torch.backends.cudnn.fastest = cudnn
torch.cuda.set_device(device)
correct = 0.0
tp = 0.0
size = 0.0
# map_location: map cuda gpu numbers (specially for transfering from different workstations)
model = torch.load(filename_model,
map_location=dict(
('cuda:' + str(k), 'cuda:' + str(device))
for k in range(0, 100)))
#model=torch.load(filename_model)
model.train(False)
testset = GenomeDataset(filename=filename_testset,
seq_name=['seq_ref_1', 'seq_ref_2'],
encode_mode=encode_mode)
testloader = DataLoader(testset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
print('[INFO] testloader generated')
probs_list = []
labels_list = []
rsids_list = []
query_snp_rsids_list = []
for data in tqdm(testloader):
inputs1, inputs2, labels, rsids, query_snp_rsids = data
elif scheduler == 'ReduceLROnPlateau':
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, **scheduler_param)
if loss == 'CrossEntropyLoss':
criterion = nn.CrossEntropyLoss(weight=class_imbalance)
elif loss == 'NLLLoss':
criterion = nn.NLLLoss(weight=class_imbalance)
elif loss == 'MSELoss':
criterion = nn.MSELoss(weight=class_imbalance)
elif loss == 'MSELoss_weighted':
criterion = weighted_mse_loss
# read datasets
trainset = GenomeDataset(filename=filename_trainset,
seq_name=['seq_ref_1', 'seq_ref_2'],
encode_mode=encode_mode)
trainloader = DataLoader(trainset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
valset = GenomeDataset(filename=filename_valset,
seq_name=['seq_ref_1', 'seq_ref_2'],
encode_mode=encode_mode)
valloader = DataLoader(valset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
dataloaders = {'train': trainloader, 'val': valloader}
print("[INFO] dataloaders generated")