def run(args, log_interval=5000, rerun=False):
global temp
assert not args.maml
# see if we already ran this experiment
code_root = os.path.dirname(os.path.realpath(__file__))
if not os.path.isdir('{}/{}_result_files/'.format(code_root, args.task)):
os.mkdir('{}/{}_result_files/'.format(code_root, args.task))
path = '{}/{}_result_files/'.format(
code_root, args.task) + utils.get_path_from_args(args)
if os.path.exists(path + '.pkl') and not rerun:
return utils.load_obj(path)
start_time = time.time()
utils.set_seed(args.seed)
# --- initialise everything ---
# get the task family
if args.task == 'sine':
task_family_train = tasks_sine.RegressionTasksSinusoidal()
task_family_valid = tasks_sine.RegressionTasksSinusoidal()
task_family_test = tasks_sine.RegressionTasksSinusoidal()
elif args.task == 'celeba':
task_family_train = tasks_celebA.CelebADataset('train',
device=args.device)
task_family_valid = tasks_celebA.CelebADataset('valid',
device=args.device)
task_family_test = tasks_celebA.CelebADataset('test',
device=args.device)
elif args.task == 'multi':
task_family_train = multi()
task_family_valid = multi()
task_family_test = multi()
else:
raise NotImplementedError
# initialise network
model = CaviaModel(n_in=task_family_train.num_inputs,
n_out=task_family_train.num_outputs,
num_context_params=args.num_context_params,
n_hidden=args.num_hidden_layers,
device=args.device).to(args.device)
# intitialise meta-optimiser
# (only on shared params - context parameters are *not* registered parameters of the model)
meta_optimiser = optim.Adam(model.parameters(), args.lr_meta)
encoder = pool_encoder().to(args.device)
encoder_optimiser = optim.Adam(encoder.parameters(), lr=1e-3)
decoder = pool_decoder().to(args.device)
decoder_optimiser = optim.Adam(decoder.parameters(), lr=1e-3)
#encoder.load_state_dict(torch.load('./model/encoder'))
p_encoder = place().to(args.device)
p_optimiser = optim.Adam(p_encoder.parameters(), lr=1e-3)
# initialise loggers
logger = Logger()
logger.best_valid_model = copy.deepcopy(model)
# --- main training loop ---
for i_iter in range(args.n_iter):
# initialise meta-gradient
meta_gradient = [0 for _ in range(len(model.state_dict()))]
place_gradient = [0 for _ in range(len(p_encoder.state_dict()))]
encoder_gradient = [0 for _ in range(len(encoder.state_dict()))]
#print(meta_gradient)
# sample tasks
target_functions, ty = task_family_train.sample_tasks(
args.tasks_per_metaupdate, True)
# --- inner loop ---
for t in range(args.tasks_per_metaupdate):
# reset private network weights
model.reset_context_params()
# get data for current task
x = task_family_train.sample_inputs(
args.k_meta_train, args.use_ordered_pixels).to(args.device)
y = target_functions[t](x)
train_inputs = torch.cat([x, y], dim=1)
a = encoder(train_inputs)
#embedding,_ = torch.max(a,dim=0)
embedding = torch.mean(a, dim=0)
logits = p_encoder(embedding)
logits = logits.reshape([latent_dim, categorical_dim])
y = gumbel_softmax(logits, temp, hard=True)
y = y[:, 1]
#print(temp)
#model.set_context_params(embedding)
#print(model.context_params)
for _ in range(args.num_inner_updates):
# forward through model
train_outputs = model(x)
# get targets
train_targets = target_functions[t](x)
# ------------ update on current task ------------
# compute loss for current task
task_loss = F.mse_loss(train_outputs, train_targets)
# compute gradient wrt context params
task_gradients = \
torch.autograd.grad(task_loss, model.context_params, create_graph=not args.first_order)[0]
# update context params (this will set up the computation graph correctly)
model.context_params = model.context_params - args.lr_inner * task_gradients * y
#print(model.context_params)
# ------------ compute meta-gradient on test loss of current task ------------
# get test data
test_inputs = task_family_train.sample_inputs(
args.k_meta_test, args.use_ordered_pixels).to(args.device)
# get outputs after update
test_outputs = model(test_inputs)
# get the correct targets
test_targets = target_functions[t](test_inputs)
# compute loss after updating context (will backprop through inner loop)
loss_meta = F.mse_loss(test_outputs, test_targets)
#print(torch.norm(y,1)/1000)
#loss_meta += torch.norm(y,1)/700
qy = F.softmax(logits, dim=-1)
log_ratio = torch.log(qy * categorical_dim + 1e-20)
KLD = torch.sum(qy * log_ratio, dim=-1).mean() / 5
# print(KLD)
loss_meta += KLD
# compute gradient + save for current task
task_grad = torch.autograd.grad(loss_meta,
model.parameters(),
retain_graph=True)
for i in range(len(task_grad)):
# clip the gradient
meta_gradient[i] += task_grad[i].detach().clamp_(-10, 10)
task_grad_place = torch.autograd.grad(loss_meta,
p_encoder.parameters(),
retain_graph=True)
for i in range(len(task_grad_place)):
# clip the gradient
place_gradient[i] += task_grad_place[i].detach().clamp_(
-10, 10)
task_grad_encoder = torch.autograd.grad(loss_meta,
encoder.parameters())
for i in range(len(task_grad_encoder)):
# clip the gradient
encoder_gradient[i] += task_grad_encoder[i].detach().clamp_(
-10, 10)
# ------------ meta update ------------
# assign meta-gradient
for i, param in enumerate(model.parameters()):
param.grad = meta_gradient[i] / args.tasks_per_metaupdate
meta_optimiser.step()
# do update step on shared model
for i, param in enumerate(p_encoder.parameters()):
param.grad = place_gradient[i] / args.tasks_per_metaupdate
p_optimiser.step()
for i, param in enumerate(encoder.parameters()):
param.grad = encoder_gradient[i] / args.tasks_per_metaupdate
encoder_optimiser.step()
# reset context params
model.reset_context_params()
if i_iter % 350 == 1:
temp = np.maximum(temp * np.exp(-ANNEAL_RATE * i_iter), 0.5)
print(temp)
# ------------ logging ------------
if i_iter % log_interval == 0:
# evaluate on training set
loss_mean, loss_conf = eval_cavia(
args,
copy.deepcopy(model),
task_family=task_family_train,
num_updates=args.num_inner_updates,
encoder=encoder,
p_encoder=p_encoder)
logger.train_loss.append(loss_mean)
logger.train_conf.append(loss_conf)
# evaluate on test set
loss_mean, loss_conf = eval_cavia(
args,
copy.deepcopy(model),
task_family=task_family_valid,
num_updates=args.num_inner_updates,
encoder=encoder,
p_encoder=p_encoder)
logger.valid_loss.append(loss_mean)
logger.valid_conf.append(loss_conf)
# evaluate on validation set
if i_iter % log_interval == 0:
loss_mean, loss_conf = eval_cavia(
args,
copy.deepcopy(model),
task_family=task_family_test,
num_updates=args.num_inner_updates,
encoder=encoder,
p_encoder=p_encoder)
logger.test_loss.append(loss_mean)
logger.test_conf.append(loss_conf)
# save logging results
utils.save_obj(logger, path)
# save best model
if logger.valid_loss[-1] == np.min(logger.valid_loss):
print('saving best model at iter', i_iter)
logger.best_valid_model = copy.deepcopy(model)
logger.best_encoder_valid_model = copy.deepcopy(encoder)
logger.best_place_valid_model = copy.deepcopy(p_encoder)
if i_iter % (4 * log_interval) == 0:
print('saving model at iter', i_iter)
logger.valid_model.append(copy.deepcopy(model))
logger.encoder_valid_model.append(copy.deepcopy(encoder))
logger.place_valid_model.append(copy.deepcopy(p_encoder))
# visualise results
if args.task == 'celeba':
task_family_train.visualise(
task_family_train, task_family_test,
copy.deepcopy(logger.best_valid_model), args, i_iter)
# print current results
logger.print_info(i_iter, start_time)
start_time = time.time()
return logger
def run(args, log_interval=5000, rerun=False):
assert not args.maml
# see if we already ran this experiment
code_root = os.path.dirname(os.path.realpath(__file__))
if not os.path.isdir('{}/{}_result_files/'.format(code_root, args.task)):
os.mkdir('{}/{}_result_files/'.format(code_root, args.task))
path = '{}/{}_result_files/'.format(
code_root, args.task) + utils.get_path_from_args(args)
if os.path.exists(path + '.pkl') and not rerun:
return utils.load_obj(path)
start_time = time.time()
utils.set_seed(args.seed)
# --- initialise everything ---
# get the task family
if args.task == 'sine':
task_family_train = tasks_sine.RegressionTasksSinusoidal()
task_family_valid = tasks_sine.RegressionTasksSinusoidal()
task_family_test = tasks_sine.RegressionTasksSinusoidal()
elif args.task == 'celeba':
task_family_train = tasks_celebA.CelebADataset('train',
device=args.device)
task_family_valid = tasks_celebA.CelebADataset('valid',
device=args.device)
task_family_test = tasks_celebA.CelebADataset('test',
device=args.device)
else:
raise NotImplementedError
# initialise network
model = CaviaModel(n_in=task_family_train.num_inputs,
n_out=task_family_train.num_outputs,
num_context_params=args.num_context_params,
n_hidden=args.num_hidden_layers,
device=args.device).to(args.device)
# intitialise meta-optimiser
# (only on shared params - context parameters are *not* registered parameters of the model)
meta_optimiser = optim.Adam(model.parameters(), args.lr_meta)
# initialise loggers
logger = Logger()
logger.best_valid_model = copy.deepcopy(model)
# --- main training loop ---
for i_iter in range(args.n_iter):
# initialise meta-gradient
meta_gradient = [0 for _ in range(len(model.state_dict()))]
# sample tasks
target_functions = task_family_train.sample_tasks(
args.tasks_per_metaupdate)
# --- inner loop ---
for t in range(args.tasks_per_metaupdate):
# reset private network weights
model.reset_context_params()
# get data for current task
train_inputs = task_family_train.sample_inputs(
args.k_meta_train, args.use_ordered_pixels).to(args.device)
for _ in range(args.num_inner_updates):
# forward through model
train_outputs = model(train_inputs)
# get targets
train_targets = target_functions[t](train_inputs)
# ------------ update on current task ------------
# compute loss for current task
task_loss = F.mse_loss(train_outputs, train_targets)
# compute gradient wrt context params
task_gradients = \
torch.autograd.grad(task_loss, model.context_params, create_graph=not args.first_order)[0]
# update context params (this will set up the computation graph correctly)
model.context_params = model.context_params - args.lr_inner * task_gradients
# ------------ compute meta-gradient on test loss of current task ------------
# get test data
test_inputs = task_family_train.sample_inputs(
args.k_meta_test, args.use_ordered_pixels).to(args.device)
# get outputs after update
test_outputs = model(test_inputs)
# get the correct targets
test_targets = target_functions[t](test_inputs)
# compute loss after updating context (will backprop through inner loop)
loss_meta = F.mse_loss(test_outputs, test_targets)
# compute gradient + save for current task
task_grad = torch.autograd.grad(loss_meta, model.parameters())
for i in range(len(task_grad)):
# clip the gradient
meta_gradient[i] += task_grad[i].detach().clamp_(-10, 10)
# ------------ meta update ------------
# assign meta-gradient
for i, param in enumerate(model.parameters()):
param.grad = meta_gradient[i] / args.tasks_per_metaupdate
# do update step on shared model
meta_optimiser.step()
# reset context params
model.reset_context_params()
# ------------ logging ------------
if i_iter % log_interval == 0:
# evaluate on training set
loss_mean, loss_conf = eval_cavia(
args,
copy.deepcopy(model),
task_family=task_family_train,
num_updates=args.num_inner_updates)
logger.train_loss.append(loss_mean)
logger.train_conf.append(loss_conf)
# evaluate on test set
loss_mean, loss_conf = eval_cavia(
args,
copy.deepcopy(model),
task_family=task_family_valid,
num_updates=args.num_inner_updates)
logger.valid_loss.append(loss_mean)
logger.valid_conf.append(loss_conf)
# evaluate on validation set
loss_mean, loss_conf = eval_cavia(
args,
copy.deepcopy(model),
task_family=task_family_test,
num_updates=args.num_inner_updates)
logger.test_loss.append(loss_mean)
logger.test_conf.append(loss_conf)
# save logging results
utils.save_obj(logger, path)
# save best model
if logger.valid_loss[-1] == np.min(logger.valid_loss):
print('saving best model at iter', i_iter)
logger.best_valid_model = copy.deepcopy(model)
# visualise results
if args.task == 'celeba':
task_family_train.visualise(
task_family_train, task_family_test,
copy.deepcopy(logger.best_valid_model), args, i_iter)
# print current results
logger.print_info(i_iter, start_time)
start_time = time.time()
return logger
def run(args, log_interval=5000, rerun=False):
assert args.maml
# see if we already ran this experiment
code_root = os.path.dirname(os.path.realpath(__file__))
if not os.path.isdir('{}/{}_result_files/'.format(code_root, args.task)):
os.mkdir('{}/{}_result_files/'.format(code_root, args.task))
path = '{}/{}_result_files/'.format(code_root, args.task) + utils.get_path_from_args(args)
if os.path.exists(path + '.pkl') and not rerun:
return utils.load_obj(path)
start_time = time.time()
# correctly seed everything
utils.set_seed(args.seed)
# --- initialise everything ---
# get the task family
if args.task == 'sine':
task_family_train = tasks_sine.RegressionTasksSinusoidal()
task_family_valid = tasks_sine.RegressionTasksSinusoidal()
task_family_test = tasks_sine.RegressionTasksSinusoidal()
elif args.task == 'celeba':
task_family_train = tasks_celebA.CelebADataset('train', args.device)
task_family_valid = tasks_celebA.CelebADataset('valid', args.device)
task_family_test = tasks_celebA.CelebADataset('test', args.device)
else:
raise NotImplementedError
#initialize transformer
transformer = FCNet(task_family_train.num_inputs, 3, 128, 128).to(args.device)
# initialise network
model_inner = MamlModel(128,
task_family_train.num_outputs,
n_weights=args.num_hidden_layers,
num_context_params=args.num_context_params,
device=args.device
).to(args.device)
model_outer = copy.deepcopy(model_inner)
print("MAML: ", model_outer)
print("Transformer: ", transformer)
# intitialise meta-optimiser
meta_optimiser = optim.Adam(model_outer.weights + model_outer.biases + [model_outer.task_context],
args.lr_meta)
opt_transformer = torch.optim.Adam(transformer.parameters(), 0.01)
# initialise loggers
logger = Logger()
logger.best_valid_model = copy.deepcopy(model_outer)
for i_iter in range(args.n_iter):
#meta_train_error = 0.0
# copy weights of network
copy_weights = [w.clone() for w in model_outer.weights]
copy_biases = [b.clone() for b in model_outer.biases]
copy_context = model_outer.task_context.clone()
# get all shared parameters and initialise cumulative gradient
meta_gradient = [0 for _ in range(len(copy_weights + copy_biases) + 1)]
# sample tasks
target_functions = task_family_train.sample_tasks(args.tasks_per_metaupdate)
for t in range(args.tasks_per_metaupdate):
#gradient initialization for transformer
acc_grads = fsn.phi_gradients(transformer, args.device)
# reset network weights
model_inner.weights = [w.clone() for w in copy_weights]
model_inner.biases = [b.clone() for b in copy_biases]
model_inner.task_context = copy_context.clone()
# get data for current task
train_inputs = task_family_train.sample_inputs(args.k_meta_train, args.use_ordered_pixels).to(args.device)
# get test data
test_inputs = task_family_train.sample_inputs(args.k_meta_test, args.use_ordered_pixels).to(args.device)
transformed_train_inputs = transformer(train_inputs)#.to(args.device)
transformed_test_inputs = transformer(test_inputs)#.to(args.device)
# transformer task loss
# with torch.no_grad():
targets0 = target_functions[t](train_inputs)
L0 = F.mse_loss(model_inner(transformed_train_inputs), targets0)
targets1 = target_functions[t](test_inputs)
L1 = F.mse_loss(model_inner(transformed_test_inputs), targets1)
trans_loss = fsn.cosine_loss(L0, L1, model_inner, args.device)
#trans_loss = evaluation_error + trans_loss
for step in range(args.num_inner_updates):
# print("iteration:" , i_iter, "innerstep: ", step)
outputs = model_inner(transformed_train_inputs)
# make prediction using the current model
#outputs = model_inner(train_inputs)
# get targets
targets = target_functions[t](train_inputs)
# ------------ update on current task ------------
# compute loss for current task
loss_task = F.mse_loss(outputs, targets)
# compute the gradient wrt current model
params = [w for w in model_inner.weights] + [b for b in model_inner.biases] + [model_inner.task_context]
grads = torch.autograd.grad(loss_task, params, create_graph=True, retain_graph=True)
# make an update on the inner model using the current model (to build up computation graph)
for i in range(len(model_inner.weights)):
if not args.first_order:
model_inner.weights[i] = model_inner.weights[i] - args.lr_inner * grads[i].clamp_(-10, 10)
else:
model_inner.weights[i] = model_inner.weights[i] - args.lr_inner * grads[i].detach().clamp_(-10, 10)
for j in range(len(model_inner.biases)):
if not args.first_order:
model_inner.biases[j] = model_inner.biases[j] - args.lr_inner * grads[i + j + 1].clamp_(-10, 10)
else:
model_inner.biases[j] = model_inner.biases[j] - args.lr_inner * grads[i + j + 1].detach().clamp_(-10, 10)
if not args.first_order:
model_inner.task_context = model_inner.task_context - args.lr_inner * grads[i + j + 2].clamp_(-10, 10)
else:
model_inner.task_context = model_inner.task_context - args.lr_inner * grads[i + j + 2].detach().clamp_(-10, 10)
# ------------ compute meta-gradient on test loss of current task ------------
# get outputs after update
test_outputs = model_inner(transformed_test_inputs)
# get the correct targets
test_targets = target_functions[t](test_inputs)
# compute loss (will backprop through inner loop)
loss_meta = F.mse_loss(test_outputs, test_targets)
#meta_train_error += loss_meta.item()
# transformer gradients
trans_loss = loss_meta
grads_phi = list(torch.autograd.grad(trans_loss, transformer.parameters(), retain_graph=True, create_graph=True))
for p, l in zip(acc_grads, grads_phi):
l = l
p.data = torch.add(p, (1 / args.tasks_per_metaupdate), l.detach().clamp_(-10,10))
# compute gradient w.r.t. *outer model*
task_grads = torch.autograd.grad(loss_meta,
model_outer.weights + model_outer.biases + [model_outer.task_context])
for i in range(len(model_inner.weights + model_inner.biases) + 1):
meta_gradient[i] += task_grads[i].detach().clamp_(-10, 10)
# ------------ meta update ------------
opt_transformer.zero_grad()
meta_optimiser.zero_grad()
# parameter gradient attributes of transformer updated
for k, p in zip(transformer.parameters(), acc_grads):
k.grad = p
# print(meta_gradient)
# assign meta-gradient
for i in range(len(model_outer.weights)):
model_outer.weights[i].grad = meta_gradient[i] / args.tasks_per_metaupdate
meta_gradient[i] = 0
for j in range(len(model_outer.biases)):
model_outer.biases[j].grad = meta_gradient[i + j + 1] / args.tasks_per_metaupdate
meta_gradient[i + j + 1] = 0
model_outer.task_context.grad = meta_gradient[i + j + 2] / args.tasks_per_metaupdate
meta_gradient[i + j + 2] = 0
# do update step on outer model
meta_optimiser.step()
opt_transformer.step()
# ------------ logging ------------
if i_iter % log_interval == 0:# and i_iter > 0:
# evaluate on training set
loss_mean, loss_conf = eval(args, copy.copy(model_outer), task_family=task_family_train,
num_updates=args.num_inner_updates, transformer=transformer)
logger.train_loss.append(loss_mean)
logger.train_conf.append(loss_conf)
# evaluate on test set
loss_mean, loss_conf = eval(args, copy.copy(model_outer), task_family=task_family_valid,
num_updates=args.num_inner_updates, transformer=transformer)
logger.valid_loss.append(loss_mean)
logger.valid_conf.append(loss_conf)
# evaluate on validation set
loss_mean, loss_conf = eval(args, copy.copy(model_outer), task_family=task_family_test,
num_updates=args.num_inner_updates, transformer=transformer)
logger.test_loss.append(loss_mean)
logger.test_conf.append(loss_conf)
# save logging results
utils.save_obj(logger, path)
# save best model
if logger.valid_loss[-1] == np.min(logger.valid_loss):
print('saving best model at iter', i_iter)
logger.best_valid_model = copy.copy(model_outer)
# visualise results
if args.task == 'celeba':
task_family_train.visualise(task_family_train, task_family_test, copy.copy(logger.best_valid_model),
args, i_iter, transformer)
# print current results
logger.print_info(i_iter, start_time)
start_time = time.time()
return logger
def run(args, log_interval=5000, rerun=False):
assert args.maml
# see if we already ran this experiment
code_root = os.path.dirname(os.path.realpath(__file__))
if not os.path.isdir('{}/{}_result_files/'.format(code_root, args.task)):
os.mkdir('{}/{}_result_files/'.format(code_root, args.task))
path = '{}/{}_result_files/'.format(
code_root, args.task) + utils.get_path_from_args(args)
if os.path.exists(path + '.pkl') and not rerun:
return utils.load_obj(path)
start_time = time.time()
# correctly seed everything
utils.set_seed(args.seed)
# --- initialise everything ---
# get the task family
if args.task == 'sine':
task_family_train = tasks_sine.RegressionTasksSinusoidal()
task_family_valid = tasks_sine.RegressionTasksSinusoidal()
task_family_test = tasks_sine.RegressionTasksSinusoidal()
elif args.task == 'celeba':
task_family_train = tasks_celebA.CelebADataset('train')
task_family_valid = tasks_celebA.CelebADataset('valid')
task_family_test = tasks_celebA.CelebADataset('test')
else:
raise NotImplementedError
# initialise network
model_inner = MamlModel(task_family_train.num_inputs,
task_family_train.num_outputs,
n_weights=args.num_hidden_layers,
num_context_params=args.num_context_params,
device=args.device).to(args.device)
model_outer = copy.deepcopy(model_inner)
# intitialise meta-optimiser
meta_optimiser = optim.Adam(
model_outer.weights + model_outer.biases + [model_outer.task_context],
args.lr_meta)
# initialise loggers
logger = Logger()
logger.best_valid_model = copy.deepcopy(model_outer)
for i_iter in range(args.n_iter):
# copy weights of network
copy_weights = [w.clone() for w in model_outer.weights]
copy_biases = [b.clone() for b in model_outer.biases]
copy_context = model_outer.task_context.clone()
# get all shared parameters and initialise cumulative gradient
meta_gradient = [0 for _ in range(len(copy_weights + copy_biases) + 1)]
# sample tasks
target_functions = task_family_train.sample_tasks(
args.tasks_per_metaupdate)
for t in range(args.tasks_per_metaupdate):
# reset network weights
model_inner.weights = [w.clone() for w in copy_weights]
model_inner.biases = [b.clone() for b in copy_biases]
model_inner.task_context = copy_context.clone()
# get data for current task
train_inputs = task_family_train.sample_inputs(
args.k_meta_train, args.use_ordered_pixels).to(args.device)
for _ in range(args.num_inner_updates):
# forward through network
outputs = model_outer(train_inputs)
# get targets
targets = target_functions[t](train_inputs)
# ------------ update on current task ------------
# compute loss for current task
loss_task = F.mse_loss(outputs, targets)
# update private parts of network and keep correct computation graph
params = [w for w in model_outer.weights] + [
b for b in model_outer.biases
] + [model_outer.task_context]
grads = torch.autograd.grad(loss_task,
params,
create_graph=True,
retain_graph=True)
for i in range(len(model_inner.weights)):
if not args.first_order:
model_inner.weights[i] = model_outer.weights[
i] - args.lr_inner * grads[i]
else:
model_inner.weights[i] = model_outer.weights[
i] - args.lr_inner * grads[i].detach()
for j in range(len(model_inner.biases)):
if not args.first_order:
model_inner.biases[j] = model_outer.biases[
j] - args.lr_inner * grads[i + j + 1]
else:
model_inner.biases[j] = model_outer.biases[
j] - args.lr_inner * grads[i + j + 1].detach()
if not args.first_order:
model_inner.task_context = model_outer.task_context - args.lr_inner * grads[
i + j + 2]
else:
model_inner.task_context = model_outer.task_context - args.lr_inner * grads[
i + j + 2].detach()
# ------------ compute meta-gradient on test loss of current task ------------
# get test data
test_inputs = task_family_train.sample_inputs(
args.k_meta_test, args.use_ordered_pixels).to(args.device)
# get outputs after update
test_outputs = model_inner(test_inputs)
# get the correct targets
test_targets = target_functions[t](test_inputs)
# compute loss (will backprop through inner loop)
loss_meta = F.mse_loss(test_outputs, test_targets)
# compute gradient w.r.t. *outer model*
task_grads = torch.autograd.grad(
loss_meta, model_outer.weights + model_outer.biases +
[model_outer.task_context])
for i in range(len(model_inner.weights + model_inner.biases) + 1):
meta_gradient[i] += task_grads[i].detach()
# ------------ meta update ------------
meta_optimiser.zero_grad()
# print(meta_gradient)
# assign meta-gradient
for i in range(len(model_outer.weights)):
model_outer.weights[
i].grad = meta_gradient[i] / args.tasks_per_metaupdate
meta_gradient[i] = 0
for j in range(len(model_outer.biases)):
model_outer.biases[j].grad = meta_gradient[
i + j + 1] / args.tasks_per_metaupdate
meta_gradient[i + j + 1] = 0
model_outer.task_context.grad = meta_gradient[
i + j + 2] / args.tasks_per_metaupdate
meta_gradient[i + j + 2] = 0
# do update step on outer model
meta_optimiser.step()
# ------------ logging ------------
if i_iter % log_interval == 0:
# evaluate on training set
loss_mean, loss_conf = eval(args,
copy.deepcopy(model_outer),
task_family=task_family_train,
num_updates=args.num_inner_updates)
logger.train_loss.append(loss_mean)
logger.train_conf.append(loss_conf)
# evaluate on test set
loss_mean, loss_conf = eval(args,
copy.deepcopy(model_outer),
task_family=task_family_valid,
num_updates=args.num_inner_updates)
logger.valid_loss.append(loss_mean)
logger.valid_conf.append(loss_conf)
# evaluate on validation set
loss_mean, loss_conf = eval(args,
copy.deepcopy(model_outer),
task_family=task_family_test,
num_updates=args.num_inner_updates)
logger.test_loss.append(loss_mean)
logger.test_conf.append(loss_conf)
# save logging results
utils.save_obj(logger, path)
# save best model
if logger.valid_loss[-1] == np.min(logger.valid_loss):
print('saving best model at iter', i_iter)
logger.best_valid_model = copy.deepcopy(model_outer)
# visualise results
if args.task == 'celeba':
tasks_celebA.visualise(task_family_train, task_family_test,
copy.deepcopy(logger.best_valid_model),
args, i_iter)
# print current results
logger.print_info(i_iter, start_time)
start_time = time.time()
return logger
