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()