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cifar_h.py
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import torch
import numpy as np
import os
import random
import sys
import torch
import logging
import sys
sys.path.append("../")
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch.nn.functional as F
from datasetsdefer.generic_dataset import GenericImageExpertDataset
import requests
from .basedataset import BaseDataset
# https://github.com/jcpeterson/cifar-10h
class Cifar10h(BaseDataset):
"""CIFAR-10H dataset with seperate human annotations on the test set of CIFAR-10"""
def __init__(
self,
use_data_aug,
data_dir,
test_split=0.2,
val_split=0.1,
batch_size=1000,
transforms=None,
):
"""
data_dir: where to save files for model
use_data_aug: whether to use data augmentation (bool)
test_split: percentage of test data
val_split: percentage of data to be used for validation (from training set)
batch_size: batch size for training
transforms: data transforms
"""
self.data_dir = data_dir
self.use_data_aug = use_data_aug
self.test_split = test_split
self.val_split = val_split
self.batch_size = batch_size
self.n_dataset = 10
self.train_split = 1 - test_split - val_split
self.transforms = transforms
self.generate_data()
def metrics_cifar10h(self, exp_preds, labels):
correct = 0
total = 0
j = 0
self.class_conditional_acc = [0] * 10
class_counts = [0] * 10
for i in range(len(exp_preds)):
total += 1
j += 1
correct += exp_preds[i] == labels[i]
self.class_conditional_acc[labels[i]] += exp_preds[i] == labels[i]
class_counts[labels[i]] += 1
for i in range(0, 10):
self.class_conditional_acc[i] = (
100 * self.class_conditional_acc[i] / class_counts[i]
)
self.human_accuracy = 100 * correct / total
def generate_data(self):
"""
generate data for training, validation and test sets
: "airplane": 0, "automobile": 1, "bird": 2, "cat": 3, "deer": 4, "dog": 5, "frog": 6, "horse": 7, "ship": 8, "truck": 9
"""
# download cifar10h data
# check if file already exists
# check if file already exists
if not os.path.exists(self.data_dir + "/cifar10h-probs.npy"):
logging.info("Downloading cifar10h data")
r = requests.get(
"https://github.com/jcpeterson/cifar-10h/raw/master/data/cifar10h-probs.npy",
allow_redirects=True,
)
with open(self.data_dir + "/cifar10h-probs.npy", "wb") as f:
f.write(r.content)
logging.info("Finished Downloading cifar10h data")
try:
cifar10h = np.load(self.data_dir + "/cifar10h-probs.npy")
except:
logging.error("Failed to load cifar10h data")
raise
else:
logging.info("Loading cifar10h data")
try:
cifar10h = np.load(self.data_dir + "/cifar10h-probs.npy")
except:
logging.error("Failed to load cifar10h data")
raise
human_predictions = np.array(
[
np.argmax(np.random.multinomial(1, cifar10h[i]))
for i in range(len(cifar10h))
]
)
normalize = transforms.Normalize(
mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]],
)
if self.use_data_aug:
transform_train = transforms.Compose(
[
transforms.ToTensor(),
transforms.Lambda(
lambda x: F.pad(
x.unsqueeze(0), (4, 4, 4, 4), mode="reflect"
).squeeze()
),
transforms.ToPILImage(),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]
)
else:
transform_train = transforms.Compose(
[
transforms.ToTensor(),
normalize,
]
)
transform_test = transforms.Compose([transforms.ToTensor(), normalize])
dataset = "cifar10"
kwargs = {"num_workers": 0, "pin_memory": True}
train_dataset_all = datasets.__dict__[dataset.upper()](
"../data", train=False, download=True, transform=transform_test
)
labels_all = train_dataset_all.targets
self.metrics_cifar10h(human_predictions, labels_all)
test_size = int(self.test_split * len(train_dataset_all))
val_size = int(self.val_split * len(train_dataset_all))
train_size = len(train_dataset_all) - test_size - val_size
train_x = train_dataset_all.data
train_y = train_dataset_all.targets
train_y = np.array(train_y)
random_seed = random.randrange(10000)
train_x, val_x, test_x = torch.utils.data.random_split(
train_x,
[train_size, val_size, test_size],
generator=torch.Generator().manual_seed(random_seed),
)
train_y, val_y, test_y = torch.utils.data.random_split(
train_y,
[train_size, val_size, test_size],
generator=torch.Generator().manual_seed(random_seed),
)
train_h, val_h, test_h = torch.utils.data.random_split(
human_predictions,
[train_size, val_size, test_size],
generator=torch.Generator().manual_seed(random_seed),
)
data_train = GenericImageExpertDataset(
train_x.dataset[train_x.indices],
train_y.dataset[train_y.indices],
train_h.dataset[train_h.indices],
transform_train,
)
data_val = GenericImageExpertDataset(
val_x.dataset[val_x.indices],
val_y.dataset[val_y.indices],
val_h.dataset[val_h.indices],
transform_test,
)
data_test = GenericImageExpertDataset(
test_x.dataset[test_x.indices],
test_y.dataset[test_y.indices],
test_h.dataset[test_h.indices],
transform_test,
)
self.data_train_loader = torch.utils.data.DataLoader(
data_train,
batch_size=self.batch_size,
shuffle=True,
num_workers=0,
pin_memory=True,
)
self.data_val_loader = torch.utils.data.DataLoader(
data_val,
batch_size=self.batch_size,
shuffle=False,
num_workers=0,
pin_memory=True,
)
self.data_test_loader = torch.utils.data.DataLoader(
data_test,
batch_size=self.batch_size,
shuffle=False,
num_workers=0,
pin_memory=True,
)