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utils.py
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utils.py
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# -*- coding:utf-8 -*-
# @Time :2023/4/27 上午10.55
# @AUTHOR :Jiaqing Zhang
# @FileName :utils.py
import torch
import torch.backends.cudnn as cudnn
from sklearn.metrics import confusion_matrix
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
# from calen import calen
class OhemCELoss(nn.Module):
def __init__(self, thresh, ignore_lb=255):
super(OhemCELoss, self).__init__()
self.thresh = -torch.log(torch.tensor(thresh, requires_grad=False, dtype=torch.float))
self.ignore_lb = ignore_lb
self.criteria = nn.CrossEntropyLoss(ignore_index=ignore_lb, reduction='none')
def forward(self, logits, labels):
n_min = labels[labels != self.ignore_lb].numel() // 16
loss = self.criteria(logits, labels).view(-1)
loss_hard = loss[loss > self.thresh]
# print(min(loss))
if loss_hard.numel() < n_min:
loss_hard, _ = loss.topk(n_min)
return torch.mean(loss_hard)
class FocalLoss(nn.Module):
def __init__(self, loss_weight,alpha=None, gamma=2, reduction='mean'):
super(FocalLoss, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self, inputs, targets):
ce_loss = F.cross_entropy(inputs, targets,weight = self.loss_weight, reduction= 'none')
pt = torch.exp(-ce_loss)
focal_loss = ((1 - pt) ** self.gamma) * ce_loss
if self.alpha is not None:
alpha_t = self.alpha[targets]
focal_loss = alpha_t * focal_loss
if self.reduction == 'mean':
return torch.mean(focal_loss)
elif self.reduction == 'sum':
return torch.sum(focal_loss)
else:
return focal_loss
def normalize(input2):
input2_normalize = np.zeros(input2.shape)
for i in range(input2.shape[2]):
input2_max = np.max(input2[:, :, i])
input2_min = np.min(input2[:, :, i])
input2_normalize[:, :, i] = (input2[:, :, i] - input2_min) / (input2_max - input2_min)
return input2_normalize
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.avg = 0
self.sum = 0
self.cnt = 0
def update(self, val, n=1):
self.sum += val * n
self.cnt += n
self.avg = self.sum / self.cnt
def accuracy(output, target, topk=(1,)):
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res, target, pred.squeeze()
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
torch.backends.cudnn.deterministic = True
cudnn.benchmark = False
def train_patch_tsne(Data1, Data2, patchsize, pad_width, Label):
[m1, n1, l1] = np.shape(Data1)
Data2 = Data2.reshape([m1, n1, -1])
[m2, n2, l2] = np.shape(Data2)
for i in range(l1):
Data1[:, :, i] = (Data1[:, :, i] - Data1[:, :, i].min()) / (Data1[:, :, i].max() - Data1[:, :, i].min())
x1 = Data1
for i in range(l2):
Data2[:, :, i] = (Data2[:, :, i] - Data2[:, :, i].min()) / (Data2[:, :, i].max() - Data2[:, :, i].min())
x2 = Data2
x1_pad = np.empty((m1 + patchsize, n1 + patchsize, l1), dtype='float32')
x2_pad = np.empty((m2 + patchsize, n2 + patchsize, l2), dtype='float32')
for i in range(l1):
temp = x1[:, :, i]
temp2 = np.pad(temp, pad_width, 'symmetric')
x1_pad[:, :, i] = temp2
for i in range(l2):
temp = x2[:, :, i]
temp2 = np.pad(temp, pad_width, 'symmetric')
x2_pad[:, :, i] = temp2
# construct the training and testing set
# ind1 = np.zeros(int(100*Label.max()))
# ind2 = np.zeros(int(100*Label.max()))
# for i in range(1,Label.max()+1):
# [ind_1, ind_2] = np.where(Label == i)
# ind1[(i-1)*100:i*100] = ind_1[:100]
# ind2[(i-1)*100:i*100] = ind_2[:100]
TrainNum = len(ind1)
TrainPatch1 = np.empty((TrainNum, l1, patchsize, patchsize), dtype='float32')
TrainPatch2 = np.empty((TrainNum, l2, patchsize, patchsize), dtype='float32')
TrainLabel = np.empty(TrainNum)
ind3 = ind1 + pad_width
ind4 = ind2 + pad_width
for i in range(len(ind1)):
patch1 = x1_pad[(ind3[i] - pad_width):(ind3[i] + pad_width), (ind4[i] - pad_width):(ind4[i] + pad_width), :]
patch1 = np.transpose(patch1, (2, 0, 1))
TrainPatch1[i, :, :, :] = patch1
patch2 = x2_pad[(ind3[i] - pad_width):(ind3[i] + pad_width), (ind4[i] - pad_width):(ind4[i] + pad_width), :]
patch2 = np.transpose(patch2, (2, 0, 1))
TrainPatch2[i, :, :, :] = patch2
patchlabel = Label[ind1[i], ind2[i]]
TrainLabel[i] = patchlabel
# change data to the input type of PyTorch
TrainPatch1 = torch.from_numpy(TrainPatch1)
TrainPatch2 = torch.from_numpy(TrainPatch2)
TrainLabel = torch.from_numpy(TrainLabel) - 1
TrainLabel = TrainLabel.long()
return TrainPatch1, TrainPatch2, TrainLabel
def train_patch(Data1, Data2, patchsize, pad_width, Label):
[m1, n1, l1] = np.shape(Data1)
Data2 = Data2.reshape([m1, n1, -1])
[m2, n2, l2] = np.shape(Data2)
for i in range(l1):
Data1[:, :, i] = (Data1[:, :, i] - Data1[:, :, i].min()) / (Data1[:, :, i].max() - Data1[:, :, i].min())
x1 = Data1
for i in range(l2):
Data2[:, :, i] = (Data2[:, :, i] - Data2[:, :, i].min()) / (Data2[:, :, i].max() - Data2[:, :, i].min())
x2 = Data2
x1_pad = np.empty((m1 + patchsize, n1 + patchsize, l1), dtype='float32')
x2_pad = np.empty((m2 + patchsize, n2 + patchsize, l2), dtype='float32')
for i in range(l1):
temp = x1[:, :, i]
temp2 = np.pad(temp, pad_width, 'symmetric')
x1_pad[:, :, i] = temp2
for i in range(l2):
temp = x2[:, :, i]
temp2 = np.pad(temp, pad_width, 'symmetric')
x2_pad[:, :, i] = temp2
# construct the training and testing set
[ind1, ind2] = np.where(Label > 0)
TrainNum = len(ind1)
TrainPatch1 = np.empty((TrainNum, l1, patchsize, patchsize), dtype='float32')
TrainPatch2 = np.empty((TrainNum, l2, patchsize, patchsize), dtype='float32')
TrainLabel = np.empty(TrainNum)
ind3 = ind1 + pad_width
ind4 = ind2 + pad_width
for i in range(len(ind1)):
patch1 = x1_pad[(ind3[i] - pad_width):(ind3[i] + pad_width), (ind4[i] - pad_width):(ind4[i] + pad_width), :]
patch1 = np.transpose(patch1, (2, 0, 1))
TrainPatch1[i, :, :, :] = patch1
patch2 = x2_pad[(ind3[i] - pad_width):(ind3[i] + pad_width), (ind4[i] - pad_width):(ind4[i] + pad_width), :]
patch2 = np.transpose(patch2, (2, 0, 1))
TrainPatch2[i, :, :, :] = patch2
patchlabel = Label[ind1[i], ind2[i]]
TrainLabel[i] = patchlabel
# change data to the input type of PyTorch
TrainPatch1 = torch.from_numpy(TrainPatch1)
TrainPatch2 = torch.from_numpy(TrainPatch2)
TrainLabel = torch.from_numpy(TrainLabel) - 1
TrainLabel = TrainLabel.long()
return TrainPatch1, TrainPatch2, TrainLabel
def test_patch(Data1, Data2, patchsize, pad_width, Label):
[m1, n1, l1] = np.shape(Data1)
Data2 = Data2.reshape([m1, n1, -1])
[m2, n2, l2] = np.shape(Data2)
# for i in range(l1):
# Data1[:, :, i] = (Data1[:, :, i] - Data1[:, :, i].min()) / (Data1[:, :, i].max() - Data1[:, :, i].min())
x1 = Data1
# for i in range(l2):
# Data2[:, :, i] = (Data2[:, :, i] - Data2[:, :, i].min()) / (Data2[:, :, i].max() - Data2[:, :, i].min())
x2 = Data2
x1_pad = np.empty((m1 + patchsize, n1 + patchsize, l1), dtype='float32')
x2_pad = np.empty((m2 + patchsize, n2 + patchsize, l2), dtype='float32')
for i in range(l1):
temp = x1[:, :, i]
temp2 = np.pad(temp, pad_width, 'symmetric')
x1_pad[:, :, i] = temp2
for i in range(l2):
temp = x2[:, :, i]
temp2 = np.pad(temp, pad_width, 'symmetric')
x2_pad[:, :, i] = temp2
# construct the training and testing set
[ind1, ind2] = np.where(Label > 0)
TrainNum = len(ind1)
TrainPatch1 = np.empty((TrainNum, l1, patchsize, patchsize), dtype='float32')
TrainPatch2 = np.empty((TrainNum, l2, patchsize, patchsize), dtype='float32')
TrainLabel = np.empty(TrainNum)
ind3 = ind1 + pad_width
ind4 = ind2 + pad_width
for i in range(len(ind1)):
patch1 = x1_pad[(ind3[i] - pad_width):(ind3[i] + pad_width), (ind4[i] - pad_width):(ind4[i] + pad_width), :]
patch1 = np.transpose(patch1, (2, 0, 1))
TrainPatch1[i, :, :, :] = patch1
patch2 = x2_pad[(ind3[i] - pad_width):(ind3[i] + pad_width), (ind4[i] - pad_width):(ind4[i] + pad_width), :]
patch2 = np.transpose(patch2, (2, 0, 1))
TrainPatch2[i, :, :, :] = patch2
patchlabel = Label[ind1[i], ind2[i]]
TrainLabel[i] = patchlabel
# change data to the input type of PyTorch
TrainPatch1 = torch.from_numpy(TrainPatch1)
TrainPatch2 = torch.from_numpy(TrainPatch2)
TrainLabel = torch.from_numpy(TrainLabel) - 1
TrainLabel = TrainLabel.long()
return TrainPatch1, TrainPatch2, TrainLabel
# def test_patch(Data1, Data2, patchsize, pad_width, Label):
# [m1, n1, l1] = np.shape(Data1)
# Data2 = Data2.reshape([m1, n1, -1])
# [m2, n2, l2] = np.shape(Data2)
# for i in range(l1):
# Data1[:, :, i] = (Data1[:, :, i] - Data1[:, :, i].min()) / (Data1[:, :, i].max() - Data1[:, :, i].min())
# x1 = Data1
# for i in range(l2):
# Data2[:, :, i] = (Data2[:, :, i] - Data2[:, :, i].min()) / (Data2[:, :, i].max() - Data2[:, :, i].min())
# x2 = Data2
# x1_pad = np.empty((m1 + patchsize, n1 + patchsize, l1), dtype='float32')
# x2_pad = np.empty((m2 + patchsize, n2 + patchsize, l2), dtype='float32')
# for i in range(l1):
# temp = x1[:, :, i]
# temp2 = np.pad(temp, pad_width, 'symmetric')
# x1_pad[:, :, i] = temp2
# for i in range(l2):
# temp = x2[:, :, i]
# temp2 = np.pad(temp, pad_width, 'symmetric')
# x2_pad[:, :, i] = temp2
# # construct the training and testing set
# [ind1, ind2] = np.where(Label > 0)
# TrainNum = len(ind1)
# TrainPatch1 = np.empty((TrainNum, l1, patchsize, patchsize), dtype='float32')
# TrainPatch2 = np.empty((TrainNum, l2, patchsize, patchsize), dtype='float32')
# TrainLabel = np.empty(TrainNum)
# ind3 = ind1 + pad_width
# ind4 = ind2 + pad_width
# for i in range(len(ind1)):
# patch1 = x1_pad[(ind3[i] - pad_width):(ind3[i] + pad_width), (ind4[i] - pad_width):(ind4[i] + pad_width), :]
# patch1 = np.transpose(patch1, (2, 0, 1))
# TrainPatch1[i, :, :, :] = patch1
# patch2 = x2_pad[(ind3[i] - pad_width):(ind3[i] + pad_width), (ind4[i] - pad_width):(ind4[i] + pad_width), :]
# patch2 = np.transpose(patch2, (2, 0, 1))
# TrainPatch2[i, :, :, :] = patch2
# patchlabel = Label[ind1[i], ind2[i]]
# TrainLabel[i] = patchlabel
# # change data to the input type of PyTorch
# TrainPatch1 = torch.from_numpy(TrainPatch1)
# TrainPatch2 = torch.from_numpy(TrainPatch2)
# TrainLabel = torch.from_numpy(TrainLabel) - 1
# TrainLabel = TrainLabel.long()
# return TrainPatch1, TrainPatch2, TrainLabel
def output_metric(tar, pre):
matrix = confusion_matrix(tar, pre)
OA, AA_mean, Kappa, AA = cal_results(matrix)
return OA, AA_mean, Kappa, AA
def cal_results(matrix):
shape = np.shape(matrix)
number = 0
sum = 0
AA = np.zeros([shape[0]], dtype=np.float64)
for i in range(shape[0]):
number += matrix[i, i]
AA[i] = matrix[i, i] / np.sum(matrix[i, :])
sum += np.sum(matrix[i, :]) * np.sum(matrix[:, i])
OA = number / np.sum(matrix)
AA_mean = np.mean(AA)
pe = sum / (np.sum(matrix) ** 2)
Kappa = (OA - pe) / (1 - pe)
return OA, AA_mean, Kappa, AA
def print_args(args):
for k, v in zip(args.keys(), args.values()):
print("{0}: {1}".format(k, v))
def cal_loss(f_s, f_t,reduction='sum'):
p_s = F.log_softmax(f_s, dim=1)
p_t = F.softmax(f_t, dim=1)
loss = F.kl_div(p_s, p_t, reduction=reduction) / f_t.shape[0]
return loss
def adjust(init, fin, step, fin_step):
if fin_step == 0:
return fin
deta = fin - init
adj = min(init + deta * step / fin_step, fin)
return adj
def train_epoch(model, train_loader, loss_weight, optimizer,distillation,epoch,num_epochs,datasetname,lambda1=5,lambda2=0.1,lambda3=0.3,lambda4=1):
objs = AverageMeter()
top1 = AverageMeter()
tar = np.array([])
pre = np.array([])
# MSE = nn.MSELoss()
# L1 = nn.L1Loss()
# Ohem = OhemCELoss(0.9)
criterion = FocalLoss(loss_weight,gamma=2,alpha=None) #更换新的损失函数
# CE = torch.nn.BCELoss(reduction='mean')
for batch_idx, (batch_data11, batch_data21, batch_data12, batch_data22, batch_data13, batch_data23, batch_target) in enumerate(train_loader):
batch_data11 = batch_data11.cuda()
batch_data21 = batch_data21.cuda()
batch_data12 = batch_data12.cuda()
batch_data22 = batch_data22.cuda()
batch_data13 = batch_data13.cuda()
batch_data23 = batch_data23.cuda()
batch_target = batch_target.cuda()
optimizer.zero_grad()
batch_pred,x_cls_cnn,x_cls_trans,x1_out,x2_out, con_loss,x1c_out,loss_ml,x_fuse1,x_fuse2,x_transfusion = model(batch_data11, batch_data21, batch_data12, batch_data22, batch_data13, batch_data23)
# x_cls,x_cls_cnn,x_cls_trans,x1_out,x2_out, con_loss,x1c_out,mutual_loss,x_fuse1,x_fuse2,x_transfusion
# if batch_pred.equal(batch_pred_2):
# if datasetname=='Augsburg':
# loss = criterion(batch_pred, batch_target) #为什么在重建损失前面加了一个5??? 如果不做重建的话效果如何
# else:
loss = criterion(batch_pred, batch_target) + lambda1*con_loss
# else:
# loss = criterion(batch_pred, batch_target) + criterion(batch_pred_2, batch_target)+ 5*con_loss
if distillation==1:
# if datasetname=='Augsburg':
# loss += 0.3*criterion(x1_out, batch_target)
# loss += 0.3*criterion(x2_out, batch_target)
# loss += 0.3*criterion(x1c_out, batch_target)
# #distillation loss
# loss += cal_loss(x1_out,batch_pred) #蒸馏损失
# loss += cal_loss(x2_out,batch_pred)
# loss += cal_loss(x1c_out,batch_pred)
# # cross-entropy loss
# else:
loss += lambda3*criterion(x1_out, batch_target)
loss += lambda3*criterion(x2_out, batch_target)
loss += lambda3*criterion(x1c_out, batch_target)
# loss += lambda3*criterion(x_cls_cnn, batch_target)
# loss += lambda3*criterion(x_cls_trans, batch_target)
#distillation loss
loss += lambda4*cal_loss(x1_out,batch_pred) #蒸馏损失
loss += lambda4*cal_loss(x2_out,batch_pred)
loss += lambda4*cal_loss(x1c_out,batch_pred)
# loss += lambda4*cal_loss(x_cls_cnn,batch_pred)
# loss += lambda4*cal_loss(x_cls_trans,batch_pred)
# mutual information loss
# if datasetname=='Augsburg':
# loss += loss_ml * 0.01 #* adjust(0, 1, epoch, num_epochs)
# else:
# loss += loss_ml * 0.1
loss += loss_ml * lambda2
loss.backward()
optimizer.step()
prec1, t, p = accuracy(batch_pred, batch_target, topk=(1,))
n = batch_data11.shape[0]
objs.update(loss.data, n)
top1.update(prec1[0].data, n)
tar = np.append(tar, t.data.cpu().numpy())
pre = np.append(pre, p.data.cpu().numpy())
return top1.avg, objs.avg, tar, pre
def valid_epoch(model, valid_loader, loss_weight,pred_flag):
objs = AverageMeter()
top1 = AverageMeter()
objs2 = AverageMeter()
top12 = AverageMeter()
tar = np.array([])
pre = np.array([])
pre1 = np.array([])
emb = np.array([])
criterion = FocalLoss(loss_weight,gamma=2,alpha=None)
# labels = []
embs = []
embs1 = []
embs2 = []
for batch_idx, (batch_data11, batch_data21, batch_data12, batch_data22, batch_data13, batch_data23, batch_target) in enumerate(valid_loader):
batch_data11 = batch_data11.cuda()
batch_data21 = batch_data21.cuda()
batch_data12 = batch_data12.cuda()
batch_data22 = batch_data22.cuda()
batch_data13 = batch_data13.cuda()
batch_data23 = batch_data23.cuda()
batch_target = batch_target.cuda()
batch_pred,x_cls_cnn,x_cls_trans ,x1_out,x2_out, con_loss,x1c_out, loss_ml,x_fuse1,x_fuse2,x_transfusion = model(batch_data11, batch_data21, batch_data12, batch_data22, batch_data13, batch_data23)
if pred_flag == 'o_fuse':
choice_pred = batch_pred
elif pred_flag == 'o_1':
choice_pred = x1_out
elif pred_flag == 'o_2':
choice_pred = x2_out
elif pred_flag == 'o_cnn':
choice_pred = x_cls_cnn
elif pred_flag == 'o_trans':
choice_pred = x_cls_trans
loss = criterion(choice_pred, batch_target) + con_loss #+ criterion(batch_pred_2, batch_target)
# if batch_idx == 70:
# calen(x_fuse1,x_fuse2,"co_1_2.png")
# calen(x_fuse1,x_transfusion,"co_1_f.png")
# calen(x_fuse2,x_transfusion,"co_2_f.png")
prec1, t, p = accuracy(choice_pred, batch_target, topk=(1,))
n = batch_data11.shape[0]
objs.update(loss.data, n)
top1.update(prec1[0].data, n)
tar = np.append(tar, t.data.cpu().numpy()) #(29395,)
pre = np.append(pre, p.data.cpu().numpy())
#emb = np.append(emb, batch_pred.data.cpu().numpy()) #(176370,)
# embs.append(batch_pred.data.cpu().numpy())
x_transfusion = x_transfusion.reshape(x_transfusion.shape[0],-1)
embs.append(x_transfusion.data.cpu().numpy())
x_fuse1 = x_fuse1.reshape(x_fuse1.shape[0],-1)
embs1.append(x_fuse1.data.cpu().numpy())
x_fuse2 = x_fuse2.reshape(x_fuse2.shape[0],-1)
embs2.append(x_fuse2.data.cpu().numpy())
embs = np.concatenate(embs)
embs1 = np.concatenate(embs1)
embs2 = np.concatenate(embs2)
return top1.avg,tar, pre, pre1,embs,embs1,embs2
def test_epoch(model, valid_loader, loss_weight):
objs = AverageMeter()
top1 = AverageMeter()
objs2 = AverageMeter()
top12 = AverageMeter()
tar = np.array([])
pre = np.array([])
pre1 = np.array([])
emb = np.array([])
criterion = FocalLoss(loss_weight,gamma=2,alpha=None)
# labels = []
embs = []
for batch_idx, (batch_data11, batch_data21, batch_data12, batch_data22, batch_data13, batch_data23, batch_target) in enumerate(valid_loader):
batch_data11 = batch_data11.cuda()
batch_data21 = batch_data21.cuda()
batch_data12 = batch_data12.cuda()
batch_data22 = batch_data22.cuda()
batch_data13 = batch_data13.cuda()
batch_data23 = batch_data23.cuda()
batch_target = batch_target.cuda()
batch_pred = model(batch_data11, batch_data21, batch_data12, batch_data22, batch_data13, batch_data23)[0]
loss = criterion(batch_pred, batch_target) #+ criterion(batch_pred_2, batch_target)
prec1, t, p = accuracy(batch_pred, batch_target, topk=(1,))
n = batch_data11.shape[0]
objs.update(loss.data, n)
top1.update(prec1[0].data, n)
tar = np.append(tar, t.data.cpu().numpy()) #(29395,)
pre = np.append(pre, p.data.cpu().numpy())
#emb = np.append(emb, batch_pred.data.cpu().numpy()) #(176370,)
embs.append(batch_pred.data.cpu().numpy())
embs = np.concatenate(embs)
return top1.avg,tar, pre, pre1,embs
## To show the last all classification result
def test(model,batch_data11, batch_data21, batch_data12, batch_data22, batch_data13, batch_data23, TestLabel, Classes,height1, width1):
pred_y = np.empty((len(TestLabel)), dtype='float32')
number = len(TestLabel) // 100
for i in range(number):
temp1_1 = batch_data11[i * 100:(i + 1) * 100, :, :, :]
temp2_1 = batch_data21[i * 100:(i + 1) * 100, :, :, :]
temp1_2 = batch_data12[i * 100:(i + 1) * 100, :, :, :]
temp2_2 = batch_data22[i * 100:(i + 1) * 100, :, :, :]
temp1_3 = batch_data13[i * 100:(i + 1) * 100, :, :, :]
temp2_3 = batch_data23[i * 100:(i + 1) * 100, :, :, :]
temp1_1 = temp1_1.cuda()
temp2_1 = temp2_1.cuda()
temp1_2 = temp1_2.cuda()
temp2_2 = temp2_2.cuda()
temp1_3 = temp1_3.cuda()
temp2_3 = temp2_3.cuda()
temp2 = model(temp1_1, temp2_1, temp1_2, temp2_2, temp1_3, temp2_3)[0]
temp3 = torch.max(temp2, 1)[1].squeeze()
pred_y[i * 100:(i + 1) * 100] = temp3.cpu()
print(i)
del temp1_1, temp1_2, temp2, temp3
if (i + 1) * 100 < len(TestLabel):
temp1_1 = batch_data11[(i + 1) * 100:len(TestLabel), :, :, :]
temp2_1 = batch_data21[(i + 1) * 100:len(TestLabel), :, :, :]
temp1_2 = batch_data12[(i + 1) * 100:len(TestLabel), :, :, :]
temp2_2 = batch_data22[(i + 1) * 100:len(TestLabel), :, :, :]
temp1_3 = batch_data13[(i + 1) * 100:len(TestLabel), :, :, :]
temp2_3 = batch_data23[(i + 1) * 100:len(TestLabel), :, :, :]
temp1_1 = temp1_1.cuda()
temp2_1 = temp2_1.cuda()
temp1_2 = temp1_2.cuda()
temp2_2 = temp2_2.cuda()
temp1_3 = temp1_3.cuda()
temp2_3 = temp2_3.cuda()
temp2 = model(temp1_1, temp2_1, temp1_2, temp2_2, temp1_3, temp2_3)[0]
temp3 = torch.max(temp2, 1)[1].squeeze()
pred_y[(i + 1) * 100:len(TestLabel)] = temp3.cpu()
del temp1_1, temp1_2, temp2, temp3
pred_y = torch.from_numpy(pred_y).long()
# Classes = np.unique(TestLabel)
if Classes == 15:
colormap = colormap_houston
elif Classes == 6:
colormap = colormap_trento
elif Classes == 10:
colormap = colormap_rochester
elif Classes == 11:
colormap = colormap_muufl
# num_class = 15
colormap = colormap * 1.0 / 255
X_result = np.zeros((height1*width1, 3)) # 创建一个RGB的图像
for i in range(0, Classes):
index = np.where(pred_y == i)[0] # 取出所有值等于i的元素在X_result数组中的下标
X_result[index, 0] = colormap[i, 0] # 将这些元素的第0列赋值为colormap中第i个元素的第0列 R
X_result[index, 1] = colormap[i, 1] # 将这些元素的第1列赋值为colormap中第i个元素的第1列 G
X_result[index, 2] = colormap[i, 2] # 将这些元素的第2列赋值为colormap中第i个元素的第2列 B
X_result = np.reshape(X_result, (height1, width1, 3))
return X_result
def loss_weight_calculation(TrainLabel):
loss_weight = torch.ones(int(TrainLabel.max())+1)
sum_num = 0
for i in range(TrainLabel.max()+1):
loss_weight[i] = len(torch.where(TrainLabel ==i)[0])
sum_num = sum_num + len(torch.where(TrainLabel ==i)[0])
sum_mean = sum_num/(int(TrainLabel.max())+1)
print(loss_weight)
print(sum_num)
print(sum_mean)
weight_out = (sum_mean-loss_weight)/loss_weight
weight_out[torch.where(weight_out <1)] = 1
return weight_out#(1-loss_weight/sum)/((1-loss_weight/sum).sum())
def loss_weight_calculation_np(TrainLabel):
loss_weight = np.ones(int((TrainLabel).max()))
sum = 0
for i in range(1,TrainLabel.max()+1):
loss_weight[i-1] = len(np.where(TrainLabel ==i)[0])
sum = sum + len(np.where(TrainLabel ==i)[0])
print(loss_weight)
print(sum)
return (1-loss_weight/sum)/((1-loss_weight/sum).sum())
colormap_houston = np.array([[0, 0, 205],
[0, 8, 255],
[0, 77, 255],
[0, 145, 255],
[0, 212, 255],
[41, 255, 206],
[96, 255, 151],
[151, 255, 96],
[206, 255, 41],
[255, 230, 0],
[255, 167, 0],
[255, 104, 0],
[255, 41, 0],
[205, 0, 0],
[128, 0, 0]])
# colormap_trento = np.array([[61, 86, 168],
# [80, 200, 235],
# [154, 204, 105],
# [255, 209, 12],
# [238, 52, 39],
# [124, 21, 22],
# ])
# colormap_rochester = np.array([[34, 0, 255],
# #[0, 255, 0],
# [0, 128, 1],
# [0, 255, 255],
# [255, 0, 0],
# [255, 0, 255],
# [253, 255, 0],
# # [253, 255, 0],
# [128,128,128],
# [128,128,255],
# [128,255,128],
# [255,128,128],
# ])
colormap_rochester = np.array([[34, 0, 255],
[0, 128, 1],
[0, 255, 255],
[255, 0, 0],
[255, 0, 255],
[253, 255, 0],
[128,128,128],
[128,128,255],
[128,255,128],
[255,128,128],
])
# colormap_muufl = np.array([[0,128,1],
# [0, 255, 1],
# [0, 255, 255],
# [254, 203, 0],
# [252, 0, 49],
# [2, 1, 203],
# [102,1,205],
# [254,126,151],
# [201,102,0],
# [254,254,0],
# [204,26,100],
# ])
#ldx
colormap_muufl = np.array([[0,0,205],
[0,8,255],
[0,77,255],
[0,145,255],
[0,212,255],
[41,255,206],
[96,255,151],
[151,255,96],
[206,255,41],
[255,230,0],
[255,167,0],
[255,104,0],
[255,41,0],
[205,0,0],
[128,0,0]])
colormap_trento = colormap_muufl
colormap_Augsburg = np.array([
[32,109,45],
[249, 20, 10],
[228, 241, 33],
[137, 208, 5],
[109, 225, 3],
[154, 242, 236],
[70,134,198],
])