polish pool infer shape (#20038)

* fix pool infershape. test=develop

* fix unittest converage. test=develop

* fix format. test=develop

paddle/fluid/operators/pool_op.cc

@@ -78,10 +78,14 @@ void PoolOp::InferShape(framework::InferShapeContext* ctx) const {
     output_shape.insert(output_shape.end(), ksize.begin(), ksize.end());
   } else {
     for (size_t i = 0; i < ksize.size(); ++i) {
+      if (!ctx->IsRuntime() && in_x_dims[i + 2] <= 0) {
+        output_shape.push_back(-1);
+      } else {
         output_shape.push_back(PoolOutputSize(
             in_x_dims[i + 2], ksize[i], paddings[i], strides[i], ceil_mode));
       }
     }
+  }
   ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
   ctx->ShareLoD("X", "Out");
 }

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

@@ -1354,6 +1354,25 @@ class TestBook(LayerTest):
             return (layers.pool2d(
                 x, pool_size=[5, 3], pool_stride=[1, 2], pool_padding=(2, 1)))
 
+    def make_pool2d_infershape(self):
+        with program_guard(fluid.default_main_program(),
+                           fluid.default_startup_program()):
+            theta = self._get_data("theta", shape=[2, 3], dtype='float32')
+            x = fluid.layers.affine_grid(theta, out_shape=[2, 3, 244, 244])
+            return (layers.pool2d(
+                x, pool_size=[5, 3], pool_stride=[1, 2], pool_padding=(2, 1)))
+
+    def make_pool3d(self):
+        with program_guard(fluid.default_main_program(),
+                           fluid.default_startup_program()):
+            x = self._get_data(
+                name='x', shape=[3, 244, 244, 244], dtype='float32')
+            return (layers.pool3d(
+                x,
+                pool_size=[5, 3, 2],
+                pool_stride=[1, 2, 3],
+                pool_padding=(2, 1, 1)))
+
     def make_adaptive_pool2d(self):
         with program_guard(fluid.default_main_program(),
                            fluid.default_startup_program()):