finetune conv2d navie func

python/paddle/v2/framework/tests/test_conv2d_op.py

@@ -11,19 +11,19 @@ def conv2d_forward_naive(input, filter, group, conv_param):
     sub_out_c = out_c / group
 
     stride, pad = conv_param['stride'], conv_param['pad']
-    out_h = 1 + (in_h + 2 * pad - f_h) / stride
-    out_w = 1 + (in_w + 2 * pad - f_w) / stride
+    out_h = 1 + (in_h + 2 * pad[0] - f_h) / stride[0]
+    out_w = 1 + (in_w + 2 * pad[1] - f_w) / stride[1]
     out = np.zeros((in_n, out_c, out_h, out_w))
 
-    input_pad = np.pad(input, ((0, ), (0, ), (pad, ), (pad, )),
+    input_pad = np.pad(input, ((0, ), (0, ), (pad[0], ), (pad[1], )),
                        mode='constant',
                        constant_values=0)
     for i in range(out_h):
         for j in range(out_w):
             for g in range(group):
                 input_pad_masked = input_pad[:, g * f_c:(
-                    g + 1) * f_c, i * stride:i * stride + f_h, j * stride:j *
-                                             stride + f_w]
+                    g + 1) * f_c, i * stride[0]:i * stride[0] + f_h, j * stride[
+                        1]:j * stride[1] + f_w]
                 f_sub = filter[g * sub_out_c:(g + 1) * sub_out_c, :, :, :]
                 for k in range(sub_out_c):
                     out[:, g * sub_out_c + k, i, j] = np.sum(input_pad_masked *
@@ -37,11 +37,14 @@ class TestConv2dOp(OpTest):
     def setUp(self):
         self.init_groups()
         self.op_type = "conv2d"
+        pad = [0, 0]
+        stride = [1, 1]
         input_size = [2, 3, 5, 5]  # NCHW
         assert np.mod(input_size[1], self.groups) == 0
         f_c = input_size[1] / self.groups
         filter_size = [6, f_c, 3, 3]
-        conv2d_param = {'stride': 1, 'pad': 0}
+
+        conv2d_param = {'stride': stride, 'pad': pad}
 
         input = np.random.random(input_size).astype("float32")
         filter = np.random.random(filter_size).astype("float32")
@@ -49,11 +52,7 @@ class TestConv2dOp(OpTest):
         output = conv2d_forward_naive(input, filter, self.groups, conv2d_param)
 
         self.inputs = {'Input': input, 'Filter': filter}
-        self.attrs = {
-            'strides': [1, 1],
-            'paddings': [0, 0],
-            'groups': self.groups
-        }
+        self.attrs = {'strides': stride, 'paddings': pad, 'groups': self.groups}
         self.outputs = {'Output': output}
 
     def test_check_output(self):