Hotfix master (#310)

Author: yalaudah

docker/Dockerfile

@@ -13,17 +13,19 @@ ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
 ENV PATH /opt/conda/bin:$PATH
 SHELL ["/bin/bash", "-c"] 
 
+WORKDIR /home/username
+
 # Install Anaconda and download the seismic-deeplearning repo
 RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
     /bin/bash ~/miniconda.sh -b -p /opt/conda && \
     rm ~/miniconda.sh && \
     ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
     echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
     apt-get install -y zip && \
-    wget --quiet https://github.com/microsoft/seismic-deeplearning/archive/staging.zip -O staging.zip && \
-    unzip staging.zip && rm staging.zip
+    wget --quiet https://github.com/microsoft/seismic-deeplearning/archive/master.zip -O master.zip && \
+    unzip master.zip && rm master.zip
 
-RUN cd seismic-deeplearning-staging && \
+RUN cd seismic-deeplearning-master && \
     conda env create -n seismic-interpretation --file environment/anaconda/local/environment.yml && \
     source activate seismic-interpretation && \
     python -m ipykernel install --user --name seismic-interpretation && \
@@ -32,7 +34,7 @@ RUN cd seismic-deeplearning-staging && \
 
 # TODO: add back in later when Penobscot notebook is available
 # Download Penobscot dataset:
-# RUN cd seismic-deeplearning-staging && \
+# RUN cd seismic-deeplearning-master && \
 #    data_dir="/home/username/data/penobscot" && \
 #    mkdir -p "$data_dir" && \
 #    ./scripts/download_penobscot.sh "$data_dir"  && \
@@ -42,7 +44,7 @@ RUN cd seismic-deeplearning-staging && \
 #    cd ..
 
 # Download F3 dataset:
-RUN cd seismic-deeplearning-staging && \
+RUN cd seismic-deeplearning-master && \
     data_dir="/home/username/data/dutch" && \
     mkdir -p "$data_dir" && \
     ./scripts/download_dutch_f3.sh "$data_dir" && \

docker/README.md

@@ -22,9 +22,9 @@ This process will take a few minutes to complete.
 # Run the Docker image:
 Once the Docker image is built, you can run it anytime using the following command:
 ```bash
-sudo docker run --rm -it -p 9000:9000 -p 9001:9001 --gpus=all --shm-size 11G --network host --mount type=bind,source=$PWD/hrnetv2_w48_imagenet_pretrained.pth,target=/home/models/hrnetv2_w48_imagenet_pretrained.pth -v ~/:/home/username seismic-deeplearning
+sudo docker run --rm -it -p 9000:9000 -p 9001:9001 --gpus=all --shm-size 11G --mount type=bind,source=$PWD/hrnetv2_w48_imagenet_pretrained.pth,target=/home/models/hrnetv2_w48_imagenet_pretrained.pth seismic-deeplearning
 ```
-If you have saved the pretrained model in a different directory, make sure you replace `$PWD/hrnetv2_w48_imagenet_pretrained.pth` with the **absolute** path to the pretrained HRNet model. The command above will run a Jupyter Lab instance that you can access by clicking on the link in your terminal. You can then navigate to the notebook or script that you would like to run. By default, running the command above would mount your home directory to the Docker container, allowing you to access your files and data from within Jupyter Lab. 
+If you have saved the pretrained model in a different directory, make sure you replace `$PWD/hrnetv2_w48_imagenet_pretrained.pth` with the **absolute** path to the pretrained HRNet model. The command above will run a Jupyter Lab instance that you can access by clicking on the link in your terminal. You can then navigate to the notebook or script that you would like to run. 
 
 # Run TensorBoard:
 To run Tensorboard to visualize the logged metrics and results, open a terminal in Jupyter Lab, navigate to the parent of the `output` directory of your model, and run the following command: 

docs/README.md

@@ -1,8 +1,3 @@
 The folder contains notebook examples illustrating the use of segmentation algorithms on openly available datasets. Make sure you have followed the [set up instructions](../README.md) before running these examples. We provide the following notebook examples 
 
-* [Dutch F3 dataset](notebooks/F3_block_training_and_evaluation_local.ipynb): This notebook illustrates section and patch based segmentation approaches on the [Dutch F3](https://terranubis.com/datainfo/Netherlands-Offshore-F3-Block-Complete) open dataset. This notebook uses denconvolution based segmentation algorithm on 2D patches. The notebook will guide you through visualization of the input volume, setting up model training and evaluation. 
-
-
-* [Penobscot dataset](notebooks/HRNet_Penobscot_demo_notebook.ipynb): 
-In this notebook, we demonstrate how to train an [HRNet](https://github.com/HRNet/HRNet-Semantic-Segmentation) model for facies prediction using [Penobscot](https://terranubis.com/datainfo/Penobscot) dataset. The Penobscot 3D seismic dataset was acquired in the Scotian shelf, offshore Nova Scotia, Canada. This notebook illustrates the use of HRNet based segmentation algorithm on the dataset. Details of HRNet based model can be found [here](https://arxiv.org/abs/1904.04514)
-
+* [Dutch F3 dataset](notebooks/Dutch_F3_patch_model_training_and_evaluation.ipynb): This notebook illustrates section and patch based segmentation approaches on the [Dutch F3](https://terranubis.com/datainfo/Netherlands-Offshore-F3-Block-Complete) open dataset. This notebook uses denconvolution based segmentation algorithm on 2D patches. The notebook will guide you through visualization of the input volume, setting up model training and evaluation.