90 lines
3.0 KiB
Python
Executable File
90 lines
3.0 KiB
Python
Executable File
#!/usr/bin/env python3
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#
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# Copyright 2015-2016 Carnegie Mellon University
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import matplotlib as mpl
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mpl.use('Agg')
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import matplotlib.pyplot as plt
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plt.style.use('bmh')
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import pandas as pd
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import os
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import sys
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scriptDir = os.path.dirname(os.path.realpath(__file__))
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plotDir = os.path.join(scriptDir, 'plots')
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# workDir = os.path.join(scriptDir, 'work')
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def plot(workDirs):
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trainDfs = []
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testDfs = []
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for d in workDirs:
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trainF = os.path.join(d, 'train.log')
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testF = os.path.join(d, 'test.log')
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trainDfs.append(pd.read_csv(trainF, sep='\t'))
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testDfs.append(pd.read_csv(testF, sep='\t'))
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if len(trainDfs[-1]) != len(testDfs[-1]):
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print("Error: Train/test dataframe shapes "
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"for '{}' don't match: {}, {}".format(
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d, trainDfs[-1].shape, testDfs[-1].shape))
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sys.exit(-1)
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trainDf = pd.concat(trainDfs, ignore_index=True)
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testDf = pd.concat(testDfs, ignore_index=True)
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# print("train, test:")
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# print("\n".join(["{:0.2e}, {:0.2e}".format(x, y) for (x, y) in
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# zip(trainDf['avg triplet loss (train set)'].values[-5:],
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# testDf['avg triplet loss (test set)'].values[-5:])]))
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fig, ax = plt.subplots(1, 1)
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trainDf.index += 1
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trainDf['avg triplet loss (train set)'].plot(ax=ax)
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plt.xlabel("Epoch")
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plt.ylabel("Average Triplet Loss, Training")
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plt.ylim(ymin=0)
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# plt.xlim(xmin=1)
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plt.grid(b=True, which='major', color='k', linestyle='-')
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plt.grid(b=True, which='minor', color='k', linestyle='-', alpha=0.2)
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plt.minorticks_on()
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# ax.set_yscale('log')
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d = os.path.join(plotDir, "train-loss.pdf")
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fig.savefig(d)
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print("Created {}".format(d))
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fig, ax = plt.subplots(1, 1)
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testDf.index += 1
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testDf['lfwAcc'].plot(ax=ax)
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plt.xlabel("Epoch")
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plt.ylabel("LFW Accuracy")
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plt.ylim(ymin=0, ymax=1)
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plt.grid(b=True, which='major', color='k', linestyle='-')
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plt.grid(b=True, which='minor', color='k', linestyle='-', alpha=0.2)
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plt.minorticks_on()
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# plt.xlim(xmin=1)
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# ax.set_yscale('log')
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d = os.path.join(plotDir, "lfw-accuracy.pdf")
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fig.savefig(d)
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print("Created {}".format(d))
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if __name__ == '__main__':
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os.makedirs(plotDir, exist_ok=True)
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parser = argparse.ArgumentParser()
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parser.add_argument('workDirs', type=str, nargs='+')
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args = parser.parse_args()
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plot(args.workDirs)
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