sbrl-github/research-rainfallradar
1
0
Fork 0
mirror of https://github.com/sbrl/research-rainfallradar synced 2024-11-22 09:13:01 +00:00
Code Issues Projects Releases Packages Wiki Activity

dlr: slurm, load checkpoint

Browse source
This commit is contained in:
Starbeamrainbowlabs 2023-01-11 17:26:57 +00:00
parent 93e663e45d
commit a69c809008
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 3 additions and 1 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

1
aimodel/slurm-TEST-deeplabv3p-rainfall.job
View file

@ -30,6 +30,7 @@ show_help() {
echo -e " DIR_RAINFALLWATER The path to the directory the .tfrecord files containing the rainfall radar / water depth data." >&2; echo -e " DIR_RAINFALLWATER The path to the directory the .tfrecord files containing the rainfall radar / water depth data." >&2;
echo -e " PATH_HEIGHTMAP The path to the heightmap jsonl file to read in." >&2; echo -e " PATH_HEIGHTMAP The path to the heightmap jsonl file to read in." >&2;
echo -e " PATH_COLOURMAP The path to the colourmap for predictive purposes." >&2; echo -e " PATH_COLOURMAP The path to the colourmap for predictive purposes." >&2;
echo -e " PATH_CHECKPOINT The path to a checkcpoint to load. If specified, a model will be loaded instead of being trained." >&2;
echo -e " STEPS_PER_EPOCH The number of steps to consider an epoch. Defaults to None, which means use the entire dataset." >&2; echo -e " STEPS_PER_EPOCH The number of steps to consider an epoch. Defaults to None, which means use the entire dataset." >&2;
echo -e " POSTFIX Postfix to append to the output dir (auto calculated)." >&2; echo -e " POSTFIX Postfix to append to the output dir (auto calculated)." >&2;
echo -e " ARGS Optional. Any additional arguments to pass to the python program." >&2; echo -e " ARGS Optional. Any additional arguments to pass to the python program." >&2;

3
aimodel/src/deeplabv3_plus_test_rainfall.py
View file

@ -132,7 +132,8 @@ if PATH_CHECKPOINT is None:
model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES, num_channels=8) model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES, num_channels=8)
summarywriter(model, os.path.join(DIR_OUTPUT, "summary.txt")) summarywriter(model, os.path.join(DIR_OUTPUT, "summary.txt"))
else:
model = tf.keras.models.load_model(PATH_CHECKPOINT)