| """ Convolution with Weight Standardization (StdConv and ScaledStdConv) |
| |
| StdConv: |
| @article{weightstandardization, |
| author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille}, |
| title = {Weight Standardization}, |
| journal = {arXiv preprint arXiv:1903.10520}, |
| year = {2019}, |
| } |
| Code: https://github.com/joe-siyuan-qiao/WeightStandardization |
| |
| ScaledStdConv: |
| Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` |
| - https://arxiv.org/abs/2101.08692 |
| Official Deepmind JAX code: https://github.com/deepmind/deepmind-research/tree/master/nfnets |
| |
| Hacked together by / copyright Ross Wightman, 2021. |
| """ |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| from .padding import get_padding, get_padding_value, pad_same |
|
|
|
|
| class StdConv2d(nn.Conv2d): |
| """Conv2d with Weight Standardization. Used for BiT ResNet-V2 models. |
| |
| Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` - |
| https://arxiv.org/abs/1903.10520v2 |
| """ |
| def __init__( |
| self, in_channel, out_channels, kernel_size, stride=1, padding=None, |
| dilation=1, groups=1, bias=False, eps=1e-6): |
| if padding is None: |
| padding = get_padding(kernel_size, stride, dilation) |
| super().__init__( |
| in_channel, out_channels, kernel_size, stride=stride, |
| padding=padding, dilation=dilation, groups=groups, bias=bias) |
| self.eps = eps |
|
|
| def forward(self, x): |
| weight = F.batch_norm( |
| self.weight.reshape(1, self.out_channels, -1), None, None, |
| training=True, momentum=0., eps=self.eps).reshape_as(self.weight) |
| x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups) |
| return x |
|
|
|
|
| class StdConv2dSame(nn.Conv2d): |
| """Conv2d with Weight Standardization. TF compatible SAME padding. Used for ViT Hybrid model. |
| |
| Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` - |
| https://arxiv.org/abs/1903.10520v2 |
| """ |
| def __init__( |
| self, in_channel, out_channels, kernel_size, stride=1, padding='SAME', |
| dilation=1, groups=1, bias=False, eps=1e-6): |
| padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation) |
| super().__init__( |
| in_channel, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, |
| groups=groups, bias=bias) |
| self.same_pad = is_dynamic |
| self.eps = eps |
|
|
| def forward(self, x): |
| if self.same_pad: |
| x = pad_same(x, self.kernel_size, self.stride, self.dilation) |
| weight = F.batch_norm( |
| self.weight.reshape(1, self.out_channels, -1), None, None, |
| training=True, momentum=0., eps=self.eps).reshape_as(self.weight) |
| x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups) |
| return x |
|
|
|
|
| class ScaledStdConv2d(nn.Conv2d): |
| """Conv2d layer with Scaled Weight Standardization. |
| |
| Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` - |
| https://arxiv.org/abs/2101.08692 |
| |
| NOTE: the operations used in this impl differ slightly from the DeepMind Haiku impl. The impact is minor. |
| """ |
|
|
| def __init__( |
| self, in_channels, out_channels, kernel_size, stride=1, padding=None, |
| dilation=1, groups=1, bias=True, gamma=1.0, eps=1e-6, gain_init=1.0): |
| if padding is None: |
| padding = get_padding(kernel_size, stride, dilation) |
| super().__init__( |
| in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, |
| groups=groups, bias=bias) |
| self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init)) |
| self.scale = gamma * self.weight[0].numel() ** -0.5 |
| self.eps = eps |
|
|
| def forward(self, x): |
| weight = F.batch_norm( |
| self.weight.reshape(1, self.out_channels, -1), None, None, |
| weight=(self.gain * self.scale).view(-1), |
| training=True, momentum=0., eps=self.eps).reshape_as(self.weight) |
| return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups) |
|
|
|
|
| class ScaledStdConv2dSame(nn.Conv2d): |
| """Conv2d layer with Scaled Weight Standardization and Tensorflow-like SAME padding support |
| |
| Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` - |
| https://arxiv.org/abs/2101.08692 |
| |
| NOTE: the operations used in this impl differ slightly from the DeepMind Haiku impl. The impact is minor. |
| """ |
|
|
| def __init__( |
| self, in_channels, out_channels, kernel_size, stride=1, padding='SAME', |
| dilation=1, groups=1, bias=True, gamma=1.0, eps=1e-6, gain_init=1.0): |
| padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation) |
| super().__init__( |
| in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, |
| groups=groups, bias=bias) |
| self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init)) |
| self.scale = gamma * self.weight[0].numel() ** -0.5 |
| self.same_pad = is_dynamic |
| self.eps = eps |
|
|
| def forward(self, x): |
| if self.same_pad: |
| x = pad_same(x, self.kernel_size, self.stride, self.dilation) |
| weight = F.batch_norm( |
| self.weight.reshape(1, self.out_channels, -1), None, None, |
| weight=(self.gain * self.scale).view(-1), |
| training=True, momentum=0., eps=self.eps).reshape_as(self.weight) |
| return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups) |
|
|
