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[Paddle Inference]Add Roi_align op TRT converter unittest (#35549)
* add_roi_align * add_roi_align * add_roi_align_teller * add_roi_align * add-roi_align
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‎paddle/fluid/inference/tensorrt/op_teller.cc‎

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@@ -793,6 +793,36 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
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"the roi_align will change the batch size.";
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return false;
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}
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std::vector<std::string> attrs{"pooled_height", "pooled_width",
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"spatial_scale", "sampling_ratio"};
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for (auto const attr : attrs) {
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if (!desc.HasAttr(attr)) return false;
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}
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const auto pooled_height =
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BOOST_GET_CONST(int, desc.GetAttr("pooled_height"));
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if (pooled_height <= 0) return false;
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const auto pooled_width =
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BOOST_GET_CONST(int, desc.GetAttr("pooled_width"));
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if (pooled_width <= 0) return false;
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const auto spatial_scale =
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BOOST_GET_CONST(float, desc.GetAttr("spatial_scale"));
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if (spatial_scale <= 0.f) return false;
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const auto sampling_ratio =
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BOOST_GET_CONST(int, desc.GetAttr("sampling_ratio"));
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const auto aligned = BOOST_GET_CONST(bool, desc.GetAttr("aligned"));
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if (sampling_ratio == -1 && aligned == true) return false;
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auto roi_align_inputs = desc.Inputs();
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if (roi_align_inputs.find("RoisNum") != roi_align_inputs.end()) {
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if (desc.Input("RoisNum").size() >= 1) {
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return false;
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}
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}
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}
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if (op_type == "shuffle_channel") {

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

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
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from program_config import TensorConfig, ProgramConfig
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import numpy as np
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import paddle.inference as paddle_infer
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from functools import partial
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from typing import Optional, List, Callable, Dict, Any, Set
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class TrtConvertRoiAlignTest(TrtLayerAutoScanTest):
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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return True
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def sample_program_configs(self):
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def generate_input1(attrs: List[Dict[str, Any]], batch):
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return np.ones([batch, 256, 32, 32]).astype(np.float32)
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def generate_input2(attrs: List[Dict[str, Any]], batch):
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return np.random.random([3, 4]).astype(np.float32)
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def generate_input3(attrs: List[Dict[str, Any]], batch):
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return np.random.random([batch]).astype(np.int32)
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for num_input in [0, 1]:
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for batch in [1, 2, 4]:
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for spatial_scale in [0.5, 0.6]:
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for pooled_height in [7, 1]:
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for pooled_width in [7, 1]:
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for sampling_ratio in [-1, 4, 8]:
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for aligned in [True, False]:
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self.num_input = num_input
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if num_input == 1:
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batch = 1
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dics = [{
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"spatial_scale": spatial_scale,
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"pooled_height": pooled_height,
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"pooled_width": pooled_width,
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"sampling_ratio": sampling_ratio,
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"aligned": aligned
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}, {}]
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dics_input = [{
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"X": ["roi_align_input"],
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"ROIs": ["ROIs"],
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"RoisNum": ["RoisNum"]
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}, {
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"X": ["roi_align_input"],
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"ROIs": ["ROIs"]
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}]
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program_input = [{
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"roi_align_input": TensorConfig(
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data_gen=partial(generate_input1,
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dics, batch)),
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"ROIs": TensorConfig(data_gen=partial(
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generate_input2, dics, batch)),
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"RoisNum": TensorConfig(
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data_gen=partial(generate_input3,
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dics, batch))
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}, {
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"roi_align_input": TensorConfig(
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data_gen=partial(generate_input1,
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dics, batch)),
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"ROIs": TensorConfig(
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data_gen=partial(generate_input2,
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dics, batch),
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lod=[[32, 3]])
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}]
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ops_config = [{
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"op_type": "roi_align",
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"op_inputs": dics_input[num_input],
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"op_outputs": {
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"Out": ["roi_align_out"]
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},
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"op_attrs": dics[0]
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}]
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ops = self.generate_op_config(ops_config)
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program_config = ProgramConfig(
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ops=ops,
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weights={},
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inputs=program_input[num_input],
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outputs=["roi_align_out"])
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yield program_config
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def sample_predictor_configs(
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self, program_config) -> (paddle_infer.Config, List[int], float):
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def generate_dynamic_shape(attrs):
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if self.num_input == 0:
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self.dynamic_shape.min_input_shape = {
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"roi_align_input": [1, 256, 32, 32],
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"ROIs": [3, 4],
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"RoisNum": [1]
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}
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self.dynamic_shape.max_input_shape = {
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"roi_align_input": [1, 256, 64, 64],
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"ROIs": [3, 4],
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"RoisNum": [1]
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}
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self.dynamic_shape.opt_input_shape = {
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"roi_align_input": [1, 256, 64, 64],
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"ROIs": [3, 4],
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"RoisNum": [1]
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}
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elif self.num_input == 1:
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self.dynamic_shape.min_input_shape = {
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"roi_align_input": [1, 256, 32, 32],
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"ROIs": [3, 4]
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}
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self.dynamic_shape.max_input_shape = {
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"roi_align_input": [1, 256, 64, 64],
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"ROIs": [3, 4]
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}
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self.dynamic_shape.opt_input_shape = {
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"roi_align_input": [1, 256, 64, 64],
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"ROIs": [3, 4]
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}
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def clear_dynamic_shape():
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self.dynamic_shape.min_input_shape = {}
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self.dynamic_shape.max_input_shape = {}
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self.dynamic_shape.opt_input_shape = {}
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def generate_trt_nodes_num(attrs, dynamic_shape):
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if self.num_input == 0:
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if dynamic_shape == True:
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return 0, 5
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elif self.num_input == 1:
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if dynamic_shape == True:
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return 1, 3
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else:
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return 0, 4
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attrs = [
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program_config.ops[i].attrs
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for i in range(len(program_config.ops))
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]
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# for static_shape
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clear_dynamic_shape()
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self.trt_param.precision = paddle_infer.PrecisionType.Float32
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yield self.create_inference_config(), generate_trt_nodes_num(
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attrs, False), 1e-5
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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yield self.create_inference_config(), generate_trt_nodes_num(
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attrs, False), 1e-5
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# for dynamic_shape
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generate_dynamic_shape(attrs)
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self.trt_param.precision = paddle_infer.PrecisionType.Float32
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yield self.create_inference_config(), generate_trt_nodes_num(attrs,
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True), 1e-5
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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yield self.create_inference_config(), generate_trt_nodes_num(attrs,
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True), 1e-5
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def add_skip_trt_case(self):
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def teller1(program_config, predictor_config):
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if len(program_config.inputs) == 3:
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return True
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return False
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self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT,
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"INPUT RoisNum NOT SUPPORT")
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def teller2(program_config, predictor_config):
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if (program_config.ops[0].attrs['sampling_ratio'] == -1 and
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program_config.ops[0].attrs['aligned'] == True):
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return True
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return False
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self.add_skip_case(
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teller2, SkipReasons.TRT_NOT_SUPPORT,
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"SAMPLING_RATIO EQUAL TO - 1 WHEN ALIGNED IS TRUE IS NOT SUPPORT")
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def test(self):
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self.add_skip_trt_case()
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self.run_test()
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if __name__ == "__main__":
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unittest.main()

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