DaSE-Computer-Vision-2021/assignment1/daseCV/data_utils.py

from __future__ import print_function

from builtins import range
from six.moves import cPickle as pickle
import numpy as np
import os
from imageio import imread
import platform

def load_pickle(f):
    version = platform.python_version_tuple()
    if version[0] == '2':
        return  pickle.load(f)
    elif version[0] == '3':
        return  pickle.load(f, encoding='latin1')
    raise ValueError("invalid python version: {}".format(version))

def load_CIFAR_batch(filename):
    """ load single batch of cifar """
    with open(filename, 'rb') as f:
        datadict = load_pickle(f)
        X = datadict['data']
        Y = datadict['labels']
        X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("float")
        Y = np.array(Y)
        return X, Y

def load_CIFAR10(ROOT):
    """ load all of cifar """
    xs = []
    ys = []
    for b in range(1,6):
        f = os.path.join(ROOT, 'data_batch_%d' % (b, ))
        X, Y = load_CIFAR_batch(f)
        xs.append(X)
        ys.append(Y)
    Xtr = np.concatenate(xs)
    Ytr = np.concatenate(ys)
    del X, Y
    Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))
    return Xtr, Ytr, Xte, Yte


def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000,
                     subtract_mean=True):
    """
    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare
    it for classifiers. These are the same steps as we used for the SVM, but
    condensed to a single function.
    """
    # Load the raw CIFAR-10 data
    cifar10_dir = 'daseCV/datasets/cifar-10-batches-py'
    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)

    # Subsample the data
    mask = list(range(num_training, num_training + num_validation))
    X_val = X_train[mask]
    y_val = y_train[mask]
    mask = list(range(num_training))
    X_train = X_train[mask]
    y_train = y_train[mask]
    mask = list(range(num_test))
    X_test = X_test[mask]
    y_test = y_test[mask]

    # Normalize the data: subtract the mean image
    if subtract_mean:
        mean_image = np.mean(X_train, axis=0)
        X_train -= mean_image
        X_val -= mean_image
        X_test -= mean_image

    # Transpose so that channels come first
    X_train = X_train.transpose(0, 3, 1, 2).copy()
    X_val = X_val.transpose(0, 3, 1, 2).copy()
    X_test = X_test.transpose(0, 3, 1, 2).copy()

    # Package data into a dictionary
    return {
      'X_train': X_train, 'y_train': y_train,
      'X_val': X_val, 'y_val': y_val,
      'X_test': X_test, 'y_test': y_test,
    }


def load_tiny_imagenet(path, dtype=np.float32, subtract_mean=True):
    """
    Load TinyImageNet. Each of TinyImageNet-100-A, TinyImageNet-100-B, and
    TinyImageNet-200 have the same directory structure, so this can be used
    to load any of them.

    Inputs:
    - path: String giving path to the directory to load.
    - dtype: numpy datatype used to load the data.
    - subtract_mean: Whether to subtract the mean training image.

    Returns: A dictionary with the following entries:
    - class_names: A list where class_names[i] is a list of strings giving the
      WordNet names for class i in the loaded dataset.
    - X_train: (N_tr, 3, 64, 64) array of training images
    - y_train: (N_tr,) array of training labels
    - X_val: (N_val, 3, 64, 64) array of validation images
    - y_val: (N_val,) array of validation labels
    - X_test: (N_test, 3, 64, 64) array of testing images.
    - y_test: (N_test,) array of test labels; if test labels are not available
      (such as in student code) then y_test will be None.
    - mean_image: (3, 64, 64) array giving mean training image
    """
    # First load wnids
    with open(os.path.join(path, 'wnids.txt'), 'r') as f:
        wnids = [x.strip() for x in f]

    # Map wnids to integer labels
    wnid_to_label = {wnid: i for i, wnid in enumerate(wnids)}

    # Use words.txt to get names for each class
    with open(os.path.join(path, 'words.txt'), 'r') as f:
        wnid_to_words = dict(line.split('\t') for line in f)
        for wnid, words in wnid_to_words.items():
            wnid_to_words[wnid] = [w.strip() for w in words.split(',')]
    class_names = [wnid_to_words[wnid] for wnid in wnids]

    # Next load training data.
    X_train = []
    y_train = []
    for i, wnid in enumerate(wnids):
        if (i + 1) % 20 == 0:
            print('loading training data for synset %d / %d'
                  % (i + 1, len(wnids)))
        # To figure out the filenames we need to open the boxes file
        boxes_file = os.path.join(path, 'train', wnid, '%s_boxes.txt' % wnid)
        with open(boxes_file, 'r') as f:
            filenames = [x.split('\t')[0] for x in f]
        num_images = len(filenames)

        X_train_block = np.zeros((num_images, 3, 64, 64), dtype=dtype)
        y_train_block = wnid_to_label[wnid] * \
                        np.ones(num_images, dtype=np.int64)
        for j, img_file in enumerate(filenames):
            img_file = os.path.join(path, 'train', wnid, 'images', img_file)
            img = imread(img_file)
            if img.ndim == 2:
        ## grayscale file
                img.shape = (64, 64, 1)
            X_train_block[j] = img.transpose(2, 0, 1)
        X_train.append(X_train_block)
        y_train.append(y_train_block)

    # We need to concatenate all training data
    X_train = np.concatenate(X_train, axis=0)
    y_train = np.concatenate(y_train, axis=0)

    # Next load validation data
    with open(os.path.join(path, 'val', 'val_annotations.txt'), 'r') as f:
        img_files = []
        val_wnids = []
        for line in f:
            img_file, wnid = line.split('\t')[:2]
            img_files.append(img_file)
            val_wnids.append(wnid)
        num_val = len(img_files)
        y_val = np.array([wnid_to_label[wnid] for wnid in val_wnids])
        X_val = np.zeros((num_val, 3, 64, 64), dtype=dtype)
        for i, img_file in enumerate(img_files):
            img_file = os.path.join(path, 'val', 'images', img_file)
            img = imread(img_file)
            if img.ndim == 2:
                img.shape = (64, 64, 1)
            X_val[i] = img.transpose(2, 0, 1)

    # Next load test images
    # Students won't have test labels, so we need to iterate over files in the
    # images directory.
    img_files = os.listdir(os.path.join(path, 'test', 'images'))
    X_test = np.zeros((len(img_files), 3, 64, 64), dtype=dtype)
    for i, img_file in enumerate(img_files):
        img_file = os.path.join(path, 'test', 'images', img_file)
        img = imread(img_file)
        if img.ndim == 2:
            img.shape = (64, 64, 1)
        X_test[i] = img.transpose(2, 0, 1)

    y_test = None
    y_test_file = os.path.join(path, 'test', 'test_annotations.txt')
    if os.path.isfile(y_test_file):
        with open(y_test_file, 'r') as f:
            img_file_to_wnid = {}
            for line in f:
                line = line.split('\t')
                img_file_to_wnid[line[0]] = line[1]
        y_test = [wnid_to_label[img_file_to_wnid[img_file]]
                  for img_file in img_files]
        y_test = np.array(y_test)

    mean_image = X_train.mean(axis=0)
    if subtract_mean:
        X_train -= mean_image[None]
        X_val -= mean_image[None]
        X_test -= mean_image[None]

    return {
      'class_names': class_names,
      'X_train': X_train,
      'y_train': y_train,
      'X_val': X_val,
      'y_val': y_val,
      'X_test': X_test,
      'y_test': y_test,
      'class_names': class_names,
      'mean_image': mean_image,
    }


def load_models(models_dir):
    """
    Load saved models from disk. This will attempt to unpickle all files in a
    directory; any files that give errors on unpickling (such as README.txt)
    will be skipped.

    Inputs:
    - models_dir: String giving the path to a directory containing model files.
      Each model file is a pickled dictionary with a 'model' field.

    Returns:
    A dictionary mapping model file names to models.
    """
    models = {}
    for model_file in os.listdir(models_dir):
        with open(os.path.join(models_dir, model_file), 'rb') as f:
            try:
                models[model_file] = load_pickle(f)['model']
            except pickle.UnpicklingError:
                continue
    return models


def load_imagenet_val(num=None):
    """Load a handful of validation images from ImageNet.

    Inputs:
    - num: Number of images to load (max of 25)

    Returns:
    - X: numpy array with shape [num, 224, 224, 3]
    - y: numpy array of integer image labels, shape [num]
    - class_names: dict mapping integer label to class name
    """
    imagenet_fn = 'daseCV/datasets/imagenet_val_25.npz'
    if not os.path.isfile(imagenet_fn):
      print('file %s not found' % imagenet_fn)
      print('Run the following:')
      print('cd daseCV/datasets')
      print('bash get_imagenet_val.sh')
      assert False, 'Need to download imagenet_val_25.npz'
    f = np.load(imagenet_fn)
    X = f['X']
    y = f['y']
    class_names = f['label_map'].item()
    if num is not None:
        X = X[:num]
        y = y[:num]
    return X, y, class_names