未验证 提交 3d232b29 编写于 作者: C ccrrong 提交者: GitHub

add bilinear interp v2 converter (#43307)

* add bilinear_interp_v2 converter
上级 b2b78f8e
......@@ -1943,6 +1943,7 @@ USE_TRT_CONVERTER(multiclass_nms);
USE_TRT_CONVERTER(multiclass_nms3);
USE_TRT_CONVERTER(nearest_interp);
USE_TRT_CONVERTER(nearest_interp_v2);
USE_TRT_CONVERTER(bilinear_interp_v2);
USE_TRT_CONVERTER(reshape);
USE_TRT_CONVERTER(reduce_sum);
USE_TRT_CONVERTER(gather_nd);
......
......@@ -52,6 +52,7 @@ list(
conv3d_op.cc
mish_op.cc
nearest_interp_v2_op.cc
bilinear_interp_v2_op.cc
pool3d_op.cc
deformable_conv_op.cc
preln_emb_eltwise_layernorm.cc
......
/* Copyright (c) 2022 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. */
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
namespace paddle {
namespace framework {
class Scope;
namespace proto {
class OpDesc;
} // namespace proto
} // namespace framework
} // namespace paddle
namespace paddle {
namespace inference {
namespace tensorrt {
class BilinearInterpolateV2OpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(3) << "convert a fluid bilinear_interp_v2 op";
framework::OpDesc op_desc(op, nullptr);
std::string input_name = op_desc.Input("X").front();
std::string output_name = op_desc.Output("Out").front();
auto input = engine_->GetITensor(input_name);
auto data_layout = framework::StringToDataLayout(
BOOST_GET_CONST(std::string, op_desc.GetAttr("data_layout")));
auto interp_method =
BOOST_GET_CONST(std::string, op_desc.GetAttr("interp_method"));
bool align_corners =
BOOST_GET_CONST(bool, op_desc.GetAttr("align_corners"));
auto align_mode = BOOST_GET_CONST(int, op_desc.GetAttr("align_mode"));
auto resize_inputs = op_desc.Inputs();
auto input_names = op_desc.Input("X");
auto out_h = BOOST_GET_CONST(int, op_desc.GetAttr("out_h"));
auto out_w = BOOST_GET_CONST(int, op_desc.GetAttr("out_w"));
auto layer = TRT_ENGINE_ADD_LAYER(engine_, Resize, *input);
if (align_mode == 0 && !align_corners) {
layer->setResizeMode(nvinfer1::ResizeMode::kLINEAR);
}
auto in_dim = input->getDimensions();
float scale_h = 1.f;
float scale_w = 1.f;
// Scales Priority: Scale(tensor) > scale(attr) > out_d/out_h/out_w(attr)
bool has_scale_input_attr =
(resize_inputs.find("Scale") != resize_inputs.end());
bool has_scale_input =
has_scale_input_attr && (op_desc.Input("Scale").size() > 0);
if (has_scale_input) {
auto* scale_var = scope.FindVar(op_desc.Input("Scale")[0]);
auto* scale_tensor = scale_var->GetMutable<framework::LoDTensor>();
auto* scale_d = scale_tensor->data<float>();
scale_h = scale_d[0];
scale_w = scale_d[1];
} else {
const std::vector<float> scale_attr =
BOOST_GET_CONST(std::vector<float>, op_desc.GetAttr("scale"));
if (scale_attr.size() > 1) {
scale_h = scale_attr[0];
scale_w = scale_attr[1];
}
}
// axis are different in static/dynamic mode
bool with_dynamic = engine_->with_dynamic_shape();
int h_axis = (data_layout == framework::DataLayout::kNCHW) + with_dynamic;
int w_axis =
(data_layout == framework::DataLayout::kNCHW) + 1 + with_dynamic;
if (scale_w > 0. && scale_h > 0.) {
out_h = static_cast<int>(in_dim.d[h_axis] * scale_h);
out_w = static_cast<int>(in_dim.d[w_axis] * scale_w);
}
if (out_h > 0 && out_w > 0) {
scale_h =
static_cast<float>(out_h) / static_cast<float>(in_dim.d[h_axis]);
scale_w =
static_cast<float>(out_w) / static_cast<float>(in_dim.d[w_axis]);
}
std::vector<float> scales;
if (engine_->with_dynamic_shape()) {
scales.push_back(1.f);
}
if (data_layout == framework::DataLayout::kNCHW) {
scales.push_back(1.f);
scales.push_back(scale_h);
scales.push_back(scale_w);
} else if (data_layout == framework::DataLayout::kNHWC) {
scales.push_back(scale_h);
scales.push_back(scale_w);
scales.push_back(1.f);
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"Data layout must be NCHW or NHWC."));
}
layer->setScales(scales.data(), scales.size());
RreplenishLayerAndOutput(layer, "bilinear_interp_v2", {output_name},
test_mode);
}
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
REGISTER_TRT_OP_CONVERTER(bilinear_interp_v2, BilinearInterpolateV2OpConverter);
......@@ -144,6 +144,7 @@ struct SimpleOpTypeSetTeller : public Teller {
"conv3d_transpose",
"mish",
"nearest_interp_v2",
"bilinear_interp_v2",
"pool3d",
"deformable_conv",
"relu6",
......@@ -239,6 +240,7 @@ struct SimpleOpTypeSetTeller : public Teller {
"conv3d",
"conv3d_transpose",
"mish",
"bilinear_interp_v2",
"nearest_interp_v2",
"pool3d",
"deformable_conv",
......@@ -875,6 +877,99 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
}
}
if (op_type == "bilinear_interp_v2") {
std::vector<std::string> attrs{"data_layout", "interp_method",
"align_corners", "scale",
"out_h", "out_w"};
for (auto const attr : attrs) {
if (!desc.HasAttr(attr)) {
VLOG(3) << "The op_type " << op_type << " doesn't have the attr "
<< attr << " and return false";
return false;
}
}
auto resize_inputs = desc.Inputs();
if (resize_inputs.find("SizeTensor") != resize_inputs.end()) {
if (desc.Input("SizeTensor").size() >= 1) {
VLOG(3)
<< "The Paddle-TRT doesn't support the SizeTensor for op_type "
<< op_type;
return false;
}
}
if (resize_inputs.find("OutSize") != resize_inputs.end()) {
if (desc.Input("OutSize").size() >= 1) {
VLOG(3) << "The Paddle-TRT doesn't support the OutSize for op_type "
<< op_type;
return false;
}
}
auto data_layout = framework::StringToDataLayout(
BOOST_GET_CONST(std::string, desc.GetAttr("data_layout")));
if (data_layout != framework::DataLayout::kNCHW &&
data_layout != framework::DataLayout::kNHWC) {
VLOG(3) << "The op_type " << op_type
<< " is not NCHW or NHWC return false";
return false;
}
auto interp_method =
BOOST_GET_CONST(std::string, desc.GetAttr("interp_method"));
if (interp_method != "bilinear") {
VLOG(3) << "The interp_method of op_type " << op_type
<< " is not bilinear";
return false;
}
auto align_corners = BOOST_GET_CONST(bool, desc.GetAttr("align_corners"));
if (align_corners != false) {
VLOG(3)
<< "The bilinear_interp_v2 only supports align_corners with false.";
return false;
}
bool has_scale_input_size =
(resize_inputs.find("Scale") != resize_inputs.end());
if (has_scale_input_size && desc.Input("Scale").size() != 1) {
const std::vector<float> scale =
BOOST_GET_CONST(std::vector<float>, desc.GetAttr("scale"));
if (scale.size() <= 1) {
if (!desc.HasAttr("out_h") || !desc.HasAttr("out_w")) {
VLOG(3) << "The op_type " << op_type
<< " doesn't have Scale and the scale size <=1 and without "
"out_h / out_w, it will return false";
return false;
}
auto out_h = BOOST_GET_CONST(int, desc.GetAttr("out_h"));
auto out_w = BOOST_GET_CONST(int, desc.GetAttr("out_w"));
if (!(out_h <= 0 && out_w <= 0)) {
if (out_h <= 0) {
VLOG(3) << "The op_type " << op_type
<< "'s out_h must be greater than 0 if scale is not set.";
return false;
}
if (out_w <= 0) {
VLOG(3) << "The op_type " << op_type
<< "'s out_w must be greater than 0 if scale is not set.";
return false;
}
}
} else {
for (size_t i = 0; i < scale.size(); i++) {
if (scale[i] <= 0 && with_dynamic_shape) {
VLOG(3) << "dynamic shape not support Attr(scale[" << i << "]) "
<< scale[i]
<< " less than 1 and Input(Scale) vector not set.";
return false;
}
}
}
}
}
if (op_type == "hard_swish") {
if (desc.Input("X").size() != 1) {
VLOG(3) << "HardSwish op has only 1 input, but got "
......
# Copyright (c) 2022 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
import unittest
class TrtConvertBilinearInterpV2Test(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))
]
return True
def sample_program_configs(self):
def generate_input1(attrs: List[Dict[str, Any]]):
return np.ones([1, 3, 64, 64]).astype(np.float32)
def generate_input2(attrs: List[Dict[str, Any]]):
return np.random.uniform(low=0.5, high=6.0,
size=(2)).astype("float32")
for data_layout in ["NCHW", "NHWC"]:
for scale_y in [2.0, -1.0, 0.0]:
for scale_x in [2.0, -1.0, 0.0]:
scale = [scale_y, scale_x]
for out_h in [32, 64, 128, 192]:
for out_w in [32, 64]:
dics = [{
"data_layout": data_layout,
"interp_method": "bilinear",
"align_corners": False,
"align_mode": 0,
"scale": scale,
"out_h": out_h,
"out_w": out_w
}]
ops_config = [{
"op_type": "bilinear_interp_v2",
"op_inputs": {
"X": ["input_data"],
"Scale": ["input_scale"]
},
"op_outputs": {
"Out": ["bilinear_interp_v2_output_data"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"input_scale":
TensorConfig(
data_gen=partial(generate_input2, dics))
},
inputs={
"input_data":
TensorConfig(
data_gen=partial(generate_input1, dics))
},
outputs=["bilinear_interp_v2_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, 64, 64]}
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, 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-2
# 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-2
def test(self):
self.run_test()
if __name__ == "__main__":
unittest.main()
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