[条记] rknn Toolkit1.6=＞2.0 API变更注释
RKNNToolkit2 API Difference With Toolkit1.6原始文档出处参见上面的标题。下面会对重要的变更内容做标记。
rknn.config
[*] Toolkit1:
config(batch_size=100, # abandoned
caffe_mean_file=None, # abandoned
dtype='float32', # abandoned
epochs=-1, # abandoned
force_gray=None, # abandoned
input_fitting='scale', # abandoned
input_normalization=None, # abandoned
mean_file=None, # abandoned
model_data_format='zone', # abandoned
model_quantize=None, # abandoned
optimize='Default', # abandoned
quantized_dtype='asymmetric_affine-u8',
quantized_moving_alpha=0.01, # abandoned
quantized_algorithm='normal',
quantized_divergence_nbins=1024, # abandoned
mmse_epoch=3, # abandoned
random_brightness=None, # abandoned
random_contrast=None, # abandoned
random_crop=None, # abandoned
random_flip=None, # abandoned
random_mirror=None, # abandoned
reorder_channel=None, # abandoned
restart=False, # abandoned
samples=-1, # abandoned
need_horizontal_merge=False, # abandoned
deconv_merge=True, # abandoned
conv_mul_merge=True, # abandoned
quantized_hybrid=False, # abandoned
output_optimize=0, # abandoned
remove_tensorflow_output_permute=False, # abandoned
optimization_level=3,
target_platform=None,
mean_values=None,
std_values=None,
channel_mean_value=None, # abandoned
force_builtin_perm=False, # abandoned
do_sparse_network=True, # abandoned
merge_dequant_layer_and_output_node=False, # abandoned
quantize_input_node=False, # abandoned
inputs_scale_range=None) # abandoned
[*] Toolkit2:
注意量化八月份吧
config(mean_values=None,
std_values=None,
quantized_dtype='asymmetric_quantized-8',
quantized_algorithm='normal',
quantized_method='channel', # new
target_platform=None,
quant_img_RGB2BGR=False, # new
float_dtype='float16', # new
optimization_level=3,
custom_string=None, # new
remove_weight=False, # new
compress_weight=False, # new
inputs_yuv_fmt=None, # new
single_core_mode=False) # new
[*] In addition to the above abandoned/new items, there are other differences:
quantized_dtype:
toolkit1: asymmetric_affine-u8, dynamic_fixed_point-i8, dynamic_fixed_point-i16
toolkit2: asymmetric_quantized-8
quantized_algorithm:
toolkit1: normal(default), mmse, kl_divergence, moving_average
toolkit2: normal(default), mmse
target_platform:
toolkit1: rk1808, rk3399pro, rv1109, rv1126
toolkit2: rk3566, rk3568, rk3588, rk3588s, rv1103, rv1106, rk3562 and newer
rknn.load_tensorflow
[*] Toolkit1:
load_tensorflow(tf_pb,
inputs,
input_size_list,
outputs,
predef_file=None, # abandoned
mean_values=None, # abandoned
std_values=None, # abandoned
size_with_batch=None) # abandoned
[*] Toolkit2:
load_tensorflow(tf_pb,
inputs,
input_size_list,
outputs)
[*] In addition to the above abandoned items, there are other differences:
inputs:
toolkit1: node list (layer name)
toolkit2: node list (operand name)
outputs:
toolkit1: node list (layer name)
toolkit2: node list (operand name)
rknn.load_caffe
[*] Toolkit1:
load_caffe(model,
proto, # abandoned
blobs=None)
[*] Toolkit2:
load_caffe(model,
blobs=None,
input_name=None) # new
rknn.load_keras
[*] Toolkit1:
load_keras(model, convert_engine='Keras')
[*] Toolkit2:
Not supported yet
!
rknn.load_pytorch
[*] Toolkit1:
load_pytorch(model,
input_size_list=None,
inputs=None, # abandoned
outputs=None, # abandoned
convert_engine='torch') # abandoned
[*] Toolkit2:
load_pytorch(model,
input_size_list)
rknn.load_mxnet
[*] Toolkit1:
load_mxnet(symbol, params, input_size_list=None)
[*] Toolkit2:
Not supported yet
!
rknn.build
[*] Toolkit1:
build(do_quantization=True,
dataset='dataset.txt',
pre_compile=False, # abandoned
rknn_batch_size=-1)
[*] Toolkit2:
build(do_quantization=True,
dataset='dataset.txt',
rknn_batch_size=-1):
rknn.direct_build
[*] Toolkit1:
direct_build(model_input, data_input, model_quantize=None, pre_compile=False)
[*] Toolkit2:
Not supported yet
!
rknn.hybrid_quantization_step1
[*] Toolkit1:
hybrid_quantization_step1(dataset=None)
[*] Toolkit2:
hybrid_quantization_step1(dataset=None,
rknn_batch_size=-1, # new
proposal=False, # new
proposal_dataset_size=1) # new
rknn.hybrid_quantization_step2
[*] Toolkit1:
hybrid_quantization_step2(model_input,
data_input,
model_quantization_cfg,
dataset, # abandoned
pre_compile=False) # abandoned
[*] Toolkit2:
hybrid_quantization_step2(model_input,
data_input,
model_quantization_cfg)
rknn.accuracy_analysis
[*] Toolkit1:
accuracy_analysis(inputs,
output_dir='./snapshot',
calc_qnt_error=True, # abandoned
target=None,
device_id=None,
dump_file_type='tensor') # abandoned
[*] Toolkit2:
accuracy_analysis(inputs,
output_dir='./snapshot',
target=None,
device_id=None)
rknn.load_rknn
[*] Toolkit1:
load_rknn(path,
load_model_in_npu=False) # abandoned
[*] Toolkit2:
load_rknn(path)
rknn.export_rknn
[*] Toolkit1:
export_rknn(export_path)
[*] Toolkit2:
export_rknn(export_path,
**kwargs) # new
rknn.load_firmware
[*] Toolkit1:
load_firmware(fw_dir=None)
[*] Toolkit2:
Not supported yet
!
rknn.init_runtime
[*] Toolkit1:
init_runtime(target=None,
target_sub_class=None,
device_id=None,
perf_debug=False,
eval_mem=False,
async_mode=False,
rknn2precompile=False) # abandoned
[*] Toolkit2:
init_runtime(target=None,
target_sub_class=None,
device_id=None,
perf_debug=False,
eval_mem=False,
async_mode=False)
[*] In addition to the above abandoned items, there are other differences:
target:
toolkit1: None(simulator), RK3399Pro, RK1808
toolkit2: None(simulator), RK3566, RK3568, RK3588, RK3562
rknn.inference
[*] Toolkit1:
inference(inputs,
data_type=None, # abandoned
data_format=None,
inputs_pass_through=None,
get_frame_id=False)
[*] Toolkit2:
inference(inputs,
data_format=None,
inputs_pass_through=None,
get_frame_id=False)
rknn.eval_perf
[*] Toolkit1:
eval_perf(inputs=None, # abandoned
data_type=None, # abandoned
data_format=None, # abandoned
is_print=True,
loop_cnt=1) # abandoned
[*] Toolkit2:
eval_perf(is_print=True)
rknn.export_rknn_precompile_model
[*] Toolkit1:
export_rknn_precompile_model(export_path=None)
[*] Toolkit2:
Abandoned
rknn.export_rknn_sync_model
[*] Toolkit1:
export_rknn_sync_model(input_model=None, sync_uids=None, output_model=None)
[*] Toolkit2:
Abandoned
rknn.register_op
[*] Toolkit1:
register_op(op_path)
[*] Toolkit2:
Not supported yet
rknn.fetch_rknn_model_config
[*] Toolkit1:
fetch_rknn_model_config(model_path)
[*] Toolkit2:
Not supported yet
rknn.list_support_target_platform
[*] Toolkit1:
list_support_target_platform(rknn_model=None)
[*] Toolkit2:
Not supported yet
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