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eval_corruptionwise_img3d.py
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from glob import glob
import numpy as np
import re
ordered_corr = ['near_focus', 'far_focus', 'bit_error', 'color_quant',
'flash', 'fog_3d', 'h265_abr', 'h265_crf',
'iso_noise', 'low_light', 'xy_motion_blur', 'z_motion_blur'] # 12 corruptions in ImageNet-3DCC
def read_file(filename):
lines = open(filename, "r").readlines()
acc = []
bri = []
nll = []
corr_name_all = []
for line in lines:
if "error : " in line:
# print(line.split(":")[-3].split(",")[0].strip())
corr_name = line.split(" ")[6][1:-1].split("_")[0]
corr_name = re.sub(r'\d+', '', corr_name)
# assert False
acc.append(float(line.split(":")[-3].split(",")[0].strip()[:-1]))
bri.append(float(line.split(":")[-2].split(",")[0].strip()))
nll.append(float(line.split(":")[-1].strip()))
corr_name_all.append(corr_name)
assert len(acc)==len(ordered_corr)==12
# print(corr_name_all)
return np.mean(np.array(acc)), np.mean(np.array(bri)), np.mean(np.array(nll))
def read_file_corr(filename, acc, bri, nll):
lines = open(filename, "r").readlines()
# corr_name_all = []
for line in lines:
if "error : " in line:
# print(line.split(":")[-3].split(",")[0].strip())
# print("line:",line)
# print("line.split(" ")[6][1:-1]:",line.split(" ")[6][1:-1])
# corr_name = line.split(" ")[6][1:-1].split("_")[0]
corr_name = line.split(" ")[6][1:-2]#.split("_")[0]
# print("corr_name:",corr_name)
# corr_name = re.sub(r'\d+', '', corr_name)
# assert False
if corr_name in acc:
acc[corr_name].append(float(line.split(":")[-3].split(",")[0].strip()[:-1]))
bri[corr_name].append(float(line.split(":")[-2].split(",")[0].strip()))
nll[corr_name].append(float(line.split(":")[-1].strip()))
else:
acc[corr_name] = [float(line.split(":")[-3].split(",")[0].strip()[:-1])]
bri[corr_name] = [float(line.split(":")[-2].split(",")[0].strip())]
nll[corr_name] = [float(line.split(":")[-1].strip())]
# corr_name_all.append(corr_name)
# print(corr_name_all)
# print(acc)
assert len(acc)==len(ordered_corr)==12
return acc, bri, nll
def read_files(files):
acc = {}
bri = {}
nll = {}
if len(files) == 1:
for f in files:
acc, bri, nll = read_file_corr(f, acc, bri, nll)
# accs.append(acc)
# bris.append(bri)
# nlls.append(nll)
print("read", len(files), "files.")
for key in acc:
acc[key] = acc[key][0]
# print(acc)
# print("Brier")
for key in bri:
bri[key] = bri[key][0]
# print(bri)
# print("NLL")
for key in nll:
nll[key] = nll[key][0]
# res={}
# res["acc"] = [np.mean(np.array(accs)), np.std(np.array(accs))]
# res["bri"] = [np.mean(np.array(bris)), np.std(np.array(bris))]
# res["nll"] = [np.mean(np.array(nlls)), np.std(np.array(nlls))]
return acc, bri, nll
else:
for f in files:
acc, bri, nll = read_file_corr(f, acc, bri, nll)
# accs.append(acc)
# bris.append(bri)
# nlls.append(nll)
print("read", len(files), "files.")
# print("Error")
for key in acc:
acc[key] = np.mean(np.array(acc[key]))
# print(acc)
# print("Brier")
for key in bri:
bri[key] = np.mean(np.array(bri[key]))
# print(bri)
# print("NLL")
for key in nll:
nll[key] = np.mean(np.array(nll[key]))
# print(nll)
return acc, bri, nll
# res={}
# res["acc"] = [np.mean(np.array(accs)), np.std(np.array(accs))]
# res["bri"] = [np.mean(np.array(bris)), np.std(np.array(bris))]
# res["nll"] = [np.mean(np.array(nlls)), np.std(np.array(nlls))]
print("Reading ImageNet3DCC petalfim files...")
# result = read_files(glob("cotta[0-9]_*.txt"))
acc, bri, nll = read_files(glob("output/imagenet3d/petalfim/petalfim[0-9]_*.txt"))
print("\nError")
for c in ordered_corr:
print(c + "," + str(round(acc[c],2)))
print("\nBrier")
for c in ordered_corr:
print(c + "," + str(round(bri[c],4)))
print("\nNLL")
for c in ordered_corr:
print(c + "," + str(round(nll[c],4)))
# for key in result:
# print("Error:", "Mean:",result["acc"][0], "Std:",result["acc"][1])
# print("Brier:", "Mean:",result["bri"][0], "Std:",result["bri"][1])
# print("NLL:", "Mean:",result["nll"][0], "Std:",result["nll"][1])
# print(result)