Spaces:
Running
Running
File size: 12,621 Bytes
9bf4bd7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 |
# Copyright (c) OpenMMLab. 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 InstanceData, LabelData
from mmocr.registry import TRANSFORMS
from mmocr.structures import (KIEDataSample, TextDetDataSample,
TextRecogDataSample)
@TRANSFORMS.register_module()
class PackTextDetInputs(BaseTransform):
"""Pack the inputs data for text detection.
The type of outputs is `dict`:
- inputs: image converted to tensor, whose shape is (C, H, W).
- data_samples: Two components of ``TextDetDataSample`` will be updated:
- gt_instances (InstanceData): Depending on annotations, a subset of the
following keys will be updated:
- bboxes (torch.Tensor((N, 4), dtype=torch.float32)): The groundtruth
of bounding boxes in the form of [x1, y1, x2, y2]. Renamed from
'gt_bboxes'.
- labels (torch.LongTensor(N)): The labels of instances.
Renamed from 'gt_bboxes_labels'.
- polygons(list[np.array((2k,), dtype=np.float32)]): The
groundtruth of polygons in the form of [x1, y1,..., xk, yk]. Each
element in polygons may have different number of points. Renamed from
'gt_polygons'. Using numpy instead of tensor is that polygon usually
is not the output of model and operated on cpu.
- ignored (torch.BoolTensor((N,))): The flag indicating whether the
corresponding instance should be ignored. Renamed from
'gt_ignored'.
- texts (list[str]): The groundtruth texts. Renamed from 'gt_texts'.
- metainfo (dict): 'metainfo' is always populated. The contents of the
'metainfo' depends on ``meta_keys``. By default it includes:
- "img_path": Path to the image file.
- "img_shape": Shape of the image input to the network as a tuple
(h, w). Note that the image may be zero-padded afterward on the
bottom/right if the batch tensor is larger than this shape.
- "scale_factor": A tuple indicating the ratio of width and height
of the preprocessed image to the original one.
- "ori_shape": Shape of the preprocessed image as a tuple
(h, w).
- "pad_shape": Image shape after padding (if any Pad-related
transform involved) as a tuple (h, w).
- "flip": A boolean indicating if the image has been flipped.
- ``flip_direction``: the flipping direction.
Args:
meta_keys (Sequence[str], optional): Meta keys to be converted to
the metainfo of ``TextDetSample``. Defaults to ``('img_path',
'ori_shape', 'img_shape', 'scale_factor', 'flip',
'flip_direction')``.
"""
mapping_table = {
'gt_bboxes': 'bboxes',
'gt_bboxes_labels': 'labels',
'gt_polygons': 'polygons',
'gt_texts': 'texts',
'gt_ignored': 'ignored'
}
def __init__(self,
meta_keys=('img_path', 'ori_shape', 'img_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`): Data for model forwarding.
- 'data_samples' (obj:`DetDataSample`): The annotation info of the
sample.
"""
packed_results = dict()
if 'img' in results:
img = results['img']
if len(img.shape) < 3:
img = np.expand_dims(img, -1)
# A simple trick to speedup formatting by 3-5 times when
# OMP_NUM_THREADS != 1
# Refer to https://github.com/open-mmlab/mmdetection/pull/9533
# for more details
if img.flags.c_contiguous:
img = to_tensor(img)
img = img.permute(2, 0, 1).contiguous()
else:
img = np.ascontiguousarray(img.transpose(2, 0, 1))
img = to_tensor(img)
packed_results['inputs'] = img
data_sample = TextDetDataSample()
instance_data = InstanceData()
for key in self.mapping_table.keys():
if key not in results:
continue
if key in ['gt_bboxes', 'gt_bboxes_labels', 'gt_ignored']:
instance_data[self.mapping_table[key]] = to_tensor(
results[key])
else:
instance_data[self.mapping_table[key]] = results[key]
data_sample.gt_instances = instance_data
img_meta = {}
for key in self.meta_keys:
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
@TRANSFORMS.register_module()
class PackTextRecogInputs(BaseTransform):
"""Pack the inputs data for text recognition.
The type of outputs is `dict`:
- inputs: Image as a tensor, whose shape is (C, H, W).
- data_samples: Two components of ``TextRecogDataSample`` will be updated:
- gt_text (LabelData):
- item(str): The groundtruth of text. Rename from 'gt_texts'.
- metainfo (dict): 'metainfo' is always populated. The contents of the
'metainfo' depends on ``meta_keys``. By default it includes:
- "img_path": Path to the image file.
- "ori_shape": Shape of the preprocessed image as a tuple
(h, w).
- "img_shape": Shape of the image input to the network as a tuple
(h, w). Note that the image may be zero-padded afterward on the
bottom/right if the batch tensor is larger than this shape.
- "valid_ratio": The proportion of valid (unpadded) content of image
on the x-axis. It defaults to 1 if not set in pipeline.
Args:
meta_keys (Sequence[str], optional): Meta keys to be converted to
the metainfo of ``TextRecogDataSampel``. Defaults to
``('img_path', 'ori_shape', 'img_shape', 'pad_shape',
'valid_ratio')``.
"""
def __init__(self,
meta_keys=('img_path', 'ori_shape', 'img_shape', 'pad_shape',
'valid_ratio')):
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`): Data for model forwarding.
- 'data_samples' (obj:`TextRecogDataSample`): The annotation info
of the sample.
"""
packed_results = dict()
if 'img' in results:
img = results['img']
if len(img.shape) < 3:
img = np.expand_dims(img, -1)
# A simple trick to speedup formatting by 3-5 times when
# OMP_NUM_THREADS != 1
# Refer to https://github.com/open-mmlab/mmdetection/pull/9533
# for more details
if img.flags.c_contiguous:
img = to_tensor(img)
img = img.permute(2, 0, 1).contiguous()
else:
img = np.ascontiguousarray(img.transpose(2, 0, 1))
img = to_tensor(img)
packed_results['inputs'] = img
data_sample = TextRecogDataSample()
gt_text = LabelData()
if results.get('gt_texts', None):
assert len(
results['gt_texts']
) == 1, 'Each image sample should have one text annotation only'
gt_text.item = results['gt_texts'][0]
data_sample.gt_text = gt_text
img_meta = {}
for key in self.meta_keys:
if key == 'valid_ratio':
img_meta[key] = results.get('valid_ratio', 1)
else:
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
@TRANSFORMS.register_module()
class PackKIEInputs(BaseTransform):
"""Pack the inputs data for key information extraction.
The type of outputs is `dict`:
- inputs: image converted to tensor, whose shape is (C, H, W).
- data_samples: Two components of ``TextDetDataSample`` will be updated:
- gt_instances (InstanceData): Depending on annotations, a subset of the
following keys will be updated:
- bboxes (torch.Tensor((N, 4), dtype=torch.float32)): The groundtruth
of bounding boxes in the form of [x1, y1, x2, y2]. Renamed from
'gt_bboxes'.
- labels (torch.LongTensor(N)): The labels of instances.
Renamed from 'gt_bboxes_labels'.
- edge_labels (torch.LongTensor(N, N)): The edge labels.
Renamed from 'gt_edges_labels'.
- texts (list[str]): The groundtruth texts. Renamed from 'gt_texts'.
- metainfo (dict): 'metainfo' is always populated. The contents of the
'metainfo' depends on ``meta_keys``. By default it includes:
- "img_path": Path to the image file.
- "img_shape": Shape of the image input to the network as a tuple
(h, w). Note that the image may be zero-padded afterward on the
bottom/right if the batch tensor is larger than this shape.
- "scale_factor": A tuple indicating the ratio of width and height
of the preprocessed image to the original one.
- "ori_shape": Shape of the preprocessed image as a tuple
(h, w).
Args:
meta_keys (Sequence[str], optional): Meta keys to be converted to
the metainfo of ``TextDetSample``. Defaults to ``('img_path',
'ori_shape', 'img_shape', 'scale_factor', 'flip',
'flip_direction')``.
"""
mapping_table = {
'gt_bboxes': 'bboxes',
'gt_bboxes_labels': 'labels',
'gt_edges_labels': 'edge_labels',
'gt_texts': 'texts',
}
def __init__(self, meta_keys=()):
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`): Data for model forwarding.
- 'data_samples' (obj:`DetDataSample`): The annotation info of the
sample.
"""
packed_results = dict()
if 'img' in results:
img = results['img']
if len(img.shape) < 3:
img = np.expand_dims(img, -1)
# A simple trick to speedup formatting by 3-5 times when
# OMP_NUM_THREADS != 1
# Refer to https://github.com/open-mmlab/mmdetection/pull/9533
# for more details
if img.flags.c_contiguous:
img = to_tensor(img)
img = img.permute(2, 0, 1).contiguous()
else:
img = np.ascontiguousarray(img.transpose(2, 0, 1))
img = to_tensor(img)
packed_results['inputs'] = img
else:
packed_results['inputs'] = torch.FloatTensor().reshape(0, 0, 0)
data_sample = KIEDataSample()
instance_data = InstanceData()
for key in self.mapping_table.keys():
if key not in results:
continue
if key in ['gt_bboxes', 'gt_bboxes_labels', 'gt_edges_labels']:
instance_data[self.mapping_table[key]] = to_tensor(
results[key])
else:
instance_data[self.mapping_table[key]] = results[key]
data_sample.gt_instances = instance_data
img_meta = {}
for key in self.meta_keys:
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
|