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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from .registry import CONV_LAYERS
def conv_ws_2d(input,
weight,
bias=None,
stride=1,
padding=0,
dilation=1,
groups=1,
eps=1e-5):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1)
mean = weight_flat.mean(dim=1, keepdim=True).view(c_in, 1, 1, 1)
std = weight_flat.std(dim=1, keepdim=True).view(c_in, 1, 1, 1)
weight = (weight - mean) / (std + eps)
return F.conv2d(input, weight, bias, stride, padding, dilation, groups)
@CONV_LAYERS.register_module('ConvWS')
class ConvWS2d(nn.Conv2d):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
eps=1e-5):
super(ConvWS2d, self).__init__(
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias)
self.eps = eps
def forward(self, x):
return conv_ws_2d(x, self.weight, self.bias, self.stride, self.padding,
self.dilation, self.groups, self.eps)
@CONV_LAYERS.register_module(name='ConvAWS')
class ConvAWS2d(nn.Conv2d):
"""AWS (Adaptive Weight Standardization)
This is a variant of Weight Standardization
(https://arxiv.org/pdf/1903.10520.pdf)
It is used in DetectoRS to avoid NaN
(https://arxiv.org/pdf/2006.02334.pdf)
Args:
in_channels (int): Number of channels in the input image
out_channels (int): Number of channels produced by the convolution
kernel_size (int or tuple): Size of the conv kernel
stride (int or tuple, optional): Stride of the convolution. Default: 1
padding (int or tuple, optional): Zero-padding added to both sides of
the input. Default: 0
dilation (int or tuple, optional): Spacing between kernel elements.
Default: 1
groups (int, optional): Number of blocked connections from input
channels to output channels. Default: 1
bias (bool, optional): If set True, adds a learnable bias to the
output. Default: True
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True):
super().__init__(
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias)
self.register_buffer('weight_gamma',
torch.ones(self.out_channels, 1, 1, 1))
self.register_buffer('weight_beta',
torch.zeros(self.out_channels, 1, 1, 1))
def _get_weight(self, weight):
weight_flat = weight.view(weight.size(0), -1)
mean = weight_flat.mean(dim=1).view(-1, 1, 1, 1)
std = torch.sqrt(weight_flat.var(dim=1) + 1e-5).view(-1, 1, 1, 1)
weight = (weight - mean) / std
weight = self.weight_gamma * weight + self.weight_beta
return weight
def forward(self, x):
weight = self._get_weight(self.weight)
return F.conv2d(x, weight, self.bias, self.stride, self.padding,
self.dilation, self.groups)
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
"""Override default load function.
AWS overrides the function _load_from_state_dict to recover
weight_gamma and weight_beta if they are missing. If weight_gamma and
weight_beta are found in the checkpoint, this function will return
after super()._load_from_state_dict. Otherwise, it will compute the
mean and std of the pretrained weights and store them in weight_beta
and weight_gamma.
"""
self.weight_gamma.data.fill_(-1)
local_missing_keys = []
super()._load_from_state_dict(state_dict, prefix, local_metadata,
strict, local_missing_keys,
unexpected_keys, error_msgs)
if self.weight_gamma.data.mean() > 0:
for k in local_missing_keys:
missing_keys.append(k)
return
weight = self.weight.data
weight_flat = weight.view(weight.size(0), -1)
mean = weight_flat.mean(dim=1).view(-1, 1, 1, 1)
std = torch.sqrt(weight_flat.var(dim=1) + 1e-5).view(-1, 1, 1, 1)
self.weight_beta.data.copy_(mean)
self.weight_gamma.data.copy_(std)
missing_gamma_beta = [
k for k in local_missing_keys
if k.endswith('weight_gamma') or k.endswith('weight_beta')
]
for k in missing_gamma_beta:
local_missing_keys.remove(k)
for k in local_missing_keys:
missing_keys.append(k)
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