. |
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num_heads (List[int], optional): List of number of attention heads for each layer. Defaults to [3, 6, 12, 24]. |
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window_sizes (List[int], optional): List of window sizes for each layer. Defaults to [7, 7, 14, 7]. |
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mlp_ratio (float, optional): Ratio of MLP hidden dimension to embedding dimension. Defaults to 4. |
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drop_rate (float, optional): Dropout rate. Defaults to 0. |
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drop_path_rate (float, optional): Drop path rate for stochastic depth. Defaults to 0.1. |
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use_checkpoint (bool, optional): Whether to use checkpointing for efficient memory usage. Defaults to False. |
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mbconv_expand_ratio (float, optional): Expansion ratio for MBConv layer. Defaults to 4.0. |
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local_conv_size (int, optional): Local convolution kernel size. Defaults to 3. |
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layer_lr_decay (float, optional): Layer-wise learning rate decay. Defaults to 1.0. |
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""" |
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super().__init__() |
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self.img_size = img_size |
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self.num_classes = num_classes |
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self.depths = depths |
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self.num_layers = len(depths) |
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self.mlp_ratio = mlp_ratio |
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|
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activation = nn.GELU |
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|
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self.patch_embed = PatchEmbed(in_chans=in_chans, |
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embed_dim=embed_dims[0], |
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resolution=img_size, |
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activation=activation) |
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|
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patches_resolution = self.patch_embed.patches_resolution |
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self.patches_resolution = patches_resolution |
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|
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# Stochastic depth |
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule |
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|
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# Build layers |
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self.layers = nn.ModuleList() |
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for i_layer in range(self.num_layers): |
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kwargs = dict( |
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dim=embed_dims[i_layer], |
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input_resolution=(patches_resolution[0] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)), |
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patches_resolution[1] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer))), |
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# input_resolution=(patches_resolution[0] // (2 ** i_layer), |
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# patches_resolution[1] // (2 ** i_layer)), |
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depth=depths[i_layer], |
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drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], |
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downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, |
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use_checkpoint=use_checkpoint, |
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out_dim=embed_dims[min(i_layer + 1, |
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len(embed_dims) - 1)], |
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activation=activation, |
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) |
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if i_layer == 0: |
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layer = ConvLayer(conv_expand_ratio=mbconv_expand_ratio, **kwargs) |
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else: |
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layer = BasicLayer(num_heads=num_heads[i_layer], |
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window_size=window_sizes[i_layer], |
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mlp_ratio=self.mlp_ratio, |
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drop=drop_rate, |
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local_conv_size=local_conv_size, |
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**kwargs) |
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self.layers.append(layer) |
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|
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# Classifier head |
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self.norm_head = nn.LayerNorm(embed_dims[-1]) |
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self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else torch.nn.Identity() |
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|
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# Init weights |
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self.apply(self._init_weights) |
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self.set_layer_lr_decay(layer_lr_decay) |
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self.neck = nn.Sequential( |
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nn.Conv2d( |
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embed_dims[-1], |
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256, |
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kernel_size=1, |
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bias=False, |
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), |
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LayerNorm2d(256), |
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nn.Conv2d( |
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256, |
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256, |
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kernel_size=3, |
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padding=1, |
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bias=False, |
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), |
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LayerNorm2d(256), |
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) |
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|
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def set_layer_lr_decay(self, layer_lr_decay): |
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"""Sets the learning rate decay for each layer in the TinyViT model.""" |
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decay_rate = layer_lr_decay |
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|
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# Layers -> blocks (depth) |
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depth = sum(self.depths) |
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lr_scales = [decay_rate ** (depth - i - 1) for i in range(depth)] |
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|
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def _set_lr_scale(m, scale): |
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"""Sets the learning rate scale for each layer in the model based on the layer's depth.""" |
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for p in m.parameters(): |
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p.lr_scale = scale |
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|
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self.patch_embed.apply(lambda x: _set_lr_scale(x, lr_scales[0])) |
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i = 0 |
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for layer in self.layers: |
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for block in layer.blocks: |
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block.apply(lambda x: _set_lr_scale(x, lr_scales[i])) |
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i += 1 |
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if layer.downsample is not None: |
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layer.downsample.apply(lambda x: _set_lr_scale(x, lr_scales[i - 1])) |
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assert i == depth |
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for m in [self.norm_head, self.head]: |
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m.apply(lambda x: _set_lr_scale(x, lr_scales[-1])) |
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|
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for k, p in self.named_parameters(): |
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p.param_name = k |
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|
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def _check_lr_scale(m): |
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"""Checks if the learning rate scale attribute is present in module's parameters.""" |
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for p in m.parameters(): |
|
assert hasattr(p, 'lr_scale'), p.param_name |
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|
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self.apply(_check_lr_scale) |
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|
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def _init_weights(self, m): |
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"""Initializes weights for linear layers and layer normalization in the given module.""" |
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if isinstance(m, nn.Linear): |
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# NOTE: This initialization is needed only for training. |
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# trunc_normal_(m.weight, std=.02) |
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if m.bias is not None: |
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nn.init.constant_(m.bias, 0) |
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elif isinstance(m, nn.LayerNorm): |
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nn.init.constant_(m.bias, 0) |
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nn.init.constant_(m.weight, 1.0) |
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|
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@torch.jit.ignore |
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def no_weight_decay_keywords(self): |
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"""Returns a dictionary of parameter names where weight decay should not be applied.""" |
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return {'attention_biases'} |
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|
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def forward_features(self, x): |
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"""Runs the input through the model layers and returns the transformed output.""" |
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x = self.patch_embed(x) # x input is (N, C, H, W) |
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|
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x = self.layers[0](x) |
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start_i = 1 |
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|
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for i in range(start_i, len(self.layers)): |
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layer = self.layers[i] |
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x = layer(x) |
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B, _, C = x.size() |
|
x = x.view(B, 64, 64, C) |
|
x = x.permute(0, 3, 1, 2) |
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return self.neck(x) |
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|
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def forward(self, x): |
|
"""Executes a forward pass on the input tensor through the constructed model layers.""" |
|
return self.forward_features(x)
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|
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