Add operator implementation and tests for IBILINEAR CHW microkernel
PiperOrigin-RevId: 339348062
diff --git a/test/resize-bilinear-operator-tester.h b/test/resize-bilinear-operator-tester.h
index 8223277..d3fa46b 100644
--- a/test/resize-bilinear-operator-tester.h
+++ b/test/resize-bilinear-operator-tester.h
@@ -301,6 +301,91 @@
}
}
+ void TestNCHWxF32() const {
+ if (align_corners()) {
+ ASSERT_FALSE(tf_legacy_mode());
+ }
+
+ std::random_device random_device;
+ auto rng = std::mt19937(random_device());
+ auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
+
+ std::vector<float> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
+ std::vector<float> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels());
+ std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
+ for (size_t iteration = 0; iteration < iterations(); iteration++) {
+ std::generate(input.begin(), input.end(), std::ref(f32rng));
+ std::fill(output.begin(), output.end(), std::nanf(""));
+
+ // Compute reference results.
+ const float offset = (tf_legacy_mode() || align_corners()) ? 0.0f : 0.5f;
+ const int64_t input_num_pixels = input_height() * input_width();
+ const int64_t input_num_elements = input_num_pixels * input_pixel_stride();
+ const int64_t output_num_pixels = output_height() * output_width();
+ const int64_t output_num_elements = output_num_pixels * channels();
+ for (size_t batch_index = 0; batch_index < batch_size(); batch_index++) {
+ for (size_t output_y = 0; output_y < output_height(); output_y++) {
+ const float input_y = (float(output_y) + offset) * height_scale() - offset;
+ const int64_t input_y_top = std::max<int64_t>(int64_t(std::floor(input_y)), 0);
+ const int64_t input_y_bottom = std::min<int64_t>(int64_t(std::ceil(input_y)), input_height() - 1);
+ const float y_alpha = input_y - std::floor(input_y);
+ for (size_t output_x = 0; output_x < output_width(); output_x++) {
+ const float input_x = (float(output_x) + offset) * width_scale() - offset;
+ const int64_t input_x_left = std::max<int64_t>(int64_t(std::floor(input_x)), 0);
+ const int64_t input_x_right = std::min<int64_t>(int64_t(std::ceil(input_x)), input_width() - 1);
+ const float x_alpha = input_x - std::floor(input_x);
+ for (size_t c = 0; c < channels(); c++) {
+ output_ref[batch_index * output_num_elements + c * output_num_pixels + output_y * output_width() + output_x] =
+ input[batch_index * input_num_elements + c * input_num_pixels + input_y_top * input_width() + input_x_left] * (1.0f - y_alpha) * (1.0f - x_alpha) +
+ input[batch_index * input_num_elements + c * input_num_pixels + input_y_top * input_width() + input_x_right] * (1.0f - y_alpha) * x_alpha +
+ input[batch_index * input_num_elements + c * input_num_pixels + input_y_bottom * input_width() + input_x_left] * y_alpha * (1.0f - x_alpha) +
+ input[batch_index * input_num_elements + c * input_num_pixels + input_y_bottom * input_width() + input_x_right] * y_alpha * x_alpha;
+ }
+ }
+ }
+ }
+
+ // Create, setup, run, and destroy Resize Bilinear operator.
+ ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
+ xnn_operator_t resize_bilinear_op = nullptr;
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_create_resize_bilinear2d_nchw_f32(
+ channels(), input_pixel_stride(), output_pixel_stride(),
+ (align_corners() ? XNN_FLAG_ALIGN_CORNERS : 0) | (tf_legacy_mode() ? XNN_FLAG_TENSORFLOW_LEGACY_MODE : 0),
+ &resize_bilinear_op));
+ ASSERT_NE(nullptr, resize_bilinear_op);
+
+ // Smart pointer to automatically delete resize_bilinear_op.
+ std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_resize_bilinear_op(resize_bilinear_op, xnn_delete_operator);
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_setup_resize_bilinear2d_nchw_f32(
+ resize_bilinear_op,
+ batch_size(), input_height(), input_width(),
+ output_height(), output_width(),
+ input.data(), output.data(),
+ nullptr /* thread pool */));
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_run_operator(resize_bilinear_op, nullptr /* thread pool */));
+
+ // Verify results.
+ for (size_t i = 0; i < batch_size(); i++) {
+ for (size_t y = 0; y < output_height(); y++) {
+ for (size_t x = 0; x < output_width(); x++) {
+ for (size_t c = 0; c < channels(); c++) {
+ ASSERT_NEAR(output[i * output_num_elements + c * output_num_pixels + y * output_width() + x],
+ output_ref[i * output_num_elements + c * output_num_pixels + y * output_width() + x],
+ 1.0e-6f) <<
+ "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
+ }
+ }
+ }
+ }
+ }
+ }
+
// void TestSetupF32() const {
// std::random_device random_device;
// auto rng = std::mt19937(random_device());