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import os
import json
import numpy as np
import pandas as pd
import matplotlib as mpl
import seaborn as sns
def main():
# hyperparameters
dataset = "cifar10"
EXP_NUM = 20
selected_epochs=[24, 100,200]
k=15
exps = list(range(EXP_NUM))
col = np.array(["metric", "method", "hue", "period", "eval"])
data = np.array([])
# NN
metric = "NN"
# DeepDebugger segments
eval_path = "/home/xianglin/projects/DVI_data/resnet18_{}/Model/test_evaluation_hybrid.json".format(dataset)
with open(eval_path, "r") as f:
eval = json.load(f)
for epoch_id in range(3):
epoch = selected_epochs[epoch_id]
nn_train = round(eval["nn_train"][str(epoch)][str(k)], 3)
nn_test = round(eval["nn_test"][str(epoch)][str(k)], 3)
if len(data) == 0:
data = np.array([[metric, "DeepDebugger", "DeepDebugger(Train)", "{}".format(str(epoch_id)), nn_train]])
else:
data = np.concatenate((data, np.array([[metric, "DeepDebugger", "DeepDebugger(Train)", "{}".format(str(epoch_id)), nn_train]])), axis=0)
data = np.concatenate((data, np.array([[metric, "DeepDebugger", "DeepDebugger(Test)", "{}".format(str(epoch_id)), nn_test]])), axis=0)
for epoch_id in range(3):
for exp in exps:
eval_path = "/home/xianglin/projects/DVI_data/resnet18_{}/Model/exp_{}/test_evaluation_hybrid.json".format(dataset, str(exp))
with open(eval_path, "r") as f:
eval = json.load(f)
epoch = selected_epochs[epoch_id]
nn_train = round(eval["nn_train"][str(epoch)][str(k)], 3)
nn_test = round(eval["nn_test"][str(epoch)][str(k)], 3)
data = np.concatenate((data, np.array([[metric, "-OS", "-OS(Train)", "{}".format(str(epoch_id)), nn_train]])), axis=0)
data = np.concatenate((data, np.array([[metric, "-OS", "-OS(Test)", "{}".format(str(epoch_id)), nn_test]])), axis=0)
# INV
metric = "INV"
# DeepDebugger segments
eval_path = "/home/xianglin/projects/DVI_data/resnet18_{}/Model/test_evaluation_hybrid.json".format(dataset)
with open(eval_path, "r") as f:
eval = json.load(f)
for epoch_id in range(3):
epoch = selected_epochs[epoch_id]
ppr_train = round(eval["ppr_train"][str(epoch)], 3)
ppr_test = round(eval["ppr_test"][str(epoch)], 3)
data = np.concatenate((data, np.array([[metric, "DeepDebugger", "DeepDebugger(Train)", "{}".format(str(epoch_id)), ppr_train]])), axis=0)
data = np.concatenate((data, np.array([[metric, "DeepDebugger", "DeepDebugger(Test)", "{}".format(str(epoch_id)), ppr_test]])), axis=0)
# DeepDebugger Random segments
for epoch_id in range(3):
for exp in exps:
eval_path = "/home/xianglin/projects/DVI_data/resnet18_{}/Model/exp_{}/test_evaluation_hybrid.json".format(dataset, str(exp))
with open(eval_path, "r") as f:
eval = json.load(f)
epoch = selected_epochs[epoch_id]
nn_train = round(eval["ppr_train"][str(epoch)], 3)
nn_test = round(eval["ppr_test"][str(epoch)], 3)
data = np.concatenate((data, np.array([[metric, "-OS", "-OS(Train)", "{}".format(str(epoch_id)), nn_train]])), axis=0)
data = np.concatenate((data, np.array([[metric, "-OS", "-OS(Test)", "{}".format(str(epoch_id)), nn_test]])), axis=0)
# TLR
metric= "TLR"
# DeepDebugger segments
eval_path = "/home/xianglin/projects/DVI_data/resnet18_{}/Model/test_evaluation_hybrid.json".format(dataset)
with open(eval_path, "r") as f:
eval = json.load(f)
for epoch_id in range(3):
epoch = selected_epochs[epoch_id]
nn_train = round(eval["tlr_train"][str(epoch)], 3)
nn_test = round(eval["tlr_test"][str(epoch)], 3)
data = np.concatenate((data, np.array([[metric, "DeepDebugger", "DeepDebugger(Train)","{}".format(str(epoch_id)), nn_train]])), axis=0)
data = np.concatenate((data, np.array([[metric, "DeepDebugger", "DeepDebugger(Test)", "{}".format(str(epoch_id)), nn_test]])), axis=0)
# DeepDebugger Random segments
for epoch_id in range(3):
for exp in exps:
eval_path = "/home/xianglin/projects/DVI_data/resnet18_{}/Model/exp_{}/test_evaluation_hybrid.json".format(dataset, str(exp))
with open(eval_path, "r") as f:
eval = json.load(f)
epoch = selected_epochs[epoch_id]
nn_train = round(eval["tlr_train"][str(epoch)], 3)
nn_test = round(eval["tlr_test"][str(epoch)], 3)
data = np.concatenate((data, np.array([[metric, "-OS", "-OS(Train)","{}".format(str(epoch_id)), nn_train]])), axis=0)
data = np.concatenate((data, np.array([[metric, "-OS", "-OS(Test)", "{}".format(str(epoch_id)), nn_test]])), axis=0)
df = pd.DataFrame(data, columns=col)
df[["period"]] = df[["period"]].astype(int)
df[["eval"]] = df[["eval"]].astype(float)
# df.to_excel("./plot_results/cifar10_segment.xlsx")
pal20c = sns.color_palette('tab20c', 20)
sns.set_theme(style="whitegrid", palette=pal20c)
hue_dict = {
"-OS(Train)": pal20c[0],
"DeepDebugger(Train)": pal20c[8],
"-OS(Test)": pal20c[3],
"DeepDebugger(Test)": pal20c[11],
}
sns.palplot([hue_dict[i] for i in hue_dict.keys()])
axes = {'labelsize': 15,
'titlesize': 15,}
mpl.rc('axes', **axes)
mpl.rcParams['xtick.labelsize'] = 15
hue_list = ["-OS(Train)", "-OS(Test)", "DeepDebugger(Train)", "DeepDebugger(Test)"]
fg = sns.catplot(
x="period",
y="eval",
hue="hue",
hue_order=hue_list,
# order = [1, 2, 3, 4, 5],
# row="method",
col="metric",
ci=0.001,
height=2.5, #2.65,
aspect=1.0,#3,
data=df,
sharey=False,
kind="box",
palette=[hue_dict[i] for i in hue_list],
legend=True
)
sns.move_legend(fg, "lower center", bbox_to_anchor=(.42, 0.92), ncol=2, title=None, frameon=False)
mpl.pyplot.setp(fg._legend.get_texts(), fontsize='15')
axs = fg.axes[0]
# max_ = df_tmp["eval"].max()
# min_ = df["eval"].min()
# axs[0].set_ylim(0., max_*1.1)
axs[0].set_title("NN")
axs[1].set_title("INV")
axs[2].set_title("Temporal")
(fg.despine(bottom=False, right=False, left=False, top=False)
.set_xticklabels(['Early', 'Mid', 'Late'])
.set_axis_labels("", "")
)
# fg.fig.suptitle("NN preserving property")
fg.savefig(
"./plot_results/cifar10_segment.png",
dpi=300,
bbox_inches="tight",
pad_inches=0.0,
transparent=True,
)
if __name__ == "__main__":
main()
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