diff --git a/UNet/old_Train_model.sh b/UNet/#Train_model.sh# similarity index 69% rename from UNet/old_Train_model.sh rename to UNet/#Train_model.sh# index e3c351e697ab96f69bb2c00b0f445ed962a75433..a1d7573ff67c4b081dce70e53860045ac1496ced 100644 --- a/UNet/old_Train_model.sh +++ b/UNet/#Train_model.sh# @@ -1,21 +1,23 @@ #!/usr/local_rwth/bin/zsh ### Project account -#SBATCH --account=thes1075 +#SBATCH --account=rwth0744 ### Cluster Partition #SBATCH --partition=c18g #SBATCH -J training_model -#SBATCH -o Sim_logs/UNet_V1_%J.log +#SBATCH -o Sim_logs/UNet_64_V14_%J.log #SBATCH --gres=gpu:1 #SBATCH --time=90:00:00 ### Request memory you need for your job in MB -#SBATCH --mem-per-cpu=10000 +#SBATCH --mem-per-cpu=15000 #SBATCH --mem-per-gpu=16000 module load cuda module load python/3.7.11 pip3 install --user -Iv -q torch==1.10.1 -time python3 ./UNet_V1.py +#time python3 ./UNet_V12.py +#time python3 ./UNet_V13.py +time python3 ./UNet_V14.py #print GPU Information #$CUDA_ROOT/extras/demo_suite/deviceQuery -noprompt diff --git a/UNet/.#Train_model.sh b/UNet/.#Train_model.sh new file mode 120000 index 0000000000000000000000000000000000000000..d0dfaea869caedd8207c2c126db76e1ea917a51f --- /dev/null +++ b/UNet/.#Train_model.sh @@ -0,0 +1 @@ +yk138599@login18-x-1.hpc.itc.rwth-aachen.de.71560:1644816141 \ No newline at end of file diff --git a/UNet/2_Train_model.sh b/UNet/2_Train_model.sh index 77deaabbb09bc91efef7c2360d7df51c38288f97..2bd0b5d45b92e05ea5f349bd249a9ae810bd0a19 100644 --- a/UNet/2_Train_model.sh +++ b/UNet/2_Train_model.sh @@ -6,7 +6,7 @@ #SBATCH --partition=c18g #SBATCH -J training_model -#SBATCH -o Sim_logs/UNet_V9_1_%J.log +#SBATCH -o Sim_logs/UNet_V10_%J.log #SBATCH --gres=gpu:1 #SBATCH --time=50:00:00 @@ -17,6 +17,6 @@ module load cuda module load python/3.7.11 echo "9.1 k=7 lr=1e-06" pip3 install --user -Iv -q torch==1.10.1 -time python3 ./UNet_V9_1.py +time python3 ./UNet_V10.py #print GPU Information #$CUDA_ROOT/extras/demo_suite/deviceQuery -noprompt diff --git a/UNet/Sim_logs/UNet_64_V12_25613707.log b/UNet/Sim_logs/UNet_64_V12_25613707.log new file mode 100644 index 0000000000000000000000000000000000000000..f56025323deea037f4cda0a0918777a8be8234d2 --- /dev/null +++ b/UNet/Sim_logs/UNet_64_V12_25613707.log @@ -0,0 +1,520 @@ +(OK) Loading cuda 10.2.89 +(OK) Loading python 3.7.11 +(!!) 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val_loss: 0.033977, val_acc: 21.191273 +Epoch [494], train_loss: 0.021018, val_loss: 0.033479, val_acc: 21.808832 +Epoch [495], train_loss: 0.021050, val_loss: 0.033667, val_acc: 21.455084 +Epoch [496], train_loss: 0.020994, val_loss: 0.033534, val_acc: 21.676916 +Epoch [497], train_loss: 0.021076, val_loss: 0.033944, val_acc: 21.197302 +Epoch [498], train_loss: 0.020961, val_loss: 0.033739, val_acc: 21.529356 +Epoch [499], train_loss: 0.020999, val_loss: 0.033579, val_acc: 21.536421 +python3 ./UNet_V12.py 53311.68s user 52238.68s system 99% cpu 29:20:00.40 total diff --git a/UNet/Sim_logs/UNet_64_V12_25614663.log b/UNet/Sim_logs/UNet_64_V12_25614663.log new file mode 100644 index 0000000000000000000000000000000000000000..29d23bf339e625e0ae5adb38d1fe8741ae15fac3 --- /dev/null +++ b/UNet/Sim_logs/UNet_64_V12_25614663.log @@ -0,0 +1,46 @@ +(OK) Loading cuda 10.2.89 +(OK) Loading python 3.7.11 +(!!) The SciPy Stack is available: http://www.scipy.org/stackspec.html + Built with GCC compilers. +Collecting torch==1.10.1 + Using cached torch-1.10.1-cp37-cp37m-manylinux1_x86_64.whl (881.9 MB) +Collecting typing-extensions + Using cached typing_extensions-4.1.1-py3-none-any.whl (26 kB) +Installing collected packages: typing-extensions, torch + WARNING: The scripts convert-caffe2-to-onnx, convert-onnx-to-caffe2 and torchrun are installed in '/home/yk138599/.local/bin' which is not on PATH. + Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. +Successfully installed torch-1.10.1 typing-extensions-4.1.1 +WARNING: You are using pip version 21.2.4; however, version 22.0.3 is available. +You should consider upgrading via the '/usr/local_rwth/sw/python/3.7.11/x86_64/bin/python3.7 -m pip install --upgrade pip' command. +number auf epochs: 500 +batchsize: 32 +learning rate: 3e-05 +kernel size is: 9 + seed is: 2518441936 +Traceback (most recent call last): + File "./UNet_V12.py", line 250, in <module> + history = fit(num_epochs, lr, model, train_dl, valid_dl,f'{path_to_rep}/UNet/output', opt_func) + File "./UNet_V12.py", line 165, in fit + loss = model.training_step(batch) + File "./UNet_V12.py", line 108, in training_step + out = self(input) # Generate predictions + File "/home/yk138599/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl + return forward_call(*input, **kwargs) + File "./UNet_V12.py", line 147, in forward + out = self.decoder(enc_ftrs[::-1][0], enc_ftrs[::-1][1:]) + File "/home/yk138599/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl + return forward_call(*input, **kwargs) + File "./UNet_V12.py", line 93, in forward + x = self.dec_blocks[i](x) + File "/home/yk138599/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl + return forward_call(*input, **kwargs) + File "./UNet_V12.py", line 29, in forward + x = self.batch_norm_1(self.relu(self.pointwise_1(self.depthwise_1(x)))) + File "/home/yk138599/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl + return forward_call(*input, **kwargs) + File "/home/yk138599/.local/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 590, in forward + return self._conv_forward(input, self.weight, self.bias) + File "/home/yk138599/.local/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 586, in _conv_forward + input, weight, bias, self.stride, self.padding, self.dilation, self.groups +RuntimeError: CUDA out of memory. Tried to allocate 512.00 MiB (GPU 0; 15.78 GiB total capacity; 14.15 GiB already allocated; 280.50 MiB free; 14.16 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF +python3 ./UNet_V12.py 4.92s user 5.82s system 16% cpu 1:06.40 total diff --git a/UNet/Sim_logs/UNet_64_V13_25614318.log b/UNet/Sim_logs/UNet_64_V13_25614318.log new file mode 100644 index 0000000000000000000000000000000000000000..e8371e6132b07b777ef4e56fa76ca95625257f57 --- /dev/null +++ b/UNet/Sim_logs/UNet_64_V13_25614318.log @@ -0,0 +1,38 @@ +(OK) Loading cuda 10.2.89 +(OK) Loading python 3.7.11 +(!!) The SciPy Stack is available: http://www.scipy.org/stackspec.html + Built with GCC compilers. +Collecting torch==1.10.1 + Using cached torch-1.10.1-cp37-cp37m-manylinux1_x86_64.whl (881.9 MB) +Collecting typing-extensions + Using cached typing_extensions-4.1.1-py3-none-any.whl (26 kB) +Installing collected packages: typing-extensions, torch + WARNING: The scripts convert-caffe2-to-onnx, convert-onnx-to-caffe2 and torchrun are installed in '/home/yk138599/.local/bin' which is not on PATH. + Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. +Successfully installed torch-1.10.1 typing-extensions-4.1.1 +WARNING: You are using pip version 21.2.4; however, version 22.0.3 is available. +You should consider upgrading via the '/usr/local_rwth/sw/python/3.7.11/x86_64/bin/python3.7 -m pip install --upgrade pip' command. +number auf epochs: 500 +batchsize: 16 +learning rate: 3e-05 +kernel size is: 9 + seed is: 2628832979 +Epoch [0], train_loss: 0.271159, val_loss: 0.268970, val_acc: 0.014685 +Epoch [1], train_loss: 0.269396, val_loss: 0.270057, val_acc: 0.073417 +Epoch [2], train_loss: 0.268085, val_loss: 0.279366, val_acc: 0.120659 +Epoch [3], train_loss: 0.266099, val_loss: 0.254583, val_acc: 0.435022 +Epoch [4], train_loss: 0.263552, val_loss: 0.256586, val_acc: 0.376657 +Epoch [5], train_loss: 0.261619, val_loss: 0.242178, val_acc: 0.313965 +Epoch [6], train_loss: 0.260539, val_loss: 0.247519, val_acc: 0.305485 +Epoch [7], train_loss: 0.259419, val_loss: 0.248480, val_acc: 0.254837 +Epoch [8], train_loss: 0.258631, val_loss: 0.247978, val_acc: 0.210317 +Epoch [9], train_loss: 0.257922, val_loss: 0.255808, val_acc: 0.172337 +Epoch [10], train_loss: 0.257285, val_loss: 0.252549, val_acc: 0.182081 +Epoch [11], train_loss: 0.256655, val_loss: 0.258195, val_acc: 0.166881 +Epoch [12], train_loss: 0.256037, val_loss: 0.265417, val_acc: 0.211055 +Epoch [13], train_loss: 0.255511, val_loss: 0.254048, val_acc: 0.176106 +Epoch [14], train_loss: 0.254910, val_loss: 0.249992, val_acc: 0.237055 +Epoch [15], train_loss: 0.254372, val_loss: 0.251587, val_acc: 0.127559 +Epoch [16], train_loss: 0.253764, val_loss: 0.260919, val_acc: 0.167581 +Epoch [17], train_loss: 0.253268, val_loss: 0.259768, val_acc: 0.206201 +python3 ./UNet_V13.py 1570.35s user 1560.30s system 96% cpu 53:54.66 total diff --git a/UNet/Sim_logs/UNet_V9_1_25611080.log b/UNet/Sim_logs/UNet_64_V13_25614634.log similarity index 69% rename from UNet/Sim_logs/UNet_V9_1_25611080.log rename to UNet/Sim_logs/UNet_64_V13_25614634.log index 0bc401d89ac2e7761fead0617ffac115edc92959..67b01f6da2c580256eb6c6b3ad669a9f695ed1c4 100644 --- a/UNet/Sim_logs/UNet_V9_1_25611080.log +++ b/UNet/Sim_logs/UNet_64_V13_25614634.log @@ -2,7 +2,6 @@ (OK) Loading python 3.7.11 (!!) The SciPy Stack is available: http://www.scipy.org/stackspec.html Built with GCC compilers. -9.1 k=7 lr=1e-06 Collecting torch==1.10.1 Using cached torch-1.10.1-cp37-cp37m-manylinux1_x86_64.whl (881.9 MB) Collecting typing-extensions @@ -13,5 +12,10 @@ Installing collected packages: typing-extensions, torch Successfully installed torch-1.10.1 typing-extensions-4.1.1 WARNING: You are using pip version 21.2.4; however, version 22.0.3 is available. You should consider upgrading via the '/usr/local_rwth/sw/python/3.7.11/x86_64/bin/python3.7 -m pip install --upgrade pip' command. -python3: can't open file './UNet_V9_1py': [Errno 2] No such file or directory -python3 ./UNet_V9_1py 0.02s user 0.00s system 43% cpu 0.052 total +Traceback (most recent call last): + File "./UNet_V14.py", line 10, in <module> + import torch + File "/home/yk138599/.local/lib/python3.7/site-packages/torch/__init__.py", line 197, in <module> + from torch._C import * # noqa: F403 +ImportError: /home/yk138599/.local/lib/python3.7/site-packages/torch/lib/libtorch_cuda.so: cannot read file data +python3 ./UNet_V14.py 0.14s user 0.06s system 47% cpu 0.420 total diff --git a/UNet/Sim_logs/UNet_64_V14_25617675.log b/UNet/Sim_logs/UNet_64_V14_25617675.log new file mode 100644 index 0000000000000000000000000000000000000000..ac6eb10c86cae42afacf8afec9d347cc803cf6d8 --- /dev/null +++ b/UNet/Sim_logs/UNet_64_V14_25617675.log @@ -0,0 +1,47 @@ +(OK) Loading cuda 10.2.89 +(OK) Loading python 3.7.11 +(!!) The SciPy Stack is available: http://www.scipy.org/stackspec.html + Built with GCC compilers. +Collecting torch==1.10.1 + Using cached torch-1.10.1-cp37-cp37m-manylinux1_x86_64.whl (881.9 MB) +Collecting typing-extensions + Using cached typing_extensions-4.1.1-py3-none-any.whl (26 kB) +Installing collected packages: typing-extensions, torch + WARNING: The scripts convert-caffe2-to-onnx, convert-onnx-to-caffe2 and torchrun are installed in '/home/yk138599/.local/bin' which is not on PATH. + Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. +Successfully installed torch-1.10.1 typing-extensions-4.1.1 +WARNING: You are using pip version 21.2.4; however, version 22.0.3 is available. +You should consider upgrading via the '/usr/local_rwth/sw/python/3.7.11/x86_64/bin/python3.7 -m pip install --upgrade pip' command. +number auf epochs: 500 +batchsize: 32 +learning rate: 3e-05 +kernel size is: 9 + seed is: 1197567716 +Traceback (most recent call last): + File "./UNet_V14.py", line 249, in <module> +Traceback (most recent call last): + File "/rwthfs/rz/SW/UTIL.common/Python/3.7.11/x86_64/lib/python3.7/multiprocessing/queues.py", line 242, in _feed + send_bytes(obj) + File "/rwthfs/rz/SW/UTIL.common/Python/3.7.11/x86_64/lib/python3.7/multiprocessing/connection.py", line 200, in send_bytes + self._send_bytes(m[offset:offset + size]) + File "/rwthfs/rz/SW/UTIL.common/Python/3.7.11/x86_64/lib/python3.7/multiprocessing/connection.py", line 404, in _send_bytes + self._send(header + buf) + File "/rwthfs/rz/SW/UTIL.common/Python/3.7.11/x86_64/lib/python3.7/multiprocessing/connection.py", line 368, in _send + n = write(self._handle, buf) +BrokenPipeError: [Errno 32] Broken pipe + history = fit(num_epochs, lr, model, train_dl, valid_dl,f'{path_to_rep}/UNet/output', opt_func) + File "./UNet_V14.py", line 163, in fit + for batch in train_loader: + File "./UNet_V14.py", line 201, in __iter__ + yield to_device(b, self.device) + File "./UNet_V14.py", line 189, in to_device + return [to_device(x, device) for x in data] + File "./UNet_V14.py", line 189, in <listcomp> + return [to_device(x, device) for x in data] + File "./UNet_V14.py", line 190, in to_device + return data.to(device, non_blocking=True) + File "/home/yk138599/.local/lib/python3.7/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler + _error_if_any_worker_fails() +RuntimeError: DataLoader worker (pid 53817) is killed by signal: Killed. +python3 ./UNet_V14.py 6.29s user 14.51s system 17% cpu 2:00.50 total +slurmstepd: error: Detected 1 oom-kill event(s) in step 25617675.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler. diff --git a/UNet/Sim_logs/UNet_64_V14_25621929.log b/UNet/Sim_logs/UNet_64_V14_25621929.log new file mode 100644 index 0000000000000000000000000000000000000000..b0e5b7de331116b7ff4290d8c850441576782811 --- /dev/null +++ b/UNet/Sim_logs/UNet_64_V14_25621929.log @@ -0,0 +1,35 @@ +(OK) Loading cuda 10.2.89 +(OK) Loading python 3.7.11 +(!!) The SciPy Stack is available: http://www.scipy.org/stackspec.html + Built with GCC compilers. +Collecting torch==1.10.1 + Using cached torch-1.10.1-cp37-cp37m-manylinux1_x86_64.whl (881.9 MB) +Collecting typing-extensions + Using cached typing_extensions-4.1.1-py3-none-any.whl (26 kB) +Installing collected packages: typing-extensions, torch + WARNING: The scripts convert-caffe2-to-onnx, convert-onnx-to-caffe2 and torchrun are installed in '/home/yk138599/.local/bin' which is not on PATH. + Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. +Successfully installed torch-1.10.1 typing-extensions-4.1.1 +WARNING: You are using pip version 21.2.4; however, version 22.0.3 is available. +You should consider upgrading via the '/usr/local_rwth/sw/python/3.7.11/x86_64/bin/python3.7 -m pip install --upgrade pip' command. +number auf epochs: 500 +batchsize: 32 +learning rate: 3e-05 +kernel size is: 9 + seed is: 1383180841 +Traceback (most recent call last): + File "./UNet_V14.py", line 249, in <module> + history = fit(num_epochs, lr, model, train_dl, valid_dl,f'{path_to_rep}/UNet/output', opt_func) + File "./UNet_V14.py", line 170, in fit + result = evaluate(model, val_loader) + File "/home/yk138599/.local/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context + return func(*args, **kwargs) + File "./UNet_V14.py", line 153, in evaluate + outputs = [model.validation_step(batch) for batch in val_loader] + File "./UNet_V14.py", line 153, in <listcomp> + outputs = [model.validation_step(batch) for batch in val_loader] + File "./UNet_V14.py", line 115, in validation_step + acc = accuracy(out.detach(), labels.detach()) # Calculate accuracy +TypeError: accuracy() missing 1 required positional argument: 'normalization' +terminate called without an active exception +python3 ./UNet_V14.py 42.18s user 50.52s system 45% cpu 3:24.39 total diff --git a/UNet/Sim_logs/UNet_64_V14_25622923.log b/UNet/Sim_logs/UNet_64_V14_25622923.log new file mode 100644 index 0000000000000000000000000000000000000000..9246fc361e5d6c0e9fb47777952f5f57b889564a --- /dev/null +++ b/UNet/Sim_logs/UNet_64_V14_25622923.log @@ -0,0 +1,530 @@ +(OK) Loading cuda 10.2.89 +(OK) Loading python 3.7.11 +(!!) 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b/UNet/Sim_logs/UNet_64_V15_25617886.log @@ -0,0 +1,1020 @@ +(OK) Loading cuda 10.2.89 +(OK) Loading python 3.7.11 +(!!) 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cuda 10.2.89 +(OK) Loading python 3.7.11 +(!!) 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train_loss: 0.076035, val_loss: 0.076118, val_acc: 22.878208 +Epoch [990], train_loss: 0.076001, val_loss: 0.073679, val_acc: 23.062403 +Epoch [991], train_loss: 0.076040, val_loss: 0.074624, val_acc: 22.976173 +Epoch [992], train_loss: 0.075915, val_loss: 0.075120, val_acc: 22.965166 +Epoch [993], train_loss: 0.075918, val_loss: 0.074991, val_acc: 22.925604 +Epoch [994], train_loss: 0.075855, val_loss: 0.073124, val_acc: 23.210239 +Epoch [995], train_loss: 0.075882, val_loss: 0.074131, val_acc: 22.986351 +Epoch [996], train_loss: 0.075701, val_loss: 0.076325, val_acc: 22.835835 +Epoch [997], train_loss: 0.075737, val_loss: 0.076386, val_acc: 22.930254 +Epoch [998], train_loss: 0.075710, val_loss: 0.077822, val_acc: 22.755323 +Epoch [999], train_loss: 0.075787, val_loss: 0.072759, val_acc: 23.129541 +python3 ./UNet_V9_1.py 22986.71s user 21373.45s system 99% cpu 12:19:42.33 total diff --git a/UNet/Train_model.sh b/UNet/Train_model.sh index c9969a09599aa13ae9ab0c32bfa74966a80beb7b..c609f15161b36418fd589aa0d4b368f191fb9641 100644 --- a/UNet/Train_model.sh +++ b/UNet/Train_model.sh @@ -6,19 +6,18 @@ #SBATCH --partition=c18g #SBATCH -J training_model -#SBATCH -o Sim_logs/UNet_32_V9_V10_J.log +#SBATCH -o Sim_logs/UNet_64_V14_%J.log #SBATCH --gres=gpu:1 #SBATCH --time=90:00:00 ### Request memory you need for your job in MB -#SBATCH --mem-per-cpu=10000 +#SBATCH --mem-per-cpu=20000 #SBATCH --mem-per-gpu=16000 module load cuda module load python/3.7.11 pip3 install --user -Iv -q torch==1.10.1 -time python3 ./UNet_V9_1.py -time python3 ./UNet_V9_2.py -time python3 ./UNet_V9_3.py -time python3 ./UNet_V10.py +#time python3 ./UNet_V12.py +#time python3 ./UNet_V13.py +time python3 ./UNet_V14.py #print GPU Information #$CUDA_ROOT/extras/demo_suite/deviceQuery -noprompt diff --git a/UNet/Train_model15.sh b/UNet/Train_model15.sh new file mode 100644 index 0000000000000000000000000000000000000000..cf9477de13891c1ff3c2c25f29cefb774478a102 --- /dev/null +++ b/UNet/Train_model15.sh @@ -0,0 +1,23 @@ +#!/usr/local_rwth/bin/zsh +### Project account +#SBATCH --account=rwth0744 + +### Cluster Partition +#SBATCH --partition=c18g + +#SBATCH -J training_model +#SBATCH -o Sim_logs/UNet_64_V15_%J.log + +#SBATCH --gres=gpu:1 +#SBATCH --time=90:00:00 +### Request memory you need for your job in MB +#SBATCH --mem-per-cpu=15000 +#SBATCH --mem-per-gpu=16000 +module load cuda +module load python/3.7.11 +pip3 install --user -Iv -q torch==1.10.1 +#time python3 ./UNet_V12.py +#time python3 ./UNet_V13.py +time python3 ./UNet_V15.py +#print GPU Information +#$CUDA_ROOT/extras/demo_suite/deviceQuery -noprompt diff --git a/UNet/Train_model2.sh b/UNet/Train_model2.sh new file mode 100644 index 0000000000000000000000000000000000000000..8588d548a921811924c8208a82e9868ed73f146f --- /dev/null +++ b/UNet/Train_model2.sh @@ -0,0 +1,23 @@ +#!/usr/local_rwth/bin/zsh +### Project account +#SBATCH --account=rwth0744 + +### Cluster Partition +#SBATCH --partition=c18g + +#SBATCH -J training_model +#SBATCH -o Sim_logs/UNet_64_V16_%J.log + +#SBATCH --gres=gpu:1 +#SBATCH --time=90:00:00 +### Request memory you need for your job in MB +#SBATCH --mem-per-cpu=20000 +#SBATCH --mem-per-gpu=16000 +module load cuda +module load python/3.7.11 +pip3 install --user -Iv -q torch==1.10.1 +time python3 ./UNet_V16.py +#time python3 ./UNet_V13.py +#time python3 ./UNet_V14.py +#print GPU Information +#$CUDA_ROOT/extras/demo_suite/deviceQuery -noprompt diff --git a/UNet/UNet_V10.py b/UNet/UNet_V10.py index ed8320cc84c52d9d3eba9e067282eb266e71a0ad..05519330a19cd212e2d7f9fb93290f19879af87d 100644 --- a/UNet/UNet_V10.py +++ b/UNet/UNet_V10.py @@ -224,11 +224,11 @@ if __name__ == '__main__': path_to_rep = '/home/yk138599/Hiwi/damask3' use_seeds = True seed = 2193910023 - num_epochs = 500 + num_epochs = 230 b_size = 32 opt_func = torch.optim.Adam - lr = 0.00001 - kernel = 5 + lr = 0.00003 + kernel = 7 print(f'number auf epochs: {num_epochs}') print(f'batchsize: {b_size}') print(f'learning rate: {lr}') diff --git a/UNet/UNet_V12.py b/UNet/UNet_V12.py index 122915551774b1c8662f3f27e66a485ec1024e96..f4d06e02744e7606f0ab77be748c80a780c612ba 100644 --- a/UNet/UNet_V12.py +++ b/UNet/UNet_V12.py @@ -134,7 +134,7 @@ def accuracy(outputs, labels,normalization, threshold = 0.05): return percentage class UNet(UNetBase): - def __init__(self,kernel_size = 9, enc_chs=((6,16,32), (32,32,64), (64,64,128)), dec_chs_up=(128, 128, 64), dec_chs_conv=((192,128, 128),(160,64,64),(70,32,32)),normalization=np.array([0,1])): + def __init__(self,kernel_size = 9, enc_chs=((2,16,32), (32,32,64), (64,64,128)), dec_chs_up=(128, 128, 64), dec_chs_conv=((192,128, 128),(160,64,64),(66,32,32)),normalization=np.array([0,1])): super().__init__() self.encoder = Encoder(kernel_size = kernel_size, chs = enc_chs) self.decoder = Decoder(kernel_size = kernel_size, chs_upsampling = dec_chs_up, chs_conv = dec_chs_conv) @@ -226,7 +226,7 @@ if __name__ == '__main__': use_seeds = False seed = 373686838 num_epochs = 500 - b_size = 8 + b_size = 32 opt_func = torch.optim.Adam lr = 0.00003 kernel = 9 @@ -241,8 +241,8 @@ if __name__ == '__main__': random.seed(seed) np.random.seed(seed) device = get_default_device() - normalization = np.load(f'{path_to_rep}/UNet/Trainingsdata/Norm_min_max_64_angles.npy', allow_pickle = True) - train_dl, valid_dl = Create_Dataloader(f'{path_to_rep}/UNet/Trainingsdata/TD_norm_64_angles.pt', batch_size= b_size ) + normalization = np.load(f'{path_to_rep}/UNet/Trainingsdata/Norm_min_max_64_phase.npy', allow_pickle = True) + train_dl, valid_dl = Create_Dataloader(f'{path_to_rep}/UNet/Trainingsdata/TD_norm_64_phase.pt', batch_size= b_size ) train_dl = DeviceDataLoader(train_dl, device) valid_dl = DeviceDataLoader(valid_dl, device) diff --git a/UNet/UNet_V13.py b/UNet/UNet_V13.py index 9e961c38534944e82f011f6fe4003d7e10d67f2b..c4960ceb6d6ca8fb5ad5a6e6d283ca3eeeed72af 100644 --- a/UNet/UNet_V13.py +++ b/UNet/UNet_V13.py @@ -134,7 +134,7 @@ def accuracy(outputs, labels,normalization, threshold = 0.05): return percentage class UNet(UNetBase): - def __init__(self,kernel_size = 5, enc_chs=((6,6,16), (16,16,32), (32,32,64), (64,128,128)), dec_chs_up=(192, 256, 128, 64), dec_chs_conv=((192,64,64),(96,32,32),(48,16,16),(22,16,16)),normalization=np.array([0,1])): + def __init__(self,kernel_size = 5, enc_chs=((6,6,16), (16,16,32), (32,32,64), (64,128,128)), dec_chs_up=(128, 128, 64, 32), dec_chs_conv=((192,128,128),(160,64,64),(80,32,32),(38,16,1)),normalization=np.array([0,1])): super().__init__() self.encoder = Encoder(kernel_size = kernel_size, chs = enc_chs) self.decoder = Decoder(kernel_size = kernel_size, chs_upsampling = dec_chs_up, chs_conv = dec_chs_conv) @@ -172,8 +172,8 @@ def fit(epochs, lr, model, train_loader, val_loader, path, opt_func=torch.optim. result['train_loss'] = torch.stack(train_losses).mean().item() model.epoch_end(epoch, result) history.append(result) - torch.save(model.state_dict(),f'{path}/Unet_dict_V13.pth') - torch.save(history,f'{path}/history_V13.pt') + torch.save(model.state_dict(),f'{path}/Unet_dict_V13_2.pth') + torch.save(history,f'{path}/history_V13_2.pt') return history def get_default_device(): @@ -226,7 +226,7 @@ if __name__ == '__main__': use_seeds = False seed = 373686838 num_epochs = 500 - b_size = 8 + b_size = 16 opt_func = torch.optim.Adam lr = 0.00003 kernel = 9 diff --git a/UNet/UNet_V14.py b/UNet/UNet_V14.py index e32d3668177d6adaa1dbe0ed6e456d276aba02d0..708b27da78319d4d85037bab8c6c93ccca3caccf 100644 --- a/UNet/UNet_V14.py +++ b/UNet/UNet_V14.py @@ -112,7 +112,7 @@ class UNetBase(nn.Module): input, labels = batch out = self(input) # Generate predictions loss = F.l1_loss(out, labels) # Calculate loss - acc = accuracy(out.detach(), labels.detach()) # Calculate accuracy + acc = accuracy(out.detach(), labels.detach(),self.normalization) # Calculate accuracy return {'val_loss': loss.detach(), 'val_acc': acc} def validation_epoch_end(self, outputs): @@ -225,10 +225,10 @@ if __name__ == '__main__': use_seeds = False seed = 373686838 num_epochs = 500 - b_size = 8 + b_size = 32 opt_func = torch.optim.Adam lr = 0.00003 - kernel = 7 + kernel = 9 print(f'number auf epochs: {num_epochs}') print(f'batchsize: {b_size}') print(f'learning rate: {lr}') diff --git a/UNet/UNet_V9_1_nopadding.py b/UNet/UNet_V15.py similarity index 94% rename from UNet/UNet_V9_1_nopadding.py rename to UNet/UNet_V15.py index 592aa4048169bb301ba798f3b35d1899443b0aae..1395565b527211a328fe08a8802fd8122243afcf 100644 --- a/UNet/UNet_V9_1_nopadding.py +++ b/UNet/UNet_V15.py @@ -25,12 +25,13 @@ class depthwise_separable_conv(nn.Module): self.pointwise_1 = nn.Conv3d(in_c, out_1_c, kernel_size=1, bias=True) self.batch_norm_1 = nn.BatchNorm3d(out_1_c) self.relu = nn.ReLU() + self.droptout = nn.Dropout3d(p=0.25) self.depthwise_2 = nn.Conv3d(out_1_c, out_1_c, kernel_size= kernel_size, padding=padding[1], groups=out_1_c, bias=True) self.pointwise_2 = nn.Conv3d(out_1_c, out_2_c, kernel_size=1, bias=True) self.batch_norm_2 = nn.BatchNorm3d(out_2_c) def forward(self, x): - x = self.batch_norm_1(self.relu(self.pointwise_1(self.depthwise_1(x)))) - return self.batch_norm_2(self.relu(self.pointwise_2(self.depthwise_2(x)))) + x = self.batch_norm_1(self.relu(self.droptout(self.pointwise_1(self.depthwise_1(x))))) + return self.batch_norm_2(self.relu(self.droptout(self.pointwise_2(self.depthwise_2(x))))) class convolution_Layer(nn.Module): def __init__(self, in_c, out_1_c, out_2_c, padding, kernel_size): @@ -54,7 +55,7 @@ class head_layer(nn.Module): #return self.sig(self.pointwise(self.depthwise(x))) #convolution class Encoder(nn.Module): - def __init__(self,kernel_size, chs, padding=(("same","same"),("same","same"),("same","same"))): + def __init__(self,kernel_size, chs, padding=((0,"same"),("same","same"),("same","same"))): super().__init__() self.channels = chs self.enc_blocks = nn.ModuleList([depthwise_separable_conv(chs[i][0], chs[i][1], chs[i][2], kernel_size=kernel_size, padding=padding[i]) for i in range(len(chs))]) @@ -65,7 +66,7 @@ class Encoder(nn.Module): def forward(self, x): ftrs = [] - #x = self.periodic_upsample(x) + x = self.periodic_upsample(x) for i in range(len(self.channels)): ftrs.append(x) x =self.enc_blocks[i](x) @@ -175,8 +176,8 @@ def fit(epochs, lr, model, train_loader, val_loader, path, opt_func=torch.optim. result['train_loss'] = torch.stack(train_losses).mean().item() model.epoch_end(epoch, result) history.append(result) - torch.save(model.state_dict(),f'{path}/Unet_dict_V9_1.pth') - torch.save(history,f'{path}/history_V9_1.pt') + torch.save(model.state_dict(),f'{path}/Unet_dict_V15.pth') + torch.save(history,f'{path}/history_V15.pt') return history def get_default_device(): @@ -226,18 +227,17 @@ def Create_Dataloader(path, batch_size = 100, percent_val = 0.2): if __name__ == '__main__': #os.chdir('F:/RWTH/HiWi_IEHK/DAMASK3/UNet/Trainingsdata') path_to_rep = '/home/yk138599/Hiwi/damask3' - use_seeds = True + use_seeds = False seed = 373686838 - num_epochs = 200 + num_epochs = 1000 b_size = 32 opt_func = torch.optim.Adam - lr = 0.00001 - kernel = 5 + lr = 0.00003 + kernel = 7 print(f'number auf epochs: {num_epochs}') print(f'batchsize: {b_size}') print(f'learning rate: {lr}') print(f'kernel size is: {kernel}') - print('no reflecting padding') if not use_seeds: seed = random.randrange(2**32 - 1) print(f' seed is: {seed}') diff --git a/UNet/UNet_V16.py b/UNet/UNet_V16.py new file mode 100644 index 0000000000000000000000000000000000000000..2f80c4e1beb078620fd98885e843becf365073b5 --- /dev/null +++ b/UNet/UNet_V16.py @@ -0,0 +1,255 @@ + +"""UNet_V6.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1yvtk3lFo_x0ZiqtFdnR8jgcjPKy3nZA4 +""" + +import torch +import torch.nn as nn +import numpy as np +import random +from torch.utils.data.sampler import SubsetRandomSampler +from torch.utils.data.dataloader import DataLoader +from torch.utils.data import TensorDataset +import torch.nn.functional as F +from torch.utils.data import random_split +from torch.nn.modules.activation import ReLU + +class depthwise_separable_conv(nn.Module): + def __init__(self, in_c, out_1_c, out_2_c, padding, kernel_size): + super(depthwise_separable_conv, self).__init__() + self.depthwise_1 = nn.Conv3d(in_c, in_c, kernel_size= kernel_size, padding=padding[0], groups=in_c, bias=True) + self.pointwise_1 = nn.Conv3d(in_c, out_1_c, kernel_size=1, bias=True) + self.batch_norm_1 = nn.BatchNorm3d(out_1_c) + self.relu = nn.ReLU() + self.droptout = nn.Dropout3d(p=0.5) + + self.depthwise_2 = nn.Conv3d(out_1_c, out_1_c, kernel_size= kernel_size, padding=padding[1], groups=out_1_c, bias=True) + self.pointwise_2 = nn.Conv3d(out_1_c, out_2_c, kernel_size=1, bias=True) + self.batch_norm_2 = nn.BatchNorm3d(out_2_c) + def forward(self, x): + x = self.batch_norm_1(self.relu(self.droptout(self.pointwise_1(self.depthwise_1(x))))) + return self.batch_norm_2(self.relu(self.droptout(self.pointwise_2(self.depthwise_2(x))))) + +class convolution_Layer(nn.Module): + def __init__(self, in_c, out_1_c, out_2_c, padding, kernel_size): + super(convolution_Layer, self).__init__() + self.conv_1 = nn.Conv3d(in_c, out_1_c, kernel_size= kernel_size, padding=padding[0], bias=True) + self.batch_norm_1 = nn.BatchNorm3d(out_1_c) + self.relu = nn.ReLU() + self.conv_2 = nn.Conv3d(out_1_c, out_2_c, kernel_size= kernel_size, padding=padding[1], bias=True) + self.batch_norm_2 = nn.BatchNorm3d(out_2_c) + def forward(self, x): + x = self.batch_norm_1(self.relu(self.conv_1(x))) + return self.batch_norm_2(self.relu(self.relu(self.conv_2(x)))) + +class head_layer(nn.Module): + def __init__(self, in_c, out_c = 1, padding = "same"): + super(head_layer, self).__init__() + self.conv = nn.Conv3d(in_c, out_c, kernel_size=1, bias=True) + self.sig = nn.Sigmoid() + def forward(self, x): + return self.sig(self.conv(x)) #convolution + #return self.sig(self.pointwise(self.depthwise(x))) #convolution + +class Encoder(nn.Module): + def __init__(self,kernel_size, chs, padding=((0,"same"),("same","same"),("same","same"))): + super().__init__() + self.channels = chs + self.enc_blocks = nn.ModuleList([depthwise_separable_conv(chs[i][0], chs[i][1], chs[i][2], kernel_size=kernel_size, padding=padding[i]) for i in range(len(chs))]) + self.pool = nn.MaxPool3d(kernel_size=2, stride=2) + #self.batch_norm = nn.ModuleList([nn.BatchNorm3d( chs[i][2]) for i in range(len(chs))]) + self.periodic_upsample = nn.ReflectionPad3d(int((kernel_size-1)/2)) + + + def forward(self, x): + ftrs = [] + x = self.periodic_upsample(x) + for i in range(len(self.channels)): + ftrs.append(x) + x =self.enc_blocks[i](x) + #print(f'size of ftrs: {ftrs[i].size()}') + x = self.pool(x) + #print(f'size of x after pooling{x.size()}') + ftrs.append(x) + #print(f'size of ftrs: {ftrs[3].size()}') + #print(f'length of ftrs: {len(ftrs)}') + return ftrs + +class Decoder(nn.Module): + def __init__(self,kernel_size, chs_upsampling, chs_conv, padding=(("same","same"),("same","same"),("same","same"))): + super().__init__() + assert len(chs_conv) == len(chs_upsampling) + self.chs = chs_upsampling + self.upconvs = nn.ModuleList([nn.ConvTranspose3d(chs_upsampling[i], chs_upsampling[i], 2, 2) for i in range(len(chs_upsampling))]) + self.dec_blocks = nn.ModuleList([depthwise_separable_conv(chs_conv[i][0], chs_conv[i][1], chs_conv[i][2], kernel_size=kernel_size, padding=padding[i]) for i in range(len(chs_conv))]) + self.head = head_layer(chs_conv[-1][2]) + def forward(self, x, encoder_features): + for i in range(len(self.chs)): + x = self.upconvs[i](x) + #print(f'size after upsampling: {x.size()}') + enc_ftrs = self.crop(encoder_features[i], x) + x = torch.cat([x, enc_ftrs], dim=1) + #print(f'size after cropping&cat: {x.size()}') + + x = self.dec_blocks[i](x) + #print(f'size after convolution: {x.size()}') + x = self.head(x) + return x + + def crop(self, tensor, target_tensor): + target_size = target_tensor.size()[2] + tensor_size = tensor.size()[2] + delta = tensor_size - target_size + delta = delta // 2 + return tensor[:,:,delta:tensor_size-delta,delta:tensor_size-delta,delta:tensor_size-delta] + +class UNetBase(nn.Module): + def training_step(self, batch): + input, labels = batch + out = self(input) # Generate predictions + loss = F.l1_loss(out, labels) # Calculate loss + return loss + + def validation_step(self, batch): + input, labels = batch + out = self(input) # Generate predictions + loss = F.l1_loss(out, labels) # Calculate loss + acc = accuracy(out.detach(), labels.detach(),normalization=self.normalization) # Calculate accuracy + return {'val_loss': loss.detach(), 'val_acc': acc} + + def validation_epoch_end(self, outputs): + batch_losses = [x['val_loss'] for x in outputs] + epoch_loss = torch.stack(batch_losses).mean() # Combine losses + batch_accs = [x['val_acc'] for x in outputs] + epoch_acc = torch.stack(batch_accs).mean() # Combine accuracies + return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()} + + def epoch_end(self, epoch, result): + print("Epoch [{}], train_loss: {:.6f}, val_loss: {:.6f}, val_acc: {:.6f}".format( + epoch, result['train_loss'], result['val_loss'], result['val_acc'])) + +def accuracy(outputs, labels,normalization, threshold = 0.05): + error = (abs((outputs) - (labels)))/(outputs+normalization[0]/normalization[1]) + right_predic = torch.sum(error < threshold) + percentage = ((right_predic/torch.numel(error))*100.) + return percentage + +class UNet(UNetBase): + def __init__(self,kernel_size = 5, enc_chs=((6,16,32), (32,32,64), (64,64,128)), dec_chs_up=(128, 128, 64), dec_chs_conv=((192,128, 128),(160,64,64),(70,32,32)),normalization=np.array([0,1])): + super().__init__() + self.encoder = Encoder(kernel_size = kernel_size, chs = enc_chs) + self.decoder = Decoder(kernel_size = kernel_size, chs_upsampling = dec_chs_up, chs_conv = dec_chs_conv) + #self.head = depthwise_separable_conv(1, 1, padding = "same", kernel_size=1) + self.normalization = normalization + + + def forward(self, x): + enc_ftrs = self.encoder(x) + out = self.decoder(enc_ftrs[::-1][0], enc_ftrs[::-1][1:]) + #out = self.head(out) + return out + +@torch.no_grad() +def evaluate(model, val_loader): + model.eval() + outputs = [model.validation_step(batch) for batch in val_loader] + return model.validation_epoch_end(outputs) + +def fit(epochs, lr, model, train_loader, val_loader, path, opt_func=torch.optim.Adam): + history = [] + optimizer = opt_func(model.parameters(), lr, eps=1e-07) + for epoch in range(epochs): + # Training Phase + model.train() + train_losses = [] + for batch in train_loader: + loss = model.training_step(batch) + train_losses.append(loss) + loss.backward() + optimizer.step() + optimizer.zero_grad() + # Validation phase + result = evaluate(model, val_loader) + result['train_loss'] = torch.stack(train_losses).mean().item() + model.epoch_end(epoch, result) + history.append(result) + torch.save(model.state_dict(),f'{path}/Unet_dict_V11.pth') + torch.save(history,f'{path}/history_V11.pt') + return history + +def get_default_device(): + """Pick GPU if available, else CPU""" + if torch.cuda.is_available(): + return torch.device('cuda') + else: + print('no GPU found') + return torch.device('cpu') + +def to_device(data, device): + """Move tensor(s) to chosen device""" + if isinstance(data, (list,tuple)): + return [to_device(x, device) for x in data] + return data.to(device, non_blocking=True) + +class DeviceDataLoader(): + """Wrap a dataloader to move data to a device""" + def __init__(self, dl, device): + self.dl = dl + self.device = device + + def __iter__(self): + """Yield a batch of data after moving it to device""" + for b in self.dl: + yield to_device(b, self.device) + + def __len__(self): + """Number of batches""" + return len(self.dl) + +def Create_Dataloader(path, batch_size = 100, percent_val = 0.2): + dataset = torch.load(path) # create the pytorch dataset + #size_data = 500 #shrink dataset for colab + #rest = len(dataset) -size_data + #dataset,_ = torch.utils.data.random_split(dataset, [size_data, rest]) + val_size = int(len(dataset) * percent_val) + train_size = len(dataset) - val_size + + train_ds, val_ds = random_split(dataset, [train_size, val_size]) + # Create DataLoader + train_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=1, pin_memory=True) + valid_dl = DataLoader(val_ds, batch_size, num_workers=1, pin_memory=True) + + return train_dl, valid_dl + +if __name__ == '__main__': + #os.chdir('F:/RWTH/HiWi_IEHK/DAMASK3/UNet/Trainingsdata') + path_to_rep = '/home/yk138599/Hiwi/damask3' + use_seeds = True + seed = 373686838 + num_epochs = 10000 + b_size = 32 + opt_func = torch.optim.Adam + lr = 0.00003 + kernel = 7 + print(f'number auf epochs: {num_epochs}') + print(f'batchsize: {b_size}') + print(f'learning rate: {lr}') + print(f'kernel size is: {kernel}') + if not use_seeds: + seed = random.randrange(2**32 - 1) + print(f' seed is: {seed}') + torch.manual_seed(seed) + random.seed(seed) + np.random.seed(seed) + device = get_default_device() + normalization = np.load(f'{path_to_rep}/UNet/Trainingsdata/Norm_min_max_32_angles.npy', allow_pickle = True) + train_dl, valid_dl = Create_Dataloader(f'{path_to_rep}/UNet/Trainingsdata/TD_norm_32_angles.pt', batch_size= b_size ) + train_dl = DeviceDataLoader(train_dl, device) + valid_dl = DeviceDataLoader(valid_dl, device) + + model = to_device(UNet(kernel_size=kernel,normalization=normalization).double(), device) + history = fit(num_epochs, lr, model, train_dl, valid_dl,f'{path_to_rep}/UNet/output', opt_func) diff --git a/UNet/UNet_V9_1.py b/UNet/UNet_V9_1.py index 675b6d135852edcde26d9fac56b93ffe1661720a..ea851deabbf1de277ddfd6837ba07ef8cdfca4f7 100644 --- a/UNet/UNet_V9_1.py +++ b/UNet/UNet_V9_1.py @@ -54,18 +54,16 @@ class head_layer(nn.Module): #return self.sig(self.pointwise(self.depthwise(x))) #convolution class Encoder(nn.Module): - def __init__(self,kernel_size, chs, padding=((0,"same"),("same","same"),("same","same"))): + def __init__(self,kernel_size, chs, padding=(("same","same"),("same","same"),("same","same"))): super().__init__() self.channels = chs self.enc_blocks = nn.ModuleList([depthwise_separable_conv(chs[i][0], chs[i][1], chs[i][2], kernel_size=kernel_size, padding=padding[i]) for i in range(len(chs))]) self.pool = nn.MaxPool3d(kernel_size=2, stride=2) #self.batch_norm = nn.ModuleList([nn.BatchNorm3d( chs[i][2]) for i in range(len(chs))]) - self.periodic_upsample = nn.ReflectionPad3d(int((kernel_size-1)/2)) def forward(self, x): ftrs = [] - x = self.periodic_upsample(x) for i in range(len(self.channels)): ftrs.append(x) x =self.enc_blocks[i](x) diff --git a/UNet/core.ncg05.hpc.itc.rwth-aachen.de.120012.7 b/UNet/core.ncg05.hpc.itc.rwth-aachen.de.120012.7 new file mode 100644 index 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