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Merge pull request #633 from kwagyeman/kwabena/add_mobilenet_examples
Kwabena/add mobilenet examples
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# TensorFlow Lite Mobilenet V1 Example
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#
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# Google's Mobilenet V1 detects 1000 classes of objects
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#
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# WARNING: Mobilenet is trained on ImageNet and isn't meant to classify anything
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# in the real world. It's just designed to score well on the ImageNet dataset.
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# This example just shows off running mobilenet on the OpenMV Cam. However, the
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# default model is not really usable for anything. You have to use transfer
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# learning to apply the model to a target problem by re-training the model.
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#
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# NOTE: This example only works on the OpenMV Cam H7 Pro (that has SDRAM) and better!
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# To get the models please see the CNN Network library in OpenMV IDE under
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# Tools -> Machine Vision. The labels are there too.
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#
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# In this example we slide the detector window over the image and get a list
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# of activations. Note that use a CNN with a sliding window is extremely compute
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# expensive so for an exhaustive search do not expect the CNN to be real-time.
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import sensor, image, time, os, tf
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sensor.reset() # Reset and initialize the sensor.
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sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
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sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
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sensor.set_windowing((240, 240)) # Set 240x240 window.
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sensor.skip_frames(time=2000) # Let the camera adjust.
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mobilenet_version = "1" # 1
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mobilenet_width = "0.5" # 1.0, 0.75, 0.50, 0.25
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mobilenet_resolution = "128" # 224, 192, 160, 128
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mobilenet = "mobilenet_v%s_%s_%s_quant.tflite" % (mobilenet_version, mobilenet_width, mobilenet_resolution)
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labels = [line.rstrip('\n') for line in open("mobilenet_labels.txt")]
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clock = time.clock()
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while(True):
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clock.tick()
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img = sensor.snapshot()
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# net.classify() will run the network on an roi in the image (or on the whole image if the roi is not
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# specified). A classification score output vector will be generated for each location. At each scale the
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# detection window is moved around in the ROI using x_overlap (0-1) and y_overlap (0-1) as a guide.
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# If you set the overlap to 0.5 then each detection window will overlap the previous one by 50%. Note
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# the computational work load goes WAY up the more overlap. Finally, for multi-scale matching after
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# sliding the network around in the x/y dimensions the detection window will shrink by scale_mul (0-1)
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# down to min_scale (0-1). For example, if scale_mul is 0.5 the detection window will shrink by 50%.
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# Note that at a lower scale there's even more area to search if x_overlap and y_overlap are small...
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# default settings just do one detection... change them to search the image...
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for obj in tf.classify(mobilenet, img, min_scale=1.0, scale_mul=0.5, x_overlap=0.0, y_overlap=0.0):
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print("**********\nTop 5 Detections at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
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img.draw_rectangle(obj.rect())
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# This combines the labels and confidence values into a list of tuples
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# and then sorts that list by the confidence values.
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sorted_list = sorted(zip(labels, obj.output()), key = lambda x: x[1], reverse = True)
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for i in range(5):
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print("%s = %f" % (sorted_list[i][0], sorted_list[i][1]))
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print(clock.fps(), "fps")
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# TensorFlow Lite Mobilenet V1 Example
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#
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# Google's Mobilenet V1 detects 1000 classes of objects
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#
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# WARNING: Mobilenet is trained on ImageNet and isn't meant to classify anything
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# in the real world. It's just designed to score well on the ImageNet dataset.
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# This example just shows off running mobilenet on the OpenMV Cam. However, the
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# default model is not really usable for anything. You have to use transfer
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# learning to apply the model to a target problem by re-training the model.
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#
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# NOTE: This example only works on the OpenMV Cam H7 Pro (that has SDRAM) and better!
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# To get the models please see the CNN Network library in OpenMV IDE under
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# Tools -> Machine Vision. The labels are there too.
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#
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# In this example we slide the detector window over the image and get a list
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# of activations. Note that use a CNN with a sliding window is extremely compute
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# expensive so for an exhaustive search do not expect the CNN to be real-time.
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import sensor, image, time, os, tf
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sensor.reset() # Reset and initialize the sensor.
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sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
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sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
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sensor.set_windowing((240, 240)) # Set 240x240 window.
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sensor.skip_frames(time=2000) # Let the camera adjust.
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mobilenet_version = "1" # 1
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mobilenet_width = "0.5" # 1.0, 0.75, 0.50, 0.25
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mobilenet_resolution = "128" # 224, 192, 160, 128
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mobilenet = "mobilenet_v%s_%s_%s_quant.tflite" % (mobilenet_version, mobilenet_width, mobilenet_resolution)
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labels = [line.rstrip('\n') for line in open("mobilenet_labels.txt")]
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clock = time.clock()
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while(True):
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clock.tick()
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img = sensor.snapshot()
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# net.classify() will run the network on an roi in the image (or on the whole image if the roi is not
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# specified). A classification score output vector will be generated for each location. At each scale the
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# detection window is moved around in the ROI using x_overlap (0-1) and y_overlap (0-1) as a guide.
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# If you set the overlap to 0.5 then each detection window will overlap the previous one by 50%. Note
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# the computational work load goes WAY up the more overlap. Finally, for multi-scale matching after
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# sliding the network around in the x/y dimensions the detection window will shrink by scale_mul (0-1)
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# down to min_scale (0-1). For example, if scale_mul is 0.5 the detection window will shrink by 50%.
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# Note that at a lower scale there's even more area to search if x_overlap and y_overlap are small...
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# Setting x_overlap=-1 forces the window to stay centered in the ROI in the x direction always. If
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# y_overlap is not -1 the method will search in all vertical positions.
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# Setting y_overlap=-1 forces the window to stay centered in the ROI in the y direction always. If
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# x_overlap is not -1 the method will serach in all horizontal positions.
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# default settings just do one detection... change them to search the image...
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for obj in tf.classify(mobilenet, img, min_scale=1.0, scale_mul=0.5, x_overlap=-1, y_overlap=-1):
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print("**********\nTop 5 Detections at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
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img.draw_rectangle(obj.rect())
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# This combines the labels and confidence values into a list of tuples
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# and then sorts that list by the confidence values.
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sorted_list = sorted(zip(labels, obj.output()), key = lambda x: x[1], reverse = True)
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for i in range(5):
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print("%s = %f" % (sorted_list[i][0], sorted_list[i][1]))
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print(clock.fps(), "fps")
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@ -137,6 +137,7 @@ STATIC mp_obj_t int_py_tf_load(mp_obj_t path_obj, bool mode)
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}
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fb_alloc_mark();
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uint32_t tensor_arena_size;
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uint8_t *tensor_arena = fb_alloc_all(&tensor_arena_size, FB_ALLOC_PREFER_SIZE);
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@ -153,6 +154,21 @@ STATIC mp_obj_t int_py_tf_load(mp_obj_t path_obj, bool mode)
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return tf_model;
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}
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STATIC mp_obj_t py_tf_load_xalloc(mp_obj_t path_obj)
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{
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return int_py_tf_load(path_obj, false);
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}
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STATIC MP_DEFINE_CONST_FUN_OBJ_1(py_tf_load_obj, py_tf_load_xalloc);
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STATIC py_tf_model_obj_t *py_tf_load_fb_alloc(mp_obj_t path_obj)
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{
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if (MP_OBJ_IS_TYPE(path_obj, &py_tf_model_type)) {
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return (py_tf_model_obj_t *) path_obj;
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} else {
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return (py_tf_model_obj_t *) int_py_tf_load(path_obj, true);
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}
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}
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typedef struct py_tf_input_data_callback_data {
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image_t *img;
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rectangle_t *roi;
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STATIC mp_obj_t py_tf_classify(uint n_args, const mp_obj_t *args, mp_map_t *kw_args)
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{
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py_tf_model_obj_t *arg_model;
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fb_alloc_mark();
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py_tf_model_obj_t *arg_model = py_tf_load_fb_alloc(args[0]);
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image_t *arg_img = py_helper_arg_to_image_mutable(args[1]);
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rectangle_t roi;
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float arg_y_overlap = py_helper_keyword_float(n_args, args, 6, kw_args, MP_OBJ_NEW_QSTR(MP_QSTR_y_overlap), 0.0f);
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PY_ASSERT_TRUE_MSG(((0.0f <= arg_y_overlap) && (arg_y_overlap < 1.0f)) || (arg_y_overlap == -1.0f), "0 <= y_overlap < 1");
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fb_alloc_mark();
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if (MP_OBJ_IS_TYPE(args[0], &py_tf_model_type)) {
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arg_model = (py_tf_model_obj_t *) args[0];
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} else {
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arg_model = int_py_tf_load(args[0], true);
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}
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uint32_t tensor_arena_size;
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uint8_t *tensor_arena = fb_alloc_all(&tensor_arena_size, FB_ALLOC_PREFER_SIZE);
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@ -404,20 +414,14 @@ STATIC void py_tf_segment_output_data_callback(void *callback_data,
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STATIC mp_obj_t py_tf_segment(uint n_args, const mp_obj_t *args, mp_map_t *kw_args)
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{
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py_tf_model_obj_t *arg_model;
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fb_alloc_mark();
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py_tf_model_obj_t *arg_model = py_tf_load_fb_alloc(args[0]);
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image_t *arg_img = py_helper_arg_to_image_mutable(args[1]);
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rectangle_t roi;
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py_helper_keyword_rectangle_roi(arg_img, n_args, args, 2, kw_args, &roi);
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fb_alloc_mark();
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if (MP_OBJ_IS_TYPE(args[0], &py_tf_model_type)) {
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arg_model = (py_tf_model_obj_t *) args[0];
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} else {
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arg_model = int_py_tf_load(args[0], true);
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}
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uint32_t tensor_arena_size;
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uint8_t *tensor_arena = fb_alloc_all(&tensor_arena_size, FB_ALLOC_PREFER_SIZE);
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@ -470,12 +474,6 @@ STATIC const mp_obj_type_t py_tf_model_type = {
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.locals_dict = (mp_obj_t) &locals_dict
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};
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STATIC mp_obj_t py_tf_load(mp_obj_t path_obj)
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{
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return int_py_tf_load(path_obj, false);
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}
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STATIC MP_DEFINE_CONST_FUN_OBJ_1(py_tf_load_obj, py_tf_load);
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#endif // IMLIB_ENABLE_TF
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STATIC const mp_rom_map_elem_t globals_dict_table[] = {
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