def main_cs2sg_one(csnum: int,
hs_indices: List[int],
model_struct: tuple,
num_epoch: int = 1):
print("crystal system: {}".format(csnum))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = base_model.get_cs2sg(*model_struct)
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.SetCs2Sg(csnum, hs_indices, [
"data/actual/spacegroup_list_{}.txt".format(i)
for i in crystal.spacegroup_number_range(csnum)
], 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.append_guess_spacegroup_in_crystal_list_files(
device, model, csnum, hs_indices,
"data/actual/crystal_list_{}.txt".format(csnum), "data/guess/")
def main_crys2sg_one(crysnum: int, num_epoch: int = 1):
print("crystal system: {}".format(crysnum))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
margins = [2, 15, 74, 142, 167, 194, 230]
model = base_model.get_crys2sg(
# 360, 100, 100, margins[crysnum - 1] - margins[crysnum - 2] if crysnum > 1 else 2
1200,
128,
128,
margins[crysnum - 1] - margins[crysnum - 2] +
1 if crysnum > 1 else 3 # <<<<<<
)
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.SetCrys2Sg([
"data/actual/spacegroup_list_{}.txt".format(i)
for i in crystal.spacegroup_number_range(crysnum)
], crysnum, 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.append_guess_spacegroup_in_crystal_list_files(
device, model, crysnum,
"data/guess/crystal_list_{}.txt".format(crysnum), "data/guess/")
def append_guess_spacegroup_in_crystal_list_files(device, model, csnum,
hs_indices, in_list_path,
out_list_dir):
if os.stat(in_list_path).st_size == 0:
return
file_paths = numpy.loadtxt(in_list_path, "U60")
for i, file_path in enumerate(file_paths):
with open(file_path, "r") as file:
data_json = json.load(file)
data_input_np = numpy.array(data_json["bands"])
data_input_np = data_input_np[:, hs_indices]
data_input_np = data_input_np.flatten().T
data_input = torch.from_numpy(data_input_np).float()
output = model(data_input.to(device))
sgnum = torch.max(
output,
0)[1].item() + 1 + crystal.spacegroup_index_lower(csnum)
if sgnum not in crystal.spacegroup_number_range(csnum):
continue
with open(out_list_dir + "spacegroup_list_{}.txt".format(sgnum),
"a") as file_out:
file_out.write(file_path + "\n")
print("\r\tcreate guess list: {}/{}".format(i, len(file_paths)),
end="")
print("\rcreate guess list: {}".format(len(file_paths)))
# prepare actual data # (Do this everytime dataset is changed)
# data_processing.create_valid_list_files(100, "data/input_data_test06/", "data/actual/valid_list.txt") # <<<<
# data_processing.create_actual_crystal_list_files("data/actual/valid_list.txt", "data/actual/")
# data_processing.create_actual_spacegroup_list_files("data/actual/valid_list.txt", "data/actual/")
# generate guess data #
# main_bs2sg(num_epoch=30)
# main_bs2crys(num_epoch=15)
main_crys2sg_all(num_epoch=20)
# analyse result #
# analysis.print_result(range(1, 8), "data/guess/", "data/actual/", "crystal_list_{}.txt", plt=True) # into crys
# analysis.print_result(range(1, 231), "data/guess/", "data/actual/", "spacegroup_list_{}.txt") # into sg
for c in range(1, 8): # into crys and sg
print("crystal system {}".format(c))
print("\nbandstructure to crystal system result")
analysis.print_result([c], "data/guess/", "data/actual/",
"crystal_list_{}.txt")
print("\ncrystal to spacegroup system result")
analysis.print_result(crystal.spacegroup_number_range(c),
"data/guess/", "data/actual/",
"spacegroup_list_{}.txt")
print("\n")
torch.cuda.empty_cache()
import winsound
duration = 500 # milliseconds
freq = 200 # Hz
winsound.Beep(freq, duration)