57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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fid_data = {
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"4encoder8decoder":[64.16, 48.04, 39.88, 35.41],
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"6encoder4decoder":[67.71, 48.26, 39.30, 34.91],
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"8encoder4decoder":[69.4, 49.7, 41.56, 36.76],
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}
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sfid_data = {
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"4encoder8decoder":[7.86, 7.48, 7.15, 7.07],
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"6encoder4decoder":[8.54, 8.11, 7.40, 7.40],
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"8encoder4decoder":[8.42, 8.27, 8.10, 7.69],
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}
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is_data = {
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"4encoder8decoder":[20.37, 29.41, 36.88, 41.32],
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"6encoder4decoder":[20.04, 30.13, 38.17, 43.84],
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"8encoder4decoder":[19.98, 29.54, 35.93, 42.025],
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}
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pr_data = {
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"4encoder8decoder":[0.3935, 0.4687, 0.5047, 0.5271],
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"6encoder4decoder":[0.3767, 0.4686, 0.50876, 0.5266],
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"8encoder4decoder":[0.37, 0.45676, 0.49602, 0.5162],
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}
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recall_data = {
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"4encoder8decoder":[0.5604, 0.5941, 0.6244, 0.6338],
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"6encoder4decoder":[0.5295, 0.595, 0.6287, 0.6378],
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"8encoder4decoder":[0.51, 0.596, 0.6242, 0.6333],
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}
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x = [100, 200, 300, 400]
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colors = ["#70d6ff", "#ff70a6", "#ff9770", "#ffd670", "#e9ff70"]
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metric_data = {
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"FID" : fid_data,
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# "SFID" : sfid_data,
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"InceptionScore" : is_data,
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"Precision" : pr_data,
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"Recall" : recall_data,
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}
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for key, data in metric_data.items():
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for i, (name, v) in enumerate(data.items()):
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name = name.replace("encoder", "En")
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name = name.replace("decoder", "De")
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plt.plot(x, v, label=name, color=colors[i], linewidth=3, marker="o")
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plt.legend()
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plt.xticks(x)
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plt.ylabel(key, weight="bold")
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plt.xlabel("Training iterations(K steps)", weight="bold")
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plt.savefig("output/base_{}.pdf".format(key), bbox_inches='tight')
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plt.close() |