Source code for aimet_torch.visualize_serialized_data

# /usr/bin/env python3.5
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""" Class for visualizing after compression is completed"""
import pickle
import pandas as pd
from bokeh.models import ColumnDataSource, DataTable, TableColumn
from aimet_common.compression_algo import CompressionAlgo
from aimet_common.bokeh_plots import BokehServerSession
from aimet_common import plotting_utils


[docs]class VisualizeCompression: """ Updates bokeh server session document and publishes graphs/tables to the server with session id compression. """ def __init__(self, visualization_url): self.bokeh_session = BokehServerSession(visualization_url, session_id="compression") self.__document = self.bokeh_session.document
[docs] def display_eval_scores(self, saved_eval_scores_dict_path): """ Publishes the evaluation scores table to the server. :param saved_eval_scores_dict_path: file path to the evaluation scores for each layer :return: None """ infile = open(saved_eval_scores_dict_path, 'rb') eval_scores_dict = pickle.load(infile) infile.close() eval_scores_data_frame = pd.DataFrame.from_dict(eval_scores_dict).T eval_scores_data_frame.columns = eval_scores_data_frame.columns.map(str) eval_scores_data_frame.insert(0, 'layers', eval_scores_data_frame.index) source = ColumnDataSource(data=eval_scores_data_frame) columns = [TableColumn(field=Ci, title=Ci) for Ci in eval_scores_data_frame.columns] # bokeh columns eval_scores_data_table = DataTable(source=source, columns=columns, width=1500) self.__document.add_root(eval_scores_data_table)
[docs] def display_comp_ratio_plot(self, comp_ratio_list_path): """ Publishes the optimal compression ratios to the server. :param comp_ratio_list_path: Path to the pkl file with compression ratios for each layer :return: None """ layer_comp_ratio_list = CompressionAlgo.unpickle_comp_ratios_list(comp_ratio_list_path=comp_ratio_list_path) # visualize comp ratios vs layers in a plot and add it to a server session document. comp_ratios = [] layer_names = [] for layer_name, comp_ratio in layer_comp_ratio_list: comp_ratios.append(comp_ratio) layer_names.append(layer_name) plot = plotting_utils.plot_optimal_compression_ratios(comp_ratios, layer_names) self.__document.add_root(plot)