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build multiple suffixes and socs at once

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MarcA711
2023-11-17 22:06:11 +01:00
parent 85c3796720
commit f0cea8b81f
1 changed files with 55 additions and 52 deletions
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107
conv.py
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@@ -1,60 +1,63 @@
from rknn.api import RKNN
import os
INPUT_MODEL = "yolov8x.onnx"
WIDTH = 320
HEIGHT = 320
OUTPUT_MODEL_BASENAME = 'yolov8x'
QUANTIZATION = False
DATASET = './dataset_coco10.txt'
for suffix in ["n", "s", "m", "l", "x"]:
for soc in ["rk3562","rk3566", "rk3568", "rk3588"]:
INPUT_MODEL = 'yolov8{}.onnx'.format(suffix)
WIDTH = 320
HEIGHT = 320
OUTPUT_MODEL_BASENAME = 'yolov8{}'.format(suffix)
QUANTIZATION = False
DATASET = './dataset_coco10.txt'
# Config
MEAN_VALUES = [[0, 0, 0]]
STD_VALUES = [[255, 255, 255]]
QUANT_IMG_RGB2BGR = True
QUANTIZED_DTYPE = "asymmetric_quantized-8"
QUANTIZED_ALGORITHM = "normal"
QUANTIZED_METHOD = "channel"
FLOAT_DTYPE = "float16"
OPTIMIZATION_LEVEL = 2
TARGET_PLATFORM = "rk3588"
CUSTOM_STRING = None
REMOVE_WEIGHT = None
COMPRESS_WEIGHT = False
SINGLE_CORE_MODE = False
MODEL_PRUNNING = False
OP_TARGET = None
DYNAMIC_INPUT = None
# Config
MEAN_VALUES = [[0, 0, 0]]
STD_VALUES = [[255, 255, 255]]
QUANT_IMG_RGB2BGR = True
QUANTIZED_DTYPE = "asymmetric_quantized-8"
QUANTIZED_ALGORITHM = "normal"
QUANTIZED_METHOD = "channel"
FLOAT_DTYPE = "float16"
OPTIMIZATION_LEVEL = 2
TARGET_PLATFORM = soc
CUSTOM_STRING = None
REMOVE_WEIGHT = None
COMPRESS_WEIGHT = False
SINGLE_CORE_MODE = False
MODEL_PRUNNING = False
OP_TARGET = None
DYNAMIC_INPUT = None
OUTPUT_MODEL_FILE = "./output/{}/{}-{}x{}-{}.rknn".format(soc, OUTPUT_MODEL_BASENAME, WIDTH, HEIGHT, soc)
os.makedirs("./output/{}".format(soc), exist_ok=True)
OUTPUT_MODEL = OUTPUT_MODEL_BASENAME + '-' + str(WIDTH) + 'x' + str(HEIGHT) + ".rknn"
rknn = RKNN()
rknn.config(mean_values=MEAN_VALUES,
std_values=STD_VALUES,
quant_img_RGB2BGR=QUANT_IMG_RGB2BGR,
quantized_dtype=QUANTIZED_DTYPE,
quantized_algorithm=QUANTIZED_ALGORITHM,
quantized_method=QUANTIZED_METHOD,
float_dtype=FLOAT_DTYPE,
optimization_level=OPTIMIZATION_LEVEL,
target_platform=TARGET_PLATFORM,
custom_string=CUSTOM_STRING,
remove_weight=REMOVE_WEIGHT,
compress_weight=COMPRESS_WEIGHT,
single_core_mode=SINGLE_CORE_MODE,
model_pruning=MODEL_PRUNNING,
op_target=OP_TARGET,
dynamic_input=DYNAMIC_INPUT)
rknn = RKNN()
rknn.config(mean_values=MEAN_VALUES,
std_values=STD_VALUES,
quant_img_RGB2BGR=QUANT_IMG_RGB2BGR,
quantized_dtype=QUANTIZED_DTYPE,
quantized_algorithm=QUANTIZED_ALGORITHM,
quantized_method=QUANTIZED_METHOD,
float_dtype=FLOAT_DTYPE,
optimization_level=OPTIMIZATION_LEVEL,
target_platform=TARGET_PLATFORM,
custom_string=CUSTOM_STRING,
remove_weight=REMOVE_WEIGHT,
compress_weight=COMPRESS_WEIGHT,
single_core_mode=SINGLE_CORE_MODE,
model_pruning=MODEL_PRUNNING,
op_target=OP_TARGET,
dynamic_input=DYNAMIC_INPUT)
# if rknn.load_pytorch("./input/" + INPUT_MODEL, [[HEIGHT, WIDTH, 3]]) != 0:
if rknn.load_onnx("./input/" + INPUT_MODEL) != 0:
print('Error loading model.')
exit()
# if rknn.load_pytorch("./input/" + INPUT_MODEL, [[HEIGHT, WIDTH, 3]]) != 0:
if rknn.load_onnx("./input/" + INPUT_MODEL) != 0:
print('Error loading model.')
exit()
if rknn.build(do_quantization=QUANTIZATION, dataset=DATASET) != 0:
print('Error building model.')
exit()
if rknn.build(do_quantization=QUANTIZATION, dataset=DATASET) != 0:
print('Error building model.')
exit()
if rknn.export_rknn("./output/" + OUTPUT_MODEL) != 0:
print('Error exporting rknn model.')
exit()
if rknn.export_rknn(OUTPUT_MODEL_FILE) != 0:
print('Error exporting rknn model.')
exit()