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https://github.com/zebrajr/pytorch.git
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50 lines
1.6 KiB
Python
Executable File
50 lines
1.6 KiB
Python
Executable File
# 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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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 __future__ import unicode_literals
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from caffe2.python import core
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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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class TestLars(hu.HypothesisTestCase):
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@given(offset=st.floats(min_value=0, max_value=100), **hu.gcs)
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def test_lars(self, offset, dc, gc):
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X = np.random.rand(6, 7, 8, 9).astype(np.float32)
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dX = np.random.rand(6, 7, 8, 9).astype(np.float32)
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def ref_lars(X, dX):
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return [1. / (np.linalg.norm(dX) / np.linalg.norm(X) + offset)]
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op = core.CreateOperator(
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"Lars",
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["X", "dX"],
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["rescale_factor"],
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offset=offset
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X, dX],
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reference=ref_lars
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)
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