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https://github.com/zebrajr/pytorch.git
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f6df18f6ca
Summary: See https://github.com/pytorch/pytorch/issues/42919 Pull Request resolved: https://github.com/pytorch/pytorch/pull/53349 Reviewed By: malfet Differential Revision: D27039089 Pulled By: bugra fbshipit-source-id: 8063dc184248604506a8dbb1bcb73da8ec85bb18
82 lines
3.1 KiB
Python
82 lines
3.1 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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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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from caffe2.python import core, workspace
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from hypothesis import given, settings
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class TestComputeEqualizationScaleOp(hu.HypothesisTestCase):
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@settings(max_examples=10)
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@given(
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m=st.integers(1, 50),
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n=st.integers(1, 50),
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k=st.integers(1, 50),
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rnd_seed=st.integers(1, 5),
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**hu.gcs_cpu_only
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)
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def test_compute_equalization_scale(self, m, n, k, rnd_seed, gc, dc):
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np.random.seed(rnd_seed)
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W = np.random.rand(n, k).astype(np.float32) - 0.5
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X = np.random.rand(m, k).astype(np.float32) - 0.5
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def ref_compute_equalization_scale(X, W):
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S = np.ones([X.shape[1]])
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for j in range(W.shape[1]):
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WcolMax = np.absolute(W[:, j]).max()
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XcolMax = np.absolute(X[:, j]).max()
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if WcolMax and XcolMax:
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S[j] = np.sqrt(WcolMax / XcolMax)
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return S
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net = core.Net("test")
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ComputeEqualizationScaleOp = core.CreateOperator(
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"ComputeEqualizationScale", ["X", "W"], ["S"]
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)
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net.Proto().op.extend([ComputeEqualizationScaleOp])
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self.ws.create_blob("X").feed(X, device_option=gc)
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self.ws.create_blob("W").feed(W, device_option=gc)
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self.ws.run(net)
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S = self.ws.blobs["S"].fetch()
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S_ref = ref_compute_equalization_scale(X, W)
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np.testing.assert_allclose(S, S_ref, atol=1e-3, rtol=1e-3)
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def test_compute_equalization_scale_shape_inference(self):
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X = np.array([[1, 2], [2, 4], [6, 7]]).astype(np.float32)
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W = np.array([[2, 3], [5, 4], [8, 2]]).astype(np.float32)
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ComputeEqualizationScaleOp = core.CreateOperator(
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"ComputeEqualizationScale", ["X", "W"], ["S"]
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)
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workspace.FeedBlob("X", X)
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workspace.FeedBlob("W", W)
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net = core.Net("test_shape_inference")
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net.Proto().op.extend([ComputeEqualizationScaleOp])
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shapes, types = workspace.InferShapesAndTypes(
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[net],
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blob_dimensions={"X": X.shape, "W": W.shape},
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blob_types={"X": core.DataType.FLOAT, "W": core.DataType.FLOAT},
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)
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assert (
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"S" in shapes and "S" in types
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), "Failed to infer the shape or type of output"
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self.assertEqual(shapes["S"], [1, 2])
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self.assertEqual(types["S"], core.DataType.FLOAT)
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