Merge pull request #7 from BaiYM0117/new_API

Clip op implemented in new API

ngraph/frontend/paddlepaddle/src/op/clip.cpp

@@ -0,0 +1,34 @@
+//*****************************************************************************
+// Copyright 2017-2021 Intel Corporation
+//
+// 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.
+//*****************************************************************************
+
+#include <ngraph/opsets/opset6.hpp>
+#include "clip.hpp"
+#include <paddlepaddle_frontend/utility.hpp>
+
+namespace ngraph {
+namespace frontend {
+namespace pdpd {
+namespace op {
+
+NamedOutputs clip (const NodeContext& node) {
+    auto data = node.get_ng_input("X");
+    auto min = node.get_attribute<float>("min");
+    auto max = node.get_attribute<float>("max");
+    PDPD_ASSERT(max >= min, "clip: max value must greater than min value!");
+    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Clamp>(data, min, max)}, {"Out"});     
+}
+
+}}}}

ngraph/frontend/paddlepaddle/src/op/clip.hpp

@@ -0,0 +1,27 @@
+//*****************************************************************************
+// Copyright 2017-2021 Intel Corporation
+//
+// 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.
+//*****************************************************************************
+
+#pragma once
+#include "node_context.hpp"
+
+namespace ngraph {
+namespace frontend {
+namespace pdpd {
+namespace op {
+
+NamedOutputs clip (const NodeContext& node);
+
+}}}}

ngraph/frontend/paddlepaddle/src/op_table.cpp

@@ -46,10 +46,10 @@
 #include "op/unsqueeze.hpp"
 #include "op/slice.hpp"
 #include "op/hard_swish.hpp"
+#include "op/clip.hpp"
 #include "op/greater_equal.hpp"
 #include "op/log.hpp"
 #include "op/fill_constant_batch_size_like.hpp"
-
 #include "op_table.hpp"
 
 
@@ -101,6 +101,7 @@ std::map<std::string, CreatorFunction> get_supported_ops() {
             {"unsqueeze2", op::unsqueeze},
             {"slice", op::slice},
             {"hard_swish", op::hard_swish},
+            {"clip", op::clip},
             {"greater_equal", op::greater_equal},
             {"log", op::log},
             {"fill_constant_batch_size_like", op::fill_constant_batch_size_like}

ngraph/test/files/paddlepaddle/gen_scripts/generate_clip.py

@@ -0,0 +1,39 @@
+#
+# clip paddle model generator
+#
+import numpy as np
+from save_model import saveModel
+
+
+def clip(name: str, x, min, max):
+    import paddle as pdpd
+    pdpd.enable_static()
+
+    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
+        node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
+        out = pdpd.fluid.layers.clip(node_x, min=min, max=max)
+
+        cpu = pdpd.static.cpu_places(1)
+        exe = pdpd.static.Executor(cpu[0])
+        # startup program will call initializer to initialize the parameters.
+        exe.run(pdpd.static.default_startup_program())
+
+        outs = exe.run(
+            feed={'x': x},
+            fetch_list=[out])
+
+        saveModel(name, exe, feedkeys=['x'], fetchlist=[out], inputs=[x], outputs=[outs[0]])
+
+    return outs[0]
+
+
+def main():
+    data = np.random.random([2, 3, 4]).astype('float32')
+    min = 0
+    max = 0.8
+
+    clip("clip", data, min, max)
+
+
+if __name__ == "__main__":
+    main()