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tanaka_adaptation_network.py
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tanaka_adaptation_network.py
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# -*- coding: utf-8 -*-
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from shakedrop import ShakeDrop
class ShakeBasicBlock(nn.Module):
def __init__(self, in_ch, out_ch, stride=1, p_shakedrop=1.0):
super(ShakeBasicBlock, self).__init__()
self.downsampled = stride == 2
self.branch = self._make_branch(in_ch, out_ch, stride=stride)
self.shortcut = not self.downsampled and None or nn.AvgPool2d(2)
self.shake_drop = ShakeDrop(p_shakedrop)
def forward(self, x):
h = self.branch(x)
h = self.shake_drop(h)
h0 = x if not self.downsampled else self.shortcut(x)
pad_zero = Variable(torch.zeros(h0.size(0), h.size(1) - h0.size(1), h0.size(2), h0.size(3)).float()).cuda()
h0 = torch.cat([h0, pad_zero], dim=1)
return h + h0
def _make_branch(self, in_ch, out_ch, stride=1):
return nn.Sequential(
nn.BatchNorm2d(in_ch),
nn.Conv2d(in_ch, out_ch, 3, padding=1, stride=stride, bias=False),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1, stride=1, bias=False),
nn.BatchNorm2d(out_ch))
class ShakePyramidNet(nn.Module):
def __init__(self, depth=110, alpha=270, label=10):
super(ShakePyramidNet, self).__init__()
in_ch = 16
# for BasicBlock
n_units = (depth - 2) // 6
in_chs = [in_ch] + [in_ch + math.ceil((alpha / (3 * n_units)) * (i + 1)) for i in range(3 * n_units)]
block = ShakeBasicBlock
self.in_chs, self.u_idx = in_chs, 0
self.ps_shakedrop = [1 - (1.0 - (0.5 / (3 * n_units)) * (i + 1)) for i in range(3 * n_units)]
self.ll = nn.LeakyReLU(0.1)
self.conv0 = nn.Conv2d(3, 4*4*16, kernel_size=4, stride=4, padding=0, bias=False)
self.bn0 = nn.BatchNorm2d(4*4*16)
self.conv1 = nn.Conv2d(4*4*3, 4*4*3, kernel_size=1, stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(3*16)
self.conv2 = nn.Conv2d(4*4*3, 16*16, kernel_size=1, stride=1, padding=0, bias=False)
self.bn2 = nn.BatchNorm2d(16*16)
self.pixelshuffle = nn.PixelShuffle(4)
self.c_in = nn.Conv2d(16, in_chs[0], 3, padding=1)
self.bn_in = nn.BatchNorm2d(in_chs[0])
self.layer1 = self._make_layer(n_units, block, 1)
self.layer2 = self._make_layer(n_units, block, 2)
self.layer3 = self._make_layer(n_units, block, 2)
self.bn_out = nn.BatchNorm2d(in_chs[-1])
self.fc_out = nn.Linear(in_chs[-1], label)
# Initialize paramters
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):
m.bias.data.zero_()
def forward(self, x):
x_stack = None
idx = 0
for i in range(8):
tmp = None
for j in range(8):
out = self.ll(self.bn0(self.conv0(x[:,:,i*4:(i+1)*4,j*4:(j+1)*4])))
out = self.pixelshuffle(out)
if tmp is None:
tmp = out
else:
tmp = torch.cat([tmp,out],dim=3)
idx = idx + 1
if x_stack is None:
x_stack = tmp
else:
x_stack = torch.cat([x_stack,tmp],dim=2)
h = x_stack
feature = h
h = self.bn_in(self.c_in(h))
h = self.layer1(h)
h = self.layer2(h)
h = self.layer3(h)
h = F.relu(self.bn_out(h))
h = F.avg_pool2d(h, 8)
h = h.view(h.size(0), -1)
h = self.fc_out(h)
return h
def _make_layer(self, n_units, block, stride=1):
layers = []
for i in range(int(n_units)):
layers.append(block(self.in_chs[self.u_idx], self.in_chs[self.u_idx+1],
stride, self.ps_shakedrop[self.u_idx]))
self.u_idx, stride = self.u_idx + 1, 1
return nn.Sequential(*layers)