Compressor(Manual Compression)¶

Upload Model¶

`netspresso.compressor.v2.compressor.CompressorV2.upload_model(input_model_path, input_shapes=None, framework=Framework.PYTORCH)` ¶

Upload a model for compression.

Parameters:

Name	Type	Description	Default
`input_model_path`	`str`	The file path where the model is located.	required
`input_shapes`	`List[Dict[str, int]]`	Input shapes of the model. Defaults to [].	`None`
`framework`	`Framework`	The framework of the model.	`PYTORCH`

Raises:

Type	Description
`e`	If an error occurs while uploading the model.

Returns:

Name	Type	Description
`ModelBase`	`ModelBase`	Uploaded model object.

Details of Parameters¶

Framework¶

Available Framework¶

Name	Description
TENSORFLOW_KERAS	TensorFlow-Keras
PYTORCH	PyTorch GraphModule
ONNX	ONNX

Example¶

from netspresso.enums import Framework

FRAMEWORK = Framework.PYTORCH

Note

ONNX (.onnx)
- Supported version: PyTorch >= 1.11.x, ONNX >= 1.10.x.
- If a model is defined in PyTorch, it should be converted into the ONNX format before being uploaded.
- How-to-guide for the conversion of PyTorch into ONNX
PyTorch GraphModule (.pt)
- Supported version: PyTorch >= 1.11.x.
- If a model is defined in PyTorch, it should be converted into the GraphModule before being uploaded.
- The model must contain not only the status dictionary but also the structure of the model (do not use state_dict).
- How-to-guide for the conversion of PyTorch into GraphModule
TensorFlow-Keras (.h5, .zip)
- Supported version: TensorFlow 2.3.x ~ 2.8.x.
- Custom layer must not be included in Keras H5 (.h5) format.
- The model must contain not only weights but also the structure of the model (do not use save_weights).
- If there is a custom layer in the model, please upload TensorFlow SavedModel format (.zip).

Input Shapes¶

Note

For input shapes, use the same values that you used to train the model.
- If the input shapes of the model is dynamic, input shapes is required.
- If the input shapes of the model is static, input shapes is not required.

For example, batch=1, channel=3, height=768, width=1024.

input_shapes = [{"batch": 1, "channel": 3, "dimension": [768, 1024]}]

Currently, only single input models are supported.

Example¶

from netspresso import NetsPresso


netspresso = NetsPresso(email="YOUR_EMAIL", password="YOUR_PASSWORD")

compressor = netspresso.compressor_v2()
model = compressor.upload_model(
    input_model_path="./examples/sample_models/mobilenetv1.h5",
    input_shapes=[{"batch": 1, "channel": 3, "dimension": [224, 224]}],
)

Select Compression Method¶

`netspresso.compressor.v2.compressor.CompressorV2.select_compression_method(model_id, compression_method, options=Options())` ¶

Select a compression method for a model.

Parameters:

Name	Type	Description	Default
`model_id`	`str`	The ID of the model.	required
`compression_method`	`CompressionMethod`	The selected compression method.	required
`options(Options,`	`optional`	The options for pruning method.	required

Raises:

Type	Description
`e`	If an error occurs while selecting the compression method.

Returns:

Name	Type	Description
`ResponseSelectMethod`	`ResponseSelectMethod`	The compression information for the selected compression method.

Details of Parameters¶

Compression Method¶

Available Compression Method¶

Name	Description
PR_L2	L2 Norm Pruning
PR_GM	GM Pruning
PR_NN	Nuclear Norm Pruning
PR_SNP	Structured Neuron-level Pruning
PR_ID	Pruning By Index
FD_TK	Tucker Decomposition
FD_SVD	Singular Value Decomposition
FD_CP	CP Decomposition

Example¶

from netspresso.enums import CompressionMethod

COMPRESSION_METHOD = CompressionMethod.PR_L2

Warning

Nuclear Norm is only supported in the Tensorflow-Keras framework.
Structured Neuron-level is only supported in the PyTorch and ONNX frameworks.

Note

Click on the link to learn more about the information. (Compression Method)

Options¶

netspresso.enums.Policy - Policy for handling connected filters
netspresso.enums.LayerNorm - Layer normalization method
netspresso.enums.GroupPolicy - Group policy for group convolutions
netspresso.enums.StepOp - Step operator for rounding

Example¶

from netspresso.enums import Policy, LayerNorm, GroupPolicy
from netspresso.clients.compressor.v2.schemas import Options

OPTIONS = Options(
    policy=Policy.AVERAGE,
    layer_norm=LayerNorm.TSS_NORM,
    group_policy=GroupPolicy.COUNT,
    reshape_channel_axis=-1
)

Note

Click the link for more information. (Pruning Options)

Note

This parameter applies only to the Pruning Method (PR_L2, PR_GM, PR_NN, PR_SNP).

Details of Returns¶

Example¶

from netspresso import NetsPresso
from netspresso.enums import CompressionMethod, Policy, LayerNorm, GroupPolicy
from netspresso.clients.compressor.v2.schemas import Options


netspresso = NetsPresso(email="YOUR_EMAIL", password="YOUR_PASSWORD")

compressor = netspresso.compressor_v2()
compression_info = compressor.select_compression_method(
    model_id="YOUR_UPLOADED_MODEL_ID",
    compression_method=CompressionMethod.PR_L2,
    options=Options(
        policy=Policy.AVERAGE,
        layer_norm=LayerNorm.STANDARD_SCORE,
        group_policy=GroupPolicy.AVERAGE,
        reshape_channel_axis=-1,
    ),
)

Output¶

>>> compression_info
CompressionInfo(
    compression_method="PR_L2", 
    available_layers=[
        AvailableLayer(name='conv1', values=[""], channels=[32]), 
        AvailableLayer(name='layers.0.conv2', values=[""], channels=[64]), 
        AvailableLayer(name='layers.1.conv2', values=[""], channels=[128]), 
        AvailableLayer(name='layers.2.conv2', values=[""], channels=[128]), 
        AvailableLayer(name='layers.3.conv2', values=[""], channels=[256]), 
        AvailableLayer(name='layers.4.conv2', values=[""], channels=[256]), 
        AvailableLayer(name='layers.5.conv2', values=[""], channels=[512]), 
        AvailableLayer(name='layers.6.conv2', values=[""], channels=[512]), 
        AvailableLayer(name='layers.7.conv2', values=[""], channels=[512]), 
        AvailableLayer(name='layers.8.conv2', values=[""], channels=[512]), 
        AvailableLayer(name='layers.9.conv2', values=[""], channels=[512]), 
        AvailableLayer(name='layers.10.conv2', values=[""], channels=[512]), 
        AvailableLayer(name='layers.11.conv2', values=[""], channels=[1024]), 
        AvailableLayer(name='layers.12.conv2', values=[""], channels=[1024])
    ], 
    options={'reshape_channel_axis': -1, 'policy': 'average', 'layer_norm': 'tss_norm', 'group_policy': 'average'}
    original_model_id="YOUR_UPLOADED_MODEL_ID",
    compressed_model_id="", 
    compression_id="", 
)

Set Compression Params¶

Details of Parameters¶

Values of available layer¶

Compression Method	Number of Values	Type	Range
PR_L2	1	Float	0.0 < ratio < 1.0
PR_GM	1	Float	0.0 < ratio < 1.0
PR_NN	1	Float	0.0 < ratio < 1.0
PR_SNP	1	Float	0.0 < ratio < 1.0
PR_ID	(Num of Out Channels - 1)	Int	0 ≤ channels < Num of Out Channels
FD_TK	2	Int	0 < rank ≤ (Num of In Channels or Num of Out Channels)
FD_CP	1	Int	0 < rank ≤ min(Num of In Channels or Num of Out Channels)
FD_SVD	1	Int	0 < rank ≤ min(Num of In Channels or Num of Out Channels)

Example¶

for available_layer in compression_info.available_layers:
   available_layer.values = [0.2]

Output¶

>>> compression_info
CompressionInfo(
   compression_method="PR_L2", 
   available_layers=[
      AvailableLayer(name='conv1', values=[0.2], channels=[32]), 
      AvailableLayer(name='layers.0.conv2', values=[0.2], channels=[64]), 
      AvailableLayer(name='layers.1.conv2', values=[0.2], channels=[128]), 
      AvailableLayer(name='layers.2.conv2', values=[0.2], channels=[128]), 
      AvailableLayer(name='layers.3.conv2', values=[0.2], channels=[256]), 
      AvailableLayer(name='layers.4.conv2', values=[0.2], channels=[256]), 
      AvailableLayer(name='layers.5.conv2', values=[0.2], channels=[512]), 
      AvailableLayer(name='layers.6.conv2', values=[0.2], channels=[512]), 
      AvailableLayer(name='layers.7.conv2', values=[0.2], channels=[512]), 
      AvailableLayer(name='layers.8.conv2', values=[0.2], channels=[512]), 
      AvailableLayer(name='layers.9.conv2', values=[0.2], channels=[512]), 
      AvailableLayer(name='layers.10.conv2', values=[0.2], channels=[512]), 
      AvailableLayer(name='layers.11.conv2', values=[0.2], channels=[1024]), 
      AvailableLayer(name='layers.12.conv2', values=[0.2], channels=[1024])
   ], 
   options={'reshape_channel_axis': -1, 'policy': 'average', 'layer_norm': 'tss_norm', 'group_policy': 'average'}
   original_model_id="YOUR_UPLOADED_MODEL_ID",
   compressed_model_id="", 
   compression_id="", 
)

Compress Model¶

`netspresso.compressor.v2.compressor.CompressorV2.compress_model(compression, output_dir, dataset_path=None)` ¶

Compress a model using the provided compression information.

Parameters:

Name	Type	Description	Default
`compression`	`CompressionInfo`	The information about the compression.	required
`output_dir`	`str`	The local path to save the compressed model.	required
`dataset_path`	`str`	The path of the dataset used for nuclear norm compression method. Default is None.	`None`

Raises:

Type	Description
`e`	If an error occurs while compressing the model.

Returns:

Name	Type	Description
`CompressorMetadata`	`CompressorMetadata`	Compress metadata.

Example¶

compressed_model = compressor.compress_model(
    compression=compression_info,
    output_dir="./outputs/compressed/graphmodule_manual",
)

Full Example¶

from netspresso import NetsPresso
from netspresso.enums import CompressionMethod, GroupPolicy, LayerNorm, Policy
from netspresso.clients.compressor.v2.schemas import Options


netspresso = NetsPresso(email="YOUR_EMAIL", password="YOUR_PASSWORD")

# 1. Declare compressor
compressor = netspresso.compressor_v2()

# 2. Upload model
model = compressor.upload_model(
    input_model_path="./examples/sample_models/graphmodule.pt",
    input_shapes=[{"batch": 1, "channel": 3, "dimension": [224, 224]}],
)

# 3. Select compression method
compression_info = compressor.select_compression_method(
    model_id=model.ai_model_id,
    compression_method=CompressionMethod.PR_L2,
    options=Options(
        policy=Policy.AVERAGE,
        layer_norm=LayerNorm.STANDARD_SCORE,
        group_policy=GroupPolicy.AVERAGE,
        reshape_channel_axis=-1,
    ),
)

# 4. Set params for compression(ratio or rank)
for available_layer in compression_info.available_layers[:5]:
    available_layer.values = [0.2]

# 5. Compress model
compressed_model = compressor.compress_model(
    compression=compression_info,
    output_dir="./outputs/compressed/graphmodule_manual",
)

Compressor(Manual Compression)¶

Upload Model¶

netspresso.compressor.v2.compressor.CompressorV2.upload_model(input_model_path, input_shapes=None, framework=Framework.PYTORCH) ¶

Details of Parameters¶

Framework¶

Available Framework¶

Example¶

Input Shapes¶

Example¶

Select Compression Method¶

netspresso.compressor.v2.compressor.CompressorV2.select_compression_method(model_id, compression_method, options=Options()) ¶

Details of Parameters¶

Compression Method¶

Available Compression Method¶

Example¶

Options¶

Example¶

Details of Returns¶

Example¶

Output¶

Set Compression Params¶

Details of Parameters¶

Values of available layer¶

Example¶

Output¶

Compress Model¶

netspresso.compressor.v2.compressor.CompressorV2.compress_model(compression, output_dir, dataset_path=None) ¶

Example¶

Full Example¶

`netspresso.compressor.v2.compressor.CompressorV2.upload_model(input_model_path, input_shapes=None, framework=Framework.PYTORCH)` ¶

`netspresso.compressor.v2.compressor.CompressorV2.select_compression_method(model_id, compression_method, options=Options())` ¶

`netspresso.compressor.v2.compressor.CompressorV2.compress_model(compression, output_dir, dataset_path=None)` ¶