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MLUDA_sh.py
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MLUDA_sh.py
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# -*- coding:utf-8 -*-
# Author：Mingshuo Cai
# Create_time：2023-08-01
# Updata_time：2024-03-15
# Usage：Implementation of the MLUDA method on the SH2HZ cross-domain dataset
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import mmd
import numpy as np
from sklearn import neighbors
from sklearn import metrics
from net2 import DSANSS
import time
import utils
from torch.utils.data import TensorDataset, DataLoader
from contrastive_loss import SupConLoss
from config_SH2HZ import *
from net2 import DSANSS
from sklearn import svm
from UtilsCMS import *
##################################
file_path = './datasets/Shanghai-Hangzhou/DataCube.mat'
data_s, data_t, label_s, label_t = utils.cubeData(file_path)
data_s,data_t = ILDA(data_s,data_t,pca_n,radius)
# Loss Function
crossEntropy = nn.CrossEntropyLoss().cuda()
ContrastiveLoss_s = SupConLoss(temperature=0.1).cuda()
ContrastiveLoss_t = SupConLoss(temperature=0.1).cuda()
DSH_loss = utils.Domain_Occ_loss().cuda()
acc = np.zeros([nDataSet, 1])
A = np.zeros([nDataSet, CLASS_NUM])
k = np.zeros([nDataSet, 1])
best_predict_all = []
best_acc_all = 0.0
best_G,best_RandPerm,best_Row, best_Column,best_nTrain = None,None,None,None,None
for iDataSet in range(nDataSet):
print('#######################idataset######################## ', iDataSet)
utils.set_seed(seeds[iDataSet])
trainX, trainY = utils.get_sample_data(data_s, label_s, HalfWidth, 180)
testID, testX, testY, G, RandPerm, Row, Column = utils.get_all_data(data_t, label_t, HalfWidth)
train_dataset = TensorDataset(torch.tensor(trainX), torch.tensor(trainY))
test_dataset = TensorDataset(torch.tensor(testX), torch.tensor(testY))
train_loader_s = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
train_loader_t = DataLoader(test_dataset,batch_size=BATCH_SIZE,shuffle=True,drop_last=True)
test_loader = DataLoader(test_dataset,batch_size=BATCH_SIZE,shuffle=False,drop_last=True)
len_source_loader = len(train_loader_s)
len_target_loader = len(train_loader_t)
# model
feature_encoder = DSANSS(nBand, patch_size, CLASS_NUM).cuda()
print("Training...")
last_accuracy = 0.0
best_episdoe = 0
train_loss = []
test_acc = []
running_D_loss, running_F_loss = 0.0, 0.0
running_label_loss = 0
running_domain_loss = 0
total_hit, total_num = 0.0, 0.0
size = 0.0
test_acc_list = []
train_start = time.time()
#loss plot
loss1 = []
loss2 = []
loss3 = []
for epoch in range(1, epochs + 1):
LEARNING_RATE = lr #/ math.pow((1 + 10 * (epoch - 1) / epochs), 0.75)
print('learning rate{: .4f}'.format(LEARNING_RATE))
optimizer = torch.optim.SGD([
{'params': feature_encoder.feature_layers.parameters(),},
{'params': feature_encoder.fc1.parameters(), 'lr': LEARNING_RATE},
{'params': feature_encoder.fc2.parameters(), 'lr': LEARNING_RATE},
{'params': feature_encoder.head1.parameters(), 'lr': LEARNING_RATE},
{'params': feature_encoder.head2.parameters(), 'lr': LEARNING_RATE},
], lr=LEARNING_RATE , momentum=momentum, weight_decay=l2_decay)
feature_encoder.train()
iter_source = iter(train_loader_s)
iter_target = iter(train_loader_t)
num_iter = len_source_loader
for i in range(1,num_iter):
source_data, source_label = next(iter_source)
target_data, target_label = next(iter_target)
if i % len_target_loader == 0:
iter_target = iter(train_loader_t)
# 0
source_data0 = utils.radiation_noise(source_data)
source_data0 = source_data0.type(torch.FloatTensor)
# 1
source_data1 = utils.flip_augmentation(source_data)
# 2
target_data0 = utils.radiation_noise(target_data)
target_data0 = target_data0.type(torch.FloatTensor)
# 3
target_data1 = utils.flip_augmentation(target_data)
(source_features, source1, _, source_outputs, source_out,
target_features,_, target1, target_outputs, target_out) = feature_encoder(source_data.cuda(),target_data.cuda())
(_, source2, _, source_outputs2 ,_,
_, _, target2, t1, _) = feature_encoder(source_data0.cuda(),target_data0.cuda())
(_, source3, _, source_outputs3,_,
_, _, target3, t2, _) = feature_encoder(source_data1.cuda(),target_data1.cuda())
softmax_output_t = nn.Softmax(dim=1)(target_outputs).detach()
_, pseudo_label_t = torch.max(softmax_output_t, 1)
entropy_loss = mmd.EntropyLoss(softmax_output_t)
# Supervised Contrastive Loss
all_source_con_features = torch.cat([source2.unsqueeze(1), source3.unsqueeze(1)],dim=1)
all_target_con_features = torch.cat([target2.unsqueeze(1), target3.unsqueeze(1)], dim=1)
# Loss Cls
cls_loss = crossEntropy(source_outputs, source_label.cuda())
# Loss Lmmd
lmmd_loss = mmd.lmmd(source_features, target_features, source_label,
torch.nn.functional.softmax(target_outputs, dim=1), BATCH_SIZE=BATCH_SIZE,
CLASS_NUM=CLASS_NUM)
lambd = 2 / (1 + math.exp(-10 * (epoch) / epochs)) - 1
# Loss Con_s
contrastive_loss_s = ContrastiveLoss_s(all_source_con_features, source_label)
# Loss Con_t
contrastive_loss_t = ContrastiveLoss_t(all_target_con_features, pseudo_label_t)
loss = cls_loss + 0.01 * lambd * lmmd_loss + contrastive_loss_t + contrastive_loss_s
# Update parameters
optimizer.zero_grad()
loss.backward()
optimizer.step()
pred = source_outputs.data.max(1)[1]
total_hit += pred.eq(source_label.data.cuda()).sum()
size += source_label.data.size()[0]
test_accuracy = 100. * float(total_hit) / size
print('epoch {:>3d}: cls loss: {:6.4f},lmmd loss:{:6f},con_s loss:{:6f}, con_t loss:{:6f},acc {:6.4f}, total loss: {:6.4f}'
.format(epoch , cls_loss.item(),lmmd_loss.item(), contrastive_loss_s.item(),contrastive_loss_t.item(),
total_hit / size,loss.item()))
train_end = time.time()
if epoch % epochs == 0:
feature_encoder.eval()
total_rewards = 0
counter = 0
accuracies = []
predict = np.array([], dtype=np.int64)
labels = np.array([], dtype=np.int64)
with torch.no_grad():
for test_datas, test_labels in test_loader:
batch_size = test_labels.shape[0]
source_features, source1, _, source_outputs, source_out, test_features, _, _, test_outputs, _ = feature_encoder(
Variable(source_data).cuda(), Variable(test_datas).cuda())
pred = test_outputs.data.max(1)[1]
test_labels = test_labels.numpy()
rewards = [1 if pred[j] == test_labels[j] else 0 for j in range(batch_size)]
total_rewards += np.sum(rewards)
counter += batch_size
predict = np.append(predict, pred.cpu().numpy())
labels = np.append(labels, test_labels)
accuracy = total_rewards / 1.0 / counter #
accuracies.append(accuracy)
test_accuracy = 100. * total_rewards / len(test_loader.dataset)
acc[iDataSet] = 100. * total_rewards / len(test_loader.dataset)
OA = acc
C = metrics.confusion_matrix(labels, predict)
A[iDataSet, :] = np.diag(C) / np.sum(C, 1, dtype=np.float64)
k[iDataSet] = metrics.cohen_kappa_score(labels, predict)
print('\t\tAccuracy: {}/{} ({:.2f}%)\n'.format(total_rewards, len(test_loader.dataset),
100. * total_rewards / len(test_loader.dataset)))
test_end = time.time()
# Training mode
if test_accuracy > last_accuracy:
# save networks
# torch.save(feature_encoder.state_dict(),str("../checkpoints/DFSL_feature_encoder_" + "houston_cl_lmmd_dis_attention" +str(iDataSet) +".pkl"))
print("save networks for epoch:", epoch + 1)
last_accuracy = test_accuracy
best_episdoe = epoch
best_predict_all = predict
best_G, best_RandPerm, best_Row, best_Column = G, RandPerm, Row, Column
print('best epoch:[{}], best accuracy={}'.format(best_episdoe + 1, last_accuracy))
print('iter:{} best epoch:[{}], best accuracy={}'.format(iDataSet, best_episdoe + 1, last_accuracy))
print('***********************************************************************************')
AA = np.mean(A, 1)
AAMean = np.mean(AA,0)
AAStd = np.std(AA)
AMean = np.mean(A, 0)
AStd = np.std(A, 0)
OAMean = np.mean(acc)
OAStd = np.std(acc)
kMean = np.mean(k)
kStd = np.std(k)
print ("train time per DataSet(s): " + "{:.5f}".format(train_end-train_start))
print("test time per DataSet(s): " + "{:.5f}".format(test_end-train_end))
print ("average OA: " + "{:.2f}".format( OAMean) + " +- " + "{:.2f}".format( OAStd))
print ("average AA: " + "{:.2f}".format(100 * AAMean) + " +- " + "{:.2f}".format(100 * AAStd))
print ("average kappa: " + "{:.4f}".format(100 *kMean) + " +- " + "{:.4f}".format(100 *kStd))
print ("accuracy for each class: ")
for i in range(CLASS_NUM):
print ("Class " + str(i) + ": " + "{:.2f}".format(100 * AMean[i]) + " +- " + "{:.2f}".format(100 * AStd[i]))
best_iDataset = 0
for i in range(len(acc)):
print('{}:{}'.format(i, acc[i]))
if acc[i] > acc[best_iDataset]:
best_iDataset = i
print('best acc all={}'.format(acc[best_iDataset]))
#################classification map################################
for i in range(len(best_predict_all)): # predict ndarray <class 'tuple'>: (9729,)
best_G[best_Row[best_RandPerm[ i]]][best_Column[best_RandPerm[ i]]] = best_predict_all[i] + 1
hsi_pic = np.zeros((best_G.shape[0], best_G.shape[1], 3))
for i in range(best_G.shape[0]):
for j in range(best_G.shape[1]):
if best_G[i][j] == 0:
hsi_pic[i, j, :] = [0, 0, 0]
if best_G[i][j] == 1:
hsi_pic[i, j, :] = [0, 0, 1]
if best_G[i][j] == 2:
hsi_pic[i, j, :] = [0, 1, 0]
if best_G[i][j] == 3:
hsi_pic[i, j, :] = [0, 1, 1]
if best_G[i][j] == 4:
hsi_pic[i, j, :] = [1, 0, 0]
if best_G[i][j] == 5:
hsi_pic[i, j, :] = [1, 0, 1]
if best_G[i][j] == 6:
hsi_pic[i, j, :] = [1, 1, 0]
if best_G[i][j] == 7:
hsi_pic[i, j, :] = [0.5, 0.5, 1]
# utils.classification_map(hsi_pic[4:-4, 4:-4, :], best_G[4:-4, 4:-4], 24, "classificationMap/housotn18.png")