mirror of
https://github.com/saymrwulf/pytorch.git
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8286ce1e3a
Summary: Closes https://github.com/caffe2/caffe2/pull/1260 Differential Revision: D5906739 Pulled By: Yangqing fbshipit-source-id: e482ba9ba60b5337d9165f28f7ec68d4518a0902
162 lines
4.3 KiB
Python
162 lines
4.3 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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from caffe2.python import core
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from hypothesis import assume, 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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from caffe2.proto import caffe2_pb2
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class TestReductionOps(hu.HypothesisTestCase):
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@given(n=st.integers(5, 8), **hu.gcs)
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def test_elementwise_sum(self, n, gc, dc):
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X = np.random.rand(n).astype(np.float32)
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def sum_op(X):
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return [np.sum(X)]
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op = core.CreateOperator(
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"SumElements",
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["X"],
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["y"]
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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],
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reference=sum_op,
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)
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self.assertGradientChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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outputs_to_check=0,
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outputs_with_grads=[0],
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)
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@given(n=st.integers(1, 65536),
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dtype=st.sampled_from([np.float32, np.float16]),
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**hu.gcs)
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def test_elementwise_sqrsum(self, n, dtype, gc, dc):
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if dtype == np.float16:
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# fp16 is only supported with CUDA
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assume(gc.device_type == caffe2_pb2.CUDA)
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dc = [d for d in dc if d.device_type == caffe2_pb2.CUDA]
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X = np.random.rand(n).astype(dtype)
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def sumsqr_op(X):
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return [np.sum(X * X)]
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op = core.CreateOperator(
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"SumSqrElements",
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["X"],
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["y"]
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)
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threshold = 0.01 if dtype == np.float16 else 0.005
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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],
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reference=sumsqr_op,
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threshold=threshold,
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)
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@given(n=st.integers(5, 8), **hu.gcs)
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def test_elementwise_avg(self, n, gc, dc):
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X = np.random.rand(n).astype(np.float32)
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def avg_op(X):
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return [np.mean(X)]
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op = core.CreateOperator(
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"SumElements",
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["X"],
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["y"],
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average=1
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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],
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reference=avg_op,
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)
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self.assertGradientChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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outputs_to_check=0,
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outputs_with_grads=[0],
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)
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@given(batch_size=st.integers(1, 3),
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m=st.integers(1, 3),
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n=st.integers(1, 4),
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**hu.gcs)
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def test_rowwise_max(self, batch_size, m, n, gc, dc):
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X = np.random.rand(batch_size, m, n).astype(np.float32)
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def rowwise_max(X):
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return [np.max(X, axis=2)]
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op = core.CreateOperator(
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"RowwiseMax",
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["x"],
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["y"]
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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],
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reference=rowwise_max,
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)
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@given(batch_size=st.integers(1, 3),
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m=st.integers(1, 3),
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n=st.integers(1, 4),
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**hu.gcs)
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def test_columnwise_max(self, batch_size, m, n, gc, dc):
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X = np.random.rand(batch_size, m, n).astype(np.float32)
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def columnwise_max(X):
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return [np.max(X, axis=1)]
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op = core.CreateOperator(
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"ColwiseMax",
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["x"],
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["y"]
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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],
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reference=columnwise_max,
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)
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