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
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
|
import unittest
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, workspace
from hypothesis import given, settings
class TestSelfBinningHistogramBase(object):
def __init__(self, bin_spacing, dtype, abs=False):
self.bin_spacing = bin_spacing
self.dtype = dtype
self.abs = abs
def _check_histogram(self, arrays, num_bins, expected_values=None, expected_counts=None):
# Check that sizes match and counts add up.
values = workspace.FetchBlob("histogram_values")
counts = workspace.FetchBlob("histogram_counts")
self.assertTrue(np.size(values) == num_bins)
self.assertTrue(np.size(counts) == num_bins)
self.assertTrue(np.sum(counts) == sum([np.size(array) for array in arrays]))
# Check counts
if expected_counts is None:
# Check that counts are correct for the returned values if expected_counts is not given.
expected_counts = np.zeros(num_bins, dtype='i')
for array in arrays:
for input_val in array:
input_val = abs(input_val) if self.abs else input_val
found = False
for pos in range(np.size(values)):
if values[pos] > input_val:
found = True
break
self.assertTrue(found, f"input value must fit inside values array: "
f"input={input_val}, last_value={values[-1]}")
if self.bin_spacing == "linear":
self.assertTrue(pos > 0,
f"input should not be smaller than the first bin value: "
f"input={input_val}, 1st bin value={values[pos]}")
if pos == 0:
self.assertEqual(self.bin_spacing, "logarithmic")
expected_counts[pos] += 1
else:
expected_counts[pos - 1] += 1
self.assertTrue(np.array_equal(expected_counts, counts), f"expected:{expected_counts}\ncounts:{counts}")
# Check values
if expected_values is not None:
self.assertTrue(np.allclose(expected_values, values, rtol=1e-02, atol=1e-05),
f"expected:{expected_values}\nvalues:{values}")
# Ideally, the output values are sorted in a non-decreasing order.
for idx in range(len(values) - 1):
self.assertTrue(values[idx] <= values[idx + 1])
if self.abs:
self.assertTrue(values[0] >= 0)
def _run_single_op_net(self, arrays, num_bins, logspacing_start=None):
for i in range(len(arrays)):
workspace.FeedBlob(
"X{}".format(i), arrays[i]
)
net = core.Net("test_net")
if logspacing_start is not None:
net.SelfBinningHistogram(
["X{}".format(i) for i in range(len(arrays))],
["histogram_values", "histogram_counts"],
num_bins=num_bins,
bin_spacing=self.bin_spacing,
logspacing_start=logspacing_start,
abs=self.abs
)
else:
net.SelfBinningHistogram(
["X{}".format(i) for i in range(len(arrays))],
["histogram_values", "histogram_counts"],
num_bins=num_bins,
bin_spacing=self.bin_spacing,
abs=self.abs
)
workspace.RunNetOnce(net)
@given(rows=st.integers(1, 1000), cols=st.integers(1, 1000), **hu.gcs_cpu_only)
@settings(deadline=10000)
def test_histogram_device_consistency(self, rows, cols, gc, dc):
X = np.random.rand(rows, cols)
op = core.CreateOperator(
"SelfBinningHistogram",
["X"],
["histogram_values", "histogram_counts"],
num_bins=1000,
bin_spacing=self.bin_spacing,
)
self.assertDeviceChecks(dc, op, [X], [0])
def test_histogram_bin_to_fewer(self):
X = np.array([-2.0, -2.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 9.0], dtype=self.dtype)
if self.bin_spacing == 'linear':
if not self.abs:
expected_values = [-2., 0.2, 2.4, 4.6, 6.8, 9.]
expected_counts = [5, 2, 2, 1, 1, 0]
else:
expected_values = [0., 1.8, 3.6, 5.4, 7.2, 9.]
expected_counts = [4, 4, 1, 1, 1, 0]
else:
expected_values = [1.e-24, 9.8e-20, 9.6e-15, 9.4e-10, 9.2e-05, 9.]
if not self.abs:
expected_counts = [5, 0, 0, 0, 6, 0]
else:
expected_counts = [3, 0, 0, 0, 8, 0]
self._run_single_op_net([X], 5)
self._check_histogram(
[X],
6,
expected_values=expected_values,
expected_counts=expected_counts
)
def test_histogram_bin_to_more(self):
X = np.array([-2.0, -2.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 9.0], dtype=self.dtype)
self._run_single_op_net([X], 100)
self._check_histogram(
[X],
101,
)
def test_histogram_bin_to_two(self):
"""This test roughly tests [min,max+EPSILON] and [N,0]"""
X = np.array([-2.0, -2.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 9.0], dtype=self.dtype)
if self.bin_spacing == 'linear':
if not self.abs:
expected_values = [-2., 9.]
else:
expected_values = [0., 9.]
else:
expected_values = [1.e-24, 9.]
expected_counts = [11, 0]
self._run_single_op_net([X], 1)
self._check_histogram(
[X],
2,
expected_values=expected_values,
expected_counts=expected_counts
)
def test_histogram_min_max_equal(self):
"""This test uses exact value match, so is only relevant for float type."""
X = np.array([0., 0., 0., 0., 0.], dtype='f')
logspacing_start = np.float(1e-24)
self._run_single_op_net([X], 3, logspacing_start)
if self.bin_spacing == "linear":
self._check_histogram(
[X],
4,
expected_values=np.array([0., 0., 0., 0.], dtype='f'),
expected_counts=[5, 0, 0, 0]
)
else:
self.assertEqual(self.bin_spacing, "logarithmic")
self._check_histogram(
[X],
4,
expected_values=np.array([logspacing_start] * 4, dtype='f'),
expected_counts=[5, 0, 0, 0],
)
def test_histogram_min_max_equal_nonzero(self):
X = np.array([1., 1., 1., 1., 1.], dtype=self.dtype)
logspacing_start = 1e-24
self._run_single_op_net([X], 3, logspacing_start)
self._check_histogram(
[X],
4,
expected_values=[1., 1., 1., 1.],
expected_counts=[5, 0, 0, 0]
)
def test_histogram_empty_input_tensor(self):
X = np.array([], dtype=self.dtype)
self._run_single_op_net([X], 1)
self._check_histogram(
[X],
2,
expected_values=[0., 0.],
expected_counts=[0, 0]
)
self._run_single_op_net([X], 10)
self._check_histogram(
[X],
11,
expected_values=[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
expected_counts=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
)
def test_histogram_multi_input(self):
X1 = np.array([-2.0, -2.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 9.0], dtype=self.dtype)
X2 = np.array([-5.0, -3.0, 7, 7, 0.0, 1.0, 2.0, -3.0, 4.0, 6.0, 9.0], dtype=self.dtype)
if self.bin_spacing == 'linear':
if not self.abs:
expected_values = [-5., -2.2, 0.6, 3.4, 6.2, 9.]
expected_counts = [3, 6, 5, 4, 4, 0]
else:
expected_values = [0., 1.8, 3.6, 5.4, 7.2, 9.]
expected_counts = [6, 7, 3, 4, 2, 0]
else:
expected_values = [1.e-24, 9.8e-20, 9.6e-15, 9.4e-10, 9.2e-05, 9.]
if not self.abs:
expected_counts = [9, 0, 0, 0, 13, 0]
else:
expected_counts = [4, 0, 0, 0, 18, 0]
self._run_single_op_net([X1, X2], 5)
self._check_histogram(
[X1, X2],
6,
expected_values=expected_values,
expected_counts=expected_counts
)
def test_histogram_very_small_range_for_stride_underflow(self):
"""Tests a large number of bins for a very small range of values.
This test uses float type. 1-e302 is very small, and with 1M bins, it
causes numeric underflow. This test is to show that this is handled.
Note: this test was flaky due to how compiler and OS handls floats.
Previously, 1-e38 does not induce overflow and cuases test error for some
combinations of compiler and OS. Now 1-e302 should be small enough.
"""
X = np.array([0, 1e-302], dtype='f')
large_bin_number = 1000000
self._run_single_op_net([X], large_bin_number)
self._check_histogram(
[X],
large_bin_number + 1,
expected_counts=[2] + [0] * large_bin_number # [2, 0, 0, ..., 0]
)
def test_histogram_insufficient_bins(self):
with self.assertRaisesRegex(
RuntimeError, "Number of bins must be greater than or equal to 1."
):
self._run_single_op_net([np.random.rand(111)], 0)
class TestSelfBinningHistogramLinear(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
def __init__(self, *args, **kwargs):
TestSelfBinningHistogramBase.__init__(self, bin_spacing="linear", dtype='d')
hu.HypothesisTestCase.__init__(self, *args, **kwargs)
class TestSelfBinningHistogramLogarithmic(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
def __init__(self, *args, **kwargs):
TestSelfBinningHistogramBase.__init__(self, bin_spacing="logarithmic", dtype='d')
hu.HypothesisTestCase.__init__(self, *args, **kwargs)
class TestSelfBinningHistogramLinearFloat(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
def __init__(self, *args, **kwargs):
TestSelfBinningHistogramBase.__init__(self, bin_spacing="linear", dtype='f')
hu.HypothesisTestCase.__init__(self, *args, **kwargs)
class TestSelfBinningHistogramLogarithmicFloat(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
def __init__(self, *args, **kwargs):
TestSelfBinningHistogramBase.__init__(self, bin_spacing="logarithmic", dtype='f')
hu.HypothesisTestCase.__init__(self, *args, **kwargs)
class TestSelfBinningHistogramLinearWithAbs(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
def __init__(self, *args, **kwargs):
TestSelfBinningHistogramBase.__init__(self, bin_spacing="linear", dtype='d', abs=True)
hu.HypothesisTestCase.__init__(self, *args, **kwargs)
class TestSelfBinningHistogramLogarithmicWithAbs(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
def __init__(self, *args, **kwargs):
TestSelfBinningHistogramBase.__init__(self, bin_spacing="logarithmic", dtype='d', abs=True)
hu.HypothesisTestCase.__init__(self, *args, **kwargs)
class TestSelfBinningHistogramLinearFloatWithAbs(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
def __init__(self, *args, **kwargs):
TestSelfBinningHistogramBase.__init__(self, bin_spacing="linear", dtype='f', abs=True)
hu.HypothesisTestCase.__init__(self, *args, **kwargs)
class TestSelfBinningHistogramLogarithmicFloatWithAbs(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
def __init__(self, *args, **kwargs):
TestSelfBinningHistogramBase.__init__(self, bin_spacing="logarithmic", dtype='f', abs=True)
hu.HypothesisTestCase.__init__(self, *args, **kwargs)
class TestSelfBinningHistogramLinearWithNoneAbs(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
def __init__(self, *args, **kwargs):
TestSelfBinningHistogramBase.__init__(self, bin_spacing="linear", dtype='d', abs=None)
hu.HypothesisTestCase.__init__(self, *args, **kwargs)
class TestSelfBinningHistogramLinearFloatWithNoneAbs(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
def __init__(self, *args, **kwargs):
TestSelfBinningHistogramBase.__init__(self, bin_spacing="linear", dtype='f', abs=None)
hu.HypothesisTestCase.__init__(self, *args, **kwargs)
if __name__ == "__main__":
global_options = ["caffe2"]
core.GlobalInit(global_options)
unittest.main()
|
