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https://github.com/saymrwulf/pytorch.git
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9945fd7253
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49980 From ``` ./python/libcst/libcst codemod remove_unused_imports.RemoveUnusedImportsWithGlean --no-format caffe2/ ``` Test Plan: Standard sandcastle tests Reviewed By: xush6528 Differential Revision: D25727359 fbshipit-source-id: c4f60005b10546423dc093d31d46deb418352286
130 lines
4.3 KiB
Python
130 lines
4.3 KiB
Python
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from caffe2.python import core, workspace
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import caffe2.python.hypothesis_test_util as hu
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import numpy as np
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class TestPercentileOp(hu.HypothesisTestCase):
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def _test_percentile_op(
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self,
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original_inp,
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value_to_pct_map,
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dist_lengths,
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expected_values
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):
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op = core.CreateOperator(
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'Percentile',
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['original_values', 'value_to_pct_map', 'dist_lengths'],
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['percentile_values']
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)
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workspace.FeedBlob('original_values', np.array(
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original_inp, dtype=np.float32))
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workspace.FeedBlob(
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'value_to_pct_map', np.array(value_to_pct_map, dtype=np.float32))
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workspace.FeedBlob('dist_lengths', np.array(
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dist_lengths, dtype=np.int32))
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workspace.RunOperatorOnce(op)
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np.testing.assert_array_almost_equal(
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workspace.FetchBlob('percentile_values'),
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np.array(expected_values),
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decimal=5
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)
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self._test_shape_inference(
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original_inp,
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value_to_pct_map,
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dist_lengths,
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expected_values
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)
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def _test_shape_inference(
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self,
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original_inp,
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value_to_pct_map,
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dist_lengths,
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expected_values
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):
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net = core.Net('test_shape_inference')
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result = net.Percentile(
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['original_values', 'value_to_pct_map', 'dist_lengths'],
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['percentile_values']
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)
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workspace.FeedBlob('original_values', np.array(
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original_inp, dtype=np.float32))
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workspace.FeedBlob(
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'value_to_pct_map', np.array(value_to_pct_map, dtype=np.float32))
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workspace.FeedBlob('dist_lengths', np.array(
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dist_lengths, dtype=np.int32))
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(shapes, types) = workspace.InferShapesAndTypes([net])
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workspace.RunNetOnce(net)
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self.assertEqual(shapes[result], list(workspace.blobs[result].shape))
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self.assertEqual(shapes[result], list(workspace.blobs['original_values'].shape))
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self.assertEqual(types[result], core.DataType.FLOAT)
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def test_percentile_op_with_only_one_dist(self):
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self._test_percentile_op(
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original_inp=[[5]],
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value_to_pct_map=[[5, 0.4]],
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dist_lengths=[1],
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expected_values=[[0.4]]
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)
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def test_percentile_op_with_all_elements_in_map(self):
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self._test_percentile_op(
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original_inp=[[3, 4], [10, 4]],
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value_to_pct_map=[[3, 0.3], [4, 0.6],
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[10, 0.8], [4, 0.5], [5, 0.6]],
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dist_lengths=[3, 2],
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expected_values=[[0.3, 0.5], [0.8, 0.5]],
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)
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def test_percentile_op_with_same_value(self):
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self._test_percentile_op(
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original_inp=[[1, 1], [1, 2]],
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value_to_pct_map=[[1, 0.1], [4, 0.4], [2, 0.5]],
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dist_lengths=[2, 1],
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expected_values=[[0.1, 0.0], [0.1, 0.5]]
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)
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def test_percentile_op_with_elements_bigger_than_map_range(self):
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self._test_percentile_op(
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original_inp=[[1, 5], [3, 4]],
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value_to_pct_map=[[1, 0.1], [4, 0.4], [2, 0.1], [3, 0.3]],
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dist_lengths=[2, 2],
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expected_values=[[0.1, 1.], [0.3, 1.0]]
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)
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def test_percentile_op_with_elements_smaller_than_map_range(self):
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self._test_percentile_op(
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original_inp=[[1], [5], [6]],
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value_to_pct_map=[[2, 0.2], [5, 0.5], [7, 0.5]],
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dist_lengths=[3],
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expected_values=[[0.0], [0.5], [0.5]]
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)
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def test_percentile_op_with_interpolation(self):
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self._test_percentile_op(
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original_inp=[[3, 2, 5], [6, 7, 8]],
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value_to_pct_map=[[1, 0.1], [4, 0.7], [4.5, 0.8],
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[6, 0.5], [8, 0.9],
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[8, 0.6]],
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dist_lengths=[3, 2, 1],
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expected_values=[[0.5, 0.0, 0.0], [1.0, 0.7, 0.6]]
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)
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def test_percentile_op_with_large_sample_size_per_dist(self):
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self._test_percentile_op(
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original_inp=[[3, 1], [5, 7]],
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value_to_pct_map=[[3, 0.5], [4, 0.6], [5, 0.7],
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[1, 0.2], [2, 0.3], [5, 0.8]],
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dist_lengths=[3, 3],
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expected_values=[[0.5, 0.2], [0.7, 1.0]]
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)
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if __name__ == "__main__":
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import unittest
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unittest.main()
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