change_detector.py 42 KB

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  1. # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
  2. #
  3. # Licensed under the Apache License, Version 2.0 (the "License");
  4. # you may not use this file except in compliance with the License.
  5. # You may obtain a copy of the License at
  6. #
  7. # http://www.apache.org/licenses/LICENSE-2.0
  8. #
  9. # Unless required by applicable law or agreed to in writing, software
  10. # distributed under the License is distributed on an "AS IS" BASIS,
  11. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12. # See the License for the specific language governing permissions and
  13. # limitations under the License.
  14. import math
  15. import os
  16. import os.path as osp
  17. from collections import OrderedDict
  18. from operator import attrgetter
  19. import cv2
  20. import numpy as np
  21. import paddle
  22. import paddle.nn.functional as F
  23. from paddle.static import InputSpec
  24. import paddlers
  25. import paddlers.custom_models.cd as cmcd
  26. import paddlers.utils.logging as logging
  27. import paddlers.models.ppseg as paddleseg
  28. from paddlers.transforms import Resize, decode_image
  29. from paddlers.utils import get_single_card_bs, DisablePrint
  30. from paddlers.utils.checkpoint import seg_pretrain_weights_dict
  31. from .base import BaseModel
  32. from .utils import seg_metrics as metrics
  33. __all__ = [
  34. "CDNet", "FCEarlyFusion", "FCSiamConc", "FCSiamDiff", "STANet", "BIT",
  35. "SNUNet", "DSIFN", "DSAMNet", "ChangeStar"
  36. ]
  37. class BaseChangeDetector(BaseModel):
  38. def __init__(self,
  39. model_name,
  40. num_classes=2,
  41. use_mixed_loss=False,
  42. **params):
  43. self.init_params = locals()
  44. if 'with_net' in self.init_params:
  45. del self.init_params['with_net']
  46. super(BaseChangeDetector, self).__init__('changedetector')
  47. if model_name not in __all__:
  48. raise Exception("ERROR: There's no model named {}.".format(
  49. model_name))
  50. self.model_name = model_name
  51. self.num_classes = num_classes
  52. self.use_mixed_loss = use_mixed_loss
  53. self.losses = None
  54. self.labels = None
  55. if params.get('with_net', True):
  56. params.pop('with_net', None)
  57. self.net = self.build_net(**params)
  58. self.find_unused_parameters = True
  59. def build_net(self, **params):
  60. # TODO: add other model
  61. net = cmcd.__dict__[self.model_name](num_classes=self.num_classes,
  62. **params)
  63. return net
  64. def _fix_transforms_shape(self, image_shape):
  65. if hasattr(self, 'test_transforms'):
  66. if self.test_transforms is not None:
  67. has_resize_op = False
  68. resize_op_idx = -1
  69. normalize_op_idx = len(self.test_transforms.transforms)
  70. for idx, op in enumerate(self.test_transforms.transforms):
  71. name = op.__class__.__name__
  72. if name == 'Normalize':
  73. normalize_op_idx = idx
  74. if 'Resize' in name:
  75. has_resize_op = True
  76. resize_op_idx = idx
  77. if not has_resize_op:
  78. self.test_transforms.transforms.insert(
  79. normalize_op_idx, Resize(target_size=image_shape))
  80. else:
  81. self.test_transforms.transforms[resize_op_idx] = Resize(
  82. target_size=image_shape)
  83. def _get_test_inputs(self, image_shape):
  84. if image_shape is not None:
  85. if len(image_shape) == 2:
  86. image_shape = [1, 3] + image_shape
  87. self._fix_transforms_shape(image_shape[-2:])
  88. else:
  89. image_shape = [None, 3, -1, -1]
  90. self.fixed_input_shape = image_shape
  91. return [
  92. InputSpec(
  93. shape=image_shape, name='image', dtype='float32'), InputSpec(
  94. shape=image_shape, name='image2', dtype='float32')
  95. ]
  96. def run(self, net, inputs, mode):
  97. net_out = net(inputs[0], inputs[1])
  98. logit = net_out[0]
  99. outputs = OrderedDict()
  100. if mode == 'test':
  101. origin_shape = inputs[2]
  102. if self.status == 'Infer':
  103. label_map_list, score_map_list = self._postprocess(
  104. net_out, origin_shape, transforms=inputs[3])
  105. else:
  106. logit_list = self._postprocess(
  107. logit, origin_shape, transforms=inputs[3])
  108. label_map_list = []
  109. score_map_list = []
  110. for logit in logit_list:
  111. logit = paddle.transpose(logit, perm=[0, 2, 3, 1]) # NHWC
  112. label_map_list.append(
  113. paddle.argmax(
  114. logit, axis=-1, keepdim=False, dtype='int32')
  115. .squeeze().numpy())
  116. score_map_list.append(
  117. F.softmax(
  118. logit, axis=-1).squeeze().numpy().astype('float32'))
  119. outputs['label_map'] = label_map_list
  120. outputs['score_map'] = score_map_list
  121. if mode == 'eval':
  122. if self.status == 'Infer':
  123. pred = paddle.unsqueeze(net_out[0], axis=1) # NCHW
  124. else:
  125. pred = paddle.argmax(logit, axis=1, keepdim=True, dtype='int32')
  126. label = inputs[2]
  127. if label.ndim == 3:
  128. paddle.unsqueeze_(label, axis=1)
  129. if label.ndim != 4:
  130. raise ValueError("Expected label.ndim == 4 but got {}".format(
  131. label.ndim))
  132. origin_shape = [label.shape[-2:]]
  133. pred = self._postprocess(
  134. pred, origin_shape, transforms=inputs[3])[0] # NCHW
  135. intersect_area, pred_area, label_area = paddleseg.utils.metrics.calculate_area(
  136. pred, label, self.num_classes)
  137. outputs['intersect_area'] = intersect_area
  138. outputs['pred_area'] = pred_area
  139. outputs['label_area'] = label_area
  140. outputs['conf_mat'] = metrics.confusion_matrix(pred, label,
  141. self.num_classes)
  142. if mode == 'train':
  143. if hasattr(net, 'USE_MULTITASK_DECODER') and \
  144. net.USE_MULTITASK_DECODER is True:
  145. # CD+Seg
  146. if len(inputs) != 5:
  147. raise ValueError(
  148. "Cannot perform loss computation with {} inputs.".
  149. format(len(inputs)))
  150. labels_list = [
  151. inputs[2 + idx]
  152. for idx in map(attrgetter('value'), net.OUT_TYPES)
  153. ]
  154. loss_list = metrics.multitask_loss_computation(
  155. logits_list=net_out,
  156. labels_list=labels_list,
  157. losses=self.losses)
  158. else:
  159. loss_list = metrics.loss_computation(
  160. logits_list=net_out, labels=inputs[2], losses=self.losses)
  161. loss = sum(loss_list)
  162. outputs['loss'] = loss
  163. return outputs
  164. def default_loss(self):
  165. if isinstance(self.use_mixed_loss, bool):
  166. if self.use_mixed_loss:
  167. losses = [
  168. paddleseg.models.CrossEntropyLoss(),
  169. paddleseg.models.LovaszSoftmaxLoss()
  170. ]
  171. coef = [.8, .2]
  172. loss_type = [
  173. paddleseg.models.MixedLoss(
  174. losses=losses, coef=coef),
  175. ]
  176. else:
  177. loss_type = [paddleseg.models.CrossEntropyLoss()]
  178. else:
  179. losses, coef = list(zip(*self.use_mixed_loss))
  180. if not set(losses).issubset(
  181. ['CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss']):
  182. raise ValueError(
  183. "Only 'CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss' are supported."
  184. )
  185. losses = [getattr(paddleseg.models, loss)() for loss in losses]
  186. loss_type = [
  187. paddleseg.models.MixedLoss(
  188. losses=losses, coef=list(coef))
  189. ]
  190. loss_coef = [1.0]
  191. losses = {'types': loss_type, 'coef': loss_coef}
  192. return losses
  193. def default_optimizer(self,
  194. parameters,
  195. learning_rate,
  196. num_epochs,
  197. num_steps_each_epoch,
  198. lr_decay_power=0.9):
  199. decay_step = num_epochs * num_steps_each_epoch
  200. lr_scheduler = paddle.optimizer.lr.PolynomialDecay(
  201. learning_rate, decay_step, end_lr=0, power=lr_decay_power)
  202. optimizer = paddle.optimizer.Momentum(
  203. learning_rate=lr_scheduler,
  204. parameters=parameters,
  205. momentum=0.9,
  206. weight_decay=4e-5)
  207. return optimizer
  208. def train(self,
  209. num_epochs,
  210. train_dataset,
  211. train_batch_size=2,
  212. eval_dataset=None,
  213. optimizer=None,
  214. save_interval_epochs=1,
  215. log_interval_steps=2,
  216. save_dir='output',
  217. pretrain_weights=None,
  218. learning_rate=0.01,
  219. lr_decay_power=0.9,
  220. early_stop=False,
  221. early_stop_patience=5,
  222. use_vdl=True,
  223. resume_checkpoint=None):
  224. """
  225. Train the model.
  226. Args:
  227. num_epochs(int): The number of epochs.
  228. train_dataset(paddlers.dataset): Training dataset.
  229. train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 2.
  230. eval_dataset(paddlers.dataset, optional):
  231. Evaluation dataset. If None, the model will not be evaluated furing training process. Defaults to None.
  232. optimizer(paddle.optimizer.Optimizer or None, optional):
  233. Optimizer used in training. If None, a default optimizer is used. Defaults to None.
  234. save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
  235. log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
  236. save_dir(str, optional): Directory to save the model. Defaults to 'output'.
  237. pretrain_weights(str or None, optional):
  238. None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to None.
  239. learning_rate(float, optional): Learning rate for training. Defaults to .025.
  240. lr_decay_power(float, optional): Learning decay power. Defaults to .9.
  241. early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
  242. early_stop_patience(int, optional): Early stop patience. Defaults to 5.
  243. use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
  244. resume_checkpoint(str or None, optional): The path of the checkpoint to resume training from.
  245. If None, no training checkpoint will be resumed. At most one of `resume_checkpoint` and
  246. `pretrain_weights` can be set simultaneously. Defaults to None.
  247. """
  248. if self.status == 'Infer':
  249. logging.error(
  250. "Exported inference model does not support training.",
  251. exit=True)
  252. if pretrain_weights is not None and resume_checkpoint is not None:
  253. logging.error(
  254. "pretrain_weights and resume_checkpoint cannot be set simultaneously.",
  255. exit=True)
  256. self.labels = train_dataset.labels
  257. if self.losses is None:
  258. self.losses = self.default_loss()
  259. if optimizer is None:
  260. num_steps_each_epoch = train_dataset.num_samples // train_batch_size
  261. self.optimizer = self.default_optimizer(
  262. self.net.parameters(), learning_rate, num_epochs,
  263. num_steps_each_epoch, lr_decay_power)
  264. else:
  265. self.optimizer = optimizer
  266. if pretrain_weights is not None and not osp.exists(pretrain_weights):
  267. if pretrain_weights not in seg_pretrain_weights_dict[
  268. self.model_name]:
  269. logging.warning(
  270. "Path of pretrain_weights('{}') does not exist!".format(
  271. pretrain_weights))
  272. logging.warning("Pretrain_weights is forcibly set to '{}'. "
  273. "If don't want to use pretrain weights, "
  274. "set pretrain_weights to be None.".format(
  275. seg_pretrain_weights_dict[self.model_name][
  276. 0]))
  277. pretrain_weights = seg_pretrain_weights_dict[self.model_name][0]
  278. elif pretrain_weights is not None and osp.exists(pretrain_weights):
  279. if osp.splitext(pretrain_weights)[-1] != '.pdparams':
  280. logging.error(
  281. "Invalid pretrain weights. Please specify a '.pdparams' file.",
  282. exit=True)
  283. pretrained_dir = osp.join(save_dir, 'pretrain')
  284. is_backbone_weights = pretrain_weights == 'IMAGENET'
  285. self.net_initialize(
  286. pretrain_weights=pretrain_weights,
  287. save_dir=pretrained_dir,
  288. resume_checkpoint=resume_checkpoint,
  289. is_backbone_weights=is_backbone_weights)
  290. self.train_loop(
  291. num_epochs=num_epochs,
  292. train_dataset=train_dataset,
  293. train_batch_size=train_batch_size,
  294. eval_dataset=eval_dataset,
  295. save_interval_epochs=save_interval_epochs,
  296. log_interval_steps=log_interval_steps,
  297. save_dir=save_dir,
  298. early_stop=early_stop,
  299. early_stop_patience=early_stop_patience,
  300. use_vdl=use_vdl)
  301. def quant_aware_train(self,
  302. num_epochs,
  303. train_dataset,
  304. train_batch_size=2,
  305. eval_dataset=None,
  306. optimizer=None,
  307. save_interval_epochs=1,
  308. log_interval_steps=2,
  309. save_dir='output',
  310. learning_rate=0.0001,
  311. lr_decay_power=0.9,
  312. early_stop=False,
  313. early_stop_patience=5,
  314. use_vdl=True,
  315. resume_checkpoint=None,
  316. quant_config=None):
  317. """
  318. Quantization-aware training.
  319. Args:
  320. num_epochs(int): The number of epochs.
  321. train_dataset(paddlers.dataset): Training dataset.
  322. train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 2.
  323. eval_dataset(paddlers.dataset, optional):
  324. Evaluation dataset. If None, the model will not be evaluated furing training process. Defaults to None.
  325. optimizer(paddle.optimizer.Optimizer or None, optional):
  326. Optimizer used in training. If None, a default optimizer is used. Defaults to None.
  327. save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
  328. log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
  329. save_dir(str, optional): Directory to save the model. Defaults to 'output'.
  330. learning_rate(float, optional): Learning rate for training. Defaults to .025.
  331. lr_decay_power(float, optional): Learning decay power. Defaults to .9.
  332. early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
  333. early_stop_patience(int, optional): Early stop patience. Defaults to 5.
  334. use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
  335. quant_config(dict or None, optional): Quantization configuration. If None, a default rule of thumb
  336. configuration will be used. Defaults to None.
  337. resume_checkpoint(str or None, optional): The path of the checkpoint to resume quantization-aware training
  338. from. If None, no training checkpoint will be resumed. Defaults to None.
  339. """
  340. self._prepare_qat(quant_config)
  341. self.train(
  342. num_epochs=num_epochs,
  343. train_dataset=train_dataset,
  344. train_batch_size=train_batch_size,
  345. eval_dataset=eval_dataset,
  346. optimizer=optimizer,
  347. save_interval_epochs=save_interval_epochs,
  348. log_interval_steps=log_interval_steps,
  349. save_dir=save_dir,
  350. pretrain_weights=None,
  351. learning_rate=learning_rate,
  352. lr_decay_power=lr_decay_power,
  353. early_stop=early_stop,
  354. early_stop_patience=early_stop_patience,
  355. use_vdl=use_vdl,
  356. resume_checkpoint=resume_checkpoint)
  357. def evaluate(self, eval_dataset, batch_size=1, return_details=False):
  358. """
  359. Evaluate the model.
  360. Args:
  361. eval_dataset(paddlers.dataset): Evaluation dataset.
  362. batch_size(int, optional): Total batch size among all cards used for evaluation. Defaults to 1.
  363. return_details(bool, optional): Whether to return evaluation details. Defaults to False.
  364. Returns:
  365. collections.OrderedDict with key-value pairs:
  366. For binary change detection (number of classes == 2), the key-value pairs are like:
  367. {"iou": `intersection over union for the change class`,
  368. "f1": `F1 score for the change class`,
  369. "oacc": `overall accuracy`,
  370. "kappa": ` kappa coefficient`}.
  371. For multi-class change detection (number of classes > 2), the key-value pairs are like:
  372. {"miou": `mean intersection over union`,
  373. "category_iou": `category-wise mean intersection over union`,
  374. "oacc": `overall accuracy`,
  375. "category_acc": `category-wise accuracy`,
  376. "kappa": ` kappa coefficient`,
  377. "category_F1-score": `F1 score`}.
  378. """
  379. self._check_transforms(eval_dataset.transforms, 'eval')
  380. self.net.eval()
  381. nranks = paddle.distributed.get_world_size()
  382. local_rank = paddle.distributed.get_rank()
  383. if nranks > 1:
  384. # Initialize parallel environment if not done.
  385. if not (paddle.distributed.parallel.parallel_helper.
  386. _is_parallel_ctx_initialized()):
  387. paddle.distributed.init_parallel_env()
  388. batch_size_each_card = get_single_card_bs(batch_size)
  389. if batch_size_each_card > 1:
  390. batch_size_each_card = 1
  391. batch_size = batch_size_each_card * paddlers.env_info['num']
  392. logging.warning(
  393. "ChangeDetector only supports batch_size=1 for each gpu/cpu card " \
  394. "during evaluation, so batch_size " \
  395. "is forcibly set to {}.".format(batch_size)
  396. )
  397. self.eval_data_loader = self.build_data_loader(
  398. eval_dataset, batch_size=batch_size, mode='eval')
  399. intersect_area_all = 0
  400. pred_area_all = 0
  401. label_area_all = 0
  402. conf_mat_all = []
  403. logging.info(
  404. "Start to evaluate(total_samples={}, total_steps={})...".format(
  405. eval_dataset.num_samples,
  406. math.ceil(eval_dataset.num_samples * 1.0 / batch_size)))
  407. with paddle.no_grad():
  408. for step, data in enumerate(self.eval_data_loader):
  409. data.append(eval_dataset.transforms.transforms)
  410. outputs = self.run(self.net, data, 'eval')
  411. pred_area = outputs['pred_area']
  412. label_area = outputs['label_area']
  413. intersect_area = outputs['intersect_area']
  414. conf_mat = outputs['conf_mat']
  415. # Gather from all ranks
  416. if nranks > 1:
  417. intersect_area_list = []
  418. pred_area_list = []
  419. label_area_list = []
  420. conf_mat_list = []
  421. paddle.distributed.all_gather(intersect_area_list,
  422. intersect_area)
  423. paddle.distributed.all_gather(pred_area_list, pred_area)
  424. paddle.distributed.all_gather(label_area_list, label_area)
  425. paddle.distributed.all_gather(conf_mat_list, conf_mat)
  426. # Some image has been evaluated and should be eliminated in last iter
  427. if (step + 1) * nranks > len(eval_dataset):
  428. valid = len(eval_dataset) - step * nranks
  429. intersect_area_list = intersect_area_list[:valid]
  430. pred_area_list = pred_area_list[:valid]
  431. label_area_list = label_area_list[:valid]
  432. conf_mat_list = conf_mat_list[:valid]
  433. intersect_area_all += sum(intersect_area_list)
  434. pred_area_all += sum(pred_area_list)
  435. label_area_all += sum(label_area_list)
  436. conf_mat_all.extend(conf_mat_list)
  437. else:
  438. intersect_area_all = intersect_area_all + intersect_area
  439. pred_area_all = pred_area_all + pred_area
  440. label_area_all = label_area_all + label_area
  441. conf_mat_all.append(conf_mat)
  442. class_iou, miou = paddleseg.utils.metrics.mean_iou(
  443. intersect_area_all, pred_area_all, label_area_all)
  444. # TODO 确认是按oacc还是macc
  445. class_acc, oacc = paddleseg.utils.metrics.accuracy(intersect_area_all,
  446. pred_area_all)
  447. kappa = paddleseg.utils.metrics.kappa(intersect_area_all, pred_area_all,
  448. label_area_all)
  449. category_f1score = metrics.f1_score(intersect_area_all, pred_area_all,
  450. label_area_all)
  451. if len(class_acc) > 2:
  452. eval_metrics = OrderedDict(
  453. zip([
  454. 'miou', 'category_iou', 'oacc', 'category_acc', 'kappa',
  455. 'category_F1-score'
  456. ], [miou, class_iou, oacc, class_acc, kappa, category_f1score]))
  457. else:
  458. eval_metrics = OrderedDict(
  459. zip(['iou', 'f1', 'oacc', 'kappa'],
  460. [class_iou[1], category_f1score[1], oacc, kappa]))
  461. if return_details:
  462. conf_mat = sum(conf_mat_all)
  463. eval_details = {'confusion_matrix': conf_mat.tolist()}
  464. return eval_metrics, eval_details
  465. return eval_metrics
  466. def predict(self, img_file, transforms=None):
  467. """
  468. Do inference.
  469. Args:
  470. Args:
  471. img_file (list[tuple] | tuple[str | np.ndarray]):
  472. Tuple of image paths or decoded image data for bi-temporal images, which also could constitute a list,
  473. meaning all image pairs to be predicted as a mini-batch.
  474. transforms(paddlers.transforms.Compose or None, optional):
  475. Transforms for inputs. If None, the transforms for evaluation process will be used. Defaults to None.
  476. Returns:
  477. If img_file is a tuple of string or np.array, the result is a dict with key-value pairs:
  478. {"label map": `label map`, "score_map": `score map`}.
  479. If img_file is a list, the result is a list composed of dicts with the corresponding fields:
  480. label_map(np.ndarray): the predicted label map (HW)
  481. score_map(np.ndarray): the prediction score map (HWC)
  482. """
  483. if transforms is None and not hasattr(self, 'test_transforms'):
  484. raise Exception("transforms need to be defined, now is None.")
  485. if transforms is None:
  486. transforms = self.test_transforms
  487. if isinstance(img_file, tuple):
  488. if not len(img_file) == 2 and any(
  489. map(lambda obj: not isinstance(obj, (str, np.ndarray)),
  490. img_file)):
  491. raise TypeError
  492. images = [img_file]
  493. else:
  494. images = img_file
  495. batch_im1, batch_im2, batch_origin_shape = self._preprocess(
  496. images, transforms, self.model_type)
  497. self.net.eval()
  498. data = (batch_im1, batch_im2, batch_origin_shape, transforms.transforms)
  499. outputs = self.run(self.net, data, 'test')
  500. label_map_list = outputs['label_map']
  501. score_map_list = outputs['score_map']
  502. if isinstance(img_file, list):
  503. prediction = [{
  504. 'label_map': l,
  505. 'score_map': s
  506. } for l, s in zip(label_map_list, score_map_list)]
  507. else:
  508. prediction = {
  509. 'label_map': label_map_list[0],
  510. 'score_map': score_map_list[0]
  511. }
  512. return prediction
  513. def slider_predict(self,
  514. img_file,
  515. save_dir,
  516. block_size,
  517. overlap=36,
  518. transforms=None):
  519. """
  520. Do inference.
  521. Args:
  522. Args:
  523. img_file(list[str]):
  524. List of image paths.
  525. save_dir(str):
  526. Directory that contains saved geotiff file.
  527. block_size(list[int] | tuple[int] | int, optional):
  528. Size of block.
  529. overlap(list[int] | tuple[int] | int, optional):
  530. Overlap between two blocks. Defaults to 36.
  531. transforms(paddlers.transforms.Compose or None, optional):
  532. Transforms for inputs. If None, the transforms for evaluation process will be used. Defaults to None.
  533. """
  534. try:
  535. from osgeo import gdal
  536. except:
  537. import gdal
  538. if len(img_file) != 2:
  539. raise ValueError("`img_file` must be a list of length 2.")
  540. if isinstance(block_size, int):
  541. block_size = (block_size, block_size)
  542. elif isinstance(block_size, (tuple, list)) and len(block_size) == 2:
  543. block_size = tuple(block_size)
  544. else:
  545. raise ValueError(
  546. "`block_size` must be a tuple/list of length 2 or an integer.")
  547. if isinstance(overlap, int):
  548. overlap = (overlap, overlap)
  549. elif isinstance(overlap, (tuple, list)) and len(overlap) == 2:
  550. overlap = tuple(overlap)
  551. else:
  552. raise ValueError(
  553. "`overlap` must be a tuple/list of length 2 or an integer.")
  554. src1_data = gdal.Open(img_file[0])
  555. src2_data = gdal.Open(img_file[1])
  556. width = src1_data.RasterXSize
  557. height = src1_data.RasterYSize
  558. bands = src1_data.RasterCount
  559. driver = gdal.GetDriverByName("GTiff")
  560. file_name = osp.splitext(osp.normpath(img_file[0]).split(os.sep)[-1])[
  561. 0] + ".tif"
  562. if not osp.exists(save_dir):
  563. os.makedirs(save_dir)
  564. save_file = osp.join(save_dir, file_name)
  565. dst_data = driver.Create(save_file, width, height, 1, gdal.GDT_Byte)
  566. dst_data.SetGeoTransform(src1_data.GetGeoTransform())
  567. dst_data.SetProjection(src1_data.GetProjection())
  568. band = dst_data.GetRasterBand(1)
  569. band.WriteArray(255 * np.ones((height, width), dtype="uint8"))
  570. step = np.array(block_size) - np.array(overlap)
  571. for yoff in range(0, height, step[1]):
  572. for xoff in range(0, width, step[0]):
  573. xsize, ysize = block_size
  574. if xoff + xsize > width:
  575. xsize = int(width - xoff)
  576. if yoff + ysize > height:
  577. ysize = int(height - yoff)
  578. im1 = src1_data.ReadAsArray(
  579. int(xoff), int(yoff), xsize, ysize).transpose((1, 2, 0))
  580. im2 = src2_data.ReadAsArray(
  581. int(xoff), int(yoff), xsize, ysize).transpose((1, 2, 0))
  582. # fill
  583. h, w = im1.shape[:2]
  584. im1_fill = np.zeros(
  585. (block_size[1], block_size[0], bands), dtype=im1.dtype)
  586. im2_fill = im1_fill.copy()
  587. im1_fill[:h, :w, :] = im1
  588. im2_fill[:h, :w, :] = im2
  589. im_fill = (im1_fill, im2_fill)
  590. # predict
  591. pred = self.predict(im_fill,
  592. transforms)["label_map"].astype("uint8")
  593. # overlap
  594. rd_block = band.ReadAsArray(int(xoff), int(yoff), xsize, ysize)
  595. mask = (rd_block == pred[:h, :w]) | (rd_block == 255)
  596. temp = pred[:h, :w].copy()
  597. temp[mask == False] = 0
  598. band.WriteArray(temp, int(xoff), int(yoff))
  599. dst_data.FlushCache()
  600. dst_data = None
  601. print("GeoTiff saved in {}.".format(save_file))
  602. def _preprocess(self, images, transforms, to_tensor=True):
  603. self._check_transforms(transforms, 'test')
  604. batch_im1, batch_im2 = list(), list()
  605. batch_ori_shape = list()
  606. for im1, im2 in images:
  607. if isinstance(im1, str) or isinstance(im2, str):
  608. im1 = decode_image(im1, to_rgb=False)
  609. im2 = decode_image(im2, to_rgb=False)
  610. ori_shape = im1.shape[:2]
  611. # XXX: sample do not contain 'image_t1' and 'image_t2'.
  612. sample = {'image': im1, 'image2': im2}
  613. im1, im2 = transforms(sample)[:2]
  614. batch_im1.append(im1)
  615. batch_im2.append(im2)
  616. batch_ori_shape.append(ori_shape)
  617. if to_tensor:
  618. batch_im1 = paddle.to_tensor(batch_im1)
  619. batch_im2 = paddle.to_tensor(batch_im2)
  620. else:
  621. batch_im1 = np.asarray(batch_im1)
  622. batch_im2 = np.asarray(batch_im2)
  623. return batch_im1, batch_im2, batch_ori_shape
  624. @staticmethod
  625. def get_transforms_shape_info(batch_ori_shape, transforms):
  626. batch_restore_list = list()
  627. for ori_shape in batch_ori_shape:
  628. restore_list = list()
  629. h, w = ori_shape[0], ori_shape[1]
  630. for op in transforms:
  631. if op.__class__.__name__ == 'Resize':
  632. restore_list.append(('resize', (h, w)))
  633. h, w = op.target_size
  634. elif op.__class__.__name__ == 'ResizeByShort':
  635. restore_list.append(('resize', (h, w)))
  636. im_short_size = min(h, w)
  637. im_long_size = max(h, w)
  638. scale = float(op.short_size) / float(im_short_size)
  639. if 0 < op.max_size < np.round(scale * im_long_size):
  640. scale = float(op.max_size) / float(im_long_size)
  641. h = int(round(h * scale))
  642. w = int(round(w * scale))
  643. elif op.__class__.__name__ == 'ResizeByLong':
  644. restore_list.append(('resize', (h, w)))
  645. im_long_size = max(h, w)
  646. scale = float(op.long_size) / float(im_long_size)
  647. h = int(round(h * scale))
  648. w = int(round(w * scale))
  649. elif op.__class__.__name__ == 'Pad':
  650. if op.target_size:
  651. target_h, target_w = op.target_size
  652. else:
  653. target_h = int(
  654. (np.ceil(h / op.size_divisor) * op.size_divisor))
  655. target_w = int(
  656. (np.ceil(w / op.size_divisor) * op.size_divisor))
  657. if op.pad_mode == -1:
  658. offsets = op.offsets
  659. elif op.pad_mode == 0:
  660. offsets = [0, 0]
  661. elif op.pad_mode == 1:
  662. offsets = [(target_h - h) // 2, (target_w - w) // 2]
  663. else:
  664. offsets = [target_h - h, target_w - w]
  665. restore_list.append(('padding', (h, w), offsets))
  666. h, w = target_h, target_w
  667. batch_restore_list.append(restore_list)
  668. return batch_restore_list
  669. def _postprocess(self, batch_pred, batch_origin_shape, transforms):
  670. batch_restore_list = BaseChangeDetector.get_transforms_shape_info(
  671. batch_origin_shape, transforms)
  672. if isinstance(batch_pred, (tuple, list)) and self.status == 'Infer':
  673. return self._infer_postprocess(
  674. batch_label_map=batch_pred[0],
  675. batch_score_map=batch_pred[1],
  676. batch_restore_list=batch_restore_list)
  677. results = []
  678. if batch_pred.dtype == paddle.float32:
  679. mode = 'bilinear'
  680. else:
  681. mode = 'nearest'
  682. for pred, restore_list in zip(batch_pred, batch_restore_list):
  683. pred = paddle.unsqueeze(pred, axis=0)
  684. for item in restore_list[::-1]:
  685. h, w = item[1][0], item[1][1]
  686. if item[0] == 'resize':
  687. pred = F.interpolate(
  688. pred, (h, w), mode=mode, data_format='NCHW')
  689. elif item[0] == 'padding':
  690. x, y = item[2]
  691. pred = pred[:, :, y:y + h, x:x + w]
  692. else:
  693. pass
  694. results.append(pred)
  695. return results
  696. def _infer_postprocess(self, batch_label_map, batch_score_map,
  697. batch_restore_list):
  698. label_maps = []
  699. score_maps = []
  700. for label_map, score_map, restore_list in zip(
  701. batch_label_map, batch_score_map, batch_restore_list):
  702. if not isinstance(label_map, np.ndarray):
  703. label_map = paddle.unsqueeze(label_map, axis=[0, 3])
  704. score_map = paddle.unsqueeze(score_map, axis=0)
  705. for item in restore_list[::-1]:
  706. h, w = item[1][0], item[1][1]
  707. if item[0] == 'resize':
  708. if isinstance(label_map, np.ndarray):
  709. label_map = cv2.resize(
  710. label_map, (w, h), interpolation=cv2.INTER_NEAREST)
  711. score_map = cv2.resize(
  712. score_map, (w, h), interpolation=cv2.INTER_LINEAR)
  713. else:
  714. label_map = F.interpolate(
  715. label_map, (h, w),
  716. mode='nearest',
  717. data_format='NHWC')
  718. score_map = F.interpolate(
  719. score_map, (h, w),
  720. mode='bilinear',
  721. data_format='NHWC')
  722. elif item[0] == 'padding':
  723. x, y = item[2]
  724. if isinstance(label_map, np.ndarray):
  725. label_map = label_map[..., y:y + h, x:x + w]
  726. score_map = score_map[..., y:y + h, x:x + w]
  727. else:
  728. label_map = label_map[:, :, y:y + h, x:x + w]
  729. score_map = score_map[:, :, y:y + h, x:x + w]
  730. else:
  731. pass
  732. label_map = label_map.squeeze()
  733. score_map = score_map.squeeze()
  734. if not isinstance(label_map, np.ndarray):
  735. label_map = label_map.numpy()
  736. score_map = score_map.numpy()
  737. label_maps.append(label_map.squeeze())
  738. score_maps.append(score_map.squeeze())
  739. return label_maps, score_maps
  740. def _check_transforms(self, transforms, mode):
  741. super()._check_transforms(transforms, mode)
  742. if not isinstance(transforms.arrange,
  743. paddlers.transforms.ArrangeChangeDetector):
  744. raise TypeError(
  745. "`transforms.arrange` must be an ArrangeChangeDetector object.")
  746. class CDNet(BaseChangeDetector):
  747. def __init__(self,
  748. num_classes=2,
  749. use_mixed_loss=False,
  750. in_channels=6,
  751. **params):
  752. params.update({'in_channels': in_channels})
  753. super(CDNet, self).__init__(
  754. model_name='CDNet',
  755. num_classes=num_classes,
  756. use_mixed_loss=use_mixed_loss,
  757. **params)
  758. class FCEarlyFusion(BaseChangeDetector):
  759. def __init__(self,
  760. num_classes=2,
  761. use_mixed_loss=False,
  762. in_channels=6,
  763. use_dropout=False,
  764. **params):
  765. params.update({'in_channels': in_channels, 'use_dropout': use_dropout})
  766. super(FCEarlyFusion, self).__init__(
  767. model_name='FCEarlyFusion',
  768. num_classes=num_classes,
  769. use_mixed_loss=use_mixed_loss,
  770. **params)
  771. class FCSiamConc(BaseChangeDetector):
  772. def __init__(self,
  773. num_classes=2,
  774. use_mixed_loss=False,
  775. in_channels=3,
  776. use_dropout=False,
  777. **params):
  778. params.update({'in_channels': in_channels, 'use_dropout': use_dropout})
  779. super(FCSiamConc, self).__init__(
  780. model_name='FCSiamConc',
  781. num_classes=num_classes,
  782. use_mixed_loss=use_mixed_loss,
  783. **params)
  784. class FCSiamDiff(BaseChangeDetector):
  785. def __init__(self,
  786. num_classes=2,
  787. use_mixed_loss=False,
  788. in_channels=3,
  789. use_dropout=False,
  790. **params):
  791. params.update({'in_channels': in_channels, 'use_dropout': use_dropout})
  792. super(FCSiamDiff, self).__init__(
  793. model_name='FCSiamDiff',
  794. num_classes=num_classes,
  795. use_mixed_loss=use_mixed_loss,
  796. **params)
  797. class STANet(BaseChangeDetector):
  798. def __init__(self,
  799. num_classes=2,
  800. use_mixed_loss=False,
  801. in_channels=3,
  802. att_type='BAM',
  803. ds_factor=1,
  804. **params):
  805. params.update({
  806. 'in_channels': in_channels,
  807. 'att_type': att_type,
  808. 'ds_factor': ds_factor
  809. })
  810. super(STANet, self).__init__(
  811. model_name='STANet',
  812. num_classes=num_classes,
  813. use_mixed_loss=use_mixed_loss,
  814. **params)
  815. class BIT(BaseChangeDetector):
  816. def __init__(self,
  817. num_classes=2,
  818. use_mixed_loss=False,
  819. in_channels=3,
  820. backbone='resnet18',
  821. n_stages=4,
  822. use_tokenizer=True,
  823. token_len=4,
  824. pool_mode='max',
  825. pool_size=2,
  826. enc_with_pos=True,
  827. enc_depth=1,
  828. enc_head_dim=64,
  829. dec_depth=8,
  830. dec_head_dim=8,
  831. **params):
  832. params.update({
  833. 'in_channels': in_channels,
  834. 'backbone': backbone,
  835. 'n_stages': n_stages,
  836. 'use_tokenizer': use_tokenizer,
  837. 'token_len': token_len,
  838. 'pool_mode': pool_mode,
  839. 'pool_size': pool_size,
  840. 'enc_with_pos': enc_with_pos,
  841. 'enc_depth': enc_depth,
  842. 'enc_head_dim': enc_head_dim,
  843. 'dec_depth': dec_depth,
  844. 'dec_head_dim': dec_head_dim
  845. })
  846. super(BIT, self).__init__(
  847. model_name='BIT',
  848. num_classes=num_classes,
  849. use_mixed_loss=use_mixed_loss,
  850. **params)
  851. class SNUNet(BaseChangeDetector):
  852. def __init__(self,
  853. num_classes=2,
  854. use_mixed_loss=False,
  855. in_channels=3,
  856. width=32,
  857. **params):
  858. params.update({'in_channels': in_channels, 'width': width})
  859. super(SNUNet, self).__init__(
  860. model_name='SNUNet',
  861. num_classes=num_classes,
  862. use_mixed_loss=use_mixed_loss,
  863. **params)
  864. class DSIFN(BaseChangeDetector):
  865. def __init__(self,
  866. num_classes=2,
  867. use_mixed_loss=False,
  868. use_dropout=False,
  869. **params):
  870. params.update({'use_dropout': use_dropout})
  871. super(DSIFN, self).__init__(
  872. model_name='DSIFN',
  873. num_classes=num_classes,
  874. use_mixed_loss=use_mixed_loss,
  875. **params)
  876. def default_loss(self):
  877. if self.use_mixed_loss is False:
  878. return {
  879. # XXX: make sure the shallow copy works correctly here.
  880. 'types': [paddleseg.models.CrossEntropyLoss()] * 5,
  881. 'coef': [1.0] * 5
  882. }
  883. else:
  884. raise ValueError(
  885. f"Currently `use_mixed_loss` must be set to False for {self.__class__}"
  886. )
  887. class DSAMNet(BaseChangeDetector):
  888. def __init__(self,
  889. num_classes=2,
  890. use_mixed_loss=False,
  891. in_channels=3,
  892. ca_ratio=8,
  893. sa_kernel=7,
  894. **params):
  895. params.update({
  896. 'in_channels': in_channels,
  897. 'ca_ratio': ca_ratio,
  898. 'sa_kernel': sa_kernel
  899. })
  900. super(DSAMNet, self).__init__(
  901. model_name='DSAMNet',
  902. num_classes=num_classes,
  903. use_mixed_loss=use_mixed_loss,
  904. **params)
  905. def default_loss(self):
  906. if self.use_mixed_loss is False:
  907. return {
  908. 'types': [
  909. paddleseg.models.CrossEntropyLoss(),
  910. paddleseg.models.DiceLoss(), paddleseg.models.DiceLoss()
  911. ],
  912. 'coef': [1.0, 0.05, 0.05]
  913. }
  914. else:
  915. raise ValueError(
  916. f"Currently `use_mixed_loss` must be set to False for {self.__class__}"
  917. )
  918. class ChangeStar(BaseChangeDetector):
  919. def __init__(self,
  920. num_classes=2,
  921. use_mixed_loss=False,
  922. mid_channels=256,
  923. inner_channels=16,
  924. num_convs=4,
  925. scale_factor=4.0,
  926. **params):
  927. params.update({
  928. 'mid_channels': mid_channels,
  929. 'inner_channels': inner_channels,
  930. 'num_convs': num_convs,
  931. 'scale_factor': scale_factor
  932. })
  933. super(ChangeStar, self).__init__(
  934. model_name='ChangeStar',
  935. num_classes=num_classes,
  936. use_mixed_loss=use_mixed_loss,
  937. **params)
  938. def default_loss(self):
  939. if self.use_mixed_loss is False:
  940. return {
  941. # XXX: make sure the shallow copy works correctly here.
  942. 'types': [paddleseg.models.CrossEntropyLoss()] * 4,
  943. 'coef': [1.0] * 4
  944. }
  945. else:
  946. raise ValueError(
  947. f"Currently `use_mixed_loss` must be set to False for {self.__class__}"
  948. )