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wideresnet_wavelet.py
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wideresnet_wavelet.py
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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from DWT_IDWT_layer import *
class Downsample_v1(nn.Module):
def __init__(self, wavename = 'haar'):
super(Downsample_v1, self).__init__()
self.dwt = DWT_2D(wavename = wavename)
def forward(self, input):
LL, LH, HL, HH = self.dwt(input)
return torch.cat((LL, LH, HL, HH), dim = 1)
class Downsample_v2(nn.Module):
def __init__(self, wavename = 'haar'):
super(Downsample_v2, self).__init__()
self.dwt = DWT_2D(wavename = wavename)
def forward(self, input):
LL, LH, HL, HH = self.dwt(input)
return (LL + LH + HL + HH) / 4
class Downsample_v3(nn.Module):
def __init__(self, wavename = 'haar'):
super(Downsample_v3, self).__init__()
self.dwt = DWT_2D(wavename = wavename)
def forward(self, input):
LL, LH, HL, HH = self.dwt(input)
return LL
class UpSampling_v1(nn.Module):
def __init__(self, wavename = 'haar'):
super(UpSampling_v1, self).__init__()
self.idwt = IDWT_2D(wavename = wavename)
def forward(self, LL, LH, HL, HH):
return self.idwt(LL, LH, HL, HH)
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0, activation='ReLU', softplus_beta=1):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_planes)
#self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
# padding=1, bias=False)
if (stride ==1):
self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)
else:
self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)
if activation == 'ReLU':
self.relu1 = nn.ReLU(inplace=True)
self.relu2 = nn.ReLU(inplace=True)
print('R')
elif activation == 'Softplus':
self.relu1 = nn.Softplus(beta=softplus_beta, threshold=20)
self.relu2 = nn.Softplus(beta=softplus_beta, threshold=20)
print('S')
elif activation == 'GELU':
self.relu1 = nn.GELU()
self.relu2 = nn.GELU()
print('G')
elif activation == 'ELU':
self.relu1 = nn.ELU(alpha=1.0, inplace=True)
self.relu2 = nn.ELU(alpha=1.0, inplace=True)
print('E')
self.droprate = dropRate
self.equalInOut = (in_planes == out_planes)
if stride == 1:
convShortCut = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False)
else:
convShortCut = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False)
self.convShortcut = (not self.equalInOut) and convShortCut or None
def forward(self, x):
if not self.equalInOut:
x = self.relu1(self.bn1(x))
else:
out = self.relu1(self.bn1(x))
out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
if self.droprate > 0:
out = F.dropout(out, p=self.droprate, training=self.training)
out = self.conv2(out)
return torch.add(x if self.equalInOut else self.convShortcut(x), out)
class NetworkBlock(nn.Module):
def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0, activation='ReLU', softplus_beta=1):
super(NetworkBlock, self).__init__()
self.activation = activation
self.softplus_beta = softplus_beta
self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate)
def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate):
layers = []
for i in range(int(nb_layers)):
layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate,
self.activation, self.softplus_beta))
return nn.Sequential(*layers)
def forward(self, x):
return self.layer(x)
class WideResNetWavelet(nn.Module):
def __init__(self, depth=34, num_classes=10, widen_factor=10, dropRate=0.0, normalize=False, activation='ReLU', softplus_beta=1):
super(WideResNetWavelet, self).__init__()
nChannels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
assert ((depth - 4) % 6 == 0)
n = (depth - 4) / 6
block = BasicBlock
self.normalize = normalize
#self.scale = scale
# 1st conv before any network block
self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1,
padding=1, bias=False)
# 1st block
self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate, activation=activation, softplus_beta=softplus_beta)
# 1st sub-block
self.sub_block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate, activation=activation, softplus_beta=softplus_beta)
# 2nd block
self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate, activation=activation, softplus_beta=softplus_beta)
# 3rd block
self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate, activation=activation, softplus_beta=softplus_beta)
# global average pooling and classifier
self.bn1 = nn.BatchNorm2d(nChannels[3])
self.upsampling = UpSampling_v1(wavename = 'haar')
if activation == 'ReLU':
self.relu = nn.ReLU(inplace=True)
elif activation == 'Softplus':
self.relu = nn.Softplus(beta=softplus_beta, threshold=20)
elif activation == 'GELU':
self.relu = nn.GELU()
elif activation == 'ELU':
self.relu = nn.ELU(alpha=1.0, inplace=True)
print('Use activation of ' + activation)
self.avgpool = Downsample_v2(wavename = 'haar')
if self.normalize:
self.fc = nn.Linear(nChannels[3], num_classes, bias = False)
else:
self.fc = nn.Linear(nChannels[3], num_classes)
self.nChannels = nChannels[3]
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear) and not self.normalize:
m.bias.data.zero_()
def forward(self, x):
#print(x.shape)
#x = self.avgpool(x)
#print(x.shape)
#x = x.view(128, 3, 32, 32)
#x = self.avgpool(x)
#x = self.upsampling(x[0:32], x[32:64], x[64:96], x[96:128])
#x = x.view(128, 3, 32, 32)
out = self.conv1(x)
out = self.block1(out)
out = self.block2(out)
out = self.block3(out)
out = self.relu(self.bn1(out))
#print(out.shape)
#out = F.avg_pool2d(out, 8)
out = self.avgpool(out)
#out = self.avgpool(out)
#print(out.shape)
#dwt_shape = out.shape[0]
#print(out_intermediate.shape)
#out = self.upsampling(out, x1[32: 64],
# x1[64: 96], x1[96: 128])
#out =out.view(128, 640, 8, 8)
out = F.avg_pool2d(out, 4)
#print(out.shape)
#print(out.shape)
#print(self.nChannels)
out = out.view(-1, self.nChannels)
#print(out.shape)
if self.normalize:
out = F.normalize(out, p=2, dim=1)
for _, module in self.fc.named_modules():
if isinstance(module, nn.Linear):
module.weight.data = F.normalize(module.weight, p=2, dim=1)
return self.fc(out)