Tiger Model/IS.py

# Copyright 2024 Hui Lu, Fang Dai, Siqiong Yao.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from datasets import *

import torch.nn as nn
import torch.nn.functional as F
import torch
import numpy as np
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.autograd import Variable
from torch.nn import functional as F
import torch.utils.data
from scipy.stats import entropy
from torchvision.models.inception import inception_v3

import os
import glob
import random
import os
import numpy as np

from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms

class ISImageDataset(Dataset):
    def __init__(self, root, transforms_=None):
        self.transform = transforms.Compose(transforms_)

        self.files = sorted(glob.glob(os.path.join(root) + "/*.png"))

    def __getitem__(self, index):
        img = Image.open(self.files[index % len(self.files)]).convert('RGB')      
        item_image = self.transform(img)
        return item_image

    def __len__(self):
        return len(self.files)

path = '.../Figure/'
count = 0
for root,dirs,files in os.walk(path):   
      for each in files:
             count += 1   
print(count)
batch_size = 64
transforms_ = [
    transforms.Resize((256, 256)),
    transforms.ToTensor(),
    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
]

val_dataloader = DataLoader(
    ISImageDataset(path, transforms_=transforms_),
    batch_size = batch_size,
)

cuda = False if torch.cuda.is_available() else False
print('cuda: ',cuda)
tensor = torch.cuda.FloatTensor

inception_model = inception_v3(pretrained=True, transform_input=False).cuda()
inception_model.eval()
up = nn.Upsample(size=(299, 299), mode='bilinear', align_corners=False).cuda()

def get_pred(x):
    if True:
        x = up(x)
    x = inception_model(x)
    return F.softmax(x, dim=1).data.cpu().numpy()

print('Computing predictions using inception v3 model')
preds = np.zeros((count, 1000))

for i, data in enumerate(val_dataloader):
    data = data.type(tensor)
    batch_size_i = data.size()[0]
    preds[i * batch_size:i * batch_size + batch_size_i] = get_pred(data)

print('Computing KL Divergence')
split_scores = []
splits=10
N = count
for k in range(splits):
    part = preds[k * (N // splits): (k + 1) * (N // splits), :] 
    py = np.mean(part, axis=0) 
    scores = []
    for i in range(part.shape[0]):
        pyx = part[i, :]  
        scores.append(entropy(pyx, py))  
    split_scores.append(np.exp(np.mean(scores)))


mean, std  = np.mean(split_scores), np.std(split_scores)
print('IS is %.4f' % mean)
print('The std is %.4f' % std)