herbarium_19.py

import os

import torchvision
import numpy as np
from copy import deepcopy

from data.data_utils import subsample_instances
from config import herbarium_dataroot

class HerbariumDataset19(torchvision.datasets.ImageFolder):

    def __init__(self, *args, **kwargs):

        # Process metadata json for training images into a DataFrame
        super().__init__(*args, **kwargs)

        self.uq_idxs = np.array(range(len(self)))

    def __getitem__(self, idx):

        img, label = super().__getitem__(idx)
        uq_idx = self.uq_idxs[idx]

        return img, label, uq_idx


def subsample_dataset(dataset, idxs):

    mask = np.zeros(len(dataset)).astype('bool')
    mask[idxs] = True

    dataset.samples = np.array(dataset.samples)[mask].tolist()
    dataset.targets = np.array(dataset.targets)[mask].tolist()

    dataset.uq_idxs = dataset.uq_idxs[mask]

    dataset.samples = [[x[0], int(x[1])] for x in dataset.samples]
    dataset.targets = [int(x) for x in dataset.targets]

    return dataset


def subsample_classes(dataset, include_classes=range(250)):

    cls_idxs = [x for x, l in enumerate(dataset.targets) if l in include_classes]

    target_xform_dict = {}
    for i, k in enumerate(include_classes):
        target_xform_dict[k] = i

    dataset = subsample_dataset(dataset, cls_idxs)

    dataset.target_transform = lambda x: target_xform_dict[x]

    return dataset


def get_train_val_indices(train_dataset, val_instances_per_class=5):

    train_classes = list(set(train_dataset.targets))

    # Get train/test indices
    train_idxs = []
    val_idxs = []
    for cls in train_classes:

        cls_idxs = np.where(np.array(train_dataset.targets) == cls)[0]

        # Have a balanced test set
        v_ = np.random.choice(cls_idxs, replace=False, size=(val_instances_per_class,))
        t_ = [x for x in cls_idxs if x not in v_]

        train_idxs.extend(t_)
        val_idxs.extend(v_)

    return train_idxs, val_idxs


def get_herbarium_datasets(train_transform, test_transform, train_classes=range(500), prop_train_labels=0.8,
                            seed=0, split_train_val=False):

    np.random.seed(seed)

    # Init entire training set
    train_dataset = HerbariumDataset19(transform=train_transform,
                                            root=os.path.join(herbarium_dataroot, 'small-train'))

    # Get labelled training set which has subsampled classes, then subsample some indices from that
    # TODO: Subsampling unlabelled set in uniform random fashion from training data, will contain many instances of dominant class
    train_dataset_labelled = subsample_classes(deepcopy(train_dataset), include_classes=train_classes)
    subsample_indices = subsample_instances(train_dataset_labelled, prop_indices_to_subsample=prop_train_labels)
    train_dataset_labelled = subsample_dataset(train_dataset_labelled, subsample_indices)

    # Split into training and validation sets
    if split_train_val:

        train_idxs, val_idxs = get_train_val_indices(train_dataset_labelled,
                                                     val_instances_per_class=5)
        train_dataset_labelled_split = subsample_dataset(deepcopy(train_dataset_labelled), train_idxs)
        val_dataset_labelled_split = subsample_dataset(deepcopy(train_dataset_labelled), val_idxs)
        val_dataset_labelled_split.transform = test_transform

    else:

        train_dataset_labelled_split, val_dataset_labelled_split = None, None

    # Get unlabelled data
    unlabelled_indices = set(train_dataset.uq_idxs) - set(train_dataset_labelled.uq_idxs)
    train_dataset_unlabelled = subsample_dataset(deepcopy(train_dataset), np.array(list(unlabelled_indices)))

    # Get test dataset
    test_dataset = HerbariumDataset19(transform=test_transform,
                                            root=os.path.join(herbarium_dataroot, 'small-validation'))

    # Transform dict
    unlabelled_classes = list(set(train_dataset.targets) - set(train_classes))
    target_xform_dict = {}
    for i, k in enumerate(list(train_classes) + unlabelled_classes):
        target_xform_dict[k] = i

    test_dataset.target_transform = lambda x: target_xform_dict[x]
    train_dataset_unlabelled.target_transform = lambda x: target_xform_dict[x]

    # Either split train into train and val or use test set as val
    train_dataset_labelled = train_dataset_labelled_split if split_train_val else train_dataset_labelled
    val_dataset_labelled = val_dataset_labelled_split if split_train_val else None

    all_datasets = {
        'train_labelled': train_dataset_labelled,
        'train_unlabelled': train_dataset_unlabelled,
        'val': val_dataset_labelled,
        'test': test_dataset,
    }

    return all_datasets

if __name__ == '__main__':

    np.random.seed(0)
    train_classes = np.random.choice(range(683,), size=(int(683 / 2)), replace=False)

    x = get_herbarium_datasets(None, None, train_classes=train_classes,
                               prop_train_labels=0.5)

    assert set(x['train_unlabelled'].targets) == set(range(683))

    print('Printing lens...')
    for k, v in x.items():
        if v is not None:
            print(f'{k}: {len(v)}')

    print('Printing labelled and unlabelled overlap...')
    print(set.intersection(set(x['train_labelled'].uq_idxs), set(x['train_unlabelled'].uq_idxs)))
    print('Printing total instances in train...')
    print(len(set(x['train_labelled'].uq_idxs)) + len(set(x['train_unlabelled'].uq_idxs)))
    print('Printing number of labelled classes...')
    print(len(set(x['train_labelled'].targets)))
    print('Printing total number of classes...')
    print(len(set(x['train_unlabelled'].targets)))

    print(f'Num Labelled Classes: {len(set(x["train_labelled"].targets))}')
    print(f'Num Unabelled Classes: {len(set(x["train_unlabelled"].targets))}')
    print(f'Len labelled set: {len(x["train_labelled"])}')
    print(f'Len unlabelled set: {len(x["train_unlabelled"])}')