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dataset.py
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dataset.py
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from torch.utils.data import Dataset
from torchvision import transforms
from PIL import Image
import torch
import numpy as np
import pandas as pd
class ImageDataSet(Dataset):
'''图片加载和处理'''
def __init__(self,df,transform):
self.df=df
self.transform=transform
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
image_path = self.df.iloc[idx].path
image_id = self.df.iloc[idx].image_id
order = self.df.iloc[idx].category_id
if self.df.iloc[idx].is_train == 1:
image = image_path
else:
image = Image.open(image_path).convert('RGBA').convert('RGB')
vector=np.zeros(137,dtype=float)
vector[order]=1.0
label = torch.from_numpy(vector)
image=self.transform(image)
return image, label, order, image_id
class ImageDataSet2(Dataset):
'''图片加载和处理'''
def __init__(self,df,transform):
self.df=df
self.transform=transform
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
image_path = self.df.iloc[idx].path
image_id = self.df.iloc[idx].image_id
order = self.df.iloc[idx].category_id
image = Image.open(image_path).convert('RGBA').convert('RGB')
label = torch.from_numpy(np.array(order))
image=self.transform(image)
return image, label, order, image_id