#define DEFAULT_FRAMEWORK "tensorflow-lite"
#define CONFIGURED_INPUT_TENSOR_SIZE 1
-#define CONFIGURED_INPUT_TENSOR_DIMS 2
+#define CONFIGURED_INPUT_TENSOR_DIMS 4
// TODO:
// We have to update the sizeMap with more precise values for each of image format
}
// NOTE:
- // WIDTH and HEIGHT, dimension size is always 2
+ // BATCH, WIDTH, HEIGHT and CHANNEL, dimension size is always 4
info->dims = static_cast<beyond_tensor_info::dimensions *>(malloc(sizeof(beyond_tensor_info::dimensions) + sizeof(int) * CONFIGURED_INPUT_TENSOR_DIMS));
if (info->dims == nullptr) {
ErrPrintCode(errno, "malloc");
// This array information should be updated for each image format specification
// In case of the YUV, it could be manipulatd as a 3 dimensional array buffer
// At least now, image format related buffer layout will be treated as a 2-dimensional
- // array (width and height) buffer
+ // array (height and width) buffer
info->dims->size = CONFIGURED_INPUT_TENSOR_DIMS;
- info->dims->data[0] = imageConfig->width;
+ info->dims->data[0] = 1;
info->dims->data[1] = imageConfig->height;
+ info->dims->data[2] = imageConfig->width;
+ info->dims->data[3] = it->second;
return info;
}
ASSERT_EQ(input_info->type, BEYOND_TENSOR_TYPE_UINT8);
ASSERT_EQ(input_info->size, 320 * 192 * 3);
ASSERT_NE(input_info->dims, nullptr);
- ASSERT_EQ(input_info->dims->size, 2);
- ASSERT_EQ(input_info->dims->data[0], 320);
+ ASSERT_EQ(input_info->dims->size, 4);
+ ASSERT_EQ(input_info->dims->data[0], 1);
ASSERT_EQ(input_info->dims->data[1], 192);
+ ASSERT_EQ(input_info->dims->data[2], 320);
+ ASSERT_EQ(input_info->dims->data[3], 3);
(void)beyond_peer_deactivate(peerEdgeHandle);
(void)beyond_inference_remove_peer(inferenceHandle, peerHandle);