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80 lines
2.8 KiB
Python
80 lines
2.8 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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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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import numpy as np
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from scipy.sparse import coo_matrix
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from hypothesis import given
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import hypothesis.strategies as st
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from caffe2.python import core
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import caffe2.python.hypothesis_test_util as hu
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class TestFunHash(hu.HypothesisTestCase):
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@given(n_out=st.integers(min_value=5, max_value=20),
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n_in=st.integers(min_value=10, max_value=20),
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n_data=st.integers(min_value=2, max_value=8),
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n_weight=st.integers(min_value=8, max_value=15),
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n_alpha=st.integers(min_value=3, max_value=8),
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sparsity=st.floats(min_value=0.1, max_value=1.0),
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**hu.gcs)
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def test_funhash(self, n_out, n_in, n_data, n_weight, n_alpha, sparsity,
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gc, dc):
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A = np.random.rand(n_data, n_in)
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A[A > sparsity] = 0
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A_coo = coo_matrix(A)
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val, key, seg = A_coo.data, A_coo.col, A_coo.row
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weight = np.random.rand(n_weight).astype(np.float32)
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alpha = np.random.rand(n_alpha).astype(np.float32)
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val = val.astype(np.float32)
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key = key.astype(np.int64)
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seg = seg.astype(np.int32)
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op = core.CreateOperator(
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'FunHash',
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['val', 'key', 'seg', 'weight', 'alpha'],
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['out'],
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num_outputs=n_out)
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# Check over multiple devices
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self.assertDeviceChecks(
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dc, op, [val, key, seg, weight, alpha], [0])
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# Gradient check wrt weight
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self.assertGradientChecks(
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gc, op, [val, key, seg, weight, alpha], 3, [0])
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# Gradient check wrt alpha
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self.assertGradientChecks(
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gc, op, [val, key, seg, weight, alpha], 4, [0])
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op2 = core.CreateOperator(
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'FunHash',
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['val', 'key', 'seg', 'weight'],
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['out'],
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num_outputs=n_out)
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# Check over multiple devices
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self.assertDeviceChecks(
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dc, op2, [val, key, seg, weight], [0])
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# Gradient check wrt weight
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self.assertGradientChecks(
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gc, op2, [val, key, seg, weight], 3, [0])
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