def evaluate_from_model(model_name):
models_dir = 'C:/Users/Omar/OneDrive - Duke University/Padilla Group/Manuscripts/Lorentz DNN/DNN models/Lorentz/06-2021'
model_dir = os.path.join(models_dir, model_name)
pytorch_dir = 'C:/Users/Omar/PycharmProjects/DL_AEM/forward/lorentz_model'
os.chdir(pytorch_dir)
flags = fr.read_flag()
# flags = fr.load_flags(model_dir)
flags.eval_model = model_name
print("Load data:")
train_loader, test_loader = tdu.generate_torch_dataloader(x_range=flags.x_range,
y_range=flags.y_range,
geoboundary=flags.geoboundary,
batch_size=flags.batch_size,
normalize_input=flags.normalize_input,
data_dir=flags.data_dir,
test_ratio=0.999, shuffle=False)
ntwk = Network(LorentzDNN, flags, train_loader, test_loader, inference_mode=True, saved_model=flags.eval_model)
# Evaluation process
print("Start eval now:")
pred_file, truth_file = ntwk.evaluate()
# Plot the MSE distribution
plotMSELossDistrib_eval(pred_file, truth_file, flags)
print("Evaluation finished")
def continue_training_model(flags):
eval_model = flags.eval_model
# Retrieve flag object
print("Retrieving flag object for parameters")
old_model_dir = os.path.join("models", eval_model)
flags = fr.load_flags(old_model_dir)
flags.model_name = eval_model + '_retrain'
train_loader, test_loader = tdu.generate_torch_dataloader(
x_range=flags.x_range,
y_range=flags.y_range,
geoboundary=flags.geoboundary,
data_dir=flags.data_dir,
batch_size=flags.batch_size,
normalize_input=flags.normalize_input,
test_ratio=flags.test_ratio,
shuffle=True,
dataset_size=flags.data_reduce)
print("Loading pre-trained network now")
# Make Network
ntwk = Network(LorentzDNN, flags, train_loader, test_loader)
new_model_dir = ntwk.ckpt_dir
ntwk.ckpt_dir = old_model_dir
ntwk.load()
ntwk.ckpt_dir = new_model_dir
# Training process
print("Continue training model now...")
ntwk.train()
write_flags_and_BVE(flags, ntwk.best_mse_loss, ntwk.ckpt_dir)
def evaluate_from_model(model_dir):
"""
Evaluating interface. 1. Retreive the flags 2. get data 3. initialize network 4. eval
:param model_dir: The folder to retrieve the model
:return: None
"""
# Retrieve the flag object
if (model_dir.startswith("models")):
model_dir = model_dir[7:]
print("after removing prefix models/, now model_dir is:", model_dir)
print("Retrieving flag object for parameters")
flags = fr.load_flags(os.path.join("models", model_dir))
flags.eval_model = model_dir # Reset the eval mode
# Get the data
# train_loader, test_loader = datareader.read_data(flags)
train_loader, test_loader = tdu.generate_torch_dataloader(
x_range=flags.x_range,
y_range=flags.y_range,
geoboundary=flags.geoboundary,
batch_size=flags.batch_size,
normalize_input=flags.normalize_input,
data_dir=flags.data_dir,
test_ratio=0,
shuffle=False)
print("Making network now")
# Make Network
ntwk = Network(LorentzDNN,
flags,
train_loader,
test_loader,
inference_mode=True,
saved_model=flags.eval_model)
# Evaluation process
print("Start eval now:")
pred_T_file, truth_T_file, pred_R_file, truth_R_file = ntwk.evaluate()
# eps_re_file, eps_im_file, mu_re_file, mu_im_file ,eps_eff_re_file, eps_eff_im_file, \
# mu_eff_re_file, mu_eff_im_file, n_eff_re_file, n_eff_im_file, theta_re_file, theta_im_file, adv_re_file, \
# adv_im_file = ntwk.evaluate_all_output()
# Plot the MSE distribution
# plotMSELossDistrib_eval(pred_T_file, truth_T_file, flags)
print("Evaluation finished")
def training_from_flag(flags):
"""
Training interface. 1. Read in data
2. Initialize network
3. Train network
4. Record flags
:param flag: The training flags read from command line or parameter.py
:return: None
"""
if flags.use_cpu_only:
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# # Import the data
train_loader, test_loader = tdu.generate_torch_dataloader(
x_range=flags.x_range,
y_range=flags.y_range,
geoboundary=flags.geoboundary,
data_dir=flags.data_dir,
batch_size=flags.batch_size,
normalize_input=flags.normalize_input,
test_ratio=flags.test_ratio,
shuffle=True,
dataset_size=flags.data_reduce)
# Reset the boundary if normalized
if flags.normalize_input:
flags.geoboundary_norm = [-1, 1, -1, 1]
print("Geometry boundary is set to:", flags.geoboundary)
# Make Network
print("Making network now")
ntwk = Network(LorentzDNN, flags, train_loader, test_loader)
# Training process
print("Start training now...")
ntwk.train()
# ntwk.evaluate()
# Do the house keeping, write the parameters and put into folder, also use pickle to save the flags object
write_flags_and_BVE(flags, ntwk.best_validation_loss, ntwk.best_mse_loss,
ntwk.ckpt_dir)