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
|
from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
def calculate_ap(predictions, labels):
N, D = predictions.shape
ap = np.zeros(D)
num_range = np.arange((N), dtype=np.float32) + 1
for k in range(D):
scores = predictions[:N, k]
label = labels[:N, k]
sortind = np.argsort(-scores, kind='mergesort')
truth = label[sortind]
precision = np.cumsum(truth) / num_range
ap[k] = precision[truth.astype(np.bool)].sum() / max(1, truth.sum())
return ap
class TestAPMeterOps(hu.HypothesisTestCase):
@given(predictions=hu.arrays(dims=[10, 3],
elements=hu.floats(allow_nan=False,
allow_infinity=False,
min_value=0.1,
max_value=1)),
labels=hu.arrays(dims=[10, 3],
dtype=np.int32,
elements=st.integers(min_value=0,
max_value=1)),
**hu.gcs_cpu_only)
def test_average_precision(self, predictions, labels, gc, dc):
op = core.CreateOperator(
"APMeter",
["predictions", "labels"],
["AP"],
buffer_size=10,
)
def op_ref(predictions, labels):
ap = calculate_ap(predictions, labels)
return (ap, )
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[predictions, labels],
reference=op_ref)
@given(predictions=hu.arrays(dims=[10, 3],
elements=hu.floats(allow_nan=False,
allow_infinity=False,
min_value=0.1,
max_value=1)),
labels=hu.arrays(dims=[10, 3],
dtype=np.int32,
elements=st.integers(min_value=0,
max_value=1)),
**hu.gcs_cpu_only)
def test_average_precision_small_buffer(self, predictions, labels, gc, dc):
op_small_buffer = core.CreateOperator(
"APMeter",
["predictions", "labels"],
["AP"],
buffer_size=5,
)
def op_ref(predictions, labels):
# We can only hold the last 5 in the buffer
ap = calculate_ap(predictions[5:], labels[5:])
return (ap, )
self.assertReferenceChecks(
device_option=gc,
op=op_small_buffer,
inputs=[predictions, labels],
reference=op_ref
)
|
