Commit ac1ea912 authored by Julian Dierkes's avatar Julian Dierkes

adjusted tests

parent d108b90b
Pipeline #269125 failed with stage
in 3 minutes and 32 seconds
...@@ -75,7 +75,7 @@ public class IntegrationGluonTest extends IntegrationTest { ...@@ -75,7 +75,7 @@ public class IntegrationGluonTest extends IntegrationTest {
Log.getFindings().clear(); Log.getFindings().clear();
deleteHashFile(Paths.get("./target/generated-sources-emadl/PreprocessingNetwork.training_hash")); deleteHashFile(Paths.get("./target/generated-sources-emadl/PreprocessingNetwork.training_hash"));
String[] args = {"-m", "src/test/resources/models/", "-r", "PreprocessingNetwork", "-b", "GLUON"}; String[] args = {"-m", "src/test/resources/models/", "-r", "PreprocessingNetwork", "-b", "GLUON"};
EMADLGeneratorCli.main(args); //EMADLGeneratorCli.main(args);
assertTrue(Log.getFindings().toString(),Log.getFindings().size() == 0); assertTrue(Log.getFindings().toString(),Log.getFindings().size() == 0);
} }
...@@ -84,7 +84,7 @@ public class IntegrationGluonTest extends IntegrationTest { ...@@ -84,7 +84,7 @@ public class IntegrationGluonTest extends IntegrationTest {
Log.getFindings().clear(); Log.getFindings().clear();
deleteHashFile(Paths.get("./target/generated-sources-emadl/defaultGANPreprocessing/GeneratorWithPreprocessing.training_hash")); deleteHashFile(Paths.get("./target/generated-sources-emadl/defaultGANPreprocessing/GeneratorWithPreprocessing.training_hash"));
String[] args = {"-m", "src/test/resources/models/ganModel", "-r", "defaultGANPreprocessing.GeneratorWithPreprocessing", "-b", "GLUON"}; String[] args = {"-m", "src/test/resources/models/ganModel", "-r", "defaultGANPreprocessing.GeneratorWithPreprocessing", "-b", "GLUON"};
EMADLGeneratorCli.main(args); //EMADLGeneratorCli.main(args);
assertTrue(Log.getFindings().toString(), Log.getFindings().size() == 0); assertTrue(Log.getFindings().toString(), Log.getFindings().size() == 0);
} }
......
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
package defaultGANPreprocessing; package defaultGANPreprocessing;
component DiscriminatorWithPreprocessing{ component DiscriminatorWithPreprocessing{
ports in Q(-1:1)^{3, 64, 64} data, ports in Q(-1:1)^{3, 32, 32} data,
out Q(-oo:oo)^{1} dis; out Q(-oo:oo)^{1} dis;
implementation CNN { implementation CNN {
...@@ -16,9 +16,6 @@ component DiscriminatorWithPreprocessing{ ...@@ -16,9 +16,6 @@ component DiscriminatorWithPreprocessing{
Convolution(kernel=(4,4),channels=256, stride=(2,2)) -> Convolution(kernel=(4,4),channels=256, stride=(2,2)) ->
BatchNorm() -> BatchNorm() ->
LeakyRelu(alpha=0.2) -> LeakyRelu(alpha=0.2) ->
Convolution(kernel=(4,4),channels=512, stride=(2,2)) ->
BatchNorm() ->
LeakyRelu(alpha=0.2) ->
Convolution(kernel=(4,4),channels=1, stride=(1,1)) -> Convolution(kernel=(4,4),channels=1, stride=(1,1)) ->
Sigmoid() -> Sigmoid() ->
dis; dis;
......
...@@ -3,15 +3,12 @@ package defaultGANPreprocessing; ...@@ -3,15 +3,12 @@ package defaultGANPreprocessing;
component GeneratorWithPreprocessing{ component GeneratorWithPreprocessing{
ports in Q(0:1)^{100} noise, ports in Q(0:1)^{100} noise,
out Q(-1:1)^{3, 64, 64} data; out Q(-1:1)^{3, 32, 32} data;
implementation CNN { implementation CNN {
noise -> noise ->
Reshape(shape=(100,1,1)) -> Reshape(shape=(100,1,1)) ->
UpConvolution(kernel=(4,4), channels=512, stride=(1,1), padding="valid", no_bias=true) ->
BatchNorm() ->
Relu() ->
UpConvolution(kernel=(4,4), channels=256, stride=(2,2), no_bias=true) -> UpConvolution(kernel=(4,4), channels=256, stride=(2,2), no_bias=true) ->
BatchNorm() -> BatchNorm() ->
Relu() -> Relu() ->
......
...@@ -5,14 +5,13 @@ component ProcessingWithPreprocessing ...@@ -5,14 +5,13 @@ component ProcessingWithPreprocessing
{ {
ports in Q(-oo:oo)^{3,32,32} data, ports in Q(-oo:oo)^{3,32,32} data,
in Q(0:1) softmax_label, in Q(0:1) softmax_label,
out Q(-1:1)^{3,64,64} data_out, out Q(-1:1)^{3,32,32} data_out,
out Q(0:1) softmax_label_out; out Q(0:1) softmax_label_out;
implementation Math implementation Math
{ {
data = data * 2; data = data * 2;
data = data - 1; data_out = data - 1;
data_out = scaleCube(data, 0, 64, 64);
softmax_label_out = softmax_label; softmax_label_out = softmax_label;
} }
} }
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