add global avgpool unittest

python/oneflow/test/modules/test_consistent_avgpool.py

@@ -0,0 +1,144 @@
+"""
+Copyright 2020 The OneFlow Authors. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import unittest
+
+import oneflow as flow
+import oneflow.unittest
+
+from oneflow.test_utils.automated_test_util import *
+
+
+@autotest(n=1, check_graph=False)
+def _test_avgpool1d_with_random_data(test_case, placement, sbp):
+    m = torch.nn.AvgPool1d(
+        kernel_size=random(4, 6),
+        stride=random(1, 3),
+        padding=random(1, 3),
+        ceil_mode=random(),
+        count_include_pad=random(),
+    )
+    m.train(random())
+    m.to_global(placement=placement, sbp=sbp)
+    ndim = 3
+    dims = [random(1, 3) * 8 for _ in range(ndim)]
+    x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp)
+    y = m(x)
+    return y
+
+
+@autotest(n=1, check_graph=False)
+def _test_avgpool2d_with_random_data(test_case, placement, sbp):
+    m = torch.nn.AvgPool2d(
+        kernel_size=random(4, 6),
+        stride=random(1, 3),
+        padding=random(1, 3),
+        ceil_mode=random(),
+        count_include_pad=random(),
+        divisor_override=random().to(int),
+    )
+    m.train(random())
+    m.to_global(placement=placement, sbp=sbp)
+    ndim = 4
+    dims = [random(1, 3) * 8 for _ in range(ndim)]
+    x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp)
+    y = m(x)
+    return y
+
+
+@autotest(n=1, check_graph=False)
+def _test_avgpool3d_with_random_data(test_case, placement, sbp):
+    m = torch.nn.AvgPool3d(
+        kernel_size=random(4, 6),
+        stride=random(1, 3),
+        padding=random(1, 3),
+        ceil_mode=random(),
+        count_include_pad=random(),
+        divisor_override=random().to(int),
+    )
+    m.train(random())
+    m.to_global(placement=placement, sbp=sbp)
+    ndim = 5
+    dims = [random(1, 3) * 8 for _ in range(ndim)]
+    x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp)
+    y = m(x)
+    return y
+
+
+@autotest(n=1, check_graph=False)
+def _test_functional_avgpool1d_with_random_data(test_case, placement, sbp):
+    ndim = 3
+    dims = [random(1, 3) * 8 for _ in range(ndim)]
+    x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp)
+    y = torch.nn.functional.avg_pool1d(
+        x,
+        kernel_size=random(1, 6).to(int),
+        stride=random(1, 3).to(int),
+        padding=random(1, 3).to(int),
+        ceil_mode=random_bool(),
+        count_include_pad=random_bool(),
+    )
+    return y
+
+
+@autotest(n=1, check_graph=False)
+def _test_functional_avgpool2d_with_random_data(test_case, placement, sbp):
+    ndim = 4
+    dims = [random(1, 3) * 8 for _ in range(ndim)]
+    x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp)
+    y = torch.nn.functional.avg_pool2d(
+        x,
+        kernel_size=random(1, 6).to(int),
+        stride=random(1, 3).to(int),
+        padding=random(1, 3).to(int),
+        ceil_mode=random_bool(),
+        count_include_pad=random_bool(),
+    )
+    return y
+
+
+@autotest(n=1, check_graph=False)
+def _test_functional_avgpool3d_with_random_data(test_case, placement, sbp):
+    ndim = 5
+    dims = [random(1, 3) * 8 for _ in range(ndim)]
+    x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp)
+    y = torch.nn.functional.avg_pool3d(
+        x,
+        kernel_size=random(1, 6).to(int),
+        stride=random(1, 3).to(int),
+        padding=random(1, 3).to(int),
+        ceil_mode=random_bool(),
+        count_include_pad=random_bool(),
+    )
+    return y
+
+
+class TestAvgPoolingModule(flow.unittest.TestCase):
+    @globaltest
+    def test_avg_pooling(test_case):
+        for placement in all_placement():
+            for sbp in all_sbp(placement, max_dim=2):
+                _test_avgpool1d_with_random_data(test_case, placement, sbp)
+                _test_functional_avgpool1d_with_random_data(test_case, placement, sbp)
+            for sbp in all_sbp(placement, max_dim=2):
+                _test_avgpool2d_with_random_data(test_case, placement, sbp)
+                _test_functional_avgpool2d_with_random_data(test_case, placement, sbp)
+            for sbp in all_sbp(placement, max_dim=2):
+                _test_avgpool3d_with_random_data(test_case, placement, sbp)
+                _test_functional_avgpool3d_with_random_data(test_case, placement, sbp)
+
+
+if __name__ == "__main__":
+    unittest.main()