mmdet/models/data_preprocessors/HSIdata_preprocessor.py

# Copyright (c) OpenMMLab. All rights reserved.
import random
from numbers import Number
from typing import List, Optional, Sequence, Tuple, Union

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmengine.dist import barrier, broadcast, get_dist_info
from mmengine.logging import MessageHub
from mmengine.model import BaseDataPreprocessor
from mmengine.structures import PixelData
from mmengine.utils import is_seq_of
from mmengine.model.utils import stack_batch
from torch import Tensor

from mmdet.models.utils import unfold_wo_center
from mmdet.models.utils.misc import samplelist_boxtype2tensor
from mmdet.registry import MODELS
from mmdet.structures import DetDataSample
from mmdet.structures.mask import BitmapMasks
from mmdet.utils import ConfigType
import mmcv
import math

try:
    import skimage
except ImportError:
    skimage = None





@MODELS.register_module()
class HSIImgDataPreprocessor(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,
                 non_blocking: Optional[bool] = False):
        super().__init__(non_blocking)
        assert (mean is None) == (std is None), (
            'mean and std should be both None or tuple')
        if mean is not None:
            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.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.
        """
        data = self.cast_data(data)  # type: ignore
        _batch_inputs = data['inputs']
        # Process data with `pseudo_collate`.
        if is_seq_of(_batch_inputs, torch.Tensor):
            batch_inputs = []
            for _batch_input in _batch_inputs:
                # efficiency
                _batch_input = _batch_input.float()
                # Normalization.
                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}')
            # Convert to float after channel conversion to ensure
            # efficiency
            _batch_inputs = _batch_inputs.float()
            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 dict of '
                            'list/tuple with inputs and data_samples, '
                            f'but got {type(data)}： {data}')
        data['inputs'] = batch_inputs
        data.setdefault('data_samples', None)
        return data

@MODELS.register_module()
class HSIDetDataPreprocessor(HSIImgDataPreprocessor):
    """Image pre-processor for detection tasks.

    Comparing with the :class:`mmengine.ImgDataPreprocessor`,

    1. It supports batch augmentations.
    2. It will additionally append batch_input_shape and pad_shape
    to data_samples considering the object detection task.

    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 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.
        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.
        pad_seg (bool): Whether to pad semantic segmentation maps.
            Defaults to False.
        seg_pad_value (int): The padded pixel value for semantic
            segmentation maps. Defaults to 255.
        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.
        boxtype2tensor (bool): Whether to keep the ``BaseBoxes`` type of
            bboxes data or not. Defaults to True.
        non_blocking (bool): Whether block current process
            when transferring data to device. Defaults to False.
        batch_augments (list[dict], optional): Batch-level augmentations
    """

    def __init__(self,
                 mean: Sequence[Number] = None,
                 std: Sequence[Number] = None,
                 pad_size_divisor: int = 1,
                 pad_value: Union[float, int] = 0,
                 pad_mask: bool = False,
                 mask_pad_value: int = 0,
                 pad_seg: bool = False,
                 seg_pad_value: int = 255,
                 boxtype2tensor: bool = True,
                 non_blocking: Optional[bool] = False,
                 batch_augments: Optional[List[dict]] = None):
        super().__init__(
            mean=mean,
            std=std,
            pad_size_divisor=pad_size_divisor,
            pad_value=pad_value,
            non_blocking=non_blocking)
        if batch_augments is not None:
            self.batch_augments = nn.ModuleList(
                [MODELS.build(aug) for aug in batch_augments])
        else:
            self.batch_augments = None
        self.pad_mask = pad_mask
        self.mask_pad_value = mask_pad_value
        self.pad_seg = pad_seg
        self.seg_pad_value = seg_pad_value
        self.boxtype2tensor = boxtype2tensor

    def forward(self, data: dict, training: bool = False) -> dict:
        """Perform normalization、padding and bgr2rgb conversion 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.
        """
        batch_pad_shape = self._get_pad_shape(data)
        data = super().forward(data=data, training=training)
        inputs, data_samples = data['inputs'], data['data_samples']

        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[0].size()[-2:])
            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 hasattr(data_samples[0].gt_instances, 'masks') and training:
                self.pad_gt_masks(data_samples)
            if hasattr(data_samples[0], 'gt_pixel') and training:
                self.pad_gt_pixel(data_samples)
            # if self.pad_mask and training:
            #     self.pad_gt_masks(data_samples)
            if self.pad_seg and training:
                self.pad_gt_sem_seg(data_samples)

        if training and self.batch_augments is not None:
            for batch_aug in self.batch_augments:
                inputs, data_samples = batch_aug(inputs, data_samples)

        return {'inputs': inputs, 'data_samples': data_samples}

    def _get_pad_shape(self, data: dict) -> List[tuple]:
        """Get the pad_shape of each image based on data and
        pad_size_divisor."""
        _batch_inputs = data['inputs']
        # Process data with `pseudo_collate`.
        if is_seq_of(_batch_inputs, torch.Tensor):
            batch_pad_shape = []
            for ori_input in _batch_inputs:
                pad_h = int(
                    np.ceil(ori_input.shape[1] /
                            self.pad_size_divisor)) * self.pad_size_divisor
                pad_w = int(
                    np.ceil(ori_input.shape[2] /
                            self.pad_size_divisor)) * self.pad_size_divisor
                batch_pad_shape.append((pad_h, pad_w))
        # 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}')
            pad_h = int(
                np.ceil(_batch_inputs.shape[1] /
                        self.pad_size_divisor)) * self.pad_size_divisor
            pad_w = int(
                np.ceil(_batch_inputs.shape[2] /
                        self.pad_size_divisor)) * self.pad_size_divisor
            batch_pad_shape = [(pad_h, pad_w)] * _batch_inputs.shape[0]
        else:
            raise TypeError('Output of `cast_data` should be a dict '
                            'or a tuple with inputs and data_samples, but got'
                            f'{type(data)}： {data}')
        return batch_pad_shape

    def pad_gt_masks(self,
                     batch_data_samples: Sequence[DetDataSample]) -> None:
        """Pad gt_masks to shape of batch_input_shape."""
        if 'masks' in batch_data_samples[0].gt_instances:
            for data_samples in batch_data_samples:
                masks = data_samples.gt_instances.masks
                data_samples.gt_instances.masks = masks.pad(
                    data_samples.batch_input_shape,
                    pad_val=self.mask_pad_value)

    def pad_gt_pixel(self,
                     batch_data_samples: Sequence[DetDataSample]) -> None:
        """Pad gt_masks to shape of batch_input_shape."""
        for data_samples in batch_data_samples:
            seg=data_samples.gt_pixel.seg
            h, w = data_samples.gt_pixel.shape[-2:]
            pad_h, pad_w = data_samples.batch_input_shape
            seg = F.pad(
                seg,
                pad=(0, max(pad_w - w, 0), 0, max(pad_h - h, 0)),
                mode='constant',
                value=0)
            if hasattr(data_samples.gt_pixel, 'abu'):
                abu = data_samples.gt_pixel.abu
                abu = F.pad(
                    abu,
                    pad=(0, max(pad_w - w, 0), 0, max(pad_h - h, 0)),
                    mode='constant',
                    value=0)
                data_samples.gt_pixel = PixelData(seg=seg, abu=abu)
            else:
                data_samples.gt_pixel = PixelData(seg=seg,)


    def pad_gt_sem_seg(self,
                       batch_data_samples: Sequence[DetDataSample]) -> None:
        """Pad gt_sem_seg to shape of batch_input_shape."""
        if 'gt_sem_seg' in batch_data_samples[0]:
            for data_samples in batch_data_samples:
                gt_sem_seg = data_samples.gt_sem_seg.sem_seg
                h, w = gt_sem_seg.shape[-2:]
                pad_h, pad_w = data_samples.batch_input_shape
                gt_sem_seg = F.pad(
                    gt_sem_seg,
                    pad=(0, max(pad_w - w, 0), 0, max(pad_h - h, 0)),
                    mode='constant',
                    value=self.seg_pad_value)
                data_samples.gt_sem_seg = PixelData(sem_seg=gt_sem_seg)