aboutsummaryrefslogtreecommitdiffhomepage
path: root/tensorflow/contrib/lite/kernels/transpose_conv_test.cc
diff options
context:
space:
mode:
Diffstat (limited to 'tensorflow/contrib/lite/kernels/transpose_conv_test.cc')
-rw-r--r--tensorflow/contrib/lite/kernels/transpose_conv_test.cc121
1 files changed, 13 insertions, 108 deletions
diff --git a/tensorflow/contrib/lite/kernels/transpose_conv_test.cc b/tensorflow/contrib/lite/kernels/transpose_conv_test.cc
index c741df19de..55df897180 100644
--- a/tensorflow/contrib/lite/kernels/transpose_conv_test.cc
+++ b/tensorflow/contrib/lite/kernels/transpose_conv_test.cc
@@ -14,7 +14,6 @@ limitations under the License.
==============================================================================*/
#include <cstdarg>
#include <gtest/gtest.h>
-#include "absl/memory/memory.h"
#include "tensorflow/contrib/lite/interpreter.h"
#include "tensorflow/contrib/lite/kernels/register.h"
#include "tensorflow/contrib/lite/kernels/test_util.h"
@@ -25,49 +24,9 @@ namespace {
using ::testing::ElementsAreArray;
-class ConstTransposeConvOpModel : public SingleOpModel {
- // Just to be extra confusing, transpose_conv has an _input_ named
- // "output_shape". This input sets the shape of the output tensor of the op.
- // In this version of the test class, "output_shape" is a constant that must
- // be specified in the constructor.
- public:
- ConstTransposeConvOpModel(TfLiteRegistration* registration,
- std::initializer_list<int> input_shape,
- std::initializer_list<int> filter_shape,
- std::initializer_list<int> output_shape_data,
- Padding padding, int stride_w, int stride_h) {
- output_shape_ = AddConstInput(TensorType_INT32, output_shape_data,
- {static_cast<int>(output_shape_data.size())});
- filter_ = AddInput(TensorType_FLOAT32);
- input_ = AddInput(TensorType_FLOAT32);
- output_ = AddOutput(TensorType_FLOAT32);
- SetBuiltinOp(
- BuiltinOperator_TRANSPOSE_CONV, BuiltinOptions_TransposeConvOptions,
- CreateTransposeConvOptions(builder_, padding, stride_w, stride_h)
- .Union());
- resolver_ = absl::make_unique<SingleOpResolver>(
- BuiltinOperator_TRANSPOSE_CONV, registration);
- BuildInterpreter({{4}, filter_shape, input_shape});
- }
-
- int output_shape() { return output_shape_; }
- int filter() { return filter_; }
- int input() { return input_; }
-
- std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
- std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
-
- private:
- int output_shape_;
- int filter_;
- int input_;
- int output_;
-};
-
class TransposeConvOpModel : public SingleOpModel {
public:
- TransposeConvOpModel(TfLiteRegistration* registration,
- std::initializer_list<int> input_shape,
+ TransposeConvOpModel(std::initializer_list<int> input_shape,
std::initializer_list<int> filter_shape, Padding padding,
int stride_w, int stride_h) {
output_shape_ = AddInput(TensorType_INT32);
@@ -78,8 +37,6 @@ class TransposeConvOpModel : public SingleOpModel {
BuiltinOperator_TRANSPOSE_CONV, BuiltinOptions_TransposeConvOptions,
CreateTransposeConvOptions(builder_, padding, stride_w, stride_h)
.Union());
- resolver_ = absl::make_unique<SingleOpResolver>(
- BuiltinOperator_TRANSPOSE_CONV, registration);
BuildInterpreter({{4}, filter_shape, input_shape});
}
@@ -97,15 +54,6 @@ class TransposeConvOpModel : public SingleOpModel {
int output_;
};
-const auto kKernelMap = new std::map<string, TfLiteRegistration*>({});
-
-class TransposeConvOpTest : public SingleOpTest {
- protected:
- const std::map<string, TfLiteRegistration*>& GetKernelMap() override {
- return *kKernelMap;
- }
-};
-
// Test case:
// output = tf.nn.conv2d_backprop_input(
// tf.constant([ 1, 4, 4, 1 ]),
@@ -113,9 +61,8 @@ class TransposeConvOpTest : public SingleOpTest {
// tf.constant(np.arange(1, 17), shape=[ 1, 4, 4, 1 ], dtype=tf.float32),
// [1, 1, 1, 1 ],
// "SAME")
-TEST_P(TransposeConvOpTest, SimpleTest) {
- TransposeConvOpModel m(GetRegistration(), {1, 4, 4, 1}, {1, 3, 3, 1},
- Padding_SAME, 1, 1);
+TEST(TransposeConvOpModelTest, SimpleTest) {
+ TransposeConvOpModel m({1, 4, 4, 1}, {1, 3, 3, 1}, Padding_SAME, 1, 1);
m.PopulateTensor<int>(m.output_shape(), {1, 4, 4, 1});
m.PopulateTensor<float>(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9});
m.PopulateTensor<float>(
@@ -128,21 +75,6 @@ TEST_P(TransposeConvOpTest, SimpleTest) {
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 4, 1}));
}
-// Test case: Same as above, but with a const "output_shape"
-TEST_P(TransposeConvOpTest, ConstSimpleTest) {
- ConstTransposeConvOpModel m(GetRegistration(), {1, 4, 4, 1}, {1, 4, 4, 1},
- {1, 3, 3, 1}, Padding_SAME, 1, 1);
- m.PopulateTensor<float>(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9});
- m.PopulateTensor<float>(
- m.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
- m.Invoke();
-
- EXPECT_THAT(m.GetOutput(),
- ElementsAreArray({29, 62, 83, 75, 99, 192, 237, 198, 207, 372,
- 417, 330, 263, 446, 485, 365}));
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 4, 1}));
-}
-
// Test case:
// filter = tf.constant(np.arange(1, 19),
// shape=[ 3, 3, 1, 2 ],
@@ -155,9 +87,8 @@ TEST_P(TransposeConvOpTest, ConstSimpleTest) {
// "SAME")
// And filter value is derived by:
// filter = tf.reshape(tf.transpose(filter, perm=[3, 0, 1, 2]), shape=[18, 1])
-TEST_P(TransposeConvOpTest, TwoFiltersTest) {
- TransposeConvOpModel m(GetRegistration(), {1, 4, 4, 2}, {1, 3, 3, 2},
- Padding_SAME, 1, 1);
+TEST(TransposeConvOpModelTest, TwoFiltersTest) {
+ TransposeConvOpModel m({1, 4, 4, 2}, {1, 3, 3, 2}, Padding_SAME, 1, 1);
m.PopulateTensor<int>(m.output_shape(), {1, 4, 4, 1});
m.PopulateTensor<float>(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18});
@@ -185,9 +116,8 @@ TEST_P(TransposeConvOpTest, TwoFiltersTest) {
// "VALID")
// And filter value is derived by:
// filter = tf.reshape(tf.transpose(filter, perm=[3, 0, 1, 2]), shape=[1, 18])
-TEST_P(TransposeConvOpTest, PaddingValidTest) {
- TransposeConvOpModel m(GetRegistration(), {1, 4, 4, 2}, {1, 3, 3, 2},
- Padding_VALID, 1, 1);
+TEST(TransposeConvOpModelTest, PaddingValidTest) {
+ TransposeConvOpModel m({1, 4, 4, 2}, {1, 3, 3, 2}, Padding_VALID, 1, 1);
m.PopulateTensor<int>(m.output_shape(), {1, 6, 6, 1});
m.PopulateTensor<float>(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18});
@@ -216,9 +146,8 @@ TEST_P(TransposeConvOpTest, PaddingValidTest) {
// tf.constant(np.arange(1, 5), shape=[ 1, 2, 2, 1 ], dtype=tf.float32),
// [1, 2, 2, 1 ],
// "VALID")
-TEST_P(TransposeConvOpTest, StrideValidTest) {
- TransposeConvOpModel m(GetRegistration(), {1, 2, 2, 1}, {1, 3, 3, 1},
- Padding_VALID, 2, 2);
+TEST(TransposeConvOpModelTest, StrideValidTest) {
+ TransposeConvOpModel m({1, 2, 2, 1}, {1, 3, 3, 1}, Padding_VALID, 2, 2);
m.PopulateTensor<int>(m.output_shape(), {1, 5, 5, 1});
m.PopulateTensor<float>(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9});
m.PopulateTensor<float>(m.input(), {1, 2, 3, 4});
@@ -241,9 +170,8 @@ TEST_P(TransposeConvOpTest, StrideValidTest) {
// tf.constant(np.arange(1, 5), shape=[ 1, 2, 2, 1 ], dtype=tf.float32),
// [1, 2, 2, 1 ],
// "VALID")
-TEST_P(TransposeConvOpTest, MultiChannelTest) {
- TransposeConvOpModel m(GetRegistration(), {1, 2, 2, 1}, {2, 3, 3, 1},
- Padding_VALID, 2, 2);
+TEST(TransposeConvOpModelTest, MultiChannelTest) {
+ TransposeConvOpModel m({1, 2, 2, 1}, {2, 3, 3, 1}, Padding_VALID, 2, 2);
m.PopulateTensor<int>(m.output_shape(), {1, 5, 5, 2});
m.PopulateTensor<float>(m.filter(), {1, 3, 5, 7, 9, 11, 13, 15, 17, 2, 4, 6,
8, 10, 12, 14, 16, 18});
@@ -259,24 +187,6 @@ TEST_P(TransposeConvOpTest, MultiChannelTest) {
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 5, 5, 2}));
}
-// Test case: Same as above, but with a const "output_shape"
-TEST_P(TransposeConvOpTest, ConstMultiChannelTest) {
- ConstTransposeConvOpModel m(GetRegistration(), {1, 2, 2, 1}, {2, 3, 3, 1},
- {1, 5, 5, 2}, Padding_VALID, 2, 2);
- m.PopulateTensor<float>(m.filter(), {1, 3, 5, 7, 9, 11, 13, 15, 17, 2, 4, 6,
- 8, 10, 12, 14, 16, 18});
- m.PopulateTensor<float>(m.input(), {1, 2, 3, 4});
- m.Invoke();
-
- EXPECT_THAT(
- m.GetOutput(),
- ElementsAreArray({1, 2, 3, 4, 7, 10, 6, 8, 10, 12, 7, 8, 9,
- 10, 25, 28, 18, 20, 22, 24, 16, 20, 24, 28, 62, 72,
- 42, 48, 54, 60, 21, 24, 27, 30, 61, 68, 36, 40, 44,
- 48, 39, 42, 45, 48, 103, 110, 60, 64, 68, 72}));
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 5, 5, 2}));
-}
-
// Test case:
// filter = tf.constant(np.random.randint(1, 10, size=9),
// shape=[ 3, 3, 1, 1 ],
@@ -289,9 +199,8 @@ TEST_P(TransposeConvOpTest, ConstMultiChannelTest) {
// "SAME")
// And filter value is derived by:
// filter = tf.reshape(tf.transpose(filter, perm=[3, 0, 1, 2]), shape=[-1])
-TEST_P(TransposeConvOpTest, AccuracyTest) {
- TransposeConvOpModel m(GetRegistration(), {1, 1, 2, 1}, {1, 3, 3, 1},
- Padding_SAME, 3, 3);
+TEST(TransposeConvOpModelTest, AccuracyTest) {
+ TransposeConvOpModel m({1, 1, 2, 1}, {1, 3, 3, 1}, Padding_SAME, 3, 3);
m.PopulateTensor<int>(m.output_shape(), {1, 3, 4, 1});
m.PopulateTensor<float>(m.filter(), {9, 5, 6, 9, 8, 5, 3, 1, 4});
m.PopulateTensor<float>(m.input(), {323, 521});
@@ -303,10 +212,6 @@ TEST_P(TransposeConvOpTest, AccuracyTest) {
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 4, 1}));
}
-INSTANTIATE_TEST_CASE_P(
- TransposeConvOpTest, TransposeConvOpTest,
- ::testing::ValuesIn(SingleOpTest::GetKernelTags(*kKernelMap)));
-
} // namespace
} // namespace tflite
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-02 02:09:52 +0000