File size: 6,727 Bytes
8711d24 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from pylab import setp
import argparse
import os
from glob import glob
import pickle as pkl
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
parser = argparse.ArgumentParser()
parser.add_argument('--folder', help='Folder of the runs. Either resnet or resnet_augment',
required=False, default="resnet")
parser.add_argument('--repeats', help='The number of times each model was trained', required=False, default="10")
args = parser.parse_args()
cur_folder = str(args.folder)
repeats = int(args.repeats)
colors = {0: "#1f77b4", 1: "#ff7f0e", 2: "#2ca02c", 3: "#d62728", 4: "#9467bd", 5: "#8c564b", 6: "#e377c2",
7: "#7f7f7f", 8: "#bcbd22", 9: "#17becf", 10: "#000000", 11: "#000080"}
# Plot Accuracy and F2-Score overview
dp = "./results_" + cur_folder + "/results/summary.txt"
dp2 = "./results_" + cur_folder + "/results/summary_rev.txt"
sp = "./results_" + cur_folder + "/images/"
if not os.path.exists("./results_" + cur_folder + "/images/"):
os.makedirs("./results_" + cur_folder + "/images/")
with open(dp, "r") as f:
data = f.readlines()
data = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data])
data[np.isnan(data)] = 0.0
data = data.reshape((6, repeats, 6, 7))
data_mean = np.mean(data, axis=1)
with open(dp2, "r") as f:
data2 = f.readlines()
data2 = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data2])
data2[np.isnan(data2)] = 0.0
data2 = data2.reshape((6, repeats, 6, 5))
data_mean2 = np.mean(data2, axis=1)
poses = np.arange(3, 36, 6)
w = 0.5
poses = np.array([[x - 3*w, x - 1.8*w, x - 0.6*w, x + 0.6*w, x + 1.8*w, x + 3*w] for x in poses]).flatten()
data_sum = np.zeros((6, repeats, 6, 2))
data_sum[:, :, :, 0] = data[:, :, :, 0]
data_sum[:, :, :, 1] = (data[:, :, :, 3] + data2[:, :, :, 1]) / 2.
data_sum = data_sum.transpose((1, 0, 2, 3)).reshape((repeats, 36, 2)) * 100
plt.figure(figsize=(10, 4))
plt.rcParams["font.family"] = "serif"
bp = plt.boxplot(data_sum[:, :, 0], positions=poses, widths=w)
for i in range(6):
if i < 3:
cur_col = colors[i]
else:
cur_col = colors[i - 1]
for j in range(6):
setp(bp['boxes'][j*6 + i], color=cur_col)
setp(bp['caps'][(j*6 + i) * 2], color=cur_col)
setp(bp['caps'][(j*6 + i) * 2 + 1], color=cur_col)
setp(bp['whiskers'][(j*6 + i) * 2], color=cur_col)
setp(bp['whiskers'][(j*6 + i) * 2 + 1], color=cur_col)
setp(bp['fliers'][j*6 + i], markeredgecolor=cur_col)
plt.ylabel("Accuracy [%]", fontsize=12)
plt.xticks(np.arange(3, 36, 6), ["Real-world\nHighway 1", "Real-world\nHighway 2",
"Controlled\nlarge-scale 1+2\n[low]", "Controlled\nlarge-scale 2\n[high]",
"Controlled\n[large+small]-scale\n[high]", "Combined"])
for tick_label, tick_color in zip(plt.gca().get_xticklabels(), [colors[x] for x in range(5)] + ["black"]):
tick_label.set_color(tick_color)
plt.tight_layout()
plt.savefig(sp + "/overview_accuracy.pdf")
plt.close()
plt.figure(figsize=(10, 4))
plt.rcParams["font.family"] = "serif"
bp = plt.boxplot(data_sum[:, :, 1], positions=poses, widths=w)
for i in range(6):
if i < 3:
cur_col = colors[i]
else:
cur_col = colors[i - 1]
for j in range(6):
setp(bp['boxes'][j*6 + i], color=cur_col)
setp(bp['caps'][(j*6 + i) * 2], color=cur_col)
setp(bp['caps'][(j*6 + i) * 2 + 1], color=cur_col)
setp(bp['whiskers'][(j*6 + i) * 2], color=cur_col)
setp(bp['whiskers'][(j*6 + i) * 2 + 1], color=cur_col)
setp(bp['fliers'][j*6 + i], markeredgecolor=cur_col)
plt.ylabel("F2-Score", fontsize=12)
plt.xticks(np.arange(3, 36, 6), ["Real-world\nHighway 1", "Real-world\nHighway 2",
"Controlled\nlarge-scale 1+2\n[low]", "Controlled\nlarge-scale 2\n[high]",
"Controlled\n[large+small]-scale\n[high]", "Combined"])
for tick_label, tick_color in zip(plt.gca().get_xticklabels(), [colors[x] for x in range(5)] + ["black"]):
tick_label.set_color(tick_color)
plt.tight_layout()
plt.savefig(sp + "overview_f2.pdf")
plt.close()
# Plot Confusion Matrices and Uncertainty
base_path = "./results_" + cur_folder + "/uncertainties/"
dp = base_path + "*_confusion.pkl"
sp = "./results_" + cur_folder + "/images/%06d_%02d_plot.pdf"
files = glob(dp)
files.sort()
files = [x.replace("\\", "/") for x in files]
nums = [int(x.split("/")[-1].split("_")[0]) for x in files]
classes = ["0", "1", "2", "3", "4", "5", "6", "7", "8"]
font = {'family': 'normal',
'weight': 'bold',
'size': 10}
data_keys = ["T0", "T1", "T2", "T3", "T4", "T5"]
for cur_num in nums:
with open(base_path + "%06d_aleatoric_diag.pkl" % cur_num, "rb") as f:
aleatoric = pkl.load(f)
with open(base_path + "%06d_epistemic_diag.pkl" % cur_num, "rb") as f:
epistemic = pkl.load(f)
with open(base_path + "%06d_confusion.pkl" % cur_num, "rb") as f:
confusion_mat = pkl.load(f)
for ik, key in enumerate(data_keys):
aleatoric[key][np.isnan(aleatoric[key])] = 0.0
epistemic[key][np.isnan(epistemic[key])] = 0.0
plt.figure(figsize=(9, 3))
plt.rcParams["font.family"] = "serif"
num_c = len(confusion_mat[key])
plt.subplot(1, 3, 1)
plt.imshow(confusion_mat[key], cmap="rainbow", norm=matplotlib.colors.LogNorm(vmin=1.0))
for ci, c in enumerate(confusion_mat[key].flatten()):
cii = ci % len(confusion_mat[key])
cij = ci // len(confusion_mat[key])
plt.text(cii, cij, np.minimum(int(c), 9999), ha='center', va='center', size=8)
plt.xlabel("Class [Prediction]", fontsize=12)
plt.ylabel("Class [Ground Truth]", fontsize=12)
plt.xticks(np.arange(num_c), classes, fontsize=12)
plt.yticks(np.arange(num_c), classes, fontsize=12)
plt.colorbar(shrink=0.7)
plt.subplot(1, 3, 2)
plt.imshow(aleatoric[key], cmap="rainbow", vmin=0.0, vmax=0.25)
plt.xlabel("Class [Prediction]", fontsize=12)
plt.xticks(np.arange(num_c), classes, fontsize=12)
plt.yticks(np.arange(num_c), classes, fontsize=12)
plt.colorbar(shrink=0.7)
plt.subplot(1, 3, 3)
plt.imshow(epistemic[key], cmap="rainbow", vmin=0.0, vmax=np.max(epistemic[key]))
plt.xlabel("Class [Prediction]", fontsize=12)
plt.xticks(np.arange(num_c), classes, fontsize=12)
plt.yticks(np.arange(num_c), classes, fontsize=12)
plt.colorbar(shrink=0.7)
plt.tight_layout()
plt.savefig(sp % (cur_num, ik))
plt.close()
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