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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
87 lines
3.2 KiB
Python
87 lines
3.2 KiB
Python
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import unittest
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import caffe2.python.hypothesis_test_util as hu
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import numpy as np
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from caffe2.python import core, workspace
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class TestQuantile(hu.HypothesisTestCase):
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def _test_quantile(self, inputs, quantile, abs, tol):
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net = core.Net("test_net")
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net.Proto().type = "dag"
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input_tensors = []
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for i, input in enumerate(inputs):
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workspace.FeedBlob("t_{}".format(i), input)
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input_tensors.append("t_{}".format(i))
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net.Quantile(
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input_tensors, ["quantile_value"], quantile=quantile, abs=abs, tol=tol
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)
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workspace.RunNetOnce(net)
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quantile_value_blob = workspace.FetchBlob("quantile_value")
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assert np.size(quantile_value_blob) == 1
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quantile_value = quantile_value_blob[0]
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input_cat = np.concatenate([input.flatten() for input in inputs])
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input_cat = np.abs(input_cat) if abs == 1 else input_cat
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target_cnt = np.ceil(np.size(input_cat) * quantile)
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actual_cnt = np.sum(input_cat <= quantile_value)
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# prune with return value will remove no less than
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# "quantile" portion of elements
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assert actual_cnt >= target_cnt
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# Expect that (hi-lo) < tol * (|lo| + |hi|)
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# if tol < 1.0 -> hi * lo > 0, then we are expecting
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# 1. if hi >0,
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# |hi|-|lo| < tol * (|lo| + |hi|)
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# hi - lo < (2 tol) /(1 + tol) |hi| < 2 tol |hi|
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# 2. if hi < 0,
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# |lo|- |hi| < tol * (|lo| + |hi|)
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# hi - lo < (2 tol) /(1 - tol) |hi| < 2.5 tol |hi| if tol < 0.2
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quantile_value_lo = quantile_value - 2.5 * tol * np.abs(quantile_value)
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lo_cnt = np.sum(input_cat <= quantile_value_lo)
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# prune with a slightly smaller value will remove
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# less than "quantile" portion of elements
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assert lo_cnt <= target_cnt
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def test_quantile_1(self):
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inputs = []
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num_tensors = 5
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for i in range(num_tensors):
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dim = np.random.randint(5, 100)
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inputs.append(np.random.rand(dim))
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self._test_quantile(inputs=inputs, quantile=0.2, abs=1, tol=1e-4)
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def test_quantile_2(self):
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inputs = []
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num_tensors = 5
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for i in range(num_tensors):
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dim = np.random.randint(5, 100)
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inputs.append(np.random.rand(dim))
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self._test_quantile(inputs=inputs, quantile=1e-6, abs=0, tol=1e-3)
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def test_quantile_3(self):
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inputs = []
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num_tensors = 5
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for i in range(num_tensors):
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dim1 = np.random.randint(5, 100)
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dim2 = np.random.randint(5, 100)
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inputs.append(np.random.rand(dim1, dim2))
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self._test_quantile(inputs=inputs, quantile=1 - 1e-6, abs=1, tol=1e-5)
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def test_quantile_4(self):
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inputs = []
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num_tensors = 5
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for i in range(num_tensors):
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dim1 = np.random.randint(5, 100)
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dim2 = np.random.randint(5, 100)
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inputs.append(np.random.rand(dim1, dim2))
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inputs.append(np.random.rand(dim1))
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self._test_quantile(inputs=inputs, quantile=0.168, abs=1, tol=1e-4)
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if __name__ == "__main__":
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global_options = ["caffe2"]
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core.GlobalInit(global_options)
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unittest.main()
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