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ican_v2.py
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import torch.nn as nn
import torch
import torch.nn.init as init
import torch.nn.functional as F
import math
import copy
from collections import OrderedDict
from torch.autograd import Variable
NUM_CLASS = 31
BATCH_SIZE = 16
INI_DISC_WEIGHT_SCALE = (200)**4
INI_DISC_BIAS = 0.5
LAST_WEIGHT_LIMIT = -2
class Contrast_ReLU_activate(nn.Module):
def __init__(self, initWeightScale, initBias):
super(Contrast_ReLU_activate, self).__init__()
self.dom_func_weight = nn.Parameter(torch.ones(1),requires_grad=True)
self.dom_func_bias = torch.FloatTensor([0]).cuda()
self.weight_scale = initWeightScale
self.add_bias = initBias
def forward(self, dom_res, dom_label):
w = (self.dom_func_weight * self.weight_scale)
b = (self.dom_func_bias + self.add_bias)
dom_prob = F.sigmoid(dom_res).squeeze()
dom_variance = torch.abs(dom_prob - 0.5)
act_weight = 0.8 - w * dom_variance**4 + b
# Minimise function to zero(target)
zeros_var = b
f_weight = torch.max(act_weight, zeros_var)
final_weight = f_weight
return final_weight, float(w.squeeze()), float(b.squeeze())
class Discriminator_Weights_Adjust(nn.Module):
def __init__(self):
super(Discriminator_Weights_Adjust, self).__init__()
self.main_var = torch.FloatTensor([0]).cuda()
self.l1_var = nn.Parameter(torch.zeros(1),requires_grad=True)
self.l2_var = nn.Parameter(torch.zeros(1),requires_grad=True)
self.k_var = torch.FloatTensor([-0.9]).cuda()
self.l1_rev = torch.ones(1).cuda()
self.l2_rev = torch.ones(1).cuda()
self.l3_rev = torch.ones(1).cuda()
def forward(self, main_weight, l1_weight, l2_weight, l3_weight):
w_main = main_weight + self.main_var
w_l1 = l1_weight + self.l1_var
w_l2 = l2_weight + self.l2_var
w_l3 = (w_main - self.k_var) - w_l1 - w_l2
l1_rev = self.l1_rev
l2_rev = self.l2_rev
l3_rev = self.l3_rev
if float(w_l1) < 0:
l1_rev[0] = -1
if float(w_l2) < 0:
l2_rev[0] = -1
if float(w_l3) < 0:
l3_rev[0] = -1
return torch.abs(w_main), torch.abs(w_l1), torch.abs(w_l2), torch.abs(w_l3), l1_rev, l2_rev, l3_rev
class ICAN(nn.Module):
def __init__(self, pre_trained):
super(ICAN, self).__init__()
self.num_class = NUM_CLASS
self.disc_activate = Contrast_ReLU_activate(INI_DISC_WEIGHT_SCALE, INI_DISC_BIAS)
self.disc_weight = Discriminator_Weights_Adjust()
self.conv1 = pre_trained.conv1
self.bn1 = pre_trained.bn1
self.relu = pre_trained.relu
self.maxpool = pre_trained.maxpool
self.layer1 = pre_trained.layer1
self.layer2 = pre_trained.layer2
self.layer3 = pre_trained.layer3
self.layer4 = pre_trained.layer4
self.domain_pred = nn.Sequential(nn.Linear(256, 3072), nn.ReLU(True), nn.Dropout(),
nn.Linear(3072, 2048), nn.ReLU(True), nn.Dropout(),
nn.Linear(2048, 1))
self.domain_pred_l1 = nn.Sequential(nn.Linear(256, 3072), nn.ReLU(True), nn.Dropout(),
nn.Linear(3072, 2048), nn.ReLU(True), nn.Dropout(),
nn.Linear(2048, 1))
self.domain_pred_l2 = nn.Sequential(nn.Linear(256, 3072), nn.ReLU(True), nn.Dropout(),
nn.Linear(3072, 2048), nn.ReLU(True), nn.Dropout(),
nn.Linear(2048, 1))
self.domain_pred_l3 = nn.Sequential(nn.Linear(256, 3072), nn.ReLU(True), nn.Dropout(),
nn.Linear(3072, 2048), nn.ReLU(True), nn.Dropout(),
nn.Linear(2048, 1))
self.process = pre_trained.avgpool
self.process_l1 = nn.AvgPool2d(kernel_size=56)
self.process_l2 = nn.AvgPool2d(kernel_size=28)
self.process_l3 = nn.AvgPool2d(kernel_size=14)
self.source_bottleneck = nn.Sequential(nn.Linear(pre_trained.fc.in_features, 256))
self.l1_bottleneck = nn.Sequential(nn.Linear(256, 256))
self.l2_bottleneck = nn.Sequential(nn.Linear(512, 256))
self.l3_bottleneck = nn.Sequential(nn.Linear(1024, 256))
self.source_classifier = nn.Sequential(nn.Linear(256, self.num_class))
# ----- data parallel (multi-gpu) -------
self.conv1 = nn.DataParallel(self.conv1)
self.layer1 = nn.DataParallel(self.layer1)
self.layer2 = nn.DataParallel(self.layer2)
self.layer3 = nn.DataParallel(self.layer3)
self.layer4 = nn.DataParallel(self.layer4)
self.source_bottleneck = nn.DataParallel(self.source_bottleneck)
self.l1_bottleneck = nn.DataParallel(self.l1_bottleneck)
self.l2_bottleneck = nn.DataParallel(self.l2_bottleneck)
self.l3_bottleneck = nn.DataParallel(self.l3_bottleneck)
self.domain_pred = nn.DataParallel(self.domain_pred)
self.domain_pred_l1 = nn.DataParallel(self.domain_pred_l1)
self.domain_pred_l2 = nn.DataParallel(self.domain_pred_l2)
self.domain_pred_l3 = nn.DataParallel(self.domain_pred_l3)
def forward(self, cond, x1, x2=None, l=None, dom_label=None,
init_weight=None, init_w_main=None, init_w_l1=None,
init_w_l2=None, init_w_l3=None):
base1 = self.conv1(x1)
base1 = self.bn1(base1)
base1 = self.relu(base1)
base1 = self.maxpool(base1)
l1 = self.layer1(base1)
l2 = self.layer2(l1)
l3 = self.layer3(l2)
l4 = self.layer4(l3)
process = self.process(l4).view(l4.size(0), -1)
if (cond == 'source_train' or cond == 'pretrain' or cond == 'source_pseudo_train'):
bottle = self.source_bottleneck(process)
class_pred = self.source_classifier(bottle)
base2 = self.conv1(x2)
base2 = self.bn1(base2)
base2 = self.relu(base2)
base2 = self.maxpool(base2)
l1_2 = self.layer1(base2)
l2_2 = self.layer2(l1_2)
l3_2 = self.layer3(l2_2)
l4_2 = self.layer4(l3_2)
process_2 = self.process(l4_2)
process_2 = process_2.view(l4_2.size(0), -1)
bottle_2 = self.source_bottleneck(process_2)
grad_inverse_hook_2 = bottle_2.register_hook(lambda grad: grad * -1*l)
process_l1 = self.process_l1(l1_2).view(l1_2.size(0), -1)
bottle_l1 = self.l1_bottleneck(process_l1)
process_l2 = self.process_l2(l2_2).view(l2_2.size(0), -1)
bottle_l2 = self.l2_bottleneck(process_l2)
process_l3 = self.process_l3(l3_2).view(l3_2.size(0), -1)
bottle_l3 = self.l3_bottleneck(process_l3)
disc_main, disc_l1, disc_l2, disc_l3, l1_rev, l2_rev, l3_rev = self.disc_weight(init_w_main, init_w_l1,
init_w_l2, init_w_l3)
grad_hook_l1 = bottle_l1.register_hook(lambda grad: grad *l*l1_rev)
grad_hook_l2 = bottle_l2.register_hook(lambda grad: grad *l*l2_rev)
grad_hook_l3 = bottle_l3.register_hook(lambda grad: grad *l*l3_rev)
dom_pred = self.domain_pred(bottle_2)
dom_pred_l1 = self.domain_pred_l1(bottle_l1)
dom_pred_l2 = self.domain_pred_l2(bottle_l2)
dom_pred_l3 = self.domain_pred_l3(bottle_l3)
# print("process: ", process.grad)
return class_pred, dom_pred.squeeze(1), dom_pred_l1.squeeze(1), \
dom_pred_l2.squeeze(1), dom_pred_l3.squeeze(1), \
disc_main, disc_l1, disc_l2, disc_l3, l1_rev, l2_rev, l3_rev
elif (cond == 'pseudo_discriminator'):
bottle_2 = self.source_bottleneck(process)
class_pred = self.source_classifier(bottle_2)
dom_pred = self.domain_pred(bottle_2)
disc_w, weight, bias = self.disc_activate(dom_pred, dom_label)
return class_pred, dom_pred.squeeze(), disc_w, weight, bias
else:
# test and pseudo label
bottle = self.source_bottleneck(process)
class_pred = self.source_classifier(bottle)
return class_pred