def test(config):
"""Test point cloud data loader.
"""
from torch.utils.data import DataLoader
from lib.utils import Timer
timer = Timer()
DatasetClass = StanfordVoxelization2cmDataset
transformations = [
t.RandomHorizontalFlip(DatasetClass.ROTATION_AXIS,
DatasetClass.IS_TEMPORAL),
t.ChromaticAutoContrast(),
t.ChromaticTranslation(config.data_aug_color_trans_ratio),
t.ChromaticJitter(config.data_aug_color_jitter_std),
t.HueSaturationTranslation(config.data_aug_hue_max,
config.data_aug_saturation_max),
]
dataset = DatasetClass(config,
input_transform=t.Compose(transformations),
augment_data=True,
cache=True,
elastic_distortion=True)
data_loader = DataLoader(
dataset=dataset,
collate_fn=t.cfl_collate_fn_factory(limit_numpoints=False),
batch_size=4,
shuffle=True)
# Start from index 1
iter = data_loader.__iter__()
for i in range(100):
timer.tic()
data = iter.next()
print(timer.toc())
def test(config, intensity=False):
"""Test point cloud data loader.
"""
from torch.utils.data import DataLoader
from lib.utils import Timer
import open3d as o3d
def make_pcd(coords, feats):
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(coords[:, :3].float().numpy())
pcd.colors = o3d.utility.Vector3dVector(feats[:, :3].numpy() / 255)
if intensity:
pcd.intensities = o3d.utility.Vector3dVector(feats[:, 3:3].numpy())
return pcd
timer = Timer()
DatasetClass = FacilityArea5Dataset
transformations = [
t.RandomHorizontalFlip(DatasetClass.ROTATION_AXIS,
DatasetClass.IS_TEMPORAL),
t.ChromaticAutoContrast(),
t.ChromaticTranslation(config.data_aug_color_trans_ratio),
t.ChromaticJitter(config.data_aug_color_jitter_std),
]
dataset = DatasetClass(config,
prevoxel_transform=t.ElasticDistortion(
DatasetClass.ELASTIC_DISTORT_PARAMS),
input_transform=t.Compose(transformations),
augment_data=True,
cache=True,
elastic_distortion=True)
data_loader = DataLoader(
dataset=dataset,
collate_fn=t.cfl_collate_fn_factory(limit_numpoints=False),
batch_size=1,
shuffle=True)
# Start from index 1
iter = data_loader.__iter__()
for i in range(100):
timer.tic()
coords, feats, labels = iter.next()
pcd = make_pcd(coords, feats)
o3d.visualization.draw_geometries([pcd])
print(timer.toc())
def test(self):
from torch.utils.data import DataLoader
from lib.utils import Timer
from config import get_config
config = get_config()
dataset = SynthiaVoxelizationDataset(config)
timer = Timer()
data_loader = DataLoader(
dataset=dataset,
collate_fn=cfl_collate_fn_factory(limit_numpoints=False),
num_workers=0,
batch_size=4,
shuffle=True)
# Start from index 1
# for i, batch in enumerate(data_loader, 1):
iter = data_loader.__iter__()
for i in range(100):
timer.tic()
batch = iter.next()
print(batch, timer.toc())
class Solver(object):
"""Solver for generic networks.
"""
def __init__(self, net, graph, is_training):
self.net = net
self.graph = graph
self.is_training = is_training
self.num_epochs = cfg.TRAIN.NUM_EPOCHS
self.train_timer = Timer()
self.data_timer = Timer()
self.global_step = slim.get_or_create_global_step()
# Build basic ops and tensors
if self.is_training:
self.net.build_train_ops(self.global_step)
if isinstance(net, Classifier):
self.saver = tf.train.Saver(var_list=net.vars_to_restore,
name='saver')
else:
self.saver = tf.train.Saver(max_to_keep=None,
name='saver_all_var') # Save all vars
self.init_ops = self.build_init_ops()
self.val_loss_ph = tf.placeholder(tf.float32,
shape=(),
name='val_loss_ph')
self.net.build_summary_ops(self.graph)
self.val_loss_summary = tf.summary.scalar(name='val_loss',
tensor=self.val_loss_ph)
print('saver variables:')
print_tensor_shapes(net.vars_to_restore, prefix='-->')
def build_init_ops(self):
"""Builds the init ops.
Returns:
init_op: Initialization op.
ready_op: Initialization op.
local_init_op: Initialization op.
"""
with tf.name_scope('init_ops'):
init_op = tf.global_variables_initializer()
ready_op = tf.report_uninitialized_variables()
local_init_op = tf.group(tf.local_variables_initializer(),
tf.tables_initializer())
return init_op, ready_op, local_init_op
def restore_checkpoint(self, sess):
"""Restores the network to a previously saved checkpoint if a path is provided from the
config.
Args:
sess: Current session.
"""
if cfg.DIR.CKPT_PATH is not None:
tf.logging.info('Restoring checkpoint.')
self.saver.restore(sess, cfg.DIR.CKPT_PATH)
else:
tf.logging.info('Using network with random weights.')
def train_step(self, sess, train_queue, step):
"""Executes a train step, including saving the summaries if appropriate.
Args:
sess: Current session.
train_queue: Data queue containing train set minibatches.
step: The current training iteration (0-based indexing).
Returns:
print_dict: Dictionary of items such as losses (for just one minibatch) to print.
"""
print_dict = self.net.train_step(sess,
train_queue,
step,
data_timer=self.data_timer)
return print_dict
def val_step(self, sess, val_queue):
"""Executes a validation step, which simply computes the loss.
Args:
sess: Current session.
val_queue: Data queue containing validation set minibatches.
Returns:
val_loss: Loss for a single minibatch of validation data.
"""
raise NotImplementedError('Must be implemented by a subclass.')
def validate(self, sess, val_queue, step, num_val_iter):
raise NotImplementedError('Must be implemented by a subclass.')
def train(self,
train_iters_per_epoch,
train_queue,
val_iters_per_epoch=None,
val_queue=None):
"""Train the network, computing the validation loss if val_iters_per_epoch and val_queue are
provided.
Args:
train_iters_per_epoch: Number of iterations in a single epoch of train data, as computed
by the data process.
train_queue: Data queue containing minibatches of train data.
val_iters_per_epoch: Optional input describing the number of iterations in a single
epoch of validation data, as computed by the data process.
val_queue: Optional input representing the data queue containing minibatches of
validation data.
"""
if (val_iters_per_epoch is None and val_queue is not None) or \
(val_iters_per_epoch is not None and val_queue is None):
raise ValueError('Need to input both val size and val queue.')
if val_iters_per_epoch is not None and val_queue is not None:
run_validation = True
else:
run_validation = False
print('-------------- BEGIN TRAINING --------------')
num_train_iter = get_num_iterations(train_iters_per_epoch,
num_epochs=cfg.TRAIN.NUM_EPOCHS,
disp=True)
num_val_iter = 20000 // cfg.CONST.BATCH_SIZE # Evaluate on roughly 20000 samples
if val_iters_per_epoch is not None:
num_val_iter = min(num_val_iter, val_iters_per_epoch)
with tf.Session() as sess:
sess.run(self.init_ops)
self.restore_checkpoint(sess)
# Train loop
for step in range(num_train_iter):
# For randomized model
# self.save(sess, step)
# break
self.train_timer.tic()
print_dict = self.train_step(sess, train_queue, step)
self.train_timer.toc()
if (step + 1) % cfg.CONST.PRINT_FREQ == 0:
print_dict['queue size'] = (str(train_queue.qsize()) +
'/' +
str(cfg.CONST.QUEUE_CAPACITY))
print_dict[
'data fetch (sec/step)'] = '%.2f' % self.data_timer.average_time
print_dict[
'train step (sec/step)'] = '%.2f' % self.train_timer.average_time
print_train_step_data(print_dict, step)
# Reset timers
self.data_timer.reset()
self.train_timer.reset()
if (run_validation is True) and (
(step + 1) % cfg.TRAIN.VALIDATION_FREQ == 0):
validation_val = self.validate(sess, val_queue, step,
num_val_iter)
if validation_val == -1: # Training termination flag
tf.logging.info(
'Terminating train loop due to decreasing validation performance.'
)
break
else:
val_summary = sess.run(
self.val_loss_summary,
feed_dict={self.val_loss_ph: validation_val})
self.net.summary_writer.add_summary(
val_summary, (step + 1))
if (step + 1) % cfg.TRAIN.CKPT_FREQ == 0:
self.save(sess, step)
# save model after training
self.save(sess, step)
def forward_pass_batches(self, sess, minibatch_generator):
"""Forward pass a series of minibatches from the minibatch generator.
"""
minibatch_list = []
outputs_list = []
for step, minibatch in enumerate(minibatch_generator):
np.random.seed(1234)
try:
outputs = self.net.forward_pass(sess, minibatch)
except KeyError:
outputs = self.net.forward_pass(sess,
minibatch,
full_feed_dict=True)
# Reduce size of minibatch so we can pass through entire val set
if isinstance(self.net, LBA):
minibatch_save = {
'raw_embedding_batch': minibatch['raw_embedding_batch'],
'caption_label_batch': minibatch['caption_label_batch'],
'category_list': minibatch['category_list'],
'model_list': minibatch['model_list'],
}
minibatch = minibatch_save
if isinstance(self.net, Classifier):
minibatch_save = {
'class_label_batch': minibatch['class_label_batch'],
'model_id_list': minibatch['model_id_list'],
}
minibatch = minibatch_save
minibatch_list.append(minibatch)
outputs_list.append(outputs)
if (step + 1) % 100 == 0:
tf.logging.info('%s Step: %d' %
(str(datetime.now()), step + 1))
return minibatch_list, outputs_list
def val_phase_minibatch_generator(self, val_queue, num_val_iter):
"""Return a minibatch generator for the test phase.
"""
for step in range(num_val_iter):
minibatch = val_queue.get()
minibatch['test_queue'] = True
yield minibatch
def evaluate(self, minibatch_list, outputs_list):
"""Do some evaluation of the outputs.
"""
pass
def test(self,
test_process,
test_queue,
num_minibatches=None,
save_outputs=False):
"""Compute (and optionally save) the outputs for the test set. This function only computes
the outputs for num_minibatches minibatches.
Args:
test_process: Data process for the test data.
test_queue: Queue containing minibatches of test data.
num_minibatches: Number of minibatches to compute the outputs for.
save_outputs: Boolean flag for whether or not to save the outputs.
"""
with tf.Session() as sess:
if cfg.DIR.CKPT_PATH is None:
raise ValueError('Please provide a checkpoint.')
sess.run(self.init_ops)
else:
self.restore_checkpoint(sess)
def test_phase_minibatch_generator():
for step, minibatch in enumerate(
get_while_running(test_process, test_queue)):
if (num_minibatches is not None) and (step
== num_minibatches):
break
yield minibatch
minibatch_generator = test_phase_minibatch_generator()
minibatch_list, outputs_list = self.forward_pass_batches(
sess, minibatch_generator)
self.evaluate(minibatch_list, outputs_list)
if save_outputs:
self.save_outputs(minibatch_list, outputs_list)
def save(self, sess, step):
"""Save a checkpoint.
"""
ckpt_path = os.path.join(cfg.DIR.LOG_PATH, 'model.ckpt')
tf.logging.info('Saving checkpoint (step %d).', (step + 1))
self.saver.save(sess, ckpt_path, global_step=(step + 1))
def save_outputs(self, minibatch_list, outputs_list, filename=None):
"""Save the outputs (from the self.test).
"""
raise NotImplementedError('Must be implemented by a subclass.')
def train(self, train_loader, val_loader=None):
''' Given data queues, train the network '''
# Parameter directory
save_dir = os.path.join(cfg.DIR.OUT_PATH)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# Timer for the training op and parallel data loading op.
train_timer = Timer()
data_timer = Timer()
training_losses = []
# Setup learning rates
lr_steps = [int(k) for k in cfg.TRAIN.LEARNING_RATES.keys()]
#Setup the lr_scheduler
self.lr_scheduler = lr_scheduler.MultiStepLR(self.optimizer,
lr_steps,
gamma=0.1)
start_iter = 0
# Resume training
if cfg.TRAIN.RESUME_TRAIN:
self.load(cfg.CONST.WEIGHTS)
start_iter = cfg.TRAIN.INITIAL_ITERATION
# Main training loop
train_loader_iter = iter(train_loader)
for train_ind in range(start_iter, cfg.TRAIN.NUM_ITERATION + 1):
self.lr_scheduler.step()
data_timer.tic()
try:
batch_img, batch_voxel = train_loader_iter.next()
except StopIteration:
train_loader_iter = iter(train_loader)
batch_img, batch_voxel = train_loader_iter.next()
data_timer.toc()
if self.net.is_x_tensor4:
batch_img = batch_img[0]
# Apply one gradient step
train_timer.tic()
loss = self.train_loss(batch_img, batch_voxel)
train_timer.toc()
training_losses.append(loss.item())
# Decrease learning rate at certain points
if train_ind in lr_steps:
#for pytorch optimizer, learning rate can only be set when the optimizer is created
#or using torch.optim.lr_scheduler
print('Learing rate decreased to %f: ' %
cfg.TRAIN.LEARNING_RATES[str(train_ind)])
# Debugging modules
#
# Print status, run validation, check divergence, and save model.
if train_ind % cfg.TRAIN.PRINT_FREQ == 0:
# Print the current loss
print('%s Iter: %d Loss: %f' %
(datetime.now(), train_ind, loss))
if train_ind % cfg.TRAIN.VALIDATION_FREQ == 0 and val_loader is not None:
# Print test loss and params to check convergence every N iterations
val_losses = 0
val_num_iter = min(cfg.TRAIN.NUM_VALIDATION_ITERATIONS,
len(val_loader))
val_loader_iter = iter(val_loader)
for i in range(val_num_iter):
batch_img, batch_voxel = val_loader_iter.next()
val_loss = self.train_loss(batch_img, batch_voxel)
val_losses += val_loss
var_losses_mean = val_losses / val_num_iter
print('%s Test loss: %f' % (datetime.now(), var_losses_mean))
if train_ind % cfg.TRAIN.NAN_CHECK_FREQ == 0:
# Check that the network parameters are all valid
nan_or_max_param = max_or_nan(self.net.parameters())
if has_nan(nan_or_max_param):
print('NAN detected')
break
if (train_ind % cfg.TRAIN.SAVE_FREQ == 0 and not train_ind == 0) or \
(train_ind == cfg.TRAIN.NUM_ITERATION):
# Save the checkpoint every a few iterations or at the end.
self.save(training_losses, save_dir, train_ind)
#loss is a Variable containing torch.FloatTensor of size 1
if loss.item() > cfg.TRAIN.LOSS_LIMIT:
print("Cost exceeds the threshold. Stop training")
break
def train(model, data_loader, val_data_loader, config, transform_data_fn=None):
device = config.device_id
distributed = get_world_size() > 1
# Set up the train flag for batch normalization
model.train()
# Configuration
writer = SummaryWriter(log_dir=config.log_dir)
data_timer, iter_timer = Timer(), Timer()
fw_timer, bw_timer, ddp_timer = Timer(), Timer(), Timer()
data_time_avg, iter_time_avg = AverageMeter(), AverageMeter()
fw_time_avg, bw_time_avg, ddp_time_avg = AverageMeter(), AverageMeter(
), AverageMeter()
losses, scores = AverageMeter(), AverageMeter()
optimizer = initialize_optimizer(model.parameters(), config)
scheduler = initialize_scheduler(optimizer, config)
criterion = nn.CrossEntropyLoss(ignore_index=config.ignore_label)
writer = SummaryWriter(log_dir=config.log_dir)
# Train the network
logging.info('===> Start training on {} GPUs, batch-size={}'.format(
get_world_size(), config.batch_size * get_world_size()))
best_val_miou, best_val_iter, curr_iter, epoch, is_training = 0, 0, 1, 1, True
if config.resume:
checkpoint_fn = config.resume + '/weights.pth'
if osp.isfile(checkpoint_fn):
logging.info("=> loading checkpoint '{}'".format(checkpoint_fn))
state = torch.load(
checkpoint_fn,
map_location=lambda s, l: default_restore_location(s, 'cpu'))
curr_iter = state['iteration'] + 1
epoch = state['epoch']
load_state(model, state['state_dict'])
if config.resume_optimizer:
scheduler = initialize_scheduler(optimizer,
config,
last_step=curr_iter)
optimizer.load_state_dict(state['optimizer'])
if 'best_val' in state:
best_val_miou = state['best_val']
best_val_iter = state['best_val_iter']
logging.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_fn, state['epoch']))
else:
raise ValueError(
"=> no checkpoint found at '{}'".format(checkpoint_fn))
data_iter = data_loader.__iter__() # (distributed) infinite sampler
while is_training:
for iteration in range(len(data_loader) // config.iter_size):
optimizer.zero_grad()
data_time, batch_loss, batch_score = 0, 0, 0
iter_timer.tic()
# set random seed for every iteration for trackability
_set_seed(config, curr_iter)
for sub_iter in range(config.iter_size):
# Get training data
data_timer.tic()
coords, input, target = data_iter.next()
# For some networks, making the network invariant to even, odd coords is important
coords[:, :3] += (torch.rand(3) * 100).type_as(coords)
# Preprocess input
color = input[:, :3].int()
if config.normalize_color:
input[:, :3] = input[:, :3] / 255. - 0.5
sinput = SparseTensor(input, coords).to(device)
data_time += data_timer.toc(False)
# Feed forward
fw_timer.tic()
inputs = (sinput, ) if config.wrapper_type == 'None' else (
sinput, coords, color)
# model.initialize_coords(*init_args)
soutput = model(*inputs)
# The output of the network is not sorted
target = target.long().to(device)
loss = criterion(soutput.F, target.long())
# Compute and accumulate gradient
loss /= config.iter_size
pred = get_prediction(data_loader.dataset, soutput.F, target)
score = precision_at_one(pred, target)
fw_timer.toc(False)
bw_timer.tic()
# bp the loss
loss.backward()
bw_timer.toc(False)
# gather information
logging_output = {
'loss': loss.item(),
'score': score / config.iter_size
}
ddp_timer.tic()
if distributed:
logging_output = all_gather_list(logging_output)
logging_output = {
w: np.mean([a[w] for a in logging_output])
for w in logging_output[0]
}
batch_loss += logging_output['loss']
batch_score += logging_output['score']
ddp_timer.toc(False)
# Update number of steps
optimizer.step()
scheduler.step()
data_time_avg.update(data_time)
iter_time_avg.update(iter_timer.toc(False))
fw_time_avg.update(fw_timer.diff)
bw_time_avg.update(bw_timer.diff)
ddp_time_avg.update(ddp_timer.diff)
losses.update(batch_loss, target.size(0))
scores.update(batch_score, target.size(0))
if curr_iter >= config.max_iter:
is_training = False
break
if curr_iter % config.stat_freq == 0 or curr_iter == 1:
lrs = ', '.join(
['{:.3e}'.format(x) for x in scheduler.get_lr()])
debug_str = "===> Epoch[{}]({}/{}): Loss {:.4f}\tLR: {}\t".format(
epoch, curr_iter,
len(data_loader) // config.iter_size, losses.avg, lrs)
debug_str += "Score {:.3f}\tData time: {:.4f}, Forward time: {:.4f}, Backward time: {:.4f}, DDP time: {:.4f}, Total iter time: {:.4f}".format(
scores.avg, data_time_avg.avg, fw_time_avg.avg,
bw_time_avg.avg, ddp_time_avg.avg, iter_time_avg.avg)
logging.info(debug_str)
# Reset timers
data_time_avg.reset()
iter_time_avg.reset()
# Write logs
writer.add_scalar('training/loss', losses.avg, curr_iter)
writer.add_scalar('training/precision_at_1', scores.avg,
curr_iter)
writer.add_scalar('training/learning_rate',
scheduler.get_lr()[0], curr_iter)
losses.reset()
scores.reset()
# Save current status, save before val to prevent occational mem overflow
if curr_iter % config.save_freq == 0:
checkpoint(model, optimizer, epoch, curr_iter, config,
best_val_miou, best_val_iter)
# Validation
if curr_iter % config.val_freq == 0:
val_miou = validate(model, val_data_loader, writer, curr_iter,
config, transform_data_fn)
if val_miou > best_val_miou:
best_val_miou = val_miou
best_val_iter = curr_iter
checkpoint(model, optimizer, epoch, curr_iter, config,
best_val_miou, best_val_iter, "best_val")
logging.info("Current best mIoU: {:.3f} at iter {}".format(
best_val_miou, best_val_iter))
# Recover back
model.train()
if curr_iter % config.empty_cache_freq == 0:
# Clear cache
torch.cuda.empty_cache()
# End of iteration
curr_iter += 1
epoch += 1
# Explicit memory cleanup
if hasattr(data_iter, 'cleanup'):
data_iter.cleanup()
# Save the final model
checkpoint(model, optimizer, epoch, curr_iter, config, best_val_miou,
best_val_iter)
val_miou = validate(model, val_data_loader, writer, curr_iter, config,
transform_data_fn)
if val_miou > best_val_miou:
best_val_miou = val_miou
best_val_iter = curr_iter
checkpoint(model, optimizer, epoch, curr_iter, config, best_val_miou,
best_val_iter, "best_val")
logging.info("Current best mIoU: {:.3f} at iter {}".format(
best_val_miou, best_val_iter))
def test(model,
data_loader,
config,
transform_data_fn=None,
has_gt=True,
validation=None,
epoch=None):
device = get_torch_device(config.is_cuda)
dataset = data_loader.dataset
num_labels = dataset.NUM_LABELS
global_timer, data_timer, iter_timer = Timer(), Timer(), Timer()
criterion = nn.CrossEntropyLoss(ignore_index=config.ignore_label)
alpha, gamma, eps = 1, 2, 1e-6 # Focal Loss parameters
losses, scores, ious = AverageMeter(), AverageMeter(), 0
aps = np.zeros((0, num_labels))
hist = np.zeros((num_labels, num_labels))
if not config.is_train:
checkpoint_fn = config.resume + '/weights.pth'
if osp.isfile(checkpoint_fn):
logging.info("=> loading checkpoint '{}'".format(checkpoint_fn))
state = torch.load(checkpoint_fn)
model.load_state_dict(state['state_dict'])
logging.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_fn, state['epoch']))
else:
raise ValueError(
"=> no checkpoint found at '{}'".format(checkpoint_fn))
if validation:
logging.info('===> Start validating')
else:
logging.info('===> Start testing')
global_timer.tic()
data_iter = data_loader.__iter__()
max_iter = len(data_loader)
max_iter_unique = max_iter
all_preds, all_labels, batch_losses, batch_loss = [], [], {}, 0
# Fix batch normalization running mean and std
model.eval()
# Clear cache (when run in val mode, cleanup training cache)
torch.cuda.empty_cache()
if config.save_prediction or config.test_original_pointcloud:
if config.save_prediction:
save_pred_dir = config.save_pred_dir
os.makedirs(save_pred_dir, exist_ok=True)
else:
save_pred_dir = tempfile.mkdtemp()
if os.listdir(save_pred_dir):
raise ValueError(f'Directory {save_pred_dir} not empty. '
'Please remove the existing prediction.')
with torch.no_grad():
for iteration in range(max_iter):
data_timer.tic()
if config.return_transformation:
coords, input, target, transformation = data_iter.next()
else:
coords, input, target = data_iter.next()
transformation = None
data_time = data_timer.toc(False)
# Preprocess input
iter_timer.tic()
if config.wrapper_type != 'None':
color = input[:, :3].int()
if config.normalize_color:
input[:, :3] = input[:, :3] / 255. - 0.5
sinput = SparseTensor(input, coords).to(device)
# Feed forward
inputs = (sinput, ) if config.wrapper_type == 'None' else (sinput,
coords,
color)
soutput = model(*inputs)
output = soutput.F
pred = get_prediction(dataset, output, target).int()
iter_time = iter_timer.toc(False)
all_preds.append(pred.cpu().detach().numpy())
all_labels.append(target.cpu().detach().numpy())
if config.save_prediction or config.test_original_pointcloud:
save_predictions(coords, pred, transformation, dataset, config,
iteration, save_pred_dir)
if has_gt:
if config.evaluate_original_pointcloud:
raise NotImplementedError('pointcloud')
output, pred, target = permute_pointcloud(
coords, pointcloud, transformation, dataset.label_map,
output, pred)
target_np = target.numpy()
num_sample = target_np.shape[0]
target = target.to(device)
"""# focal loss
input_soft = nn.functional.softmax(output, dim=1) + eps
focal_weight = torch.pow(-input_soft + 1., gamma)
loss = (-alpha * focal_weight * torch.log(input_soft)).mean()"""
loss = criterion(output, target.long())
batch_loss += loss
losses.update(float(loss), num_sample)
scores.update(precision_at_one(pred, target), num_sample)
hist += fast_hist(pred.cpu().numpy().flatten(),
target_np.flatten(), num_labels)
ious = per_class_iu(hist) * 100
prob = torch.nn.functional.softmax(output, dim=1)
ap = average_precision(prob.cpu().detach().numpy(), target_np)
aps = np.vstack((aps, ap))
# Due to heavy bias in class, there exists class with no test label at all
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
ap_class = np.nanmean(aps, 0) * 100.
if iteration % config.test_stat_freq == 0 and iteration > 0:
preds = np.concatenate(all_preds)
targets = np.concatenate(all_labels)
to_ignore = [
i for i in range(len(targets)) if targets[i] == 255
]
preds_trunc = [
preds[i] for i in range(len(preds)) if i not in to_ignore
]
targets_trunc = [
targets[i] for i in range(len(targets))
if i not in to_ignore
]
cm = confusion_matrix(targets_trunc,
preds_trunc,
normalize='true')
np.savetxt(config.log_dir + '/cm_epoch_{0}.txt'.format(epoch),
cm)
reordered_ious = dataset.reorder_result(ious)
reordered_ap_class = dataset.reorder_result(ap_class)
class_names = dataset.get_classnames()
print_info(iteration,
max_iter_unique,
data_time,
iter_time,
has_gt,
losses,
scores,
reordered_ious,
hist,
reordered_ap_class,
class_names=class_names)
if iteration % config.empty_cache_freq == 0:
# Clear cache
torch.cuda.empty_cache()
batch_losses[epoch] = batch_loss
global_time = global_timer.toc(False)
reordered_ious = dataset.reorder_result(ious)
reordered_ap_class = dataset.reorder_result(ap_class)
class_names = dataset.get_classnames()
print_info(iteration,
max_iter_unique,
data_time,
iter_time,
has_gt,
losses,
scores,
reordered_ious,
hist,
reordered_ap_class,
class_names=class_names)
if not config.is_train:
preds = np.concatenate(all_preds)
targets = np.concatenate(all_labels)
to_ignore = [i for i in range(len(targets)) if targets[i] == 255]
preds_trunc = [
preds[i] for i in range(len(preds)) if i not in to_ignore
]
targets_trunc = [
targets[i] for i in range(len(targets)) if i not in to_ignore
]
cm = confusion_matrix(targets_trunc, preds_trunc, normalize='true')
np.savetxt(config.log_dir + '/cm.txt', cm)
if config.test_original_pointcloud:
logging.info('===> Start testing on original pointcloud space.')
dataset.test_pointcloud(save_pred_dir)
logging.info("Finished test. Elapsed time: {:.4f}".format(global_time))
if validation:
loss_file_name = "/val_loss.txt"
with open(config.log_dir + loss_file_name, 'a') as val_loss_file:
for key in batch_losses:
val_loss_file.writelines('{0}, {1}\n'.format(
batch_losses[key], key))
val_loss_file.close()
return losses.avg, scores.avg, np.nanmean(ap_class), np.nanmean(
per_class_iu(hist)) * 100, batch_losses
else:
return losses.avg, scores.avg, np.nanmean(ap_class), np.nanmean(
per_class_iu(hist)) * 100
def train(self, train_queue, val_queue=None):
''' Given data queues, train the network '''
# Parameter directory
save_dir = os.path.join(cfg.DIR.OUT_PATH)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# Timer for the training op and parallel data loading op.
train_timer = Timer()
data_timer = Timer()
training_losses = []
# Setup learning rates
lr_steps = [int(k) for k in cfg.TRAIN.LEARNING_RATES.keys()]
#Setup the lr_scheduler
self.lr_scheduler = lr_scheduler.MultiStepLR(self.optimizer,
lr_steps,
gamma=0.1) # gamma为下降系数
start_iter = 0
# Resume training
if cfg.TRAIN.RESUME_TRAIN:
self.load(cfg.CONST.WEIGHTS)
start_iter = cfg.TRAIN.INITIAL_ITERATION
if cfg.TRAIN.SHOW_LOSS: # 要动态打印
import matplotlib.pyplot as plot
plot.figure(1, figsize=(12, 5))
plot.ion()
# Main training loop
for train_ind in range(start_iter, cfg.TRAIN.NUM_ITERATION + 1):
self.lr_scheduler.step()
data_timer.tic()
batch_img, batch_voxel = train_queue.get()
data_timer.toc()
if self.net.is_x_tensor4:
batch_img = batch_img[0]
# Apply one gradient step
train_timer.tic()
loss = self.train_loss(batch_img, batch_voxel)
train_timer.toc()
# print(loss)
# training_losses.append(loss.data) 转换为numpy数组
# print(type(loss))
# print(loss.data.numpy())
# print(loss.data.numpy().shape)
# print(type(loss.data.numpy()))
if (torch.cuda.is_available()):
training_losses.append(loss.cpu().data.numpy())
else:
training_losses.append(loss.data.numpy())
# Decrease learning rate at certain points
if train_ind in lr_steps:
#for pytorch optimizer, learning rate can only be set when the optimizer is created
#or using torch.optim.lr_scheduler
print('Learing rate decreased to %f: ' %
cfg.TRAIN.LEARNING_RATES[str(train_ind)])
# '''
# Debugging modules
# '''
# Print status, run validation, check divergence, and save model.
if train_ind % cfg.TRAIN.PRINT_FREQ == 0: #40
# Print the current loss
print('%s Iter: %d Loss: %f' %
(datetime.now(), train_ind, loss))
'''
@TODO(dingyadong): loss dynamic Visualization
'''
# plot
if (train_ind != 0):
steps = np.linspace(0,
train_ind,
train_ind + 1,
dtype=np.float32)
if cfg.TRAIN.SHOW_LOSS: # 要动态打印
plot.plot(steps, training_losses, 'b-')
plot.draw()
# plot.pause(0.05)
if train_ind % cfg.TRAIN.VALIDATION_FREQ == 0 and val_queue is not None:
# Print test loss and params to check convergence every N iterations
val_losses = 0
for i in range(cfg.TRAIN.NUM_VALIDATION_ITERATIONS):
batch_img, batch_voxel = val_queue.get()
val_loss = self.train_loss(batch_img, batch_voxel)
val_losses += val_loss
var_losses_mean = val_losses / cfg.TRAIN.NUM_VALIDATION_ITERATIONS
print('%s Test loss: %f' % (datetime.now(), var_losses_mean))
if train_ind % cfg.TRAIN.NAN_CHECK_FREQ == 0:
# Check that the network parameters are all valid
nan_or_max_param = max_or_nan(self.net.parameters())
if has_nan(nan_or_max_param):
print('NAN detected')
break
if train_ind % cfg.TRAIN.SAVE_FREQ == 0 and not train_ind == 0:
self.save(training_losses, save_dir, train_ind)
#loss is a Variable containing torch.FloatTensor of size 1
if loss.data > cfg.TRAIN.LOSS_LIMIT:
print("Cost exceeds the threshold. Stop training")
break
if cfg.TRAIN.SHOW_LOSS: # 要动态打印ƒ
plot.ioff()
plot.show()
def test(model, data_loader, config, transform_data_fn=None, has_gt=True):
device = get_torch_device(config.is_cuda)
dataset = data_loader.dataset
num_labels = dataset.NUM_LABELS
global_timer, data_timer, iter_timer = Timer(), Timer(), Timer()
criterion = nn.CrossEntropyLoss(ignore_index=config.ignore_label)
losses, scores, ious = AverageMeter(), AverageMeter(), 0
aps = np.zeros((0, num_labels))
hist = np.zeros((num_labels, num_labels))
logging.info('===> Start testing')
global_timer.tic()
data_iter = data_loader.__iter__()
max_iter = len(data_loader)
max_iter_unique = max_iter
# Fix batch normalization running mean and std
model.eval()
# Clear cache (when run in val mode, cleanup training cache)
torch.cuda.empty_cache()
if config.save_prediction or config.test_original_pointcloud:
if config.save_prediction:
save_pred_dir = config.save_pred_dir
os.makedirs(save_pred_dir, exist_ok=True)
else:
save_pred_dir = tempfile.mkdtemp()
if os.listdir(save_pred_dir):
raise ValueError(f'Directory {save_pred_dir} not empty. '
'Please remove the existing prediction.')
with torch.no_grad():
for iteration in range(max_iter):
data_timer.tic()
if config.return_transformation:
coords, input, target, transformation = data_iter.next()
else:
coords, input, target = data_iter.next()
transformation = None
data_time = data_timer.toc(False)
# Preprocess input
iter_timer.tic()
if config.wrapper_type != 'None':
color = input[:, :3].int()
if config.normalize_color:
input[:, :3] = input[:, :3] / 255. - 0.5
sinput = SparseTensor(input, coords).to(device)
# Feed forward
inputs = (sinput, ) if config.wrapper_type == 'None' else (sinput,
coords,
color)
soutput = model(*inputs)
output = soutput.F
pred = get_prediction(dataset, output, target).int()
iter_time = iter_timer.toc(False)
if config.save_prediction or config.test_original_pointcloud:
save_predictions(coords, pred, transformation, dataset, config,
iteration, save_pred_dir)
if has_gt:
if config.evaluate_original_pointcloud:
raise NotImplementedError('pointcloud')
output, pred, target = permute_pointcloud(
coords, pointcloud, transformation, dataset.label_map,
output, pred)
target_np = target.numpy()
num_sample = target_np.shape[0]
target = target.to(device)
cross_ent = criterion(output, target.long())
losses.update(float(cross_ent), num_sample)
scores.update(precision_at_one(pred, target), num_sample)
hist += fast_hist(pred.cpu().numpy().flatten(),
target_np.flatten(), num_labels)
ious = per_class_iu(hist) * 100
prob = torch.nn.functional.softmax(output, dim=1)
ap = average_precision(prob.cpu().detach().numpy(), target_np)
aps = np.vstack((aps, ap))
# Due to heavy bias in class, there exists class with no test label at all
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
ap_class = np.nanmean(aps, 0) * 100.
if iteration % config.test_stat_freq == 0 and iteration > 0:
reordered_ious = dataset.reorder_result(ious)
reordered_ap_class = dataset.reorder_result(ap_class)
class_names = dataset.get_classnames()
print_info(iteration,
max_iter_unique,
data_time,
iter_time,
has_gt,
losses,
scores,
reordered_ious,
hist,
reordered_ap_class,
class_names=class_names)
if iteration % config.empty_cache_freq == 0:
# Clear cache
torch.cuda.empty_cache()
global_time = global_timer.toc(False)
reordered_ious = dataset.reorder_result(ious)
reordered_ap_class = dataset.reorder_result(ap_class)
class_names = dataset.get_classnames()
print_info(iteration,
max_iter_unique,
data_time,
iter_time,
has_gt,
losses,
scores,
reordered_ious,
hist,
reordered_ap_class,
class_names=class_names)
if config.test_original_pointcloud:
logging.info('===> Start testing on original pointcloud space.')
dataset.test_pointcloud(save_pred_dir)
logging.info("Finished test. Elapsed time: {:.4f}".format(global_time))
return losses.avg, scores.avg, np.nanmean(ap_class), np.nanmean(
per_class_iu(hist)) * 100
def train(model, data_loader, val_data_loader, config, transform_data_fn=None):
all_losses = []
device = get_torch_device(config.is_cuda)
# Set up the train flag for batch normalization
model.train()
# Configuration
writer = SummaryWriter(log_dir=config.log_dir)
data_timer, iter_timer = Timer(), Timer()
data_time_avg, iter_time_avg = AverageMeter(), AverageMeter()
losses, scores, batch_losses = AverageMeter(), AverageMeter(), {}
optimizer = initialize_optimizer(model.parameters(), config)
scheduler = initialize_scheduler(optimizer, config)
criterion = nn.CrossEntropyLoss(ignore_index=config.ignore_label)
alpha, gamma, eps = 1, 2, 1e-6
writer = SummaryWriter(log_dir=config.log_dir)
# Train the network
logging.info('===> Start training')
best_val_miou, best_val_iter, curr_iter, epoch, is_training = 0, 0, 1, 1, True
if config.resume:
checkpoint_fn = config.resume + '/weights.pth'
if osp.isfile(checkpoint_fn):
logging.info("=> loading checkpoint '{}'".format(checkpoint_fn))
state = torch.load(checkpoint_fn)
curr_iter = state['iteration'] + 1
epoch = state['epoch']
model.load_state_dict(state['state_dict'])
if config.resume_optimizer:
scheduler = initialize_scheduler(optimizer,
config,
last_step=curr_iter)
optimizer.load_state_dict(state['optimizer'])
if 'best_val' in state:
best_val_miou = state['best_val']
best_val_iter = state['best_val_iter']
logging.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_fn, state['epoch']))
else:
raise ValueError(
"=> no checkpoint found at '{}'".format(checkpoint_fn))
data_iter = data_loader.__iter__()
while is_training:
print(
"********************************** epoch N° {0} ************************"
.format(epoch))
for iteration in range(len(data_loader) // config.iter_size):
print("####### Iteration N° {0}".format(iteration))
optimizer.zero_grad()
data_time, batch_loss = 0, 0
iter_timer.tic()
for sub_iter in range(config.iter_size):
print("------------- Sub_iteration N° {0}".format(sub_iter))
# Get training data
data_timer.tic()
coords, input, target = data_iter.next()
print("len of coords : {0}".format(len(coords)))
# For some networks, making the network invariant to even, odd coords is important
coords[:, :3] += (torch.rand(3) * 100).type_as(coords)
# Preprocess input
color = input[:, :3].int()
if config.normalize_color:
input[:, :3] = input[:, :3] / 255. - 0.5
sinput = SparseTensor(input, coords).to(device)
data_time += data_timer.toc(False)
# Feed forward
inputs = (sinput, ) if config.wrapper_type == 'None' else (
sinput, coords, color)
# model.initialize_coords(*init_args)
soutput = model(*inputs)
# The output of the network is not sorted
target = target.long().to(device)
print("count of classes : {0}".format(
np.unique(target.cpu().numpy(), return_counts=True)))
print("target : {0}\ntarget_len : {1}".format(
target, len(target)))
print("target [0]: {0}".format(target[0]))
input_soft = nn.functional.softmax(soutput.F, dim=1) + eps
print("input_soft[0] : {0}".format(input_soft[0]))
focal_weight = torch.pow(-input_soft + 1., gamma)
print("focal_weight : {0}\nweight[0] : {1}".format(
focal_weight, focal_weight[0]))
focal_loss = (-alpha * focal_weight *
torch.log(input_soft)).mean()
loss = criterion(soutput.F, target.long())
print("focal_loss :{0}\nloss : {1}".format(focal_loss, loss))
# Compute and accumulate gradient
loss /= config.iter_size
#batch_loss += loss
batch_loss += loss.item()
print("batch_loss : {0}".format(batch_loss))
loss.backward()
# Update number of steps
optimizer.step()
scheduler.step()
data_time_avg.update(data_time)
iter_time_avg.update(iter_timer.toc(False))
pred = get_prediction(data_loader.dataset, soutput.F, target)
score = precision_at_one(pred, target)
losses.update(batch_loss, target.size(0))
scores.update(score, target.size(0))
if curr_iter >= config.max_iter:
is_training = False
break
if curr_iter % config.stat_freq == 0 or curr_iter == 1:
lrs = ', '.join(
['{:.3e}'.format(x) for x in scheduler.get_lr()])
debug_str = "===> Epoch[{}]({}/{}): Loss {:.4f}\tLR: {}\t".format(
epoch, curr_iter,
len(data_loader) // config.iter_size, losses.avg, lrs)
debug_str += "Score {:.3f}\tData time: {:.4f}, Total iter time: {:.4f}".format(
scores.avg, data_time_avg.avg, iter_time_avg.avg)
logging.info(debug_str)
# Reset timers
data_time_avg.reset()
iter_time_avg.reset()
# Write logs
writer.add_scalar('training/loss', losses.avg, curr_iter)
writer.add_scalar('training/precision_at_1', scores.avg,
curr_iter)
writer.add_scalar('training/learning_rate',
scheduler.get_lr()[0], curr_iter)
losses.reset()
scores.reset()
# Save current status, save before val to prevent occational mem overflow
if curr_iter % config.save_freq == 0:
checkpoint(model, optimizer, epoch, curr_iter, config,
best_val_miou, best_val_iter)
# Validation
if curr_iter % config.val_freq == 0:
val_miou, val_losses = validate(model, val_data_loader, writer,
curr_iter, config,
transform_data_fn, epoch)
if val_miou > best_val_miou:
best_val_miou = val_miou
best_val_iter = curr_iter
checkpoint(model, optimizer, epoch, curr_iter, config,
best_val_miou, best_val_iter, "best_val")
logging.info("Current best mIoU: {:.3f} at iter {}".format(
best_val_miou, best_val_iter))
# Recover back
model.train()
if curr_iter % config.empty_cache_freq == 0:
# Clear cache
torch.cuda.empty_cache()
batch_losses[epoch] = batch_loss
# End of iteration
curr_iter += 1
with open(config.log_dir + "/train_loss.txt", 'a') as train_loss_log:
train_loss_log.writelines('{0}, {1}\n'.format(
batch_losses[epoch], epoch))
train_loss_log.close()
epoch += 1
# Explicit memory cleanup
if hasattr(data_iter, 'cleanup'):
data_iter.cleanup()
# Save the final model
checkpoint(model, optimizer, epoch, curr_iter, config, best_val_miou,
best_val_iter)
val_miou = validate(model, val_data_loader, writer, curr_iter, config,
transform_data_fn, epoch)[0]
if val_miou > best_val_miou:
best_val_miou = val_miou
best_val_iter = curr_iter
checkpoint(model, optimizer, epoch, curr_iter, config, best_val_miou,
best_val_iter, "best_val")
logging.info("Current best mIoU: {:.3f} at iter {}".format(
best_val_miou, best_val_iter))
def train_worker(gpu,
num_devices,
NetClass,
data_loader,
val_data_loader,
config,
transform_data_fn=None):
if gpu is not None:
print("Use GPU: {} for training".format(gpu))
rank = gpu
addr = 23491
dist.init_process_group(backend="nccl",
init_method="tcp://127.0.0.1:{}".format(addr),
world_size=num_devices,
rank=rank)
# replace with DistributedSampler
if config.multiprocess:
from lib.dataloader_dist import InfSampler
sampler = InfSampler(data_loader.dataset)
data_loader = DataLoader(dataset=data_loader.dataset,
num_workers=data_loader.num_workers,
batch_size=data_loader.batch_size,
collate_fn=data_loader.collate_fn,
worker_init_fn=data_loader.worker_init_fn,
sampler=sampler)
if data_loader.dataset.NUM_IN_CHANNEL is not None:
num_in_channel = data_loader.dataset.NUM_IN_CHANNEL
else:
num_in_channel = 3
num_labels = data_loader.dataset.NUM_LABELS
# load model
if config.pure_point:
model = NetClass(num_class=config.num_labels,
N=config.num_points,
normal_channel=config.num_in_channel)
else:
if config.model == 'MixedTransformer':
model = NetClass(config,
num_class=num_labels,
N=config.num_points,
normal_channel=num_in_channel)
elif config.model == 'MinkowskiVoxelTransformer':
model = NetClass(config, num_in_channel, num_labels)
elif config.model == 'MinkowskiTransformerNet':
model = NetClass(config, num_in_channel, num_labels)
elif "Res" in config.model:
model = NetClass(num_in_channel, num_labels, config)
else:
model = NetClass(num_in_channel, num_labels, config)
if config.weights == 'modelzoo':
model.preload_modelzoo()
elif config.weights.lower() != 'none':
state = torch.load(config.weights)
# delete the keys containing the attn since it raises size mismatch
d = {k: v for k, v in state['state' '_dict'].items() if 'map' not in k}
if config.weights_for_inner_model:
model.model.load_state_dict(d)
else:
if config.lenient_weight_loading:
matched_weights = load_state_with_same_shape(
model, state['state_dict'])
model_dict = model.state_dict()
model_dict.update(matched_weights)
model.load_state_dict(model_dict)
else:
model.load_state_dict(d, strict=False)
torch.cuda.set_device(gpu)
model.cuda(gpu)
# use model with DDP
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[gpu], find_unused_parameters=False)
# Synchronized batch norm
model = ME.MinkowskiSyncBatchNorm.convert_sync_batchnorm(model)
# Set up the train flag for batch normalization
model.train()
# Configuration
data_timer, iter_timer = Timer(), Timer()
data_time_avg, iter_time_avg = AverageMeter(), AverageMeter()
regs, losses, scores = AverageMeter(), AverageMeter(), AverageMeter()
optimizer = initialize_optimizer(model.parameters(), config)
scheduler = initialize_scheduler(optimizer, config)
criterion = nn.CrossEntropyLoss(ignore_index=config.ignore_label)
# Train the network
if rank == 0:
setup_logger(config)
logging.info('===> Start training')
best_val_miou, best_val_iter, curr_iter, epoch, is_training = 0, 0, 1, 1, True
if config.resume:
# Test loaded ckpt first
v_loss, v_score, v_mAP, v_mIoU = test(model, val_data_loader, config)
checkpoint_fn = config.resume + '/weights.pth'
if osp.isfile(checkpoint_fn):
logging.info("=> loading checkpoint '{}'".format(checkpoint_fn))
state = torch.load(checkpoint_fn)
curr_iter = state['iteration'] + 1
epoch = state['epoch']
# we skip attention maps because the shape won't match because voxel number is different
# e.g. copyting a param with shape (23385, 8, 4) to (43529, 8, 4)
d = {
k: v
for k, v in state['state_dict'].items() if 'map' not in k
}
# handle those attn maps we don't load from saved dict
for k in model.state_dict().keys():
if k in d.keys(): continue
d[k] = model.state_dict()[k]
model.load_state_dict(d)
if config.resume_optimizer:
scheduler = initialize_scheduler(optimizer,
config,
last_step=curr_iter)
optimizer.load_state_dict(state['optimizer'])
if 'best_val' in state:
best_val_miou = state['best_val']
best_val_iter = state['best_val_iter']
logging.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_fn, state['epoch']))
else:
raise ValueError(
"=> no checkpoint found at '{}'".format(checkpoint_fn))
data_iter = data_loader.__iter__()
device = gpu # multitrain fed in the device
if config.dataset == "SemanticKITTI":
num_class = 19
config.normalize_color = False
config.xyz_input = False
val_freq_ = config.val_freq
config.val_freq = config.val_freq * 10 # origianl val_freq_
elif config.dataset == 'S3DIS':
num_class = 13
config.normalize_color = False
config.xyz_input = False
val_freq_ = config.val_freq
elif config.dataset == "Nuscenes":
num_class = 16
config.normalize_color = False
config.xyz_input = False
val_freq_ = config.val_freq
config.val_freq = config.val_freq * 50
else:
val_freq_ = config.val_freq
num_class = 20
while is_training:
total_correct_class = torch.zeros(num_class, device=device)
total_iou_deno_class = torch.zeros(num_class, device=device)
for iteration in range(len(data_loader) // config.iter_size):
optimizer.zero_grad()
data_time, batch_loss = 0, 0
iter_timer.tic()
if curr_iter >= config.max_iter:
# if curr_iter >= max(config.max_iter, config.epochs*(len(data_loader) // config.iter_size):
is_training = False
break
elif curr_iter >= config.max_iter * (2 / 3):
config.val_freq = val_freq_ * 2 # valid more freq on lower half
for sub_iter in range(config.iter_size):
# Get training data
data_timer.tic()
if config.return_transformation:
coords, input, target, _, _, pointcloud, transformation = data_iter.next(
)
else:
coords, input, target, _, _ = data_iter.next(
) # ignore unique_map and inverse_map
if config.use_aux:
assert target.shape[1] == 2
aux = target[:, 1]
target = target[:, 0]
else:
aux = None
# For some networks, making the network invariant to even, odd coords is important
coords[:, 1:] += (torch.rand(3) * 100).type_as(coords)
# Preprocess input
if config.normalize_color:
input[:, :3] = input[:, :3] / input[:, :3].max() - 0.5
coords_norm = coords[:, 1:] / coords[:, 1:].max() - 0.5
# cat xyz into the rgb feature
if config.xyz_input:
input = torch.cat([coords_norm, input], dim=1)
# print(device)
sinput = SparseTensor(input, coords, device=device)
# d = {}
# d['coord'] = sinput.C
# d['feat'] = sinput.F
# torch.save(d, 'voxel.pth')
# import ipdb; ipdb.set_trace()
data_time += data_timer.toc(False)
# model.initialize_coords(*init_args)
if aux is not None:
soutput = model(sinput, aux)
elif config.enable_point_branch:
soutput = model(sinput,
iter_=curr_iter / config.max_iter,
enable_point_branch=True)
else:
soutput = model(
sinput, iter_=curr_iter / config.max_iter
) # feed in the progress of training for annealing inside the model
# soutput = model(sinput)
# The output of the network is not sorted
target = target.view(-1).long().to(device)
loss = criterion(soutput.F, target.long())
# ====== other loss regs =====
cur_loss = torch.tensor([0.], device=device)
if hasattr(model, 'module.block1'):
cur_loss = torch.tensor([0.], device=device)
if hasattr(model.module.block1[0], 'vq_loss'):
if model.block1[0].vq_loss is not None:
cur_loss = torch.tensor([0.], device=device)
for n, m in model.named_children():
if 'block' in n:
cur_loss += m[
0].vq_loss # m is the nn.Sequential obj, m[0] is the TRBlock
logging.info(
'Cur Loss: {}, Cur vq_loss: {}'.format(
loss, cur_loss))
loss += cur_loss
if hasattr(model.module.block1[0], 'diverse_loss'):
if model.block1[0].diverse_loss is not None:
cur_loss = torch.tensor([0.], device=device)
for n, m in model.named_children():
if 'block' in n:
cur_loss += m[
0].diverse_loss # m is the nn.Sequential obj, m[0] is the TRBlock
logging.info(
'Cur Loss: {}, Cur diverse _loss: {}'.format(
loss, cur_loss))
loss += cur_loss
if hasattr(model.module.block1[0], 'label_reg'):
if model.block1[0].label_reg is not None:
cur_loss = torch.tensor([0.], device=device)
for n, m in model.named_children():
if 'block' in n:
cur_loss += m[
0].label_reg # m is the nn.Sequential obj, m[0] is the TRBlock
# logging.info('Cur Loss: {}, Cur diverse _loss: {}'.format(loss, cur_loss))
loss += cur_loss
# Compute and accumulate gradient
loss /= config.iter_size
batch_loss += loss.item()
if not config.use_sam:
loss.backward()
else:
with model.no_sync():
loss.backward()
# Update number of steps
if not config.use_sam:
optimizer.step()
else:
optimizer.first_step(zero_grad=True)
soutput = model(sinput,
iter_=curr_iter / config.max_iter,
aux=starget)
criterion(soutput.F, target.long()).backward()
optimizer.second_step(zero_grad=True)
if config.lr_warmup is None:
scheduler.step()
else:
if curr_iter >= config.lr_warmup:
scheduler.step()
else:
for g in optimizer.param_groups:
g['lr'] = config.lr * (iteration +
1) / config.lr_warmup
# CLEAR CACHE!
torch.cuda.empty_cache()
data_time_avg.update(data_time)
iter_time_avg.update(iter_timer.toc(False))
pred = get_prediction(data_loader.dataset, soutput.F, target)
score = precision_at_one(pred, target, ignore_label=-1)
regs.update(cur_loss.item(), target.size(0))
losses.update(batch_loss, target.size(0))
scores.update(score, target.size(0))
# calc the train-iou
for l in range(num_class):
total_correct_class[l] += ((pred == l) & (target == l)).sum()
total_iou_deno_class[l] += (((pred == l) & (target != -1)) |
(target == l)).sum()
if curr_iter % config.stat_freq == 0 or curr_iter == 1:
lrs = ', '.join(
['{:.3e}'.format(g['lr']) for g in optimizer.param_groups])
IoU = ((total_correct_class) /
(total_iou_deno_class + 1e-6)).mean() * 100.
debug_str = "===> Epoch[{}]({}/{}): Loss {:.4f}\tLR: {}\t".format(
epoch, curr_iter,
len(data_loader) // config.iter_size, losses.avg, lrs)
debug_str += "Score {:.3f}\tIoU {:.3f}\tData time: {:.4f}, Iter time: {:.4f}".format(
scores.avg, IoU.item(), data_time_avg.avg,
iter_time_avg.avg)
if regs.avg > 0:
debug_str += "\n Additional Reg Loss {:.3f}".format(
regs.avg)
if rank == 0:
logging.info(debug_str)
# Reset timers
data_time_avg.reset()
iter_time_avg.reset()
# Write logs
losses.reset()
scores.reset()
# only save status on the 1st gpu
if rank == 0:
# Save current status, save before val to prevent occational mem overflow
if curr_iter % config.save_freq == 0:
checkpoint(model,
optimizer,
epoch,
curr_iter,
config,
best_val_miou,
best_val_iter,
save_inter=True)
# Validation
if curr_iter % config.val_freq == 0:
val_miou = validate(model, val_data_loader, None,
curr_iter, config, transform_data_fn
) # feedin None for SummaryWriter args
if val_miou > best_val_miou:
best_val_miou = val_miou
best_val_iter = curr_iter
checkpoint(model,
optimizer,
epoch,
curr_iter,
config,
best_val_miou,
best_val_iter,
"best_val",
save_inter=True)
if rank == 0:
logging.info(
"Current best mIoU: {:.3f} at iter {}".format(
best_val_miou, best_val_iter))
# Recover back
model.train()
# End of iteration
curr_iter += 1
IoU = (total_correct_class) / (total_iou_deno_class + 1e-6)
if rank == 0:
logging.info('train point avg class IoU: %f' %
((IoU).mean() * 100.))
epoch += 1
# Explicit memory cleanup
if hasattr(data_iter, 'cleanup'):
data_iter.cleanup()
# Save the final model
if rank == 0:
checkpoint(model, optimizer, epoch, curr_iter, config, best_val_miou,
best_val_iter)
v_loss, v_score, v_mAP, val_mIoU = test(model, val_data_loader, config)
if val_miou > best_val_miou and rank == 0:
best_val_miou = val_miou
best_val_iter = curr_iter
logging.info("Final best miou: {} at iter {} ".format(
val_miou, curr_iter))
checkpoint(model, optimizer, epoch, curr_iter, config,
best_val_miou, best_val_iter, "best_val")
logging.info("Current best mIoU: {:.3f} at iter {}".format(
best_val_miou, best_val_iter))
def test(model, data_loader, config, transform_data_fn=None, has_gt=True, save_pred=False, split=None, submit_dir=None):
device = get_torch_device(config.is_cuda)
dataset = data_loader.dataset
num_labels = dataset.NUM_LABELS
global_timer, data_timer, iter_timer = Timer(), Timer(), Timer()
criterion = nn.CrossEntropyLoss(ignore_index=config.ignore_label)
losses, scores, ious = AverageMeter(), AverageMeter(), 0
aps = np.zeros((0, num_labels))
hist = np.zeros((num_labels, num_labels))
# some cfgs concerning the usage of instance-level information
config.save_pred = save_pred
if split is not None:
assert save_pred
if config.save_pred:
save_dict = {}
save_dict['pred'] = []
save_dict['coord'] = []
logging.info('===> Start testing')
global_timer.tic()
data_iter = data_loader.__iter__()
max_iter = len(data_loader)
max_iter_unique = max_iter
# Fix batch normalization running mean and std
model.eval()
# Clear cache (when run in val mode, cleanup training cache)
torch.cuda.empty_cache()
# semantic kitti label inverse mapping
if config.submit:
remap_lut = Remap().getRemapLUT()
with torch.no_grad():
# Calc of the iou
total_correct = np.zeros(num_labels)
total_seen = np.zeros(num_labels)
total_positive = np.zeros(num_labels)
point_nums = np.zeros([19])
for iteration in range(max_iter):
data_timer.tic()
if config.return_transformation:
coords, input, target, unique_map_list, inverse_map_list, pointcloud, transformation, filename = data_iter.next()
else:
coords, input, target, unique_map_list, inverse_map_list, filename = data_iter.next()
data_time = data_timer.toc(False)
if config.use_aux:
assert target.shape[1] == 2
aux = target[:,1]
target = target[:,0]
else:
aux = None
# Preprocess input
iter_timer.tic()
if config.normalize_color:
input[:, :3] = input[:, :3] / input[:,:3].max() - 0.5
coords_norm = coords[:,1:] / coords[:,1:].max() - 0.5
XYZ_INPUT = config.xyz_input
# cat xyz into the rgb feature
if XYZ_INPUT:
input = torch.cat([coords_norm, input], dim=1)
sinput = ME.SparseTensor(input, coords, device=device)
# Feed forward
if aux is not None:
soutput = model(sinput)
else:
soutput = model(sinput, iter_ = iteration / max_iter, enable_point_branch=config.enable_point_branch)
output = soutput.F
if torch.isnan(output).sum() > 0:
import ipdb; ipdb.set_trace()
pred = get_prediction(dataset, output, target).int()
assert sum([int(t.shape[0]) for t in unique_map_list]) == len(pred), "number of points in unique_map doesn't match predition, do not enable preprocessing"
iter_time = iter_timer.toc(False)
if config.save_pred or config.submit:
# troublesome processing for splitting each batch's data, and export
batch_ids = sinput.C[:,0]
splits_at = torch.stack([torch.where(batch_ids == i)[0][-1] for i in torch.unique(batch_ids)]).int()
splits_at = splits_at + 1
splits_at_leftshift_one = splits_at.roll(shifts=1)
splits_at_leftshift_one[0] = 0
# len_per_batch = splits_at - splits_at_leftshift_one
len_sum = 0
batch_id = 0
for start, end in zip(splits_at_leftshift_one, splits_at):
len_sum += len(pred[int(start):int(end)])
pred_this_batch = pred[int(start):int(end)]
coord_this_batch = pred[int(start):int(end)]
if config.save_pred:
save_dict['pred'].append(pred_this_batch[inverse_map_list[batch_id]])
else: # save submit result
submission_path = filename[batch_id].replace(config.semantic_kitti_path, submit_dir).replace('velodyne', 'predictions').replace('.bin', '.label')
parent_dir = Path(submission_path).parent.absolute()
if not os.path.exists(parent_dir):
os.makedirs(parent_dir)
label_pred = pred_this_batch[inverse_map_list[batch_id]].cpu().numpy()
label_pred = remap_lut[label_pred].astype(np.uint32)
label_pred.tofile(submission_path)
print(submission_path)
batch_id += 1
assert len_sum == len(pred)
# Unpack it to original length
REVERT_WHOLE_POINTCLOUD = True
# print('{}/{}'.format(iteration, max_iter))
if REVERT_WHOLE_POINTCLOUD:
whole_pred = []
whole_target = []
for batch_ in range(config.batch_size):
batch_mask_ = (soutput.C[:,0] == batch_).cpu().numpy()
if batch_mask_.sum() == 0: # for empty batch, skip em
continue
try:
whole_pred_ = soutput.F[batch_mask_][inverse_map_list[batch_]]
except:
import ipdb; ipdb.set_trace()
whole_target_ = target[batch_mask_][inverse_map_list[batch_]]
whole_pred.append(whole_pred_)
whole_target.append(whole_target_)
whole_pred = torch.cat(whole_pred, dim=0)
whole_target = torch.cat(whole_target, dim=0)
pred = get_prediction(dataset, whole_pred, whole_target).int()
output = whole_pred
target = whole_target
if has_gt:
target_np = target.numpy()
num_sample = target_np.shape[0]
target = target.to(device)
output = output.to(device)
cross_ent = criterion(output, target.long())
losses.update(float(cross_ent), num_sample)
scores.update(precision_at_one(pred, target), num_sample)
hist += fast_hist(pred.cpu().numpy().flatten(), target_np.flatten(), num_labels) # within fast hist, mark label should >=0 & < num_label to filter out 255 / -1
ious = per_class_iu(hist) * 100
prob = torch.nn.functional.softmax(output, dim=-1)
pred = pred[target != -1]
target = target[target != -1]
# for _ in range(num_labels): # debug for SemKITTI: spvnas way of calc miou
# total_seen[_] += torch.sum(target == _)
# total_correct[_] += torch.sum((pred == target) & (target == _))
# total_positive[_] += torch.sum(pred == _)
# ious_ = []
# for _ in range(num_labels):
# if total_seen[_] == 0:
# ious_.append(1)
# else:
# ious_.append(total_correct[_]/(total_seen[_] + total_positive[_] - total_correct[_]))
# ious_ = torch.stack(ious_, dim=-1).cpu().numpy()*100
# print(np.nanmean(per_class_iu(hist)), np.nanmean(ious_))
# ious = np.array(ious_)*100
# calc the ratio of total points
# for i_ in range(19):
# point_nums[i_] += (target == i_).sum().detach()
# skip calculating aps
ap = average_precision(prob.cpu().detach().numpy(), target_np)
aps = np.vstack((aps, ap))
# Due to heavy bias in class, there exists class with no test label at all
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
ap_class = np.nanmean(aps, 0) * 100.
if iteration % config.test_stat_freq == 0 and iteration > 0 and not config.submit:
reordered_ious = dataset.reorder_result(ious)
reordered_ap_class = dataset.reorder_result(ap_class)
# dirty fix for semnaticcKITTI has no getclassnames
if hasattr(dataset, "class_names"):
class_names = dataset.get_classnames()
else: # semnantic KITTI
class_names = None
print_info(
iteration,
max_iter_unique,
data_time,
iter_time,
has_gt,
losses,
scores,
reordered_ious,
hist,
reordered_ap_class,
class_names=class_names)
if iteration % 5 == 0:
# Clear cache
torch.cuda.empty_cache()
if config.save_pred:
# torch.save(save_dict, os.path.join(config.log_dir, 'preds_{}_with_coord.pth'.format(split)))
torch.save(save_dict, os.path.join(config.log_dir, 'preds_{}.pth'.format(split)))
print("===> saved prediction result")
global_time = global_timer.toc(False)
save_map(model, config)
reordered_ious = dataset.reorder_result(ious)
reordered_ap_class = dataset.reorder_result(ap_class)
if hasattr(dataset, "class_names"):
class_names = dataset.get_classnames()
else:
class_names = None
print_info(
iteration,
max_iter_unique,
data_time,
iter_time,
has_gt,
losses,
scores,
reordered_ious,
hist,
reordered_ap_class,
class_names=class_names)
logging.info("Finished test. Elapsed time: {:.4f}".format(global_time))
# Explicit memory cleanup
if hasattr(data_iter, 'cleanup'):
data_iter.cleanup()
return losses.avg, scores.avg, np.nanmean(ap_class), np.nanmean(per_class_iu(hist)) * 100
def train(self, train_queue, val_queue=None):
''' Given data queues, train the network '''
# Parameter directory
save_dir = os.path.join(cfg.DIR.OUT_PATH)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# Timer for the training op and parallel data loading op.
train_timer = Timer()
data_timer = Timer()
training_losses = []
start_iter = 0
# Resume training
if cfg.TRAIN.RESUME_TRAIN:
self.net.load(cfg.CONST.WEIGHTS)
start_iter = cfg.TRAIN.INITIAL_ITERATION
# Setup learning rates
lr = cfg.TRAIN.DEFAULT_LEARNING_RATE
lr_steps = [int(k) for k in cfg.TRAIN.LEARNING_RATES.keys()]
print('Set the learning rate to %f.' % lr)
self.set_lr(lr)
# Main training loop
for train_ind in range(start_iter, cfg.TRAIN.NUM_ITERATION + 1):
data_timer.tic()
batch_img, batch_voxel = train_queue.get()
data_timer.toc()
if self.net.is_x_tensor4:
batch_img = batch_img[0]
# Apply one gradient step
train_timer.tic()
loss = self.train_loss(batch_img, batch_voxel)
train_timer.toc()
training_losses.append(loss)
# Decrease learning rate at certain points
if train_ind in lr_steps:
# edict only takes string for key. Hacky way
self.set_lr(np.float(cfg.TRAIN.LEARNING_RATES[str(train_ind)]))
print('Learing rate decreased to %f: ' % self.lr.get_value())
# Debugging modules
#
# Print status, run validation, check divergence, and save model.
if train_ind % cfg.TRAIN.PRINT_FREQ == 0:
# Print the current loss
print('%s Iter: %d Loss: %f' %
(datetime.now(), train_ind, loss))
if train_ind % cfg.TRAIN.VALIDATION_FREQ == 0 and val_queue is not None:
# Print test loss and params to check convergence every N iterations
val_losses = []
for i in range(cfg.TRAIN.NUM_VALIDATION_ITERATIONS):
batch_img, batch_voxel = val_queue.get()
_, val_loss, _ = self.test_output(batch_img, batch_voxel)
val_losses.append(val_loss)
print('%s Test loss: %f' %
(datetime.now(), np.mean(val_losses)))
if train_ind % cfg.TRAIN.NAN_CHECK_FREQ == 0:
# Check that the network parameters are all valid
max_param = max_or_nan(self.net.params)
if np.isnan(max_param):
print('NAN detected')
break
if train_ind % cfg.TRAIN.SAVE_FREQ == 0 and not train_ind == 0:
self.save(training_losses, save_dir, train_ind)
if loss > cfg.TRAIN.LOSS_LIMIT:
print("Cost exceeds the threshold. Stop training")
break
def train(model, data_loader, val_data_loader, config, transform_data_fn=None):
device = get_torch_device(config.is_cuda)
# Set up the train flag for batch normalization
model.train()
# Configuration
writer = SummaryWriter(log_dir=config.log_dir)
data_timer, iter_timer = Timer(), Timer()
data_time_avg, iter_time_avg = AverageMeter(), AverageMeter()
losses, scores = AverageMeter(), AverageMeter()
optimizer = initialize_optimizer(model.parameters(), config)
scheduler = initialize_scheduler(optimizer, config)
criterion = nn.CrossEntropyLoss(ignore_index=config.ignore_label)
writer = SummaryWriter(log_dir=config.log_dir)
# Train the network
logging.info('===> Start training')
best_val_miou, best_val_iter, curr_iter, epoch, is_training = 0, 0, 1, 1, True
if config.resume:
checkpoint_fn = config.resume + '/weights.pth'
if osp.isfile(checkpoint_fn):
logging.info("=> loading checkpoint '{}'".format(checkpoint_fn))
state = torch.load(checkpoint_fn)
curr_iter = state['iteration'] + 1
epoch = state['epoch']
model.load_state_dict(state['state_dict'])
if config.resume_optimizer:
scheduler = initialize_scheduler(optimizer,
config,
last_step=curr_iter)
optimizer.load_state_dict(state['optimizer'])
if 'best_val' in state:
best_val_miou = state['best_val']
best_val_iter = state['best_val_iter']
logging.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_fn, state['epoch']))
else:
raise ValueError(
"=> no checkpoint found at '{}'".format(checkpoint_fn))
data_iter = data_loader.__iter__()
while is_training:
for iteration in range(len(data_loader) // config.iter_size):
optimizer.zero_grad()
data_time, batch_loss = 0, 0
iter_timer.tic()
for sub_iter in range(config.iter_size):
# Get training data
data_timer.tic()
if config.return_transformation:
coords, input, target, pointcloud, transformation = data_iter.next(
)
else:
coords, input, target = data_iter.next()
# For some networks, making the network invariant to even, odd coords is important
coords[:, 1:] += (torch.rand(3) * 100).type_as(coords)
# Preprocess input
if config.normalize_color:
input[:, :3] = input[:, :3] / 255. - 0.5
sinput = SparseTensor(input, coords).to(device)
data_time += data_timer.toc(False)
# model.initialize_coords(*init_args)
soutput = model(sinput)
# The output of the network is not sorted
target = target.long().to(device)
loss = criterion(soutput.F, target.long())
# Compute and accumulate gradient
loss /= config.iter_size
batch_loss += loss.item()
loss.backward()
# Update number of steps
optimizer.step()
scheduler.step()
data_time_avg.update(data_time)
iter_time_avg.update(iter_timer.toc(False))
pred = get_prediction(data_loader.dataset, soutput.F, target)
score = precision_at_one(pred, target)
losses.update(batch_loss, target.size(0))
scores.update(score, target.size(0))
if curr_iter >= config.max_iter:
is_training = False
break
if curr_iter % config.stat_freq == 0 or curr_iter == 1:
lrs = ', '.join(
['{:.3e}'.format(x) for x in scheduler.get_lr()])
debug_str = "===> Epoch[{}]({}/{}): Loss {:.4f}\tLR: {}\t".format(
epoch, curr_iter,
len(data_loader) // config.iter_size, losses.avg, lrs)
debug_str += "Score {:.3f}\tData time: {:.4f}, Iter time: {:.4f}".format(
scores.avg, data_time_avg.avg, iter_time_avg.avg)
logging.info(debug_str)
# Reset timers
data_time_avg.reset()
iter_time_avg.reset()
# Write logs
writer.add_scalar('training/loss', losses.avg, curr_iter)
writer.add_scalar('training/precision_at_1', scores.avg,
curr_iter)
writer.add_scalar('training/learning_rate',
scheduler.get_lr()[0], curr_iter)
losses.reset()
scores.reset()
# Save current status, save before val to prevent occational mem overflow
if curr_iter % config.save_freq == 0:
checkpoint(model, optimizer, epoch, curr_iter, config,
best_val_miou, best_val_iter)
# Validation
if curr_iter % config.val_freq == 0:
val_miou = validate(model, val_data_loader, writer, curr_iter,
config, transform_data_fn)
if val_miou > best_val_miou:
best_val_miou = val_miou
best_val_iter = curr_iter
checkpoint(model, optimizer, epoch, curr_iter, config,
best_val_miou, best_val_iter, "best_val")
logging.info("Current best mIoU: {:.3f} at iter {}".format(
best_val_miou, best_val_iter))
# Recover back
model.train()
# End of iteration
curr_iter += 1
epoch += 1
# Explicit memory cleanup
if hasattr(data_iter, 'cleanup'):
data_iter.cleanup()
# Save the final model
checkpoint(model, optimizer, epoch, curr_iter, config, best_val_miou,
best_val_iter)
val_miou = validate(model, val_data_loader, writer, curr_iter, config,
transform_data_fn)
if val_miou > best_val_miou:
best_val_miou = val_miou
best_val_iter = curr_iter
checkpoint(model, optimizer, epoch, curr_iter, config, best_val_miou,
best_val_iter, "best_val")
logging.info("Current best mIoU: {:.3f} at iter {}".format(
best_val_miou, best_val_iter))
def train(model, data_loader, val_data_loader, config, transform_data_fn=None):
device = get_torch_device(config.is_cuda)
# Set up the train flag for batch normalization
model.train()
# Configuration
data_timer, iter_timer = Timer(), Timer()
data_time_avg, iter_time_avg = AverageMeter(), AverageMeter()
regs, losses, scores = AverageMeter(), AverageMeter(), AverageMeter()
optimizer = initialize_optimizer(model.parameters(), config)
scheduler = initialize_scheduler(optimizer, config)
criterion = nn.CrossEntropyLoss(ignore_index=config.ignore_label)
# Train the network
logging.info('===> Start training')
best_val_miou, best_val_iter, curr_iter, epoch, is_training = 0, 0, 1, 1, True
if config.resume:
# Test loaded ckpt first
v_loss, v_score, v_mAP, v_mIoU = test(model, val_data_loader, config)
checkpoint_fn = config.resume + '/weights.pth'
if osp.isfile(checkpoint_fn):
logging.info("=> loading checkpoint '{}'".format(checkpoint_fn))
state = torch.load(checkpoint_fn)
curr_iter = state['iteration'] + 1
epoch = state['epoch']
# we skip attention maps because the shape won't match because voxel number is different
# e.g. copyting a param with shape (23385, 8, 4) to (43529, 8, 4)
d = {
k: v
for k, v in state['state_dict'].items() if 'map' not in k
}
# handle those attn maps we don't load from saved dict
for k in model.state_dict().keys():
if k in d.keys(): continue
d[k] = model.state_dict()[k]
model.load_state_dict(d)
if config.resume_optimizer:
scheduler = initialize_scheduler(optimizer,
config,
last_step=curr_iter)
optimizer.load_state_dict(state['optimizer'])
if 'best_val' in state:
best_val_miou = state['best_val']
best_val_iter = state['best_val_iter']
logging.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_fn, state['epoch']))
else:
raise ValueError(
"=> no checkpoint found at '{}'".format(checkpoint_fn))
data_iter = data_loader.__iter__()
if config.dataset == "SemanticKITTI":
num_class = 19
config.normalize_color = False
config.xyz_input = False
val_freq_ = config.val_freq
config.val_freq = config.val_freq * 10
elif config.dataset == "S3DIS":
num_class = 13
config.normalize_color = False
config.xyz_input = False
val_freq_ = config.val_freq
config.val_freq = config.val_freq
elif config.dataset == "Nuscenes":
num_class = 16
config.normalize_color = False
config.xyz_input = False
val_freq_ = config.val_freq
config.val_freq = config.val_freq * 50
else:
num_class = 20
val_freq_ = config.val_freq
while is_training:
total_correct_class = torch.zeros(num_class, device=device)
total_iou_deno_class = torch.zeros(num_class, device=device)
for iteration in range(len(data_loader) // config.iter_size):
optimizer.zero_grad()
data_time, batch_loss = 0, 0
iter_timer.tic()
if curr_iter >= config.max_iter:
# if curr_iter >= max(config.max_iter, config.epochs*(len(data_loader) // config.iter_size):
is_training = False
break
elif curr_iter >= config.max_iter * (2 / 3):
config.val_freq = val_freq_ * 2 # valid more freq on lower half
for sub_iter in range(config.iter_size):
# Get training data
data_timer.tic()
pointcloud = None
if config.return_transformation:
coords, input, target, _, _, pointcloud, transformation, _ = data_iter.next(
)
else:
coords, input, target, _, _, _ = data_iter.next(
) # ignore unique_map and inverse_map
if config.use_aux:
assert target.shape[1] == 2
aux = target[:, 1]
target = target[:, 0]
else:
aux = None
# For some networks, making the network invariant to even, odd coords is important
coords[:, 1:] += (torch.rand(3) * 100).type_as(coords)
# Preprocess input
if config.normalize_color:
input[:, :3] = input[:, :3] / input[:, :3].max() - 0.5
coords_norm = coords[:, 1:] / coords[:, 1:].max() - 0.5
# cat xyz into the rgb feature
if config.xyz_input:
input = torch.cat([coords_norm, input], dim=1)
sinput = SparseTensor(input, coords, device=device)
starget = SparseTensor(
target.unsqueeze(-1).float(),
coordinate_map_key=sinput.coordinate_map_key,
coordinate_manager=sinput.coordinate_manager,
device=device
) # must share the same coord-manager to align for sinput
data_time += data_timer.toc(False)
# model.initialize_coords(*init_args)
# d = {}
# d['c'] = sinput.C
# d['l'] = starget.F
# torch.save('./plot/test-label.pth')
# import ipdb; ipdb.set_trace()
# Set up profiler
# memory_profiler = CUDAMemoryProfiler(
# [model, criterion],
# filename="cuda_memory.profile"
# )
# sys.settrace(memory_profiler)
# threading.settrace(memory_profiler)
# with torch.autograd.profiler.profile(enabled=True, use_cuda=True, record_shapes=False, profile_memory=True) as prof0:
if aux is not None:
soutput = model(sinput, aux)
elif config.enable_point_branch:
soutput = model(sinput,
iter_=curr_iter / config.max_iter,
enable_point_branch=True)
else:
# label-aux, feed it in as additional reg
soutput = model(
sinput, iter_=curr_iter / config.max_iter, aux=starget
) # feed in the progress of training for annealing inside the model
# The output of the network is not sorted
target = target.view(-1).long().to(device)
loss = criterion(soutput.F, target.long())
# ====== other loss regs =====
if hasattr(model, 'block1'):
cur_loss = torch.tensor([0.], device=device)
if hasattr(model.block1[0], 'vq_loss'):
if model.block1[0].vq_loss is not None:
cur_loss = torch.tensor([0.], device=device)
for n, m in model.named_children():
if 'block' in n:
cur_loss += m[
0].vq_loss # m is the nn.Sequential obj, m[0] is the TRBlock
logging.info(
'Cur Loss: {}, Cur vq_loss: {}'.format(
loss, cur_loss))
loss += cur_loss
if hasattr(model.block1[0], 'diverse_loss'):
if model.block1[0].diverse_loss is not None:
cur_loss = torch.tensor([0.], device=device)
for n, m in model.named_children():
if 'block' in n:
cur_loss += m[
0].diverse_loss # m is the nn.Sequential obj, m[0] is the TRBlock
logging.info(
'Cur Loss: {}, Cur diverse _loss: {}'.format(
loss, cur_loss))
loss += cur_loss
if hasattr(model.block1[0], 'label_reg'):
if model.block1[0].label_reg is not None:
cur_loss = torch.tensor([0.], device=device)
for n, m in model.named_children():
if 'block' in n:
cur_loss += m[
0].label_reg # m is the nn.Sequential obj, m[0] is the TRBlock
# logging.info('Cur Loss: {}, Cur diverse _loss: {}'.format(loss, cur_loss))
loss += cur_loss
# Compute and accumulate gradient
loss /= config.iter_size
batch_loss += loss.item()
loss.backward()
# soutput = model(sinput)
# Update number of steps
if not config.use_sam:
optimizer.step()
else:
optimizer.first_step(zero_grad=True)
soutput = model(sinput,
iter_=curr_iter / config.max_iter,
aux=starget)
criterion(soutput.F, target.long()).backward()
optimizer.second_step(zero_grad=True)
if config.lr_warmup is None:
scheduler.step()
else:
if curr_iter >= config.lr_warmup:
scheduler.step()
for g in optimizer.param_groups:
g['lr'] = config.lr * (iteration + 1) / config.lr_warmup
# CLEAR CACHE!
torch.cuda.empty_cache()
data_time_avg.update(data_time)
iter_time_avg.update(iter_timer.toc(False))
pred = get_prediction(data_loader.dataset, soutput.F, target)
score = precision_at_one(pred, target, ignore_label=-1)
regs.update(cur_loss.item(), target.size(0))
losses.update(batch_loss, target.size(0))
scores.update(score, target.size(0))
# calc the train-iou
for l in range(num_class):
total_correct_class[l] += ((pred == l) & (target == l)).sum()
total_iou_deno_class[l] += (((pred == l) & (target != -1)) |
(target == l)).sum()
if curr_iter % config.stat_freq == 0 or curr_iter == 1:
lrs = ', '.join(
['{:.3e}'.format(x) for x in scheduler.get_lr()])
IoU = ((total_correct_class) /
(total_iou_deno_class + 1e-6)).mean() * 100.
debug_str = "[{}] ===> Epoch[{}]({}/{}): Loss {:.4f}\tLR: {}\t".format(
config.log_dir.split('/')[-2], epoch, curr_iter,
len(data_loader) // config.iter_size, losses.avg, lrs)
debug_str += "Score {:.3f}\tIoU {:.3f}\tData time: {:.4f}, Iter time: {:.4f}".format(
scores.avg, IoU.item(), data_time_avg.avg,
iter_time_avg.avg)
if regs.avg > 0:
debug_str += "\n Additional Reg Loss {:.3f}".format(
regs.avg)
# print(debug_str)
logging.info(debug_str)
# Reset timers
data_time_avg.reset()
iter_time_avg.reset()
# Write logs
losses.reset()
scores.reset()
# Save current status, save before val to prevent occational mem overflow
if curr_iter % config.save_freq == 0:
checkpoint(model,
optimizer,
epoch,
curr_iter,
config,
best_val_miou,
best_val_iter,
save_inter=True)
# Validation
if curr_iter % config.val_freq == 0:
val_miou = validate(model, val_data_loader, None, curr_iter,
config, transform_data_fn)
if val_miou > best_val_miou:
best_val_miou = val_miou
best_val_iter = curr_iter
checkpoint(model,
optimizer,
epoch,
curr_iter,
config,
best_val_miou,
best_val_iter,
"best_val",
save_inter=True)
logging.info("Current best mIoU: {:.3f} at iter {}".format(
best_val_miou, best_val_iter))
# print("Current best mIoU: {:.3f} at iter {}".format(best_val_miou, best_val_iter))
# Recover back
model.train()
# End of iteration
curr_iter += 1
IoU = (total_correct_class) / (total_iou_deno_class + 1e-6)
logging.info('train point avg class IoU: %f' % ((IoU).mean() * 100.))
epoch += 1
# Explicit memory cleanup
if hasattr(data_iter, 'cleanup'):
data_iter.cleanup()
# Save the final model
checkpoint(model, optimizer, epoch, curr_iter, config, best_val_miou,
best_val_iter)
v_loss, v_score, v_mAP, val_miou = test(model, val_data_loader, config)
if val_miou > best_val_miou:
best_val_miou = val_miou
best_val_iter = curr_iter
checkpoint(model, optimizer, epoch, curr_iter, config, best_val_miou,
best_val_iter, "best_val")
logging.info("Current best mIoU: {:.3f} at iter {}".format(
best_val_miou, best_val_iter))
def train(pipeline_model, data_loader, val_data_loader, config):
# Set up the train flag for batch normalization
pipeline_model.train()
num_devices = torch.cuda.device_count()
num_devices = min(config.max_ngpu, num_devices)
devices = list(range(num_devices))
target_device = devices[0]
pipeline_model.to(target_device)
if num_devices > 1:
pipeline_model = ME.MinkowskiSyncBatchNorm.convert_sync_batchnorm(
pipeline_model, devices)
# Configuration
writer = SummaryWriter(logdir=config.log_dir)
data_timer, iter_timer = Timer(), Timer()
data_time_avg, iter_time_avg = AverageMeter(), AverageMeter()
meters = collections.defaultdict(AverageMeter)
hists = pipeline_model.initialize_hists()
optimizer = pipeline_model.initialize_optimizer(config)
scheduler = pipeline_model.initialize_scheduler(optimizer, config)
writer = SummaryWriter(logdir=config.log_dir)
# Train the network
logging.info('===> Start training')
best_val, best_val_iter, curr_iter, epoch, is_training = 0, 0, 1, 1, True
if config.resume:
if osp.isfile(config.resume):
logging.info("=> loading checkpoint '{}'".format(config.resume))
state = torch.load(config.resume)
curr_iter = state['iteration'] + 1
epoch = state['epoch']
pipeline_model.load_state_dict(state['state_dict'])
if config.resume_optimizer:
curr_iter = state['iteration'] + 1
scheduler = pipeline_model.initialize_scheduler(
optimizer, config, last_step=curr_iter)
pipeline_model.load_optimizer(optimizer, state['optimizer'])
if 'best_val' in state:
best_val = state['best_val']
best_val_iter = state['best_val_iter']
logging.info("=> loaded checkpoint '{}' (epoch {})".format(
config.resume, state['epoch']))
else:
logging.info("=> no checkpoint found at '{}'".format(
config.resume))
data_iter = data_loader.__iter__()
while is_training:
for iteration in range(len(data_loader)):
pipeline_model.reset_gradient(optimizer)
iter_timer.tic()
pipelines = parallel.replicate(pipeline_model, devices)
# Get training data
data_timer.tic()
inputs = []
for pipeline, device in zip(pipelines, devices):
with torch.cuda.device(device):
while True:
datum = pipeline.load_datum(data_iter, has_gt=True)
num_boxes = sum(box.shape[0]
for box in datum['bboxes_coords'])
if config.skip_empty_boxes and num_boxes == 0:
continue
break
inputs.append(datum)
data_time_avg.update(data_timer.toc(False))
outputs = parallel.parallel_apply(pipelines,
[(x, True) for x in inputs],
devices=devices)
losses = parallel.parallel_apply(
[pipeline.loss for pipeline in pipelines],
tuple(zip(inputs, outputs)),
devices=devices)
losses = parallel.gather(losses, target_device)
losses = dict([(k, v.mean()) for k, v in losses.items()])
meters, hists = pipeline_model.update_meters(meters, hists, losses)
# Compute and accumulate gradient
losses['loss'].backward()
# Update number of steps
pipeline_model.step_optimizer(losses, optimizer, scheduler,
iteration)
iter_time_avg.update(iter_timer.toc(False))
if curr_iter >= config.max_iter:
is_training = False
break
if curr_iter % config.stat_freq == 0 or curr_iter == 1:
lrs = ', '.join([
'{:.3e}'.format(x) for x in scheduler['default'].get_lr()
])
debug_str = "===> Epoch[{}]({}/{}): LR: {}\n".format(
epoch, curr_iter, len(data_loader), lrs)
debug_str += log_meters(meters, log_perclass_meters=False)
debug_str += f"\n data time: {data_time_avg.avg:.3f}"
debug_str += f" iter time: {iter_time_avg.avg:.3f}"
logging.info(debug_str)
# Reset timers
data_time_avg.reset()
iter_time_avg.reset()
# Write logs
update_writer(writer, meters, curr_iter, 'training')
writer.add_scalar('training/learning_rate',
scheduler['default'].get_lr()[0], curr_iter)
# Reset meters
reset_meters(meters, hists)
# Save current status, save before val to prevent occational mem overflow
if curr_iter % config.save_freq == 0:
checkpoint(pipeline_model, optimizer, epoch, curr_iter, config,
best_val, best_val_iter)
if config.heldout_save_freq > 0 and curr_iter % config.heldout_save_freq == 0:
checkpoint(pipeline_model,
optimizer,
epoch,
curr_iter,
config,
best_val,
best_val_iter,
heldout_save=True)
# Validation
if curr_iter % config.val_freq == 0:
if num_devices > 1:
unconvert_sync_batchnorm(pipeline_model)
best_val, best_val_iter = validate(pipeline_model,
val_data_loader, config,
writer, curr_iter, best_val,
best_val_iter, optimizer,
epoch)
if num_devices > 1:
pipeline_model = ME.MinkowskiSyncBatchNorm.convert_sync_batchnorm(
pipeline_model, devices)
if curr_iter % config.empty_cache_freq == 0:
# Clear cache
torch.cuda.empty_cache()
# End of iteration
curr_iter += 1
epoch += 1
# Explicit memory cleanup
if hasattr(data_iter, 'cleanup'):
data_iter.cleanup()
# Save the final model
if num_devices > 1:
unconvert_sync_batchnorm(pipeline_model)
validate(pipeline_model, val_data_loader, config, writer, curr_iter,
best_val, best_val_iter, optimizer, epoch)
if num_devices > 1:
pipeline_model = ME.MinkowskiSyncBatchNorm.convert_sync_batchnorm(
pipeline_model, devices)
checkpoint(pipeline_model, optimizer, epoch, curr_iter, config, best_val,
best_val_iter)
def train_distill(model,
data_loader,
val_data_loader,
config,
transform_data_fn=None):
'''
the distillation training
some cfgs here
'''
# distill_lambda = 1
# distill_lambda = 0.33
distill_lambda = 0.67
# TWO_STAGE=True: Transformer is first trained with L2 loss to match ResNet's activation, and then it fintunes like normal training on the second stage.
# TWO_STAGE=False: Transformer trains with combined loss
TWO_STAGE = False
# STAGE_PERCENTAGE = 0.7
device = get_torch_device(config.is_cuda)
# Set up the train flag for batch normalization
model.train()
# Configuration
data_timer, iter_timer = Timer(), Timer()
data_time_avg, iter_time_avg = AverageMeter(), AverageMeter()
losses, scores = AverageMeter(), AverageMeter()
optimizer = initialize_optimizer(model.parameters(), config)
scheduler = initialize_scheduler(optimizer, config)
criterion = nn.CrossEntropyLoss(ignore_index=config.ignore_label)
# Train the network
logging.info('===> Start training')
best_val_miou, best_val_iter, curr_iter, epoch, is_training = 0, 0, 1, 1, True
# TODO:
# load the sub-model only
# FIXME: some dirty hard-written stuff, only supporting current state
tch_model_cls = load_model('Res16UNet18A')
tch_model = tch_model_cls(3, 20, config).to(device)
# checkpoint_fn = "/home/zhaotianchen/project/point-transformer/SpatioTemporalSegmentation-ScanNet/outputs/ScannetSparseVoxelizationDataset/Res16UNet18A/resnet_base/weights.pth"
checkpoint_fn = "/home/zhaotianchen/project/point-transformer/SpatioTemporalSegmentation-ScanNet/outputs/ScannetSparseVoxelizationDataset/Res16UNet18A/Res18A/weights.pth" # voxel-size: 0.05
assert osp.isfile(checkpoint_fn)
logging.info("=> loading checkpoint '{}'".format(checkpoint_fn))
state = torch.load(checkpoint_fn)
d = {k: v for k, v in state['state_dict'].items() if 'map' not in k}
tch_model.load_state_dict(d)
if 'best_val' in state:
best_val_miou = state['best_val']
best_val_iter = state['best_val_iter']
logging.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_fn, state['epoch']))
if config.resume:
raise NotImplementedError
# Test loaded ckpt first
# checkpoint_fn = config.resume + '/weights.pth'
# if osp.isfile(checkpoint_fn):
# logging.info("=> loading checkpoint '{}'".format(checkpoint_fn))
# state = torch.load(checkpoint_fn)
# curr_iter = state['iteration'] + 1
# epoch = state['epoch']
# d = {k:v for k,v in state['state_dict'].items() if 'map' not in k }
# model.load_state_dict(d)
# if config.resume_optimizer:
# scheduler = initialize_scheduler(optimizer, config, last_step=curr_iter)
# optimizer.load_state_dict(state['optimizer'])
# if 'best_val' in state:
# best_val_miou = state['best_val']
# best_val_iter = state['best_val_iter']
# logging.info("=> loaded checkpoint '{}' (epoch {})".format(checkpoint_fn, state['epoch']))
# else:
# raise ValueError("=> no checkpoint found at '{}'".format(checkpoint_fn))
# test after loading the ckpt
v_loss, v_score, v_mAP, v_mIoU = test(tch_model, val_data_loader, config)
logging.info('Tch model tested, bes_miou: {}'.format(v_mIoU))
data_iter = data_loader.__iter__()
while is_training:
num_class = 20
total_correct_class = torch.zeros(num_class, device=device)
total_iou_deno_class = torch.zeros(num_class, device=device)
total_iteration = len(data_loader) // config.iter_size
for iteration in range(total_iteration):
# NOTE: for single stage distillation, L2 loss might be too large at first
# so we added a warmup training that don't use L2 loss
if iteration < 0:
use_distill = False
else:
use_distill = True
# Stage 1 / Stage 2 boundary
if TWO_STAGE:
stage_boundary = int(total_iteration * STAGE_PERCENTAGE)
optimizer.zero_grad()
data_time, batch_loss = 0, 0
iter_timer.tic()
for sub_iter in range(config.iter_size):
# Get training data
data_timer.tic()
if config.return_transformation:
coords, input, target, _, _, pointcloud, transformation = data_iter.next(
)
else:
coords, input, target, _, _ = data_iter.next(
) # ignore unique_map and inverse_map
if config.use_aux:
assert target.shape[1] == 2
aux = target[:, 1]
target = target[:, 0]
else:
aux = None
# For some networks, making the network invariant to even, odd coords is important
coords[:, 1:] += (torch.rand(3) * 100).type_as(coords)
# Preprocess input
if config.normalize_color:
input[:, :3] = input[:, :3] / 255. - 0.5
coords_norm = coords[:, 1:] / coords[:, 1:].max() - 0.5
# cat xyz into the rgb feature
if config.xyz_input:
input = torch.cat([coords_norm, input], dim=1)
sinput = SparseTensor(input, coords, device=device)
# TODO: return both-models
# in order to not breaking the valid interface, use a get_loss to get the regsitered loss
data_time += data_timer.toc(False)
# model.initialize_coords(*init_args)
if aux is not None:
raise NotImplementedError
# flatten ground truth tensor
target = target.view(-1).long().to(device)
if TWO_STAGE:
if iteration < stage_boundary:
# Stage 1: train transformer on L2 loss
soutput, anchor = model(sinput, save_anchor=True)
# Make sure gradient don't flow to teacher model
with torch.no_grad():
_, tch_anchor = tch_model(sinput, save_anchor=True)
loss = DistillLoss(tch_anchor, anchor)
else:
# Stage 2: finetune transformer on Cross-Entropy
soutput = model(sinput)
loss = criterion(soutput.F, target.long())
else:
if use_distill: # after warm up
soutput, anchor = model(sinput, save_anchor=True)
# if pretrained teacher, do not let the grad flow to teacher to update its params
with torch.no_grad():
tch_soutput, tch_anchor = tch_model(
sinput, save_anchor=True)
else: # warming up
soutput = model(sinput)
# The output of the network is not sorted
loss = criterion(soutput.F, target.long())
# Add L2 loss if use distillation
if use_distill:
distill_loss = DistillLoss(tch_anchor,
anchor) * distill_lambda
loss += distill_loss
# Compute and accumulate gradient
loss /= config.iter_size
batch_loss += loss.item()
loss.backward()
# Update number of steps
optimizer.step()
scheduler.step()
# CLEAR CACHE!
torch.cuda.empty_cache()
data_time_avg.update(data_time)
iter_time_avg.update(iter_timer.toc(False))
pred = get_prediction(data_loader.dataset, soutput.F, target)
score = precision_at_one(pred, target, ignore_label=-1)
losses.update(batch_loss, target.size(0))
scores.update(score, target.size(0))
# calc the train-iou
for l in range(num_class):
total_correct_class[l] += ((pred == l) & (target == l)).sum()
total_iou_deno_class[l] += (((pred == l) & (target != -1)) |
(target == l)).sum()
if curr_iter >= config.max_iter:
is_training = False
break
if curr_iter % config.stat_freq == 0 or curr_iter == 1:
lrs = ', '.join(
['{:.3e}'.format(x) for x in scheduler.get_lr()])
debug_str = "[{}] ===> Epoch[{}]({}/{}): Loss {:.4f}\tLR: {}\t".format(
config.log_dir, epoch, curr_iter,
len(data_loader) // config.iter_size, losses.avg, lrs)
debug_str += "Score {:.3f}\tData time: {:.4f}, Iter time: {:.4f}".format(
scores.avg, data_time_avg.avg, iter_time_avg.avg)
logging.info(debug_str)
if use_distill and not TWO_STAGE:
logging.info('Loss {} Distill Loss:{}'.format(
loss, distill_loss))
# Reset timers
data_time_avg.reset()
iter_time_avg.reset()
losses.reset()
scores.reset()
# Save current status, save before val to prevent occational mem overflow
if curr_iter % config.save_freq == 0:
checkpoint(model,
optimizer,
epoch,
curr_iter,
config,
best_val_miou,
best_val_iter,
save_inter=True)
# Validation
if curr_iter % config.val_freq == 0:
val_miou = validate(model, val_data_loader, None, curr_iter,
config, transform_data_fn)
if val_miou > best_val_miou:
best_val_miou = val_miou
best_val_iter = curr_iter
checkpoint(model,
optimizer,
epoch,
curr_iter,
config,
best_val_miou,
best_val_iter,
"best_val",
save_inter=True)
logging.info("Current best mIoU: {:.3f} at iter {}".format(
best_val_miou, best_val_iter))
# Recover back
model.train()
# End of iteration
curr_iter += 1
IoU = (total_correct_class) / (total_iou_deno_class + 1e-6)
logging.info('train point avg class IoU: %f' % ((IoU).mean() * 100.))
epoch += 1
# Explicit memory cleanup
if hasattr(data_iter, 'cleanup'):
data_iter.cleanup()
# Save the final model
checkpoint(model, optimizer, epoch, curr_iter, config, best_val_miou,
best_val_iter)
v_loss, v_score, v_mAP, val_miou = test(model, val_data_loader, config)
if val_miou > best_val_miou:
best_val_miou = val_miou
best_val_iter = curr_iter
checkpoint(model, optimizer, epoch, curr_iter, config, best_val_miou,
best_val_iter, "best_val")
logging.info("Current best mIoU: {:.3f} at iter {}".format(
best_val_miou, best_val_iter))
def test(pipeline_model, data_loader, config, has_gt=True):
global_timer, data_timer, iter_timer = Timer(), Timer(), Timer()
meters = collections.defaultdict(AverageMeter)
hists = pipeline_model.initialize_hists()
logging.info('===> Start testing')
global_timer.tic()
data_iter = data_loader.__iter__()
max_iter = len(data_loader)
# Fix batch normalization running mean and std
pipeline_model.eval()
# Clear cache (when run in val mode, cleanup training cache)
torch.cuda.empty_cache()
if config.save_prediction or config.test_original_pointcloud:
if config.save_prediction:
save_pred_dir = config.save_pred_dir
os.makedirs(save_pred_dir, exist_ok=True)
else:
save_pred_dir = tempfile.mkdtemp()
if os.listdir(save_pred_dir):
raise ValueError(f'Directory {save_pred_dir} not empty. '
'Please remove the existing prediction.')
with torch.no_grad():
for iteration in range(max_iter):
iter_timer.tic()
data_timer.tic()
datum = pipeline_model.load_datum(data_iter, has_gt=has_gt)
data_time = data_timer.toc(False)
output_dict = pipeline_model(datum, False)
iter_time = iter_timer.toc(False)
if config.save_prediction or config.test_original_pointcloud:
pipeline_model.save_prediction(datum, output_dict,
save_pred_dir, iteration)
if config.visualize and iteration % config.visualize_freq == 0:
pipeline_model.visualize_predictions(datum, output_dict,
iteration)
if has_gt:
loss_dict = pipeline_model.loss(datum, output_dict)
if config.visualize and iteration % config.visualize_freq == 0:
pipeline_model.visualize_groundtruth(datum, iteration)
loss_dict.update(pipeline_model.evaluate(datum, output_dict))
meters, hists = pipeline_model.update_meters(
meters, hists, loss_dict)
if iteration % config.test_stat_freq == 0 and iteration > 0:
debug_str = "===> {}/{}\n".format(iteration, max_iter)
debug_str += log_meters(meters, log_perclass_meters=True)
debug_str += f"\n data time: {data_time:.3f} iter time: {iter_time:.3f}"
logging.info(debug_str)
if iteration % config.empty_cache_freq == 0:
# Clear cache
torch.cuda.empty_cache()
global_time = global_timer.toc(False)
debug_str = "===> Final test results:\n"
debug_str += log_meters(meters, log_perclass_meters=True)
logging.info(debug_str)
if config.test_original_pointcloud:
pipeline_model.test_original_pointcloud(save_pred_dir)
logging.info('Finished test. Elapsed time: {:.4f}'.format(global_time))
# Explicit memory cleanup
if hasattr(data_iter, 'cleanup'):
data_iter.cleanup()
return meters
def train(self, generator_queue, discriminator_queue):
''' Given data queues, train the network '''
# Parameter directory
save_dir = os.path.join(cfg.DIR.OUT_PATH)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# Timer for the training op and parallel data loading op.
train_timer = Timer()
data_timer = Timer()
start_iter = 0
# Resume training
if cfg.TRAIN.RESUME_TRAIN:
self.net.load(cfg.CONST.WEIGHTS)
start_iter = cfg.TRAIN.INITIAL_ITERATION
# Setup learning rates
lr = cfg.TRAIN.DEFAULT_LEARNING_RATE
lr_steps = [int(k) for k in cfg.TRAIN.LEARNING_RATES.keys()]
print('Set the learning rate to %f.' % lr)
gen_lr = lr
discriminator_losses = []
generator_losses = []
mask_losses = []
generator_idx = 0
voxel_loss = 0
generator_loss = 0
mask_loss = 0
discriminator_loss = 0
# Main training loop
for train_ind in range(start_iter, cfg.TRAIN.NUM_ITERATION + 1):
self.net.noise.set_value(max(1 - float(train_ind) / 20000., 1e-8))
data_timer.tic()
gen_img, gen_camera, _ = generator_queue.get()
data_timer.toc()
data_timer.tic()
_, _, disc_voxel = discriminator_queue.get()
data_timer.toc()
# Apply one gradient step
train_timer.tic()
if self.net.discriminator_loss is not None:
error_F, error_R = self.evaluate_discriminator(
gen_img, gen_camera, disc_voxel)
if error_F > 0.2 or error_R > 0.2:
self.set_lr(gen_lr / 100.)
discriminator_loss = self.discriminator_train_loss(
gen_img, gen_camera, disc_voxel)
discriminator_losses.append(discriminator_loss)
self.set_lr(gen_lr)
results = self.generator_train_loss(gen_img, gen_camera)
generator_loss = results[0]
generator_losses.append(generator_loss)
generator_idx += 1
mask_loss = results[1]
mask_losses.append(mask_loss)
activations = results[2:]
train_timer.toc()
# Decrease learning rate at certain points
if train_ind in lr_steps:
# edict only takes string for key. Hacky way
gen_lr = np.float(cfg.TRAIN.LEARNING_RATES[str(train_ind)])
print('Learing rate decreased to %f: ' % gen_lr)
# Debugging modules
#
# Print status, run validation, check divergence, and save model.
if train_ind % cfg.TRAIN.PRINT_FREQ == 0:
# Print the current loss
get_mean = lambda x, y: np.mean(x) if x else y
print("""%s Iter %d: Discriminator loss %f, Generator """
"""loss %f, Mask loss %f""" %
(datetime.now(), train_ind,
get_mean(discriminator_losses, discriminator_loss),
get_mean(generator_losses, generator_loss),
get_mean(mask_losses, mask_loss)))
discriminator_losses = []
generator_losses = []
mask_losses = []
if train_ind % cfg.TRAIN.NAN_CHECK_FREQ == 0:
# Check that the network parameters are all valid
max_param = max_or_nan(self.net.all_params)
if np.isnan(max_param):
print('NAN detected')
break
if train_ind % cfg.TRAIN.SAVE_FREQ == 0 and not train_ind == 0:
self.save(mask_losses, save_dir, train_ind)
