Newer
Older
# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Mapping, Optional, Sequence, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmengine.registry import MODELS
from mmengine.structures import BaseDataElement
from mmengine.utils import is_list_of
from ..utils import stack_batch
CastData = Union[tuple, dict, BaseDataElement, torch.Tensor, list, bytes, str,
None]
@MODELS.register_module()
class BaseDataPreprocessor(nn.Module):
"""Base data pre-processor used for copying data to the target device.
Subclasses inherit from ``BaseDataPreprocessor`` could override the
forward method to implement custom data pre-processing, such as
batch-resize, MixUp, or CutMix.
Args:
non_blocking (bool): Whether block current process
when transferring data to device.
New in version 0.3.0.
Note:
Data dictionary returned by dataloader must be a dict and at least
contain the ``inputs`` key.
def __init__(self, non_blocking: Optional[bool] = False):
super().__init__()
self._non_blocking = non_blocking
self._device = torch.device('cpu')
def cast_data(self, data: CastData) -> CastData:
"""Copying data to the target device.
data (dict): Data returned by ``DataLoader``.
CollatedResult: Inputs and data sample at target device.
if isinstance(data, Mapping):
return {key: self.cast_data(data[key]) for key in data}
elif isinstance(data, (str, bytes)) or data is None:
return data
elif isinstance(data, tuple) and hasattr(data, '_fields'):
# namedtuple
return type(data)(*(self.cast_data(sample) for sample in data)) # type: ignore # noqa: E501 # yapf:disable
elif isinstance(data, Sequence):
return type(data)(self.cast_data(sample) for sample in data) # type: ignore # noqa: E501 # yapf:disable
elif isinstance(data, (torch.Tensor, BaseDataElement)):
return data.to(self.device, non_blocking=self._non_blocking)
raise TypeError(
'`BaseDataPreprocessor.cast_data`: batch data must contain '
'tensors, numpy arrays, numbers, dicts or lists, but '
f'found {type(data)}')
def forward(self, data: dict, training: bool = False) -> Union[dict, list]:
"""Preprocesses the data into the model input format.
After the data pre-processing of :meth:`cast_data`, ``forward``
will stack the input tensor list to a batch tensor at the first
dimension.
Args:
data (dict): Data returned by dataloader
training (bool): Whether to enable training time augmentation.
Returns:
dict or list: Data in the same format as the model input.
return self.cast_data(data) # type: ignore
@property
def device(self):
return self._device
Qian Zhao
committed
def to(self, *args, **kwargs) -> nn.Module:
"""Overrides this method to set the :attr:`device`
Returns:
nn.Module: The model itself.
"""
Qian Zhao
committed
device = torch._C._nn._parse_to(*args, **kwargs)[0]
if device is not None:
self._device = torch.device(device)
return super().to(*args, **kwargs)
def cuda(self, *args, **kwargs) -> nn.Module:
"""Overrides this method to set the :attr:`device`
Returns:
nn.Module: The model itself.
"""
self._device = torch.device(torch.cuda.current_device())
return super().cuda()
def cpu(self, *args, **kwargs) -> nn.Module:
"""Overrides this method to set the :attr:`device`
Returns:
nn.Module: The model itself.
"""
self._device = torch.device('cpu')
return super().cpu()
@MODELS.register_module()
class ImgDataPreprocessor(BaseDataPreprocessor):
"""Image pre-processor for normalization and bgr to rgb conversion.
Accepts the data sampled by the dataloader, and preprocesses it into the
format of the model input. ``ImgDataPreprocessor`` provides the
basic data pre-processing as follows
- Collates and moves data to the target device.
- Converts inputs from bgr to rgb if the shape of input is (3, H, W).
- Normalizes image with defined std and mean.
- Pads 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.
For ``ImgDataPreprocessor``, the dimension of the single inputs must be
(3, H, W).
Note:
``ImgDataPreprocessor`` and its subclass is built in the
constructor of :class:`BaseDataset`.
Args:
mean (Sequence[float or int], optional): The pixel mean of image
channels. If ``bgr_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
None.
std (Sequence[float or int], optional): The pixel standard deviation of
image channels. If ``bgr_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 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.
bgr_to_rgb (bool): whether to convert image from BGR to RGB.
Defaults to False.
rgb_to_bgr (bool): whether to convert image from RGB to RGB.
Defaults to False.
non_blocking (bool): Whether block current process
when transferring data to device.
New in version v0.3.0.
Note:
if images do not need to be normalized, `std` and `mean` should be
both set to None, otherwise both of them should be set to a tuple of
corresponding values.
"""
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,
bgr_to_rgb: bool = False,
rgb_to_bgr: bool = False,
non_blocking: Optional[bool] = False):
super().__init__(non_blocking)
assert not (bgr_to_rgb and rgb_to_bgr), (
'`bgr2rgb` and `rgb2bgr` cannot be set to True at the same time')
assert (mean is None) == (std is None), (
'mean and std should be both None or tuple')
if mean is not None:
assert len(mean) == 3 or len(mean) == 1, (
'`mean` should have 1 or 3 values, to be compatible with '
f'RGB or gray image, but got {len(mean)} values')
assert len(std) == 3 or len(std) == 1, ( # type: ignore
'`std` should have 1 or 3 values, to be compatible with RGB ' # type: ignore # noqa: E501
f'or gray image, but got {len(std)} values') # type: ignore
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
self._channel_conversion = rgb_to_bgr or bgr_to_rgb
self.pad_size_divisor = pad_size_divisor
self.pad_value = pad_value
def forward(self, data: dict, training: bool = False) -> Union[dict, list]:
"""Performs normalization、padding and bgr2rgb conversion based on
``BaseDataPreprocessor``.
Args:
data (dict): Data sampled from dataset. If the collate
function of DataLoader is :obj:`pseudo_collate`, data will be a
list of dict. If collate function is :obj:`default_collate`,
data will be a tuple with batch input tensor and list of data
samples.
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:
dict or list: Data in the same format as the model input.
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
data = self.cast_data(data) # type: ignore
_batch_inputs = data['inputs']
# Process data with `pseudo_collate`.
if is_list_of(_batch_inputs, torch.Tensor):
batch_inputs = []
for _batch_input in _batch_inputs:
# channel transform
if self._channel_conversion:
_batch_input = _batch_input[[2, 1, 0], ...]
# Convert to float after channel conversion to ensure
# efficiency
_batch_input = _batch_input.float()
# Normalization.
if self._enable_normalize:
if self.mean.shape[0] == 3:
assert _batch_input.dim(
) == 3 and _batch_input.shape[0] == 3, (
'If the mean has 3 values, the input tensor '
'should in shape of (3, H, W), but got the tensor '
f'with shape {_batch_input.shape}')
_batch_input = (_batch_input - self.mean) / self.std
batch_inputs.append(_batch_input)
# Pad and stack Tensor.
batch_inputs = stack_batch(batch_inputs, self.pad_size_divisor,
self.pad_value)
# Process data with `default_collate`.
elif isinstance(_batch_inputs, torch.Tensor):
assert _batch_inputs.dim() == 4, (
'The input of `ImgDataPreprocessor` should be a NCHW tensor '
'or a list of tensor, but got a tensor with shape: '
f'{_batch_inputs.shape}')
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()
_batch_inputs = (_batch_inputs - self.mean) / self.std
h, w = _batch_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
batch_inputs = F.pad(_batch_inputs, (0, pad_w, 0, pad_h),
'constant', self.pad_value)
else:
raise TypeError('Output of `cast_data` should be a list of dict '
'or a tuple with inputs and data_samples, but got'
f'{type(data)}： {data}')
data['inputs'] = batch_inputs
data.setdefault('data_samples', None)
return data