-
Notifications
You must be signed in to change notification settings - Fork 7
/
Copy pathevaluate.py
139 lines (118 loc) · 6.07 KB
/
evaluate.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
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