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utils_data.py
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203 lines (179 loc) · 7.72 KB
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from torch.utils.data import Dataset, DataLoader
from PIL import Image
from torchvision import transforms
import torch
import glob
import os
import numpy as np
import xml.etree.ElementTree as ET
transform_train = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(
brightness=0.4, contrast=0.4, saturation=0.4, hue=0),
])
resnet_transform = transforms.Normalize(
mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711])
class AircraftTrainDataset(Dataset):
def __init__(
self,
class_range_train,
all_classes,
data_dir,
desc_path,
fewshot,
preprocess,
img_size=(224, 224),
classes_sublist=None
):
with open(os.path.join(data_dir,"fgvc-aircraft-2013b/data/images_variant_train.txt")) as f:
data = f.readlines()
self.class_range = class_range_train
data = [line.rstrip('\n') for line in data]
self.img_path_list = []
self.lbl_list = []
for data_i in data:
if data_i.split(" ", 1)[1] in np.array(all_classes)[self.class_range].tolist():
self.img_path_list.append(data_i.split(" ", 1)[0]+'.jpg')
self.lbl_list.append(data_i.split(" ", 1)[1])
self.img_size = img_size
self.classes_sublist = classes_sublist
self.im_dir = os.path.join(data_dir,"fgvc-aircraft-2013b/data/images")
self.preprocess = preprocess
self.all_classes = all_classes
self.desc_path = desc_path
def __len__(self):
return len(self.img_path_list)
def __getitem__(self, index):
im_path = os.path.join(self.im_dir, self.img_path_list[index])
im = Image.open(im_path).convert('RGB')
class_id = np.asarray([self.all_classes.index(self.lbl_list[index])])
im = transform_train(im)
im = self.preprocess(im)
with open(os.path.join(self.desc_path, self.lbl_list[index].replace('/','SLASH')+'.txt')) as f:
texts_class = f.readlines()
texts_class = ["a photo of a " + self.lbl_list[index] + ", a type of" + line.rstrip('\n')[2:] for line in texts_class if line.strip()]
text_i = texts_class[np.random.randint(0,len(texts_class))]
return im, torch.from_numpy(class_id), text_i
class ImageNetTrainDataset(Dataset):
def __init__(
self,
class_range_train,
all_classes_ids,
all_classes_names,
data_dir,
desc_path,
fewshot,
preprocess,
img_size=(224, 224),
classes_sublist=None
):
self.im_dir = os.path.join(data_dir, 'ILSVRC/Data/CLS-LOC/train')
self.class_range = class_range_train
self.label_list = []
self.img_path_list = []
for idx in self.class_range:
if fewshot:
files_folder = glob.glob(os.path.join(self.im_dir,all_classes_ids[idx]+"/*"))[0:16]
else:
files_folder = glob.glob(os.path.join(self.im_dir,all_classes_ids[idx]+"/*"))
self.img_path_list.extend(files_folder)
self.label_list.extend([idx]*len(files_folder))
self.img_size = img_size
self.classes_sublist = classes_sublist
self.all_classes_ids = all_classes_ids
self.all_classes_names = all_classes_names
self.desc_path = desc_path
self.preprocess = preprocess
def __len__(self):
return len(self.img_path_list)
def __getitem__(self, index):
im_path = os.path.join(self.img_path_list[index])
im = Image.open(im_path).convert('RGB')
class_id = np.asarray([self.label_list[index]])
im = transform_train(im)
im = self.preprocess(im)
with open(os.path.join(self.desc_path, self.all_classes_ids[self.label_list[index]]+'.txt')) as f:
texts_class = f.readlines()
texts_class = ["a photo of a " + self.all_classes_names[self.label_list[index]] + line.rstrip('\n')[9:] for line in texts_class if line.strip()]
text_i = texts_class[np.random.randint(0,len(texts_class))]
if len(text_i.split())>30:
text_i = min((text_i.split(delim)[0] for delim in ";:,"), key=len) + "."
return im, torch.from_numpy(class_id), text_i
class ImageNetTestDataset(Dataset):
def __init__(
self,
all_classes_ids,
data_dir,
img_size=(224, 224),
classes_sublist=None
):
self.im_dir = os.path.join(data_dir, 'ILSVRC/Data/CLS-LOC/val')
self.label_list = []
self.img_path_list = []
xml_dir = os.path.join(data_dir, 'ILSVRC/Annotations/CLS-LOC/val/')
self.all_classes_ids = all_classes_ids
files_folder = glob.glob(self.im_dir+"/*")
for file_i in files_folder:
tree = ET.parse(os.path.join(xml_dir, file_i.split("/")[-1][:-4]+"xml"))
root = tree.getroot()
name_element = root.find('.//name')
name_text = name_element.text if name_element is not None else "None"
if name_text not in all_classes_ids:
continue
self.img_path_list.append(file_i)
self.label_list.append(name_text)
self.img_size = img_size
self.classes_sublist = classes_sublist
def __len__(self):
return len(self.img_path_list)
def __getitem__(self, index):
im_path = os.path.join(self.img_path_list[index])
im = Image.open(im_path).convert('RGB')
class_id = np.asarray([self.all_classes_ids.index(self.label_list[index])])
im = self.transform(im)
# import pdb;pdb.set_trace()
return im, torch.from_numpy(np.asarray(class_id))
def transform(self, img):
im_shape = (min(int(img.size[0]*0.995), int(img.size[1]*0.995)), min(int(img.size[0]*0.995), int(img.size[1]*0.995)))
img = transforms.CenterCrop(im_shape)(img)
img = img.resize(self.img_size)
img = transforms.ToTensor()(img)
img = resnet_transform(img)
return img
class AircraftTestDataset(Dataset):
def __init__(
self,
all_classes,
data_dir,
img_size=(224, 224),
classes_sublist=None
):
with open(os.path.join(data_dir, "fgvc-aircraft-2013b/data/images_variant_test.txt")) as f:
data = f.readlines()
data = [line.rstrip('\n') for line in data]
self.img_path_list = []
self.lbl_list = []
for data_i in data:
if data_i.split(" ", 1)[1] in np.array(all_classes).tolist():
self.img_path_list.append(data_i.split(" ", 1)[0]+'.jpg')
self.lbl_list.append(data_i.split(" ", 1)[1])
self.img_size = img_size
self.im_dir = os.path.join(data_dir, "fgvc-aircraft-2013b/data/images")
self.all_classes = all_classes
def __len__(self):
return len(self.img_path_list)
def __getitem__(self, index):
im_path = os.path.join(self.im_dir, self.img_path_list[index])
im = Image.open(im_path).convert('RGB')
class_id = np.asarray([self.all_classes.index(self.lbl_list[index])])
im = self.transform(im)
return im, torch.from_numpy(class_id)
def transform(self, img):
im_shape = (min(int(img.size[0]*0.995), int(img.size[1]*0.995)), min(int(img.size[0]*0.995), int(img.size[1]*0.995)))
img = transforms.CenterCrop(im_shape)(img)
img = img.resize(self.img_size)
img = transforms.ToTensor()(img)
img = resnet_transform(img)
return img