tensorflow/core/ops/image_ops_test.cc - platform/external/tensorflow - Git at Google

 /* Copyright 2016 The TensorFlow Authors. All Rights Reserved.

 Licensed under the Apache License, Version 2.0 (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 "tensorflow/core/framework/node_def_builder.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/shape_inference_testutil.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/platform/test.h"

 namespace tensorflow {

 TEST(ImageOpsTest, SampleDistortedBoundingBox_ShapeFn) {
   ShapeInferenceTestOp op("SampleDistortedBoundingBox");
   INFER_OK(op, "?;?", "[3];[3];[1,1,4]");
 }

 TEST(ImageOpsTest, Resize_ShapeFn) {
   for (const char* op_name : {"ResizeArea", "ResizeBicubic", "ResizeBilinear",
                               "ResizeNearestNeighbor"}) {
     ShapeInferenceTestOp op(op_name);
     op.input_tensors.resize(2);

     // Inputs are images and size.

     // Rank and size checks.
     INFER_ERROR("Shape must be rank 4 but is rank 5", op, "[1,2,3,4,5];?");
     INFER_ERROR("Shape must be rank 4 but is rank 3", op, "[1,2,3];?");
     INFER_ERROR("Shape must be rank 1 but is rank 0", op, "?;[]");
     INFER_ERROR("Dimension must be 2 but is 3", op, "?;[3]");

     // When the size tensor is not a constant, the middle dims are unknown.
     INFER_OK(op, "[1,?,3,?];[2]", "[d0_0,?,?,d0_3]");

     Tensor size_tensor = test::AsTensor<int32>({20, 30});
     op.input_tensors[1] = &size_tensor;
     INFER_OK(op, "[1,?,3,?];[2]", "[d0_0,20,30,d0_3]");
   }
 }

 TEST(ImageOpsTest, DecodeGif) {
   ShapeInferenceTestOp op("DecodeGif");

   // Rank check.
   INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1]");

   // Output is always ?,?,?,3.
   INFER_OK(op, "?", "[?,?,?,3]");
   INFER_OK(op, "[]", "[?,?,?,3]");
 }

 TEST(ImageOpsTest, DecodeImage_ShapeFn) {
   for (const char* op_name : {"DecodeJpeg", "DecodePng"}) {
     ShapeInferenceTestOp op(op_name);

     // Rank check.
     INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1]");

     // Set the channel to zero - output is not known.
     TF_ASSERT_OK(NodeDefBuilder("test", op_name)
                      .Input({"a", 0, DT_STRING})
                      .Finalize(&op.node_def));
     INFER_OK(op, "[]", "[?,?,?]");

     // Set the channel and so that part of output shape is known.
     TF_ASSERT_OK(NodeDefBuilder("test", op_name)
                      .Input({"a", 0, DT_STRING})
                      .Attr("channels", 4)
                      .Finalize(&op.node_def));
     INFER_OK(op, "[]", "[?,?,4]");

     // Negative channel value is rejected.
     TF_ASSERT_OK(NodeDefBuilder("test", op_name)
                      .Input({"a", 0, DT_STRING})
                      .Attr("channels", -1)
                      .Finalize(&op.node_def));
     INFER_ERROR("channels must be non-negative, got -1", op, "[]");
   }
 }

 TEST(ImageOpsTest, DecodeAndCropJpeg_ShapeFn) {
   const char* op_name = "DecodeAndCropJpeg";
   ShapeInferenceTestOp op(op_name);

   // Check the number of inputs.
   INFER_ERROR("Wrong number of inputs passed: 1 while 2 expected", op, "[1]");

   // Rank check.
   INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1];?");

   // Set the channel to zero - output is not known.
   TF_ASSERT_OK(NodeDefBuilder("test", op_name)
                    .Input({"img", 0, DT_STRING})
                    .Input({"crop_window", 1, DT_INT32})
                    .Finalize(&op.node_def));
   INFER_OK(op, "[];[?]", "[?,?,?]");

   // Set the channel, so that part of output shape is known.
   TF_ASSERT_OK(NodeDefBuilder("test", op_name)
                    .Input({"img", 0, DT_STRING})
                    .Input({"crop_window", 1, DT_INT32})
                    .Attr("channels", 4)
                    .Finalize(&op.node_def));
   INFER_OK(op, "[];[?]", "[?,?,4]");

   // Negative channel value is rejected.
   TF_ASSERT_OK(NodeDefBuilder("test", op_name)
                    .Input({"img", 0, DT_STRING})
                    .Input({"crop_window", 1, DT_INT32})
                    .Attr("channels", -1)
                    .Finalize(&op.node_def));
   INFER_ERROR("channels must be non-negative, got -1", op, "[];[]");
 }

 TEST(ImageOpsTest, DecodeAndCropJpeg_InvalidCropWindow) {
   const char* op_name = "DecodeAndCropJpeg";
   ShapeInferenceTestOp op(op_name);

   // Check the number of inputs.
   INFER_ERROR("Wrong number of inputs passed: 1 while 2 expected", op, "[1]");

   // Rank check.
   INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1];?");

   // Set the channel to zero - output is not known.
   TF_ASSERT_OK(NodeDefBuilder("test", op_name)
                    .Input({"img", 0, DT_STRING})
                    .Input({"crop_window", 1, DT_INT32})
                    .Finalize(&op.node_def));
   INFER_OK(op, "[];[?]", "[?,?,?]");
 }

 TEST(ImageOpsTest, EncodeImage_ShapeFn) {
   for (const char* op_name : {"EncodeJpeg", "EncodePng"}) {
     ShapeInferenceTestOp op(op_name);

     // Rank check.
     INFER_ERROR("Shape must be rank 3 but is rank 2", op, "[1,2]");

     INFER_OK(op, "[1,?,3]", "[]");  // output is always scalar.
   }
 }

 TEST(ImageOpsTest, ExtractJpegShape_ShapeFn) {
   ShapeInferenceTestOp op("ExtractJpegShape");

   // Rank check.
   INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1]");

   // Only specify input data. Output must be a 1-D tensor with 3 elements.
   INFER_OK(op, "?", "[3]");
 }

 TEST(ImageOpsTest, Colorspace_ShapeFn) {
   for (const char* op_name : {"HSVToRGB", "RGBToHSV"}) {
     ShapeInferenceTestOp op(op_name);

     // Rank check.
     INFER_ERROR("Shape must be at least rank 1 but is rank 0", op, "[]");

     // Input's last dim is required to be 3.
     INFER_ERROR("Dimension must be 3 but is 4", op, "[1,2,4]");
     INFER_OK(op, "[1,2,3]", "[d0_0,d0_1,d0_2]");
     INFER_OK(op, "[1,2,?]", "[d0_0,d0_1,3]");
     INFER_OK(op, "?", "?");
   }
 }

 TEST(ImageOpsTest, ExtractGlimpse_ShapeFn) {
   ShapeInferenceTestOp op("ExtractGlimpse");
   op.input_tensors.resize(2);

   // Inputs are input, size, offsets.
   TF_ASSERT_OK(NodeDefBuilder("test", "ExtractGlimpse")
                    .Input({"input", 0, DT_FLOAT})
                    .Input({"size", 1, DT_INT32})
                    .Input({"offsets", 2, DT_FLOAT})
                    .Attr("uniform_noise", true)
                    .Attr("noise", "")
                    .Finalize(&op.node_def));

   // Rank and size checks.
   INFER_ERROR("Shape must be rank 4 but is rank 5", op, "[1,2,3,4,5];?;?");
   INFER_ERROR("Shape must be rank 4 but is rank 3", op, "[1,2,3];?;?");
   INFER_ERROR("Shape must be rank 1 but is rank 0", op, "?;[];?");
   INFER_ERROR("Dimension must be 2 but is 3", op, "?;[3];?");
   INFER_ERROR("Shape must be rank 2 but is rank 3", op, "?;?;[1,2,3]");

   // When the size tensor is not a constant, the middle dims are unknown.
   INFER_OK(op, "[1,?,3,?];[2];?", "[d0_0,?,?,d0_3]");

   Tensor size_tensor = test::AsTensor<int32>({20, 30});
   op.input_tensors[1] = &size_tensor;
   INFER_OK(op, "[1,?,3,?];[2];?", "[d0_0,20,30,d0_3]");

   // input.dim(0) and offsets.dim(0) are both the batch dimension.
   INFER_OK(op, "[?,?,3,?];[2];[1,?]", "[d2_0,20,30,d0_3]");
   INFER_OK(op, "[1,?,3,?];[2];[1,?]", "[d0_0|d2_0,20,30,d_0|d0_3]");
   INFER_ERROR("Dimensions must be equal, but are 10 and 1", op,
               "[10,?,?,?];?;[1,2]");
 }

 TEST(ImageOpsTest, CropAndResize_ShapeFn) {
   ShapeInferenceTestOp op("CropAndResize");
   op.input_tensors.resize(4);

   // Inputs are:  input, boxes, box_ind, and crop_size.

   // Rank and size checks.
   INFER_ERROR("Shape must be rank 4 but is rank 5", op, "[1,2,3,4,5];?;?;?");
   INFER_ERROR("Shape must be rank 4 but is rank 3", op, "[1,2,3];?;?;?");
   INFER_ERROR("Shape must be rank 2 but is rank 3", op, "?;[1,2,3];?;?");
   INFER_ERROR("Shape must be rank 1 but is rank 2", op, "?;?;[1,2];?");
   INFER_ERROR("Shape must be rank 1 but is rank 2", op, "?;?;?;[1,2]");
   INFER_ERROR("Dimension must be 2 but is 1", op, "?;?;?;[1]");

   // When the size tensor is not a constant, the middle dims are unknown.
   INFER_OK(op, "[1,?,3,?];?;?;[2]", "[?,?,?,d0_3]");

   Tensor size_tensor = test::AsTensor<int32>({20, 30});
   op.input_tensors[3] = &size_tensor;
   INFER_OK(op, "[1,?,3,?];?;?;[2]", "[?,20,30,d0_3]");

   // boxes.dim(0) and box_ind.dim(0) are both the num_boxes dim.
   INFER_OK(op, "[1,?,3,?];[2,4];?;[2]", "[d1_0,20,30,d0_3]");
   INFER_OK(op, "[1,?,3,?];?;[2];[2]", "[d2_0,20,30,d0_3]");
   INFER_OK(op, "[1,?,3,?];[?,4];[?];[2]", "[d1_0|d3_0,20,30,d0_3]");
   INFER_ERROR("Dimensions must be equal, but are 2 and 1", op, "?;[2,?];[1];?");

   // boxes.dim(1) must be 4.
   INFER_ERROR("Dimension must be 4 but is 3", op, "?;[?,3];?;?");
 }

 TEST(ImageOpsTest, ResizeNearestNeighborGrad_ShapeFn) {
   ShapeInferenceTestOp op("ResizeNearestNeighborGrad");
   op.input_tensors.resize(2);

   // Rank and size checks.
   INFER_ERROR("Shape must be rank 4 but is rank 3", op, "[1,2,3];?");
   INFER_ERROR("Shape must be rank 1 but is rank 2", op, "?;[1,2]")
   INFER_ERROR("Dimension must be 2 but is 1", op, "?;[1]");

   // When the size tensor is not a constant, the middle dims are unknown.
   INFER_OK(op, "[1,?,3,?];[2]", "[d0_0,?,?,d0_3]");

   Tensor size_tensor = test::AsTensor<int32>({20, 30});
   op.input_tensors[1] = &size_tensor;
   INFER_OK(op, "[1,?,3,?];[2]", "[d0_0,20,30,d0_3]");
 }

 TEST(ImageOpsTest, CropAndResizeGradImage_ShapeFn) {
   ShapeInferenceTestOp op("CropAndResizeGradImage");
   op.input_tensors.resize(4);

   // Rank checks.
   INFER_ERROR("Shape must be rank 1 but is rank 2", op, "?;?;?;[1,2]");

   // Unknown image_size should result in output of rank 4 with unknown dims.
   INFER_OK(op, "?;?;?;?", "[?,?,?,?]");

   // Known image_size should result in full shape information.
   Tensor image_size = test::AsTensor<int32>({10, 20, 30, 40});
   op.input_tensors[3] = &image_size;
   INFER_OK(op, "?;?;?;[1]", "[10, 20, 30, 40]");
 }

 TEST(ImageOpsTest, RandomCrop_ShapeFn) {
   ShapeInferenceTestOp op("RandomCrop");
   op.input_tensors.resize(2);

   // Rank checks.
   INFER_ERROR("must be rank 3", op, "[1,2];?");
   INFER_ERROR("must be equal", op, "?;[3]");
   INFER_ERROR("must be equal", op, "?;[1,2]");

   // Unknown size tensor values.
   INFER_OK(op, "[?,?,?];[2]", "[?,?,d0_2]");

   // Known size should result in full shape information.
   Tensor size = test::AsTensor<int64>({10, 20});
   op.input_tensors[1] = &size;
   INFER_OK(op, "[?,?,?];[2]", "[10,20,d0_2]");
 }

 TEST(ImageOpsTest, QuantizedResizeBilinear_ShapeFn) {
   ShapeInferenceTestOp op("QuantizedResizeBilinear");
   op.input_tensors.resize(4);

   NodeDefBuilder builder =
       NodeDefBuilder("test", "QuantizedResizeBilinear")
           .Input(NodeDefBuilder::NodeOut{"images", 0, DT_QINT32})
           .Input(NodeDefBuilder::NodeOut{"size", 0, DT_INT32})
           .Input(NodeDefBuilder::NodeOut{"min", 0, DT_FLOAT})
           .Input(NodeDefBuilder::NodeOut{"max", 0, DT_FLOAT})
           .Attr("T", DT_QINT32)
           .Attr("Toutput", DT_QINT32);
   TF_ASSERT_OK(builder.Finalize(&op.node_def));

   // When the size tensor is not a constant, the middle dims are unknown.
   INFER_OK(op, "[1,?,3,?];[2];[];[]",
            "[d0_0,?,?,d0_3];[];[]");  // output rank unknown
   INFER_ERROR("must be rank 0", op, "[1,?,3,?];[2];[?];[]");
   INFER_ERROR("must be rank 0", op, "[1,?,3,?];[2];[];[?]");

   const Tensor size_tensor = test::AsTensor<int32>({20, 30});
   op.input_tensors.at(1) = &size_tensor;
   INFER_OK(op, "[1,?,3,?];[2];[];[]", "[d0_0,20,30,d0_3];[];[]");
 }

 TEST(ImageOpsTest, DrawBoundingBoxes_ShapeFn) {
   ShapeInferenceTestOp op("DrawBoundingBoxes");
   op.input_tensors.resize(2);

   // Check images.
   INFER_ERROR("must be rank 4", op, "[1,?,3];?");
   INFER_ERROR("should be either 1 (GRY), 3 (RGB), or 4 (RGBA)",
       op, "[1,?,?,5];?");

   // Check boxes.
   INFER_ERROR("must be rank 3", op, "[1,?,?,4];[1,4]");
   INFER_ERROR("Dimension must be 4", op, "[1,?,?,4];[1,2,2]");

   // OK shapes.
   INFER_OK(op, "[4,?,?,4];?", "in0");
   INFER_OK(op, "[?,?,?,?];[?,?,?]", "in0");
   INFER_OK(op, "[4,?,?,4];[?,?,?]", "in0");
   INFER_OK(op, "[4,?,?,4];[?,?,4]", "in0");
 }
 }  // end namespace tensorflow
	/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.

	Licensed under the Apache License, Version 2.0 (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 "tensorflow/core/framework/node_def_builder.h"
	#include "tensorflow/core/framework/op.h"
	#include "tensorflow/core/framework/shape_inference_testutil.h"
	#include "tensorflow/core/framework/tensor_testutil.h"
	#include "tensorflow/core/lib/core/status_test_util.h"
	#include "tensorflow/core/platform/test.h"

	namespace tensorflow {

	TEST(ImageOpsTest, SampleDistortedBoundingBox_ShapeFn) {
	ShapeInferenceTestOp op("SampleDistortedBoundingBox");
	INFER_OK(op, "?;?", "[3];[3];[1,1,4]");
	}

	TEST(ImageOpsTest, Resize_ShapeFn) {
	for (const char* op_name : {"ResizeArea", "ResizeBicubic", "ResizeBilinear",
	"ResizeNearestNeighbor"}) {
	ShapeInferenceTestOp op(op_name);
	op.input_tensors.resize(2);

	// Inputs are images and size.

	// Rank and size checks.
	INFER_ERROR("Shape must be rank 4 but is rank 5", op, "[1,2,3,4,5];?");
	INFER_ERROR("Shape must be rank 4 but is rank 3", op, "[1,2,3];?");
	INFER_ERROR("Shape must be rank 1 but is rank 0", op, "?;[]");
	INFER_ERROR("Dimension must be 2 but is 3", op, "?;[3]");

	// When the size tensor is not a constant, the middle dims are unknown.
	INFER_OK(op, "[1,?,3,?];[2]", "[d0_0,?,?,d0_3]");

	Tensor size_tensor = test::AsTensor<int32>({20, 30});
	op.input_tensors[1] = &size_tensor;
	INFER_OK(op, "[1,?,3,?];[2]", "[d0_0,20,30,d0_3]");
	}
	}

	TEST(ImageOpsTest, DecodeGif) {
	ShapeInferenceTestOp op("DecodeGif");

	// Rank check.
	INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1]");

	// Output is always ?,?,?,3.
	INFER_OK(op, "?", "[?,?,?,3]");
	INFER_OK(op, "[]", "[?,?,?,3]");
	}

	TEST(ImageOpsTest, DecodeImage_ShapeFn) {
	for (const char* op_name : {"DecodeJpeg", "DecodePng"}) {
	ShapeInferenceTestOp op(op_name);

	// Rank check.
	INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1]");

	// Set the channel to zero - output is not known.
	TF_ASSERT_OK(NodeDefBuilder("test", op_name)
	.Input({"a", 0, DT_STRING})
	.Finalize(&op.node_def));
	INFER_OK(op, "[]", "[?,?,?]");

	// Set the channel and so that part of output shape is known.
	TF_ASSERT_OK(NodeDefBuilder("test", op_name)
	.Input({"a", 0, DT_STRING})
	.Attr("channels", 4)
	.Finalize(&op.node_def));
	INFER_OK(op, "[]", "[?,?,4]");

	// Negative channel value is rejected.
	TF_ASSERT_OK(NodeDefBuilder("test", op_name)
	.Input({"a", 0, DT_STRING})
	.Attr("channels", -1)
	.Finalize(&op.node_def));
	INFER_ERROR("channels must be non-negative, got -1", op, "[]");
	}
	}

	TEST(ImageOpsTest, DecodeAndCropJpeg_ShapeFn) {
	const char* op_name = "DecodeAndCropJpeg";
	ShapeInferenceTestOp op(op_name);

	// Check the number of inputs.
	INFER_ERROR("Wrong number of inputs passed: 1 while 2 expected", op, "[1]");

	// Rank check.
	INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1];?");

	// Set the channel to zero - output is not known.
	TF_ASSERT_OK(NodeDefBuilder("test", op_name)
	.Input({"img", 0, DT_STRING})
	.Input({"crop_window", 1, DT_INT32})
	.Finalize(&op.node_def));
	INFER_OK(op, "[];[?]", "[?,?,?]");

	// Set the channel, so that part of output shape is known.
	TF_ASSERT_OK(NodeDefBuilder("test", op_name)
	.Input({"img", 0, DT_STRING})
	.Input({"crop_window", 1, DT_INT32})
	.Attr("channels", 4)
	.Finalize(&op.node_def));
	INFER_OK(op, "[];[?]", "[?,?,4]");

	// Negative channel value is rejected.
	TF_ASSERT_OK(NodeDefBuilder("test", op_name)
	.Input({"img", 0, DT_STRING})
	.Input({"crop_window", 1, DT_INT32})
	.Attr("channels", -1)
	.Finalize(&op.node_def));
	INFER_ERROR("channels must be non-negative, got -1", op, "[];[]");
	}

	TEST(ImageOpsTest, DecodeAndCropJpeg_InvalidCropWindow) {
	const char* op_name = "DecodeAndCropJpeg";
	ShapeInferenceTestOp op(op_name);

	// Check the number of inputs.
	INFER_ERROR("Wrong number of inputs passed: 1 while 2 expected", op, "[1]");

	// Rank check.
	INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1];?");

	// Set the channel to zero - output is not known.
	TF_ASSERT_OK(NodeDefBuilder("test", op_name)
	.Input({"img", 0, DT_STRING})
	.Input({"crop_window", 1, DT_INT32})
	.Finalize(&op.node_def));
	INFER_OK(op, "[];[?]", "[?,?,?]");
	}

	TEST(ImageOpsTest, EncodeImage_ShapeFn) {
	for (const char* op_name : {"EncodeJpeg", "EncodePng"}) {
	ShapeInferenceTestOp op(op_name);

	// Rank check.
	INFER_ERROR("Shape must be rank 3 but is rank 2", op, "[1,2]");

	INFER_OK(op, "[1,?,3]", "[]"); // output is always scalar.
	}
	}

	TEST(ImageOpsTest, ExtractJpegShape_ShapeFn) {
	ShapeInferenceTestOp op("ExtractJpegShape");

	// Rank check.
	INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1]");

	// Only specify input data. Output must be a 1-D tensor with 3 elements.
	INFER_OK(op, "?", "[3]");
	}

	TEST(ImageOpsTest, Colorspace_ShapeFn) {
	for (const char* op_name : {"HSVToRGB", "RGBToHSV"}) {
	ShapeInferenceTestOp op(op_name);

	// Rank check.
	INFER_ERROR("Shape must be at least rank 1 but is rank 0", op, "[]");

	// Input's last dim is required to be 3.
	INFER_ERROR("Dimension must be 3 but is 4", op, "[1,2,4]");
	INFER_OK(op, "[1,2,3]", "[d0_0,d0_1,d0_2]");
	INFER_OK(op, "[1,2,?]", "[d0_0,d0_1,3]");
	INFER_OK(op, "?", "?");
	}
	}

	TEST(ImageOpsTest, ExtractGlimpse_ShapeFn) {
	ShapeInferenceTestOp op("ExtractGlimpse");
	op.input_tensors.resize(2);

	// Inputs are input, size, offsets.
	TF_ASSERT_OK(NodeDefBuilder("test", "ExtractGlimpse")
	.Input({"input", 0, DT_FLOAT})
	.Input({"size", 1, DT_INT32})
	.Input({"offsets", 2, DT_FLOAT})
	.Attr("uniform_noise", true)
	.Attr("noise", "")
	.Finalize(&op.node_def));

	// Rank and size checks.
	INFER_ERROR("Shape must be rank 4 but is rank 5", op, "[1,2,3,4,5];?;?");
	INFER_ERROR("Shape must be rank 4 but is rank 3", op, "[1,2,3];?;?");
	INFER_ERROR("Shape must be rank 1 but is rank 0", op, "?;[];?");
	INFER_ERROR("Dimension must be 2 but is 3", op, "?;[3];?");
	INFER_ERROR("Shape must be rank 2 but is rank 3", op, "?;?;[1,2,3]");

	// When the size tensor is not a constant, the middle dims are unknown.
	INFER_OK(op, "[1,?,3,?];[2];?", "[d0_0,?,?,d0_3]");

	Tensor size_tensor = test::AsTensor<int32>({20, 30});
	op.input_tensors[1] = &size_tensor;
	INFER_OK(op, "[1,?,3,?];[2];?", "[d0_0,20,30,d0_3]");

	// input.dim(0) and offsets.dim(0) are both the batch dimension.
	INFER_OK(op, "[?,?,3,?];[2];[1,?]", "[d2_0,20,30,d0_3]");
	INFER_OK(op, "[1,?,3,?];[2];[1,?]", "[d0_0\|d2_0,20,30,d_0\|d0_3]");
	INFER_ERROR("Dimensions must be equal, but are 10 and 1", op,
	"[10,?,?,?];?;[1,2]");
	}

	TEST(ImageOpsTest, CropAndResize_ShapeFn) {
	ShapeInferenceTestOp op("CropAndResize");
	op.input_tensors.resize(4);

	// Inputs are: input, boxes, box_ind, and crop_size.

	// Rank and size checks.
	INFER_ERROR("Shape must be rank 4 but is rank 5", op, "[1,2,3,4,5];?;?;?");
	INFER_ERROR("Shape must be rank 4 but is rank 3", op, "[1,2,3];?;?;?");
	INFER_ERROR("Shape must be rank 2 but is rank 3", op, "?;[1,2,3];?;?");
	INFER_ERROR("Shape must be rank 1 but is rank 2", op, "?;?;[1,2];?");
	INFER_ERROR("Shape must be rank 1 but is rank 2", op, "?;?;?;[1,2]");
	INFER_ERROR("Dimension must be 2 but is 1", op, "?;?;?;[1]");

	// When the size tensor is not a constant, the middle dims are unknown.
	INFER_OK(op, "[1,?,3,?];?;?;[2]", "[?,?,?,d0_3]");

	Tensor size_tensor = test::AsTensor<int32>({20, 30});
	op.input_tensors[3] = &size_tensor;
	INFER_OK(op, "[1,?,3,?];?;?;[2]", "[?,20,30,d0_3]");

	// boxes.dim(0) and box_ind.dim(0) are both the num_boxes dim.
	INFER_OK(op, "[1,?,3,?];[2,4];?;[2]", "[d1_0,20,30,d0_3]");
	INFER_OK(op, "[1,?,3,?];?;[2];[2]", "[d2_0,20,30,d0_3]");
	INFER_OK(op, "[1,?,3,?];[?,4];[?];[2]", "[d1_0\|d3_0,20,30,d0_3]");
	INFER_ERROR("Dimensions must be equal, but are 2 and 1", op, "?;[2,?];[1];?");

	// boxes.dim(1) must be 4.
	INFER_ERROR("Dimension must be 4 but is 3", op, "?;[?,3];?;?");
	}

	TEST(ImageOpsTest, ResizeNearestNeighborGrad_ShapeFn) {
	ShapeInferenceTestOp op("ResizeNearestNeighborGrad");
	op.input_tensors.resize(2);

	// Rank and size checks.
	INFER_ERROR("Shape must be rank 4 but is rank 3", op, "[1,2,3];?");
	INFER_ERROR("Shape must be rank 1 but is rank 2", op, "?;[1,2]")
	INFER_ERROR("Dimension must be 2 but is 1", op, "?;[1]");

	// When the size tensor is not a constant, the middle dims are unknown.
	INFER_OK(op, "[1,?,3,?];[2]", "[d0_0,?,?,d0_3]");

	Tensor size_tensor = test::AsTensor<int32>({20, 30});
	op.input_tensors[1] = &size_tensor;
	INFER_OK(op, "[1,?,3,?];[2]", "[d0_0,20,30,d0_3]");
	}

	TEST(ImageOpsTest, CropAndResizeGradImage_ShapeFn) {
	ShapeInferenceTestOp op("CropAndResizeGradImage");
	op.input_tensors.resize(4);

	// Rank checks.
	INFER_ERROR("Shape must be rank 1 but is rank 2", op, "?;?;?;[1,2]");

	// Unknown image_size should result in output of rank 4 with unknown dims.
	INFER_OK(op, "?;?;?;?", "[?,?,?,?]");

	// Known image_size should result in full shape information.
	Tensor image_size = test::AsTensor<int32>({10, 20, 30, 40});
	op.input_tensors[3] = &image_size;
	INFER_OK(op, "?;?;?;[1]", "[10, 20, 30, 40]");
	}

	TEST(ImageOpsTest, RandomCrop_ShapeFn) {
	ShapeInferenceTestOp op("RandomCrop");
	op.input_tensors.resize(2);

	// Rank checks.
	INFER_ERROR("must be rank 3", op, "[1,2];?");
	INFER_ERROR("must be equal", op, "?;[3]");
	INFER_ERROR("must be equal", op, "?;[1,2]");

	// Unknown size tensor values.
	INFER_OK(op, "[?,?,?];[2]", "[?,?,d0_2]");

	// Known size should result in full shape information.
	Tensor size = test::AsTensor<int64>({10, 20});
	op.input_tensors[1] = &size;
	INFER_OK(op, "[?,?,?];[2]", "[10,20,d0_2]");
	}

	TEST(ImageOpsTest, QuantizedResizeBilinear_ShapeFn) {
	ShapeInferenceTestOp op("QuantizedResizeBilinear");
	op.input_tensors.resize(4);

	NodeDefBuilder builder =
	NodeDefBuilder("test", "QuantizedResizeBilinear")
	.Input(NodeDefBuilder::NodeOut{"images", 0, DT_QINT32})
	.Input(NodeDefBuilder::NodeOut{"size", 0, DT_INT32})
	.Input(NodeDefBuilder::NodeOut{"min", 0, DT_FLOAT})
	.Input(NodeDefBuilder::NodeOut{"max", 0, DT_FLOAT})
	.Attr("T", DT_QINT32)
	.Attr("Toutput", DT_QINT32);
	TF_ASSERT_OK(builder.Finalize(&op.node_def));

	// When the size tensor is not a constant, the middle dims are unknown.
	INFER_OK(op, "[1,?,3,?];[2];[];[]",
	"[d0_0,?,?,d0_3];[];[]"); // output rank unknown
	INFER_ERROR("must be rank 0", op, "[1,?,3,?];[2];[?];[]");
	INFER_ERROR("must be rank 0", op, "[1,?,3,?];[2];[];[?]");

	const Tensor size_tensor = test::AsTensor<int32>({20, 30});
	op.input_tensors.at(1) = &size_tensor;
	INFER_OK(op, "[1,?,3,?];[2];[];[]", "[d0_0,20,30,d0_3];[];[]");
	}

	TEST(ImageOpsTest, DrawBoundingBoxes_ShapeFn) {
	ShapeInferenceTestOp op("DrawBoundingBoxes");
	op.input_tensors.resize(2);

	// Check images.
	INFER_ERROR("must be rank 4", op, "[1,?,3];?");
	INFER_ERROR("should be either 1 (GRY), 3 (RGB), or 4 (RGBA)",
	op, "[1,?,?,5];?");

	// Check boxes.
	INFER_ERROR("must be rank 3", op, "[1,?,?,4];[1,4]");
	INFER_ERROR("Dimension must be 4", op, "[1,?,?,4];[1,2,2]");

	// OK shapes.
	INFER_OK(op, "[4,?,?,4];?", "in0");
	INFER_OK(op, "[?,?,?,?];[?,?,?]", "in0");
	INFER_OK(op, "[4,?,?,4];[?,?,?]", "in0");
	INFER_OK(op, "[4,?,?,4];[?,?,4]", "in0");
	}
	} // end namespace tensorflow