del framework test_maxout_op

paddle/operators/math/maxouting.cu

@@ -37,7 +37,7 @@ __global__ void KernelMaxOut(const int nthreads, const T* input_data,
       (batch_idx * size + channel_idx * feat_len) * groups + feat_idx;
     T ele = static_cast<T>(-FLT_MAX);
     for (int g = 0; g < groups; ++g) {
-      T x=input_data[data_idx + g * feat_len];
+      T x = input_data[data_idx + g * feat_len];
       ele = ele > x ? ele : x;
     }
     output_data[i] = ele;

python/paddle/v2/framework/tests/test_maxout_op.py

-import unittest
-import numpy as np
-from op_test import OpTest
-
-
-def maxout_forward_naive(input, groups,num_channels):
-    s0, s1, s2, s3 = input.shape
-    return np.ndarray([s0, s1 / groups, groups, s2, s3], \
-        buffer = input, dtype=input.dtype).max(axis=(2))
-
-
-class TestMaxOutOp(OpTest):
-    def setUp(self):
-        self.op_type = "maxout"
-        self.init_test_case()
-        input = np.random.random(self.shape).astype("float32")
-        output = self.MaxOut_forward_naive(input, self.groups,
-                self.num_channels).astype("float32")
-
-        self.inputs = {'X': input}
-        self.attrs = {'groups': self.groups, 'num_channels': self.num_channels}
-
-        self.outputs = {'Out': output.astype('float32')}
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
-
-    def init_test_case(self):
-        self.MaxOut_forward_naive = maxout_forward_naive
-        self.shape = [100, 6, 2, 2]
-        self.groups=2
-        self.num_channels=6
-
-
-
-
-if __name__ == '__main__':
-    unittest.main()