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from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import numpy as np
class TestPercentileOp(hu.HypothesisTestCase):
def _test_percentile_op(
self,
original_inp,
value_to_pct_map,
dist_lengths,
expected_values
):
op = core.CreateOperator(
'Percentile',
['original_values', 'value_to_pct_map', 'dist_lengths'],
['percentile_values']
)
workspace.FeedBlob('original_values', np.array(
original_inp, dtype=np.float32))
workspace.FeedBlob(
'value_to_pct_map', np.array(value_to_pct_map, dtype=np.float32))
workspace.FeedBlob('dist_lengths', np.array(
dist_lengths, dtype=np.int32))
workspace.RunOperatorOnce(op)
np.testing.assert_array_almost_equal(
workspace.FetchBlob('percentile_values'),
np.array(expected_values),
decimal=5
)
self._test_shape_inference(
original_inp,
value_to_pct_map,
dist_lengths,
expected_values
)
def _test_shape_inference(
self,
original_inp,
value_to_pct_map,
dist_lengths,
expected_values
):
net = core.Net('test_shape_inference')
result = net.Percentile(
['original_values', 'value_to_pct_map', 'dist_lengths'],
['percentile_values']
)
workspace.FeedBlob('original_values', np.array(
original_inp, dtype=np.float32))
workspace.FeedBlob(
'value_to_pct_map', np.array(value_to_pct_map, dtype=np.float32))
workspace.FeedBlob('dist_lengths', np.array(
dist_lengths, dtype=np.int32))
(shapes, types) = workspace.InferShapesAndTypes([net])
workspace.RunNetOnce(net)
self.assertEqual(shapes[result], list(workspace.blobs[result].shape))
self.assertEqual(shapes[result], list(workspace.blobs['original_values'].shape))
self.assertEqual(types[result], core.DataType.FLOAT)
def test_percentile_op_with_only_one_dist(self):
self._test_percentile_op(
original_inp=[[5]],
value_to_pct_map=[[5, 0.4]],
dist_lengths=[1],
expected_values=[[0.4]]
)
def test_percentile_op_with_all_elements_in_map(self):
self._test_percentile_op(
original_inp=[[3, 4], [10, 4]],
value_to_pct_map=[[3, 0.3], [4, 0.6],
[10, 0.8], [4, 0.5], [5, 0.6]],
dist_lengths=[3, 2],
expected_values=[[0.3, 0.5], [0.8, 0.5]],
)
def test_percentile_op_with_same_value(self):
self._test_percentile_op(
original_inp=[[1, 1], [1, 2]],
value_to_pct_map=[[1, 0.1], [4, 0.4], [2, 0.5]],
dist_lengths=[2, 1],
expected_values=[[0.1, 0.0], [0.1, 0.5]]
)
def test_percentile_op_with_elements_bigger_than_map_range(self):
self._test_percentile_op(
original_inp=[[1, 5], [3, 4]],
value_to_pct_map=[[1, 0.1], [4, 0.4], [2, 0.1], [3, 0.3]],
dist_lengths=[2, 2],
expected_values=[[0.1, 1.], [0.3, 1.0]]
)
def test_percentile_op_with_elements_smaller_than_map_range(self):
self._test_percentile_op(
original_inp=[[1], [5], [6]],
value_to_pct_map=[[2, 0.2], [5, 0.5], [7, 0.5]],
dist_lengths=[3],
expected_values=[[0.0], [0.5], [0.5]]
)
def test_percentile_op_with_interpolation(self):
self._test_percentile_op(
original_inp=[[3, 2, 5], [6, 7, 8]],
value_to_pct_map=[[1, 0.1], [4, 0.7], [4.5, 0.8],
[6, 0.5], [8, 0.9],
[8, 0.6]],
dist_lengths=[3, 2, 1],
expected_values=[[0.5, 0.0, 0.0], [1.0, 0.7, 0.6]]
)
def test_percentile_op_with_large_sample_size_per_dist(self):
self._test_percentile_op(
original_inp=[[3, 1], [5, 7]],
value_to_pct_map=[[3, 0.5], [4, 0.6], [5, 0.7],
[1, 0.2], [2, 0.3], [5, 0.8]],
dist_lengths=[3, 3],
expected_values=[[0.5, 0.2], [0.7, 1.0]]
)
if __name__ == "__main__":
import unittest
unittest.main()
|
