add pool2d convert test

paddle/fluid/inference/tensorrt/convert/pool2d_op.cc

@@ -87,6 +87,10 @@ class Pool2dOpConverter : public OpConverter {
     bool adaptive = false;
     if (op_desc.HasAttr("adaptive"))
       adaptive = BOOST_GET_CONST(bool, op_desc.GetAttr("adaptive"));
+    std::string padding_algorithm = "EXPLICIT";
+    if (op_desc.HasAttr("padding_algorithm"))
+      padding_algorithm =
+          BOOST_GET_CONST(std::string, op_desc.GetAttr("padding_algorithm"));
 
     nvinfer1::PoolingType nv_pool_type = nvinfer1::PoolingType::kMAX;
     nvinfer1::ReduceOperation reduce_operation =
@@ -124,6 +128,9 @@ class Pool2dOpConverter : public OpConverter {
         pool_layer->setStride(nv_strides);
         pool_layer->setPadding(nv_paddings);
         pool_layer->setAverageCountExcludesPadding(exclusive);
+        if (padding_algorithm == "SAME") {
+          pool_layer->setPaddingMode(nvinfer1::PaddingMode::kSAME_UPPER);
+        }
         layer = pool_layer;
       } else if (global_pooling) {
         auto *reduce_layer = TRT_ENGINE_ADD_LAYER(engine_, Reduce, *input1,
@@ -159,6 +166,9 @@ class Pool2dOpConverter : public OpConverter {
       auto output_name = op_desc.Output("Out")[0];
       pool_layer->setStride(nv_strides);
       pool_layer->setPadding(nv_paddings);
+      if (padding_algorithm == "SAME") {
+        pool_layer->setPaddingMode(nvinfer1::PaddingMode::kSAME_UPPER);
+      }
       pool_layer->setAverageCountExcludesPadding(exclusive);
       pool_layer->setName(("pool2d (Output: " + output_name + ")").c_str());
       pool_layer->getOutput(0)->setName(output_name.c_str());
@@ -198,6 +208,9 @@ class Pool2dOpConverter : public OpConverter {
                           "trt pool layer in converter could not be created."));
       pool_layer->setStride(nv_strides);
       pool_layer->setPadding(nv_paddings);
+      if (padding_algorithm == "SAME") {
+        pool_layer->setPaddingMode(nvinfer1::PaddingMode::kSAME_UPPER);
+      }
       pool_layer->setAverageCountExcludesPadding(exclusive);
       layer = pool_layer;
     } else {

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -179,6 +179,25 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
       std::vector<int> paddings =
           BOOST_GET_CONST(std::vector<int>, desc.GetAttr("paddings"));
       if (paddings.size() > 2) return false;
+      if (desc.HasAttr("exclusive")) {
+        if (BOOST_GET_CONST(bool, desc.GetAttr("exclusive"))) {
+          std::vector<int> ksize =
+              BOOST_GET_CONST(std::vector<int>, desc.GetAttr("ksize"));
+          for (size_t i = 0; i < ksize.size(); i++) {
+            if (ksize[i] <= paddings[i]) {
+              VLOG(3) << "the padding size should be less than the filter size "
+                         "for exclusive-counting pooling.";
+              return false;
+            }
+          }
+        }
+      }
+      if (desc.HasAttr("global_pooling")) {
+        if (BOOST_GET_CONST(bool, desc.GetAttr("global_pooling"))) return false;
+      }
+      if (desc.HasAttr("ceil_mode")) {
+        if (BOOST_GET_CONST(bool, desc.GetAttr("ceil_mode"))) return false;
+      }
       if (desc.Input("X").size() != 1) {
         VLOG(3) << "TRT Pool2d expect 1 input, but got "
                 << desc.Input("X").size();
@@ -442,6 +461,10 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
       }
     }
 
+    if (op_type == "anchor_generator") {
+      if (!with_dynamic_shape) return false;
+    }
+
     if (op_type == "yolo_box") {
       if (with_dynamic_shape) return false;
       bool has_attrs =

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_anchor_generator.py

@@ -0,0 +1,116 @@
+# Copyright (c) 2021 PaddlePaddle 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.
+
+from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
+from program_config import TensorConfig, ProgramConfig
+import numpy as np
+import paddle.inference as paddle_infer
+from functools import partial
+from typing import Optional, List, Callable, Dict, Any, Set
+
+
+class TrtConvertAnchorGeneratorTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(batch, attrs: List[Dict[str, Any]]):
+            return np.random.random([batch, 3, 64, 64]).astype(np.float32)
+
+        for batch in [1, 2, 4]:
+            for anchor_sizes in [[64.0, 128.0, 256.0, 512.0]]:
+                for aspect_ratios in [[0.5, 1, 2], [0.4, 1.2, 3]]:
+                    for variances in [[1.0, 1.0, 1.0, 1.0],
+                                      [0.5, 1.0, 0.5, 1.0]]:
+                        for stride in [[16.0, 16.0], [16.0, 32.0]]:
+                            for offset in [0.5, 0.8]:
+                                dics = [{
+                                    "anchor_sizes": anchor_sizes,
+                                    "aspect_ratios": aspect_ratios,
+                                    "variances": variances,
+                                    "stride": stride,
+                                    "offset": offset
+                                }]
+
+                                ops_config = [{
+                                    "op_type": "anchor_generator",
+                                    "op_inputs": {
+                                        "Input": ["input_data"]
+                                    },
+                                    "op_outputs": {
+                                        "Anchors": ["output_anchors"],
+                                        "Variances": ["output_variances"]
+                                    },
+                                    "op_attrs": dics[0]
+                                }]
+                                ops = self.generate_op_config(ops_config)
+
+                                program_config = ProgramConfig(
+                                    ops=ops,
+                                    weights={},
+                                    inputs={
+                                        "input_data": TensorConfig(
+                                            data_gen=partial(generate_input1,
+                                                             batch, dics))
+                                    },
+                                    outputs=[
+                                        "output_anchors", "output_variances"
+                                    ])
+
+                                yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]}
+            self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]}
+            self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        def generate_trt_nodes_num(attrs, dynamic_shape):
+            return 1, 3
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        # for static_shape
+        clear_dynamic_shape()
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, False), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, False), 1e-5
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), generate_trt_nodes_num(attrs,
+                                                                     True), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), generate_trt_nodes_num(attrs,
+                                                                     True), 1e-5
+
+    def test(self):
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()