Line data Source code
1 : /* SPDX-License-Identifier: Apache-2.0 */
2 : /**
3 : * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved.
4 : *
5 : * @file ml-api-inference-single.c
6 : * @date 29 Aug 2019
7 : * @brief NNStreamer/Single C-API Wrapper.
8 : * This allows to invoke individual input frame with NNStreamer.
9 : * @see https://github.com/nnstreamer/nnstreamer
10 : * @author MyungJoo Ham <myungjoo.ham@samsung.com>
11 : * @author Parichay Kapoor <pk.kapoor@samsung.com>
12 : * @bug No known bugs except for NYI items
13 : */
14 :
15 : #include <string.h>
16 : #include <nnstreamer-single.h>
17 : #include <nnstreamer-tizen-internal.h> /* Tizen platform header */
18 : #include <nnstreamer_internal.h>
19 : #include <nnstreamer_plugin_api_util.h>
20 : #include <tensor_filter_single.h>
21 :
22 : #include "ml-api-inference-internal.h"
23 : #include "ml-api-internal.h"
24 : #include "ml-api-inference-single-internal.h"
25 :
26 : #define ML_SINGLE_MAGIC 0xfeedfeed
27 :
28 : /**
29 : * @brief Default time to wait for an output in milliseconds (0 will wait for the output).
30 : */
31 : #define SINGLE_DEFAULT_TIMEOUT 0
32 :
33 : /**
34 : * @brief Global lock for single shot API
35 : * @detail This lock ensures that ml_single_close is thread safe. All other API
36 : * functions use the mutex from the single handle. However for close,
37 : * single handle mutex cannot be used as single handle is destroyed at
38 : * close
39 : * @note This mutex is automatically initialized as it is statically declared
40 : */
41 : G_LOCK_DEFINE_STATIC (magic);
42 :
43 : /**
44 : * @brief Get valid handle after magic verification
45 : * @note handle's mutex (single_h->mutex) is acquired after this
46 : * @param[out] single_h The handle properly casted: (ml_single *).
47 : * @param[in] single The handle to be validated: (void *).
48 : * @param[in] reset Set TRUE if the handle is to be reset (magic = 0).
49 : */
50 : #define ML_SINGLE_GET_VALID_HANDLE_LOCKED(single_h, single, reset) do { \
51 : G_LOCK (magic); \
52 : single_h = (ml_single *) single; \
53 : if (G_UNLIKELY(single_h->magic != ML_SINGLE_MAGIC)) { \
54 : _ml_error_report \
55 : ("The given param, %s (ml_single_h), is invalid. It is not a single_h instance or the user thread has modified it.", \
56 : #single); \
57 : G_UNLOCK (magic); \
58 : return ML_ERROR_INVALID_PARAMETER; \
59 : } \
60 : if (G_UNLIKELY(reset)) \
61 : single_h->magic = 0; \
62 : g_mutex_lock (&single_h->mutex); \
63 : G_UNLOCK (magic); \
64 : } while (0)
65 :
66 : /**
67 : * @brief This is for the symmetricity with ML_SINGLE_GET_VALID_HANDLE_LOCKED
68 : * @param[in] single_h The casted handle (ml_single *).
69 : */
70 : #define ML_SINGLE_HANDLE_UNLOCK(single_h) g_mutex_unlock (&single_h->mutex);
71 :
72 : /** define string names for input/output */
73 : #define INPUT_STR "input"
74 : #define OUTPUT_STR "output"
75 : #define TYPE_STR "type"
76 : #define NAME_STR "name"
77 :
78 : /** concat string from #define */
79 : #define CONCAT_MACRO_STR(STR1,STR2) STR1 STR2
80 :
81 : /** States for invoke thread */
82 : typedef enum
83 : {
84 : IDLE = 0, /**< ready to accept next input */
85 : RUNNING, /**< running an input, cannot accept more input */
86 : JOIN_REQUESTED /**< should join the thread, will exit soon */
87 : } thread_state;
88 :
89 : /**
90 : * @brief The name of sub-plugin for defined neural net frameworks.
91 : * @note The sub-plugin for Android is not declared (e.g., snap)
92 : */
93 : static const char *ml_nnfw_subplugin_name[] = {
94 : [ML_NNFW_TYPE_ANY] = "any", /* DO NOT use this name ('any') to get the sub-plugin */
95 : [ML_NNFW_TYPE_CUSTOM_FILTER] = "custom",
96 : [ML_NNFW_TYPE_TENSORFLOW_LITE] = "tensorflow-lite",
97 : [ML_NNFW_TYPE_TENSORFLOW] = "tensorflow",
98 : [ML_NNFW_TYPE_NNFW] = "nnfw",
99 : [ML_NNFW_TYPE_MVNC] = "movidius-ncsdk2",
100 : [ML_NNFW_TYPE_OPENVINO] = "openvino",
101 : [ML_NNFW_TYPE_VIVANTE] = "vivante",
102 : [ML_NNFW_TYPE_EDGE_TPU] = "edgetpu",
103 : [ML_NNFW_TYPE_ARMNN] = "armnn",
104 : [ML_NNFW_TYPE_SNPE] = "snpe",
105 : [ML_NNFW_TYPE_PYTORCH] = "pytorch",
106 : [ML_NNFW_TYPE_NNTR_INF] = "nntrainer",
107 : [ML_NNFW_TYPE_VD_AIFW] = "vd_aifw",
108 : [ML_NNFW_TYPE_TRIX_ENGINE] = "trix-engine",
109 : [ML_NNFW_TYPE_MXNET] = "mxnet",
110 : [ML_NNFW_TYPE_TVM] = "tvm",
111 : [ML_NNFW_TYPE_ONNX_RUNTIME] = "onnxruntime",
112 : [ML_NNFW_TYPE_NCNN] = "ncnn",
113 : [ML_NNFW_TYPE_TENSORRT] = "tensorrt",
114 : [ML_NNFW_TYPE_QNN] = "qnn",
115 : [ML_NNFW_TYPE_LLAMACPP] = "llamacpp",
116 : [ML_NNFW_TYPE_TIZEN_HAL] = "tizen-hal",
117 : NULL
118 : };
119 :
120 : /** ML single api data structure for handle */
121 : typedef struct
122 : {
123 : GTensorFilterSingleClass *klass; /**< tensor filter class structure*/
124 : GTensorFilterSingle *filter; /**< tensor filter element */
125 : GstTensorsInfo in_info; /**< info about input */
126 : GstTensorsInfo out_info; /**< info about output */
127 : ml_nnfw_type_e nnfw; /**< nnfw type for this filter */
128 : guint magic; /**< code to verify valid handle */
129 :
130 : GThread *thread; /**< thread for invoking */
131 : GMutex mutex; /**< mutex for synchronization */
132 : GCond cond; /**< condition for synchronization */
133 : ml_tensors_data_h input; /**< input received from user */
134 : ml_tensors_data_h output; /**< output to be sent back to user */
135 : guint timeout; /**< timeout for invoking */
136 : thread_state state; /**< current state of the thread */
137 : gboolean free_output; /**< true if output tensors are allocated in single-shot */
138 : int status; /**< status of processing */
139 : gboolean invoking; /**< invoke running flag */
140 : ml_tensors_data_h in_tensors; /**< input tensor wrapper for processing */
141 : ml_tensors_data_h out_tensors; /**< output tensor wrapper for processing */
142 :
143 : GList *destroy_data_list; /**< data to be freed by filter */
144 : gboolean invoke_dynamic; /**< true to invoke flexible tensor */
145 : gboolean invoke_async; /**< true to invoke and return result asynchronously */
146 : } ml_single;
147 :
148 : /**
149 : * @brief Internal function to get the nnfw type.
150 : */
151 : ml_nnfw_type_e
152 96 : _ml_get_nnfw_type_by_subplugin_name (const char *name)
153 : {
154 96 : ml_nnfw_type_e nnfw_type = ML_NNFW_TYPE_ANY;
155 96 : int idx = -1;
156 :
157 96 : if (name == NULL)
158 2 : return ML_NNFW_TYPE_ANY;
159 :
160 94 : idx = find_key_strv (ml_nnfw_subplugin_name, name);
161 94 : if (idx < 0) {
162 : /* check sub-plugin for android */
163 2 : if (g_ascii_strcasecmp (name, "snap") == 0)
164 1 : nnfw_type = ML_NNFW_TYPE_SNAP;
165 : else
166 1 : _ml_error_report ("Cannot find nnfw, %s is an invalid name.",
167 : _STR_NULL (name));
168 : } else {
169 92 : nnfw_type = (ml_nnfw_type_e) idx;
170 : }
171 :
172 94 : return nnfw_type;
173 : }
174 :
175 : /**
176 : * @brief Internal function to get the sub-plugin name.
177 : */
178 : const char *
179 370 : _ml_get_nnfw_subplugin_name (ml_nnfw_type_e nnfw)
180 : {
181 : /* check sub-plugin for android */
182 370 : if (nnfw == ML_NNFW_TYPE_SNAP)
183 1 : return "snap";
184 :
185 369 : return ml_nnfw_subplugin_name[nnfw];
186 : }
187 :
188 : /**
189 : * @brief Convert c-api based hw to internal representation
190 : */
191 : accl_hw
192 271 : _ml_nnfw_to_accl_hw (const ml_nnfw_hw_e hw)
193 : {
194 271 : switch (hw) {
195 249 : case ML_NNFW_HW_ANY:
196 249 : return ACCL_DEFAULT;
197 3 : case ML_NNFW_HW_AUTO:
198 3 : return ACCL_AUTO;
199 5 : case ML_NNFW_HW_CPU:
200 5 : return ACCL_CPU;
201 : #if defined (__aarch64__) || defined (__arm__)
202 : case ML_NNFW_HW_CPU_NEON:
203 : return ACCL_CPU_NEON;
204 : #else
205 2 : case ML_NNFW_HW_CPU_SIMD:
206 2 : return ACCL_CPU_SIMD;
207 : #endif
208 3 : case ML_NNFW_HW_GPU:
209 3 : return ACCL_GPU;
210 2 : case ML_NNFW_HW_NPU:
211 2 : return ACCL_NPU;
212 2 : case ML_NNFW_HW_NPU_MOVIDIUS:
213 2 : return ACCL_NPU_MOVIDIUS;
214 1 : case ML_NNFW_HW_NPU_EDGE_TPU:
215 1 : return ACCL_NPU_EDGE_TPU;
216 1 : case ML_NNFW_HW_NPU_VIVANTE:
217 1 : return ACCL_NPU_VIVANTE;
218 1 : case ML_NNFW_HW_NPU_SLSI:
219 1 : return ACCL_NPU_SLSI;
220 2 : case ML_NNFW_HW_NPU_SR:
221 : /** @todo how to get srcn npu */
222 2 : return ACCL_NPU_SR;
223 0 : default:
224 0 : return ACCL_AUTO;
225 : }
226 : }
227 :
228 : /**
229 : * @brief Checks the availability of the given execution environments with custom option.
230 : */
231 : int
232 193 : ml_check_nnfw_availability_full (ml_nnfw_type_e nnfw, ml_nnfw_hw_e hw,
233 : const char *custom, bool *available)
234 : {
235 193 : const char *fw_name = NULL;
236 :
237 193 : check_feature_state (ML_FEATURE_INFERENCE);
238 :
239 193 : if (!available)
240 2 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
241 : "The parameter, available (bool *), is NULL. It should be a valid pointer of bool. E.g., bool a; ml_check_nnfw_availability_full (..., &a);");
242 :
243 : /* init false */
244 191 : *available = false;
245 :
246 191 : if (nnfw == ML_NNFW_TYPE_ANY)
247 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
248 : "The parameter, nnfw (ml_nnfw_type_e), is ML_NNFW_TYPE_ANY. It should specify the framework to be probed for the hardware availability.");
249 :
250 190 : fw_name = _ml_get_nnfw_subplugin_name (nnfw);
251 :
252 190 : if (fw_name) {
253 190 : if (nnstreamer_filter_find (fw_name) != NULL) {
254 189 : accl_hw accl = _ml_nnfw_to_accl_hw (hw);
255 :
256 189 : if (gst_tensor_filter_check_hw_availability (fw_name, accl, custom)) {
257 180 : *available = true;
258 : } else {
259 9 : _ml_logi ("%s is supported but not with the specified hardware.",
260 : fw_name);
261 : }
262 : } else {
263 1 : _ml_logi ("%s is not supported.", fw_name);
264 : }
265 : } else {
266 0 : _ml_logw ("Cannot get the name of sub-plugin for given nnfw.");
267 : }
268 :
269 190 : return ML_ERROR_NONE;
270 : }
271 :
272 : /**
273 : * @brief Checks the availability of the given execution environments.
274 : */
275 : int
276 191 : ml_check_nnfw_availability (ml_nnfw_type_e nnfw, ml_nnfw_hw_e hw,
277 : bool *available)
278 : {
279 191 : return ml_check_nnfw_availability_full (nnfw, hw, NULL, available);
280 : }
281 :
282 : /**
283 : * @brief setup input and output tensor memory to pass to the tensor_filter.
284 : * @note this tensor memory wrapper will be reused for each invoke.
285 : */
286 : static void
287 98 : __setup_in_out_tensors (ml_single * single_h)
288 : {
289 : guint i;
290 98 : ml_tensors_data_s *in_tensors = (ml_tensors_data_s *) single_h->in_tensors;
291 98 : ml_tensors_data_s *out_tensors = (ml_tensors_data_s *) single_h->out_tensors;
292 :
293 : /* Setup input buffer */
294 98 : if (in_tensors) {
295 20 : _ml_tensors_info_free (in_tensors->info);
296 20 : _ml_tensors_info_copy_from_gst (in_tensors->info, &single_h->in_info);
297 : } else {
298 : ml_tensors_info_h info;
299 :
300 78 : _ml_tensors_info_create_from_gst (&info, &single_h->in_info);
301 78 : _ml_tensors_data_create_no_alloc (info, &single_h->in_tensors);
302 :
303 78 : ml_tensors_info_destroy (info);
304 78 : in_tensors = (ml_tensors_data_s *) single_h->in_tensors;
305 : }
306 :
307 98 : in_tensors->num_tensors = single_h->in_info.num_tensors;
308 229 : for (i = 0; i < in_tensors->num_tensors; i++) {
309 : /** memory will be allocated by tensor_filter_single */
310 131 : in_tensors->tensors[i].data = NULL;
311 131 : in_tensors->tensors[i].size =
312 131 : gst_tensors_info_get_size (&single_h->in_info, i);
313 : }
314 :
315 : /* Setup output buffer */
316 98 : if (out_tensors) {
317 20 : _ml_tensors_info_free (out_tensors->info);
318 20 : _ml_tensors_info_copy_from_gst (out_tensors->info, &single_h->out_info);
319 : } else {
320 : ml_tensors_info_h info;
321 :
322 78 : _ml_tensors_info_create_from_gst (&info, &single_h->out_info);
323 78 : _ml_tensors_data_create_no_alloc (info, &single_h->out_tensors);
324 :
325 78 : ml_tensors_info_destroy (info);
326 78 : out_tensors = (ml_tensors_data_s *) single_h->out_tensors;
327 : }
328 :
329 98 : out_tensors->num_tensors = single_h->out_info.num_tensors;
330 227 : for (i = 0; i < out_tensors->num_tensors; i++) {
331 : /** memory will be allocated by tensor_filter_single */
332 129 : out_tensors->tensors[i].data = NULL;
333 129 : out_tensors->tensors[i].size =
334 129 : gst_tensors_info_get_size (&single_h->out_info, i);
335 : }
336 98 : }
337 :
338 : /**
339 : * @brief To call the framework to destroy the allocated output data
340 : */
341 : static inline void
342 0 : __destroy_notify (gpointer data_h, gpointer single_data)
343 : {
344 : ml_single *single_h;
345 : ml_tensors_data_s *data;
346 :
347 0 : data = (ml_tensors_data_s *) data_h;
348 0 : single_h = (ml_single *) single_data;
349 :
350 0 : if (G_LIKELY (single_h->filter)) {
351 0 : if (single_h->klass->allocate_in_invoke (single_h->filter)) {
352 0 : single_h->klass->destroy_notify (single_h->filter, data->tensors);
353 : }
354 : }
355 :
356 : /* reset callback function */
357 0 : data->destroy = NULL;
358 0 : }
359 :
360 : /**
361 : * @brief Wrapper function for __destroy_notify
362 : */
363 : static int
364 0 : ml_single_destroy_notify_cb (void *handle, void *user_data)
365 : {
366 0 : ml_tensors_data_h data = (ml_tensors_data_h) handle;
367 0 : ml_single_h single = (ml_single_h) user_data;
368 : ml_single *single_h;
369 0 : int status = ML_ERROR_NONE;
370 :
371 0 : if (G_UNLIKELY (!single))
372 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
373 : "Failed to destroy data buffer. Callback function argument from _ml_tensors_data_destroy_internal is invalid. The given 'user_data' is NULL. It appears to be an internal error of ML-API or the user thread has touched private data structure.");
374 0 : if (G_UNLIKELY (!data))
375 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
376 : "Failed to destroy data buffer. Callback function argument from _ml_tensors_data_destroy_internal is invalid. The given 'handle' is NULL. It appears to be an internal error of ML-API or the user thread has touched private data structure.");
377 :
378 0 : ML_SINGLE_GET_VALID_HANDLE_LOCKED (single_h, single, 0);
379 :
380 0 : if (G_UNLIKELY (!single_h->filter)) {
381 0 : status = ML_ERROR_INVALID_PARAMETER;
382 0 : _ml_error_report
383 : ("Failed to destroy the data buffer. The handle instance (single_h) is invalid. It appears to be an internal error of ML-API of the user thread has touched private data structure.");
384 0 : goto exit;
385 : }
386 :
387 0 : single_h->destroy_data_list =
388 0 : g_list_remove (single_h->destroy_data_list, data);
389 0 : __destroy_notify (data, single_h);
390 :
391 0 : exit:
392 0 : ML_SINGLE_HANDLE_UNLOCK (single_h);
393 :
394 0 : return status;
395 : }
396 :
397 : /**
398 : * @brief setup the destroy notify for the allocated output data.
399 : * @note this stores the data entry in the single list.
400 : * @note this has not overhead if the allocation of output is not performed by
401 : * the framework but by tensor filter element.
402 : */
403 : static void
404 78 : set_destroy_notify (ml_single * single_h, ml_tensors_data_s * data,
405 : gboolean add)
406 : {
407 78 : if (single_h->klass->allocate_in_invoke (single_h->filter)) {
408 0 : data->destroy = ml_single_destroy_notify_cb;
409 0 : data->user_data = single_h;
410 0 : add = TRUE;
411 : }
412 :
413 78 : if (add) {
414 4 : single_h->destroy_data_list = g_list_append (single_h->destroy_data_list,
415 : (gpointer) data);
416 : }
417 78 : }
418 :
419 : /**
420 : * @brief Internal function to call subplugin's invoke
421 : */
422 : static inline int
423 80 : __invoke (ml_single * single_h, ml_tensors_data_h in, ml_tensors_data_h out,
424 : gboolean alloc_output)
425 : {
426 : ml_tensors_data_s *in_data, *out_data;
427 80 : int status = ML_ERROR_NONE;
428 :
429 80 : in_data = (ml_tensors_data_s *) in;
430 80 : out_data = (ml_tensors_data_s *) out;
431 :
432 : /* Prevent error case when input or output is null in invoke thread. */
433 80 : if (!in_data || !out_data) {
434 0 : _ml_error_report ("Failed to invoke a model, invalid data handle.");
435 0 : return ML_ERROR_STREAMS_PIPE;
436 : }
437 :
438 : /* Invoke the thread. */
439 80 : if (!single_h->klass->invoke (single_h->filter, in_data->tensors,
440 80 : out_data->tensors, alloc_output)) {
441 0 : const char *fw_name = _ml_get_nnfw_subplugin_name (single_h->nnfw);
442 0 : _ml_error_report
443 : ("Failed to invoke the tensors. The invoke callback of the tensor-filter subplugin '%s' has failed. Please contact the author of tensor-filter-%s (nnstreamer-%s) or review its source code. Note that this usually happens when the designated framework does not support the given model (e.g., trying to run tf-lite 2.6 model with tf-lite 1.13).",
444 : fw_name, fw_name, fw_name);
445 0 : status = ML_ERROR_STREAMS_PIPE;
446 : }
447 :
448 80 : return status;
449 : }
450 :
451 : /**
452 : * @brief Internal function to post-process given output.
453 : * @note Do not call this if single_h->free_output is false (output data is not allocated in single-shot).
454 : */
455 : static inline void
456 75 : __process_output (ml_single * single_h, ml_tensors_data_h output)
457 : {
458 : ml_tensors_data_s *out_data;
459 :
460 75 : if (g_list_find (single_h->destroy_data_list, output)) {
461 : /**
462 : * Caller of the invoke thread has returned back with timeout.
463 : * So, free the memory allocated by the invoke as their is no receiver.
464 : */
465 1 : single_h->destroy_data_list =
466 1 : g_list_remove (single_h->destroy_data_list, output);
467 1 : ml_tensors_data_destroy (output);
468 : } else {
469 74 : out_data = (ml_tensors_data_s *) output;
470 74 : set_destroy_notify (single_h, out_data, FALSE);
471 : }
472 75 : }
473 :
474 : /**
475 : * @brief thread to execute calls to invoke
476 : *
477 : * @details The thread behavior is detailed as below:
478 : * - Starting with IDLE state, the thread waits for an input or change
479 : * in state externally.
480 : * - If state is not RUNNING, exit this thread, else process the
481 : * request.
482 : * - Process input, call invoke, process output. Any error in this
483 : * state sets the status to be used by ml_single_invoke().
484 : * - State is set back to IDLE and thread moves back to start.
485 : *
486 : * State changes performed by this function when:
487 : * RUNNING -> IDLE - processing is finished.
488 : * JOIN_REQUESTED -> IDLE - close is requested.
489 : *
490 : * @note Error while processing an input is provided back to requesting
491 : * function, and further processing of invoke_thread is not affected.
492 : */
493 : static void *
494 82 : invoke_thread (void *arg)
495 : {
496 : ml_single *single_h;
497 : ml_tensors_data_h input, output;
498 82 : gboolean alloc_output = FALSE;
499 :
500 82 : single_h = (ml_single *) arg;
501 :
502 82 : g_mutex_lock (&single_h->mutex);
503 :
504 101 : while (single_h->state <= RUNNING) {
505 101 : int status = ML_ERROR_NONE;
506 :
507 : /** wait for data */
508 124 : while (single_h->state != RUNNING) {
509 101 : g_cond_wait (&single_h->cond, &single_h->mutex);
510 99 : if (single_h->state == JOIN_REQUESTED)
511 76 : goto exit;
512 : }
513 :
514 23 : input = single_h->input;
515 23 : output = single_h->output;
516 : /* Set null to prevent double-free. */
517 23 : single_h->input = single_h->output = NULL;
518 :
519 23 : single_h->invoking = TRUE;
520 23 : alloc_output = single_h->free_output;
521 23 : g_mutex_unlock (&single_h->mutex);
522 23 : status = __invoke (single_h, input, output, alloc_output);
523 23 : g_mutex_lock (&single_h->mutex);
524 : /* Clear input data after invoke is done. */
525 23 : ml_tensors_data_destroy (input);
526 23 : single_h->invoking = FALSE;
527 :
528 23 : if (status != ML_ERROR_NONE || single_h->state == JOIN_REQUESTED) {
529 4 : if (alloc_output) {
530 4 : single_h->destroy_data_list =
531 4 : g_list_remove (single_h->destroy_data_list, output);
532 4 : ml_tensors_data_destroy (output);
533 : }
534 :
535 4 : if (single_h->state == JOIN_REQUESTED)
536 4 : goto exit;
537 0 : goto wait_for_next;
538 : }
539 :
540 19 : if (alloc_output)
541 19 : __process_output (single_h, output);
542 :
543 : /** loop over to wait for the next element */
544 0 : wait_for_next:
545 19 : single_h->status = status;
546 19 : if (single_h->state == RUNNING)
547 19 : single_h->state = IDLE;
548 19 : g_cond_broadcast (&single_h->cond);
549 : }
550 :
551 0 : exit:
552 : /* Do not set IDLE if JOIN_REQUESTED */
553 80 : if (single_h->state == JOIN_REQUESTED) {
554 : /* Release input and output data */
555 80 : if (single_h->input)
556 0 : ml_tensors_data_destroy (single_h->input);
557 :
558 80 : if (alloc_output && single_h->output) {
559 0 : single_h->destroy_data_list =
560 0 : g_list_remove (single_h->destroy_data_list, single_h->output);
561 0 : ml_tensors_data_destroy (single_h->output);
562 : }
563 :
564 80 : single_h->input = single_h->output = NULL;
565 0 : } else if (single_h->state == RUNNING)
566 0 : single_h->state = IDLE;
567 80 : g_mutex_unlock (&single_h->mutex);
568 80 : return NULL;
569 : }
570 :
571 : /**
572 : * @brief Sets the information (tensor dimension, type, name and so on) of required input data for the given model, and get updated output data information.
573 : * @details Note that a model/framework may not support setting such information.
574 : * @since_tizen 6.0
575 : * @param[in] single The model handle.
576 : * @param[in] in_info The handle of input tensors information.
577 : * @param[out] out_info The handle of output tensors information. The caller is responsible for freeing the information with ml_tensors_info_destroy().
578 : * @return @c 0 on success. Otherwise a negative error value.
579 : * @retval #ML_ERROR_NONE Successful
580 : * @retval #ML_ERROR_NOT_SUPPORTED This implies that the given framework does not support dynamic dimensions.
581 : * Use ml_single_get_input_info() and ml_single_get_output_info() instead for this framework.
582 : * @retval #ML_ERROR_INVALID_PARAMETER Fail. The parameter is invalid.
583 : */
584 : static int
585 7 : ml_single_update_info (ml_single_h single,
586 : const ml_tensors_info_h in_info, ml_tensors_info_h * out_info)
587 : {
588 7 : if (!single)
589 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
590 : "The parameter, single (ml_single_h), is NULL. It should be a valid ml_single_h instance, usually created by ml_single_open().");
591 7 : if (!in_info)
592 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
593 : "The parameter, in_info (const ml_tensors_info_h), is NULL. It should be a valid instance of ml_tensors_info_h, usually created by ml_tensors_info_create() and configured by the application.");
594 7 : if (!out_info)
595 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
596 : "The parameter, out_info (ml_tensors_info_h *), is NULL. It should be a valid pointer to an instance ml_tensors_info_h, usually created by ml_tensors_info_h(). Note that out_info is supposed to be overwritten by this API call.");
597 :
598 : /* init null */
599 7 : *out_info = NULL;
600 :
601 7 : _ml_error_report_return_continue_iferr (ml_single_set_input_info (single,
602 : in_info),
603 : "Configuring the neural network model with the given input information has failed with %d error code. The given input information ('in_info' parameter) might be invalid or the given neural network cannot accept it as its input data.",
604 : _ERRNO);
605 :
606 5 : __setup_in_out_tensors (single);
607 5 : _ml_error_report_return_continue_iferr (ml_single_get_output_info (single,
608 : out_info),
609 : "Fetching output info after configuring input information has failed with %d error code.",
610 : _ERRNO);
611 :
612 5 : return ML_ERROR_NONE;
613 : }
614 :
615 : /**
616 : * @brief Internal function to get the gst info from tensor-filter.
617 : */
618 : static void
619 169 : ml_single_get_gst_info (ml_single * single_h, gboolean is_input,
620 : GstTensorsInfo * gst_info)
621 : {
622 : const gchar *prop_prefix, *prop_name, *prop_type;
623 : gchar *val;
624 : guint num;
625 :
626 169 : if (is_input) {
627 89 : prop_prefix = INPUT_STR;
628 89 : prop_type = CONCAT_MACRO_STR (INPUT_STR, TYPE_STR);
629 89 : prop_name = CONCAT_MACRO_STR (INPUT_STR, NAME_STR);
630 : } else {
631 80 : prop_prefix = OUTPUT_STR;
632 80 : prop_type = CONCAT_MACRO_STR (OUTPUT_STR, TYPE_STR);
633 80 : prop_name = CONCAT_MACRO_STR (OUTPUT_STR, NAME_STR);
634 : }
635 :
636 169 : gst_tensors_info_init (gst_info);
637 :
638 : /* get dimensions */
639 169 : g_object_get (single_h->filter, prop_prefix, &val, NULL);
640 169 : num = gst_tensors_info_parse_dimensions_string (gst_info, val);
641 169 : g_free (val);
642 :
643 : /* set the number of tensors */
644 169 : gst_info->num_tensors = num;
645 :
646 : /* get types */
647 169 : g_object_get (single_h->filter, prop_type, &val, NULL);
648 169 : num = gst_tensors_info_parse_types_string (gst_info, val);
649 169 : g_free (val);
650 :
651 169 : if (gst_info->num_tensors != num) {
652 0 : _ml_logw ("The number of tensor type is mismatched in filter.");
653 : }
654 :
655 : /* get names */
656 169 : g_object_get (single_h->filter, prop_name, &val, NULL);
657 169 : num = gst_tensors_info_parse_names_string (gst_info, val);
658 169 : g_free (val);
659 :
660 169 : if (gst_info->num_tensors != num) {
661 8 : _ml_logw ("The number of tensor name is mismatched in filter.");
662 : }
663 :
664 169 : if (single_h->invoke_dynamic) {
665 : /* flexible tensor stream */
666 0 : gst_info->format = _NNS_TENSOR_FORMAT_FLEXIBLE;
667 :
668 : /** @todo Consider multiple input tensors while invoking a model. */
669 0 : if (gst_info->num_tensors == 0) {
670 0 : gst_info->num_tensors = 1;
671 : }
672 : }
673 169 : }
674 :
675 : /**
676 : * @brief Internal function to set the gst info in tensor-filter.
677 : */
678 : static int
679 21 : ml_single_set_gst_info (ml_single * single_h, const GstTensorsInfo * in_info)
680 : {
681 : GstTensorsInfo out_info;
682 21 : int status = ML_ERROR_NONE;
683 21 : int ret = -EINVAL;
684 :
685 21 : gst_tensors_info_init (&out_info);
686 21 : ret = single_h->klass->set_input_info (single_h->filter, in_info, &out_info);
687 21 : if (ret == 0) {
688 15 : gst_tensors_info_free (&single_h->in_info);
689 15 : gst_tensors_info_free (&single_h->out_info);
690 15 : gst_tensors_info_copy (&single_h->in_info, in_info);
691 15 : gst_tensors_info_copy (&single_h->out_info, &out_info);
692 :
693 15 : __setup_in_out_tensors (single_h);
694 6 : } else if (ret == -ENOENT) {
695 0 : status = ML_ERROR_NOT_SUPPORTED;
696 : } else {
697 6 : status = ML_ERROR_INVALID_PARAMETER;
698 : }
699 :
700 21 : gst_tensors_info_free (&out_info);
701 :
702 21 : return status;
703 : }
704 :
705 : /**
706 : * @brief Set the info for input/output tensors
707 : */
708 : static int
709 0 : ml_single_set_inout_tensors_info (GObject * object,
710 : const gboolean is_input, ml_tensors_info_s * tensors_info)
711 : {
712 0 : int status = ML_ERROR_NONE;
713 : GstTensorsInfo info;
714 : gchar *str_dim, *str_type, *str_name;
715 : const gchar *str_type_name, *str_name_name;
716 : const gchar *prefix;
717 :
718 0 : if (is_input) {
719 0 : prefix = INPUT_STR;
720 0 : str_type_name = CONCAT_MACRO_STR (INPUT_STR, TYPE_STR);
721 0 : str_name_name = CONCAT_MACRO_STR (INPUT_STR, NAME_STR);
722 : } else {
723 0 : prefix = OUTPUT_STR;
724 0 : str_type_name = CONCAT_MACRO_STR (OUTPUT_STR, TYPE_STR);
725 0 : str_name_name = CONCAT_MACRO_STR (OUTPUT_STR, NAME_STR);
726 : }
727 :
728 0 : _ml_error_report_return_continue_iferr
729 : (_ml_tensors_info_copy_from_ml (&info, tensors_info),
730 : "Cannot fetch tensor-info from the given information. Error code: %d",
731 : _ERRNO);
732 :
733 : /* Set input option */
734 0 : str_dim = gst_tensors_info_get_dimensions_string (&info);
735 0 : str_type = gst_tensors_info_get_types_string (&info);
736 0 : str_name = gst_tensors_info_get_names_string (&info);
737 :
738 0 : if (!str_dim || !str_type || !str_name) {
739 0 : if (!str_dim)
740 0 : _ml_error_report
741 : ("Cannot fetch specific tensor-info from the given information: cannot fetch tensor dimension information.");
742 0 : if (!str_type)
743 0 : _ml_error_report
744 : ("Cannot fetch specific tensor-info from the given information: cannot fetch tensor type information.");
745 0 : if (!str_name)
746 0 : _ml_error_report
747 : ("Cannot fetch specific tensor-info from the given information: cannot fetch tensor name information. Even if tensor names are not defined, this should be able to fetch a list of empty strings.");
748 :
749 0 : status = ML_ERROR_INVALID_PARAMETER;
750 : } else {
751 0 : g_object_set (object, prefix, str_dim, str_type_name, str_type,
752 : str_name_name, str_name, NULL);
753 : }
754 :
755 0 : g_free (str_dim);
756 0 : g_free (str_type);
757 0 : g_free (str_name);
758 :
759 0 : gst_tensors_info_free (&info);
760 :
761 0 : return status;
762 : }
763 :
764 : /**
765 : * @brief Internal static function to set tensors info in the handle.
766 : */
767 : static gboolean
768 162 : ml_single_set_info_in_handle (ml_single_h single, gboolean is_input,
769 : ml_tensors_info_s * tensors_info)
770 : {
771 : int status;
772 : ml_single *single_h;
773 : GstTensorsInfo *dest;
774 162 : gboolean configured = FALSE;
775 162 : gboolean is_valid = FALSE;
776 : GObject *filter_obj;
777 :
778 162 : single_h = (ml_single *) single;
779 162 : filter_obj = G_OBJECT (single_h->filter);
780 :
781 162 : if (is_input) {
782 82 : dest = &single_h->in_info;
783 82 : configured = single_h->klass->input_configured (single_h->filter);
784 : } else {
785 80 : dest = &single_h->out_info;
786 80 : configured = single_h->klass->output_configured (single_h->filter);
787 : }
788 :
789 162 : if (configured) {
790 : /* get configured info and compare with input info */
791 : GstTensorsInfo gst_info;
792 162 : ml_tensors_info_h info = NULL;
793 :
794 162 : ml_single_get_gst_info (single_h, is_input, &gst_info);
795 162 : _ml_tensors_info_create_from_gst (&info, &gst_info);
796 :
797 162 : gst_tensors_info_free (&gst_info);
798 :
799 162 : if (tensors_info && !ml_tensors_info_is_equal (tensors_info, info)) {
800 : /* given input info is not matched with configured */
801 5 : ml_tensors_info_destroy (info);
802 5 : if (is_input) {
803 : /* try to update tensors info */
804 3 : status = ml_single_update_info (single, tensors_info, &info);
805 3 : if (status != ML_ERROR_NONE)
806 4 : goto done;
807 : } else {
808 2 : goto done;
809 : }
810 : }
811 :
812 158 : gst_tensors_info_free (dest);
813 158 : _ml_tensors_info_copy_from_ml (dest, info);
814 158 : ml_tensors_info_destroy (info);
815 0 : } else if (tensors_info) {
816 : status =
817 0 : ml_single_set_inout_tensors_info (filter_obj, is_input, tensors_info);
818 0 : if (status != ML_ERROR_NONE)
819 0 : goto done;
820 :
821 0 : gst_tensors_info_free (dest);
822 0 : _ml_tensors_info_copy_from_ml (dest, tensors_info);
823 : }
824 :
825 158 : is_valid = gst_tensors_info_validate (dest);
826 :
827 162 : done:
828 162 : return is_valid;
829 : }
830 :
831 : /**
832 : * @brief Internal function to create and initialize the single handle.
833 : */
834 : static ml_single *
835 82 : ml_single_create_handle (ml_nnfw_type_e nnfw)
836 : {
837 : ml_single *single_h;
838 : GError *error;
839 82 : gboolean created = FALSE;
840 :
841 82 : single_h = g_new0 (ml_single, 1);
842 82 : if (single_h == NULL)
843 82 : _ml_error_report_return (NULL,
844 : "Failed to allocate memory for the single_h handle. Out of memory?");
845 :
846 82 : single_h->filter = g_object_new (G_TYPE_TENSOR_FILTER_SINGLE, NULL);
847 82 : if (single_h->filter == NULL) {
848 0 : _ml_error_report
849 : ("Failed to create a new instance for filter. Out of memory?");
850 0 : g_free (single_h);
851 0 : return NULL;
852 : }
853 :
854 82 : single_h->magic = ML_SINGLE_MAGIC;
855 82 : single_h->timeout = SINGLE_DEFAULT_TIMEOUT;
856 82 : single_h->nnfw = nnfw;
857 82 : single_h->state = IDLE;
858 82 : single_h->thread = NULL;
859 82 : single_h->input = NULL;
860 82 : single_h->output = NULL;
861 82 : single_h->destroy_data_list = NULL;
862 82 : single_h->invoking = FALSE;
863 :
864 82 : gst_tensors_info_init (&single_h->in_info);
865 82 : gst_tensors_info_init (&single_h->out_info);
866 82 : g_mutex_init (&single_h->mutex);
867 82 : g_cond_init (&single_h->cond);
868 :
869 82 : single_h->klass = g_type_class_ref (G_TYPE_TENSOR_FILTER_SINGLE);
870 82 : if (single_h->klass == NULL) {
871 0 : _ml_error_report
872 : ("Failed to get class of the tensor-filter of single API. This binary is not compiled properly or required libraries are not loaded.");
873 0 : goto done;
874 : }
875 :
876 82 : single_h->thread =
877 82 : g_thread_try_new (NULL, invoke_thread, (gpointer) single_h, &error);
878 82 : if (single_h->thread == NULL) {
879 0 : _ml_error_report
880 : ("Failed to create the invoke thread of single API, g_thread_try_new has reported an error: %s.",
881 : error->message);
882 0 : g_clear_error (&error);
883 0 : goto done;
884 : }
885 :
886 82 : created = TRUE;
887 :
888 82 : done:
889 82 : if (!created) {
890 0 : ml_single_close (single_h);
891 0 : single_h = NULL;
892 : }
893 :
894 82 : return single_h;
895 : }
896 :
897 : /**
898 : * @brief Validate arguments for open
899 : */
900 : static int
901 91 : _ml_single_open_custom_validate_arguments (ml_single_h * single,
902 : ml_single_preset * info)
903 : {
904 91 : if (!single)
905 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
906 : "The parameter, 'single' (ml_single_h *), is NULL. It should be a valid pointer to an instance of ml_single_h.");
907 90 : if (!info)
908 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
909 : "The parameter, 'info' (ml_single_preset *), is NULL. It should be a valid pointer to a valid instance of ml_single_preset.");
910 :
911 : /* Validate input tensor info. */
912 90 : if (info->input_info && !ml_tensors_info_is_valid (info->input_info))
913 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
914 : "The parameter, 'info' (ml_single_preset *), is not valid. It has 'input_info' entry that cannot be validated. ml_tensors_info_is_valid(info->input_info) has failed while info->input_info exists.");
915 :
916 : /* Validate output tensor info. */
917 89 : if (info->output_info && !ml_tensors_info_is_valid (info->output_info))
918 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
919 : "The parameter, 'info' (ml_single_preset *), is not valid. It has 'output_info' entry that cannot be validated. ml_tensors_info_is_valid(info->output_info) has failed while info->output_info exists.");
920 :
921 88 : if (!info->models)
922 2 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
923 : "The parameter, 'info' (ml_single_preset *), is not valid. Its models entry if NULL (info->models is NULL).");
924 :
925 86 : return ML_ERROR_NONE;
926 : }
927 :
928 : /**
929 : * @brief Internal function to convert accelerator as tensor_filter property format.
930 : * @note returned value must be freed by the caller
931 : * @note More details on format can be found in gst_tensor_filter_install_properties() in tensor_filter_common.c.
932 : */
933 : char *
934 82 : _ml_nnfw_to_str_prop (const ml_nnfw_hw_e hw)
935 : {
936 : const gchar *hw_name;
937 82 : const gchar *use_accl = "true:";
938 82 : gchar *str_prop = NULL;
939 :
940 82 : hw_name = get_accl_hw_str (_ml_nnfw_to_accl_hw (hw));
941 82 : str_prop = g_strdup_printf ("%s%s", use_accl, hw_name);
942 :
943 82 : return str_prop;
944 : }
945 :
946 : /**
947 : * @brief Opens an ML model with the custom options and returns the instance as a handle.
948 : */
949 : int
950 91 : ml_single_open_custom (ml_single_h * single, ml_single_preset * info)
951 : {
952 : ml_single *single_h;
953 : GObject *filter_obj;
954 91 : int status = ML_ERROR_NONE;
955 : ml_tensors_info_s *in_tensors_info, *out_tensors_info;
956 : ml_nnfw_type_e nnfw;
957 : ml_nnfw_hw_e hw;
958 : const gchar *fw_name;
959 91 : g_autofree gchar *converted_models = NULL;
960 : gchar **list_models;
961 : guint i, num_models;
962 : char *hw_name;
963 :
964 91 : check_feature_state (ML_FEATURE_INFERENCE);
965 :
966 : /* Validate the params */
967 91 : _ml_error_report_return_continue_iferr
968 : (_ml_single_open_custom_validate_arguments (single, info),
969 : "The parameter, 'info' (ml_single_preset *), cannot be validated. Please provide valid information for this object.");
970 :
971 : /* init null */
972 86 : *single = NULL;
973 :
974 86 : in_tensors_info = (ml_tensors_info_s *) info->input_info;
975 86 : out_tensors_info = (ml_tensors_info_s *) info->output_info;
976 86 : nnfw = info->nnfw;
977 86 : hw = info->hw;
978 86 : fw_name = _ml_get_nnfw_subplugin_name (nnfw);
979 86 : converted_models = _ml_convert_predefined_entity (info->models);
980 :
981 : /**
982 : * 1. Determine nnfw and validate model file
983 : */
984 86 : list_models = g_strsplit (converted_models, ",", -1);
985 86 : num_models = g_strv_length (list_models);
986 172 : for (i = 0; i < num_models; i++)
987 86 : g_strstrip (list_models[i]);
988 :
989 86 : status = _ml_validate_model_file ((const char **) list_models, num_models,
990 : &nnfw);
991 86 : if (status != ML_ERROR_NONE) {
992 4 : _ml_error_report_continue
993 : ("Cannot validate the model (1st model: %s. # models: %d). Error code: %d",
994 : list_models[0], num_models, status);
995 4 : g_strfreev (list_models);
996 4 : return status;
997 : }
998 :
999 82 : g_strfreev (list_models);
1000 :
1001 : /**
1002 : * 2. Determine hw
1003 : * (Supposed CPU only) Support others later.
1004 : */
1005 82 : if (!_ml_nnfw_is_available (nnfw, hw)) {
1006 0 : _ml_error_report_return (ML_ERROR_NOT_SUPPORTED,
1007 : "The given nnfw, '%s', is not supported. There is no corresponding tensor-filter subplugin available or the given hardware requirement is not supported for the given nnfw.",
1008 : fw_name);
1009 : }
1010 :
1011 : /* Create ml_single object */
1012 82 : if ((single_h = ml_single_create_handle (nnfw)) == NULL) {
1013 0 : _ml_error_report_return_continue (ML_ERROR_OUT_OF_MEMORY,
1014 : "Cannot create handle for the given nnfw, %s", fw_name);
1015 : }
1016 :
1017 82 : single_h->invoke_dynamic = info->invoke_dynamic;
1018 82 : single_h->invoke_async = info->invoke_async;
1019 :
1020 82 : filter_obj = G_OBJECT (single_h->filter);
1021 :
1022 : /**
1023 : * 3. Construct a direct connection with the nnfw.
1024 : * Note that we do not construct a pipeline since 2019.12.
1025 : */
1026 82 : if (nnfw == ML_NNFW_TYPE_TENSORFLOW || nnfw == ML_NNFW_TYPE_SNAP ||
1027 82 : nnfw == ML_NNFW_TYPE_PYTORCH || nnfw == ML_NNFW_TYPE_TRIX_ENGINE ||
1028 82 : nnfw == ML_NNFW_TYPE_NCNN) {
1029 : /* set input and output tensors information */
1030 0 : if (in_tensors_info && out_tensors_info) {
1031 : status =
1032 0 : ml_single_set_inout_tensors_info (filter_obj, TRUE, in_tensors_info);
1033 0 : if (status != ML_ERROR_NONE) {
1034 0 : _ml_error_report_continue
1035 : ("Input tensors info is given; however, failed to set input tensors info. Error code: %d",
1036 : status);
1037 0 : goto error;
1038 : }
1039 :
1040 : status =
1041 0 : ml_single_set_inout_tensors_info (filter_obj, FALSE,
1042 : out_tensors_info);
1043 0 : if (status != ML_ERROR_NONE) {
1044 0 : _ml_error_report_continue
1045 : ("Output tensors info is given; however, failed to set output tensors info. Error code: %d",
1046 : status);
1047 0 : goto error;
1048 : }
1049 : } else {
1050 0 : _ml_error_report
1051 : ("To run the given nnfw, '%s', with a neural network model, both input and output information should be provided.",
1052 : fw_name);
1053 0 : status = ML_ERROR_INVALID_PARAMETER;
1054 0 : goto error;
1055 : }
1056 82 : } else if (nnfw == ML_NNFW_TYPE_ARMNN) {
1057 : /* set input and output tensors information, if available */
1058 0 : if (in_tensors_info) {
1059 : status =
1060 0 : ml_single_set_inout_tensors_info (filter_obj, TRUE, in_tensors_info);
1061 0 : if (status != ML_ERROR_NONE) {
1062 0 : _ml_error_report_continue
1063 : ("With nnfw '%s', input tensors info is optional. However, the user has provided an invalid input tensors info. Error code: %d",
1064 : fw_name, status);
1065 0 : goto error;
1066 : }
1067 : }
1068 0 : if (out_tensors_info) {
1069 : status =
1070 0 : ml_single_set_inout_tensors_info (filter_obj, FALSE,
1071 : out_tensors_info);
1072 0 : if (status != ML_ERROR_NONE) {
1073 0 : _ml_error_report_continue
1074 : ("With nnfw '%s', output tensors info is optional. However, the user has provided an invalid output tensors info. Error code: %d",
1075 : fw_name, status);
1076 0 : goto error;
1077 : }
1078 : }
1079 : }
1080 :
1081 : /* set accelerator, framework, model files and custom option */
1082 82 : if (info->fw_name) {
1083 33 : fw_name = (const char *) info->fw_name;
1084 : } else {
1085 49 : fw_name = _ml_get_nnfw_subplugin_name (nnfw); /* retry for "auto" */
1086 : }
1087 82 : hw_name = _ml_nnfw_to_str_prop (hw);
1088 :
1089 82 : g_object_set (filter_obj, "framework", fw_name, "accelerator", hw_name,
1090 : "model", converted_models, "invoke-dynamic", single_h->invoke_dynamic,
1091 : "invoke-async", single_h->invoke_async, NULL);
1092 82 : g_free (hw_name);
1093 :
1094 82 : if (info->custom_option) {
1095 0 : g_object_set (filter_obj, "custom", info->custom_option, NULL);
1096 : }
1097 :
1098 : /* 4. Start the nnfw to get inout configurations if needed */
1099 82 : if (!single_h->klass->start (single_h->filter)) {
1100 0 : _ml_error_report
1101 : ("Failed to start NNFW, '%s', to get inout configurations. Subplugin class method has failed to start.",
1102 : fw_name);
1103 0 : status = ML_ERROR_STREAMS_PIPE;
1104 0 : goto error;
1105 : }
1106 :
1107 82 : if (nnfw == ML_NNFW_TYPE_NNTR_INF) {
1108 0 : if (!in_tensors_info || !out_tensors_info) {
1109 0 : if (!in_tensors_info) {
1110 : GstTensorsInfo in_info;
1111 :
1112 0 : gst_tensors_info_init (&in_info);
1113 :
1114 : /* ml_single_set_input_info() can't be done as it checks num_tensors */
1115 0 : status = ml_single_set_gst_info (single_h, &in_info);
1116 0 : if (status != ML_ERROR_NONE) {
1117 0 : _ml_error_report_continue
1118 : ("NNTrainer-inference-single cannot configure single_h handle instance with the given in_info. This might be an ML-API / NNTrainer internal error. Error Code: %d",
1119 : status);
1120 0 : goto error;
1121 : }
1122 : } else {
1123 0 : status = ml_single_set_input_info (single_h, in_tensors_info);
1124 0 : if (status != ML_ERROR_NONE) {
1125 0 : _ml_error_report_continue
1126 : ("NNTrainer-inference-single cannot configure single_h handle instance with the given in_info from the user. Error code: %d",
1127 : status);
1128 0 : goto error;
1129 : }
1130 : }
1131 : }
1132 : }
1133 :
1134 : /* 5. Set in/out configs and metadata */
1135 82 : if (!ml_single_set_info_in_handle (single_h, TRUE, in_tensors_info)) {
1136 2 : _ml_error_report
1137 : ("The input tensors info is invalid. Cannot configure single_h handle with the given input tensors info.");
1138 2 : status = ML_ERROR_INVALID_PARAMETER;
1139 2 : goto error;
1140 : }
1141 :
1142 80 : if (!ml_single_set_info_in_handle (single_h, FALSE, out_tensors_info)) {
1143 2 : _ml_error_report
1144 : ("The output tensors info is invalid. Cannot configure single_h handle with the given output tensors info.");
1145 2 : status = ML_ERROR_INVALID_PARAMETER;
1146 2 : goto error;
1147 : }
1148 :
1149 : /* Setup input and output memory buffers for invoke */
1150 78 : __setup_in_out_tensors (single_h);
1151 :
1152 78 : *single = single_h;
1153 78 : return ML_ERROR_NONE;
1154 :
1155 4 : error:
1156 4 : ml_single_close (single_h);
1157 4 : return status;
1158 : }
1159 :
1160 : /**
1161 : * @brief Opens an ML model and returns the instance as a handle.
1162 : */
1163 : int
1164 53 : ml_single_open (ml_single_h * single, const char *model,
1165 : const ml_tensors_info_h input_info, const ml_tensors_info_h output_info,
1166 : ml_nnfw_type_e nnfw, ml_nnfw_hw_e hw)
1167 : {
1168 53 : return ml_single_open_full (single, model, input_info, output_info, nnfw, hw,
1169 : NULL);
1170 : }
1171 :
1172 : /**
1173 : * @brief Opens an ML model and returns the instance as a handle.
1174 : */
1175 : int
1176 53 : ml_single_open_full (ml_single_h * single, const char *model,
1177 : const ml_tensors_info_h input_info, const ml_tensors_info_h output_info,
1178 : ml_nnfw_type_e nnfw, ml_nnfw_hw_e hw, const char *custom_option)
1179 : {
1180 53 : ml_single_preset info = { 0, };
1181 :
1182 53 : info.input_info = input_info;
1183 53 : info.output_info = output_info;
1184 53 : info.nnfw = nnfw;
1185 53 : info.hw = hw;
1186 53 : info.models = (char *) model;
1187 53 : info.custom_option = (char *) custom_option;
1188 :
1189 53 : return ml_single_open_custom (single, &info);
1190 : }
1191 :
1192 : /**
1193 : * @brief Open new single handle with given option.
1194 : */
1195 : int
1196 39 : ml_single_open_with_option (ml_single_h * single, const ml_option_h option)
1197 : {
1198 : void *value;
1199 39 : ml_single_preset info = { 0, };
1200 :
1201 78 : check_feature_state (ML_FEATURE_INFERENCE);
1202 :
1203 39 : if (!option) {
1204 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1205 : "The parameter, 'option' is NULL. It should be a valid ml_option_h, which should be created by ml_option_create().");
1206 : }
1207 :
1208 38 : if (!single)
1209 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1210 : "The parameter, 'single' (ml_single_h), is NULL. It should be a valid ml_single_h instance, usually created by ml_single_open().");
1211 :
1212 38 : if (ML_ERROR_NONE == ml_option_get (option, "input_info", &value))
1213 16 : info.input_info = value;
1214 38 : if (ML_ERROR_NONE == ml_option_get (option, "output_info", &value))
1215 16 : info.output_info = value;
1216 38 : if (ML_ERROR_NONE == ml_option_get (option, "nnfw", &value))
1217 2 : info.nnfw = *((ml_nnfw_type_e *) value);
1218 38 : if (ML_ERROR_NONE == ml_option_get (option, "hw", &value))
1219 0 : info.hw = *((ml_nnfw_hw_e *) value);
1220 38 : if (ML_ERROR_NONE == ml_option_get (option, "models", &value))
1221 37 : info.models = (gchar *) value;
1222 38 : if (ML_ERROR_NONE == ml_option_get (option, "custom", &value))
1223 0 : info.custom_option = (gchar *) value;
1224 38 : if (ML_ERROR_NONE == ml_option_get (option, "framework_name", &value) ||
1225 5 : ML_ERROR_NONE == ml_option_get (option, "framework", &value))
1226 33 : info.fw_name = (gchar *) value;
1227 38 : if (ML_ERROR_NONE == ml_option_get (option, "invoke_dynamic", &value)) {
1228 0 : if (g_ascii_strcasecmp ((gchar *) value, "true") == 0)
1229 0 : info.invoke_dynamic = TRUE;
1230 : }
1231 38 : if (ML_ERROR_NONE == ml_option_get (option, "invoke_async", &value)) {
1232 0 : if (g_ascii_strcasecmp ((gchar *) value, "true") == 0)
1233 0 : info.invoke_async = TRUE;
1234 : }
1235 :
1236 38 : return ml_single_open_custom (single, &info);
1237 : }
1238 :
1239 : /**
1240 : * @brief Closes the opened model handle.
1241 : *
1242 : * @details State changes performed by this function:
1243 : * ANY STATE -> JOIN REQUESTED - on receiving a request to close
1244 : *
1245 : * Once requested to close, invoke_thread() will exit after processing
1246 : * the current input (if any).
1247 : */
1248 : int
1249 82 : ml_single_close (ml_single_h single)
1250 : {
1251 : ml_single *single_h;
1252 : gboolean invoking;
1253 :
1254 82 : check_feature_state (ML_FEATURE_INFERENCE);
1255 :
1256 82 : if (!single)
1257 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1258 : "The parameter, 'single' (ml_single_h), is NULL. It should be a valid ml_single_h instance, usually created by ml_single_open().");
1259 :
1260 81 : ML_SINGLE_GET_VALID_HANDLE_LOCKED (single_h, single, 1);
1261 :
1262 80 : single_h->state = JOIN_REQUESTED;
1263 80 : g_cond_broadcast (&single_h->cond);
1264 80 : invoking = single_h->invoking;
1265 80 : ML_SINGLE_HANDLE_UNLOCK (single_h);
1266 :
1267 : /** Wait until invoke process is finished */
1268 1697 : while (invoking) {
1269 1617 : _ml_logd ("Wait 1 ms until invoke is finished and close the handle.");
1270 1617 : g_usleep (1000);
1271 1617 : invoking = single_h->invoking;
1272 : /**
1273 : * single_h->invoking is the only protected value here and we are
1274 : * doing a read-only operation and do not need to project its value
1275 : * after the assignment.
1276 : * Thus, we do not need to lock single_h here.
1277 : */
1278 : }
1279 :
1280 80 : if (single_h->thread != NULL)
1281 80 : g_thread_join (single_h->thread);
1282 :
1283 : /** locking ensures correctness with parallel calls on close */
1284 80 : if (single_h->filter) {
1285 80 : g_list_foreach (single_h->destroy_data_list, __destroy_notify, single_h);
1286 80 : g_list_free (single_h->destroy_data_list);
1287 :
1288 80 : if (single_h->klass)
1289 80 : single_h->klass->stop (single_h->filter);
1290 :
1291 80 : g_object_unref (single_h->filter);
1292 80 : single_h->filter = NULL;
1293 : }
1294 :
1295 80 : if (single_h->klass) {
1296 80 : g_type_class_unref (single_h->klass);
1297 80 : single_h->klass = NULL;
1298 : }
1299 :
1300 80 : gst_tensors_info_free (&single_h->in_info);
1301 80 : gst_tensors_info_free (&single_h->out_info);
1302 :
1303 80 : ml_tensors_data_destroy (single_h->in_tensors);
1304 80 : ml_tensors_data_destroy (single_h->out_tensors);
1305 :
1306 80 : g_cond_clear (&single_h->cond);
1307 80 : g_mutex_clear (&single_h->mutex);
1308 :
1309 80 : g_free (single_h);
1310 80 : return ML_ERROR_NONE;
1311 : }
1312 :
1313 : /**
1314 : * @brief Internal function to validate input/output data.
1315 : */
1316 : static int
1317 92 : _ml_single_invoke_validate_data (ml_single_h single,
1318 : const ml_tensors_data_h data, const gboolean is_input)
1319 : {
1320 : ml_single *single_h;
1321 : ml_tensors_data_s *_data;
1322 : ml_tensors_data_s *_model;
1323 : guint i;
1324 : size_t raw_size;
1325 :
1326 92 : single_h = (ml_single *) single;
1327 92 : _data = (ml_tensors_data_s *) data;
1328 :
1329 92 : if (G_UNLIKELY (!_data))
1330 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1331 : "(internal function) The parameter, 'data' (const ml_tensors_data_h), is NULL. It should be a valid instance of ml_tensors_data_h.");
1332 :
1333 92 : if (is_input)
1334 91 : _model = (ml_tensors_data_s *) single_h->in_tensors;
1335 : else
1336 1 : _model = (ml_tensors_data_s *) single_h->out_tensors;
1337 :
1338 92 : if (G_UNLIKELY (_data->num_tensors != _model->num_tensors))
1339 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1340 : "(internal function) The number of %s tensors is not compatible with model. Given: %u, Expected: %u.",
1341 : (is_input) ? "input" : "output", _data->num_tensors,
1342 : _model->num_tensors);
1343 :
1344 335 : for (i = 0; i < _data->num_tensors; i++) {
1345 247 : if (G_UNLIKELY (!_data->tensors[i].data))
1346 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1347 : "The %d-th input tensor is not valid. There is no valid dimension metadata for this tensor.",
1348 : i);
1349 :
1350 246 : if (single_h->invoke_dynamic) {
1351 : /* If tensor is not static, we cannot check tensor data size. */
1352 0 : continue;
1353 : }
1354 :
1355 246 : raw_size = _model->tensors[i].size;
1356 246 : if (G_UNLIKELY (_data->tensors[i].size != raw_size))
1357 2 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1358 : "The size of %d-th %s tensor is not compatible with model. Given: %zu, Expected: %zu.",
1359 : i, (is_input) ? "input" : "output", _data->tensors[i].size, raw_size);
1360 : }
1361 :
1362 88 : return ML_ERROR_NONE;
1363 : }
1364 :
1365 : /**
1366 : * @brief Internal function to invoke the model.
1367 : *
1368 : * @details State changes performed by this function:
1369 : * IDLE -> RUNNING - on receiving a valid request
1370 : *
1371 : * Invoke returns error if the current state is not IDLE.
1372 : * If IDLE, then invoke is requested to the thread.
1373 : * Invoke waits for the processing to be complete, and returns back
1374 : * the result once notified by the processing thread.
1375 : *
1376 : * @note IDLE is the valid thread state before and after this function call.
1377 : */
1378 : static int
1379 104 : _ml_single_invoke_internal (ml_single_h single,
1380 : const ml_tensors_data_h input, ml_tensors_data_h * output,
1381 : const gboolean need_alloc)
1382 : {
1383 : ml_single *single_h;
1384 : ml_tensors_data_h _in, _out;
1385 : gint64 end_time;
1386 104 : int status = ML_ERROR_NONE;
1387 :
1388 208 : check_feature_state (ML_FEATURE_INFERENCE);
1389 :
1390 104 : if (G_UNLIKELY (!single))
1391 2 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1392 : "(internal function) The parameter, single (ml_single_h), is NULL. It should be a valid instance of ml_single_h, usually created by ml_single_open().");
1393 :
1394 102 : if (G_UNLIKELY (!input))
1395 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1396 : "(internal function) The parameter, input (ml_tensors_data_h), is NULL. It should be a valid instance of ml_tensors_data_h.");
1397 :
1398 101 : if (G_UNLIKELY (!output))
1399 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1400 : "(internal function) The parameter, output (ml_tensors_data_h *), is NULL. It should be a valid pointer to an instance of ml_tensors_data_h to store the inference results.");
1401 :
1402 100 : ML_SINGLE_GET_VALID_HANDLE_LOCKED (single_h, single, 0);
1403 :
1404 91 : if (G_UNLIKELY (!single_h->filter)) {
1405 0 : _ml_error_report
1406 : ("The tensor_filter element of this single handle (single_h) is not valid. It appears that the handle (ml_single_h single) is not appropriately created by ml_single_open(), user thread has touched its internal data, or the handle is already closed or freed by user.");
1407 0 : status = ML_ERROR_INVALID_PARAMETER;
1408 0 : goto exit;
1409 : }
1410 :
1411 : /* Validate input/output data */
1412 91 : status = _ml_single_invoke_validate_data (single, input, TRUE);
1413 91 : if (status != ML_ERROR_NONE) {
1414 4 : _ml_error_report_continue
1415 : ("The input data for the inference is not valid: error code %d. Please check the dimensions, type, number-of-tensors, and size information of the input data.",
1416 : status);
1417 4 : goto exit;
1418 : }
1419 :
1420 87 : if (!need_alloc) {
1421 1 : status = _ml_single_invoke_validate_data (single, *output, FALSE);
1422 1 : if (status != ML_ERROR_NONE) {
1423 0 : _ml_error_report_continue
1424 : ("The output data buffer provided by the user is not valid for the given neural network mode: error code %d. Please check the dimensions, type, number-of-tensors, and size information of the output data buffer.",
1425 : status);
1426 0 : goto exit;
1427 : }
1428 : }
1429 :
1430 87 : if (single_h->state != IDLE) {
1431 7 : if (G_UNLIKELY (single_h->state == JOIN_REQUESTED)) {
1432 0 : _ml_error_report
1433 : ("The handle (single_h single) is closed or being closed awaiting for the last ongoing invocation. Invoking with such a handle is not allowed. Please open another single_h handle to invoke.");
1434 0 : status = ML_ERROR_STREAMS_PIPE;
1435 0 : goto exit;
1436 : }
1437 7 : _ml_error_report
1438 : ("The handle (single_h single) is busy. There is another thread waiting for inference results with this handle. Please retry invoking again later when the handle becomes idle after completing the current inference task.");
1439 7 : status = ML_ERROR_TRY_AGAIN;
1440 7 : goto exit;
1441 : }
1442 :
1443 : /* prepare output data */
1444 80 : if (need_alloc) {
1445 79 : *output = NULL;
1446 :
1447 79 : status = _ml_tensors_data_clone_no_alloc (single_h->out_tensors, &_out);
1448 79 : if (status != ML_ERROR_NONE)
1449 0 : goto exit;
1450 : } else {
1451 1 : _out = *output;
1452 : }
1453 :
1454 : /**
1455 : * Clone input data here to prevent use-after-free case.
1456 : * We should release single_h->input after calling __invoke() function.
1457 : */
1458 80 : status = ml_tensors_data_clone (input, &_in);
1459 80 : if (status != ML_ERROR_NONE)
1460 0 : goto exit;
1461 :
1462 80 : single_h->state = RUNNING;
1463 80 : single_h->free_output = need_alloc;
1464 80 : single_h->input = _in;
1465 80 : single_h->output = _out;
1466 :
1467 80 : if (single_h->timeout > 0) {
1468 : /* Wake up "invoke_thread" */
1469 23 : g_cond_broadcast (&single_h->cond);
1470 :
1471 : /* set timeout */
1472 23 : end_time = g_get_monotonic_time () +
1473 23 : single_h->timeout * G_TIME_SPAN_MILLISECOND;
1474 :
1475 23 : if (g_cond_wait_until (&single_h->cond, &single_h->mutex, end_time)) {
1476 19 : status = single_h->status;
1477 : } else {
1478 4 : _ml_logw ("Wait for invoke has timed out");
1479 4 : status = ML_ERROR_TIMED_OUT;
1480 : /** This is set to notify invoke_thread to not process if timed out */
1481 4 : if (need_alloc)
1482 4 : set_destroy_notify (single_h, _out, TRUE);
1483 : }
1484 : } else {
1485 : /**
1486 : * Don't worry. We have locked single_h->mutex, thus there is no
1487 : * other thread with ml_single_invoke function on the same handle
1488 : * that are in this if-then-else block, which means that there is
1489 : * no other thread with active invoke-thread (calling __invoke())
1490 : * with the same handle. Thus we can call __invoke without
1491 : * having yet another mutex for __invoke.
1492 : */
1493 57 : single_h->invoking = TRUE;
1494 57 : status = __invoke (single_h, _in, _out, need_alloc);
1495 57 : ml_tensors_data_destroy (_in);
1496 57 : single_h->invoking = FALSE;
1497 57 : single_h->state = IDLE;
1498 :
1499 57 : if (status != ML_ERROR_NONE) {
1500 0 : if (need_alloc)
1501 0 : ml_tensors_data_destroy (_out);
1502 0 : goto exit;
1503 : }
1504 :
1505 57 : if (need_alloc)
1506 56 : __process_output (single_h, _out);
1507 : }
1508 :
1509 1 : exit:
1510 91 : if (status == ML_ERROR_NONE) {
1511 76 : if (need_alloc)
1512 75 : *output = _out;
1513 : }
1514 :
1515 91 : single_h->input = single_h->output = NULL;
1516 91 : ML_SINGLE_HANDLE_UNLOCK (single_h);
1517 91 : return status;
1518 : }
1519 :
1520 : /**
1521 : * @brief Invokes the model with the given input data.
1522 : */
1523 : int
1524 103 : ml_single_invoke (ml_single_h single,
1525 : const ml_tensors_data_h input, ml_tensors_data_h * output)
1526 : {
1527 103 : return _ml_single_invoke_internal (single, input, output, TRUE);
1528 : }
1529 :
1530 : /**
1531 : * @brief Invokes the model with the given input data and fills the output data handle.
1532 : */
1533 : int
1534 1 : ml_single_invoke_fast (ml_single_h single,
1535 : const ml_tensors_data_h input, ml_tensors_data_h output)
1536 : {
1537 1 : return _ml_single_invoke_internal (single, input, &output, FALSE);
1538 : }
1539 :
1540 : /**
1541 : * @brief Gets the tensors info for the given handle.
1542 : * @param[out] info A pointer to a NULL (unallocated) instance.
1543 : */
1544 : static int
1545 61 : ml_single_get_tensors_info (ml_single_h single, gboolean is_input,
1546 : ml_tensors_info_h * info)
1547 : {
1548 : ml_single *single_h;
1549 61 : int status = ML_ERROR_NONE;
1550 :
1551 61 : check_feature_state (ML_FEATURE_INFERENCE);
1552 :
1553 61 : if (!single)
1554 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1555 : "(internal function) The parameter, 'single' (ml_single_h), is NULL. It should be a valid ml_single_h instance, usually created by ml_single_open().");
1556 61 : if (!info)
1557 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1558 : "(internal function) The parameter, 'info' (ml_tensors_info_h *) is NULL. It should be a valid pointer to an empty (NULL) instance of ml_tensor_info_h, which is supposed to be filled with the fetched info by this function.");
1559 :
1560 61 : ML_SINGLE_GET_VALID_HANDLE_LOCKED (single_h, single, 0);
1561 :
1562 61 : if (is_input)
1563 39 : status = _ml_tensors_info_create_from_gst (info, &single_h->in_info);
1564 : else
1565 22 : status = _ml_tensors_info_create_from_gst (info, &single_h->out_info);
1566 :
1567 61 : if (status != ML_ERROR_NONE) {
1568 0 : _ml_error_report_continue
1569 : ("(internal function) Failed to create an entry for the ml_tensors_info_h instance. Error code: %d",
1570 : status);
1571 : }
1572 :
1573 61 : ML_SINGLE_HANDLE_UNLOCK (single_h);
1574 61 : return status;
1575 : }
1576 :
1577 : /**
1578 : * @brief Gets the information of required input data for the given handle.
1579 : * @note information = (tensor dimension, type, name and so on)
1580 : */
1581 : int
1582 39 : ml_single_get_input_info (ml_single_h single, ml_tensors_info_h * info)
1583 : {
1584 39 : return ml_single_get_tensors_info (single, TRUE, info);
1585 : }
1586 :
1587 : /**
1588 : * @brief Gets the information of output data for the given handle.
1589 : * @note information = (tensor dimension, type, name and so on)
1590 : */
1591 : int
1592 22 : ml_single_get_output_info (ml_single_h single, ml_tensors_info_h * info)
1593 : {
1594 22 : return ml_single_get_tensors_info (single, FALSE, info);
1595 : }
1596 :
1597 : /**
1598 : * @brief Sets the maximum amount of time to wait for an output, in milliseconds.
1599 : */
1600 : int
1601 19 : ml_single_set_timeout (ml_single_h single, unsigned int timeout)
1602 : {
1603 : ml_single *single_h;
1604 :
1605 19 : check_feature_state (ML_FEATURE_INFERENCE);
1606 :
1607 19 : if (!single)
1608 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1609 : "The parameter, single (ml_single_h), is NULL. It should be a valid instance of ml_single_h, which is usually created by ml_single_open().");
1610 :
1611 19 : ML_SINGLE_GET_VALID_HANDLE_LOCKED (single_h, single, 0);
1612 :
1613 19 : single_h->timeout = (guint) timeout;
1614 :
1615 19 : ML_SINGLE_HANDLE_UNLOCK (single_h);
1616 19 : return ML_ERROR_NONE;
1617 : }
1618 :
1619 : /**
1620 : * @brief Sets the information (tensor dimension, type, name and so on) of required input data for the given model.
1621 : */
1622 : int
1623 17 : ml_single_set_input_info (ml_single_h single, const ml_tensors_info_h info)
1624 : {
1625 : ml_single *single_h;
1626 : GstTensorsInfo gst_info;
1627 17 : int status = ML_ERROR_NONE;
1628 :
1629 34 : check_feature_state (ML_FEATURE_INFERENCE);
1630 :
1631 17 : if (!single)
1632 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1633 : "The parameter, single (ml_single_h), is NULL. It should be a valid instance of ml_single_h, which is usually created by ml_single_open().");
1634 17 : if (!info)
1635 2 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1636 : "The parameter, info (const ml_tensors_info_h), is NULL. It should be a valid instance of ml_tensors_info_h, which is usually created by ml_tensors_info_create() or other APIs.");
1637 :
1638 15 : if (!ml_tensors_info_is_valid (info))
1639 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1640 : "The parameter, info (const ml_tensors_info_h), is not valid. Although it is not NULL, the content of 'info' is invalid. If it is created by ml_tensors_info_create(), which creates an empty instance, it should be filled by users afterwards. Please check if 'info' has all elements filled with valid values.");
1641 :
1642 14 : ML_SINGLE_GET_VALID_HANDLE_LOCKED (single_h, single, 0);
1643 14 : _ml_tensors_info_copy_from_ml (&gst_info, info);
1644 14 : status = ml_single_set_gst_info (single_h, &gst_info);
1645 14 : gst_tensors_info_free (&gst_info);
1646 14 : ML_SINGLE_HANDLE_UNLOCK (single_h);
1647 :
1648 14 : if (status != ML_ERROR_NONE)
1649 5 : _ml_error_report_continue
1650 : ("ml_single_set_gst_info() has failed to configure the single_h handle with the given info. Error code: %d",
1651 : status);
1652 :
1653 14 : return status;
1654 : }
1655 :
1656 : /**
1657 : * @brief Invokes the model with the given input data with the given info.
1658 : */
1659 : int
1660 9 : ml_single_invoke_dynamic (ml_single_h single,
1661 : const ml_tensors_data_h input, const ml_tensors_info_h in_info,
1662 : ml_tensors_data_h * output, ml_tensors_info_h * out_info)
1663 : {
1664 : int status;
1665 9 : ml_tensors_info_h cur_in_info = NULL;
1666 :
1667 9 : if (!single)
1668 9 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1669 : "The parameter, single (ml_single_h), is NULL. It should be a valid instance of ml_single_h, which is usually created by ml_single_open().");
1670 8 : if (!input)
1671 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1672 : "The parameter, input (const ml_tensors_data_h), is NULL. It should be a valid instance of ml_tensors_data_h with input data frame for inference.");
1673 7 : if (!in_info)
1674 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1675 : "The parameter, in_info (const ml_tensors_info_h), is NULL. It should be a valid instance of ml_tensor_info_h that describes metadata of the given input for inference (input).");
1676 6 : if (!output)
1677 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1678 : "The parameter, output (ml_tensors_data_h *), is NULL. It should be a pointer to an empty (NULL or do-not-care) instance of ml_tensors_data_h, which is filled by this API with the result of inference.");
1679 5 : if (!out_info)
1680 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1681 : "The parameter, out_info (ml_tensors_info_h *), is NULL. It should be a pointer to an empty (NULL or do-not-care) instance of ml_tensors_info_h, which is filled by this API with the neural network model info.");
1682 :
1683 : /* init null */
1684 4 : *output = NULL;
1685 4 : *out_info = NULL;
1686 :
1687 4 : status = ml_single_get_input_info (single, &cur_in_info);
1688 4 : if (status != ML_ERROR_NONE) {
1689 0 : _ml_error_report_continue
1690 : ("Failed to get input metadata configured by the opened single_h handle instance. Error code: %d.",
1691 : status);
1692 0 : goto exit;
1693 : }
1694 4 : status = ml_single_update_info (single, in_info, out_info);
1695 4 : if (status != ML_ERROR_NONE) {
1696 0 : _ml_error_report_continue
1697 : ("Failed to reconfigure the opened single_h handle instance with the updated input/output metadata. Error code: %d.",
1698 : status);
1699 0 : goto exit;
1700 : }
1701 :
1702 4 : status = ml_single_invoke (single, input, output);
1703 4 : if (status != ML_ERROR_NONE) {
1704 0 : ml_single_set_input_info (single, cur_in_info);
1705 0 : if (status != ML_ERROR_TRY_AGAIN) {
1706 : /* If it's TRY_AGAIN, ml_single_invoke() has already gave enough info. */
1707 0 : _ml_error_report_continue
1708 : ("Invoking the given neural network has failed. Error code: %d.",
1709 : status);
1710 : }
1711 : }
1712 :
1713 4 : exit:
1714 4 : if (cur_in_info)
1715 4 : ml_tensors_info_destroy (cur_in_info);
1716 :
1717 4 : if (status != ML_ERROR_NONE) {
1718 0 : if (*out_info) {
1719 0 : ml_tensors_info_destroy (*out_info);
1720 0 : *out_info = NULL;
1721 : }
1722 : }
1723 :
1724 4 : return status;
1725 : }
1726 :
1727 : /**
1728 : * @brief Sets the property value for the given model.
1729 : */
1730 : int
1731 13 : ml_single_set_property (ml_single_h single, const char *name, const char *value)
1732 : {
1733 : ml_single *single_h;
1734 13 : int status = ML_ERROR_NONE;
1735 13 : char *old_value = NULL;
1736 :
1737 26 : check_feature_state (ML_FEATURE_INFERENCE);
1738 :
1739 13 : if (!single)
1740 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1741 : "The parameter, single (ml_single_h), is NULL. It should be a valid instance of ml_single_h, which is usually created by ml_single_open().");
1742 13 : if (!name)
1743 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1744 : "The parameter, name (const char *), is NULL. It should be a valid string representing a property key.");
1745 :
1746 : /* get old value, also check the property is updatable. */
1747 12 : _ml_error_report_return_continue_iferr
1748 : (ml_single_get_property (single, name, &old_value),
1749 : "Cannot fetch the previous value for the given property name, '%s'. It appears that the property key, '%s', is invalid (not supported).",
1750 : name, name);
1751 :
1752 : /* if sets same value, do not change. */
1753 11 : if (old_value && value && g_ascii_strcasecmp (old_value, value) == 0) {
1754 1 : g_free (old_value);
1755 1 : return ML_ERROR_NONE;
1756 : }
1757 :
1758 10 : ML_SINGLE_GET_VALID_HANDLE_LOCKED (single_h, single, 0);
1759 :
1760 : /* update property */
1761 10 : if (g_str_equal (name, "is-updatable")) {
1762 2 : if (!value)
1763 0 : goto error;
1764 :
1765 : /* boolean */
1766 2 : if (g_ascii_strcasecmp (value, "true") == 0) {
1767 1 : if (g_ascii_strcasecmp (old_value, "true") != 0)
1768 1 : g_object_set (G_OBJECT (single_h->filter), name, (gboolean) TRUE, NULL);
1769 1 : } else if (g_ascii_strcasecmp (value, "false") == 0) {
1770 1 : if (g_ascii_strcasecmp (old_value, "false") != 0)
1771 1 : g_object_set (G_OBJECT (single_h->filter), name, (gboolean) FALSE,
1772 : NULL);
1773 : } else {
1774 0 : _ml_error_report
1775 : ("The property value, '%s', is not appropriate for a boolean property 'is-updatable'. It should be either 'true' or 'false'.",
1776 : value);
1777 0 : status = ML_ERROR_INVALID_PARAMETER;
1778 : }
1779 8 : } else if (g_str_equal (name, "input") || g_str_equal (name, "inputtype")
1780 0 : || g_str_equal (name, "inputname") || g_str_equal (name, "output")
1781 7 : || g_str_equal (name, "outputtype") || g_str_equal (name, "outputname")) {
1782 : GstTensorsInfo gst_info;
1783 8 : gboolean is_input = g_str_has_prefix (name, "input");
1784 : guint num;
1785 :
1786 8 : if (!value)
1787 1 : goto error;
1788 :
1789 7 : ml_single_get_gst_info (single_h, is_input, &gst_info);
1790 :
1791 7 : if (g_str_has_suffix (name, "type"))
1792 0 : num = gst_tensors_info_parse_types_string (&gst_info, value);
1793 7 : else if (g_str_has_suffix (name, "name"))
1794 0 : num = gst_tensors_info_parse_names_string (&gst_info, value);
1795 : else
1796 7 : num = gst_tensors_info_parse_dimensions_string (&gst_info, value);
1797 :
1798 7 : if (num == gst_info.num_tensors) {
1799 : /* change configuration */
1800 7 : status = ml_single_set_gst_info (single_h, &gst_info);
1801 : } else {
1802 0 : _ml_error_report
1803 : ("The property value, '%s', is not appropriate for the given property key, '%s'. The API has failed to parse the given property value.",
1804 : value, name);
1805 0 : status = ML_ERROR_INVALID_PARAMETER;
1806 : }
1807 :
1808 7 : gst_tensors_info_free (&gst_info);
1809 : } else {
1810 0 : g_object_set (G_OBJECT (single_h->filter), name, value, NULL);
1811 : }
1812 9 : goto done;
1813 1 : error:
1814 1 : _ml_error_report
1815 : ("The parameter, value (const char *), is NULL. It should be a valid string representing the value to be set for the given property key, '%s'",
1816 : name);
1817 1 : status = ML_ERROR_INVALID_PARAMETER;
1818 10 : done:
1819 10 : ML_SINGLE_HANDLE_UNLOCK (single_h);
1820 :
1821 10 : g_free (old_value);
1822 10 : return status;
1823 : }
1824 :
1825 : /**
1826 : * @brief Gets the property value for the given model.
1827 : */
1828 : int
1829 27 : ml_single_get_property (ml_single_h single, const char *name, char **value)
1830 : {
1831 : ml_single *single_h;
1832 27 : int status = ML_ERROR_NONE;
1833 :
1834 27 : check_feature_state (ML_FEATURE_INFERENCE);
1835 :
1836 27 : if (!single)
1837 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1838 : "The parameter, single (ml_single_h), is NULL. It should be a valid instance of ml_single_h, which is usually created by ml_single_open().");
1839 27 : if (!name)
1840 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1841 : "The parameter, name (const char *), is NULL. It should be a valid string representing a property key.");
1842 26 : if (!value)
1843 1 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1844 : "The parameter, value (const char *), is NULL. It should be a valid string representing the value to be set for the given property key, '%s'",
1845 : name);
1846 :
1847 : /* init null */
1848 25 : *value = NULL;
1849 :
1850 25 : ML_SINGLE_GET_VALID_HANDLE_LOCKED (single_h, single, 0);
1851 :
1852 25 : if (g_str_equal (name, "input") || g_str_equal (name, "output") ||
1853 8 : g_str_equal (name, "inputtype") || g_str_equal (name, "inputname") ||
1854 8 : g_str_equal (name, "inputlayout") || g_str_equal (name, "outputtype") ||
1855 7 : g_str_equal (name, "outputname") || g_str_equal (name, "outputlayout") ||
1856 7 : g_str_equal (name, "accelerator") || g_str_equal (name, "custom")) {
1857 : /* string */
1858 18 : g_object_get (G_OBJECT (single_h->filter), name, value, NULL);
1859 7 : } else if (g_str_equal (name, "is-updatable")) {
1860 5 : gboolean bool_value = FALSE;
1861 :
1862 : /* boolean */
1863 5 : g_object_get (G_OBJECT (single_h->filter), name, &bool_value, NULL);
1864 10 : *value = (bool_value) ? g_strdup ("true") : g_strdup ("false");
1865 : } else {
1866 2 : _ml_error_report
1867 : ("The property key, '%s', is not available for get_property and not recognized by the API. It should be one of {input, inputtype, inputname, inputlayout, output, outputtype, outputname, outputlayout, accelerator, custom, is-updatable}.",
1868 : name);
1869 2 : status = ML_ERROR_NOT_SUPPORTED;
1870 : }
1871 :
1872 25 : ML_SINGLE_HANDLE_UNLOCK (single_h);
1873 25 : return status;
1874 : }
1875 :
1876 : /**
1877 : * @brief Internal helper function to validate model files.
1878 : */
1879 : static int
1880 90 : __ml_validate_model_file (const char *const *model,
1881 : const unsigned int num_models, gboolean * is_dir)
1882 : {
1883 : guint i;
1884 :
1885 90 : if (!model)
1886 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1887 : "The parameter, model, is NULL. It should be a valid array of strings, where each string is a valid file path for a neural network model file.");
1888 90 : if (num_models < 1)
1889 0 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1890 : "The parameter, num_models, is 0. It should be the number of files for the given neural network model.");
1891 :
1892 90 : if (g_file_test (model[0], G_FILE_TEST_IS_DIR)) {
1893 4 : *is_dir = TRUE;
1894 4 : return ML_ERROR_NONE;
1895 : }
1896 :
1897 169 : for (i = 0; i < num_models; i++) {
1898 86 : if (!model[i] ||
1899 86 : !g_file_test (model[i], G_FILE_TEST_EXISTS | G_FILE_TEST_IS_REGULAR)) {
1900 3 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1901 : "The given param, model path [%d] = \"%s\" is invalid or the file is not found or accessible.",
1902 : i, _STR_NULL (model[i]));
1903 : }
1904 : }
1905 :
1906 83 : *is_dir = FALSE;
1907 :
1908 83 : return ML_ERROR_NONE;
1909 : }
1910 :
1911 : /**
1912 : * @brief Validates the nnfw model file.
1913 : * @since_tizen 5.5
1914 : * @param[in] model The path of model file.
1915 : * @param[in/out] nnfw The type of NNFW.
1916 : * @return @c 0 on success. Otherwise a negative error value.
1917 : * @retval #ML_ERROR_NONE Successful
1918 : * @retval #ML_ERROR_NOT_SUPPORTED Not supported, or framework to support this model file is unavailable in the environment.
1919 : * @retval #ML_ERROR_INVALID_PARAMETER Given parameter is invalid.
1920 : */
1921 : int
1922 90 : _ml_validate_model_file (const char *const *model,
1923 : const unsigned int num_models, ml_nnfw_type_e * nnfw)
1924 : {
1925 90 : int status = ML_ERROR_NONE;
1926 90 : ml_nnfw_type_e detected = ML_NNFW_TYPE_ANY;
1927 90 : gboolean is_dir = FALSE;
1928 : gchar *pos, *fw_name;
1929 90 : gchar **file_ext = NULL;
1930 : guint i;
1931 :
1932 90 : if (!nnfw)
1933 90 : _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
1934 : "The parameter, nnfw, is NULL. It should be a valid pointer of ml_nnfw_type_e.");
1935 :
1936 90 : _ml_error_report_return_continue_iferr (__ml_validate_model_file (model,
1937 : num_models, &is_dir),
1938 : "The parameters, model and num_models, are not valid.");
1939 :
1940 : /**
1941 : * @note detect-fw checks the file ext and returns proper fw name for given models.
1942 : * If detected fw and given nnfw are same, we don't need to check the file extension.
1943 : * If any condition for auto detection is added later, below code also should be updated.
1944 : */
1945 87 : fw_name = gst_tensor_filter_detect_framework (model, num_models, TRUE);
1946 87 : detected = _ml_get_nnfw_type_by_subplugin_name (fw_name);
1947 87 : g_free (fw_name);
1948 :
1949 87 : if (*nnfw == ML_NNFW_TYPE_ANY) {
1950 37 : if (detected == ML_NNFW_TYPE_ANY) {
1951 0 : _ml_error_report
1952 : ("The given neural network model (1st path is \"%s\", and there are %d paths declared) has unknown or unsupported extension. Please check its corresponding neural network framework and try to specify it instead of \"ML_NNFW_TYPE_ANY\".",
1953 : model[0], num_models);
1954 0 : status = ML_ERROR_INVALID_PARAMETER;
1955 : } else {
1956 37 : _ml_logi ("The given model is supposed a %s model.",
1957 : _ml_get_nnfw_subplugin_name (detected));
1958 37 : *nnfw = detected;
1959 : }
1960 :
1961 37 : goto done;
1962 50 : } else if (is_dir && *nnfw != ML_NNFW_TYPE_NNFW) {
1963 : /* supposed it is ONE if given model is directory */
1964 2 : _ml_error_report
1965 : ("The given model (1st path is \"%s\", and there are %d paths declared) is directory, which is allowed by \"NNFW (One Runtime)\" only, Please check the model and framework.",
1966 : model[0], num_models);
1967 2 : status = ML_ERROR_INVALID_PARAMETER;
1968 2 : goto done;
1969 48 : } else if (detected == *nnfw) {
1970 : /* Expected framework, nothing to do. */
1971 43 : goto done;
1972 : }
1973 :
1974 : /* Handle mismatched case, check file extension. */
1975 5 : file_ext = g_malloc0 (sizeof (char *) * (num_models + 1));
1976 10 : for (i = 0; i < num_models; i++) {
1977 5 : if ((pos = strrchr (model[i], '.')) == NULL) {
1978 0 : _ml_error_report ("The given model [%d]=\"%s\" has invalid extension.", i,
1979 : model[i]);
1980 0 : status = ML_ERROR_INVALID_PARAMETER;
1981 0 : goto done;
1982 : }
1983 :
1984 5 : file_ext[i] = g_ascii_strdown (pos, -1);
1985 : }
1986 :
1987 : /** @todo Make sure num_models is correct for each nnfw type */
1988 5 : switch (*nnfw) {
1989 4 : case ML_NNFW_TYPE_NNFW:
1990 : case ML_NNFW_TYPE_TVM:
1991 : case ML_NNFW_TYPE_ONNX_RUNTIME:
1992 : case ML_NNFW_TYPE_NCNN:
1993 : case ML_NNFW_TYPE_TENSORRT:
1994 : case ML_NNFW_TYPE_QNN:
1995 : case ML_NNFW_TYPE_LLAMACPP:
1996 : case ML_NNFW_TYPE_TIZEN_HAL:
1997 : /**
1998 : * We cannot check the file ext with NNFW.
1999 : * NNFW itself will validate metadata and model file.
2000 : */
2001 4 : break;
2002 0 : case ML_NNFW_TYPE_MVNC:
2003 : case ML_NNFW_TYPE_OPENVINO:
2004 : case ML_NNFW_TYPE_EDGE_TPU:
2005 : /**
2006 : * @todo Need to check method to validate model
2007 : * Although nnstreamer supports these frameworks,
2008 : * ML-API implementation is not ready.
2009 : */
2010 0 : _ml_error_report
2011 : ("Given NNFW is not supported by ML-API Inference.Single, yet, although it is supported by NNStreamer. If you have such NNFW integrated into your machine and want to access via ML-API, please update the corresponding implementation or report and discuss at github.com/nnstreamer/nnstreamer/issues.");
2012 0 : status = ML_ERROR_NOT_SUPPORTED;
2013 0 : break;
2014 0 : case ML_NNFW_TYPE_VD_AIFW:
2015 0 : if (!g_str_equal (file_ext[0], ".nb") &&
2016 0 : !g_str_equal (file_ext[0], ".ncp") &&
2017 0 : !g_str_equal (file_ext[0], ".tvn") &&
2018 0 : !g_str_equal (file_ext[0], ".bin")) {
2019 0 : status = ML_ERROR_INVALID_PARAMETER;
2020 : }
2021 0 : break;
2022 0 : case ML_NNFW_TYPE_SNAP:
2023 : #if !defined (__ANDROID__)
2024 0 : _ml_error_report ("SNAP is supported by Android/arm64-v8a devices only.");
2025 0 : status = ML_ERROR_NOT_SUPPORTED;
2026 : #endif
2027 : /* SNAP requires multiple files, set supported if model file exists. */
2028 0 : break;
2029 0 : case ML_NNFW_TYPE_ARMNN:
2030 0 : if (!g_str_equal (file_ext[0], ".caffemodel") &&
2031 0 : !g_str_equal (file_ext[0], ".tflite") &&
2032 0 : !g_str_equal (file_ext[0], ".pb") &&
2033 0 : !g_str_equal (file_ext[0], ".prototxt")) {
2034 0 : _ml_error_report
2035 : ("ARMNN accepts .caffemodel, .tflite, .pb, and .prototxt files only. Please support correct file extension. You have specified: \"%s\"",
2036 : file_ext[0]);
2037 0 : status = ML_ERROR_INVALID_PARAMETER;
2038 : }
2039 0 : break;
2040 0 : case ML_NNFW_TYPE_MXNET:
2041 0 : if (!g_str_equal (file_ext[0], ".params") &&
2042 0 : !g_str_equal (file_ext[0], ".json")) {
2043 0 : status = ML_ERROR_INVALID_PARAMETER;
2044 : }
2045 0 : break;
2046 1 : default:
2047 1 : _ml_error_report
2048 : ("You have designated an incorrect neural network framework (out of bound).");
2049 1 : status = ML_ERROR_INVALID_PARAMETER;
2050 1 : break;
2051 : }
2052 :
2053 87 : done:
2054 87 : if (status == ML_ERROR_NONE) {
2055 84 : if (!_ml_nnfw_is_available (*nnfw, ML_NNFW_HW_ANY)) {
2056 1 : status = ML_ERROR_NOT_SUPPORTED;
2057 1 : _ml_error_report
2058 : ("The subplugin for tensor-filter \"%s\" is not available. Please install the corresponding tensor-filter subplugin file (usually, \"libnnstreamer_filter_${NAME}.so\") at the correct path. Please use \"nnstreamer-check\" utility to check related configurations. If you do not have the utility ready, build and install \"confchk\", which is located at ${nnstreamer_source}/tools/development/confchk/ .",
2059 : _ml_get_nnfw_subplugin_name (*nnfw));
2060 : }
2061 : } else {
2062 3 : _ml_error_report
2063 : ("The given model file, \"%s\" (1st of %d files), is invalid.",
2064 : model[0], num_models);
2065 : }
2066 :
2067 87 : g_strfreev (file_ext);
2068 87 : return status;
2069 : }
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