/
imagenet_dataset.py
61 lines (53 loc) · 1.76 KB
/
imagenet_dataset.py
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import torch
import torch.utils.data
import numpy as np
import json
from PIL import Image
class DogCatDataset(torch.utils.data.Dataset):
def __init__(self, train, transform, path='./raw_data/dog_and_cat/train'):
self.train = train
self.transform = transform
self.path = path
if train:
self.st = 0
self.N = 20000
else:
self.st = 20000
self.N = 5000
self.dataset = []
for idx in range(self.N):
n = (idx+self.st) // 2
lab = (idx+self.st) % 2
lab_str = 'cat' if lab == 0 else 'dog'
path = self.path + '/%s.%d.jpg'%(lab_str, n)
img = Image.open(path).convert("RGB")
self.dataset.append((self.transform(img), lab))
print (self.N, 'Dataset loaded')
def __len__(self):
return self.N
def __getitem__(self, idx):
return self.dataset[idx]
class DogFishDataset(torch.utils.data.Dataset):
def __init__(self, train, transform, path='./raw_data/dog_and_fish/train'):
self.train = train
self.transform = transform
self.path = path
if train:
self.st = 0
self.N = 10000
else:
self.st = 10000
self.N = 2000
self.dataset = []
for idx in range(self.N):
n = (idx+self.st) // 2
lab = (idx+self.st) % 2
lab_str = 'fish' if lab == 0 else 'dog'
path = self.path + '/%s.%d.jpg'%(lab_str, n)
img = Image.open(path,'rb').convert("RGB")
self.dataset.append((self.transform(img), lab))
print (self.N, 'Dataset loaded')
def __len__(self):
return self.N
def __getitem__(self, idx):
return self.dataset[idx]