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View Code? Open in Web Editor NEWPyTorch implementation of DANN (Domain-Adversarial Training of Neural Networks)
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Why this model's loss not care about the taget dataset's label
1.Classification loss
class_pred = classifier(source_feature)
class_loss = classifier_criterion(class_pred, source_label)
2. Domain loss
domain_pred = discriminator(combined_feature, alpha)
domain_source_labels = torch.zeros(source_label.shape[0]).type(torch.LongTensor)
domain_target_labels = torch.ones(target_label.shape[0]).type(torch.LongTensor)
domain_combined_label = torch.cat((domain_source_labels, domain_target_labels), 0).cuda()
domain_loss = discriminator_criterion(domain_pred, domain_combined_label)
total_loss = class_loss + domain_loss
total_loss.backward()
i've seen many implementation of DANN...but every of it just get the loss with three parts .
could you tell me why this model is not care about the loss of taget dataset's label??
optim was repeatedly defined in for loop - train.py
Regarding softmax layer
The softmax layer makes models hard to train. If you faced the same problem try to remove the last softmax layer in Classifier and Discriminator.
RuntimeError: Output shape doesn't match broadcast shape.
Setup:
$ conda list
# packages in environment at C:\Users\$username\.conda\envs\pytorch1:
#
# Name Version Build Channel
blas 1.0 mkl
brotli 1.0.9 ha925a31_2
brotlipy 0.7.0 py39h2bbff1b_1003
ca-certificates 2022.3.29 haa95532_0
certifi 2021.10.8 py39haa95532_2
cffi 1.15.0 py39h2bbff1b_1
charset-normalizer 2.0.4 pyhd3eb1b0_0
cryptography 36.0.0 py39h21b164f_0
cudatoolkit 11.3.1 h59b6b97_2
cycler 0.11.0 pyhd3eb1b0_0
fonttools 4.25.0 pyhd3eb1b0_0
freetype 2.10.4 hd328e21_0
icc_rt 2019.0.0 h0cc432a_1
icu 58.2 ha925a31_3
idna 3.3 pyhd3eb1b0_0
intel-openmp 2021.4.0 haa95532_3556
joblib 1.1.0 pyhd3eb1b0_0
jpeg 9d h2bbff1b_0
kiwisolver 1.3.2 py39hd77b12b_0
libpng 1.6.37 h2a8f88b_0
libtiff 4.2.0 hd0e1b90_0
libuv 1.40.0 he774522_0
libwebp 1.2.2 h2bbff1b_0
lz4-c 1.9.3 h2bbff1b_1
matplotlib 3.5.1 py39haa95532_1
matplotlib-base 3.5.1 py39hd77b12b_1
mkl 2021.4.0 haa95532_640
mkl-service 2.4.0 py39h2bbff1b_0
mkl_fft 1.3.1 py39h277e83a_0
mkl_random 1.2.2 py39hf11a4ad_0
munkres 1.1.4 py_0
numpy 1.21.5 py39ha4e8547_0
numpy-base 1.21.5 py39hc2deb75_0
openssl 1.1.1n h2bbff1b_0
packaging 21.3 pyhd3eb1b0_0
pillow 9.0.1 py39hdc2b20a_0
pip 21.2.4 py39haa95532_0
pycparser 2.21 pyhd3eb1b0_0
pyopenssl 22.0.0 pyhd3eb1b0_0
pyparsing 3.0.4 pyhd3eb1b0_0
pyqt 5.9.2 py39hd77b12b_6
pysocks 1.7.1 py39haa95532_0
python 3.9.11 h6244533_1
python-dateutil 2.8.2 pyhd3eb1b0_0
pytorch 1.11.0 py3.9_cuda11.3_cudnn8_0 pytorch
pytorch-mutex 1.0 cuda pytorch
qt 5.9.7 vc14h73c81de_0
requests 2.27.1 pyhd3eb1b0_0
scikit-learn 1.0.2 py39hf11a4ad_1
scipy 1.7.3 py39h0a974cb_0
setuptools 58.0.4 py39haa95532_0
sip 4.19.13 py39hd77b12b_0
six 1.16.0 pyhd3eb1b0_1
sqlite 3.38.0 h2bbff1b_0
threadpoolctl 2.2.0 pyh0d69192_0
tk 8.6.11 h2bbff1b_0
torchaudio 0.11.0 py39_cu113 pytorch
torchvision 0.12.0 py39_cu113 pytorch
tornado 6.1 py39h2bbff1b_0
typing_extensions 4.1.1 pyh06a4308_0
tzdata 2021e hda174b7_0
urllib3 1.26.8 pyhd3eb1b0_0
vc 14.2 h21ff451_1
vs2015_runtime 14.27.29016 h5e58377_2
wheel 0.37.1 pyhd3eb1b0_0
win_inet_pton 1.1.0 py39haa95532_0
wincertstore 0.2 py39haa95532_2
xz 5.2.5 h62dcd97_0
zlib 1.2.11 hbd8134f_5
zstd 1.4.9 h19a0ad4_0Error received;
Running GPU : 0
Source-only training
Epoch : 0
Traceback (most recent call last):
File "E:\GitProjects\PyTorchTests\MNIST-m_DANN\pytorch_DANN\main.py", line 30, in <module>
main()
File "E:\GitProjects\PyTorchTests\MNIST-m_DANN\pytorch_DANN\main.py", line 22, in main
train.source_only(encoder, classifier, source_train_loader, target_train_loader, save_name)
File "E:\GitProjects\PyTorchTests\MNIST-m_DANN\pytorch_DANN\train.py", line 34, in source_only
for batch_idx, (source_data, target_data) in enumerate(zip(source_train_loader, target_train_loader)):
File "C:\Users\$username\.conda\envs\pytorch1\lib\site-packages\torch\utils\data\dataloader.py", line 530, in __next__
data = self._next_data()
File "C:\Users\$username\.conda\envs\pytorch1\lib\site-packages\torch\utils\data\dataloader.py", line 570, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "C:\Users\$username\.conda\envs\pytorch1\lib\site-packages\torch\utils\data\_utils\fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Users\$username\.conda\envs\pytorch1\lib\site-packages\torch\utils\data\_utils\fetch.py", line 49, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Users\$username\.conda\envs\pytorch1\lib\site-packages\torchvision\datasets\mnist.py", line 145, in __getitem__
img = self.transform(img)
File "C:\Users\$username\.conda\envs\pytorch1\lib\site-packages\torchvision\transforms\transforms.py", line 95, in __call__
img = t(img)
File "C:\Users\$username\.conda\envs\pytorch1\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "C:\Users\$username\.conda\envs\pytorch1\lib\site-packages\torchvision\transforms\transforms.py", line 270, in forward
return F.normalize(tensor, self.mean, self.std, self.inplace)
File "C:\Users\$username\.conda\envs\pytorch1\lib\site-packages\torchvision\transforms\functional.py", line 363, in normalize
tensor.sub_(mean).div_(std)
RuntimeError: output with shape [1, 28, 28] doesn't match the broadcast shape [3, 28, 28]d
why use cross entropy loss and not BCE loss?
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