diff --git a/src/lib/tflm/tflm_backend.cc b/src/lib/tflm/tflm_backend.cc index 879b14e57..5072c8e54 100644 --- a/src/lib/tflm/tflm_backend.cc +++ b/src/lib/tflm/tflm_backend.cc @@ -56,15 +56,15 @@ static bool ml_backend_valid_dataype(TfLiteType type) { type == kTfLiteFloat32); } -static py_ml_dtype_t ml_backend_map_dtype(TfLiteType type) { +static char ml_backend_map_dtype(TfLiteType type) { if (type == kTfLiteUInt8) { - return PY_ML_DTYPE_UINT8; + return 'B'; } else if (type == kTfLiteInt8) { - return PY_ML_DTYPE_INT8; + return 'b'; } else if (type == kTfLiteInt16) { - return PY_ML_DTYPE_INT16; + return 'h'; } else { - return PY_ML_DTYPE_FLOAT; + return 'f'; } } diff --git a/src/omv/modules/py_ml.c b/src/omv/modules/py_ml.c index c0bf49c81..f6402661c 100644 --- a/src/omv/modules/py_ml.c +++ b/src/omv/modules/py_ml.c @@ -25,32 +25,6 @@ #include "tflm_builtin_models.h" #include "ulab/code/ndarray.h" -#define PY_ML_GRAYSCALE_RANGE ((COLOR_GRAYSCALE_MAX) -(COLOR_GRAYSCALE_MIN)) -#define PY_ML_GRAYSCALE_MID (((PY_ML_GRAYSCALE_RANGE) +1) / 2) - -static const char *py_ml_map_dtype(py_ml_dtype_t dtype) { - if (dtype == PY_ML_DTYPE_UINT8) { - return "uint8"; - } else if (dtype == PY_ML_DTYPE_INT8) { - return "int8"; - } else if (dtype == PY_ML_DTYPE_INT16) { - return "int16"; - } else { - return "float"; - } -} - -// TF Input/Output callback functions. -typedef mp_obj_t py_ml_output_data_t; - -typedef struct _py_ml_input_callback_data { - void *data; - rectangle_t roi; - py_ml_scale_t scale; - float mean[3]; - float stdev[3]; -} py_ml_input_data_t; - static size_t py_ml_tuple_sum(mp_obj_tuple_t *o) { if (o->len < 1) { mp_raise_msg(&mp_type_ValueError, MP_ERROR_TEXT("Unexpected tensor shape")); @@ -63,175 +37,67 @@ static size_t py_ml_tuple_sum(mp_obj_tuple_t *o) { return size; } -static void py_ml_tuple_hwc(mp_obj_tuple_t *o, size_t *h, size_t *w, size_t *c) { - if (o->len != 1 || ((mp_obj_tuple_t *) MP_OBJ_TO_PTR(o->items[0]))->len != 4) { - mp_raise_msg(&mp_type_ValueError, MP_ERROR_TEXT("Unexpected tensor shape")); - } - o = MP_OBJ_TO_PTR(o->items[0]); - *h = mp_obj_get_int(o->items[1]); - *w = mp_obj_get_int(o->items[2]); - *c = mp_obj_get_int(o->items[3]); -} - +// TF Input/Output callback functions. static void py_ml_input_callback(py_ml_model_obj_t *model, void *arg) { - // TODO we assume that there's a single input. - void *model_input = ml_backend_get_input(model, 0); - py_ml_input_data_t *input_data = (py_ml_input_data_t *) arg; + mp_obj_list_t *input_list = MP_OBJ_TO_PTR(*((mp_obj_t *) arg)); - // TODO we assume that the input shape is (1, h, w, c) - size_t input_height = 0, input_width = 0, input_channels = 0; - py_ml_tuple_hwc(model->input_shape, &input_height, &input_width, &input_channels); + for (size_t i = 0; i < model->inputs_size; i++) { + void *input_buffer = ml_backend_get_input(model, i); + size_t input_size = py_ml_tuple_sum(MP_OBJ_TO_PTR(model->input_shape->items[i])); + mp_obj_tuple_t *input_shape = MP_OBJ_TO_PTR(model->input_shape->items[i]); + mp_obj_t input_arg = input_list->items[i]; - int shift = (model->input_dtype == PY_ML_DTYPE_INT8) ? PY_ML_GRAYSCALE_MID : 0; - float fscale = 1.0f, fadd = 0.0f; + if (mp_obj_is_callable(input_arg)) { + // Input is a callable. Call the object and pass the tensor buffer and dtype. + mp_obj_t fargs[3] = { + mp_obj_new_bytearray_by_ref(input_size, input_buffer), + MP_OBJ_FROM_PTR(input_shape), + mp_obj_new_int(model->input_dtype) + }; + mp_call_function_n_kw(input_arg, 3, 0, fargs); + } else if (MP_OBJ_IS_TYPE(input_arg, &ulab_ndarray_type)) { + // Input is an ndarry. The input is converted and copied to the tensor buffer. + ndarray_obj_t *input_array = MP_OBJ_TO_PTR(input_arg); - switch (input_data->scale) { - case PY_ML_SCALE_0_1: // convert 0->255 to 0->1 - fscale = 1.0f / 255.0f; - break; - case PY_ML_SCALE_S1_1: // convert 0->255 to -1->1 - fscale = 2.0f / 255.0f; - fadd = -1.0f; - break; - case PY_ML_SCALE_S128_127: // convert 0->255 to -128->127 - fadd = -128.0f; - break; - case PY_ML_SCALE_NONE: // convert 0->255 to 0->255 - default: - break; - } - - float fscale_r = fscale, fadd_r = fadd; - float fscale_g = fscale, fadd_g = fadd; - float fscale_b = fscale, fadd_b = fadd; - - // To normalize the input image we need to subtract the mean and divide by the standard deviation. - // We can do this by applying the normalization to fscale and fadd outside the loop. - // Red - fadd_r = (fadd_r - input_data->mean[0]) / input_data->stdev[0]; - fscale_r /= input_data->stdev[0]; - - // Green - fadd_g = (fadd_g - input_data->mean[1]) / input_data->stdev[1]; - fscale_g /= input_data->stdev[1]; - - // Blue - fadd_b = (fadd_b - input_data->mean[2]) / input_data->stdev[2]; - fscale_b /= input_data->stdev[2]; - - // Grayscale -> Y = 0.299R + 0.587G + 0.114B - float mean = (input_data->mean[0] * 0.299f) + (input_data->mean[1] * 0.587f) + (input_data->mean[2] * 0.114f); - float std = (input_data->stdev[0] * 0.299f) + (input_data->stdev[1] * 0.587f) + (input_data->stdev[2] * 0.114f); - fadd = (fadd - mean) / std; - fscale /= std; - - image_t dst_img; - dst_img.w = input_width; - dst_img.h = input_height; - dst_img.data = (uint8_t *) model_input; - - if (input_channels == 1) { - dst_img.pixfmt = PIXFORMAT_GRAYSCALE; - } else if (input_channels == 3) { - dst_img.pixfmt = PIXFORMAT_RGB565; - } else { - mp_raise_msg(&mp_type_ValueError, MP_ERROR_TEXT("Expected model input channels to be 1 or 3!")); - } - - imlib_draw_image(&dst_img, input_data->data, 0, 0, 1.0f, 1.0f, &input_data->roi, - -1, 256, NULL, NULL, IMAGE_HINT_BILINEAR | IMAGE_HINT_CENTER | - IMAGE_HINT_SCALE_ASPECT_EXPAND | IMAGE_HINT_BLACK_BACKGROUND, NULL, NULL, NULL); - - int size = (input_width * input_height) - 1; // must be int per countdown loop - - if (input_channels == 1) { - // GRAYSCALE - if (model->input_dtype == PY_ML_DTYPE_FLOAT) { - // convert u8 -> f32 - uint8_t *model_input_u8 = (uint8_t *) model_input; - float *model_input_f32 = (float *) model_input; - for (; size >= 0; size -= 1) { - model_input_f32[size] = (model_input_u8[size] * fscale) + fadd; + if (input_array->ndim != input_shape->len) { + mp_raise_msg(&mp_type_ValueError, + MP_ERROR_TEXT("Input shape does not match the model input shape")); } - } else { - if (shift) { - // convert u8 -> s8 - uint8_t *model_input_8 = (uint8_t *) model_input; - #if (__ARM_ARCH > 6) - for (; size >= 3; size -= 4) { - *((uint32_t *) (model_input_8 + size - 3)) ^= 0x80808080; - } - #endif - for (; size >= 0; size -= 1) { - model_input_8[size] ^= PY_ML_GRAYSCALE_MID; + + for (size_t i = 0; i < input_array->ndim; i++) { + if (input_array->shape[i] != mp_obj_get_int(input_shape->items[i])) { + mp_raise_msg(&mp_type_ValueError, + MP_ERROR_TEXT("Input shape does not match the model input shape")); } } - } - } else if (input_channels == 3) { - // RGB888 - int rgb_size = size * 3; // must be int per countdown loop - if (model->input_dtype == PY_ML_DTYPE_FLOAT) { - uint16_t *model_input_u16 = (uint16_t *) model_input; - float *model_input_f32 = (float *) model_input; - for (; size >= 0; size -= 1, rgb_size -= 3) { - int pixel = model_input_u16[size]; - model_input_f32[rgb_size] = (COLOR_RGB565_TO_R8(pixel) * fscale_r) + fadd_r; - model_input_f32[rgb_size + 1] = (COLOR_RGB565_TO_G8(pixel) * fscale_g) + fadd_g; - model_input_f32[rgb_size + 2] = (COLOR_RGB565_TO_B8(pixel) * fscale_b) + fadd_b; + + if (model->input_dtype == 'f') { + float *model_input_float = (float *) input_buffer; + for (size_t i = 0; i < input_array->len; i++) { + float value = ndarray_get_float_index(input_array->array, input_array->dtype, i); + model_input_float[i] = value; + } + } else if (model->input_dtype == 'b') { + int8_t *model_input_8 = (int8_t *) input_buffer; + for (size_t i = 0; i < input_array->len; i++) { + float value = ndarray_get_float_index(input_array->array, input_array->dtype, i); + model_input_8[i] = (int8_t) ((value / model->input_scale) + model->input_zero_point); + } + } else if (model->input_dtype == 'B') { + uint8_t *model_input_8 = (uint8_t *) input_buffer; + for (size_t i = 0; i < input_array->len; i++) { + float value = ndarray_get_float_index(input_array->array, input_array->dtype, i); + model_input_8[i] = (uint8_t) ((value / model->input_scale) + model->input_zero_point); + } + } else { + int16_t *model_input_16 = (int16_t *) input_buffer; + for (size_t i = 0; i < input_array->len; i++) { + float value = ndarray_get_float_index(input_array->array, input_array->dtype, i); + model_input_16[i] = (int16_t) ((value / model->input_scale) + model->input_zero_point); + } } } else { - uint16_t *model_input_u16 = (uint16_t *) model_input; - uint8_t *model_input_8 = (uint8_t *) model_input; - for (; size >= 0; size -= 1, rgb_size -= 3) { - int pixel = model_input_u16[size]; - model_input_8[rgb_size] = COLOR_RGB565_TO_R8(pixel) ^ shift; - model_input_8[rgb_size + 1] = COLOR_RGB565_TO_G8(pixel) ^ shift; - model_input_8[rgb_size + 2] = COLOR_RGB565_TO_B8(pixel) ^ shift; - } - } - } -} - -static void py_ml_input_callback_regression(py_ml_model_obj_t *model, void *arg) { - // TODO we assume that there's a single input. - void *model_input = ml_backend_get_input(model, 0); - py_ml_input_data_t *input_data = (py_ml_input_data_t *) arg; - - mp_obj_tuple_t *input_shape = MP_OBJ_TO_PTR(model->input_shape->items[0]); - ndarray_obj_t *input_array = MP_OBJ_TO_PTR(*((mp_obj_t *) input_data->data)); - - if (input_array->ndim != input_shape->len) { - mp_raise_msg(&mp_type_ValueError, MP_ERROR_TEXT("Input shape does not match the model input shape")); - } - for (size_t i = 0; i < input_array->ndim; i++) { - if (input_array->shape[i] != mp_obj_get_int(input_shape->items[i])) { - mp_raise_msg(&mp_type_ValueError, MP_ERROR_TEXT("Input shape does not match the model input shape")); - } - } - - if (model->input_dtype == PY_ML_DTYPE_FLOAT) { - float *model_input_float = (float *) model_input; - for (size_t i = 0; i < input_array->len; i++) { - float value = ndarray_get_float_index(input_array->array, input_array->dtype, i); - model_input_float[i] = value; - } - } else if (model->input_dtype == PY_ML_DTYPE_INT8) { - int8_t *model_input_8 = (int8_t *) model_input; - for (size_t i = 0; i < input_array->len; i++) { - float value = ndarray_get_float_index(input_array->array, input_array->dtype, i); - model_input_8[i] = (int8_t) ((value / model->input_scale) + model->input_zero_point); - } - } else if (model->input_dtype == PY_ML_DTYPE_UINT8) { - uint8_t *model_input_8 = (uint8_t *) model_input; - for (size_t i = 0; i < input_array->len; i++) { - float value = ndarray_get_float_index(input_array->array, input_array->dtype, i); - model_input_8[i] = (uint8_t) ((value / model->input_scale) + model->input_zero_point); - } - } else { - int16_t *model_input_16 = (int16_t *) model_input; - for (size_t i = 0; i < input_array->len; i++) { - float value = ndarray_get_float_index(input_array->array, input_array->dtype, i); - model_input_16[i] = (int16_t) ((value / model->input_scale) + model->input_zero_point); + mp_raise_msg(&mp_type_ValueError, MP_ERROR_TEXT("Unsupported input type")); } } } @@ -243,16 +109,16 @@ static void py_ml_output_callback(py_ml_model_obj_t *model, void *arg) { size_t size = py_ml_tuple_sum(MP_OBJ_TO_PTR(model->output_shape->items[i])); mp_obj_tuple_t *output = MP_OBJ_TO_PTR(mp_obj_new_tuple(size, NULL)); - if (model->output_dtype == PY_ML_DTYPE_FLOAT) { + if (model->output_dtype == 'f') { for (size_t j = 0; j < size; j++) { output->items[j] = mp_obj_new_float(((float *) model_output)[j]); } - } else if (model->output_dtype == PY_ML_DTYPE_INT8) { + } else if (model->output_dtype == 'b') { for (size_t j = 0; j < size; j++) { float v = (((int8_t *) model_output)[j] - model->output_zero_point); output->items[j] = mp_obj_new_float(v * model->output_scale); } - } else if (model->output_dtype == PY_ML_DTYPE_UINT8) { + } else if (model->output_dtype == 'B') { for (size_t j = 0; j < size; j++) { float v = (((uint8_t *) model_output)[j] - model->output_zero_point); output->items[j] = mp_obj_new_float(v * model->output_scale); @@ -265,7 +131,7 @@ static void py_ml_output_callback(py_ml_model_obj_t *model, void *arg) { } output_list->items[i] = MP_OBJ_FROM_PTR(output); } - *((py_ml_output_data_t *) arg) = MP_OBJ_FROM_PTR(output_list); + *((mp_obj_t *) arg) = MP_OBJ_FROM_PTR(output_list); } // TF Model Object. @@ -274,21 +140,18 @@ static const mp_obj_type_t py_ml_model_type; static void py_ml_model_print(const mp_print_t *print, mp_obj_t self_in, mp_print_kind_t kind) { py_ml_model_obj_t *self = MP_OBJ_TO_PTR(self_in); mp_printf(print, - "{size: %d, ram: %d, inputs_size: %d, input_dtype: %s, input_scale: %f, input_zero_point: %d, " - "outputs_size: %d output_dtype: %s, output_scale: %f, output_zero_point: %d}", - self->size, self->memory_size, self->inputs_size, py_ml_map_dtype(self->input_dtype), - (double) self->input_scale, self->input_zero_point, self->outputs_size, py_ml_map_dtype(self->output_dtype), + "{size: \"%d\", ram: \"%d\"," + " inputs_size: \"%d\", input_dtype: \"%c\", input_scale: \"%f\", input_zero_point: \"%d\"," + " outputs_size: \"%d\" output_dtype: \"%c\", output_scale: \"%f\", output_zero_point: \"%d\"}", + self->size, self->memory_size, self->inputs_size, self->input_dtype, + (double) self->input_scale, self->input_zero_point, self->outputs_size, self->output_dtype, (double) self->output_scale, self->output_zero_point); } static mp_obj_t py_ml_model_predict(uint n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) { - enum { ARG_roi, ARG_callback, ARG_scale, ARG_mean, ARG_stdev }; + enum { ARG_callback }; static const mp_arg_t allowed_args[] = { - { MP_QSTR_roi, MP_ARG_OBJ | MP_ARG_KW_ONLY, {.u_rom_obj = MP_ROM_NONE} }, { MP_QSTR_callback, MP_ARG_OBJ | MP_ARG_KW_ONLY, {.u_rom_obj = MP_ROM_NONE} }, - { MP_QSTR_scale, MP_ARG_INT | MP_ARG_KW_ONLY, {.u_int = PY_ML_SCALE_0_1} }, - { MP_QSTR_mean, MP_ARG_OBJ | MP_ARG_KW_ONLY, {.u_rom_obj = MP_ROM_NONE} }, - { MP_QSTR_stdev, MP_ARG_OBJ | MP_ARG_KW_ONLY, {.u_rom_obj = MP_ROM_NONE} }, }; // Parse args. @@ -297,41 +160,22 @@ static mp_obj_t py_ml_model_predict(uint n_args, const mp_obj_t *pos_args, mp_ma py_ml_model_obj_t *model = MP_OBJ_TO_PTR(pos_args[0]); - py_ml_input_data_t input_data = { - .scale = args[ARG_scale].u_int, - .mean = {0.0f, 0.0f, 0.0f}, - .stdev = {1.0f, 1.0f, 1.0f} - }; + mp_obj_t input_data = pos_args[1]; ml_backend_input_callback_t input_callback = py_ml_input_callback; - py_ml_output_data_t output_data; + mp_obj_t output_data; ml_backend_output_callback_t output_callback = py_ml_output_callback; - if (MP_OBJ_IS_TYPE(pos_args[1], &ulab_ndarray_type)) { - input_data.data = (void *) &pos_args[1]; - input_callback = py_ml_input_callback_regression; - } else if (MP_OBJ_IS_TYPE(pos_args[1], &py_image_type)) { - input_data.data = py_helper_arg_to_image(pos_args[1], ARG_IMAGE_ANY); - input_data.roi = py_helper_arg_to_roi(args[ARG_roi].u_obj, input_data.data); - py_helper_arg_to_float_array(args[ARG_mean].u_obj, input_data.mean, 3); - py_helper_arg_to_float_array(args[ARG_stdev].u_obj, input_data.stdev, 3); - } else { - mp_raise_msg(&mp_type_ValueError, MP_ERROR_TEXT("Unsupported input type")); + if (!MP_OBJ_IS_TYPE(input_data, &mp_type_list)) { + mp_raise_msg(&mp_type_ValueError, MP_ERROR_TEXT("Unsupported input type. Expected a list")); } ml_backend_run_inference(model, input_callback, &input_data, output_callback, &output_data); if (args[ARG_callback].u_obj != mp_const_none) { - mp_obj_t rect = mp_obj_new_tuple(4, (mp_obj_t []) { mp_obj_new_int(input_data.roi.x), - mp_obj_new_int(input_data.roi.y), - mp_obj_new_int(input_data.roi.w), - mp_obj_new_int(input_data.roi.h) }); - mp_obj_t fun_args[3] = { MP_OBJ_FROM_PTR(model), output_data, rect }; - if (!MP_OBJ_IS_TYPE(pos_args[1], &py_image_type)) { - output_data = mp_call_function_n_kw(args[ARG_callback].u_obj, 2, 0, fun_args); - } else { - output_data = mp_call_function_n_kw(args[ARG_callback].u_obj, 3, 0, fun_args); - } + // Pass model, inputs, outputs to the post-processing callback. + mp_obj_t fargs[3] = { MP_OBJ_FROM_PTR(model), pos_args[1], output_data }; + output_data = mp_call_function_n_kw(args[ARG_callback].u_obj, 3, 0, fargs); } return output_data; @@ -340,7 +184,7 @@ static MP_DEFINE_CONST_FUN_OBJ_KW(py_ml_model_predict_obj, 2, py_ml_model_predic static void py_ml_model_attr(mp_obj_t self_in, qstr attr, mp_obj_t *dest) { py_ml_model_obj_t *self = MP_OBJ_TO_PTR(self_in); - const char *str; + if (dest[0] == MP_OBJ_NULL) { // Load attribute. switch (attr) { @@ -354,8 +198,7 @@ static void py_ml_model_attr(mp_obj_t self_in, qstr attr, mp_obj_t *dest) { dest[0] = MP_OBJ_FROM_PTR(self->input_shape); break; case MP_QSTR_input_dtype: - str = py_ml_map_dtype(self->input_dtype); - dest[0] = mp_obj_new_str(str, strlen(str)); + dest[0] = mp_obj_new_str(&self->input_dtype, 1); break; case MP_QSTR_input_scale: dest[0] = mp_obj_new_float(self->input_scale); @@ -367,8 +210,7 @@ static void py_ml_model_attr(mp_obj_t self_in, qstr attr, mp_obj_t *dest) { dest[0] = MP_OBJ_FROM_PTR(self->output_shape); break; case MP_QSTR_output_dtype: - str = py_ml_map_dtype(self->output_dtype); - dest[0] = mp_obj_new_str(str, strlen(str)); + dest[0] = mp_obj_new_str(&self->output_dtype, 1); break; case MP_QSTR_output_scale: dest[0] = mp_obj_new_float(self->output_scale); @@ -490,10 +332,6 @@ static const mp_rom_map_elem_t py_ml_globals_dict_table[] = { { MP_ROM_QSTR(MP_QSTR___name__), MP_OBJ_NEW_QSTR(MP_QSTR_ml) }, { MP_ROM_QSTR(MP_QSTR_Model), MP_ROM_PTR(&py_ml_model_type) }, { MP_ROM_QSTR(MP_QSTR_NMS), MP_ROM_PTR(&py_ml_nms_type) }, - { MP_ROM_QSTR(MP_QSTR_SCALE_NONE), MP_ROM_INT(PY_ML_SCALE_NONE) }, - { MP_ROM_QSTR(MP_QSTR_SCALE_0_1), MP_ROM_INT(PY_ML_SCALE_0_1) }, - { MP_ROM_QSTR(MP_QSTR_SCALE_S1_1), MP_ROM_INT(PY_ML_SCALE_S1_1) }, - { MP_ROM_QSTR(MP_QSTR_SCALE_S128_127), MP_ROM_INT(PY_ML_SCALE_S128_127) }, }; static MP_DEFINE_CONST_DICT(py_ml_globals_dict, py_ml_globals_dict_table); diff --git a/src/omv/modules/py_ml.h b/src/omv/modules/py_ml.h index 941cfc2f9..d44be3f8a 100644 --- a/src/omv/modules/py_ml.h +++ b/src/omv/modules/py_ml.h @@ -10,20 +10,6 @@ */ #ifndef __PY_ML_H__ #define __PY_ML_H__ -typedef enum { - PY_ML_SCALE_NONE, - PY_ML_SCALE_0_1, - PY_ML_SCALE_S1_1, - PY_ML_SCALE_S128_127 -} py_ml_scale_t; - -typedef enum py_ml_dtype { - PY_ML_DTYPE_INT8, - PY_ML_DTYPE_UINT8, - PY_ML_DTYPE_INT16, - PY_ML_DTYPE_FLOAT -} py_ml_dtype_t; - // TF Model Object. typedef struct py_ml_model_obj { mp_obj_base_t base; @@ -35,12 +21,12 @@ typedef struct py_ml_model_obj { mp_obj_tuple_t *input_shape; float input_scale; int input_zero_point; - py_ml_dtype_t input_dtype; + char input_dtype; size_t outputs_size; mp_obj_tuple_t *output_shape; float output_scale; int output_zero_point; - py_ml_dtype_t output_dtype; + char output_dtype; void *state; // Private context for the backend. } py_ml_model_obj_t;