y_pred = []
y_true = []
with torch.set_grad_enabled(False):
for local_batch, local_labels in validation_generator:
y_true.extend(local_labels.numpy().tolist())
local_batch, local_labels = local_batch.to(
device), local_labels.to(device)
output = model.forward(local_batch)
val_loss = criterion(output, local_labels)
output = model.softmax(output)
output = torch.max(output, 1)[1]
val_acc = pred_acc(local_labels.cpu(), output.cpu())
writer.add_scalar('Validation/Accuracy', val_acc, global_val_step)
writer.add_scalar('Validation/Loss', val_loss, global_val_step)
global_val_step += params['batch_size']
y_pred.extend(output.cpu().numpy().tolist())
tqdm.write(
classification_report(y_true=y_true,
y_pred=y_pred,
target_names=["No Findings", "Pneumonia"]))
torch.save(
model.state_dict(),
os.path.join(
model_dir,
'{0}-epoch-{1}.pt'.format(today.strftime("%b%d_%H-%M-%S"), epoch)))
if (use_cuda):
model.cuda()
