def main_bs2sg(num_epoch: int = 1):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = base_model.get_bs2sg()
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=1,
gamma=0.75)
criterion = torch.nn.CrossEntropyLoss()
dataset = data_loader.SetBs2Sg([
"data/actual/spacegroup_list_{}.txt".format(i) for i in range(1, 231)
], 0.1)
train_loader, valid_loader = data_loader.get_valid_train_loader(
dataset, 32)
network.validate_train_loop(device, model, optimizer, scheduler, criterion,
valid_loader, train_loader, num_epoch)
data_processing.create_empty_list_files(230, "data/guess/",
"spacegroup_list_{}.txt")
data_processing.create_guess_list_files(device, model, 230,
"data/actual/valid_list.txt",
"data/guess/",
"spacegroup_list_{}.txt")
def main_cs2sg_all(num_epoch: int = 1):
data_processing.create_empty_list_files(230, "data/guess/",
"spacegroup_list_{}.txt")
input_sizes = [800, 2800, 2100, 1000, 1300, 600, 1100]
cs_sizes = crystal.crystal_system_sizes()
output_sizes = [
cs_sizes[i - 1] - cs_sizes[i - 2] + 1 if i > 1 else 3
for i in range(1, 8)
]
for i in range(1, 8):
main_cs2sg_one(i, (input_sizes[i], 128, 128, output_sizes[i]),
num_epoch)
def main_crys2sg_all(num_epoch: int = 1):
data_processing.create_empty_list_files(230, "data/guess/",
"spacegroup_list_{}.txt")
for i in range(1, 8):
main_crys2sg_one(i, num_epoch)
