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https://github.com/saymrwulf/pytorch.git
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cd2112717c
* Update ReduceMean Add reduce mean to math Add reduce mean to math * sync reduce_ops_test * Update math_gpu.cu
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from caffe2.python import core, workspace
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from hypothesis import given
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import caffe2.python.hypothesis_test_util as hu
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import hypothesis.strategies as st
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import numpy as np
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import unittest
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class TestTransposeOp(hu.HypothesisTestCase):
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@given(X=hu.tensor(dtype=np.float32), use_axes=st.booleans(), **hu.gcs)
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def test_transpose(self, X, use_axes, gc, dc):
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ndim = len(X.shape)
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axes = np.arange(ndim)
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np.random.shuffle(axes)
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if (use_axes):
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op = core.CreateOperator(
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"Transpose", ["X"], ["Y"], axes=axes, device_option=gc)
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else:
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op = core.CreateOperator(
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"Transpose", ["X"], ["Y"], device_option=gc)
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def transpose_ref(X):
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if use_axes:
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return [np.transpose(X, axes=axes)]
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else:
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return [np.transpose(X)]
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self.assertReferenceChecks(gc, op, [X], transpose_ref)
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self.assertDeviceChecks(dc, op, [X], [0])
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self.assertGradientChecks(gc, op, [X], 0, [0])
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@unittest.skipIf(not workspace.has_gpu_support, "no gpu support")
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@given(X=hu.tensor(dtype=np.float32), use_axes=st.booleans(),
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**hu.gcs_gpu_only)
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def test_transpose_cudnn(self, X, use_axes, gc, dc):
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ndim = len(X.shape)
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axes = np.arange(ndim)
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np.random.shuffle(axes)
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if (use_axes):
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op = core.CreateOperator(
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"Transpose", ["X"], ["Y"], axes=axes, engine="CUDNN",
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device_option=hu.gpu_do)
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else:
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op = core.CreateOperator(
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"Transpose", ["X"], ["Y"], engine="CUDNN",
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device_option=hu.gpu_do)
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def transpose_ref(X):
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if use_axes:
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return [np.transpose(X, axes=axes)]
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else:
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return [np.transpose(X)]
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self.assertReferenceChecks(hu.gpu_do, op, [X], transpose_ref)
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self.assertGradientChecks(hu.gpu_do, op, [X], 0, [0])
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if __name__ == "__main__":
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unittest.main()
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