diff options
Diffstat (limited to 'tensorflow/contrib/lite/kernels/pooling.cc')
| -rw-r--r-- | tensorflow/contrib/lite/kernels/pooling.cc | 99 |
1 files changed, 63 insertions, 36 deletions
diff --git a/tensorflow/contrib/lite/kernels/pooling.cc b/tensorflow/contrib/lite/kernels/pooling.cc index 7240fe04cc..29a5be0683 100644 --- a/tensorflow/contrib/lite/kernels/pooling.cc +++ b/tensorflow/contrib/lite/kernels/pooling.cc @@ -12,7 +12,6 @@ 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 <unistd.h> #include <cassert> #include <cmath> #include <cstdio> @@ -126,13 +125,19 @@ void AverageEvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRange(params->activation, &activation_min, &activation_max); -#define TF_LITE_AVERAGE_POOL(type) \ - type::AveragePool(GetTensorData<float>(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, \ - activation_min, activation_max, \ - GetTensorData<float>(output), GetTensorShape(output)) +#define TF_LITE_AVERAGE_POOL(type) \ + tflite::PoolParams op_params; \ + op_params.stride_height = params->stride_height; \ + op_params.stride_width = params->stride_width; \ + op_params.filter_height = params->filter_height; \ + op_params.filter_width = params->filter_width; \ + op_params.padding_values.height = data->padding.height; \ + op_params.padding_values.width = data->padding.width; \ + op_params.float_activation_min = activation_min; \ + op_params.float_activation_max = activation_max; \ + type::AveragePool(op_params, GetTensorShape(input), \ + GetTensorData<float>(input), GetTensorShape(output), \ + GetTensorData<float>(output)) if (kernel_type == kReference) { TF_LITE_AVERAGE_POOL(reference_ops); } else { @@ -149,13 +154,19 @@ void AverageEvalQuantized(TfLiteContext* context, TfLiteNode* node, int32_t activation_max; CalculateActivationRangeUint8(params->activation, output, &activation_min, &activation_max); -#define TF_LITE_AVERAGE_POOL(type) \ - type::AveragePool(GetTensorData<uint8_t>(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, \ - activation_min, activation_max, \ - GetTensorData<uint8_t>(output), GetTensorShape(output)) +#define TF_LITE_AVERAGE_POOL(type) \ + tflite::PoolParams op_params; \ + op_params.stride_height = params->stride_height; \ + op_params.stride_width = params->stride_width; \ + op_params.filter_height = params->filter_height; \ + op_params.filter_width = params->filter_width; \ + op_params.padding_values.height = data->padding.height; \ + op_params.padding_values.width = data->padding.width; \ + op_params.quantized_activation_min = activation_min; \ + op_params.quantized_activation_max = activation_max; \ + type::AveragePool(op_params, GetTensorShape(input), \ + GetTensorData<uint8_t>(input), GetTensorShape(output), \ + GetTensorData<uint8_t>(output)) if (kernel_type == kReference) { TF_LITE_AVERAGE_POOL(reference_ops); } else { @@ -171,13 +182,18 @@ void MaxEvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRange(params->activation, &activation_min, &activation_max); -#define TF_LITE_MAX_POOL(type) \ - type::MaxPool(GetTensorData<float>(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData<float>(output), \ - GetTensorShape(output)) +#define TF_LITE_MAX_POOL(type) \ + tflite::PoolParams op_params; \ + op_params.stride_height = params->stride_height; \ + op_params.stride_width = params->stride_width; \ + op_params.filter_height = params->filter_height; \ + op_params.filter_width = params->filter_width; \ + op_params.padding_values.height = data->padding.height; \ + op_params.padding_values.width = data->padding.width; \ + op_params.float_activation_min = activation_min; \ + op_params.float_activation_max = activation_max; \ + type::MaxPool(op_params, GetTensorShape(input), GetTensorData<float>(input), \ + GetTensorShape(output), GetTensorData<float>(output)) if (kernel_type == kReference) { TF_LITE_MAX_POOL(reference_ops); } else { @@ -194,13 +210,19 @@ void MaxEvalQuantized(TfLiteContext* context, TfLiteNode* node, int32_t activation_max; CalculateActivationRangeUint8(params->activation, output, &activation_min, &activation_max); -#define TF_LITE_MAX_POOL(type) \ - type::MaxPool(GetTensorData<uint8_t>(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData<uint8_t>(output), \ - GetTensorShape(output)) +#define TF_LITE_MAX_POOL(type) \ + tflite::PoolParams op_params; \ + op_params.stride_height = params->stride_height; \ + op_params.stride_width = params->stride_width; \ + op_params.filter_height = params->filter_height; \ + op_params.filter_width = params->filter_width; \ + op_params.padding_values.height = data->padding.height; \ + op_params.padding_values.width = data->padding.width; \ + op_params.quantized_activation_min = activation_min; \ + op_params.quantized_activation_max = activation_max; \ + type::MaxPool(op_params, GetTensorShape(input), \ + GetTensorData<uint8_t>(input), GetTensorShape(output), \ + GetTensorData<uint8_t>(output)) if (kernel_type == kReference) { TF_LITE_MAX_POOL(reference_ops); } else { @@ -216,13 +238,18 @@ void L2EvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRange(params->activation, &activation_min, &activation_max); -#define TF_LITE_L2_POOL(type) \ - type::L2Pool(GetTensorData<float>(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData<float>(output), \ - GetTensorShape(output)) +#define TF_LITE_L2_POOL(type) \ + tflite::PoolParams op_params; \ + op_params.stride_height = params->stride_height; \ + op_params.stride_width = params->stride_width; \ + op_params.filter_height = params->filter_height; \ + op_params.filter_width = params->filter_width; \ + op_params.padding_values.height = data->padding.height; \ + op_params.padding_values.width = data->padding.width; \ + op_params.float_activation_min = activation_min; \ + op_params.float_activation_max = activation_max; \ + type::L2Pool(op_params, GetTensorShape(input), GetTensorData<float>(input), \ + GetTensorShape(output), GetTensorData<float>(output)) if (kernel_type == kReference) { TF_LITE_L2_POOL(reference_ops); } else { |