def eval_one_epoch(stack, model, criterion, device, val_writer):
num_batches = VALIDATION_DATASET.get_num_batches(BATCH_SIZE_VAL)
# Reset metrics
loss_sum = 0
confusion_matrix = metric.ConfusionMatrix(num_classes)
log_string(str(datetime.now()))
log_string("---- EPOCH %03d EVALUATION ----" % (EPOCH_CNT))
update_progress(0)
for batch_idx in range(num_batches):
progress = float(batch_idx) / float(num_batches)
update_progress(round(progress, 2))
batch_data, batch_label, batch_weights = stack.get()
# Get predicted labels
points_tensor = torch.from_numpy(batch_data).to(
device, dtype=torch.float32) # (B, sample_num, 3)
batch_label_tensor = torch.from_numpy(batch_label).to(
device, dtype=torch.long) # (B, sample_num)
model = model.eval()
with torch.no_grad():
points_prob = run_model(
model, points_tensor, PARAMS,
MODEL_NAME) # (B, sample_num, num_classes), (B, sample_num, 3)
batch_loss = criterion(points_prob, batch_label_tensor)
_, points_pred = torch.max(points_prob, dim=2) # (B, sample_num)
# Update metrics
pred_val = points_pred.cpu().numpy()
for i in range(len(pred_val)):
for j in range(len(pred_val[i])):
confusion_matrix.increment(batch_label[i][j], pred_val[i][j])
loss_sum += batch_loss.cpu().numpy()
update_progress(1)
iou_per_class = confusion_matrix.get_per_class_ious()
# Display metrics
log_string("mean loss: %f" % (loss_sum / float(num_batches)))
log_string("Overall accuracy : %f" % (confusion_matrix.get_accuracy()))
log_string("Average IoU : %f" % (confusion_matrix.get_mean_iou()))
val_writer.add_scalar("%s mean loss" % DATASET_NAME,
loss_sum / float(num_batches), EPOCH_CNT)
val_writer.add_scalar("%s overall accuracy" % DATASET_NAME,
confusion_matrix.get_accuracy(), EPOCH_CNT)
val_writer.add_scalar("%s average IoU" % DATASET_NAME,
confusion_matrix.get_mean_iou(), EPOCH_CNT)
iou_per_class = [0] + iou_per_class # label 0 is ignored
for i in range(1, num_classes):
log_string("IoU of %s : %f" %
(VALIDATION_DATASET.labels_names[i], iou_per_class[i]))
return confusion_matrix.get_mean_iou()
def train_one_epoch(sess, ops, train_writer, stack):
"""Train one epoch
Args:
sess (tf.Session): the session to evaluate Tensors and ops
ops (dict of tf.Operation): contain multiple operation mapped with with strings
train_writer (tf.FileSaver): enable to log the training with TensorBoard
compute_class_iou (bool): it takes time to compute the iou per class, so you can disable it here
"""
is_training = True
num_batches = TRAIN_DATASET.get_num_batches(BATCH_SIZE)
log_string(str(datetime.now()))
update_progress(0)
# Reset metrics
loss_sum = 0
confusion_matrix = metric.ConfusionMatrix(NUM_CLASSES)
# Train over num_batches batches
for batch_idx in range(num_batches):
# Refill more batches if empty
progress = float(batch_idx) / float(num_batches)
update_progress(round(progress, 2))
batch_data, batch_label, batch_weights = stack.get()
# Get predicted labels
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['labels_pl']: batch_label,
ops['smpws_pl']: batch_weights,
ops['is_training_pl']: is_training,
}
summary, step, _, loss_val, pred_val, _ = sess.run([
ops['merged'], ops['step'], ops['train_op'], ops['loss'],
ops['pred'], ops['update_iou']
],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 2)
# Update metrics
for i in range(len(pred_val)):
for j in range(len(pred_val[i])):
confusion_matrix.count_predicted(batch_label[i][j],
pred_val[i][j])
loss_sum += loss_val
update_progress(1)
log_string('mean loss: %f' % (loss_sum / float(num_batches)))
log_string("Overall accuracy : %f" %
(confusion_matrix.get_overall_accuracy()))
log_string("Average IoU : %f" %
(confusion_matrix.get_average_intersection_union()))
iou_per_class = confusion_matrix.get_intersection_union_per_class()
for i in range(1, NUM_CLASSES):
log_string("IoU of %s : %f" %
(TRAIN_DATASET.labels_names[i], iou_per_class[i]))
def train_one_epoch(stack, scheduler, model, criterion, device, train_writer):
global EPOCH_CNT
num_batches = TRAIN_DATASET.get_num_batches(BATCH_SIZE_TRAIN)
log_string(str(datetime.now()))
update_progress(0)
# Reset metrics
loss_sum = 0
confusion_matrix = metric.ConfusionMatrix(num_classes)
# Train over num_batches batches
for batch_idx in range(num_batches):
# Refill more batches if empty
progress = float(batch_idx) / float(num_batches)
update_progress(round(progress, 2))
batch_data, batch_label, batch_weights = stack.get()
# Get predicted labels
points_tensor = torch.from_numpy(batch_data).to(
device, dtype=torch.float32) # (B, sample_num, 3)
batch_label_tensor = torch.from_numpy(batch_label).to(
device, dtype=torch.long) # (B, sample_num)
scheduler.optimizer.zero_grad()
model = model.train()
points_prob = run_model(
model, points_tensor, PARAMS,
MODEL_NAME) # (B, sample_num, num_classes), (B, sample_num, 3)
batch_loss = criterion(points_prob, batch_label_tensor)
_, points_pred = torch.max(points_prob, dim=2) # (B, sample_num)
batch_loss.backward()
scheduler.optimizer.step()
scheduler.step()
# Update metrics
pred_val = points_pred.cpu().numpy()
for i in range(len(pred_val)):
for j in range(len(pred_val[i])):
confusion_matrix.increment(batch_label[i][j], pred_val[i][j])
loss_sum += batch_loss.cpu().detach().numpy()
update_progress(1)
EPOCH_CNT += 1
log_string("mean loss: %f" % (loss_sum / float(num_batches)))
log_string("Overall accuracy : %f" % (confusion_matrix.get_accuracy()))
log_string("Average IoU : %f" % (confusion_matrix.get_mean_iou()))
train_writer.add_scalar("%s mean loss" % DATASET_NAME,
loss_sum / float(num_batches), EPOCH_CNT)
train_writer.add_scalar("%s overall accuracy" % DATASET_NAME,
confusion_matrix.get_accuracy(), EPOCH_CNT)
train_writer.add_scalar("%s average IoU" % DATASET_NAME,
confusion_matrix.get_mean_iou(), EPOCH_CNT)
iou_per_class = confusion_matrix.get_per_class_ious()
iou_per_class = [0] + iou_per_class # label 0 is ignored
for i in range(1, num_classes):
log_string("IoU of %s : %f" %
(TRAIN_DATASET.labels_names[i], iou_per_class[i]))
def eval_one_epoch(sess, ops, test_writer):
"""Evaluate one epoch
Args:
sess (tf.Session): the session to evaluate tensors and operations
ops (tf.Operation): the dict of operations
test_writer (tf.summary.FileWriter): enable to log the evaluation on TensorBoard
Returns:
float: the overall accuracy computed on the test set
"""
global EPOCH_CNT
is_training = False
test_idxs = np.arange(0, len(TEST_DATASET))
num_batches = len(TEST_DATASET) / BATCH_SIZE
# Reset metrics
loss_sum = 0
confusion_matrix = metric.ConfusionMatrix(NUM_CLASSES)
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION ----' % (EPOCH_CNT))
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
batch_data, batch_label, batch_smpw = get_batch(
TEST_DATASET, test_idxs, start_idx, end_idx)
aug_data = provider.rotate_point_cloud(batch_data)
feed_dict = {
ops['pointclouds_pl']: aug_data,
ops['labels_pl']: batch_label,
ops['smpws_pl']: batch_smpw,
ops['is_training_pl']: is_training
}
summary, step, loss_val, pred_val = sess.run(
[ops['merged'], ops['step'], ops['loss'], ops['pred']],
feed_dict=feed_dict)
test_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 2) # BxN
# Update metrics
for i in range(len(pred_val)):
for j in range(len(pred_val[i])):
confusion_matrix.count_predicted(batch_label[i][j],
pred_val[i][j])
loss_sum += loss_val
iou_per_class = confusion_matrix.get_intersection_union_per_class()
# Display metrics
log_string('eval mean loss: %f' % (loss_sum / float(num_batches)))
log_string("Overall accuracy : %f" %
(confusion_matrix.get_overall_accuracy()))
log_string("Average IoU : %f" %
(confusion_matrix.get_average_intersection_union()))
for i in range(1, NUM_CLASSES):
log_string("IoU of %s : %f" % (data.LABELS_NAMES[i], iou_per_class[i]))
EPOCH_CNT += 5
return confusion_matrix.get_overall_accuracy()
def train_one_epoch(sess, ops, train_writer):
"""Train one epoch
Args:
sess (tf.Session): the session to evaluate Tensors and ops
ops (dict of tf.Operation): contain multiple operation mapped with with strings
train_writer (tf.FileSaver): enable to log the training with TensorBoard
"""
is_training = True
# Shuffle train samples
train_idxs = np.arange(0, len(TRAIN_DATASET))
np.random.shuffle(train_idxs)
num_batches = len(TRAIN_DATASET) / BATCH_SIZE
log_string(str(datetime.now()))
# Reset metrics
loss_sum = 0
confusion_matrix = metric.ConfusionMatrix(NUM_CLASSES)
# Train over num_batches batches
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
batch_data, batch_label, batch_smpw = get_batch(
TRAIN_DATASET, train_idxs, start_idx, end_idx, True, INPUT_DROPOUT)
# Augment batched point clouds by z-axis rotation
aug_data = provider.rotate_point_cloud(batch_data)
# Get predicted labels
feed_dict = {
ops['pointclouds_pl']: aug_data,
ops['labels_pl']: batch_label,
ops['smpws_pl']: batch_smpw,
ops['is_training_pl']: is_training,
}
summary, step, _, loss_val, pred_val, _ = sess.run([
ops['merged'], ops['step'], ops['train_op'], ops['loss'],
ops['pred'], ops['update_iou']
],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 2)
# Update metrics
for i in range(len(pred_val)):
for j in range(len(pred_val[i])):
confusion_matrix.count_predicted(batch_label[i][j],
pred_val[i][j])
loss_sum += loss_val
# Every 10 batches, print metrics and reset them
if (batch_idx + 1) % 10 == 0:
log_string(' -- %03d / %03d --' % (batch_idx + 1, num_batches))
log_string('mean loss: %f' % (loss_sum / 10))
log_string("Overall accuracy : %f" %
(confusion_matrix.get_overall_accuracy()))
log_string("Average IoU : %f" %
(confusion_matrix.get_average_intersection_union()))
iou_per_class = confusion_matrix.get_intersection_union_per_class()
for i in range(1, NUM_CLASSES):
log_string("IoU of %s : %f" %
(data.LABELS_NAMES[i], iou_per_class[i]))
loss_sum = 0
confusion_matrix = metric.ConfusionMatrix(NUM_CLASSES)
def eval_one_epoch(sess, ops, test_writer, stack):
"""Evaluate one epoch
Args:
sess (tf.Session): the session to evaluate tensors and operations
ops (tf.Operation): the dict of operations
test_writer (tf.summary.FileWriter): enable to log the evaluation on TensorBoard
Returns:
float: the overall accuracy computed on the test set
"""
global EPOCH_CNT
is_training = False
num_batches = TEST_DATASET.get_num_batches(BATCH_SIZE)
# Reset metrics
loss_sum = 0
confusion_matrix = metric.ConfusionMatrix(NUM_CLASSES)
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT))
update_progress(0)
for batch_idx in range(num_batches):
progress = float(batch_idx)/float(num_batches)
update_progress(round(progress,2))
batch_data, batch_label, batch_weights = stack.get()
feed_dict = {ops['pointclouds_pl']: batch_data,
ops['labels_pl']: batch_label,
ops['smpws_pl']: batch_weights,
ops['is_training_pl']: is_training}
summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
ops['loss'], ops['pred']], feed_dict=feed_dict)
test_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 2) # BxN
# Update metrics
for i in range(len(pred_val)):
for j in range(len(pred_val[i])):
confusion_matrix.count_predicted(batch_label[i][j], pred_val[i][j])
loss_sum += loss_val
update_progress(1)
iou_per_class = confusion_matrix.get_intersection_union_per_class()
# Display metrics
log_string('mean loss: %f' % (loss_sum / float(num_batches)))
log_string("Overall accuracy : %f" %(confusion_matrix.get_overall_accuracy()))
log_string("Average IoU : %f" %(confusion_matrix.get_average_intersection_union()))
for i in range(1,NUM_CLASSES):
log_string("IoU of %s : %f" % (TEST_DATASET.labels_names[i],iou_per_class[i]))
EPOCH_CNT += 5
return confusion_matrix.get_overall_accuracy()
def train_one_epoch(sess, ops, train_writer):
"""Train one epoch
Args:
sess (tf.Session): the session to evaluate Tensors and ops
ops (dict of tf.Operation): contain multiple operation mapped with with strings
train_writer (tf.FileSaver): enable to log the training with TensorBoard
"""
is_training = True
num_batches = int(TRAIN_DATASET.get_total_num_points() /
(BATCH_SIZE * NUM_POINT)) * 2
log_string(str(datetime.now()))
# Reset metrics
loss_sum = 0
confusion_matrix = metric.ConfusionMatrix(NUM_CLASSES)
# Train over num_batches batches
for batch_idx in range(num_batches):
batch_data, batch_label, batch_weights = TRAIN_DATASET.next_batch(
BATCH_SIZE, True, False)
# Get predicted labels
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['labels_pl']: batch_label,
ops['smpws_pl']: batch_weights,
ops['is_training_pl']: is_training,
}
summary, step, _, loss_val, pred_val, _ = sess.run([
ops['merged'], ops['step'], ops['train_op'], ops['loss'],
ops['pred'], ops['update_iou']
],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 2)
# Update metrics
for i in range(len(pred_val)):
for j in range(len(pred_val[i])):
confusion_matrix.count_predicted(batch_label[i][j],
pred_val[i][j])
loss_sum += loss_val
# Every 10 batches, print metrics and reset them
if (batch_idx + 1) % 10 == 0:
log_string(' -- %03d / %03d --' % (batch_idx + 1, num_batches))
log_string('mean loss: %f' % (loss_sum / 10))
log_string("Overall accuracy : %f" %
(confusion_matrix.get_overall_accuracy()))
log_string("Average IoU : %f" %
(confusion_matrix.get_average_intersection_union()))
iou_per_class = confusion_matrix.get_intersection_union_per_class()
for i in range(1, NUM_CLASSES):
log_string("IoU of %s : %f" %
(data.LABELS_NAMES[i], iou_per_class[i]))
loss_sum = 0
confusion_matrix = metric.ConfusionMatrix(NUM_CLASSES)