bring-your-own-container/pytorch_extending_our_containers/utils/utils_cifar.py

# Copyright 2017-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
#     http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file 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 matplotlib.pyplot as plt
import numpy as np
import torch
import torchvision
import torchvision.transforms as transforms

classes = ("plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck")


def _get_transform():
    return transforms.Compose(
        [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
    )


def get_train_data_loader(data_dir="/tmp/pytorch/cifar-10-data"):
    transform = _get_transform()
    trainset = torchvision.datasets.CIFAR10(
        root=data_dir, train=True, download=True, transform=transform
    )
    return torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)


def get_test_data_loader(data_dir="/tmp/pytorch/cifar-10-data"):
    transform = _get_transform()
    testset = torchvision.datasets.CIFAR10(
        root=data_dir, train=False, download=True, transform=transform
    )
    return torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)


# function to show an image
def imshow(img):
    img = img / 2 + 0.5  # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))