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# https://stackoverflow.com/questions/63761717/load-image-dataset
# https://stackoverflow.com/questions/60655280/how-to-split-an-image-dataset-in-x-train-y-train-x-test-y-test-by-tensorflow

import tensorflow as tf
import pandas as pd

train_df = pd.read_csv('train.csv')
train_df['class'] = train_df['class'].apply(str)

train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(horizontal_flip=True, vertical_flip=True,)

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import numpy as np
from sklearn import metrics
from sklearn.metrics import roc_auc_score
y = np.array([1, 1, 2, 2])
#scores可以是模型預測結果(Label)
#scores也可以是模型預測的confidence(softmax probability)
scores = np.array([1, 1, 2, 2])
scores = np.array([0.1, 0.4, 0.35, 0.8])
area_under_curve = roc_auc_score(y, scores)

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 def eval(loader, gt_labels_t, output_file="output.txt"):
    G.eval()  # 特徵提取器
    F1.eval() # 分類器
    size = 0
    correct = 0
    
    y_pred=[]
    y_true=[]
    pred_prob = None
    pred_result = None

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import torch.nn.functional as F
import torch
import numpy as np
from torch.autograd import Function
import torch.nn as nn
from pdb import set_trace as breakpoint
import sys
import math
from torch.nn.parameter import Parameter
from torch.nn import init

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# LabelSmoothing.py

# https://www.aiuai.cn/aifarm1333.html 示例 3
# From: Github - NVIDIA/DeepLearningExamples/PyTorch/Classification
# smoothing.py
import torch
import torch.nn as nn

# 一般版本LabelSmoothing
class LabelSmoothing(nn.Module):

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# https://stackoverflow.com/a/45925049/13369757
import matplotlib.pyplot as plt 
import numpy as np

fig, host = plt.subplots(figsize=(12,10)) # (width, height) in inches
    
par1 = host.twinx()
par2 = host.twinx()

host.set_xlabel("Threshold")

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import numpy as np
import torch
import pickle
from torch.utils.data import Dataset, DataLoader

# 提取特徵
def extract(loader):
    featureExtractor.eval()
    features = None
    with torch.no_grad():

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import os
import shutil

fileListingFile = "missclassified_file_path.txt"
outputDir = "./"

# sample path in txt: /root/notebooks/XXXXX/datasets/XXXXXXXX.jpg

with open(fileListingFile, "r") as file:
    fileNames = [file.strip() for file in file.readlines()]

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batch_iterator = iter(dataloader)

images, targets = next(batch_iterator)
try:
	images, targets = next(batch_iterator)
except StopIteration:
	batch_iterator = iter(data_loader)
	images, targets = next(batch_iterator)

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# https://github.com/rajatkoner08/oodformer/blob/master/networks/resnet.py
'''ResNet in PyTorch.
BasicBlock and Bottleneck module is from the original ResNet paper:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Deep Residual Learning for Image Recognition. arXiv:1512.03385
PreActBlock and PreActBottleneck module is from the later paper:
[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Identity Mappings in Deep Residual Networks. arXiv:1603.05027
Original code is from https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py
'''