Updating the path to test-data in the test suite and redirecting work…

…flow_test to Be_model

test/all_lazy_loading_test.py

@@ -7,9 +7,7 @@
 import torch
 import pytest
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # This test compares the data scaling using the regular scaling procedure and
 # the lazy-loading one (incremental fitting).

test/basic_gpu_test.py

@@ -6,9 +6,9 @@
 which MALA relies on). Two things are tested:
 
 1. Whether or not your system has GPU support.
-2. Whether or not the GPU does what it is supposed to. For this, 
+2. Whether or not the GPU does what it is supposed to. For this,
 a training is performed. It is measured whether or not the utilization
-of the GPU results in a speed up. 
+of the GPU results in a speed up.
 """
 import os
 import time
@@ -19,9 +19,7 @@
 import pytest
 import torch
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 test_checkpoint_name = "test"
 

test/checkpoint_hyperopt_test.py

@@ -4,9 +4,7 @@
 from mala import printout
 import numpy as np
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 checkpoint_name = "test_ho"
 

test/checkpoint_training_test.py

@@ -4,9 +4,8 @@
 from mala import printout
 import numpy as np
 
-from mala.datahandling.data_repo import data_repo_path
+from mala.datahandling.data_repo import data_path
 
-data_path = os.path.join(data_repo_path, "Be2")
 test_checkpoint_name = "test"
 
 # Define the accuracy used in the tests.

test/complete_interfaces_test.py

@@ -8,9 +8,7 @@
 import pytest
 
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 
 # This test checks whether MALA interfaces to other codes, mainly the ASE

test/descriptor_test.py

@@ -6,9 +6,7 @@
 import numpy as np
 import pytest
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # Accuracy of test.
 accuracy_descriptors = 5e-8

test/hyperopt_test.py

@@ -7,9 +7,7 @@
 import mala
 import numpy as np
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # Control how much the loss should be better after hyperopt compared to
 # before. This value is fairly high, but we're training on absolutely

test/inference_test.py

@@ -3,10 +3,8 @@
 import numpy as np
 from mala import Tester, Runner
 
-from mala.datahandling.data_repo import data_repo_path
+from mala.datahandling.data_repo import data_path
 
-data_path = os.path.join(data_repo_path, "Be2")
-param_path = os.path.join(data_repo_path, "workflow_test/")
 accuracy_strict = 1e-16
 accuracy_coarse = 5e-7
 accuracy_very_coarse = 3
@@ -18,7 +16,7 @@ class TestInference:
     def test_unit_conversion(self):
         """Test that RAM inexpensive unit conversion works."""
         parameters, network, data_handler = Runner.load_run(
-            "workflow_test", load_runner=False, path=param_path
+            "Be_model", load_runner=False, path=data_path
         )
         parameters.data.use_lazy_loading = False
         parameters.running.mini_batch_size = 50
@@ -99,7 +97,7 @@ def test_inference_lazy_loading(self):
     def __run(use_lazy_loading=False, batchsize=46):
         # First we load Parameters and network.
         parameters, network, data_handler, tester = Tester.load_run(
-            "workflow_test", path=param_path
+            "Be_model", path=data_path
         )
         parameters.data.use_lazy_loading = use_lazy_loading
         parameters.running.mini_batch_size = batchsize

test/integration_test.py

@@ -6,7 +6,7 @@
 import scipy as sp
 import pytest
 
-from mala.datahandling.data_repo import data_repo_path
+from mala.datahandling.data_repo import data_path
 
 # In order to test the integration capabilities of MALA we need a
 # QuantumEspresso
@@ -18,7 +18,6 @@
 # Scripts to reproduce the data files used in this test script can be found
 # in the data repo.
 
-data_path = os.path.join(data_repo_path, "Be2")
 path_to_out = os.path.join(data_path, "Be_snapshot0.out")
 path_to_ldos_npy = os.path.join(data_path, "Be_snapshot0.out.npy")
 path_to_dos_npy = os.path.join(data_path, "Be_snapshot0.dos.npy")

test/parallel_run_test.py

@@ -6,9 +6,7 @@
 from ase.io import read
 import pytest
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # Control the various accuracies..
 accuracy_snaps = 1e-4

test/scaling_test.py

@@ -4,9 +4,7 @@
 import numpy as np
 import torch
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # This test checks that all scaling options are working and are not messing
 # up the data.

test/shuffling_test.py

@@ -3,9 +3,7 @@
 import mala
 import numpy as np
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # Accuracy for the shuffling test.
 accuracy = np.finfo(float).eps

test/tensor_memory_test.py

@@ -5,9 +5,7 @@
 from torch.utils.data import TensorDataset
 from torch.utils.data import DataLoader
 
-from mala.datahandling.data_repo import data_repo_path
-
-data_path = os.path.join(data_repo_path, "Be2")
+from mala.datahandling.data_repo import data_path
 
 # Define the accuracy used in the tests.
 accuracy = 1e-5

test/workflow_test.py

@@ -5,9 +5,8 @@
 import numpy as np
 import pytest
 
-from mala.datahandling.data_repo import data_repo_path
+from mala.datahandling.data_repo import data_path
 
-data_path = os.path.join(data_repo_path, "Be2")
 # Control how much the loss should be better after training compared to
 # before. This value is fairly high, but we're training on absolutely
 # minimal amounts of data.
@@ -382,7 +381,7 @@ def test_training_with_postprocessing_data_repo(self):
         """
         # Load parameters, network and data scalers.
         parameters, network, data_handler, tester = mala.Tester.load_run(
-            "workflow_test", path=os.path.join(data_repo_path, "workflow_test")
+            "Be_model", path=data_path
         )
 
         parameters.targets.target_type = "LDOS"
@@ -431,7 +430,7 @@ def test_predictions(self):
         ####################
 
         parameters, network, data_handler, tester = mala.Tester.load_run(
-            "workflow_test", path=os.path.join(data_repo_path, "workflow_test")
+            "Be_model", path=data_path
         )
         parameters.targets.target_type = "LDOS"
         parameters.targets.ldos_gridsize = 11
@@ -518,7 +517,7 @@ def test_total_energy_predictions(self):
         ####################
 
         parameters, network, data_handler, predictor = mala.Predictor.load_run(
-            "workflow_test", path=os.path.join(data_repo_path, "workflow_test")
+            "Be_model", path=data_path
         )
         parameters.targets.target_type = "LDOS"
         parameters.targets.ldos_gridsize = 11