[笔记] rknn Toolkit1.6=＞2.0 API变动注释

69 阅读 0 评论 0 点赞

RKNNToolkit2 API Difference With Toolkit1.6

原始文档出处参见上面的标题。下面会对重要的变动内容做标记。

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
```

本站资源均来自互联网，仅供研究学习，禁止违法使用和商用，产生法律纠纷本站概不负责！如果侵犯了您的权益请与我们联系！

转载请注明出处：免费源码网-免费的源码资源网站 » [笔记] rknn Toolkit1.6=＞2.0 API变动注释

点赞(0) 打赏

本文分类：文章资讯
本文标签：[笔记] rknn Toolkit1.6=＞2.0 API变动注释
浏览次数：69 次浏览
本文链接：https://freeymw.com/article/11529.html

上一篇 > 苹果不会在WWDC 2024中推出任何搭载M4芯片的Mac电脑
下一篇 > docker 部署kafka，zokeeper，快速开发模式

评论列表共有 0 条评论

暂无评论

[笔记] rknn Toolkit1.6=＞2.0 API变动注释

RKNNToolkit2 API Difference With Toolkit1.6

rknn.config

rknn.load_tensorflow

rknn.load_caffe

rknn.load_keras

rknn.load_pytorch

rknn.load_mxnet

rknn.build

rknn.direct_build

rknn.hybrid_quantization_step1

rknn.hybrid_quantization_step2

rknn.accuracy_analysis

rknn.load_rknn

rknn.export_rknn

rknn.load_firmware

rknn.init_runtime

rknn.inference

rknn.eval_perf

rknn.export_rknn_precompile_model

rknn.export_rknn_sync_model

rknn.register_op

rknn.fetch_rknn_model_config

rknn.list_support_target_platform

评论列表 共有 0 条评论

发表评论 取消回复

评论列表共有 0 条评论

发表评论取消回复