SCM tf_set_attr_type(SCM scm_description, SCM scm_name, SCM scm_type)
{
struct tf_description_t *self = get_tf_description(scm_description);
char *name = scm_to_locale_string(scm_name);
TF_SetAttrType(self->description, name, scm_to_int(scm_type));
free(name);
return SCM_UNDEFINED;
}
static TF_Operation *add_pad_op(TFModel *tf_model, TF_Operation *input_op, int32_t pad)
{
TF_OperationDescription *op_desc;
TF_Operation *op;
TF_Tensor *tensor;
TF_Output input;
int32_t *pads;
int64_t pads_shape[] = {4, 2};
op_desc = TF_NewOperation(tf_model->graph, "Const", "pads");
TF_SetAttrType(op_desc, "dtype", TF_INT32);
tensor = TF_AllocateTensor(TF_INT32, pads_shape, 2, 4 * 2 * sizeof(int32_t));
pads = (int32_t *)TF_TensorData(tensor);
pads[0] = 0; pads[1] = 0;
pads[2] = pad; pads[3] = pad;
pads[4] = pad; pads[5] = pad;
pads[6] = 0; pads[7] = 0;
TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return NULL;
}
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return NULL;
}
op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
input.oper = input_op;
input.index = 0;
TF_AddInput(op_desc, input);
input.oper = op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
TF_SetAttrType(op_desc, "Tpaddings", TF_INT32);
TF_SetAttrString(op_desc, "mode", "SYMMETRIC", 9);
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return NULL;
}
return op;
}
static DNNReturnType add_depth_to_space_layer(TFModel *tf_model, TF_Operation **cur_op,
DepthToSpaceParams *params, const int layer)
{
TF_OperationDescription *op_desc;
TF_Output input;
char name_buffer[NAME_BUFFER_SIZE];
snprintf(name_buffer, NAME_BUFFER_SIZE, "depth_to_space%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "DepthToSpace", name_buffer);
input.oper = *cur_op;
input.index = 0;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
TF_SetAttrInt(op_desc, "block_size", params->block_size);
*cur_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return DNN_ERROR;
}
return DNN_SUCCESS;
}
// extern void TF_SetAttrType(TF_OperationDescription* desc, const char* attr_name,
// TF_DataType value);
static PHP_METHOD(TensorFlow_OperationDescription, setAttrType)
{
zend_string *name;
zend_long dtype;
ZEND_PARSE_PARAMETERS_START(2, 2)
Z_PARAM_STR(name)
Z_PARAM_LONG(dtype)
ZEND_PARSE_PARAMETERS_END();
if (!valid_dtype(dtype)) {
zend_throw_exception(spl_ce_InvalidArgumentException, "dtype must be from 1 to 20", 0);
return;
}
// this
t_tf_operation_description_object* intern = TF_OPERATION_DESCRIPTION_P_ZV(getThis());
t_tf_operation_description* node = intern->ptr;
TF_SetAttrType(node->src, name->val, dtype);
}
static TF_Operation *add_const_op(TFModel *tf_model, const float *values, const int64_t *dims, int dims_len, const char *name)
{
int dim;
TF_OperationDescription *op_desc;
TF_Tensor *tensor;
size_t len;
op_desc = TF_NewOperation(tf_model->graph, "Const", name);
TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
len = sizeof(float);
for (dim = 0; dim < dims_len; ++dim){
len *= dims[dim];
}
tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, len);
memcpy(TF_TensorData(tensor), values, len);
TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return NULL;
}
return TF_FinishOperation(op_desc, tf_model->status);
}
static DNNReturnType add_conv_layer(TFModel *tf_model, TF_Operation *transpose_op, TF_Operation **cur_op,
ConvolutionalParams* params, const int layer)
{
TF_Operation *op;
TF_OperationDescription *op_desc;
TF_Output input;
int64_t strides[] = {1, 1, 1, 1};
TF_Tensor *tensor;
int64_t dims[4];
int dims_len;
char name_buffer[NAME_BUFFER_SIZE];
int32_t size;
size = params->input_num * params->output_num * params->kernel_size * params->kernel_size;
input.index = 0;
snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_kernel%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
dims[0] = params->output_num;
dims[1] = params->kernel_size;
dims[2] = params->kernel_size;
dims[3] = params->input_num;
dims_len = 4;
tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, size * sizeof(float));
memcpy(TF_TensorData(tensor), params->kernel, size * sizeof(float));
TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return DNN_ERROR;
}
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return DNN_ERROR;
}
snprintf(name_buffer, NAME_BUFFER_SIZE, "transpose%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer);
input.oper = op;
TF_AddInput(op_desc, input);
input.oper = transpose_op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
TF_SetAttrType(op_desc, "Tperm", TF_INT32);
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return DNN_ERROR;
}
snprintf(name_buffer, NAME_BUFFER_SIZE, "conv2d%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer);
input.oper = *cur_op;
TF_AddInput(op_desc, input);
input.oper = op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
TF_SetAttrIntList(op_desc, "strides", strides, 4);
TF_SetAttrString(op_desc, "padding", "VALID", 5);
*cur_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return DNN_ERROR;
}
snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_biases%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
dims[0] = params->output_num;
dims_len = 1;
tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, params->output_num * sizeof(float));
memcpy(TF_TensorData(tensor), params->biases, params->output_num * sizeof(float));
TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return DNN_ERROR;
}
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return DNN_ERROR;
}
snprintf(name_buffer, NAME_BUFFER_SIZE, "bias_add%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer);
input.oper = *cur_op;
TF_AddInput(op_desc, input);
input.oper = op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
*cur_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return DNN_ERROR;
}
snprintf(name_buffer, NAME_BUFFER_SIZE, "activation%d", layer);
switch (params->activation){
case RELU:
op_desc = TF_NewOperation(tf_model->graph, "Relu", name_buffer);
break;
case TANH:
op_desc = TF_NewOperation(tf_model->graph, "Tanh", name_buffer);
break;
case SIGMOID:
op_desc = TF_NewOperation(tf_model->graph, "Sigmoid", name_buffer);
break;
default:
return DNN_ERROR;
}
input.oper = *cur_op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
*cur_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return DNN_ERROR;
}
return DNN_SUCCESS;
}
DNNModel *ff_dnn_load_default_model_tf(DNNDefaultModel model_type)
{
DNNModel *model = NULL;
TFModel *tf_model = NULL;
TF_OperationDescription *op_desc;
TF_Operation *op;
TF_Output input;
static const int64_t input_shape[] = {1, -1, -1, 1};
static const char tanh[] = "Tanh";
static const char sigmoid[] = "Sigmoid";
static const char relu[] = "Relu";
static const float *srcnn_consts[] = {
srcnn_conv1_kernel,
srcnn_conv1_bias,
srcnn_conv2_kernel,
srcnn_conv2_bias,
srcnn_conv3_kernel,
srcnn_conv3_bias
};
static const long int *srcnn_consts_dims[] = {
srcnn_conv1_kernel_dims,
srcnn_conv1_bias_dims,
srcnn_conv2_kernel_dims,
srcnn_conv2_bias_dims,
srcnn_conv3_kernel_dims,
srcnn_conv3_bias_dims
};
static const int srcnn_consts_dims_len[] = {
4,
1,
4,
1,
4,
1
};
static const char *srcnn_activations[] = {
relu,
relu,
relu
};
static const float *espcn_consts[] = {
espcn_conv1_kernel,
espcn_conv1_bias,
espcn_conv2_kernel,
espcn_conv2_bias,
espcn_conv3_kernel,
espcn_conv3_bias
};
static const long int *espcn_consts_dims[] = {
espcn_conv1_kernel_dims,
espcn_conv1_bias_dims,
espcn_conv2_kernel_dims,
espcn_conv2_bias_dims,
espcn_conv3_kernel_dims,
espcn_conv3_bias_dims
};
static const int espcn_consts_dims_len[] = {
4,
1,
4,
1,
4,
1
};
static const char *espcn_activations[] = {
tanh,
tanh,
sigmoid
};
input.index = 0;
model = av_malloc(sizeof(DNNModel));
if (!model){
return NULL;
}
tf_model = av_malloc(sizeof(TFModel));
if (!tf_model){
av_freep(&model);
return NULL;
}
tf_model->session = NULL;
tf_model->input_tensor = NULL;
tf_model->output_data = NULL;
tf_model->graph = TF_NewGraph();
tf_model->status = TF_NewStatus();
#define CLEANUP_ON_ERROR(tf_model, model) { \
TF_DeleteGraph(tf_model->graph); \
TF_DeleteStatus(tf_model->status); \
av_freep(&tf_model); \
av_freep(&model); \
return NULL; \
}
op_desc = TF_NewOperation(tf_model->graph, "Placeholder", "x");
TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
TF_SetAttrShape(op_desc, "shape", input_shape, 4);
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
CLEANUP_ON_ERROR(tf_model, model);
}
switch (model_type){
case DNN_SRCNN:
op = add_pad_op(tf_model, op, 6);
if (!op){
CLEANUP_ON_ERROR(tf_model, model);
}
op = add_conv_layers(tf_model, srcnn_consts,
srcnn_consts_dims, srcnn_consts_dims_len,
srcnn_activations, op, 3);
if (!op){
CLEANUP_ON_ERROR(tf_model, model);
}
break;
case DNN_ESPCN:
op = add_pad_op(tf_model, op, 4);
if (!op){
CLEANUP_ON_ERROR(tf_model, model);
}
op = add_conv_layers(tf_model, espcn_consts,
espcn_consts_dims, espcn_consts_dims_len,
espcn_activations, op, 3);
if (!op){
CLEANUP_ON_ERROR(tf_model, model);
}
op_desc = TF_NewOperation(tf_model->graph, "DepthToSpace", "depth_to_space");
input.oper = op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
TF_SetAttrInt(op_desc, "block_size", 2);
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
CLEANUP_ON_ERROR(tf_model, model);
}
break;
default:
CLEANUP_ON_ERROR(tf_model, model);
}
op_desc = TF_NewOperation(tf_model->graph, "Identity", "y");
input.oper = op;
TF_AddInput(op_desc, input);
TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
CLEANUP_ON_ERROR(tf_model, model);
}
model->model = (void *)tf_model;
model->set_input_output = &set_input_output_tf;
return model;
}
static TF_Operation* add_conv_layers(TFModel *tf_model, const float **consts, const int64_t **consts_dims,
const int *consts_dims_len, const char **activations,
TF_Operation *input_op, int layers_num)
{
int i;
TF_OperationDescription *op_desc;
TF_Operation *op;
TF_Operation *transpose_op;
TF_Output input;
int64_t strides[] = {1, 1, 1, 1};
int32_t *transpose_perm;
TF_Tensor *tensor;
int64_t transpose_perm_shape[] = {4};
#define NAME_BUFF_SIZE 256
char name_buffer[NAME_BUFF_SIZE];
op_desc = TF_NewOperation(tf_model->graph, "Const", "transpose_perm");
TF_SetAttrType(op_desc, "dtype", TF_INT32);
tensor = TF_AllocateTensor(TF_INT32, transpose_perm_shape, 1, 4 * sizeof(int32_t));
transpose_perm = (int32_t *)TF_TensorData(tensor);
transpose_perm[0] = 1;
transpose_perm[1] = 2;
transpose_perm[2] = 3;
transpose_perm[3] = 0;
TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return NULL;
}
transpose_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return NULL;
}
input.index = 0;
for (i = 0; i < layers_num; ++i){
snprintf(name_buffer, NAME_BUFF_SIZE, "conv_kernel%d", i);
op = add_const_op(tf_model, consts[i << 1], consts_dims[i << 1], consts_dims_len[i << 1], name_buffer);
if (TF_GetCode(tf_model->status) != TF_OK || op == NULL){
return NULL;
}
snprintf(name_buffer, NAME_BUFF_SIZE, "transpose%d", i);
op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer);
input.oper = op;
TF_AddInput(op_desc, input);
input.oper = transpose_op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
TF_SetAttrType(op_desc, "Tperm", TF_INT32);
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return NULL;
}
snprintf(name_buffer, NAME_BUFF_SIZE, "conv2d%d", i);
op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer);
input.oper = input_op;
TF_AddInput(op_desc, input);
input.oper = op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
TF_SetAttrIntList(op_desc, "strides", strides, 4);
TF_SetAttrString(op_desc, "padding", "VALID", 5);
input_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return NULL;
}
snprintf(name_buffer, NAME_BUFF_SIZE, "conv_biases%d", i);
op = add_const_op(tf_model, consts[(i << 1) + 1], consts_dims[(i << 1) + 1], consts_dims_len[(i << 1) + 1], name_buffer);
if (TF_GetCode(tf_model->status) != TF_OK || op == NULL){
return NULL;
}
snprintf(name_buffer, NAME_BUFF_SIZE, "bias_add%d", i);
op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer);
input.oper = input_op;
TF_AddInput(op_desc, input);
input.oper = op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
input_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return NULL;
}
snprintf(name_buffer, NAME_BUFF_SIZE, "activation%d", i);
op_desc = TF_NewOperation(tf_model->graph, activations[i], name_buffer);
input.oper = input_op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
input_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return NULL;
}
}
return input_op;
}
