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Improve predictor module #562

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merged 8 commits into from
Oct 18, 2024
Merged

Improve predictor module #562

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43c7b31
predictor: remove wrong module doc string
elcorto Apr 21, 2024
da0ce13
predictor: remove some code duplications (#508)
elcorto Apr 21, 2024
8b24c09
predictor: format remaining code
elcorto Apr 22, 2024
31e0e5f
predictor: simplify code (use slice)
elcorto Apr 23, 2024
3de8619
predictor: add Tensor type check
elcorto Apr 23, 2024
d087714
Add module doc string in predictor
elcorto Jul 29, 2024
8c78a62
Remove random type check in Predictor
elcorto Oct 18, 2024
e8ba079
Fix linter complaint in Predictor
elcorto Oct 18, 2024
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32 changes: 12 additions & 20 deletions mala/network/predictor.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
"""Tester class for testing a network."""
"""Predictor class."""

import numpy as np
import torch
Expand Down Expand Up @@ -59,13 +59,6 @@ def predict_from_qeout(self, path_to_file, gather_ldos=False):
predicted_ldos : numpy.array
Precicted LDOS for these atomic positions.
"""
self.data.grid_dimension = self.parameters.inference_data_grid
self.data.grid_size = (
self.data.grid_dimension[0]
* self.data.grid_dimension[1]
* self.data.grid_dimension[2]
)

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self.data.target_calculator.read_additional_calculation_data(
path_to_file, "espresso-out"
)
Expand Down Expand Up @@ -230,18 +223,17 @@ def _forward_snap_descriptors(
)

for i in range(0, self.number_of_batches_per_snapshot):
inputs = snap_descriptors[
i
* self.parameters.mini_batch_size : (i + 1)
* self.parameters.mini_batch_size
]
inputs = inputs.to(self.parameters._configuration["device"])
predicted_outputs[
i
* self.parameters.mini_batch_size : (i + 1)
* self.parameters.mini_batch_size
] = self.data.output_data_scaler.inverse_transform(
self.network(inputs).to("cpu"), as_numpy=True
sl = slice(
i * self.parameters.mini_batch_size,
(i + 1) * self.parameters.mini_batch_size,
)
inputs = snap_descriptors[sl].to(
self.parameters._configuration["device"]
)
predicted_outputs[sl] = (
self.data.output_data_scaler.inverse_transform(
self.network(inputs).to("cpu"), as_numpy=True
)
)

# Restricting the actual quantities to physical meaningful values,
Expand Down