AIMET Visualization Compression API

Top-level API Compression

class aimet_torch.visualize_serialized_data.VisualizeCompression(visualization_url)

Updates bokeh server session document and publishes graphs/tables to the server with session id compression.

VisualizeCompression.display_eval_scores(saved_eval_scores_dict_path)

Publishes the evaluation scores table to the server.

Parameters

saved_eval_scores_dict_path – file path to the evaluation scores for each layer

Returns

None

VisualizeCompression.display_comp_ratio_plot(comp_ratio_list_path)

Publishes the optimal compression ratios to the server.

Parameters

comp_ratio_list_path – Path to the pkl file with compression ratios for each layer

Returns

None

Code Examples

Required imports

from decimal import Decimal
import torch
from torchvision import models
import aimet_common.defs
import aimet_torch.defs
import aimet_torch.utils
from aimet_common.utils import start_bokeh_server_session
from aimet_torch.compress import ModelCompressor
from aimet_torch.visualize_serialized_data import VisualizeCompression
from aimet_torch.examples.imagenet_dataloader import ImageNetDataLoader
from aimet_torch.examples.supervised_classification_pipeline import \
    create_stand_alone_supervised_classification_evaluator
from aimet_torch.utils import IterFirstX

Model Compression with Visualization Parameter

def model_compression_with_visualization():
    """
    Code example for compressing a model with a visualization url provided.
    """
    visualization_url, process = start_bokeh_server_session(8002)

    ImageNetDataLoader(image_dir, image_size, batch_size, num_workers)
    input_shape = (1, 3, 224, 224)
    model = models.resnet18(pretrained=True).to(torch.device('cuda'))

    modules_to_ignore = [model.conv1]

    greedy_params = aimet_common.defs.GreedySelectionParameters(target_comp_ratio=Decimal(0.65),
                                                                num_comp_ratio_candidates=10,
                                                                saved_eval_scores_dict=
                                                               '../data/resnet18_eval_scores.pkl')

    auto_params = aimet_torch.defs.SpatialSvdParameters.AutoModeParams(greedy_params,
                                                                       modules_to_ignore=modules_to_ignore)

    params = aimet_torch.defs.SpatialSvdParameters(aimet_torch.defs.SpatialSvdParameters.Mode.auto, auto_params,
                                                   multiplicity=8)

    # If no visualization URL is provided, during model compression execution no visualizations will be published.
    ModelCompressor.compress_model(model=model, eval_callback=evaluate, eval_iterations=5,
                                   input_shape=input_shape,
                                   compress_scheme=aimet_common.defs.CompressionScheme.spatial_svd,
                                   cost_metric=aimet_common.defs.CostMetric.mac, parameters=params,
                                   visualization_url=None)

    comp_ratios_file_path = './data/greedy_selection_comp_ratios_list.pkl'
    eval_scores_path = '../data/resnet18_eval_scores.pkl'

    # A user can visualize the eval scores dictionary and optimal compression ratios by executing the following code.
    compression_visualizations = VisualizeCompression(visualization_url)
    compression_visualizations.display_eval_scores(eval_scores_path)
    compression_visualizations.display_comp_ratio_plot(comp_ratios_file_path)