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# Copyright (c) Open-CD. All rights reserved.
import numpy as np
import torch
from mmcv.transforms import to_tensor
from mmcv.transforms.base import BaseTransform
from mmengine.structures import PixelData
from mmseg.structures import SegDataSample
from opencd.registry import TRANSFORMS
@TRANSFORMS.register_module()
class MultiImgPackSegInputs(BaseTransform):
"""Pack the inputs data for the semantic segmentation.
The ``img_meta`` item is always populated. The contents of the
``img_meta`` dictionary depends on ``meta_keys``. By default this includes:
- ``img_path``: filename of the image
- ``ori_shape``: original shape of the image as a tuple (h, w, c)
- ``img_shape``: shape of the image input to the network as a tuple \
(h, w, c). Note that images may be zero padded on the \
bottom/right if the batch tensor is larger than this shape.
- ``pad_shape``: shape of padded images
- ``scale_factor``: a float indicating the preprocessing scale
- ``flip``: a boolean indicating if image flip transform was used
- ``flip_direction``: the flipping direction
Args:
meta_keys (Sequence[str], optional): Meta keys to be packed from
``SegDataSample`` and collected in ``data[img_metas]``.
Default: ``('img_path', 'ori_shape',
'img_shape', 'pad_shape', 'scale_factor', 'flip',
'flip_direction')``
"""
def __init__(self,
meta_keys=('img_path', 'seg_map_path', 'seg_map_path_from',
'seg_map_path_to', 'ori_shape','img_shape',
'pad_shape', 'scale_factor', 'flip',
'flip_direction')):
self.meta_keys = meta_keys
def transform(self, results: dict) -> dict:
"""Method to pack the input data.
Args:
results (dict): Result dict from the data pipeline.
Returns:
dict:
- 'inputs' (obj:`torch.Tensor`): The forward data of models.
- 'data_sample' (obj:`SegDataSample`): The annotation info of the
sample.
"""
packed_results = dict()
if 'img' in results:
def _transform_img(img):
if len(img.shape) < 3:
img = np.expand_dims(img, -1)
if not img.flags.c_contiguous:
img = to_tensor(np.ascontiguousarray(img.transpose(2, 0, 1)))
else:
img = img.transpose(2, 0, 1)
img = to_tensor(img).contiguous()
return img
imgs = [_transform_img(img) for img in results['img']]
imgs = torch.cat(imgs, axis=0) # -> (6, H, W)
packed_results['inputs'] = imgs
data_sample = SegDataSample()
if 'gt_seg_map' in results:
gt_sem_seg_data = dict(
data=to_tensor(results['gt_seg_map'][None,
...].astype(np.int64)))
data_sample.gt_sem_seg = PixelData(**gt_sem_seg_data)
if 'gt_edge_map' in results:
gt_edge_data = dict(
data=to_tensor(results['gt_edge_map'][None,
...].astype(np.int64)))
data_sample.set_data(dict(gt_edge_map=PixelData(**gt_edge_data)))
if 'gt_seg_map_from' in results:
gt_sem_seg_data_from = dict(
data=to_tensor(results['gt_seg_map_from'][None,
...].astype(np.int64)))
data_sample.set_data(dict(gt_sem_seg_from=PixelData(**gt_sem_seg_data_from)))
if 'gt_seg_map_to' in results:
gt_sem_seg_data_to = dict(
data=to_tensor(results['gt_seg_map_to'][None,
...].astype(np.int64)))
data_sample.set_data(dict(gt_sem_seg_to=PixelData(**gt_sem_seg_data_to)))
img_meta = {}
for key in self.meta_keys:
if key in results:
img_meta[key] = results[key]
data_sample.set_metainfo(img_meta)
packed_results['data_samples'] = data_sample
return packed_results
def __repr__(self) -> str:
repr_str = self.__class__.__name__
repr_str += f'(meta_keys={self.meta_keys})'
return repr_str
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