/
meta_task_generator.py
executable file
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/
meta_task_generator.py
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import torchvision
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch
from torch.utils.data import DataLoader,Dataset
import random
import os
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from torch.utils.data.sampler import Sampler
def imshow(img):
npimg = img.numpy()
plt.axis("off")
plt.imshow(np.transpose(npimg,(1,2,0)))
plt.show()
class Rotate(object):
def __init__(self, angle):
self.angle = angle
def __call__(self, x, mode="reflect"):
x = x.rotate(self.angle)
return x
def generate_folders(dataset):
train_folder = './datas/' + dataset + '/train'
test_folder = './datas/' + dataset + '/val'
metatrain_folders = [os.path.join(train_folder, label) \
for label in os.listdir(train_folder) \
if os.path.isdir(os.path.join(train_folder, label)) \
]
metatest_folders = [os.path.join(test_folder, label) \
for label in os.listdir(test_folder) \
if os.path.isdir(os.path.join(test_folder, label)) \
]
random.seed(1)
random.shuffle(metatrain_folders)
random.shuffle(metatest_folders)
return metatrain_folders,metatest_folders
class ARTask(object):
def __init__(self, character_folders, num_classes, train_num, test_num, frame_num):
self.character_folders = character_folders
self.num_classes = num_classes
self.train_num = train_num
self.test_num = test_num
self.frame_num = frame_num
class_folders = random.sample(self.character_folders,self.num_classes)
labels = np.array(range(len(class_folders)))
labels = dict(zip(class_folders, labels))
samples = dict()
self.train_roots = []
self.test_roots = []
for c in class_folders:
temp = [os.path.join(c, x) for x in os.listdir(c)]
samples[c] = random.sample(temp, len(temp))
random.shuffle(samples[c])
self.train_roots += samples[c][:train_num]
self.test_roots += samples[c][train_num:train_num+test_num]
self.train_labels = [labels[self.get_class(x)] for x in self.train_roots]
self.test_labels = [labels[self.get_class(x)] for x in self.test_roots]
def get_class(self, sample):
return os.path.join(*sample.split('/')[:-1])
class FewShotDataset(Dataset):
def __init__(self, task, split='train', transform=None, target_transform=None):
self.transform = transform # Torch operations on the input image
self.target_transform = target_transform
self.task = task
self.split = split
self.image_roots = self.task.train_roots if self.split == 'train' else self.task.test_roots
self.labels = self.task.train_labels if self.split == 'train' else self.task.test_labels
def __len__(self):
return len(self.image_roots)
def __getitem__(self, idx):
raise NotImplementedError("This is an abstract class. Subclass this class for your particular dataset.")
class ARDataset(FewShotDataset):
def __init__(self, *args, **kwargs):
super(ARDataset, self).__init__(*args, **kwargs)
self.frame_num = int(args[0].frame_num)
def __getitem__(self, idx):
image_root = self.image_roots[idx]
img_name = (os.listdir(image_root))
img_name.sort()
frame = self.frame_num
video = torch.Tensor(frame, 3, 128, 128)
if len(img_name) > frame:
img_name = random.sample(img_name, frame)
img_name.sort()
num_frame = frame
else:
num_frame = len(img_name)
for n in range(len(img_name)):
image = Image.open(image_root + '/' + img_name[n])
image = image.convert('RGB')
if self.transform is not None:
image = self.transform(image)
video[n,:,:,:] = image
label = self.labels[idx]
if self.target_transform is not None:
label = self.target_transform(label)
return video, label, num_frame
class ClassBalancedSampler(Sampler):
''' Samples 'num_inst' examples each from 'num_cl' pools
of examples of size 'num_per_class' '''
def __init__(self, num_per_class, num_cl, num_inst,shuffle=True):
self.num_per_class = num_per_class
self.num_cl = num_cl
self.num_inst = num_inst
self.shuffle = shuffle
def __iter__(self):
# return a single list of indices, assuming that items will be grouped by class
if self.shuffle:
batch = [[i+j*self.num_inst for i in torch.randperm(self.num_inst)[:self.num_per_class]] for j in range(self.num_cl)]
else:
batch = [[i+j*self.num_inst for i in range(self.num_inst)[:self.num_per_class]] for j in range(self.num_cl)]
batch = [item for sublist in batch for item in sublist]
if self.shuffle:
random.shuffle(batch)
return iter(batch)
def __len__(self):
return 1
def get_ar_data_loader(task, num_per_class=1, split='train',shuffle = False):
normalize = transforms.Normalize(mean=[0.3474, 0.3474, 0.3474], std=[0.2100, 0.2100, 0.2100])
dataset = ARDataset(task,split=split,transform=transforms.Compose([transforms.Resize([128,128]), transforms.ToTensor(), normalize]))
if split == 'train':
sampler = ClassBalancedSampler(num_per_class, task.num_classes, task.train_num,shuffle=shuffle)
else:
sampler = ClassBalancedSampler(num_per_class, task.num_classes, task.test_num,shuffle=shuffle)
loader = DataLoader(dataset, batch_size=num_per_class*task.num_classes, sampler=sampler)
return loader