186 lines
7.7 KiB
Python
186 lines
7.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import math
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from numbers import Number
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from typing import Optional, Sequence
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import torch
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import torch.nn.functional as F
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from mmengine.model import BaseDataPreprocessor, stack_batch
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from mmcls.registry import MODELS
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from mmcls.structures import (batch_label_to_onehot, cat_batch_labels,
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stack_batch_scores, tensor_split)
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from .batch_augments import RandomBatchAugment
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@MODELS.register_module()
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class ClsDataPreprocessor(BaseDataPreprocessor):
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"""Image pre-processor for classification tasks.
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Comparing with the :class:`mmengine.model.ImgDataPreprocessor`,
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1. It won't do normalization if ``mean`` is not specified.
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2. It does normalization and color space conversion after stacking batch.
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3. It supports batch augmentations like mixup and cutmix.
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It provides the data pre-processing as follows
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- Collate and move data to the target device.
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- Pad inputs to the maximum size of current batch with defined
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``pad_value``. The padding size can be divisible by a defined
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``pad_size_divisor``
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- Stack inputs to batch_inputs.
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- Convert inputs from bgr to rgb if the shape of input is (3, H, W).
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- Normalize image with defined std and mean.
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- Do batch augmentations like Mixup and Cutmix during training.
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Args:
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mean (Sequence[Number], optional): The pixel mean of R, G, B channels.
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Defaults to None.
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std (Sequence[Number], optional): The pixel standard deviation of
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R, G, B channels. Defaults to None.
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pad_size_divisor (int): The size of padded image should be
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divisible by ``pad_size_divisor``. Defaults to 1.
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pad_value (Number): The padded pixel value. Defaults to 0.
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to_rgb (bool): whether to convert image from BGR to RGB.
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Defaults to False.
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to_onehot (bool): Whether to generate one-hot format gt-labels and set
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to data samples. Defaults to False.
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num_classes (int, optional): The number of classes. Defaults to None.
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batch_augments (dict, optional): The batch augmentations settings,
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including "augments" and "probs". For more details, see
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:class:`mmcls.models.RandomBatchAugment`.
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"""
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def __init__(self,
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mean: Sequence[Number] = None,
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std: Sequence[Number] = None,
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pad_size_divisor: int = 1,
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pad_value: Number = 0,
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to_rgb: bool = False,
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to_onehot: bool = False,
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num_classes: Optional[int] = None,
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batch_augments: Optional[dict] = None):
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super().__init__()
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self.pad_size_divisor = pad_size_divisor
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self.pad_value = pad_value
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self.to_rgb = to_rgb
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self.to_onehot = to_onehot
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self.num_classes = num_classes
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if mean is not None:
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assert std is not None, 'To enable the normalization in ' \
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'preprocessing, please specify both `mean` and `std`.'
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# Enable the normalization in preprocessing.
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self._enable_normalize = True
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self.register_buffer('mean',
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torch.tensor(mean).view(-1, 1, 1), False)
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self.register_buffer('std',
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torch.tensor(std).view(-1, 1, 1), False)
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else:
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self._enable_normalize = False
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if batch_augments is not None:
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self.batch_augments = RandomBatchAugment(**batch_augments)
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if not self.to_onehot:
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from mmengine.logging import MMLogger
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MMLogger.get_current_instance().info(
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'Because batch augmentations are enabled, the data '
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'preprocessor automatically enables the `to_onehot` '
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'option to generate one-hot format labels.')
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self.to_onehot = True
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else:
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self.batch_augments = None
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def forward(self, data: dict, training: bool = False) -> dict:
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"""Perform normalization, padding, bgr2rgb conversion and batch
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augmentation based on ``BaseDataPreprocessor``.
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Args:
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data (dict): data sampled from dataloader.
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training (bool): Whether to enable training time augmentation.
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Returns:
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dict: Data in the same format as the model input.
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"""
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inputs = self.cast_data(data['inputs'])
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if isinstance(inputs, torch.Tensor):
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# The branch if use `default_collate` as the collate_fn in the
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# dataloader.
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# ------ To RGB ------
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if self.to_rgb and inputs.size(1) == 3:
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inputs = inputs.flip(1)
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# -- Normalization ---
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inputs = inputs.float()
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if self._enable_normalize:
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inputs = (inputs - self.mean) / self.std
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# ------ Padding -----
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if self.pad_size_divisor > 1:
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h, w = inputs.shape[-2:]
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target_h = math.ceil(
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h / self.pad_size_divisor) * self.pad_size_divisor
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target_w = math.ceil(
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w / self.pad_size_divisor) * self.pad_size_divisor
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pad_h = target_h - h
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pad_w = target_w - w
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inputs = F.pad(inputs, (0, pad_w, 0, pad_h), 'constant',
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self.pad_value)
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else:
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# The branch if use `pseudo_collate` as the collate_fn in the
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# dataloader.
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processed_inputs = []
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for input_ in inputs:
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# ------ To RGB ------
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if self.to_rgb and input_.size(0) == 3:
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input_ = input_.flip(0)
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# -- Normalization ---
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input_ = input_.float()
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if self._enable_normalize:
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input_ = (input_ - self.mean) / self.std
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processed_inputs.append(input_)
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# Combine padding and stack
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inputs = stack_batch(processed_inputs, self.pad_size_divisor,
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self.pad_value)
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data_samples = data.get('data_samples', None)
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if data_samples is not None:
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gt_labels = [sample.gt_label for sample in data_samples]
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batch_label, label_indices = cat_batch_labels(
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gt_labels, device=self.device)
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batch_score = stack_batch_scores(gt_labels, device=self.device)
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if batch_score is None and self.to_onehot:
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assert batch_label is not None, \
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'Cannot generate onehot format labels because no labels.'
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num_classes = self.num_classes or data_samples[0].get(
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'num_classes')
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assert num_classes is not None, \
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'Cannot generate one-hot format labels because not set ' \
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'`num_classes` in `data_preprocessor`.'
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batch_score = batch_label_to_onehot(batch_label, label_indices,
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num_classes)
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# ----- Batch Augmentations ----
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if training and self.batch_augments is not None:
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inputs, batch_score = self.batch_augments(inputs, batch_score)
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# ----- scatter labels and scores to data samples ---
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if batch_label is not None:
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for sample, label in zip(
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data_samples, tensor_split(batch_label,
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label_indices)):
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sample.set_gt_label(label)
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if batch_score is not None:
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for sample, score in zip(data_samples, batch_score):
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sample.set_gt_score(score)
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return {'inputs': inputs, 'data_samples': data_samples}
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