openface/training/plot-loss.py

#!/usr/bin/env python3
#
# Copyright 2015-2016 Carnegie Mellon University
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('bmh')
import pandas as pd

import os
import sys

scriptDir = os.path.dirname(os.path.realpath(__file__))
plotDir = os.path.join(scriptDir, 'plots')
# workDir = os.path.join(scriptDir, 'work')


def plot(workDirs):
    trainDfs = []
    testDfs = []
    for d in workDirs:
        trainF = os.path.join(d, 'train.log')
        testF = os.path.join(d, 'test.log')
        trainDfs.append(pd.read_csv(trainF, sep='\t'))
        testDfs.append(pd.read_csv(testF, sep='\t'))
        if len(trainDfs[-1]) != len(testDfs[-1]):
            print("Error: Train/test dataframe shapes "
                  "for '{}' don't match: {}, {}".format(
                      d, trainDfs[-1].shape, testDfs[-1].shape))
            sys.exit(-1)
    trainDf = pd.concat(trainDfs, ignore_index=True)
    testDf = pd.concat(testDfs, ignore_index=True)

    # print("train, test:")
    # print("\n".join(["{:0.2e}, {:0.2e}".format(x, y) for (x, y) in
    #                  zip(trainDf['avg triplet loss (train set)'].values[-5:],
    #                      testDf['avg triplet loss (test set)'].values[-5:])]))

    fig, ax = plt.subplots(1, 1)
    trainDf.index += 1
    trainDf['avg triplet loss (train set)'].plot(ax=ax)
    plt.xlabel("Epoch")
    plt.ylabel("Average Triplet Loss, Training")
    plt.ylim(ymin=0)
    # plt.xlim(xmin=1)
    plt.grid(b=True, which='major', color='k', linestyle='-')
    plt.grid(b=True, which='minor', color='k', linestyle='-', alpha=0.2)
    plt.minorticks_on()
    # ax.set_yscale('log')
    d = os.path.join(plotDir, "train-loss.pdf")
    fig.savefig(d)
    print("Created {}".format(d))

    fig, ax = plt.subplots(1, 1)
    testDf.index += 1
    testDf['lfwAcc'].plot(ax=ax)
    plt.xlabel("Epoch")
    plt.ylabel("LFW Accuracy")
    plt.ylim(ymin=0, ymax=1)
    plt.grid(b=True, which='major', color='k', linestyle='-')
    plt.grid(b=True, which='minor', color='k', linestyle='-', alpha=0.2)
    plt.minorticks_on()
    # plt.xlim(xmin=1)
    # ax.set_yscale('log')
    d = os.path.join(plotDir, "lfw-accuracy.pdf")
    fig.savefig(d)
    print("Created {}".format(d))

if __name__ == '__main__':
    os.makedirs(plotDir, exist_ok=True)
    parser = argparse.ArgumentParser()
    parser.add_argument('workDirs', type=str, nargs='+')
    args = parser.parse_args()
    plot(args.workDirs)