113 lines
4.3 KiB
Python
113 lines
4.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
from numbers import Number
|
|
from typing import List, Optional, Sequence, Tuple
|
|
|
|
import torch
|
|
from mmengine.model import BaseDataPreprocessor, stack_batch
|
|
|
|
from mmcls.registry import MODELS
|
|
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.
|
|
batch_augments (dict, optional): The batch augmentations settings,
|
|
including "augments" and "probs". For more details, see
|
|
:class:`mmcls.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,
|
|
batch_augments: Optional[List[dict]] = None):
|
|
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
|
|
|
|
if batch_augments is not None:
|
|
self.batch_augments = RandomBatchAugment(batch_augments)
|
|
else:
|
|
self.batch_augments = None
|
|
|
|
def forward(self,
|
|
data: Sequence[dict],
|
|
training: bool = False) -> Tuple[torch.Tensor, list]:
|
|
"""Perform normalization, padding, bgr2rgb conversion and batch
|
|
augmentation based on ``BaseDataPreprocessor``.
|
|
|
|
Args:
|
|
data (Sequence[dict]): data sampled from dataloader.
|
|
training (bool): Whether to enable training time augmentation.
|
|
|
|
Returns:
|
|
Tuple[torch.Tensor, list]: Data in the same format as the model
|
|
input.
|
|
"""
|
|
inputs, batch_data_samples = self.collate_data(data)
|
|
|
|
# --- Pad and stack --
|
|
batch_inputs = stack_batch(inputs, self.pad_size_divisor,
|
|
self.pad_value)
|
|
|
|
# ------ To RGB ------
|
|
if self.to_rgb and batch_inputs.size(1) == 3:
|
|
batch_inputs = batch_inputs[:, [2, 1, 0], ...]
|
|
|
|
# -- Normalization ---
|
|
if self._enable_normalize:
|
|
batch_inputs = (batch_inputs - self.mean) / self.std
|
|
else:
|
|
batch_inputs = batch_inputs.to(torch.float32)
|
|
|
|
# ----- Batch Aug ----
|
|
if training and self.batch_augments is not None:
|
|
batch_inputs, batch_data_samples = self.batch_augments(
|
|
batch_inputs, batch_data_samples)
|
|
|
|
return batch_inputs, batch_data_samples
|