/* Copyright 2015 The TensorFlow 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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==============================================================================*/
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#include <functional>
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#include <memory>
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#include "tensorflow/core/framework/allocator.h"
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#include "tensorflow/core/framework/fake_input.h"
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#include "tensorflow/core/framework/node_def_builder.h"
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#include "tensorflow/core/framework/op_kernel.h"
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#include "tensorflow/core/framework/summary.pb.h"
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#include "tensorflow/core/framework/tensor.h"
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#include "tensorflow/core/framework/types.h"
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#include "tensorflow/core/kernels/ops_testutil.h"
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#include "tensorflow/core/kernels/ops_util.h"
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#include "tensorflow/core/lib/core/status_test_util.h"
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#include "tensorflow/core/lib/histogram/histogram.h"
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#include "tensorflow/core/lib/strings/strcat.h"
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#include "tensorflow/core/platform/env.h"
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#include "tensorflow/core/platform/logging.h"
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#include "tensorflow/core/platform/protobuf.h"
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#include "tensorflow/core/platform/test.h"
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namespace tensorflow {
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namespace {
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static void EXPECT_SummaryMatches(const Summary& actual,
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const string& expected_str) {
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Summary expected;
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CHECK(protobuf::TextFormat::ParseFromString(expected_str, &expected));
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EXPECT_EQ(expected.DebugString(), actual.DebugString());
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}
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// --------------------------------------------------------------------------
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// SummaryImageOp
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// --------------------------------------------------------------------------
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class SummaryImageOpTest : public OpsTestBase {
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protected:
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void MakeOp(int max_images) {
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TF_ASSERT_OK(NodeDefBuilder("myop", "ImageSummary")
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.Input(FakeInput())
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.Input(FakeInput())
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.Attr("max_images", max_images)
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.Finalize(node_def()));
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TF_ASSERT_OK(InitOp());
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}
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void CheckAndRemoveEncodedImages(Summary* summary) {
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for (int i = 0; i < summary->value_size(); ++i) {
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Summary::Value* value = summary->mutable_value(i);
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ASSERT_TRUE(value->has_image()) << "No image for value: " << value->tag();
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ASSERT_FALSE(value->image().encoded_image_string().empty())
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<< "No encoded_image_string for value: " << value->tag();
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if (VLOG_IS_ON(2)) {
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// When LOGGING, output the images to disk for manual inspection.
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TF_CHECK_OK(WriteStringToFile(
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Env::Default(), strings::StrCat("/tmp/", value->tag(), ".png"),
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value->image().encoded_image_string()));
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}
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value->mutable_image()->clear_encoded_image_string();
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}
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}
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};
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TEST_F(SummaryImageOpTest, ThreeGrayImagesOutOfFive4dInput) {
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MakeOp(3 /* max images */);
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// Feed and run
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AddInputFromArray<string>(TensorShape({}), {"tag"});
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AddInputFromArray<float>(TensorShape({5, 2, 1, 1}),
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{0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0});
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TF_ASSERT_OK(RunOpKernel());
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// Check the output size.
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Tensor* out_tensor = GetOutput(0);
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ASSERT_EQ(0, out_tensor->dims());
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Summary summary;
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ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
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CheckAndRemoveEncodedImages(&summary);
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EXPECT_SummaryMatches(summary, R"(
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value { tag: 'tag/image/0' image { width: 1 height: 2 colorspace: 1} }
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value { tag: 'tag/image/1' image { width: 1 height: 2 colorspace: 1} }
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value { tag: 'tag/image/2' image { width: 1 height: 2 colorspace: 1} }
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)");
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}
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TEST_F(SummaryImageOpTest, OneGrayImage4dInput) {
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MakeOp(1 /* max images */);
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// Feed and run
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AddInputFromArray<string>(TensorShape({}), {"tag"});
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AddInputFromArray<float>(TensorShape({5 /*batch*/, 2, 1, 1 /*depth*/}),
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{0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0});
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TF_ASSERT_OK(RunOpKernel());
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// Check the output size.
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Tensor* out_tensor = GetOutput(0);
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ASSERT_EQ(0, out_tensor->dims());
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Summary summary;
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ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
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CheckAndRemoveEncodedImages(&summary);
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EXPECT_SummaryMatches(summary, R"(
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value { tag: 'tag/image' image { width: 1 height: 2 colorspace: 1} })");
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}
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TEST_F(SummaryImageOpTest, OneColorImage4dInput) {
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MakeOp(1 /* max images */);
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// Feed and run
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AddInputFromArray<string>(TensorShape({}), {"tag"});
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AddInputFromArray<float>(
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TensorShape({1 /*batch*/, 5 /*rows*/, 2 /*columns*/, 3 /*depth*/}),
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{
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/* r0, c0, RGB */ 1.0f, 0.1f, 0.2f,
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/* r0, c1, RGB */ 1.0f, 0.3f, 0.4f,
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/* r1, c0, RGB */ 0.0f, 1.0f, 0.0f,
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/* r1, c1, RGB */ 0.0f, 1.0f, 0.0f,
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/* r2, c0, RGB */ 0.0f, 0.0f, 1.0f,
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/* r2, c1, RGB */ 0.0f, 0.0f, 1.0f,
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/* r3, c0, RGB */ 1.0f, 1.0f, 0.0f,
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/* r3, c1, RGB */ 1.0f, 0.0f, 1.0f,
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/* r4, c0, RGB */ 1.0f, 1.0f, 0.0f,
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/* r4, c1, RGB */ 1.0f, 0.0f, 1.0f,
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});
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TF_ASSERT_OK(RunOpKernel());
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// Check the output size.
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Tensor* out_tensor = GetOutput(0);
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ASSERT_EQ(0, out_tensor->dims());
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Summary summary;
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ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
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CheckAndRemoveEncodedImages(&summary);
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EXPECT_SummaryMatches(summary, R"(
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value { tag: 'tag/image' image { width: 2 height: 5 colorspace: 3} })");
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}
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} // namespace
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} // namespace tensorflow
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