68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
|
|
# All rights reserved.
|
|
|
|
# This source code is licensed under the license found in the
|
|
# LICENSE file in the root directory of this source tree.
|
|
|
|
from __future__ import print_function
|
|
from PIL import Image
|
|
from typing import Any, Callable, Optional, Tuple
|
|
|
|
import numpy as np
|
|
import os
|
|
import os.path
|
|
import pickle
|
|
import scipy.io
|
|
|
|
from torchvision.datasets.vision import VisionDataset
|
|
|
|
|
|
class Flowers(VisionDataset):
|
|
|
|
def __init__(
|
|
self,
|
|
root,
|
|
train=True,
|
|
transform=None,
|
|
target_transform=None,
|
|
download=False,
|
|
):
|
|
|
|
super(Flowers, self).__init__(root, transform=transform,
|
|
target_transform=target_transform)
|
|
|
|
base_folder = root
|
|
self.image_folder = os.path.join(base_folder, "jpg")
|
|
label_file = os.path.join(base_folder, "imagelabels.mat")
|
|
setid_file = os.path.join(base_folder, "setid.mat")
|
|
|
|
self.train = train
|
|
|
|
self.labels = scipy.io.loadmat(label_file)["labels"][0]
|
|
train_list = scipy.io.loadmat(setid_file)["trnid"][0]
|
|
val_list = scipy.io.loadmat(setid_file)["valid"][0]
|
|
test_list = scipy.io.loadmat(setid_file)["tstid"][0]
|
|
trainval_list = np.concatenate([train_list, val_list])
|
|
|
|
if self.train:
|
|
self.img_files = trainval_list
|
|
else:
|
|
self.img_files = test_list
|
|
|
|
|
|
def __getitem__(self, index):
|
|
img_name = "image_%05d.jpg" % self.img_files[index]
|
|
target = self.labels[self.img_files[index] - 1] - 1
|
|
img = Image.open(os.path.join(self.image_folder, img_name))
|
|
|
|
if self.transform is not None:
|
|
img = self.transform(img)
|
|
|
|
if self.target_transform is not None:
|
|
target = self.target_transform(target)
|
|
|
|
return img, target
|
|
|
|
def __len__(self):
|
|
return len(self.img_files)
|