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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
137 lines
4.2 KiB
Python
137 lines
4.2 KiB
Python
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import unittest
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import hypothesis.strategies as st
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from hypothesis import given
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import numpy as np
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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 caffe2.python.ideep_test_util as mu
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@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
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class ExpandDimsSqueezeTest(hu.HypothesisTestCase):
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@given(
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squeeze_dims=st.lists(st.integers(0, 3), min_size=1, max_size=3),
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inplace=st.booleans(),
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**mu.gcs
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)
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def test_squeeze(self, squeeze_dims, inplace, gc, dc):
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shape = [
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1 if dim in squeeze_dims else np.random.randint(1, 5)
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for dim in range(4)
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]
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X = np.random.rand(*shape).astype(np.float32)
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op = core.CreateOperator(
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"Squeeze", "X", "X" if inplace else "Y", dims=squeeze_dims
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)
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self.assertDeviceChecks(dc, op, [X], [0])
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@given(
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squeeze_dims=st.lists(st.integers(0, 3), min_size=1, max_size=3),
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inplace=st.booleans(),
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**mu.gcs_cpu_ideep
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)
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def test_squeeze_fallback(self, squeeze_dims, inplace, gc, dc):
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shape = [
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1 if dim in squeeze_dims else np.random.randint(1, 5)
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for dim in range(4)
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]
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X = np.random.rand(*shape).astype(np.float32)
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op0 = core.CreateOperator(
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"Squeeze",
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"X0",
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"X0" if inplace else "Y0",
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dims=squeeze_dims,
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device_option=dc[0]
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)
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workspace.FeedBlob('X0', X, dc[0])
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workspace.RunOperatorOnce(op0)
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Y0 = workspace.FetchBlob("X0" if inplace else "Y0")
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op1 = core.CreateOperator(
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"Squeeze",
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"X1",
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"X1" if inplace else "Y1",
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dims=squeeze_dims,
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device_option=dc[1]
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)
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workspace.FeedBlob('X1', X, dc[0])
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workspace.RunOperatorOnce(op1)
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Y1 = workspace.FetchBlob("X1" if inplace else "Y1")
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if not np.allclose(Y0, Y1, atol=0.01, rtol=0.01):
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print(Y1.flatten())
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print(Y0.flatten())
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print(np.max(np.abs(Y1 - Y0)))
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self.assertTrue(False)
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@given(
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squeeze_dims=st.lists(st.integers(0, 3), min_size=1, max_size=3),
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inplace=st.booleans(),
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**mu.gcs
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)
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def test_expand_dims(self, squeeze_dims, inplace, gc, dc):
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oshape = [
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1 if dim in squeeze_dims else np.random.randint(2, 5)
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for dim in range(4)
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]
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nshape = [s for s in oshape if s!=1]
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expand_dims = [i for i in range(len(oshape)) if oshape[i]==1]
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X = np.random.rand(*nshape).astype(np.float32)
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op = core.CreateOperator(
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"ExpandDims", "X", "X" if inplace else "Y", dims=expand_dims
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)
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self.assertDeviceChecks(dc, op, [X], [0])
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@given(
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squeeze_dims=st.lists(st.integers(0, 3), min_size=1, max_size=3),
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inplace=st.booleans(),
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**mu.gcs_cpu_ideep
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)
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def test_expand_dims_fallback(self, squeeze_dims, inplace, gc, dc):
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oshape = [
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1 if dim in squeeze_dims else np.random.randint(2, 5)
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for dim in range(4)
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]
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nshape = [s for s in oshape if s!=1]
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expand_dims = [i for i in range(len(oshape)) if oshape[i]==1]
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X = np.random.rand(*nshape).astype(np.float32)
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op0 = core.CreateOperator(
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"ExpandDims",
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"X0",
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"X0" if inplace else "Y0",
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dims=expand_dims,
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device_option=dc[0]
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)
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workspace.FeedBlob('X0', X, dc[0])
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workspace.RunOperatorOnce(op0)
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Y0 = workspace.FetchBlob("X0" if inplace else "Y0")
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op1 = core.CreateOperator(
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"ExpandDims",
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"X1",
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"X1" if inplace else "Y1",
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dims=expand_dims,
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device_option=dc[1]
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)
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workspace.FeedBlob('X1', X, dc[0])
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workspace.RunOperatorOnce(op1)
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Y1 = workspace.FetchBlob("X1" if inplace else "Y1")
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if not np.allclose(Y0, Y1, atol=0.01, rtol=0.01):
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print(Y1.flatten())
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print(Y0.flatten())
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print(np.max(np.abs(Y1 - Y0)))
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self.assertTrue(False)
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if __name__ == "__main__":
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unittest.main()
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