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kneron-mmclassification/regnetx_plus_demo/0201.log

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+https://github.com/kneron/kneron-mmclassification/blob/main/docs_kneron/regnet_step_by_step.md
+
+Step 6 (For Kneron AI Competition 2022): Run NEF model on KL720 USB accelerator
+
+python KL720DemoGenericInferenceRegNetX_BypassHwPreProc.py -nef ./example_RegNetX_720.nef -img shark.jpg
+
+"""
+(kneron) C:\Users\ytlWin\Downloads\kneron\kneron-mmclassification\regnetx_plus_demo>python KL720DemoGenericInferenceRegNetX_BypassHwPreProc.py -nef ./example_RegNetX_720.nef -img shark.jpg
+[Connect Device]
+ - Success
+[Set Device Timeout]
+ - Success
+[Upload Model]
+ - Success
+======== NEF Info =========
+
+Toolchain ver= kneron/toolchain:v0.17.2
+
+Schema ver   = v0.9.1
+
+============================
+[Read Image]
+ - Success
+[Starting Inference Work]
+ - Starting inference loop 1 times
+ - .
+[Retrieve Inference Node Output ]
+ - Success
+[Result]
+ - Top-1 class id : 2  (imagenet 1000 classes idx)
+
+(kneron) C:\Users\ytlWin\Downloads\kneron\kneron-mmclassification\regnetx_plus_demo>
+"""

kneron-mmclassification/regnetx_plus_demo/KL720DemoGenericInferenceRegNetX_BypassHwPreProc.py

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+# ******************************************************************************
+#  Copyright (c) 2022. Kneron Inc. All rights reserved.                        *
+# ******************************************************************************
+
+import os
+import sys
+import argparse
+
+import kp
+import cv2
+import numpy as np
+import math
+from utils import NEF_Parser_lite
+
+LOOP_TIME = 1
+
+def softmax(x):
+    """Compute softmax values for each sets of scores in x."""
+    return np.exp(x) / np.sum(np.exp(x), axis=0)
+
+def convert_numpy_to_rgba_and_width_align_4(data):
+    """Converts the numpy data into RGBA.
+
+    720 input is 4 byte width aligned.
+
+    """
+
+    height, width, channel = data.shape
+
+    width_aligned = 4 * math.ceil(width / 4.0)
+    aligned_data = np.zeros((height, width_aligned, 4), dtype=np.int8)
+    aligned_data[:height, :width, :channel] = data
+    aligned_data = aligned_data.flatten()
+
+    return aligned_data.tobytes()
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='KL720 Kneron Model Zoo Generic Inference Example - RegNetX.')
+    parser.add_argument('-p',
+                        '--port_id',
+                        help='Using specified port ID for connecting device (Default: port ID of first scanned Kneron '
+                             'device)',
+                        default=0,
+                        type=int)
+    parser.add_argument('-img',
+                        '--img_path',
+                        help='input image path',
+                        type=str)
+    parser.add_argument('-nef',
+                        '--nef_model_path',
+                        help='input NEF model path',
+                        type=str)
+
+    args = parser.parse_args()
+
+    assert args.img_path is not None, "need to set input image but got None"
+    assert args.nef_model_path is not None, "need to set nef model path but got None"
+
+    usb_port_id = args.port_id
+    nef_model_path = args.nef_model_path
+    image_file_path = args.img_path
+
+
+    """
+    connect the device
+    """
+    try:
+        print('[Connect Device]')
+        device_group = kp.core.connect_devices(usb_port_ids=[usb_port_id])
+        print(' - Success')
+    except kp.ApiKPException as exception:
+        print('Error: connect device fail, port ID = \'{}\', error msg: [{}]'.format(usb_port_id,
+                                                                                     str(exception)))
+        exit(0)
+
+    """
+    check PLUS version
+    """
+    assert kp.core.get_version() == '1.3.0', f"Only verified on PLUS version 1.3.0, but got {kp.core.get_version()}"
+
+    """
+    setting timeout of the usb communication with the device
+    """
+    print('[Set Device Timeout]')
+    kp.core.set_timeout(device_group=device_group, milliseconds=5000)
+    print(' - Success')
+
+    """
+    upload model to device
+    """
+    try:
+        print('[Upload Model]')
+        model_nef_descriptor = kp.core.load_model_from_file(device_group=device_group,
+                                                            file_path=nef_model_path)
+        print(' - Success')
+    except kp.ApiKPException as exception:
+        print('Error: upload model failed, error = \'{}\''.format(str(exception)))
+        exit(0)
+
+    """
+    extract input radix from NEF
+    """
+    nef_radix = NEF_Parser_lite.extract_input_radix_from_kl720_nef(nef_model_path) # only support single model NEF
+
+    """
+    prepare the image
+    """
+    nef_model_width = model_nef_descriptor.models[0].width
+    nef_model_height = model_nef_descriptor.models[0].height
+    print('[Read Image]')
+    img = cv2.imread(filename=image_file_path)
+    img_height, img_width, img_channels = img.shape
+
+    # resize to model input size
+    img = cv2.resize(img, ( nef_model_width,  nef_model_height), interpolation=cv2.INTER_AREA)
+
+    # to rgb
+    img_input = cv2.cvtColor(src=img, code=cv2.COLOR_BGR2RGB)
+
+    # this model trained with normalize method: (data - 128)/256 , 
+    img_input = img_input/256.
+    img_input -= 0.5
+
+    # toolchain calculate the radix value from input data (after normalization), and set it into NEF model.
+    # NPU will divide input data "2^radix" automaticly, so, we have to scaling the input data here due to this reason.
+    img_input *= (1 << nef_radix)
+
+    # convert rgb to rgba and width align 4, due to npu requirement.
+    img_buffer = convert_numpy_to_rgba_and_width_align_4(img_input)
+
+    print(' - Success')
+
+    """
+    prepare app generic inference config
+    """
+    generic_raw_image_header = kp.GenericRawBypassPreProcImageHeader(
+        model_id=model_nef_descriptor.models[0].id,
+        image_buffer_size=len(img_buffer),
+        inference_number=0
+    )
+
+    """
+    starting inference work
+    """
+    print('[Starting Inference Work]')
+    print(' - Starting inference loop {} times'.format(LOOP_TIME))
+    print(' - ', end='')
+    for i in range(LOOP_TIME):
+        try:
+            kp.inference.generic_raw_inference_bypass_pre_proc_send(
+                device_group=device_group,
+                generic_raw_image_header=generic_raw_image_header,
+                image_buffer=img_buffer)
+
+            generic_raw_bypass_pre_proc_result = kp.inference.generic_raw_inference_bypass_pre_proc_receive(
+                device_group=device_group,
+                generic_raw_image_header=generic_raw_image_header,
+                model_nef_descriptor=model_nef_descriptor)
+        except kp.ApiKPException as exception:
+            print(' - Error: inference failed, error = {}'.format(exception))
+            exit(0)
+
+        print('.', end='', flush=True)
+    print()
+
+    """
+    retrieve inference node output 
+    """
+    print('[Retrieve Inference Node Output ]')
+    inf_node_output_list = []
+    for node_idx in range(generic_raw_bypass_pre_proc_result.header.num_output_node):
+        inference_float_node_output = kp.inference.generic_inference_retrieve_float_node(
+            node_idx=node_idx,
+            generic_raw_result=generic_raw_bypass_pre_proc_result,
+            channels_ordering=kp.ChannelOrdering.KP_CHANNEL_ORDERING_CHW)
+        inf_node_output_list.append(inference_float_node_output)
+
+    print(' - Success')
+
+# get class id with highest score
+pred = inf_node_output_list[0].ndarray.squeeze() # should only one output node
+pred = softmax(pred)
+cls_num = np.argmax(pred)
+
+print("[Result]")
+print(" - Top-1 class id : " + str(cls_num) + "  (imagenet 1000 classes idx)")
+

kneron-mmclassification/regnetx_plus_demo/README.txt

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+
+Python libs requirement:
+	1. Kneron plus v0.13.0
+	2. Numpy
+	3. OpenCV-Python
+
+
+
+How to run this example:
+
+	python KL720DemoGenericInferenceRegNetX_BypassHwPreProc.py -nef example_RegNetX_720.nef -img shark.jpg

kneron-mmclassification/regnetx_plus_demo/requirements.txt

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+numpy
+opencv-python
+flatbuffers