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122 lines
5.3 KiB
122 lines
5.3 KiB
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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from .padding import get_padding, get_padding_value, pad_same
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def get_weight(module):
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std, mean = torch.std_mean(module.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
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weight = (module.weight - mean) / (std + module.eps)
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return weight
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class StdConv2d(nn.Conv2d):
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"""Conv2d with Weight Standardization. Used for BiT ResNet-V2 models.
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Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` -
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https://arxiv.org/abs/1903.10520v2
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"""
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def __init__(
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self, in_channel, out_channels, kernel_size, stride=1, padding=None,
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dilation=1, groups=1, bias=False, eps=1e-6):
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if padding is None:
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padding = get_padding(kernel_size, stride, dilation)
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super().__init__(
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in_channel, out_channels, kernel_size, stride=stride,
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padding=padding, dilation=dilation, groups=groups, bias=bias)
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self.eps = eps
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def forward(self, x):
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weight = F.batch_norm(
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self.weight.view(1, self.out_channels, -1), None, None,
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eps=self.eps, training=True, momentum=0.).reshape_as(self.weight)
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x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
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return x
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class StdConv2dSame(nn.Conv2d):
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"""Conv2d with Weight Standardization. TF compatible SAME padding. Used for ViT Hybrid model.
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Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` -
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https://arxiv.org/abs/1903.10520v2
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"""
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def __init__(
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self, in_channel, out_channels, kernel_size, stride=1, padding='SAME',
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dilation=1, groups=1, bias=False, eps=1e-6):
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padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
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super().__init__(
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in_channel, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
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groups=groups, bias=bias)
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self.same_pad = is_dynamic
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self.eps = eps
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def forward(self, x):
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if self.same_pad:
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x = pad_same(x, self.kernel_size, self.stride, self.dilation)
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weight = F.batch_norm(
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self.weight.view(1, self.out_channels, -1), None, None,
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eps=self.eps, training=True, momentum=0.).reshape_as(self.weight)
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x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
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return x
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class ScaledStdConv2d(nn.Conv2d):
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"""Conv2d layer with Scaled Weight Standardization.
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Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` -
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https://arxiv.org/abs/2101.08692
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NOTE: the operations used in this impl differ slightly from the DeepMind Haiku impl. The impact is minor.
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"""
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def __init__(
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self, in_channels, out_channels, kernel_size, stride=1, padding=None,
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dilation=1, groups=1, bias=True, gamma=1.0, eps=1e-6, gain_init=1.0):
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if padding is None:
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padding = get_padding(kernel_size, stride, dilation)
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super().__init__(
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in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
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groups=groups, bias=bias)
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self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init))
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self.scale = gamma * self.weight[0].numel() ** -0.5 # gamma * 1 / sqrt(fan-in)
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self.eps = eps
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def forward(self, x):
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weight = F.batch_norm(
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self.weight.view(1, self.out_channels, -1), None, None,
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weight=(self.gain * self.scale).view(-1),
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eps=self.eps, training=True, momentum=0.).reshape_as(self.weight)
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return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
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class ScaledStdConv2dSame(nn.Conv2d):
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"""Conv2d layer with Scaled Weight Standardization and Tensorflow-like SAME padding support
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Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` -
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https://arxiv.org/abs/2101.08692
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NOTE: the operations used in this impl differ slightly from the DeepMind Haiku impl. The impact is minor.
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"""
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def __init__(
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self, in_channels, out_channels, kernel_size, stride=1, padding='SAME',
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dilation=1, groups=1, bias=True, gamma=1.0, eps=1e-6, gain_init=1.0):
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padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
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super().__init__(
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in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
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groups=groups, bias=bias)
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self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init))
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self.scale = gamma * self.weight[0].numel() ** -0.5
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self.same_pad = is_dynamic
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self.eps = eps
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def forward(self, x):
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if self.same_pad:
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x = pad_same(x, self.kernel_size, self.stride, self.dilation)
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weight = F.batch_norm(
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self.weight.view(1, self.out_channels, -1), None, None,
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weight=(self.gain * self.scale).view(-1),
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eps=self.eps, training=True, momentum=0.).reshape_as(self.weight)
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return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
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