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Update construct_single_presto_input to unmask NDVI if its calculated #18

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merged 3 commits into from
Oct 13, 2023
Merged

Update construct_single_presto_input to unmask NDVI if its calculated #18

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f42d6c0
Update construct_single_presto_input to unmask NDVI if its calculated
gabrieltseng Oct 13, 2023
43341dd
Test that NDVI isn't added if normalize isn't called
gabrieltseng Oct 13, 2023
46e314d
Add comment explaining that normalize removes some bands
gabrieltseng Oct 13, 2023
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11 changes: 9 additions & 2 deletions presto/dataops/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
from .pipelines.s1_s2_era5_srtm import (
BANDS,
ERA5_BANDS,
NORMED_BANDS,
REMOVED_BANDS,
S1_BANDS,
S1_S2_ERA5_SRTM,
Expand Down Expand Up @@ -60,8 +61,14 @@ def construct_single_presto_input(
if dynamic_world is None:
dynamic_world = torch.ones(num_timesteps) * (DynamicWorld2020_2021.class_amount)

keep_indices = [idx for idx, val in enumerate(BANDS) if val != "B9"]
mask = mask[:, keep_indices]

if normalize:
keep_indices = [idx for idx, val in enumerate(BANDS) if val != "B9"]
mask = mask[:, keep_indices]
x = S1_S2_ERA5_SRTM.normalize(x)
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Can you add a comment here that .normalize includes x = x[:, keep_indices]? :)

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added!

if s2_bands is not None:
if ("B8" in s2_bands) and ("B4" in s2_bands):
mask[:, NORMED_BANDS.index("NDVI")] = 0
else:
x = x[:, keep_indices]
return x, mask, dynamic_world
26 changes: 25 additions & 1 deletion tests/test_dataops_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,15 +4,39 @@

from presto import construct_single_presto_input
from presto.dataops.pipelines.dynamicworld import DynamicWorld2020_2021
from presto.dataops.pipelines.s1_s2_era5_srtm import NORMED_BANDS


class TestDatopsUtils(TestCase):
def test_construct_single_presto_input(self):
input_bands = ["B2", "B3", "B4", "B8"]
x, mask, dw = construct_single_presto_input(
s2=torch.ones(2, 3), s2_bands=["B2", "B3", "B4"], normalize=False
s2=torch.ones(2, 4), s2_bands=input_bands, normalize=False
)
self.assertTrue(torch.equal(dw, torch.ones_like(dw) * DynamicWorld2020_2021.class_amount))
self.assertEqual(len(dw), x.shape[0])
self.assertEqual(x.shape, mask.shape)
self.assertTrue((x[mask == 1] == 0).all())
self.assertTrue((x[mask == 0] != 0).all())
for idx, band in enumerate(NORMED_BANDS):
if band in input_bands:
self.assertTrue((mask[:, idx] == 0).all())
else:
self.assertTrue((mask[:, idx] == 1).all())

def test_construct_single_presto_input_ndvi(self):
input_bands = ["B2", "B3", "B4", "B8"]
x, mask, dw = construct_single_presto_input(
s2=torch.tensor([[1, 2, 3, 4], [1, 2, 3, 4]]).float(),
s2_bands=input_bands,
normalize=True,
)
self.assertTrue(torch.equal(dw, torch.ones_like(dw) * DynamicWorld2020_2021.class_amount))
self.assertEqual(len(dw), x.shape[0])
self.assertEqual(x.shape, mask.shape)
# we can't test for equality to 0 since we normalize;
# that's tested above
self.assertTrue((x[mask == 0] != 0).all())
for idx, band in enumerate(NORMED_BANDS):
if band in input_bands + ["NDVI"]:
self.assertTrue((mask[:, idx] == 0).all())
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