def load_model(test_arguments):
rcnn = RCNN(test_arguments.pos_loss_method, test_arguments.loss_weight_lambda).cuda()
rcnn.load_state_dict(t.load(test_arguments.model_path))
rcnn.eval() # dropout rate = 0
return rcnn
# print(output)
# print(target)
loss = criterion(output, labels)
loss.backward()
optimizer.step()
# scheduler.step(loss)
loss_to_append = loss.clone().cpu().view(1).data.numpy()[0]
print("Epoch : {}, Mini-Epoch : {}, Loss: {}".format(i+1,mini_count,loss_to_append))
mini_count += 1
loss_each_epoch.append(loss_to_append)
loss_trend.append(sum(loss_each_epoch))
for images,labels in dataloader['test']:
net = net.eval()
if use_gpu:
images = images.cuda()
labels = labels.cuda()
output = net(images)
predicted_labels = torch.argmax(output, dim=1)
minibatch_accuracy = torch.eq(predicted_labels,labels).cpu().sum().view(1).numpy()[0]
running_accuracy.append(minibatch_accuracy)
accuracy_trend.append( sum(running_accuracy)/test_dataset_len )
print('##### Epoch {} #####'.format(i+1))
print('Loss : {}'.format(sum(loss_each_epoch)))
print('Accuracy : {}'.format( sum(running_accuracy)/test_dataset_len ))
