fix div 0 error in conv1/2/3 (#49999)

paddle/phi/infermeta/binary.cc

@@ -467,6 +467,13 @@ void ConvInferMeta(const MetaTensor& input,
   const bool channel_last = (config.is_run_mkldnn_kernel == false) &&
                             (data_format == "NHWC" || data_format == "NDHWC");
 
+  for (int i = 0; i < 2; ++i) {
+    PADDLE_ENFORCE_NE(in_dims[i],
+                      0,
+                      phi::errors::InvalidArgument(
+                          "The size of Op(Conv) inputs should not be 0."));
+  }
+
   PADDLE_ENFORCE_EQ(
       in_dims.size() == 4 || in_dims.size() == 5,
       true,

python/paddle/fluid/tests/unittests/test_functional_conv1d.py

@@ -70,5 +70,17 @@ def setUp(self):
         self.data_format = "NCL"
 
 
+class TestFunctionalConv1DErrorCase2(TestFunctionalConv1DError):
+    def setUp(self):
+        self.input = np.random.randn(0, 0, 0)
+        self.filter = np.random.randn(1, 0, 0)
+        self.bias = None
+        self.padding = 0
+        self.stride = 1
+        self.dilation = 1
+        self.groups = 1
+        self.data_format = "NCL"
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_functional_conv2d.py

@@ -569,6 +569,20 @@ def setUp(self):
         self.data_format = "NCHW"
 
 
+class TestFunctionalConv2DErrorCase14(TestFunctionalConv2DErrorCase12):
+    def setUp(self):
+        self.input = np.random.randn(0, 0, 0, 0)
+        self.filter = np.random.randn(1, 0, 0, 0)
+        self.num_filters = 0
+        self.filter_size = 0
+        self.bias = None
+        self.padding = 0
+        self.stride = 1
+        self.dilation = 1
+        self.groups = 1
+        self.data_format = "NCHW"
+
+
 if __name__ == "__main__":
     paddle.enable_static()
     unittest.main()

python/paddle/fluid/tests/unittests/test_functional_conv3d.py

@@ -544,6 +544,20 @@ def setUp(self):
         self.data_format = "NCDHW"
 
 
+class TestFunctionalConv3DErrorCase13(TestFunctionalConv3DErrorCase11):
+    def setUp(self):
+        self.input = np.random.randn(0, 0, 0, 0, 0)
+        self.filter = np.random.randn(1, 0, 0, 0, 0)
+        self.num_filters = 1
+        self.filter_size = 1
+        self.bias = None
+        self.padding = 0
+        self.stride = 1
+        self.dilation = 1
+        self.groups = 1
+        self.data_format = "NCDHW"
+
+
 if __name__ == "__main__":
     paddle.enable_static()
     unittest.main()