netspresso 0.1.3

python client for the NetsPresso

NetsPresso

Installation

To install this package, please use Python 3.8 or higher.

Install from Source:

git clone https://github.com/nota-github/netspresso-python.git
pip install -e .

Install from PyPi (Recommended):

To be updated later.

Quickstart

Login

Please enter your email and password to log in to your NetsPresso account.

from netspresso.compressor import ModelCompressor

compressor = ModelCompressor(email="YOUR_EMAIL", password="YOUR_PASSWORD")

Model

Upload

from netspresso.compressor import Task, Framework

UPLOAD_MODEL_NAME = "tensorflow_keras_mv1"
TASK = Task.IMAGE_CLASSIFICATION
FRAMEWORK = Framework.TENSORFLOW_KERAS
UPLOAD_MODEL_PATH = "./mobilenetv1.h5"
INPUT_LAYERS = [{"batch": 1, "channel": 3, "dimension": [32, 32]}]

model = compressor.upload_modfel(
    model_name=UPLOAD_MODEL_NAME,
    task=TASK,
    framework=FRAMEWORK,
    file_path=UPLOAD_MODEL_PATH,
    input_layers=INPUT_LAYERS,
)
print(f"model_id: {model.model_id}")

Get

model = compressor.get_model(model_id="UPLOADED_MODEL_ID")
print(f"model_id: {model.model_id}")

Compression

Manual

# Select Compression Method
COMPRESSION_METHOD = CompressionMethod.PR_L2.value
compression_1 = compressor.select_compression_method(
    model=model,
    compression_method=COMPRESSION_METHOD
)
print(f"compression method: {compression_1.compression_method}")
print(f"available layers: {compression_1.available_layers}")

# Set Compression Params
for available_layer in compression_1.available_layers[:5]:
    available_layer.values = [0.2]

# Compress Model
COMPRESSED_MODEL_NAME = "l2norm_manual"
OUTPUT_PATH = "./mobilenetv1_cifar100_manual.h5"
compressed_model = compressor.compress_model(
    compression=compression_1,
    model_name=COMPRESSED_MODEL_NAME,
    output_path=OUTPUT_PATH,
)
print(f"compressed model id: {compressed_model.model_id}")

Recommendation

COMPRESSED_MODEL_NAME = "recommendation_model"
COMPRESSION_METHOD = CompressionMethod.PR_L2
RECOMMENDATION_METHOD = RecommendationMethod.LAMP
RECOMMENDATION_RATIO = 0.6
OUTPUT_PATH = "./mobilenetv1_cifar100_recommendation.h5"
compressed_model = compressor.recommendation_compression(
    model=model,
    model_name=COMPRESSED_MODEL_NAME,
    compression_method=COMPRESSION_METHOD,
    recommendation_method=RECOMMENDATION_METHOD,
    recommendation_ratio=RECOMMENDATION_RATIO,
    output_path=OUTPUT_PATH,
)
print(f"compressed model id: {compressed_model.model_id}")

Automatic

AUTO_COMPRESSED_MODEL_NAME = "test_auto_compress"
OUTPUT_PATH = "./mobilenetv1_cifar100_automatic.h5"
COMPRESSION_RATIO = 0.5
compressed_model = compressor.automatic_compression(
    model=model,
    model_name=AUTO_COMPRESSED_MODEL_NAME,
    compression_ratio=COMPRESSION_RATIO,
    output_path=OUTPUT_PATH,
)
print(f"compressed model id: {compressed_model.model_id}")