# coding=utf-8
# Copyright 2019 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import norms
import numpy as np
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
def conv_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.xavier_uniform(m.weight, gain=np.sqrt(2))
init.constant(m.bias, 0)
elif classname.find('BatchNorm') != -1:
init.constant(m.weight, 1)
init.constant(m.bias, 0)
class Identity(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
def forward(self, x):
return x
class wide_basic(nn.Module):
def __init__(self, in_planes, planes, dropout_rate, stride=1, norm=None, leak=.2):
super(wide_basic, self).__init__()
self.lrelu = nn.LeakyReLU(leak)
self.bn1 = get_norm(in_planes, norm)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, bias=True)
self.dropout = Identity() if dropout_rate == 0.0 else nn.Dropout(p=dropout_rate)
self.bn2 = get_norm(planes, norm)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=True),
)
def forward(self, x):
out = self.dropout(self.conv1(self.lrelu(self.bn1(x))))
out = self.conv2(self.lrelu(self.bn2(out)))
out += self.shortcut(x)
return out
def get_norm(n_filters, norm):
if norm is None:
return Identity()
elif norm == "batch":
return nn.BatchNorm2d(n_filters, momentum=0.9)
elif norm == "instance":
return nn.InstanceNorm2d(n_filters, affine=True)
elif norm == "layer":
return nn.GroupNorm(1, n_filters)
elif norm == "act":
return norms.ActNorm(n_filters, False)
class Wide_ResNet(nn.Module):
def __init__(self, depth, widen_factor, num_classes=10, input_channels=3,
sum_pool=False, norm=None, leak=.2, dropout_rate=0.0):
super(Wide_ResNet, self).__init__()
self.leak = leak
self.in_planes = 16
self.sum_pool = sum_pool
self.norm = norm
self.lrelu = nn.LeakyReLU(leak)
assert ((depth-4)%6 ==0), 'Wide-resnet depth should be 6n+4'
n = (depth-4)//6
k = widen_factor
print('| Wide-Resnet %dx%d' %(depth, k))
nStages = [16, 16*k, 32*k, 64*k]
self.conv1 = conv3x3(input_channels, nStages[0])
self.layer1 = self._wide_layer(wide_basic, nStages[1], n, dropout_rate, stride=1)
self.layer2 = self._wide_layer(wide_basic, nStages[2], n, dropout_rate, stride=2)
self.layer3 = self._wide_layer(wide_basic, nStages[3], n, dropout_rate, stride=2)
self.bn1 = get_norm(nStages[3], self.norm)
self.last_dim = nStages[3]
self.linear = nn.Linear(nStages[3], num_classes)
def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, dropout_rate, stride, norm=self.norm))
self.in_planes = planes
return nn.Sequential(*layers)
def forward(self, x, vx=None):
out = self.conv1(x)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.lrelu(self.bn1(out))
if self.sum_pool:
out = out.view(out.size(0), out.size(1), -1).sum(2)
else:
out = F.avg_pool2d(out, 8)
out = out.view(out.size(0), -1)
return out
