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import os
import torch
from torch.utils.data import Dataset
from torchvision.transforms import transforms
import numpy as np
import collections
from PIL import Image
import csv
import random
class MiniImagenet(Dataset):
"""
put mini-imagenet files as :
root :
|- images/*.jpg includes all imgeas
|- train.csv
|- test.csv
|- val.csv
NOTICE: meta-learning is different from general supervised learning, especially the concept of batch and set.
batch: contains several sets
sets: conains n_way * k_shot for meta-train set, n_way * n_query for meta-test set.
"""
def __init__(self, root, mode, batch_size, n_way, k_shot, k_query, resize, split=1, startidx=0):
"""
:param root: root path of mini-imagenet
:param mode: train, val or test
:param batch_size: batch size of sets, not batch of imgs
:param n_way:
:param k_shot:
:param k_query: num of qeruy imgs per class
:param resize: resize to
:param startidx: start to index label from startidx
"""
self.batch_size = batch_size # batch of set, not batch of imgs
self.n_way = n_way # n-way
self.k_shot = k_shot # k-shot
self.k_query = k_query # for evaluation
self.set_size = self.n_way * self.k_shot # num of samples per set
self.query_size = self.n_way * self.k_query # number of samples per set for evaluation
self.resize = resize # resize to
self.split = split
self.startidx = startidx # index label not from 0, but from startidx
print('shuffle DB :%s, b:%d, %d-way, %d-shot, %d-query, resize:%d' % (
mode, batch_size, n_way, k_shot, k_query, resize))
if mode == 'train':
self.transform = transforms.Compose([lambda x: Image.open(x).convert('RGB'),
transforms.Resize((self.resize, self.resize)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
else:
self.transform = transforms.Compose([lambda x: Image.open(x).convert('RGB'),
transforms.Resize((self.resize, self.resize)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
self.path = os.path.join(root, 'images') # image path
csvdata = self.loadCSV(os.path.join(root, mode + '.csv')) # csv path
self.data = [] # list of list
self.img2label = {} # img2label dict
for i, (k, v) in enumerate(csvdata.items()):
self.data.append(v) # [[img1, img2, ...], [img601, ...]]
self.img2label[k] = i + self.startidx # {"img_name[:9]":label}
self.cls_num = len(self.data) # 64
''' split train data '''
if self.split != 1:
data = list(zip(*self.data))
data_list = [x for data_tuple in data for x in data_tuple]
label_list = list(range(self.cls_num)) * len(data)
self.data_target = list(zip(data_list, label_list))
data_split = self.data_target[int(self.split[0] * len(data_list)):int(self.split[1] * len(data_list))]
data_dict = {}
for v in data_split:
d = v[0]
lbl = v[1]
if lbl in data_dict.keys():
data_dict[lbl].append(d)
else:
data_dict[lbl] = [d]
self.data = []
for i in range(self.cls_num):
self.data.append(data_dict[i]) # list of list
self.create_batch(self.batch_size)
def loadCSV(self, csvf):
"""
return a dict saving the information of csv
:param splitFile: csv file name
:return: {label:[file1, file2 ...]}
"""
dictLabels = {}
with open(csvf) as csvfile:
csvreader = csv.reader(csvfile, delimiter=',')
next(csvreader, None) # skip (filename, label)
for i, row in enumerate(csvreader):
filename = row[0]
label = row[1]
# append filename to current label
if label in dictLabels.keys():
dictLabels[label].append(filename)
else:
dictLabels[label] = [filename]
return dictLabels
def create_batch(self, batch_size):
"""
create batch for meta-learning.
episode here means batch, and it means how many sets we want to retain.
:param episodes: batch size
:return:
"""
self.support_x_batch = [] # support set batch
self.query_x_batch = [] # query set batch
for b in range(batch_size): # for each batch
# 1.select n_way classes randomly, choose 5-way from 64 classes, no duplicate
selected_cls = np.random.choice(self.cls_num, self.n_way, replace=False)
np.random.shuffle(selected_cls)
support_x = [] # list of list
query_x = [] # list of list
for cls in selected_cls:
# 2. select k_shot + k_query for each class
selected_imgs_idx = np.random.choice(len(self.data[cls]), self.k_shot + self.k_query, replace=False)
np.random.shuffle(selected_imgs_idx)
indexDtrain = np.array(selected_imgs_idx[:self.k_shot]) # idx for Dtrain
indexDtest = np.array(selected_imgs_idx[self.k_shot:]) # idx for Dtest
support_x.append(
np.array(self.data[cls])[indexDtrain].tolist()) # get all images filename for current Dtrain
query_x.append(np.array(self.data[cls])[indexDtest].tolist())
# shuffle the correponding relation between support set and query set
random.shuffle(support_x) # 5*1
random.shuffle(query_x) # 5*15
self.support_x_batch.append(support_x) # append set to current sets, 10000*5*1
self.query_x_batch.append(query_x) # append sets to current sets, 10000*5*15
def __getitem__(self, index):
support_x = torch.FloatTensor(self.set_size, 3, self.resize, self.resize)
query_x = torch.FloatTensor(self.query_size, 3, self.resize, self.resize)
flatten_support_x = [] # list of path
for sublist in self.support_x_batch[index]:
for item in sublist:
flatten_support_x.append(os.path.join(self.path, item))
support_y = []
for sublist in self.support_x_batch[index]:
for item in sublist:
support_y.append(self.img2label[item[:9]])
support_y = np.array(support_y)
flatten_query_x = [os.path.join(self.path, item)
for sublist in self.query_x_batch[index] for item in sublist]
query_y = np.array([self.img2label[item[:9]]
for sublist in self.query_x_batch[index] for item in sublist]).astype(np.int32)
# print('global:', support_y, query_y)
# support_y: [set_size]
# query_y: [query_size]
# unique: [n-way], sorted
unique = np.unique(support_y)
random.shuffle(unique)
# relative means the label ranges from 0 to n-way
support_y_relative = np.zeros(self.set_size) # 5*1
query_y_relative = np.zeros(self.query_size) # 5*15
for idx, label in enumerate(unique):
support_y_relative[support_y == label] = idx
query_y_relative[query_y == label] = idx
# print('relative:', support_y_relative, query_y_relative)
for i, path in enumerate(flatten_support_x):
support_x[i] = self.transform(path)
for i, path in enumerate(flatten_query_x):
query_x[i] = self.transform(path)
return support_x, torch.LongTensor(support_y_relative), query_x, torch.LongTensor(query_y_relative)
def __len__(self):
# as we have built up to batch_size of sets, you can sample some small batch size of sets.
return self.batch_size
if __name__ == '__main__':
# the following episode is to view one set of images via tensorboard.
from torchvision.utils import make_grid
from matplotlib import pyplot as plt
from tensorboardX import SummaryWriter
import time
plt.ion()
tb = SummaryWriter('runs', 'mini-imagenet')
mini = MiniImagenet('../mini-imagenet/', mode='train', n_way=5, k_shot=1, k_query=1, batch_size=1000, resize=168)
for i, set_ in enumerate(mini):
# support_x: [k_shot*n_way, 3, 84, 84]
support_x, support_y, query_x, query_y = set_
support_x = make_grid(support_x, nrow=2)
query_x = make_grid(query_x, nrow=2)
plt.figure(1)
plt.imshow(support_x.transpose(2, 0).numpy())
plt.pause(0.5)
plt.figure(2)
plt.imshow(query_x.transpose(2, 0).numpy())
plt.pause(0.5)
tb.add_image('support_x', support_x)
tb.add_image('query_x', query_x)
time.sleep(5)
tb.close()