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61 lines
2.0 KiB
Python
61 lines
2.0 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 hypothesis import given
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import hypothesis.strategies as st
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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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class TestTTPad(hu.HypothesisTestCase):
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@given(K=st.integers(min_value=2, max_value=10),
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M=st.integers(min_value=10, max_value=20),
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N=st.integers(min_value=10, max_value=20),
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**hu.gcs)
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def test_tt_pad(self, K, M, N, gc, dc):
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op = core.CreateOperator(
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'TTPad',
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['A'],
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['A', 'dim0'],
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scale=(K))
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A = np.random.rand(M, N).astype(np.float32)
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workspace.FeedBlob('A', A)
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workspace.RunOperatorOnce(op)
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def tt_pad_ref(A_):
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M_ = A_.shape[0]
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if M_ % K == 0:
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new_dim0 = M_
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else:
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new_dim0 = (M_ // K + 1) * K
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return (np.vstack((A_, np.zeros((new_dim0 - M_, A_.shape[1])))),
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np.array([A.shape[0]]))
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# Check against numpy reference
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self.assertReferenceChecks(gc, op, [A], tt_pad_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [A], [0])
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# Gradient check wrt A
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self.assertGradientChecks(gc, op, [A], 0, [0])
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