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144 lines
6.4 KiB
144 lines
6.4 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, dilation=1,
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groups=1, bias=False, eps=1e-5):
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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 get_weight(self):
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std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
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weight = (self.weight - mean) / (std + self.eps)
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return weight
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def forward(self, x):
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x = F.conv2d(x, self.get_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', dilation=1,
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groups=1, bias=False, eps=1e-5):
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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 get_weight(self):
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std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
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weight = (self.weight - mean) / (std + self.eps)
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return weight
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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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x = F.conv2d(x, self.get_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, dilation=1, groups=1,
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bias=True, gamma=1.0, eps=1e-5, use_layernorm=False):
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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.ones(self.out_channels, 1, 1, 1))
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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 ** 2 if use_layernorm else eps
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self.use_layernorm = use_layernorm # experimental, slightly faster/less GPU memory to hijack LN kernel
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def get_weight(self):
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if self.use_layernorm:
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weight = self.scale * F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
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else:
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std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
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weight = self.scale * (self.weight - mean) / (std + self.eps)
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return self.gain * weight
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def forward(self, x):
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return F.conv2d(x, self.get_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', dilation=1, groups=1,
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bias=True, gamma=1.0, eps=1e-5, use_layernorm=False):
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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.ones(self.out_channels, 1, 1, 1))
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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 ** 2 if use_layernorm else eps
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self.use_layernorm = use_layernorm # experimental, slightly faster/less GPU memory to hijack LN kernel
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# NOTE an alternate formulation to consider, closer to DeepMind Haiku impl but doesn't seem
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# to make much numerical difference (+/- .002 to .004) in top-1 during eval.
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# def get_weight(self):
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# var, mean = torch.var_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
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# scale = torch.rsqrt((self.weight[0].numel() * var).clamp_(self.eps)) * self.gain
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# weight = (self.weight - mean) * scale
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# return self.gain * weight
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def get_weight(self):
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if self.use_layernorm:
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weight = self.scale * F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
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else:
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std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
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weight = self.scale * (self.weight - mean) / (std + self.eps)
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return self.gain * weight
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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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return F.conv2d(x, self.get_weight(), self.bias, self.stride, self.padding, self.dilation, self.groups)
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