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5f858bc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | #!/usr/bin/env python
# coding: utf-8
import datetime
from rknn.api import RKNN
from sys import exit
ONNX_MODEL="decoder.onnx"
RKNN_MODEL=ONNX_MODEL.replace(".onnx",".rknn")
DATASET=""
QUANTIZE=False
detailed_performance_log = True
timedate_iso = datetime.datetime.now().isoformat()
rknn = RKNN(verbose=True)
rknn.config(
# mean_values=[x * 255 for x in [0.485, 0.456, 0.406]],
# std_values=[x * 255 for x in [0.229, 0.224, 0.225]],
quantized_dtype='w8a8',
quantized_algorithm='normal',
quantized_method='channel',
quantized_hybrid_level=0,
target_platform='rk3588',
quant_img_RGB2BGR = False,
float_dtype='float16',
optimization_level=3,
custom_string=f"converted at {timedate_iso}",
remove_weight=False,
compress_weight=False,
inputs_yuv_fmt=None,
single_core_mode=False,
dynamic_input=None,
model_pruning=False,
op_target=None,
quantize_weight=False,
remove_reshape=False,
sparse_infer=False,
enable_flash_attention=False,
)
ret = rknn.load_onnx(model=ONNX_MODEL)
ret = rknn.build(do_quantization=QUANTIZE, dataset=DATASET, rknn_batch_size=None)
ret = rknn.export_rknn(RKNN_MODEL)
# ret = rknn.init_runtime(target='rk3588',device_id='cbb956772bf5dac9',core_mask=RKNN.NPU_CORE_0,perf_debug=detailed_performance_log)
# rknn.eval_perf()
# ret = rknn.accuracy_analysis(inputs=['../embeddings.npy','../state.npy','../scale_ratio.npy'], target='rk3588', device_id=device_id)
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