add repeated fc relu unit test

test=develop

paddle/fluid/operators/fused/fusion_repeated_fc_relu_op.cc

@@ -120,7 +120,7 @@ class FusionRepeatedFCReluKernel : public framework::OpKernel<T> {
       int m = i_dims[0];
       int n = w_dims[1];
       int k = w_dims[0];
-      relus[i - 1]->Resize({m, n});
+      relus[i]->Resize({m, n});
       fc_relu(relus[i - 1]->data<T>(), weights[i]->data<T>(),
               biases[i]->data<T>(), relus[i]->mutable_data<T>(place), m, n, k);
     }

python/paddle/fluid/tests/unittests/test_fusion_repeated_fc_relu_op.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
+from op_test import OpTest
+from test_fc_op import fc_refer, MatrixGenerate
+
+
+class TestFusionRepeatedFCReluOp(OpTest):
+    def setUp(self):
+        self.bs = 3
+        self.ic = 9
+        self.oc = [2, 4, 3]
+        assert len(self.oc) > 1, 'Should larger than 1'
+        self.set_conf()
+        self.op_type = 'fusion_repeated_fc_relu'
+        sz = len(self.oc)
+        ics = [self.ic] + self.oc[0:sz - 1]
+        assert len(ics) == len(self.oc)
+        weights = []
+        biases = []
+        outs = []
+
+        i = 0
+        matrix = MatrixGenerate(self.bs, ics[i], self.oc[i], 1, 1)
+        inp = np.reshape(matrix.input, [self.bs, ics[i]])
+        weights.append(('W_{0}'.format(i), np.reshape(matrix.weights,
+                                                      [ics[i], self.oc[i]])))
+        biases.append(('B_{0}'.format(i), matrix.bias))
+        outs.append(
+            np.reshape(
+                np.maximum(fc_refer(matrix, True), 0), [self.bs, self.oc[i]]))
+
+        for i in range(sz - 1):
+            matrix = MatrixGenerate(self.bs, ics[i + 1], self.oc[i + 1], 1, 1)
+            matrix.input = np.reshape(outs[i], [self.bs, ics[i + 1], 1, 1])
+            out = fc_refer(matrix, True)
+            weights.append(
+                ('W_{0}'.format(i + 1),
+                 np.reshape(matrix.weights, [ics[i + 1], self.oc[i + 1]])))
+            biases.append(('B_{0}'.format(i + 1), matrix.bias))
+            outs.append(
+                np.reshape(np.maximum(out, 0), [self.bs, self.oc[i + 1]]))
+
+        relu_outs = []
+        for i in range(sz - 1):
+            relu_outs.append(('ReluOut_{0}'.format(i), outs[i]))
+
+        self.inputs = {
+            'X': inp,
+            'W': weights,
+            'Bias': biases,
+        }
+
+        self.outputs = {'Out': outs[-1], 'ReluOut': relu_outs}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def set_conf(self):
+        pass
+
+
+class TestFusionRepeatedFCReluOpBS1(TestFusionRepeatedFCReluOp):
+    def set_conf(self):
+        self.bs = 1
+        self.oc = [4, 2, 7, 5]
+
+
+if __name__ == '__main__':
+    unittest.main()