/
solver.py
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solver.py
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from skimage import io
import os
import glob
import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn import init
from tqdm import tqdm
import torchnet as tnt
import ConvNet1_int
import tools
import Myresnet1
import Rs2
import Rs1
import ConvNet1
import matplotlib.pyplot as plt
import unet
import unet1
import new_u
import un
IDSv=np.load('./trainvaltest/IDSv_train.npy')
LABSv=np.load('./trainvaltest/LABSv_train.npy')
val_IDSv = np.load('./trainvaltest/IDSv_val.npy')
val_LABSv = np.load('./trainvaltest/LABSv_val.npy')
weight_tensor=torch.FloatTensor(5)
weight_tensor[0]= 1.1
weight_tensor[1]= 0.8
weight_tensor[2]= 0.6
weight_tensor[3]= 0.9
weight_tensor[4]= 0.3
epochs=15
model=tools.to_cuda(un.UNet())
optimizer = optim.Adam(model.parameters(), lr=0.0001)
#model=tools.to_cuda(new_u.UNet(epoch))
#model2=tools.to_cuda(new_u.U_Net2())
#optimizer = optim.Adam(model.parameters(), lr=0.0001)
criterion2=tools.to_cuda(nn.CrossEntropyLoss(tools.to_cuda(weight_tensor)))
criterion1 =tools.to_cuda(nn.MSELoss())
confusion_matrix = tnt.meter.ConfusionMeter(5)
t_acc=[]
t_Loss=[]
v_acc=[]
v_Loss=[]
ff=open('./models/progress.txt','w')
batchsize=2
for epoch in range(1,epochs+1):
model.train()
p = tools.shuffle(IDSv)
IDSv = IDSv[p]
LABSv = LABSv[p]
train_losses = []
train_losses_recon = []
cnt=0
###############################################################################
for t in tqdm(range(0, len(IDSv)-batchsize, batchsize)):
iter=t/batchsize
inputs=[]
targets=[]
retargets=[]
for i in range(t, min(t+batchsize, len(IDSv))):
input = tools.make_patch(IDSv[i])
lab=LABSv[i]
retarget=input
inputs.append(input)
targets.append(lab)
retargets.append(retarget)
inputs=np.asarray(inputs)
targets=np.asarray(targets)
retargets=np.asarray(retargets)
inputs=tools.to_cuda(torch.from_numpy(inputs).float())
targets=tools.to_cuda(torch.from_numpy(targets).long())
retargets=tools.to_cuda(torch.from_numpy(retargets).float())
optimizer.zero_grad()
outputs1,outputs2=model(inputs)
loss1=criterion1(outputs1, retargets)
confusion_matrix.add(outputs2.data.squeeze(), targets)
loss2=criterion2(outputs2, targets)
loss=0.3*loss1 + 0.7*loss2
loss.backward()
train_losses.append(loss.item())
if cnt % 10 == 0:
print('Train (epoch {}/{}) [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, epochs, cnt, len(IDSv),100.*cnt/len(IDSv),loss.item()))
cnt=cnt+1
optimizer.step()
del(inputs,targets,retargets, loss)
train_Loss = np.mean(train_losses)
t_Loss.append(train_Loss)
print(confusion_matrix.conf)
train_acc=(np.trace(confusion_matrix.conf)/float(np.ndarray.sum(confusion_matrix.conf))) *100
t_acc.append(train_acc)
print('TRAIN_OA', '%.3f' % train_acc)
confusion_matrix.reset()
##VALIDATION
with torch.no_grad():
model.eval()
val_losses=[]
for t in tqdm(range(0, len(val_IDSv)-batchsize, batchsize)):
iter=t/batchsize
inputs=[]
targets=[]
retargets=[]
for i in range(t, min(t+batchsize, len(val_IDSv))):
input = tools.make_patch(val_IDSv[i])
lab=val_LABSv[i]
retarget=input
inputs.append(input)
targets.append(lab)
retargets.append(retarget)
inputs=np.asarray(inputs)
targets=np.asarray(targets)
retargets=np.asarray(retargets)
inputs=tools.to_cuda(torch.from_numpy(inputs).float())
targets=tools.to_cuda(torch.from_numpy(targets).long())
retargets=tools.to_cuda(torch.from_numpy(retargets).float())
outputs1,outputs2=model(inputs)
confusion_matrix.add(outputs2.data.squeeze(), targets)
loss1=criterion1(outputs1, retargets)
loss2=criterion2(outputs2, targets)
loss=0.3*loss1 + 0.7*loss2
val_losses.append(loss.item())
if cnt % 10 == 0:
print('Train (epoch {}/{}) [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, epochs, cnt, len(val_IDSv),100.*cnt/len(val_IDSv), loss.item()))
cnt =cnt+1
######################################################################
del(inputs, targets, retargets, loss)
print(confusion_matrix.conf)
val_Loss = np.mean(val_losses)
v_Loss.append(val_Loss)
val_acc=(np.trace(confusion_matrix.conf)/float(np.ndarray.sum(confusion_matrix.conf))) *100
v_acc.append(val_acc)
print('VAL_OA', '%.3f' % val_acc)
print('Train_Loss: ', np.mean(train_Loss))
print('Val_Loss: ', np.mean(val_Loss))
tools.write_results(train_Loss, val_Loss, train_acc, val_acc, epoch)
torch.save(model.state_dict(), './models/model_{}.pt'.format(epoch))
confusion_matrix.reset()
plt.figure(1)
plt.plot(t_Loss)
plt.plot(v_Loss)
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train loss','val loss'], loc='upper left')
plt.savefig("plots/Myfile.png",format="png")
plt.figure(2)
plt.plot(t_acc)
plt.plot(v_acc)
plt.title('Model Accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train acc', 'val acc'], loc='upper left')
plt.show()
plt.savefig("plots/Myfile1.png",format="png")