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environment and import fix
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robogast committed Jan 11, 2022
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279 changes: 137 additions & 142 deletions environment.yml
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name: pytorch1.7
name: py38
channels:
- simpleitk
- pytorch-nightly
- conda-forge
- pytorch
- defaults
- conda-forge
dependencies:
- _libgcc_mutex=0.1=main
- _pytorch_select=0.2=gpu_0
- absl-py=0.9.0=py38h32f6830_1
- _openmp_mutex=4.5=1_gnu
- absl-py=0.15.0=pyhd3eb1b0_0
- aiohttp=3.8.1=py38h7f8727e_0
- aiosignal=1.2.0=pyhd3eb1b0_0
- async-timeout=4.0.1=pyhd3eb1b0_0
- attrs=21.2.0=pyhd3eb1b0_0
- blas=1.0=mkl
- blinker=1.4=py_1
- brotlipy=0.7.0=py38h1e0a361_1000
- blinker=1.4=py38h06a4308_0
- brotli=1.0.9=he6710b0_2
- brotlipy=0.7.0=py38h27cfd23_1003
- bzip2=1.0.8=h7b6447c_0
- c-ares=1.16.1=h516909a_2
- ca-certificates=2021.1.19=h06a4308_0
- cachetools=4.1.1=py_0
- certifi=2020.12.5=py38h06a4308_0
- cffi=1.14.1=py38he30daa8_0
- chardet=3.0.4=py38h32f6830_1006
- click=7.1.2=pyh9f0ad1d_0
- cryptography=3.0=py38h766eaa4_0
- cudatoolkit=11.0.221=h6bb024c_0
- cycler=0.10.0=py_2
- dbus=1.13.16=hb2f20db_0
- einops=0.3.0=py_0
- expat=2.2.9=he6710b0_2
- ffmpeg=4.2.2=h20bf706_0
- filelock=3.0.12=pyhd3eb1b0_1
- fontconfig=2.13.0=h9420a91_0
- freetype=2.10.4=h5ab3b9f_0
- future=0.18.2=py38h32f6830_1
- gdcm=2.8.9=py38h138f8cb_1
- glib=2.65.0=h3eb4bd4_0
- gmp=6.1.2=h6c8ec71_1
- gnutls=3.6.5=h71b1129_1002
- google-auth=1.20.1=py_0
- google-auth-oauthlib=0.4.1=py_2
- grpcio=1.31.0=py38h2c89da0_0
- gst-plugins-base=1.14.0=hbbd80ab_1
- gstreamer=1.14.0=hb31296c_0
- h5py=2.10.0=py38hd6299e0_1
- hdf5=1.10.6=hb1b8bf9_0
- c-ares=1.17.1=h27cfd23_0
- ca-certificates=2021.10.26=h06a4308_2
- cached-property=1.5.2=hd8ed1ab_1
- cached_property=1.5.2=pyha770c72_1
- cachetools=4.2.2=pyhd3eb1b0_0
- certifi=2021.10.8=py38h06a4308_0
- cffi=1.14.6=py38h400218f_0
- charset-normalizer=2.0.4=pyhd3eb1b0_0
- click=8.0.3=pyhd3eb1b0_0
- cryptography=3.4.8=py38hd23ed53_0
- cudatoolkit=11.3.1=h2bc3f7f_2
- cycler=0.11.0=pyhd3eb1b0_0
- dataclasses=0.8=pyh6d0b6a4_7
- dbus=1.13.18=hb2f20db_0
- einops=0.3.2=pyhd8ed1ab_0
- expat=2.4.1=h2531618_2
- ffmpeg=4.3=hf484d3e_0
- filelock=3.4.2=pyhd8ed1ab_1
- fontconfig=2.13.1=h6c09931_0
- fonttools=4.25.0=pyhd3eb1b0_0
- freetype=2.11.0=h70c0345_0
- frozenlist=1.2.0=py38h7f8727e_0
- fsspec=2021.10.1=pyhd3eb1b0_0
- future=0.18.2=py38_1
- giflib=5.2.1=h7b6447c_0
- glib=2.69.1=h5202010_0
- gmp=6.2.1=h2531618_2
- gnutls=3.6.15=he1e5248_0
- google-auth=1.33.0=pyhd3eb1b0_0
- google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
- grpcio=1.42.0=py38hce63b2e_0
- gst-plugins-base=1.14.0=h8213a91_2
- gstreamer=1.14.0=h28cd5cc_2
- h5py=3.6.0=py38ha0f2276_0
- hdf5=1.10.6=nompi_h3c11f04_101
- icu=58.2=he6710b0_3
- idna=2.10=pyh9f0ad1d_0
- importlib-metadata=1.7.0=py38h32f6830_0
- intel-openmp=2020.1=217
- jpeg=9b=h024ee3a_2
- json-c=0.13.1=h1bed415_0
- kiwisolver=1.2.0=py38hbf85e49_0
- idna=3.3=pyhd3eb1b0_0
- importlib-metadata=4.8.2=py38h06a4308_0
- intel-openmp=2021.4.0=h06a4308_3561
- jpeg=9d=h7f8727e_0
- kiwisolver=1.3.1=py38h2531618_0
- lame=3.100=h7b6447c_0
- lcms2=2.11=h396b838_0
- ld_impl_linux-64=2.33.1=h53a641e_7
- libedit=3.1.20181209=hc058e9b_0
- libffi=3.3=he6710b0_1
- libgcc-ng=9.1.0=hdf63c60_0
- libgfortran-ng=7.3.0=hdf63c60_0
- libjpeg-turbo=2.0.5=h516909a_0
- libopus=1.3.1=h7b6447c_0
- lcms2=2.12=h3be6417_0
- ld_impl_linux-64=2.35.1=h7274673_9
- libffi=3.3=he6710b0_2
- libgcc-ng=9.3.0=h5101ec6_17
- libgfortran-ng=7.5.0=ha8ba4b0_17
- libgfortran4=7.5.0=ha8ba4b0_17
- libgomp=9.3.0=h5101ec6_17
- libiconv=1.15=h63c8f33_5
- libidn2=2.3.2=h7f8727e_0
- libpng=1.6.37=hbc83047_0
- libprotobuf=3.13.0=h8b12597_0
- libstdcxx-ng=9.1.0=hdf63c60_0
- libtiff=4.1.0=h2733197_1
- libuuid=1.0.3=h1bed415_2
- libprotobuf=3.19.1=h4ff587b_0
- libstdcxx-ng=9.3.0=hd4cf53a_17
- libtasn1=4.16.0=h27cfd23_0
- libtiff=4.2.0=h85742a9_0
- libunistring=0.9.10=h27cfd23_0
- libuuid=1.0.3=h7f8727e_2
- libuv=1.40.0=h7b6447c_0
- libvpx=1.7.0=h439df22_0
- libwebp=1.2.0=h89dd481_0
- libwebp-base=1.2.0=h27cfd23_0
- libxcb=1.14=h7b6447c_0
- libxml2=2.9.10=he19cac6_1
- lz4-c=1.9.2=he6710b0_0
- markdown=3.2.2=py_0
- matplotlib=3.1.3=py38_0
- matplotlib-base=3.1.3=py38hef1b27d_0
- mkl=2020.1=217
- mkl-service=2.3.0=py38he904b0f_0
- mkl_fft=1.1.0=py38h23d657b_0
- mkl_random=1.1.1=py38h0573a6f_0
- ncurses=6.2=he6710b0_1
- nettle=3.4.1=hbb512f6_0
- ninja=1.9.0=py38hfd86e86_0
- numpy=1.18.1=py38h4f9e942_0
- numpy-base=1.18.1=py38hde5b4d6_1
- oauthlib=3.0.1=py_0
- olefile=0.46=py_0
- openh264=2.1.0=hd408876_0
- openjpeg=2.3.1=h981e76c_3
- openssl=1.1.1i=h27cfd23_0
- packaging=20.4=py_0
- pcre=8.44=he6710b0_0
- pillow=8.0.1=py38he98fc37_0
- protobuf=3.13.0=py38h950e882_0
- pyasn1=0.4.8=py_0
- pyasn1-modules=0.2.7=py_0
- pycparser=2.20=pyh9f0ad1d_2
- pydicom=2.1.0=pyhd3deb0d_0
- pyjwt=1.7.1=py_0
- libxml2=2.9.12=h03d6c58_0
- lz4-c=1.9.3=h295c915_1
- markdown=3.3.4=py38h06a4308_0
- matplotlib=3.5.0=py38h06a4308_0
- matplotlib-base=3.5.0=py38h3ed280b_0
- mkl=2021.4.0=h06a4308_640
- mkl-service=2.4.0=py38h7f8727e_0
- mkl_fft=1.3.1=py38hd3c417c_0
- mkl_random=1.2.2=py38h51133e4_0
- monai=0.8.0=pyhd8ed1ab_1
- multidict=5.2.0=py38h7f8727e_2
- munkres=1.1.4=py_0
- ncurses=6.3=h7f8727e_2
- nettle=3.7.3=hbbd107a_1
- numpy=1.21.2=py38h20f2e39_0
- numpy-base=1.21.2=py38h79a1101_0
- oauthlib=3.1.1=pyhd3eb1b0_0
- olefile=0.46=pyhd3eb1b0_0
- openh264=2.1.1=h4ff587b_0
- openssl=1.1.1l=h7f8727e_0
- packaging=21.3=pyhd3eb1b0_0
- pcre=8.45=h295c915_0
- pillow=8.4.0=py38h5aabda8_0
- pip=21.2.4=py38h06a4308_0
- protobuf=3.19.1=py38h295c915_0
- pyasn1=0.4.8=pyhd3eb1b0_0
- pyasn1-modules=0.2.8=py_0
- pycparser=2.21=pyhd3eb1b0_0
- pydicom=2.2.2=pyh6c4a22f_0
- pyjwt=2.1.0=py38h06a4308_0
- pynrrd=0.4.2=pyh95af2a2_0
- pyopenssl=19.1.0=py_1
- pyparsing=2.4.7=pyh9f0ad1d_0
- pyopenssl=21.0.0=pyhd3eb1b0_1
- pyparsing=3.0.4=pyhd3eb1b0_0
- pyqt=5.9.2=py38h05f1152_4
- pysocks=1.7.1=py38h32f6830_1
- python=3.8.3=hcff3b4d_0
- python-dateutil=2.8.1=py_0
- python-lmdb=0.98=py38he6710b0_0
- python_abi=3.8=1_cp38
- pytorch=1.8.0.dev20210122=py3.8_cuda11.0.221_cudnn8.0.5_0
- pyyaml=5.3.1=py38h1e0a361_0
- pysocks=1.7.1=py38h06a4308_0
- python=3.8.12=h12debd9_0
- python-dateutil=2.8.2=pyhd3eb1b0_0
- python-lmdb=0.99=py38h709712a_0
- python_abi=3.8=2_cp38
- pytorch=1.10.1=py3.8_cuda11.3_cudnn8.2.0_0
- pytorch-lightning=1.2.10=pyhd8ed1ab_0
- pytorch-mutex=1.0=cuda
- pyyaml=6.0=py38h7f8727e_1
- qt=5.9.7=h5867ecd_1
- readline=8.0=h7b6447c_0
- requests=2.24.0=pyh9f0ad1d_0
- requests-oauthlib=1.3.0=pyh9f0ad1d_0
- rsa=4.6=pyh9f0ad1d_0
- setuptools=51.0.0=py38h06a4308_2
- simpleitk=2.0rc3=py38hf484d3e_0
- readline=8.1.2=h7f8727e_0
- requests=2.27.1=pyhd3eb1b0_0
- requests-oauthlib=1.3.0=py_0
- rsa=4.7.2=pyhd3eb1b0_1
- scipy=1.7.3=py38hc147768_0
- setuptools=58.0.4=py38h06a4308_0
- sip=4.19.13=py38he6710b0_0
- six=1.15.0=py_0
- sqlite=3.31.1=h62c20be_1
- tensorboard=2.3.0=pyh4dce500_0
- tensorboard-plugin-wit=1.6.0=pyh9f0ad1d_0
- tk=8.6.8=hbc83047_0
- torchvision=0.9.0.dev20210122=py38_cu110
- tornado=6.0.4=py38h1e0a361_1
- tqdm=4.48.2=pyh9f0ad1d_0
- typing_extensions=3.7.4.3=py_0
- urllib3=1.25.10=py_0
- werkzeug=1.0.1=pyh9f0ad1d_0
- wheel=0.36.2=pyhd3eb1b0_0
- x264=1!157.20191217=h7b6447c_0
- six=1.16.0=pyhd3eb1b0_0
- sqlite=3.37.0=hc218d9a_0
- tensorboard=2.4.0=pyhc547734_0
- tensorboard-plugin-wit=1.6.0=py_0
- tk=8.6.11=h1ccaba5_0
- torchaudio=0.10.1=py38_cu113
- torchmetrics=0.6.2=pyhd8ed1ab_0
- torchvision=0.11.2=py38_cu113
- tornado=6.1=py38h27cfd23_0
- tqdm=4.62.3=pyhd3eb1b0_1
- typing-extensions=3.10.0.2=hd3eb1b0_0
- typing_extensions=3.10.0.2=pyh06a4308_0
- urllib3=1.26.7=pyhd3eb1b0_0
- werkzeug=2.0.2=pyhd3eb1b0_0
- wheel=0.37.1=pyhd3eb1b0_0
- xz=5.2.5=h7b6447c_0
- yaml=0.2.5=h516909a_0
- zipp=3.1.0=py_0
- zlib=1.2.11=h7b6447c_3
- zstd=1.4.4=h0b5b093_3
- pip:
- aiohttp==3.7.3
- async-timeout==3.0.1
- attrs==20.3.0
- dataclasses==0.6
- fairscale==0.1.5
- fsspec==0.8.4
- itk==5.1.1
- itk-core==5.1.1
- itk-filtering==5.1.1
- itk-io==5.1.1
- itk-numerics==5.1.1
- itk-registration==5.1.1
- itk-segmentation==5.1.1
- monai==0.3.0
- multidict==5.1.0
- nibabel==3.1.1
- omegaconf==2.0.5
- onnxruntime==1.6.0
- pip==21.0.1
- python-graphviz==0.16
- pytorch-lightning==1.2.0.dev0
- torchviz==0.0.1
- yarl==1.6.3
prefix: /home/robertsc/.conda/envs/pytorch1.7

- yaml=0.2.5=h7b6447c_0
- yarl=1.6.3=py38h27cfd23_0
- zipp=3.7.0=pyhd3eb1b0_0
- zlib=1.2.11=h7f8727e_4
- zstd=1.4.9=haebb681_0
prefix: /home/robertsc/.conda/envs/py38
File renamed without changes.
4 changes: 2 additions & 2 deletions pixel-model/pixelcnn.py → pixel_model/pixelcnn.py
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Expand Up @@ -17,10 +17,10 @@
from pytorch_lightning.metrics import Accuracy, Precision, Recall
from tqdm import tqdm

from layers import FixupCausalResBlock, PreActFixupCausalResBlock, input_to_stack, stack_to_output, GatedResBlock
from pixel_model.layers import FixupCausalResBlock, PreActFixupCausalResBlock, input_to_stack, stack_to_output, GatedResBlock
from pixel_model.train_helpers import idx_to_one_hot, bits_per_dim, mixup_data, mixup_criterion
from utils.logging_helpers import sub_metric_log_dict
from utils.argparse_helpers import booltype
from train_helpers import idx_to_one_hot, bits_per_dim, mixup_data, mixup_criterion



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4 changes: 2 additions & 2 deletions pixel-model/pixelsnail.py → pixel_model/pixelsnail.py
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Expand Up @@ -17,10 +17,10 @@
from pytorch_lightning.metrics import Accuracy, Precision, Recall
from tqdm import tqdm

from layers import CausalAttentionPixelBlock, PreActFixupCausalResBlock, input_to_stack, stack_to_output, GatedResBlock
from pixel_model.layers import CausalAttentionPixelBlock, PreActFixupCausalResBlock, input_to_stack, stack_to_output, GatedResBlock
from pixel_model.train_helpers import idx_to_one_hot, bits_per_dim, mixup_data, mixup_criterion
from utils.logging_helpers import sub_metric_log_dict
from utils.argparse_helpers import booltype
from train_helpers import idx_to_one_hot, bits_per_dim, mixup_data, mixup_criterion



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Expand Up @@ -12,8 +12,8 @@
from tqdm import tqdm
from filelock import FileLock

from pixelcnn import PixelCNN
from pixelsnail import PixelSNAIL
from pixel_model.pixelcnn import PixelCNN
from pixel_model.pixelsnail import PixelSNAIL

GPU = torch.device('cuda')

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2 changes: 1 addition & 1 deletion pixel-model/test.py → pixel_model/test.py
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Expand Up @@ -2,7 +2,7 @@
import matplotlib.pyplot as plt
from torchvision.utils import make_grid

from model import CausalConv3dAdd
from pixel_model.layers import CausalConv3dAdd

if __name__ == '__main__':
inp = torch.zeros((1, 3, 20,20,20))
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4 changes: 2 additions & 2 deletions pixel-model/train.py → pixel_model/train.py
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Expand Up @@ -4,8 +4,8 @@
import torch
import pytorch_lightning as pl

from pixelcnn import PixelCNN
from pixelsnail import PixelSNAIL
from pixel_model.pixelcnn import PixelCNN
from pixel_model.pixelsnail import PixelSNAIL
from utils.load_lmdb_dataset import LMDBDataModule

def parse_arguments():
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2 changes: 1 addition & 1 deletion vqvae/calc_ssim_from_checkpoint.py
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Expand Up @@ -10,7 +10,7 @@
from einops import rearrange
import torch.nn as nn

from model import VQVAE
from vqvae.model import VQVAE
from utils import CTDataModule
from metrics.evaluate import SSIM3DSlices

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2 changes: 1 addition & 1 deletion vqvae/decode_embeddings.py
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Expand Up @@ -8,7 +8,7 @@
import torch.nn.functional as F
from torch.utils.data import DataLoader

from model import VQVAE
from vqvae.model import VQVAE
from utils import CTScanDataset, DepthPadAndCrop

def inverse_softplus(x):
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2 changes: 1 addition & 1 deletion vqvae/extract_embeddings.py
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Expand Up @@ -7,7 +7,7 @@
import numpy as np
from tqdm import tqdm

from model import VQVAE
from vqvae.model import VQVAE
from utils import CTDataModule


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2 changes: 1 addition & 1 deletion vqvae/layers.py
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Expand Up @@ -8,7 +8,7 @@
import torch.nn.functional as F
from torch import nn

from evonorm import EvoNorm3DS0
from vqvae.evonorm import EvoNorm3DS0


class EvonormResBlock(nn.Module):
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2 changes: 1 addition & 1 deletion vqvae/model.py
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Expand Up @@ -8,7 +8,7 @@
from torch import nn
from torch.distributions.normal import Normal

from layers import Encoder, Decoder, FixupResBlock, PreActFixupResBlock, EvonormResBlock, Encoder2
from vqvae.layers import Encoder, Decoder, FixupResBlock, PreActFixupResBlock, EvonormResBlock, Encoder2
from utils import ExtractCenterCylinder
from metrics.distribution import Logistic, mixture_nll_loss, generic_nll_loss
from metrics.evaluate import nmse, psnr, SSIM3DSlices
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