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dataloader.py
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import glob
import pandas as pd
import numpy as np
from PIL import Image
import torch
from torchvision import transforms
from torch.utils.data import Dataset
import torch.nn.functional as F
class DVSDataset(Dataset):
def __init__(self, data_txt):
self.data_txt = data_txt
with open(self.data_txt,"r") as f:
self.data_list = f.readlines()
self.transform = transforms.Compose([
transforms.RandomApply(
[transforms.RandomCrop(size=(270, 240)), transforms.Resize(size=(288, 256),antialias=True)],p=0.3),
])
def __len__(self):
return len(self.data_list)
def __getitem__(self, idx):
"""
Load the input and label
input shape [T, C, H, W]
"""
data = self.data_list[idx].split(" ")
frame1 = np.array(Image.open(data[0]))
frame2 = np.array(Image.open(data[1]))
label = int(data[2])
f1_polarized = -frame1[:,-256:,0]/255 + frame1[:,-256:,2]/255
f2_polarized = -frame2[:,-256:,0]/255 + frame2[:,-256:,2]/255
input = torch.tensor(np.vstack((f1_polarized[np.newaxis,np.newaxis,:,:],f2_polarized[np.newaxis,np.newaxis,:,:])),dtype=torch.float32)
if self.data_txt == "./train.txt":
input = self.transform(input)
label = torch.tensor(label,dtype=torch.float32)
return input, label
def get_DVSDataloader(data_txt,batch_size, num_workers=4, shuffle=True):
return torch.utils.data.DataLoader(
DVSDataset(data_txt=data_txt),
batch_size=batch_size,
num_workers=num_workers,
shuffle=shuffle
)
class RGBDataset(Dataset):
def __init__(self, data_txt):
self.data_txt = data_txt
with open(self.data_txt,"r") as f:
self.data_list = f.readlines()
self.transform = transforms.Compose([
transforms.RandomApply(
[transforms.RandomCrop(size=(270, 240)), transforms.Resize(size=(288, 256),antialias=True)],p=0.3),
])
def __len__(self):
return len(self.data_list)
def __getitem__(self, idx):
"""
Load the input and label
input shape [T, C, H, W]
"""
data = self.data_list[idx].split(" ")
frame1 = np.array(Image.open(data[0]))
frame2 = np.array(Image.open(data[1]))
label = int(data[2])
input = torch.tensor(np.concatenate((frame1[:,-256:,:],frame2[:,-256:,:]), axis=2),dtype=torch.float32).permute(2,0,1)
if self.data_txt == "./train.txt":
input = self.transform(input)
label = torch.tensor(label,dtype=torch.float32)
return input, label
def get_RGBDataloader(data_txt,batch_size, num_workers=4, shuffle=True):
return torch.utils.data.DataLoader(
RGBDataset(data_txt=data_txt),
batch_size=batch_size,
num_workers=num_workers,
shuffle=shuffle
)
class ImageDataset(Dataset):
def __init__(self, data_txt):
self.data_txt = data_txt
with open(self.data_txt,"r") as f:
self.data_list = f.readlines()
self.transform = transforms.Compose([
transforms.RandomApply(
[transforms.RandomCrop(size=(270, 240)), transforms.Resize(size=(288, 256),antialias=True)],p=0.3),
])
def __len__(self):
return len(self.data_list)
def __getitem__(self, idx):
"""
Load the input and label
"""
data = self.data_list[idx].split(" ")
frame1 = np.array(Image.open(data[0]))
frame2 = np.array(Image.open(data[1]))
label = int(data[2])
input = torch.tensor(np.concatenate((frame1[:,-256:,:],frame2[:,-256:,:]), axis=2),dtype=torch.float32).permute(2,0,1)
if self.data_txt == "./train.txt":
input = self.transform(input)
label = torch.tensor(label,dtype=torch.float32)
return input, label
def get_ImageDataloader(data_txt,batch_size, num_workers=4, shuffle=True):
return torch.utils.data.DataLoader(
ImageDataset(data_txt=data_txt),
batch_size=batch_size,
num_workers=num_workers,
shuffle=shuffle
)