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dataloading.py
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dataloading.py
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# Copyright 2018-2020 Stanislav Pidhorskyi
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import torch.utils.data
from net import *
import pickle
import numpy as np
from os import path
import dlutils
import warnings
class Dataset:
@staticmethod
def list_of_pairs_to_numpy(l):
return np.asarray([x[1] for x in l], np.float32), np.asarray([x[0] for x in l], np.int)
def __init__(self, data):
self.x, self.y = Dataset.list_of_pairs_to_numpy(data)
def __getitem__(self, index):
if isinstance(index, slice):
return self.y[index.start:index.stop], self.x[index.start:index.stop]
return self.y[index], self.x[index]
def __len__(self):
return len(self.y)
def shuffle(self):
permutation = np.random.permutation(self.y.shape[0])
for x in [self.y, self.x]:
np.take(x, permutation, axis=0, out=x)
def make_datasets(cfg, folding_id, inliner_classes):
data_train = []
data_valid = []
for i in range(cfg.DATASET.FOLDS_COUNT):
if i != folding_id:
with open(path.join(cfg.DATASET.PATH, 'data_fold_%d.pkl' % i), 'rb') as pkl:
fold = pickle.load(pkl)
if len(data_valid) == 0:
data_valid = fold
else:
data_train += fold
outlier_classes = []
for i in range(cfg.DATASET.TOTAL_CLASS_COUNT):
if i not in inliner_classes:
outlier_classes.append(i)
data_train = [x for x in data_train if x[0] in inliner_classes]
with open(path.join(cfg.DATASET.PATH, 'data_fold_%d.pkl') % folding_id, 'rb') as pkl:
data_test = pickle.load(pkl)
train_set = Dataset(data_train)
valid_set = Dataset(data_valid)
test_set = Dataset(data_test)
return train_set, valid_set, test_set
def make_dataloader(dataset, batch_size, device):
class BatchCollator(object):
def __init__(self, device):
self.device = device
def __call__(self, batch):
with torch.no_grad():
y, x = batch
x = torch.tensor(x / 255.0, requires_grad=True, dtype=torch.float32, device=self.device)
y = torch.tensor(y, dtype=torch.int32, device=self.device)
return y, x
data_loader = dlutils.batch_provider(dataset, batch_size, BatchCollator(device))
return data_loader
def create_set_with_outlier_percentage(dataset, inliner_classes, target_percentage, concervative=True):
np.random.seed(0)
dataset.shuffle()
dataset_outlier = [x for x in dataset if x[0] not in inliner_classes]
dataset_inliner = [x for x in dataset if x[0] in inliner_classes]
def increase_length(data_list, target_length):
repeat = (target_length + len(data_list) - 1) // len(data_list)
data_list = data_list * repeat
data_list = data_list[:target_length]
return data_list
if not concervative:
inliner_count = len(dataset_inliner)
outlier_count = inliner_count * target_percentage // (100 - target_percentage)
if len(dataset_outlier) > outlier_count:
dataset_outlier = dataset_outlier[:outlier_count]
else:
outlier_count = len(dataset_outlier)
inliner_count = outlier_count * (100 - target_percentage) // target_percentage
dataset_inliner = dataset_inliner[:inliner_count]
else:
inliner_count = len(dataset_inliner)
outlier_count = len(dataset_outlier)
current_percentage = outlier_count * 100 / (outlier_count + inliner_count)
if current_percentage < target_percentage: # we don't have enought outliers
outlier_count = int(inliner_count * target_percentage / (100.0 - target_percentage))
dataset_outlier = increase_length(dataset_outlier, outlier_count)
else: # we don't have enought inliers
inlier_count = int(outlier_count * (100.0 - target_percentage) / target_percentage)
dataset_inliner = increase_length(dataset_inliner, inlier_count)
dataset = Dataset(dataset_outlier + dataset_inliner)
dataset.shuffle()
# Post checks
outlier_count = len([1 for x in dataset if x[0] not in inliner_classes])
inliner_count = len([1 for x in dataset if x[0] in inliner_classes])
real_percetage = outlier_count * 100.0 / (outlier_count + inliner_count)
assert abs(real_percetage - target_percentage) < 0.01, "Didn't create dataset with requested percentage of outliers"
return dataset