!9910 [MD][Perf] MindData add NormalizePad for GPU performance
From: @xiefangqi Reviewed-by: Signed-off-by:pull/9910/MERGE
mindspore-ci-bot
4 years ago
committed by
Gitee
commit
c4f284b928
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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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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*/
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#include "minddata/dataset/kernels/image/normalize_pad_op.h"
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#include <random>
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#include "minddata/dataset/kernels/image/image_utils.h"
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#include "minddata/dataset/util/status.h"
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namespace mindspore {
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namespace dataset {
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NormalizePadOp::NormalizePadOp(float mean_r, float mean_g, float mean_b, float std_r, float std_g, float std_b,
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std::string dtype) {
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Status s = Tensor::CreateFromVector<float>({mean_r, mean_g, mean_b}, &mean_);
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if (s.IsError()) {
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MS_LOG(ERROR) << "Could not create mean tensor.";
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}
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s = Tensor::CreateFromVector<float>({std_r, std_g, std_b}, &std_);
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if (s.IsError()) {
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MS_LOG(ERROR) << "Could not create std tensor.";
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}
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dtype_ = dtype;
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}
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Status NormalizePadOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
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IO_CHECK(input, output);
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// Doing the normalization + pad
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return NormalizePad(input, output, mean_, std_, dtype_);
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}
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void NormalizePadOp::Print(std::ostream &out) const {
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out << "NormalizeOp, mean: " << *(mean_.get()) << std::endl << "std: " << *(std_.get()) << std::endl;
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}
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} // namespace dataset
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} // namespace mindspore
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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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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*/
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#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_NORMALIZE_PAD_OP_H_
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#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_NORMALIZE_PAD_OP_H_
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#include <memory>
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#include <string>
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#include "minddata/dataset/core/tensor.h"
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#include "minddata/dataset/kernels/tensor_op.h"
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#include "minddata/dataset/util/status.h"
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namespace mindspore {
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namespace dataset {
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class NormalizePadOp : public TensorOp {
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public:
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NormalizePadOp(float mean_r, float mean_g, float mean_b, float std_r, float std_g, float std_b,
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std::string dtype = "float32");
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~NormalizePadOp() override = default;
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void Print(std::ostream &out) const override;
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Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
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std::string Name() const override { return kNormalizePadOp; }
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private:
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std::shared_ptr<Tensor> mean_;
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std::shared_ptr<Tensor> std_;
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std::string dtype_;
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};
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} // namespace dataset
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_NORMALIZE_OP_H_
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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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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*/
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#include "common/common.h"
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#include "common/cvop_common.h"
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#include "minddata/dataset/kernels/image/normalize_pad_op.h"
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#include "minddata/dataset/core/cv_tensor.h"
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#include "utils/log_adapter.h"
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#include <opencv2/opencv.hpp>
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using namespace mindspore::dataset;
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using mindspore::MsLogLevel::INFO;
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using mindspore::ExceptionType::NoExceptionType;
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using mindspore::LogStream;
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class MindDataTestNormalizePadOP : public UT::CVOP::CVOpCommon {
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public:
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MindDataTestNormalizePadOP() : CVOpCommon() {}
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};
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TEST_F(MindDataTestNormalizePadOP, TestFloat32) {
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MS_LOG(INFO) << "Doing TestNormalizePadOp::TestFloat32.";
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std::shared_ptr<Tensor> output_tensor;
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// Numbers are from the resnet50 model implementation
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float mean[3] = {121.0, 115.0, 100.0};
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float std[3] = {70.0, 68.0, 71.0};
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// NormalizePad Op
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std::unique_ptr<NormalizePadOp> op(new NormalizePadOp(mean[0], mean[1], mean[2], std[0], std[1], std[2], "float32"));
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EXPECT_TRUE(op->OneToOne());
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Status s = op->Compute(input_tensor_, &output_tensor);
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EXPECT_TRUE(s.IsOk());
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}
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TEST_F(MindDataTestNormalizePadOP, TestFloat16) {
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MS_LOG(INFO) << "Doing TestNormalizePadOp::TestFloat16.";
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std::shared_ptr<Tensor> output_tensor;
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// Numbers are from the resnet50 model implementation
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float mean[3] = {121.0, 115.0, 100.0};
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float std[3] = {70.0, 68.0, 71.0};
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// NormalizePad Op
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std::unique_ptr<NormalizePadOp> op(new NormalizePadOp(mean[0], mean[1], mean[2], std[0], std[1], std[2], "float16"));
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EXPECT_TRUE(op->OneToOne());
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Status s = op->Compute(input_tensor_, &output_tensor);
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EXPECT_TRUE(s.IsOk());
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}
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ==============================================================================
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"""
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Testing Normalize op in DE
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"""
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import numpy as np
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.py_transforms
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import mindspore.dataset.vision.c_transforms as c_vision
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import mindspore.dataset.vision.py_transforms as py_vision
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from mindspore import log as logger
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from util import diff_mse, visualize_image
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DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
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SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
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GENERATE_GOLDEN = False
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def normalizepad_np(image, mean, std):
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"""
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Apply the normalize+pad
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"""
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# DE decodes the image in RGB by deafult, hence
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# the values here are in RGB
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image = np.array(image, np.float32)
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image = image - np.array(mean)
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image = image * (1.0 / np.array(std))
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zeros = np.zeros([image.shape[0], image.shape[1], 1], dtype=np.float32)
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output = np.concatenate((image, zeros), axis=2)
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return output
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def test_normalizepad_op_c(plot=False):
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"""
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Test NormalizePad in cpp transformations
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"""
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logger.info("Test Normalize in cpp")
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mean = [121.0, 115.0, 100.0]
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std = [70.0, 68.0, 71.0]
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# define map operations
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decode_op = c_vision.Decode()
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normalizepad_op = c_vision.NormalizePad(mean, std)
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data1 = data1.map(operations=decode_op, input_columns=["image"])
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data1 = data1.map(operations=normalizepad_op, input_columns=["image"])
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data2 = data2.map(operations=decode_op, input_columns=["image"])
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num_iter = 0
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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image_de_normalized = item1["image"]
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image_original = item2["image"]
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image_np_normalized = normalizepad_np(image_original, mean, std)
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mse = diff_mse(image_de_normalized, image_np_normalized)
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logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
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assert mse < 0.01
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if plot:
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visualize_image(image_original, image_de_normalized, mse, image_np_normalized)
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num_iter += 1
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def test_normalizepad_op_py(plot=False):
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"""
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Test NormalizePad in python transformations
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"""
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logger.info("Test Normalize in python")
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mean = [0.475, 0.45, 0.392]
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std = [0.275, 0.267, 0.278]
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# define map operations
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transforms = [
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py_vision.Decode(),
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py_vision.ToTensor()
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]
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transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
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normalizepad_op = py_vision.NormalizePad(mean, std)
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data1 = data1.map(operations=transform, input_columns=["image"])
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data1 = data1.map(operations=normalizepad_op, input_columns=["image"])
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data2 = data2.map(operations=transform, input_columns=["image"])
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num_iter = 0
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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image_de_normalized = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_np_normalized = (normalizepad_np(item2["image"].transpose(1, 2, 0), mean, std) * 255).astype(np.uint8)
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image_original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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mse = diff_mse(image_de_normalized, image_np_normalized)
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logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
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assert mse < 0.01
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if plot:
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visualize_image(image_original, image_de_normalized, mse, image_np_normalized)
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num_iter += 1
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def test_decode_normalizepad_op():
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"""
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Test Decode op followed by NormalizePad op
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"""
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logger.info("Test [Decode, Normalize] in one Map")
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image", "label"], num_parallel_workers=1,
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shuffle=False)
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# define map operations
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decode_op = c_vision.Decode()
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normalizepad_op = c_vision.NormalizePad([121.0, 115.0, 100.0], [70.0, 68.0, 71.0], "float16")
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# apply map operations on images
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data1 = data1.map(operations=[decode_op, normalizepad_op], input_columns=["image"])
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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logger.info("Looping inside iterator {}".format(num_iter))
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assert item["image"].dtype == np.float16
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num_iter += 1
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def test_normalizepad_exception_unequal_size_c():
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"""
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Test NormalizePad in c transformation: len(mean) != len(std)
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expected to raise ValueError
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"""
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logger.info("test_normalize_exception_unequal_size_c")
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try:
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_ = c_vision.NormalizePad([100, 250, 125], [50, 50, 75, 75])
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Length of mean and std must be equal."
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try:
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_ = c_vision.NormalizePad([100, 250, 125], [50, 50, 75], 1)
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "dtype should be string."
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try:
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_ = c_vision.NormalizePad([100, 250, 125], [50, 50, 75], "")
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "dtype only support float32 or float16."
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def test_normalizepad_exception_unequal_size_py():
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"""
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Test NormalizePad in python transformation: len(mean) != len(std)
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expected to raise ValueError
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"""
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logger.info("test_normalizepad_exception_unequal_size_py")
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try:
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_ = py_vision.NormalizePad([0.50, 0.30, 0.75], [0.18, 0.32, 0.71, 0.72])
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Length of mean and std must be equal."
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try:
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_ = py_vision.NormalizePad([0.50, 0.30, 0.75], [0.18, 0.32, 0.71], 1)
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "dtype should be string."
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try:
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_ = py_vision.NormalizePad([0.50, 0.30, 0.75], [0.18, 0.32, 0.71], "")
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "dtype only support float32 or float16."
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def test_normalizepad_exception_invalid_range_py():
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"""
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Test NormalizePad in python transformation: value is not in range [0,1]
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expected to raise ValueError
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"""
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logger.info("test_normalizepad_exception_invalid_range_py")
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try:
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_ = py_vision.NormalizePad([0.75, 1.25, 0.5], [0.1, 0.18, 1.32])
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Input mean_value is not within the required interval of (0.0 to 1.0)." in str(e)
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