def predict_from_model(pre_trained_model, Xpred_file, shrink_factor=1, save_name=''):
"""
Predicting 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
print("This is doing the prediction for file", Xpred_file)
print("Retrieving flag object for parameters")
if (pre_trained_model.startswith("models")):
eval_model = pre_trained_model[7:]
print("after removing prefix models/, now model_dir is:", eval_model)
flags = load_flags(pre_trained_model) # Get the pre-trained model
flags.eval_model = eval_model # Reset the eval mode
# Get the data, this part is useless in prediction but just for simplicity
train_loader, test_loader = data_reader.read_data(flags)
print("Making network now")
# Make Network
ntwk = Network(Backprop, flags, train_loader, test_loader, inference_mode=True, saved_model=flags.eval_model)
print("number of trainable parameters is :")
pytorch_total_params = sum(p.numel() for p in ntwk.model.parameters() if p.requires_grad)
print(pytorch_total_params)
# Evaluation process
print("Start eval now:")
pred_file, truth_file = ntwk.predict(Xpred_file, save_prefix=save_name + 'shrink_factor' + str(shrink_factor), shrink_factor=shrink_factor)
def predict(model_dir, Ytruth_file, multi_flag=False):
"""
Predict the output from given spectra
"""
print("Retrieving flag object for parameters")
if (model_dir.startswith("models")):
model_dir = model_dir[7:]
print("after removing prefix models/, now model_dir is:", model_dir)
if model_dir.startswith('/'): # It is a absolute path
flags = helper_functions.load_flags(model_dir)
else:
flags = helper_functions.load_flags(os.path.join("models", model_dir))
flags.eval_model = model_dir # Reset the eval mode
ntwk = Network(INN,
flags,
train_loader=None,
test_loader=None,
inference_mode=True,
saved_model=flags.eval_model)
print("number of trainable parameters is :")
pytorch_total_params = sum(p.numel() for p in ntwk.model.parameters()
if p.requires_grad)
print(pytorch_total_params)
# Evaluation process
pred_file, truth_file = ntwk.predict(Ytruth_file)
if 'Yang' not in flags.data_set:
plotMSELossDistrib(pred_file, truth_file, flags)
def infer(pre_trained_model, Xpred_file, no_plot=True):
# Retrieve the flag object
print("This is doing the prediction for file", Xpred_file)
print("Retrieving flag object for parameters")
if (pre_trained_model.startswith("models")):
eval_model = pre_trained_model[7:]
print("after removing prefix models/, now model_dir is:", eval_model)
flags = load_flags(pre_trained_model) # Get the pre-trained model
flags.eval_model = pre_trained_model # Reset the eval mode
flags.test_ratio = 0.1 # useless number
# Get the data, this part is useless in prediction but just for simplicity
train_loader, test_loader = data_reader.read_data(flags)
print("Making network now")
# Make Network
ntwk = Network(Backprop,
flags,
train_loader,
test_loader,
inference_mode=True,
saved_model=flags.eval_model)
print("number of trainable parameters is :")
pytorch_total_params = sum(p.numel() for p in ntwk.model.parameters()
if p.requires_grad)
print(pytorch_total_params)
# Evaluation process
print("Start eval now:")
if not no_plot:
# Plot the MSE distribution
pred_file, truth_file = ntwk.predict(Xpred_file, no_save=False)
flags.eval_model = pred_file.replace(
'.', '_') # To make the plot name different
plotMSELossDistrib(pred_file, truth_file, flags)
else:
pred_file, truth_file = ntwk.predict(Xpred_file, no_save=True)
print("Evaluation finished")
return pred_file, truth_file, flags