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1020 lines
42 KiB
1020 lines
42 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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|
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import math |
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import os |
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import os.path as osp |
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from collections import OrderedDict |
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from operator import attrgetter |
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|
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import cv2 |
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import numpy as np |
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import paddle |
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import paddle.nn.functional as F |
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from paddle.static import InputSpec |
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|
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import paddlers |
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import paddlers.rs_models.cd as cmcd |
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import paddlers.utils.logging as logging |
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import paddlers.models.ppseg as paddleseg |
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from paddlers.transforms import Resize, decode_image |
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from paddlers.utils import get_single_card_bs, DisablePrint |
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from paddlers.utils.checkpoint import seg_pretrain_weights_dict |
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from .base import BaseModel |
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from .utils import seg_metrics as metrics |
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|
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__all__ = [ |
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"CDNet", "FCEarlyFusion", "FCSiamConc", "FCSiamDiff", "STANet", "BIT", |
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"SNUNet", "DSIFN", "DSAMNet", "ChangeStar" |
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] |
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|
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class BaseChangeDetector(BaseModel): |
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def __init__(self, |
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model_name, |
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num_classes=2, |
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use_mixed_loss=False, |
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**params): |
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self.init_params = locals() |
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if 'with_net' in self.init_params: |
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del self.init_params['with_net'] |
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super(BaseChangeDetector, self).__init__('change_detector') |
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if model_name not in __all__: |
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raise ValueError("ERROR: There is no model named {}.".format( |
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model_name)) |
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self.model_name = model_name |
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self.num_classes = num_classes |
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self.use_mixed_loss = use_mixed_loss |
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self.losses = None |
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self.labels = None |
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if params.get('with_net', True): |
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params.pop('with_net', None) |
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self.net = self.build_net(**params) |
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self.find_unused_parameters = True |
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|
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def build_net(self, **params): |
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# TODO: add other model |
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net = cmcd.__dict__[self.model_name](num_classes=self.num_classes, |
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**params) |
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return net |
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|
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def _fix_transforms_shape(self, image_shape): |
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if hasattr(self, 'test_transforms'): |
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if self.test_transforms is not None: |
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has_resize_op = False |
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resize_op_idx = -1 |
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normalize_op_idx = len(self.test_transforms.transforms) |
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for idx, op in enumerate(self.test_transforms.transforms): |
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name = op.__class__.__name__ |
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if name == 'Normalize': |
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normalize_op_idx = idx |
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if 'Resize' in name: |
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has_resize_op = True |
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resize_op_idx = idx |
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|
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if not has_resize_op: |
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self.test_transforms.transforms.insert( |
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normalize_op_idx, Resize(target_size=image_shape)) |
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else: |
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self.test_transforms.transforms[resize_op_idx] = Resize( |
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target_size=image_shape) |
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|
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def _get_test_inputs(self, image_shape): |
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if image_shape is not None: |
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if len(image_shape) == 2: |
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image_shape = [1, 3] + image_shape |
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self._fix_transforms_shape(image_shape[-2:]) |
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else: |
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image_shape = [None, 3, -1, -1] |
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self.fixed_input_shape = image_shape |
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return [ |
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InputSpec( |
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shape=image_shape, name='image', dtype='float32'), InputSpec( |
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shape=image_shape, name='image2', dtype='float32') |
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] |
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|
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def run(self, net, inputs, mode): |
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net_out = net(inputs[0], inputs[1]) |
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logit = net_out[0] |
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outputs = OrderedDict() |
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if mode == 'test': |
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origin_shape = inputs[2] |
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if self.status == 'Infer': |
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label_map_list, score_map_list = self._postprocess( |
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net_out, origin_shape, transforms=inputs[3]) |
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else: |
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logit_list = self._postprocess( |
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logit, origin_shape, transforms=inputs[3]) |
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label_map_list = [] |
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score_map_list = [] |
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for logit in logit_list: |
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logit = paddle.transpose(logit, perm=[0, 2, 3, 1]) # NHWC |
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label_map_list.append( |
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paddle.argmax( |
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logit, axis=-1, keepdim=False, dtype='int32') |
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.squeeze().numpy()) |
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score_map_list.append( |
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F.softmax( |
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logit, axis=-1).squeeze().numpy().astype('float32')) |
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outputs['label_map'] = label_map_list |
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outputs['score_map'] = score_map_list |
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|
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if mode == 'eval': |
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if self.status == 'Infer': |
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pred = paddle.unsqueeze(net_out[0], axis=1) # NCHW |
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else: |
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pred = paddle.argmax(logit, axis=1, keepdim=True, dtype='int32') |
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label = inputs[2] |
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if label.ndim == 3: |
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paddle.unsqueeze_(label, axis=1) |
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if label.ndim != 4: |
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raise ValueError("Expected label.ndim == 4 but got {}".format( |
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label.ndim)) |
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origin_shape = [label.shape[-2:]] |
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pred = self._postprocess( |
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pred, origin_shape, transforms=inputs[3])[0] # NCHW |
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intersect_area, pred_area, label_area = paddleseg.utils.metrics.calculate_area( |
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pred, label, self.num_classes) |
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outputs['intersect_area'] = intersect_area |
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outputs['pred_area'] = pred_area |
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outputs['label_area'] = label_area |
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outputs['conf_mat'] = metrics.confusion_matrix(pred, label, |
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self.num_classes) |
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if mode == 'train': |
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if hasattr(net, 'USE_MULTITASK_DECODER') and \ |
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net.USE_MULTITASK_DECODER is True: |
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# CD+Seg |
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if len(inputs) != 5: |
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raise ValueError( |
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"Cannot perform loss computation with {} inputs.". |
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format(len(inputs))) |
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labels_list = [ |
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inputs[2 + idx] |
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for idx in map(attrgetter('value'), net.OUT_TYPES) |
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] |
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loss_list = metrics.multitask_loss_computation( |
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logits_list=net_out, |
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labels_list=labels_list, |
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losses=self.losses) |
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else: |
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loss_list = metrics.loss_computation( |
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logits_list=net_out, labels=inputs[2], losses=self.losses) |
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loss = sum(loss_list) |
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outputs['loss'] = loss |
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return outputs |
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|
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def default_loss(self): |
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if isinstance(self.use_mixed_loss, bool): |
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if self.use_mixed_loss: |
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losses = [ |
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paddleseg.models.CrossEntropyLoss(), |
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paddleseg.models.LovaszSoftmaxLoss() |
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] |
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coef = [.8, .2] |
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loss_type = [ |
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paddleseg.models.MixedLoss( |
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losses=losses, coef=coef), |
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] |
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else: |
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loss_type = [paddleseg.models.CrossEntropyLoss()] |
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else: |
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losses, coef = list(zip(*self.use_mixed_loss)) |
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if not set(losses).issubset( |
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['CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss']): |
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raise ValueError( |
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"Only 'CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss' are supported." |
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) |
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losses = [getattr(paddleseg.models, loss)() for loss in losses] |
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loss_type = [ |
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paddleseg.models.MixedLoss( |
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losses=losses, coef=list(coef)) |
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] |
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loss_coef = [1.0] |
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losses = {'types': loss_type, 'coef': loss_coef} |
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return losses |
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|
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def default_optimizer(self, |
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parameters, |
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learning_rate, |
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num_epochs, |
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num_steps_each_epoch, |
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lr_decay_power=0.9): |
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decay_step = num_epochs * num_steps_each_epoch |
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lr_scheduler = paddle.optimizer.lr.PolynomialDecay( |
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learning_rate, decay_step, end_lr=0, power=lr_decay_power) |
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optimizer = paddle.optimizer.Momentum( |
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learning_rate=lr_scheduler, |
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parameters=parameters, |
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momentum=0.9, |
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weight_decay=4e-5) |
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return optimizer |
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|
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def train(self, |
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num_epochs, |
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train_dataset, |
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train_batch_size=2, |
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eval_dataset=None, |
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optimizer=None, |
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save_interval_epochs=1, |
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log_interval_steps=2, |
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save_dir='output', |
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pretrain_weights=None, |
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learning_rate=0.01, |
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lr_decay_power=0.9, |
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early_stop=False, |
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early_stop_patience=5, |
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use_vdl=True, |
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resume_checkpoint=None): |
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""" |
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Train the model. |
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Args: |
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num_epochs(int): The number of epochs. |
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train_dataset(paddlers.dataset): Training dataset. |
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train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 2. |
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eval_dataset(paddlers.dataset, optional): |
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Evaluation dataset. If None, the model will not be evaluated furing training process. Defaults to None. |
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optimizer(paddle.optimizer.Optimizer or None, optional): |
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Optimizer used in training. If None, a default optimizer is used. Defaults to None. |
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save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1. |
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log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10. |
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save_dir(str, optional): Directory to save the model. Defaults to 'output'. |
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pretrain_weights(str or None, optional): |
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None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to None. |
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learning_rate(float, optional): Learning rate for training. Defaults to .025. |
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lr_decay_power(float, optional): Learning decay power. Defaults to .9. |
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early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False. |
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early_stop_patience(int, optional): Early stop patience. Defaults to 5. |
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use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True. |
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resume_checkpoint(str or None, optional): The path of the checkpoint to resume training from. |
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If None, no training checkpoint will be resumed. At most one of `resume_checkpoint` and |
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`pretrain_weights` can be set simultaneously. Defaults to None. |
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|
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""" |
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if self.status == 'Infer': |
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logging.error( |
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"Exported inference model does not support training.", |
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exit=True) |
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if pretrain_weights is not None and resume_checkpoint is not None: |
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logging.error( |
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"pretrain_weights and resume_checkpoint cannot be set simultaneously.", |
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exit=True) |
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self.labels = train_dataset.labels |
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if self.losses is None: |
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self.losses = self.default_loss() |
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|
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if optimizer is None: |
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num_steps_each_epoch = train_dataset.num_samples // train_batch_size |
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self.optimizer = self.default_optimizer( |
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self.net.parameters(), learning_rate, num_epochs, |
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num_steps_each_epoch, lr_decay_power) |
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else: |
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self.optimizer = optimizer |
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|
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if pretrain_weights is not None and not osp.exists(pretrain_weights): |
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if pretrain_weights not in seg_pretrain_weights_dict[ |
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self.model_name]: |
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logging.warning( |
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"Path of pretrain_weights('{}') does not exist!".format( |
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pretrain_weights)) |
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logging.warning("Pretrain_weights is forcibly set to '{}'. " |
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"If don't want to use pretrain weights, " |
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"set pretrain_weights to be None.".format( |
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seg_pretrain_weights_dict[self.model_name][ |
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0])) |
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pretrain_weights = seg_pretrain_weights_dict[self.model_name][0] |
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elif pretrain_weights is not None and osp.exists(pretrain_weights): |
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if osp.splitext(pretrain_weights)[-1] != '.pdparams': |
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logging.error( |
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"Invalid pretrain weights. Please specify a '.pdparams' file.", |
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exit=True) |
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pretrained_dir = osp.join(save_dir, 'pretrain') |
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is_backbone_weights = pretrain_weights == 'IMAGENET' |
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self.net_initialize( |
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pretrain_weights=pretrain_weights, |
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save_dir=pretrained_dir, |
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resume_checkpoint=resume_checkpoint, |
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is_backbone_weights=is_backbone_weights) |
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|
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self.train_loop( |
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num_epochs=num_epochs, |
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train_dataset=train_dataset, |
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train_batch_size=train_batch_size, |
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eval_dataset=eval_dataset, |
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save_interval_epochs=save_interval_epochs, |
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log_interval_steps=log_interval_steps, |
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save_dir=save_dir, |
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early_stop=early_stop, |
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early_stop_patience=early_stop_patience, |
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use_vdl=use_vdl) |
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|
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def quant_aware_train(self, |
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num_epochs, |
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train_dataset, |
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train_batch_size=2, |
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eval_dataset=None, |
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optimizer=None, |
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save_interval_epochs=1, |
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log_interval_steps=2, |
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save_dir='output', |
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learning_rate=0.0001, |
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lr_decay_power=0.9, |
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early_stop=False, |
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early_stop_patience=5, |
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use_vdl=True, |
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resume_checkpoint=None, |
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quant_config=None): |
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""" |
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Quantization-aware training. |
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Args: |
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num_epochs(int): The number of epochs. |
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train_dataset(paddlers.dataset): Training dataset. |
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train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 2. |
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eval_dataset(paddlers.dataset, optional): |
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Evaluation dataset. If None, the model will not be evaluated furing training process. Defaults to None. |
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optimizer(paddle.optimizer.Optimizer or None, optional): |
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Optimizer used in training. If None, a default optimizer is used. Defaults to None. |
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save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1. |
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log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10. |
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save_dir(str, optional): Directory to save the model. Defaults to 'output'. |
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learning_rate(float, optional): Learning rate for training. Defaults to .025. |
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lr_decay_power(float, optional): Learning decay power. Defaults to .9. |
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early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False. |
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early_stop_patience(int, optional): Early stop patience. Defaults to 5. |
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use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True. |
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quant_config(dict or None, optional): Quantization configuration. If None, a default rule of thumb |
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configuration will be used. Defaults to None. |
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resume_checkpoint(str or None, optional): The path of the checkpoint to resume quantization-aware training |
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from. If None, no training checkpoint will be resumed. Defaults to None. |
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""" |
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self._prepare_qat(quant_config) |
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self.train( |
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num_epochs=num_epochs, |
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train_dataset=train_dataset, |
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train_batch_size=train_batch_size, |
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eval_dataset=eval_dataset, |
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optimizer=optimizer, |
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save_interval_epochs=save_interval_epochs, |
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log_interval_steps=log_interval_steps, |
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save_dir=save_dir, |
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pretrain_weights=None, |
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learning_rate=learning_rate, |
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lr_decay_power=lr_decay_power, |
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early_stop=early_stop, |
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early_stop_patience=early_stop_patience, |
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use_vdl=use_vdl, |
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resume_checkpoint=resume_checkpoint) |
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|
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def evaluate(self, eval_dataset, batch_size=1, return_details=False): |
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""" |
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Evaluate the model. |
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Args: |
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eval_dataset(paddlers.dataset): Evaluation dataset. |
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batch_size(int, optional): Total batch size among all cards used for evaluation. Defaults to 1. |
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return_details(bool, optional): Whether to return evaluation details. Defaults to False. |
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|
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Returns: |
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collections.OrderedDict with key-value pairs: |
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For binary change detection (number of classes == 2), the key-value pairs are like: |
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{"iou": `intersection over union for the change class`, |
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"f1": `F1 score for the change class`, |
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"oacc": `overall accuracy`, |
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"kappa": ` kappa coefficient`}. |
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For multi-class change detection (number of classes > 2), the key-value pairs are like: |
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{"miou": `mean intersection over union`, |
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"category_iou": `category-wise mean intersection over union`, |
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"oacc": `overall accuracy`, |
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"category_acc": `category-wise accuracy`, |
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"kappa": ` kappa coefficient`, |
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"category_F1-score": `F1 score`}. |
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|
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""" |
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self._check_transforms(eval_dataset.transforms, 'eval') |
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|
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self.net.eval() |
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nranks = paddle.distributed.get_world_size() |
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local_rank = paddle.distributed.get_rank() |
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if nranks > 1: |
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# Initialize parallel environment if not done. |
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if not (paddle.distributed.parallel.parallel_helper. |
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_is_parallel_ctx_initialized()): |
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paddle.distributed.init_parallel_env() |
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|
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batch_size_each_card = get_single_card_bs(batch_size) |
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if batch_size_each_card > 1: |
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batch_size_each_card = 1 |
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batch_size = batch_size_each_card * paddlers.env_info['num'] |
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logging.warning( |
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"ChangeDetector only supports batch_size=1 for each gpu/cpu card " \ |
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"during evaluation, so batch_size " \ |
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"is forcibly set to {}.".format(batch_size) |
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) |
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self.eval_data_loader = self.build_data_loader( |
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eval_dataset, batch_size=batch_size, mode='eval') |
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|
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intersect_area_all = 0 |
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pred_area_all = 0 |
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label_area_all = 0 |
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conf_mat_all = [] |
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logging.info( |
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"Start to evaluate(total_samples={}, total_steps={})...".format( |
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eval_dataset.num_samples, |
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math.ceil(eval_dataset.num_samples * 1.0 / batch_size))) |
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with paddle.no_grad(): |
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for step, data in enumerate(self.eval_data_loader): |
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data.append(eval_dataset.transforms.transforms) |
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outputs = self.run(self.net, data, 'eval') |
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pred_area = outputs['pred_area'] |
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label_area = outputs['label_area'] |
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intersect_area = outputs['intersect_area'] |
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conf_mat = outputs['conf_mat'] |
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|
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# Gather from all ranks |
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if nranks > 1: |
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intersect_area_list = [] |
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pred_area_list = [] |
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label_area_list = [] |
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conf_mat_list = [] |
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paddle.distributed.all_gather(intersect_area_list, |
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intersect_area) |
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paddle.distributed.all_gather(pred_area_list, pred_area) |
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paddle.distributed.all_gather(label_area_list, label_area) |
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paddle.distributed.all_gather(conf_mat_list, conf_mat) |
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|
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# Some image has been evaluated and should be eliminated in last iter |
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if (step + 1) * nranks > len(eval_dataset): |
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valid = len(eval_dataset) - step * nranks |
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intersect_area_list = intersect_area_list[:valid] |
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pred_area_list = pred_area_list[:valid] |
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label_area_list = label_area_list[:valid] |
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conf_mat_list = conf_mat_list[:valid] |
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|
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intersect_area_all += sum(intersect_area_list) |
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pred_area_all += sum(pred_area_list) |
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label_area_all += sum(label_area_list) |
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conf_mat_all.extend(conf_mat_list) |
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|
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else: |
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intersect_area_all = intersect_area_all + intersect_area |
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pred_area_all = pred_area_all + pred_area |
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label_area_all = label_area_all + label_area |
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conf_mat_all.append(conf_mat) |
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class_iou, miou = paddleseg.utils.metrics.mean_iou( |
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intersect_area_all, pred_area_all, label_area_all) |
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# TODO 确认是按oacc还是macc |
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class_acc, oacc = paddleseg.utils.metrics.accuracy(intersect_area_all, |
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pred_area_all) |
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kappa = paddleseg.utils.metrics.kappa(intersect_area_all, pred_area_all, |
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label_area_all) |
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category_f1score = metrics.f1_score(intersect_area_all, pred_area_all, |
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label_area_all) |
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|
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if len(class_acc) > 2: |
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eval_metrics = OrderedDict( |
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zip([ |
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'miou', 'category_iou', 'oacc', 'category_acc', 'kappa', |
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'category_F1-score' |
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], [miou, class_iou, oacc, class_acc, kappa, category_f1score])) |
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else: |
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eval_metrics = OrderedDict( |
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zip(['iou', 'f1', 'oacc', 'kappa'], |
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[class_iou[1], category_f1score[1], oacc, kappa])) |
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|
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if return_details: |
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conf_mat = sum(conf_mat_all) |
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eval_details = {'confusion_matrix': conf_mat.tolist()} |
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return eval_metrics, eval_details |
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return eval_metrics |
|
|
|
def predict(self, img_file, transforms=None): |
|
""" |
|
Do inference. |
|
Args: |
|
Args: |
|
img_file (list[tuple] | tuple[str | np.ndarray]): |
|
Tuple of image paths or decoded image data for bi-temporal images, which also could constitute a list, |
|
meaning all image pairs to be predicted as a mini-batch. |
|
transforms(paddlers.transforms.Compose or None, optional): |
|
Transforms for inputs. If None, the transforms for evaluation process will be used. Defaults to None. |
|
|
|
Returns: |
|
If img_file is a tuple of string or np.array, the result is a dict with key-value pairs: |
|
{"label map": `label map`, "score_map": `score map`}. |
|
If img_file is a list, the result is a list composed of dicts with the corresponding fields: |
|
label_map(np.ndarray): the predicted label map (HW) |
|
score_map(np.ndarray): the prediction score map (HWC) |
|
|
|
""" |
|
if transforms is None and not hasattr(self, 'test_transforms'): |
|
raise Exception("transforms need to be defined, now is None.") |
|
if transforms is None: |
|
transforms = self.test_transforms |
|
if isinstance(img_file, tuple): |
|
if not len(img_file) == 2 and any( |
|
map(lambda obj: not isinstance(obj, (str, np.ndarray)), |
|
img_file)): |
|
raise TypeError |
|
images = [img_file] |
|
else: |
|
images = img_file |
|
batch_im1, batch_im2, batch_origin_shape = self._preprocess( |
|
images, transforms, self.model_type) |
|
self.net.eval() |
|
data = (batch_im1, batch_im2, batch_origin_shape, transforms.transforms) |
|
outputs = self.run(self.net, data, 'test') |
|
label_map_list = outputs['label_map'] |
|
score_map_list = outputs['score_map'] |
|
if isinstance(img_file, list): |
|
prediction = [{ |
|
'label_map': l, |
|
'score_map': s |
|
} for l, s in zip(label_map_list, score_map_list)] |
|
else: |
|
prediction = { |
|
'label_map': label_map_list[0], |
|
'score_map': score_map_list[0] |
|
} |
|
return prediction |
|
|
|
def slider_predict(self, |
|
img_file, |
|
save_dir, |
|
block_size, |
|
overlap=36, |
|
transforms=None): |
|
""" |
|
Do inference. |
|
Args: |
|
Args: |
|
img_file(list[str]): |
|
List of image paths. |
|
save_dir(str): |
|
Directory that contains saved geotiff file. |
|
block_size(list[int] | tuple[int] | int, optional): |
|
Size of block. |
|
overlap(list[int] | tuple[int] | int, optional): |
|
Overlap between two blocks. Defaults to 36. |
|
transforms(paddlers.transforms.Compose or None, optional): |
|
Transforms for inputs. If None, the transforms for evaluation process will be used. Defaults to None. |
|
""" |
|
try: |
|
from osgeo import gdal |
|
except: |
|
import gdal |
|
|
|
if len(img_file) != 2: |
|
raise ValueError("`img_file` must be a list of length 2.") |
|
if isinstance(block_size, int): |
|
block_size = (block_size, block_size) |
|
elif isinstance(block_size, (tuple, list)) and len(block_size) == 2: |
|
block_size = tuple(block_size) |
|
else: |
|
raise ValueError( |
|
"`block_size` must be a tuple/list of length 2 or an integer.") |
|
if isinstance(overlap, int): |
|
overlap = (overlap, overlap) |
|
elif isinstance(overlap, (tuple, list)) and len(overlap) == 2: |
|
overlap = tuple(overlap) |
|
else: |
|
raise ValueError( |
|
"`overlap` must be a tuple/list of length 2 or an integer.") |
|
|
|
src1_data = gdal.Open(img_file[0]) |
|
src2_data = gdal.Open(img_file[1]) |
|
width = src1_data.RasterXSize |
|
height = src1_data.RasterYSize |
|
bands = src1_data.RasterCount |
|
|
|
driver = gdal.GetDriverByName("GTiff") |
|
file_name = osp.splitext(osp.normpath(img_file[0]).split(os.sep)[-1])[ |
|
0] + ".tif" |
|
if not osp.exists(save_dir): |
|
os.makedirs(save_dir) |
|
save_file = osp.join(save_dir, file_name) |
|
dst_data = driver.Create(save_file, width, height, 1, gdal.GDT_Byte) |
|
dst_data.SetGeoTransform(src1_data.GetGeoTransform()) |
|
dst_data.SetProjection(src1_data.GetProjection()) |
|
band = dst_data.GetRasterBand(1) |
|
band.WriteArray(255 * np.ones((height, width), dtype="uint8")) |
|
|
|
step = np.array(block_size) - np.array(overlap) |
|
for yoff in range(0, height, step[1]): |
|
for xoff in range(0, width, step[0]): |
|
xsize, ysize = block_size |
|
if xoff + xsize > width: |
|
xsize = int(width - xoff) |
|
if yoff + ysize > height: |
|
ysize = int(height - yoff) |
|
im1 = src1_data.ReadAsArray( |
|
int(xoff), int(yoff), xsize, ysize).transpose((1, 2, 0)) |
|
im2 = src2_data.ReadAsArray( |
|
int(xoff), int(yoff), xsize, ysize).transpose((1, 2, 0)) |
|
# fill |
|
h, w = im1.shape[:2] |
|
im1_fill = np.zeros( |
|
(block_size[1], block_size[0], bands), dtype=im1.dtype) |
|
im2_fill = im1_fill.copy() |
|
im1_fill[:h, :w, :] = im1 |
|
im2_fill[:h, :w, :] = im2 |
|
im_fill = (im1_fill, im2_fill) |
|
# predict |
|
pred = self.predict(im_fill, |
|
transforms)["label_map"].astype("uint8") |
|
# overlap |
|
rd_block = band.ReadAsArray(int(xoff), int(yoff), xsize, ysize) |
|
mask = (rd_block == pred[:h, :w]) | (rd_block == 255) |
|
temp = pred[:h, :w].copy() |
|
temp[mask == False] = 0 |
|
band.WriteArray(temp, int(xoff), int(yoff)) |
|
dst_data.FlushCache() |
|
dst_data = None |
|
print("GeoTiff saved in {}.".format(save_file)) |
|
|
|
def _preprocess(self, images, transforms, to_tensor=True): |
|
self._check_transforms(transforms, 'test') |
|
batch_im1, batch_im2 = list(), list() |
|
batch_ori_shape = list() |
|
for im1, im2 in images: |
|
if isinstance(im1, str) or isinstance(im2, str): |
|
im1 = decode_image(im1, to_rgb=False) |
|
im2 = decode_image(im2, to_rgb=False) |
|
ori_shape = im1.shape[:2] |
|
# XXX: sample do not contain 'image_t1' and 'image_t2'. |
|
sample = {'image': im1, 'image2': im2} |
|
im1, im2 = transforms(sample)[:2] |
|
batch_im1.append(im1) |
|
batch_im2.append(im2) |
|
batch_ori_shape.append(ori_shape) |
|
if to_tensor: |
|
batch_im1 = paddle.to_tensor(batch_im1) |
|
batch_im2 = paddle.to_tensor(batch_im2) |
|
else: |
|
batch_im1 = np.asarray(batch_im1) |
|
batch_im2 = np.asarray(batch_im2) |
|
|
|
return batch_im1, batch_im2, batch_ori_shape |
|
|
|
@staticmethod |
|
def get_transforms_shape_info(batch_ori_shape, transforms): |
|
batch_restore_list = list() |
|
for ori_shape in batch_ori_shape: |
|
restore_list = list() |
|
h, w = ori_shape[0], ori_shape[1] |
|
for op in transforms: |
|
if op.__class__.__name__ == 'Resize': |
|
restore_list.append(('resize', (h, w))) |
|
h, w = op.target_size |
|
elif op.__class__.__name__ == 'ResizeByShort': |
|
restore_list.append(('resize', (h, w))) |
|
im_short_size = min(h, w) |
|
im_long_size = max(h, w) |
|
scale = float(op.short_size) / float(im_short_size) |
|
if 0 < op.max_size < np.round(scale * im_long_size): |
|
scale = float(op.max_size) / float(im_long_size) |
|
h = int(round(h * scale)) |
|
w = int(round(w * scale)) |
|
elif op.__class__.__name__ == 'ResizeByLong': |
|
restore_list.append(('resize', (h, w))) |
|
im_long_size = max(h, w) |
|
scale = float(op.long_size) / float(im_long_size) |
|
h = int(round(h * scale)) |
|
w = int(round(w * scale)) |
|
elif op.__class__.__name__ == 'Pad': |
|
if op.target_size: |
|
target_h, target_w = op.target_size |
|
else: |
|
target_h = int( |
|
(np.ceil(h / op.size_divisor) * op.size_divisor)) |
|
target_w = int( |
|
(np.ceil(w / op.size_divisor) * op.size_divisor)) |
|
|
|
if op.pad_mode == -1: |
|
offsets = op.offsets |
|
elif op.pad_mode == 0: |
|
offsets = [0, 0] |
|
elif op.pad_mode == 1: |
|
offsets = [(target_h - h) // 2, (target_w - w) // 2] |
|
else: |
|
offsets = [target_h - h, target_w - w] |
|
restore_list.append(('padding', (h, w), offsets)) |
|
h, w = target_h, target_w |
|
|
|
batch_restore_list.append(restore_list) |
|
return batch_restore_list |
|
|
|
def _postprocess(self, batch_pred, batch_origin_shape, transforms): |
|
batch_restore_list = BaseChangeDetector.get_transforms_shape_info( |
|
batch_origin_shape, transforms) |
|
if isinstance(batch_pred, (tuple, list)) and self.status == 'Infer': |
|
return self._infer_postprocess( |
|
batch_label_map=batch_pred[0], |
|
batch_score_map=batch_pred[1], |
|
batch_restore_list=batch_restore_list) |
|
results = [] |
|
if batch_pred.dtype == paddle.float32: |
|
mode = 'bilinear' |
|
else: |
|
mode = 'nearest' |
|
for pred, restore_list in zip(batch_pred, batch_restore_list): |
|
pred = paddle.unsqueeze(pred, axis=0) |
|
for item in restore_list[::-1]: |
|
h, w = item[1][0], item[1][1] |
|
if item[0] == 'resize': |
|
pred = F.interpolate( |
|
pred, (h, w), mode=mode, data_format='NCHW') |
|
elif item[0] == 'padding': |
|
x, y = item[2] |
|
pred = pred[:, :, y:y + h, x:x + w] |
|
else: |
|
pass |
|
results.append(pred) |
|
return results |
|
|
|
def _infer_postprocess(self, batch_label_map, batch_score_map, |
|
batch_restore_list): |
|
label_maps = [] |
|
score_maps = [] |
|
for label_map, score_map, restore_list in zip( |
|
batch_label_map, batch_score_map, batch_restore_list): |
|
if not isinstance(label_map, np.ndarray): |
|
label_map = paddle.unsqueeze(label_map, axis=[0, 3]) |
|
score_map = paddle.unsqueeze(score_map, axis=0) |
|
for item in restore_list[::-1]: |
|
h, w = item[1][0], item[1][1] |
|
if item[0] == 'resize': |
|
if isinstance(label_map, np.ndarray): |
|
label_map = cv2.resize( |
|
label_map, (w, h), interpolation=cv2.INTER_NEAREST) |
|
score_map = cv2.resize( |
|
score_map, (w, h), interpolation=cv2.INTER_LINEAR) |
|
else: |
|
label_map = F.interpolate( |
|
label_map, (h, w), |
|
mode='nearest', |
|
data_format='NHWC') |
|
score_map = F.interpolate( |
|
score_map, (h, w), |
|
mode='bilinear', |
|
data_format='NHWC') |
|
elif item[0] == 'padding': |
|
x, y = item[2] |
|
if isinstance(label_map, np.ndarray): |
|
label_map = label_map[..., y:y + h, x:x + w] |
|
score_map = score_map[..., y:y + h, x:x + w] |
|
else: |
|
label_map = label_map[:, :, y:y + h, x:x + w] |
|
score_map = score_map[:, :, y:y + h, x:x + w] |
|
else: |
|
pass |
|
label_map = label_map.squeeze() |
|
score_map = score_map.squeeze() |
|
if not isinstance(label_map, np.ndarray): |
|
label_map = label_map.numpy() |
|
score_map = score_map.numpy() |
|
label_maps.append(label_map.squeeze()) |
|
score_maps.append(score_map.squeeze()) |
|
return label_maps, score_maps |
|
|
|
def _check_transforms(self, transforms, mode): |
|
super()._check_transforms(transforms, mode) |
|
if not isinstance(transforms.arrange, |
|
paddlers.transforms.ArrangeChangeDetector): |
|
raise TypeError( |
|
"`transforms.arrange` must be an ArrangeChangeDetector object.") |
|
|
|
|
|
class CDNet(BaseChangeDetector): |
|
def __init__(self, |
|
num_classes=2, |
|
use_mixed_loss=False, |
|
in_channels=6, |
|
**params): |
|
params.update({'in_channels': in_channels}) |
|
super(CDNet, self).__init__( |
|
model_name='CDNet', |
|
num_classes=num_classes, |
|
use_mixed_loss=use_mixed_loss, |
|
**params) |
|
|
|
|
|
class FCEarlyFusion(BaseChangeDetector): |
|
def __init__(self, |
|
num_classes=2, |
|
use_mixed_loss=False, |
|
in_channels=6, |
|
use_dropout=False, |
|
**params): |
|
params.update({'in_channels': in_channels, 'use_dropout': use_dropout}) |
|
super(FCEarlyFusion, self).__init__( |
|
model_name='FCEarlyFusion', |
|
num_classes=num_classes, |
|
use_mixed_loss=use_mixed_loss, |
|
**params) |
|
|
|
|
|
class FCSiamConc(BaseChangeDetector): |
|
def __init__(self, |
|
num_classes=2, |
|
use_mixed_loss=False, |
|
in_channels=3, |
|
use_dropout=False, |
|
**params): |
|
params.update({'in_channels': in_channels, 'use_dropout': use_dropout}) |
|
super(FCSiamConc, self).__init__( |
|
model_name='FCSiamConc', |
|
num_classes=num_classes, |
|
use_mixed_loss=use_mixed_loss, |
|
**params) |
|
|
|
|
|
class FCSiamDiff(BaseChangeDetector): |
|
def __init__(self, |
|
num_classes=2, |
|
use_mixed_loss=False, |
|
in_channels=3, |
|
use_dropout=False, |
|
**params): |
|
params.update({'in_channels': in_channels, 'use_dropout': use_dropout}) |
|
super(FCSiamDiff, self).__init__( |
|
model_name='FCSiamDiff', |
|
num_classes=num_classes, |
|
use_mixed_loss=use_mixed_loss, |
|
**params) |
|
|
|
|
|
class STANet(BaseChangeDetector): |
|
def __init__(self, |
|
num_classes=2, |
|
use_mixed_loss=False, |
|
in_channels=3, |
|
att_type='BAM', |
|
ds_factor=1, |
|
**params): |
|
params.update({ |
|
'in_channels': in_channels, |
|
'att_type': att_type, |
|
'ds_factor': ds_factor |
|
}) |
|
super(STANet, self).__init__( |
|
model_name='STANet', |
|
num_classes=num_classes, |
|
use_mixed_loss=use_mixed_loss, |
|
**params) |
|
|
|
|
|
class BIT(BaseChangeDetector): |
|
def __init__(self, |
|
num_classes=2, |
|
use_mixed_loss=False, |
|
in_channels=3, |
|
backbone='resnet18', |
|
n_stages=4, |
|
use_tokenizer=True, |
|
token_len=4, |
|
pool_mode='max', |
|
pool_size=2, |
|
enc_with_pos=True, |
|
enc_depth=1, |
|
enc_head_dim=64, |
|
dec_depth=8, |
|
dec_head_dim=8, |
|
**params): |
|
params.update({ |
|
'in_channels': in_channels, |
|
'backbone': backbone, |
|
'n_stages': n_stages, |
|
'use_tokenizer': use_tokenizer, |
|
'token_len': token_len, |
|
'pool_mode': pool_mode, |
|
'pool_size': pool_size, |
|
'enc_with_pos': enc_with_pos, |
|
'enc_depth': enc_depth, |
|
'enc_head_dim': enc_head_dim, |
|
'dec_depth': dec_depth, |
|
'dec_head_dim': dec_head_dim |
|
}) |
|
super(BIT, self).__init__( |
|
model_name='BIT', |
|
num_classes=num_classes, |
|
use_mixed_loss=use_mixed_loss, |
|
**params) |
|
|
|
|
|
class SNUNet(BaseChangeDetector): |
|
def __init__(self, |
|
num_classes=2, |
|
use_mixed_loss=False, |
|
in_channels=3, |
|
width=32, |
|
**params): |
|
params.update({'in_channels': in_channels, 'width': width}) |
|
super(SNUNet, self).__init__( |
|
model_name='SNUNet', |
|
num_classes=num_classes, |
|
use_mixed_loss=use_mixed_loss, |
|
**params) |
|
|
|
|
|
class DSIFN(BaseChangeDetector): |
|
def __init__(self, |
|
num_classes=2, |
|
use_mixed_loss=False, |
|
use_dropout=False, |
|
**params): |
|
params.update({'use_dropout': use_dropout}) |
|
super(DSIFN, self).__init__( |
|
model_name='DSIFN', |
|
num_classes=num_classes, |
|
use_mixed_loss=use_mixed_loss, |
|
**params) |
|
|
|
def default_loss(self): |
|
if self.use_mixed_loss is False: |
|
return { |
|
# XXX: make sure the shallow copy works correctly here. |
|
'types': [paddleseg.models.CrossEntropyLoss()] * 5, |
|
'coef': [1.0] * 5 |
|
} |
|
else: |
|
raise ValueError( |
|
f"Currently `use_mixed_loss` must be set to False for {self.__class__}" |
|
) |
|
|
|
|
|
class DSAMNet(BaseChangeDetector): |
|
def __init__(self, |
|
num_classes=2, |
|
use_mixed_loss=False, |
|
in_channels=3, |
|
ca_ratio=8, |
|
sa_kernel=7, |
|
**params): |
|
params.update({ |
|
'in_channels': in_channels, |
|
'ca_ratio': ca_ratio, |
|
'sa_kernel': sa_kernel |
|
}) |
|
super(DSAMNet, self).__init__( |
|
model_name='DSAMNet', |
|
num_classes=num_classes, |
|
use_mixed_loss=use_mixed_loss, |
|
**params) |
|
|
|
def default_loss(self): |
|
if self.use_mixed_loss is False: |
|
return { |
|
'types': [ |
|
paddleseg.models.CrossEntropyLoss(), |
|
paddleseg.models.DiceLoss(), paddleseg.models.DiceLoss() |
|
], |
|
'coef': [1.0, 0.05, 0.05] |
|
} |
|
else: |
|
raise ValueError( |
|
f"Currently `use_mixed_loss` must be set to False for {self.__class__}" |
|
) |
|
|
|
|
|
class ChangeStar(BaseChangeDetector): |
|
def __init__(self, |
|
num_classes=2, |
|
use_mixed_loss=False, |
|
mid_channels=256, |
|
inner_channels=16, |
|
num_convs=4, |
|
scale_factor=4.0, |
|
**params): |
|
params.update({ |
|
'mid_channels': mid_channels, |
|
'inner_channels': inner_channels, |
|
'num_convs': num_convs, |
|
'scale_factor': scale_factor |
|
}) |
|
super(ChangeStar, self).__init__( |
|
model_name='ChangeStar', |
|
num_classes=num_classes, |
|
use_mixed_loss=use_mixed_loss, |
|
**params) |
|
|
|
def default_loss(self): |
|
if self.use_mixed_loss is False: |
|
return { |
|
# XXX: make sure the shallow copy works correctly here. |
|
'types': [paddleseg.models.CrossEntropyLoss()] * 4, |
|
'coef': [1.0] * 4 |
|
} |
|
else: |
|
raise ValueError( |
|
f"Currently `use_mixed_loss` must be set to False for {self.__class__}" |
|
)
|
|
|