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dataset.py
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dataset.py
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# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import os
import copy
from typing import List, Dict
import zipfile
import json
import random
from typing import Tuple
import numpy as np
import PIL.Image
import torch
from src import dnnlib
from omegaconf import DictConfig, OmegaConf
from src.training.layers import sample_frames
try:
import pyspng
except ImportError:
pyspng = None
#----------------------------------------------------------------------------
NUMPY_INTEGER_TYPES = [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64]
NUMPY_FLOAT_TYPES = [np.float16, np.float32, np.float64, np.single, np.double]
#----------------------------------------------------------------------------
class Dataset(torch.utils.data.Dataset):
def __init__(self,
name, # Name of the dataset.
raw_shape, # Shape of the raw image data (NCHW).
max_size = None, # Artificially limit the size of the dataset. None = no limit. Applied before xflip.
use_labels = False, # Enable conditioning labels? False = label dimension is zero.
xflip = False, # Artificially double the size of the dataset via x-flips. Applied after max_size.
random_seed = 0, # Random seed to use when applying max_size.
):
self._name = name
self._raw_shape = list(raw_shape)
self._use_labels = use_labels
self._raw_labels = None
self._label_shape = None
# Apply max_size.
self._raw_idx = np.arange(self._raw_shape[0], dtype=np.int64)
if (max_size is not None) and (self._raw_idx.size > max_size):
np.random.RandomState(random_seed).shuffle(self._raw_idx)
self._raw_idx = np.sort(self._raw_idx[:max_size])
# Apply xflip.
self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8)
if xflip:
self._raw_idx = np.tile(self._raw_idx, 2)
self._xflip = np.concatenate([self._xflip, np.ones_like(self._xflip)])
@staticmethod
def _file_ext(fname):
return os.path.splitext(fname)[1].lower()
def _get_raw_labels(self):
if self._raw_labels is None:
self._raw_labels = self._load_raw_labels() if self._use_labels else None
if self._raw_labels is None:
self._raw_labels = np.zeros([self._raw_shape[0], 0], dtype=np.float32)
assert isinstance(self._raw_labels, np.ndarray)
assert self._raw_labels.shape[0] == self._raw_shape[0]
assert self._raw_labels.dtype in [np.float32, np.int64]
if self._raw_labels.dtype == np.int64:
assert np.all(self._raw_labels >= 0)
return self._raw_labels
def close(self): # to be overridden by subclass
pass
def _load_raw_image(self, raw_idx): # to be overridden by subclass
raise NotImplementedError
def _load_raw_labels(self): # to be overridden by subclass
raise NotImplementedError
def __getstate__(self):
return dict(self.__dict__, _raw_labels=None)
def __del__(self):
try:
self.close()
except:
pass
def __len__(self):
return self._raw_idx.size
def __getitem__(self, idx):
image = self._load_raw_image(self._raw_idx[idx])
assert isinstance(image, np.ndarray)
assert list(image.shape) == self.image_shape
assert image.dtype == np.uint8
if self._xflip[idx]:
assert image.ndim == 3 # CHW
image = image[:, :, ::-1]
return {
'image': image.copy(),
'label': self.get_label(idx),
}
def get_label(self, idx):
label = self._get_raw_labels()[self._raw_idx[idx]]
if label.dtype == np.int64:
onehot = np.zeros(self.label_shape, dtype=np.float32)
onehot[label] = 1
label = onehot
return label.copy()
def get_details(self, idx):
d = dnnlib.EasyDict()
d.raw_idx = int(self._raw_idx[idx])
d.xflip = (int(self._xflip[idx]) != 0)
d.raw_label = self._get_raw_labels()[d.raw_idx].copy()
return d
@property
def name(self):
return self._name
@property
def image_shape(self):
return list(self._raw_shape[1:])
@property
def num_channels(self):
assert len(self.image_shape) == 3 # CHW
return self.image_shape[0]
@property
def resolution(self):
assert len(self.image_shape) == 3 # CHW
assert self.image_shape[1] == self.image_shape[2]
return self.image_shape[1]
@property
def label_shape(self):
if self._label_shape is None:
raw_labels = self._get_raw_labels()
if raw_labels.dtype == np.int64:
self._label_shape = [int(np.max(raw_labels)) + 1]
else:
self._label_shape = raw_labels.shape[1:]
return list(self._label_shape)
@property
def label_dim(self):
assert len(self.label_shape) == 1, f"Labels must be 1-dimensional: {self.label_shape} to use `.label_dim`"
return self.label_shape[0]
@property
def has_labels(self):
return any(x != 0 for x in self.label_shape)
@property
def has_onehot_labels(self):
return self._get_raw_labels().dtype == np.int64
#----------------------------------------------------------------------------
class ImageFolderDataset(Dataset):
def __init__(self,
path, # Path to directory or zip.
resolution = None, # Ensure specific resolution, None = highest available.
**super_kwargs, # Additional arguments for the Dataset base class.
):
self._path = path
self._zipfile = None
if os.path.isdir(self._path):
self._type = 'dir'
self._all_fnames = {os.path.relpath(os.path.join(root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files}
elif self._file_ext(self._path) == '.zip':
self._type = 'zip'
self._all_fnames = set(self._get_zipfile().namelist())
else:
raise IOError('Path must point to a directory or zip')
PIL.Image.init()
self._image_fnames = sorted(fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION)
if len(self._image_fnames) == 0:
raise IOError('No image files found in the specified path')
name = os.path.splitext(os.path.basename(self._path))[0]
raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0).shape)
if resolution is not None and (raw_shape[2] != resolution or raw_shape[3] != resolution):
raise IOError(f'Image files do not match the specified resolution. Resolution is {resolution}, shape is {raw_shape}')
super().__init__(name=name, raw_shape=raw_shape, **super_kwargs)
def _get_zipfile(self):
assert self._type == 'zip'
if self._zipfile is None:
self._zipfile = zipfile.ZipFile(self._path)
return self._zipfile
def _open_file(self, fname):
if self._type == 'dir':
return open(os.path.join(self._path, fname), 'rb')
if self._type == 'zip':
return self._get_zipfile().open(fname, 'r')
return None
def close(self):
try:
if self._zipfile is not None:
self._zipfile.close()
finally:
self._zipfile = None
def __getstate__(self):
return dict(super().__getstate__(), _zipfile=None)
def _load_raw_image(self, raw_idx):
fname = self._image_fnames[raw_idx]
with self._open_file(fname) as f:
use_pyspng = pyspng is not None and self._file_ext(fname) == '.png'
image = load_image_from_buffer(f, use_pyspng=use_pyspng)
return image
def _load_raw_labels(self):
fname = 'dataset.json'
labels_files = [f for f in self._all_fnames if f.endswith(fname)]
if len(labels_files) == 0:
return None
assert len(labels_files) == 1, f"There can be only a single {fname} file"
with self._open_file(labels_files[0]) as f:
labels = json.load(f)['labels']
if labels is None:
return None
labels = dict(labels)
labels = [labels[remove_root(fname, self._name).replace('\\', '/')] for fname in self._image_fnames]
labels = np.array(labels)
if labels.dtype in NUMPY_INTEGER_TYPES:
labels = labels.astype(np.int64)
elif labels.dtype in NUMPY_FLOAT_TYPES:
labels = labels.astype(np.float32)
else:
raise NotImplementedError(f"Unsupported label dtype: {labels.dtype}")
return labels
#----------------------------------------------------------------------------
class VideoFramesFolderDataset(Dataset):
def __init__(self,
path, # Path to directory or zip.
cfg: DictConfig, # Config
resolution=None, # Unused arg for backward compatibility
load_n_consecutive: int=None, # Should we load first N frames for each video?
load_n_consecutive_random_offset: bool=True, # Should we use a random offset when loading consecutive frames?
subsample_factor: int=1, # Sampling factor, i.e. decreasing the temporal resolution
discard_short_videos: bool=False, # Should we discard videos that are shorter than `load_n_consecutive`?
**super_kwargs, # Additional arguments for the Dataset base class.
):
self.sampling_dict = OmegaConf.to_container(OmegaConf.create({**cfg.sampling})) if 'sampling' in cfg else None
self.max_num_frames = cfg.max_num_frames
self._path = path
self._zipfile = None
self.load_n_consecutive = load_n_consecutive
self.load_n_consecutive_random_offset = load_n_consecutive_random_offset
self.subsample_factor = subsample_factor
self.discard_short_videos = discard_short_videos
if self.subsample_factor > 1 and self.load_n_consecutive is None:
raise NotImplementedError("Can do subsampling only when loading consecutive frames.")
listdir_full_paths = lambda d: sorted([os.path.join(d, x) for x in os.listdir(d)])
name = os.path.splitext(os.path.basename(self._path))[0]
if os.path.isdir(self._path):
self._type = 'dir'
# We assume that the depth is 2
self._all_objects = {o for d in listdir_full_paths(self._path) for o in (([d] + listdir_full_paths(d)) if os.path.isdir(d) else [d])}
self._all_objects = {os.path.relpath(o, start=os.path.dirname(self._path)) for o in {self._path}.union(self._all_objects)}
elif self._file_ext(self._path) == '.zip':
self._type = 'zip'
self._all_objects = set(self._get_zipfile().namelist())
else:
raise IOError('Path must be either a directory or point to a zip archive')
PIL.Image.init()
self._video_dir2frames = {}
objects = sorted([d for d in self._all_objects])
root_path_depth = len(os.path.normpath(objects[0]).split(os.path.sep))
curr_d = objects[1] # Root path is the first element
for o in objects[1:]:
curr_obj_depth = len(os.path.normpath(o).split(os.path.sep))
if self._file_ext(o) in PIL.Image.EXTENSION:
assert o.startswith(curr_d), f"Object {o} is out of sync. It should lie inside {curr_d}"
assert curr_obj_depth == root_path_depth + 2, "Frame images should be inside directories"
if not curr_d in self._video_dir2frames:
self._video_dir2frames[curr_d] = []
self._video_dir2frames[curr_d].append(o)
elif self._file_ext(o) == 'json':
assert curr_obj_depth == root_path_depth + 1, "Classes info file should be inside the root dir"
pass
else:
# We encountered a new directory
assert curr_obj_depth == root_path_depth + 1, f"Video directories should be inside the root dir. {o} is not."
if curr_d in self._video_dir2frames:
sorted_files = sorted(self._video_dir2frames[curr_d])
self._video_dir2frames[curr_d] = sorted_files
curr_d = o
if self.discard_short_videos:
self._video_dir2frames = {d: fs for d, fs in self._video_dir2frames.items() if len(fs) >= self.load_n_consecutive * self.subsample_factor}
self._video_idx2frames = [frames for frames in self._video_dir2frames.values()]
if len(self._video_idx2frames) == 0:
raise IOError('No videos found in the specified archive')
raw_shape = [len(self._video_idx2frames)] + list(self._load_raw_frames(0, [0])[0][0].shape)
super().__init__(name=name, raw_shape=raw_shape, **super_kwargs)
def _get_zipfile(self):
assert self._type == 'zip'
if self._zipfile is None:
self._zipfile = zipfile.ZipFile(self._path)
return self._zipfile
def _open_file(self, fname):
if self._type == 'dir':
return open(os.path.join(os.path.dirname(self._path), fname), 'rb')
if self._type == 'zip':
return self._get_zipfile().open(fname, 'r')
return None
def close(self):
try:
if self._zipfile is not None:
self._zipfile.close()
finally:
self._zipfile = None
def __getstate__(self):
return dict(super().__getstate__(), _zipfile=None)
def _load_raw_labels(self):
"""
We leave the `dataset.json` file in the same format as in the original SG2-ADA repo:
it's `labels` field is a hashmap of filename-label pairs.
"""
fname = 'dataset.json'
labels_files = [f for f in self._all_objects if f.endswith(fname)]
if len(labels_files) == 0:
return None
assert len(labels_files) == 1, f"There can be only a single {fname} file"
with self._open_file(labels_files[0]) as f:
labels = json.load(f)['labels']
if labels is None:
return None
labels = dict(labels)
# The `dataset.json` file defines a label for each image and
# For the video dataset, this is both inconvenient and redundant.
# So let's redefine this
video_labels = {}
for filename, label in labels.items():
dirname = os.path.dirname(filename)
if dirname in video_labels:
assert video_labels[dirname] == label
else:
video_labels[dirname] = label
labels = video_labels
labels = [labels[os.path.normpath(dname).split(os.path.sep)[-1]] for dname in self._video_dir2frames]
labels = np.array(labels)
if labels.dtype in NUMPY_INTEGER_TYPES:
labels = labels.astype(np.int64)
elif labels.dtype in NUMPY_FLOAT_TYPES:
labels = labels.astype(np.float32)
else:
raise NotImplementedError(f"Unsupported label dtype: {labels.dtype}")
return labels
def __getitem__(self, idx: int) -> Dict:
if self.load_n_consecutive:
num_frames_available = len(self._video_idx2frames[self._raw_idx[idx]])
assert num_frames_available - self.load_n_consecutive * self.subsample_factor >= 0, f"We have only {num_frames_available} frames available, cannot load {self.load_n_consecutive} frames."
if self.load_n_consecutive_random_offset:
random_offset = random.randint(0, num_frames_available - self.load_n_consecutive * self.subsample_factor + self.subsample_factor - 1)
else:
random_offset = 0
frames_idx = np.arange(0, self.load_n_consecutive * self.subsample_factor, self.subsample_factor) + random_offset
else:
frames_idx = None
frames, times = self._load_raw_frames(self._raw_idx[idx], frames_idx=frames_idx)
assert isinstance(frames, np.ndarray)
assert list(frames[0].shape) == self.image_shape
assert frames.dtype == np.uint8
assert len(frames) == len(times)
if self._xflip[idx]:
assert frames.ndim == 4 # TCHW
frames = frames[:, :, :, ::-1]
return {
'image': frames.copy(),
'label': self.get_label(idx),
'times': times,
'video_len': self.get_video_len(idx),
}
def get_video_len(self, idx: int) -> int:
return min(self.max_num_frames, len(self._video_idx2frames[self._raw_idx[idx]]))
def _load_raw_frames(self, raw_idx: int, frames_idx: List[int]=None) -> Tuple[np.ndarray, np.ndarray]:
frame_paths = self._video_idx2frames[raw_idx]
total_len = len(frame_paths)
offset = 0
images = []
if frames_idx is None:
assert not self.sampling_dict is None, f"The dataset was created without `cfg.sampling` config and cannot sample frames on its own."
if total_len > self.max_num_frames:
offset = random.randint(0, total_len - self.max_num_frames)
frames_idx = sample_frames(self.sampling_dict, total_video_len=min(total_len, self.max_num_frames)) + offset
else:
frames_idx = np.array(frames_idx)
for frame_idx in frames_idx:
with self._open_file(frame_paths[frame_idx]) as f:
images.append(load_image_from_buffer(f))
return np.array(images), frames_idx - offset
def compute_max_num_frames(self) -> int:
return max(len(frames) for frames in self._video_idx2frames)
#----------------------------------------------------------------------------
def load_image_from_buffer(f, use_pyspng: bool=False) -> np.ndarray:
if use_pyspng:
image = pyspng.load(f.read())
else:
image = np.array(PIL.Image.open(f))
if image.ndim == 2:
image = image[:, :, np.newaxis] # HW => HWC
image = image.transpose(2, 0, 1) # HWC => CHW
return image
#----------------------------------------------------------------------------
def video_to_image_dataset_kwargs(video_dataset_kwargs: dnnlib.EasyDict) -> dnnlib.EasyDict:
"""Converts video dataset kwargs to image dataset kwargs"""
return dnnlib.EasyDict(
class_name='training.dataset.ImageFolderDataset',
path=video_dataset_kwargs.path,
use_labels=video_dataset_kwargs.use_labels,
xflip=video_dataset_kwargs.xflip,
resolution=video_dataset_kwargs.resolution,
random_seed=video_dataset_kwargs.get('random_seed'),
# Explicitly ignoring the max size, since we are now interested
# in the number of images instead of the number of videos
# max_size=video_dataset_kwargs.max_size,
)
#----------------------------------------------------------------------------
def remove_root(fname: os.PathLike, root_name: os.PathLike):
"""`root_name` should NOT start with '/'"""
if fname == root_name or fname == ('/' + root_name):
return ''
elif fname.startswith(root_name + '/'):
return fname[len(root_name) + 1:]
elif fname.startswith('/' + root_name + '/'):
return fname[len(root_name) + 2:]
else:
return fname
#----------------------------------------------------------------------------