def _data_parallel(self, batch):
"""
Do the forward pass using multiple GPUs. This is a simplification
of torch.nn.parallel.data_parallel to support the allennlp model
interface.
"""
inputs, module_kwargs = scatter_kwargs((), batch, self._cuda_devices, 0)
used_device_ids = self._cuda_devices[:len(inputs)]
replicas = replicate(self._model, used_device_ids)
outputs = parallel_apply(replicas, inputs, module_kwargs, used_device_ids)
# Only the 'loss' is needed.
# a (num_gpu, ) tensor with loss on each GPU
losses = gather([output['loss'].unsqueeze(0) for output in outputs], used_device_ids[0], 0)
return {'loss': losses.mean()}
def test_tensor_sharing(self):
module = self.Msm1(self.Msm()).cuda()
replica = dp.replicate(module, {0, 1})
optimizer = optim.SGD(module.parameters(), lr=1, momentum=1)
x = torch.ones(2, 2, requires_grad=True).cuda()
first_forward = module.forward(x)
first_forward.sum().backward()
optimizer.step()
second_forward = module.forward(first_forward)
# replica which is on the same GPU has a shallow copy of the original
# params and buffers
r0_forward = replica[0].forward(x)
self.assertEqual(second_forward, r0_forward)
# replica which is on a different GPU has a deep copy of the original
# params and buffers
x1 = torch.ones(2, 2, requires_grad=True).cuda(device=1)
r1_forward = replica[1].forward(x1)
self.assertEqual(first_forward, r1_forward)
def my_replicate(model, source_device_id, target_device_id):
"""
1. deep copy the mode to gpu@device_id
2. eihter .cuda() and replicate objects among gpus,
but Module.cuda() will edit the origianl object
function relicate can copy multiply copies, so it returns a list; Here, we only generate one copy
3. the original replicate function won't generate new copies if target_devcie_id == source_device_id. This assist function fix that.
"""
# [source_device_id, target1, ...]
# return the models identical to the devices list including input
with cuda.device(target_device_id):
copies = replicate(model, [source_device_id, target_device_id])
orig_copy = copies[0]
new_copy = copies[1]
if source_device_id == target_device_id:
for param_name, module_tensor in new_copy.named_parameters():
module_tensor.data = module_tensor.data.new(
module_tensor.data.size()).copy_(module_tensor.data)
del orig_copy
return new_copy
def calc_distill_loss(self):
losses = []
for i, netA in enumerate(self.netAs):
assert isinstance(netA, nn.Conv2d)
n = self.mapping_layers[i]
netA_replicas = replicate(netA, self.gpu_ids)
Sacts = parallel_apply(
netA_replicas,
tuple([
self.Sacts[key] for key in sorted(self.Sacts.keys())
if n in key
]))
Tacts = [
self.Tacts[key] for key in sorted(self.Tacts.keys())
if n in key
]
loss = [F.mse_loss(Sact, Tact) for Sact, Tact in zip(Sacts, Tacts)]
loss = gather(loss, self.gpu_ids[0]).sum()
setattr(self, 'loss_G_distill%d' % i, loss)
losses.append(loss)
return sum(losses)
def parallel_chain_loss(model, inputs, den_graph):
"""
inputs: list of input tuple ((mfcc, inputs), supervision) on different gpus
"""
from torch.nn.parallel import replicate, parallel_apply, gather
model = ForwardParallelChain(model, den_graph, args)
device_ids = list(range(torch.cuda.device_count()))
assert len(inputs) == len(device_ids)
output_device = device_ids[0]
used_device_ids = device_ids[:len(inputs)]
replicas = replicate(model, used_device_ids)
model_kwargs = None
outputs = parallel_apply(replicas, inputs, model_kwargs, used_device_ids)
dim = 0
ret = gather(outputs, output_device, dim)
loss = ret[:, 0]
weights = ret[:, -1]
nummerator = loss * weights
results = ChainResults()
results.data = ret[:, 1:].sum(dim=0)
return numerator.sum() / weights.sum(), results
def test_tensor_sharing(self):
module = self.Msm1(self.Msm()).cuda()
replica = dp.replicate(module, {0, 1})
def assert_share_data(t1, t2):
# Only checks that they point to the same memory on the same device.
if t1.device != t2.device:
return False
if t1.storage().data_ptr() != t2.storage().data_ptr():
return False
return True
for p1, p2 in zip(module.parameters(), replica[0].parameters()):
self.assertTrue(assert_share_data(p1, p2))
for p1, p2 in zip(module.buffers(), replica[0].buffers()):
self.assertTrue(assert_share_data(p1, p2))
for p1, p2 in zip(module.parameters(), replica[1].parameters()):
self.assertFalse(assert_share_data(p1, p2))
for p1, p2 in zip(module.buffers(), replica[1].buffers()):
self.assertFalse(assert_share_data(p1, p2))
def test_tensor_sharing_with_forward(self):
module = self.Msm1(self.Msm()).cuda()
replica = dp.replicate(module, {0, 1})
x = torch.ones(2, 2, requires_grad=True).cuda()
first_forward = module(x)
first_forward.sum().backward()
with torch.no_grad():
for p in module.parameters():
# Use .data here to avoid version counter bump.
# The graph created by the following forward will be wrong but
# we never backward through them so it's fine
p.data -= 1. * p.grad
second_forward = module(x)
# replica which is on the same GPU has a shallow copy of the original
# params and buffers
r0_forward = replica[0](x)
self.assertEqual(second_forward, r0_forward)
# replica which is on a different GPU has a deep copy of the original
# params and buffers
x1 = torch.ones(2, 2, requires_grad=True).cuda(device=1)
r1_forward = replica[1](x1)
self.assertEqual(first_forward, r1_forward)
def data_parallel(batch_group: List[TensorDict],
model: Model,
cuda_devices: List) -> Dict[str, torch.Tensor]:
"""
Performs a forward pass using multiple GPUs. This is a simplification
of torch.nn.parallel.data_parallel to support the allennlp model
interface.
"""
assert len(batch_group) <= len(cuda_devices)
moved = [nn_util.move_to_device(batch, device)
for batch, device in zip(batch_group, cuda_devices)]
used_device_ids = cuda_devices[:len(moved)]
replicas = replicate(model, used_device_ids)
# We pass all our arguments as kwargs. Create a list of empty tuples of the
# correct shape to serve as (non-existent) positional arguments.
inputs = [()] * len(batch_group)
outputs = parallel_apply(replicas, inputs, moved, used_device_ids)
# Only the 'loss' is needed.
# a (num_gpu, ) tensor with loss on each GPU
losses = gather([output['loss'].unsqueeze(0) for output in outputs], used_device_ids[0], 0)
return {'loss': losses.mean()}
def main(args):
def log_string(str):
logger.info(str)
print(str)
'''HYPER PARAMETER'''
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
'''CREATE DIR'''
timestr = str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M'))
experiment_dir = Path('./log/')
experiment_dir.mkdir(exist_ok=True)
experiment_dir = experiment_dir.joinpath('part_seg')
experiment_dir.mkdir(exist_ok=True)
if args.log_dir is None:
experiment_dir = experiment_dir.joinpath(timestr)
else:
experiment_dir = experiment_dir.joinpath(args.log_dir)
experiment_dir.mkdir(exist_ok=True)
checkpoints_dir = experiment_dir.joinpath('checkpoints/')
checkpoints_dir.mkdir(exist_ok=True)
log_dir = experiment_dir.joinpath('logs/')
log_dir.mkdir(exist_ok=True)
'''LOG'''
args = parse_args()
logger = logging.getLogger("Model")
logger.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler = logging.FileHandler('%s/%s.txt' % (log_dir, args.model))
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
log_string('PARAMETER ...')
log_string(args)
root = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/'
file_list = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/train2.list'
val_list = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/val2.list'
TRAIN_DATASET = KittiDataset(root=root,
file_list=file_list,
npoints=args.npoint,
training=True,
augment=True)
trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET,
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
num_workers=2)
TEST_DATASET = KittiDataset(root=root,
file_list=val_list,
npoints=args.npoint,
training=False,
augment=False)
testDataLoader = torch.utils.data.DataLoader(TEST_DATASET,
batch_size=args.batch_size,
shuffle=False,
drop_last=True,
num_workers=2)
log_string("The number of training data is: %d" % len(TRAIN_DATASET))
log_string("The number of test data is: %d" % len(TEST_DATASET))
# num_classes = 16
'''MODEL LOADING'''
shutil.copy('models/%s.py' % args.model, str(experiment_dir))
shutil.copy('models/pointnet_util.py', str(experiment_dir))
num_devices = args.num_gpus #torch.cuda.device_count()
# assert num_devices > 1, "Cannot detect more than 1 GPU."
# print(num_devices)
devices = list(range(num_devices))
target_device = devices[0]
# MODEL = importlib.import_module(args.model)
net = FusionNet(args.npoint, 4, 20, nPlanes)
# net = MODEL.get_model(num_classes, normal_channel=args.normal)
net = net.to(target_device)
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
if m.weight is not None:
torch.nn.init.xavier_normal_(m.weight.data)
if m.bias is not None:
torch.nn.init.constant_(m.bias.data, 0.0)
elif classname.find('Linear') != -1:
if m.weight is not None:
torch.nn.init.xavier_normal_(m.weight.data)
if m.bias is not None:
torch.nn.init.constant_(m.bias.data, 0.0)
try:
checkpoint = torch.load(
str(experiment_dir) + '/checkpoints/best_model.pth')
start_epoch = checkpoint['epoch']
net.load_state_dict(checkpoint['model_state_dict'])
log_string('Use pretrain model')
except:
log_string('No existing model, starting training from scratch...')
start_epoch = 0
net = net.apply(weights_init)
if args.optimizer == 'Adam':
optimizer = torch.optim.Adam(net.parameters(),
lr=args.learning_rate,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=args.decay_rate)
else:
optimizer = torch.optim.SGD(net.parameters(),
lr=1e-1,
momentum=0.9,
weight_decay=1e-4,
nesterov=True)
# optimizer = torch.optim.SGD(net.parameters(), lr=args.learning_rate, momentum=0.9)
def bn_momentum_adjust(m, momentum):
if isinstance(m, torch.nn.BatchNorm2d) or isinstance(
m, torch.nn.BatchNorm1d):
m.momentum = momentum
LEARNING_RATE_CLIP = 1e-5
MOMENTUM_ORIGINAL = 0.1
MOMENTUM_DECCAY = 0.5
MOMENTUM_DECCAY_STEP = 20 / 2 # args.step_size
best_acc = 0
global_epoch = 0
best_class_avg_iou = 0
best_inctance_avg_iou = 0
# criterion = MODEL.get_loss()
criterion = nn.CrossEntropyLoss()
criterions = parallel.replicate(criterion, devices)
# The raw version of the parallel_apply
# replicas = parallel.replicate(net, devices)
# input_coding = scn.InputLayer(dimension, torch.LongTensor(spatialSize), mode=4)
for epoch in range(start_epoch, args.epoch):
log_string('Epoch %d (%d/%s):' %
(global_epoch + 1, epoch + 1, args.epoch))
'''Adjust learning rate and BN momentum'''
# lr = max(args.learning_rate * (args.lr_decay ** (epoch // args.step_size)), LEARNING_RATE_CLIP)
# lr = args.learning_rate * \
# math.exp((1 - epoch) * args.lr_decay)
# log_string('Learning rate:%f' % lr)
# for param_group in optimizer.param_groups:
# param_group['lr'] = lr
# for param_group in optimizer.param_groups:
# param_group['lr'] = lr
mean_correct = []
if 1:
momentum = MOMENTUM_ORIGINAL * (MOMENTUM_DECCAY
**(epoch // MOMENTUM_DECCAY_STEP))
if momentum < 0.01:
momentum = 0.01
print('BN momentum updated to: %f' % momentum)
net = net.apply(lambda x: bn_momentum_adjust(x, momentum))
'''learning one epoch'''
net.train()
# for iteration, data in tqdm(enumerate(trainDataLoader), total=len(trainDataLoader), smoothing=0.9):
for iteration, data in enumerate(trainDataLoader):
#adjust learing rate.
if (iteration) % 320 == 0:
lr_count = epoch * 6 + (iteration) / 320
lr = args.learning_rate * math.exp(
(1 - lr_count) * args.lr_decay)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
log_string('Learning rate:%f' % lr)
optimizer.zero_grad()
if iteration > 1920:
break
points, target, ins, mask = data
# print(torch.max(points[:, :, :3], 1)[0])
# print(torch.min(points[:, :, :3], 1)[0])
valid = mask > 0
total_points = valid.sum()
orgs = points
points = points.data.numpy()
# print(total_points)
inputs, targets, masks = [], [], []
coords = []
for i in range(num_devices):
start = int(i * (args.batch_size / num_devices))
end = int((i + 1) * (args.batch_size / num_devices))
batch = provider.transform_for_sparse(
points[start:end, :, :3], points[start:end, :, 3:],
target[start:end, :].data.numpy(),
mask[start:end, :].data.numpy(), scale, spatialSize)
batch['x'][1] = batch['x'][1].type(torch.FloatTensor)
batch['x'][0] = batch['x'][0].type(torch.IntTensor)
batch['y'] = batch['y'].type(torch.LongTensor)
org_xyz = orgs[start:end, :, :3].transpose(1, 2).contiguous()
org_feas = orgs[start:end, :, 3:].transpose(1, 2).contiguous()
label = Variable(batch['y'], requires_grad=False)
maski = batch['mask'].type(torch.IntTensor)
# print(torch.max(batch['x'][0], 0)[0])
# print(torch.min(batch['x'][0], 0)[0])
# locs, feas = input_layer(batch['x'][0].to(devices[i]), batch['x'][1].to(devices[i]))
locs, feas = input_layer(batch['x'][0].cuda(),
batch['x'][1].cuda())
# print(locs.size(), feas.size(), batch['x'][0].size())
# print(inputi.size(), batch['x'][1].size())
with torch.cuda.device(devices[i]):
org_coords = batch['x'][0].to(devices[i])
inputi = ME.SparseTensor(feas.cpu(), locs).to(
devices[i]) #input_coding(batch['x'])
org_xyz = org_xyz.to(devices[i])
org_feas = org_feas.to(devices[i])
maski = maski.to(devices[i])
inputs.append(
[inputi, org_coords, org_xyz, org_feas, maski])
targets.append(label.to(devices[i]))
# masks.append(maski.contiguous().to(devices[i]))
replicas = parallel.replicate(net, devices)
predictions = parallel.parallel_apply(replicas,
inputs,
devices=devices)
count = 0
# print("end ...")
results = []
labels = []
match = 0
for i in range(num_devices):
# temp = predictions[i]['output1'].F#.view(-1, num_classes)
temp = predictions[i]
# temp = output_layer(locs, predictions[i]['output1'].F, coords[i])
temp = temp[targets[i] > 0, :]
results.append(temp)
temp = targets[i]
temp = temp[targets[i] > 0]
labels.append(temp)
# print(prediction2[i].size(), prediction1[i].size(), targets[i].size())
outputi = results[
i] #prediction2[i].contiguous().view(-1, num_classes)
num_points = labels[i].size(0)
count += num_points
_, pred_choice = outputi.data.max(1) #[1]
# print(pred_choice)
correct = pred_choice.eq(labels[i].data).cpu().sum()
match += correct.item()
mean_correct.append(correct.item() / num_points)
# print(prediction2, labels)
losses = parallel.parallel_apply(criterions,
tuple(zip(results, labels)),
devices=devices)
loss = parallel.gather(losses, target_device, dim=0).mean()
loss.backward()
optimizer.step()
# assert(count1 == count2 and total_points == count1)
log_string(
"===> Epoch[{}]({}/{}) Valid points:{}/{} Loss: {:.4f} Accuracy: {:.4f}"
.format(epoch, iteration, len(trainDataLoader), count,
total_points, loss.item(), match / count))
# sys.stdout.flush()
train_instance_acc = np.mean(mean_correct)
log_string('Train accuracy is: %.5f' % train_instance_acc)
# continue
with torch.no_grad():
net.eval()
evaluator = iouEval(num_classes, ignore)
evaluator.reset()
for iteration, (points, target, ins,
mask) in tqdm(enumerate(testDataLoader),
total=len(testDataLoader),
smoothing=0.9):
cur_batch_size, NUM_POINT, _ = points.size()
# points, label, target, mask = points.float().cuda(), label.long().cuda(), target.long().cuda(), mask.float().cuda()
if iteration > 192:
break
if 0:
points = points.data.numpy()
points[:, :, 0:3], norm = provider.pc_normalize(
points[:, :, :3], mask.data.numpy())
points = torch.Tensor(points)
orgs = points
points = points.data.numpy()
inputs, targets, masks = [], [], []
coords = []
for i in range(num_devices):
start = int(i * (cur_batch_size / num_devices))
end = int((i + 1) * (cur_batch_size / num_devices))
batch = provider.transform_for_test(
points[start:end, :, :3], points[start:end, :, 3:],
target[start:end, :].data.numpy(),
mask[start:end, :].data.numpy(), scale, spatialSize)
batch['x'][1] = batch['x'][1].type(torch.FloatTensor)
batch['x'][0] = batch['x'][0].type(torch.IntTensor)
batch['y'] = batch['y'].type(torch.LongTensor)
org_xyz = orgs[start:end, :, :3].transpose(1,
2).contiguous()
org_feas = orgs[start:end, :,
3:].transpose(1, 2).contiguous()
label = Variable(batch['y'], requires_grad=False)
maski = batch['mask'].type(torch.IntTensor)
locs, feas = input_layer(batch['x'][0].cuda(),
batch['x'][1].cuda())
# print(locs.size(), feas.size(), batch['x'][0].size())
# print(inputi.size(), batch['x'][1].size())
with torch.cuda.device(devices[i]):
org_coords = batch['x'][0].to(devices[i])
inputi = ME.SparseTensor(feas.cpu(), locs).to(
devices[i]) #input_coding(batch['x'])
org_xyz = org_xyz.to(devices[i])
org_feas = org_feas.to(devices[i])
maski = maski.to(devices[i])
inputs.append(
[inputi, org_coords, org_xyz, org_feas, maski])
targets.append(label.to(devices[i]))
# masks.append(maski.contiguous().to(devices[i]))
replicas = parallel.replicate(net, devices)
outputs = parallel.parallel_apply(replicas,
inputs,
devices=devices)
# net = net.eval()
# seg_pred = classifier(points, to_categorical(label, num_classes))
seg_pred = outputs[0].cpu()
# mask = masks[0].cpu()
target = targets[0].cpu()
loc = locs[0].cpu()
for i in range(1, num_devices):
seg_pred = torch.cat((seg_pred, outputs[i].cpu()), 0)
# mask = torch.cat((mask, masks[i].cpu()), 0)
target = torch.cat((target, targets[i].cpu()), 0)
seg_pred = seg_pred[target > 0, :]
target = target[target > 0]
_, seg_pred = seg_pred.data.max(1) #[1]
target = target.data.numpy()
evaluator.addBatch(seg_pred, target)
# when I am done, print the evaluation
m_accuracy = evaluator.getacc()
m_jaccard, class_jaccard = evaluator.getIoU()
log_string('Validation set:\n'
'Acc avg {m_accuracy:.3f}\n'
'IoU avg {m_jaccard:.3f}'.format(m_accuracy=m_accuracy,
m_jaccard=m_jaccard))
# print also classwise
for i, jacc in enumerate(class_jaccard):
if i not in ignore:
log_string(
'IoU class {i:} [{class_str:}] = {jacc:.3f}'.format(
i=i,
class_str=class_strings[class_inv_remap[i]],
jacc=jacc))
log_string('Epoch %d test Accuracy: %f mean avg mIOU: %f' %
(epoch + 1, m_accuracy, m_jaccard))
if (m_jaccard >= best_class_avg_iou):
# logger.info('Save model...')
log_string('Saveing model...')
savepath = str(checkpoints_dir) + '/best_model.pth'
log_string('Saving at %s' % savepath)
state = {
'epoch': epoch,
'train_acc': train_instance_acc,
'test_acc': m_accuracy,
'class_avg_iou': m_jaccard,
'model_state_dict': net.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}
torch.save(state, savepath)
# log_string('Saving model....')
if m_accuracy > best_acc:
best_acc = m_accuracy
if m_jaccard > best_class_avg_iou:
best_class_avg_iou = m_jaccard
log_string('Best accuracy is: %.5f' % best_acc)
log_string('Best class avg mIOU is: %.5f' % best_class_avg_iou)
global_epoch += 1
def main(args):
def log_string(str):
# logger.info(str)
print(str)
'''HYPER PARAMETER'''
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
'''CREATE DIR'''
timestr = str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M'))
experiment_dir = Path('./log/')
experiment_dir.mkdir(exist_ok=True)
experiment_dir = experiment_dir.joinpath('part_seg')
experiment_dir.mkdir(exist_ok=True)
if args.log_dir is None:
experiment_dir = experiment_dir.joinpath(timestr)
else:
experiment_dir = experiment_dir.joinpath(args.log_dir)
experiment_dir.mkdir(exist_ok=True)
checkpoints_dir = experiment_dir.joinpath('checkpoints/')
checkpoints_dir.mkdir(exist_ok=True)
log_dir = experiment_dir.joinpath('logs/')
log_dir.mkdir(exist_ok=True)
'''LOG'''
args = parse_args()
logger = logging.getLogger("Model")
logger.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler = logging.FileHandler('%s/%s.txt' % (log_dir, args.model))
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
log_string('PARAMETER ...')
log_string(args)
root = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/'
# file_list = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/train2.list'
val_list = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/val2.list'
# TRAIN_DATASET = KittiDataset(root = root, file_list=file_list, npoints=args.npoint, training=True, augment=True)
# trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=2)
TEST_DATASET = KittiDataset(root=root,
file_list=val_list,
npoints=args.npoint,
training=False,
augment=False)
testDataLoader = torch.utils.data.DataLoader(TEST_DATASET,
batch_size=args.batch_size,
shuffle=False,
drop_last=True,
num_workers=2)
# log_string("The number of training data is: %d" % len(TRAIN_DATASET))
log_string("The number of test data is: %d" % len(TEST_DATASET))
# num_classes = 16
num_devices = args.num_gpus #torch.cuda.device_count()
# assert num_devices > 1, "Cannot detect more than 1 GPU."
# print(num_devices)
devices = list(range(num_devices))
target_device = devices[0]
# MODEL = importlib.import_module(args.model)
net = UNet(4, 20, nPlanes)
# net = MODEL.get_model(num_classes, normal_channel=args.normal)
net = net.to(target_device)
try:
checkpoint = torch.load(
str(experiment_dir) + '/checkpoints/best_model.pth')
start_epoch = checkpoint['epoch']
net.load_state_dict(checkpoint['model_state_dict'])
log_string('Use pretrain model')
except:
log_string('No existing model, starting training from scratch...')
quit()
if 1:
with torch.no_grad():
net.eval()
evaluator = iouEval(num_classes, ignore)
evaluator.reset()
# for iteration, (points, target, ins, mask) in tqdm(enumerate(testDataLoader), total=len(testDataLoader), smoothing=0.9):
for iteration, (points, target, ins,
mask) in enumerate(testDataLoader):
evaone = iouEval(num_classes, ignore)
evaone.reset()
cur_batch_size, NUM_POINT, _ = points.size()
if iteration > 128:
break
inputs, targets, masks = [], [], []
coords = []
for i in range(num_devices):
start = int(i * (cur_batch_size / num_devices))
end = int((i + 1) * (cur_batch_size / num_devices))
with torch.cuda.device(devices[i]):
pc = points[start:end, :, :].to(devices[i])
#feas = points[start:end,:,3:].to(devices[i])
targeti = target[start:end, :].to(devices[i])
maski = mask[start:end, :].to(devices[i])
locs, feas, label, maski, offsets = input_layer(
pc, targeti, maski, scale.to(devices[i]),
spatialSize.to(devices[i]), True)
# print(locs.size(), feas.size(), label.size(), maski.size(), offsets.size())
org_coords = locs[1]
label = Variable(label, requires_grad=False)
inputi = ME.SparseTensor(feas.cpu(), locs[0].cpu())
inputs.append([inputi.to(devices[i]), org_coords])
targets.append(label)
masks.append(maski)
replicas = parallel.replicate(net, devices)
outputs = parallel.parallel_apply(replicas,
inputs,
devices=devices)
seg_pred = outputs[0].cpu()
mask = masks[0].cpu()
target = targets[0].cpu()
loc = locs[0].cpu()
for i in range(1, num_devices):
seg_pred = torch.cat((seg_pred, outputs[i].cpu()), 0)
mask = torch.cat((mask, masks[i].cpu()), 0)
target = torch.cat((target, targets[i].cpu()), 0)
seg_pred = seg_pred[target > 0, :]
target = target[target > 0]
_, seg_pred = seg_pred.data.max(1) #[1]
target = target.data.numpy()
evaluator.addBatch(seg_pred, target)
evaone.addBatch(seg_pred, target)
cur_accuracy = evaone.getacc()
cur_jaccard, class_jaccard = evaone.getIoU()
print('%.4f %.4f' % (cur_accuracy, cur_jaccard))
m_accuracy = evaluator.getacc()
m_jaccard, class_jaccard = evaluator.getIoU()
log_string('Validation set:\n'
'Acc avg {m_accuracy:.3f}\n'
'IoU avg {m_jaccard:.3f}'.format(m_accuracy=m_accuracy,
m_jaccard=m_jaccard))
# print also classwise
for i, jacc in enumerate(class_jaccard):
if i not in ignore:
log_string(
'IoU class {i:} [{class_str:}] = {jacc:.3f}'.format(
i=i,
class_str=class_strings[class_inv_remap[i]],
jacc=jacc))
def replicate(self, device_ids): return replicate(self.model, device_ids)
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(
self,
train_dataset,
*,
progress_bar=True,
resume=False,
device=None,
):
"""
A simplified training loop::
for epoch in range(1, ...):
for example in train_iterator:
model_out = self.model(example)
review = self.model.review(example, model_out)
review = maybe_add_loss_from_losses(review)
review['loss'].backward()
self.optimizer.step()
add_review_to_tensorboardX(review)
The remaining code takes care about calling validation and save the
result to tensorboard (if a validation_hook is registered), save
checkpoints, cleanup checkpoints that are stale (not best according
to metric and not last) and display a progessbar.
The code is designed that many aspects can be customized.
(e.g. see test_runtime_tests.py DictTrainer for multi model trainer)
Args:
train_iterator:
The train_iterator is python iterable (e.g. tuple, list, ...)
that can consumed multiple times (i.e. not generator).
Usually it will be paderbox.database.BaseIterator that is
returned from a database in paderbox.database.
progress_bar: flag whether to show a progress bar or not.
resume:
Whether to resume a training or start a fresh one.
device:
Defines the device which shall be used ('cpu', 0, 1, ...).
If None, it selects device 0 if CUDA is available and 'cpu'
if CUDA is not available.
"""
if torch.cuda.is_available():
if device is None:
device = 0
else:
if device is None:
warnings.warn(
'CUDA is not available in this environment! The training '
'will run on the CPU! This might be caused by a damaged '
'installation or a version mismatch between PyTorch and '
'your CUDA installation.')
device = 'cpu'
elif device != 'cpu':
raise RuntimeError(
'CUDA is not available in this environment, but you set '
'device to use a GPU! This might be caused by a damaged '
'installation or a version mismatch between PyTorch and '
'your CUDA installation.')
if resume:
assert resume is True, resume
self.load_checkpoint()
else:
assert not self.checkpoint_dir.exists(),\
f'A checkpoint directory already exists. If you want to ' \
f'restart the training set resume to True.'
self.iteration = 0
self.epoch = 0
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
# Change model to train mode (e.g. activate dropout)
self.model.train()
if isinstance(device, (tuple, list)):
assert all([isinstance(d, int) for d in device]), device
# multiple devises e.g. [0, 1], [0, 1, 2, 3], ...
# torch.nn.parallel.DataParallel moves everything to the first gpu.
# We do then the same thing.
self.to(device[0])
device = list(device)
else:
self.to(device)
device = [device]
# Reset all gradients
self.optimizer_zero_grad()
self.writer = self.writer_cls(str(self.storage_dir))
hooks = [*self.hooks]
if progress_bar:
try:
max_it_len = len(train_dataset)
except TypeError:
# TypeError: object of type '...' has no len()
max_it_len = None
hooks.append(ProgressBarHook(self._stop_trigger, max_it_len))
hooks = sorted(hooks, key=lambda h: h.priority, reverse=True)
if len(device) >= 2:
import textwrap
print(
'WARNING: You called padertorch.Trainer.train with multiple\n'
+ textwrap.indent(
'devices. With this the trainer will use data parallel to\n'
'utilize the multiple GPUs to speedup your training.\n'
'We observed some problems with some versions of pytorch.\n'
'In 1.4 the performance on a NN was quite bad and accoring to\n'
'https://github.com/pytorch/pytorch/issues/33552\n'
'this was because the RNNs get no gradients.\n'
'In 1.5 the training got stuck, the reason is unclear in the'
'moment.\n'
'With Pytorch <= 1.3 we have not tested the code.\n'
f'Your pytorch version is: {torch.__version__}', ' ' *
len('WARNING: ')))
assert self.virtual_minibatch_size % len(device) == 0, (
self.virtual_minibatch_size, device)
assert len(device) > 0, (self.virtual_minibatch_size, device)
# ================ MAIN TRAINING LOOP! ===================
try:
train_iterable = None
while True:
new_epoch = False
if train_iterable is None:
new_epoch = True
# Call pre_step between the epochs.
# We call it here, so it is done, before the iteration
# over the train_dataset starts.
for hook in hooks:
hook.pre_step(self)
train_iterable = iter(train_dataset)
optimize = True
with self.train_timer['time_per_iteration'] as timer:
for minibatch_index in range(self.virtual_minibatch_size //
len(device)):
with self.train_timer['time_per_data_loading']:
example = list(
itertools.islice(train_iterable, len(device)))
if len(example) == 0:
train_iterable = None
self.epoch += 1
if minibatch_index == 0:
optimize = False
break # end minibatch loop
if new_epoch:
new_epoch = False
elif minibatch_index == 0:
# Call pre_step after getting the next example,
# to correctly detect the next epoch
with timer.pause():
for hook in hooks:
hook.pre_step(self)
if len(device) == 1:
assert len(example) == 1, (len(example), example)
example = example[0]
loss, example, model_output, review = \
self.train_step(self.model, example, device[0])
with timer.pause():
for hook in hooks:
hook.post_step(self, example, model_output,
review)
# Release pytorch object to reduce memory footprint
del example
del model_output
del review
with self.train_timer['time_per_backward']:
loss.backward(retain_graph=False)
del loss
else:
# The data parallel idea here follows the idea from
# torch.nn.parallel.DataParallel.
# We also use the same functions
# (i.e. replicate, parallel_apply and gather).
#
# The difference is, that we need no scatter,
# because we simply use multiple examples and
# the gather must only be applied on the loss.
# Move copies of the model to each GPU
with self.train_timer['time_per_replicate']:
replicas = replicate(self.model,
device[:len(example)])
# Use threads to call train_step. Each thread
# processes one example on one GPU.
with self.train_timer['time_per_parallel_apply']:
outputs = parallel_apply(
[self.train_step] * len(example),
list(
zip(
replicas,
example,
device[:len(example)],
)),
)
del replicas
# Take the sum of all losses. Since they are on
# different GPUs, use gather.
with self.train_timer['time_per_gather']:
loss = gather([
loss.view(1) for loss, _, _, _ in outputs
], device[0]).sum()
with timer.pause():
for _, example, model_output, review in outputs:
for hook in hooks:
hook.post_step(self, example,
model_output, review)
# Release pytorch object to reduce memory footprint
del example
del model_output
del review
with self.train_timer['time_per_backward']:
loss.backward(retain_graph=False)
del loss
# Only the summary hook will use optimizer_review
if optimize:
with self.train_timer['time_per_optimize']:
optimizer_summary = self.optimizer_step()
for hook in hooks:
hook.post_optimize(self, optimizer_summary)
del optimizer_summary
self.iteration += 1
except StopTraining:
pass
finally:
try:
for hook in hooks:
hook.close(self)
except Exception:
print('Exception in finally. May hide actual exception!!!\n'
'You may comment this finally block for debugging.')
raise
self.writer.close()
self.writer = None
num_devices * config.batch_size)
# For copying the final loss back to one GPU
target_device = devices[0]
# Copy the network to GPU
net = MinkUNet34C(3, 20, D=3)
net = net.to(target_device)
# Synchronized batch norm
net = ME.MinkowskiSyncBatchNorm.convert_sync_batchnorm(net)
optimizer = SGD(net.parameters(), lr=1e-1)
# Copy the loss layer
criterion = nn.CrossEntropyLoss()
criterions = parallel.replicate(criterion, devices)
min_time = np.inf
for iteration in range(10):
optimizer.zero_grad()
# Get new data
inputs, all_labels = [], []
for i in range(num_devices):
coordinates, features, labels = generate_input(config.file_name,
voxel_size=0.05)
with torch.cuda.device(devices[i]):
inputs.append(
ME.SparseTensor(features - 0.5,
coords=coordinates).to(devices[i]))
all_labels.append(labels.long().to(devices[i]))
def test_shared_module(self):
s = self.Msm()
p1 = self.Mpy1(s)
module = self.Mpy2(p1, s).cuda()
replicas = dp.replicate(module, {0, 1})
self.check_replicas(module, replicas)
def test_python_submodule_script(self):
module = self.Mpy1(self.Msm()).cuda()
replicas = dp.replicate(module, {0, 1})
self.check_replicas(module, replicas)
def test_traced_module(self):
module = torch.jit.trace(self.Mpy1(self.Mpy()), torch.ones(2,
2)).cuda()
replicas = dp.replicate(module, {0, 1})
self.check_replicas(module, replicas)
def parallel_tensor_dict(
tensor_dicts: List[Mapping],
model: Model,
device_ids: List,
loss_key='loss',
atom_types=(str, )) -> Dict[str, torch.Tensor]:
"""
Performs a forward pass using multiple GPUs. This is a simplification
of torch.nn.parallel.data_parallel to support the allennlp model
interface.
"""
if len(tensor_dicts) > len(device_ids):
raise ValueError(
"the number of tensor dicts must be the same as the number of device ids"
)
# region 1 - copy data and model to multiple GPUS
# NOTE, there can be fewer tensor dicts,
# and in this case the number of used device ids might be less than the number of provided device ids
moved = [
move_tensor_dict_to_device(tensor_dict, device_id)
for tensor_dict, device_id in zip(tensor_dicts, device_ids)
]
used_device_ids = device_ids[:len(moved)]
# must replicate the model to the GPUs every time, because its parameters have been updated
replicas = nnP.replicate(model, used_device_ids)
# endregion
# region 2 - get the outputs
# the outputs must be a dictionary of results returned by each GPU
outputs = nnP.parallel_apply(
replicas,
[()] * len(tensor_dicts), # no positional argument
moved, # the tensor dict as named arguments
used_device_ids)
# endregion
# region 3 - gather the results on the first GPU
result = {}
for k, v in outputs[0].items():
if k == loss_key: # special treatment for the loss key
result[k] = nnP.gather(
[output[k].unsqueeze(0) for output in outputs],
target_device=used_device_ids[0],
dim=0).mean()
else:
if isinstance(v, torch.Tensor):
result[k] = [
nnP.gather([output[k]],
target_device=used_device_ids[0],
dim=0) for output in outputs
]
elif gx.iterable__(v, atom_types=atom_types):
result[k] = tuple(chain([output[k] for output in outputs]))
else:
result[k] = tuple(output[k] for output in outputs)
# endregion
return result
def _ddp_init_helper(self):
"""
Initialization helper function that does the following:
(1) replicating the module from device[0] to the other devices
(2) bucketing the parameters for reductions
(3) resetting the bucketing states
(4) registering the grad hooks
(5) passing a handle of DDP to SyncBatchNorm Layer
"""
if len(self.device_ids) > 1:
# TODO: we don't need to replicate params in here. they're always going to
# be broadcasted using larger blocks in broadcast_coalesced, so it might be
# better to not pollute the caches with these small blocks
self._module_copies = replicate(self.module,
self.device_ids,
detach=True)
self._module_copies[0] = self.module
for module_copy in self._module_copies[1:]:
for param, copy_param in zip(self.module.parameters(),
module_copy.parameters()):
copy_param.requires_grad = param.requires_grad
else:
self._module_copies = [self.module]
self.modules_params = [
list(m.parameters()) for m in self._module_copies
]
self.modules_buffers = [list(m.buffers()) for m in self._module_copies]
# This is a triply-nested list where the "dimensions" are: devices, buckets, bucket_elems
param_buckets = []
# Split the parameters into buckets and by types as well
# We only need to bucket and reduce parameters that require grad and
# this is also true for backward since only the backward hooks for
# parameters that require grad will be registered with gradient
# reduction functions
params_to_bucket = [[] for _ in self._module_copies]
for dev_idx, m in enumerate(self._module_copies):
for p in m.parameters():
if p.requires_grad:
params_to_bucket[dev_idx].append(p)
param_buckets = [
dist._dist_bucket_tensors(dev_params_to_bucket,
int(self.bucket_bytes_cap),
fine_grained=False)
for dev_params_to_bucket in params_to_bucket
]
self.bucket_sizes = []
self.bucket_map = {}
# We transpose param_buckets, so the loop is over buckets.
# param_buckets_tuple is a doubly-nested list with "dims": devices, bucket_elems
for bucket_idx, param_buckets_tuple in enumerate(zip(*param_buckets)):
self.bucket_sizes.append(0)
# Now, we transpose again, so we iterate over bucket_elems, but getting tuples
# of params from each device.
for param_tuple in zip(*param_buckets_tuple):
if not param_tuple[0].requires_grad:
continue
for p in param_tuple:
self.bucket_map[p] = (bucket_idx,
self.bucket_sizes[bucket_idx])
self.bucket_sizes[bucket_idx] += 1
self.buckets = [[[None for _ in range(self.bucket_sizes[i])]
for _ in range(len(self.device_ids))]
for i in range(len(self.bucket_sizes))]
# The number of params ready in each bucket
self.buckets_ready_size = [[0 for _ in range(len(self.device_ids))]
for i in range(len(self.bucket_sizes))]
# coalesced bucket for only device 0
self.buckets_coalesced = [[] for _ in range(len(self.bucket_sizes))]
# We will always reduce the bucket following the reverse order
# that is, alway reduces following the order of: n - 1, n - 2, ..., 0
self.next_bucket = len(self.bucket_sizes) - 1
# When all buckets are reduced, this will be set to True. This flag is
# useful for sanity checks to ensure that each iteration's backward has
# always reduced all buckets
self.all_buckets_reduced = False
self.check_previous_reduction = False
self.ready_buckets_not_reduced = set()
self.reduction_works = [None for _ in range(len(self.bucket_sizes))]
self.devs_ready = [0 for _ in range(len(self.bucket_sizes))]
self._register_grad_hooks()
# passing a handle to torch.nn.SyncBatchNorm layer
self._passing_sync_batchnorm_handle(self._module_copies)
