marshall-dteach
commented
1 year ago 应该是SwinUNet的配置中的输入img_size会固定成224，修改的话要对应修改（我记得应该是位置编码啥的有关系）
------------------ 原始邮件 ------------------ 发件人: "marshall-dteach/SwinUNet" @.>; 发送时间: 2023年4月15日(星期六) 晚上8:47 @.>; @.***>; 主题: [marshall-dteach/SwinUNet] SwinUnet可以使用[1,512,512]训练吗？ (Issue #2)
作者您好， 我正在Medicalseg上使用您基于paddlepaddle复现的模型SwinUnet， 但是我希望以更大的尺寸[1,512,512]进行训练，而不是以[1,224,224].我直接修改了配置文件中 transforms-Resize3D的size参数，但是不能正常工作。报错信息如下。所以我想寻求您的帮助，如果使用[1,512,512]进行训练，需要在您的代码里做怎样的参数修改呢？
AiStudio环境： 硬件信息 CPU:4核 AI加速卡:v100 总显存:32GB 总内存:32 GB 总硬盘:100 GB 环境配置 Python:3.7.4 框架信息 框架版本:PaddlePaddle 2.3.2
配置文件：
data_root: /tmp/data train_dataset: type: Abdomen dataset_root: abdomen/abdomen_phase0 result_dir: abdomen/abdomen_phase1 transforms: - type: Resize3D size: [1, 512, 512] mode: train num_classes: 16 val_dataset: type: Abdomen dataset_root: abdomen/abdomen_phase0 result_dir: abdomen/abdomen_phase1 num_classes: 16 transforms: - type: Resize3D size: [1, 512, 512] mode: val dataset_json_path: data/abdomen/abdomen_raw/dataset.json test_dataset: type: Abdomen dataset_root: abdomen/abdomen_phase0 result_dir: abdomen/abdomen_phase1 num_classes: 16 transforms: - type: Resize3D size: [1, 512, 512] mode: val dataset_json_path: data/abdomen/abdomen_raw/dataset.json batch_size: 48 iters: 100000 model: type: SwinUNet backbone: type: SwinTransformer_tinier_patch4_window7_224 num_classes: 16 optimizer: type: sgd momentum: 0.9 weight_decay: 1.0e-4 lr_scheduler: learning_rate: 0.10 type: PolynomialDecay end_lr: 0 power: 0.9 loss: types: - type: MixedLoss losses: - type: CrossEntropyLoss - type: DiceLoss coef: [0.4, 0.6] coef: [1] export: transforms: - type: Resize3D size: [1, 512, 512] inference_helper: type: InferenceHelper2D
报错信息：
Traceback (most recent call last): File "train.py", line 232, in <module> main(args) File "train.py", line 227, in main has_dataset_json=args.has_dataset_json) File "/home/aistudio/PaddleSeg-release-2.7/contrib/MedicalSeg/medicalseg/core/train.py", line 138, in train logits_list = model(images) File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in call return self._dygraph_call_func(*inputs, kwargs) File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func outputs = self.forward(*inputs, *kwargs) File "/home/aistudio/PaddleSeg-release-2.7/contrib/MedicalSeg/medicalseg/models/swinunet.py", line 200, in forward x, x_downsample = self.backbone(x) File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in call return self._dygraph_call_func(inputs, kwargs) File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func outputs = self.forward(*inputs, kwargs) File "/home/aistudio/PaddleSeg-release-2.7/contrib/MedicalSeg/medicalseg/models/backbones/swin_transformer.py", line 670, in forward out = self.forward_features(x) File "/home/aistudio/PaddleSeg-release-2.7/contrib/MedicalSeg/medicalseg/models/backbones/swin_transformer.py", line 662, in forward_features x = layer(x) File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in call return self._dygraph_call_func(*inputs, *kwargs) File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func outputs = self.forward(inputs, kwargs) File "/home/aistudio/PaddleSeg-release-2.7/contrib/MedicalSeg/medicalseg/models/backbones/swin_transformer.py", line 481, in forward x = blk(x) File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in call return self._dygraph_call_func(*inputs, *kwargs) File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func outputs = self.forward(inputs, *kwargs) File "/home/aistudio/PaddleSeg-release-2.7/contrib/MedicalSeg/medicalseg/models/backbones/swin_transformer.py", line 329, in forward assert L == H W, "input feature has wrong size" AssertionError: input feature has wrong size
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SurvivorNo1
LebronMX-A
dumm-kd
congcongwy51
作者您好， 我正在Medicalseg上使用您基于paddlepaddle复现的模型SwinUnet， 但是我希望以更大的尺寸[1,512,512]进行训练，而不是以[1,224,224].我直接修改了配置文件中 transforms-Resize3D的size参数，但是不能正常工作。报错信息如下。所以我想寻求您的帮助，如果使用[1,512,512]进行训练，需要在您的代码里做怎样的参数修改呢？
AiStudio环境：
配置文件：
报错信息：