def evalnet(net, signals, stages, epoch, plot_result={}):
# net.eval()
confusion_mat = np.zeros((opt.label, opt.label), dtype=int)
for i, (signal, stage) in enumerate(zip(signals, stages), 1):
signal = transformer.ToInputShape(signal,
opt.model_name,
test_flag=True)
signal, stage = transformer.ToTensor(signal,
stage,
no_cuda=opt.no_cuda)
with torch.no_grad():
out = net(signal)
pred = torch.max(out, 1)[1]
pred = pred.data.cpu().numpy()
stage = stage.data.cpu().numpy()
for x in range(len(pred)):
confusion_mat[stage[x]][pred[x]] += 1
recall, acc, sp, err, k = statistics.result(confusion_mat)
plot_result['test'].append(err)
heatmap.draw(confusion_mat, opt.label_name, opt.label_name, name='test')
print('recall,acc,sp,err,k: ' + str(statistics.result(confusion_mat)))
return plot_result, confusion_mat
def evalnet(net,
signals,
stages,
sequences,
epoch,
plot_result={},
mode='part'):
# net.eval()
if mode == 'part':
transformer.shuffledata(signals, stages)
signals = signals[0:int(len(stages) / 2)]
stages = stages[0:int(len(stages) / 2)]
confusion_mat = np.zeros((5, 5), dtype=int)
for i, sequence in enumerate(sequences, 1):
signal = transformer.ToInputShape(signals[sequence],
opt.model_name,
test_flag=True)
signal, stage = transformer.ToTensor(signal,
stages[sequence],
no_cuda=opt.no_cuda)
with torch.no_grad():
out = net(signal)
pred = torch.max(out, 1)[1]
pred = pred.data.cpu().numpy()
stage = stage.data.cpu().numpy()
for x in range(len(pred)):
confusion_mat[stage[x]][pred[x]] += 1
if mode == 'part':
plot_result['test'].append(statistics.result(confusion_mat)[0])
else:
recall, acc, error = statistics.result(confusion_mat)
plot_result['test'].append(recall)
heatmap.draw(confusion_mat, name='test')
print('test avg_recall:', '%.4f' % recall, 'avg_acc:', '%.4f' % acc,
'error:', '%.4f' % error)
#util.writelog('epoch:'+str(epoch)+' test avg_recall:'+str(round(recall,4))+' avg_acc:'+str(round(acc,4))+' error:'+str(round(error,4)))
return plot_result, confusion_mat
no_cuda=opt.no_cuda)
out = net(signal)
loss = criterion(out, stage)
pred = torch.max(out, 1)[1]
optimizer.zero_grad()
loss.backward()
optimizer.step()
pred = pred.data.cpu().numpy()
stage = stage.data.cpu().numpy()
for x in range(len(pred)):
confusion_mat[stage[x]][pred[x]] += 1
if i % show_freq == 0:
plot_result['train'].append(
statistics.result(confusion_mat)[0])
heatmap.draw(confusion_mat, name='train')
# plot_result=evalnet(net,signals_eval,stages_eval,plot_result,show_freq,mode = 'part')
statistics.show(plot_result, epoch + i / (batch_length * 0.8))
confusion_mat[:] = 0
plot_result, confusion_mat = evalnet(net,
signals,
stages,
test_sequences[fold],
epoch + 1,
plot_result,
mode='all')
confusion_mats.append(confusion_mat)
# scheduler.step()
stage,
no_cuda=opt.no_cuda)
out = net(signal)
loss = criterion(out, stage)
pred = torch.max(out, 1)[1]
optimizer.zero_grad()
loss.backward()
optimizer.step()
pred = pred.data.cpu().numpy()
stage = stage.data.cpu().numpy()
for x in range(len(pred)):
confusion_mat[stage[x]][pred[x]] += 1
if i % show_freq == 0:
plot_result['train'].append(statistics.result(confusion_mat)[3])
heatmap.draw(confusion_mat,
opt.label_name,
opt.label_name,
name='train')
statistics.show(plot_result, epoch + i / (batch_length * 0.8))
confusion_mat[:] = 0
plot_result, confusion_mat = evalnet(net, signals_test, labels_test,
epoch + 1, plot_result)
confusion_mats.append(confusion_mat)
# scheduler.step()
torch.save(net.cpu().state_dict(), './checkpoints/last.pth')
if (epoch + 1) % opt.network_save_freq == 0:
torch.save(