def __init__(self, filename, benchmark, organization):
self.mllogger = mllog.get_mllogger()
self.comm_rank = comm.get_rank()
self.comm_size = comm.get_size()
self.constants = constants
# create logging dir if it does not exist
logdir = os.path.dirname(filename)
if self.comm_rank == 0:
if not os.path.isdir(logdir):
os.makedirs(logdir)
if torch.distributed.is_available(
) and torch.distributed.is_initialized():
torch.distributed.barrier()
# create config
mllog.config(filename=filename)
self.mllogger.logger.propagate = False
self.log_event(key=constants.SUBMISSION_BENCHMARK, value=benchmark)
self.log_event(key=constants.SUBMISSION_ORG, value=organization)
self.log_event(key=constants.SUBMISSION_DIVISION, value='closed')
self.log_event(key=constants.SUBMISSION_STATUS, value='onprem')
self.log_event(
key=constants.SUBMISSION_PLATFORM,
value=f'{self.comm_size}xSUBMISSION_PLATFORM_PLACEHOLDER')
def main(pargs):
# this should be global
global have_wandb
#init distributed training
comm.init(pargs.wireup_method)
comm_rank = comm.get_rank()
comm_local_rank = comm.get_local_rank()
comm_size = comm.get_size()
# set up logging
pargs.logging_frequency = max([pargs.logging_frequency, 1])
log_file = os.path.normpath(
os.path.join(pargs.output_dir, "logs", pargs.run_tag + ".log"))
logger = mll.mlperf_logger(log_file, "deepcam", "Umbrella Corp.")
logger.log_start(key="init_start", sync=True)
logger.log_event(key="cache_clear")
#set seed
seed = 333
logger.log_event(key="seed", value=seed)
# Some setup
torch.manual_seed(seed)
if torch.cuda.is_available():
device = torch.device("cuda", comm_local_rank)
torch.cuda.manual_seed(seed)
#necessary for AMP to work
torch.cuda.set_device(device)
# TEST: allowed? Valuable?
#torch.backends.cudnn.benchark = True
else:
device = torch.device("cpu")
#visualize?
visualize = (pargs.training_visualization_frequency >
0) or (pargs.validation_visualization_frequency > 0)
#set up directories
root_dir = os.path.join(pargs.data_dir_prefix)
output_dir = pargs.output_dir
plot_dir = os.path.join(output_dir, "plots")
if comm_rank == 0:
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
if visualize and not os.path.isdir(plot_dir):
os.makedirs(plot_dir)
# Setup WandB
if not pargs.enable_wandb:
have_wandb = False
if have_wandb and (comm_rank == 0):
# get wandb api token
certfile = os.path.join(pargs.wandb_certdir, ".wandbirc")
try:
with open(certfile) as f:
token = f.readlines()[0].replace("\n", "").split()
wblogin = token[0]
wbtoken = token[1]
except IOError:
print("Error, cannot open WandB certificate {}.".format(certfile))
have_wandb = False
if have_wandb:
# log in: that call can be blocking, it should be quick
sp.call(["wandb", "login", wbtoken])
#init db and get config
resume_flag = pargs.run_tag if pargs.resume_logging else False
wandb.init(entity=wblogin,
project='deepcam',
name=pargs.run_tag,
id=pargs.run_tag,
resume=resume_flag)
config = wandb.config
#set general parameters
config.root_dir = root_dir
config.output_dir = pargs.output_dir
config.max_epochs = pargs.max_epochs
config.local_batch_size = pargs.local_batch_size
config.num_workers = comm_size
config.channels = pargs.channels
config.optimizer = pargs.optimizer
config.start_lr = pargs.start_lr
config.adam_eps = pargs.adam_eps
config.weight_decay = pargs.weight_decay
config.model_prefix = pargs.model_prefix
config.amp_opt_level = pargs.amp_opt_level
config.loss_weight_pow = pargs.loss_weight_pow
config.lr_warmup_steps = pargs.lr_warmup_steps
config.lr_warmup_factor = pargs.lr_warmup_factor
# lr schedule if applicable
if pargs.lr_schedule:
for key in pargs.lr_schedule:
config.update(
{"lr_schedule_" + key: pargs.lr_schedule[key]},
allow_val_change=True)
# Logging hyperparameters
logger.log_event(key="global_batch_size",
value=(pargs.local_batch_size * comm_size))
logger.log_event(key="opt_name", value=pargs.optimizer)
logger.log_event(key="opt_base_learning_rate",
value=pargs.start_lr * pargs.lr_warmup_factor)
logger.log_event(key="opt_learning_rate_warmup_steps",
value=pargs.lr_warmup_steps)
logger.log_event(key="opt_learning_rate_warmup_factor",
value=pargs.lr_warmup_factor)
logger.log_event(key="opt_epsilon", value=pargs.adam_eps)
# Define architecture
n_input_channels = len(pargs.channels)
n_output_channels = 3
net = deeplab_xception.DeepLabv3_plus(n_input=n_input_channels,
n_classes=n_output_channels,
os=16,
pretrained=False,
rank=comm_rank)
net.to(device)
#select loss
loss_pow = pargs.loss_weight_pow
#some magic numbers
class_weights = [
0.986267818390377**loss_pow, 0.0004578708870701058**loss_pow,
0.01327431072255291**loss_pow
]
fpw_1 = 2.61461122397522257612
fpw_2 = 1.71641974795896018744
criterion = losses.fp_loss
#select optimizer
optimizer = None
if pargs.optimizer == "Adam":
optimizer = optim.Adam(net.parameters(),
lr=pargs.start_lr,
eps=pargs.adam_eps,
weight_decay=pargs.weight_decay)
elif pargs.optimizer == "AdamW":
optimizer = optim.AdamW(net.parameters(),
lr=pargs.start_lr,
eps=pargs.adam_eps,
weight_decay=pargs.weight_decay)
elif have_apex and (pargs.optimizer == "LAMB"):
optimizer = aoptim.FusedLAMB(net.parameters(),
lr=pargs.start_lr,
eps=pargs.adam_eps,
weight_decay=pargs.weight_decay)
else:
raise NotImplementedError("Error, optimizer {} not supported".format(
pargs.optimizer))
if have_apex:
#wrap model and opt into amp
net, optimizer = amp.initialize(net,
optimizer,
opt_level=pargs.amp_opt_level)
#make model distributed
net = DDP(net)
#restart from checkpoint if desired
#if (comm_rank == 0) and (pargs.checkpoint):
#load it on all ranks for now
if pargs.checkpoint:
checkpoint = torch.load(pargs.checkpoint, map_location=device)
start_step = checkpoint['step']
start_epoch = checkpoint['epoch']
optimizer.load_state_dict(checkpoint['optimizer'])
net.load_state_dict(checkpoint['model'])
if have_apex:
amp.load_state_dict(checkpoint['amp'])
else:
start_step = 0
start_epoch = 0
#select scheduler
if pargs.lr_schedule:
scheduler_after = ph.get_lr_schedule(pargs.start_lr,
pargs.lr_schedule,
optimizer,
last_step=start_step)
# LR warmup
if pargs.lr_warmup_steps > 0:
if have_warmup_scheduler:
scheduler = GradualWarmupScheduler(
optimizer,
multiplier=pargs.lr_warmup_factor,
total_epoch=pargs.lr_warmup_steps,
after_scheduler=scheduler_after)
# Throw an error if the package is not found
else:
raise Exception(
f'Requested {pargs.lr_warmup_steps} LR warmup steps '
'but warmup scheduler not found. Install it from '
'https://github.com/ildoonet/pytorch-gradual-warmup-lr')
else:
scheduler = scheduler_after
#broadcast model and optimizer state
steptens = torch.tensor(np.array([start_step, start_epoch]),
requires_grad=False).to(device)
dist.broadcast(steptens, src=0)
##broadcast model and optimizer state
#hvd.broadcast_parameters(net.state_dict(), root_rank = 0)
#hvd.broadcast_optimizer_state(optimizer, root_rank = 0)
#unpack the bcasted tensor
start_step = steptens.cpu().numpy()[0]
start_epoch = steptens.cpu().numpy()[1]
# Set up the data feeder
# train
train_dir = os.path.join(root_dir, "train")
train_set = cam.CamDataset(train_dir,
statsfile=os.path.join(root_dir, 'stats.h5'),
channels=pargs.channels,
allow_uneven_distribution=False,
shuffle=True,
preprocess=True,
comm_size=comm_size,
comm_rank=comm_rank)
train_loader = DataLoader(
train_set,
pargs.local_batch_size,
num_workers=min([pargs.max_inter_threads, pargs.local_batch_size]),
pin_memory=True,
drop_last=True)
# validation: we only want to shuffle the set if we are cutting off validation after a certain number of steps
validation_dir = os.path.join(root_dir, "validation")
validation_set = cam.CamDataset(validation_dir,
statsfile=os.path.join(
root_dir, 'stats.h5'),
channels=pargs.channels,
allow_uneven_distribution=True,
shuffle=(pargs.max_validation_steps
is not None),
preprocess=True,
comm_size=comm_size,
comm_rank=comm_rank)
# use batch size = 1 here to make sure that we do not drop a sample
validation_loader = DataLoader(
validation_set,
1,
num_workers=min([pargs.max_inter_threads, pargs.local_batch_size]),
pin_memory=True,
drop_last=True)
# log size of datasets
logger.log_event(key="train_samples", value=train_set.global_size)
if pargs.max_validation_steps is not None:
val_size = min([
validation_set.global_size,
pargs.max_validation_steps * pargs.local_batch_size * comm_size
])
else:
val_size = validation_set.global_size
logger.log_event(key="eval_samples", value=val_size)
# do sanity check
if pargs.max_validation_steps is not None:
logger.log_event(key="invalid_submission")
#for visualization
#if visualize:
# viz = vizc.CamVisualizer()
# Train network
if have_wandb and (comm_rank == 0):
wandb.watch(net)
step = start_step
epoch = start_epoch
current_lr = pargs.start_lr if not pargs.lr_schedule else scheduler.get_last_lr(
)[0]
stop_training = False
net.train()
# start trining
logger.log_end(key="init_stop", sync=True)
logger.log_start(key="run_start", sync=True)
# training loop
while True:
# start epoch
logger.log_start(key="epoch_start",
metadata={
'epoch_num': epoch + 1,
'step_num': step
},
sync=True)
# epoch loop
for inputs, label, filename in train_loader:
# send to device
inputs = inputs.to(device)
label = label.to(device)
# forward pass
outputs = net.forward(inputs)
# Compute loss and average across nodes
loss = criterion(outputs,
label,
weight=class_weights,
fpw_1=fpw_1,
fpw_2=fpw_2)
# Backprop
optimizer.zero_grad()
if have_apex:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
# step counter
step += 1
if pargs.lr_schedule:
current_lr = scheduler.get_last_lr()[0]
scheduler.step()
#visualize if requested
#if (step % pargs.training_visualization_frequency == 0) and (comm_rank == 0):
# # Compute predictions
# predictions = torch.max(outputs, 1)[1]
#
# # extract sample id and data tensors
# sample_idx = np.random.randint(low=0, high=label.shape[0])
# plot_input = inputs.detach()[sample_idx, 0,...].cpu().numpy()
# plot_prediction = predictions.detach()[sample_idx,...].cpu().numpy()
# plot_label = label.detach()[sample_idx,...].cpu().numpy()
#
# # create filenames
# outputfile = os.path.basename(filename[sample_idx]).replace("data-", "training-").replace(".h5", ".png")
# outputfile = os.path.join(plot_dir, outputfile)
#
# # plot
# viz.plot(filename[sample_idx], outputfile, plot_input, plot_prediction, plot_label)
#
# #log if requested
# if have_wandb:
# img = Image.open(outputfile)
# wandb.log({"train_examples": [wandb.Image(img, caption="Prediction vs. Ground Truth")]}, step = step)
#log if requested
if (step % pargs.logging_frequency == 0):
# allreduce for loss
loss_avg = loss.detach()
dist.reduce(loss_avg, dst=0, op=dist.ReduceOp.SUM)
loss_avg_train = loss_avg.item() / float(comm_size)
# Compute score
predictions = torch.max(outputs, 1)[1]
iou = utils.compute_score(predictions,
label,
device_id=device,
num_classes=3)
iou_avg = iou.detach()
dist.reduce(iou_avg, dst=0, op=dist.ReduceOp.SUM)
iou_avg_train = iou_avg.item() / float(comm_size)
logger.log_event(key="learning_rate",
value=current_lr,
metadata={
'epoch_num': epoch + 1,
'step_num': step
})
logger.log_event(key="train_accuracy",
value=iou_avg_train,
metadata={
'epoch_num': epoch + 1,
'step_num': step
})
logger.log_event(key="train_loss",
value=loss_avg_train,
metadata={
'epoch_num': epoch + 1,
'step_num': step
})
if have_wandb and (comm_rank == 0):
wandb.log(
{"train_loss": loss_avg.item() / float(comm_size)},
step=step)
wandb.log(
{"train_accuracy": iou_avg.item() / float(comm_size)},
step=step)
wandb.log({"learning_rate": current_lr}, step=step)
wandb.log({"epoch": epoch + 1}, step=step)
# validation step if desired
if (step % pargs.validation_frequency == 0):
logger.log_start(key="eval_start",
metadata={'epoch_num': epoch + 1})
#eval
net.eval()
count_sum_val = torch.Tensor([0.]).to(device)
loss_sum_val = torch.Tensor([0.]).to(device)
iou_sum_val = torch.Tensor([0.]).to(device)
# disable gradients
with torch.no_grad():
# iterate over validation sample
step_val = 0
# only print once per eval at most
visualized = False
for inputs_val, label_val, filename_val in validation_loader:
#send to device
inputs_val = inputs_val.to(device)
label_val = label_val.to(device)
# forward pass
outputs_val = net.forward(inputs_val)
# Compute loss and average across nodes
loss_val = criterion(outputs_val,
label_val,
weight=class_weights,
fpw_1=fpw_1,
fpw_2=fpw_2)
loss_sum_val += loss_val
#increase counter
count_sum_val += 1.
# Compute score
predictions_val = torch.max(outputs_val, 1)[1]
iou_val = utils.compute_score(predictions_val,
label_val,
device_id=device,
num_classes=3)
iou_sum_val += iou_val
# Visualize
#if (step_val % pargs.validation_visualization_frequency == 0) and (not visualized) and (comm_rank == 0):
# #extract sample id and data tensors
# sample_idx = np.random.randint(low=0, high=label_val.shape[0])
# plot_input = inputs_val.detach()[sample_idx, 0,...].cpu().numpy()
# plot_prediction = predictions_val.detach()[sample_idx,...].cpu().numpy()
# plot_label = label_val.detach()[sample_idx,...].cpu().numpy()
#
# #create filenames
# outputfile = os.path.basename(filename[sample_idx]).replace("data-", "validation-").replace(".h5", ".png")
# outputfile = os.path.join(plot_dir, outputfile)
#
# #plot
# viz.plot(filename[sample_idx], outputfile, plot_input, plot_prediction, plot_label)
# visualized = True
#
# #log if requested
# if have_wandb:
# img = Image.open(outputfile)
# wandb.log({"eval_examples": [wandb.Image(img, caption="Prediction vs. Ground Truth")]}, step = step)
#increase eval step counter
step_val += 1
if (pargs.max_validation_steps is not None
) and step_val > pargs.max_validation_steps:
break
# average the validation loss
dist.all_reduce(count_sum_val, op=dist.ReduceOp.SUM)
dist.all_reduce(loss_sum_val, op=dist.ReduceOp.SUM)
dist.all_reduce(iou_sum_val, op=dist.ReduceOp.SUM)
loss_avg_val = loss_sum_val.item() / count_sum_val.item()
iou_avg_val = iou_sum_val.item() / count_sum_val.item()
# print results
logger.log_event(key="eval_accuracy",
value=iou_avg_val,
metadata={
'epoch_num': epoch + 1,
'step_num': step
})
logger.log_event(key="eval_loss",
value=loss_avg_val,
metadata={
'epoch_num': epoch + 1,
'step_num': step
})
# log in wandb
if have_wandb and (comm_rank == 0):
wandb.log({"eval_loss": loss_avg_val}, step=step)
wandb.log({"eval_accuracy": iou_avg_val}, step=step)
if (iou_avg_val >= pargs.target_iou):
logger.log_event(key="target_accuracy_reached",
value=pargs.target_iou,
metadata={
'epoch_num': epoch + 1,
'step_num': step
})
stop_training = True
# set to train
net.train()
logger.log_end(key="eval_stop",
metadata={'epoch_num': epoch + 1})
#save model if desired
if (pargs.save_frequency > 0) and (step % pargs.save_frequency
== 0):
logger.log_start(key="save_start",
metadata={
'epoch_num': epoch + 1,
'step_num': step
},
sync=True)
if comm_rank == 0:
checkpoint = {
'step': step,
'epoch': epoch,
'model': net.state_dict(),
'optimizer': optimizer.state_dict()
}
if have_apex:
checkpoint['amp'] = amp.state_dict()
torch.save(
checkpoint,
os.path.join(
output_dir, pargs.model_prefix + "_step_" +
str(step) + ".cpt"))
logger.log_end(key="save_stop",
metadata={
'epoch_num': epoch + 1,
'step_num': step
},
sync=True)
# Stop training?
if stop_training:
break
# log the epoch
logger.log_end(key="epoch_stop",
metadata={
'epoch_num': epoch + 1,
'step_num': step
},
sync=True)
epoch += 1
# are we done?
if epoch >= pargs.max_epochs or stop_training:
break
# run done
logger.log_end(key="run_stop", sync=True, metadata={'status': 'success'})
def main(pargs):
#init distributed training
comm_local_group = comm.init(pargs.wireup_method,
pargs.batchnorm_group_size)
comm_rank = comm.get_rank()
comm_local_rank = comm.get_local_rank()
comm_size = comm.get_size()
comm_local_size = comm.get_local_size()
# set up logging
pargs.logging_frequency = max([pargs.logging_frequency, 1])
log_file = os.path.normpath(
os.path.join(pargs.output_dir, "logs", pargs.run_tag + ".log"))
logger = mll.mlperf_logger(log_file, "deepcam", "Umbrella Corp.")
logger.log_start(key="init_start", sync=True)
logger.log_event(key="cache_clear")
#set seed
seed = pargs.seed
logger.log_event(key="seed", value=seed)
# Some setup
torch.manual_seed(seed)
if torch.cuda.is_available():
device = torch.device("cuda", comm_local_rank)
torch.cuda.manual_seed(seed)
torch.cuda.set_device(device)
torch.backends.cudnn.benchmark = True
else:
device = torch.device("cpu")
#set up directories
root_dir = os.path.join(pargs.data_dir_prefix)
output_dir = pargs.output_dir
plot_dir = os.path.join(output_dir, "plots")
if comm_rank == 0:
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
# logging of rank information
logger.log_event(key="number_of_ranks", value=comm_size)
logger.log_event(key="number_of_nodes",
value=(comm_size // comm_local_size))
logger.log_event(key="accelerators_per_node", value=comm_local_size)
# Logging hyperparameters
logger.log_event(key="global_batch_size",
value=(pargs.local_batch_size * comm_size))
logger.log_event(key="batchnorm_group_size",
value=pargs.batchnorm_group_size)
logger.log_event(key="gradient_accumulation_frequency",
value=pargs.gradient_accumulation_frequency)
logger.log_event(key="checkpoint", value=pargs.checkpoint)
# Define architecture
n_input_channels = len(pargs.channels)
n_output_channels = 3
net = deeplab_xception.DeepLabv3_plus(n_input=n_input_channels,
n_classes=n_output_channels,
os=16,
pretrained=False,
rank=comm_rank,
process_group=comm_local_group)
net.to(device)
#select loss
#some magic numbers
loss_pow = -0.125
class_weights = [
0.986267818390377**loss_pow, 0.0004578708870701058**loss_pow,
0.01327431072255291**loss_pow
]
# extract loss
criterion = losses.CELoss(class_weights).to(device)
criterion = torch.jit.script(criterion)
#select optimizer
optimizer = oh.get_optimizer(pargs, net, logger)
#restart from checkpoint if desired
if pargs.checkpoint is not None:
checkpoint = torch.load(pargs.checkpoint, map_location=device)
start_step = checkpoint['step']
start_epoch = checkpoint['epoch']
optimizer.load_state_dict(checkpoint['optimizer'])
net.load_state_dict(checkpoint['model'])
else:
start_step = 0
start_epoch = 0
#broadcast model and optimizer state
steptens = torch.tensor(np.array([start_step, start_epoch]),
requires_grad=False).to(device)
if dist.is_initialized():
dist.broadcast(steptens, src=0)
#unpack the bcasted tensor
start_step = int(steptens.cpu().numpy()[0])
start_epoch = int(steptens.cpu().numpy()[1])
#select scheduler
scheduler = None
if pargs.lr_schedule:
pargs.lr_schedule["lr_warmup_steps"] = pargs.lr_warmup_steps
pargs.lr_schedule["lr_warmup_factor"] = pargs.lr_warmup_factor
scheduler = oh.get_lr_schedule(pargs.start_lr,
pargs.lr_schedule,
optimizer,
logger,
last_step=start_step)
# print parameters
if comm_rank == 0:
print(net)
print("Total number of elements:",
sum(p.numel() for p in net.parameters() if p.requires_grad))
# get input shapes for the upcoming model preprocessing
# input_shape:
tshape, _ = get_datashapes(pargs, root_dir)
input_shape = tuple([tshape[2], tshape[0], tshape[1]])
#distributed model parameters
bucket_cap_mb = 25
if pargs.batchnorm_group_size > 1:
bucket_cap_mb = 220
# get stream, relevant for graph capture
ddp_net = DDP(net,
device_ids=[device.index],
output_device=device.index,
find_unused_parameters=False,
broadcast_buffers=False,
bucket_cap_mb=bucket_cap_mb,
gradient_as_bucket_view=False)
# get stats handler here
bnstats_handler = bns.BatchNormStatsSynchronize(ddp_net,
reduction="mean",
inplace=True)
# create handles
net_validate = ddp_net
net_train = ddp_net
# Set up the data feeder
train_loader, train_size, validation_loader, validation_size = get_dataloaders(
pargs, root_dir, device, seed, comm_size, comm_rank)
# log size of datasets
logger.log_event(key="train_samples", value=train_size)
val_size = validation_size
logger.log_event(key="eval_samples", value=val_size)
# get start steps
step = start_step
epoch = start_epoch
current_lr = pargs.start_lr if not pargs.lr_schedule else scheduler.get_last_lr(
)[0]
stop_training = False
net_train.train()
# start trining
logger.log_end(key="init_stop", sync=True)
logger.log_start(key="run_start", sync=True)
# training loop
while True:
# start epoch
logger.log_start(key="epoch_start",
metadata={
'epoch_num': epoch + 1,
'step_num': step
},
sync=True)
train_loader.sampler.set_epoch(epoch)
# training
step = train_step(pargs, comm_rank, comm_size, device, step, epoch,
net_train, criterion, optimizer, scheduler,
train_loader, logger)
# average BN stats
bnstats_handler.synchronize()
# validation
stop_training = validate(pargs, comm_rank, comm_size, device, step,
epoch, net_validate, criterion,
validation_loader, logger)
# log the epoch
logger.log_end(key="epoch_stop",
metadata={
'epoch_num': epoch + 1,
'step_num': step
},
sync=True)
epoch += 1
#save model if desired
if (pargs.save_frequency > 0) and (epoch % pargs.save_frequency == 0):
logger.log_start(key="save_start",
metadata={
'epoch_num': epoch + 1,
'step_num': step
},
sync=True)
if comm_rank == 0:
checkpoint = {
'step': step,
'epoch': epoch,
'model': net_train.state_dict(),
'optimizer': optimizer.state_dict()
}
torch.save(
checkpoint,
os.path.join(
output_dir,
pargs.model_prefix + "_step_" + str(step) + ".cpt"))
logger.log_end(key="save_stop",
metadata={
'epoch_num': epoch + 1,
'step_num': step
},
sync=True)
# are we done?
if (epoch >= pargs.max_epochs) or stop_training:
break
# run done
logger.log_end(key="run_stop", sync=True, metadata={'status': 'success'})
def main(pargs):
#init distributed training
comm.init(pargs.wireup_method)
comm_rank = comm.get_rank()
comm_local_rank = comm.get_local_rank()
comm_size = comm.get_size()
#set seed
seed = 333
# Some setup
torch.manual_seed(seed)
if torch.cuda.is_available():
printr("Using GPUs", 0)
device = torch.device("cuda", comm_local_rank)
torch.cuda.manual_seed(seed)
#necessary for AMP to work
torch.cuda.set_device(device)
else:
printr("Using CPUs", 0)
device = torch.device("cpu")
#set up directories
root_dir = os.path.join(pargs.data_dir_prefix)
output_dir = pargs.output_dir
plot_dir = os.path.join(output_dir, "plots")
if comm_rank == 0:
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
# Define architecture
n_input_channels = len(pargs.channels)
n_output_channels = 3
net = deeplab_xception.DeepLabv3_plus(n_input=n_input_channels,
n_classes=n_output_channels,
os=16,
pretrained=False,
rank=comm_rank)
net.to(device)
#select loss
loss_pow = pargs.loss_weight_pow
#some magic numbers
class_weights = [
0.986267818390377**loss_pow, 0.0004578708870701058**loss_pow,
0.01327431072255291**loss_pow
]
fpw_1 = 2.61461122397522257612
fpw_2 = 1.71641974795896018744
criterion = losses.fp_loss
#select optimizer
optimizer = None
if pargs.optimizer == "Adam":
optimizer = optim.Adam(net.parameters(),
lr=pargs.start_lr,
eps=pargs.adam_eps,
weight_decay=pargs.weight_decay)
elif pargs.optimizer == "AdamW":
optimizer = optim.AdamW(net.parameters(),
lr=pargs.start_lr,
eps=pargs.adam_eps,
weight_decay=pargs.weight_decay)
elif have_apex and (pargs.optimizer == "LAMB"):
optimizer = aoptim.FusedLAMB(net.parameters(),
lr=pargs.start_lr,
eps=pargs.adam_eps,
weight_decay=pargs.weight_decay)
else:
raise NotImplementedError("Error, optimizer {} not supported".format(
pargs.optimizer))
if have_apex:
#wrap model and opt into amp
net, optimizer = amp.initialize(net,
optimizer,
opt_level=pargs.amp_opt_level)
#make model distributed
net = DDP(net)
#select scheduler
if pargs.lr_schedule:
scheduler = ph.get_lr_schedule(pargs.start_lr,
pargs.lr_schedule,
optimizer,
last_step=0)
# Set up the data feeder
# train
train_dir = os.path.join(root_dir, "train")
train_set = cam.CamDataset(train_dir,
statsfile=os.path.join(root_dir, 'stats.h5'),
channels=pargs.channels,
shuffle=True,
preprocess=True,
comm_size=comm_size,
comm_rank=comm_rank)
train_loader = DataLoader(
train_set,
pargs.local_batch_size,
num_workers=min([pargs.max_inter_threads, pargs.local_batch_size]),
drop_last=True)
printr(
'{:14.4f} REPORT: starting warmup'.format(
dt.datetime.now().timestamp()), 0)
step = 0
current_lr = pargs.start_lr if not pargs.lr_schedule else scheduler.get_last_lr(
)[0]
current_lr = pargs.start_lr
net.train()
while True:
#for inputs_raw, labels, source in train_loader:
for inputs, label, filename in train_loader:
# Print status
if step == pargs.num_warmup_steps:
printr(
'{:14.4f} REPORT: starting profiling'.format(
dt.datetime.now().timestamp()), 0)
# Forward pass
with Profile(pargs, "Forward", step):
#send data to device
inputs = inputs.to(device)
label = label.to(device)
# Compute output
outputs = net.forward(inputs)
# Compute loss
loss = criterion(outputs,
label,
weight=class_weights,
fpw_1=fpw_1,
fpw_2=fpw_2)
# allreduce for loss
loss_avg = loss.detach()
dist.reduce(loss_avg, dst=0, op=dist.ReduceOp.SUM)
# Compute score
predictions = torch.max(outputs, 1)[1]
iou = utils.compute_score(predictions,
label,
device_id=device,
num_classes=3)
iou_avg = iou.detach()
dist.reduce(iou_avg, dst=0, op=dist.ReduceOp.SUM)
# Backprop
with Profile(pargs, "Backward", step):
# reset grads
optimizer.zero_grad()
# compute grads
if have_apex:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# weight update
with Profile(pargs, "Optimizer", step):
# update weights
optimizer.step()
# advance the scheduler
if pargs.lr_schedule:
current_lr = scheduler.get_last_lr()[0]
scheduler.step()
#step counter
step += 1
#are we done?
if step >= (pargs.num_warmup_steps + pargs.num_profile_steps):
break
#need to check here too
if step >= (pargs.num_warmup_steps + pargs.num_profile_steps):
break
printr(
'{:14.4f} REPORT: finishing profiling'.format(
dt.datetime.now().timestamp()), 0)
def main(pargs):
#init distributed training
comm.init(pargs.wireup_method)
comm_rank = comm.get_rank()
comm_local_rank = comm.get_local_rank()
comm_size = comm.get_size()
#set seed
seed = 333
# Some setup
torch.manual_seed(seed)
if torch.cuda.is_available():
printr("Using GPUs", 0)
device = torch.device("cuda", comm_local_rank)
torch.cuda.manual_seed(seed)
#necessary for AMP to work
torch.cuda.set_device(device)
else:
printr("Using CPUs", 0)
device = torch.device("cpu")
#visualize?
visualize = (pargs.training_visualization_frequency >
0) or (pargs.validation_visualization_frequency > 0)
#set up directories
root_dir = os.path.join(pargs.data_dir_prefix)
output_dir = pargs.output_dir
plot_dir = os.path.join(output_dir, "plots")
if comm_rank == 0:
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
if visualize and not os.path.isdir(plot_dir):
os.makedirs(plot_dir)
# Setup WandB
if (pargs.logging_frequency > 0) and (comm_rank == 0):
# get wandb api token
with open(os.path.join(pargs.wandb_certdir, ".wandbirc")) as f:
token = f.readlines()[0].replace("\n", "").split()
wblogin = token[0]
wbtoken = token[1]
# log in: that call can be blocking, it should be quick
sp.call(["wandb", "login", wbtoken])
#init db and get config
resume_flag = pargs.run_tag if pargs.resume_logging else False
wandb.init(entity=wblogin,
project='deepcam',
name=pargs.run_tag,
id=pargs.run_tag,
resume=resume_flag)
config = wandb.config
#set general parameters
config.root_dir = root_dir
config.output_dir = pargs.output_dir
config.max_epochs = pargs.max_epochs
config.local_batch_size = pargs.local_batch_size
config.num_workers = comm_size
config.channels = pargs.channels
config.optimizer = pargs.optimizer
config.start_lr = pargs.start_lr
config.adam_eps = pargs.adam_eps
config.weight_decay = pargs.weight_decay
config.model_prefix = pargs.model_prefix
config.amp_opt_level = pargs.amp_opt_level
config.loss_weight_pow = pargs.loss_weight_pow
config.lr_warmup_steps = pargs.lr_warmup_steps
config.lr_warmup_factor = pargs.lr_warmup_factor
# lr schedule if applicable
if pargs.lr_schedule:
for key in pargs.lr_schedule:
config.update({"lr_schedule_" + key: pargs.lr_schedule[key]},
allow_val_change=True)
# Define architecture
n_input_channels = len(pargs.channels)
n_output_channels = 3
net = deeplab_xception.DeepLabv3_plus(n_input=n_input_channels,
n_classes=n_output_channels,
os=16,
pretrained=False,
rank=comm_rank)
net.to(device)
#select loss
loss_pow = pargs.loss_weight_pow
#some magic numbers
class_weights = [
0.986267818390377**loss_pow, 0.0004578708870701058**loss_pow,
0.01327431072255291**loss_pow
]
fpw_1 = 2.61461122397522257612
fpw_2 = 1.71641974795896018744
criterion = losses.fp_loss
#select optimizer
optimizer = None
if pargs.optimizer == "Adam":
optimizer = optim.Adam(net.parameters(),
lr=pargs.start_lr,
eps=pargs.adam_eps,
weight_decay=pargs.weight_decay)
elif pargs.optimizer == "AdamW":
optimizer = optim.AdamW(net.parameters(),
lr=pargs.start_lr,
eps=pargs.adam_eps,
weight_decay=pargs.weight_decay)
elif have_apex and (pargs.optimizer == "LAMB"):
optimizer = aoptim.FusedLAMB(net.parameters(),
lr=pargs.start_lr,
eps=pargs.adam_eps,
weight_decay=pargs.weight_decay)
else:
raise NotImplementedError("Error, optimizer {} not supported".format(
pargs.optimizer))
if have_apex:
#wrap model and opt into amp
net, optimizer = amp.initialize(net,
optimizer,
opt_level=pargs.amp_opt_level)
#make model distributed
net = DDP(net)
#restart from checkpoint if desired
#if (comm_rank == 0) and (pargs.checkpoint):
#load it on all ranks for now
if pargs.checkpoint:
checkpoint = torch.load(pargs.checkpoint, map_location=device)
start_step = checkpoint['step']
start_epoch = checkpoint['epoch']
optimizer.load_state_dict(checkpoint['optimizer'])
net.load_state_dict(checkpoint['model'])
if have_apex:
amp.load_state_dict(checkpoint['amp'])
else:
start_step = 0
start_epoch = 0
#select scheduler
if pargs.lr_schedule:
scheduler_after = ph.get_lr_schedule(pargs.start_lr,
pargs.lr_schedule,
optimizer,
last_step=start_step)
if pargs.lr_warmup_steps > 0:
scheduler = GradualWarmupScheduler(
optimizer,
multiplier=pargs.lr_warmup_factor,
total_epoch=pargs.lr_warmup_steps,
after_scheduler=scheduler_after)
else:
scheduler = scheduler_after
#broadcast model and optimizer state
steptens = torch.tensor(np.array([start_step, start_epoch]),
requires_grad=False).to(device)
dist.broadcast(steptens, src=0)
##broadcast model and optimizer state
#hvd.broadcast_parameters(net.state_dict(), root_rank = 0)
#hvd.broadcast_optimizer_state(optimizer, root_rank = 0)
#unpack the bcasted tensor
start_step = steptens.cpu().numpy()[0]
start_epoch = steptens.cpu().numpy()[1]
# Set up the data feeder
# train
train_dir = os.path.join(root_dir, "train")
train_set = cam.CamDataset(train_dir,
statsfile=os.path.join(root_dir, 'stats.h5'),
channels=pargs.channels,
shuffle=True,
preprocess=True,
comm_size=comm_size,
comm_rank=comm_rank)
train_loader = DataLoader(
train_set,
pargs.local_batch_size,
num_workers=min([pargs.max_inter_threads, pargs.local_batch_size]),
drop_last=True)
# validation: we only want to shuffle the set if we are cutting off validation after a certain number of steps
validation_dir = os.path.join(root_dir, "validation")
validation_set = cam.CamDataset(validation_dir,
statsfile=os.path.join(
root_dir, 'stats.h5'),
channels=pargs.channels,
shuffle=(pargs.max_validation_steps
is not None),
preprocess=True,
comm_size=comm_size,
comm_rank=comm_rank)
validation_loader = DataLoader(
validation_set,
pargs.local_batch_size,
num_workers=min([pargs.max_inter_threads, pargs.local_batch_size]),
drop_last=True)
#for visualization
if visualize:
viz = vizc.CamVisualizer()
# Train network
if (pargs.logging_frequency > 0) and (comm_rank == 0):
wandb.watch(net)
printr(
'{:14.4f} REPORT: starting training'.format(
dt.datetime.now().timestamp()), 0)
step = start_step
epoch = start_epoch
current_lr = pargs.start_lr if not pargs.lr_schedule else scheduler.get_last_lr(
)[0]
net.train()
while True:
printr(
'{:14.4f} REPORT: starting epoch {}'.format(
dt.datetime.now().timestamp(), epoch), 0)
#for inputs_raw, labels, source in train_loader:
for inputs, label, filename in train_loader:
#send to device
inputs = inputs.to(device)
label = label.to(device)
# forward pass
outputs = net.forward(inputs)
# Compute loss and average across nodes
loss = criterion(outputs,
label,
weight=class_weights,
fpw_1=fpw_1,
fpw_2=fpw_2)
# allreduce for loss
loss_avg = loss.detach()
dist.reduce(loss_avg, dst=0, op=dist.ReduceOp.SUM)
# Compute score
predictions = torch.max(outputs, 1)[1]
iou = utils.compute_score(predictions,
label,
device_id=device,
num_classes=3)
iou_avg = iou.detach()
dist.reduce(iou_avg, dst=0, op=dist.ReduceOp.SUM)
# Backprop
optimizer.zero_grad()
if have_apex:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
#step counter
step += 1
if pargs.lr_schedule:
current_lr = scheduler.get_last_lr()[0]
scheduler.step()
#print some metrics
printr(
'{:14.4f} REPORT training: step {} loss {} iou {} LR {}'.
format(dt.datetime.now().timestamp(), step,
loss_avg.item() / float(comm_size),
iou_avg.item() / float(comm_size), current_lr), 0)
#visualize if requested
if (step % pargs.training_visualization_frequency
== 0) and (comm_rank == 0):
#extract sample id and data tensors
sample_idx = np.random.randint(low=0, high=label.shape[0])
plot_input = inputs.detach()[sample_idx, 0, ...].cpu().numpy()
plot_prediction = predictions.detach()[sample_idx,
...].cpu().numpy()
plot_label = label.detach()[sample_idx, ...].cpu().numpy()
#create filenames
outputfile = os.path.basename(filename[sample_idx]).replace(
"data-", "training-").replace(".h5", ".png")
outputfile = os.path.join(plot_dir, outputfile)
#plot
viz.plot(filename[sample_idx], outputfile, plot_input,
plot_prediction, plot_label)
#log if requested
if pargs.logging_frequency > 0:
img = Image.open(outputfile)
wandb.log(
{
"Training Examples": [
wandb.Image(
img, caption="Prediction vs. Ground Truth")
]
},
step=step)
#log if requested
if (pargs.logging_frequency > 0) and (
step % pargs.logging_frequency == 0) and (comm_rank == 0):
wandb.log(
{"Training Loss": loss_avg.item() / float(comm_size)},
step=step)
wandb.log({"Training IoU": iou_avg.item() / float(comm_size)},
step=step)
wandb.log({"Current Learning Rate": current_lr}, step=step)
# validation step if desired
if (step % pargs.validation_frequency == 0):
#eval
net.eval()
count_sum_val = torch.Tensor([0.]).to(device)
loss_sum_val = torch.Tensor([0.]).to(device)
iou_sum_val = torch.Tensor([0.]).to(device)
# disable gradients
with torch.no_grad():
# iterate over validation sample
step_val = 0
# only print once per eval at most
visualized = False
for inputs_val, label_val, filename_val in validation_loader:
#send to device
inputs_val = inputs_val.to(device)
label_val = label_val.to(device)
# forward pass
outputs_val = net.forward(inputs_val)
# Compute loss and average across nodes
loss_val = criterion(outputs_val,
label_val,
weight=class_weights)
loss_sum_val += loss_val
#increase counter
count_sum_val += 1.
# Compute score
predictions_val = torch.max(outputs_val, 1)[1]
iou_val = utils.compute_score(predictions_val,
label_val,
device_id=device,
num_classes=3)
iou_sum_val += iou_val
# Visualize
if (step_val % pargs.validation_visualization_frequency
== 0) and (not visualized) and (comm_rank
== 0):
#extract sample id and data tensors
sample_idx = np.random.randint(
low=0, high=label_val.shape[0])
plot_input = inputs_val.detach()[
sample_idx, 0, ...].cpu().numpy()
plot_prediction = predictions_val.detach()[
sample_idx, ...].cpu().numpy()
plot_label = label_val.detach()[sample_idx,
...].cpu().numpy()
#create filenames
outputfile = os.path.basename(
filename[sample_idx]).replace(
"data-",
"validation-").replace(".h5", ".png")
outputfile = os.path.join(plot_dir, outputfile)
#plot
viz.plot(filename[sample_idx], outputfile,
plot_input, plot_prediction, plot_label)
visualized = True
#log if requested
if pargs.logging_frequency > 0:
img = Image.open(outputfile)
wandb.log(
{
"Validation Examples": [
wandb.Image(
img,
caption=
"Prediction vs. Ground Truth")
]
},
step=step)
#increase eval step counter
step_val += 1
if (pargs.max_validation_steps is not None
) and step_val > pargs.max_validation_steps:
break
# average the validation loss
dist.reduce(count_sum_val, dst=0, op=dist.ReduceOp.SUM)
dist.reduce(loss_sum_val, dst=0, op=dist.ReduceOp.SUM)
dist.reduce(iou_sum_val, dst=0, op=dist.ReduceOp.SUM)
loss_avg_val = loss_sum_val.item() / count_sum_val.item()
iou_avg_val = iou_sum_val.item() / count_sum_val.item()
# print results
printr(
'{:14.4f} REPORT validation: step {} loss {} iou {}'.
format(dt.datetime.now().timestamp(), step, loss_avg_val,
iou_avg_val), 0)
# log in wandb
if (pargs.logging_frequency > 0) and (comm_rank == 0):
wandb.log({"Validation Loss": loss_avg_val}, step=step)
wandb.log({"Validation IoU": iou_avg_val}, step=step)
# set to train
net.train()
#save model if desired
if (step % pargs.save_frequency == 0) and (comm_rank == 0):
checkpoint = {
'step': step,
'epoch': epoch,
'model': net.state_dict(),
'optimizer': optimizer.state_dict()
}
if have_apex:
checkpoint['amp'] = amp.state_dict()
torch.save(
checkpoint,
os.path.join(
output_dir,
pargs.model_prefix + "_step_" + str(step) + ".cpt"))
#do some after-epoch prep, just for the books
epoch += 1
if comm_rank == 0:
# Save the model
checkpoint = {
'step': step,
'epoch': epoch,
'model': net.state_dict(),
'optimizer': optimizer.state_dict()
}
if have_apex:
checkpoint['amp'] = amp.state_dict()
torch.save(
checkpoint,
os.path.join(
output_dir,
pargs.model_prefix + "_epoch_" + str(epoch) + ".cpt"))
#are we done?
if epoch >= pargs.max_epochs:
break
printr(
'{:14.4f} REPORT: finishing training'.format(
dt.datetime.now().timestamp()), 0)