add pool2d convert test (#35923)

* add pool2d convert test

* modify error

* modify error

* modify error

* modify error

* modify error

* modify error

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

@@ -172,6 +172,22 @@ 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("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();
@@ -440,6 +456,10 @@ 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

+# 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()

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_conv2d_transpose.py

+# 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 TrtConvertConv2dTransposeTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        weights = program_config.weights
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[
+                1] * attrs[0]['groups']:
+            return False
+
+        if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[0]:
+            return False
+
+        return True
+
+    def sample_program_configs(self):
+        self.trt_param.workspace_size = 1073741824
+
+        def generate_input1(batch, num_channels, attrs: List[Dict[str, Any]]):
+            return np.ones([batch, num_channels, 64, 64]).astype(np.float32)
+
+        def generate_weight1(num_channels, attrs: List[Dict[str, Any]]):
+            if attrs[0]['groups'] == 1:
+                return np.random.random(
+                    [num_channels, num_channels, 3, 3]).astype(np.float32)
+            else:
+                return np.random.random(
+                    [num_channels, int(num_channels / 2), 3, 3]).astype(
+                        np.float32)
+
+        for num_channels in [2, 4, 6]:
+            for batch in [1, 2, 4]:
+                for strides in [[1, 1], [2, 2], [1, 2]]:
+                    for paddings in [[0, 3], [1, 2, 3, 4]]:
+                        for groups in [2]:
+                            for padding_algorithm in [
+                                    'EXPLICIT', 'SAME', 'VALID'
+                            ]:
+                                for dilations in [[1, 1], [2, 2], [1, 2]]:
+                                    for data_format in ['NCHW']:
+
+                                        self.num_channels = num_channels
+                                        dics = [{
+                                            "data_fromat": data_format,
+                                            "dilations": dilations,
+                                            "padding_algorithm":
+                                            padding_algorithm,
+                                            "groups": groups,
+                                            "paddings": paddings,
+                                            "strides": strides,
+                                            "data_format": data_format,
+                                            "output_size": [],
+                                            "output_padding": []
+                                        }]
+
+                                        ops_config = [{
+                                            "op_type": "conv2d_transpose",
+                                            "op_inputs": {
+                                                "Input": ["input_data"],
+                                                "Filter": ["conv2d_weight"]
+                                            },
+                                            "op_outputs": {
+                                                "Output": ["output_data"]
+                                            },
+                                            "op_attrs": dics[0]
+                                        }]
+                                        ops = self.generate_op_config(
+                                            ops_config)
+
+                                        program_config = ProgramConfig(
+                                            ops=ops,
+                                            weights={
+                                                "conv2d_weight":
+                                                TensorConfig(data_gen=partial(
+                                                    generate_weight1,
+                                                    num_channels, dics))
+                                            },
+                                            inputs={
+                                                "input_data":
+                                                TensorConfig(data_gen=partial(
+                                                    generate_input1, batch,
+                                                    num_channels, dics))
+                                            },
+                                            outputs=["output_data"])
+
+                                        yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            if self.num_channels == 2:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data": [1, 2, 32, 32],
+                    "output_data": [1, 24, 32, 32]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [4, 2, 64, 64],
+                    "output_data": [4, 24, 64, 64]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data": [1, 2, 64, 64],
+                    "output_data": [1, 24, 64, 64]
+                }
+            elif self.num_channels == 4:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data": [1, 4, 32, 32],
+                    "output_data": [1, 24, 32, 32]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [4, 4, 64, 64],
+                    "output_data": [4, 24, 64, 64]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data": [1, 4, 64, 64],
+                    "output_data": [1, 24, 64, 64]
+                }
+            else:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data": [1, 6, 32, 32],
+                    "output_data": [1, 24, 32, 32]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [4, 6, 64, 64],
+                    "output_data": [4, 24, 64, 64]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data": [1, 6, 64, 64],
+                    "output_data": [1, 24, 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, 2
+
+        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, 1e-5)
+        self.trt_param.precision = paddle_infer.PrecisionType.Int8
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, False), (1e-5, 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, 1e-5)
+        self.trt_param.precision = paddle_infer.PrecisionType.Int8
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, True), (1e-5, 1e-5)
+
+    def add_skip_trt_case(self):
+        def teller1(program_config, predictor_config):
+            if program_config.ops[0].attrs[
+                    'padding_algorithm'] == "SAME" or program_config.ops[
+                        0].attrs['padding_algorithm'] == "VALID":
+                return True
+            return False
+
+        self.add_skip_case(
+            teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
+            "When padding_algorithm is 'SAME' or 'VALID', Trt dose not support. In this case, trt build error is caused by scale op."
+        )
+
+        def teller2(program_config, predictor_config):
+            if program_config.ops[0].attrs['dilations'][
+                    0] != 1 or program_config.ops[0].attrs['dilations'][1] != 1:
+                return True
+            return False
+
+        self.add_skip_case(
+            teller2, SkipReasons.TRT_NOT_IMPLEMENTED,
+            "When dilations's element is not equal 1, there are different behaviors between Trt and Paddle."
+        )
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+    def test_quant(self):
+        self.add_skip_trt_case()
+        self.run_test(quant=True)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_depthwise_conv2d.py

+# 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 TrtConvertDepthwiseConv2dTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        weights = program_config.weights
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[
+                1] * attrs[0]['groups']:
+            return False
+
+        return True
+
+    def sample_program_configs(self):
+        self.trt_param.workspace_size = 1073741824
+
+        def generate_input1(batch, attrs: List[Dict[str, Any]]):
+            if attrs[0]['groups'] == 1:
+                return np.ones([batch, 1, 64, 64]).astype(np.float32)
+            elif attrs[0]['groups'] == 2:
+                return np.ones([batch, 2, 64, 64]).astype(np.float32)
+            else:
+                return np.ones([batch, 3, 64, 64]).astype(np.float32)
+
+        def generate_weight1(attrs: List[Dict[str, Any]]):
+            return np.random.random([24, 1, 3, 3]).astype(np.float32)
+
+        for batch in [1, 2, 4]:
+            for strides in [[1, 1], [2, 2], [1, 2]]:
+                for paddings in [[0, 3], [1, 2, 3, 4]]:
+                    for groups in [1, 2, 3]:
+                        for padding_algorithm in ['EXPLICIT', 'SAME', 'VALID']:
+                            for dilations in [[1, 1], [2, 2], [1, 2]]:
+                                for data_format in ['NCHW']:
+
+                                    dics = [{
+                                        "data_fromat": data_format,
+                                        "dilations": dilations,
+                                        "padding_algorithm": padding_algorithm,
+                                        "groups": groups,
+                                        "paddings": paddings,
+                                        "strides": strides,
+                                        "data_format": data_format
+                                    }]
+
+                                    ops_config = [{
+                                        "op_type": "depthwise_conv2d",
+                                        "op_inputs": {
+                                            "Input": ["input_data"],
+                                            "Filter": ["conv2d_weight"]
+                                        },
+                                        "op_outputs": {
+                                            "Output": ["output_data"]
+                                        },
+                                        "op_attrs": dics[0]
+                                    }]
+                                    ops = self.generate_op_config(ops_config)
+
+                                    program_config = ProgramConfig(
+                                        ops=ops,
+                                        weights={
+                                            "conv2d_weight":
+                                            TensorConfig(data_gen=partial(
+                                                generate_weight1, dics))
+                                        },
+                                        inputs={
+                                            "input_data":
+                                            TensorConfig(data_gen=partial(
+                                                generate_input1, batch, dics))
+                                        },
+                                        outputs=["output_data"])
+
+                                    yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            if attrs[0]['groups'] == 1:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data": [1, 1, 32, 32],
+                    "output_data": [1, 24, 32, 32]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [4, 1, 64, 64],
+                    "output_data": [4, 24, 64, 64]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data": [1, 1, 64, 64],
+                    "output_data": [1, 24, 64, 64]
+                }
+            elif attrs[0]['groups'] == 2:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data": [1, 2, 32, 32],
+                    "output_data": [1, 24, 32, 32]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [4, 2, 64, 64],
+                    "output_data": [4, 24, 64, 64]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data": [1, 2, 64, 64],
+                    "output_data": [1, 24, 64, 64]
+                }
+            else:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data": [1, 3, 32, 32],
+                    "output_data": [1, 24, 32, 32]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [4, 3, 64, 64],
+                    "output_data": [4, 24, 64, 64]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data": [1, 3, 64, 64],
+                    "output_data": [1, 24, 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, 2
+
+        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, 1e-5)
+        self.trt_param.precision = paddle_infer.PrecisionType.Int8
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, False), (1e-5, 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, 1e-5)
+        self.trt_param.precision = paddle_infer.PrecisionType.Int8
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, True), (1e-5, 1e-5)
+
+    def add_skip_trt_case(self):
+        def teller1(program_config, predictor_config):
+            if program_config.ops[0].attrs[
+                    'padding_algorithm'] == "SAME" or program_config.ops[
+                        0].attrs['padding_algorithm'] == "VALID":
+                return True
+            return False
+
+        self.add_skip_case(
+            teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
+            "When padding_algorithm is 'SAME' or 'VALID', Trt dose not support. In this case, trt build error is caused by scale op."
+        )
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+    def test_quant(self):
+        self.add_skip_trt_case()
+        self.run_test(quant=True)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_depthwise_conv2d_transpose.py

+# 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 TrtConvertDepthwiseConv2dTransposeTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        weights = program_config.weights
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[
+                1] * attrs[0]['groups']:
+            return False
+
+        if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[1]:
+            return False
+
+        if inputs['input_data'].shape[1] != attrs[0]['groups']:
+            return False
+
+        return True
+
+    def sample_program_configs(self):
+        self.trt_param.workspace_size = 1073741824
+
+        def generate_input1(batch, attrs: List[Dict[str, Any]]):
+            return np.ones(
+                [batch, attrs[0]['groups'], 64, 64]).astype(np.float32)
+
+        def generate_weight1(attrs: List[Dict[str, Any]]):
+            return np.random.random(
+                [attrs[0]['groups'], 1, 3, 3]).astype(np.float32)
+
+        for batch in [1, 2, 4]:
+            for strides in [[1, 1], [2, 2], [1, 2]]:
+                for paddings in [[0, 3], [1, 2, 3, 4]]:
+                    for groups in [1, 2, 3]:
+                        for padding_algorithm in ['EXPLICIT', 'SAME', 'VALID']:
+                            for dilations in [[1, 1], [2, 2], [1, 2]]:
+                                for data_format in ['NCHW']:
+
+                                    dics = [{
+                                        "data_fromat": data_format,
+                                        "dilations": dilations,
+                                        "padding_algorithm": padding_algorithm,
+                                        "groups": groups,
+                                        "paddings": paddings,
+                                        "strides": strides,
+                                        "data_format": data_format,
+                                        "output_size": [],
+                                        "output_padding": []
+                                    }]
+
+                                    ops_config = [{
+                                        "op_type": "conv2d_transpose",
+                                        "op_inputs": {
+                                            "Input": ["input_data"],
+                                            "Filter": ["conv2d_weight"]
+                                        },
+                                        "op_outputs": {
+                                            "Output": ["output_data"]
+                                        },
+                                        "op_attrs": dics[0]
+                                    }]
+                                    ops = self.generate_op_config(ops_config)
+
+                                    program_config = ProgramConfig(
+                                        ops=ops,
+                                        weights={
+                                            "conv2d_weight":
+                                            TensorConfig(data_gen=partial(
+                                                generate_weight1, dics))
+                                        },
+                                        inputs={
+                                            "input_data":
+                                            TensorConfig(data_gen=partial(
+                                                generate_input1, batch, dics))
+                                        },
+                                        outputs=["output_data"])
+
+                                    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, attrs[0]['groups'], 32, 32],
+                "output_data": [1, attrs[0]['groups'], 32, 32]
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data": [4, attrs[0]['groups'], 64, 64],
+                "output_data": [4, attrs[0]['groups'], 64, 64]
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data": [1, attrs[0]['groups'], 64, 64],
+                "output_data": [1, attrs[0]['groups'], 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, 2
+
+        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, 1e-5)
+        self.trt_param.precision = paddle_infer.PrecisionType.Int8
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, False), (1e-5, 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, 1e-5)
+        self.trt_param.precision = paddle_infer.PrecisionType.Int8
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, True), (1e-5, 1e-5)
+
+    def add_skip_trt_case(self):
+        def teller1(program_config, predictor_config):
+            if program_config.ops[0].attrs[
+                    'padding_algorithm'] == "SAME" or program_config.ops[
+                        0].attrs['padding_algorithm'] == "VALID":
+                return True
+            return False
+
+        self.add_skip_case(
+            teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
+            "When padding_algorithm is 'SAME' or 'VALID', Trt dose not support. In this case, trt build error is caused by scale op."
+        )
+
+        def teller2(program_config, predictor_config):
+            if program_config.ops[0].attrs['dilations'][
+                    0] != 1 or program_config.ops[0].attrs['dilations'][1] != 1:
+                return True
+            return False
+
+        self.add_skip_case(
+            teller2, SkipReasons.TRT_NOT_IMPLEMENTED,
+            "When dilations's element is not equal 1, there are different behaviors between Trt and Paddle."
+        )
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+    def test_quant(self):
+        self.add_skip_trt_case()
+        self.run_test(quant=True)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pool2d.py

+# 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 TrtConvertPool2dTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        self.trt_param.workspace_size = 1073741824
+
+        def generate_input1(attrs: List[Dict[str, Any]]):
+            return np.ones([1, 3, 64, 64]).astype(np.float32)
+
+        def generate_weight1(attrs: List[Dict[str, Any]]):
+            return np.random.random([24, 3, 3, 3]).astype(np.float32)
+
+        for strides in [[1, 1], [2, 2], [1, 2]]:
+            for paddings in [[0, 2], [0, 3], [1, 2, 3, 4]]:
+                for pooling_type in ['max', 'avg']:
+                    for padding_algotithm in ['EXPLICIT', 'SAME', 'VAILD']:
+                        for ksize in [[2, 3], [3, 3]]:
+                            for data_format in ['NCHW']:
+                                for global_pooling in [True, False]:
+                                    for exclusive in [True, False]:
+                                        for adaptive in [True, False]:
+                                            for ceil_mode in [True, False]:
+                                                self.paddings = paddings
+
+                                                dics = [{
+                                                    "pooling_type":
+                                                    pooling_type,
+                                                    "ksize": ksize,
+                                                    "data_fromat": data_format,
+                                                    "padding_algorithm":
+                                                    padding_algotithm,
+                                                    "paddings": paddings,
+                                                    "strides": strides,
+                                                    "data_format": data_format,
+                                                    "global_pooling":
+                                                    global_pooling,
+                                                    "exclusive": exclusive,
+                                                    "adaptive": adaptive,
+                                                    "ceil_mode": ceil_mode
+                                                }]
+
+                                                ops_config = [{
+                                                    "op_type": "pool2d",
+                                                    "op_inputs": {
+                                                        "X": ["input_data"],
+                                                    },
+                                                    "op_outputs": {
+                                                        "Out": ["output_data"]
+                                                    },
+                                                    "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,
+                                                                dics))
+                                                    },
+                                                    outputs=["output_data"])
+
+                                                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):
+            if self.paddings == [0, 3] or attrs[0][
+                    'global_pooling'] == True or attrs[0]['ceil_mode'] == True:
+                return 0, 3
+            return 1, 2
+
+        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 add_skip_trt_case(self):
+        def teller1(program_config, predictor_config):
+            if len(program_config.ops[0].attrs['paddings']) == 4:
+                return True
+            return False
+
+        self.add_skip_case(teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
+                           "4-dims paddings are not support for trt now.")
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()