Automatic Compression

automatic_compression(self, model_id: str, model_name: str, output_path: str, compression_ratio: float = 0.5) → netspresso.compressor.core.model.CompressedModel

Compress a model automatically based on the given compression ratio.

Parameters

model_id (str) – The ID of the model.
model_name (str) – The name of the compressed model.
output_path (str) – The local path to save the compressed model.
compression_ratio (float) – The compression ratio for automatic compression. Defaults to 0.5.

Raises

e – If an error occurs while performing automatic compression.

Returns

The compressed model.

Return type

CompressedModel

Details of Parameters

Compression Ratio

Note

As the compression ratio increases, you can get more lighter and faster compressed models, but with greater lost of accuracy.
Therefore, it is necessary to find an appropriate ratio for your requirements. It might require a few trials and errors.
The range of available values is as follows.

$https://latex.codecogs.com/svg.image?0< ratio \leq 1$

Details of Returns

class CompressedModel(model_id: str, model_name: str, task: str, framework: str, model_size: float, flops: float, trainable_parameters: float, non_trainable_parameters: float, number_of_layers: int, input_shapes: List[netspresso.compressor.core.model.InputShape] = <factory>, compression_id: str = '', original_model_id: str = '')[source]

Bases: netspresso.compressor.core.model.Model

Represents a compressed model.

compression_id

The ID of the compression.

Type: str

original_model_id

The ID of the uploaded model.

Type: str

Example

from netspresso.compressor import ModelCompressor


compressor = ModelCompressor(email="YOUR_EMAIL", password="YOUR_PASSWORD")
compressed_model = compressor.automatic_compression(
    model_id="YOUR_UPLOADED_MODEL_ID",
    model_name="YOUR_COMPRESSED_MODEL_NAME",
    output_path="OUTPUT_PATH",  # ex) ./compressed_model.h5
    compression_ratio=0.5,
)

Output

>>> compressed_model
CompressedModel(
    model_id="78f65510-1f99-4856-99d9-60902373bd1d",
    model_name="YOUR_COMPRESSED_MODEL_NAME",
    task="image_classification",
    framework="tensorflow_keras",
    input_shapes=[InputShape(batch=1, channel=3, dimension=[32, 32])],
    model_size=2.9439,
    flops=24.1811,
    trainable_parameters=0.6933,
    non_trainable_parameters=0.01,
    number_of_layers=0,
    compression_id="b9feccee-d69e-4074-a225-5417d41aa572",
    original_model_id="YOUR_UPLOADED_MODEL_ID"
)