# Copyright (c) OpenMMLab. All rights reserved.
import math
from numbers import Number
from typing import List, Optional, Sequence, Tuple, Union
import torch
import torch.nn.functional as F
from mmengine.model import (BaseDataPreprocessor, ImgDataPreprocessor,
stack_batch)
from mmpretrain.registry import MODELS
from mmpretrain.structures import (DataSample, MultiTaskDataSample,
batch_label_to_onehot, cat_batch_labels,
tensor_split)
from .batch_augments import RandomBatchAugment
@MODELS.register_module()
class ClsDataPreprocessor(BaseDataPreprocessor):
"""Image pre-processor for classification tasks.
Comparing with the :class:`mmengine.model.ImgDataPreprocessor`,
1. It won't do normalization if ``mean`` is not specified.
2. It does normalization and color space conversion after stacking batch.
3. It supports batch augmentations like mixup and cutmix.
It provides the data pre-processing as follows
- Collate and move data to the target device.
- Pad inputs to the maximum size of current batch with defined
``pad_value``. The padding size can be divisible by a defined
``pad_size_divisor``
- Stack inputs to batch_inputs.
- Convert inputs from bgr to rgb if the shape of input is (3, H, W).
- Normalize image with defined std and mean.
- Do batch augmentations like Mixup and Cutmix during training.
Args:
mean (Sequence[Number], optional): The pixel mean of R, G, B channels.
Defaults to None.
std (Sequence[Number], optional): The pixel standard deviation of
R, G, B channels. Defaults to None.
pad_size_divisor (int): The size of padded image should be
divisible by ``pad_size_divisor``. Defaults to 1.
pad_value (Number): The padded pixel value. Defaults to 0.
to_rgb (bool): whether to convert image from BGR to RGB.
Defaults to False.
to_onehot (bool): Whether to generate one-hot format gt-labels and set
to data samples. Defaults to False.
num_classes (int, optional): The number of classes. Defaults to None.
batch_augments (dict, optional): The batch augmentations settings,
including "augments" and "probs". For more details, see
:class:`mmpretrain.models.RandomBatchAugment`.
"""
def __init__(self,
mean: Sequence[Number] = None,
std: Sequence[Number] = None,
pad_size_divisor: int = 1,
pad_value: Number = 0,
to_rgb: bool = False,
to_onehot: bool = False,
num_classes: Optional[int] = None,
batch_augments: Optional[dict] = None):
super().__init__()
self.pad_size_divisor = pad_size_divisor
self.pad_value = pad_value
self.to_rgb = to_rgb
self.to_onehot = to_onehot
self.num_classes = num_classes
if mean is not None:
assert std is not None, 'To enable the normalization in ' \
'preprocessing, please specify both `mean` and `std`.'
# Enable the normalization in preprocessing.
self._enable_normalize = True
self.register_buffer('mean',
torch.tensor(mean).view(-1, 1, 1), False)
self.register_buffer('std',
torch.tensor(std).view(-1, 1, 1), False)
else:
self._enable_normalize = False
if batch_augments:
self.batch_augments = RandomBatchAugment(**batch_augments)
if not self.to_onehot:
from mmengine.logging import MMLogger
MMLogger.get_current_instance().info(
'Because batch augmentations are enabled, the data '
'preprocessor automatically enables the `to_onehot` '
'option to generate one-hot format labels.')
self.to_onehot = True
else:
self.batch_augments = None
def forward(self, data: dict, training: bool = False) -> dict:
"""Perform normalization, padding, bgr2rgb conversion and batch
augmentation based on ``BaseDataPreprocessor``.
Args:
data (dict): data sampled from dataloader.
training (bool): Whether to enable training time augmentation.
Returns:
dict: Data in the same format as the model input.
"""
inputs = self.cast_data(data['inputs'])
if isinstance(inputs, torch.Tensor):
# The branch if use `default_collate` as the collate_fn in the
# dataloader.
# ------ To RGB ------
if self.to_rgb and inputs.size(1) == 3:
inputs = inputs.flip(1)
# -- Normalization ---
inputs = inputs.float()
if self._enable_normalize:
inputs = (inputs - self.mean) / self.std
# ------ Padding -----
if self.pad_size_divisor > 1:
h, w = inputs.shape[-2:]
target_h = math.ceil(
h / self.pad_size_divisor) * self.pad_size_divisor
target_w = math.ceil(
w / self.pad_size_divisor) * self.pad_size_divisor
pad_h = target_h - h
pad_w = target_w - w
inputs = F.pad(inputs, (0, pad_w, 0, pad_h), 'constant',
self.pad_value)
else:
# The branch if use `pseudo_collate` as the collate_fn in the
# dataloader.
processed_inputs = []
for input_ in inputs:
# ------ To RGB ------
if self.to_rgb and input_.size(0) == 3:
input_ = input_.flip(0)
# -- Normalization ---
input_ = input_.float()
if self._enable_normalize:
input_ = (input_ - self.mean) / self.std
processed_inputs.append(input_)
# Combine padding and stack
inputs = stack_batch(processed_inputs, self.pad_size_divisor,
self.pad_value)
data_samples = data.get('data_samples', None)
sample_item = data_samples[0] if data_samples is not None else None
if isinstance(sample_item, DataSample):
batch_label = None
batch_score = None
if 'gt_label' in sample_item:
gt_labels = [sample.gt_label for sample in data_samples]
batch_label, label_indices = cat_batch_labels(gt_labels)
batch_label = batch_label.to(self.device)
if 'gt_score' in sample_item:
gt_scores = [sample.gt_score for sample in data_samples]
batch_score = torch.stack(gt_scores).to(self.device)
elif self.to_onehot and 'gt_label' in sample_item:
assert batch_label is not None, \
'Cannot generate onehot format labels because no labels.'
num_classes = self.num_classes or sample_item.get(
'num_classes')
assert num_classes is not None, \
'Cannot generate one-hot format labels because not set ' \
'`num_classes` in `data_preprocessor`.'
batch_score = batch_label_to_onehot(
batch_label, label_indices, num_classes).to(self.device)
# ----- Batch Augmentations ----
if (training and self.batch_augments is not None
and batch_score is not None):
inputs, batch_score = self.batch_augments(inputs, batch_score)
# ----- scatter labels and scores to data samples ---
if batch_label is not None:
for sample, label in zip(
data_samples, tensor_split(batch_label,
label_indices)):
sample.set_gt_label(label)
if batch_score is not None:
for sample, score in zip(data_samples, batch_score):
sample.set_gt_score(score)
elif isinstance(sample_item, MultiTaskDataSample):
data_samples = self.cast_data(data_samples)
return {'inputs': inputs, 'data_samples': data_samples}
@MODELS.register_module()
class SelfSupDataPreprocessor(ImgDataPreprocessor):
"""Image pre-processor for operations, like normalization and bgr to rgb.
Compared with the :class:`mmengine.ImgDataPreprocessor`, this module
supports ``inputs`` as torch.Tensor or a list of torch.Tensor.
"""
def __init__(self,
mean: Optional[Sequence[Union[float, int]]] = None,
std: Optional[Sequence[Union[float, int]]] = None,
pad_size_divisor: int = 1,
pad_value: Union[float, int] = 0,
to_rgb: bool = False,
bgr_to_rgb: bool = False,
rgb_to_bgr: bool = False,
non_blocking: Optional[bool] = False):
super().__init__(
mean=mean,
std=std,
pad_size_divisor=pad_size_divisor,
pad_value=pad_value,
bgr_to_rgb=bgr_to_rgb,
rgb_to_bgr=rgb_to_bgr,
non_blocking=non_blocking)
self._channel_conversion = to_rgb or bgr_to_rgb or rgb_to_bgr
def forward(
self,
data: dict,
training: bool = False
) -> Tuple[List[torch.Tensor], Optional[list]]:
"""Performs normalization and bgr2rgb conversion based on
``BaseDataPreprocessor``.
Args:
data (dict): data sampled from dataloader.
training (bool): Whether to enable training time augmentation. If
subclasses override this method, they can perform different
preprocessing strategies for training and testing based on the
value of ``training``.
Returns:
Tuple[torch.Tensor, Optional[list]]: Data in the same format as the
model input.
"""
assert isinstance(data,
dict), 'Please use default_collate in dataloader, \
instead of pseudo_collate.'
data = [val for _, val in data.items()]
batch_inputs, batch_data_samples = self.cast_data(data)
# Here is what is different from :class:`mmengine.ImgDataPreprocessor`
# Since there are multiple views for an image for some algorithms,
# e.g. SimCLR, each item in inputs is a list, containing multi-views
# for an image.
if isinstance(batch_inputs, list):
# channel transform
if self._channel_conversion:
batch_inputs = [
_input[:, [2, 1, 0], ...] for _input in batch_inputs
]
# convert to float after channel conversion to ensure efficiency
batch_inputs = [_input.float() for _input in batch_inputs]
# normalization.
if self._enable_normalize:
batch_inputs = [(_input - self.mean) / self.std
for _input in batch_inputs]
else:
# channel transform
if self._channel_conversion:
batch_inputs = batch_inputs[:, [2, 1, 0], ...]
# convert to float after channel conversion to ensure efficiency
batch_inputs = batch_inputs.float()
# normalization.
if self._enable_normalize:
batch_inputs = (batch_inputs - self.mean) / self.std
return {'inputs': batch_inputs, 'data_samples': batch_data_samples}
@MODELS.register_module()
class TwoNormDataPreprocessor(SelfSupDataPreprocessor):
"""Image pre-processor for CAE, BEiT v1/v2, etc.
Compared with the :class:`mmselfsup.SelfSupDataPreprocessor`, this module
will normalize the prediction image and target image with different
normalization parameters.
Args:
mean (Sequence[float or int], optional): The pixel mean of image
channels. If ``to_rgb=True`` it means the mean value of R, G, B
channels. If the length of `mean` is 1, it means all channels have
the same mean value, or the input is a gray image. If it is not
specified, images will not be normalized. Defaults to None.
std (Sequence[float or int], optional): The pixel standard deviation of
image channels. If ``to_rgb=True`` it means the standard deviation
of R, G, B channels. If the length of `std` is 1, it means all
channels have the same standard deviation, or the input is a gray
image. If it is not specified, images will not be normalized.
Defaults to None.
second_mean (Sequence[float or int], optional): The description is
like ``mean``, it can be customized for targe image. Defaults to
None.
second_std (Sequence[float or int], optional): The description is
like ``std``, it can be customized for targe image. Defaults to
None.
pad_size_divisor (int): The size of padded image should be
divisible by ``pad_size_divisor``. Defaults to 1.
pad_value (float or int): The padded pixel value. Defaults to 0.
to_rgb (bool): whether to convert image from BGR to RGB.
Defaults to False.
non_blocking (bool): Whether block current process when transferring
data to device. Defaults to False.
"""
def __init__(self,
mean: Optional[Sequence[Union[float, int]]] = None,
std: Optional[Sequence[Union[float, int]]] = None,
second_mean: Sequence[Union[float, int]] = None,
second_std: Sequence[Union[float, int]] = None,
pad_size_divisor: int = 1,
pad_value: Union[float, int] = 0,
to_rgb: bool = False,
non_blocking: Optional[bool] = False):
super().__init__(
mean=mean,
std=std,
pad_size_divisor=pad_size_divisor,
pad_value=pad_value,
to_rgb=to_rgb,
non_blocking=non_blocking)
assert (second_mean is not None) and (second_std is not None), (
'mean and std should not be None while using '
'`TwoNormDataPreprocessor`')
assert len(second_mean) == 3 or len(second_mean) == 1, (
'`mean` should have 1 or 3 values, to be compatible with '
f'RGB or gray image, but got {len(second_mean)} values')
assert len(second_std) == 3 or len(second_std) == 1, (
'`std` should have 1 or 3 values, to be compatible with RGB '
f'or gray image, but got {len(std)} values')
self.register_buffer('second_mean',
torch.tensor(second_mean).view(-1, 1, 1), False)
self.register_buffer('second_std',
torch.tensor(second_std).view(-1, 1, 1), False)
def forward(
self,
data: dict,
training: bool = False
) -> Tuple[List[torch.Tensor], Optional[list]]:
"""Performs normalization and bgr2rgb conversion based on
``BaseDataPreprocessor``. The ``batch_inputs`` in forward function is a
list.
Args:
data (dict): data sampled from dataloader.
training (bool): Whether to enable training time augmentation. If
subclasses override this method, they can perform different
preprocessing strategies for training and testing based on the
value of ``training``.
Returns:
Tuple[torch.Tensor, Optional[list]]: Data in the same format as the
model input.
"""
data = [val for _, val in data.items()]
batch_inputs, batch_data_samples = self.cast_data(data)
# channel transform
if self._channel_conversion:
batch_inputs = [
_input[:, [2, 1, 0], ...] for _input in batch_inputs
]
# convert to float after channel conversion to ensure efficiency
batch_inputs = [_input.float() for _input in batch_inputs]
# Normalization. Here is what is different from
# :class:`mmselfsup.SelfSupDataPreprocessor`. Normalize the target
# image and prediction image with different normalization params
if self._enable_normalize:
batch_inputs = [
(batch_inputs[0] - self.mean) / self.std,
(batch_inputs[1] - self.second_mean) / self.second_std
]
return {'inputs': batch_inputs, 'data_samples': batch_data_samples}
@MODELS.register_module()
class VideoDataPreprocessor(BaseDataPreprocessor):
"""Video pre-processor for operations, like normalization and bgr to rgb
conversion .
Compared with the :class:`mmaction.ActionDataPreprocessor`, this module
supports ``inputs`` as torch.Tensor or a list of torch.Tensor.
Args:
mean (Sequence[float or int, optional): The pixel mean of channels
of images or stacked optical flow. Defaults to None.
std (Sequence[float or int], optional): The pixel standard deviation
of channels of images or stacked optical flow. Defaults to None.
pad_size_divisor (int): The size of padded image should be
divisible by ``pad_size_divisor``. Defaults to 1.
pad_value (float or int): The padded pixel value. Defaults to 0.
to_rgb (bool): Whether to convert image from BGR to RGB.
Defaults to False.
format_shape (str): Format shape of input data.
Defaults to ``'NCHW'``.
"""
def __init__(self,
mean: Optional[Sequence[Union[float, int]]] = None,
std: Optional[Sequence[Union[float, int]]] = None,
pad_size_divisor: int = 1,
pad_value: Union[float, int] = 0,
to_rgb: bool = False,
format_shape: str = 'NCHW') -> None:
super().__init__()
self.pad_size_divisor = pad_size_divisor
self.pad_value = pad_value
self.to_rgb = to_rgb
self.format_shape = format_shape
if mean is not None:
assert std is not None, 'To enable the normalization in ' \
'preprocessing, please specify both ' \
'`mean` and `std`.'
# Enable the normalization in preprocessing.
self._enable_normalize = True
if self.format_shape == 'NCHW':
normalizer_shape = (-1, 1, 1)
elif self.format_shape == 'NCTHW':
normalizer_shape = (-1, 1, 1, 1)
else:
raise ValueError(f'Invalid format shape: {format_shape}')
self.register_buffer(
'mean',
torch.tensor(mean, dtype=torch.float32).view(normalizer_shape),
False)
self.register_buffer(
'std',
torch.tensor(std, dtype=torch.float32).view(normalizer_shape),
False)
else:
self._enable_normalize = False
def forward(
self,
data: dict,
training: bool = False
) -> Tuple[List[torch.Tensor], Optional[list]]:
"""Performs normalization、padding and bgr2rgb conversion based on
``BaseDataPreprocessor``.
Args:
data (dict): data sampled from dataloader.
training (bool): Whether to enable training time augmentation. If
subclasses override this method, they can perform different
preprocessing strategies for training and testing based on the
value of ``training``.
Returns:
Tuple[List[torch.Tensor], Optional[list]]: Data in the same format
as the model input.
"""
data = [val for _, val in data.items()]
batch_inputs, batch_data_samples = self.cast_data(data)
if isinstance(batch_inputs, list):
# channel transform
if self.to_rgb:
if self.format_shape == 'NCHW':
batch_inputs = [
_input[..., [2, 1, 0], :, :] for _input in batch_inputs
]
elif self.format_shape == 'NCTHW':
batch_inputs = [
_input[..., [2, 1, 0], :, :, :]
for _input in batch_inputs
]
else:
raise ValueError(
f'Invalid format shape: {self.format_shape}')
# convert to float after channel conversion to ensure efficiency
batch_inputs = [_input.float() for _input in batch_inputs]
# normalization
if self._enable_normalize:
batch_inputs = [(_input - self.mean) / self.std
for _input in batch_inputs]
else:
# channel transform
if self.to_rgb:
if self.format_shape == 'NCHW':
batch_inputs = batch_inputs[..., [2, 1, 0], :, :]
elif self.format_shape == 'NCTHW':
batch_inputs = batch_inputs[..., [2, 1, 0], :, :, :]
else:
raise ValueError(
f'Invalid format shape: {self.format_shape}')
# convert to float after channel conversion to ensure efficiency
batch_inputs = batch_inputs.float()
# normalization
if self._enable_normalize:
batch_inputs = (batch_inputs - self.mean) / self.std
return {'inputs': batch_inputs, 'data_samples': batch_data_samples}
@MODELS.register_module()
class MultiModalDataPreprocessor(BaseDataPreprocessor):
"""Data pre-processor for image-text multimodality tasks.
It provides the data pre-processing as follows
- Collate and move data to the target device.
- Pad inputs to the maximum size of current batch with defined
``pad_value``. The padding size can be divisible by a defined
``pad_size_divisor``
- Stack inputs to batch_inputs.
- Convert inputs from bgr to rgb if the shape of input is (3, H, W).
- Normalize image with defined std and mean.
Args:
mean (Sequence[Number], optional): The pixel mean of R, G, B channels.
Defaults to None.
std (Sequence[Number], optional): The pixel standard deviation of
R, G, B channels. Defaults to None.
pad_size_divisor (int): The size of padded image should be
divisible by ``pad_size_divisor``. Defaults to 1.
pad_value (Number): The padded pixel value. Defaults to 0.
to_rgb (bool): whether to convert image from BGR to RGB.
Defaults to False.
"""
def __init__(
self,
mean: Sequence[Number] = None,
std: Sequence[Number] = None,
pad_size_divisor: int = 1,
pad_value: Number = 0,
to_rgb: bool = False,
):
super().__init__()
self.pad_size_divisor = pad_size_divisor
self.pad_value = pad_value
self.to_rgb = to_rgb
if mean is not None:
assert std is not None, 'To enable the normalization in ' \
'preprocessing, please specify both `mean` and `std`.'
# Enable the normalization in preprocessing.
self._enable_normalize = True
self.register_buffer('mean',
torch.tensor(mean).view(-1, 1, 1), False)
self.register_buffer('std',
torch.tensor(std).view(-1, 1, 1), False)
else:
self._enable_normalize = False
def forward(self, data: dict, training: bool = False) -> dict:
"""Perform normalization, padding, bgr2rgb conversion and batch
augmentation based on ``BaseDataPreprocessor``.
Args:
data (dict): data sampled from dataloader.
training (bool): Whether to enable training time augmentation.
Returns:
dict: Data in the same format as the model input.
"""
data = self.cast_data(data)
imgs = data.get('inputs', None)
def _process_img(img):
# ------ To RGB ------
if self.to_rgb and img.size(1) == 3:
img = img.flip(1)
# -- Normalization ---
img = img.float()
if self._enable_normalize:
img = (img - self.mean) / self.std
# ------ Padding -----
if self.pad_size_divisor > 1:
h, w = img.shape[-2:]
target_h = math.ceil(
h / self.pad_size_divisor) * self.pad_size_divisor
target_w = math.ceil(
w / self.pad_size_divisor) * self.pad_size_divisor
pad_h = target_h - h
pad_w = target_w - w
img = F.pad(img, (0, pad_w, 0, pad_h), 'constant',
self.pad_value)
return img
if isinstance(imgs, torch.Tensor):
imgs = _process_img(imgs)
elif isinstance(imgs, Sequence):
# B, T, C, H, W
imgs = torch.stack([_process_img(img) for img in imgs], dim=1)
elif imgs is not None:
raise ValueError(f'{type(imgs)} is not supported for imgs inputs.')
data_samples = data.get('data_samples', None)
return {'images': imgs, 'data_samples': data_samples}