remove adaptive pool2d shape check in ngraph paddle frontend (#7074)

* remove adaptive pool2d shape check in ngraph paddle frontend

* add ngraph paddle frontend dynamic pool2d test

ngraph/frontend/paddlepaddle/src/op/pool2d.cpp

@@ -100,8 +100,6 @@ NamedOutputs pool2d(const NodeContext& node) {
                                                       {"Out"});
         }
     } else if (adaptive) {
-        PDPD_ASSERT(input_shape[2].is_static() && input_shape[3].is_static(),
-                    "pool2d: spatial dim must be static when using adaptive pool");
         auto pool_size = std::vector<int64_t>(2, 0);
 
         if (kernel_shape.size() == 1) {
@@ -187,4 +185,4 @@ NamedOutputs pool2d(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ngraph

ngraph/test/frontend/paddlepaddle/convert_model.cpp

@@ -18,6 +18,7 @@ static const std::vector<std::string> models{
     std::string("2in_2out/2in_2out.pdmodel"),
     std::string("multi_tensor_split/multi_tensor_split.pdmodel"),
     std::string("2in_2out_dynbatch/2in_2out_dynbatch.pdmodel"),
+    std::string("pool2d_dyn_hw/pool2d_dyn_hw.pdmodel"),
 };
 
 INSTANTIATE_TEST_SUITE_P(PDPDConvertModelTest,

ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_dynamic_pool2d.py

@@ -0,0 +1,32 @@
+#
+# pool2d paddle dynamic model generator
+#
+
+import paddle
+from paddle import fluid
+import numpy as np
+import sys
+import os
+from save_model import saveModel
+
+paddle.enable_static()
+inp_blob1 = np.random.randn(1, 1, 224, 224).astype(np.float32)
+
+x1 = fluid.data(name='inputX1', shape=[1, 1, -1, -1], dtype='float32')
+
+adative_pool2d = paddle.fluid.layers.adaptive_pool2d(
+    input=x1,
+    pool_size=[3,3],
+    pool_type='avg',
+    require_index=False)
+
+cpu = paddle.static.cpu_places(1)
+exe = paddle.static.Executor(cpu[0])
+# startup program will call initializer to initialize the parameters.
+exe.run(paddle.static.default_startup_program())
+
+outs = exe.run(
+    feed={'inputX1': inp_blob1},
+    fetch_list=[adative_pool2d])
+
+saveModel("pool2d_dyn_hw", exe, feedkeys=['inputX1'], fetchlist=adative_pool2d, inputs=[inp_blob1], outputs=outs, target_dir=sys.argv[1])