Fixes for development inside the docker container (#335)

* Fix the mound command for the HRNet pretrained model in the docker readme

* Properly catch InvalidGitRepository exception

* make repo paths consistent with non-docker runs -- this way configs paths do not need to be changed

* Properly catch InvalidGitRepository exception in train.py

docker/Dockerfile

@@ -25,7 +25,8 @@ RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86
     wget --quiet https://github.com/microsoft/seismic-deeplearning/archive/master.zip -O master.zip && \
     unzip master.zip && rm master.zip
 
-RUN cd seismic-deeplearning-master && \
+RUN mv seismic-deeplearning-master seismic-deeplearning && \
+    cd seismic-deeplearning && \
     conda env create -n seismic-interpretation --file environment/anaconda/local/environment.yml && \
     source activate seismic-interpretation && \
     python -m ipykernel install --user --name seismic-interpretation && \
@@ -34,7 +35,7 @@ RUN cd seismic-deeplearning-master && \
 
 # TODO: add back in later when Penobscot notebook is available
 # Download Penobscot dataset:
-# RUN cd seismic-deeplearning-master && \
+# RUN cd seismic-deeplearning && \
 #    data_dir="/home/username/data/penobscot" && \
 #    mkdir -p "$data_dir" && \
 #    ./scripts/download_penobscot.sh "$data_dir"  && \
@@ -44,7 +45,7 @@ RUN cd seismic-deeplearning-master && \
 #    cd ..
 
 # Download F3 dataset:
-RUN cd seismic-deeplearning-master && \
+RUN cd seismic-deeplearning && \
     data_dir="/home/username/data/dutch" && \
     mkdir -p "$data_dir" && \
     ./scripts/download_dutch_f3.sh "$data_dir" && \

docker/README.md

@@ -22,7 +22,7 @@ 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 --mount type=bind,source=$PWD/hrnetv2_w48_imagenet_pretrained.pth,target=/home/models/hrnetv2_w48_imagenet_pretrained.pth seismic-deeplearning
+sudo docker run --rm -it -p 9000:9000 -p 9001:9001 --gpus=all --shm-size 11G --mount type=bind,source=$PWD/hrnetv2_w48_imagenet_pretrained.pth,target=/home/username/seismic-deeplearning/docker/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. 
 

experiments/interpretation/dutchf3_patch/local/test.py

@@ -245,6 +245,7 @@ def _patch_label_2d(
     output = output_p[:, :, ps:-ps, ps:-ps]
     return output
 
+
 def _evaluate_split(
     split, section_aug, model, pre_processing, output_processing, device, running_metrics_overall, config, debug=False,
 ):
@@ -273,7 +274,7 @@ def _evaluate_split(
         output_dir = generate_path(
             f"debug/{config.OUTPUT_DIR}_test_{split}", git_branch(), git_hash(), config.MODEL.NAME, current_datetime(),
         )
-    except TypeError:
+    except:
         output_dir = generate_path(f"debug/{config.OUTPUT_DIR}_test_{split}", config.MODEL.NAME, current_datetime(),)
 
     running_metrics_split = runningScore(n_classes)

experiments/interpretation/dutchf3_patch/local/train.py

@@ -72,7 +72,7 @@ def run(*options, cfg=None, debug=False):
         output_dir = generate_path(
             config.OUTPUT_DIR, git_branch(), git_hash(), config_file_name, config.TRAIN.MODEL_DIR, current_datetime(),
         )
-    except TypeError:
+    except:
         output_dir = generate_path(config.OUTPUT_DIR, config_file_name, config.TRAIN.MODEL_DIR, current_datetime(),)
 
     # Logging: