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evaluate.py
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evaluate.py
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import torch
import utils
####-----------------------------####
####----model evaluation----####
####-----------------------------####
def validate(model, dataset, batch_size=32, test_size=1024, verbose=True, allowed_classes=None,
with_exemplars=False, task=None):
'''Evaluate precision (= accuracy or proportion correct) of a classifier ([model]) on [dataset].
[allowed_classes] None or <list> containing all "active classes" between which should be chosen
(these "active classes" are assumed to be contiguous)'''
# Set model to eval()-mode
mode = model.training
model.eval()
# Loop over batches in [dataset]
data_loader = utils.get_data_loader(dataset, batch_size, cuda=model._is_on_cuda())
total_tested = total_correct = 0
for data, labels in data_loader:
# -break on [test_size] (if "None", full dataset is used)
if test_size:
if total_tested >= test_size:
break
# -evaluate model (if requested, only on [allowed_classes])
data, labels = data.to(model._device()), labels.to(model._device())
# labels = labels - allowed_classes[0] if (allowed_classes is not None) else labels
with torch.no_grad():
if with_exemplars:
predicted = model.classify_with_exemplars(data, allowed_classes=allowed_classes)
# - in case of Domain-IL scenario, collapse all corresponding domains into same class
if max(predicted).item() >= model.classes:
predicted = predicted % model.classes
else:
scores = model(data) if (allowed_classes is None) else model(data)[:, allowed_classes]
_, predicted = torch.max(scores, 1)
# -update statistics
total_correct += (predicted == labels).sum().item()
total_tested += len(data)
precision = total_correct / total_tested
# Set model back to its initial mode, print result on screen (if requested) and return it
model.train(mode=mode)
if verbose:
print('=> precision: {:.3f}'.format(precision))
return precision
def validate5(model, dataset, batch_size=32, test_size=1024, verbose=True, allowed_classes=None,
with_exemplars=False, task=None):
'''Evaluate precision (= accuracy or proportion correct) of a classifier ([model]) on [dataset].
[allowed_classes] None or <list> containing all "active classes" between which should be chosen
(these "active classes" are assumed to be contiguous)'''
# Set model to eval()-mode
mode = model.training
model.eval()
# Loop over batches in [dataset]
data_loader = utils.get_data_loader(dataset, batch_size, cuda=model._is_on_cuda())
total_tested = total_correct = 0
for data, labels in data_loader:
# -break on [test_size] (if "None", full dataset is used)
if test_size:
if total_tested >= test_size:
break
# -evaluate model (if requested, only on [allowed_classes])
data, labels = data.to(model._device()), labels.to(model._device())
# labels = labels - allowed_classes[0] if (allowed_classes is not None) else labels
with torch.no_grad():
if with_exemplars:
predicted = model.classify_with_exemplars(data, allowed_classes=allowed_classes)
# - in case of Domain-IL scenario, collapse all corresponding domains into same class
if max(predicted).item() >= model.classes:
predicted = predicted % model.classes
else:
scores = model(data) if (allowed_classes is None) else model(data)[:, allowed_classes]
_, predicted = scores.topk(1, -1)
# -update statistics
for i in range(5):
total_correct += (predicted[:, i] == labels).sum().item()
total_tested += len(data)
precision = total_correct / total_tested
# Set model back to its initial mode, print result on screen (if requested) and return it
model.train(mode=mode)
if verbose:
print('=> precision: {:.3f}'.format(precision))
return precision
def initiate_precision_dict(n_tasks):
'''Initiate <dict> with all precision-measures to keep track of.'''
precision = {}
precision["all_tasks"] = [[] for _ in range(n_tasks)]
precision["average"] = []
precision["x_iteration"] = []
precision["x_task"] = []
return precision
def precision(model, datasets, current_task, iteration, classes_per_task=None,
precision_dict=None, test_size=None, verbose=False, summary_graph=True,
with_exemplars=False):
'''Evaluate precision of a classifier (=[model]) on all tasks so far (= up to [current_task]) using [datasets].
[precision_dict] None or <dict> of all measures to keep track of, to which results will be appended to
[classes_per_task] <int> number of active classes er task'''
# Evaluate accuracy of model predictions for all tasks so far (reporting "0" for future tasks)
n_tasks = len(datasets)
precs = []
for i in range(n_tasks):
if i + 1 <= current_task:
allowed_classes = None
precs.append(validate(model, datasets[i], test_size=test_size, verbose=verbose,
allowed_classes=allowed_classes, with_exemplars=with_exemplars,
task=i + 1))
else:
precs.append(0)
average_precs = sum([precs[task_id] for task_id in range(current_task)]) / current_task
# Print results on screen
if verbose:
print(' => ave precision: {:.3f}'.format(average_precs))
names = ['task {}'.format(i + 1) for i in range(n_tasks)]
# Append results to [progress]-dictionary and return
if precision_dict is not None:
for task_id, _ in enumerate(names):
precision_dict["all_tasks"][task_id].append(precs[task_id])
precision_dict["average"].append(average_precs)
precision_dict["x_iteration"].append(iteration)
precision_dict["x_task"].append(current_task)
return precision_dict