is_parallel = False
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
problem = None
if args.problem.startswith("genotyping:"):
problem = SbiGenotypingProblem(args.mini_batch_size,
code=args.problem,
num_workers=args.num_workers)
elif args.problem.startswith("struct_genotyping:"):
# struct_genotyping does not support multiprocessing data loading:
problem = StructuredSbiGenotypingProblem(args.mini_batch_size,
code=args.problem,
num_workers=1)
elif args.problem.startswith("somatic:"):
problem = SbiSomaticProblem(args.mini_batch_size,
code=args.problem,
num_workers=args.num_workers)
else:
print("Unsupported problem: " + args.problem)
exit(1)
def get_metric_value(all_perfs, query_metric_name):
for perf in all_perfs:
metric = perf.get_metric(query_metric_name)
if metric is not None:
return metric
def train_once(train_args, train_problem, train_device, train_use_cuda):
train_problem.describe()
training_loop_method = None
testing_loop_method = None
except FileNotFoundError:
print("Unable to load model {} from checkpoint".format(
args.checkpoint_key))
exit(1)
if checkpoint is not None:
model = checkpoint['model']
problem = None
if args.problem.startswith("genotyping:"):
problem = SbiGenotypingProblem(args.mini_batch_size,
code=args.problem,
drop_last_batch=False,
num_workers=args.num_workers)
elif args.problem.startswith("somatic:"):
problem = SbiSomaticProblem(args.mini_batch_size,
code=args.problem,
drop_last_batch=False,
num_workers=args.num_workers)
elif args.problem.startswith("struct_genotyping:"):
problem = StructuredSbiGenotypingProblem(args.mini_batch_size,
code=args.problem,
drop_last_batch=False,
num_workers=args.num_workers)
else:
print("Unsupported problem: " + args.problem)
exit(1)
if problem is None or model is None:
print("no problem or model, aborting")
exit(1)
domain_descriptor = checkpoint[