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Merge branch 'main' of github.com:sbrl/research-rainfallradar

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Starbeamrainbowlabs 2024-12-19 15:21:51 +00:00
commit edfc2721a8
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 4 additions and 3 deletions
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1
aimodel/src/deeplabv3_plus_test_rainfall.py
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@ -250,7 +250,6 @@ if PATH_CHECKPOINT is None:
mean_iou(),
sensitivity(), # How many true positives were accurately predicted
specificity # How many true negatives were accurately predicted?
# TODO: Add IoU, F1, Precision, Recall, here.
],
steps_per_execution=STEPS_PER_EXECUTION,
jit_compile=JIT_COMPILE

6
aimodel/src/lib/dataset/dataset_mono.py
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@ -23,7 +23,7 @@ def parse_item(metadata, output_size=100, input_size="same", water_threshold=0.1
metadata (dict): Metadata about the shapes of the dataset - rainfall radar, water depth data etc. This should be read automaticallyfrom the metadata.json file that's generated by previous pipeline steps that I forget at this time.
output_size (int): The desired output size of the water depth data.
input_size (str or int): The desired input size of the rainfall radar data. If "same", it will be set to the same as the output_size.
water_threshold (float): The threshold to use for binarizing the water depth data.
water_threshold (float|None): The threshold to use for binarizing the water depth data. If None, then no thresholding will be done. IMPORTANT: setting `water_threshold=None` will NOT remove the channels! You gotta do that yourself!
water_bins (int): The number of bins to use for the water depth data (e.g. for one-hot encoding).
heightmap (tf.Tensor): An optional heightmap to include as an additional channel in the rainfall radar data.
rainfall_scale_up (int): A factor to scale up the rainfall radar data.
@ -113,7 +113,9 @@ def parse_item(metadata, output_size=100, input_size="same", water_threshold=0.1
# water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.int32)
# water = tf.one_hot(water, water_bins, axis=-1, dtype=tf.int32)
# SPARSE [LOSS dice / sparse cross entropy]
water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.float32)
if water_threshold is not None: # if water_threshold=None, then regression mode
water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.float32)
# BUG it may be a problem we're [height, width, channel] here rather than [height, width], depending on how dlr works
if do_remove_isolated_pixels:
water = remove_isolated_pixels(water)