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https://github.com/saymrwulf/pytorch.git
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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
63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
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import hypothesis.strategies as st
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import numpy as np
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from caffe2.python import core
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from hypothesis import given, settings
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.serialized_test.serialized_test_util as serial
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class TestArgOps(serial.SerializedTestCase):
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@given(
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X=hu.tensor(dtype=np.float32), axis=st.integers(-1, 5),
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keepdims=st.booleans(), **hu.gcs)
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@settings(deadline=None)
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def test_argmax(self, X, axis, keepdims, gc, dc):
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if axis >= len(X.shape):
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axis %= len(X.shape)
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op = core.CreateOperator(
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"ArgMax", ["X"], ["Indices"], axis=axis, keepdims=keepdims,
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device_option=gc)
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def argmax_ref(X):
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indices = np.argmax(X, axis=axis)
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if keepdims:
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out_dims = list(X.shape)
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out_dims[axis] = 1
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indices = indices.reshape(tuple(out_dims))
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return [indices]
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self.assertReferenceChecks(gc, op, [X], argmax_ref)
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self.assertDeviceChecks(dc, op, [X], [0])
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@given(
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X=hu.tensor(dtype=np.float32), axis=st.integers(-1, 5),
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keepdims=st.booleans(), **hu.gcs)
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@settings(deadline=None)
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def test_argmin(self, X, axis, keepdims, gc, dc):
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if axis >= len(X.shape):
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axis %= len(X.shape)
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op = core.CreateOperator(
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"ArgMin", ["X"], ["Indices"], axis=axis, keepdims=keepdims,
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device_option=gc)
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def argmin_ref(X):
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indices = np.argmin(X, axis=axis)
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if keepdims:
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out_dims = list(X.shape)
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out_dims[axis] = 1
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indices = indices.reshape(tuple(out_dims))
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return [indices]
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self.assertReferenceChecks(gc, op, [X], argmin_ref)
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self.assertDeviceChecks(dc, op, [X], [0])
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if __name__ == "__main__":
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import unittest
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unittest.main()
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