add pad op

examples/oneflow2onnx/nodes/test_math.py

@@ -37,6 +37,7 @@ class MathOps(flow.nn.Module):
 
 
 math_ops = MathOps()
+math_ops = math_ops.to("cuda")
 class MathOpGraph(flow.nn.Graph):
     def __init__(self):
         super().__init__()
@@ -50,7 +51,7 @@ class MathOpGraph(flow.nn.Graph):
 def test_math_ops():
     
     math_ops_graph = MathOpGraph()
-    math_ops_graph._compile(flow.randn(1, 3, 224, 224))
+    math_ops_graph._compile(flow.randn(1, 3, 224, 224).to("cuda"))
     # print(math_ops_graph._full_graph_proto)
 
     with tempfile.TemporaryDirectory() as tmpdirname:

examples/oneflow2onnx/nodes/test_pad.py

@@ -13,14 +13,35 @@ 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.
 """
+import tempfile
 import oneflow as flow
-import oneflow.typing as tp
 from oneflow_onnx.oneflow2onnx.util import convert_to_onnx_and_check
 
+class Pad(flow.nn.Module):
+    def __init__(self) -> None:
+        super(Pad, self).__init__()
 
-def test_pad_float():
-    @flow.global_function()
-    def pad(x: tp.Numpy.Placeholder((3, 5))):
-        return flow.pad(x, [(1, 2), (3, 4)], 1)
+    def forward(self, x: flow.Tensor) -> flow.Tensor:
+        return flow.nn.functional.pad(x, (1, 1))
 
-    convert_to_onnx_and_check(pad)
+pad = Pad()
+class padOpGraph(flow.nn.Graph):
+    def __init__(self):
+        super().__init__()
+        self.m = pad
+
+    def build(self, x):
+        out = self.m(x)
+        return out
+
+
+def test_pad():
+    
+    pad_graph = padOpGraph()
+    pad_graph._compile(flow.randn(1, 3, 224, 224))
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        flow.save(pad.state_dict(), tmpdirname)
+        convert_to_onnx_and_check(pad_graph, flow_weight_dir=tmpdirname, onnx_model_path="/tmp")
+
+test_pad()