RKNNToolkit2 API Difference With Toolkit1

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:

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
       output_tensor_type=None)                         # 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

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

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,
          data_type=None,                               # abandoned
          data_format=None,
          is_print=True,
          loop_cnt=1)                                   # abandoned

Toolkit2:

eval_perf(inputs=None,
          data_format=None,
          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
```