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data.py
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from __future__ import print_function
from torchtools import *
import torch.utils.data as data
import random
import os
import numpy as np
from PIL import Image as pil_image
import pickle
from itertools import islice
from torchvision import transforms
class MiniImagenetLoader(data.Dataset):
def __init__(self, root, partition='train'):
super(MiniImagenetLoader, self).__init__()
# set dataset information
self.root = root
self.partition = partition
if tt.arg.features:
self.data_size = [640]
else:
self.data_size = [3, 84, 84]
# set normalizer
mean_pix = [x / 255.0 for x in [120.39586422, 115.59361427, 104.54012653]]
std_pix = [x / 255.0 for x in [70.68188272, 68.27635443, 72.54505529]]
normalize = transforms.Normalize(mean=mean_pix, std=std_pix)
# set transformer
if self.partition == 'train':
self.transform = transforms.Compose([transforms.RandomCrop(84, padding=4),
transforms.RandomHorizontalFlip(),
lambda x: np.asarray(x),
transforms.ToTensor(),
normalize])
else: # 'val' or 'test' ,
self.transform = transforms.Compose([lambda x: np.asarray(x),
transforms.ToTensor(),
normalize])
# load data
self.data = self.load_dataset()
def load_dataset(self):
if tt.arg.features:
dataset_path = os.path.join(self.root, 'WRN_%s.pickle' % self.partition)
with open(dataset_path, 'rb') as handle:
data = pickle.load(handle)
return data
# load data
dataset_path = os.path.join(self.root, 'compacted_datasets/mini_imagenet_%s.pickle' % self.partition)
with open(dataset_path, 'rb') as handle:
data = pickle.load(handle)
# for each class
for c_idx in data:
# for each image
for i_idx in range(len(data[c_idx])):
# resize
image_data = pil_image.fromarray(np.uint8(data[c_idx][i_idx]))
image_data = image_data.resize((self.data_size[2], self.data_size[1]))
#image_data = np.array(image_data, dtype='float32')
#image_data = np.transpose(image_data, (2, 0, 1))
# save
data[c_idx][i_idx] = image_data
return data
def get_task_batch(self,
num_tasks=5,
num_ways=20,
num_shots=1,
num_queries=1,
seed=None):
if seed is not None:
random.seed(seed)
# init task batch data
support_data, support_label, query_data, query_label = [], [], [], []
for _ in range(num_ways * num_shots):
data = np.zeros(shape=[num_tasks] + self.data_size,
dtype='float32')
label = np.zeros(shape=[num_tasks],
dtype='float32')
support_data.append(data)
support_label.append(label)
for _ in range(num_ways * num_queries):
data = np.zeros(shape=[num_tasks] + self.data_size,
dtype='float32')
label = np.zeros(shape=[num_tasks],
dtype='float32')
query_data.append(data)
query_label.append(label)
# get full class list in dataset
full_class_list = list(self.data.keys())
label_list = list(range(0,5))
random.shuffle(label_list)
# for each task
for t_idx in range(num_tasks):
# define task by sampling classes (num_ways)
task_class_list = random.sample(full_class_list, num_ways)
# for each sampled class in task
for c_idx in range(num_ways):
# sample data for support and query (num_shots + num_queries)
class_data_list = random.sample(self.data[task_class_list[c_idx]], num_shots + num_queries)
# load sample for support set
for i_idx in range(num_shots):
# set data
if tt.arg.features:
support_data[i_idx + c_idx * num_shots][t_idx] = class_data_list[i_idx]
else:
support_data[i_idx + c_idx * num_shots][t_idx] = self.transform(class_data_list[i_idx])
support_label[i_idx + c_idx * num_shots][t_idx] = label_list[c_idx]
# load sample for query set
for i_idx in range(num_queries):
if tt.arg.features:
query_data[i_idx + c_idx * num_queries][t_idx] = class_data_list[num_shots + i_idx]
else:
query_data[i_idx + c_idx * num_queries][t_idx] = self.transform(class_data_list[num_shots + i_idx])
query_label[i_idx + c_idx * num_queries][t_idx] = label_list[c_idx]
# convert to tensor (num_tasks x (num_ways * (num_supports + num_queries)) x ...)
support_data = torch.stack([torch.from_numpy(data).float().to(tt.arg.device) for data in support_data], 1)
support_label = torch.stack([torch.from_numpy(label).float().to(tt.arg.device) for label in support_label], 1)
query_data = torch.stack([torch.from_numpy(query_data[i]).float().to(tt.arg.device) for i in label_list], 1)
query_label = torch.stack([torch.from_numpy(query_label[i]).float().to(tt.arg.device) for i in label_list], 1)
return [support_data, support_label, query_data, query_label]
class TieredImagenetLoader(data.Dataset):
def __init__(self, root, partition='train'):
print("Tiered")
super(TieredImagenetLoader, self).__init__()
# set dataset information
self.root = root
self.partition = partition
if tt.arg.features:
self.data_size = [640]
else:
self.data_size = [3, 84, 84]
# set normalizer
mean_pix = [x / 255.0 for x in [120.45, 115.59361427, 104.54012653]]
std_pix = [x / 255.0 for x in [70.68188272, 68.27635443, 72.54505529]]
normalize = transforms.Normalize(mean=mean_pix, std=std_pix)
# set transformer
if self.partition == 'train':
self.transform = transforms.Compose([transforms.RandomCrop(84, padding=4),
transforms.RandomHorizontalFlip(),
lambda x: np.asarray(x),
transforms.ToTensor(),
normalize])
else: # 'val' or 'test' ,
self.transform = transforms.Compose([lambda x: np.asarray(x),
transforms.ToTensor(),
normalize])
# load data
self.data = self.load_dataset()
def load_dataset(self):
print(tt.arg.features)
if tt.arg.features:
dataset_path = os.path.join(self.root, 'tiered_WRN_eval_%s.pickle' % self.partition)
with open(dataset_path, 'rb') as handle:
data = pickle.load(handle)
return data
# load data
image_dataset_path = os.path.join(self.root, 'tiered-imagenet/',
'%s_images_png.pkl' % self.partition)
label_dataset_path = os.path.join(self.root, 'tiered-imagenet/',
'%s_labels.pkl' % self.partition)
# for each class
resized_image_dataset_path = os.path.join(self.root, 'tiered-imagenet/',
'resized_%s_images_png.pkl' % self.partition)
if os.path.isfile(resized_image_dataset_path):
with open(resized_image_dataset_path, 'rb') as handle:
resized_data = pickle.load(handle)
else:
with open(image_dataset_path, 'rb') as handle:
data = pickle.load(handle)
with open(label_dataset_path, 'rb') as handle:
label = pickle.load(handle)
class_list = np.unique(label['label_specific'])
resized_data = {key: [] for key in class_list}
for i_idx, item in tqdm(enumerate(data), desc='decompress'):
# resize
c_idx = label['label_specific'][i_idx]
image_data = cv2.imdecode(data[i_idx], 1)
# image_data = cv2.resize(image_data, dsize=(self.data_size[2], self.data_size[1]))
image_data = pil_image.fromarray(np.uint8(image_data))
# image_data = image_data.resize((self.data_size[2], self.data_size[1]))
# image_data = np.array(image_data, dtype='float32')
# image_data = np.transpose(image_data, (2, 0, 1))
# save
resized_data[c_idx].append(image_data)
print('decode %s image finished'.format(self.partition))
with open(resized_image_dataset_path, 'wb') as f:
pickle.dump(resized_data, f)
return resized_data
def get_task_batch(self,
num_tasks=5,
num_ways=20,
num_shots=1,
num_queries=1,
seed=None):
if seed is not None:
random.seed(seed)
# init task batch data
support_data, support_label, query_data, query_label = [], [], [], []
for _ in range(num_ways * num_shots):
data = np.zeros(shape=[num_tasks] + self.data_size,
dtype='float32')
label = np.zeros(shape=[num_tasks],
dtype='float32')
support_data.append(data)
support_label.append(label)
for _ in range(num_ways * num_queries):
data = np.zeros(shape=[num_tasks] + self.data_size,
dtype='float32')
label = np.zeros(shape=[num_tasks],
dtype='float32')
query_data.append(data)
query_label.append(label)
# get full class list in dataset
full_class_list = list(self.data.keys())
label_list = list(range(0,5))
random.shuffle(label_list)
# for each task
for t_idx in range(num_tasks):
# define task by sampling classes (num_ways)
task_class_list = random.sample(full_class_list, num_ways)
# for each sampled class in task
for c_idx in range(num_ways):
# sample data for support and query (num_shots + num_queries)
class_data_list = random.sample(list(self.data[task_class_list[c_idx]]), num_shots + num_queries)
# load sample for support set
for i_idx in range(num_shots):
# set data
if tt.arg.features:
support_data[i_idx + c_idx * num_shots][t_idx] = class_data_list[i_idx]
else:
support_data[i_idx + c_idx * num_shots][t_idx] = self.transform(class_data_list[i_idx])
support_label[i_idx + c_idx * num_shots][t_idx] = label_list[c_idx]
# load sample for query set
for i_idx in range(num_queries):
if tt.arg.features:
query_data[i_idx + c_idx * num_queries][t_idx] = class_data_list[num_shots + i_idx]
else:
query_data[i_idx + c_idx * num_queries][t_idx] = self.transform(class_data_list[num_shots + i_idx])
query_label[i_idx + c_idx * num_queries][t_idx] = label_list[c_idx]
# convert to tensor (num_tasks x (num_ways * (num_supports + num_queries)) x ...)
support_data = torch.stack([torch.from_numpy(data).float().cuda() for data in support_data], 1)
support_label = torch.stack([torch.from_numpy(label).float().cuda() for label in support_label], 1)
query_data = torch.stack([torch.from_numpy(query_data[i]).float().to(tt.arg.device) for i in label_list], 1)
query_label = torch.stack([torch.from_numpy(query_label[i]).float().to(tt.arg.device) for i in label_list], 1)
return [support_data, support_label, query_data, query_label]