def set_up_experiment(args,
input_,
probs,
labels):
# Set up objective function
cross_entropy = lbann.CrossEntropy([probs, labels])
layers = list(lbann.traverse_layer_graph(input_))
weights = set()
for l in layers:
weights.update(l.weights)
# scale = weight decay
l2_reg = lbann.L2WeightRegularization(weights=weights, scale=1e-4)
objective_function = lbann.ObjectiveFunction([cross_entropy, l2_reg])
# Set up model
top1 = lbann.CategoricalAccuracy([probs, labels])
top5 = lbann.TopKCategoricalAccuracy([probs, labels], k=5)
metrics = [lbann.Metric(top1, name='top-1 accuracy', unit='%'),
lbann.Metric(top5, name='top-5 accuracy', unit='%')]
callbacks = [lbann.CallbackPrint(),
lbann.CallbackTimer(),
lbann.CallbackDropFixedLearningRate(
drop_epoch=[30, 60], amt=0.1)]
model = lbann.Model(args.mini_batch_size,
args.num_epochs,
layers=layers,
weights=weights,
objective_function=objective_function,
metrics=metrics,
callbacks=callbacks)
# Load data reader from prototext
data_reader_proto = lbann.lbann_pb2.LbannPB()
with open(args.data_reader, 'r') as f:
txtf.Merge(f.read(), data_reader_proto)
data_reader_proto = data_reader_proto.data_reader
# Set up optimizer
if args.optimizer == 'sgd':
print('Creating sgd optimizer')
optimizer = lbann.optimizer.SGD(
learn_rate=args.optimizer_learning_rate,
momentum=0.9,
nesterov=True
)
else:
optimizer = lbann.contrib.args.create_optimizer(args)
# Save prototext to args.prototext
if args.prototext:
lbann.proto.save_prototext(args.prototext,
model=model,
optimizer=optimizer,
data_reader=data_reader_proto)
return model, data_reader_proto, optimizer
def set_up_experiment(args, input_, probs, labels):
# Set up objective function
cross_entropy = lbann.CrossEntropy([probs, labels])
layers = list(lbann.traverse_layer_graph(input_))
l2_reg_weights = set()
for l in layers:
if type(l) == lbann.Convolution or type(l) == lbann.FullyConnected:
l2_reg_weights.update(l.weights)
# scale = weight decay
l2_reg = lbann.L2WeightRegularization(weights=l2_reg_weights, scale=1e-4)
objective_function = lbann.ObjectiveFunction([cross_entropy, l2_reg])
# Set up model
top1 = lbann.CategoricalAccuracy([probs, labels])
top5 = lbann.TopKCategoricalAccuracy([probs, labels], k=5)
metrics = [
lbann.Metric(top1, name='top-1 accuracy', unit='%'),
lbann.Metric(top5, name='top-5 accuracy', unit='%')
]
callbacks = [
lbann.CallbackPrint(),
lbann.CallbackTimer(),
lbann.CallbackDropFixedLearningRate(drop_epoch=[30, 60], amt=0.1)
]
model = lbann.Model(args.num_epochs,
layers=layers,
objective_function=objective_function,
metrics=metrics,
callbacks=callbacks)
# Set up data reader
data_reader = data.imagenet.make_data_reader(num_classes=args.num_classes)
# Set up optimizer
if args.optimizer == 'sgd':
print('Creating sgd optimizer')
optimizer = lbann.optimizer.SGD(
learn_rate=args.optimizer_learning_rate,
momentum=0.9,
nesterov=True)
else:
optimizer = lbann.contrib.args.create_optimizer(args)
# Setup trainer
trainer = lbann.Trainer(mini_batch_size=args.mini_batch_size)
return trainer, model, data_reader, optimizer
else:
# Some other Wide ResNet.
resnet = resnet_variant_dict[args.resnet](
imagenet_labels,
bn_statistics_group_size=args.bn_statistics_group_size,
width=args.width)
# Construct layer graph
input_ = lbann.Input(target_mode='classification')
images = lbann.Identity(input_)
labels = lbann.Identity(input_)
preds = resnet(images)
probs = lbann.Softmax(preds)
cross_entropy = lbann.CrossEntropy(probs, labels)
top1 = lbann.CategoricalAccuracy(probs, labels)
top5 = lbann.TopKCategoricalAccuracy(probs, labels, k=5)
layers = list(lbann.traverse_layer_graph(input_))
# Setup tensor core operations (just to demonstrate enum usage)
tensor_ops_mode = lbann.ConvTensorOpsMode.NO_TENSOR_OPS
for l in layers:
if type(l) == lbann.Convolution:
l.conv_tensor_op_mode = tensor_ops_mode
# Setup objective function
l2_reg_weights = set()
for l in layers:
if type(l) == lbann.Convolution or type(l) == lbann.FullyConnected:
l2_reg_weights.update(l.weights)
l2_reg = lbann.L2WeightRegularization(weights=l2_reg_weights, scale=1e-4)
obj = lbann.ObjectiveFunction([cross_entropy, l2_reg])
