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wrn_mixup_model.py
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wrn_mixup_model.py
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### dropout has been removed in this code. original code had dropout#####
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Variable
import sys, os
import numpy as np
import random
act = torch.nn.ReLU()
import backbone
import math
from torch.nn.utils.weight_norm import WeightNorm
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_planes)
self.relu2 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
padding=1, bias=False)
self.droprate = dropRate
self.equalInOut = (in_planes == out_planes)
self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
padding=0, bias=False) 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):
super(NetworkBlock, self).__init__()
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))
return nn.Sequential(*layers)
def forward(self, x):
return self.layer(x)
def to_one_hot(inp,num_classes):
y_onehot = torch.FloatTensor(inp.size(0), num_classes)
if torch.cuda.is_available():
y_onehot = y_onehot.cuda()
y_onehot.zero_()
x = inp.type(torch.LongTensor)
if torch.cuda.is_available():
x = x.cuda()
x = torch.unsqueeze(x , 1)
y_onehot.scatter_(1, x , 1)
return Variable(y_onehot,requires_grad=False)
# return y_onehot
def mixup_data(x, y, lam):
'''Compute the mixup data. Return mixed inputs, pairs of targets, and lambda'''
batch_size = x.size()[0]
index = torch.randperm(batch_size)
if torch.cuda.is_available():
index = index.cuda()
mixed_x = lam * x + (1 - lam) * x[index,:]
y_a, y_b = y, y[index]
return mixed_x, y_a, y_b, lam
emb_2d = False # False | True # for MultiDigitMNIST: doesn't work
class WideResNet(nn.Module):
def __init__(self,
depth=28,
widen_factor=10,
num_classes= 200 ,
loss_type = 'dist',
per_img_std = False,
stride = 1 ,
dropRate=0.5 ):
flatten = True
super(WideResNet, self).__init__()
nChannels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor]
assert((depth - 4) % 6 == 0)
n = (depth - 4) / 6
block = BasicBlock
# 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, stride, dropRate)
# 2nd block
self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate)
# 3rd block
self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate)
# global average pooling and linear
self.bn1 = nn.BatchNorm2d(nChannels[3])
self.relu = nn.ReLU(inplace=True)
self.nChannels = nChannels[3]
if loss_type == 'softmax':
self.linear = nn.Linear(nChannels[3], int(num_classes))
self.linear.bias.data.fill_(0)
else:
if emb_2d:
self.emb_2d = nn.Linear(nChannels[3], 2)
self.linear = backbone.distLinear(2, int(num_classes))
else:
self.classifier = backbone.distLinear(nChannels[3], int(num_classes)) # classifier | linear
self.num_classes = num_classes
if flatten:
self.final_feat_dim = 640
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_()
def forward(self, x, target= None, mixup=False, mixup_hidden=True, mixup_alpha=None , lam = 0.4):
if target is not None:
if mixup_hidden:
layer_mix = random.randint(0,3)
elif mixup:
layer_mix = 0
else:
layer_mix = None
out = x
target_a = target_b = target
if layer_mix == 0:
out, target_a , target_b , lam = mixup_data(out, target, lam=lam)
out = self.conv1(out)
out = self.block1(out)
if layer_mix == 1:
out, target_a , target_b , lam = mixup_data(out, target, lam=lam)
out = self.block2(out)
if layer_mix == 2:
out, target_a , target_b , lam = mixup_data(out, target, lam=lam)
out = self.block3(out)
if layer_mix == 3:
out, target_a , target_b , lam = mixup_data(out, target, lam=lam)
out = self.relu(self.bn1(out))
out = F.avg_pool2d(out, out.size()[2:])
out = out.view(out.size(0), -1)
if emb_2d:
out = self.emb_2d(out)
out1 = self.classifier(out) # classifier | linear
return out , out1 , target_a , target_b
else:
out = x
out = self.conv1(out)
out = self.block1(out)
out = self.block2(out)
out = self.block3(out)
out = self.relu(self.bn1(out))
out = F.avg_pool2d(out, out.size()[2:])
out = out.view(out.size(0), -1)
if emb_2d:
out = self.emb_2d(out)
out1 = self.classifier(out) # classifier | linear
return out, out1
def wrn28_10(num_classes=10 , drop_rate= 0.5, loss_type = 'dist'):
model = WideResNet(depth=28, widen_factor=10, num_classes=num_classes, loss_type = loss_type , per_img_std = False, stride = 1 ,dropRate = drop_rate)
return model