Source code for imaginaire.datasets.unpaired_images

# Copyright (C) 2021 NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, check out LICENSE.md
import random

from imaginaire.datasets.base import BaseDataset


[docs]class Dataset(BaseDataset): r"""Unpaired image dataset for use in MUNIT. Args: cfg (Config): Loaded config object. is_inference (bool): In train or inference mode? """ def __init__(self, cfg, is_inference=False, is_test=False): self.paired = False super(Dataset, self).__init__(cfg, is_inference, is_test) def _create_mapping(self): r"""Creates mapping from idx to key in LMDB. Returns: (tuple): - self.mapping (dict): Dict with data type as key mapping idx to LMDB key. - self.epoch_length (int): Number of samples in an epoch. """ idx_to_key = {} for lmdb_idx, sequence_list in enumerate(self.sequence_lists): for data_type, data_type_sequence_list in sequence_list.items(): if data_type not in idx_to_key: idx_to_key[data_type] = [] for sequence_name, filenames in data_type_sequence_list.items(): for filename in filenames: idx_to_key[data_type].append({ 'lmdb_root': self.lmdb_roots[lmdb_idx], 'lmdb_idx': lmdb_idx, 'sequence_name': sequence_name, 'filename': filename, }) self.mapping = idx_to_key self.epoch_length = max([len(lmdb_keys) for _, lmdb_keys in self.mapping.items()]) return self.mapping, self.epoch_length def _sample_keys(self, index): r"""Gets files to load for this sample. Args: index (int): Index in [0, len(dataset)]. Returns: keys (dict): Each key of this dict is a data type. lmdb_key (dict): lmdb_idx (int): Chosen LMDB dataset root. sequence_name (str): Chosen sequence in chosen dataset. filename (str): Chosen filename in chosen sequence. """ keys = {} for data_type in self.data_types: lmdb_keys = self.mapping[data_type] if self.is_inference: # Modulo ensures valid indexing in case A and B have different # number of files. keys[data_type] = lmdb_keys[index % len(lmdb_keys)] else: keys[data_type] = random.choice(lmdb_keys) return keys def __getitem__(self, index): r"""Gets selected files. Args: index (int): Index into dataset. concat (bool): Concatenate all items in labels? Returns: data (dict): Dict with all chosen data_types. """ # Select a sample from the available data. keys_per_data_type = self._sample_keys(index) # Get keys and lmdbs. keys, lmdbs = {}, {} for data_type in self.dataset_data_types: # Unpack keys. lmdb_idx = keys_per_data_type[data_type]['lmdb_idx'] sequence_name = keys_per_data_type[data_type]['sequence_name'] filename = keys_per_data_type[data_type]['filename'] keys[data_type] = '%s/%s' % (sequence_name, filename) lmdbs[data_type] = self.lmdbs[data_type][lmdb_idx] # Load all data for this index. data = self.load_from_dataset(keys, lmdbs) # Apply ops pre augmentation. data = self.apply_ops(data, self.pre_aug_ops) # Do augmentations for images. data, is_flipped = self.perform_augmentation(data, paired=False, augment_ops=self.augmentor.augment_ops) # Apply ops post augmentation. data = self.apply_ops(data, self.post_aug_ops) data = self.apply_ops(data, self.full_data_post_aug_ops, full_data=True) # Convert images to tensor. data = self.to_tensor(data) # Remove any extra dimensions. for data_type in self.image_data_types: data[data_type] = data[data_type][0] # Package output. data['is_flipped'] = is_flipped data['key'] = keys_per_data_type return data