dummy

CMakeLists_files.cmake

@@ -475,7 +475,7 @@ list (APPEND TEST_SOURCE_FILES
       tests/material/test_spline.cpp
       tests/material/test_tabulation.cpp
       tests/test_Visitor.cpp
-      opm/ml/keras_model_test.cpp
+      tests/ml/keras_model_test.cpp
 )
 
 # tests that need to be linked to dune-common
@@ -648,6 +648,15 @@ list (APPEND TEST_DATA_FILES
       tests/material/co2_unittest_below_sat.json
       tests/material/h2o_unittest.json
       tests/material/h2_unittest.json
+      tests/ml/ml_tools/models/test_dense_1x1.model
+      tests/ml/ml_tools/models/test_dense_2x2.model
+      tests/ml/ml_tools/models/test_dense_10x1.model
+      tests/ml/ml_tools/models/test_dense_10x10.model
+      tests/ml/ml_tools/models/test_dense_10x10x10.model
+      tests/ml/ml_tools/models/test_dense_relu_10.model
+      tests/ml/ml_tools/models/test_dense_tanh_10.model
+      tests/ml/ml_tools/models/test_relu_10.model
+      tests/ml/ml_tools/models/test_scalingdense_10x1.model
 )
 if(ENABLE_ECL_OUTPUT)
   list (APPEND TEST_DATA_FILES

opm/ml/ml_tools/scalertest.py

@@ -16,8 +16,6 @@
 #   You should have received a copy of the GNU General Public License
 #   along with OPM.  If not, see <http://www.gnu.org/licenses/>.
 
-
-
 from __future__ import annotations
 
 import pathlib
@@ -39,58 +37,14 @@
 
 data: np.ndarray = np.random.uniform(-500, 500, (5, 1))
 
-# model: keras.Model = keras.Sequential(
-#
-#     [
-#
-#         keras.layers.Input([10]),
-#
-#         MinMaxScalerLayer(feature_range=feature_ranges[0]),
-#
-#         keras.layers.Dense(units=10),
-#
-#         MinMaxUnScalerLayer(feature_range=feature_ranges[1]),
-#
-#     ]
-#
-# )
-
 model = Sequential()
 model.add(keras.layers.Input([1]))
 model.add(MinMaxScalerLayer(feature_range=(0.0, 1.0)))
-# model.add(Flatten())
 model.add(Dense(1, input_dim=1))
 model.add(Dense(1, input_dim=1))
 model.add(Dense(1, input_dim=1))
 model.add(Dense(1, input_dim=1))
 
 model.add(MinMaxUnScalerLayer(feature_range=(-3.7, -1.0)))
-#
-# model.get_layer(model.layers[0].name).adapt(data=data)
-# model.get_layer(model.layers[-1].name).adapt(data=data)
-
-# model.add(Dense(1, input_dim=1))
-
-# model: keras.Model = keras.Sequential(
-#
-#     [
-#
-#         keras.layers.Input([1]),
-#
-#         MinMaxScalerLayer(feature_range=(0.0, 1.0)),
-#
-#         # keras.layers.Dense(1, input_dim=1),
-#
-#         # MinMaxUnScalerLayer(feature_range=(0.0, 1.0)),
-#
-#     ]
-#
-# )
-
-
-#
-# model.get_layer(model.layers[0].name).adapt(data=data)
-# #
-# model.get_layer(model.layers[-1].name).adapt(data=data)
 
 export_model(model, str(savepath))

opm/ml/keras_model_test.cpp → tests/ml/keras_model_test.cpp

@@ -24,18 +24,18 @@
   along with OPM.  If not, see <http://www.gnu.org/licenses/>.
 */
 
-#include "keras_model.hpp"
+#include <opm/ml/keras_model.hpp>
 #include <iostream>
 #include <stdio.h>
-#include "ml_tools/include/test_dense_10x1.h"
-#include "ml_tools/include/test_dense_10x10.h"
-#include "ml_tools/include/test_dense_10x10x10.h"
-#include "ml_tools/include/test_dense_1x1.h"
-#include "ml_tools/include/test_dense_2x2.h"
-#include "ml_tools/include/test_relu_10.h"
-#include "ml_tools/include/test_dense_relu_10.h"
-#include "ml_tools/include/test_dense_tanh_10.h"
-#include "ml_tools/include/test_scalingdense_1x1.h"
+#include <tests/ml/ml_tools/include/test_dense_10x1.hpp>
+#include <tests/ml/ml_tools/include/test_dense_10x10.hpp>
+#include <tests/ml/ml_tools/include/test_dense_10x10x10.hpp>
+#include <tests/ml/ml_tools/include/test_dense_1x1.hpp>
+#include <tests/ml/ml_tools/include/test_dense_2x2.hpp>
+#include <tests/ml/ml_tools/include/test_relu_10.hpp>
+#include <tests/ml/ml_tools/include/test_dense_relu_10.hpp>
+#include <tests/ml/ml_tools/include/test_dense_tanh_10.hpp>
+#include <tests/ml/ml_tools/include/test_scalingdense_10x1.hpp>
 
 
 namespace Opm {
@@ -148,9 +148,9 @@ bool tensor_test() {
     return true;
 }
 
-
 }
 
+
 int main() {
     typedef Opm::DenseAd::Evaluation<double, 1> Evaluation;
 
@@ -193,7 +193,7 @@ int main() {
         return 1;
     }
 
-    if (!test_scalingdense_1x1<Evaluation>(&load_time, &apply_time)) {
+    if (!test_scalingdense_10x1<Evaluation>(&load_time, &apply_time)) {
         return 1;
     }
 

opm/ml/ml_tools/generateunittests.py → tests/ml/ml_tools/generateunittests.py

@@ -14,14 +14,16 @@
 #   You should have received a copy of the GNU General Public License
 #   along with OPM.  If not, see <http://www.gnu.org/licenses/>.
 
-
 import numpy as np
 import pprint
-import os
+import os, sys
 from tensorflow import keras
 
 from keras.models import Sequential
 from keras.layers import Conv2D, Dense, Flatten, Activation, MaxPooling2D, Dropout, BatchNormalization, ELU, Embedding, LSTM
+
+sys.path.append('../../../opm/ml/ml_tools')
+
 from kerasify import export_model
 from scaler_layers import MinMaxScalerLayer, MinMaxUnScalerLayer
 
@@ -110,20 +112,17 @@ def to_cpp(ndarray):
 }
 '''
 
-
 directory = os.getcwd()
 directory1 = "models"
 directory2 = "include"
 
-# path1=os.path.abspath(directory)
 if os.path.isdir(directory1):
     print(f"{directory1} exists.")
 else:
     print(f"{directory1} does not exist.")
     path1 = os.path.join(directory, directory1)
     os.makedirs(path1)
 
-
 if os.path.isdir(directory2):
     print(f"{directory2} exists.")
 else:
@@ -139,17 +138,16 @@ def output_testcase(model, test_x, test_y, name, eps):
     print(model.summary())
 
     export_model(model, 'models/test_%s.model' % name)
-    path = os.path.abspath(f'../../opm-common/opm/ml/ml_tools/models/test_{name}.model')
+    path = os.path.abspath(f'tests/ml/ml_tools/models/test_{name}.model')
     relative_path = os.path.relpath(path) 
-    with open('include/test_%s.h' % name, 'w') as f:
+    with open('include/test_%s.hpp' % name, 'w') as f:
         x_shape, x_data = c_array(test_x[0])
         y_shape, y_data = c_array(predict_y[0])
 
         f.write(TEST_CASE % (name, name, x_shape, x_data, y_shape, y_data, relative_path, eps))
 
 
-
-# scaling 1x1
+# scaling 10x1
 data: np.ndarray = np.random.uniform(-500, 500, (5, 1))
 feature_ranges: list[tuple[float, float]] = [(0.0, 1.0), (-3.7, 0.0)]
 test_x = np.random.rand(10, 10).astype('f')
@@ -166,7 +164,7 @@ def output_testcase(model, test_x, test_y, name, eps):
 # #
 model.get_layer(model.layers[0].name).adapt(data=data)
 model.get_layer(model.layers[-1].name).adapt(data=data)
-output_testcase(model, test_x, test_y, 'scalingdense_1x1', '1e-3')
+output_testcase(model, test_x, test_y, 'scalingdense_10x1', '1e-3')
 
 # Dense 1x1
 test_x = np.arange(10)
@@ -230,5 +228,4 @@ def output_testcase(model, test_x, test_y, name, eps):
 model.add(Dense(10, input_dim=10, activation='tanh'))
 model.add(Dense(10, input_dim=10, activation='tanh'))
 model.add(Dense(10, input_dim=10, activation='tanh'))
-output_testcase(model, test_x, test_y, 'dense_tanh_10', '1e-6')
-
+output_testcase(model, test_x, test_y, 'dense_tanh_10', '1e-6')

opm/ml/ml_tools/include/test_dense_10x1.h → tests/ml/ml_tools/include/test_dense_10x1.hpp

@@ -39,17 +39,17 @@ bool test_dense_10x1(Evaluation* load_time, Evaluation* apply_time)
     KASSERT(apply_time, "Invalid Evaluation");
 
     Opm::Tensor<Evaluation> in{10};
-    in.data_ = {0.74998796,0.21858868,0.23070294,0.070500664,0.46532258,0.49146336,
-0.3717498,0.45293888,0.6332608,0.43785256};
+    in.data_ = {0.17291813,0.55951893,0.55021375,0.8738541,0.2227031,0.90520567,
+0.32658377,0.9591912,0.81037205,0.9161704};
 
     Opm::Tensor<Evaluation> out{1};
-    out.data_ = {0.49467176};
+    out.data_ = {-0.19687384};
 
     KerasTimer load_timer;
     load_timer.Start();
 
     KerasModel<Evaluation> model;
-    KASSERT(model.LoadModel("../../opm-common/opm/ml/ml_tools/models/test_dense_10x1.model"), "Failed to load model");
+    KASSERT(model.LoadModel("tests/ml/ml_tools/models/test_dense_10x1.model"), "Failed to load model");
 
     *load_time = load_timer.Stop();
 

opm/ml/ml_tools/include/test_dense_10x10.h → tests/ml/ml_tools/include/test_dense_10x10.hpp

@@ -39,17 +39,17 @@ bool test_dense_10x10(Evaluation* load_time, Evaluation* apply_time)
     KASSERT(apply_time, "Invalid Evaluation");
 
     Opm::Tensor<Evaluation> in{10};
-    in.data_ = {0.46586692,0.9036544,0.5005982,0.5969117,0.09755054,0.60282475,
-0.9040883,0.6297268,0.12081735,0.42354217};
+    in.data_ = {0.7425694,0.8231955,0.43356442,0.1050322,0.68590504,0.004866237,
+0.2682057,0.2863745,0.51028156,0.4160977};
 
     Opm::Tensor<Evaluation> out{1};
-    out.data_ = {0.04306573};
+    out.data_ = {0.42263064};
 
     KerasTimer load_timer;
     load_timer.Start();
 
     KerasModel<Evaluation> model;
-    KASSERT(model.LoadModel("../../opm-common/opm/ml/ml_tools/models/test_dense_10x10.model"), "Failed to load model");
+    KASSERT(model.LoadModel("tests/ml/ml_tools/models/test_dense_10x10.model"), "Failed to load model");
 
     *load_time = load_timer.Stop();
 

opm/ml/ml_tools/include/test_dense_10x10x10.h → tests/ml/ml_tools/include/test_dense_10x10x10.hpp

@@ -39,18 +39,18 @@ bool test_dense_10x10x10(Evaluation* load_time, Evaluation* apply_time)
     KASSERT(apply_time, "Invalid Evaluation");
 
     Opm::Tensor<Evaluation> in{10};
-    in.data_ = {0.5250269,0.20885888,0.2868855,0.11718734,0.56410795,0.3253708,
-0.63131493,0.44084042,0.6677203,0.29525173};
+    in.data_ = {0.19410305,0.7003953,0.8654097,0.52044004,0.56675416,0.3896138,
+0.39529598,0.28999075,0.8540118,0.2849137};
 
     Opm::Tensor<Evaluation> out{10};
-    out.data_ = {0.054961562,-0.45561615,-0.2112424,0.0062360223,0.3164827,
-0.746866,-0.41519523,0.004653876,0.58843577,-0.3870317};
+    out.data_ = {0.17738305,0.66652817,-0.06525068,0.71002805,0.044267435,
+0.5738672,0.44073245,-0.22361669,0.49488887,-0.064859115};
 
     KerasTimer load_timer;
     load_timer.Start();
 
     KerasModel<Evaluation> model;
-    KASSERT(model.LoadModel("../../opm-common/opm/ml/ml_tools/models/test_dense_10x10x10.model"), "Failed to load model");
+    KASSERT(model.LoadModel("tests/ml/ml_tools/models/test_dense_10x10x10.model"), "Failed to load model");
 
     *load_time = load_timer.Stop();
 

opm/ml/ml_tools/include/test_dense_1x1.h → tests/ml/ml_tools/include/test_dense_1x1.hpp

@@ -48,7 +48,7 @@ bool test_dense_1x1(Evaluation* load_time, Evaluation* apply_time)
     load_timer.Start();
 
     KerasModel<Evaluation> model;
-    KASSERT(model.LoadModel("../../opm-common/opm/ml/ml_tools/models/test_dense_1x1.model"), "Failed to load model");
+    KASSERT(model.LoadModel("tests/ml/ml_tools/models/test_dense_1x1.model"), "Failed to load model");
 
     *load_time = load_timer.Stop();
 

opm/ml/ml_tools/include/test_dense_2x2.h → tests/ml/ml_tools/include/test_dense_2x2.hpp

@@ -39,16 +39,16 @@ bool test_dense_2x2(Evaluation* load_time, Evaluation* apply_time)
     KASSERT(apply_time, "Invalid Evaluation");
 
     Opm::Tensor<Evaluation> in{2};
-    in.data_ = {0.31118634,0.7765746};
+    in.data_ = {0.401759,0.21791744};
 
     Opm::Tensor<Evaluation> out{1};
-    out.data_ = {-0.18889335};
+    out.data_ = {0.40147796};
 
     KerasTimer load_timer;
     load_timer.Start();
 
     KerasModel<Evaluation> model;
-    KASSERT(model.LoadModel("../../opm-common/opm/ml/ml_tools/models/test_dense_2x2.model"), "Failed to load model");
+    KASSERT(model.LoadModel("tests/ml/ml_tools/models/test_dense_2x2.model"), "Failed to load model");
 
     *load_time = load_timer.Stop();
 

opm/ml/ml_tools/include/test_dense_relu_10.h → tests/ml/ml_tools/include/test_dense_relu_10.hpp

@@ -39,18 +39,18 @@ bool test_dense_relu_10(Evaluation* load_time, Evaluation* apply_time)
     KASSERT(apply_time, "Invalid Evaluation");
 
     Opm::Tensor<Evaluation> in{10};
-    in.data_ = {0.8129924,0.75172913,0.5933609,0.15881199,0.6235366,0.87480235,
-0.7488489,0.11598817,0.17794482,0.53804207};
+    in.data_ = {0.26160437,0.12646836,0.738134,0.21118933,0.8889372,0.5010538,
+0.9388751,0.34989306,0.13648356,0.076768965};
 
     Opm::Tensor<Evaluation> out{10};
-    out.data_ = {0.13795315,0.,0.074093476,0.3255115,0.64427626,0.,
-0.014938377,0.,0.,0.};
+    out.data_ = {0.4369418,1.300816,0.7456924,0.,0.,0.21714777,
+0.062389035,0.,0.,0.};
 
     KerasTimer load_timer;
     load_timer.Start();
 
     KerasModel<Evaluation> model;
-    KASSERT(model.LoadModel("../../opm-common/opm/ml/ml_tools/models/test_dense_relu_10.model"), "Failed to load model");
+    KASSERT(model.LoadModel("tests/ml/ml_tools/models/test_dense_relu_10.model"), "Failed to load model");
 
     *load_time = load_timer.Stop();
 

opm/ml/ml_tools/include/test_dense_tanh_10.h → tests/ml/ml_tools/include/test_dense_tanh_10.hpp

@@ -39,18 +39,18 @@ bool test_dense_tanh_10(Evaluation* load_time, Evaluation* apply_time)
     KASSERT(apply_time, "Invalid Evaluation");
 
     Opm::Tensor<Evaluation> in{10};
-    in.data_ = {0.31477565,0.54144824,0.679132,0.85510814,0.96523064,0.32442212,
-0.25378492,0.45629957,0.75501525,0.3410805};
+    in.data_ = {0.94085747,0.1746581,0.09927705,0.012826765,0.040825576,0.14897323,
+0.6822367,0.91748506,0.67433727,0.5048738};
 
     Opm::Tensor<Evaluation> out{10};
-    out.data_ = {-0.24370436,-0.15176499,-0.48301288,-0.071210645,0.31381485,
-0.20028076,-0.4273302,0.20151882,0.26926264,-0.2015895};
+    out.data_ = {-0.08283218,0.022714693,-0.15881601,-0.31616235,-0.20223989,
+0.4720518,-0.13530512,-0.18132511,-0.51301044,0.8691121};
 
     KerasTimer load_timer;
     load_timer.Start();
 
     KerasModel<Evaluation> model;
-    KASSERT(model.LoadModel("../../opm-common/opm/ml/ml_tools/models/test_dense_tanh_10.model"), "Failed to load model");
+    KASSERT(model.LoadModel("tests/ml/ml_tools/models/test_dense_tanh_10.model"), "Failed to load model");
 
     *load_time = load_timer.Stop();
 

opm/ml/ml_tools/include/test_relu_10.h → tests/ml/ml_tools/include/test_relu_10.hpp

@@ -39,18 +39,18 @@ bool test_relu_10(Evaluation* load_time, Evaluation* apply_time)
     KASSERT(apply_time, "Invalid Evaluation");
 
     Opm::Tensor<Evaluation> in{10};
-    in.data_ = {0.52176595,0.39513543,0.9489158,0.8337155,0.21538404,0.9417168,
-0.20235209,0.06845642,0.97452354,0.67260426};
+    in.data_ = {0.9424436,0.8319943,0.91191685,0.74209344,0.40372875,0.7391152,
+0.02710398,0.4579188,0.45213553,0.97500265};
 
     Opm::Tensor<Evaluation> out{10};
-    out.data_ = {0.,0.38307053,0.27412605,0.,0.,0.8103977,
-0.6584701,0.,0.62901723,0.};
+    out.data_ = {0.255042,0.303974,0.32974565,0.,0.,0.18417864,
+0.,0.,0.,0.};
 
     KerasTimer load_timer;
     load_timer.Start();
 
     KerasModel<Evaluation> model;
-    KASSERT(model.LoadModel("../../opm-common/opm/ml/ml_tools/models/test_relu_10.model"), "Failed to load model");
+    KASSERT(model.LoadModel("tests/ml/ml_tools/models/test_relu_10.model"), "Failed to load model");
 
     *load_time = load_timer.Stop();