fix cpu conv2d padding

mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/mkl_cpu_kernel.cc

@@ -37,13 +37,17 @@ void MKLCPUKernel::GetPadding(const CNodePtr &kernel_node, const std::string &pa
   if (pad_mode == PAD_MODE_LOWER_SAME || pad_mode == PAD_MODE_UPPER_SAME) {
     for (size_t i = 0; i < weight_height.size(); ++i) {
       auto wh = weight_height[i];
-      int rad = kernel_size[i] / 2;
-      int need_pad = kernel_size[i] - 1;
-      int re = (wh - 1) % stride;
-      int pad = std::max(rad - (re / 2), 0);
-      padding_r->emplace_back(pad);
-      pad = std::max(need_pad - pad - re, 0);
-      padding_l->emplace_back(pad);
+      int re = wh % stride;
+      if (re == 0) {
+        re = stride;
+      }
+      int pad = kernel_size[i] - re;
+      padding_l->emplace_back(pad / 2);
+      if (pad % 2 == 0) {
+        padding_r->emplace_back(pad / 2);
+      } else {
+        padding_r->emplace_back(pad / 2 + 1);
+      }
     }
   } else if (pad_mode == PAD_MODE_LOWER_VALID || pad_mode == PAD_MODE_UPPER_VALID) {
     MS_LOG(INFO) << "pad valid";

tests/st/ops/cpu/test_conv2d_op.py

@@ -55,13 +55,13 @@ def test_conv2d():
     conv2d = NetConv2d()
     output = conv2d()
     print("================================")
-#   expect output:
-#   [[[[ 45.  48.  51.]
-#      [ 54.  57.  60.]
-#      [ 63.  66.  69.]]
-#     [[126. 138. 150.]
-#      [162. 174. 186.]
-#      [198. 210. 222.]]]]
+    #   expect output:
+    #   [[[[ 45.  48.  51.]
+    #      [ 54.  57.  60.]
+    #      [ 63.  66.  69.]]
+    #     [[126. 138. 150.]
+    #      [162. 174. 186.]
+    #      [198. 210. 222.]]]]
     expect = np.array([[[[45, 48, 51],
                          [54, 57, 60],
                          [63, 66, 69]],
@@ -70,3 +70,105 @@ def test_conv2d():
                          [198, 210, 222]]]]).astype(np.float32)
     print(output)
     assert (output.asnumpy() == expect).all()
+
+
+class NetConv(nn.Cell):
+    def __init__(self, weight, x):
+        super(NetConv, self).__init__()
+        self.conv = nn.Conv2d(in_channels=3,
+                              out_channels=3,
+                              kernel_size=(5, 3),
+                              stride=2,
+                              pad_mode='same',
+                              padding=(0, 0, 0, 0),
+                              dilation=(1, 1),
+                              group=1,
+                              has_bias=False,
+                              weight_init=Tensor(weight)
+                              )
+        self.x = Parameter(initializer(Tensor(x), [1, 3, 4, 2]), name="x")
+
+    def construct(self):
+        return self.conv(self.x)
+
+
+def test_conv():
+    weight = np.array([[[[0.38968208, 0.14398979, 0.7962463],
+                         [-2.1836321, -0.63823014, -0.50588065],
+                         [0.6660469, 0.64673275, -0.13160042],
+                         [1.3683757, 1.4005762, -0.37235805],
+                         [-0.22638111, 0.45427424, -0.10293389]],
+                        [[1.4985064, -0.29318333, -0.92694616],
+                         [1.539068, 0.8937254, -1.2598171],
+                         [0.9658142, -0.63945454, -0.23185322],
+                         [1.363089, -0.41694695, -2.2750475],
+                         [-0.4865508, -1.6938025, 0.609849]],
+                        [[1.1844803, 0.99874926, -1.9475793],
+                         [0.4987858, 0.5307887, -0.04226681],
+                         [0.4529779, -1.1960793, 0.9456575],
+                         [3.133675, 0.2309789, -0.29201075],
+                         [-0.59632736, -0.0789804, -0.69486314]]],
+                       [[[-0.5606142, 0.6420862, 0.2478745],
+                         [0.02717604, 1.5483379, -0.9373383],
+                         [-1.1017276, -0.259478, 1.0311872],
+                         [1.8387799, 0.16468556, 0.33392152],
+                         [-1.8781787, 1.0158662, 1.6527579]],
+
+                        [[0.45696944, -0.5652523, -1.5618048],
+                         [-0.30304828, 0.1331878, -0.36955845],
+                         [0.91655576, 0.66612357, 0.3068175],
+                         [-0.45732066, 0.8923335, 1.0542952],
+                         [-0.73519516, 1.0518405, -1.0273266]],
+
+                        [[-0.79712886, -0.26814285, 0.12779616],
+                         [1.0367643, -1.6180774, 0.42999932],
+                         [-0.81818223, -0.81502074, 0.882194],
+                         [0.53640485, 0.4178927, 1.6037121],
+                         [0.9256354, -1.1006796, 0.16614541]]],
+
+                       [[[-1.5216796, -1.2473261, 0.6549515],
+                         [0.63627815, 0.7221449, 0.02977821],
+                         [-0.61331123, -0.49451825, 0.33852202],
+                         [1.4510741, -1.3818305, -0.791747],
+                         [0.6989747, 0.49558765, 1.0813237]],
+
+                        [[-0.03969796, 0.71586496, 0.8326594],
+                         [-0.15443641, 1.0389746, -0.59301984],
+                         [0.7197836, 0.03257621, 1.8398637],
+                         [0.6111736, -0.16166899, -2.4869773],
+                         [1.3066711, -1.8003578, 0.17412892]],
+
+                        [[-0.31470737, -0.5938182, -1.1311078],
+                         [-0.99081016, 0.4005125, 0.44154453],
+                         [1.0876914, -2.5958562, -0.5914863],
+                         [1.3759689, -0.7741513, 0.19928917],
+                         [1.6792973, 2.2744863, -0.04308867]]]]).astype(np.float32)
+    x = np.array([[[[-1.4311737, 1.015344],
+                    [0.04431088, -2.2886624],
+                    [1.4832113, 1.240908],
+                    [0.67040104, 0.15266363]],
+
+                   [[0.44226435, 1.1461105],
+                    [1.194218, 1.5547837],
+                    [0.23152256, 1.5911953],
+                    [0.11206784, 0.17978816]],
+
+                   [[-0.57803905, 0.8039611],
+                    [0.0823025, -0.6134477],
+                    [-1.4171146, 1.6269946],
+                    [0.48878875, 0.9117505]]]]).astype(np.float32)
+    conv2d = NetConv(weight, x)
+    output = conv2d()
+    expected = np.array([[[[2.3498724],
+                           [-1.9199573]],
+                          [[5.376562],
+                           [-5.425745]],
+                          [[5.9105043],
+                           [7.469034]]]]).astype(np.float32)
+    loss = np.abs(expected - output.asnumpy())
+    error = 1e-4 * np.ones(loss.shape)
+    assert (loss < error).all()
+
+
+test_conv2d()
+test_conv()