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# 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
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
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