Fix conv transpose (#28629)

* fix conv_transpose unittest. test=develop

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -75,8 +75,6 @@ if(NOT WITH_GPU OR WIN32)
     LIST(REMOVE_ITEM TEST_OPS test_collective_allgather_api)
 endif()
 
-#TODO(sunxiaolong01): Fix this unitest failed on GCC8.
-LIST(REMOVE_ITEM TEST_OPS test_conv2d_transpose_op)
 if(WIN32)
     LIST(REMOVE_ITEM TEST_OPS test_boxps)
     LIST(REMOVE_ITEM TEST_OPS test_trainer_desc)
@@ -341,7 +339,6 @@ list(REMOVE_ITEM TEST_OPS test_fuse_bn_act_pass)
 list(REMOVE_ITEM TEST_OPS test_fuse_bn_add_act_pass)
 list(REMOVE_ITEM TEST_OPS test_imperative_static_runner_mnist)
 list(REMOVE_ITEM TEST_OPS test_imperative_static_runner_while)
-list(REMOVE_ITEM TEST_OPS test_conv3d_transpose_op)
 
 # disable this unittest temporarily
 list(REMOVE_ITEM TEST_OPS test_imperative_data_loader_exception)
@@ -697,7 +694,9 @@ set_tests_properties(test_eager_deletion_lstm_net PROPERTIES TIMEOUT 120)
 set_tests_properties(test_parallel_executor_mnist PROPERTIES TIMEOUT 120)
 set_tests_properties(test_imperative_ptb_rnn PROPERTIES TIMEOUT 120)
 set_tests_properties(test_imperative_save_load_v2 PROPERTIES TIMEOUT 120)
+set_tests_properties(test_conv2d_transpose_op PROPERTIES TIMEOUT 120)
 set_tests_properties(test_conv3d_transpose_part2_op PROPERTIES TIMEOUT 120)
+set_tests_properties(test_conv3d_transpose_op PROPERTIES TIMEOUT 120)
 set_tests_properties(test_prroi_pool_op PROPERTIES TIMEOUT 120)
 set_tests_properties(test_multiprocess_dataloader_iterable_dataset_static PROPERTIES TIMEOUT 120)
 set_tests_properties(test_lstm_cudnn_op PROPERTIES TIMEOUT 120)

python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py

@@ -17,6 +17,8 @@ from __future__ import print_function
 import unittest
 import numpy as np
 
+import paddle
+paddle.enable_static()
 import paddle.fluid.core as core
 import paddle.fluid as fluid
 from op_test import OpTest
@@ -654,101 +656,6 @@ class TestCUDNNWithEvenUpsample_NHWC(TestWithEvenUpsample):
         self.op_type = "conv2d_transpose"
 
 
-class TestDepthwiseConvTranspose(TestConv2DTransposeOp):
-    def init_test_case(self):
-        self.pad = [1, 1]
-        self.stride = [2, 2]
-        self.dilations = [1, 1]
-        self.input_size = [2, 8, 16, 16]  # NCHW
-        self.groups = 8
-        assert np.mod(self.input_size[1], self.groups) == 0
-        f_c = self.input_size[1] // self.groups
-        self.filter_size = [self.input_size[1], f_c, 4, 4]
-        self.op_type = "depthwise_conv2d_transpose"
-
-
-class TestDepthwiseConvTransposeAsymmetricPad(TestConv2DTransposeOp):
-    def init_test_case(self):
-        self.pad = [1, 0, 1, 2]
-        self.stride = [2, 2]
-        self.dilations = [1, 1]
-        self.input_size = [2, 8, 16, 16]  # NCHW
-        self.groups = 8
-        assert np.mod(self.input_size[1], self.groups) == 0
-        f_c = self.input_size[1] // self.groups
-        self.filter_size = [self.input_size[1], f_c, 3, 3]
-        self.op_type = "depthwise_conv2d_transpose"
-        self.data_format = 'NCHW'
-
-
-class TestDepthwiseConvTransposeSAMEPad(TestConv2DTransposeOp):
-    def init_test_case(self):
-        self.stride = [2, 2]
-        self.dilations = [1, 1]
-        self.input_size = [2, 8, 16, 16]  # NHWC
-        self.groups = 8
-        assert np.mod(self.input_size[1], self.groups) == 0
-        f_c = self.input_size[1] // self.groups
-        self.filter_size = [self.input_size[1], f_c, 3, 3]
-        self.op_type = "depthwise_conv2d_transpose"
-        self.padding_algorithm = 'SAME'
-
-
-class TestDepthwiseConvTransposeVALIDPad(TestConv2DTransposeOp):
-    def init_test_case(self):
-        self.stride = [2, 2]
-        self.dilations = [1, 1]
-        self.input_size = [2, 8, 16, 16]  # NHWC
-        self.groups = 8
-        assert np.mod(self.input_size[1], self.groups) == 0
-        f_c = self.input_size[1] // self.groups
-        self.filter_size = [self.input_size[1], f_c, 3, 3]
-        self.op_type = "depthwise_conv2d_transpose"
-        self.padding_algorithm = 'VALID'
-
-
-class TestDepthwiseConvTranspose_NHWC_4x4kernel(TestConv2DTransposeOp):
-    def init_test_case(self):
-        self.pad = [1, 1]
-        self.stride = [2, 2]
-        self.dilations = [1, 1]
-        self.input_size = [2, 16, 16, 8]  # NHWC
-        self.groups = 8
-        assert np.mod(self.input_size[3], self.groups) == 0
-        f_c = self.input_size[3] // self.groups
-        self.filter_size = [self.input_size[3], f_c, 4, 4]
-        self.op_type = "depthwise_conv2d_transpose"
-        self.data_format = 'NHWC'
-
-
-class TestDepthwiseConvTranspose_NHWC_3x3kernel(TestConv2DTransposeOp):
-    def init_test_case(self):
-        self.pad = [1, 1]
-        self.stride = [2, 2]
-        self.dilations = [1, 1]
-        self.input_size = [2, 16, 16, 8]  # NHWC
-        self.groups = 8
-        assert np.mod(self.input_size[3], self.groups) == 0
-        f_c = self.input_size[3] // self.groups
-        self.filter_size = [self.input_size[3], f_c, 3, 3]
-        self.op_type = "depthwise_conv2d_transpose"
-        self.data_format = 'NHWC'
-
-
-class TestDepthwiseConvTransposeAsymmetricPad_NHWC(TestConv2DTransposeOp):
-    def init_test_case(self):
-        self.pad = [1, 0, 1, 2]
-        self.stride = [2, 2]
-        self.dilations = [1, 1]
-        self.input_size = [2, 16, 16, 8]  # NHWC
-        self.groups = 8
-        assert np.mod(self.input_size[3], self.groups) == 0
-        f_c = self.input_size[3] // self.groups
-        self.filter_size = [self.input_size[3], f_c, 3, 3]
-        self.op_type = "depthwise_conv2d_transpose"
-        self.data_format = 'NHWC'
-
-
 @unittest.skipIf(not core.is_compiled_with_cuda(),
                  "core is not compiled with CUDA")
 class TestCUDNN_FP16(TestConv2DTransposeOp):

python/paddle/fluid/tests/unittests/test_conv2d_transpose_op_depthwise_conv.py

+#   Copyright (c) 2018 PaddlePaddle 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+
+import paddle
+paddle.enable_static()
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from op_test import OpTest
+from test_conv2d_transpose_op import TestConv2DTransposeOp
+
+
+class TestDepthwiseConvTranspose(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.input_size = [1, 8, 4, 4]  # NCHW
+        self.groups = 8
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [self.input_size[1], f_c, 4, 4]
+        self.op_type = "depthwise_conv2d_transpose"
+
+
+class TestDepthwiseConvTransposeAsymmetricPad(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1, 1, 2]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.input_size = [1, 8, 4, 4]  # NCHW
+        self.groups = 8
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [self.input_size[1], f_c, 3, 3]
+        self.op_type = "depthwise_conv2d_transpose"
+        self.data_format = 'NCHW'
+
+
+class TestDepthwiseConvTransposeSAMEPad(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.input_size = [1, 8, 4, 4]  # NHWC
+        self.groups = 8
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [self.input_size[1], f_c, 3, 3]
+        self.op_type = "depthwise_conv2d_transpose"
+        self.padding_algorithm = 'SAME'
+
+
+class TestDepthwiseConvTransposeVALIDPad(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.input_size = [1, 8, 4, 4]  # NHWC
+        self.groups = 8
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [self.input_size[1], f_c, 3, 3]
+        self.op_type = "depthwise_conv2d_transpose"
+        self.padding_algorithm = 'VALID'
+
+
+class TestDepthwiseConvTranspose_NHWC_3x3kernel(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.input_size = [1, 4, 4, 8]  # NHWC
+        self.groups = 8
+        assert np.mod(self.input_size[3], self.groups) == 0
+        f_c = self.input_size[3] // self.groups
+        self.filter_size = [self.input_size[3], f_c, 3, 3]
+        self.op_type = "depthwise_conv2d_transpose"
+        self.data_format = 'NHWC'
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_conv3d_transpose_op.py

@@ -17,6 +17,8 @@ from __future__ import print_function
 import unittest
 import numpy as np
 
+import paddle
+paddle.enable_static()
 import paddle.fluid.core as core
 import paddle.fluid as fluid
 from op_test import OpTest
@@ -207,7 +209,7 @@ class TestWithSymmetricPad(TestConv3DTransposeOp):
         self.stride = [1, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 1
-        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
+        self.input_size = [1, 2, 5, 5, 5]  # NCDHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 6, 3, 3, 3]
 
@@ -218,7 +220,7 @@ class TestWithAsymmetricPad(TestConv3DTransposeOp):
         self.stride = [1, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 1
-        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
+        self.input_size = [1, 2, 5, 5, 5]  # NCDHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 6, 3, 3, 3]
 
@@ -228,7 +230,7 @@ class TestWithSAMEPad(TestConv3DTransposeOp):
         self.stride = [1, 1, 2]
         self.dilations = [1, 2, 1]
         self.groups = 1
-        self.input_size = [2, 3, 5, 5, 6]  # NCDHW
+        self.input_size = [1, 2, 5, 5, 6]  # NCDHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 6, 3, 3, 4]
         self.padding_algorithm = 'SAME'
@@ -239,7 +241,7 @@ class TestWithVALIDPad(TestConv3DTransposeOp):
         self.stride = [2, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 1
-        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
+        self.input_size = [1, 2, 5, 5, 5]  # NCDHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 6, 3, 4, 3]
         self.padding_algorithm = 'VALID'
@@ -252,7 +254,7 @@ class TestWithStride(TestConv3DTransposeOp):
         self.stride = [2, 2, 2]
         self.dilations = [1, 1, 1]
         self.groups = 1
-        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
+        self.input_size = [1, 2, 5, 5, 5]  # NCDHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 6, 3, 3, 3]
 
@@ -263,7 +265,7 @@ class TestWithGroups(TestConv3DTransposeOp):
         self.stride = [1, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 2
-        self.input_size = [2, 4, 5, 5, 5]  # NCHW
+        self.input_size = [1, 2, 5, 5, 5]  # NCHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 3, 3, 3, 3]
 
@@ -274,7 +276,7 @@ class TestWithDilation(TestConv3DTransposeOp):
         self.stride = [1, 1, 1]
         self.dilations = [2, 2, 2]
         self.groups = 1
-        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
+        self.input_size = [1, 2, 5, 5, 5]  # NCDHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 6, 3, 3, 3]
 
@@ -285,7 +287,7 @@ class Test_NHWC(TestConv3DTransposeOp):
         self.stride = [1, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 1
-        self.input_size = [2, 5, 5, 5, 3]  # NDHWC
+        self.input_size = [1, 5, 5, 5, 2]  # NDHWC
         f_c = self.input_size[-1]
         self.filter_size = [f_c, 6, 3, 3, 3]
         self.data_format = 'NHWC'
@@ -308,7 +310,7 @@ class TestCUDNNWithSymmetricPad(TestWithSymmetricPad):
         self.stride = [1, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 1
-        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
+        self.input_size = [1, 2, 5, 5, 5]  # NCDHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 6, 3, 3, 3]
 
@@ -325,7 +327,7 @@ class TestCUDNNWithAsymmetricPad(TestWithAsymmetricPad):
         self.stride = [1, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 1
-        self.input_size = [2, 3, 4, 4, 4]  # NCDHW
+        self.input_size = [1, 2, 4, 4, 4]  # NCDHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 6, 3, 3, 3]
 
@@ -341,7 +343,7 @@ class TestCUDNNWithSAMEPad(TestWithSAMEPad):
         self.stride = [1, 1, 2]
         self.dilations = [1, 2, 1]
         self.groups = 1
-        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
+        self.input_size = [1, 2, 5, 5, 5]  # NCDHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 6, 3, 4, 3]
         self.padding_algorithm = 'SAME'
@@ -358,7 +360,7 @@ class TestCUDNNWithVALIDPad(TestWithVALIDPad):
         self.stride = [1, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 1
-        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
+        self.input_size = [1, 2, 5, 5, 5]  # NCDHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 6, 3, 3, 3]
         self.padding_algorithm = 'VALID'
@@ -376,7 +378,7 @@ class TestCUDNNWithStride(TestWithStride):
         self.stride = [2, 2, 2]
         self.dilations = [1, 1, 1]
         self.groups = 1
-        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
+        self.input_size = [1, 2, 5, 5, 5]  # NCDHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 6, 3, 3, 3]
 
@@ -393,7 +395,7 @@ class TestCUDNNWithGroups(TestWithGroups):
         self.stride = [1, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 2
-        self.input_size = [2, 4, 5, 5, 5]  # NCHW
+        self.input_size = [1, 2, 5, 5, 5]  # NCHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 3, 3, 3, 3]
 
@@ -425,7 +427,7 @@ class TestCUDNN_NHWC(TestConv3DTransposeOp):
         self.stride = [1, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 1
-        self.input_size = [2, 5, 5, 5, 3]  # NDHWC
+        self.input_size = [1, 5, 5, 5, 2]  # NDHWC
         f_c = self.input_size[-1]
         self.filter_size = [f_c, 6, 3, 3, 3]
         self.data_format = 'NHWC'
@@ -443,7 +445,7 @@ class TestCUDNNWithSymmetricPad_NHWC(TestWithSymmetricPad):
         self.stride = [1, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 1
-        self.input_size = [2, 5, 5, 5, 3]  # NDHWC
+        self.input_size = [1, 5, 5, 5, 2]  # NDHWC
         f_c = self.input_size[-1]
         self.filter_size = [f_c, 6, 3, 3, 3]
         self.data_format = 'NHWC'
@@ -461,7 +463,7 @@ class TestCUDNNWithAsymmetricPad_NHWC(TestWithAsymmetricPad):
         self.stride = [1, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 1
-        self.input_size = [2, 5, 5, 5, 3]  # NDHWC
+        self.input_size = [1, 5, 5, 5, 2]  # NDHWC
         f_c = self.input_size[-1]
         self.filter_size = [f_c, 6, 3, 3, 3]
         self.data_format = 'NHWC'
@@ -479,7 +481,7 @@ class TestCUDNNWithStride_NHWC(TestWithStride):
         self.stride = [2, 2, 2]
         self.dilations = [1, 1, 1]
         self.groups = 1
-        self.input_size = [2, 5, 5, 5, 3]  # NCDHW
+        self.input_size = [1, 5, 5, 5, 2]  # NDHWC
         f_c = self.input_size[-1]
         self.filter_size = [f_c, 6, 3, 3, 3]
         self.data_format = 'NHWC'
@@ -497,7 +499,7 @@ class TestCUDNNWithGroups_NHWC(TestWithGroups):
         self.stride = [1, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 2
-        self.input_size = [2, 5, 5, 5, 4]  # NCHW
+        self.input_size = [1, 5, 5, 5, 2]  # NDHWC
         f_c = self.input_size[-1]
         self.filter_size = [f_c, 3, 3, 3, 3]
         self.data_format = 'NHWC'