1374 lines
46 KiB
Plaintext
1374 lines
46 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example Inference"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Set Up\n",
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"prerequisites\n",
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"- python 3.12/11\n",
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"\n",
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"``` shell\n",
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"$ cd ./package/{platform}/\n",
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"$ pip install KneronPLUS-{version}-py3-none-any.whl\n",
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"$ pip install opencv-python\n",
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"\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Connect KL520"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"import kp"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"13\n"
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]
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}
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],
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"source": [
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"device_descriptors = kp.core.scan_devices()\n",
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"\n",
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"if 0 < device_descriptors.device_descriptor_number:\n",
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" usb_port_id = device_descriptors.device_descriptor_list[0].usb_port_id\n",
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" print(usb_port_id)\n",
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"else:\n",
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" print('Error: no Kneron device connect.')\n",
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" exit(0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"device_group = kp.core.connect_devices(usb_port_ids=[22])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"kp.core.set_timeout(device_group=device_group,\n",
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" milliseconds=5000)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Load firmware, model, and test image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"SCPU_FW_PATH = '../../res/firmware/KL520/fw_scpu.bin'\n",
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"NCPU_FW_PATH = '../../res/firmware/KL520/fw_ncpu.bin'\n",
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"kp.core.load_firmware_from_file(device_group=device_group,\n",
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" scpu_fw_path=SCPU_FW_PATH,\n",
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" ncpu_fw_path=NCPU_FW_PATH)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"MODEL_FILE_PATH = './declutter_520.nef'\n",
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"model_nef_descriptor = kp.core.load_model_from_file(device_group=device_group,\n",
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" file_path=MODEL_FILE_PATH)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### pre-processing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"IMAGE_FILE_PATH = './test.jpg'\n",
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"\n",
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"img = cv2.imread(filename=IMAGE_FILE_PATH)\n",
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"img_bgr565 = cv2.cvtColor(src=img, code=cv2.COLOR_BGR2BGR565)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"generic_inference_input_descriptor = kp.GenericImageInferenceDescriptor(\n",
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" model_id=model_nef_descriptor.models[0].id,\n",
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" inference_number=0,\n",
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" input_node_image_list=[\n",
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" kp.GenericInputNodeImage(\n",
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" image=img_bgr565,\n",
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" image_format=kp.ImageFormat.KP_IMAGE_FORMAT_RGB565,\n",
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" resize_mode=kp.ResizeMode.KP_RESIZE_ENABLE,\n",
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" padding_mode=kp.PaddingMode.KP_PADDING_CORNER,\n",
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" normalize_mode=kp.NormalizeMode.KP_NORMALIZE_KNERON\n",
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" )\n",
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" ]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"kp.inference.generic_image_inference_send(device_group=device_group,\n",
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" generic_inference_input_descriptor=generic_inference_input_descriptor)\n",
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"generic_raw_result = kp.inference.generic_image_inference_receive(device_group=device_group)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[{\n",
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" \"width\": 1,\n",
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" \"height\": 1,\n",
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" \"channel\": 512,\n",
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" \"channels_ordering\": \"ChannelOrdering.KP_CHANNEL_ORDERING_CHW\",\n",
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" \"num_data\": 512,\n",
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" \"ndarray\": [\n",
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" \" [[0.5060123 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.70841724]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.80961967]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.5566135 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.60721475]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.05060123]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.20240492]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.30360737]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.657816 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.40480983]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.35420862]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.20240492]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.15180369]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.60721475]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[1.7204418 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.35420862]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.40480983]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.30360737]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.8602209 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.60721475]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.70841724]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.15180369]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.657816 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.8602209 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.25300616]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.40480983]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.30360737]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.45541108]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[1.0120246 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.5566135 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.5566135 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.25300616]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.30360737]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.80961967]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.35420862]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.05060123]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[1.7710431 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.60721475]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.7590185 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.40480983]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.25300616]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.657816 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.7590185 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.25300616]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.25300616]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.05060123]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.05060123]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.5060123 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.25300616]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.5060123 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.657816 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.30360737]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.05060123]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.25300616]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[1.518037 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.15180369]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.25300616]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.45541108]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.45541108]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.60721475]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.657816 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.45541108]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.5060123 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.40480983]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.05060123]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.15180369]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.20240492]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.5060123 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.30360737]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.5060123 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.25300616]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.5060123 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.45541108]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.5566135 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.05060123]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[1.113227 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.05060123]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.25300616]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.35420862]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.25300616]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.15180369]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.35420862]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.10120246]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[1.315632 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.30360737]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.5060123 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.40480983]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.5566135 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.20240492]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0. ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.05060123]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[1.7204418 ]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.80961967]]\",\n",
|
|
" \"\",\n",
|
|
" \" [[0.05060123]]]]\"\n",
|
|
" ]\n",
|
|
"}]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inf_node_output_list = []\n",
|
|
"\n",
|
|
"for node_idx in range(generic_raw_result.header.num_output_node):\n",
|
|
" inference_float_node_output = kp.inference.generic_inference_retrieve_float_node(node_idx=node_idx,\n",
|
|
" generic_raw_result=generic_raw_result,\n",
|
|
" channels_ordering=kp.ChannelOrdering.KP_CHANNEL_ORDERING_CHW)\n",
|
|
" inf_node_output_list.append(inference_float_node_output)\n",
|
|
"\n",
|
|
"print(inf_node_output_list)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### post-processing"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"dimension of feature tensor: 512\n",
|
|
"\n",
|
|
"[0.5060123 0. 0.30360737 0.5060123 0.25300616 0.\n",
|
|
" 0.05060123 1.113227 1.1638283 0.05060123 0.30360737 0.10120246\n",
|
|
" 0.5060123 0.20240492 0.05060123 0.5566135 0.05060123 0.25300616\n",
|
|
" 0.25300616 0.10120246 0.05060123 0.7590185 0.5566135 0.\n",
|
|
" 0.05060123 0.15180369 0.30360737 0.7590185 0.657816 0.05060123\n",
|
|
" 0. 0.5566135 0.05060123 0.25300616 0. 0.05060123\n",
|
|
" 0.35420862 0.05060123 2.3782578 0.60721475 0.10120246 0.10120246\n",
|
|
" 0.30360737 0.70841724 0.45541108 0.35420862 0.25300616 0.30360737\n",
|
|
" 0.25300616 0.05060123 0.657816 0.05060123 0.45541108 0.10120246\n",
|
|
" 0.15180369 0.15180369 0.657816 0.25300616 0. 0.10120246\n",
|
|
" 0. 0.20240492 0.20240492 0.40480983 0.80961967 0.25300616\n",
|
|
" 0.25300616 0.05060123 0.05060123 0. 0.05060123 0.45541108\n",
|
|
" 0.45541108 0.40480983 0.10120246 0. 1.4674357 0.60721475\n",
|
|
" 0.10120246 0.15180369 0.15180369 0.7590185 0.45541108 0.\n",
|
|
" 0. 0.35420862 0.05060123 0.5566135 0.15180369 0.10120246\n",
|
|
" 0.05060123 0.40480983 0. 0.30360737 0.25300616 0.\n",
|
|
" 3.036074 0.15180369 0.05060123 0. 0.10120246 0.\n",
|
|
" 0. 0. 3.4408836 0.5566135 0.05060123 0.\n",
|
|
" 0.10120246 0.30360737 0.40480983 0.5060123 0.15180369 0.\n",
|
|
" 0. 0.05060123 0.5060123 0.25300616 0.05060123 0.25300616\n",
|
|
" 1.2650307 0.5060123 0.30360737 0.5566135 0. 0.25300616\n",
|
|
" 0.20240492 0.30360737 2.3782578 0. 0.20240492 0.35420862\n",
|
|
" 0. 0. 0. 0.7590185 0. 0.70841724\n",
|
|
" 0. 0.15180369 1.0626259 0.25300616 0.80961967 0.5060123\n",
|
|
" 0.05060123 0.05060123 0.05060123 2.6818652 0.5060123 0.20240492\n",
|
|
" 0.657816 0.20240492 0.5060123 0.05060123 0.35420862 0.20240492\n",
|
|
" 0.05060123 0.30360737 0.10120246 0.25300616 0.30360737 0.\n",
|
|
" 0.15180369 0.80961967 0.05060123 0.30360737 0. 0.\n",
|
|
" 0.45541108 0.20240492 0. 0.30360737 0. 0.\n",
|
|
" 0.7590185 0.5566135 0.10120246 0. 0.35420862 0.\n",
|
|
" 0.10120246 0.30360737 1.6698406 0.20240492 2.1252518 0.45541108\n",
|
|
" 0.30360737 0.5060123 0.15180369 0.35420862 0.20240492 0.20240492\n",
|
|
" 1.2144295 0.91082215 0.05060123 0. 0.20240492 0.80961967\n",
|
|
" 0.8602209 0.25300616 0.05060123 0.30360737 0.80961967 0.25300616\n",
|
|
" 0. 0.10120246 0.45541108 0.35420862 1.2650307 0.25300616\n",
|
|
" 0. 0.05060123 0.35420862 0.45541108 0.15180369 0.40480983\n",
|
|
" 1.315632 0.5060123 0.20240492 0.70841724 0.05060123 0.35420862\n",
|
|
" 0.20240492 0.60721475 0.30360737 0.20240492 0.8602209 0.05060123\n",
|
|
" 0.05060123 0.45541108 0.20240492 0. 0.40480983 0.10120246\n",
|
|
" 0.7590185 0.05060123 1.0626259 0.35420862 0. 0.10120246\n",
|
|
" 0.05060123 0.25300616 0.9614234 0.9614234 0.10120246 0.10120246\n",
|
|
" 0.5566135 0.20240492 0.10120246 1.2650307 0.45541108 0.20240492\n",
|
|
" 0.20240492 0.05060123 0.45541108 0.60721475 0.5060123 0.\n",
|
|
" 0.40480983 0. 0.25300616 0.40480983 0.70841724 0.45541108\n",
|
|
" 0.35420862 2.3276565 0. 0.657816 0.25300616 0.35420862\n",
|
|
" 0.05060123 0.5060123 0.5060123 2.226454 0.10120246 0.20240492\n",
|
|
" 0.70841724 0.45541108 0.10120246 0.20240492 0.5060123 0.10120246\n",
|
|
" 0.45541108 0.10120246 0. 0.657816 0.10120246 0.25300616\n",
|
|
" 0.25300616 0.10120246 0.40480983 0. 0.5060123 0.35420862\n",
|
|
" 0. 0.40480983 0.20240492 0. 0.15180369 0.15180369\n",
|
|
" 1.3662332 0.35420862 0.10120246 0.20240492 0.91082215 0.\n",
|
|
" 0. 0.8602209 0.05060123 0. 0.5060123 0.7590185\n",
|
|
" 0.05060123 0.05060123 0.25300616 0. 0. 0.\n",
|
|
" 0.15180369 0.25300616 0.20240492 0.05060123 0. 0.10120246\n",
|
|
" 0.30360737 0.05060123 0. 0. 0.25300616 0.\n",
|
|
" 0.657816 0. 0.30360737 0.20240492 0.20240492 0.20240492\n",
|
|
" 3.1372762 0. 0. 0.05060123 0.30360737 0.5566135\n",
|
|
" 0.5060123 2.4794602 1.9228468 0.10120246 0.05060123 0.7590185\n",
|
|
" 0.80961967 0.40480983 0.60721475 0.05060123 0.5566135 0.\n",
|
|
" 0.25300616 0.5060123 0.10120246 0.20240492 0.40480983 0.20240492\n",
|
|
" 0.60721475 0.05060123 0.35420862 0. 1.518037 0.10120246\n",
|
|
" 0.60721475 0.35420862 1.0626259 0. 0.05060123 0.\n",
|
|
" 0. 0. 1.0120246 0.5060123 0.91082215 0.80961967\n",
|
|
" 0.5060123 0.15180369 0.05060123 0. 3.4914849 0.60721475\n",
|
|
" 0.657816 0. 0.10120246 0.30360737 0. 0.5060123\n",
|
|
" 0.70841724 0.80961967 0.5566135 0.60721475 0.05060123 0.20240492\n",
|
|
" 0.30360737 0.657816 0.40480983 0. 0.35420862 0.20240492\n",
|
|
" 0.15180369 0.60721475 1.7204418 0.10120246 0.35420862 0.40480983\n",
|
|
" 0.10120246 0. 0.30360737 0.8602209 0.60721475 0.70841724\n",
|
|
" 0.10120246 0.15180369 0.657816 0.8602209 0.25300616 0.\n",
|
|
" 0.40480983 0.30360737 0. 0.45541108 1.0120246 0.\n",
|
|
" 0.5566135 0.5566135 0.25300616 0.30360737 0. 0.80961967\n",
|
|
" 0.35420862 0.05060123 0.10120246 1.7710431 0.10120246 0.10120246\n",
|
|
" 0.60721475 0.7590185 0.10120246 0.40480983 0.25300616 0.657816\n",
|
|
" 0.7590185 0.25300616 0.25300616 0.05060123 0.05060123 0.\n",
|
|
" 0.5060123 0. 0.10120246 0.25300616 0.5060123 0.657816\n",
|
|
" 0.30360737 0.05060123 0.25300616 1.518037 0.15180369 0.25300616\n",
|
|
" 0.45541108 0. 0.45541108 0.60721475 0.657816 0.\n",
|
|
" 0.45541108 0.5060123 0.40480983 0.05060123 0. 0.15180369\n",
|
|
" 0.20240492 0.5060123 0.10120246 0.30360737 0.5060123 0.25300616\n",
|
|
" 0.5060123 0.45541108 0.5566135 0.05060123 1.113227 0.10120246\n",
|
|
" 0.05060123 0.25300616 0.35420862 0. 0.10120246 0.\n",
|
|
" 0. 0.10120246 0.25300616 0. 0.10120246 0.15180369\n",
|
|
" 0. 0.35420862 0.10120246 1.315632 0.30360737 0.5060123\n",
|
|
" 0.40480983 0.5566135 0.20240492 0. 0.05060123 1.7204418\n",
|
|
" 0.80961967 0.05060123]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import numpy as np\n",
|
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"import re\n",
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"\n",
|
|
"data = inf_node_output_list[0]\n",
|
|
"\n",
|
|
"raw_ndarray = data.ndarray\n",
|
|
"\n",
|
|
"if isinstance(raw_ndarray, np.ndarray):\n",
|
|
" ndarray_np = raw_ndarray\n",
|
|
"else:\n",
|
|
" ndarray_np = np.array(raw_ndarray)\n",
|
|
"\n",
|
|
"ndarray_np = ndarray_np.flatten() \n",
|
|
"# ndarray_np = ndarray_np.reshape((data.channel, data.height, data.width))\n",
|
|
"\n",
|
|
"print(f\"dimension of feature tensor: {ndarray_np.size}\\n\")\n",
|
|
"print(ndarray_np)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|