add mapping for "fill_" kernel

examples/oneflow2onnx/nodes/CPU/test_fill.py

@@ -0,0 +1,50 @@
+"""
+Copyright 2020 The OneFlow Authors. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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
+from oneflow_onnx.oneflow2onnx.util import convert_to_onnx_and_check
+
+
+class FillModule(flow.nn.Module):
+    def __init__(self) -> None:
+        super(FillModule, self).__init__()
+
+    def forward(self, x: flow.Tensor) -> flow.Tensor:
+        return x.fill_(5)
+
+
+m = FillModule()
+
+
+class fillOpGraph(flow.nn.Graph):
+    def __init__(self):
+        super().__init__()
+        self.m = m
+
+    def build(self, x):
+        out = self.m(x)
+        return out
+
+
+def test_fill():
+
+    fill_graph = fillOpGraph()
+    fill_graph._compile(flow.randn(1, 5))
+
+    convert_to_onnx_and_check(fill_graph, onnx_model_path="/tmp")
+
+
+test_fill()

examples/oneflow2onnx/nodes/GPU/test_fill.py

@@ -0,0 +1,50 @@
+"""
+Copyright 2020 The OneFlow Authors. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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
+from oneflow_onnx.oneflow2onnx.util import convert_to_onnx_and_check
+
+
+class FillModule(flow.nn.Module):
+    def __init__(self) -> None:
+        super(FillModule, self).__init__()
+
+    def forward(self, x: flow.Tensor) -> flow.Tensor:
+        return x.fill_(5)
+
+
+m = FillModule().to("cuda")
+
+
+class fillOpGraph(flow.nn.Graph):
+    def __init__(self):
+        super().__init__()
+        self.m = m
+
+    def build(self, x):
+        out = self.m(x)
+        return out
+
+
+def test_fill():
+
+    fill_graph = fillOpGraph()
+    fill_graph._compile(flow.randn(1, 5).to("cuda"))
+
+    convert_to_onnx_and_check(fill_graph, onnx_model_path="/tmp", device="gpu")
+
+
+test_fill()

oneflow_onnx/oneflow2onnx/handlers/math.py

@@ -739,3 +739,32 @@ def Version_13(cls, ctx, node, **kwargs):
             var_node = ctx.MakeNode(
                 "ReduceMean", [sqr_sub], op_name_scope=node.name, name="var", dtypes=dtypes, attr={"axes": origin_dim, "keepdims": keepdim_mean}, outputs=[node.output_tensor_names[0]]
             )
+
+
+@flow_op("fill_", onnx_op="Constant")
+class Fill:
+    @classmethod
+    def Version_1(cls, ctx, node, **kwargs):
+        is_floating_value = node.attrs["is_floating_value"]
+        output_name = node.output_tensor_names[0]
+        out_shape = ctx.get_shape(output_name)
+
+        if is_floating_value:
+            values = np.full(shape=out_shape, fill_value=node.attrs["floating_value"], dtype=np.float32)
+        else:
+            values = np.full(shape=out_shape, fill_value=node.attrs["integral_value"], dtype=np.float32)
+
+        ctx.RemoveNode(node.name)
+        ctx.MakeConst(output_name, values)
+
+    @classmethod
+    def Version_9(cls, ctx, node, **kwargs):
+        cls.Version_1(ctx, node, **kwargs)
+
+    @classmethod
+    def Version_11(cls, ctx, node, **kwargs):
+        cls.Version_1(ctx, node, **kwargs)
+
+    @classmethod
+    def Version_13(cls, ctx, node, **kwargs):
+        cls.Version_1(ctx, node, **kwargs)