Grad-CAM does not write to file

I managed to train a SlowFast model (8x8) for the Kinetics data, now I am trying to run the demo for this model. The goal is to write the Grad-CAM results for 1 video to a file.

As suggested in the documentation, I run the following command:
```
python tools/demo_net.py \
--cfg configs/Kinetics/SLOWFAST_8x8_R50_DEMO.yaml \
NUM_GPUS 1 \
TRAIN.ENABLE False \
DATA.PATH_TO_DATA_DIR /work/pe432442/slowfast/slowfast/data/Kinetics \
TEST.CHECKPOINT_FILE_PATH /work/pe432442/slowfast/slowfast/checkpoints/SLOWFAST_8X8_R50_epoch_00014.pyth
```

Besides that I have also added the "DEMO" part to the `.yaml` config file. This looks as follows:

```
OUTPUT_DIR: /work/pe432442/slowfast/slowfast/output_8x8

TENSORBOARD:
ENABLE: True
CONFUSION_MATRIX:
ENABLE: True
HISTOGRAM:
ENABLE: False
MODEL_VIS:
ENABLE: True
MODEL_WEIGHTS: True
ACTIVATIONS: True
INPUT_VIDEO: True
GRAD_CAM:
ENABLE: True

DEMO:
ENABLE: True
LABEL_FILE_PATH: /work/pe432442/slowfast/slowfast/data/Kinetics/classids.json
INPUT_VIDEO: /home/pe432442/Desktop/some_video.mp4
OUTPUT_FILE: /home/pe432442/Desktop/sf-out
THREAD_ENABLE: True
NUM_VIS_INSTANCES: 1
NUM_CLIPS_SKIP: 2
```

Then slowfast/defaults.py looks as follows regarding the Tensorboard visualization section and the demo section:

```
# -----------------------------------------------------------------------------
# Tensorboard Visualization Options
# -----------------------------------------------------------------------------
_C.TENSORBOARD = CfgNode()

# Log to summary writer, this will automatically.
# log loss, lr and metrics during train/eval.
_C.TENSORBOARD.ENABLE = True
# Provide path to prediction results for visualization.
# This is a pickle file of [prediction_tensor, label_tensor]
_C.TENSORBOARD.PREDICTIONS_PATH = "/work/pe432442/slowfast/slowfast/output_8x8/TENSOR"
# Path to directory for tensorboard logs.
# Default to to cfg.OUTPUT_DIR/runs-{cfg.TRAIN.DATASET}.
_C.TENSORBOARD.LOG_DIR = "/work/pe432442/slowfast/slowfast/output_8x8/TENSOR"
# Path to a json file providing class_name - id mapping
# in the format {"class_name1": id1, "class_name2": id2, ...}.
# This file must be provided to enable plotting confusion matrix
# by a subset or parent categories.
_C.TENSORBOARD.CLASS_NAMES_PATH = "/work/pe432442/slowfast/slowfast/data/Kinetics/classids.json"

# Path to a json file for categories -> classes mapping
# in the format {"parent_class": ["child_class1", "child_class2",...], ...}.
_C.TENSORBOARD.CATEGORIES_PATH = ""

# Config for confusion matrices visualization.
_C.TENSORBOARD.CONFUSION_MATRIX = CfgNode()
# Visualize confusion matrix.
_C.TENSORBOARD.CONFUSION_MATRIX.ENABLE = True
# Figure size of the confusion matrices plotted.
_C.TENSORBOARD.CONFUSION_MATRIX.FIGSIZE = [8, 8]
# Path to a subset of categories to visualize.
# File contains class names separated by newline characters.
_C.TENSORBOARD.CONFUSION_MATRIX.SUBSET_PATH = "/work/pe432442/slowfast/slowfast/data/Kinetics/classes.txt"

# Config for histogram visualization.
_C.TENSORBOARD.HISTOGRAM = CfgNode()
# Visualize histograms.
_C.TENSORBOARD.HISTOGRAM.ENABLE = True
# Path to a subset of classes to plot histograms.
# Class names must be separated by newline characters.
_C.TENSORBOARD.HISTOGRAM.SUBSET_PATH = "/work/pe432442/slowfast/slowfast/data/Kinetics/classes.txt"
# Visualize top-k most predicted classes on histograms for each
# chosen true label.
_C.TENSORBOARD.HISTOGRAM.TOPK = 8
# Figure size of the histograms plotted.
_C.TENSORBOARD.HISTOGRAM.FIGSIZE = [8, 8]

# Config for layers' weights and activations visualization.
# _C.TENSORBOARD.ENABLE must be True.
_C.TENSORBOARD.MODEL_VIS = CfgNode()

# If False, skip model visualization.
_C.TENSORBOARD.MODEL_VIS.ENABLE = True

# If False, skip visualizing model weights.
_C.TENSORBOARD.MODEL_VIS.MODEL_WEIGHTS = False

# If False, skip visualizing model activations.
_C.TENSORBOARD.MODEL_VIS.ACTIVATIONS = False

# If False, skip visualizing input videos.
_C.TENSORBOARD.MODEL_VIS.INPUT_VIDEO = True

# List of strings containing data about layer names and their indexing to
# visualize weights and activations for. The indexing is meant for
# choosing a subset of activations outputed by a layer for visualization.
# If indexing is not specified, visualize all activations outputed by the layer.
# For each string, layer name and indexing is separated by whitespaces.
# e.g.: [layer1 1,2;1,2, layer2, layer3 150,151;3,4]; this means for each array `arr`
# along the batch dimension in `layer1`, we take arr[[1, 2], [1, 2]]
_C.TENSORBOARD.MODEL_VIS.LAYER_LIST = ['s5/pathway1_res2', 's5/pathway0_res2']
# Top-k predictions to plot on videos
_C.TENSORBOARD.MODEL_VIS.TOPK_PREDS = 1
# Colormap to for text boxes and bounding boxes colors
_C.TENSORBOARD.MODEL_VIS.COLORMAP = "Pastel2"
# Config for visualization video inputs with Grad-CAM.
# _C.TENSORBOARD.ENABLE must be True.
_C.TENSORBOARD.MODEL_VIS.GRAD_CAM = CfgNode()
# Whether to run visualization using Grad-CAM technique.
_C.TENSORBOARD.MODEL_VIS.GRAD_CAM.ENABLE = True
# CNN layers to use for Grad-CAM. The number of layers must be equal to
# number of pathway(s).
_C.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST = ['s5/pathway1_res2', 's5/pathway0_res2']
# If True, visualize Grad-CAM using true labels for each instances.
# If False, use the highest predicted class.
_C.TENSORBOARD.MODEL_VIS.GRAD_CAM.USE_TRUE_LABEL = True
# Colormap to for text boxes and bounding boxes colors
_C.TENSORBOARD.MODEL_VIS.GRAD_CAM.COLORMAP = "viridis"

# Config for visualization for wrong prediction visualization.
# _C.TENSORBOARD.ENABLE must be True.
_C.TENSORBOARD.WRONG_PRED_VIS = CfgNode()
_C.TENSORBOARD.WRONG_PRED_VIS.ENABLE = False
# Folder tag to origanize model eval videos under.
_C.TENSORBOARD.WRONG_PRED_VIS.TAG = "Incorrectly classified videos."
# Subset of labels to visualize. Only wrong predictions with true labels
# within this subset is visualized.
_C.TENSORBOARD.WRONG_PRED_VIS.SUBSET_PATH = ""

# ---------------------------------------------------------------------------- #
# Demo options
# ---------------------------------------------------------------------------- #
_C.DEMO = CfgNode()

# Run model in DEMO mode.
_C.DEMO.ENABLE = False

# Path to a json file providing class_name - id mapping
# in the format {"class_name1": id1, "class_name2": id2, ...}.
_C.DEMO.LABEL_FILE_PATH = ""

# Specify a camera device as input. This will be prioritized
# over input video if set.
# If -1, use input video instead.
_C.DEMO.WEBCAM = -1

# Path to input video for demo.
_C.DEMO.INPUT_VIDEO = ""
# Custom width for reading input video data.
_C.DEMO.DISPLAY_WIDTH = 0
# Custom height for reading input video data.
_C.DEMO.DISPLAY_HEIGHT = 0
# Path to Detectron2 object detection model configuration,
# only used for detection tasks.
_C.DEMO.DETECTRON2_CFG = "COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"
# Path to Detectron2 object detection model pre-trained weights.
_C.DEMO.DETECTRON2_WEIGHTS = "detectron2://COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
# Threshold for choosing predicted bounding boxes by Detectron2.
_C.DEMO.DETECTRON2_THRESH = 0.9
# Number of overlapping frames between 2 consecutive clips.
# Increase this number for more frequent action predictions.
# The number of overlapping frames cannot be larger than
# half of the sequence length `cfg.DATA.NUM_FRAMES * cfg.DATA.SAMPLING_RATE`
_C.DEMO.BUFFER_SIZE = 0
# If specified, the visualized outputs will be written this a video file of
# this path. Otherwise, the visualized outputs will be displayed in a window.
_C.DEMO.OUTPUT_FILE = "/work/pe432442/slowfast/slowfast/output_8x8/"
# Frames per second rate for writing to output video file.
# If not set (-1), use fps rate from input file.
_C.DEMO.OUTPUT_FPS = -1
# Input format from demo video reader ("RGB" or "BGR").
_C.DEMO.INPUT_FORMAT = "BGR"
# Draw visualization frames in [keyframe_idx - CLIP_VIS_SIZE, keyframe_idx + CLIP_VIS_SIZE] inclusively.
_C.DEMO.CLIP_VIS_SIZE = 10
# Number of processes to run video visualizer.
_C.DEMO.NUM_VIS_INSTANCES = 2
```

I am very confused why my video is not being written to file, when running the code with above specifications, the terminal only returns the following:
```
(OK) Unloading intelmpi 2018.4.274
(OK) Unloading Intel Suite 19.0.1.144
(OK) Loading gcc 7.3.0
(!!) OpenACC / OpenMP offload to Pascal GPUs might be slow. Use
(!!) $FLAGS_OFFLOAD_OPENMP or $FLAGS_OFFLOAD_OPENACC envvars.
(OK) Intel MPI Suite 2018.4.274 loaded.
(OK) Loading cuda 10.2.89
(OK) Loading cudnn 8.0
(OK) Loading NVIDIA v. 20.9 suite: compilers (C/C++/FORTRAN), CUDA (10.2), GPU math libaries, and Nsight tools
** fvcore version of PathManager will be deprecated soon. **
** Please migrate to the version in iopath repo. **
https://github.com/facebookresearch/iopath

** fvcore version of PathManager will be deprecated soon. **
** Please migrate to the version in iopath repo. **
https://github.com/facebookresearch/iopath
```
After which it terminates without throwing any errors. An important detail is that I am working from a compute cluster and thus have no screen available when the job is running. However, since I provide a directory to write the output to, this shouldn't be an issue. I have also ran experiments with the Detectron2 repository and this works fine (i.e. writes files to output directory).