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dataset_tiered.py
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dataset_tiered.py
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from __future__ import print_function
import numpy as np
from PIL import Image
import pickle as pkl
import os
import glob
import csv
from tqdm import tqdm
import cv2
class dataset_tiered(object):
def __init__(self, n_examples, n_episodes, split, args):
self.im_width, self.im_height, self.channels = list(map(int, args['x_dim'].split(',')))
self.n_examples = n_examples
self.n_episodes = n_episodes
self.split = split
self.ratio = args['ratio']
self.seed = args['seed']
self.root_dir = './data/tieredImagenet'
self.iamge_data = []
self.dict_index_label = []
self.dict_index_unlabel = []
def load_data_pkl(self):
"""
load the pkl processed tieredImagenet into label,unlabel
maintain label,unlabel data dictionary for indexes
"""
labels_name = '{}/data/{}_labels.pkl'.format(self.root_dir, self.split)
images_name = '{}/data/{}_images.npz'.format(self.root_dir, self.split)
print('labels:',labels_name)
print('images:',images_name)
# decompress images if npz not exits
if not os.path.exists(images_name):
png_pkl = images_name[:-4] + '_png.pkl'
if os.path.exists(png_pkl):
decompress(images_name, png_pkl)
else:
raise ValueError('path png_pkl not exits')
if os.path.exists(images_name) and os.path.exists(labels_name):
try:
with open(labels_name) as f:
data = pkl.load(f)
label_specific = data["label_specific"]
#label_general = data["label_general"]
#label_specific_str = data["label_specific_str"]
#label_general_str = data["label_general_str"]
except:
with open(labels_name, 'rb') as f:
data = pkl.load(f, encoding='bytes')
label_specific = data[b'label_specific']
#label_general = data[b"label_general"]
#label_specific_str = data[b"label_specific_str"]
#label_general_str = data[b"label_general_str"]
print('read label data:{}'.format(len(label_specific)))
labels = label_specific
with np.load(images_name, mmap_mode="r", encoding='latin1') as data:
image_data = data["images"]
print('read image data:{}'.format(image_data.shape))
n_classes = np.max(labels)+1
print('n_classes:{}, n_label:{}%, n_unlabel:{}%'.format(n_classes,self.ratio*100,(1-self.ratio)*100))
dict_index_label = {} # key:label, value:idxs
dict_index_unlabel = {}
for cls in range(n_classes):
idxs = np.where(labels==cls)[0]
nums = idxs.shape[0]
np.random.RandomState(self.seed).shuffle(idxs) # fix the seed to keep label,unlabel fixed
n_label = int(self.ratio*nums)
n_unlabel = nums-n_label
dict_index_label[cls] = idxs[0:n_label]
dict_index_unlabel[cls] = idxs[n_label:]
self.image_data = image_data
self.dict_index_label = dict_index_label
self.dict_index_unlabel = dict_index_unlabel
self.n_classes = n_classes
print(dict_index_label[0])
print(dict_index_unlabel[0])
def next_data(self, n_way, n_shot, n_query, num_unlabel=0, n_distractor=0, train=True):
"""
get support,query,unlabel data from n_way
get unlabel data from n_distractor
"""
support = np.zeros([n_way, n_shot, self.im_height, self.im_width, self.channels], dtype=np.float32)
query = np.zeros([n_way, n_query, self.im_height, self.im_width, self.channels], dtype=np.float32)
if num_unlabel>0:
unlabel = np.zeros([n_way+n_distractor, num_unlabel, self.im_height, self.im_width, self.channels], dtype=np.float32)
else:
unlabel = []
n_distractor = 0
selected_classes = np.random.permutation(self.n_classes)[:n_way+n_distractor]
for i, cls in enumerate(selected_classes[0:n_way]): # train way
# labled data
idx = self.dict_index_label[cls]
np.random.RandomState().shuffle(idx)
idx1 = idx[0:n_shot + n_query]
support[i] = self.image_data[idx1[:n_shot]]
query[i] = self.image_data[idx1[n_shot:]]
# unlabel
if num_unlabel>0:
idx = self.dict_index_unlabel[cls]
np.random.RandomState().shuffle(idx)
idx2 = idx[0:num_unlabel]
unlabel[i] = self.image_data[idx2]
for j,cls in enumerate(selected_classes[self.n_classes:]): # distractor way
idx = self.dict_index_unlabel[cls]
np.random.RandomState().shuffle(idx)
idx3 = idx[0:num_unlabel]
unlabel[i+j] = self.image_data[idx3]
support_labels = np.tile(np.arange(n_way)[:, np.newaxis], (1, n_shot)).astype(np.uint8)
query_labels = np.tile(np.arange(n_way)[:, np.newaxis], (1, n_query)).astype(np.uint8)
# unlabel_labels = np.tile(np.arange(n_way+n_distractor)[:, np.newaxis], (1, num_unlabel)).astype(np.uint8)
return support, support_labels, query, query_labels, unlabel
def compress(path, output):
with np.load(path, mmap_mode="r") as data:
images = data["images"]
array = []
for ii in tqdm(six.moves.xrange(images.shape[0]), desc='compress'):
im = images[ii]
im_str = cv2.imencode('.png', im)[1]
array.append(im_str)
with open(output, 'wb') as f:
pkl.dump(array, f, protocol=pkl.HIGHEST_PROTOCOL)
def decompress(path, output):
with open(output, 'rb') as f:
array = pkl.load(f, encoding='bytes')
images = np.zeros([len(array), 84, 84, 3], dtype=np.uint8)
for ii, item in tqdm(enumerate(array), desc='decompress'):
im = cv2.imdecode(item, 1)
images[ii] = im
np.savez(path, images=images)