From 7beeadf90d737e70dce43fa1e2f22e6e09c40f81 Mon Sep 17 00:00:00 2001 From: meiqihu <104488679+meiqihu@users.noreply.github.com> Date: Wed, 27 Apr 2022 16:00:28 +0800 Subject: [PATCH] Add files via upload --- Train_func.py | 77 ++++++++++++++++++++++++++++++++++++ common_func.py | 84 ++++++++++++++++++++++++++++++++++++++++ groundtruth_samples.mat | Bin 0 -> 176416 bytes maincode.py | 77 ++++++++++++++++++++++++++++++++++++ models.py | 37 ++++++++++++++++++ pretrain_samples.mat | Bin 0 -> 88416 bytes random_samples.mat | Bin 0 -> 176416 bytes ref_EX1.bmp | Bin 0 -> 170278 bytes ref_EX2.bmp | Bin 0 -> 170278 bytes 9 files changed, 275 insertions(+) create mode 100644 Train_func.py create mode 100644 common_func.py create mode 100644 groundtruth_samples.mat create mode 100644 maincode.py create mode 100644 models.py create mode 100644 pretrain_samples.mat create mode 100644 random_samples.mat create mode 100644 ref_EX1.bmp create mode 100644 ref_EX2.bmp diff --git a/Train_func.py b/Train_func.py new file mode 100644 index 0000000..e262b7c --- /dev/null +++ b/Train_func.py @@ -0,0 +1,77 @@ +# coding=utf-8 +import torch +from torch import nn +import numpy as np +from torch.autograd import Variable +from torch.utils.data import DataLoader,TensorDataset + + +def train_predict(x_3d, y_3d, model, un_idx_train, un_idx_valid, + epoch, batch_sz, learn_rate, w_decay,use_gpu): + img_channel, img_height, img_width = x_3d.shape + # x_3d : [img_channel,img_height,img_width] + x_2d = np.transpose(x_3d.reshape(img_channel, img_height * img_width)) # [num,band] + y_2d = np.transpose(y_3d.reshape(img_channel, img_height * img_width)) + train_dataset = TensorDataset(torch.tensor(x_2d[un_idx_train, :], dtype=torch.float32), + torch.tensor(y_2d[un_idx_train, :], dtype=torch.float32)) + valid_label_x = torch.tensor(x_2d[un_idx_valid, :], dtype=torch.float32) + valid_label_y = torch.tensor(y_2d[un_idx_valid, :], dtype=torch.float32) + data_loader = DataLoader(train_dataset, batch_size=batch_sz, shuffle=True) + iter_num = un_idx_train.size // batch_sz + loss_fc = nn.MSELoss() + # While constructing the network, transfer the model to GPU (pytorch) + if (use_gpu): + model = model.cuda() + loss_fc = loss_fc.cuda() + + optimizer = torch.optim.Adam \ + (model.parameters(), lr=learn_rate, betas=(0.9, 0.99), weight_decay=w_decay) + + # Training loss & Valid loss + Tra_ls, Val_ls = [], [] + for _epoch in range(0, epoch): + model.train() + tra_ave_ls = 0 + for i, data in enumerate(data_loader): + train_x, train_y = data + # While traning, transfer the data to GPU + if (use_gpu): + train_x, train_y = train_x.cuda(), train_y.cuda() + predict_y = model(Variable(train_x)) + loss = loss_fc(model(Variable(train_x)), train_y) + optimizer.zero_grad() + loss.backward() + optimizer.step() + tra_ave_ls += loss.item() + tra_ave_ls /= iter_num + Tra_ls.append(tra_ave_ls) + model.eval() + if (use_gpu): + valid_label_x, valid_label_y = valid_label_x.cuda(), valid_label_y.cuda() + val_ls = loss_fc(model(valid_label_x), valid_label_y).item() + Val_ls.append(val_ls) + # print('epoch [{}/{}],train:{:.4f}, valid:{:.4f}'. + # format(_epoch + 1, epoch, tra_ave_ls, val_ls)) + # # if _epoch % 5 == 0 : print('epoch [{}/{}],train:{:.4f}, valid:{:.4f}'. + # format(_epoch + 1, epoch, tra_ave_ls,val_ls)) + + # Prediction + model.eval() + x_2d = torch.tensor(x_2d, dtype=torch.float32) + if (use_gpu): + x_2d = x_2d.cuda() + prediction_y = model(x_2d) # [num, band] + loss_fn = torch.nn.MSELoss(reduce=False, size_average=False) + if (use_gpu): + loss_fn = loss_fn.cuda() + input_y = torch.autograd.Variable(torch.from_numpy(y_2d)).float() # [num, band] + if (use_gpu): + input_y = input_y.cuda() + loss = loss_fn(input_y, prediction_y) + if (use_gpu): + loss,prediction_y= loss.cpu(),prediction_y.cpu() + loss_m1 = np.sum(loss.detach().numpy(), axis=1).reshape(img_height, img_width) # axis=1,[num, 1] + prediction_y = prediction_y.detach().numpy().transpose(). \ + reshape([img_channel, img_height, img_width, ]) + + return model, loss_m1, prediction_y, Tra_ls, Val_ls diff --git a/common_func.py b/common_func.py new file mode 100644 index 0000000..865550c --- /dev/null +++ b/common_func.py @@ -0,0 +1,84 @@ +# coding=utf-8 +import numpy as np +from torch import nn +from torch.nn import init + + +def initNetParams(net): + '''Init net parameters.''' + for m in net.modules(): + if isinstance(m, nn.Conv2d): + m.weight.data.normal_(0, 0.001) + #init.xavier_uniform(m.weight) + init.constant(m.bias, 0) + #if m.bias: + + elif isinstance(m, nn.BatchNorm2d): + init.constant(m.weight, 1) + init.constant(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.kaiming_normal_(m.weight.data) + m.bias.data.fill_(0) + + +# for evaluating the performance of the anomaly change detection result +def plot_roc(predict, ground_truth): + """ + INPUTS: + predict - anomalous change intensity map + ground_truth - 0or1 + OUTPUTS: + X, Y for ROC plotting + auc + """ + max_value = np.max(ground_truth) + if max_value != 1: + ground_truth = ground_truth / max_value + + # initial point(1.0, 1.0) + x = 1.0 + y = 1.0 + hight_g, width_g = ground_truth.shape + hight_p, width_p = predict.shape + if hight_p != hight_g: + predict = np.transpose(predict) + + ground_truth = ground_truth.reshape(-1) + equals_one1 = np.where(ground_truth == 1) + predict = predict.reshape(-1) + # compuate the number of positive and negagtive pixels of the ground_truth + pos_num = np.sum(ground_truth == 1) + neg_num = np.sum(ground_truth == 0) + # step in axis of X and Y + x_step = 1.0 / neg_num + y_step = 1.0 / pos_num + # ranking the result map + index = np.argsort(list(predict)) + # predict = sorted(predict) + ground_truth = ground_truth[index] + equals_one2 = np.where(ground_truth == 1) + """ + for i in ground_truth: + when ground_truth[i] = 1, TP minus 1,one y_step in the y axis, go down + when ground_truth[i] = 0, FP minus 1,one x_step in the x axis, go left + """ + X = np.zeros(ground_truth.shape) + Y = np.zeros(ground_truth.shape) + for idx in range(0, hight_g * width_g): + if ground_truth[idx] == 1: + y = y - y_step + else: + x = x - x_step + X[idx] = x + Y[idx] = y + + auc = -np.trapz(Y, X) + if auc < 0.5: + auc = -np.trapz(X, Y) + t = X + X = Y + Y = t + + return X, Y, auc + + diff --git a/groundtruth_samples.mat b/groundtruth_samples.mat new file mode 100644 index 0000000000000000000000000000000000000000..e5efd4a195546295597d3ade4bdd7d42382a1bcb GIT binary patch literal 176416 zcmbT6hhNWc`~EW$icr}jBdciGBMl=`WEQ0)AvCN|MukN7-ei-JEwgN8WQXkRJ+uAp z`};iZf8h6dJU)-ZajxrnU9amp&+~e{^K|L%;o@r9sJ@-0r%U&G144!dTY3!*962C- zcxZFWi0~01V{9y24-XC;8QkA8Jgm88x4@B>-GU=5?Hw%b?V2}e(A>_>(!tKb&fx#! zzkD4$i~q+ht!X}kLjS?S|M~yF{J;Mh{^$Sy@_hgKzeFvt*nj>#Dl8zR|CoT0!vjOY z?Ek|}{_}s#fBYlP{MXXJAb#-wfx-D2;GfX^|Mw4<7_jOzFuyHW>^^pBbKy;{6DM#J z_~O44`_F&oox$sCksrGUR@;aC+6~a?FSv0j_~9;g7stb|dVxEygI{VhZp+H>Z=bL~ z9EkjxJD7DFeg6UIYfJ*&wO~loxumbX=Xzbo;-G)UWuYUo#-EQm;)<^CT33szaA2AXBa=GG| 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z2rfxSKVEq|Jp=z@Q;_=^fky(t`Kps$)VJqqzqe|Pe#l#JZ2<9ucEJsmPcErGFHqj! z^pxlQQJu3G2cN6{s&dV5ZIu(aLMr?;tO$y1Pp2Gg;@euiF^M7JdIG$_vNUN8Y=If4n~!77V7SpDV69 zl2M6qHuoc5vhr|&QP^Km-EFTv?Wy)dsMh&Dt*f$O*q<0k97|s?G>G_le}3t~eB^n5 QKhKaG_$6pP3^@V+Kfos*R{#J2 literal 0 HcmV?d00001 diff --git a/maincode.py b/maincode.py new file mode 100644 index 0000000..85ba134 --- /dev/null +++ b/maincode.py @@ -0,0 +1,77 @@ +# coding=utf-8 +import numpy as np +import torch +import time +import scipy.io as sio +from PIL import Image +import os +import models +import common_func +import Train_func + + +def train(x, y, idx_tra, idx_vld,h1, h2, lrn_rt,w_decay,ground_truth): + model_1 = models.AutoEncoder(in_dim=127, hid_dim1=h1, hid_dim2=h2) + model_2 = models.AutoEncoder(in_dim=127, hid_dim1=h1, hid_dim2=h2) + model_1.apply(common_func.initNetParams) + model_2.apply(common_func.initNetParams) # function:initNetParams + epoch, bth_sz, = 200, 256 + use_gpu = torch.cuda.is_available() # False + model_1, ls_m1, prdt_y, T_ls_1, V_ls_1 = Train_func.train_predict( + x, y, model_1, idx_tra, idx_vld, epoch, bth_sz, lrn_rt, w_decay,use_gpu) + model_2, ls_m2, prdt_x, T_ls_2, V_ls_2 = Train_func.train_predict( + y, x, model_2, idx_tra, idx_vld, epoch, bth_sz, lrn_rt, w_decay,use_gpu) + loss_result = np.minimum(ls_m1, ls_m2) + X, Y, auc = common_func.plot_roc(loss_result.transpose(), ground_truth) + print("auc is ", auc,'\n') + return loss_result,ls_m1,ls_m2,prdt_y,prdt_x + + +if __name__ == '__main__': + start = time.time() + os.environ['CUDA_VISIBLE_DEVICES'] = "2" + use_gpu = torch.cuda.is_available() + + # Step1 : Read Data + path_name = '/data/meiqi.hu/PycharmProjects/HyperspectralACD/AE/ACDA/' + EX, img_2, train_smp, valid_smp = 'EX1', 'img_2', 'un_idx_train1', 'un_idx_valid1' + ground_truth = Image.open(path_name + 'ref_EX1.bmp') + if EX == 'EX2': + img_2, train_smp, valid_smp = 'img_3', 'un_idx_train2', 'un_idx_valid2' + ground_truth = Image.open(path_name + 'ref_EX2.bmp') + # read image data + # img_data : img_1,img_2,img_3(de-striping, noise-whitening and spectrally binning) + data_filename = 'img_data.mat' + data = sio.loadmat(path_name + data_filename) + img_x0 = data['img_1'] + img_y0 = data[img_2] + input_x = img_x0.transpose((2, 1, 0)) + input_y = img_y0.transpose((2, 1, 0)) + # read pre-train samples from pretraining result of USFA + # for different training strategy(only replace the training samples) + TrainSmp_filename = 'groundtruth_samples.mat' # groundtruth_samples random_samples pretrain_samples + TrainSmp = sio.loadmat(path_name + TrainSmp_filename) + un_idx_train = TrainSmp[train_smp].squeeze() + un_idx_valid = TrainSmp[valid_smp].squeeze() + img_channel, img_height, img_width = input_x.shape + + # Step2 : for experiemntal result + Loss_result = np.zeros([img_height, img_width], dtype=float) + h1, h2 = 60, 40 # 127, 127 + learn_rate, w_decay = 0.001, 0.001 + iter = 1 + Loss_result = np.zeros([img_height, img_width], dtype=float) + for i in np.arange(1, 1 + iter): + print('epoch i =', i) + loss_result, ls_m1, ls_m2, prdt_y, prdt_x = train(input_x, input_y, un_idx_train, un_idx_valid, h1, h2, + learn_rate, w_decay,ground_truth) + Loss_result = Loss_result + loss_result + Loss_result = Loss_result / iter + X, Y, auc = common_func.plot_roc(Loss_result.transpose(), ground_truth) + + print("auc is ", auc, '\n') + print("-------------Ending---------------") + print(" ") + print(EX) + end = time.time() + print("共用时", (end - start), "秒") \ No newline at end of file diff --git a/models.py b/models.py new file mode 100644 index 0000000..72fc15f --- /dev/null +++ b/models.py @@ -0,0 +1,37 @@ +#coding=utf-8 + +from torch import nn +import torch.nn.functional as F +import torch +import numpy as np +from torch.autograd import Variable +import sys +sys.path.append('/data/meiqi.hu/PycharmProjects/HyperspectralACD/MyFunction/') +import common_func +from PIL import Image +import random + + + +class AutoEncoder(nn.Module): + def __init__(self, in_dim, hid_dim1,hid_dim2): + super(AutoEncoder, self).__init__() + self.out_dim = in_dim + + self.encoder = nn.Sequential( + nn.Linear(in_dim, hid_dim1, bias=True), + nn.ReLU(), + nn.Linear(hid_dim1, hid_dim2, bias=True), + nn.ReLU(), + ) + self.decoder = nn.Sequential( + nn.Linear(hid_dim2, hid_dim1, bias=True), + nn.ReLU(), + nn.Linear(hid_dim1, in_dim, bias=True), + nn.ReLU(), + ) + + def forward(self, x): + feature = self.encoder(x) + cons_x = self.decoder(feature) + return cons_x diff --git a/pretrain_samples.mat b/pretrain_samples.mat new file mode 100644 index 0000000000000000000000000000000000000000..0d79d615f512edd64434cd5fb96d64f0a77feae3 GIT binary patch literal 88416 zcmbT;h1XTp_cic+E{!PN-Q6MG-QC^Y-AGGg0Fn|4h=hU&C?%i>Ur|vM1QY=g1q4J< z5qv+4@%sy2$6!2h?>%SlwdR~_uC>o&qiQW1R;!+^bdh4&8dYmqxOcySy|OhO*llF* zA;Sli&o*?(h<;-WWvel~SGSS9dS)9kxO}$S!~11x(Q9b7Y^AalD^|W(+43bzWGhju 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