def evaluate(self, save_dir='data/', prefix=''):
self.load() # load the model as constructed
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
# Set to evaluation mode for batch_norm layers
self.model.eval()
saved_model_str = self.saved_model.replace('/','_') + prefix
# Get the file names
Ypred_file = os.path.join(save_dir, 'test_Ypred_{}.csv'.format(saved_model_str))
Xtruth_file = os.path.join(save_dir, 'test_Xtruth_{}.csv'.format(saved_model_str))
Ytruth_file = os.path.join(save_dir, 'test_Ytruth_{}.csv'.format(saved_model_str))
Xpred_file = os.path.join(save_dir, 'test_Xpred_{}.csv'.format(saved_model_str))
tk = time_keeper(os.path.join(save_dir, 'evaluation_time.txt'))
# Open those files to append
with open(Xtruth_file, 'a') as fxt,open(Ytruth_file, 'a') as fyt,\
open(Ypred_file, 'a') as fyp, open(Xpred_file, 'a') as fxp:
# Loop through the eval data and evaluate
for ind, (geometry, spectra) in enumerate(self.test_loader):
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
Xpred = self.model.inference(spectra).cpu().data.numpy()
np.savetxt(fxt, geometry.cpu().data.numpy())
np.savetxt(fyt, spectra.cpu().data.numpy())
np.savetxt(fxp, Xpred)
if self.flags.data_set != 'meta_material':
Ypred = simulator(self.flags.data_set, Xpred)
np.savetxt(fyp, Ypred)
tk.record(1) # Record the total time of the eval period
return Ypred_file, Ytruth_file
def predict(self, Ytruth_file, save_dir='data/', prefix=''):
self.load() # load the model as constructed
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model_b.cuda()
self.model_b.eval()
saved_model_str = self.saved_model.replace('/', '_') + prefix
Ytruth = pd.read_csv(Ytruth_file, header=None, delimiter=',') # Read the input
if len(Ytruth.columns) == 1: # The file is not delimitered by ',' but ' '
Ytruth = pd.read_csv(Ytruth_file, header=None, delimiter=' ')
Ytruth_tensor = torch.from_numpy(Ytruth.values).to(torch.float)
print('shape of Ytruth tensor :', Ytruth_tensor.shape)
# Get the file names
Ypred_file = os.path.join(save_dir, 'test_Ypred_{}.csv'.format(saved_model_str))
Ytruth_file = os.path.join(save_dir, 'test_Ytruth_{}.csv'.format(saved_model_str))
Xpred_file = os.path.join(save_dir, 'test_Xpred_{}.csv'.format(saved_model_str))
# keep time
tk = time_keeper(os.path.join(save_dir, 'evaluation_time.txt'))
if cuda:
Ytruth_tensor = Ytruth_tensor.cuda()
print('model in eval:', self.model_b)
Xpred = self.model_b(Ytruth_tensor).detach().cpu().numpy()
# Open those files to append
with open(Ytruth_file, 'a') as fyt, open(Ypred_file, 'a') as fyp, open(Xpred_file, 'a') as fxp:
np.savetxt(fyt, Ytruth_tensor.cpu().data.numpy())
np.savetxt(fxp, Xpred)
if self.flags.data_set != 'Yang_sim':
Ypred = simulator(self.flags.data_set, Xpred)
np.savetxt(fyp, Ypred)
tk.record(1)
return Ypred_file, Ytruth_file
def evaluate(self, save_dir='data/', prefix=''):
self.load() # load the model as constructed
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
self.model.eval()
saved_model_str = self.saved_model.replace('/', '_') + prefix
# Get the file names
Ypred_file = os.path.join(save_dir, 'test_Ypred_{}.csv'.format(saved_model_str))
Xtruth_file = os.path.join(save_dir, 'test_Xtruth_{}.csv'.format(saved_model_str))
Ytruth_file = os.path.join(save_dir, 'test_Ytruth_{}.csv'.format(saved_model_str))
Xpred_file = os.path.join(save_dir, 'test_Xpred_{}.csv'.format(saved_model_str))
# keep time
tk = time_keeper(os.path.join(save_dir, 'evaluation_time.txt'))
# Open those files to append
with open(Xtruth_file, 'a') as fxt,open(Ytruth_file, 'a') as fyt,\
open(Ypred_file, 'a') as fyp, open(Xpred_file, 'a') as fxp:
# Loop through the eval data and evaluate
for ind, (geometry, spectra) in enumerate(self.test_loader):
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
# Initialize the geometry first
print('model in eval:', self.model)
pi, sigma, mu = self.model(spectra) # Get the output
Xpred = mdn.sample(pi, sigma, mu).detach().cpu().numpy()
tk.record(1)
return Ypred_file, Ytruth_file
def evaluate(self, save_dir='data/', save_all=False):
self.load() # load the model as constructed
try:
bs = self.flags.backprop_step # for previous code that did not incorporate this
except AttributeError:
print(
"There is no attribute backprop_step, catched error and adding this now"
)
self.flags.backprop_step = 2
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
self.model.eval()
saved_model_str = self.saved_model.replace('/', '_')
# Get the file names
Ypred_file = os.path.join(save_dir,
'test_Ypred_{}.csv'.format(saved_model_str))
Xtruth_file = os.path.join(
save_dir, 'test_Xtruth_{}.csv'.format(saved_model_str))
Ytruth_file = os.path.join(
save_dir, 'test_Ytruth_{}.csv'.format(saved_model_str))
Xpred_file = os.path.join(save_dir,
'test_Xpred_{}.csv'.format(saved_model_str))
print("evalution output pattern:", Ypred_file)
# Time keeping
tk = time_keeper(
time_keeping_file=os.path.join(save_dir, 'evaluation_time.txt'))
# Open those files to append
with open(Xtruth_file, 'a') as fxt,open(Ytruth_file, 'a') as fyt,\
open(Ypred_file, 'a') as fyp, open(Xpred_file, 'a') as fxp:
# Loop through the eval data and evaluate
for ind, (geometry, spectra) in enumerate(self.test_loader):
if self.flags.data_set == 'gaussian_mixture':
spectra = torch.nn.functional.one_hot(
spectra.to(torch.int64),
4).to(torch.float
) # Change the gaussian labels into one-hot
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
# Initialize the geometry first
Xpred, Ypred, loss = self.evaluate_one(spectra,
save_dir=save_dir,
save_all=save_all,
ind=ind)
tk.record(
ind) # Keep the time after each evaluation for backprop
# self.plot_histogram(loss, ind) # Debugging purposes
np.savetxt(fxt, geometry.cpu().data.numpy())
np.savetxt(fyt, spectra.cpu().data.numpy())
np.savetxt(fyp, Ypred)
np.savetxt(fxp, Xpred)
return Ypred_file, Ytruth_file
def evaluate_multiple_time(self, time=200, save_dir='../multi_eval/VAE/'):
"""
Make evaluation multiple time for deeper comparison for stochastic algorithms
:param save_dir: The directory to save the result
:return:
"""
tk = time_keeper(os.path.join(save_dir, 'evaluation_time.txt'))
save_dir += self.flags.data_set
for i in range(time):
self.evaluate(save_dir=save_dir, prefix='inference' + str(i))
tk.record(i)
def predict(self, Ytruth_file, save_dir='data/', prefix=''):
self.load() # load the model as constructed
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
self.model.eval()
saved_model_str = self.saved_model.replace('/', '_') + prefix
Ytruth = pd.read_csv(Ytruth_file, header=None,
delimiter=',') # Read the input
if len(Ytruth.columns
) == 1: # The file is not delimitered by ',' but ' '
Ytruth = pd.read_csv(Ytruth_file, header=None, delimiter=' ')
Ytruth_tensor = torch.from_numpy(Ytruth.values).to(torch.float)
print('shape of Ytruth tensor :', Ytruth_tensor.shape)
# Get the file names
Ypred_file = os.path.join(save_dir,
'test_Ypred_{}.csv'.format(saved_model_str))
Ytruth_file = os.path.join(
save_dir, 'test_Ytruth_{}.csv'.format(saved_model_str))
Xpred_file = os.path.join(save_dir,
'test_Xpred_{}.csv'.format(saved_model_str))
# keep time
tk = time_keeper(os.path.join(save_dir, 'evaluation_time.txt'))
dim_x = self.flags.dim_x
dim_y = self.flags.dim_y
dim_z = self.flags.dim_z
dim_tot = self.flags.dim_tot
batch_size = len(Ytruth_tensor)
# Create random value for the padding for yz
pad_yz = self.flags.zeros_noise_scale * torch.randn(
batch_size, dim_tot - dim_y - dim_z, device=device)
if cuda:
Ytruth_tensor = Ytruth_tensor.cuda()
# Create a noisy z vector with noise level same as y
z = torch.randn(batch_size, dim_z, device=device)
y_cat = torch.cat((z, pad_yz, Ytruth_tensor), dim=1)
# Initialize the x first
Xpred = self.model(y_cat, rev=True)
Xpred = Xpred[:, :dim_x].cpu().data.numpy()
# Open those files to append
with open(Ytruth_file,
'a') as fyt, open(Ypred_file,
'a') as fyp, open(Xpred_file, 'a') as fxp:
np.savetxt(fyt, Ytruth_tensor.cpu().data.numpy())
np.savetxt(fxp, Xpred)
if self.flags.data_set != 'Yang_sim':
Ypred = simulator(self.flags.data_set, Xpred)
np.savetxt(fyp, Ypred)
tk.record(1)
return Ypred_file, Ytruth_file
def evaluate(self, save_dir='data/', prefix=''):
self.load() # load the model as constructed
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
# Set to evaluation mode for batch_norm layers
self.model.eval()
# Set the dimensions
dim_x = self.flags.dim_x
dim_y = self.flags.dim_y
dim_z = self.flags.dim_z
dim_tot = self.flags.dim_tot
saved_model_str = self.saved_model.replace('/', '_') + prefix
# Get the file names
Ypred_file = os.path.join(save_dir,
'test_Ypred_{}.csv'.format(saved_model_str))
Xtruth_file = os.path.join(
save_dir, 'test_Xtruth_{}.csv'.format(saved_model_str))
Ytruth_file = os.path.join(
save_dir, 'test_Ytruth_{}.csv'.format(saved_model_str))
Xpred_file = os.path.join(save_dir,
'test_Xpred_{}.csv'.format(saved_model_str))
tk = time_keeper(os.path.join(save_dir, 'evaluation_time.txt'))
# Open those files to append
with open(Xtruth_file, 'a') as fxt,open(Ytruth_file, 'a') as fyt,\
open(Ypred_file, 'a') as fyp, open(Xpred_file, 'a') as fxp:
# Loop through the eval data and evaluate
for ind, (x, y) in enumerate(self.test_loader):
if cuda:
x = x.cuda() # Put data onto GPU
y = y.cuda() # Put data onto GPU
batch_size = len(x)
# Create random value for the padding for yz
pad_yz = self.flags.zeros_noise_scale * torch.randn(
batch_size, dim_tot - dim_y - dim_z, device=device)
# Create a noisy z vector with noise level same as y
z = torch.randn(batch_size, dim_z, device=device)
print("shape of z:", np.shape(z))
print("shape of pad_yz:", np.shape(pad_yz))
print("shape of y:", np.shape(y))
y_cat = torch.cat((z, pad_yz, y), dim=1)
# Initialize the x first
Xpred = self.model(y_cat, rev=True)
Xpred = Xpred[:, :dim_x].cpu().data.numpy()
#np.savetxt(fxt, x.cpu().data.numpy())
#np.savetxt(fyt, y.cpu().data.numpy())
if self.flags.data_set != 'meta_material':
Ypred = simulator(self.flags.data_set, Xpred)
np.savetxt(fyp, Ypred)
#np.savetxt(fxp, Xpred)
tk.record(1)
return Ypred_file, Ytruth_file
def evaluate_multiple_time(self, time=200, save_dir='/home/sr365/MM_bench_multi_eval/Tandem/'):
"""
Make evaluation multiple time for deeper comparison for stochastic algorithms
:param save_dir: The directory to save the result
:return:
"""
save_dir = os.path.join(save_dir, self.flags.data_set)
tk = time_keeper(os.path.join(save_dir, 'evaluation_time.txt'))
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
for i in range(time):
self.evaluate(save_dir=save_dir, prefix='inference' + str(i))
tk.record(i)
def evaluate_multiple_time(self,
time=2048,
save_dir='/work/sr365/forward_filter/cINN/'):
"""
Make evaluation multiple time for deeper comparison for stochastic algorithms
:param save_dir: The directory to save the result
:return:
"""
save_dir += self.flags.data_set
tk = time_keeper(os.path.join(save_dir, 'evaluation_time.txt'))
for i in range(time):
self.evaluate(save_dir=save_dir, prefix='inference' + str(i))
tk.record(i)
def evaluate(self,
save_dir='data/',
save_all=False,
MSE_Simulator=False,
save_misc=False,
save_Simulator_Ypred=False):
self.load() # load the model as constructed
try:
bs = self.flags.backprop_step # for previous code that did not incorporate this
except AttributeError:
print(
"There is no attribute backprop_step, catched error and adding this now"
)
self.flags.backprop_step = 300
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
self.model.eval()
saved_model_str = self.saved_model.replace('/', '_')
# Get the file names
Ypred_file = os.path.join(save_dir,
'test_Ypred_{}.csv'.format(saved_model_str))
Xtruth_file = os.path.join(
save_dir, 'test_Xtruth_{}.csv'.format(saved_model_str))
Ytruth_file = os.path.join(
save_dir, 'test_Ytruth_{}.csv'.format(saved_model_str))
Xpred_file = os.path.join(save_dir,
'test_Xpred_{}.csv'.format(saved_model_str))
print("evalution output pattern:", Ypred_file)
# Time keeping
tk = time_keeper(
time_keeping_file=os.path.join(save_dir, 'evaluation_time.txt'))
# Open those files to append
with open(Xtruth_file, 'a') as fxt,open(Ytruth_file, 'a') as fyt,\
open(Ypred_file, 'a') as fyp, open(Xpred_file, 'a') as fxp:
# Loop through the eval data and evaluate
for ind, (geometry, spectra) in enumerate(self.test_loader):
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
# Initialize the geometry first
logit = self.model(geometry)
tk.record(
ind) # Keep the time after each evaluation for backprop
# suppress printing to evaluate time
np.savetxt(fxt, geometry.cpu().data.numpy())
np.savetxt(fyt, spectra.cpu().data.numpy())
np.savetxt(fyp, logit.detach().cpu().numpy())
return Ypred_file, Ytruth_file
def evaluate(self, save_dir='data/', prefix=''):
self.load() # load the model as constructed
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
# Set to evaluation mode for batch_norm layers
self.model.eval()
# Set the dimensions
dim_x = self.flags.dim_x
dim_z = self.flags.dim_z
saved_model_str = self.saved_model.replace('/', '_') + prefix
# Get the file names
Ypred_file = os.path.join(save_dir,
'test_Ypred_{}.csv'.format(saved_model_str))
Xtruth_file = os.path.join(
save_dir, 'test_Xtruth_{}.csv'.format(saved_model_str))
Ytruth_file = os.path.join(
save_dir, 'test_Ytruth_{}.csv'.format(saved_model_str))
Xpred_file = os.path.join(save_dir,
'test_Xpred_{}.csv'.format(saved_model_str))
tk = time_keeper(
time_keeping_file=os.path.join(save_dir, 'evaluation time.txt'))
# Open those files to append
with open(Xtruth_file, 'a') as fxt,open(Ytruth_file, 'a') as fyt,\
open(Ypred_file, 'a') as fyp, open(Xpred_file, 'a') as fxp:
# Loop through the eval data and evaluate
for ind, (x, y) in enumerate(self.test_loader):
batch_size = len(x)
# Create a noisy z vector with noise level same as y
z = torch.randn(batch_size, dim_z, device=device)
"""
# Initialize the x first
if self.flags.data_set == 'gaussian_mixture':
y_prev = np.copy(y.data.numpy())
y = torch.nn.functional.one_hot(y.to(torch.int64), 4).to(torch.float) # Change the gaussian labels into one-hot
"""
if cuda:
x = x.cuda()
y = y.cuda()
Xpred = self.model(z, y, rev=True).cpu().data.numpy()
np.savetxt(fxt, x.cpu().data.numpy())
np.savetxt(fxp, Xpred)
np.savetxt(fyt, y.cpu().data.numpy())
if self.flags.data_set != 'meta_material':
Ypred = simulator(self.flags.data_set, Xpred)
np.savetxt(fyp, Ypred)
tk.record(1)
return Ypred_file, Ytruth_file
def predict_inverse(self,
Ytruth_file,
multi_flag,
save_dir='data/',
prefix=''):
self.load() # load the model as constructed
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
self.model.eval()
saved_model_str = self.saved_model.replace('/', '_') + prefix
Ytruth = pd.read_csv(Ytruth_file, header=None,
delimiter=',') # Read the input
if len(Ytruth.columns
) == 1: # The file is not delimitered by ',' but ' '
Ytruth = pd.read_csv(Ytruth_file, header=None, delimiter=' ')
Ytruth_tensor = torch.from_numpy(Ytruth.values).to(torch.float)
print('shape of Ytruth tensor :', Ytruth_tensor.shape)
# Get the file names
Ypred_file = os.path.join(save_dir,
'test_Ypred_{}.csv'.format(saved_model_str))
Ytruth_file = os.path.join(
save_dir, 'test_Ytruth_{}.csv'.format(saved_model_str))
Xpred_file = os.path.join(save_dir,
'test_Xpred_{}.csv'.format(saved_model_str))
# keep time
tk = time_keeper(os.path.join(save_dir, 'evaluation_time.txt'))
# Set the save_simulator_ytruth
save_Simulator_Ypred = True
if 'Yang' in self.flags.data_set:
save_Simulator_Ypred = False
if cuda:
Ytruth_tensor = Ytruth_tensor.cuda()
print('model in eval:', self.model)
# Open those files to append
with open(Ytruth_file,
'a') as fyt, open(Ypred_file,
'a') as fyp, open(Xpred_file, 'a') as fxp:
np.savetxt(fyt, Ytruth_tensor.cpu().data.numpy())
for ind in range(len(Ytruth_tensor)):
spectra = Ytruth_tensor[ind, :]
Xpred, Ypred, loss = self.evaluate_one(
spectra,
save_dir=save_dir,
save_all=multi_flag,
ind=ind,
MSE_Simulator=False,
save_misc=False,
save_Simulator_Ypred=save_Simulator_Ypred)
np.savetxt(fxp, Xpred)
if self.flags.data_set != 'Yang_sim':
Ypred = simulator(self.flags.data_set, Xpred)
np.savetxt(fyp, Ypred)
tk.record(1)
return Ypred_file, Ytruth_file
def train(self):
"""
The major training function. This would start the training using information given in the flags
:return: None
"""
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
# Construct optimizer after the model moved to GPU
self.optm = self.make_optimizer()
self.lr_scheduler = self.make_lr_scheduler(self.optm)
# Time keeping
tk = time_keeper(
time_keeping_file=os.path.join(self.ckpt_dir, 'training time.txt'))
for epoch in range(self.flags.train_step):
# Set to Training Mode
train_loss = 0
# boundary_loss = 0 # Unnecessary during training since we provide geometries
self.model.train()
for j, (geometry, spectra) in enumerate(self.train_loader):
if self.flags.data_set == 'gaussian_mixture':
spectra = torch.nn.functional.one_hot(
spectra.to(torch.int64),
4).to(torch.float
) # Change the gaussian labels into one-hot
if cuda:
geometry = geometry.cuda() # Put data onto GPU
spectra = spectra.cuda() # Put data onto GPU
self.optm.zero_grad() # Zero the gradient first
logit = self.model(geometry) # Get the output
loss = self.make_loss(logit, spectra) # Get the loss tensor
loss.backward() # Calculate the backward gradients
self.optm.step() # Move one step the optimizer
train_loss += loss # Aggregate the loss
# Calculate the avg loss of training
train_avg_loss = train_loss.cpu().data.numpy() / (j + 1)
if epoch % self.flags.eval_step: # For eval steps, do the evaluations and tensor board
# Record the training loss to the tensorboard
self.log.add_scalar('Loss/train', train_avg_loss, epoch)
# Set to Evaluation Mode
self.model.eval()
print("Doing Evaluation on the model now")
test_loss = 0
for j, (geometry, spectra) in enumerate(
self.test_loader): # Loop through the eval set
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
logit = self.model(geometry)
loss = self.make_loss(logit, spectra) # compute the loss
test_loss += loss # Aggregate the loss
# Record the testing loss to the tensorboard
test_avg_loss = test_loss.cpu().data.numpy() / (j + 1)
self.log.add_scalar('Loss/test', test_avg_loss, epoch)
print("This is Epoch %d, training loss %.5f, validation loss %.5f" \
% (epoch, train_avg_loss, test_avg_loss ))
# Model improving, save the model down
if test_avg_loss < self.best_validation_loss:
self.best_validation_loss = test_avg_loss
self.save()
print("Saving the model down...")
if self.best_validation_loss < self.flags.stop_threshold:
print("Training finished EARLIER at epoch %d, reaching loss of %.5f" %\
(epoch, self.best_validation_loss))
break
# Learning rate decay upon plateau
self.lr_scheduler.step(train_avg_loss)
self.log.close()
tk.record(1) # Record at the end of the training
def evaluate(self,
save_dir='data/',
save_all=False,
MSE_Simulator=False,
save_misc=False,
save_Simulator_Ypred=False):
"""
The function to evaluate how good the Neural Adjoint is and output results
:param save_dir: The directory to save the results
:param save_all: Save all the results instead of the best one (T_200 is the top 200 ones)
:param MSE_Simulator: Use simulator loss to sort (DO NOT ENABLE THIS, THIS IS OK ONLY IF YOUR APPLICATION IS FAST VERIFYING)
:param save_misc: save all the details that are probably useless
:param save_Simulator_Ypred: Save the Ypred that the Simulator gives
(This is useful as it gives us the true Ypred instead of the Ypred that the network "thinks" it gets, which is
usually inaccurate due to forward model error)
:return:
"""
try:
bs = self.flags.generations # for previous code that did not incorporate this
except AttributeError:
print(
"There is no attribute backprop_step, catched error and adding this now"
)
self.flags.generations = 300
cuda = True if torch.cuda.is_available() else False
#if cuda:
# self.model.cuda()
#self.model.eval()
saved_model_str = self.saved_model.replace('/', '_')
# Get the file names
Ypred_file = os.path.join(save_dir,
'test_Ypred_{}.csv'.format(saved_model_str))
Xtruth_file = os.path.join(
save_dir, 'test_Xtruth_{}.csv'.format(saved_model_str))
Ytruth_file = os.path.join(
save_dir, 'test_Ytruth_{}.csv'.format(saved_model_str))
Xpred_file = os.path.join(save_dir,
'test_Xpred_{}.csv'.format(saved_model_str))
print("evalution output pattern:", Ypred_file)
# Time keeping
tk = time_keeper(
time_keeping_file=os.path.join(save_dir, 'evaluation_time.txt'))
# Open those files to append
with open(Xtruth_file, 'w') as fxt,open(Ytruth_file, 'w') as fyt,\
open(Ypred_file, 'w') as fyp, open(Xpred_file, 'w') as fxp:
# Loop through the eval data and evaluate
for ind, (geometry, spectra) in enumerate(self.test_loader):
print("SAMPLE: {}".format(ind))
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
# Initialize the geometry first
Xpred, Ypred, loss = self.evaluate_one(
spectra,
save_dir=save_dir,
save_all=save_all,
ind=ind,
MSE_Simulator=MSE_Simulator,
save_misc=save_misc,
save_Simulator_Ypred=save_Simulator_Ypred)
tk.record(
ind) # Keep the time after each evaluation for backprop
# self.plot_histogram(loss, ind) # Debugging purposes
np.savetxt(fxt, geometry.cpu().data.numpy())
np.savetxt(fyt, spectra.cpu().data.numpy())
if self.flags.data_set != 'Yang_sim':
np.savetxt(fyp, Ypred)
np.savetxt(fxp, Xpred)
#if (ind+1)%self.flags.eval_step == 0:
# plotMSELossDistrib(Ypred_file,Ytruth_file,self.flags)
if ind > (self.flags.xtra - 1):
print("THIS BREAK IS HIT!", self.flags.xtra)
break
return Ypred_file, Ytruth_file
def train(self):
"""
The major training function. This would start the training using information given in the flags
:return: None
"""
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
# Construct optimizer after the model moved to GPU
self.optm = self.make_optimizer()
self.lr_scheduler = self.make_lr_scheduler(self.optm)
# Time keeping
tk = time_keeper(
time_keeping_file=os.path.join(self.ckpt_dir, 'training time.txt'))
for epoch in range(self.flags.train_step):
# Set to Training Mode
train_loss = 0
# boundary_loss = 0 # Unnecessary during training since we provide geometries
self.model.train()
for j, (geometry, spectra) in enumerate(self.train_loader):
if cuda:
geometry = geometry.cuda() # Put data onto GPU
spectra = spectra.cuda() # Put data onto GPU
#print('spectra = ', spectra)
#print('geometry = ', geometry)
self.optm.zero_grad() # Zero the gradient first
pi, sigma, mu = self.model(spectra) # Get the output
#print('spectra = {}, pi, sigma, mu = {}, {}, {}'.format(spectra.cpu().numpy(),
# pi.detach().cpu().numpy()[0,:],
# sigma.detach().cpu().numpy()[0,:,0],
# mu.detach().cpu().numpy()[0,:,0]))
#print('geometry shape', geometry.size())
loss = self.make_loss(pi, sigma, mu,
geometry) # Get the loss tensor
#loss = self.make_loss(pi, sigma, mu, geometry, warmup=epoch) # Get the loss tensor
#Xpred = mdn.sample(pi, sigma, mu).detach().cpu().numpy()
#Ypred = torch.tensor(simulator(self.flags.data_set, Xpred), requires_grad=False)
#if cuda:
# Ypred = Ypred.cuda()
#simulator_loss = nn.functional.mse_loss(Ypred, spectra).detach().cpu().numpy() # Get the loss tensor
#print('nll loss at epoch {}, batch {} is {} '.format(epoch, j, loss.detach().cpu().numpy()))
loss.backward() # Calculate the backward gradients
# gradient clipping
torch.nn.utils.clip_grad_value_(self.model.parameters(), 1)
self.optm.step() # Move one step the optimizer
train_loss += loss # Aggregate the loss
# boundary_loss += self.Boundary_loss # Aggregate the BDY loss
# Calculate the avg loss of training
train_avg_loss = train_loss.cpu().data.numpy() / (j + 1)
# boundary_avg_loss = boundary_loss.cpu().data.numpy() / (j + 1)
if epoch % self.flags.eval_step == 0: # For eval steps, do the evaluations and tensor board
# Record the training loss to the tensorboard
self.log.add_scalar('Loss/train', train_avg_loss, epoch)
#self.log.add_scalar('Loss/simulator_train', simulator_loss, epoch)
# self.log.add_scalar('Loss/BDY_train', boundary_avg_loss, epoch)
# Set to Evaluation Mode
self.model.eval()
print("Doing Evaluation on the model now")
test_loss = 0
for j, (geometry, spectra) in enumerate(
self.test_loader): # Loop through the eval set
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
pi, sigma, mu = self.model(spectra) # Get the output
if self.flags.data_set == 'meta_material':
loss = self.make_loss(pi, sigma, mu,
geometry) # Get the loss tensor
test_loss += loss.detach().cpu().numpy()
else:
Xpred = mdn.sample(pi, sigma, mu).numpy()
Ypred_np = simulator(self.flags.data_set, Xpred)
mae, mse = compare_truth_pred(Ypred_np,
spectra.cpu().numpy(),
cut_off_outlier_thres=10,
quiet_mode=True)
test_loss += np.mean(mse) # Aggregate the loss
break
# only get the first batch that is enough
# Record the testing loss to the tensorboard
test_avg_loss = test_loss / (j + 1)
self.log.add_scalar('Loss/test', test_avg_loss, epoch)
print("This is Epoch %d, training loss %.5f, validation loss %.5f"\
% (epoch, train_avg_loss, test_avg_loss ))
#print("This is Epoch %d, training loss %.5f, validation loss %.5f, training simulator loss %.5f" \
# % (epoch, train_avg_loss, test_avg_loss, simulator_loss ))
# Plotting the first spectra prediction for validation
# f = self.compare_spectra(Ypred=logit[0,:].cpu().data.numpy(), Ytruth=spectra[0,:].cpu().data.numpy())
# self.log.add_figure(tag='spectra compare',figure=f,global_step=epoch)
# Model improving, save the model down
if test_avg_loss < self.best_validation_loss:
self.best_validation_loss = test_avg_loss
self.save()
print("Saving the model down...")
if self.best_validation_loss < self.flags.stop_threshold:
print("Training finished EARLIER at epoch %d, reaching loss of %.5f" %\
(epoch, self.best_validation_loss))
return None
# Learning rate decay upon plateau
self.lr_scheduler.step(train_avg_loss)
self.log.close()
tk.record(1) # Record the total time of the training peroid
def evaluate(self, save_dir='data/', prefix=''):
self.load() # load the model as constructed
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model_b.cuda()
self.model_f.cuda()
# Set to evaluation mode for batch_norm layers
self.model_f.eval()
self.model_b.eval()
print('using data set simulator: ', self.flags.data_set)
saved_model_str = self.saved_model.replace('/', '_') + prefix
# Get the file names
Ypred_file = os.path.join(save_dir,
'test_Ypred_{}.csv'.format(saved_model_str))
Xtruth_file = os.path.join(
save_dir, 'test_Xtruth_{}.csv'.format(saved_model_str))
Ytruth_file = os.path.join(
save_dir, 'test_Ytruth_{}.csv'.format(saved_model_str))
Xpred_file = os.path.join(save_dir,
'test_Xpred_{}.csv'.format(saved_model_str))
# For gaussian itself
#Ypre_pred_file = os.path.join(save_dir, 'test_Ypre_pred_{}.csv'.format(saved_model_str))
#YSIM_Truth_file = os.path.join(save_dir, 'test_YSim_truth_{}.csv'.format(saved_model_str))
tk = time_keeper(os.path.join(save_dir, 'evaluation_time.txt'))
# Open those files to append
with open(Xtruth_file, 'a') as fxt,open(Ytruth_file, 'a') as fyt,\
open(Ypred_file, 'a') as fyp, open(Xpred_file, 'a') as fxp: #,\
#open(Ypre_pred_file, 'a') as fypp, open(YSIM_Truth_file, 'a') as fyst:
# Loop through the eval data and evaluate
for ind, (geometry, spectra) in enumerate(self.test_loader):
"""
Older version when we have gaussian_mixture data back then
if self.flags.data_set == 'gaussian_mixture':
spectra_origin = np.copy(spectra.cpu().data.numpy())
spectra = torch.nn.functional.one_hot(spectra.to(torch.int64), 4).to(torch.float) # Change the gaussian labels into one-hot
np.savetxt(fxt, geometry.cpu().data.numpy())
if self.flags.data_set == 'gaussian_mixture':
Xpred = self.model_b(spectra)
#Ypre_pred = self.model_f(Xpred).cpu().data.numpy()
Xpred = Xpred.cpu().data.numpy()
Ypred = simulator(self.flags.data_set, Xpred)
#Ysim_truth = simulator(self.flags.data_set, geometry.cpu().data.numpy())
#np.savetxt(fyst, Ysim_truth)
np.savetxt(fyp, Ypred)
np.savetxt(fxp, Xpred)
np.savetxt(fyt, spectra_origin)
#np.savetxt(fypp, Ypre_pred)
else:
"""
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
Xpred = self.model_b(spectra)
#Ypred = self.model_f(Xpred).cpu().data.numpy()
np.savetxt(fyt, spectra.cpu().data.numpy())
np.savetxt(fxt, geometry.cpu().data.numpy())
if self.flags.data_set != 'meta_material':
Ypred = simulator(self.flags.data_set,
Xpred.cpu().data.numpy())
np.savetxt(fyp, Ypred)
if self.flags.data_set == 'ballistics':
Xpred[:, 3] *= 15
np.savetxt(fxp, Xpred.cpu().data.numpy())
tk.record(1)
return Ypred_file, Ytruth_file
def train(self):
"""
The major training function. This would start the training using information given in the flags
:return: None
"""
"""
Forward Training part
"""
self.best_forward_validation_loss = self.best_validation_loss
# Time keeping
tk = time_keeper(
time_keeping_file=os.path.join(self.ckpt_dir, 'training time.txt'))
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model_f.cuda()
self.model_b.cuda()
if self.load_forward_ckpt_dir is None:
print("Start Forward Training now")
# Construct optimizer after the model moved to GPU
self.optm_f = self.make_optimizer_f()
self.lr_scheduler = self.make_lr_scheduler(self.optm_f)
for epoch in range(self.flags.train_step):
# Set to Training Mode
train_loss = 0
# boundary_loss = 0 # Unnecessary during training since we provide geometries
self.model_f.train()
for j, (geometry, spectra) in enumerate(self.train_loader):
if cuda:
geometry = geometry.cuda() # Put data onto GPU
spectra = spectra.cuda() # Put data onto GPU
self.optm_f.zero_grad() # Zero the gradient first
S_out = self.model_f(geometry) # Get the output
loss = self.make_loss(S_out,
spectra) # Get the loss tensor
loss.backward() # Calculate the backward gradients
self.optm_f.step() # Move one step the optimizer
train_loss += loss # Aggregate the loss
# boundary_loss += self.Boundary_loss # Aggregate the BDY loss
# Calculate the avg loss of training
train_avg_loss = train_loss.cpu().data.numpy() / (j + 1)
# boundary_avg_loss = boundary_loss.cpu().data.numpy() / (j + 1)
if epoch % self.flags.eval_step == 0: # For eval steps, do the evaluations and tensor board
# Record the training loss to the tensorboard
self.log.add_scalar('Loss/forward_train', train_avg_loss,
epoch)
# self.log.add_scalar('Loss/BDY_train', boundary_avg_loss, epoch)
# Set to Evaluation Mode
self.model_f.eval()
print("Doing Evaluation on the forward model now")
test_loss = 0
for j, (geometry, spectra) in enumerate(
self.test_loader): # Loop through the eval set
#if self.flags.data_set == 'gaussian_mixture':
# spectra = torch.nn.functional.one_hot(spectra.to(torch.int64), 4).to(torch.float) # Change the gaussian labels into one-hot
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
logit = self.model_f(geometry)
loss = self.make_loss(logit,
spectra) # compute the loss
test_loss += loss # Aggregate the loss
# Record the testing loss to the tensorboard
test_avg_loss = test_loss.cpu().data.numpy() / (j + 1)
self.log.add_scalar('Loss/forward_test', test_avg_loss,
epoch)
print("This is Epoch %d, training loss %.5f, validation loss %.5f" \
% (epoch, train_avg_loss, test_avg_loss ))
# Model improving, save the model down
if test_avg_loss < self.best_forward_validation_loss:
self.best_forward_validation_loss = test_avg_loss
self.save_f()
print("Saving the model down...")
if self.best_forward_validation_loss < self.flags.stop_threshold:
print("Training finished EARLIER at epoch %d, reaching loss of %.5f" %\
(epoch, self.best_forward_validation_loss))
break
# Learning rate decay upon plateau
self.lr_scheduler.step(train_avg_loss)
else:
print(
"Loading the pre-trained forward model instead of training it")
self.load_f()
# Set to Evaluation Mode
self.model_f.eval()
print("Doing Evaluation on the forward model now")
test_loss = 0
for j, (geometry, spectra) in enumerate(
self.test_loader): # Loop through the eval set
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
logit = self.model_f(geometry)
loss = self.make_loss(logit, spectra) # compute the loss
test_loss += loss # Aggregate the loss
# Record the testing loss to the tensorboard
test_avg_loss = test_loss.cpu().data.numpy() / (j + 1)
print("Test loss of forward model loaded is", test_avg_loss)
"""
Backward Training Part
"""
self.model_b.train()
self.model_f.eval()
print("Now, start Backward Training")
# Construct optimizer after the model moved to GPU
self.optm_b = self.make_optimizer_b()
self.lr_scheduler = self.make_lr_scheduler(self.optm_b)
for epoch in range(self.flags.train_step):
# Set to Training Mode
train_loss = 0
# boundary_loss = 0 # Unnecessary during training since we provide geometries
self.model_b.train()
self.model_f.eval()
for j, (geometry, spectra) in enumerate(self.train_loader):
if cuda:
geometry = geometry.cuda() # Put data onto GPU
spectra = spectra.cuda() # Put data onto GPU
self.optm_b.zero_grad() # Zero the gradient first
#if self.flags.data_set == 'gaussian_mixture':
# spectra = torch.nn.functional.one_hot(spectra.to(torch.int64), 4).to(torch.float) # Change the gaussian labels into one-hot
G_out = self.model_b(spectra) # Get the geometry prediction
# print("G_out.size", G_out.size())
S_out = self.model_f(G_out) # Get the spectra prediction
loss = self.make_loss(S_out, spectra,
G=G_out) # Get the loss tensor
loss.backward() # Calculate the backward gradients
self.optm_b.step() # Move one step the optimizer
train_loss += loss # Aggregate the loss
# boundary_loss += self.Boundary_loss # Aggregate the BDY loss
# Testing code #
#if epoch == self.flags.train_step - 1:
# print('Training Ypred is', S_out.cpu().data.numpy())
# print('Training Ytruth is', spectra.cpu().data.numpy())
# Calculate the avg loss of training
train_avg_loss = train_loss.cpu().data.numpy() / (j + 1)
# boundary_avg_loss = boundary_loss.cpu().data.numpy() / (j + 1)
if epoch % self.flags.eval_step == 0: # For eval steps, do the evaluations and tensor board
# Record the training loss to the tensorboard
self.log.add_scalar('Loss/backward_train', train_avg_loss,
epoch)
self.log.add_scalar('Loss/BDY_train',
self.Boundary_loss.cpu().data.numpy(),
epoch)
# Set to Evaluation Mode
self.model_b.eval()
self.model_f.eval()
print("Doing Evaluation on the backward model now")
test_loss = 0
for j, (geometry, spectra) in enumerate(
self.test_loader): # Loop through the eval set
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
G_out = self.model_b(
spectra) # Get the geometry prediction
S_out = self.model_f(G_out) # Get the spectra prediction
loss = self.make_loss(S_out, spectra,
G=G_out) # compute the loss
test_loss += loss # Aggregate the loss
# Record the testing loss to the tensorboard
test_avg_loss = test_loss.cpu().data.numpy() / (j + 1)
self.log.add_scalar('Loss/backward_test', test_avg_loss, epoch)
self.log.add_scalar('Loss/BDY_test',
self.Boundary_loss.cpu().data.numpy(),
epoch)
# Testing code #
#print('Testing Ypred is', S_out.cpu().data.numpy())
#print('Testing Ytruth is', spectra.cpu().data.numpy())
print("This is Epoch %d, training loss %.5f, validation loss %.5f" \
% (epoch, train_avg_loss, test_avg_loss))
# Model improving, save the model down
if test_avg_loss < self.best_validation_loss:
self.best_validation_loss = test_avg_loss
self.save_b()
print("Saving the backward model down...")
if self.best_validation_loss < -1: #self.flags.stop_threshold:
print("Training finished EARLIER at epoch %d, reaching loss of %.5f" % \
(epoch, self.best_validation_loss))
self.log.close()
break
# Learning rate decay upon plateau
self.lr_scheduler.step(train_avg_loss)
self.log.close()
tk.record(1) # Record the total time of the training peroid
"""
def train(self):
"""
The major training function. This would start the training using information given in the flags
:return: None
"""
print("Starting training now")
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
# Construct optimizer after the model moved to GPU
self.optm = self.make_optimizer()
self.lr_scheduler = self.make_lr_scheduler(self.optm)
dim_x = self.flags.dim_x
dim_y = self.flags.dim_y
dim_z = self.flags.dim_z
dim_tot = self.flags.dim_tot
# Time keeping
tk = time_keeper(
time_keeping_file=os.path.join(self.ckpt_dir, 'training time.txt'))
for epoch in range(self.flags.train_step):
# Set to Training Mode
train_loss = 0
self.model.train()
# If MMD on x-space is present from the start, the model can get stuck.
# Instead, ramp it up exponetially.
loss_factor = min(
1., 2. * 0.002**(1. - (float(epoch) / self.flags.train_step)))
for j, (x, y) in enumerate(self.train_loader):
batch_size = len(x)
if self.flags.data_set == 'gaussian_mixture':
y = y.unsqueeze(1)
######################
# Preparing the data #
######################
# Pad the x, y with zero_noise
y_clean = y.clone() # keep a copy of y for backward
x_pad = self.flags.zeros_noise_scale * torch.randn(
batch_size, dim_tot - dim_x)
y_pad = self.flags.zeros_noise_scale * torch.randn(
batch_size, dim_tot - dim_y - dim_z)
z = torch.randn(batch_size, dim_z)
if cuda:
x = x.cuda() # Put data onto GPU
y = y.cuda() # Put data onto GPU
x_pad = x_pad.cuda()
y_pad = y_pad.cuda()
y_clean = y_clean.cuda()
z = z.cuda()
# Concate the x and y with pads and add y with small purtubation
y += self.flags.y_noise_scale * torch.randn(
batch_size, dim_y, device=device)
x, y = torch.cat((x, x_pad), dim=1), torch.cat((z, y_pad, y),
dim=1)
################
# Forward step #
################
self.optm.zero_grad() # Zero the gradient first
ypred = self.model(x) # Get the Ypred
#y_without_pad = torch.cat((y[:, :dim_z], y[:, -dim_y:]), dim=1)
# Do the same thing for ypred
#y_block_grad = torch.cat((ypred[:, :dim_z], ypred[:, -dim_y:]), dim=1)
# Do the MSE loss for reconstruction, Doesn't compare z part (only pad and y itself)
MSE_loss_y = self.make_loss(logit=ypred[:, dim_z:],
labels=y[:, dim_z:])
# Get the MMD loss for latent
#MMD_loss_latent = self.MMD(y_block_grad, y_without_pad)
#Forward_loss = self.flags.lambda_mse * MSE_loss_y + self.flags.lambda_z * MMD_loss_latent
# Use the maximum likelihood method
log_det = self.model.log_jacobian(x=x)
#print("The log determinant is", log_det)
Forward_loss = 0.5 * (
MSE_loss_y / self.flags.lambda_mse +
torch.mean(torch.pow(z, 2))) - torch.mean(log_det)
Forward_loss.backward()
"""
For a maximum likelihood method, there is no inverse step
#################
# Backward step #
#################
# Create random value for the padding for yz
pad_yz = self.flags.zeros_noise_scale * torch.randn(batch_size,
dim_tot - dim_y - dim_z, device=device)
# Add noise to the backward y value
y = y_clean + self.flags.y_noise_scale * torch.randn(batch_size, dim_y, device=device)
# Create a noisy z vector with noise level same as y
noise_on_z = self.flags.y_noise_scale * torch.randn(batch_size, dim_z, device=device)
# Add the noise to the outcome of z
orig_z_perturbed = ypred.data[:, :dim_z] + noise_on_z
# Set up the input of reverse network
y_rev = torch.cat((orig_z_perturbed, pad_yz, y), dim=1)
rand_z = torch.randn(batch_size, dim_z, device=device)
# set up the randomized input of reverse netowrk
y_rev_rand = torch.cat((rand_z, pad_yz, y), dim=1)
# Get the output of the inverse model
xpred_rev = self.model(y_rev, rev=True)
xpred_rev_rand = self.model(y_rev_rand, rev=True)
# Set the Losses
MMD_loss_x = self.MMD(xpred_rev_rand[:, :dim_x], x[:, :dim_x])
MSE_loss_x = self.make_loss(xpred_rev, x)
Backward_loss = self.flags.lambda_mse * MSE_loss_x + \
loss_factor * self.flags.lambda_rev * MMD_loss_x
Backward_loss.backward()
"""
######################
# Gradient Clipping #
######################
for parameter in self.model.parameters():
parameter.grad.data.clamp_(-self.flags.grad_clamp,
self.flags.grad_clamp)
#########################
# Descent your gradient #
#########################
self.optm.step() # Move one step the optimizer
# L2 + MMD training
#train_loss += Backward_loss + Forward_loss # Aggregate the loss
# MLE training
train_loss += Forward_loss
# Calculate the avg loss of training
train_avg_loss = train_loss.cpu().data.numpy() / (j + 1)
if epoch % self.flags.eval_step == 0: # For eval steps, do the evaluations and tensor board
# Record the training loss to the tensorboard
self.log.add_scalar('Loss/total_train', train_avg_loss, epoch)
self.log.add_scalar('Loss/MSE_y_train', MSE_loss_y, epoch)
#self.log.add_scalar('Loss/MSE_x_train', MSE_loss_x, epoch)
#self.log.add_scalar('Loss/MMD_z_train', MMD_loss_latent, epoch)
#self.log.add_scalar('Loss/MMD_x_train', MMD_loss_x, epoch)
# Set to Evaluation Mode
self.model.eval()
print("Doing Evaluation on the model now")
test_loss = 0
for j, (x, y) in enumerate(
self.test_loader): # Loop through the eval set
batch_size = len(x)
if self.flags.data_set == 'gaussian_mixture':
y = y.unsqueeze(1)
######################
# Preparing the data #
######################
# Pad the x, y with zero_noise
y_clean = y.clone() # keep a copy of y for backward
x_pad = self.flags.zeros_noise_scale * torch.randn(
batch_size, dim_tot - dim_x)
y_pad = self.flags.zeros_noise_scale * torch.randn(
batch_size, dim_tot - dim_y - dim_z)
z = torch.randn(batch_size, dim_z)
if cuda:
x = x.cuda() # Put data onto GPU
y = y.cuda() # Put data onto GPU
x_pad = x_pad.cuda()
y_pad = y_pad.cuda()
y_clean = y_clean.cuda()
z = z.cuda()
# Concate the x and y with pads and add y with small purtubation
y += self.flags.y_noise_scale * torch.randn(
batch_size, dim_y, device=device)
x, y = torch.cat((x, x_pad), dim=1), torch.cat(
(z, y_pad, y), dim=1)
################
# Forward step #
################
self.optm.zero_grad() # Zero the gradient first
ypred = self.model(x) # Get the Ypred
#y_without_pad = torch.cat((y[:, :dim_z], y[:, -dim_y:]), dim=1)
# Do the same thing for ypred
#y_block_grad = torch.cat((ypred[:, :dim_z], ypred[:, -dim_y:]), dim=1)
# Do the MSE loss for reconstruction, Doesn't compare z part (only pad and y itself)
MSE_loss_y = self.make_loss(logit=ypred[:, dim_z:],
labels=y[:, dim_z:])
# Get the MMD loss for latent
#MMD_loss_latent = self.MMD(y_block_grad, y_without_pad)
#Forward_loss = self.flags.lambda_mse * MSE_loss_y + self.flags.lambda_z * MMD_loss_latent
log_det = self.model.log_jacobian(x=x)
#print("The log determinant is", log_det)
Forward_loss = 0.5 * (
MSE_loss_y / self.flags.lambda_mse +
torch.mean(torch.pow(z, 2))) - torch.mean(log_det)
"""
#################
# Backward step #
#################
# Create random value for the padding for yz
pad_yz = self.flags.zeros_noise_scale * torch.randn(batch_size,
dim_tot - dim_y - dim_z, device=device)
# Add noise to the backward y value
y = y_clean + self.flags.y_noise_scale * torch.randn(batch_size, dim_y, device=device)
# Create a noisy z vector with noise level same as y
noise_on_z = self.flags.y_noise_scale * torch.randn(batch_size, dim_z, device=device)
# Add the noise to the outcome of z
orig_z_perturbed = ypred.data[:, :dim_z] + noise_on_z
# Set up the input of reverse network
y_rev = torch.cat((orig_z_perturbed, pad_yz, y), dim=1)
rand_z = torch.randn(batch_size, dim_z, device=device)
# set up the randomized input of reverse network
y_rev_rand = torch.cat((rand_z, pad_yz, y), dim=1)
# Get the output of the inverse model
xpred_rev = self.model(y_rev, rev=True)
xpred_rev_rand = self.model(y_rev_rand, rev=True)
# Set the Losses
MMD_loss_x = self.MMD(xpred_rev_rand[:, :dim_x], x[:, :dim_x])
MSE_loss_x = self.make_loss(xpred_rev, x)
Backward_loss = self.flags.lambda_mse * MSE_loss_x + \
loss_factor * self.flags.lambda_rev * MMD_loss_x
test_loss += Backward_loss + Forward_loss # Aggregate the loss
"""
test_loss += Forward_loss
# Aggregate the other loss (in np form)
# Record the testing loss to the tensorboard
test_avg_loss = test_loss.cpu().data.numpy() / (j + 1)
self.log.add_scalar('Loss/total_test', test_avg_loss, epoch)
self.log.add_scalar('Loss/MSE_y_test', MSE_loss_y, epoch)
#self.log.add_scalar('Loss/MSE_x_test', MSE_loss_x, epoch)
#self.log.add_scalar('Loss/MMD_z_test', MMD_loss_latent, epoch)
#self.log.add_scalar('Loss/MMD_x_test', MMD_loss_x, epoch)
print("This is Epoch %d, training loss %.5f, validation loss %.5f" \
% (epoch, train_avg_loss, test_avg_loss ))
# Model improving, save the model down
if test_avg_loss < self.best_validation_loss:
self.best_validation_loss = train_avg_loss
self.save()
print("Saving the model down...")
if self.best_validation_loss < self.flags.stop_threshold:
print("Training finished EARLIER at epoch %d, reaching loss of %.5f" %\
(epoch, self.best_validation_loss))
break
# Learning rate decay upon plateau
self.lr_scheduler.step(train_avg_loss)
tk.record(1) # Record the total time of the training peroid
def train(self):
"""
The major training function. This would start the training using information given in the flags
:return: None
"""
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
# Construct optimizer after the model moved to GPU
self.optm = self.make_optimizer()
self.lr_scheduler = self.make_lr_scheduler(self.optm)
# Time keeping
tk = time_keeper(
time_keeping_file=os.path.join(self.ckpt_dir, 'training time.txt'))
# Set up the total number of training samples allowed to see
total_training_samples = 0
train_end_flag = False
for epoch in range(self.flags.train_step):
if train_end_flag: # Training is ended due to max sample reached
break
# Set to Training Mode
epoch_samples = 0
metrics = defaultdict(float)
self.model.train()
iou_sum_train = 0
for j, sample in enumerate(self.train_loader):
inputs = sample['image'] # Get the input
labels = sample['labels'] # Get the labels
if cuda:
inputs = inputs.cuda() # Put data onto GPU
labels = labels.cuda() # Put data onto GPU
self.optm.zero_grad() # Zero the gradient first
logit = self.model(inputs.float()) # Get the output
loss = self.make_loss(logit,
labels,
metrics,
bce_weight=self.flags.bce_weight,
boundary_weight=self.flags.
boundary_weight) # Get the loss tensor
loss.backward() # Calculate the backward gradients
self.optm.step() # Move one step the optimizer
epoch_samples += inputs.size(0)
total_training_samples += inputs.size(0)
# change from epoch base to mini-batch base
if j % self.flags.eval_step == 0:
IoU = self.compute_iou(logit, labels)
iou_sum_train += IoU
IoU_aggregate = iou_sum_train / total_training_samples
self.print_metrics(metrics, epoch_samples, 'training')
print('training IoU in current batch {} is'.format(j), IoU)
print('training IoU uptillnow {} is'.format(j),
IoU_aggregate)
self.log.add_scalar('training/bce',
metrics['bce'] / epoch_samples, j)
self.log.add_scalar('training/dice',
metrics['dice'] / epoch_samples, j)
self.log.add_scalar('training/loss',
metrics['loss'] / epoch_samples, j)
self.log.add_scalar('training/IoU', IoU, j)
# Set eval mode
self.model.eval()
# Set to Training Mode
test_epoch_samples = 0
test_metrics = defaultdict(float)
iou_sum = 0
for jj, sample in enumerate(self.test_loader):
inputs = sample['image'] # Get the input
labels = sample['labels'] # Get the labels
if cuda:
inputs = inputs.cuda() # Put data onto GPU
labels = labels.cuda() # Put data onto GPU
self.optm.zero_grad() # Zero the gradient first
logit = self.model(inputs.float()) # Get the output
loss = self.make_loss(logit,
labels,
test_metrics,
bce_weight=self.flags.bce_weight
) # Get the loss tensor
test_epoch_samples += inputs.size(0)
IoU = self.compute_iou(logit, labels)
iou_sum += IoU
if test_epoch_samples > self.flags.max_test_sample:
break
IoU = iou_sum / (jj + 1)
self.print_metrics(metrics, test_epoch_samples, 'testing')
print('IoU in current test batch is', IoU)
self.log.add_scalar(
'test/bce', test_metrics['bce'] / test_epoch_samples,
j)
self.log.add_scalar(
'test/dice', test_metrics['dice'] / test_epoch_samples,
j)
self.log.add_scalar(
'test/loss', test_metrics['loss'] / test_epoch_samples,
j)
self.log.add_scalar('test/IoU', IoU, j)
self.plot_eval_graph(inputs.cpu().numpy(),
logit.detach().cpu().numpy(),
labels.detach().cpu().numpy(), j)
#raise Exception("Testing stop point for getting shapes")
if loss.cpu().data.numpy() < self.best_validation_loss:
self.best_validation_loss = loss.cpu().data.numpy()
# Learning rate decay upon plateau
self.lr_scheduler.step(loss)
if total_training_samples > self.flags.max_train_sample:
print(
"Maximum training samples requirement meet, I have been training for more than ",
total_training_samples, " samples.")
train_end_flag = True
break
self.log.close()
tk.record(999) # Record at the end of the training
# Save the module at the end
self.save()
def train(self):
"""
The major training function. This would start the training using information given in the flags
:return: None
"""
print("Starting training now")
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
# Construct optimizer after the model moved to GPU
self.optm = self.make_optimizer()
self.lr_scheduler = self.make_lr_scheduler(self.optm)
# Time keeping
tk = time_keeper(
time_keeping_file=os.path.join(self.ckpt_dir, 'training time.txt'))
for epoch in range(self.flags.train_step):
# Set to Training Mode
train_loss = 0
loss_aggregate_list = np.array([0., 0.,
0.]) # kl_loss, mse_loss, bdy_loss
self.model.train()
for j, (geometry, spectra) in enumerate(self.train_loader):
if self.flags.data_set == 'gaussian_mixture':
spectra = spectra.unsqueeze(1)
if cuda:
geometry = geometry.cuda() # Put data onto GPU
spectra = spectra.cuda() # Put data onto GPU
self.optm.zero_grad() # Zero the gradient first
G_pred, z_mean, z_log_var = self.model(geometry,
spectra) # Get G_pred
loss, loss_list = self.make_loss(logit=G_pred,
labels=geometry,
z_mean=z_mean,
z_log_var=z_log_var)
loss.backward() # Calculate the backward gradients
self.optm.step() # Move one step the optimizer
train_loss += loss # Aggregate the loss
loss_aggregate_list += loss_list # Aggregate the other loss (in np form)
# Calculate the avg loss of training
train_avg_loss = train_loss.cpu().data.numpy() / (j + 1)
loss_aggregate_list /= (j + 1)
if epoch % self.flags.eval_step == 0: # For eval steps, do the evaluations and tensor board
# Record the training loss to the tensorboard
self.log.add_scalar('Loss/total_train', train_avg_loss, epoch)
self.log.add_scalar('Loss/kl_train', loss_aggregate_list[0],
epoch)
self.log.add_scalar('Loss/mse_train', loss_aggregate_list[1],
epoch)
self.log.add_scalar('Loss/bdy_train', loss_aggregate_list[2],
epoch)
self.log.add_histogram('z_mean',
z_mean.cpu().data.numpy(), epoch)
self.log.add_histogram('z_log_var',
z_log_var.cpu().data.numpy(), epoch)
# Set to Evaluation Mode
self.model.eval()
print("Doing Evaluation on the model now")
test_loss = 0
loss_aggregate_list = np.array(
[0., 0., 0.]) # kl_loss, mse_loss, bdy_loss
for j, (geometry, spectra) in enumerate(
self.test_loader): # Loop through the eval set
if self.flags.data_set == 'gaussian_mixture':
spectra = spectra.unsqueeze(1)
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
G_pred, z_mean, z_log_var = self.model(
geometry, spectra) # Get G_pred
loss, loss_list = self.make_loss(
logit=G_pred,
labels=geometry,
z_mean=z_mean,
z_log_var=z_log_var) # Get the loss tensor
test_loss += loss # Aggregate the loss
loss_aggregate_list += loss_list # Aggregate the other loss (in np form)
# Record the testing loss to the tensorboard
test_avg_loss = test_loss.cpu().data.numpy() / (j + 1)
loss_aggregate_list /= (j + 1)
self.log.add_scalar('Loss/total_test', test_avg_loss, epoch)
self.log.add_scalar('Loss/kl_test', loss_aggregate_list[0],
epoch)
self.log.add_scalar('Loss/mse_test', loss_aggregate_list[1],
epoch)
self.log.add_scalar('Loss/bdy_test', loss_aggregate_list[2],
epoch)
print("This is Epoch %d, training loss %.5f, validation loss %.5f" \
% (epoch, train_avg_loss, test_avg_loss ))
# Model improving, save the model down
if test_avg_loss < self.best_validation_loss:
self.best_validation_loss = test_avg_loss
self.save()
print("Saving the model down...")
if self.best_validation_loss < self.flags.stop_threshold:
print("Training finished EARLIER at epoch %d, reaching loss of %.5f" %\
(epoch, self.best_validation_loss))
break
# Learning rate decay upon plateau
self.lr_scheduler.step(train_avg_loss)
tk.record(1) # Record the total time of the training peroid
def evaluate(self,
save_dir='data/',
save_all=False,
MSE_Simulator=False,
save_misc=False,
save_Simulator_Ypred=False):
"""
The function to evaluate how good the Neural Adjoint is and output results
:param save_dir: The directory to save the results
:param save_all: Save all the results instead of the best one (T_200 is the top 200 ones)
:param MSE_Simulator: Use simulator loss to sort (DO NOT ENABLE THIS, THIS IS OK ONLY IF YOUR APPLICATION IS FAST VERIFYING)
:param save_misc: save all the details that are probably useless
:param save_Simulator_Ypred: Save the Ypred that the Simulator gives
(This is useful as it gives us the true Ypred instead of the Ypred that the network "thinks" it gets, which is
usually inaccurate due to forward model error)
:return:
"""
self.load() # load the model as constructed
try:
bs = self.flags.backprop_step # for previous code that did not incorporate this
except AttributeError:
print(
"There is no attribute backprop_step, catched error and adding this now"
)
self.flags.backprop_step = 300
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
self.model.eval()
saved_model_str = self.saved_model.replace('/', '_')
#saved_model_str = self.flags.eval_model
# Get the file names
Ypred_file = os.path.join(save_dir,
'test_Ypred_{}.csv'.format(saved_model_str))
Xtruth_file = os.path.join(
save_dir, 'test_Xtruth_{}.csv'.format(saved_model_str))
Ytruth_file = os.path.join(
save_dir, 'test_Ytruth_{}.csv'.format(saved_model_str))
Xpred_file = os.path.join(save_dir,
'test_Xpred_{}.csv'.format(saved_model_str))
print("evalution output pattern:", Ypred_file)
# Logging
tk = time_keeper(
time_keeping_file=os.path.join(save_dir, 'evaluation_time.txt'))
min_mse_hist = np.empty(len(self.test_loader))
# Open those files to append
with open(Xtruth_file, 'a') as fxt,open(Ytruth_file, 'a') as fyt,\
open(Ypred_file, 'a') as fyp, open(Xpred_file, 'a') as fxp:
# Loop through the eval data and evaluate
for ind, (geometry, spectra) in enumerate(self.test_loader):
print("Sample #:\t", ind, " / ", len(self.test_loader))
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
# Initialize the geometry first
Xpred, Ypred, loss = self.evaluate_one(
spectra,
save_dir=save_dir,
save_all=save_all,
ind=ind,
MSE_Simulator=MSE_Simulator,
save_misc=save_misc,
save_Simulator_Ypred=save_Simulator_Ypred)
tk.record(
ind) # Keep the time after each evaluation for backprop
if save_misc:
np.savetxt(
'visualize_final/point{}_Xtruth.csv'.format(ind),
geometry.cpu().data.numpy())
np.savetxt(
'visualize_final/point{}_Ytruth.csv'.format(ind),
spectra.cpu().data.numpy())
# suppress printing to evaluate time
geo = geometry.cpu().data.numpy()
spec = spectra.cpu().data.numpy()
np.savetxt(fxt, geo)
np.savetxt(fyt, spec)
if self.flags.data_set != 'meta_material':
np.savetxt(fyp, Ypred)
np.savetxt(fxp, Xpred)
min_mse = np.min(loss)
# Logging
self.log.add_scalar('NA/avg_mse', np.mean(loss), ind)
self.log.add_scalar('NA/min_mse', min_mse, ind)
'''
f = plt.figure()
for i,c in enumerate([0.2,0.5,0.8]):
plt.plot(range(len(spec[0])),worst_samples['truth_spect'][i], '-.',color=c)
plt.plot(range(len(spec[0])),worst_samples['pred_spect'][i], '-',color=c,
label=np.round(worst_samples['mse'][i],6))
plt.xlabel("Wavelength")
plt.ylabel("Spectra")
plt.legend(loc="upper left")
plt.suptitle("3 Random Spectral Fits")
plt.savefig(os.path.join('data','misc_fits_{}.png'.format(saved_model_str)))
'''
return Ypred_file, Ytruth_file
def evaluate(self,
save_dir='data/',
save_all=False,
MSE_Simulator=False,
save_misc=False,
save_Simulator_Ypred=True):
"""
The function to evaluate how good the models is (outputs validation loss)
Note that Ypred and Ytruth still refer to spectra, while Xpred and Xtruth still refer to geometries.
:return:
"""
self.load() # load the model as constructed
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
self.model.eval()
saved_model_str = self.saved_model.replace('/', '_')
# Get the file names
Ypred_file = os.path.join(save_dir, 'test_Ypred_{}.csv'.format(
saved_model_str)) #Input associated? No real value
Xtruth_file = os.path.join(save_dir, 'test_Xtruth_{}.csv'.format(
saved_model_str)) #Output to compare against
Ytruth_file = os.path.join(
save_dir,
'test_Ytruth_{}.csv'.format(saved_model_str)) #Input of Neural Net
Xpred_file = os.path.join(
save_dir,
'test_Xpred_{}.csv'.format(saved_model_str)) #Output of Neural Net
print("evalution output pattern:", Ypred_file)
# Time keeping
tk = time_keeper(
time_keeping_file=os.path.join(save_dir, 'evaluation_time.txt'))
# Open those files to append
with open(Xtruth_file, 'w') as fxt,open(Ytruth_file, 'w') as fyt,\
open(Ypred_file, 'w') as fyp, open(Xpred_file, 'w') as fxp:
# Loop through the eval data and evaluate
geometry, spectra = next(iter(self.test_loader))
if cuda:
geometry = geometry.cuda()
spectra = spectra.cuda()
# Initialize the geometry first
Xpred = self.model(spectra).cpu().data.numpy()
Ytruth = spectra.cpu().data.numpy()
if save_Simulator_Ypred and not (self.flags.data_set == 'Yang'
or self.flags.data_set
== 'Yang_sim'):
Ypred = simulator(self.flags.data_set, Xpred)
else:
Ypred = spectra.cpu().data.numpy()
MSE_List = np.mean(np.power(Ypred - Ytruth, 2), axis=1)
mse = np.mean(MSE_List)
print(mse)
np.savetxt(fxt, geometry.cpu().data.numpy())
np.savetxt(fyt, Ytruth)
if self.flags.data_set != 'Yang':
np.savetxt(fyp, Ypred)
np.savetxt(fxp, Xpred)
return Ypred_file, Ytruth_file
def train(self):
"""
The major training function. This would start the training using information given in the flags
:return: None
"""
print("Starting training now")
cuda = True if torch.cuda.is_available() else False
if cuda:
self.model.cuda()
# Construct optimizer after the model moved to GPU
self.optm = self.make_optimizer()
self.lr_scheduler = self.make_lr_scheduler(self.optm)
dim_x = self.flags.dim_x
dim_y = self.flags.dim_y
dim_z = self.flags.dim_z
# Time keeping
tk = time_keeper(
time_keeping_file=os.path.join(self.ckpt_dir, 'training time.txt'))
for epoch in range(self.flags.train_step):
# Set to Training Mode
train_loss = 0
self.model.train()
# If MMD on x-space is present from the start, the model can get stuck.
# Instead, ramp it up exponetially.
loss_factor = min(
1., 2. * 0.002**(1. - (float(epoch) / self.flags.train_step)))
for j, (x, y) in enumerate(self.train_loader):
batch_size = len(x)
if self.flags.data_set == 'gaussian_mixture':
y = torch.nn.functional.one_hot(y.to(torch.int64), 4).to(
torch.float) # Change the gaussian labels into one-hot
######################
# Preparing the data #
######################
if cuda:
x = x.cuda() # Put data onto GPU
y = y.cuda() # Put data onto GPU
################
# Forward step #
################
self.optm.zero_grad() # Zero the gradient first
z = self.model(x, y) # Get the zpred
loss, jac, zz = self.make_loss(z) # Make the z loss
loss.backward()
######################
# Gradient Clipping #
######################
for parameter in self.model.parameters():
parameter.grad.data.clamp_(-self.flags.grad_clamp,
self.flags.grad_clamp)
#########################
# Descent your gradient #
#########################
self.optm.step() # Move one step the optimizer
train_loss += loss # Aggregate the loss
# Calculate the avg loss of training
train_avg_loss = train_loss.cpu().data.numpy() / (j + 1)
if epoch % self.flags.eval_step == 0: # For eval steps, do the evaluations and tensor board
# Record the training loss to the tensorboard
self.log.add_scalar('Loss/total_train', train_avg_loss, epoch)
self.log.add_scalar('Loss/train_jac', jac, epoch)
self.log.add_scalar('Loss/train_zz', zz, epoch)
# Set to Evaluation Mode
self.model.eval()
print("Doing Evaluation on the model now")
test_loss = 0
for j, (x, y) in enumerate(
self.test_loader): # Loop through the eval set
batch_size = len(x)
if self.flags.data_set == 'gaussian_mixture':
y = torch.nn.functional.one_hot(
y.to(torch.int64),
4).to(torch.float
) # Change the gaussian labels into one-hot
######################
# Preparing the data #
######################
if cuda:
x = x.cuda() # Put data onto GPU
y = y.cuda() # Put data onto GPU
################
# Forward step #
################
self.optm.zero_grad() # Zero the gradient first
z = self.model(x, y) # Get the zpred
loss, jac, zz = self.make_loss(z) # Make the z loss
test_loss += loss # Aggregate the loss
# Record the testing loss to the tensorboard
test_avg_loss = test_loss.cpu().data.numpy() / (j + 1)
self.log.add_scalar('Loss/total_test', test_avg_loss, epoch)
self.log.add_scalar('Loss/test_jac', jac, epoch)
self.log.add_scalar('Loss/test_zz', zz, epoch)
print("This is Epoch %d, training loss %.5f, validation loss %.5f" \
% (epoch, train_avg_loss, test_avg_loss ))
# Model improving, save the model down
if test_avg_loss < self.best_validation_loss:
self.best_validation_loss = train_avg_loss
self.save()
print("Saving the model down...")
if self.best_validation_loss < self.flags.stop_threshold:
print("Training finished EARLIER at epoch %d, reaching loss of %.5f" %\
(epoch, self.best_validation_loss))
break
# Learning rate decay upon plateau
self.lr_scheduler.step(train_avg_loss)
tk.record(1) # Record the total time of the training peroid
