# Run standard learning for each task and average the parameters:
avg_param_vec = None
for i_task in range(n_train_tasks):
print('Learning train-task {} out of {}'.format(i_task+1, n_train_tasks))
data_loader = train_data_loaders[i_task]
test_err, curr_model = learn_single_standard.run_learning(data_loader, prm, verbose=0)
if i_task == 0:
avg_param_vec = parameters_to_vector(curr_model.parameters()) * (1 / n_train_tasks)
else:
avg_param_vec += parameters_to_vector(curr_model.parameters()) * (1 / n_train_tasks)
avg_model = deterministic_models.get_model(prm)
vector_to_parameters(avg_param_vec, avg_model.parameters())
# create the prior model:
prior_model = stochastic_models.get_model(prm)
prior_layers_list = [layer for layer in prior_model.modules() if isinstance(layer, StochasticLayer)]
avg_model_layers_list = [layer for layer in avg_model.modules()
if isinstance(layer, torch.nn.Conv2d) or isinstance(layer, torch.nn.Linear)]
assert len(avg_model_layers_list)==len(prior_layers_list), "lists not equal"
for i_layer, prior_layer in enumerate(prior_layers_list):
if hasattr(prior_layer, 'w'):
prior_layer.w['log_var'] = torch.nn.Parameter(zeros_gpu(1))
prior_layer.w['mean'] = avg_model_layers_list[i_layer].weight
if hasattr(prior_layer, 'b'):
prior_layer.b['log_var'] = torch.nn.Parameter(zeros_gpu(1))
prior_layer.b['mean'] = avg_model_layers_list[i_layer].bias
# save learned prior:
save_model_state(prior_model, save_path)
write_to_log('Trained prior saved in ' + save_path, prm)
else:
# In this case we observe new tasks generated from the task-distribution in each meta-iteration.
write_to_log('---- Infinite train tasks - New training tasks are '
'drawn from tasks distribution in each iteration...', prm)
# Meta-training to learn meta-prior (theta params):
prior_model = meta_train_Bayes_infinite_tasks.run_meta_learning(task_generator, prm)
elif prm.mode == 'LoadMetaModel':
# Loads previously training prior.
# First, create the model:
prior_model = get_model(prm)
prm.load_model_path = '/hdd/shiwei/meta_learning _example/PriorMetaLearning/saved/ShuffledPixels100_TasksN/log 2019-05-23 14:36:30/1/model.pt'
# prm.load_model_path = '/hdd/shiwei/meta_learning _example/PriorMetaLearning/saved/PermutedLabels_TasksN/log 2019-04-18 12:40:53/5/model.pt'
# prm.load_model_path = '/hdd/shiwei/meta_learning _example/PriorMetaLearning/saved/model.pt'
# Then load the weights:
load_model_state(prior_model, prm.load_model_path)
write_to_log('Pre-trained prior loaded from ' + prm.load_model_path, prm)
else:
raise ValueError('Invalid mode')
# -------------------------------------------------------------------------------------------
# Generate the data sets of the test tasks:
# -------------------------------------------------------------------------------------------
n_test_tasks = prm.n_test_tasks
def run_learning(task_data, prior_model, prm, init_from_prior=True, verbose=1):
# -------------------------------------------------------------------------------------------
# Setting-up
# -------------------------------------------------------------------------------------------
# Unpack parameters:
optim_func, optim_args, lr_schedule =\
prm.optim_func, prm.optim_args, prm.lr_schedule
# Loss criterion
loss_criterion = get_loss_criterion(prm.loss_type)
# Create posterior model for the new task:
post_model = get_model(prm)
if init_from_prior:
post_model.load_state_dict(prior_model.state_dict())
# prior_model_dict = prior_model.state_dict()
# post_model_dict = post_model.state_dict()
#
# # filter out unnecessary keys:
# prior_model_dict = {k: v for k, v in prior_model_dict.items() if '_log_var' in k or '_mu' in k}
# # overwrite entries in the existing state dict:
# post_model_dict.update(prior_model_dict)
#
# # # load the new state dict
# post_model.load_state_dict(post_model_dict)
# add_noise_to_model(post_model, prm.kappa_factor)
# The data-sets of the new task:
train_loader = task_data['train']
test_loader = task_data['test']
n_train_samples = len(train_loader.dataset)
n_batches = len(train_loader)
# Get optimizer:
optimizer = optim_func(post_model.parameters(), **optim_args)
# -------------------------------------------------------------------------------------------
# Training epoch function
# -------------------------------------------------------------------------------------------
def run_train_epoch(i_epoch):
log_interval = 500
post_model.train()
for batch_idx, batch_data in enumerate(train_loader):
correct_count = 0
sample_count = 0
# Monte-Carlo iterations:
n_MC = prm.n_MC
task_empirical_loss = 0
task_complexity = 0
for i_MC in range(n_MC):
# get batch:
inputs, targets = data_gen.get_batch_vars(batch_data, prm)
# Calculate empirical loss:
outputs = post_model(inputs)
curr_empirical_loss = loss_criterion(outputs, targets)
curr_empirical_loss, curr_complexity = get_bayes_task_objective(
prm,
prior_model,
post_model,
n_train_samples,
curr_empirical_loss,
noised_prior=False)
task_empirical_loss += (1 / n_MC) * curr_empirical_loss
task_complexity += (1 / n_MC) * curr_complexity
correct_count += count_correct(outputs, targets)
sample_count += inputs.size(0)
# Total objective:
total_objective = task_empirical_loss + task_complexity
# Take gradient step with the posterior:
grad_step(total_objective, optimizer, lr_schedule, prm.lr, i_epoch)
# Print status:
if batch_idx % log_interval == 0:
batch_acc = correct_count / sample_count
print(
cmn.status_string(i_epoch, prm.n_meta_test_epochs,
batch_idx, n_batches, batch_acc,
total_objective.item()) +
' Empiric Loss: {:.4}\t Intra-Comp. {:.4}'.format(
task_empirical_loss.item(), task_complexity.item()))
data_objective.append(total_objective.item())
data_accuracy.append(batch_acc)
data_emp_loss.append(task_empirical_loss.item())
data_task_comp.append(task_complexity.item())
return total_objective.item()
# -----------------------------------------------------------------------------------------------------------#
# Update Log file
if verbose == 1:
write_to_log(
'Total number of steps: {}'.format(n_batches *
prm.n_meta_test_epochs), prm)
# -------------------------------------------------------------------------------------------
# Run epochs
# -------------------------------------------------------------------------------------------
start_time = timeit.default_timer()
data_objective = []
data_accuracy = []
data_emp_loss = []
data_task_comp = []
# Training loop:
for i_epoch in range(prm.n_meta_test_epochs):
test_bound = run_train_epoch(i_epoch)
with open(
os.path.join(prm.result_dir, 'run_test_data_prior_bound_data.pkl'),
'wb') as f:
pickle.dump(
{
'data_objective': data_objective,
"data_accuracy": data_accuracy,
'data_emp_loss': data_emp_loss,
'data_task_comp': data_task_comp
}, f)
# Test:
test_acc, test_loss = run_test_Bayes(post_model, test_loader,
loss_criterion, prm)
stop_time = timeit.default_timer()
cmn.write_final_result(test_acc,
stop_time - start_time,
prm,
result_name=prm.test_type,
verbose=verbose)
test_err = 1 - test_acc
return test_err, test_loss, test_bound, post_model
def run_learning(task_data, prior_model, prm, init_from_prior=True, verbose=1):
# prm.optim_func, prm.optim_args = optim.EntropySGD, {'llr':0.01, 'lr':0.1, 'momentum':0.9, 'damp':0, 'weight_decay':1e-3, 'nesterov':True,
# 'L':20, 'eps':1e-3, 'g0':1e-4, 'g1':1e-3}
# -------------------------------------------------------------------------------------------
# Setting-up
# -------------------------------------------------------------------------------------------
# Unpack parameters:
# prm.optim_args['llr'] = 0.1
# prm.optim_args['L'] = 20
# # prm.optim_args['weight_decay'] = 1e-3
# # prm.optim_args['g1'] = 0
# prm.optim_args['g0'] = 1e-4
optim_func, optim_args, lr_schedule =\
prm.optim_func, prm.optim_args, prm.lr_schedule_test
# prm.optim_func, prm.optim_args = optim.Adam, {'lr': prm.lr} # 'weight_decay': 1e-4
# lr_schedule = {'decay_factor': 0.1, 'decay_epochs': [15, 20]}
# Loss criterion
loss_criterion = get_loss_criterion(prm.loss_type)
# Create posterior model for the new task:
post_model = get_model(prm)
if init_from_prior:
post_model.load_state_dict(prior_model.state_dict())
# prior_model_dict = prior_model.state_dict()
# post_model_dict = post_model.state_dict()
#
# # filter out unnecessary keys:
# prior_model_dict = {k: v for k, v in prior_model_dict.items() if '_log_var' in k or '_mu' in k}
# # overwrite entries in the existing state dict:
# post_model_dict.update(prior_model_dict)
#
# # # load the new state dict
# post_model.load_state_dict(post_model_dict)
# add_noise_to_model(post_model, prm.kappa_factor)
# The data-sets of the new task:
train_loader = task_data
test_loader = task_data['test']
# n_train_samples = len(train_loader['train'].dataset)
n_batches = len(train_loader)
# Get optimizer:
optimizer = optim_func(
filter(lambda p: p.requires_grad, post_model.parameters()), optim_args)
# optimizer = optim_func(filter(lambda p: p.requires_grad, post_model.parameters()), optim_args['lr'])
# -------------------------------------------------------------------------------------------
# Training epoch function
# -------------------------------------------------------------------------------------------
def run_train_epoch(i_epoch):
# log_interval = 500
post_model.train()
train_iterators = iter(train_loader['train'])
for batch_idx, batch_data in enumerate(train_loader['train']):
task_loss, info = get_objective(prior_model, prm, [train_loader],
object.feval, [train_iterators],
[post_model], loss_criterion, 1,
[0])
grad_step(task_loss[0], post_model, loss_criterion, optimizer, prm,
train_iterators, train_loader['train'], lr_schedule,
prm.optim_args['lr'], i_epoch)
# for log_var in post_model.parameters():
# if log_var.requires_grad is False:
# log_var.data = log_var.data - (i_epoch + 1) * math.log(1 + prm.gamma1)
# Print status:
log_interval = 10
if (batch_idx) % log_interval == 0:
batch_acc = info['correct_count'] / info['sample_count']
print(
cmn.status_string(i_epoch, prm.n_meta_train_epochs,
batch_idx, n_batches, batch_acc) +
' Empiric-Loss: {:.4f}'.format(info['avg_empirical_loss']))
# -----------------------------------------------------------------------------------------------------------#
# Update Log file
if verbose == 1:
write_to_log(
'Total number of steps: {}'.format(n_batches *
prm.n_meta_test_epochs), prm)
# -------------------------------------------------------------------------------------------
# Run epochs
# -------------------------------------------------------------------------------------------
start_time = timeit.default_timer()
# Training loop:
for i_epoch in range(prm.n_meta_test_epochs):
run_train_epoch(i_epoch)
# Test:
test_acc, test_loss = run_test_Bayes(post_model, test_loader,
loss_criterion, prm)
stop_time = timeit.default_timer()
cmn.write_final_result(test_acc,
stop_time - start_time,
prm,
result_name=prm.test_type,
verbose=verbose)
test_err = 1 - test_acc
return test_err, post_model
def run_meta_learning(data_loaders, prm):
# -------------------------------------------------------------------------------------------
# Setting-up
# -------------------------------------------------------------------------------------------
# Unpack parameters:
optim_func, optim_args, lr_schedule =\
prm.optim_func, prm.optim_args, prm.lr_schedule
# Loss criterion
loss_criterion = get_loss_criterion(prm.loss_type)
n_train_tasks = len(data_loaders)
# Create a 'dummy' model to generate the set of parameters of the shared prior:
prior_model = get_model(prm)
# Create posterior models for each task:
posteriors_models = [
transfer_weights(prior_model, get_model(prm))
for _ in range(n_train_tasks)
]
# Gather all tasks posterior params:
# all_post_param = sum([list(posterior_model.parameters()) for posterior_model in posteriors_models], [])
# Create optimizer for all parameters (posteriors + prior)
prior_params = filter(lambda p: p.requires_grad, prior_model.parameters())
prior_optimizer = optim_func(prior_params, optim_args)
object.grad_init(prm, prior_model, loss_criterion,
iter(data_loaders[0]['train']), data_loaders[0]['train'],
prior_optimizer)
# all_params = all_post_param + prior_params
all_posterior_optimizers = [
optim_func(
filter(lambda p: p.requires_grad,
posteriors_models[i].parameters()), optim_args)
for i in range(n_train_tasks)
]
# number of sample-batches in each task:
n_batch_list = [len(data_loader['train']) for data_loader in data_loaders]
n_batches_per_task = np.max(n_batch_list)
L = prm.optim_args['L']
# -------------------------------------------------------------------------------------------
# Training epoch function
# -------------------------------------------------------------------------------------------
def run_train_epoch(i_epoch):
# optim_args['L'] = L-5*i_epoch
# For each task, prepare an iterator to generate training batches:
train_iterators = [
iter(data_loaders[ii]['train']) for ii in range(n_train_tasks)
]
# The task order to take batches from:
# The meta-batch will be balanced - i.e, each task will appear roughly the same number of times
# note: if some tasks have less data that other tasks - it may be sampled more than once in an epoch
task_order = []
task_ids_list = list(range(n_train_tasks))
for i_batch in range(n_batches_per_task):
random.shuffle(task_ids_list)
task_order += task_ids_list
# Note: this method ensures each training sample in each task is drawn in each epoch.
# If all the tasks have the same number of sample, then each sample is drawn exactly once in an epoch.
# ----------- meta-batches loop (batches of tasks) -----------------------------------#
# each meta-batch includes several tasks
# we take a grad step with theta after each meta-batch
# meta_batch_starts = list(range(0, len(task_order), n_train_tasks))
meta_batch_starts = list(range(0, len(task_order),
prm.meta_batch_size))
n_meta_batches = len(meta_batch_starts)
for i_meta_batch in range(n_meta_batches):
meta_batch_start = meta_batch_starts[i_meta_batch]
task_ids_in_meta_batch = task_order[meta_batch_start:(
meta_batch_start + prm.meta_batch_size)]
# meta-batch size may be less than prm.meta_batch_size at the last one
# note: it is OK if some tasks appear several times in the meta-batch
mb_data_loaders = [
data_loaders[task_id] for task_id in task_ids_in_meta_batch
]
mb_iterators = [
train_iterators[task_id] for task_id in task_ids_in_meta_batch
]
mb_posteriors_models = [
posteriors_models[task_id]
for task_id in task_ids_in_meta_batch
]
#task_loss_list, info = get_objective(prior_model, prm, mb_data_loaders, object.feval,
# mb_iterators, mb_posteriors_models, loss_criterion, n_train_tasks, task_ids_in_meta_batch)
# Take gradient step with the shared prior and all tasks' posteriors:
# for i_task in range(n_train_tasks):
# prior_optimizer.zero_grad()
#for i_task in range(n_train_tasks):
# if isinstance(task_loss_list[i_task], int):
# continue
# grad_step(task_loss_list[i_task], posteriors_models[i_task], loss_criterion, all_posterior_optimizers[i_task], prm,
# train_iterators[i_task], data_loaders[i_task]['train'], lr_schedule, prm.lr, i_epoch)
#task_loss_list, info = get_objective(prior_model, prm, mb_data_loaders, object.feval,
# mb_iterators, mb_posteriors_models, loss_criterion, n_train_tasks, task_ids_in_meta_batch)
# Take gradient step with the shared prior and all tasks' posteriors:
# for i_task in range(n_train_tasks):
# prior_optimizer.zero_grad()
#for i_task in range(n_train_tasks):
# if isinstance(task_loss_list[i_task], int):
# continue
# grad_step(task_loss_list[i_task], posteriors_models[i_task], loss_criterion, all_posterior_optimizers[i_task], prm,
# train_iterators[i_task], data_loaders[i_task]['train'], lr_schedule, prm.lr, i_epoch)
# Get objective based on tasks in meta-batch:
task_loss_list, info = get_objective(prior_model, prm,
mb_data_loaders, object.feval,
mb_iterators,
mb_posteriors_models,
loss_criterion, n_train_tasks,
task_ids_in_meta_batch)
# Take gradient step with the shared prior and all tasks' posteriors:
# for i_task in range(n_train_tasks):
prior_optimizer.zero_grad()
for i_task in range(n_train_tasks):
if isinstance(task_loss_list[i_task], int):
continue
grad_step(task_loss_list[i_task], posteriors_models[i_task],
loss_criterion, all_posterior_optimizers[i_task],
prm, train_iterators[i_task],
data_loaders[i_task]['train'], lr_schedule, prm.lr,
i_epoch)
# if i_meta_batch==n_meta_batches-1:
# if i_epoch == prm.n_meta_train_epochs-1:
prior_get_grad(prior_optimizer,
all_posterior_optimizers[i_task])
# task_loss_list, info = get_objective(prior_model, prm, mb_data_loaders, object.feval,
# mb_iterators, mb_posteriors_models, loss_criterion, n_train_tasks, task_ids_in_meta_batch)
# prior_grad_step(prior_optimizer, prm.meta_batch_size, prm,prm.prior_lr_schedule, prm.prior_lr, i_epoch)
prior_updates(prior_optimizer, n_train_tasks, prm)
for post_model in posteriors_models:
post_model.load_state_dict(prior_model.state_dict())
# Print status:
log_interval = 10
if (i_meta_batch) % log_interval == 0:
batch_acc = info['correct_count'] / info['sample_count']
print(
cmn.status_string(i_epoch, prm.n_meta_train_epochs,
i_meta_batch, n_meta_batches, batch_acc)
+
' Empiric-Loss: {:.4f}'.format(info['avg_empirical_loss']))
# for i in range(20):
# prior_grad_step(prior_optimizer, prm.meta_batch_size, prm, lr_schedule, prm.prior_lr, i_epoch)
# end meta-batches loop
# end run_epoch()
# -------------------------------------------------------------------------------------------
# Test evaluation function -
# Evaluate the mean loss on samples from the test sets of the training tasks
# --------------------------------------------------------------------------------------------
def run_test():
test_acc_avg = 0.0
n_tests = 0
for i_task in range(n_train_tasks):
model = posteriors_models[i_task]
test_loader = data_loaders[i_task]['test']
if len(test_loader) > 0:
test_acc, test_loss = run_test_Bayes(model, test_loader,
loss_criterion, prm)
n_tests += 1
test_acc_avg += test_acc
n_test_samples = len(test_loader.dataset)
write_to_log(
'Train Task {}, Test set: {} - Average loss: {:.4}, Accuracy: {:.3} (of {} samples)\n'
.format(i_task, prm.test_type, test_loss, test_acc,
n_test_samples), prm)
else:
print('Train Task {}, Test set: {} - No test data'.format(
i_task, prm.test_type))
if n_tests > 0:
test_acc_avg /= n_tests
return test_acc_avg
# -----------------------------------------------------------------------------------------------------------#
# Main script
# -----------------------------------------------------------------------------------------------------------#
# Update Log file
write_to_log(cmn.get_model_string(prior_model), prm)
write_to_log('---- Meta-Training set: {0} tasks'.format(len(data_loaders)),
prm)
# -------------------------------------------------------------------------------------------
# Run epochs
# -------------------------------------------------------------------------------------------
start_time = timeit.default_timer()
# Training loop:
for i_epoch in range(prm.n_meta_train_epochs):
# if (i_epoch+1) % 50 == 0:
# prm.lr = prm.lr/2
# for post_model in posteriors_models:
# post_model.load_state_dict(prior_model.state_dict())
run_train_epoch(i_epoch)
# for post_model in posteriors_models:
# post_model.load_state_dict(prior_model.state_dict())
# prior_update(prior_optimizer,prm.meta_batch_size,prm)
stop_time = timeit.default_timer()
# Test:
test_acc_avg = run_test()
# Update Log file:
cmn.write_final_result(test_acc_avg,
stop_time - start_time,
prm,
result_name=prm.test_type)
# Return learned prior:
return prior_model
def run_learning(task_data, prior_model, prm, init_from_prior=True, verbose=1):
# -------------------------------------------------------------------------------------------
# Setting-up
# -------------------------------------------------------------------------------------------
# Unpack parameters:
optim_func, optim_args, lr_schedule =\
prm.optim_func, prm.optim_args, prm.lr_schedule
# Loss criterion
loss_criterion = get_loss_func(prm)
# Create posterior model for the new task:
post_model = get_model(prm)
if init_from_prior:
post_model.load_state_dict(prior_model.state_dict())
# prior_model_dict = prior_model.state_dict()
# post_model_dict = post_model.state_dict()
#
# # filter out unnecessary keys:
# prior_model_dict = {k: v for k, v in prior_model_dict.items() if '_log_var' in k or '_mu' in k}
# # overwrite entries in the existing state dict:
# post_model_dict.update(prior_model_dict)
#
# # # load the new state dict
# post_model.load_state_dict(post_model_dict)
# add_noise_to_model(post_model, prm.kappa_factor)
# The data-sets of the new task:
train_loader = task_data['train']
test_loader = task_data['test']
n_train_samples = len(train_loader.dataset)
n_batches = len(train_loader)
# Get optimizer:
optimizer = optim_func(post_model.parameters(), **optim_args)
# -------------------------------------------------------------------------------------------
# Training epoch function
# -------------------------------------------------------------------------------------------
def run_train_epoch(i_epoch):
log_interval = 500
post_model.train()
for batch_idx, batch_data in enumerate(train_loader):
# get batch data:
inputs, targets = data_gen.get_batch_vars(batch_data, prm)
batch_size = inputs.shape[0]
correct_count = 0
sample_count = 0
# Monte-Carlo iterations:
n_MC = prm.n_MC
avg_empiric_loss = 0
complexity_term = 0
for i_MC in range(n_MC):
# Calculate empirical loss:
outputs = post_model(inputs)
avg_empiric_loss_curr = (1 / batch_size) * loss_criterion(
outputs, targets)
# complexity_curr = get_task_complexity(prm, prior_model, post_model,
# n_train_samples, avg_empiric_loss_curr)
avg_empiric_loss += (1 / n_MC) * avg_empiric_loss_curr
# complexity_term += (1 / n_MC) * complexity_curr
correct_count += count_correct(outputs, targets)
sample_count += inputs.size(0)
# end monte-carlo loop
complexity_term = get_task_complexity(prm, prior_model, post_model,
n_train_samples,
avg_empiric_loss)
# Approximated total objective (for current batch):
if prm.complexity_type == 'Variational_Bayes':
# note that avg_empiric_loss_per_task is estimated by an average over batch samples,
# but its weight in the objective should be considered by how many samples there are total in the task
total_objective = avg_empiric_loss * (
n_train_samples) + complexity_term
else:
total_objective = avg_empiric_loss + complexity_term
# Take gradient step with the posterior:
grad_step(total_objective, optimizer, lr_schedule, prm.lr, i_epoch)
# Print status:
if batch_idx % log_interval == 0:
batch_acc = correct_count / sample_count
print(
cmn.status_string(i_epoch, prm.n_meta_test_epochs,
batch_idx, n_batches, batch_acc,
total_objective.item()) +
' Empiric Loss: {:.4}\t Intra-Comp. {:.4}'.format(
avg_empiric_loss.item(), complexity_term.item()))
# end batch loop
# end run_train_epoch()
# -----------------------------------------------------------------------------------------------------------#
# Update Log file
if verbose == 1:
write_to_log(
'Total number of steps: {}'.format(n_batches *
prm.n_meta_test_epochs), prm)
# -------------------------------------------------------------------------------------------
# Run epochs
# -------------------------------------------------------------------------------------------
start_time = timeit.default_timer()
# Training loop:
for i_epoch in range(prm.n_meta_test_epochs):
run_train_epoch(i_epoch)
# Test:
test_acc, test_loss = run_eval_Bayes(post_model, test_loader, prm)
stop_time = timeit.default_timer()
cmn.write_final_result(test_acc,
stop_time - start_time,
prm,
result_name=prm.test_type,
verbose=verbose)
test_err = 1 - test_acc
return test_err, post_model
def run_learning(data_loader, prm, prior_model=None, init_from_prior=True, verbose=1):
# -------------------------------------------------------------------------------------------
# Setting-up
# -------------------------------------------------------------------------------------------
# Unpack parameters:
optim_func, optim_args, lr_schedule = \
prm.optim_func, prm.optim_args, prm.lr_schedule
# Loss criterion
loss_criterion = get_loss_criterion(prm.loss_type)
train_loader = data_loader['train']
test_loader = data_loader['test']
n_batches = len(train_loader)
n_train_samples = data_loader['n_train_samples']
# get model:
if prior_model and init_from_prior:
# init from prior model:
post_model = deepcopy(prior_model)
else:
post_model = get_model(prm)
# post_model.set_eps_std(0.0) # DEBUG: turn off randomness
# Get optimizer:
optimizer = optim_func(post_model.parameters(), **optim_args)
# -------------------------------------------------------------------------------------------
# Training epoch function
# -------------------------------------------------------------------------------------------
def run_train_epoch(i_epoch):
# # Adjust randomness (eps_std)
# if hasattr(prm, 'use_randomness_schedeule') and prm.use_randomness_schedeule:
# if i_epoch > prm.randomness_full_epoch:
# eps_std = 1.0
# elif i_epoch > prm.randomness_init_epoch:
# eps_std = (i_epoch - prm.randomness_init_epoch) / (prm.randomness_full_epoch - prm.randomness_init_epoch)
# else:
# eps_std = 0.0 # turn off randomness
# post_model.set_eps_std(eps_std)
# post_model.set_eps_std(0.00) # debug
complexity_term = 0
post_model.train()
for batch_idx, batch_data in enumerate(train_loader):
# Monte-Carlo iterations:
empirical_loss = 0
n_MC = prm.n_MC
# get batch:
inputs, targets = data_gen.get_batch_vars(batch_data, prm)
for i_MC in range(n_MC):
# calculate objective:
outputs = post_model(inputs)
empirical_loss_c = loss_criterion(outputs, targets)
empirical_loss += (1 / n_MC) * empirical_loss_c
# complexity/prior term:
if prior_model:
empirical_loss, complexity_term = get_bayes_task_objective(
prm, prior_model, post_model, n_train_samples, empirical_loss)
else:
complexity_term = 0.0
# Total objective:
objective = empirical_loss + complexity_term
# Take gradient step:
grad_step(objective, optimizer, lr_schedule, prm.lr, i_epoch)
# Print status:
log_interval = 500
if batch_idx % log_interval == 0:
batch_acc = correct_rate(outputs, targets)
print(cmn.status_string(i_epoch, prm.num_epochs, batch_idx, n_batches, batch_acc, get_value(objective)) +
' Loss: {:.4}\t Comp.: {:.4}'.format(get_value(empirical_loss), get_value(complexity_term)))
# -------------------------------------------------------------------------------------------
# Main Script
# -------------------------------------------------------------------------------------------
# Update Log file
update_file = not verbose == 0
cmn.write_to_log(cmn.get_model_string(post_model), prm, update_file=update_file)
cmn.write_to_log('Total number of steps: {}'.format(n_batches * prm.num_epochs), prm, update_file=update_file)
cmn.write_to_log('Number of training samples: {}'.format(data_loader['n_train_samples']), prm, update_file=update_file)
start_time = timeit.default_timer()
# Run training epochs:
for i_epoch in range(prm.num_epochs):
run_train_epoch(i_epoch)
# Test:
test_acc, test_loss = run_test_Bayes(post_model, test_loader, loss_criterion, prm)
stop_time = timeit.default_timer()
cmn.write_final_result(test_acc, stop_time - start_time, prm, result_name=prm.test_type)
test_err = 1 - test_acc
return test_err, post_model
train_dataset, test_dataset, info = load_pretrain_dataset()
train_loader = DataLoader(train_dataset,
batch_size=bz,
shuffle=True,
num_workers=12,
pin_memory=True)
print(len(train_dataset), len(test_dataset))
test_loader = DataLoader(test_dataset,
batch_size=bz,
shuffle=True,
num_workers=12,
pin_memory=True)
save_dir = "pretrained_cifar100"
os.makedirs(save_dir, exist_ok=True)
net = get_model(prm)
#load_model_state(net, save_dir + "/" + "epoch-2-acc0.277.pth")
#debug()
print(net)
net = net.cuda()
loss_fn = nn.CrossEntropyLoss()
learning_rate = 1e-3
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)
for epoch in range(epoch_num):
cnt = 0
for imgs, ys in train_loader:
cnt += 1
