def MNIST_GAN(epoch, epoch_start, G, G_optim, D, D_optim, dataloader, train_fn,
history, best_stat, times, config, z_fixed):
'''
MNIST dataset training loop for GAN model. Used to train GAN as a
proof-of-concept, i.e. that the linear GAN model is able to reproduce
MNIST data, and is therefore (possibly) suited to reproducing the
LArCV1 dataset. Hopefully this will extend to other datasets...
- Args: G (Torch model): Generator model
G_optim (function): G optimizer (either adam or sgd)
D (Torch model): Discriminator model
D_optim (function): D optimizer (either adam or sgd)
Dataloader (iterable): Torch dataloader object wrapped as
tqdm progress bar for terminal output
train_fn (function): GAN training function selected in train.py
history, best_stat, times, config (dicts): dictionaries
epoch, epoch_start (ints)
z_fixed (Torch tensor): Fixed vector for sampling G at the
end of a training epoch
'''
for itr, (x, _) in enumerate(dataloader):
tr_loop_start = time.time()
metrics = train_fn(x)
history, best_stat, best = utils.train_logger(history, best_stat,
metrics)
# Save checkpoint periodically
if (itr % 2000 == 0):
# G Checkpoint
chkpt_G = utils.get_checkpoint(itr, epoch, G, G_optim)
utils.save_checkpoint(chkpt_G, best, 'G', config['weights_save'])
# D Checkpoint
chkpt_D = utils.get_checkpoint(itr, epoch, D, D_optim)
utils.save_checkpoint(chkpt_D, best, 'D', config['weights_save'])
# Save Generator output periodically
if (itr % 1000 == 0):
z_rand = torch.randn(config['sample_size'],
config['z_dim']).to(config['gpu'])
sample = G(z_rand).view(-1, 1, config['dataset'],
config['dataset'])
utils.save_sample(sample, epoch, itr, config['random_samples'])
# Log the time at the end of training loop
times['tr_loop_times'].append(time.time() - tr_loop_start)
# Log the time at the end of the training epoch
times['epoch_times'].append(time.time() - epoch_start)
# Save Generator output using fixed vector at end of epoch
sample = G(z_fixed).view(-1, 1, config['dataset'], config['dataset'])
utils.save_sample(sample, epoch, itr, config['fixed_samples'])
return history, best_stat, times
def train_mlp(G_mlp, opt_G, psi, opt_psi, dataloader, device, loader_kwargs,
config, times, losses, hist_dict, checkpt):
if config['mnist']:
import poc
poc.train_MNIST(G_mlp, opt_G, psi, opt_psi, dataloader, device,
loader_kwargs, config, times, losses, hist_dict,
checkpt)
return
# Set fixed noise vector for testing
z_fixed = utils.get_z(config, device, sample=False)
z_fixed.resize_((config['batch_size'], config['z_dim']))
# Create python version of cpp operation
if config['dist'] == 'W1':
from torch.utils.cpp_extension import load
my_ops = load(name="my_ops",
sources=[
"W1_extension/my_ops.cpp",
"W1_extension/my_ops_kernel.cu"
],
verbose=False)
import my_ops
n_dim = len(dataloader)
# Training loop
for epoch in range(config['num_epochs']):
epoch_start_time = time.time()
# Save list of losses for end-training determination
loss_memory = []
# Set up memory tensors: simple feed-forward distribution, transfer plan
mu = torch.zeros(config['mem_size'], config['batch_size'],
config['z_dim'])
transfer = torch.zeros(config['mem_size'],
config['batch_size'],
dtype=torch.long)
mem_idx = 0
# Compute Optimal Transport Solver (OTS) over every training example
ot_start = time.time()
# for ots_iter in range(0, config['dset_size']):
for iter in range(0, loader_kwargs['batch_size']):
opt_psi.zero_grad()
# Generate samples from feed-forward distribution
z_batch = utils.get_z(config, device, sample=False)
z_batch.resize_((config['batch_size'], config['z_dim']))
y_fake = G_mlp(z_batch) # [B, n_dim]
# Compute cost between sample batch and target distribution
if (config['dist'] == 'W1'):
score = -my_ops.l1_t(y_fake, dataloader) - psi
else:
score = torch.matmul(
y_fake, dataloader.t()) - psi # score: [B, N], psi: [N]
# phi, hit = torch.max(score, 1)
phi, hit = torch.min(score, 1) # [B], [B]
# Wasserstein distance computation: d(x,y)^p
if (config['dist'] == 'W1'):
loss_primal = torch.mean(
torch.abs(y_fake - dataloader[hit])) * config['out_dim']
else:
loss_primal = torch.mean(
(y_fake - dataloader[hit])**2) * config['out_dim']
# Loss computation
# loss = -(torch.mean(phi) + torch.mean(psi)) # Testing this
loss = -torch.mean(psi[hit]) # equiv. to loss?
# Backprop
loss.backward() # Gradient ascent
opt_psi.step()
# Append losses to dict
losses['ot_loss'].append(loss.item())
losses['w2_estim'].append(loss_primal.item())
# Update memory tensors
mu[mem_idx] = z_batch
transfer[mem_idx] = hit
mem_idx = (mem_idx + 1) % config['mem_size']
if (iter % 500 == 0):
print('OTS Iteration {} | Epoch {}'.format(iter, epoch))
if (iter % (config['num_epochs'] * 10) == 0):
# Display histogram stats
hist_dict, stop = utils.update_histogram(
transfer, n_dim, epoch, iter, config, losses, hist_dict)
# Emperical stopping criterion
if stop:
break
# Compute OTS time and append
ot_end = time.time()
times['ot_time'].append(ot_end - ot_start)
# Compute Fitting Optimal Transport Plan (FIT)
fit_start = time.time()
for fit_iter in range(config['mem_size']):
opt_G.zero_grad()
# Get stored batch of generated samples
z_batch = mu[fit_iter].to(device)
y_fake = G_mlp(z_batch) # G'(z)
# Get Transfer plan from OTS: T(G_{t-1}(z))
y0_hit = dataloader[transfer[fit_iter].to(device)]
# Compute Wasserstein distance between G and T
if (config['dist'] == 'W1'):
loss_g = torch.mean(
torch.abs(y0_hit - y_fake)) * config['out_dim']
else:
loss_g = torch.mean((y0_hit - y_fake)**2) * config['out_dim']
# Backprop
loss_g.backward() # Gradient descent
opt_G.step()
# Append losses to dict
losses['g_loss'].append(loss_g.item())
loss_memory.append(loss_g.item())
if (fit_iter % 500 == 0):
print(
'Fit_iter: {} | Epoch: {} | Loss: {:.2f} | Best Loss: {:.2f}'
.format(fit_iter, epoch, loss_g, checkpt['best']))
# Check if best loss value and save checkpoint
threshold = (checkpt['best'] - round(checkpt['best'] * 0.5))
best = (loss_g.item() < threshold)
if best:
checkpt['best'] = loss_g.item()
chkpt_dict = utils.checkpoint_dict(fit_iter, epoch, G_mlp,
opt_G)
utils.save_checkpoint(chkpt_dict, best, epoch, -1,
config['weights_root'])
# Save periodic checkpoint
if (fit_iter % (config['num_epochs'] * 5) == 0):
chkpt_dict = utils.checkpoint_dict(fit_iter, epoch, G_mlp,
opt_G)
utils.save_checkpoint(chkpt_dict, False, epoch, fit_iter,
config['weights_root'])
# Get random sample from G
if (fit_iter % (config['num_epochs']) == 0):
z_rand = utils.get_z(config, device, sample=True)
z_rand.resize_((config['batch_size'], config['z_dim']))
sample = G_mlp(z_rand).view(-1, 1, config['imsize'],
config['imsize'])
utils.save_sample(sample, epoch, fit_iter, config['random'])
# Check if loss is changing - stop training if no change
if (len(loss_memory) > (config['mem_size'] // 2)):
if ((loss_g <= (mean(loss_memory)*.999)) and \
(loss_g >= (mean(loss_memory)*.995))):
break
# Compute FIT time
fit_end = time.time()
times['fit_time'].append(fit_end - fit_start)
# Compute epoch time
times['epoch_times'].append(time.time() - epoch_start_time)
# Output to terminal
print('Best loss: {}'.format(checkpt['best']))
print('Epoch_time: {}'.format(time.time() - epoch_start_time))
print('Num epochs: {}'.format(epoch))
print("FIT loss: {:.2f}".format(np.mean(losses['g_loss'])))
# Save fixed sample at end of training epoch
sample = G_mlp(z_fixed).view(-1, 1, config['imsize'], config['imsize'])
utils.save_sample(sample, epoch, 0, config['fixed'])
return [times, losses, hist_dict]
def train(config):
'''
Training function for EWM generator model.
'''
# Create python version of cpp operation
# (Credit: Chen, arXiv:1906.03471, GitHub: https://github.com/chen0706/EWM)
from torch.utils.cpp_extension import load
my_ops = load(name = "my_ops",
sources = ["W1_extension/my_ops.cpp",
"W1_extension/my_ops_kernel.cu"],
verbose = False)
import my_ops
# Set up GPU device ordinal
device = torch.device(config['gpu'])
# Get model kwargs for convolutional generator
config['model'] == 'ewm_conv'
emw_kwargs = setup_model.ewm_kwargs(config)
# Setup convolutional generator model on GPU
G = ewm.ewm_convG(**emw_kwargs).to(device)
# ewm.weights_init(G)
G.train()
# Setup model optimizer
model_params = {'g_params': G.parameters()}
G_optim = utils.get_optim(config, model_params)
# Testing: MSE loss for conv_generator reconstruction
loss_fn = nn.MSELoss().to(device)
# Print G -- make sure it's right before continuing
print(G)
input('Press any key to continue')
# Setup source of structured noise on GPU (Trained EWM_MLP_Generator Model)
# Add these configs to the experiment source file
if not config['ewm_root']:
raise Exception('Path to trained EWM_MLP Model must be specified')
# Print list of evaluated EWM model
EWM_paths = []; EWM_root = config['ewm_root']
for path in os.listdir(EWM_root):
EWM_paths.append(os.path.join(EWM_root, path))
print("-"*60)
for i in range(len(EWM_paths)):
EWM_name = EWM_paths[i].split('/')[-1]
print("\n Exp_{}:".format(str(i)), EWM_name, '\n')
print("-"*60)
# Select the trained model
model_num = input('Select EWM_MLP model (enter integer): ')
EWM_dir = EWM_paths[int(model_num)]
# Create the full path to the EWM model
EWM_path = os.path.join(EWM_root, EWM_dir) + '/'
print("Path to EWM Generator Model set as: \n{}".format(EWM_path))
config_csv = EWM_path + "config.csv"
config_df = pd.read_csv(config_csv, delimiter = ",")
# Get the model architecture from config df
n_layers = int(config_df[config_df['Unnamed: 0'].str.contains("n_layers")==True]['0'].values.item())
n_hidden = int(config_df[config_df['Unnamed: 0'].str.contains("n_hidden")==True]['0'].values.item())
l_dim = int(config_df[config_df['Unnamed: 0'].str.contains("l_dim")==True]['0'].values.item())
im_size = int(config_df[config_df['Unnamed: 0'].str.contains("dataset")==True]['0'].values.item())
z_dim = int(config_df[config_df['Unnamed: 0'].str.contains("z_dim")==True]['0'].values.item())
# Model kwargs
ewm_kwargs = {'z_dim': z_dim, 'fc_sizes': [n_hidden]*n_layers, 'n_out': l_dim}
# Send model to GPU
Gz = ewm.ewm_G(**ewm_kwargs).to(device)
# Load the model checkpoint
# Get checkpoint name(s)
EWM_checkpoint_path = EWM_path + 'weights/'
EWM_checkpoint_names = []
for file in os.listdir(EWM_checkpoint_path):
EWM_checkpoint_names.append(os.path.join(EWM_checkpoint_path, file))
print("-"*60)
for i in range(len(EWM_checkpoint_names)):
name = EWM_checkpoint_names[i].split('/')[-1]
print("\n {} :".format(str(i)), name, '\n')
print("-"*60)
file_num = input("Select a checkpoint file for EWM_MLP (enter integer): ")
EWM_checkpoint = EWM_checkpoint_names[int(file_num)]
# Load the model checkpoint
# Keys: ['state_dict', 'epoch', 'optimizer']
checkpoint = torch.load(EWM_checkpoint)
# Load the model's state dictionary
Gz.load_state_dict(checkpoint['state_dict'])
# Use the code_vector model in evaluation mode -- no need for gradients here
Gz.eval()
print(Gz)
input('Press any key to continue')
# Set up full_dataloader (single batch)
dataloader = utils.get_dataloader(config).to(device) # Full Dataloader
dset_size = len(dataloader)
# Flatten the dataloader into a Tensor of shape [dset_size, l_dim]
# dataloader = dataloader.view(dset_size, -1).to(device)
# Set up psi optimizer
psi = torch.zeros(dset_size, requires_grad=True).to(device).detach().requires_grad_(True)
psi_optim = torch.optim.Adam([psi], lr=config['psi_lr'])
# Set up directories for saving training stats and outputs
config = utils.directories(config)
# Set up dict for saving checkpoints
checkpoint_kwargs = {'G':G, 'G_optim':G_optim}
# Set up stats logging
hist_dict = {'hist_min':[], 'hist_max':[], 'ot_loss':[]}
losses = {'ot_loss': [], 'fit_loss': []}
history = {'dset_size': dset_size, 'epoch': 0, 'iter': 0,
'losses' : losses, 'hist_dict': hist_dict}
config['early_end'] = (200, 320) # Empirical stopping criterion from EWM author
stop_counter = 0
# Compute how the input vectors need to be reshaped, based on conv_G input layer
in_f = G.main[0][2].in_channels; out_f = G.main[0][2].out_channels
# Set the height and width of the feature maps. Note: Manually setting this to 8 is
# hackish, but computing the actualy value requires knowing the number of layers in
# the AutoEncoder whose code layer was used to train the generator model being loaded
# here. I'm avoiding loading multiple paths and dataframes by simply setting it to 8,
# but maybe you can do better than I did...
H = W = 8
print('Vectors will be reshaped as: [{}] --> [{},{},{}]'.format(l_dim, in_f, H, W))
# Set a fixed feature tensor for testing
noise = torch.randn(config['sample_size'], config['z_dim']).to(device)
z_fixed = Gz(noise).view(-1, in_f, H, W).to(device)
# Training Loop
input('\nPress any key to launch -- good luck out there\n')
# Set up progress bar for terminal output and enumeration
epoch_bar = tqdm([i for i in range(config['num_epochs'])])
for epoch, _ in enumerate(epoch_bar):
history['epoch'] = epoch
# Set up memory lists:
# - mu: simple feed-forward distribution
# - transfer: transfer plan given by lists of indices
# Rule-of-thumb: do not save the tensors themselves: instead, save the
# data as a list and covert it to a tensor as needed.
mu = [0] * config['mem_size']
transfer = [0] * config['mem_size']
mem_idx = 0
# Compute the Optimal Transport Solver
print("\nOptimal Transport Solver")
ots_bar = tqdm([i for i in range(dset_size//10)])
for ots_iter, _ in enumerate(ots_bar):
history['iter'] = ots_iter
psi_optim.zero_grad()
# Generate samples from cove_vector distribution
z_batch = torch.randn(config['batch_size'], config['z_dim']).to(device)
z_batch = Gz(z_batch).view(-1, in_f, H, W)
# Push structured noise vector through convolutional generator
# y_fake = G(z_batch).view(config['batch_size'], -1) # Flatten the output to match dataloader
y_fake = G(z_batch)
# Compute the W1 distance between the model output and the target distribution
score = my_ops.l1_t(y_fake, dataloader) - psi
phi, hit = torch.max(score, 1)
# Standard loss computation
# This loss defines the sample mean of the marginal distribution
# of the dataset. This is the only computation that generalizes.
loss = -torch.mean(psi[hit])
# Backprop
loss.backward()
psi_optim.step()
# Update memory tensors (lists)
mu[mem_idx] = z_batch.data.cpu().numpy().tolist()
transfer[mem_idx] = hit.data.cpu().numpy().tolist()
mem_idx = (mem_idx + 1) % config['mem_size']
# Update losses
history['losses']['ot_loss'].append(loss.item())
if (ots_iter % 50 == 0):
avg_loss = np.mean(history['losses']['ot_loss'])
# print('OTS Iteration {} | Epoch {} | Avg Loss Value: {}'.format(ots_iter, epoch, round(avg_loss, 3)))
if (ots_iter % 2000 == 0):
# Occasionally save a random sample from the generator during OTS
sample = y_fake[0:config['sample_size']].view(-1, 1, config['dataset'], config['dataset'])
utils.save_sample(sample, epoch, ots_iter, config['random_samples'])
# # Display histogram stats
# hist_dict, stop = utils.update_histogram(transfer, history, config)
# # Emperical stopping criterion
# if stop:
# break
# Compute the Optimal Fitting Transport Plan
print("\nFitting Optimal Transport Plan")
fit_bar = tqdm([i for i in range(config['mem_size'])])
for fit_iter, _ in enumerate(fit_bar):
G_optim.zero_grad()
# Retrieve stored batch of generated samples
# z_batch = torch.tensor(mu[fit_iter]).view(-1, in_f, H, W).to(device)
z_batch = torch.tensor(mu[fit_iter]).to(device)
# Flatten the model output to match dataloader
# y_fake = G(z_batch).view(config['batch_size'], -1)
y_fake = G(z_batch)
# Get Transfer plan from OTS: T(G_{t-1}(Gz))
t_plan = torch.tensor(transfer[fit_iter])
y0_hit = dataloader[t_plan].to(device)
# Compute Wasserstein distance between G and T
# G_loss = torch.mean(torch.abs(y0_hit - y_fake)) * l_dim
G_loss = loss_fn(y_fake, y0_hit)
# Backprop
G_loss.backward() # Gradient descent
G_optim.step()
# Update losses
history['losses']['fit_loss'].append(G_loss.item())
# Check if best loss value and save checkpoint
if 'best_loss' not in history:
history.update({ 'best_loss' : G_loss.item() })
best = G_loss.item() < (history['best_loss'] * 0.5)
if best:
history['best_loss'] = G_loss.item()
checkpoint = utils.get_checkpoint(history['epoch'], checkpoint_kwargs, config)
utils.save_checkpoint(checkpoint, config)
if (fit_iter % 50 == 0):
avg_loss = np.mean(history['losses']['fit_loss'])
# print('FIT Iteration {} | Epoch {} | Avg Loss Value: {}'.format(fit_iter, epoch, round(avg_loss,3)))
# Save a fixed sample of the generator's output at the end of FIT
sample = G(z_fixed).view(-1, 1, config['dataset'], config['dataset'])
utils.save_sample(sample, epoch, fit_iter, config['fixed_samples'])
# Save a checkpoint at end of training
checkpoint = utils.get_checkpoint(history['epoch'], checkpoint_kwargs, config)
utils.save_checkpoint(checkpoint, config)
# Save training data to csv's after training end
utils.save_train_hist(history, config, times=None, histogram=history['hist_dict'])
print("Stop Counter Triggered {} Times".format(stop_counter))
# For Spike
print("See you, Space Cowboy")
def train(config):
'''
Training function for EWM generator model.
'''
# Create python version of cpp operation
# (Credit: Chen, arXiv:1906.03471, GitHub: https://github.com/chen0706/EWM)
from torch.utils.cpp_extension import load
my_ops = load(name = "my_ops",
sources = ["W1_extension/my_ops.cpp",
"W1_extension/my_ops_kernel.cu"],
verbose = False)
import my_ops
# Set up GPU device ordinal - if this fails, use CUDA_LAUNCH_BLOCKING environment param...
device = torch.device(config['gpu'])
# Get model kwargs
emw_kwargs = setup_model.ewm_kwargs(config)
# Setup model on GPU
G = ewm_G(**emw_kwargs).to(device)
G.weights_init()
print(G)
input('Press any key to launch')
# Setup model optimizer
model_params = {'g_params': G.parameters()}
G_optim = utils.get_optim(config, model_params)
# Set up full_dataloader (single batch)
dataloader = utils.get_dataloader(config) # Full Dataloader
dset_size = len(dataloader)
# Flatten the dataloader into a Tensor of shape [dset_size, l_dim]
dataloader = dataloader.view(dset_size, -1).to(device)
# Set up psi optimizer
psi = torch.zeros(dset_size, requires_grad=True).to(device).detach().requires_grad_(True).to(device)
psi_optim = torch.optim.Adam([psi], lr=config['psi_lr'])
# Set up directories for saving training stats and outputs
config = utils.directories(config)
# Set up dict for saving checkpoints
checkpoint_kwargs = {'G':G, 'G_optim':G_optim}
# Variance argument for the tessellation vectors
tess_var = config['tess_var']**0.5
# Compute the stopping criterion using set of test vectors
# and computing the 'ideal' loss between the test/target.
print(line(60))
print("Computing stopping criterion")
print(line(60))
stop_criterion = []
test_loader = utils.get_test_loader(config)
for _, test_vecs in enumerate(test_loader):
# Add Gaussian noise to test_vectors
test_vecs = test_vecs.view(config['batch_size'], -1).to(device) # 'Perfect' generator model
t1 = tess_var*torch.randn(test_vecs.shape[0], test_vecs.shape[1]).to(device)
test_vecs += t1
# Add Gaussian noise to target data
t2 = tess_var*torch.randn(dataloader.shape[0], dataloader.shape[1]).to(device)
test_target = dataloader + t2
# Compute the stop score
stop_score = my_ops.l1_t(test_vecs, test_target)
stop_loss = -torch.mean(stop_score)
stop_criterion.append(stop_loss.cpu().detach().numpy())
del test_loader
# Set stopping criterion variables
stop_min, stop_mean, stop_max = np.min(stop_criterion), np.mean(stop_criterion), np.max(stop_criterion)
print(line(60))
print('Stop Criterion: min: {}, mean: {}, max: {}'.format(round(stop_min, 3), round(stop_mean, 3), round(stop_max, 3)))
print(line(60))
# Set up stats logging
hist_dict = {'hist_min':[], 'hist_max':[], 'ot_loss':[]}
losses = {'ot_loss': [], 'fit_loss': []}
history = {'dset_size': dset_size, 'epoch': 0, 'iter': 0, 'losses' : losses, 'hist_dict': hist_dict}
config['early_end'] = (200, 320) # Empirical stopping criterion from EWM author
stop_counter = 0
# Set up progress bar for terminal output and enumeration
epoch_bar = tqdm([i for i in range(config['num_epochs'])])
# Training Loop
for epoch, _ in enumerate(epoch_bar):
history['epoch'] = epoch
# Set up memory lists:
# - mu: simple feed-forward distribution
# - transfer: transfer plan given by lists of indices
# Rule-of-thumb: do not save the tensors themselves: instead, save the
# data as a list and covert it to a tensor as needed.
mu = [0] * config['mem_size']
transfer = [0] * config['mem_size']
mem_idx = 0
# Compute the Optimal Transport Solver
for ots_iter in range(1, dset_size//2):
history['iter'] = ots_iter
psi_optim.zero_grad()
# Generate samples from feed-forward distribution
z_batch = torch.randn(config['batch_size'], config['z_dim']).to(device)
y_fake = G(z_batch) # [B, dset_size]
# # Add Gaussian noise to the output of the generator function and to the data with tessellation vectors
t1 = tess_var*torch.randn(y_fake.shape[0], y_fake.shape[1]).to(device)
t2 = tess_var*torch.randn(dataloader.shape[0], dataloader.shape[1]).to(device)
y_fake += t1
dataloader += t2
# Compute the W1 distance between the model output and the target distribution
score = my_ops.l1_t(y_fake, dataloader) - psi
phi, hit = torch.max(score, 1)
# Remove the tesselation from the dataloader
dataloader -= t2
# Standard loss computation
# This loss defines the sample mean of the marginal distribution
# of the dataset. This is the only computation that generalizes.
loss = -torch.mean(psi[hit])
# Backprop
loss.backward()
psi_optim.step()
# Update memory tensors
mu[mem_idx] = z_batch.data.cpu().numpy().tolist()
transfer[mem_idx] = hit.data.cpu().numpy().tolist()
mem_idx = (mem_idx + 1) % config['mem_size']
# Update losses
history['losses']['ot_loss'].append(loss.item())
if (ots_iter % 500 == 0):
avg_loss = np.mean(history['losses']['ot_loss'])
print('OTS Iteration {} | Epoch {} | Avg Loss Value: {}'.format(ots_iter, epoch, round(avg_loss, 3)))
# if (iter % 2000 == 0):
# # Display histogram stats
# hist_dict, stop = utils.update_histogram(transfer, history, config)
# # Emperical stopping criterion
# if stop:
# break
if ots_iter > (dset_size//3):
if stop_min <= np.mean(history['losses']['ot_loss']) <= stop_max:
stop_counter += 1
break
# Compute the Optimal Fitting Transport Plan
for fit_iter in range(config['mem_size']):
G_optim.zero_grad()
# Retrieve stored batch of generated samples
z_batch = torch.tensor(mu[fit_iter]).to(device)
y_fake = G(z_batch) # G'(z)
# Get Transfer plan from OTS: T(G_{t-1}(z))
t_plan = torch.tensor(transfer[fit_iter]).to(device)
y0_hit = dataloader[t_plan].to(device)
# Tesselate the output of the generator function and the data
# t1 = tess_var*torch.randn(y_fake.shape[0], y_fake.shape[1]).to(device)
# t2 = tess_var*torch.randn(y0_hit.shape[0], y0_hit.shape[1]).to(device)
# y_fake *= t1
# y0_hit *= t1
# Compute Wasserstein distance between G and T
G_loss = torch.mean(torch.abs(y0_hit - y_fake)) * config['l_dim']
# Backprop
G_loss.backward() # Gradient descent
G_optim.step()
# Update losses
history['losses']['fit_loss'].append(G_loss.item())
# Check if best loss value and save checkpoint
if 'best_loss' not in history:
history.update({ 'best_loss' : G_loss.item() })
best = G_loss.item() < (history['best_loss'] * 0.70)
if best:
history['best_loss'] = G_loss.item()
checkpoint = utils.get_checkpoint(history['epoch'], checkpoint_kwargs, config)
utils.save_checkpoint(checkpoint, config)
if (fit_iter % 500 == 0):
avg_loss = np.mean(history['losses']['fit_loss'])
print('FIT Iteration {} | Epoch {} | Avg Loss Value: {}'.format(fit_iter, epoch, round(avg_loss,3)))
# Save a checkpoint at end of training
checkpoint = utils.get_checkpoint(history['epoch'], checkpoint_kwargs, config)
utils.save_checkpoint(checkpoint, config)
# Save training data to csv's after training end
utils.save_train_hist(history, config, times=None, histogram=history['hist_dict'])
print("Stop Counter Triggered {} Times".format(stop_counter))
# For Aiur
print("I see you have an appetite for destruction.")
print("And you have learned to use your illusion.")
print("But I find your lack of control disturbing.")