[Paddle-TRT] nearest_interp op (#31626)
* nearest_interp op converter w/ dynamic/static * fix data_layout include * add trt nearest unit_test * add nearest_interp NHWC test * update trt nearest interp nhwc testcase * remove asterisk for python2 compatibility * add empty line to prevent conflict * nearest_interp op converter w/ dynamic/static * fix data_layout include * add trt nearest unit_test * add nearest_interp NHWC test * update trt nearest interp nhwc testcase * remove asterisk for python2 compatibility * add empty line to prevent conflict * change the priority of out_h, out_w2.0.1-rocm-post
zlsh80826
4 years ago
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bfced39eb6
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/fluid/framework/data_layout.h"
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#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
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namespace paddle {
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namespace framework {
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class Scope;
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namespace proto {
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class OpDesc;
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} // namespace proto
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} // namespace framework
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} // namespace paddle
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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class NearestInterpolateOpConverter : public OpConverter {
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public:
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void operator()(const framework::proto::OpDesc& op,
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const framework::Scope& scope, bool test_mode) override {
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VLOG(3) << "convert a fluid nearest_interp op";
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framework::OpDesc op_desc(op, nullptr);
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std::string input_name = op_desc.Input("X").front();
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std::string output_name = op_desc.Output("Out").front();
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auto input = engine_->GetITensor(input_name);
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auto data_layout = framework::StringToDataLayout(
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BOOST_GET_CONST(std::string, op_desc.GetAttr("data_layout")));
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auto interp_method =
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BOOST_GET_CONST(std::string, op_desc.GetAttr("interp_method"));
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bool align_corners =
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BOOST_GET_CONST(bool, op_desc.GetAttr("align_corners"));
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auto input_names = op_desc.Input("X");
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auto scale = BOOST_GET_CONST(float, op_desc.GetAttr("scale"));
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auto out_h = BOOST_GET_CONST(int, op_desc.GetAttr("out_h"));
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auto out_w = BOOST_GET_CONST(int, op_desc.GetAttr("out_w"));
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auto layer = TRT_ENGINE_ADD_LAYER(engine_, Resize, *input);
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layer->setAlignCorners(align_corners);
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auto in_dim = input->getDimensions();
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float scale_h = 1.f;
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float scale_w = 1.f;
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std::vector<float> scales;
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if (scale > 0.f && (out_h <= 0 && out_w <= 0)) {
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scale_h = scale;
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scale_w = scale;
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} else {
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// axis are different in static/dynamic mode
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PADDLE_ENFORCE_GT(
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out_h, 0, platform::errors::InvalidArgument(
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"out_h must be greater than 0 if scale is not set."));
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PADDLE_ENFORCE_GT(
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out_w, 0, platform::errors::InvalidArgument(
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"out_w must be greater than 0 if scale is not set."));
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bool with_dynamic = engine_->with_dynamic_shape();
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int h_axis = (data_layout == framework::DataLayout::kNCHW) + with_dynamic;
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int w_axis =
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(data_layout == framework::DataLayout::kNCHW) + 1 + with_dynamic;
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scale_h =
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static_cast<float>(out_h) / static_cast<float>(in_dim.d[h_axis]);
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scale_w =
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static_cast<float>(out_w) / static_cast<float>(in_dim.d[w_axis]);
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}
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if (engine_->with_dynamic_shape()) {
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scales.push_back(1.f);
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}
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if (data_layout == framework::DataLayout::kNCHW) {
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scales.push_back(1.f);
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scales.push_back(scale_h);
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scales.push_back(scale_w);
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} else if (data_layout == framework::DataLayout::kNHWC) {
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// NHWC
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scales.push_back(scale_h);
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scales.push_back(scale_w);
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scales.push_back(1.f);
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} else {
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PADDLE_THROW(platform::errors::InvalidArgument(
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"Data layout must be NCHW or NHWC."));
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}
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layer->setScales(scales.data(), scales.size());
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RreplenishLayerAndOutput(layer, "nearest_interp", {output_name}, test_mode);
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}
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};
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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REGISTER_TRT_OP_CONVERTER(nearest_interp, NearestInterpolateOpConverter);
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@ -0,0 +1,192 @@
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# Copyright (c) 2020 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 __future__ import print_function
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import unittest
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import numpy as np
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from inference_pass_test import InferencePassTest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.core import PassVersionChecker
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from paddle.fluid.core import AnalysisConfig
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class TRTNearestInterpTest(InferencePassTest):
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def setUp(self):
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self.set_params()
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with fluid.program_guard(self.main_program, self.startup_program):
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if self.data_layout == 'NCHW':
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shape = [
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-1, self.channels, self.origin_shape[0],
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self.origin_shape[1]
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]
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else:
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shape = [
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-1, self.origin_shape[0], self.origin_shape[1],
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self.channels
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]
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data = fluid.data(name='data', shape=shape, dtype='float32')
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resize_out = self.append_nearest_interp(data)
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out = fluid.layers.batch_norm(resize_out, is_test=True)
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if self.data_layout == 'NCHW':
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shape = [
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self.bs, self.channels, self.origin_shape[0],
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self.origin_shape[1]
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]
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else:
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shape = [
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self.bs, self.origin_shape[0], self.origin_shape[1],
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self.channels
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]
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self.feeds = {'data': np.random.random(shape).astype('float32'), }
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self.enable_trt = True
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self.trt_parameters = TRTNearestInterpTest.TensorRTParam(
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1 << 30, self.bs, 1, AnalysisConfig.Precision.Float32, False, False)
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self.fetch_list = [out]
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def set_params(self):
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self.bs = 4
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self.scale = 1
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self.channels = 3
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self.origin_shape = (32, 32) # HW
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self.resize_shape = (64, 64) # HW
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self.align_corners = True
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self.data_layout = 'NCHW'
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def append_nearest_interp(self, data):
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if self.scale > 0.:
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return fluid.layers.resize_nearest(
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data,
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scale=self.scale,
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align_corners=self.align_corners,
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data_format=self.data_layout)
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return fluid.layers.resize_nearest(
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data,
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out_shape=self.resize_shape,
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align_corners=self.align_corners,
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data_format=self.data_layout)
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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use_gpu = True
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self.check_output_with_option(use_gpu, flatten=True)
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self.assertTrue(
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PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))
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class TRTNearestInterpTest1(TRTNearestInterpTest):
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def set_params(self):
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self.bs = 4
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self.scale = -1
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self.channels = 3
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self.origin_shape = (32, 32) # HW
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self.resize_shape = (64, 64) # HW
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self.align_corners = True
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self.data_layout = 'NCHW'
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class TRTNearestInterpTest2(TRTNearestInterpTest):
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def set_params(self):
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self.bs = 4
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self.scale = 2.
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self.channels = 3
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self.origin_shape = (32, 32) # HW
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self.resize_shape = (64, 64) # HW
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self.align_corners = False
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self.data_layout = 'NCHW'
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class TRTNearestInterpTest3(TRTNearestInterpTest):
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def set_params(self):
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self.bs = 4
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self.scale = -1
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self.channels = 3
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self.origin_shape = (32, 32) # HW
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self.resize_shape = (64, 64) # HW
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self.align_corners = False
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self.data_layout = 'NCHW'
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class TRTNearestInterpTest4(TRTNearestInterpTest):
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def set_params(self):
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self.bs = 4
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self.scale = -1
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self.channels = 3
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self.origin_shape = (32, 32) # HW
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self.resize_shape = (47, 48) # HW
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self.align_corners = False
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self.data_layout = 'NCHW'
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class TRTNearestInterpTest5(TRTNearestInterpTest):
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def set_params(self):
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self.bs = 4
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self.scale = -1
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self.channels = 3
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self.origin_shape = (32, 32) # HW
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self.resize_shape = (64, 64) # HW
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self.align_corners = True
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self.data_layout = 'NHWC'
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class TRTNearestInterpTest6(TRTNearestInterpTest):
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def set_params(self):
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self.bs = 4
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self.scale = 2.
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self.channels = 3
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self.origin_shape = (32, 32) # HW
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self.resize_shape = (64, 64) # HW
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self.align_corners = False
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self.data_layout = 'NHWC'
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class TRTNearestInterpTest7(TRTNearestInterpTest):
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def set_params(self):
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self.bs = 4
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self.scale = -1
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self.channels = 3
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self.origin_shape = (32, 32) # HW
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self.resize_shape = (64, 64) # HW
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self.align_corners = False
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self.data_layout = 'NHWC'
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class TRTNearestInterpTest8(TRTNearestInterpTest):
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def set_params(self):
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self.bs = 4
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self.scale = -1
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self.channels = 3
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self.origin_shape = (32, 32) # HW
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self.resize_shape = (47, 48) # HW
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self.align_corners = False
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self.data_layout = 'NHWC'
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class TRTNearestInterpTest9(TRTNearestInterpTest):
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def set_params(self):
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self.bs = 4
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self.scale = -1
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self.channels = 3
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self.origin_shape = (32, 32) # HW
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self.resize_shape = (47, 48) # HW
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self.align_corners = False
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self.data_layout = 'NHWC'
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if __name__ == "__main__":
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unittest.main()
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