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targetmodel.py
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targetmodel.py
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import torch.nn as nn
from PIL import Image
from torch.utils.data import Dataset
import numpy as np
from transform_file import cut
root='/home/wang/Dataset/Caltech256/'
#root='/media/this/02ff0572-4aa8-47c6-975d-16c3b8062013/Caltech256/'
def default_loader(path):
return Image.open(path).convert('RGB')
class MyDataset(Dataset):
def __init__(self, txt, transform=None, pert=np.zeros(1), loader=default_loader):
fh = open(txt, 'r')
imgs = []
for line in fh:
line = line.rstrip()
line = line.strip('\n')
line = line.rstrip()
words = line.split()
imgs.append((words[0],int(words[1])))
self.imgs = imgs
self.transform = transform
self.loader = loader
self.pert = pert
def __getitem__(self, index):
fn, label = self.imgs[index]
img = Image.fromarray(np.clip(cut(self.loader(fn))+self.pert,0,255).astype(np.uint8))
if self.transform is not None:
img = self.transform(img)
return img,label
def __len__(self):
return len(self.imgs)
class ResNet50_ft(nn.Module):
def __init__(self, model):
super(ResNet50_ft, self).__init__()
self.resnet_layer = nn.Sequential(*list(model.children())[:-1])
self.Linear_layer = nn.Linear(2048, 257)
def forward(self, x):
x = self.resnet_layer(x)
x = x.view(x.size(0), -1)
x = self.Linear_layer(x)
return x