class MAMLFewShotClassifier(nn.Module):
def __init__(self, im_shape, device, args):
"""
Initializes a MAML few shot learning system
:param im_shape: The images input size, in batch, c, h, w shape
:param device: The device to use to use the model on.
:param args: A namedtuple of arguments specifying various hyperparameters.
"""
super(MAMLFewShotClassifier, self).__init__()
self.args = args
self.device = device
self.batch_size = args.batch_size
self.use_cuda = args.use_cuda
self.im_shape = im_shape
self.current_epoch = 0
self.rng = set_torch_seed(seed=args.seed)
if self.args.backbone == 'ResNet12':
self.classifier = ResNet12(im_shape=self.im_shape, num_output_classes=self.args.
num_classes_per_set,
args=args, device=device, meta_classifier=True).to(device=self.device)
else:
self.classifier = VGGReLUNormNetwork(im_shape=self.im_shape, num_output_classes=self.args.
num_classes_per_set,
args=args, device=device, meta_classifier=True).to(device=self.device)
self.task_learning_rate = args.init_inner_loop_learning_rate
self.inner_loop_optimizer = LSLRGradientDescentLearningRule(device=device,
init_learning_rate=self.task_learning_rate,
init_weight_decay=args.init_inner_loop_weight_decay,
total_num_inner_loop_steps=self.args.number_of_training_steps_per_iter,
use_learnable_weight_decay=self.args.alfa,
use_learnable_learning_rates=self.args.alfa,
alfa=self.args.alfa, random_init=self.args.random_init)
names_weights_copy = self.get_inner_loop_parameter_dict(self.classifier.named_parameters())
if self.args.attenuate:
num_layers = len(names_weights_copy)
self.attenuator = nn.Sequential(
nn.Linear(num_layers, num_layers),
nn.ReLU(inplace=True),
nn.Linear(num_layers, num_layers),
nn.Sigmoid()
).to(device=self.device)
self.inner_loop_optimizer.initialise(
names_weights_dict=names_weights_copy)
print("Inner Loop parameters")
for key, value in self.inner_loop_optimizer.named_parameters():
print(key, value.shape)
self.use_cuda = args.use_cuda
self.device = device
self.args = args
self.to(device)
print("Outer Loop parameters")
for name, param in self.named_parameters():
if param.requires_grad:
print(name, param.shape, param.device, param.requires_grad)
# ALFA
if self.args.alfa:
num_layers = len(names_weights_copy)
input_dim = num_layers*2
self.regularizer = nn.Sequential(
nn.Linear(input_dim, input_dim),
nn.ReLU(inplace=True),
nn.Linear(input_dim, input_dim)
).to(device=self.device)
if self.args.attenuate:
if self.args.alfa:
self.optimizer = optim.Adam([
{'params':self.classifier.parameters()},
{'params': self.inner_loop_optimizer.parameters()},
{'params': self.regularizer.parameters()},
{'params':self.attenuator.parameters()},
],lr=args.meta_learning_rate, amsgrad=False)
else:
self.optimizer = optim.Adam([
{'params':self.classifier.parameters()},
{'params':self.attenuator.parameters()},
],lr=args.meta_learning_rate, amsgrad=False)
else:
if self.args.alfa:
if self.args.random_init:
self.optimizer = optim.Adam([
{'params': self.inner_loop_optimizer.parameters()},
{'params': self.regularizer.parameters()},
], lr=args.meta_learning_rate, amsgrad=False)
else:
self.optimizer = optim.Adam([
{'params': self.classifier.parameters()},
{'params': self.inner_loop_optimizer.parameters()},
{'params': self.regularizer.parameters()},
], lr=args.meta_learning_rate, amsgrad=False)
else:
self.optimizer = optim.Adam([
{'params': self.classifier.parameters()},
], lr=args.meta_learning_rate, amsgrad=False)
self.scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer=self.optimizer, T_max=self.args.total_epochs,
eta_min=self.args.min_learning_rate)
self.device = torch.device('cpu')
if torch.cuda.is_available():
print(torch.cuda.device_count())
if torch.cuda.device_count() > 1:
self.to(torch.cuda.current_device())
self.classifier = nn.DataParallel(module=self.classifier)
else:
self.to(torch.cuda.current_device())
self.device = torch.cuda.current_device()
def get_task_embeddings(self, x_support_set_task, y_support_set_task, names_weights_copy):
# Use gradients as task embeddings
support_loss, support_preds = self.net_forward(x=x_support_set_task,
y=y_support_set_task,
weights=names_weights_copy,
backup_running_statistics=True,
training=True, num_step=0)
if torch.cuda.device_count() > 1:
self.classifier.module.zero_grad(names_weights_copy)
else:
self.classifier.zero_grad(names_weights_copy)
grads = torch.autograd.grad(support_loss, names_weights_copy.values(), create_graph=True)
layerwise_mean_grads = []
for i in range(len(grads)):
layerwise_mean_grads.append(grads[i].mean())
layerwise_mean_grads = torch.stack(layerwise_mean_grads)
return layerwise_mean_grads
def attenuate_init(self, task_embeddings, names_weights_copy):
# Generate attenuation parameters
gamma = self.attenuator(task_embeddings)
## Attenuate
updated_names_weights_copy = dict()
i = 0
for key in names_weights_copy.keys():
updated_names_weights_copy[key] = gamma[i] * names_weights_copy[key]
i+=1
return updated_names_weights_copy
def get_per_step_loss_importance_vector(self):
"""
Generates a tensor of dimensionality (num_inner_loop_steps) indicating the importance of each step's target
loss towards the optimization loss.
:return: A tensor to be used to compute the weighted average of the loss, useful for
the MSL (Multi Step Loss) mechanism.
"""
loss_weights = np.ones(shape=(self.args.number_of_training_steps_per_iter)) * (
1.0 / self.args.number_of_training_steps_per_iter)
decay_rate = 1.0 / self.args.number_of_training_steps_per_iter / self.args.multi_step_loss_num_epochs
min_value_for_non_final_losses = 0.03 / self.args.number_of_training_steps_per_iter
for i in range(len(loss_weights) - 1):
curr_value = np.maximum(loss_weights[i] - (self.current_epoch * decay_rate), min_value_for_non_final_losses)
loss_weights[i] = curr_value
curr_value = np.minimum(
loss_weights[-1] + (self.current_epoch * (self.args.number_of_training_steps_per_iter - 1) * decay_rate),
1.0 - ((self.args.number_of_training_steps_per_iter - 1) * min_value_for_non_final_losses))
loss_weights[-1] = curr_value
loss_weights = torch.Tensor(loss_weights).to(device=self.device)
return loss_weights
def get_inner_loop_parameter_dict(self, params):
"""
Returns a dictionary with the parameters to use for inner loop updates.
:param params: A dictionary of the network's parameters.
:return: A dictionary of the parameters to use for the inner loop optimization process.
"""
param_dict = dict()
for name, param in params:
if param.requires_grad:
if self.args.enable_inner_loop_optimizable_bn_params:
param_dict[name] = param.to(device=self.device)
else:
if "norm_layer" not in name:
param_dict[name] = param.to(device=self.device)
return param_dict
def apply_inner_loop_update(self, loss, names_weights_copy, generated_alpha_params, generated_beta_params, use_second_order, current_step_idx):
"""
Applies an inner loop update given current step's loss, the weights to update, a flag indicating whether to use
second order derivatives and the current step's index.
:param loss: Current step's loss with respect to the support set.
:param names_weights_copy: A dictionary with names to parameters to update.
:param use_second_order: A boolean flag of whether to use second order derivatives.
:param current_step_idx: Current step's index.
:return: A dictionary with the updated weights (name, param)
"""
num_gpus = torch.cuda.device_count()
if num_gpus > 1:
self.classifier.module.zero_grad(params=names_weights_copy)
else:
self.classifier.zero_grad(params=names_weights_copy)
grads = torch.autograd.grad(loss, names_weights_copy.values(),
create_graph=use_second_order, allow_unused=True)
names_grads_copy = dict(zip(names_weights_copy.keys(), grads))
names_weights_copy = {key: value[0] for key, value in names_weights_copy.items()}
for key, grad in names_grads_copy.items():
if grad is None:
print('Grads not found for inner loop parameter', key)
names_grads_copy[key] = names_grads_copy[key].sum(dim=0)
names_weights_copy = self.inner_loop_optimizer.update_params(names_weights_dict=names_weights_copy,
names_grads_wrt_params_dict=names_grads_copy,
generated_alpha_params=generated_alpha_params,
generated_beta_params=generated_beta_params,
num_step=current_step_idx)
num_devices = torch.cuda.device_count() if torch.cuda.is_available() else 1
names_weights_copy = {
name.replace('module.', ''): value.unsqueeze(0).repeat(
[num_devices] + [1 for i in range(len(value.shape))]) for
name, value in names_weights_copy.items()}
return names_weights_copy
def get_across_task_loss_metrics(self, total_losses, total_accuracies):
losses = dict()
losses['loss'] = torch.mean(torch.stack(total_losses))
losses['accuracy'] = np.mean(total_accuracies)
return losses
def forward(self, data_batch, epoch, use_second_order, use_multi_step_loss_optimization, num_steps, training_phase):
"""
Runs a forward outer loop pass on the batch of tasks using the MAML/++ framework.
:param data_batch: A data batch containing the support and target sets.
:param epoch: Current epoch's index
:param use_second_order: A boolean saying whether to use second order derivatives.
:param use_multi_step_loss_optimization: Whether to optimize on the outer loop using just the last step's
target loss (True) or whether to use multi step loss which improves the stability of the system (False)
:param num_steps: Number of inner loop steps.
:param training_phase: Whether this is a training phase (True) or an evaluation phase (False)
:return: A dictionary with the collected losses of the current outer forward propagation.
"""
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
[b, ncs, spc] = y_support_set.shape
self.num_classes_per_set = ncs
total_losses = []
total_accuracies = []
total_support_accuracies = [[] for i in range(num_steps)]
total_target_accuracies = [[] for i in range(num_steps)]
per_task_target_preds = [[] for i in range(len(x_target_set))]
if torch.cuda.device_count() > 1:
self.classifier.module.zero_grad()
else:
self.classifier.zero_grad()
for task_id, (x_support_set_task, y_support_set_task, x_target_set_task, y_target_set_task) in \
enumerate(zip(x_support_set,
y_support_set,
x_target_set,
y_target_set)):
task_losses = []
task_accuracies = []
per_step_support_accuracy = []
per_step_target_accuracy = []
per_step_loss_importance_vectors = self.get_per_step_loss_importance_vector()
names_weights_copy = self.get_inner_loop_parameter_dict(self.classifier.named_parameters())
num_devices = torch.cuda.device_count() if torch.cuda.is_available() else 1
names_weights_copy = {
name.replace('module.', ''): value.unsqueeze(0).repeat(
[num_devices] + [1 for i in range(len(value.shape))]) for
name, value in names_weights_copy.items()}
n, s, c, h, w = x_target_set_task.shape
x_support_set_task = x_support_set_task.view(-1, c, h, w)
y_support_set_task = y_support_set_task.view(-1)
x_target_set_task = x_target_set_task.view(-1, c, h, w)
y_target_set_task = y_target_set_task.view(-1)
# Attenuate the initialization for L2F
if self.args.attenuate:
# Obtain gradients from support set for task embedding
task_embeddings = self.get_task_embeddings(x_support_set_task=x_support_set_task,
y_support_set_task=y_support_set_task,
names_weights_copy=names_weights_copy)
names_weights_copy = self.attenuate_init(task_embeddings=task_embeddings,
names_weights_copy=names_weights_copy)
for num_step in range(num_steps):
support_loss, support_preds = self.net_forward(x=x_support_set_task,
y=y_support_set_task,
weights=names_weights_copy,
backup_running_statistics=
True if (num_step == 0) else False,
training=True, num_step=num_step)
generated_alpha_params = {}
generated_beta_params = {}
if self.args.alfa:
support_loss_grad = torch.autograd.grad(support_loss, names_weights_copy.values(), retain_graph=True)
per_step_task_embedding = []
for k, v in names_weights_copy.items():
per_step_task_embedding.append(v.mean())
for i in range(len(support_loss_grad)):
per_step_task_embedding.append(support_loss_grad[i].mean())
per_step_task_embedding = torch.stack(per_step_task_embedding)
generated_params = self.regularizer(per_step_task_embedding)
num_layers = len(names_weights_copy)
generated_alpha, generated_beta = torch.split(generated_params, split_size_or_sections=num_layers)
g = 0
for key in names_weights_copy.keys():
generated_alpha_params[key] = generated_alpha[g]
generated_beta_params[key] = generated_beta[g]
g+=1
names_weights_copy = self.apply_inner_loop_update(loss=support_loss,
names_weights_copy=names_weights_copy,
generated_beta_params=generated_beta_params,
generated_alpha_params=generated_alpha_params,
use_second_order=use_second_order,
current_step_idx=num_step)
if use_multi_step_loss_optimization and training_phase and epoch < self.args.multi_step_loss_num_epochs:
target_loss, target_preds = self.net_forward(x=x_target_set_task,
y=y_target_set_task, weights=names_weights_copy,
backup_running_statistics=False, training=True,
num_step=num_step)
task_losses.append(per_step_loss_importance_vectors[num_step] * target_loss)
else:
if num_step == (self.args.number_of_training_steps_per_iter - 1):
target_loss, target_preds = self.net_forward(x=x_target_set_task,
y=y_target_set_task, weights=names_weights_copy,
backup_running_statistics=False, training=True,
num_step=num_step)
task_losses.append(target_loss)
per_task_target_preds[task_id] = target_preds.detach().cpu().numpy()
_, predicted = torch.max(target_preds.data, 1)
accuracy = predicted.float().eq(y_target_set_task.data.float()).cpu().float()
task_losses = torch.sum(torch.stack(task_losses))
total_losses.append(task_losses)
total_accuracies.extend(accuracy)
if not training_phase:
if torch.cuda.device_count() > 1:
self.classifier.module.restore_backup_stats()
else:
self.classifier.restore_backup_stats()
losses = self.get_across_task_loss_metrics(total_losses=total_losses,
total_accuracies=total_accuracies)
for idx, item in enumerate(per_step_loss_importance_vectors):
losses['loss_importance_vector_{}'.format(idx)] = item.detach().cpu().numpy()
return losses, per_task_target_preds
def net_forward(self, x, y, weights, backup_running_statistics, training, num_step):
"""
A base model forward pass on some data points x. Using the parameters in the weights dictionary. Also requires
boolean flags indicating whether to reset the running statistics at the end of the run (if at evaluation phase).
A flag indicating whether this is the training session and an int indicating the current step's number in the
inner loop.
:param x: A data batch of shape b, c, h, w
:param y: A data targets batch of shape b, n_classes
:param weights: A dictionary containing the weights to pass to the network.
:param backup_running_statistics: A flag indicating whether to reset the batch norm running statistics to their
previous values after the run (only for evaluation)
:param training: A flag indicating whether the current process phase is a training or evaluation.
:param num_step: An integer indicating the number of the step in the inner loop.
:return: the crossentropy losses with respect to the given y, the predictions of the base model.
"""
preds = self.classifier.forward(x=x, params=weights,
training=training,
backup_running_statistics=backup_running_statistics, num_step=num_step)
loss = F.cross_entropy(input=preds, target=y)
return loss, preds
def trainable_parameters(self):
"""
Returns an iterator over the trainable parameters of the model.
"""
for param in self.parameters():
if param.requires_grad:
yield param
def train_forward_prop(self, data_batch, epoch):
"""
Runs an outer loop forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, per_task_target_preds = self.forward(data_batch=data_batch, epoch=epoch,
use_second_order=self.args.second_order and
epoch > self.args.first_order_to_second_order_epoch,
use_multi_step_loss_optimization=self.args.use_multi_step_loss_optimization,
num_steps=self.args.number_of_training_steps_per_iter,
training_phase=True)
return losses, per_task_target_preds
def evaluation_forward_prop(self, data_batch, epoch):
"""
Runs an outer loop evaluation forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, per_task_target_preds = self.forward(data_batch=data_batch, epoch=epoch, use_second_order=False,
use_multi_step_loss_optimization=True,
num_steps=self.args.number_of_evaluation_steps_per_iter,
training_phase=False)
return losses, per_task_target_preds
def meta_update(self, loss):
"""
Applies an outer loop update on the meta-parameters of the model.
:param loss: The current crossentropy loss.
"""
self.optimizer.zero_grad()
loss.backward()
#if 'imagenet' in self.args.dataset_name:
# for name, param in self.classifier.named_parameters():
# if param.requires_grad:
# param.grad.data.clamp_(-10, 10) # not sure if this is necessary, more experiments are needed
#for name, param in self.classifier.named_parameters():
# print(param.mean())
self.optimizer.step()
def run_train_iter(self, data_batch, epoch):
"""
Runs an outer loop update step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
epoch = int(epoch)
self.scheduler.step(epoch=epoch)
if self.current_epoch != epoch:
self.current_epoch = epoch
if not self.training:
self.train()
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
x_support_set = torch.Tensor(x_support_set).float().to(device=self.device)
x_target_set = torch.Tensor(x_target_set).float().to(device=self.device)
y_support_set = torch.Tensor(y_support_set).long().to(device=self.device)
y_target_set = torch.Tensor(y_target_set).long().to(device=self.device)
data_batch = (x_support_set, x_target_set, y_support_set, y_target_set)
losses, per_task_target_preds = self.train_forward_prop(data_batch=data_batch, epoch=epoch)
self.meta_update(loss=losses['loss'])
losses['learning_rate'] = self.scheduler.get_lr()[0]
self.optimizer.zero_grad()
self.zero_grad()
return losses, per_task_target_preds
def run_validation_iter(self, data_batch):
"""
Runs an outer loop evaluation step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
if self.training:
self.eval()
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
x_support_set = torch.Tensor(x_support_set).float().to(device=self.device)
x_target_set = torch.Tensor(x_target_set).float().to(device=self.device)
y_support_set = torch.Tensor(y_support_set).long().to(device=self.device)
y_target_set = torch.Tensor(y_target_set).long().to(device=self.device)
data_batch = (x_support_set, x_target_set, y_support_set, y_target_set)
losses, per_task_target_preds = self.evaluation_forward_prop(data_batch=data_batch, epoch=self.current_epoch)
losses['loss'].backward() # uncomment if you get the weird memory error
self.zero_grad()
self.optimizer.zero_grad()
return losses, per_task_target_preds
def save_model(self, model_save_dir, state):
"""
Save the network parameter state and experiment state dictionary.
:param model_save_dir: The directory to store the state at.
:param state: The state containing the experiment state and the network. It's in the form of a dictionary
object.
"""
state['network'] = self.state_dict()
torch.save(state, f=model_save_dir)
def load_model(self, model_save_dir, model_name, model_idx):
"""
Load checkpoint and return the state dictionary containing the network state params and experiment state.
:param model_save_dir: The directory from which to load the files.
:param model_name: The model_name to be loaded from the direcotry.
:param model_idx: The index of the model (i.e. epoch number or 'latest' for the latest saved model of the current
experiment)
:return: A dictionary containing the experiment state and the saved model parameters.
"""
filepath = os.path.join(model_save_dir, "{}_{}".format(model_name, model_idx))
state = torch.load(filepath)
state_dict_loaded = state['network']
self.load_state_dict(state_dict=state_dict_loaded)
return state
class MAMLFewShotClassifier(nn.Module):
def __init__(self, device, args):
"""
Initializes a MAML few shot learning system
:param im_shape: The images input size, in batch, c, h, w shape
:param device: The device to use to use the model on.
:param args: A namedtuple of arguments specifying various hyperparameters.
"""
super(MAMLFewShotClassifier, self).__init__()
self.args = args
self.device = device
self.batch_size = args.batch_size
self.use_cuda = args.use_cuda
self.current_epoch = 0
set_torch_seed(args.seed)
self.classifier = DeepDTAv2Meta(args=args,
device=device).to(device=self.device)
if self.args.pretrained != "":
self.classifier.load_from_path(self.args.pretrained)
self.task_learning_rate = args.task_learning_rate
self.inner_loop_optimizer = LSLRGradientDescentLearningRule(
device=device,
init_learning_rate=self.task_learning_rate,
total_num_inner_loop_steps=self.args.
number_of_training_steps_per_iter,
use_learnable_learning_rates=self.args.
learnable_per_layer_per_step_inner_loop_learning_rate)
self.inner_loop_optimizer.initialise(
names_weights_dict=self.get_inner_loop_parameter_dict(
params=self.classifier.named_parameters()))
print("Inner Loop parameters")
for key, value in self.inner_loop_optimizer.named_parameters():
print(key, value.shape, value.requires_grad)
self.use_cuda = args.use_cuda
self.device = device
self.args = args
self.to(device)
print("Outer Loop parameters")
for name, param in self.named_parameters():
if param.requires_grad:
print(name, param.shape, param.requires_grad)
print("All parameters")
for name, param in self.named_parameters():
print(name, param.shape, param.requires_grad)
self.optimizer = optim.Adam(self.trainable_parameters(),
lr=args.meta_learning_rate,
amsgrad=False)
self.scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer=self.optimizer,
T_max=self.args.total_epochs,
eta_min=self.args.min_learning_rate)
self.device = torch.device('cpu')
if torch.cuda.is_available():
if torch.cuda.device_count() > 1:
self.to(torch.cuda.current_device())
self.classifier = nn.DataParallel(module=self.classifier)
else:
self.to(torch.cuda.current_device())
self.device = torch.cuda.current_device()
def get_per_step_loss_importance_vector(self):
"""
Generates a tensor of dimensionality (num_inner_loop_steps) indicating the importance of each step's target
loss towards the optimization loss.
:return: A tensor to be used to compute the weighted average of the loss, useful for
the MSL (Multi Step Loss) mechanism.
"""
loss_weights = np.ones(
shape=(self.args.number_of_training_steps_per_iter)) * (
1.0 / self.args.number_of_training_steps_per_iter)
decay_rate = 1.0 / self.args.number_of_training_steps_per_iter / self.args.multi_step_loss_num_epochs
min_value_for_non_final_losses = 0.03 / self.args.number_of_training_steps_per_iter
for i in range(len(loss_weights) - 1):
curr_value = np.maximum(
loss_weights[i] - (self.current_epoch * decay_rate),
min_value_for_non_final_losses)
loss_weights[i] = curr_value
curr_value = np.minimum(
loss_weights[-1] +
(self.current_epoch *
(self.args.number_of_training_steps_per_iter - 1) * decay_rate),
1.0 - ((self.args.number_of_training_steps_per_iter - 1) *
min_value_for_non_final_losses))
loss_weights[-1] = curr_value
loss_weights = torch.Tensor(loss_weights).to(device=self.device)
return loss_weights
def get_inner_loop_parameter_dict(self, params):
"""
Returns a dictionary with the parameters to use for inner loop updates.
:param params: A dictionary of the network's parameters.
:return: A dictionary of the parameters to use for the inner loop optimization process.
"""
param_dict = dict()
for name, param in params:
if param.requires_grad:
if self.args.enable_inner_loop_optimizable_bn_params:
param_dict[name] = param.to(device=self.device)
else:
if "norm" not in name:
param_dict[name] = param.to(device=self.device)
return param_dict
def apply_inner_loop_update(self, loss, names_weights_copy,
use_second_order, current_step_idx):
"""
Applies an inner loop update given current step's loss, the weights to update, a flag indicating whether to use
second order derivatives and the current step's index.
:param loss: Current step's loss with respect to the support set.
:param names_weights_copy: A dictionary with names to parameters to update.
:param use_second_order: A boolean flag of whether to use second order derivatives.
:param current_step_idx: Current step's index.
:return: A dictionary with the updated weights (name, param)
"""
num_gpus = torch.cuda.device_count()
if num_gpus > 1:
self.classifier.module.zero_grad(params=names_weights_copy)
else:
self.classifier.zero_grad(params=names_weights_copy)
grads = torch.autograd.grad(loss,
names_weights_copy.values(),
create_graph=use_second_order,
allow_unused=True)
names_grads_copy = dict(zip(names_weights_copy.keys(), grads))
names_weights_copy = {
key: value[0]
for key, value in names_weights_copy.items()
}
for key, grad in names_grads_copy.items():
if grad is None:
print('Grads not found for inner loop parameter', key)
assert False
names_grads_copy[key] = names_grads_copy[key].sum(dim=0)
names_weights_copy = self.inner_loop_optimizer.update_params(
names_weights_dict=names_weights_copy,
names_grads_wrt_params_dict=names_grads_copy,
num_step=current_step_idx)
num_devices = torch.cuda.device_count() if torch.cuda.is_available(
) else 1
names_weights_copy = {
name.replace('module.', ''):
value.unsqueeze(0).repeat([num_devices] +
[1 for i in range(len(value.shape))])
for name, value in names_weights_copy.items()
}
return names_weights_copy
def get_across_task_loss_metrics(self, total_losses):
losses = dict()
losses['loss'] = torch.mean(torch.stack(total_losses))
return losses
def forward(self, data_batch, epoch, use_second_order,
use_multi_step_loss_optimization, num_steps, training_phase):
"""
Runs a forward outer loop pass on the batch of tasks using the MAML/++ framework.
:param data_batch: A data batch containing the support and target sets.
:param epoch: Current epoch's index
:param use_second_order: A boolean saying whether to use second order derivatives.
:param use_multi_step_loss_optimization: Whether to optimize on the outer loop using just the last step's
target loss (True) or whether to use multi step loss which improves the stability of the system (False)
:param num_steps: Number of inner loop steps.
:param training_phase: Whether this is a training phase (True) or an evaluation phase (False)
:return: A dictionary with the collected losses of the current outer forward propagation.
"""
support_ligand, support_prot, target_ligand, target_protein, support_labels, target_labels = data_batch
total_losses = []
per_task_target_preds = [[] for i in range(len(target_ligand))]
self.classifier.zero_grad()
for task_id, (support_ligand_task, support_prot_task, target_ligand_task, target_protein_task, support_labels_task, target_labels_task) in \
enumerate(zip(support_ligand,
support_prot,
target_ligand,
target_protein,
support_labels,
target_labels)):
task_losses = []
val_has_support = not(support_ligand_task.float().mean() == 0 and \
support_ligand_task.float().min() == 0 and support_ligand_task.float().max() == 0)
per_step_loss_importance_vectors = self.get_per_step_loss_importance_vector(
)
names_weights_copy = self.get_inner_loop_parameter_dict(
self.classifier.named_parameters())
num_devices = torch.cuda.device_count() if torch.cuda.is_available(
) else 1
names_weights_copy = {
name.replace('module.', ''):
value.unsqueeze(0).repeat([num_devices] +
[1 for i in range(len(value.shape))])
for name, value in names_weights_copy.items()
}
target_ligand_task = target_ligand_task.unsqueeze(0)
target_protein_task = target_protein_task.unsqueeze(0)
for num_step in range(num_steps):
x_target_set_task = (target_ligand_task, target_protein_task)
if val_has_support:
x_support_set_task = (support_ligand_task,
support_prot_task)
support_loss, support_preds = self.net_forward(
x=x_support_set_task,
y=support_labels_task,
weights=names_weights_copy,
backup_running_statistics=True if
(num_step == 0) else False,
training=True,
num_step=num_step)
names_weights_copy = self.apply_inner_loop_update(
loss=support_loss,
names_weights_copy=names_weights_copy,
use_second_order=use_second_order,
current_step_idx=num_step)
if use_multi_step_loss_optimization and training_phase and epoch < self.args.multi_step_loss_num_epochs:
target_loss, target_preds = self.net_forward(
x=x_target_set_task,
y=target_labels_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step)
task_losses.append(
per_step_loss_importance_vectors[num_step] *
target_loss)
else:
if num_step == (
self.args.number_of_training_steps_per_iter -
1):
target_loss, target_preds = self.net_forward(
x=x_target_set_task,
y=target_labels_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step)
task_losses.append(target_loss)
else:
assert not training_phase
if num_step == (
self.args.number_of_training_steps_per_iter - 1):
target_loss, target_preds = self.net_forward(
x=x_target_set_task,
y=target_labels_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step)
task_losses.append(target_loss)
per_task_target_preds[task_id] = target_preds.detach().cpu().numpy(
)
task_losses = torch.sum(torch.stack(task_losses))
total_losses.append(task_losses)
if not training_phase:
self.classifier.restore_backup_stats()
losses = self.get_across_task_loss_metrics(total_losses=total_losses)
for idx, item in enumerate(per_step_loss_importance_vectors):
losses['loss_importance_vector_{}'.format(
idx)] = item.detach().cpu().numpy()
return losses, per_task_target_preds
def net_forward(self, x, y, weights, backup_running_statistics, training,
num_step):
"""
A base model forward pass on some data points x. Using the parameters in the weights dictionary. Also requires
boolean flags indicating whether to reset the running statistics at the end of the run (if at evaluation phase).
A flag indicating whether this is the training session and an int indicating the current step's number in the
inner loop.
:param x: A data batch of shape b, c, h, w
:param y: A data targets batch of shape b, n_classes
:param weights: A dictionary containing the weights to pass to the network.
:param backup_running_statistics: A flag indicating whether to reset the batch norm running statistics to their
previous values after the run (only for evaluation)
:param training: A flag indicating whether the current process phase is a training or evaluation.
:param num_step: An integer indicating the number of the step in the inner loop.
:return: the crossentropy losses with respect to the given y, the predictions of the base model.
"""
preds = self.classifier.forward(
smiles=x[0],
protein=x[1],
params=weights,
training=training,
backup_running_statistics=backup_running_statistics,
num_step=num_step)
y = y.view(-1, 1)
loss = F.mse_loss(input=preds, target=y)
return loss, preds
def trainable_parameters(self):
"""
Returns an iterator over the trainable parameters of the model.
"""
for param in self.parameters():
if param.requires_grad:
yield param
def train_forward_prop(self, data_batch, epoch):
"""
Runs an outer loop forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, per_task_target_preds = self.forward(
data_batch=data_batch,
epoch=epoch,
use_second_order=self.args.second_order
and epoch > self.args.first_order_to_second_order_epoch,
use_multi_step_loss_optimization=self.args.
use_multi_step_loss_optimization,
num_steps=self.args.number_of_training_steps_per_iter,
training_phase=True)
return losses, per_task_target_preds
def evaluation_forward_prop(self, data_batch, epoch):
"""
Runs an outer loop evaluation forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, per_task_target_preds = self.forward(
data_batch=data_batch,
epoch=epoch,
use_second_order=False,
use_multi_step_loss_optimization=True,
num_steps=self.args.number_of_evaluation_steps_per_iter,
training_phase=False)
return losses, per_task_target_preds
def meta_update(self, loss):
"""
Applies an outer loop update on the meta-parameters of the model.
:param loss: The current crossentropy loss.
"""
self.optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.trainable_parameters(), 1)
self.optimizer.step()
def run_train_iter(self, data_batch, epoch):
"""
Runs an outer loop update step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
epoch = int(epoch)
self.scheduler.step(epoch=epoch)
if self.current_epoch != epoch:
self.current_epoch = epoch
if not self.training:
self.train()
support_ligand, support_prot, target_ligand, target_protein, support_labels, target_labels = data_batch
support_ligand = torch.LongTensor(support_ligand).to(
device=self.device)
support_prot = torch.LongTensor(support_prot).to(device=self.device)
target_ligand = torch.LongTensor(target_ligand).to(device=self.device)
target_protein = torch.LongTensor(target_protein).to(
device=self.device)
support_labels = torch.Tensor(support_labels).to(device=self.device)
target_labels = torch.Tensor(target_labels).to(device=self.device)
data_batch = (support_ligand, support_prot, target_ligand,
target_protein, support_labels, target_labels)
losses, per_task_target_preds = self.train_forward_prop(
data_batch=data_batch, epoch=epoch)
self.meta_update(loss=losses['loss'])
losses['learning_rate'] = self.scheduler.get_lr()[0]
self.optimizer.zero_grad()
self.zero_grad()
return losses, per_task_target_preds
def run_validation_iter(self, data_batch):
"""
Runs an outer loop evaluation step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
if self.training:
self.eval()
support_ligand, support_prot, target_ligand, target_protein, support_labels, target_labels = data_batch
support_ligand = torch.LongTensor(support_ligand).to(
device=self.device)
support_prot = torch.LongTensor(support_prot).to(device=self.device)
target_ligand = torch.LongTensor(target_ligand).to(device=self.device)
target_protein = torch.LongTensor(target_protein).to(
device=self.device)
support_labels = torch.Tensor(support_labels).to(device=self.device)
target_labels = torch.Tensor(target_labels).to(device=self.device)
data_batch = (support_ligand, support_prot, target_ligand,
target_protein, support_labels, target_labels)
losses, per_task_target_preds = self.evaluation_forward_prop(
data_batch=data_batch, epoch=self.current_epoch)
my_preds = np.array([x.item() for x in per_task_target_preds])
my_labels = target_labels.detach().cpu().numpy().squeeze()
return losses, per_task_target_preds, my_preds, my_labels
def save_model(self, model_save_dir, state):
"""
Save the network parameter state and experiment state dictionary.
:param model_save_dir: The directory to store the state at.
:param state: The state containing the experiment state and the network. It's in the form of a dictionary
object.
"""
state['network'] = self.state_dict()
torch.save(state, f=model_save_dir)
def load_model(self, model_save_dir, model_name, model_idx):
"""
Load checkpoint and return the state dictionary containing the network state params and experiment state.
:param model_save_dir: The directory from which to load the files.
:param model_name: The model_name to be loaded from the direcotry.
:param model_idx: The index of the model (i.e. epoch number or 'latest' for the latest saved model of the current
experiment)
:return: A dictionary containing the experiment state and the saved model parameters.
"""
filepath = os.path.join(model_save_dir,
"{}_{}".format(model_name, model_idx))
state = torch.load(filepath)
state_dict_loaded = state['network']
self.load_state_dict(state_dict=state_dict_loaded)
return state
def load_single_model(self, model_path):
"""
Load checkpoint and return the state dictionary containing the network state params and experiment state.
:param model_save_dir: The directory from which to load the files.
:param model_name: The model_name to be loaded from the direcotry.
:param model_idx: The index of the model (i.e. epoch number or 'latest' for the latest saved model of the current
experiment)
:return: A dictionary containing the experiment state and the saved model parameters.
"""
state = torch.load(model_path)
state_dict_loaded = state['network']
self.load_state_dict(state_dict=state_dict_loaded)
return state
class MAMLFewShotClassifier(nn.Module):
def __init__(self, im_shape, device, args):
"""
Initializes a MAML few shot learning system
:param im_shape: The images input size, in batch, c, h, w shape
:param device: The device to use to use the model on.
:param args: A namedtuple of arguments specifying various hyperparameters.
"""
super(MAMLFewShotClassifier, self).__init__()
self.args = args
self.device = device
self.batch_size = args.batch_size
self.use_cuda = args.use_cuda
self.im_shape = im_shape
self.current_epoch = 0
self.rng = set_torch_seed(seed=args.seed)
self.classifier = VGGReLUNormNetwork(
im_shape=self.im_shape,
num_output_classes=self.args.num_classes_per_set,
args=args,
device=device,
meta_classifier=True).to(device=self.device)
self.task_learning_rate = args.task_learning_rate # It seems that this learning rate is not changed in the json files
# This is an nn.Module and will be registered
self.inner_loop_optimizer = LSLRGradientDescentLearningRule(
device=device,
init_learning_rate=self.task_learning_rate,
total_num_inner_loop_steps=self.args.
number_of_training_steps_per_iter,
use_learnable_learning_rates=self.args.
learnable_per_layer_per_step_inner_loop_learning_rate)
self.inner_loop_optimizer.initialise(
names_weights_dict=self.get_inner_loop_parameter_dict(
params=self.classifier.named_parameters()))
print("Inner Loop parameters") # inner loop optimizer parameters
for key, value in self.inner_loop_optimizer.named_parameters():
print(key, value.shape)
self.use_cuda = args.use_cuda
self.device = device
self.args = args
self.to(device)
print("Outer Loop parameters")
for name, param in self.named_parameters():
if param.requires_grad:
print(name, param.shape, param.device, param.requires_grad)
# lizx: torch optimizers are not subclasses of nn.Modules and will not be registered
self.optimizer = optim.Adam(self.trainable_parameters(),
lr=args.meta_learning_rate,
amsgrad=False)
self.scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer=self.optimizer,
T_max=self.args.total_epochs,
eta_min=self.args.min_learning_rate)
self.device = torch.device('cpu')
if torch.cuda.is_available():
if torch.cuda.device_count() > 1:
self.to(torch.cuda.current_device())
self.classifier = nn.DataParallel(module=self.classifier)
else:
self.to(torch.cuda.current_device())
self.device = torch.cuda.current_device()
def get_per_step_loss_importance_vector(self):
"""
Generates a tensor of dimensionality (num_inner_loop_steps) indicating the importance of each step's target
loss towards the optimization loss.
:return: A tensor to be used to compute the weighted average of the loss, useful for
the MSL (Multi Step Loss) mechanism.
"""
# number_of_training_steps_per_iter is the number of inner gradient descent steps
loss_weights = np.ones(
shape=(self.args.number_of_training_steps_per_iter)) * (
1.0 / self.args.number_of_training_steps_per_iter)
decay_rate = 1.0 / self.args.number_of_training_steps_per_iter / self.args.multi_step_loss_num_epochs # lizx: only in the first `multi_step_loss_num_epochs` epochs use multistep loss
min_value_for_non_final_losses = 0.03 / self.args.number_of_training_steps_per_iter
for i in range(len(loss_weights) - 1):
curr_value = np.maximum(
loss_weights[i] - (self.current_epoch * decay_rate),
min_value_for_non_final_losses)
loss_weights[i] = curr_value
curr_value = np.minimum(
loss_weights[-1] +
(self.current_epoch *
(self.args.number_of_training_steps_per_iter - 1) * decay_rate),
1.0 - ((self.args.number_of_training_steps_per_iter - 1) *
min_value_for_non_final_losses))
loss_weights[-1] = curr_value
loss_weights = torch.Tensor(loss_weights).to(device=self.device)
return loss_weights
def get_inner_loop_parameter_dict(self, params):
"""
Returns a dictionary with the parameters to use for inner loop updates.
:param params: A dictionary of the network's parameters.
:return: A dictionary of the parameters to use for the inner loop optimization process.
"""
# lizx: in dictionary `params`, running statistics, weight, bias are present
# lizx: requires_grad will exclude running statistics
# lizx: if enable_inner_loop_optimizable_bn_params is false, will exclude weight and bias
param_dict = dict()
for name, param in params:
if param.requires_grad: # get all trainable parameters
if self.args.enable_inner_loop_optimizable_bn_params:
param_dict[name] = param.to(device=self.device)
else:
if "norm_layer" not in name: # lizx: exclude the batchnorm weight and bias
param_dict[name] = param.to(device=self.device)
return param_dict
def apply_inner_loop_update(self, loss, names_weights_copy,
use_second_order, current_step_idx):
"""
Applies an inner loop update given current step's loss, the weights to update, a flag indicating whether to use
second order derivatives and the current step's index.
:param loss: Current step's loss with respect to the support set.
:param names_weights_copy: A dictionary with names to parameters to update.
:param use_second_order: A boolean flag of whether to use second order derivatives.
:param current_step_idx: Current step's index.
:return: A dictionary with the updated weights (name, param)
"""
num_gpus = torch.cuda.device_count()
if num_gpus > 1:
self.classifier.module.zero_grad(params=names_weights_copy)
else:
self.classifier.zero_grad(params=names_weights_copy)
grads = torch.autograd.grad(loss,
names_weights_copy.values(),
create_graph=use_second_order,
allow_unused=True)
names_grads_copy = dict(zip(names_weights_copy.keys(), grads))
names_weights_copy = {
key: value[0]
for key, value in names_weights_copy.items()
}
for key, grad in names_grads_copy.items():
if grad is None:
print('Grads not found for inner loop parameter', key)
names_grads_copy[key] = names_grads_copy[key].sum(dim=0)
names_weights_copy = self.inner_loop_optimizer.update_params(
names_weights_dict=names_weights_copy,
names_grads_wrt_params_dict=names_grads_copy,
num_step=current_step_idx)
num_devices = torch.cuda.device_count() if torch.cuda.is_available(
) else 1
names_weights_copy = {
name.replace('module.', ''):
value.unsqueeze(0).repeat([num_devices] +
[1 for i in range(len(value.shape))])
for name, value in names_weights_copy.items()
}
return names_weights_copy
def get_across_task_loss_metrics(self, total_losses, total_accuracies):
losses = dict()
losses['loss'] = torch.mean(torch.stack(total_losses))
losses['accuracy'] = np.mean(total_accuracies)
return losses
def forward(self, data_batch, epoch, use_second_order,
use_multi_step_loss_optimization, num_steps, training_phase):
"""
Runs a forward outer loop pass on the batch of tasks using the MAML/++ framework.
:param data_batch: A data batch containing the support and target sets.
:param epoch: Current epoch's index
:param use_second_order: A boolean saying whether to use second order derivatives.
:param use_multi_step_loss_optimization: Whether to optimize on the outer loop using just the last step's
target loss (True) or whether to use multi step loss which improves the stability of the system (False)
:param num_steps: Number of inner loop steps.
:param training_phase: Whether this is a training phase (True) or an evaluation phase (False)
:return: A dictionary with the collected losses of the current outer forward propagation.
"""
# x_support_set = (batch_size, n_ways, n_shots, C, W, H)
# x_target_set = (batch_size, n_ways, num_target_samples, C, W, H)
# y_support_set = (batch_size, n_ways, n_shots)
# y_target_set = (batch_size, n_ways, num_target_samples)
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
[b, ncs, spc] = y_support_set.shape
# b: batch_size
# ncs: n_ways
# spc: n_shots
self.num_classes_per_set = ncs
total_losses = []
total_accuracies = []
per_task_target_preds = [[] for i in range(len(x_target_set))]
self.classifier.zero_grad()
for task_id, (x_support_set_task, y_support_set_task, x_target_set_task, y_target_set_task) in \
enumerate(zip(x_support_set,
y_support_set,
x_target_set,
y_target_set)):
task_losses = []
task_accuracies = []
# DELVEIN
per_step_loss_importance_vectors = self.get_per_step_loss_importance_vector(
)
# DELVEIN
names_weights_copy = self.get_inner_loop_parameter_dict(
self.classifier.named_parameters())
# lizx: the parameters are got at this step for inner loop update
# lizx: the inner loop update of parameters is not inplace, therefore no need to copy the parameters
num_devices = torch.cuda.device_count() if torch.cuda.is_available(
) else 1
names_weights_copy = {
name.replace('module.', ''):
value.unsqueeze(0).repeat([num_devices] + [
1 for i in range(len(value.shape))
]) # lizx: repeat the first dimension for num_devices times
for name, value in names_weights_copy.items()
}
n, s, c, h, w = x_target_set_task.shape
x_support_set_task = x_support_set_task.view(-1, c, h, w)
y_support_set_task = y_support_set_task.view(-1)
x_target_set_task = x_target_set_task.view(-1, c, h, w)
y_target_set_task = y_target_set_task.view(-1)
import meta_neural_network_architectures as arch
arch.IPDB = True
for num_step in range(
num_steps): # lizx: number of inner loop steps
# lizx: pure layer forward
support_loss, support_preds = self.net_forward(
x=x_support_set_task, # lizx: will possibly be divided across multiple GPUs
y=y_support_set_task,
weights=names_weights_copy,
backup_running_statistics=True if
(num_step == 0) else False,
# lizx: backup running statistics at the first step
# lizx: if this forward pass is not a training step, the running statistics will be restored
training=True,
num_step=num_step)
# DELVEIN
names_weights_copy = self.apply_inner_loop_update(
loss=support_loss,
names_weights_copy=names_weights_copy,
use_second_order=use_second_order,
current_step_idx=num_step)
if use_multi_step_loss_optimization and training_phase and epoch < self.args.multi_step_loss_num_epochs:
target_loss, target_preds = self.net_forward(
x=x_target_set_task,
y=y_target_set_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step)
task_losses.append(
per_step_loss_importance_vectors[num_step] *
target_loss
) # lizx: evaluate on target set at each step and loss is appended and finally averaged
else:
if num_step == (
self.args.number_of_training_steps_per_iter - 1
): # lizx: only at the last step we evaluate on target set and target_loss is computed for meta-update
target_loss, target_preds = self.net_forward(
x=x_target_set_task,
y=y_target_set_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step)
task_losses.append(target_loss)
# lizx: target_preds: (n_ways*n_images_per_class, n_ways)
per_task_target_preds[task_id] = target_preds.detach().cpu().numpy(
)
_, predicted = torch.max(
target_preds.data,
1) # lizx: max across the first dimension and get the argmax
accuracy = predicted.float().eq(
y_target_set_task.data.float()).cpu().float()
# task_losses is a list of scalar tensors, torch.stack will just concatenate them
task_losses = torch.sum(torch.stack(task_losses))
total_losses.append(task_losses) # lizx: task_losses is a scalar
total_accuracies.extend(
accuracy) # lizx: accuracy.shape: (n_ways*n_images_per_class,)
if not training_phase:
self.classifier.restore_backup_stats(
) # restore the running_mean and running_var buffer and get them ready for the next task
losses = self.get_across_task_loss_metrics(
total_losses=total_losses, total_accuracies=total_accuracies
) # This will average across batch dimension
for idx, item in enumerate(per_step_loss_importance_vectors):
losses['loss_importance_vector_{}'.format(
idx)] = item.detach().cpu().numpy()
return losses, per_task_target_preds
def net_forward(self, x, y, weights, backup_running_statistics, training,
num_step):
"""
A base model forward pass on some data points x. Using the parameters in the weights dictionary. Also requires
boolean flags indicating whether to reset the running statistics at the end of the run (if at evaluation phase).
A flag indicating whether this is the training session and an int indicating the current step's number in the
inner loop.
:param x: A data batch of shape b, c, h, w
:param y: A data targets batch of shape b, n_classes
:param weights: A dictionary containing the weights to pass to the network.
:param backup_running_statistics: A flag indicating whether to reset the batch norm running statistics to their
previous values after the run (only for evaluation)
:param training: A flag indicating whether the current process phase is a training or evaluation.
:param num_step: An integer indicating the number of the step in the inner loop.
:return: the crossentropy losses with respect to the given y, the predictions of the base model.
"""
preds = self.classifier.forward(
x=x,
params=weights,
training=training,
backup_running_statistics=backup_running_statistics,
num_step=num_step)
loss = F.cross_entropy(input=preds, target=y)
return loss, preds
def trainable_parameters(self):
"""
Returns an iterator over the trainable parameters of the model.
"""
for param in self.parameters():
if param.requires_grad:
yield param
def train_forward_prop(self, data_batch, epoch):
"""
Runs an outer loop forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, per_task_target_preds = self.forward(
data_batch=data_batch,
epoch=epoch,
use_second_order=self.args.second_order and epoch > self.args.
first_order_to_second_order_epoch, # lizx: This is the derivative order annealing
use_multi_step_loss_optimization=self.args.
use_multi_step_loss_optimization,
num_steps=self.args.number_of_training_steps_per_iter,
training_phase=True)
return losses, per_task_target_preds
def evaluation_forward_prop(self, data_batch, epoch):
"""
Runs an outer loop evaluation forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, per_task_target_preds = self.forward(
data_batch=data_batch,
epoch=epoch,
use_second_order=False,
use_multi_step_loss_optimization=True,
num_steps=self.args.number_of_evaluation_steps_per_iter,
training_phase=False)
return losses, per_task_target_preds
def meta_update(self, loss):
"""
Applies an outer loop update on the meta-parameters of the model.
:param loss: The current crossentropy loss.
"""
self.optimizer.zero_grad()
loss.backward()
if 'imagenet' in self.args.dataset_name:
for name, param in self.classifier.named_parameters():
if param.requires_grad:
param.grad.data.clamp_(
-10, 10
) # not sure if this is necessary, more experiments are needed
self.optimizer.step()
def run_train_iter(self, data_batch, epoch):
"""
Runs an outer loop update step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
epoch = int(epoch)
self.scheduler.step(epoch=epoch)
if self.current_epoch != epoch:
self.current_epoch = epoch
if not self.training:
self.train()
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
x_support_set = torch.Tensor(x_support_set).float().to(
device=self.device)
x_target_set = torch.Tensor(x_target_set).float().to(
device=self.device)
y_support_set = torch.Tensor(y_support_set).long().to(
device=self.device)
y_target_set = torch.Tensor(y_target_set).long().to(device=self.device)
data_batch = (x_support_set, x_target_set, y_support_set, y_target_set)
losses, per_task_target_preds = self.train_forward_prop(
data_batch=data_batch, epoch=epoch)
self.meta_update(loss=losses['loss'])
losses['learning_rate'] = self.scheduler.get_lr()[
0] # lizx: this is the meta-update learning rate
self.optimizer.zero_grad()
self.zero_grad()
return losses, per_task_target_preds
def run_validation_iter(self, data_batch):
"""
Runs an outer loop evaluation step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
if self.training:
self.eval()
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
x_support_set = torch.Tensor(x_support_set).float().to(
device=self.device)
x_target_set = torch.Tensor(x_target_set).float().to(
device=self.device)
y_support_set = torch.Tensor(y_support_set).long().to(
device=self.device)
y_target_set = torch.Tensor(y_target_set).long().to(device=self.device)
data_batch = (x_support_set, x_target_set, y_support_set, y_target_set)
losses, per_task_target_preds = self.evaluation_forward_prop(
data_batch=data_batch, epoch=self.current_epoch)
# losses['loss'].backward() # uncomment if you get the weird memory error
# self.zero_grad()
# self.optimizer.zero_grad()
return losses, per_task_target_preds
def save_model(self, model_save_dir, state):
"""
Save the network parameter state and experiment state dictionary.
:param model_save_dir: The directory to store the state at.
:param state: The state containing the experiment state and the network. It's in the form of a dictionary
object.
"""
state['network'] = self.state_dict()
torch.save(state, f=model_save_dir)
def load_model(self, model_save_dir, model_name, model_idx):
"""
Load checkpoint and return the state dictionary containing the network state params and experiment state.
:param model_save_dir: The directory from which to load the files.
:param model_name: The model_name to be loaded from the direcotry.
:param model_idx: The index of the model (i.e. epoch number or 'latest' for the latest saved model of the current
experiment)
:return: A dictionary containing the experiment state and the saved model parameters.
"""
filepath = os.path.join(model_save_dir,
"{}_{}".format(model_name, model_idx))
state = torch.load(filepath)
state_dict_loaded = state['network']
self.load_state_dict(state_dict=state_dict_loaded)
return state
class MAMLFewShotClassifier(nn.Module):
def __init__(self, im_shape, device, args):
"""
Initializes a MAML few shot learning system
:param im_shape: The images input size, in batch, c, h, w shape
:param device: The device to use to use the model on.
:param args: A namedtuple of arguments specifying various hyperparameters.
"""
super(MAMLFewShotClassifier, self).__init__()
self.args = args
self.device = device
self.batch_size = args.batch_size
self.use_cuda = args.use_cuda
self.im_shape = im_shape
self.current_epoch = 0
self.num_tasks = self.args.num_train_tasks
self.lr_p = self.args.p_lr #0.00001 #0.00002 for 5 way
self.p = np.ones(self.num_tasks) / self.num_tasks
if not self.args.TR_MAML:
self.TR_MAML = False
else:
self.TR_MAML = True
self.count = 0
self.max = 0
self.old_max = 9
self.old_val_max = 3
self.rng = set_torch_seed(seed=args.seed)
self.classifier = VGGReLUNormNetwork(
im_shape=self.im_shape,
num_output_classes=self.args.num_classes_per_set,
args=args,
device=device,
meta_classifier=True).to(device=self.device)
self.task_learning_rate = args.task_learning_rate
self.old_epoch = -1
self.inner_loop_optimizer = LSLRGradientDescentLearningRule(
device=device,
init_learning_rate=self.task_learning_rate,
total_num_inner_loop_steps=self.args.
number_of_training_steps_per_iter,
use_learnable_learning_rates=self.args.
learnable_per_layer_per_step_inner_loop_learning_rate)
self.inner_loop_optimizer.initialise(
names_weights_dict=self.get_inner_loop_parameter_dict(
params=self.classifier.named_parameters()))
print("Inner Loop parameters")
for key, value in self.inner_loop_optimizer.named_parameters():
print(key, value.shape)
self.use_cuda = args.use_cuda
self.device = device
self.args = args
self.to(device)
print("Outer Loop parameters")
for name, param in self.named_parameters():
if param.requires_grad:
print(name, param.shape, param.device, param.requires_grad)
self.optimizer = optim.Adam(self.trainable_parameters(),
lr=args.meta_learning_rate,
amsgrad=False)
self.scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer=self.optimizer,
T_max=self.args.total_epochs,
eta_min=self.args.min_learning_rate)
self.device = torch.device('cpu')
if torch.cuda.is_available():
if torch.cuda.device_count() > 1:
self.to(torch.cuda.current_device())
self.classifier = nn.DataParallel(module=self.classifier)
else:
self.to(torch.cuda.current_device())
self.device = torch.cuda.current_device()
def get_per_step_loss_importance_vector(self):
"""
Generates a tensor of dimensionality (num_inner_loop_steps) indicating the importance of each step's target
loss towards the optimization loss.
:return: A tensor to be used to compute the weighted average of the loss, useful for
the MSL (Multi Step Loss) mechanism.
"""
loss_weights = np.ones(
shape=(self.args.number_of_training_steps_per_iter)) * (
1.0 / self.args.number_of_training_steps_per_iter)
decay_rate = 1.0 / self.args.number_of_training_steps_per_iter / self.args.multi_step_loss_num_epochs
min_value_for_non_final_losses = 0.03 / self.args.number_of_training_steps_per_iter
for i in range(len(loss_weights) - 1):
curr_value = np.maximum(
loss_weights[i] - (self.current_epoch * decay_rate),
min_value_for_non_final_losses)
loss_weights[i] = curr_value
curr_value = np.minimum(
loss_weights[-1] +
(self.current_epoch *
(self.args.number_of_training_steps_per_iter - 1) * decay_rate),
1.0 - ((self.args.number_of_training_steps_per_iter - 1) *
min_value_for_non_final_losses))
loss_weights[-1] = curr_value
loss_weights = torch.Tensor(loss_weights).to(device=self.device)
return loss_weights
def get_inner_loop_parameter_dict(self, params):
"""
Returns a dictionary with the parameters to use for inner loop updates.
:param params: A dictionary of the network's parameters.
:return: A dictionary of the parameters to use for the inner loop optimization process.
"""
param_dict = dict()
for name, param in params:
if param.requires_grad:
if self.args.enable_inner_loop_optimizable_bn_params:
param_dict[name] = param.to(device=self.device)
else:
if "norm_layer" not in name:
param_dict[name] = param.to(device=self.device)
return param_dict
def apply_inner_loop_update(self, loss, names_weights_copy,
use_second_order, current_step_idx):
"""
Applies an inner loop update given current step's loss, the weights to update, a flag indicating whether to use
second order derivatives and the current step's index.
:param loss: Current step's loss with respect to the support set.
:param names_weights_copy: A dictionary with names to parameters to update.
:param use_second_order: A boolean flag of whether to use second order derivatives.
:param current_step_idx: Current step's index.
:return: A dictionary with the updated weights (name, param)
"""
num_gpus = torch.cuda.device_count()
if num_gpus > 1:
self.classifier.module.zero_grad(params=names_weights_copy)
else:
self.classifier.zero_grad(params=names_weights_copy)
grads = torch.autograd.grad(loss,
names_weights_copy.values(),
create_graph=use_second_order,
allow_unused=True)
names_grads_copy = dict(zip(names_weights_copy.keys(), grads))
names_weights_copy = {
key: value[0]
for key, value in names_weights_copy.items()
}
for key, grad in names_grads_copy.items():
if grad is None:
print('Grads not found for inner loop parameter', key)
names_grads_copy[key] = names_grads_copy[key].sum(dim=0)
names_weights_copy = self.inner_loop_optimizer.update_params(
names_weights_dict=names_weights_copy,
names_grads_wrt_params_dict=names_grads_copy,
num_step=current_step_idx)
num_devices = torch.cuda.device_count() if torch.cuda.is_available(
) else 1
names_weights_copy = {
name.replace('module.', ''):
value.unsqueeze(0).repeat([num_devices] +
[1 for i in range(len(value.shape))])
for name, value in names_weights_copy.items()
}
return names_weights_copy
def get_across_task_loss_metrics(self, total_losses, total_accuracies):
losses = dict()
# print("TOTAL_ACCS")
# print(total_accuracies)
losses['loss'] = torch.mean(torch.stack(total_losses))
losses['mx_loss'] = torch.max(torch.stack(total_losses))
losses['std_loss'] = torch.std(torch.stack(total_losses))
losses['accuracy'] = torch.mean(torch.stack(total_accuracies))
# losses['min_accuracy'] = np.min(total_accuracies)
# losses['std_accuracy'] = np.std(total_accuracies)
# losses['full_loosses'] = torch.stack(total_losses) # need to makes ure right datatype
return losses
def forward(self, data_batch, epoch, use_second_order,
use_multi_step_loss_optimization, num_steps, training_phase):
"""
Runs a forward outer loop pass on the batch of tasks using the MAML/++ framework.
:param data_batch: A data batch containing the support and target sets.
:param epoch: Current epoch's index
:param use_second_order: A boolean saying whether to use second order derivatives.
:param use_multi_step_loss_optimization: Whether to optimize on the outer loop using just the last step's
target loss (True) or whether to use multi step loss which improves the stability of the system (False)
:param num_steps: Number of inner loop steps.
:param training_phase: Whether this is a training phase (True) or an evaluation phase (False)
:return: A dictionary with the collected losses of the current outer forward propagation.
"""
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
[b, ncs, spc] = y_support_set.shape
self.num_classes_per_set = ncs
total_losses = []
total_accuracies = []
per_task_target_preds = [[] for i in range(len(x_target_set))]
self.classifier.zero_grad()
for task_id, (x_support_set_task, y_support_set_task, x_target_set_task, y_target_set_task) in \
enumerate(zip(x_support_set,
y_support_set,
x_target_set,
y_target_set)):
task_losses = []
task_accuracies = []
per_step_loss_importance_vectors = self.get_per_step_loss_importance_vector(
)
names_weights_copy = self.get_inner_loop_parameter_dict(
self.classifier.named_parameters())
num_devices = torch.cuda.device_count() if torch.cuda.is_available(
) else 1
names_weights_copy = {
name.replace('module.', ''):
value.unsqueeze(0).repeat([num_devices] +
[1 for i in range(len(value.shape))])
for name, value in names_weights_copy.items()
}
n, s, c, h, w = x_target_set_task.shape
x_support_set_task = x_support_set_task.view(-1, c, h, w)
y_support_set_task = y_support_set_task.view(-1)
x_target_set_task = x_target_set_task.view(-1, c, h, w)
y_target_set_task = y_target_set_task.view(-1)
for num_step in range(num_steps):
support_loss, support_preds = self.net_forward(
x=x_support_set_task,
y=y_support_set_task,
weights=names_weights_copy,
backup_running_statistics=True if
(num_step == 0) else False,
training=True,
num_step=num_step)
names_weights_copy = self.apply_inner_loop_update(
loss=support_loss,
names_weights_copy=names_weights_copy,
use_second_order=use_second_order,
current_step_idx=num_step)
if use_multi_step_loss_optimization and training_phase and epoch < self.args.multi_step_loss_num_epochs:
target_loss, target_preds = self.net_forward(
x=x_target_set_task,
y=y_target_set_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step)
task_losses.append(
per_step_loss_importance_vectors[num_step] *
target_loss)
else:
if num_step == (
self.args.number_of_training_steps_per_iter - 1):
target_loss, target_preds = self.net_forward(
x=x_target_set_task,
y=y_target_set_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step)
task_losses.append(target_loss)
per_task_target_preds[task_id] = target_preds.detach().cpu().numpy(
)
_, predicted = torch.max(target_preds.data, 1)
# print("PREDS")
# print(target_preds.data)
# print("PREDICTIONS")
# print(predicted)
# print("TRUE LABELS")
# print(y_target_set_task.data.float())
accuracy = predicted.float().eq(
y_target_set_task.data.float()).cpu().float()
task_losses = torch.sum(torch.stack(task_losses))
total_losses.append(task_losses)
total_accuracies.extend(accuracy)
if not training_phase:
self.classifier.restore_backup_stats()
losses = self.get_across_task_loss_metrics(
total_losses=total_losses, total_accuracies=total_accuracies)
for idx, item in enumerate(per_step_loss_importance_vectors):
losses['loss_importance_vector_{}'.format(
idx)] = item.detach().cpu().numpy()
return losses, total_losses, total_accuracies, per_task_target_preds
def net_forward(self, x, y, weights, backup_running_statistics, training,
num_step):
"""
A base model forward pass on some data points x. Using the parameters in the weights dictionary. Also requires
boolean flags indicating whether to reset the running statistics at the end of the run (if at evaluation phase).
A flag indicating whether this is the training session and an int indicating the current step's number in the
inner loop.
:param x: A data batch of shape b, c, h, w
:param y: A data targets batch of shape b, n_classes
:param weights: A dictionary containing the weights to pass to the network.
:param backup_running_statistics: A flag indicating whether to reset the batch norm running statistics to their
previous values after the run (only for evaluation)
:param training: A flag indicating whether the current process phase is a training or evaluation.
:param num_step: An integer indicating the number of the step in the inner loop.
:return: the crossentropy losses with respect to the given y, the predictions of the base model.
"""
preds = self.classifier.forward(
x=x,
params=weights,
training=training,
backup_running_statistics=backup_running_statistics,
num_step=num_step)
loss = F.cross_entropy(input=preds, target=y)
return loss, preds
def trainable_parameters(self):
"""
Returns an iterator over the trainable parameters of the model.
"""
for param in self.parameters():
if param.requires_grad:
yield param
def train_forward_prop(self, data_batch, epoch):
"""
Runs an outer loop forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, full_losses, full_accs, per_task_target_preds = self.forward(
data_batch=data_batch,
epoch=epoch,
use_second_order=self.args.second_order
and epoch > self.args.first_order_to_second_order_epoch,
use_multi_step_loss_optimization=self.args.
use_multi_step_loss_optimization,
num_steps=self.args.number_of_training_steps_per_iter,
training_phase=True)
return losses, full_losses, full_accs, per_task_target_preds
def evaluation_forward_prop(self, data_batch, epoch):
"""
Runs an outer loop evaluation forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, _, full_accs, per_task_target_preds = self.forward(
data_batch=data_batch,
epoch=epoch,
use_second_order=False,
use_multi_step_loss_optimization=True,
num_steps=self.args.number_of_evaluation_steps_per_iter,
training_phase=False)
return losses, full_accs, per_task_target_preds
def meta_update(self, loss):
"""
Applies an outer loop update on the meta-parameters of the model.
:param loss: The current crossentropy loss.
"""
self.optimizer.zero_grad()
loss.backward()
if 'imagenet' in self.args.dataset_name:
for name, param in self.classifier.named_parameters():
if param.requires_grad:
param.grad.data.clamp_(
-10, 10
) # not sure if this is necessary, more experiments are needed
self.optimizer.step()
# The following code is adapted from the psuedocode in https://arxiv.org/pdf/1309.1541.pdf (Wang and Perpinan 2013)
def simplex_proj(self, beta):
beta_sorted = np.flip(np.sort(beta))
rho = 1
for i in range(len(beta) - 1):
j = len(beta) - i
test = beta_sorted[j - 1] + (1 - np.sum(beta_sorted[:j])) / (j)
if test > 0:
rho = j
break
lam = (1 - np.sum(beta_sorted[:rho])) / (rho)
return np.maximum(beta + lam, 0)
def run_train_iter(self, data_batch, epoch):
"""
Runs an outer loop update step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
epoch = int(epoch)
self.scheduler.step(epoch=epoch)
if self.current_epoch != epoch:
self.current_epoch = epoch
if not self.training:
self.train()
x_support_set, x_target_set, y_support_set, y_target_set, selected_task = data_batch
# print(selected_task[0])
# print(x_support_set[0][0,:,:,:,:])
x_support_set = torch.Tensor(x_support_set).float().to(
device=self.device)
x_target_set = torch.Tensor(x_target_set).float().to(
device=self.device)
y_support_set = torch.Tensor(y_support_set).long().to(
device=self.device)
y_target_set = torch.Tensor(y_target_set).long().to(device=self.device)
# p = torch.Tensor(p).long().to(device=self.device)
data_batch = (x_support_set, x_target_set, y_support_set, y_target_set)
#print(x_support_set[0])
losses, full_losses, full_accs, per_task_target_preds = self.train_forward_prop(
data_batch=data_batch, epoch=epoch)
loss_report = np.zeros(self.num_tasks)
loss_counts = np.zeros(self.num_tasks)
for i, idx in enumerate(selected_task):
loss_report[idx] += full_losses[i]
loss_counts[idx] += 1
for idx in range(self.num_tasks):
if loss_counts[idx] > 1:
loss_report[idx] = loss_report[idx] / loss_counts[idx]
# print("TASK_train")
# print(selected_task)
# print(loss_report)
losses['check_mx'] = False
if losses['mx_loss'] > self.max:
self.max = losses['mx_loss']
self.count += 1
if self.count == 1:
losses['mx_loss'] = self.max
losses['check_mx'] = True
self.old_max = self.max
self.max = 0
self.count = 0
else:
losses['mx_loss'] = self.old_max
full_losses = torch.stack(full_losses)
losses['loss'] = torch.mean(full_losses)
if self.TR_MAML and epoch != self.old_epoch:
print(self.p)
self.old_epoch = epoch
for idx, task_idx in enumerate(selected_task):
full_losses[
idx] = self.p[task_idx] * full_losses[idx] * self.num_tasks
self.meta_update(torch.mean(full_losses))
losses['learning_rate'] = self.scheduler.get_lr()[0]
# update p
if self.TR_MAML == 1:
tmp = np.zeros(self.num_tasks)
for idxx, task_idx in enumerate(selected_task):
tmp[task_idx] += full_losses[idxx].detach().cpu().numpy()
self.p = self.simplex_proj(self.p + self.lr_p * tmp)
self.optimizer.zero_grad()
self.zero_grad()
return losses, per_task_target_preds
def run_validation_iter(self, data_batch):
"""
Runs an outer loop evaluation step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
if self.training:
self.eval()
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
x_support_set = torch.Tensor(x_support_set).float().to(
device=self.device)
x_target_set = torch.Tensor(x_target_set).float().to(
device=self.device)
y_support_set = torch.Tensor(y_support_set).long().to(
device=self.device)
y_target_set = torch.Tensor(y_target_set).long().to(device=self.device)
#print(self.p)
data_batch = (x_support_set, x_target_set, y_support_set, y_target_set)
losses, full_accs, per_task_target_preds = self.evaluation_forward_prop(
data_batch=data_batch, epoch=self.current_epoch)
if losses['mx_loss'] > self.max:
self.max = losses['mx_loss']
self.count += 1
if self.count == 5:
losses['mx_loss'] = self.max
self.old_val_max = self.max
self.max = 0
self.count = 0
else:
losses['mx_loss'] = self.old_val_max
# losses['loss'].backward() # uncomment if you get the weird memory error
# self.zero_grad()
# self.optimizer.zero_grad()
return losses, full_accs, per_task_target_preds
def save_model(self, model_save_dir, state):
"""
Save the network parameter state and experiment state dictionary.
:param model_save_dir: The directory to store the state at.
:param state: The state containing the experiment state and the network. It's in the form of a dictionary
object.
"""
state['network'] = self.state_dict()
torch.save(state, f=model_save_dir)
def load_model(self, model_save_dir, model_name, model_idx):
"""
Load checkpoint and return the state dictionary containing the network state params and experiment state.
:param model_save_dir: The directory from which to load the files.
:param model_name: The model_name to be loaded from the direcotry.
:param model_idx: The index of the model (i.e. epoch number or 'latest' for the latest saved model of the current
experiment)
:return: A dictionary containing the experiment state and the saved model parameters.
"""
filepath = os.path.join(model_save_dir,
"{}_{}".format(model_name, model_idx))
state = torch.load(filepath)
state_dict_loaded = state['network']
self.load_state_dict(state_dict=state_dict_loaded)
return state
class MAMLFewShotClassifier(nn.Module):
def __init__(self, im_shape, device, args):
"""
Initializes a MAML few shot learning system
:param im_shape: The images input size, in batch, c, h, w shape
:param device: The device to use to use the model on.
:param args: A namedtuple of arguments specifying various hyperparameters.
"""
super(MAMLFewShotClassifier, self).__init__()
self.args = args
self.device = device
self.batch_size = args.batch_size
self.use_cuda = args.use_cuda
self.im_shape = im_shape
self.current_epoch = 0
self.rng = set_torch_seed(seed=args.seed)
if not self.args.retrain:
print('search for optimal arch for meta learning')
self.classifier = MetaPruningNetwork(
args=args,
in_channels=im_shape[1],
init_channels=args.init_channels,
num_classes=self.args.num_classes_per_set,
layers=self.args.layers,
device=device).to(device=self.device)
else: # for retrain
print('retraining the arch', args.arch,
eval('meta_genotype.{}'.format(args.arch)))
self.classifier = MetaPrunedPretrainedNetwork(
args=args,
genotype=eval('meta_genotype.{}'.format(args.arch)),
in_channels=im_shape[1],
init_channels=args.init_channels,
num_classes=self.args.num_classes_per_set,
device=device).to(device=self.device)
# self.task_learning_rate = args.task_learning_rate #0.1
# self.task_learning_rate = args.init_inner_loop_learning_rate #0.1
self.inner_loop_optimizer = LSLRGradientDescentLearningRule(
device=device,
init_learning_rate=args.init_inner_loop_learning_rate,
init_learning_rate_arch=args.init_inner_arch_loop_learning_rate,
total_num_inner_loop_steps=self.args.
number_of_training_steps_per_iter,
use_learnable_learning_rates=self.args.
learnable_per_layer_per_step_inner_loop_learning_rate)
if self.args.retrain: # only weights
self.inner_loop_optimizer.initialise(
names_weights_dict=self.get_inner_loop_parameter_dict(
params=self.classifier.named_parameters(), ))
else: # arch + weights
self.inner_loop_optimizer.initialise(
names_weights_dict=self.get_inner_loop_parameter_dict(
params=self.classifier.named_parameters(),
arch_params=self.classifier.named_arch_parameters()))
self.use_cuda = args.use_cuda
self.device = device
self.args = args
self.to(device)
# print("Inner Loop parameters")
# for key, value in self.inner_loop_optimizer.named_parameters():
# print(key, value.shape)
# print("Outer Loop parameters")
# for name, param in self.named_parameters():
# if param.requires_grad:
# print(name, param.shape, param.device, param.requires_grad)
self.optimizer = optim.Adam(self.trainable_parameters(),
lr=args.meta_learning_rate,
amsgrad=False)
if not self.args.retrain: # train the arch
self.optimizer_arch = optim.Adam(
self.classifier.arch_parameters(),
lr=args.init_meta_arch_loop_learning_rate,
betas=(0.5, 0.999),
weight_decay=args.meta_arch_weights_decay)
self.scheduler_arch = optim.lr_scheduler.CosineAnnealingLR(
optimizer=self.optimizer_arch,
T_max=self.args.total_epochs,
eta_min=self.args.min_arch_learning_rate)
self.scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer=self.optimizer,
T_max=self.args.total_epochs,
eta_min=self.args.min_learning_rate)
self.device = torch.device('cpu')
if torch.cuda.is_available():
# print('torch.cuda.device_count() = ', torch.cuda.device_count())
if torch.cuda.device_count() > 1:
self.to(torch.cuda.current_device())
self.classifier = nn.DataParallel(module=self.classifier)
else:
self.to(torch.cuda.current_device())
self.device = torch.cuda.current_device()
def get_per_step_loss_importance_vector(self):
"""
Generates a tensor of dimensionality (num_inner_loop_steps) indicating the importance of each step's target
loss towards the optimization loss.
:return: A tensor to be used to compute the weighted average of the loss, useful for
the MSL (Multi Step Loss) mechanism.
"""
loss_weights = np.ones(
shape=(self.args.number_of_training_steps_per_iter)) * (
1.0 / self.args.number_of_training_steps_per_iter)
decay_rate = 1.0 / self.args.number_of_training_steps_per_iter / self.args.multi_step_loss_num_epochs
min_value_for_non_final_losses = 0.03 / self.args.number_of_training_steps_per_iter
for i in range(len(loss_weights) - 1):
curr_value = np.maximum(
loss_weights[i] - (self.current_epoch * decay_rate),
min_value_for_non_final_losses)
loss_weights[i] = curr_value
curr_value = np.minimum(
loss_weights[-1] +
(self.current_epoch *
(self.args.number_of_training_steps_per_iter - 1) * decay_rate),
1.0 - ((self.args.number_of_training_steps_per_iter - 1) *
min_value_for_non_final_losses))
loss_weights[-1] = curr_value
loss_weights = torch.Tensor(loss_weights).to(device=self.device)
return loss_weights
def get_inner_loop_parameter_dict(self, params=None, arch_params=None):
"""
Returns a dictionary with the parameters to use for inner loop updates.
:param params: A dictionary of the network's parameters.
:return: A dictionary of the parameters to use for the inner loop optimization process.
"""
param_dict = dict()
if params is not None:
for name, param in params:
if param.requires_grad:
if self.args.enable_inner_loop_optimizable_bn_params: # enable bn params to be optimized
param_dict[name] = param.to(device=self.device)
# param_dict[name] = param
else:
if "norm_layer" not in name:
param_dict[name] = param.to(device=self.device)
# param_dict[name] = param
if arch_params is not None:
for name, param in arch_params:
if param.requires_grad:
param_dict[name] = param.to(device=self.device)
return param_dict
def apply_inner_loop_update(self, loss, names_weights_copy,
use_second_order, current_step_idx):
"""
Applies an inner loop update given current step's loss, the weights to update, a flag indicating whether to use
second order derivatives and the current step's index.
:param loss: Current step's loss with respect to the support set.
:param names_weights_copy: A dictionary with names to parameters to update.
:param use_second_order: A boolean flag of whether to use second order derivatives.
:param current_step_idx: Current step's index.
:return: A dictionary with the updated weights (name, param)
"""
num_gpus = torch.cuda.device_count()
if num_gpus > 1:
self.classifier.module.zero_grad(params=names_weights_copy)
else:
self.classifier.zero_grad(params=names_weights_copy)
grads = torch.autograd.grad(loss,
names_weights_copy.values(),
create_graph=use_second_order,
allow_unused=True)
names_grads_copy = dict(zip(names_weights_copy.keys(), grads))
names_weights_copy = {
key: value[0]
for key, value in names_weights_copy.items()
}
for key, grad in names_grads_copy.items():
if grad is None: # some grads are not needed.
raise Exception('Grads not found for inner loop parameter',
key)
else:
names_grads_copy[key] = names_grads_copy[key].sum(dim=0)
names_weights_copy = self.inner_loop_optimizer.update_params(
names_weights_dict=names_weights_copy,
names_grads_wrt_params_dict=names_grads_copy,
num_step=current_step_idx)
num_devices = torch.cuda.device_count() if torch.cuda.is_available(
) else 1
names_weights_copy = {
name.replace('module.', ''):
value.unsqueeze(0).repeat([num_devices] +
[1 for i in range(len(value.shape))])
for name, value in names_weights_copy.items()
}
return names_weights_copy
def get_across_task_loss_metrics(self, total_losses, total_accuracies):
losses = dict()
losses['loss'] = torch.mean(
torch.stack(total_losses)) #use loss average
losses['accuracy'] = np.mean(total_accuracies)
return losses
def forward(self,
data_batch,
epoch,
use_second_order,
use_multi_step_loss_optimization,
num_steps,
training_phase,
param='weight'):
"""
Runs a forward outer loop pass on the batch of tasks using the MAML/++ framework.
:param data_batch: A data batch containing the support and target sets.
:param epoch: Current epoch's index
:param use_second_order: A boolean saying whether to use second order derivatives.
:param use_multi_step_loss_optimization: Whether to optimize on the outer loop using just the last step's
target loss (True) or whether to use multi step loss which improves the stability of the system (False)
:param num_steps: Number of inner loop steps.
:param training_phase: Whether this is a training phase (True) or an evaluation phase (False)
:param param: 0 for weights. 1 for alpha
:return: A dictionary with the collected losses of the current outer forward propagation.
"""
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
total_losses = []
total_accuracies = []
per_task_target_preds = [[] for i in range(len(x_target_set))]
self.classifier.zero_grad()
for task_id, (x_support_set_task, y_support_set_task, x_target_set_task, y_target_set_task) in \
enumerate(zip(x_support_set,
y_support_set,
x_target_set,
y_target_set)):
task_losses = []
task_accuracies = []
per_step_loss_importance_vectors = self.get_per_step_loss_importance_vector(
)
names_weights_copy = None
if param == 'weight':
names_weights_copy = self.get_inner_loop_parameter_dict(
self.classifier.named_parameters(), )
else: # for alpha
names_weights_copy = self.get_inner_loop_parameter_dict(
arch_params=self.classifier.named_arch_parameters(), )
num_devices = torch.cuda.device_count() if torch.cuda.is_available(
) else 1
names_weights_copy = {
name.replace('module.', ''):
value.unsqueeze(0).repeat([num_devices] +
[1 for i in range(len(value.shape))])
for name, value in names_weights_copy.items()
}
n, s, c, h, w = x_target_set_task.shape
x_support_set_task = x_support_set_task.view(-1, c, h, w)
y_support_set_task = y_support_set_task.view(-1)
x_target_set_task = x_target_set_task.view(-1, c, h, w)
y_target_set_task = y_target_set_task.view(-1)
task_max_acc = 0.0
for num_step in range(num_steps):
support_loss, support_preds = self.net_forward(
x=x_support_set_task,
y=y_support_set_task,
weights=names_weights_copy,
backup_running_statistics=True if
(num_step == 0) else False,
training=True,
num_step=num_step)
names_weights_copy = self.apply_inner_loop_update(
loss=support_loss,
names_weights_copy=names_weights_copy,
use_second_order=use_second_order,
current_step_idx=num_step)
if use_multi_step_loss_optimization and training_phase and epoch < self.args.multi_step_loss_num_epochs:
target_loss, target_preds = self.net_forward(
x=x_target_set_task,
y=y_target_set_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step)
task_losses.append(
per_step_loss_importance_vectors[num_step] *
target_loss)
else:
if num_step == (
self.args.number_of_training_steps_per_iter - 1):
target_loss, target_preds = self.net_forward(
x=x_target_set_task,
y=y_target_set_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step)
task_losses.append(target_loss)
per_task_target_preds[task_id] = target_preds.detach().cpu().numpy(
)
_, predicted = torch.max(target_preds.data, 1)
accuracy = predicted.float().eq(
y_target_set_task.data.float()).cpu().float()
task_losses = torch.sum(torch.stack(task_losses))
total_losses.append(task_losses)
total_accuracies.extend(accuracy)
if not training_phase:
self.classifier.restore_backup_stats()
losses = self.get_across_task_loss_metrics(
total_losses=total_losses, total_accuracies=total_accuracies)
# for idx, item in enumerate(per_step_loss_importance_vectors):
# losses['loss_importance_vector_{}'.format(idx)] = item.detach().cpu().numpy()
return losses, per_task_target_preds
def net_forward(self, x, y, weights, backup_running_statistics, training,
num_step):
"""
A base model forward pass on some data points x. Using the parameters in the weights dictionary. Also requires
boolean flags indicating whether to reset the running statistics at the end of the run (if at evaluation phase).
A flag indicating whether this is the training session and an int indicating the current step's number in the
inner loop.
:param x: A data batch of shape b, c, h, w
:param y: A data targets batch of shape b, n_classes
:param weights: A dictionary containing the weights to pass to the network.
:param backup_running_statistics: A flag indicating whether to reset the batch norm running statistics to their
previous values after the run (only for evaluation)
:param training: A flag indicating whether the current process phase is a training or evaluation.
:param num_step: An integer indicating the number of the step in the inner loop.
:return: the crossentropy losses with respect to the given y, the predictions of the base model.
"""
preds = self.classifier.forward(
x=x,
params=weights,
training=training,
backup_running_statistics=backup_running_statistics,
num_step=num_step,
)
loss = F.cross_entropy(input=preds, target=y)
return loss, preds
def trainable_parameters(self):
"""
Returns an iterator over the trainable parameters of the model.
"""
for param in self.parameters():
if param.requires_grad:
yield param
def train_forward_prop(self, data_batch, epoch, param='weight'):
"""
Runs an outer loop forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, per_task_target_preds = self.forward(
data_batch=data_batch,
epoch=epoch,
use_second_order=self.args.second_order
and epoch > self.args.first_order_to_second_order_epoch,
use_multi_step_loss_optimization=self.args.
use_multi_step_loss_optimization,
num_steps=self.args.number_of_training_steps_per_iter,
training_phase=True,
param=param)
return losses, per_task_target_preds
def train_arch_forward_prop(self, data_batch, epoch, param='arch'):
"""
Runs an outer loop forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, per_task_target_preds = self.forward(
data_batch=data_batch,
epoch=epoch,
use_second_order=False,
use_multi_step_loss_optimization=self.args.
use_multi_step_loss_optimization,
num_steps=self.args.number_of_training_steps_per_iter,
training_phase=True,
param=param)
return losses, per_task_target_preds
def evaluation_forward_prop(self, data_batch, epoch):
"""
Runs an outer loop evaluation forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, per_task_target_preds = self.forward(
data_batch=data_batch,
epoch=epoch,
use_second_order=False,
use_multi_step_loss_optimization=True,
num_steps=self.args.number_of_evaluation_steps_per_iter,
training_phase=False)
return losses, per_task_target_preds
def meta_update_weights(self, loss):
"""
Applies an outer loop update on the meta-parameters of the model.
:param loss: The current crossentropy loss.
"""
self.optimizer.zero_grad()
# self.optimizer_arch.zero_grad()
loss.backward()
if 'imagenet' in self.args.dataset_name:
for name, param in self.classifier.named_parameters():
if param.requires_grad:
param.grad.data.clamp_(
-10, 10
) # not sure if this is necessary, more experiments are needed
self.optimizer.step()
# self.optimizer_arch.step()
def meta_update_alphas(self, loss):
"""
Applies an outer loop update on the meta-parameters of the model.
:param loss: The current crossentropy loss.
"""
self.optimizer_arch.zero_grad()
loss.backward()
self.optimizer_arch.step()
def batch_helper(self, data_batch, split=False):
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
x_support_set = torch.Tensor(x_support_set).float().to(
device=self.device)
x_target_set = torch.Tensor(x_target_set).float().to(
device=self.device)
y_support_set = torch.Tensor(y_support_set).long().to(
device=self.device)
y_target_set = torch.Tensor(y_target_set).long().to(device=self.device)
if not split:
data_batch = (x_support_set, x_target_set, y_support_set,
y_target_set)
return data_batch
else: #split the data
task_num_split = len(x_target_set) // 2
data_batch_arch = (x_support_set[:task_num_split],
x_target_set[:task_num_split],
y_support_set[:task_num_split],
y_target_set[:task_num_split])
data_batch_weight = (x_support_set[task_num_split:],
x_target_set[task_num_split:],
y_support_set[task_num_split:],
y_target_set[task_num_split:])
return data_batch_arch, data_batch_weight
def run_train_iter(self, data_batch, sample_idx, epoch):
"""
Runs an outer loop update step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
epoch = int(epoch)
# self.step = sample_idx
self.scheduler.step(epoch=epoch)
if not self.args.retrain:
self.scheduler_arch.step(epoch=epoch)
if self.current_epoch != epoch:
self.current_epoch = epoch
# print('learning_rate = ', self.scheduler.get_last_lr()[0])
# self.scheduler.step()
if not self.training:
self.train()
if not self.args.retrain: # search process
data_batch_arch, data_batch_weight = self.batch_helper(data_batch,
split=True)
train_prop_weight = self.train_arch_forward_prop
if epoch > self.args.min_search_epoch:
if torch.sum(self.classifier.normal_candidate_flags.int()) > 0:
losses, _ = self.train_arch_forward_prop(
data_batch=data_batch_arch, epoch=epoch, param='arch')
self.meta_update_alphas(loss=losses['loss'])
self.optimizer.zero_grad()
self.optimizer_arch.zero_grad()
self.zero_grad()
else: # no need for optimize arch
train_prop_weight = self.train_forward_prop
losses, per_task_target_preds = train_prop_weight(
data_batch=data_batch_weight, epoch=epoch, param='weight')
self.meta_update_weights(loss=losses['loss'])
losses['learning_rate'] = self.scheduler.get_last_lr()[0]
self.optimizer.zero_grad()
self.optimizer_arch.zero_grad()
self.zero_grad()
else: # different batch
data_batch = self.batch_helper(data_batch)
losses, per_task_target_preds = self.train_forward_prop(
data_batch=data_batch, epoch=epoch, param='weight')
self.meta_update_weights(loss=losses['loss'])
losses['learning_rate'] = self.scheduler.get_last_lr()[0]
# print("current_learning_rate =", losses['learning_rate'])
self.optimizer.zero_grad()
self.zero_grad()
return losses, per_task_target_preds
def run_test_iter(self, data_batch):
"""
Runs an outer loop evaluation step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
if self.training:
self.eval()
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
x_support_set = torch.Tensor(x_support_set).float().to(
device=self.device)
x_target_set = torch.Tensor(x_target_set).float().to(
device=self.device)
y_support_set = torch.Tensor(y_support_set).long().to(
device=self.device)
y_target_set = torch.Tensor(y_target_set).long().to(device=self.device)
data_batch = (x_support_set, x_target_set, y_support_set, y_target_set)
losses, per_task_target_preds = self.evaluation_forward_prop(
data_batch=data_batch, epoch=self.current_epoch)
# losses['loss'].backward() # uncomment if you get the weird memory error
# self.zero_grad()
# self.optimizer.zero_grad()
return losses, per_task_target_preds
def run_check_iter(self, data_batch, logging):
"""
check weather all the weights still require grad.
"""
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
x_support_set = torch.Tensor(x_support_set).float().to(
device=self.device)
x_target_set = torch.Tensor(x_target_set).float().to(
device=self.device)
y_support_set = torch.Tensor(y_support_set).long().to(
device=self.device)
y_target_set = torch.Tensor(y_target_set).long().to(device=self.device)
if not self.training:
self.train()
for task_id, (x_support_set_task, y_support_set_task, x_target_set_task, y_target_set_task) in \
enumerate(zip(x_support_set,
y_support_set,
x_target_set,
y_target_set)):
names_weights_copy = self.get_inner_loop_parameter_dict(
self.classifier.named_parameters())
num_devices = torch.cuda.device_count() if torch.cuda.is_available(
) else 1
names_weights_copy = {
name.replace('module.', ''):
value.unsqueeze(0).repeat([num_devices] +
[1 for i in range(len(value.shape))])
for name, value in names_weights_copy.items()
}
n, s, c, h, w = x_target_set_task.shape
x_support_set_task = x_support_set_task.view(-1, c, h, w)
y_support_set_task = y_support_set_task.view(-1)
x_target_set_task = x_target_set_task.view(-1, c, h, w)
y_target_set_task = y_target_set_task.view(-1)
num_step = 0
target_loss, target_preds = self.net_forward(
x=x_target_set_task,
y=y_target_set_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step)
self.optimizer.zero_grad()
target_loss.backward()
for name, param in self.classifier.named_parameters():
# for name, param in self.named_parameters():
if param.requires_grad:
if param.grad is not None:
# print(name, param.grad.shape)
if torch.sum(param.grad) == 0:
if 'op' in name:
logging.info(
'{} requires_grad = False'.format(name))
param.requires_grad = False
else:
if 'op' in name:
logging.info(
'{} requires_grad = False'.format(name))
param.requires_grad = False
self.optimizer.zero_grad()
self.zero_grad()
break # only one iter
def save_model(self, model_save_dir, state):
"""
Save the network parameter state and experiment state dictionary.
:param model_save_dir: The directory to store the state at.
:param state: The state containing the experiment state and the network. It's in the form of a dictionary
object.
"""
state['network'] = self.state_dict()
state['classifier'] = self.classifier.state_dict()
torch.save(state, f=model_save_dir)
def load_model(self, model_save_dir, model_name, model_idx):
"""
Load checkpoint and return the state dictionary containing the network state params and experiment state.
:param model_save_dir: The directory from which to load the files.
:param model_name: The model_name to be loaded from the direcotry.
:param model_idx: The index of the model (i.e. epoch number or 'latest' for the latest saved model of the current
experiment)
:return: A dictionary containing the experiment state and the saved model parameters.
"""
filepath = os.path.join(model_save_dir,
"{}_{}".format(model_name, model_idx))
state = torch.load(filepath)
state_dict_loaded = state['network']
if not self.args.retrain:
self.classifier.load_alphas(state)
self.load_state_dict(state_dict=state_dict_loaded)
return state
def load_pretrained_model_init(self, pretrained_mode_path):
state = torch.load(pretrained_mode_path)
state_dict_loaded = state['network']
state_dict_loaded = {k[len('classifier.'):]: v for k, v in state_dict_loaded.items() \
if k.startswith('classifier.') and k[len('classifier.'):] in self.classifier.state_dict()}
# pretrained_model = dict()
for k, v in state_dict_loaded.items():
if self.args.per_step_bn_statistics:
if 'norm' in k and len(v.shape) == 1:
# print(k)
state_dict_loaded[k] = v.unsqueeze(0).repeat(
[self.args.number_of_training_steps_per_iter] +
[1 for i in range(len(v.shape))])
else:
if 'norm' in k and len(v.shape) == 2:
# unuse the per_step_bn_statistics
state_dict_loaded[k] = v[0]
self.classifier.load_state_dict(state_dict_loaded)
class MAMLFewShotClassifier(nn.Module):
def __init__(self, im_shape, device, args):
"""
Initializes a MAML few shot learning system
:param im_shape: The images input size, in batch, c, h, w shape
:param device: The device to use to use the model on.
:param args: A namedtuple of arguments specifying various hyperparameters.
"""
super(MAMLFewShotClassifier, self).__init__()
self.args = args
self.device = device
self.batch_size = args.batch_size
self.use_cuda = args.use_cuda
self.im_shape = im_shape
self.current_epoch = 0
self.rng = set_torch_seed(seed=args.seed)
if args.high_end:
self.embedding = HighEndEmbedding(device, args, 3).to(device)
self.classifier = HighEndClassifier(
device, args, self.embedding.n_out_channels).to(device)
else:
self.classifier = VGGReLUNormNetwork(
im_shape=self.im_shape,
num_output_classes=self.args.num_classes_per_set,
args=args,
device=device,
meta_classifier=True).to(device=self.device)
self.task_learning_rate = args.task_learning_rate
self.inner_loop_optimizer = LSLRGradientDescentLearningRule(
device=device,
init_learning_rate=self.task_learning_rate,
total_num_inner_loop_steps=self.args.
number_of_training_steps_per_iter + self.args.num_critic_updates,
use_learnable_learning_rates=self.args.
learnable_per_layer_per_step_inner_loop_learning_rate)
self.inner_loop_optimizer.initialise(
names_weights_dict=self.get_inner_loop_parameter_dict(
params=self.classifier.named_parameters()))
print("Inner Loop parameters")
for key, value in self.inner_loop_optimizer.named_parameters():
print(key, value.shape)
if args.use_critic:
print(
sum([
reduce(mul, p.size(), 1) for p in list(
self.get_inner_loop_parameter_dict(
self.classifier.named_parameters()).values())
]))
self.critic = Critic(n_theta=sum([
reduce(mul, p.size(), 1) for p in list(
self.get_inner_loop_parameter_dict(
self.classifier.named_parameters()).values())
]))
self.use_cuda = args.use_cuda
self.device = device
self.args = args
self.to(device)
print("Outer Loop parameters")
for name, param in self.named_parameters():
if param.requires_grad:
print(name, param.shape, param.device, param.requires_grad)
if args.high_end:
self.optimizer = optim.SGD(self.trainable_parameters(), lr=1e-4)
else:
self.optimizer = optim.Adam(self.trainable_parameters(),
lr=args.meta_learning_rate,
amsgrad=False)
if args.use_critic:
self.critic_optimizer = optim.SGD(self.critic.parameters(),
lr=1e-6)
self.scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer=self.optimizer,
T_max=self.args.total_epochs,
eta_min=self.args.min_learning_rate)
def get_per_step_loss_importance_vector(self):
"""
Generates a tensor of dimensionality (num_inner_loop_steps) indicating the importance of each step's target
loss towards the optimization loss.
:return: A tensor to be used to compute the weighted average of the loss, useful for
the MSL (Multi Step Loss) mechanism.
"""
loss_weights = np.ones(
shape=(self.args.number_of_training_steps_per_iter)) * (
1.0 / self.args.number_of_training_steps_per_iter)
decay_rate = 1.0 / self.args.number_of_training_steps_per_iter / self.args.multi_step_loss_num_epochs
min_value_for_non_final_losses = 0.03 / self.args.number_of_training_steps_per_iter
for i in range(len(loss_weights) - 1):
curr_value = np.maximum(
loss_weights[i] - (self.current_epoch * decay_rate),
min_value_for_non_final_losses)
loss_weights[i] = curr_value
curr_value = np.minimum(
loss_weights[-1] +
(self.current_epoch *
(self.args.number_of_training_steps_per_iter - 1) * decay_rate),
1.0 - ((self.args.number_of_training_steps_per_iter - 1) *
min_value_for_non_final_losses))
loss_weights[-1] = curr_value
loss_weights = torch.Tensor(loss_weights).to(device=self.device)
return loss_weights
def get_inner_loop_parameter_dict(self, params):
"""
Returns a dictionary with the parameters to use for inner loop updates.
:param params: A dictionary of the network's parameters.
:return: A dictionary of the parameters to use for the inner loop optimization process.
"""
param_dict = dict()
for name, param in params:
if param.requires_grad:
if self.args.enable_inner_loop_optimizable_bn_params:
param_dict[name] = param.to(device=self.device)
else:
if "norm_layer" not in name:
param_dict[name] = param.to(device=self.device)
return param_dict
def apply_inner_loop_update(self, loss, names_weights_copy,
use_second_order, current_step_idx):
"""
Applies an inner loop update given current step's loss, the weights to update, a flag indicating whether to use
second order derivatives and the current step's index.
:param loss: Current step's loss with respect to the support set.
:param names_weights_copy: A dictionary with names to parameters to update.
:param use_second_order: A boolean flag of whether to use second order derivatives.
:param current_step_idx: Current step's index.
:return: A dictionary with the updated weights (name, param)
"""
self.classifier.zero_grad(names_weights_copy)
grads = torch.autograd.grad(loss,
names_weights_copy.values(),
create_graph=use_second_order)
names_grads_wrt_params = dict(zip(names_weights_copy.keys(), grads))
names_weights_copy = self.inner_loop_optimizer.update_params(
names_weights_dict=names_weights_copy,
names_grads_wrt_params_dict=names_grads_wrt_params,
num_step=current_step_idx)
return names_weights_copy
def get_across_task_loss_metrics(self, total_losses, total_accuracies):
losses = dict()
losses['loss'] = torch.mean(torch.stack(total_losses))
losses['accuracy'] = np.mean(total_accuracies)
return losses
def forward(self, data_batch, epoch, use_second_order,
use_multi_step_loss_optimization, num_steps, training_phase):
"""
Runs a forward outer loop pass on the batch of tasks using the MAML/++ framework.
:param data_batch: A data batch containing the support and target sets.
:param epoch: Current epoch's index
:param use_second_order: A boolean saying whether to use second order derivatives.
:param use_multi_step_loss_optimization: Whether to optimize on the outer loop using just the last step's
target loss (True) or whether to use multi step loss which improves the stability of the system (False)
:param num_steps: Number of inner loop steps.
:param training_phase: Whether this is a training phase (True) or an evaluation phase (False)
:return: A dictionary with the collected losses of the current outer forward propagation.
"""
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
[b, ncs, spc] = y_support_set.shape
self.num_classes_per_set = ncs
total_losses = []
total_accuracies = []
per_task_target_preds = [[] for i in range(len(x_target_set))]
if self.args.high_end:
self.embedding.zero_grad()
self.classifier.zero_grad()
for task_id, (x_support_set_task, y_support_set_task, x_target_set_task, y_target_set_task) in \
enumerate(zip(x_support_set,
y_support_set,
x_target_set,
y_target_set)):
task_losses = []
task_accuracies = []
per_step_loss_importance_vectors = self.get_per_step_loss_importance_vector(
)
# this is theta_0
names_weights_copy = self.get_inner_loop_parameter_dict(
self.classifier.named_parameters())
n, s, c, h, w = x_target_set_task.shape
x_support_set_task = x_support_set_task.view(-1, c, h, w)
y_support_set_task = y_support_set_task.view(-1)
x_target_set_task = x_target_set_task.view(-1, c, h, w)
y_target_set_task = y_target_set_task.view(-1)
# Inner loop starts
if self.args.high_end:
x_support_set_task = self.embedding(x_support_set_task,
0,
training=training_phase)
x_target_set_task = self.embedding(x_target_set_task,
0,
training=training_phase)
for num_step in range(num_steps):
# operates on the support set
support_loss, support_preds = self.net_forward(
x=x_support_set_task,
y=y_support_set_task,
weights=names_weights_copy,
backup_running_statistics=True if
(num_step == 0) else False,
training=True,
num_step=num_step)
# this is update of theta from the copy of current theta_0 and onward
# i.e. inner loop optimization wrt support set
names_weights_copy = self.apply_inner_loop_update(
loss=support_loss,
names_weights_copy=names_weights_copy,
use_second_order=use_second_order,
current_step_idx=num_step)
# TODO: inner loop OPTIMIZATION wrt target set???
if use_multi_step_loss_optimization and training_phase and epoch < self.args.multi_step_loss_num_epochs:
# this is MAML++ way
target_loss, target_preds = self.net_forward(
x=x_target_set_task,
y=y_target_set_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step)
task_losses.append(
per_step_loss_importance_vectors[num_step] *
target_loss)
else:
if num_step == (
self.args.number_of_training_steps_per_iter - 1):
target_loss, target_preds = self.net_forward(
x=x_target_set_task,
y=y_target_set_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step)
task_losses.append(target_loss)
if self.args.use_critic:
for i in range(self.args.num_critic_updates):
# TODO: here must be an update using the Critic (start without g)
# F = {f(x^b_T, θ_{N+j}), θ_{N+j}, g(xS, xn)}
# θ_{N+j+1} = θ_{N+j} − \gamma * \nabla_{θ_{N+j}} C(F,W)
critic_loss, target_preds = self.net_forward_critic(
x=x_target_set_task,
y=y_target_set_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step + i)
names_weights_copy = self.apply_inner_loop_update(
loss=critic_loss,
names_weights_copy=names_weights_copy,
use_second_order=use_second_order,
current_step_idx=num_step + i)
target_loss, target_preds = self.net_forward(
x=x_target_set_task,
y=y_target_set_task,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_step)
task_losses.append(target_loss)
per_task_target_preds[task_id] = target_preds.detach().cpu().numpy(
)
_, predicted = torch.max(target_preds.data, 1)
accuracy = predicted.float().eq(
y_target_set_task.data.float()).cpu().float()
task_losses = torch.sum(torch.stack(task_losses))
total_losses.append(task_losses)
total_accuracies.extend(accuracy)
if not training_phase:
self.classifier.restore_backup_stats()
losses = self.get_across_task_loss_metrics(
total_losses=total_losses, total_accuracies=total_accuracies)
for idx, item in enumerate(per_step_loss_importance_vectors):
losses['loss_importance_vector_{}'.format(
idx)] = item.detach().cpu().numpy()
return losses, per_task_target_preds
def net_forward(self, x, y, weights, backup_running_statistics, training,
num_step):
"""
A base model forward pass on some data points x. Using the parameters in the weights dictionary. Also requires
boolean flags indicating whether to reset the running statistics at the end of the run (if at evaluation phase).
A flag indicating whether this is the training session and an int indicating the current step's number in the
inner loop.
:param x: A data batch of shape b, c, h, w
:param y: A data targets batch of shape b, n_classes
:param weights: A dictionary containing the weights to pass to the network.
:param backup_running_statistics: A flag indicating whether to reset the batch norm running statistics to their
previous values after the run (only for evaluation)
:param training: A flag indicating whether the current process phase is a training or evaluation.
:param num_step: An integer indicating the number of the step in the inner loop.
:return: the crossentropy losses with respect to the given y, the predictions of the base model.
"""
preds = self.classifier.forward(
x=x,
params=weights,
training=training,
backup_running_statistics=backup_running_statistics,
num_step=num_step)
loss = F.cross_entropy(input=preds, target=y)
return loss, preds
def net_forward_critic(self, x, y, weights, backup_running_statistics,
training, num_step):
"""
A base model forward pass on some data points x. Using the parameters in the weights dictionary. Also requires
boolean flags indicating whether to reset the running statistics at the end of the run (if at evaluation phase).
A flag indicating whether this is the training session and an int indicating the current step's number in the
inner loop.
:param x: A data batch of shape b, c, h, w
:param y: A data targets batch of shape b, n_classes
:param weights: A dictionary containing the weights to pass to the network.
:param backup_running_statistics: A flag indicating whether to reset the batch norm running statistics to their
previous values after the run (only for evaluation)
:param training: A flag indicating whether the current process phase is a training or evaluation.
:param num_step: An integer indicating the number of the step in the inner loop.
:return: the crossentropy losses with respect to the given y, the predictions of the base model.
"""
preds = self.classifier.forward(
x=x,
params=weights,
training=training,
backup_running_statistics=backup_running_statistics,
num_step=num_step)
print(weights.keys())
params1d = torch.cat(
[torch.reshape(p, (1, -1)) for p in list(weights.values())], dim=1)
print(params1d.shape)
loss = self.critic(preds, params1d)
return loss, preds
def trainable_parameters(self):
"""
Returns an iterator over the trainable parameters of the model.
"""
for param in self.parameters():
if param.requires_grad:
yield param
def train_forward_prop(self, data_batch, epoch):
"""
Runs an outer loop forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, per_task_target_preds = self.forward(
data_batch=data_batch,
epoch=epoch,
use_second_order=self.args.second_order
and epoch > self.args.first_order_to_second_order_epoch,
use_multi_step_loss_optimization=self.args.
use_multi_step_loss_optimization,
num_steps=self.args.number_of_training_steps_per_iter,
training_phase=True)
return losses, per_task_target_preds
def evaluation_forward_prop(self, data_batch, epoch):
"""
Runs an outer loop evaluation forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, per_task_target_preds = self.forward(
data_batch=data_batch,
epoch=epoch,
use_second_order=False,
use_multi_step_loss_optimization=True,
num_steps=self.args.number_of_evaluation_steps_per_iter,
training_phase=False)
return losses, per_task_target_preds
def meta_update(self, loss):
"""
Applies an outer loop update on the meta-parameters of the model.
:param loss: The current crossentropy loss.
"""
self.optimizer.zero_grad()
loss.backward(retain_graph=True)
if 'imagenet' in self.args.dataset_name:
for name, param in self.classifier.named_parameters():
if param.requires_grad:
param.grad.data.clamp_(
-10, 10
) # not sure if this is necessary, more experiments are needed
if self.args.high_end:
for name, param in self.embedding.named_parameters():
if param.requires_grad:
param.grad.data.clamp_(
-10, 10
) # not sure if this is necessary, more experiments are needed
self.optimizer.step()
def critic_meta_update(self, loss):
self.critic_optimizer.zero_grad()
loss.backward()
self.critic_optimizer.step()
def run_train_iter(self, data_batch, epoch):
"""
Runs an outer loop update step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
epoch = int(epoch)
self.scheduler.step(epoch=epoch)
if self.current_epoch != epoch:
self.current_epoch = epoch
if not self.training:
self.train()
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
x_support_set = torch.Tensor(x_support_set).float().to(
device=self.device)
x_target_set = torch.Tensor(x_target_set).float().to(
device=self.device)
y_support_set = torch.Tensor(y_support_set).long().to(
device=self.device)
y_target_set = torch.Tensor(y_target_set).long().to(device=self.device)
data_batch = (x_support_set, x_target_set, y_support_set, y_target_set)
losses, per_task_target_preds = self.train_forward_prop(
data_batch=data_batch, epoch=epoch)
self.meta_update(loss=losses['loss'])
if self.args.use_critic:
self.critic_meta_update(loss=losses['loss'])
losses['learning_rate'] = self.scheduler.get_lr()[0]
self.optimizer.zero_grad()
self.zero_grad()
return losses, per_task_target_preds
def run_validation_iter(self, data_batch):
"""
Runs an outer loop evaluation step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
if self.training:
self.eval()
x_support_set, x_target_set, y_support_set, y_target_set = data_batch
x_support_set = torch.Tensor(x_support_set).float().to(
device=self.device)
x_target_set = torch.Tensor(x_target_set).float().to(
device=self.device)
y_support_set = torch.Tensor(y_support_set).long().to(
device=self.device)
y_target_set = torch.Tensor(y_target_set).long().to(device=self.device)
data_batch = (x_support_set, x_target_set, y_support_set, y_target_set)
losses, per_task_target_preds = self.evaluation_forward_prop(
data_batch=data_batch, epoch=self.current_epoch)
# losses['loss'].backward() # uncomment if you get the weird memory error
# self.zero_grad()
# self.optimizer.zero_grad()
return losses, per_task_target_preds
def save_model(self, model_save_dir, state):
"""
Save the network parameter state and experiment state dictionary.
:param model_save_dir: The directory to store the state at.
:param state: The state containing the experiment state and the network. It's in the form of a dictionary
object.
"""
state['network'] = self.state_dict()
torch.save(state, f=model_save_dir)
def load_model(self, model_save_dir, model_name, model_idx):
"""
Load checkpoint and return the state dictionary containing the network state params and experiment state.
:param model_save_dir: The directory from which to load the files.
:param model_name: The model_name to be loaded from the direcotry.
:param model_idx: The index of the model (i.e. epoch number or 'latest' for the latest saved model of the current
experiment)
:return: A dictionary containing the experiment state and the saved model parameters.
"""
filepath = os.path.join(model_save_dir,
"{}_{}".format(model_name, model_idx))
state = torch.load(filepath)
state_dict_loaded = state['network']
self.load_state_dict(state_dict=state_dict_loaded)
return state
class MAMLRegressor(nn.Module):
def __init__(self, input_shape, device, args):
"""
Initializes a MAML few shot learning system
:param im_shape: The images input size, in batch, c, h, w shape
:param device: The device to use to use the model on.
:param args: A namedtuple of arguments specifying various hyperparameters.
"""
super(MAMLRegressor, self).__init__()
self.args = args
self.device = device
self.batch_size = args.batch_size
self.use_cuda = args.use_cuda
self.current_epoch = 0
self.input_shape = input_shape
self.rng = set_torch_seed(seed=args.seed)
self.args.rng = self.rng
self.regressor = IFCNReLUNormNetwork(input_shape=self.input_shape,
args=self.args,
device=device,
meta=True).to(device=self.device)
self.task_learning_rate = args.task_learning_rate
self.inner_loop_optimizer = LSLRGradientDescentLearningRule(
device=device,
init_learning_rate=self.task_learning_rate,
total_num_inner_loop_steps=self.args.
number_of_training_steps_per_iter,
use_learnable_learning_rates=self.args.
learnable_per_layer_per_step_inner_loop_learning_rate)
self.inner_loop_optimizer.initialise(
names_weights_dict=self.get_inner_loop_parameter_dict(
params=self.regressor.named_parameters()))
print("Inner Loop parameters")
for key, value in self.inner_loop_optimizer.named_parameters():
print(key, value.shape)
self.use_cuda = args.use_cuda
self.device = device
self.args = args
self.to(device)
print("Outer Loop parameters")
for name, param in self.named_parameters():
if param.requires_grad:
print(name, param.shape, param.device, param.requires_grad)
self.optimizer = optim.Adam(self.trainable_parameters(),
lr=args.meta_learning_rate,
amsgrad=False)
self.scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer=self.optimizer,
T_max=self.args.total_epochs,
eta_min=self.args.min_learning_rate)
self.optimizer_adapt = optim.Adam(
self.trainable_adaptation_parameters(),
lr=args.meta_learning_rate,
amsgrad=False)
self.scheduler_adapt = optim.lr_scheduler.CosineAnnealingLR(
optimizer=self.optimizer_adapt,
T_max=self.args.total_epochs,
eta_min=self.args.min_learning_rate)
def get_per_step_loss_importance_vector(self):
"""
Generates a tensor of dimensionality (num_inner_loop_steps) indicating the importance of each step's target
loss towards the optimization loss.
:return: A tensor to be used to compute the weighted average of the loss, useful for
the MSL (Multi Step Loss) mechanism.
"""
loss_weights = np.ones(
shape=(self.args.number_of_training_steps_per_iter)) * (
1.0 / self.args.number_of_training_steps_per_iter)
decay_rate = 1.0 / self.args.number_of_training_steps_per_iter / self.args.multi_step_loss_num_epochs
min_value_for_non_final_losses = 0.03 / self.args.number_of_training_steps_per_iter
for i in range(len(loss_weights) - 1):
curr_value = np.maximum(
loss_weights[i] - (self.current_epoch * decay_rate),
min_value_for_non_final_losses)
loss_weights[i] = curr_value
curr_value = np.minimum(
loss_weights[-1] +
(self.current_epoch *
(self.args.number_of_training_steps_per_iter - 1) * decay_rate),
1.0 - ((self.args.number_of_training_steps_per_iter - 1) *
min_value_for_non_final_losses))
loss_weights[-1] = curr_value
loss_weights = torch.Tensor(loss_weights).to(device=self.device)
return loss_weights
def get_inner_loop_parameter_dict(self, params):
"""
Returns a dictionary with the parameters to use for inner loop updates.
:param params: A dictionary of the network's parameters.
:return: A dictionary of the parameters to use for the inner loop optimization process.
"""
param_dict = dict()
for name, param in params:
if param.requires_grad:
if self.args.enable_inner_loop_optimizable_bn_params:
param_dict[name] = param.to(device=self.device)
else:
# if "norm_layer" not in name:
# param_dict[name] = param.to(device=self.device)
if "layer_dict.linear.bias" in name or "layer_dict.linear.weights" in name:
param_dict[name] = param.to(device=self.device)
return param_dict
def apply_inner_loop_update(self, loss, names_weights_copy,
use_second_order, current_step_idx):
"""
Applies an inner loop update given current step's loss, the weights to update, a flag indicating whether to use
second order derivatives and the current step's index.
:param loss: Current step's loss with respect to the support set.
:param names_weights_copy: A dictionary with names to parameters to update.
:param use_second_order: A boolean flag of whether to use second order derivatives.
:param current_step_idx: Current step's index.
:return: A dictionary with the updated weights (name, param)
"""
self.regressor.zero_grad(names_weights_copy)
grads = torch.autograd.grad(loss,
names_weights_copy.values(),
create_graph=use_second_order)
names_grads_wrt_params = dict(zip(names_weights_copy.keys(), grads))
names_weights_copy = self.inner_loop_optimizer.update_params(
names_weights_dict=names_weights_copy,
names_grads_wrt_params_dict=names_grads_wrt_params,
num_step=current_step_idx)
return names_weights_copy
def get_across_task_loss_metrics(self, total_losses, total_accuracies):
losses = dict()
losses['loss'] = torch.mean(torch.stack(total_losses[0]))
losses['loss_supp'] = torch.mean(torch.stack(total_losses[1]))
losses['loss_0'] = torch.mean(torch.stack(total_losses[2]))
losses['loss_supp_0'] = torch.mean(torch.stack(total_losses[3]))
if sum(~np.isnan(total_accuracies[0])) == 0:
losses['accuracy'] = 0
else:
losses['accuracy'] = np.mean(
np.array(total_accuracies[0])[~np.isnan(total_accuracies[0])])
losses['accuracy_supp'] = np.mean(
np.array(total_accuracies[1])[~np.isnan(total_accuracies[1])])
losses['accuracy_0'] = np.mean(
np.array(total_accuracies[2])[~np.isnan(total_accuracies[2])])
losses['accuracy_supp_0'] = np.mean(
np.array(total_accuracies[3])[~np.isnan(total_accuracies[3])])
return losses
def forward(self, data_batch, epoch, use_second_order,
use_multi_step_loss_optimization, num_steps, training_phase):
"""
Runs a forward outer loop pass on the batch of tasks using the MAML/++ framework.
:param data_batch: A data batch containing the support and target sets.
:param epoch: Current epoch's index
:param use_second_order: A boolean saying whether to use second order derivatives.
:param use_multi_step_loss_optimization: Whether to optimize on the outer loop using just the last step's
target loss (True) or whether to use multi step loss which improves the stability of the system (False)
:param num_steps: Number of inner loop steps.
:param training_phase: Whether this is a training phase (True) or an evaluation phase (False)
:return: A dictionary with the collected losses of the current outer forward propagation.
"""
support_set_x, support_set_y, support_set_z, support_set_assay, \
target_set_x, target_set_y, target_set_z, target_set_assay = data_batch
b = len(support_set_y)
total_losses = []
total_accuracies = []
total_losses_supp = []
total_accuracies_supp = []
total_losses_0 = []
total_accuracies_0 = []
total_losses_supp_0 = []
total_accuracies_supp_0 = []
total_losses_only = []
per_task_target_preds = [[] for i in range(len(target_set_x))]
self.regressor.zero_grad()
for task_id, (support_set_x_task, support_set_y_task, support_set_z_task, support_set_assay_task,
target_set_x_task, target_set_y_task, target_set_z_task, target_set_assay_task,) in \
enumerate(zip(support_set_x,
support_set_y,
support_set_z,
support_set_assay,
target_set_x,
target_set_y,
target_set_z,
target_set_assay)):
# first of all, put all tensors to the device
support_set_x_task = torch.Tensor(
support_set_x_task[0]).float().to(device=self.device)
support_set_y_task = torch.Tensor(
support_set_y_task[0]).float().to(device=self.device)
support_set_z_task = torch.IntTensor(
support_set_z_task[0]).int().to(device=self.device)
support_set_assay_task = torch.LongTensor(
support_set_assay_task).int().to(device=self.device)
target_set_x_task = torch.Tensor(
target_set_x_task[0]).float().to(device=self.device)
target_set_y_task = torch.Tensor(
target_set_y_task[0]).float().to(device=self.device)
target_set_z_task = torch.IntTensor(
target_set_z_task[0]).int().to(device=self.device)
target_set_assay_task = torch.LongTensor(
target_set_assay_task).int().to(device=self.device)
task_losses = []
task_accuracies = []
task_losses_supp = []
task_accuracies_supp = []
task_losses_0 = []
task_accuracies_0 = []
task_losses_supp_0 = []
task_accuracies_supp_0 = []
task_losses_only = []
per_step_loss_importance_vectors = self.get_per_step_loss_importance_vector(
)
names_weights_copy = self.get_inner_loop_parameter_dict(
self.regressor.named_parameters())
ns, fp_dim = support_set_x_task.shape
nt, _ = target_set_x_task.shape
r = np.random.uniform(-3, 3)
for num_step in range(num_steps):
support_loss, support_preds = self.net_forward(
x=support_set_x_task,
y=support_set_y_task + r,
weights=names_weights_copy,
backup_running_statistics=True if
(num_step == 0) else False,
training=True,
num_step=num_step) # , support=True)
if num_step == 0:
target_loss, target_preds = self.net_forward(
x=target_set_x_task,
y=target_set_y_task + r,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=0)
task_losses_supp_0.append(support_loss)
task_losses_0.append(target_loss)
accuracy_0 = pearson_score(
target_set_y_task.detach().cpu().numpy(),
(target_preds - r).detach().cpu().numpy())
task_losses_0 = torch.sum(torch.stack(task_losses_0))
total_losses_0.append(task_losses_0)
total_accuracies_0.append(accuracy_0)
accuracy_supp_0 = pearson_score(
support_set_y_task.detach().cpu().numpy(),
(support_preds - r).detach().cpu().numpy())
task_losses_supp_0 = torch.sum(
torch.stack(task_losses_supp_0))
total_losses_supp_0.append(task_losses_supp_0)
total_accuracies_supp_0.append(accuracy_supp_0)
names_weights_copy = self.apply_inner_loop_update(
loss=support_loss,
names_weights_copy=names_weights_copy,
use_second_order=use_second_order,
current_step_idx=num_step)
support_loss, support_preds = self.net_forward(
x=support_set_x_task,
y=support_set_y_task + r,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_steps - 1)
target_loss, target_preds = self.net_forward(
x=target_set_x_task,
y=target_set_y_task + r,
weights=names_weights_copy,
backup_running_statistics=False,
training=True,
num_step=num_steps - 1)
task_losses.append(target_loss)
task_losses_supp.append(support_loss)
per_task_target_preds[task_id] = target_preds.detach().cpu().numpy(
)
# _, predicted = torch.max(target_preds.data, 1)
accuracy = pearson_score(target_set_y_task.detach().cpu().numpy(),
(target_preds - r).detach().cpu().numpy())
task_losses = torch.sum(torch.stack(task_losses))
total_losses.append(task_losses)
total_accuracies.append(accuracy)
supp_accuracy = pearson_score(
support_set_y_task.detach().cpu().numpy(),
(support_preds - r).detach().cpu().numpy())
task_losses_supp = torch.sum(torch.stack(task_losses_supp))
total_losses_supp.append(task_losses_supp)
total_accuracies_supp.append(supp_accuracy)
# pdb.set_trace()
if not training_phase:
self.regressor.restore_backup_stats()
losses = self.get_across_task_loss_metrics(total_losses=[
total_losses, total_losses_supp, total_losses_0,
total_losses_supp_0
],
total_accuracies=[
total_accuracies,
total_accuracies_supp,
total_accuracies_0,
total_accuracies_supp_0
])
for idx, item in enumerate(per_step_loss_importance_vectors):
losses['loss_importance_vector_{}'.format(
idx)] = item.detach().cpu().numpy()
return losses, per_task_target_preds
def net_forward(self,
x,
y,
weights,
backup_running_statistics,
training,
num_step,
mixup=None,
lam=None,
support=None):
"""
A base model forward pass on some data points x. Using the parameters in the weights dictionary. Also requires
boolean flags indicating whether to reset the running statistics at the end of the run (if at evaluation phase).
A flag indicating whether this is the training session and an int indicating the current step's number in the
inner loop.
:param x: A data batch of shape b, c, h, w
:param y: A data targets batch of shape b, n_classes
:param weights: A dictionary containing the weights to pass to the network.
:param backup_running_statistics: A flag indicating whether to reset the batch norm running statistics to their
previous values after the run (only for evaluation)
:param training: A flag indicating whether the current process phase is a training or evaluation.
:param num_step: An integer indicating the number of the step in the inner loop.
:return: the crossentropy losses with respect to the given y, the predictions of the base model.
"""
preds = self.regressor.forward(
x=x,
params=weights,
training=training,
backup_running_statistics=backup_running_statistics,
num_step=num_step,
mixup=mixup,
lam=lam)
if mixup is not None:
npreds = preds.shape[0]
preds = preds[:int(npreds / 2), :]
# loss = F.cross_entropy(input=preds, target=y)
# if support is not None:
# loss = F.mse_loss(input=preds * sample_weights, target=y.unsqueeze(dim=-1))
# else:
loss = F.mse_loss(input=preds, target=y.unsqueeze(dim=-1))
return loss, preds
def trainable_parameters(self):
"""
Returns an iterator over the trainable parameters of the model.
"""
for param in self.parameters():
if param.requires_grad:
yield param
def trainable_feature_parameters(self):
for name, param in self.named_parameters():
if param.requires_grad and 'learning_rates' not in name: # 'layer_dict.linear.bias' not in name and 'layer_dict.linear.weights' not in name and 'learning_rates' not in name:
yield param
def trainable_adaptation_parameters(self):
for name, param in self.named_parameters():
if param.requires_grad and ('layer_dict.linear.bias' in name
or 'layer_dict.linear.weights' in name
or 'learning_rates' in name):
yield param
def train_forward_prop(self, data_batch, epoch):
"""
Runs an outer loop forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, per_task_target_preds = self.forward(
data_batch=data_batch,
epoch=epoch,
use_second_order=self.args.second_order
and epoch > self.args.first_order_to_second_order_epoch,
use_multi_step_loss_optimization=self.args.
use_multi_step_loss_optimization,
num_steps=self.args.number_of_training_steps_per_iter,
training_phase=True)
return losses, per_task_target_preds
def evaluation_forward_prop(self, data_batch, epoch):
"""
Runs an outer loop evaluation forward prop using the meta-model and base-model.
:param data_batch: A data batch containing the support set and the target set input, output pairs.
:param epoch: The index of the currrent epoch.
:return: A dictionary of losses for the current step.
"""
losses, per_task_target_preds = self.forward(
data_batch=data_batch,
epoch=epoch,
use_second_order=False,
use_multi_step_loss_optimization=True,
num_steps=self.args.number_of_evaluation_steps_per_iter,
training_phase=False)
return losses, per_task_target_preds
def meta_update(self, loss):
"""
Applies an outer loop update on the meta-parameters of the model.
:param loss: The current crossentropy loss.
"""
self.optimizer.zero_grad()
loss.backward()
if 'imagenet' in self.args.dataset_name:
for name, param in self.regressor.named_parameters():
if param.requires_grad:
param.grad.data.clamp_(
-10, 10
) # not sure if this is necessary, more experiments are needed
self.optimizer.step()
def run_train_iter(self, data_batch, epoch):
"""
Runs an outer loop update step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
epoch = int(epoch)
self.scheduler.step(epoch=epoch)
if self.current_epoch != epoch:
self.current_epoch = epoch
if not self.training:
self.train()
losses, per_task_target_preds = self.train_forward_prop(
data_batch=data_batch, epoch=epoch)
self.meta_update(loss=losses['loss'])
'''
if epoch < 10:
self.optimizer.zero_grad()
losses['loss'].backward()
self.optimizer.step()
else:
self.optimizer_adapt.zero_grad()
losses['loss'].backward()
self.optimizer_adapt.step()
'''
'''
self.optimizer.zero_grad()
losses['loss'].backward(retain_graph=True)
grad_loss_backup = {}
for name, param in self.named_parameters():
if param.requires_grad and name not in grad_loss_backup and 'learning_rates' not in name and "layer_dict.linear.bias" not in name or "layer_dict.linear.weights" not in name:
grad_loss_backup[name] = copy.deepcopy(param.grad)
self.optimizer.zero_grad()
losses['loss_only'].backward()
for name, param in self.named_parameters():
if param.requires_grad and 'learning_rates' not in name and "layer_dict.linear.bias" not in name or "layer_dict.linear.weights" not in name:
param.grad = copy.deepcopy(grad_loss_backup[name])
self.optimizer.step()
'''
losses['learning_rate'] = self.scheduler.get_lr()[0]
self.optimizer.zero_grad()
self.optimizer_adapt.zero_grad()
self.zero_grad()
return losses, per_task_target_preds
def run_validation_iter(self, data_batch):
"""
Runs an outer loop evaluation step on the meta-model's parameters.
:param data_batch: input data batch containing the support set and target set input, output pairs
:param epoch: the index of the current epoch
:return: The losses of the ran iteration.
"""
if self.training:
self.eval()
losses, per_task_target_preds = self.evaluation_forward_prop(
data_batch=data_batch, epoch=self.current_epoch)
# losses['loss'].backward() # uncomment if you get the weird memory error
# self.zero_grad()
# self.optimizer.zero_grad()
return losses, per_task_target_preds
def save_model(self, model_save_dir, state):
"""
Save the network parameter state and experiment state dictionary.
:param model_save_dir: The directory to store the state at.
:param state: The state containing the experiment state and the network. It's in the form of a dictionary
object.
"""
state['network'] = self.state_dict()
torch.save(state, f=model_save_dir)
def load_model(self, model_save_dir, model_name, model_idx):
"""
Load checkpoint and return the state dictionary containing the network state params and experiment state.
:param model_save_dir: The directory from which to load the files.
:param model_name: The model_name to be loaded from the direcotry.
:param model_idx: The index of the model (i.e. epoch number or 'latest' for the latest saved model of the current
experiment)
:return: A dictionary containing the experiment state and the saved model parameters.
"""
filepath = os.path.join(model_save_dir,
"{}_{}".format(model_name, model_idx))
state = torch.load(filepath)
state_dict_loaded = state['network']
self.load_state_dict(state_dict=state_dict_loaded)
return state
class MAMLFewShotClassifier(FewShotClassifier):
def __init__(self, device, args):
"""
Initializes a MAML few shot learning system
:param device: The device to use to use the model on.
:param args: A namedtuple of arguments specifying various hyperparameters.
"""
super(MAMLFewShotClassifier, self).__init__(device, args)
config = AutoConfig.from_pretrained(args.pretrained_weights)
config.num_labels = args.num_classes_per_set
model_initialization = AutoModelForSequenceClassification.from_pretrained(
args.pretrained_weights, config=config
)
slow_model = MetaBERT
# Init fast model
state_dict = model_initialization.state_dict()
config = model_initialization.config
del model_initialization
# Slow model
self.classifier = slow_model.init_from_pretrained(
state_dict,
config,
num_labels=args.num_classes_per_set,
is_distil=self.is_distil,
is_xlm=self.is_xlm,
per_step_layer_norm_weights=args.per_step_layer_norm_weights,
num_inner_loop_steps=args.number_of_training_steps_per_iter,
device=device,
)
self.classifier.to("cpu")
self.classifier.train()
self.inner_loop_optimizer = LSLRGradientDescentLearningRule(
device=torch.device("cpu"),
init_learning_rate=self.task_learning_rate,
total_num_inner_loop_steps=self.args.number_of_training_steps_per_iter,
use_learnable_learning_rates=self.args.learnable_per_layer_per_step_inner_loop_learning_rate,
init_class_head_lr_multiplier=self.args.init_class_head_lr_multiplier,
)
self.inner_loop_optimizer.initialise(
names_weights_dict=self.get_inner_loop_parameter_dict(
params=self.classifier.named_parameters()
)
)
print("Inner Loop parameters")
for key, value in self.inner_loop_optimizer.named_parameters():
print(key, value.shape)
print("Outer Loop parameters")
for name, param in self.named_parameters():
if param.requires_grad:
print(name, param.shape, param.device, param.requires_grad)
self.optimizer = Ranger(
[
{"params": self.classifier.parameters(), "lr": args.meta_learning_rate},
{
"params": self.inner_loop_optimizer.parameters(),
"lr": args.meta_inner_optimizer_learning_rate,
},
],
lr=args.meta_learning_rate,
)
self.scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer=self.optimizer,
T_max=self.args.total_epochs * self.args.total_iter_per_epoch,
eta_min=self.args.min_learning_rate,
)
self.inner_loop_optimizer.to(self.device)
self.clip_value = 1.0
# gradient clipping
for p in self.classifier.parameters():
if p.requires_grad:
p.register_hook(
lambda grad: torch.clamp(grad, -self.clip_value, self.clip_value)
)
self.num_freeze_epochs = args.num_freeze_epochs
if self.num_freeze_epochs > 0:
self.classifier.freeze()
def get_inner_loop_parameter_dict(self, params, adapter_only=False):
"""
Returns a dictionary with the parameters to use for inner loop updates.
:param params: A dictionary of the network's parameters.
:return: A dictionary of the parameters to use for the inner loop optimization process.
"""
param_dict = dict()
for name, param in params:
if param.requires_grad:
key = (
name.replace("module.", "", 1)
if name.startswith("module.")
else name
)
if self.args.enable_inner_loop_optimizable_ln_params:
param_dict[key] = param.to(device=self.device)
else:
if "LayerNorm" not in key:
if adapter_only:
if "adapter" in key or "classifier" in key:
param_dict[key] = param.to(device=self.device)
else:
print(key)
else:
param_dict[key] = param.to(device=self.device)
return param_dict
def apply_inner_loop_update(
self,
loss,
names_weights_copy,
use_second_order,
current_step_idx,
allow_unused=True,
):
"""
Applies an inner loop update given current step's loss, the weights to update, a flag indicating whether to use
second order derivatives and the current step's index.
:param loss: Current step's loss with respect to the support set.
:param names_weights_copy: A dictionary with names to parameters to update.
:param use_second_order: A boolean flag of whether to use second order derivatives.
:param current_step_idx: Current step's index.
:return: A dictionary with the updated weights (name, param)
"""
all_names = list(names_weights_copy.keys())
names_weights_copy = {
k: v for k, v in names_weights_copy.items() if v is not None
}
grads = torch.autograd.grad(
loss,
names_weights_copy.values(),
create_graph=use_second_order,
allow_unused=allow_unused,
)
names_grads_wrt_params = dict(zip(names_weights_copy.keys(), grads))
names_weights_copy = self.inner_loop_optimizer.update_params(
names_weights_dict=names_weights_copy,
names_grads_wrt_params_dict=names_grads_wrt_params,
num_step=current_step_idx,
)
del names_grads_wrt_params
for name in all_names:
if name not in names_weights_copy.keys():
names_weights_copy[name] = None
return names_weights_copy
def net_forward(
self,
x,
teacher_unary,
fast_model,
training,
num_step,
return_nr_correct=False,
mask=None,
task_name="",
):
student_logits = self.classifier(
input_ids=x, attention_mask=mask, num_step=num_step, params=fast_model
)[0]
set_kl_loss = False
if task_name in self.gold_label_tasks and self.meta_loss.lower() == "kl":
set_kl_loss = True
self.meta_loss = "ce"
loss = self.inner_loss(
student_logits, teacher_unary, return_nr_correct=return_nr_correct
)
if set_kl_loss:
self.meta_loss = "kl"
return loss
def forward(
self,
data_batch,
epoch,
use_second_order,
num_steps,
training_phase,
):
"""
Runs a forward outer loop pass on the batch of tasks using the MAML/++ framework.
:param data_batch: A data batch containing the support and target sets.
:param epoch: Current epoch's index
:param use_second_order: A boolean saying whether to use second order derivatives.
:param use_multi_step_loss_optimization: Whether to optimize on the outer loop using just the last step's
target loss (True) or whether to use multi step loss which improves the stability of the system (False)
:param num_steps: Number of inner loop steps.
:param training_phase: Whether this is a training phase (True) or an evaluation phase (False)
:return: A dictionary with the collected losses of the current outer forward propagation.
"""
(
x_support_set,
len_support_set,
x_target_set,
len_target_set,
y_support_set,
y_target_set,
teacher_names,
) = data_batch
meta_batch_size = self.args.batch_size
self.classifier.zero_grad()
if self.num_freeze_epochs <= epoch:
self.classifier.unfreeze()
losses = {"loss": 0}
task_accuracies = []
task_lang_logs = []
for (
task_id,
(
x_support_set_task,
len_support_set_task,
y_support_set_task,
x_target_set_task,
len_target_set_task,
y_target_set_task,
teacher_name,
),
) in enumerate(
zip(
x_support_set,
len_support_set,
y_support_set,
x_target_set,
len_target_set,
y_target_set,
teacher_names,
)
):
task_lang_log = [teacher_name, epoch]
task_losses = []
# freeze and unfreeze if necessary to get correct params
if epoch <= self.num_freeze_epochs:
self.classifier.unfreeze()
fast_weights = self.classifier.get_inner_loop_params()
if epoch < self.num_freeze_epochs:
self.classifier.freeze()
total_task_loss = 0
x_support_set_task = x_support_set_task.squeeze()
len_support_set_task = len_support_set_task.squeeze()
y_support_set_task = y_support_set_task.squeeze()
x_target_set_task = x_target_set_task.squeeze()
len_target_set_task = len_target_set_task.squeeze()
y_target_set_task = y_target_set_task.squeeze()
for num_step in range(num_steps):
torch.cuda.empty_cache()
support_loss, is_correct = self.net_forward(
x=x_support_set_task,
mask=len_support_set_task,
num_step=num_step,
teacher_unary=y_support_set_task,
fast_model=fast_weights,
training=True,
return_nr_correct=True,
task_name=teacher_name,
)
fast_weights = self.apply_inner_loop_update(
loss=support_loss,
names_weights_copy=fast_weights,
use_second_order=use_second_order,
current_step_idx=num_step,
)
if num_step == (self.args.number_of_training_steps_per_iter - 1):
# store support set statistics
task_lang_log.append(support_loss.detach().item())
task_lang_log.append(np.mean(is_correct))
target_loss, is_correct = self.net_forward(
x=x_target_set_task,
mask=len_target_set_task,
teacher_unary=y_target_set_task,
num_step=num_step,
fast_model=fast_weights,
training=True,
return_nr_correct=True,
task_name=teacher_name,
)
task_losses.append(target_loss)
accuracy = np.mean(is_correct)
task_accuracies.append(accuracy)
# store query set statistics
task_lang_log.append(target_loss.detach().item())
task_lang_log.append(accuracy)
# Achieve gradient accumulation by already backpropping current loss
torch.cuda.empty_cache()
task_losses = torch.sum(torch.stack(task_losses)) / meta_batch_size
task_losses.backward()
total_task_loss += task_losses.detach().cpu().item()
losses["loss"] += total_task_loss
task_lang_logs.append(task_lang_log)
torch.cuda.synchronize()
losses["accuracy"] = np.mean(task_accuracies)
if training_phase:
return losses, task_lang_logs
else:
return losses
def finetune_epoch(
self,
names_weights_copy,
model_config,
train_dataloader,
dev_dataloader,
best_loss,
eval_every,
model_save_dir,
task_name,
epoch,
train_on_cpu=False,
writer=None,
):
"""
Finetunes the meta-learned classifier on a dataset
:param train_dataloader: Dataloader with train examples
:param dev_dataloader: Dataloader with validation examples
:param best_loss: best achieved loss on dev set up till now
:param eval_every: eval on dev set after eval_every updates
:param model_save_dir: directory to save the model to
:param task_name: name of the task finetuning is performed on
:param epoch: current epoch number
:return: best_loss
"""
if train_on_cpu:
self.device = torch.device("cpu")
self.inner_loop_optimizer.requires_grad_(False)
self.inner_loop_optimizer.eval()
self.inner_loop_optimizer.to(self.device)
self.classifier.to(self.device)
if names_weights_copy is None:
if epoch <= self.num_freeze_epochs:
self.classifier.unfreeze()
# # Get fast weights
names_weights_copy = self.classifier.get_inner_loop_params()
if epoch < self.num_freeze_epochs:
self.classifier.freeze()
eval_every = (
eval_every if eval_every < len(train_dataloader) else len(train_dataloader)
)
if writer is not None: # create histogram of weights
for param_name, param in names_weights_copy.items():
writer.add_histogram(task_name + "/" + param_name, param, 0)
writer.flush()
with tqdm(
initial=0, total=eval_every * self.args.number_of_training_steps_per_iter
) as pbar_train:
for batch_idx, batch in enumerate(train_dataloader):
torch.cuda.empty_cache()
batch = tuple(t.to(self.device) for t in batch)
x, mask, y_true = batch
for train_step in range(self.args.number_of_training_steps_per_iter):
support_loss = self.net_forward(
x,
mask=mask,
teacher_unary=y_true,
num_step=train_step,
fast_model=names_weights_copy,
training=True,
)
names_weights_copy = self.apply_inner_loop_update(
loss=support_loss,
names_weights_copy=names_weights_copy,
use_second_order=False,
current_step_idx=train_step,
)
self.inner_loop_optimizer.zero_grad()
pbar_train.update(1)
pbar_train.set_description(
"finetuning phase {} -> loss: {}".format(
batch_idx * self.args.number_of_training_steps_per_iter
+ train_step
+ 1,
support_loss.item(),
)
)
if writer is not None: # create histogram of weights
for param_name, param in names_weights_copy.items():
writer.add_histogram(
task_name + "/" + param_name, param, train_step + 1
)
writer.flush()
if (batch_idx + 1) % eval_every == 0:
print("Evaluating model...")
losses = []
is_correct_preds = []
if train_on_cpu:
self.device = torch.device("cuda")
self.classifier.to(self.device)
with torch.no_grad():
for batch in tqdm(
dev_dataloader,
desc="Evaluating",
leave=False,
total=len(dev_dataloader),
):
batch = tuple(t.to(self.device) for t in batch)
x, mask, y_true = batch
loss, is_correct = self.net_forward(
x,
mask=mask,
teacher_unary=y_true,
fast_model=names_weights_copy,
training=False,
return_nr_correct=True,
num_step=train_step,
)
losses.append(loss.item())
is_correct_preds.extend(is_correct.tolist())
avg_loss = np.mean(losses)
accuracy = np.mean(is_correct_preds)
print("Accuracy", accuracy)
if avg_loss < best_loss:
best_loss = avg_loss
print(
"New best finetuned model with loss {:.05f}".format(
best_loss
)
)
torch.save(
names_weights_copy,
os.path.join(
model_save_dir,
"model_finetuned_{}".format(
task_name.replace("train/", "", 1)
.replace("val/", "", 1)
.replace("test/", "", 1)
),
),
)
return names_weights_copy, best_loss, avg_loss, accuracy