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classification_train.py
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classification_train.py
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import torch
from torch import nn
from sklearn.metrics import confusion_matrix
import numpy as np
from torchmetrics import AUROC, F1Score, Precision, Recall
import datetime
import matplotlib.pyplot as plt
from model import threeDClassModel
def train_loop_class(train_loader, val_loader, args):
input_channels = next(iter(train_loader))[0].shape[1]
num_classes = next(iter(train_loader))[1].shape[-1]
model = threeDClassModel(input_size=input_channels, num_classes=num_classes)
model.cuda()
print(model)
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr) # 0.001
datestr = str(datetime.datetime.now())
print("this run has datestr " + datestr)
tr_accs,tr_losses,tr_aucs,tr_f1ss,tr_precisions,tr_recalls = [],[],[],[],[],[]
val_accs,val_aucs,val_f1ss,val_precisions,val_recalls = [],[],[],[],[]
best_val = 0.0
for ep in range(args.epochs):
print("Epoch " + str(ep))
print("Training")
model, tr_acc, tr_loss, tr_auc, tr_f1s, tr_precision, tr_recall = train_fnc(model, train_loader, optim=optimizer)
tr_accs.append(tr_acc)
tr_losses.append(tr_loss.cpu().detach().numpy())
tr_aucs.append(tr_auc.cpu().detach().numpy())
tr_f1ss.append(tr_f1s.cpu().detach().numpy())
tr_precisions.append(tr_precision.cpu().detach().numpy())
tr_recalls.append(tr_recall.cpu().detach().numpy())
val_acc, val_auc, val_f1s, val_precision, val_recall = val_fnc(model, val_loader)
val_accs.append(val_acc)
val_aucs.append(val_auc.cpu().detach().numpy())
val_f1ss.append(val_f1s.cpu().detach().numpy())
val_precisions.append(val_precision.cpu().detach().numpy())
val_recalls.append(val_recall.cpu().detach().numpy())
if val_f1s.cpu().detach().numpy() > best_val:
print("save new best model")
torch.save(model, str(datestr) + '.pth')
best_val = val_f1s.cpu().detach().numpy()
# plt.subplot(2, 6, 1)
# plt.plot(np.arange(args.epochs), tr_losses)
# plt.title("Train Loss")
# plt.subplot(2, 6, 2)
# plt.plot(np.arange(args.epochs), tr_accs)
# plt.title("Train Accuracy")
# plt.subplot(2, 6, 3)
# plt.plot(np.arange(args.epochs), tr_aucs)
# plt.title("Train AUC")
# plt.subplot(2, 6, 4)
# plt.plot(np.arange(args.epochs), tr_f1ss)
# plt.title("Train F1")
# plt.subplot(2, 6, 5)
# plt.plot(np.arange(args.epochs), tr_precisions)
# plt.title("Train Precision")
# plt.subplot(2, 6, 6)
# plt.plot(np.arange(args.epochs), tr_recalls)
# plt.title("Train Recall")
#
# plt.subplot(2, 6, 8)
# plt.plot(np.arange(args.epochs), val_accs)
# plt.title("Val Accuracy")
# plt.subplot(2, 6, 9)
# plt.plot(np.arange(args.epochs), val_aucs)
# plt.title("Val AUC")
# plt.subplot(2, 6, 10)
# plt.plot(np.arange(args.epochs), val_f1ss)
# plt.title("Val F1")
# plt.subplot(2, 6, 11)
# plt.plot(np.arange(args.epochs), val_precisions)
# plt.title("Val Precision")
# plt.subplot(2, 6, 12)
# plt.plot(np.arange(args.epochs), val_recalls)
# plt.title("Val Recall")
# plt.show()
return str(datestr) + ".pth"
def loss_fcn(gt, pred):
L_pred = nn.CrossEntropyLoss()(torch.squeeze(pred, dim=-1), gt)
return L_pred
def train_fnc(trainmodel, data_loader, optim):
trainmodel.train()
auc,f1s,precision,recall = [],[],[],[]
correct_mal = 0
tr_loss = 0
for i, (x, y_mal) in enumerate(data_loader):
x, y_mal = x.to("cuda", dtype=torch.float), y_mal.to("cuda", dtype=torch.float)
optim.zero_grad()
pred_mal = trainmodel(x)
loss = loss_fcn(y_mal, pred_mal)
loss.backward()
optim.step()
mal_confusion_matrix = confusion_matrix(np.argmax(pred_mal.cpu().detach().numpy(), axis=1),
np.argmax(y_mal.cpu().detach().numpy(), axis=1),
labels=[0, 1])
mal_correct = sum(np.diagonal(mal_confusion_matrix, offset=0))
correct_mal += mal_correct
tr_loss += loss
auroc = AUROC(task="multiclass", num_classes=2).to("cuda")
auc.append(auroc(pred_mal, torch.argmax(y_mal, dim=1)))
f1score = F1Score(task="multiclass", num_classes=2).to("cuda")
f1s.append(f1score(pred_mal, torch.argmax(y_mal, dim=1)))
precisionscore = Precision(task="multiclass", average='macro', num_classes=2).to("cuda")
precision.append(precisionscore(pred_mal, torch.argmax(y_mal, dim=1)))
recallscore = Recall(task="multiclass", average='macro', num_classes=2).to("cuda")
recall.append(recallscore(pred_mal, torch.argmax(y_mal, dim=1)))
return trainmodel, \
correct_mal / len(data_loader.dataset), \
tr_loss / len(data_loader.dataset), \
sum(auc) / len(auc), \
sum(f1s) / len(f1s), \
sum(precision) / len(precision), \
sum(recall) / len(recall)
def val_fnc(testmodel, data_loader):
testmodel.eval()
auc,f1s,precision,recall = [],[],[],[]
correct_mal = 0
with torch.no_grad():
for i, (x, y_mal) in enumerate(data_loader):
x, y_mal = x.to("cuda", dtype=torch.float), y_mal.to("cuda", dtype=torch.float)
pred_mal = testmodel(x)
mal_confusion_matrix = confusion_matrix(np.argmax(pred_mal.cpu().detach().numpy(), axis=1),
np.argmax(y_mal.cpu().detach().numpy(), axis=1),
labels=[0, 1])
mal_correct = sum(np.diagonal(mal_confusion_matrix, offset=0))
correct_mal += mal_correct
auroc = AUROC(task="multiclass", num_classes=2).to("cuda")
auc.append(auroc(pred_mal, torch.argmax(y_mal, dim=1)))
f1score = F1Score(task="multiclass", num_classes=2).to("cuda")
f1s.append(f1score(pred_mal, torch.argmax(y_mal, dim=1)))
precisionscore = Precision(task="multiclass", average='macro', num_classes=2).to("cuda")
precision.append(precisionscore(pred_mal, torch.argmax(y_mal, dim=1)))
recallscore = Recall(task="multiclass", average='macro', num_classes=2).to("cuda")
recall.append(recallscore(pred_mal, torch.argmax(y_mal, dim=1)))
return correct_mal / len(data_loader.dataset), \
sum(auc) / len(auc), \
sum(f1s) / len(f1s), \
sum(precision) / len(precision), \
sum(recall) / len(recall)