def train(model: nn.Module,
dataset: Dataset,
validate_data: Dataset = None) -> None:
loader = DataLoader(dataset,
batch_size=dataset.BATCH_SIZE,
shuffle=dataset.BATCH_SIZE)
optimizer = getattr(torch.optim,
config.TRAIN.OPTIMIZER)(model.parameters(),
**config.TRAIN.OPTIM_PARAMS)
overall_iter = 0
evaluation = ConfusionMatrix(dataset.get_num_class())
model.train()
for epoch in range(config.TRAIN.NUM_EPOCHS):
total_loss = 0
for batch_idx, samples in enumerate(loader):
images, target = device([samples['image'], samples['mask']],
gpu=config.USE_GPU)
outputs = model(images)['out']
output_mask = outputs.argmax(1)
batch_loss = Loss.cross_entropy2D(outputs, target, False)
total_loss += batch_loss.item()
overall_loss = total_loss / ((batch_idx + 1))
evaluation.update(output_mask, target)
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
if batch_idx % config.PRINT_BATCH_FREQ == 0:
metrics = evaluation()
logger.info(f'Train Epoch: {epoch}, {batch_idx}')
logger.info(
f'Batch loss: {batch_loss.item():.6f}, Overall loss: {overall_loss:.6f}'
)
for met in beautify(metrics[0]):
logger.info(f'{met}')
logger.info(f'Classwise IoU')
for met in beautify(metrics[1]):
logger.info(f'{met}')
logger.info("\n")
overall_iter += 1
if config.SAVE_ITER_FREQ and overall_iter % config.SAVE_ITER_FREQ == 0:
torch.save(
model.state_dict(),
os.path.join(config.LOG_PATH,
config.NAME + f"-iter={overall_iter}"))
sample_batched['gender']).cuda(), Variable(
sample_batched['edu']).cuda(), Variable(
sample_batched['apoe']).cuda(), Variable(
sample_batched['dx']).view(-1).cuda()
# ===================forward====================
outputs = classifier(images, lefts, rights)
loss = criterion(outputs, labels)
predictions = torch.argmax(outputs, dim=1) # outputs are N * 2: 0 / 1
# ===================backward====================
optimizer.zero_grad()
loss.backward()
optimizer.step()
metrics.update(labels.data.cpu().numpy(),
predictions.data.cpu().numpy())
results_train = metrics.get_scores()
torch.cuda.empty_cache()
metrics.reset()
# =================== evaluation ========================
if epoch % 1 == 0:
classifier.eval()
with torch.no_grad():
for i_batch, sample_batched in enumerate(tqdm(dataloader_val)):
images, lefts, rights, ages, genders, edus, apoes, labels = Variable(
sample_batched['mri']
).cuda(), Variable(sample_batched['left']).cuda(), Variable(
sample_batched['right']).cuda(), Variable(
sample_batched['age']).cuda(), Variable(
sample_batched['gender']).cuda(), Variable(
sample_batched['edu']).cuda(), Variable(
def predict_worker(proc_id,
output_file,
classes,
model_params,
batch_size,
que,
lock,
status_que,
gpu_id=0,
evaluate=True,
framework='mxnet'):
""" get data from batch loader and make predictions, predictions will be saved in output_file
if evaluate, will evaluate recall, precision, f1_score and recall_top5 """
logging.info('Predictor #{}: Loading model...'.format(proc_id))
model = load_model(proc_id,
model_params,
batch_size,
classes,
gpu_id,
framework=framework)
if model is None:
status_que.put('Error')
raise ValueError('No model created! Exit')
logging.info('Predictor #{}: Model loaded'.format(proc_id))
status_que.put('OK')
if evaluate:
from metrics import F1, ConfusionMatrix, MisClassified, RecallTopK
evaluator = F1(len(classes))
misclassified = MisClassified(len(classes))
cm = ConfusionMatrix(classes)
recall_topk = RecallTopK(len(classes), top_k=5)
f = open(output_file, 'w')
batch_idx = 0
logging.info('Predictor #{} starts'.format(proc_id))
start = time.time()
while True:
# get a batch from data loader via a queue
lock.acquire()
batch = que.get()
lock.release()
if batch == 'FINISH':
logging.info(
'Predictor #{} has received all batches, exit'.format(proc_id))
break
# predict
im_names, batch, gt_list = batch
logging.debug('Predictor #{}: predict'.format(proc_id))
pred, prob = model.predict(batch)
pred_labels, top_probs = model.get_label_prob(top_k=5)
# write prediction to file
for im_name, label, top_prob in zip(im_names, pred_labels, top_probs):
if im_name is None:
continue
top_prob = [str(p) for p in top_prob]
f.write('{} labels:{} prob:{}\n'.format(im_name, ','.join(label),
','.join(top_prob)))
# update metrics if evaluation mode is set
if evaluate:
assert gt_list is not None and gt_list != [] and gt_list[
0] is not None
top1_int = [p[0] for p in pred]
assert len(top1_int) == len(gt_list), '{} != {}'.format(
len(top1_int), len(gt_list))
evaluator.update(top1_int, gt_list)
misclassified.update(top1_int, gt_list, prob, im_names)
cm.update(top1_int, gt_list)
top5_int = [p[:5] for p in pred]
assert len(top5_int) == len(gt_list), '{} != {}'.format(
len(top5_int), len(gt_list))
recall_topk.update(top5_int, gt_list)
batch_idx += 1
if batch_idx % 50 == 0 and batch_idx != 0:
elapsed = time.time() - start
logging.info(
'Predictor #{}: Tested {} batches of {} images, elapsed {}s'.
format(proc_id, batch_idx, batch_size, elapsed))
# evaluation after prediction if set
if evaluate:
logging.info('Evaluating...')
recall, precision, f1_score = evaluator.get()
for rec, prec, f1, cls, in zip(recall, precision, f1_score, classes):
print(
'Class {:<20}: recall: {:<12}, precsion: {:<12}, f1 score: {:<12}'
.format(cls, rec, prec, f1))
f.write(
'Class {:<20}: recall: {:<12}, precsion: {:<12}, f1 score: {:<12}\n'
.format(cls, rec, prec, f1))
topk_recall = recall_topk.get()
for rec, cls in zip(topk_recall, classes):
print('Class {:<20}: recall-top-5: {:<12}'.format(cls, rec))
f.write('Class {:<20}: recall-top-5: {:<12}\n'.format(cls, rec))
fp_images, fn_images = misclassified.get()
g = open(output_file + '.fp', 'w')
for cls, fp_cls in zip(classes, fp_images):
for fp in fp_cls:
g.write('{} pred:{} prob:{} gt:{} prob:{}\n'.format(
fp[0], cls, fp[2], classes[fp[1]], fp[3]))
g.close()
g = open(output_file + '.fn', 'w')
for cls, fn_cls in zip(classes, fn_images):
for fn in fn_cls:
g.write('{} gt:{} prob:{} pred:{} prob:{}\n'.format(
fp[0], cls, fp[3], classes[fp[1]], fp[2]))
g.close()
cm.normalize()
plt_name = output_file + '_cm.jpg'
cm.draw(plt_name)
f.close()
