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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Optional, Sequence, Union
import numpy as np
import torch
import torch.nn.functional as F
from mmengine.model.utils import stack_batch
from mmdet.models.utils.misc import samplelist_boxtype2tensor
from mmdet.registry import MODELS
from mmdet.structures import TrackDataSample
from mmdet.structures.mask import BitmapMasks
from .data_preprocessor import DetDataPreprocessor
@MODELS.register_module()
class TrackDataPreprocessor(DetDataPreprocessor):
"""Image pre-processor for tracking tasks.
Accepts the data sampled by the dataloader, and preprocesses
it into the format of the model input. ``TrackDataPreprocessor``
provides the tracking 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 inputs.
- Convert inputs from bgr to rgb if the shape of input is (1, 3, H, W).
- Normalize image with defined std and mean.
- Do batch augmentations during training.
- Record the information of ``batch_input_shape`` and ``pad_shape``.
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.
pad_mask (bool): Whether to pad instance masks. Defaults to False.
mask_pad_value (int): The padded pixel value for instance masks.
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.
use_det_processor: (bool): whether to use DetDataPreprocessor
in training phrase. This is mainly for some tracking models
fed into one image rather than a group of image in training.
Defaults to False.
. boxtype2tensor (bool): Whether to convert the ``BaseBoxes`` type of
bboxes data to ``Tensor`` type. Defaults to True.
batch_augments (list[dict], optional): Batch-level augmentations
"""
def __init__(self,
mean: Optional[Sequence[Union[float, int]]] = None,
std: Optional[Sequence[Union[float, int]]] = None,
use_det_processor: bool = False,
**kwargs):
super().__init__(mean=mean, std=std, **kwargs)
self.use_det_processor = use_det_processor
if mean is not None and not self.use_det_processor:
# overwrite the ``register_bufffer`` in ``ImgDataPreprocessor``
# since the shape of ``mean`` and ``std`` in tracking tasks must be
# (T, C, H, W), which T is the temporal length of the video.
self.register_buffer('mean',
torch.tensor(mean).view(1, -1, 1, 1), False)
self.register_buffer('std',
torch.tensor(std).view(1, -1, 1, 1), False)
def forward(self, data: dict, training: bool = False) -> Dict:
"""Perform normalization,padding and bgr2rgb conversion based on
``TrackDataPreprocessor``.
Args:
data (dict): data sampled from dataloader.
training (bool): Whether to enable training time augmentation.
Returns:
Tuple[Dict[str, List[torch.Tensor]], OptSampleList]: Data in the
same format as the model input.
"""
if self.use_det_processor and training:
batch_pad_shape = self._get_pad_shape(data)
else:
batch_pad_shape = self._get_track_pad_shape(data)
data = self.cast_data(data)
imgs, data_samples = data['inputs'], data['data_samples']
if self.use_det_processor and training:
assert imgs[0].dim() == 3, \
'Only support the 3 dims when use detpreprocessor in training'
if self._channel_conversion:
imgs = [_img[[2, 1, 0], ...] for _img in imgs]
# Convert to `float`
imgs = [_img.float() for _img in imgs]
if self._enable_normalize:
imgs = [(_img - self.mean) / self.std for _img in imgs]
inputs = stack_batch(imgs, self.pad_size_divisor, self.pad_value)
else:
assert imgs[0].dim() == 4, \
'Only support the 4 dims when use trackprocessor in training'
# The shape of imgs[0] is (T, C, H, W).
channel = imgs[0].size(1)
if self._channel_conversion and channel == 3:
imgs = [_img[:, [2, 1, 0], ...] for _img in imgs]
# change to `float`
imgs = [_img.float() for _img in imgs]
if self._enable_normalize:
imgs = [(_img - self.mean) / self.std for _img in imgs]
inputs = stack_track_batch(imgs, self.pad_size_divisor,
self.pad_value)
if data_samples is not None:
# NOTE the batched image size information may be useful, e.g.
# in DETR, this is needed for the construction of masks, which is
# then used for the transformer_head.
batch_input_shape = tuple(inputs.size()[-2:])
if self.use_det_processor and training:
for data_sample, pad_shape in zip(data_samples,
batch_pad_shape):
data_sample.set_metainfo({
'batch_input_shape': batch_input_shape,
'pad_shape': pad_shape
})
if self.boxtype2tensor:
samplelist_boxtype2tensor(data_samples)
if self.pad_mask:
self.pad_gt_masks(data_samples)
else:
for track_data_sample, pad_shapes in zip(
data_samples, batch_pad_shape):
for i in range(len(track_data_sample)):
det_data_sample = track_data_sample[i]
det_data_sample.set_metainfo({
'batch_input_shape': batch_input_shape,
'pad_shape': pad_shapes[i]
})
if self.pad_mask and training:
self.pad_track_gt_masks(data_samples)
if training and self.batch_augments is not None:
for batch_aug in self.batch_augments:
if self.use_det_processor and training:
inputs, data_samples = batch_aug(inputs, data_samples)
else:
# we only support T==1 when using batch augments.
# Only yolox need batch_aug, and yolox can only process
# (N, C, H, W) shape.
# The shape of `inputs` is (N, T, C, H, W), hence, we use
# inputs[:, 0] to change the shape to (N, C, H, W).
assert inputs.size(1) == 1 and len(
data_samples[0]
) == 1, 'Only support the number of sequence images equals to 1 when using batch augment.' # noqa: E501
det_data_samples = [
track_data_sample[0]
for track_data_sample in data_samples
]
aug_inputs, aug_det_samples = batch_aug(
inputs[:, 0], det_data_samples)
inputs = aug_inputs.unsqueeze(1)
for track_data_sample, det_sample in zip(
data_samples, aug_det_samples):
track_data_sample.video_data_samples = [det_sample]
# Note: inputs may contain large number of frames, so we must make
# sure that the mmeory is contiguous for stable forward
inputs = inputs.contiguous()
return dict(inputs=inputs, data_samples=data_samples)
def _get_track_pad_shape(self, data: dict) -> Dict[str, List]:
"""Get the pad_shape of each image based on data and pad_size_divisor.
Args:
data (dict): Data sampled from dataloader.
Returns:
Dict[str, List]: The shape of padding.
"""
batch_pad_shape = dict()
batch_pad_shape = []
for imgs in data['inputs']:
# The sequence images in one sample among a batch have the same
# original shape
pad_h = int(np.ceil(imgs.shape[-2] /
self.pad_size_divisor)) * self.pad_size_divisor
pad_w = int(np.ceil(imgs.shape[-1] /
self.pad_size_divisor)) * self.pad_size_divisor
pad_shapes = [(pad_h, pad_w)] * imgs.size(0)
batch_pad_shape.append(pad_shapes)
return batch_pad_shape
def pad_track_gt_masks(self,
data_samples: Sequence[TrackDataSample]) -> None:
"""Pad gt_masks to shape of batch_input_shape."""
if 'masks' in data_samples[0][0].get('gt_instances', None):
for track_data_sample in data_samples:
for i in range(len(track_data_sample)):
det_data_sample = track_data_sample[i]
masks = det_data_sample.gt_instances.masks
# TODO: whether to use BitmapMasks
assert isinstance(masks, BitmapMasks)
batch_input_shape = det_data_sample.batch_input_shape
det_data_sample.gt_instances.masks = masks.pad(
batch_input_shape, pad_val=self.mask_pad_value)
def stack_track_batch(tensors: List[torch.Tensor],
pad_size_divisor: int = 0,
pad_value: Union[int, float] = 0) -> torch.Tensor:
"""Stack multiple tensors to form a batch and pad the images to the max
shape use the right bottom padding mode in these images. If
``pad_size_divisor > 0``, add padding to ensure the common height and width
is divisible by ``pad_size_divisor``. The difference between this function
and ``stack_batch`` in MMEngine is that this function can process batch
sequence images with shape (N, T, C, H, W).
Args:
tensors (List[Tensor]): The input multiple tensors. each is a
TCHW 4D-tensor. T denotes the number of key/reference frames.
pad_size_divisor (int): If ``pad_size_divisor > 0``, add padding
to ensure the common height and width is divisible by
``pad_size_divisor``. This depends on the model, and many
models need a divisibility of 32. Defaults to 0
pad_value (int, float): The padding value. Defaults to 0
Returns:
Tensor: The NTCHW 5D-tensor. N denotes the batch size.
"""
assert isinstance(tensors, list), \
f'Expected input type to be list, but got {type(tensors)}'
assert len(set([tensor.ndim for tensor in tensors])) == 1, \
f'Expected the dimensions of all tensors must be the same, ' \
f'but got {[tensor.ndim for tensor in tensors]}'
assert tensors[0].ndim == 4, f'Expected tensor dimension to be 4, ' \
f'but got {tensors[0].ndim}'
assert len(set([tensor.shape[0] for tensor in tensors])) == 1, \
f'Expected the channels of all tensors must be the same, ' \
f'but got {[tensor.shape[0] for tensor in tensors]}'
tensor_sizes = [(tensor.shape[-2], tensor.shape[-1]) for tensor in tensors]
max_size = np.stack(tensor_sizes).max(0)
if pad_size_divisor > 1:
# the last two dims are H,W, both subject to divisibility requirement
max_size = (
max_size +
(pad_size_divisor - 1)) // pad_size_divisor * pad_size_divisor
padded_samples = []
for tensor in tensors:
padding_size = [
0, max_size[-1] - tensor.shape[-1], 0,
max_size[-2] - tensor.shape[-2]
]
if sum(padding_size) == 0:
padded_samples.append(tensor)
else:
padded_samples.append(F.pad(tensor, padding_size, value=pad_value))
return torch.stack(padded_samples, dim=0)
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