【论文复现赛】ChangeFormer (#13)

精度验收通过，代码符合规范，论文复现成功。

paddlers/rs_models/cd/__init__.py

@@ -22,3 +22,4 @@ from .changestar import ChangeStar
 from .fc_ef import FCEarlyFusion
 from .fc_siam_conc import FCSiamConc
 from .fc_siam_diff import FCSiamDiff
+from .changeformer import ChangeFormer

paddlers/rs_models/cd/layers/pd_timm.py

@@ -0,0 +1,71 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License..
+
+from itertools import repeat
+import collections.abc
+
+import paddle
+import paddle.nn as nn
+"""
+Droppath, reimplement from https://github.com/yueatsprograms/Stochastic_Depth
+"""
+
+
+class DropPath(nn.Layer):
+    """DropPath class"""
+
+    def __init__(self, drop_prob=None):
+        super().__init__()
+        self.drop_prob = drop_prob
+
+    def drop_path(self, inputs):
+        """drop path op
+        Args:
+            input: tensor with arbitrary shape
+            drop_prob: float number of drop path probability, default: 0.0
+            training: bool, if current mode is training, default: False
+        Returns:
+            output: output tensor after drop path
+        """
+        # if prob is 0 or eval mode, return original input
+        if self.drop_prob == 0. or not self.training:
+            return inputs
+        keep_prob = 1 - self.drop_prob
+        keep_prob = paddle.to_tensor(keep_prob, dtype='float32')
+        shape = (inputs.shape[0], ) + (1, ) * (inputs.ndim - 1
+                                               )  # shape=(N, 1, 1, 1)
+        random_tensor = keep_prob + paddle.rand(shape, dtype=inputs.dtype)
+        random_tensor = random_tensor.floor()  # mask
+        output = inputs.divide(
+            keep_prob) * random_tensor  # divide to keep same output expectation
+        return output
+
+    def forward(self, inputs):
+        return self.drop_path(inputs)
+
+
+def _ntuple(n):
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable):
+            return x
+        return tuple(repeat(x, n))
+
+    return parse
+
+
+to_1tuple = _ntuple(1)
+to_2tuple = _ntuple(2)
+to_3tuple = _ntuple(3)
+to_4tuple = _ntuple(4)
+to_ntuple = _ntuple

paddlers/tasks/change_detector.py

@@ -36,7 +36,7 @@ from .utils import seg_metrics as metrics
 
 __all__ = [
     "CDNet", "FCEarlyFusion", "FCSiamConc", "FCSiamDiff", "STANet", "BIT",
-    "SNUNet", "DSIFN", "DSAMNet", "ChangeStar"
+    "SNUNet", "DSIFN", "DSAMNet", "ChangeStar", "ChangeFormer"
 ]
 
 
@@ -1041,3 +1041,23 @@ class ChangeStar(BaseChangeDetector):
             raise ValueError(
                 f"Currently `use_mixed_loss` must be set to False for {self.__class__}"
             )
+
+
+class ChangeFormer(BaseChangeDetector):
+    def __init__(self,
+                 in_channels=3,
+                 num_classes=2,
+                 decoder_softmax=False,
+                 embed_dim=256,
+                 use_mixed_loss=False,
+                 **params):
+        params.update({
+            'in_channels': in_channels,
+            'embed_dim': embed_dim,
+            'decoder_softmax': decoder_softmax
+        })
+        super(ChangeFormer, self).__init__(
+            model_name='ChangeFormer',
+            num_classes=num_classes,
+            use_mixed_loss=use_mixed_loss,
+            **params)

test_tipc/configs/cd/changeformer/changeformer.yaml

@@ -0,0 +1,8 @@
+# Basic configurations of ChangeFormer
+
+_base_: ../_base_/airchange.yaml
+
+save_dir: ./test_tipc/output/cd/changeformer/
+
+model: !Node
+       type: ChangeFormer

test_tipc/configs/cd/changeformer/train_infer_python.txt

@@ -0,0 +1,53 @@
+===========================train_params===========================
+model_name:cd:changeformer
+python:python
+gpu_list:0|0,1
+use_gpu:null|null
+--precision:null
+--num_epochs:lite_train_lite_infer=5|lite_train_whole_infer=5|whole_train_whole_infer=10
+--save_dir:adaptive
+--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=4
+--model_path:null
+train_model_name:best_model
+train_infer_file_list:./test_tipc/data/airchange/:./test_tipc/data/airchange/eval.txt
+null:null
+##
+trainer:norm
+norm_train:test_tipc/run_task.py train cd --config ./test_tipc/configs/cd/changeformer/changeformer.yaml
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:null
+null:null
+##
+===========================export_params===========================
+--save_dir:adaptive
+--model_dir:adaptive
+--fixed_input_shape:[1,3,256,256]
+norm_export:deploy/export/export_model.py
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+===========================infer_params===========================
+infer_model:null
+infer_export:null
+infer_quant:False
+inference:test_tipc/infer.py
+--device:cpu|gpu
+--enable_mkldnn:True
+--cpu_threads:6
+--batch_size:1
+--use_trt:False
+--precision:fp32
+--model_dir:null
+--file_list:null:null
+--save_log_path:null
+--benchmark:True
+--model_name:changeformer
+null:null

tests/rs_models/test_cd_models.py

@@ -21,7 +21,8 @@ from rs_models.test_model import TestModel
 __all__ = [
     'TestBITModel', 'TestCDNetModel', 'TestChangeStarModel', 'TestDSAMNetModel',
     'TestDSIFNModel', 'TestFCEarlyFusionModel', 'TestFCSiamConcModel',
-    'TestFCSiamDiffModel', 'TestSNUNetModel', 'TestSTANetModel'
+    'TestFCSiamDiffModel', 'TestSNUNetModel', 'TestSTANetModel',
+    'TestChangeFormerModel'
 ]
 
 
@@ -215,6 +216,18 @@ class TestSTANetModel(TestCDModel):
         ]   # yapf: disable
 
 
+class TestChangeFormerModel(TestCDModel):
+    MODEL_CLASS = paddlers.rs_models.cd.ChangeFormer
+
+    def set_specs(self):
+        base_spec = dict(in_channels=3, num_classes=2)
+        self.specs = [
+            base_spec,
+            dict(**base_spec, decoder_softmax=True),
+            dict(**base_spec, embed_dim=56)
+        ]   # yapf: disable
+
+
 # HACK:FIXME: We observe an OOM error when running TestSTANetModel.test_forward() on a Windows machine.
 # Currently, we do not perform this test.
 if platform.system() == 'Windows':

tutorials/train/change_detection/changeformer.py

@@ -0,0 +1,94 @@
+#!/usr/bin/env python
+
+# 变化检测模型ChangeFormer训练示例脚本
+# 执行此脚本前，请确认已正确安装PaddleRS库
+
+import paddlers as pdrs
+from paddlers import transforms as T
+
+# 数据集存放目录
+DATA_DIR = './data/airchange/'
+# 训练集`file_list`文件路径
+TRAIN_FILE_LIST_PATH = './data/airchange/train.txt'
+# 验证集`file_list`文件路径
+EVAL_FILE_LIST_PATH = './data/airchange/eval.txt'
+# 实验目录，保存输出的模型权重和结果
+EXP_DIR = './output/changeformer/'
+
+# 下载和解压AirChange数据集
+pdrs.utils.download_and_decompress(
+    'https://paddlers.bj.bcebos.com/datasets/airchange.zip', path='./data/')
+
+# 定义训练和验证时使用的数据变换（数据增强、预处理等）
+# 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
+# API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/transforms.md
+train_transforms = T.Compose([
+    # 读取影像
+    T.DecodeImg(),
+    # 随机裁剪
+    T.RandomCrop(
+        # 裁剪区域将被缩放到256x256
+        crop_size=256,
+        # 裁剪区域的横纵比在0.5-2之间变动
+        aspect_ratio=[0.5, 2.0],
+        # 裁剪区域相对原始影像长宽比例在一定范围内变动，最小不低于原始长宽的1/5
+        scaling=[0.2, 1.0]),
+    # 以50%的概率实施随机水平翻转
+    T.RandomHorizontalFlip(prob=0.5),
+    # 将数据归一化到[-1,1]
+    T.Normalize(
+        mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ArrangeChangeDetector('train')
+])
+
+eval_transforms = T.Compose([
+    T.DecodeImg(),
+    # 验证阶段与训练阶段的数据归一化方式必须相同
+    T.Normalize(
+        mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    T.ReloadMask(),
+    T.ArrangeChangeDetector('eval')
+])
+
+# 分别构建训练和验证所用的数据集
+train_dataset = pdrs.datasets.CDDataset(
+    data_dir=DATA_DIR,
+    file_list=TRAIN_FILE_LIST_PATH,
+    label_list=None,
+    transforms=train_transforms,
+    num_workers=0,
+    shuffle=True,
+    with_seg_labels=False,
+    binarize_labels=True)
+
+eval_dataset = pdrs.datasets.CDDataset(
+    data_dir=DATA_DIR,
+    file_list=EVAL_FILE_LIST_PATH,
+    label_list=None,
+    transforms=eval_transforms,
+    num_workers=0,
+    shuffle=False,
+    with_seg_labels=False,
+    binarize_labels=True)
+
+# 使用默认参数构建ChangeFormer模型
+# 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/model_zoo.md
+# 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/change_detector.py
+model = pdrs.tasks.cd.ChangeFormer()
+
+# 执行模型训练
+model.train(
+    num_epochs=5,
+    train_dataset=train_dataset,
+    train_batch_size=4,
+    eval_dataset=eval_dataset,
+    save_interval_epochs=3,
+    # 每多少次迭代记录一次日志
+    log_interval_steps=50,
+    save_dir=EXP_DIR,
+    # 是否使用early stopping策略，当精度不再改善时提前终止训练
+    early_stop=False,
+    # 是否启用VisualDL日志功能
+    use_vdl=True,
+    # 指定从某个检查点继续训练
+    resume_checkpoint=None)