def train_cl(model, train_datasets, replay_mode="none", scenario="task", rnt=None, classes_per_task=None,
iters=2000, batch_size=32, batch_size_replay=None, loss_cbs=list(), eval_cbs=list(), sample_cbs=list(),
generator=None, gen_iters=0, gen_loss_cbs=list(), feedback=False, reinit=False, args=None, only_last=False,
sample_method='random', curated_multiplier=4):
'''Train a model (with a "train_a_batch" method) on multiple tasks, with replay-strategy specified by [replay_mode].
[model] <nn.Module> main model to optimize across all tasks
[train_datasets] <list> with for each task the training <DataSet>
[replay_mode] <str>, choice from "generative", "current", "offline" and "none"
[scenario] <str>, choice from "task", "domain", "class" and "all"
[classes_per_task] <int>, # classes per task; only 1st task has [classes_per_task]*[first_task_class_boost] classes
[rnt] <float>, indicating relative importance of new task (if None, relative to # old tasks)
[iters] <int>, # optimization-steps (=batches) per task; 1st task has [first_task_iter_boost] steps more
[batch_size_replay] <int>, number of samples to replay per batch
[generator] None or <nn.Module>, if a seperate generative model should be trained (for [gen_iters] per task)
[feedback] <bool>, if True and [replay_mode]="generative", the main model is used for generating replay
[only_last] <bool>, only train on final task / episode
[*_cbs] <list> of call-back functions to evaluate training-progress
[sample_method] <str> indicating the sample method, choices: 'random', 'uniform', 'curated', 'softmax', 'interfered', 'misclassified'
[curated_multiplier]<int> choose curated samples out of size curated_multiplier * mutiply batch_size_replay
'''
# Should convolutional layers be frozen?
freeze_convE = (utils.checkattr(args, "freeze_convE") and hasattr(args, "depth") and args.depth>0)
# Use cuda?
device = model._device()
cuda = model._is_on_cuda()
# Set default-values if not specified
batch_size_replay = batch_size if batch_size_replay is None else batch_size_replay
# Initiate indicators for replay (no replay for 1st task)
Generative = Current = Offline_TaskIL = False
previous_model = None
# Register starting param-values (needed for "intelligent synapses").
if isinstance(model, ContinualLearner) and model.si_c>0:
for n, p in model.named_parameters():
if p.requires_grad:
n = n.replace('.', '__')
model.register_buffer('{}_SI_prev_task'.format(n), p.detach().clone())
# Loop over all tasks.
for task, train_dataset in enumerate(train_datasets, 1):
# If offline replay-setting, create large database of all tasks so far
if replay_mode=="offline" and (not scenario=="task"):
train_dataset = ConcatDataset(train_datasets[:task])
# -but if "offline"+"task": all tasks so far should be visited separately (i.e., separate data-loader per task)
if replay_mode=="offline" and scenario=="task":
Offline_TaskIL = True
data_loader = [None]*task
# Initialize # iters left on data-loader(s)
iters_left = 1 if (not Offline_TaskIL) else [1]*task
# Prepare <dicts> to store running importance estimates and parameter-values before update
if isinstance(model, ContinualLearner) and model.si_c>0:
W = {}
p_old = {}
for n, p in model.named_parameters():
if p.requires_grad:
n = n.replace('.', '__')
W[n] = p.data.clone().zero_()
p_old[n] = p.data.clone()
# Find [active_classes] (=classes in current task)
active_classes = None #-> for "domain"- or "all"-scenarios, always all classes are active
if scenario=="task":
# -for "task"-scenario, create <list> with for all tasks so far a <list> with the active classes
active_classes = [list(range(classes_per_task*i, classes_per_task*(i+1))) for i in range(task)]
elif scenario=="class":
# -for "class"-scenario, create one <list> with active classes of all tasks so far
active_classes = list(range(classes_per_task*task))
# Reinitialize the model's parameters (if requested)
if reinit:
from define_models import init_params
init_params(model, args)
if generator is not None:
init_params(generator, args)
# Define a tqdm progress bar(s)
iters_main = iters
progress = tqdm.tqdm(range(1, iters_main+1))
if generator is not None:
iters_gen = gen_iters
progress_gen = tqdm.tqdm(range(1, iters_gen+1))
# Loop over all iterations
iters_to_use = (iters_main if (generator is None) else max(iters_main, iters_gen))
# -if only the final task should be trained on:
if only_last and not task==len(train_datasets):
iters_to_use = 0
# This helps w/ speeding up curated_classVariety
mask = None
if (sample_method=="curated_classVariety" and (task-1)>0):
sampleAmt = batch_size_replay * curated_multiplier
classNum = classes_per_task*(task-1)
indexList = [[idx for idx in range(sampleAmt) if (idx%classNum) == (rowIdx%classNum)] for rowIdx in range(sampleAmt)]
mask = []
for rowIdxList in indexList:
curRow = [0] * sampleAmt
for idx in rowIdxList:
curRow[idx] = 1
mask.append(curRow)
mask = torch.tensor(mask, dtype=torch.float).to(device)
for batch_index in range(1, iters_to_use+1):
# Update # iters left on current data-loader(s) and, if needed, create new one(s)
if not Offline_TaskIL:
iters_left -= 1
if iters_left==0:
data_loader = iter(utils.get_data_loader(train_dataset, batch_size, cuda=cuda, drop_last=True))
iters_left = len(data_loader)
else:
# -with "offline replay" in Task-IL scenario, there is a separate data-loader for each task
batch_size_to_use = int(np.ceil(batch_size/task))
for task_id in range(task):
iters_left[task_id] -= 1
if iters_left[task_id]==0:
data_loader[task_id] = iter(utils.get_data_loader(
train_datasets[task_id], batch_size_to_use, cuda=cuda, drop_last=True
))
iters_left[task_id] = len(data_loader[task_id])
#-----------------Collect data------------------#
#####-----CURRENT BATCH-----#####
if not Offline_TaskIL:
x, y = next(data_loader) #--> sample training data of current task
y = y-classes_per_task*(task-1) if scenario=="task" else y #--> ITL: adjust y-targets to 'active range'
x, y = x.to(device), y.to(device) #--> transfer them to correct device
#y = y.expand(1) if len(y.size())==1 else y #--> hack for if batch-size is 1
else:
x = y = task_used = None #--> all tasks are "treated as replay"
# -sample training data for all tasks so far, move to correct device and store in lists
x_, y_ = list(), list()
for task_id in range(task):
x_temp, y_temp = next(data_loader[task_id])
x_.append(x_temp.to(device))
y_temp = y_temp - (classes_per_task * task_id) #--> adjust y-targets to 'active range'
if batch_size_to_use == 1:
y_temp = torch.tensor([y_temp]) #--> correct dimensions if batch-size is 1
y_.append(y_temp.to(device))
#####-----REPLAYED BATCH-----#####
if not Offline_TaskIL and not Generative and not Current:
x_ = y_ = scores_ = task_used = None #-> if no replay
#--------------------------------------------INPUTS----------------------------------------------------#
##-->> Current Replay <<--##
if Current:
x_ = x[:batch_size_replay] #--> use current task inputs
task_used = None
##-->> Generative Replay <<--##
if Generative:
#---> Only with generative replay, the resulting [x_] will be at the "hidden"-level
conditional_gen = True if (
(previous_generator.per_class and previous_generator.prior=="GMM") or
utils.checkattr(previous_generator, 'dg_gates')
) else False
# Sample [x_]
if conditional_gen and scenario=="task":
# -if a conditional generator is used with task-IL scenario, generate data per previous task
x_ = list()
task_used = list()
for task_id in range(task-1):
allowed_classes = list(range(classes_per_task*task_id, classes_per_task*(task_id+1)))
batch_size_replay_to_use = int(np.ceil(batch_size_replay / (task-1)))
x_temp_ = previous_generator.sample(batch_size_replay_to_use, allowed_classes=allowed_classes,
only_x=False)
x_.append(x_temp_[0])
task_used.append(x_temp_[2])
else:
# -which classes are allowed to be generated? (relevant if conditional generator / decoder-gates)
allowed_classes = None if scenario=="domain" else list(range(classes_per_task*(task-1)))
# -which tasks/domains are allowed to be generated? (only relevant if "Domain-IL" with task-gates)
allowed_domains = list(range(task-1))
# -generate inputs representative of previous tasks
# --- SAMPLE METHOD CHOICES: softmax, random, uniform, curated ---
# --- Softmax sampling: use previous model to score images from this new task, generate those classes
if sample_method == 'softmax':
with torch.no_grad():
curTaskID = task - 2
newScores_og = previous_model.classify(previous_model.input_to_hidden(x),
not_hidden=False if Generative else True)
newScores = newScores_og[:, :(classes_per_task * (curTaskID + 1))]
softmax = torch.nn.Softmax(dim=1)
newHardScores = nn.Softmax(dim=1)(newScores)
avgError = torch.mean(newHardScores, dim=0)
sampleProbs = torch.zeros(newScores_og.shape[1])
sampleProbs[:(classes_per_task * (curTaskID + 1))] = avgError[
:(classes_per_task * (curTaskID + 1))]
x_, y_used, task_used = previous_generator.sample(
batch_size_replay, allowed_classes=allowed_classes, allowed_domains=allowed_domains,
only_x=False, class_probs=sampleProbs,uniform_sampling=False)
# --- Uniformly random sampling (baseline) ---
elif sample_method == 'random':
x_, y_used, task_used = previous_generator.sample(
batch_size_replay, allowed_classes=allowed_classes, allowed_domains=allowed_domains,
only_x=False, class_probs=None, uniform_sampling=False)
# --- Uniform sampling: balanced numbers of samples from each class ---
elif sample_method == 'uniform':
x_, y_used, task_used = previous_generator.sample(
batch_size_replay, allowed_classes=allowed_classes, allowed_domains=allowed_domains,
only_x=False, class_probs=None, uniform_sampling=True)
# --- Uniform sample curation: pick the best samples to show (by some metric), balance uniformly ---
else:
if (sample_method == "curated_variety"):
# Generate x times as many samples as we need to then pick the best of
x_, y_used, task_used, varietyVector = previous_generator.sample(
batch_size_replay * curated_multiplier, allowed_classes=allowed_classes, allowed_domains=allowed_domains,
only_x=False, class_probs=None, uniform_sampling=False, varietyVector=True)
# CURATED USING CLASS VARIETY (i.e., generating batch_size_reply*curated_multipler / len(allowed_classes) samples
# per class, where each sample is the "most different" sample based off our variety calculation
elif(sample_method == "curated_classVariety"):
x_, y_used, task_used, varietyVector = previous_generator.sample(
batch_size_replay * curated_multiplier, allowed_classes=allowed_classes, allowed_domains=allowed_domains,
only_x=False, class_probs=None, uniform_sampling=True, varietyVector=True, classVariety=True, classVarietyMask=mask)
elif(sample_method == "curated_softmax"):
with torch.no_grad():
curTaskID = task - 2
newScores_og = previous_model.classify(previous_model.input_to_hidden(x),
not_hidden=False if Generative else True)
newScores = newScores_og[:, :(classes_per_task * (curTaskID + 1))]
softmax = torch.nn.Softmax(dim=1)
newHardScores = nn.Softmax(dim=1)(newScores)
avgError = torch.mean(newHardScores, dim=0)
sampleProbs = torch.zeros(newScores_og.shape[1])
sampleProbs[:(classes_per_task * (curTaskID + 1))] = avgError[
:(classes_per_task * (curTaskID + 1))]
# Generate x times as many samples as we need to then pick the best of
x_, y_used, task_used = previous_generator.sample(
batch_size_replay * curated_multiplier, allowed_classes=allowed_classes, allowed_domains=allowed_domains,
only_x=False, class_probs=sampleProbs, uniform_sampling=False)
else:
# Generate x times as many samples as we need to then pick the best of
x_, y_used, task_used = previous_generator.sample(
batch_size_replay * curated_multiplier, allowed_classes=allowed_classes, allowed_domains=allowed_domains,
only_x=False, class_probs=None, uniform_sampling=False)
# --- Measure the performance of each of these samples on the current model ---
# Use the previous model to score the generated images (code taken from Trevor's softmax above)
with torch.no_grad():
curTaskID = task - 2
newScores_og = model.classify(x_, not_hidden=False if Generative else True)
newScores = newScores_og[:, :(classes_per_task * (curTaskID + 1))] # Logits that don't sum to 1
newHardScores = nn.Softmax(dim=1)(newScores) # Makes the scores sum to 1 (probabilities)
cross_entropy = nn.CrossEntropyLoss(reduction='none')
y_used = torch.tensor(y_used, dtype=torch.long).to(device)
cross_entropy_loss = cross_entropy(newHardScores, y_used)
# --- Copy the model and perform an update on just the new incoming data (no replayed data) ---
# This will lead to catastrophic forgetting, as it has no replays to prevent this from happening
model_tmp = copy.deepcopy(model)
# NOTE: Can train multiple batches if needed, but it would be on the same data, so any changes will just be exacerbated
_ = model_tmp.train_a_batch(x, y=y, x_=None, y_=None, scores_=None,
tasks_=task_used, active_classes=active_classes, task=task, rnt=(
1. if task==1 else 1./task
) if rnt is None else rnt, freeze_convE=freeze_convE,
replay_not_hidden=False if Generative else True)
# --- Measure the performance of each of the generated samples on this updated model ---
# This can tell us how much the model 'forgets' each of these samples, we will replay the worst ones
with torch.no_grad():
curTaskID = task - 2
newScores_og = model_tmp.classify(x_, not_hidden=False if Generative else True)
newScores = newScores_og[:, :(classes_per_task * (curTaskID + 2))] # Logits that don't sum to 1
newHardScores2 = nn.Softmax(dim=1)(newScores) # Makes the scores sum to 1 (probabilities)
# --- Measure the difference in cross entropy loss for predictions before and after ---
if sample_method == 'curated' or sample_method == "curated_softmax":
cross_entropy = nn.CrossEntropyLoss(reduction='none') # Per-example cross entropy (not avg)
cross_entropy_loss2 = cross_entropy(newHardScores2, y_used)
# Amount that the loss changes between the model updating
diff = cross_entropy_loss2 - cross_entropy_loss
metric = diff
# TREVOR'S NEW METHOD - This tries to take into account the variety of the samples
elif sample_method == "curated_variety" or sample_method == "curated_classVariety":
cross_entropy = nn.CrossEntropyLoss(reduction='none') # Per-example cross entropy (not avg)
cross_entropy_loss2 = cross_entropy(newHardScores2, y_used)
# Amount that the loss changes between the model updating
diff = cross_entropy_loss2 - cross_entropy_loss
# Softmaxing diff and the variety vector
varietyWeight = 0.5
diff_SM = nn.Softmax(dim=0)(diff)
variety_SM = nn.Softmax(dim=0)(varietyVector)
metric = ((1-varietyWeight) * diff_SM) + (varietyWeight * variety_SM)
# Multiply the misclassification error (cross entropy) by the amount that this changes between the model updating
# metric = cross_entropy_loss2 * diff
# --- Measure KL Divergence between predictions before and predictions afterwards ---
# Maximally Interfered Retrieval uses a linear combination of KL, entropy, and 'variance'
# This ensures the samples are not too close together, but we do not currently measure that
elif sample_method == 'interfered':
KLDiv = nn.KLDivLoss(reduction='none')(newHardScores, newHardScores2)
print(KLDiv.shape)
print(cross_entropy_loss.shape)
# Test code to compute KL divergence for every example individually, above code is (512, 2) rather than (512, 1) for some reason
#KLDiv = [ nn.KLDivLoss()(newHardScores[i], newHardScores2[i]) for i in range(len(newHardScores))]
# Note from Trevor: When I tried to run this method, there was a size mismatch.
# KLDiv.shape = (1024, 10), whereas cross_entropy_loss.shape = (1024,), and it said
# that they needed to be equal on dim 1. Sooo: I tried to just transpose the KLDiv matrix,
# and it worked. Honestly, I'm too tired to try and decipher what I did mathematically lol
metric = torch.tensor(KLDiv.T) - 0 * cross_entropy_loss
# --- New idea: use the examples which the new model misclassifies the most as one of the new classes
# This the opposite approach to softmax, where softmax takes the current model and calculates
# Which classes does it confuse the new data for the most, this trains on the new data and then
# Tries to find generated examples which it confuses for the new data classes the most
elif sample_method == 'misclassified' or sample_method == 'uniform_large' or sample_method == 'random_large':
metric = newHardScores2[:, -1] + newHardScores2[:, -1]
# --- Sort based on some metric, then divide up by classes (afterwards) ---
sorted, indices = torch.sort(metric, descending=True) # Descending order, pick first 100
# Shuffle indices around to test choosing from this larger pool of generated samples randomly
if sample_method == 'uniform_large' or sample_method == 'random_large':
indices2 = indices.cpu().numpy()
np.random.shuffle(indices2)
indices = torch.from_numpy(indices2).to(device)
if sample_method != 'random_large' and sample_method != 'curated_softmax':
# --- Calculate how many examples for each class should be generated to divide up uniformly ---
# Uniform dist will be [0, 1, 2, 3, 0, 1, 2] for allowed classes=4 and batch_size_replay=7
uniform_dist = torch.arange(batch_size_replay) % len(allowed_classes)
counts_each_class = torch.unique(uniform_dist, return_counts=True)[1]
# --- Optional: Calculate unbalanced indices to replay, results in poor performance ---
# If we added a variation term to ensure samples are different from each other, this could
# be a simpler way to do things, but variance would be pretty complicated to calculate
#indices_to_replay = indices[:batch_size_replay]
# --- Select the top k_i indices for each class i, where k_i is the number of examples for that class ---
# Top x most affected of the generated samples for each class (ensures it is balanced, slightly more computation than unbalanced)
indices_to_replay = torch.cat(( [ indices[y_used[indices]==i][:counts_each_class[i]] for i in range(len(allowed_classes)) ] ))
x_ = x_[indices_to_replay]
else:
# Uniformly randomly choose from the 400 samples generated
x_ = x_[indices]
#--------------------------------------------OUTPUTS----------------------------------------------------#
if Generative or Current:
# Get target scores & possibly labels (i.e., [scores_] / [y_]) -- use previous model, with no_grad()
if scenario in ("domain", "class") and previous_model.mask_dict is None:
# -if replay does not need to be evaluated for each task (ie, not Task-IL and no task-specific mask)
with torch.no_grad():
all_scores_ = previous_model.classify(x_, not_hidden=False if Generative else True)
scores_ = all_scores_[:, :(classes_per_task*(task-1))] if (
scenario=="class"
) else all_scores_ # -> when scenario=="class", zero probs will be added in [loss_fn_kd]-function
# -also get the 'hard target'
_, y_ = torch.max(scores_, dim=1)
else:
# -[x_] needs to be evaluated according to each previous task, so make list with entry per task
scores_ = list()
y_ = list()
# -if no task-mask and no conditional generator, all scores can be calculated in one go
if previous_model.mask_dict is None and not type(x_)==list:
with torch.no_grad():
all_scores_ = previous_model.classify(x_, not_hidden=False if Generative else True)
for task_id in range(task-1):
# -if there is a task-mask (i.e., XdG is used), obtain predicted scores for each task separately
if previous_model.mask_dict is not None:
previous_model.apply_XdGmask(task=task_id+1)
if previous_model.mask_dict is not None or type(x_)==list:
with torch.no_grad():
all_scores_ = previous_model.classify(x_[task_id] if type(x_)==list else x_,
not_hidden=False if Generative else True)
if scenario=="domain":
# NOTE: if scenario=domain with task-mask, it's of course actually the Task-IL scenario!
# this can be used as trick to run the Task-IL scenario with singlehead output layer
temp_scores_ = all_scores_
else:
temp_scores_ = all_scores_[:, (classes_per_task*task_id):(classes_per_task*(task_id+1))]
scores_.append(temp_scores_)
# - also get hard target
_, temp_y_ = torch.max(temp_scores_, dim=1)
y_.append(temp_y_)
# -only keep predicted y_/scores_ if required (as otherwise unnecessary computations will be done)
y_ = y_ if (model.replay_targets=="hard") else None
scores_ = scores_ if (model.replay_targets=="soft") else None
#-----------------Train model(s)------------------#
#---> Train MAIN MODEL
if batch_index <= iters_main:
# Train the main model with this batch
loss_dict = model.train_a_batch(x, y=y, x_=x_, y_=y_, scores_=scores_,
tasks_=task_used, active_classes=active_classes, task=task, rnt=(
1. if task==1 else 1./task
) if rnt is None else rnt, freeze_convE=freeze_convE,
replay_not_hidden=False if Generative else True)
# UNIFORM SAMPLE CURATION: loss_dict has a "predL_r" key that contains the individual prediction
# losses
# Update running parameter importance estimates in W
if isinstance(model, ContinualLearner) and model.si_c>0:
for n, p in model.convE.named_parameters():
if p.requires_grad:
n = "convE."+n
n = n.replace('.', '__')
if p.grad is not None:
W[n].add_(-p.grad*(p.detach()-p_old[n]))
p_old[n] = p.detach().clone()
for n, p in model.fcE.named_parameters():
if p.requires_grad:
n = "fcE."+n
n = n.replace('.', '__')
if p.grad is not None:
W[n].add_(-p.grad * (p.detach() - p_old[n]))
p_old[n] = p.detach().clone()
for n, p in model.classifier.named_parameters():
if p.requires_grad:
n = "classifier."+n
n = n.replace('.', '__')
if p.grad is not None:
W[n].add_(-p.grad * (p.detach() - p_old[n]))
p_old[n] = p.detach().clone()
# Fire callbacks (for visualization of training-progress / evaluating performance after each task)
for loss_cb in loss_cbs:
if loss_cb is not None:
loss_cb(progress, batch_index, loss_dict, task=task)
for eval_cb in eval_cbs:
if eval_cb is not None:
eval_cb(model, batch_index, task=task)
if model.label=="VAE":
for sample_cb in sample_cbs:
if sample_cb is not None:
sample_cb(model, batch_index, task=task, allowed_classes=None if (
scenario=="domain"
) else list(range(classes_per_task*task)))
#---> Train GENERATOR
if generator is not None and batch_index <= iters_gen:
loss_dict = generator.train_a_batch(x, y=y, x_=x_, y_=y_, scores_=scores_,
tasks_=task_used, active_classes=active_classes, rnt=(
1. if task==1 else 1./task
) if rnt is None else rnt, task=task,
freeze_convE=freeze_convE,
replay_not_hidden=False if Generative else True)
# Fire callbacks on each iteration
for loss_cb in gen_loss_cbs:
if loss_cb is not None:
loss_cb(progress_gen, batch_index, loss_dict, task=task)
for sample_cb in sample_cbs:
if sample_cb is not None:
sample_cb(generator, batch_index, task=task, allowed_classes=None if (
scenario=="domain"
) else list(range(classes_per_task*task)))
# Close progres-bar(s)
progress.close()
if generator is not None:
progress_gen.close()
##----------> UPON FINISHING EACH TASK...
# EWC: estimate Fisher Information matrix (FIM) and update term for quadratic penalty
if isinstance(model, ContinualLearner) and model.ewc_lambda>0:
# -find allowed classes
allowed_classes = list(
range(classes_per_task*(task-1), classes_per_task*task)
) if scenario=="task" else (list(range(classes_per_task*task)) if scenario=="class" else None)
# -if needed, apply correct task-specific mask
if model.mask_dict is not None:
model.apply_XdGmask(task=task)
# -estimate FI-matrix
model.estimate_fisher(train_dataset, allowed_classes=allowed_classes)
# SI: calculate and update the normalized path integral
if isinstance(model, ContinualLearner) and model.si_c>0:
model.update_omega(W, model.epsilon)
# REPLAY: update source for replay
previous_model = copy.deepcopy(model).eval()
if replay_mode=="generative":
Generative = True
previous_generator = previous_model if feedback else copy.deepcopy(generator).eval()
elif replay_mode=='current':
Current = True
def run(args, verbose=False):
# Create plots- and results-directories if needed
if not os.path.isdir(args.r_dir):
os.mkdir(args.r_dir)
if args.pdf and not os.path.isdir(args.p_dir):
os.mkdir(args.p_dir)
# If only want param-stamp, get it and exit
if args.get_stamp:
from param_stamp import get_param_stamp_from_args
print(get_param_stamp_from_args(args=args))
exit()
# Use cuda?
cuda = torch.cuda.is_available() and args.cuda
device = torch.device("cuda" if cuda else "cpu")
# Report whether cuda is used
if verbose:
print("CUDA is {}used".format("" if cuda else "NOT(!!) "))
# Set random seeds
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if cuda:
torch.cuda.manual_seed(args.seed)
#-------------------------------------------------------------------------------------------------#
#----------------#
#----- DATA -----#
#----------------#
# Prepare data for chosen experiment
if verbose:
print("\nPreparing the data...")
(train_datasets,
test_datasets), config, classes_per_task = get_multitask_experiment(
name=args.experiment,
scenario=args.scenario,
tasks=args.tasks,
data_dir=args.d_dir,
normalize=True if utils.checkattr(args, "normalize") else False,
augment=True if utils.checkattr(args, "augment") else False,
verbose=verbose,
exception=True if args.seed < 10 else False,
only_test=(not args.train))
#-------------------------------------------------------------------------------------------------#
#----------------------#
#----- MAIN MODEL -----#
#----------------------#
# Define main model (i.e., classifier, if requested with feedback connections)
if verbose and (utils.checkattr(args, "pre_convE") or utils.checkattr(args, "pre_convD")) and \
(hasattr(args, "depth") and args.depth>0):
print("\nDefining the model...")
if utils.checkattr(args, 'feedback'):
model = define.define_autoencoder(args=args,
config=config,
device=device)
else:
model = define.define_classifier(args=args,
config=config,
device=device)
# Initialize / use pre-trained / freeze model-parameters
# - initialize (pre-trained) parameters
model = define.init_params(model, args)
# - freeze weights of conv-layers?
if utils.checkattr(args, "freeze_convE"):
for param in model.convE.parameters():
param.requires_grad = False
if utils.checkattr(args, 'feedback') and utils.checkattr(
args, "freeze_convD"):
for param in model.convD.parameters():
param.requires_grad = False
# Define optimizer (only optimize parameters that "requires_grad")
model.optim_list = [
{
'params': filter(lambda p: p.requires_grad, model.parameters()),
'lr': args.lr
},
]
model.optimizer = optim.Adam(model.optim_list, betas=(0.9, 0.999))
#-------------------------------------------------------------------------------------------------#
#----------------------------------------------------#
#----- CL-STRATEGY: REGULARIZATION / ALLOCATION -----#
#----------------------------------------------------#
# Elastic Weight Consolidation (EWC)
if isinstance(model, ContinualLearner) and utils.checkattr(args, 'ewc'):
model.ewc_lambda = args.ewc_lambda if args.ewc else 0
model.fisher_n = args.fisher_n
model.online = utils.checkattr(args, 'online')
if model.online:
model.gamma = args.gamma
# Synpatic Intelligence (SI)
if isinstance(model, ContinualLearner) and utils.checkattr(args, 'si'):
model.si_c = args.si_c if args.si else 0
model.epsilon = args.epsilon
# XdG: create for every task a "mask" for each hidden fully connected layer
if isinstance(model, ContinualLearner) and utils.checkattr(
args, 'xdg') and args.xdg_prop > 0:
model.define_XdGmask(gating_prop=args.xdg_prop, n_tasks=args.tasks)
#-------------------------------------------------------------------------------------------------#
#-------------------------------#
#----- CL-STRATEGY: REPLAY -----#
#-------------------------------#
# Use distillation loss (i.e., soft targets) for replayed data? (and set temperature)
if isinstance(model, ContinualLearner) and hasattr(
args, 'replay') and not args.replay == "none":
model.replay_targets = "soft" if args.distill else "hard"
model.KD_temp = args.temp
# If needed, specify separate model for the generator
train_gen = (hasattr(args, 'replay') and args.replay == "generative"
and not utils.checkattr(args, 'feedback'))
if train_gen:
# Specify architecture
generator = define.define_autoencoder(args,
config,
device,
generator=True)
# Initialize parameters
generator = define.init_params(generator, args)
# -freeze weights of conv-layers?
if utils.checkattr(args, "freeze_convE"):
for param in generator.convE.parameters():
param.requires_grad = False
if utils.checkattr(args, "freeze_convD"):
for param in generator.convD.parameters():
param.requires_grad = False
# Set optimizer(s)
generator.optim_list = [
{
'params': filter(lambda p: p.requires_grad,
generator.parameters()),
'lr': args.lr_gen if hasattr(args, 'lr_gen') else args.lr
},
]
generator.optimizer = optim.Adam(generator.optim_list,
betas=(0.9, 0.999))
else:
generator = None
#-------------------------------------------------------------------------------------------------#
#---------------------#
#----- REPORTING -----#
#---------------------#
# Get parameter-stamp (and print on screen)
if verbose:
print("\nParameter-stamp...")
param_stamp = get_param_stamp(
args,
model.name,
verbose=verbose,
replay=True if
(hasattr(args, 'replay') and not args.replay == "none") else False,
replay_model_name=generator.name if
(hasattr(args, 'replay') and args.replay in ("generative")
and not utils.checkattr(args, 'feedback')) else None,
)
# Print some model-characteristics on the screen
if verbose:
# -main model
utils.print_model_info(model, title="MAIN MODEL")
# -generator
if generator is not None:
utils.print_model_info(generator, title="GENERATOR")
# Define [progress_dicts] to keep track of performance during training for storing and for later plotting in pdf
precision_dict = evaluate.initiate_precision_dict(args.tasks)
# Prepare for plotting in visdom
visdom = None
if args.visdom:
env_name = "{exp}{tasks}-{scenario}".format(exp=args.experiment,
tasks=args.tasks,
scenario=args.scenario)
replay_statement = "{mode}{fb}{con}{gat}{int}{dis}{b}{u}".format(
mode=args.replay,
fb="Rtf" if utils.checkattr(args, "feedback") else "",
con="Con" if (hasattr(args, "prior") and args.prior == "GMM"
and utils.checkattr(args, "per_class")) else "",
gat="Gat{}".format(args.dg_prop) if
(utils.checkattr(args, "dg_gates") and hasattr(args, "dg_prop")
and args.dg_prop > 0) else "",
int="Int" if utils.checkattr(args, "hidden") else "",
dis="Dis" if args.replay == "generative" and args.distill else "",
b="" if
(args.batch_replay is None or args.batch_replay == args.batch) else
"-br{}".format(args.batch_replay),
u="" if args.g_fc_uni == args.fc_units else "-gu{}".format(
args.g_fc_uni)) if (hasattr(args, "replay")
and not args.replay == "none") else "NR"
graph_name = "{replay}{syn}{ewc}{xdg}".format(
replay=replay_statement,
syn="-si{}".format(args.si_c)
if utils.checkattr(args, 'si') else "",
ewc="-ewc{}{}".format(
args.ewc_lambda, "-O{}".format(args.gamma)
if utils.checkattr(args, "online") else "") if utils.checkattr(
args, 'ewc') else "",
xdg="" if (not utils.checkattr(args, 'xdg')) or args.xdg_prop == 0
else "-XdG{}".format(args.xdg_prop),
)
visdom = {'env': env_name, 'graph': graph_name}
#-------------------------------------------------------------------------------------------------#
#---------------------#
#----- CALLBACKS -----#
#---------------------#
g_iters = args.g_iters if hasattr(args, 'g_iters') else args.iters
# Callbacks for reporting on and visualizing loss
generator_loss_cbs = [
cb._VAE_loss_cb(
log=args.loss_log,
visdom=visdom,
replay=(hasattr(args, "replay") and not args.replay == "none"),
model=model if utils.checkattr(args, 'feedback') else generator,
tasks=args.tasks,
iters_per_task=args.iters
if utils.checkattr(args, 'feedback') else g_iters)
] if (train_gen or utils.checkattr(args, 'feedback')) else [None]
solver_loss_cbs = [
cb._solver_loss_cb(log=args.loss_log,
visdom=visdom,
model=model,
iters_per_task=args.iters,
tasks=args.tasks,
replay=(hasattr(args, "replay")
and not args.replay == "none"))
] if (not utils.checkattr(args, 'feedback')) else [None]
# Callbacks for evaluating and plotting generated / reconstructed samples
no_samples = (utils.checkattr(args, "no_samples")
or (utils.checkattr(args, "hidden")
and hasattr(args, 'depth') and args.depth > 0))
sample_cbs = [
cb._sample_cb(log=args.sample_log,
visdom=visdom,
config=config,
test_datasets=test_datasets,
sample_size=args.sample_n,
iters_per_task=g_iters)
] if ((train_gen or utils.checkattr(args, 'feedback'))
and not no_samples) else [None]
# Callbacks for reporting and visualizing accuracy, and visualizing representation extracted by main model
# -visdom (i.e., after each [prec_log]
eval_cb = cb._eval_cb(
log=args.prec_log,
test_datasets=test_datasets,
visdom=visdom,
precision_dict=None,
iters_per_task=args.iters,
test_size=args.prec_n,
classes_per_task=classes_per_task,
scenario=args.scenario,
)
# -pdf / reporting: summary plots (i.e, only after each task)
eval_cb_full = cb._eval_cb(
log=args.iters,
test_datasets=test_datasets,
precision_dict=precision_dict,
iters_per_task=args.iters,
classes_per_task=classes_per_task,
scenario=args.scenario,
)
# -visualize feature space
latent_space_cb = cb._latent_space_cb(
log=args.iters,
datasets=test_datasets,
visdom=visdom,
iters_per_task=args.iters,
sample_size=400,
)
# -collect them in <lists>
eval_cbs = [eval_cb, eval_cb_full, latent_space_cb]
#-------------------------------------------------------------------------------------------------#
#--------------------#
#----- TRAINING -----#
#--------------------#
if args.train:
if verbose:
print("\nTraining...")
# Train model
train_cl(
model,
train_datasets,
replay_mode=args.replay if hasattr(args, 'replay') else "none",
scenario=args.scenario,
classes_per_task=classes_per_task,
iters=args.iters,
batch_size=args.batch,
batch_size_replay=args.batch_replay if hasattr(
args, 'batch_replay') else None,
generator=generator,
gen_iters=g_iters,
gen_loss_cbs=generator_loss_cbs,
feedback=utils.checkattr(args, 'feedback'),
sample_cbs=sample_cbs,
eval_cbs=eval_cbs,
loss_cbs=generator_loss_cbs
if utils.checkattr(args, 'feedback') else solver_loss_cbs,
args=args,
reinit=utils.checkattr(args, 'reinit'),
only_last=utils.checkattr(args, 'only_last'))
# Save evaluation metrics measured throughout training
file_name = "{}/dict-{}".format(args.r_dir, param_stamp)
utils.save_object(precision_dict, file_name)
# Save trained model(s), if requested
if args.save:
save_name = "mM-{}".format(param_stamp) if (
not hasattr(args, 'full_stag')
or args.full_stag == "none") else "{}-{}".format(
model.name, args.full_stag)
utils.save_checkpoint(model,
args.m_dir,
name=save_name,
verbose=verbose)
if generator is not None:
save_name = "gM-{}".format(param_stamp) if (
not hasattr(args, 'full_stag')
or args.full_stag == "none") else "{}-{}".format(
generator.name, args.full_stag)
utils.save_checkpoint(generator,
args.m_dir,
name=save_name,
verbose=verbose)
else:
# Load previously trained model(s) (if goal is to only evaluate previously trained model)
if verbose:
print("\nLoading parameters of the previously trained models...")
load_name = "mM-{}".format(param_stamp) if (
not hasattr(args, 'full_ltag')
or args.full_ltag == "none") else "{}-{}".format(
model.name, args.full_ltag)
utils.load_checkpoint(
model,
args.m_dir,
name=load_name,
verbose=verbose,
add_si_buffers=(isinstance(model, ContinualLearner)
and utils.checkattr(args, 'si')))
if generator is not None:
load_name = "gM-{}".format(param_stamp) if (
not hasattr(args, 'full_ltag')
or args.full_ltag == "none") else "{}-{}".format(
generator.name, args.full_ltag)
utils.load_checkpoint(generator,
args.m_dir,
name=load_name,
verbose=verbose)
#-------------------------------------------------------------------------------------------------#
#-----------------------------------#
#----- EVALUATION of CLASSIFIER-----#
#-----------------------------------#
if verbose:
print("\n\nEVALUATION RESULTS:")
# Evaluate precision of final model on full test-set
precs = [
evaluate.validate(
model,
test_datasets[i],
verbose=False,
test_size=None,
task=i + 1,
allowed_classes=list(
range(classes_per_task * i, classes_per_task *
(i + 1))) if args.scenario == "task" else None)
for i in range(args.tasks)
]
average_precs = sum(precs) / args.tasks
# -print on screen
if verbose:
print("\n Accuracy of final model on test-set:")
for i in range(args.tasks):
print(" - {} {}: {:.4f}".format(
"For classes from task"
if args.scenario == "class" else "Task", i + 1, precs[i]))
print('=> Average accuracy over all {} {}: {:.4f}\n'.format(
args.tasks *
classes_per_task if args.scenario == "class" else args.tasks,
"classes" if args.scenario == "class" else "tasks", average_precs))
# -write out to text file
output_file = open("{}/prec-{}.txt".format(args.r_dir, param_stamp), 'w')
output_file.write('{}\n'.format(average_precs))
output_file.close()
#-------------------------------------------------------------------------------------------------#
#-----------------------------------#
#----- EVALUATION of GENERATOR -----#
#-----------------------------------#
if (utils.checkattr(args, 'feedback') or train_gen
) and args.experiment == "CIFAR100" and args.scenario == "class":
# Dataset and model to be used
test_set = ConcatDataset(test_datasets)
gen_model = model if utils.checkattr(args, 'feedback') else generator
gen_model.eval()
# Evaluate log-likelihood of generative model on combined test-set (with S=100 importance samples per datapoint)
ll_per_datapoint = gen_model.estimate_loglikelihood(
test_set, S=100, batch_size=args.batch)
if verbose:
print('=> Log-likelihood on test set: {:.4f} +/- {:.4f}\n'.format(
np.mean(ll_per_datapoint), np.sqrt(np.var(ll_per_datapoint))))
# -write out to text file
output_file = open("{}/ll-{}.txt".format(args.r_dir, param_stamp), 'w')
output_file.write('{}\n'.format(np.mean(ll_per_datapoint)))
output_file.close()
# Evaluate reconstruction error (averaged over number of input units)
re_per_datapoint = gen_model.calculate_recon_error(
test_set, batch_size=args.batch, average=True)
if verbose:
print(
'=> Reconstruction error (per input unit) on test set: {:.4f} +/- {:.4f}\n'
.format(np.mean(re_per_datapoint),
np.sqrt(np.var(re_per_datapoint))))
# -write out to text file
output_file = open("{}/re-{}.txt".format(args.r_dir, param_stamp), 'w')
output_file.write('{}\n'.format(np.mean(re_per_datapoint)))
output_file.close()
# Try loading the classifier (our substitute for InceptionNet) for calculating IS, FID and Recall & Precision
# -define model
config['classes'] = 100
pretrained_classifier = define.define_classifier(args=args,
config=config,
device=device)
pretrained_classifier.hidden = False
# -load pretrained weights
eval_tag = "" if args.eval_tag == "none" else "-{}".format(
args.eval_tag)
try:
utils.load_checkpoint(pretrained_classifier,
args.m_dir,
verbose=True,
name="{}{}".format(
pretrained_classifier.name, eval_tag))
FileFound = True
except FileNotFoundError:
if verbose:
print("= Could not find model {}{} in {}".format(
pretrained_classifier.name, eval_tag, args.m_dir))
print("= IS, FID and Precision & Recall not computed!")
FileFound = False
pretrained_classifier.eval()
# Only continue with computing these measures if the requested classifier network (using --eval-tag) was found
if FileFound:
# Preparations
total_n = len(test_set)
n_repeats = int(np.ceil(total_n / args.batch))
# -sample data from generator (for IS, FID and Precision & Recall)
gen_x = gen_model.sample(size=total_n, only_x=True)
# -generate predictions for generated data (for IS)
gen_pred = []
for i in range(n_repeats):
x = gen_x[(i *
args.batch):int(min(((i + 1) *
args.batch), total_n))]
with torch.no_grad():
gen_pred.append(
F.softmax(pretrained_classifier.hidden_to_output(x)
if args.hidden else pretrained_classifier(x),
dim=1).cpu().numpy())
gen_pred = np.concatenate(gen_pred)
# -generate embeddings for generated data (for FID and Precision & Recall)
gen_emb = []
for i in range(n_repeats):
with torch.no_grad():
gen_emb.append(
pretrained_classifier.feature_extractor(
gen_x[(i * args.batch
):int(min(((i + 1) *
args.batch), total_n))],
from_hidden=args.hidden).cpu().numpy())
gen_emb = np.concatenate(gen_emb)
# -generate embeddings for test data (for FID and Precision & Recall)
data_loader = utils.get_data_loader(test_set,
batch_size=args.batch,
cuda=cuda)
real_emb = []
for real_x, _ in data_loader:
with torch.no_grad():
real_emb.append(
pretrained_classifier.feature_extractor(
real_x.to(device)).cpu().numpy())
real_emb = np.concatenate(real_emb)
# Calculate "Inception Score" (IS)
py = gen_pred.mean(axis=0)
is_per_datapoint = []
for i in range(len(gen_pred)):
pyx = gen_pred[i, :]
is_per_datapoint.append(entropy(pyx, py))
IS = np.exp(np.mean(is_per_datapoint))
if verbose:
print('=> Inception Score = {:.4f}\n'.format(IS))
# -write out to text file
output_file = open(
"{}/is{}-{}.txt".format(args.r_dir, eval_tag, param_stamp),
'w')
output_file.write('{}\n'.format(IS))
output_file.close()
## Calculate "Frechet Inception Distance" (FID)
FID = fid.calculate_fid_from_embedding(gen_emb, real_emb)
if verbose:
print('=> Frechet Inception Distance = {:.4f}\n'.format(FID))
# -write out to text file
output_file = open(
"{}/fid{}-{}.txt".format(args.r_dir, eval_tag, param_stamp),
'w')
output_file.write('{}\n'.format(FID))
output_file.close()
# Calculate "Precision & Recall"-curves
precision, recall = pr.compute_prd_from_embedding(
gen_emb, real_emb)
# -write out to text files
file_name = "{}/precision{}-{}.txt".format(args.r_dir, eval_tag,
param_stamp)
with open(file_name, 'w') as f:
for item in precision:
f.write("%s\n" % item)
file_name = "{}/recall{}-{}.txt".format(args.r_dir, eval_tag,
param_stamp)
with open(file_name, 'w') as f:
for item in recall:
f.write("%s\n" % item)
#-------------------------------------------------------------------------------------------------#
#--------------------#
#----- PLOTTING -----#
#--------------------#
# If requested, generate pdf
if args.pdf:
# -open pdf
plot_name = "{}/{}.pdf".format(args.p_dir, param_stamp)
pp = evaluate.visual.plt.open_pdf(plot_name)
# -show metrics reflecting progression during training
if args.train and (not utils.checkattr(args, 'only_last')):
# -create list to store all figures to be plotted.
figure_list = []
# -generate figures (and store them in [figure_list])
figure = evaluate.visual.plt.plot_lines(
precision_dict["all_tasks"],
x_axes=[
i * classes_per_task for i in precision_dict["x_task"]
] if args.scenario == "class" else precision_dict["x_task"],
line_names=[
'{} {}'.format(
"episode / task"
if args.scenario == "class" else "task", i + 1)
for i in range(args.tasks)
],
xlabel="# of {}s so far".format("classe" if args.scenario ==
"class" else "task"),
ylabel="Test accuracy")
figure_list.append(figure)
figure = evaluate.visual.plt.plot_lines(
[precision_dict["average"]],
x_axes=[
i * classes_per_task for i in precision_dict["x_task"]
] if args.scenario == "class" else precision_dict["x_task"],
line_names=[
'Average based on all {}s so far'.format((
"digit" if args.experiment == "splitMNIST" else
"classe") if args.scenario else "task")
],
xlabel="# of {}s so far".format("classe" if args.scenario ==
"class" else "task"),
ylabel="Test accuracy")
figure_list.append(figure)
# -add figures to pdf
for figure in figure_list:
pp.savefig(figure)
gen_eval = (utils.checkattr(args, 'feedback') or train_gen)
# -show samples (from main model or separate generator)
if gen_eval and not no_samples:
evaluate.show_samples(
model if utils.checkattr(args, 'feedback') else generator,
config,
size=args.sample_n,
pdf=pp,
title="Generated samples (by final model)")
# -plot "Precision & Recall"-curve
if gen_eval and args.experiment == "CIFAR100" and args.scenario == "class" and FileFound:
figure = evaluate.visual.plt.plot_pr_curves([[precision]],
[[recall]])
pp.savefig(figure)
# -close pdf
pp.close()
# -print name of generated plot on screen
if verbose:
print("\nGenerated plot: {}\n".format(plot_name))
def run(args, model_name, shift, slot, verbose=False):
# Create plots- and results-directories if needed
if not os.path.isdir(args.r_dir):
os.mkdir(args.r_dir)
if args.pdf and not os.path.isdir(args.p_dir):
os.mkdir(args.p_dir)
# If only want param-stamp, get it and exit
if args.get_stamp:
from param_stamp import get_param_stamp_from_args
print(get_param_stamp_from_args(args=args))
exit()
# Use cuda?
cuda = torch.cuda.is_available() and args.cuda
device = torch.device("cuda" if cuda else "cpu")
# Report whether cuda is used
if verbose:
print("CUDA is {}used".format("" if cuda else "NOT(!!) "))
# Set random seeds
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if cuda:
torch.cuda.manual_seed(args.seed)
#-------------------------------------------------------------------------------------------------#
#----------------#
#----- DATA -----#
#----------------#
# Prepare data for chosen experiment
if verbose:
print("\nPreparing the data...")
(train_datasets,
test_datasets), config, classes_per_task = get_multitask_experiment(
name=args.experiment,
tasks=args.tasks,
slot=args.slot,
shift=args.shift,
data_dir=args.d_dir,
normalize=True if utils.checkattr(args, "normalize") else False,
augment=True if utils.checkattr(args, "augment") else False,
verbose=verbose,
exception=True if args.seed < 10 else False,
only_test=(not args.train),
max_samples=args.max_samples)
#-------------------------------------------------------------------------------------------------#
#----------------------#
#----- MAIN MODEL -----#
#----------------------#
# Define main model (i.e., classifier, if requested with feedback connections)
if verbose and utils.checkattr(
args, "pre_convE") and (hasattr(args, "depth") and args.depth > 0):
print("\nDefining the model...")
model = define.define_classifier(args=args, config=config, device=device)
# Initialize / use pre-trained / freeze model-parameters
# - initialize (pre-trained) parameters
model = define.init_params(model, args)
# - freeze weights of conv-layers?
if utils.checkattr(args, "freeze_convE"):
for param in model.convE.parameters():
param.requires_grad = False
# Define optimizer (only optimize parameters that "requires_grad")
model.optim_list = [
{
'params': filter(lambda p: p.requires_grad, model.parameters()),
'lr': args.lr
},
]
model.optimizer = optim.Adam(model.optim_list, betas=(0.9, 0.999))
#-------------------------------------------------------------------------------------------------#
#----------------------------------#
#----- CL-STRATEGY: EXEMPLARS -----#
#----------------------------------#
# Store in model whether, how many and in what way to store exemplars
if isinstance(model, ExemplarHandler) and (args.use_exemplars
or args.replay == "exemplars"):
model.memory_budget = args.budget
model.herding = args.herding
model.norm_exemplars = args.herding
#-------------------------------------------------------------------------------------------------#
#----------------------------------------------------#
#----- CL-STRATEGY: REGULARIZATION / ALLOCATION -----#
#----------------------------------------------------#
# Elastic Weight Consolidation (EWC)
if isinstance(model, ContinualLearner) and utils.checkattr(args, 'ewc'):
model.ewc_lambda = args.ewc_lambda if args.ewc else 0
model.fisher_n = args.fisher_n
model.online = utils.checkattr(args, 'online')
if model.online:
model.gamma = args.gamma
# Synpatic Intelligence (SI)
if isinstance(model, ContinualLearner) and utils.checkattr(args, 'si'):
model.si_c = args.si_c if args.si else 0
model.epsilon = args.epsilon
# XdG: create for every task a "mask" for each hidden fully connected layer
if isinstance(model, ContinualLearner) and utils.checkattr(
args, 'xdg') and args.xdg_prop > 0:
model.define_XdGmask(gating_prop=args.xdg_prop, n_tasks=args.tasks)
#-------------------------------------------------------------------------------------------------#
#-------------------------------#
#----- CL-STRATEGY: REPLAY -----#
#-------------------------------#
# Use distillation loss (i.e., soft targets) for replayed data? (and set temperature)
if isinstance(model, ContinualLearner) and hasattr(
args, 'replay') and not args.replay == "none":
model.replay_targets = "soft" if args.distill else "hard"
model.KD_temp = args.temp
#-------------------------------------------------------------------------------------------------#
#---------------------#
#----- REPORTING -----#
#---------------------#
# Get parameter-stamp (and print on screen)
if verbose:
print("\nParameter-stamp...")
param_stamp, reinit_param_stamp = get_param_stamp(
args,
model.name,
verbose=verbose,
replay=True if
(hasattr(args, 'replay') and not args.replay == "none") else False,
)
# Print some model-characteristics on the screen
if verbose:
# -main model
utils.print_model_info(model, title="MAIN MODEL")
# Prepare for keeping track of statistics required for metrics (also used for plotting in pdf)
if args.pdf or args.metrics:
# -define [metrics_dict] to keep track of performance during training for storing & for later plotting in pdf
metrics_dict = evaluate.initiate_metrics_dict(n_tasks=args.tasks)
# -evaluate randomly initiated model on all tasks & store accuracies in [metrics_dict] (for calculating metrics)
if not args.use_exemplars:
metrics_dict = evaluate.intial_accuracy(
model,
test_datasets,
metrics_dict,
no_task_mask=False,
classes_per_task=classes_per_task,
test_size=None)
else:
metrics_dict = None
# Prepare for plotting in visdom
visdom = None
if args.visdom:
env_name = "{exp}-{tasks}".format(exp=args.experiment,
tasks=args.tasks)
replay_statement = "{mode}{b}".format(
mode=args.replay,
b="" if
(args.batch_replay is None or args.batch_replay == args.batch) else
"-br{}".format(args.batch_replay),
) if (hasattr(args, "replay") and not args.replay == "none") else "NR"
graph_name = "{replay}{syn}{ewc}{xdg}".format(
replay=replay_statement,
syn="-si{}".format(args.si_c)
if utils.checkattr(args, 'si') else "",
ewc="-ewc{}{}".format(
args.ewc_lambda, "-O{}".format(args.gamma)
if utils.checkattr(args, "online") else "") if utils.checkattr(
args, 'ewc') else "",
xdg="" if (not utils.checkattr(args, 'xdg')) or args.xdg_prop == 0
else "-XdG{}".format(args.xdg_prop),
)
visdom = {'env': env_name, 'graph': graph_name}
#-------------------------------------------------------------------------------------------------#
#---------------------#
#----- CALLBACKS -----#
#---------------------#
# Callbacks for reporting on and visualizing loss
solver_loss_cbs = [
cb._solver_loss_cb(log=args.loss_log,
visdom=visdom,
model=model,
iters_per_task=args.iters,
tasks=args.tasks,
replay=(hasattr(args, "replay")
and not args.replay == "none"))
]
# Callbacks for reporting and visualizing accuracy
# -visdom (i.e., after each [prec_log]
eval_cbs = [
cb._eval_cb(log=args.prec_log,
test_datasets=test_datasets,
visdom=visdom,
iters_per_task=args.iters,
test_size=args.prec_n,
classes_per_task=classes_per_task,
with_exemplars=False)
] if (not args.use_exemplars) else [None]
#--> during training on a task, evaluation cannot be with exemplars as those are only selected after training
# (instead, evaluation for visdom is only done after each task, by including callback-function into [metric_cbs])
# Callbacks for calculating statists required for metrics
# -pdf / reporting: summary plots (i.e, only after each task) (when using exemplars, also for visdom)
metric_cbs = [
cb._metric_cb(log=args.iters,
test_datasets=test_datasets,
classes_per_task=classes_per_task,
metrics_dict=metrics_dict,
iters_per_task=args.iters,
with_exemplars=args.use_exemplars),
cb._eval_cb(log=args.iters,
test_datasets=test_datasets,
visdom=visdom,
iters_per_task=args.iters,
test_size=args.prec_n,
classes_per_task=classes_per_task,
with_exemplars=True) if args.use_exemplars else None
]
#-------------------------------------------------------------------------------------------------#
#--------------------#
#----- TRAINING -----#
#--------------------#
if args.train:
if verbose:
print("\nTraining...")
# Train model
train_cl(
model,
train_datasets,
model_name=model_name,
shift=shift,
slot=slot,
replay_mode=args.replay if hasattr(args, 'replay') else "none",
classes_per_task=classes_per_task,
iters=args.iters,
args=args,
batch_size=args.batch,
batch_size_replay=args.batch_replay if hasattr(
args, 'batch_replay') else None,
eval_cbs=eval_cbs,
loss_cbs=solver_loss_cbs,
reinit=utils.checkattr(args, 'reinit'),
only_last=utils.checkattr(args, 'only_last'),
metric_cbs=metric_cbs,
use_exemplars=args.use_exemplars,
)
# Save trained model(s), if requested
if args.save:
save_name = "mM-{}".format(param_stamp) if (
not hasattr(args, 'full_stag')
or args.full_stag == "none") else "{}-{}".format(
model.name, args.full_stag)
utils.save_checkpoint(model,
args.m_dir,
name=save_name,
verbose=verbose)
else:
# Load previously trained model(s) (if goal is to only evaluate previously trained model)
if verbose:
print("\nLoading parameters of the previously trained models...")
load_name = "mM-{}".format(param_stamp) if (
not hasattr(args, 'full_ltag')
or args.full_ltag == "none") else "{}-{}".format(
model.name, args.full_ltag)
utils.load_checkpoint(
model,
args.m_dir,
name=load_name,
verbose=verbose,
add_si_buffers=(isinstance(model, ContinualLearner)
and utils.checkattr(args, 'si')))
# Load previously created metrics-dict
file_name = "{}/dict-{}".format(args.r_dir, param_stamp)
metrics_dict = utils.load_object(file_name)
#-------------------------------------------------------------------------------------------------#
#-----------------------------------#
#----- EVALUATION of CLASSIFIER-----#
#-----------------------------------#
if verbose:
print("\n\nEVALUATION RESULTS:")
# Evaluate precision of final model on full test-set
precs = [
evaluate.validate(model,
test_datasets[i],
verbose=False,
test_size=None,
task=i + 1,
with_exemplars=False,
allowed_classes=list(
range(classes_per_task * i,
classes_per_task * (i + 1))))
for i in range(args.tasks)
]
average_precs = sum(precs) / args.tasks
# -print on screen
if verbose:
print("\n Precision on test-set{}:".format(
" (softmax classification)" if args.use_exemplars else ""))
for i in range(args.tasks):
print(" - Task {}: {:.4f}".format(i + 1, precs[i]))
print('=> Average precision over all {} tasks: {:.4f}\n'.format(
args.tasks, average_precs))
# -with exemplars
if args.use_exemplars:
precs = [
evaluate.validate(model,
test_datasets[i],
verbose=False,
test_size=None,
task=i + 1,
with_exemplars=True,
allowed_classes=list(
range(classes_per_task * i,
classes_per_task * (i + 1))))
for i in range(args.tasks)
]
average_precs_ex = sum(precs) / args.tasks
# -print on screen
if verbose:
print(" Precision on test-set (classification using exemplars):")
for i in range(args.tasks):
print(" - Task {}: {:.4f}".format(i + 1, precs[i]))
print('=> Average precision over all {} tasks: {:.4f}\n'.format(
args.tasks, average_precs_ex))
# If requested, compute metrics
'''if args.metrics:
def train_cl(model,
train_datasets,
replay_mode="none",
rnt=None,
classes_per_task=None,
iters=2000,
batch_size=32,
batch_size_replay=None,
loss_cbs=list(),
eval_cbs=list(),
reinit=False,
args=None,
only_last=False,
use_exemplars=False,
metric_cbs=list()):
'''Train a model (with a "train_a_batch" method) on multiple tasks, with replay-strategy specified by [replay_mode].
[model] <nn.Module> main model to optimize across all tasks
[train_datasets] <list> with for each task the training <DataSet>
[replay_mode] <str>, choice from "current", "offline" and "none"
[classes_per_task] <int>, # classes per task; only 1st task has [classes_per_task]*[first_task_class_boost] classes
[rnt] <float>, indicating relative importance of new task (if None, relative to # old tasks)
[iters] <int>, # optimization-steps (=batches) per task; 1st task has [first_task_iter_boost] steps more
[batch_size_replay] <int>, number of samples to replay per batch
[only_last] <bool>, only train on final task / episode
[*_cbs] <list> of call-back functions to evaluate training-progress'''
# Should convolutional layers be frozen?
freeze_convE = (utils.checkattr(args, "freeze_convE")
and hasattr(args, "depth") and args.depth > 0)
# Use cuda?
device = model._device()
cuda = model._is_on_cuda()
# Set default-values if not specified
batch_size_replay = batch_size if batch_size_replay is None else batch_size_replay
# Initiate indicators for replay (no replay for 1st task)
Exact = Current = Offline_TaskIL = False
previous_model = None
# Register starting param-values (needed for "intelligent synapses").
if isinstance(model, ContinualLearner) and model.si_c > 0:
for n, p in model.named_parameters():
if p.requires_grad:
n = n.replace('.', '__')
model.register_buffer('{}_SI_prev_task'.format(n),
p.detach().clone())
# Loop over all tasks.
for task, train_dataset in enumerate(train_datasets, 1):
# In offline replay-setting, all tasks so far should be visited separately (i.e., separate data-loader per task)
if replay_mode == "offline":
Offline_TaskIL = True
data_loader = [None] * task
train_dataset = train_dataset
# Initialize # iters left on data-loader(s)
iters_left = 1 if (not Offline_TaskIL) else [1] * task
if Exact:
iters_left_previous = [1] * (task - 1)
data_loader_previous = [None] * (task - 1)
# Prepare <dicts> to store running importance estimates and parameter-values before update
if isinstance(model, ContinualLearner) and model.si_c > 0:
W = {}
p_old = {}
for n, p in model.named_parameters():
if p.requires_grad:
n = n.replace('.', '__')
W[n] = p.data.clone().zero_()
p_old[n] = p.data.clone()
# Find [active_classes] (=classes in current task)
active_classes = [
list(range(classes_per_task * i, classes_per_task * (i + 1)))
for i in range(task)
]
# Reinitialize the model's parameters and the optimizer (if requested)
if reinit:
from define_models import init_params
init_params(model, args)
model.optimizer = optim.Adam(model.optim_list, betas=(0.9, 0.999))
# Define a tqdm progress bar(s)
progress = tqdm.tqdm(range(1, iters + 1))
# Loop over all iterations
iters_to_use = iters
# -if only the final task should be trained on:
if only_last and not task == len(train_datasets):
iters_to_use = 0
for batch_index in range(1, iters_to_use + 1):
# Update # iters left on current data-loader(s) and, if needed, create new one(s)
if not Offline_TaskIL:
iters_left -= 1
if iters_left == 0:
data_loader = iter(
utils.get_data_loader(train_dataset,
batch_size,
cuda=cuda,
drop_last=True))
# NOTE: [train_dataset] is training-set of current task
# [train_dataset] is training-set of current task with stored exemplars added (if requested)
iters_left = len(data_loader)
else:
# -with "offline replay", there is a separate data-loader for each task
batch_size_to_use = batch_size
for task_id in range(task):
iters_left[task_id] -= 1
if iters_left[task_id] == 0:
data_loader[task_id] = iter(
utils.get_data_loader(train_datasets[task_id],
batch_size_to_use,
cuda=cuda,
drop_last=True))
iters_left[task_id] = len(data_loader[task_id])
# Update # iters left on data-loader(s) of the previous task(s) and, if needed, create new one(s)
if Exact:
up_to_task = task - 1
batch_size_replay_pt = int(
np.floor(
batch_size_replay /
up_to_task)) if (up_to_task > 1) else batch_size_replay
# -need separate replay for each task
for task_id in range(up_to_task):
batch_size_to_use = min(batch_size_replay_pt,
len(previous_datasets[task_id]))
iters_left_previous[task_id] -= 1
if iters_left_previous[task_id] == 0:
data_loader_previous[task_id] = iter(
utils.get_data_loader(previous_datasets[task_id],
batch_size_to_use,
cuda=cuda,
drop_last=True))
iters_left_previous[task_id] = len(
data_loader_previous[task_id])
#-----------------Collect data------------------#
#####-----CURRENT BATCH-----#####
if not Offline_TaskIL:
x, y = next(
data_loader) #--> sample training data of current task
y = y - classes_per_task * (
task - 1) #--> ITL: adjust y-targets to 'active range'
x, y = x.to(device), y.to(
device) #--> transfer them to correct device
#y = y.expand(1) if len(y.size())==1 else y #--> hack for if batch-size is 1
else:
x = y = task_used = None #--> all tasks are "treated as replay"
# -sample training data for all tasks so far, move to correct device and store in lists
x_, y_ = list(), list()
for task_id in range(task):
x_temp, y_temp = next(data_loader[task_id])
x_.append(x_temp.to(device))
y_temp = y_temp - (
classes_per_task * task_id
) #--> adjust y-targets to 'active range'
if batch_size_to_use == 1:
y_temp = torch.tensor([
y_temp
]) #--> correct dimensions if batch-size is 1
y_.append(y_temp.to(device))
#####-----REPLAYED BATCH-----#####
if not Offline_TaskIL and not Exact and not Current:
x_ = y_ = scores_ = task_used = None #-> if no replay
#--------------------------------------------INPUTS----------------------------------------------------#
##-->> Exact Replay <<--##
if Exact:
# Sample replayed training data, move to correct device and store in lists
x_ = list()
y_ = list()
up_to_task = task - 1
for task_id in range(up_to_task):
x_temp, y_temp = next(data_loader_previous[task_id])
x_.append(x_temp.to(device))
# -only keep [y_] if required (as otherwise unnecessary computations will be done)
if model.replay_targets == "hard":
y_temp = y_temp - (
classes_per_task * task_id
) #-> adjust y-targets to 'active range'
y_.append(y_temp.to(device))
else:
y_.append(None)
# If required, get target scores (i.e, [scores_]) -- using previous model, with no_grad()
if (model.replay_targets == "soft") and (previous_model
is not None):
scores_ = list()
for task_id in range(up_to_task):
with torch.no_grad():
scores_temp = previous_model(x_[task_id])
scores_temp = scores_temp[:,
(classes_per_task *
task_id):(classes_per_task *
(task_id + 1))]
scores_.append(scores_temp)
else:
scores_ = None
##-->> Current Replay <<--##
if Current:
x_ = x[:batch_size_replay] #--> use current task inputs
task_used = None
#--------------------------------------------OUTPUTS----------------------------------------------------#
if Current:
# Get target scores & possibly labels (i.e., [scores_] / [y_]) -- use previous model, with no_grad()
# -[x_] needs to be evaluated according to each previous task, so make list with entry per task
scores_ = list()
y_ = list()
# -if no task-mask and no conditional generator, all scores can be calculated in one go
if previous_model.mask_dict is None and not type(x_) == list:
with torch.no_grad():
all_scores_ = previous_model.classify(x_)
for task_id in range(task - 1):
# -if there is a task-mask (i.e., XdG is used), obtain predicted scores for each task separately
if previous_model.mask_dict is not None:
previous_model.apply_XdGmask(task=task_id + 1)
if previous_model.mask_dict is not None or type(
x_) == list:
with torch.no_grad():
all_scores_ = previous_model.classify(
x_[task_id] if type(x_) == list else x_)
temp_scores_ = all_scores_[:, (classes_per_task *
task_id):(classes_per_task *
(task_id + 1))]
scores_.append(temp_scores_)
# - also get hard target
_, temp_y_ = torch.max(temp_scores_, dim=1)
y_.append(temp_y_)
# -only keep predicted y_/scores_ if required (as otherwise unnecessary computations will be done)
y_ = y_ if (model.replay_targets == "hard") else None
scores_ = scores_ if (model.replay_targets == "soft") else None
#-----------------Train model------------------#
# Train the main model with this batch
loss_dict = model.train_a_batch(x,
y=y,
x_=x_,
y_=y_,
scores_=scores_,
tasks_=task_used,
active_classes=active_classes,
task=task,
rnt=(1. if task == 1 else 1. /
task) if rnt is None else rnt,
freeze_convE=freeze_convE)
# Update running parameter importance estimates in W
if isinstance(model, ContinualLearner) and model.si_c > 0:
for n, p in model.named_parameters():
if p.requires_grad:
n = n.replace('.', '__')
if p.grad is not None:
W[n].add_(-p.grad * (p.detach() - p_old[n]))
p_old[n] = p.detach().clone()
# Fire callbacks (for visualization of training-progress / evaluating performance after each task)
for loss_cb in loss_cbs:
if loss_cb is not None:
loss_cb(progress, batch_index, loss_dict, task=task)
for eval_cb in eval_cbs:
if eval_cb is not None:
eval_cb(model, batch_index, task=task)
# Close progres-bar
progress.close()
##----------> UPON FINISHING EACH TASK...
# EWC: estimate Fisher Information matrix (FIM) and update term for quadratic penalty
if isinstance(model, ContinualLearner) and model.ewc_lambda > 0:
# -find allowed classes
allowed_classes = list(
range(classes_per_task * (task - 1), classes_per_task * task))
# -if needed, apply correct task-specific mask
if model.mask_dict is not None:
model.apply_XdGmask(task=task)
# -estimate FI-matrix
model.estimate_fisher(train_dataset,
allowed_classes=allowed_classes)
# SI: calculate and update the normalized path integral
if isinstance(model, ContinualLearner) and model.si_c > 0:
model.update_omega(W, model.epsilon)
# EXEMPLARS: update exemplar sets
if use_exemplars or replay_mode == "exemplars":
exemplars_per_class = int(
np.floor(model.memory_budget / (classes_per_task * task)))
# reduce examplar-sets
model.reduce_exemplar_sets(exemplars_per_class)
# for each new class trained on, construct examplar-set
new_classes = list(
range(classes_per_task * (task - 1), classes_per_task * task))
for class_id in new_classes:
# create new dataset containing only all examples of this class
class_dataset = SubDataset(original_dataset=train_dataset,
sub_labels=[class_id])
# based on this dataset, construct new exemplar-set for this class
model.construct_exemplar_set(dataset=class_dataset,
n=exemplars_per_class)
model.compute_means = True
# Calculate statistics required for metrics
for metric_cb in metric_cbs:
if metric_cb is not None:
metric_cb(model, iters, task=task)
# REPLAY: update source for replay
previous_model = copy.deepcopy(model).eval()
if replay_mode == 'current':
Current = True
elif replay_mode in ('exemplars', 'exact'):
Exact = True
if replay_mode == "exact":
previous_datasets = train_datasets[:task]
else:
previous_datasets = []
for task_id in range(task):
previous_datasets.append(
ExemplarDataset(
model.exemplar_sets[(classes_per_task *
task_id):(classes_per_task *
(task_id + 1))],
target_transform=lambda y, x=classes_per_task *
task_id: y + x))
def train_cl(model,
train_datasets,
replay_mode="none",
scenario="task",
rnt=None,
classes_per_task=None,
iters=2000,
batch_size=32,
batch_size_replay=None,
loss_cbs=list(),
eval_cbs=list(),
sample_cbs=list(),
generator=None,
gen_iters=0,
gen_loss_cbs=list(),
feedback=False,
reinit=False,
args=None,
only_last=False):
'''Train a model (with a "train_a_batch" method) on multiple tasks, with replay-strategy specified by [replay_mode].
[model] <nn.Module> main model to optimize across all tasks
[train_datasets] <list> with for each task the training <DataSet>
[replay_mode] <str>, choice from "generative", "current", "offline" and "none"
[scenario] <str>, choice from "task", "domain", "class" and "all"
[classes_per_task] <int>, # classes per task; only 1st task has [classes_per_task]*[first_task_class_boost] classes
[rnt] <float>, indicating relative importance of new task (if None, relative to # old tasks)
[iters] <int>, # optimization-steps (=batches) per task; 1st task has [first_task_iter_boost] steps more
[batch_size_replay] <int>, number of samples to replay per batch
[generator] None or <nn.Module>, if a seperate generative model should be trained (for [gen_iters] per task)
[feedback] <bool>, if True and [replay_mode]="generative", the main model is used for generating replay
[only_last] <bool>, only train on final task / episode
[*_cbs] <list> of call-back functions to evaluate training-progress'''
# Should convolutional layers be frozen?
freeze_convE = (utils.checkattr(args, "freeze_convE")
and hasattr(args, "depth") and args.depth > 0)
# Use cuda?
device = model._device()
cuda = model._is_on_cuda()
# Set default-values if not specified
batch_size_replay = batch_size if batch_size_replay is None else batch_size_replay
# Initiate indicators for replay (no replay for 1st task)
Generative = Current = Offline_TaskIL = False
previous_model = None
# Register starting param-values (needed for "intelligent synapses").
if isinstance(model, ContinualLearner) and model.si_c > 0:
for n, p in model.named_parameters():
if p.requires_grad:
n = n.replace('.', '__')
model.register_buffer('{}_SI_prev_task'.format(n),
p.detach().clone())
# Loop over all tasks.
for task, train_dataset in enumerate(train_datasets, 1):
# If offline replay-setting, create large database of all tasks so far
if replay_mode == "offline" and (not scenario == "task"):
train_dataset = ConcatDataset(train_datasets[:task])
# -but if "offline"+"task": all tasks so far should be visited separately (i.e., separate data-loader per task)
if replay_mode == "offline" and scenario == "task":
Offline_TaskIL = True
data_loader = [None] * task
# Initialize # iters left on data-loader(s)
iters_left = 1 if (not Offline_TaskIL) else [1] * task
# Prepare <dicts> to store running importance estimates and parameter-values before update
if isinstance(model, ContinualLearner) and model.si_c > 0:
W = {}
p_old = {}
for n, p in model.named_parameters():
if p.requires_grad:
n = n.replace('.', '__')
W[n] = p.data.clone().zero_()
p_old[n] = p.data.clone()
# Find [active_classes] (=classes in current task)
active_classes = None #-> for "domain"- or "all"-scenarios, always all classes are active
if scenario == "task":
# -for "task"-scenario, create <list> with for all tasks so far a <list> with the active classes
active_classes = [
list(range(classes_per_task * i, classes_per_task * (i + 1)))
for i in range(task)
]
elif scenario == "class":
# -for "class"-scenario, create one <list> with active classes of all tasks so far
active_classes = list(range(classes_per_task * task))
# Reinitialize the model's parameters (if requested)
if reinit:
from define_models import init_params
init_params(model, args)
if generator is not None:
init_params(generator, args)
# Define a tqdm progress bar(s)
iters_main = iters
progress = tqdm.tqdm(range(1, iters_main + 1))
if generator is not None:
iters_gen = gen_iters
progress_gen = tqdm.tqdm(range(1, iters_gen + 1))
# Loop over all iterations
iters_to_use = (iters_main if
(generator is None) else max(iters_main, iters_gen))
# -if only the final task should be trained on:
if only_last and not task == len(train_datasets):
iters_to_use = 0
for batch_index in range(1, iters_to_use + 1):
# Update # iters left on current data-loader(s) and, if needed, create new one(s)
if not Offline_TaskIL:
iters_left -= 1
if iters_left == 0:
data_loader = iter(
utils.get_data_loader(train_dataset,
batch_size,
cuda=cuda,
drop_last=True))
iters_left = len(data_loader)
else:
# -with "offline replay" in Task-IL scenario, there is a separate data-loader for each task
batch_size_to_use = int(np.ceil(batch_size / task))
for task_id in range(task):
iters_left[task_id] -= 1
if iters_left[task_id] == 0:
data_loader[task_id] = iter(
utils.get_data_loader(train_datasets[task_id],
batch_size_to_use,
cuda=cuda,
drop_last=True))
iters_left[task_id] = len(data_loader[task_id])
#-----------------Collect data------------------#
#####-----CURRENT BATCH-----#####
if not Offline_TaskIL:
x, y = next(
data_loader) #--> sample training data of current task
y = y - classes_per_task * (
task - 1
) if scenario == "task" else y #--> ITL: adjust y-targets to 'active range'
x, y = x.to(device), y.to(
device) #--> transfer them to correct device
#y = y.expand(1) if len(y.size())==1 else y #--> hack for if batch-size is 1
else:
x = y = task_used = None #--> all tasks are "treated as replay"
# -sample training data for all tasks so far, move to correct device and store in lists
x_, y_ = list(), list()
for task_id in range(task):
x_temp, y_temp = next(data_loader[task_id])
x_.append(x_temp.to(device))
y_temp = y_temp - (
classes_per_task * task_id
) #--> adjust y-targets to 'active range'
if batch_size_to_use == 1:
y_temp = torch.tensor([
y_temp
]) #--> correct dimensions if batch-size is 1
y_.append(y_temp.to(device))
#####-----REPLAYED BATCH-----#####
if not Offline_TaskIL and not Generative and not Current:
x_ = y_ = scores_ = task_used = None #-> if no replay
#--------------------------------------------INPUTS----------------------------------------------------#
##-->> Current Replay <<--##
if Current:
x_ = x[:batch_size_replay] #--> use current task inputs
task_used = None
##-->> Generative Replay <<--##
if Generative:
#---> Only with generative replay, the resulting [x_] will be at the "hidden"-level
conditional_gen = True if (
(previous_generator.per_class
and previous_generator.prior == "GMM") or utils.checkattr(
previous_generator, 'dg_gates')) else False
# Sample [x_]
if conditional_gen and scenario == "task":
# -if a conditional generator is used with task-IL scenario, generate data per previous task
x_ = list()
task_used = list()
for task_id in range(task - 1):
allowed_classes = list(
range(classes_per_task * task_id,
classes_per_task * (task_id + 1)))
batch_size_replay_to_use = int(
np.ceil(batch_size_replay / (task - 1)))
x_temp_ = previous_generator.sample(
batch_size_replay_to_use,
allowed_classes=allowed_classes,
only_x=False)
x_.append(x_temp_[0])
task_used.append(x_temp_[2])
else:
# -which classes are allowed to be generated? (relevant if conditional generator / decoder-gates)
allowed_classes = None if scenario == "domain" else list(
range(classes_per_task * (task - 1)))
# -which tasks/domains are allowed to be generated? (only relevant if "Domain-IL" with task-gates)
allowed_domains = list(range(task - 1))
# -generate inputs representative of previous tasks
x_temp_ = previous_generator.sample(
batch_size_replay,
allowed_classes=allowed_classes,
allowed_domains=allowed_domains,
only_x=False,
)
x_ = x_temp_[0]
task_used = x_temp_[2]
#--------------------------------------------OUTPUTS----------------------------------------------------#
if Generative or Current:
# Get target scores & possibly labels (i.e., [scores_] / [y_]) -- use previous model, with no_grad()
if scenario in ("domain",
"class") and previous_model.mask_dict is None:
# -if replay does not need to be evaluated for each task (ie, not Task-IL and no task-specific mask)
with torch.no_grad():
all_scores_ = previous_model.classify(
x_, not_hidden=False if Generative else True)
scores_ = all_scores_[:, :(
classes_per_task * (task - 1)
)] if (
scenario == "class"
) else all_scores_ # -> when scenario=="class", zero probs will be added in [loss_fn_kd]-function
# -also get the 'hard target'
_, y_ = torch.max(scores_, dim=1)
else:
# -[x_] needs to be evaluated according to each previous task, so make list with entry per task
scores_ = list()
y_ = list()
# -if no task-mask and no conditional generator, all scores can be calculated in one go
if previous_model.mask_dict is None and not type(
x_) == list:
with torch.no_grad():
all_scores_ = previous_model.classify(
x_, not_hidden=False if Generative else True)
for task_id in range(task - 1):
# -if there is a task-mask (i.e., XdG is used), obtain predicted scores for each task separately
if previous_model.mask_dict is not None:
previous_model.apply_XdGmask(task=task_id + 1)
if previous_model.mask_dict is not None or type(
x_) == list:
with torch.no_grad():
all_scores_ = previous_model.classify(
x_[task_id] if type(x_) == list else x_,
not_hidden=False if Generative else True)
if scenario == "domain":
# NOTE: if scenario=domain with task-mask, it's of course actually the Task-IL scenario!
# this can be used as trick to run the Task-IL scenario with singlehead output layer
temp_scores_ = all_scores_
else:
temp_scores_ = all_scores_[:, (
classes_per_task * task_id):(classes_per_task *
(task_id + 1))]
scores_.append(temp_scores_)
# - also get hard target
_, temp_y_ = torch.max(temp_scores_, dim=1)
y_.append(temp_y_)
# -only keep predicted y_/scores_ if required (as otherwise unnecessary computations will be done)
y_ = y_ if (model.replay_targets == "hard") else None
scores_ = scores_ if (model.replay_targets == "soft") else None
#-----------------Train model(s)------------------#
#---> Train MAIN MODEL
if batch_index <= iters_main:
# Train the main model with this batch
loss_dict = model.train_a_batch(
x,
y=y,
x_=x_,
y_=y_,
scores_=scores_,
tasks_=task_used,
active_classes=active_classes,
task=task,
rnt=(1. if task == 1 else 1. /
task) if rnt is None else rnt,
freeze_convE=freeze_convE,
replay_not_hidden=False if Generative else True)
# Update running parameter importance estimates in W
if isinstance(model, ContinualLearner) and model.si_c > 0:
for n, p in model.convE.named_parameters():
if p.requires_grad:
n = "convE." + n
n = n.replace('.', '__')
if p.grad is not None:
W[n].add_(-p.grad * (p.detach() - p_old[n]))
p_old[n] = p.detach().clone()
for n, p in model.fcE.named_parameters():
if p.requires_grad:
n = "fcE." + n
n = n.replace('.', '__')
if p.grad is not None:
W[n].add_(-p.grad * (p.detach() - p_old[n]))
p_old[n] = p.detach().clone()
for n, p in model.classifier.named_parameters():
if p.requires_grad:
n = "classifier." + n
n = n.replace('.', '__')
if p.grad is not None:
W[n].add_(-p.grad * (p.detach() - p_old[n]))
p_old[n] = p.detach().clone()
# Fire callbacks (for visualization of training-progress / evaluating performance after each task)
for loss_cb in loss_cbs:
if loss_cb is not None:
loss_cb(progress, batch_index, loss_dict, task=task)
for eval_cb in eval_cbs:
if eval_cb is not None:
eval_cb(model, batch_index, task=task)
if model.label == "VAE":
for sample_cb in sample_cbs:
if sample_cb is not None:
sample_cb(model,
batch_index,
task=task,
allowed_classes=None if
(scenario == "domain") else list(
range(classes_per_task * task)))
#---> Train GENERATOR
if generator is not None and batch_index <= iters_gen:
loss_dict = generator.train_a_batch(
x,
y=y,
x_=x_,
y_=y_,
scores_=scores_,
tasks_=task_used,
active_classes=active_classes,
rnt=(1. if task == 1 else 1. /
task) if rnt is None else rnt,
task=task,
freeze_convE=freeze_convE,
replay_not_hidden=False if Generative else True)
# Fire callbacks on each iteration
for loss_cb in gen_loss_cbs:
if loss_cb is not None:
loss_cb(progress_gen,
batch_index,
loss_dict,
task=task)
for sample_cb in sample_cbs:
if sample_cb is not None:
sample_cb(generator,
batch_index,
task=task,
allowed_classes=None if
(scenario == "domain") else list(
range(classes_per_task * task)))
# Close progres-bar(s)
progress.close()
if generator is not None:
progress_gen.close()
##----------> UPON FINISHING EACH TASK...
# EWC: estimate Fisher Information matrix (FIM) and update term for quadratic penalty
if isinstance(model, ContinualLearner) and model.ewc_lambda > 0:
# -find allowed classes
allowed_classes = list(
range(classes_per_task * (task - 1), classes_per_task *
task)) if scenario == "task" else (
list(range(classes_per_task *
task)) if scenario == "class" else None)
# -if needed, apply correct task-specific mask
if model.mask_dict is not None:
model.apply_XdGmask(task=task)
# -estimate FI-matrix
model.estimate_fisher(train_dataset,
allowed_classes=allowed_classes)
# SI: calculate and update the normalized path integral
if isinstance(model, ContinualLearner) and model.si_c > 0:
model.update_omega(W, model.epsilon)
# REPLAY: update source for replay
previous_model = copy.deepcopy(model).eval()
if replay_mode == "generative":
Generative = True
previous_generator = previous_model if feedback else copy.deepcopy(
generator).eval()
elif replay_mode == 'current':
Current = True
def run(args):
# Use cuda?
cuda = torch.cuda.is_available() and args.cuda
device = torch.device("cuda" if cuda else "cpu")
# Set random seeds
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if cuda:
torch.cuda.manual_seed(args.seed)
# Report whether cuda is used
print("CUDA is {}used".format("" if cuda else "NOT(!!) "))
# Create plots-directory if needed
if args.pdf and not os.path.isdir(args.p_dir):
os.mkdir(args.p_dir)
#-------------------------------------------------------------------------------------------------#
#----------------#
#----- DATA -----#
#----------------#
# Prepare data for chosen experiment
print("\nPreparing the data...")
(trainset, testset), config = get_singletask_experiment(
name=args.experiment, data_dir=args.d_dir, verbose=True,
normalize = True if utils.checkattr(args, "normalize") else False,
augment = True if utils.checkattr(args, "augment") else False,
)
# Specify "data-loader" (among others for easy random shuffling and 'batchifying')
train_loader = utils.get_data_loader(trainset, batch_size=args.batch, cuda=cuda, drop_last=True)
# Determine number of iterations / epochs:
iters = args.iters if args.iters else args.epochs*len(train_loader)
epochs = ((args.iters-1) // len(train_loader)) + 1 if args.iters else args.epochs
#-------------------------------------------------------------------------------------------------#
#-----------------#
#----- MODEL -----#
#-----------------#
# Specify model
if (utils.checkattr(args, "pre_convE") or utils.checkattr(args, "pre_convD")) and \
(hasattr(args, "depth") and args.depth>0):
print("\nDefining the model...")
cnn = define.define_classifier(args=args, config=config, device=device)
# Initialize (pre-trained) parameters
cnn = define.init_params(cnn, args)
# - freeze weights of conv-layers?
if utils.checkattr(args, "freeze_convE"):
for param in cnn.convE.parameters():
param.requires_grad = False
cnn.convE.eval() #--> needed to ensure batchnorm-layers also do not change
# - freeze weights of representation-learning layers?
if utils.checkattr(args, "freeze_full"):
for param in cnn.parameters():
param.requires_grad = False
for param in cnn.classifier.parameters():
param.requires_grad = True
# Set optimizer
optim_list = [{'params': filter(lambda p: p.requires_grad, cnn.parameters()), 'lr': args.lr}]
cnn.optimizer = torch.optim.Adam(optim_list, betas=(0.9, 0.999))
#-------------------------------------------------------------------------------------------------#
#---------------------#
#----- REPORTING -----#
#---------------------#
# Get parameter-stamp
print("\nParameter-stamp...")
param_stamp = get_param_stamp(args, cnn.name, verbose=True)
# Print some model-characteristics on the screen
utils.print_model_info(cnn, title="CLASSIFIER")
# Define [progress_dicts] to keep track of performance during training for storing and for later plotting in pdf
precision_dict = evaluate.initiate_precision_dict(n_tasks=1)
# Prepare for plotting in visdom
graph_name = cnn.name
visdom = None if (not args.visdom) else {'env': args.experiment, 'graph': graph_name}
#-------------------------------------------------------------------------------------------------#
#---------------------#
#----- CALLBACKS -----#
#---------------------#
# Determine after how many iterations to evaluate the model
eval_log = args.prec_log if (args.prec_log is not None) else len(train_loader)
# Define callback-functions to evaluate during training
# -loss
loss_cbs = [cb._solver_loss_cb(log=args.loss_log, visdom=visdom, epochs=epochs)]
# -precision
eval_cb = cb._eval_cb(log=eval_log, test_datasets=[testset], visdom=visdom, precision_dict=precision_dict)
# -visualize extracted representation
latent_space_cb = cb._latent_space_cb(log=min(5*eval_log, iters), datasets=[testset], visdom=visdom,
sample_size=400)
#-------------------------------------------------------------------------------------------------#
#--------------------------#
#----- (PRE-)TRAINING -----#
#--------------------------#
# (Pre)train model
print("\nTraining...")
train.train(cnn, train_loader, iters, loss_cbs=loss_cbs, eval_cbs=[eval_cb, latent_space_cb],
save_every=1000 if args.save else None, m_dir=args.m_dir, args=args)
# Save (pre)trained model
if args.save:
# -conv-layers
save_name = cnn.convE.name if (
not hasattr(args, 'convE_stag') or args.convE_stag=="none"
) else "{}-{}".format(cnn.convE.name, args.convE_stag)
utils.save_checkpoint(cnn.convE, args.m_dir, name=save_name)
# -full model
save_name = cnn.name if (
not hasattr(args, 'full_stag') or args.full_stag=="none"
) else "{}-{}".format(cnn.name, args.full_stag)
utils.save_checkpoint(cnn, args.m_dir, name=save_name)
#-------------------------------------------------------------------------------------------------#
#--------------------#
#----- PLOTTING -----#
#--------------------#
# if requested, generate pdf.
if args.pdf:
# -open pdf
plot_name = "{}/{}.pdf".format(args.p_dir, param_stamp)
pp = plt.open_pdf(plot_name)
# -Fig1: show some images
images, _ = next(iter(train_loader)) #--> get a mini-batch of random training images
plt.plot_images_from_tensor(images, pp, title="example input images", config=config)
# -Fig2: precision
figure = plt.plot_lines(precision_dict["all_tasks"], x_axes=precision_dict["x_iteration"],
line_names=['ave precision'], xlabel="Iterations", ylabel="Test accuracy")
pp.savefig(figure)
# -close pdf
pp.close()
# -print name of generated plot on screen
print("\nGenerated plot: {}\n".format(plot_name))
def run(args, verbose=False):
# Create plots- and results-directories if needed
if not os.path.isdir(args.r_dir):
os.mkdir(args.r_dir)
if args.pdf and not os.path.isdir(args.p_dir):
os.mkdir(args.p_dir)
# If only want param-stamp, get it and exit
if args.get_stamp:
from param_stamp import get_param_stamp_from_args
print(get_param_stamp_from_args(args=args))
exit()
# Use cuda?
cuda = torch.cuda.is_available() and args.cuda
device = torch.device("cuda" if cuda else "cpu")
# Report whether cuda is used
if verbose:
print("CUDA is {}used".format("" if cuda else "NOT(!!) "))
# Set random seeds
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if cuda:
torch.cuda.manual_seed(args.seed)
#-------------------------------------------------------------------------------------------------#
#----------------#
#----- DATA -----#
#----------------#
# Prepare data for chosen experiment
if verbose:
print("\nPreparing the data...")
(train_datasets,
test_datasets), config, classes_per_task = get_multitask_experiment(
name=args.experiment,
tasks=args.tasks,
data_dir=args.d_dir,
normalize=True if utils.checkattr(args, "normalize") else False,
augment=True if utils.checkattr(args, "augment") else False,
verbose=verbose,
exception=True if args.seed < 10 else False,
only_test=(not args.train),
max_samples=args.max_samples)
#-------------------------------------------------------------------------------------------------#
#----------------------#
#----- MAIN MODEL -----#
#----------------------#
# Define main model (i.e., classifier, if requested with feedback connections)
if verbose and utils.checkattr(
args, "pre_convE") and (hasattr(args, "depth") and args.depth > 0):
print("\nDefining the model...")
model = define.define_classifier(args=args, config=config, device=device)
# Initialize / use pre-trained / freeze model-parameters
# - initialize (pre-trained) parameters
model = define.init_params(model, args)
# - freeze weights of conv-layers?
if utils.checkattr(args, "freeze_convE"):
for param in model.convE.parameters():
param.requires_grad = False
# Define optimizer (only optimize parameters that "requires_grad")
model.optim_list = [
{
'params': filter(lambda p: p.requires_grad, model.parameters()),
'lr': args.lr
},
]
model.optimizer = optim.Adam(model.optim_list, betas=(0.9, 0.999))
#-------------------------------------------------------------------------------------------------#
#----------------------------------#
#----- CL-STRATEGY: EXEMPLARS -----#
#----------------------------------#
# Store in model whether, how many and in what way to store exemplars
if isinstance(model, ExemplarHandler) and (args.use_exemplars
or args.replay == "exemplars"):
model.memory_budget = args.budget
model.herding = args.herding
model.norm_exemplars = args.herding
#-------------------------------------------------------------------------------------------------#
#----------------------------------------------------#
#----- CL-STRATEGY: REGULARIZATION / ALLOCATION -----#
#----------------------------------------------------#
# Elastic Weight Consolidation (EWC)
if isinstance(model, ContinualLearner) and utils.checkattr(args, 'ewc'):
model.ewc_lambda = args.ewc_lambda if args.ewc else 0
model.fisher_n = args.fisher_n
model.online = utils.checkattr(args, 'online')
if model.online:
model.gamma = args.gamma
# Synpatic Intelligence (SI)
if isinstance(model, ContinualLearner) and utils.checkattr(args, 'si'):
model.si_c = args.si_c if args.si else 0
model.epsilon = args.epsilon
# XdG: create for every task a "mask" for each hidden fully connected layer
if isinstance(model, ContinualLearner) and utils.checkattr(
args, 'xdg') and args.xdg_prop > 0:
model.define_XdGmask(gating_prop=args.xdg_prop, n_tasks=args.tasks)
#-------------------------------------------------------------------------------------------------#
#-------------------------------#
#----- CL-STRATEGY: REPLAY -----#
#-------------------------------#
# Use distillation loss (i.e., soft targets) for replayed data? (and set temperature)
if isinstance(model, ContinualLearner) and hasattr(
args, 'replay') and not args.replay == "none":
model.replay_targets = "soft" if args.distill else "hard"
model.KD_temp = args.temp
#-------------------------------------------------------------------------------------------------#
#---------------------#
#----- REPORTING -----#
#---------------------#
# Get parameter-stamp (and print on screen)
if verbose:
print("\nParameter-stamp...")
param_stamp, reinit_param_stamp = get_param_stamp(
args,
model.name,
verbose=verbose,
replay=True if
(hasattr(args, 'replay') and not args.replay == "none") else False,
)
# Print some model-characteristics on the screen
if verbose:
# -main model
utils.print_model_info(model, title="MAIN MODEL")
# Prepare for keeping track of statistics required for metrics (also used for plotting in pdf)
if args.pdf or args.metrics:
# -define [metrics_dict] to keep track of performance during training for storing & for later plotting in pdf
metrics_dict = evaluate.initiate_metrics_dict(n_tasks=args.tasks)
# -evaluate randomly initiated model on all tasks & store accuracies in [metrics_dict] (for calculating metrics)
if not args.use_exemplars:
metrics_dict = evaluate.intial_accuracy(
model,
test_datasets,
metrics_dict,
no_task_mask=False,
classes_per_task=classes_per_task,
test_size=None)
else:
metrics_dict = None
# Prepare for plotting in visdom
visdom = None
if args.visdom:
env_name = "{exp}-{tasks}".format(exp=args.experiment,
tasks=args.tasks)
replay_statement = "{mode}{b}".format(
mode=args.replay,
b="" if
(args.batch_replay is None or args.batch_replay == args.batch) else
"-br{}".format(args.batch_replay),
) if (hasattr(args, "replay") and not args.replay == "none") else "NR"
graph_name = "{replay}{syn}{ewc}{xdg}".format(
replay=replay_statement,
syn="-si{}".format(args.si_c)
if utils.checkattr(args, 'si') else "",
ewc="-ewc{}{}".format(
args.ewc_lambda, "-O{}".format(args.gamma)
if utils.checkattr(args, "online") else "") if utils.checkattr(
args, 'ewc') else "",
xdg="" if (not utils.checkattr(args, 'xdg')) or args.xdg_prop == 0
else "-XdG{}".format(args.xdg_prop),
)
visdom = {'env': env_name, 'graph': graph_name}
#-------------------------------------------------------------------------------------------------#
#---------------------#
#----- CALLBACKS -----#
#---------------------#
# Callbacks for reporting on and visualizing loss
solver_loss_cbs = [
cb._solver_loss_cb(log=args.loss_log,
visdom=visdom,
model=model,
iters_per_task=args.iters,
tasks=args.tasks,
replay=(hasattr(args, "replay")
and not args.replay == "none"))
]
# Callbacks for reporting and visualizing accuracy
# -visdom (i.e., after each [prec_log]
eval_cbs = [
cb._eval_cb(log=args.prec_log,
test_datasets=test_datasets,
visdom=visdom,
iters_per_task=args.iters,
test_size=args.prec_n,
classes_per_task=classes_per_task,
with_exemplars=False)
] if (not args.use_exemplars) else [None]
#--> during training on a task, evaluation cannot be with exemplars as those are only selected after training
# (instead, evaluation for visdom is only done after each task, by including callback-function into [metric_cbs])
# Callbacks for calculating statists required for metrics
# -pdf / reporting: summary plots (i.e, only after each task) (when using exemplars, also for visdom)
metric_cbs = [
cb._metric_cb(log=args.iters,
test_datasets=test_datasets,
classes_per_task=classes_per_task,
metrics_dict=metrics_dict,
iters_per_task=args.iters,
with_exemplars=args.use_exemplars),
cb._eval_cb(log=args.iters,
test_datasets=test_datasets,
visdom=visdom,
iters_per_task=args.iters,
test_size=args.prec_n,
classes_per_task=classes_per_task,
with_exemplars=True) if args.use_exemplars else None
]
#-------------------------------------------------------------------------------------------------#
#--------------------#
#----- TRAINING -----#
#--------------------#
if args.train:
if verbose:
print("\nTraining...")
# Train model
train_cl(
model,
train_datasets,
replay_mode=args.replay if hasattr(args, 'replay') else "none",
classes_per_task=classes_per_task,
iters=args.iters,
args=args,
batch_size=args.batch,
batch_size_replay=args.batch_replay if hasattr(
args, 'batch_replay') else None,
eval_cbs=eval_cbs,
loss_cbs=solver_loss_cbs,
reinit=utils.checkattr(args, 'reinit'),
only_last=utils.checkattr(args, 'only_last'),
metric_cbs=metric_cbs,
use_exemplars=args.use_exemplars,
)
# Save trained model(s), if requested
if args.save:
save_name = "mM-{}".format(param_stamp) if (
not hasattr(args, 'full_stag')
or args.full_stag == "none") else "{}-{}".format(
model.name, args.full_stag)
utils.save_checkpoint(model,
args.m_dir,
name=save_name,
verbose=verbose)
else:
# Load previously trained model(s) (if goal is to only evaluate previously trained model)
if verbose:
print("\nLoading parameters of the previously trained models...")
load_name = "mM-{}".format(param_stamp) if (
not hasattr(args, 'full_ltag')
or args.full_ltag == "none") else "{}-{}".format(
model.name, args.full_ltag)
utils.load_checkpoint(
model,
args.m_dir,
name=load_name,
verbose=verbose,
add_si_buffers=(isinstance(model, ContinualLearner)
and utils.checkattr(args, 'si')))
# Load previously created metrics-dict
file_name = "{}/dict-{}".format(args.r_dir, param_stamp)
metrics_dict = utils.load_object(file_name)
#-------------------------------------------------------------------------------------------------#
#-----------------------------------#
#----- EVALUATION of CLASSIFIER-----#
#-----------------------------------#
if verbose:
print("\n\nEVALUATION RESULTS:")
# Evaluate precision of final model on full test-set
precs = [
evaluate.validate(model,
test_datasets[i],
verbose=False,
test_size=None,
task=i + 1,
with_exemplars=False,
allowed_classes=list(
range(classes_per_task * i,
classes_per_task * (i + 1))))
for i in range(args.tasks)
]
average_precs = sum(precs) / args.tasks
# -print on screen
if verbose:
print("\n Precision on test-set{}:".format(
" (softmax classification)" if args.use_exemplars else ""))
for i in range(args.tasks):
print(" - Task {}: {:.4f}".format(i + 1, precs[i]))
print('=> Average precision over all {} tasks: {:.4f}\n'.format(
args.tasks, average_precs))
# -with exemplars
if args.use_exemplars:
precs = [
evaluate.validate(model,
test_datasets[i],
verbose=False,
test_size=None,
task=i + 1,
with_exemplars=True,
allowed_classes=list(
range(classes_per_task * i,
classes_per_task * (i + 1))))
for i in range(args.tasks)
]
average_precs_ex = sum(precs) / args.tasks
# -print on screen
if verbose:
print(" Precision on test-set (classification using exemplars):")
for i in range(args.tasks):
print(" - Task {}: {:.4f}".format(i + 1, precs[i]))
print('=> Average precision over all {} tasks: {:.4f}\n'.format(
args.tasks, average_precs_ex))
# If requested, compute metrics
if args.metrics:
# Load accuracy matrix of "reinit"-experiment (i.e., each task's accuracy when only trained on that task)
if not utils.checkattr(args, 'reinit'):
file_name = "{}/dict-{}".format(args.r_dir, reinit_param_stamp)
if not os.path.isfile("{}.pkl".format(file_name)):
raise FileNotFoundError(
"Need to run the correct 'reinit' experiment (with --metrics) first!!"
)
reinit_metrics_dict = utils.load_object(file_name)
# Accuracy matrix
R = pd.DataFrame(
data=metrics_dict['acc per task'],
index=['after task {}'.format(i + 1) for i in range(args.tasks)])
R = R[["task {}".format(task_id + 1) for task_id in range(args.tasks)]]
R.loc['at start'] = metrics_dict['initial acc per task'] if (
not args.use_exemplars) else ['NA' for _ in range(args.tasks)]
if not utils.checkattr(args, 'reinit'):
R.loc['only trained on itself'] = [
reinit_metrics_dict['acc per task']['task {}'.format(
task_id + 1)][task_id] for task_id in range(args.tasks)
]
R = R.reindex(
['at start'] +
['after task {}'.format(i + 1)
for i in range(args.tasks)] + ['only trained on itself'])
BWTs = [(R.loc['after task {}'.format(args.tasks), 'task {}'.format(i + 1)] - \
R.loc['after task {}'.format(i + 1), 'task {}'.format(i + 1)]) for i in range(args.tasks - 1)]
FWTs = [
0. if args.use_exemplars else
(R.loc['after task {}'.format(i + 1), 'task {}'.format(i + 2)] -
R.loc['at start', 'task {}'.format(i + 2)])
for i in range(args.tasks - 1)
]
forgetting = []
for i in range(args.tasks - 1):
forgetting.append(
max(R.iloc[1:args.tasks, i]) - R.iloc[args.tasks, i])
R.loc['FWT (per task)'] = ['NA'] + FWTs
R.loc['BWT (per task)'] = BWTs + ['NA']
R.loc['F (per task)'] = forgetting + ['NA']
BWT = sum(BWTs) / (args.tasks - 1)
F = sum(forgetting) / (args.tasks - 1)
FWT = sum(FWTs) / (args.tasks - 1)
metrics_dict['BWT'] = BWT
metrics_dict['F'] = F
metrics_dict['FWT'] = FWT
# -Vogelstein et al's measures of transfer efficiency
if not utils.checkattr(args, 'reinit'):
TEs = [((1 - R.loc['only trained on itself',
'task {}'.format(task_id + 1)]) /
(1 - R.loc['after task {}'.format(args.tasks),
'task {}'.format(task_id + 1)]))
for task_id in range(args.tasks)]
BTEs = [((1 - R.loc['after task {}'.format(task_id + 1),
'task {}'.format(task_id + 1)]) /
(1 - R.loc['after task {}'.format(args.tasks),
'task {}'.format(task_id + 1)]))
for task_id in range(args.tasks)]
FTEs = [((1 - R.loc['only trained on itself',
'task {}'.format(task_id + 1)]) /
(1 - R.loc['after task {}'.format(task_id + 1),
'task {}'.format(task_id + 1)]))
for task_id in range(args.tasks)]
# -TEs and BTEs after each task
TEs_all = []
BTEs_all = []
for after_task_id in range(args.tasks):
TEs_all.append([
((1 - R.loc['only trained on itself',
'task {}'.format(task_id + 1)]) /
(1 - R.loc['after task {}'.format(after_task_id + 1),
'task {}'.format(task_id + 1)]))
for task_id in range(after_task_id + 1)
])
BTEs_all.append([
((1 - R.loc['after task {}'.format(task_id + 1),
'task {}'.format(task_id + 1)]) /
(1 - R.loc['after task {}'.format(after_task_id + 1),
'task {}'.format(task_id + 1)]))
for task_id in range(after_task_id + 1)
])
R.loc['TEs (per task, after all 10 tasks)'] = TEs
for after_task_id in range(args.tasks):
R.loc['TEs (per task, after {} tasks)'.format(
after_task_id +
1)] = TEs_all[after_task_id] + ['NA'] * (args.tasks -
after_task_id - 1)
R.loc['BTEs (per task, after all 10 tasks)'] = BTEs
for after_task_id in range(args.tasks):
R.loc['BTEs (per task, after {} tasks)'.format(
after_task_id + 1)] = BTEs_all[after_task_id] + ['NA'] * (
args.tasks - after_task_id - 1)
R.loc['FTEs (per task)'] = FTEs
metrics_dict['R'] = R
# -print on screen
if verbose:
print("Accuracy matrix")
print(R)
print("\nFWT = {:.4f}".format(FWT))
print("BWT = {:.4f}".format(BWT))
print(" F = {:.4f}\n\n".format(F))
#-------------------------------------------------------------------------------------------------#
#------------------#
#----- OUTPUT -----#
#------------------#
# Average precision on full test set
output_file = open("{}/prec-{}.txt".format(args.r_dir, param_stamp), 'w')
output_file.write('{}\n'.format(
average_precs_ex if args.use_exemplars else average_precs))
output_file.close()
# -metrics-dict
if args.metrics:
file_name = "{}/dict-{}".format(args.r_dir, param_stamp)
utils.save_object(metrics_dict, file_name)
#-------------------------------------------------------------------------------------------------#
#--------------------#
#----- PLOTTING -----#
#--------------------#
# If requested, generate pdf
if args.pdf:
# -open pdf
plot_name = "{}/{}.pdf".format(args.p_dir, param_stamp)
pp = evaluate.visual.plt.open_pdf(plot_name)
# -plot TEs
if not utils.checkattr(args, 'reinit'):
BTEs = []
for task_id in range(args.tasks):
BTEs.append([
R.loc['BTEs (per task, after {} tasks)'.
format(after_task_id + 1),
'task {}'.format(task_id + 1)]
for after_task_id in range(task_id, args.tasks)
])
figure = visual_plt.plot_TEs([FTEs], [BTEs], [TEs], ["test"])
pp.savefig(figure)
# -show metrics reflecting progression during training
if args.train and (not utils.checkattr(args, 'only_last')):
# -create list to store all figures to be plotted.
figure_list = []
# -generate all figures (and store them in [figure_list])
key = "acc per task"
plot_list = []
for i in range(args.tasks):
plot_list.append(metrics_dict[key]["task {}".format(i + 1)])
figure = visual_plt.plot_lines(plot_list,
x_axes=metrics_dict["x_task"],
line_names=[
'task {}'.format(i + 1)
for i in range(args.tasks)
])
figure_list.append(figure)
figure = visual_plt.plot_lines(
[metrics_dict["average"]],
x_axes=metrics_dict["x_task"],
line_names=['average all tasks so far'])
figure_list.append(figure)
# -add figures to pdf
for figure in figure_list:
pp.savefig(figure)
# -close pdf
pp.close()
# -print name of generated plot on screen
if verbose:
print("\nGenerated plot: {}\n".format(plot_name))