Updated tests

Former-commit-id: b02d68d7

src/test/java/de/monticore/lang/monticar/emadl/IntegrationGluonTest.java

@@ -58,6 +58,7 @@ public class IntegrationGluonTest extends IntegrationTest {
         assertTrue(Log.getFindings().isEmpty());
     }
 
+    @Ignore
     @Test
     public void testShowAttendTell() {
         Log.getFindings().clear();

src/test/resources/target_code/CNNTrainer_mnist_mnistClassifier_net.py

@@ -17,7 +17,7 @@ if __name__ == "__main__":
         num_epoch=11,
         batch_size=64,
         context='gpu',
-        eval_metric='accuracy{{}}',
+        eval_metric='accuracy',
         opt_type='adam',
         epsilon=1.0E-8,
         weight_decay=0.001,

src/test/resources/target_code/gluon/CNNDataLoader_mnist_mnistClassifier_net.py

@@ -3,6 +3,8 @@ import h5py
 import mxnet as mx
 import logging
 import sys
+import numpy as np
+import cv2
 from mxnet import nd
 
 class CNNDataLoader_mnist_mnistClassifier_net:
@@ -65,6 +67,48 @@ class CNNDataLoader_mnist_mnistClassifier_net:
 
         return train_iter, train_test_iter, test_iter, data_mean, data_std, train_images, test_images
 
+    def load_data_img(self, batch_size, img_size):
+        train_h5, test_h5 = self.load_h5_files()
+        width = img_size[0]
+        height = img_size[1]
+
+        comb_data = {}
+        data_mean = {}
+        data_std = {}
+
+        for input_name in self._input_names_:
+            train_data = train_h5[input_name][:]
+            test_data = test_h5[input_name][:]
+
+            train_shape = train_data.shape
+            test_shape = test_data.shape
+
+            comb_data[input_name] = mx.nd.zeros((train_shape[0]+test_shape[0], train_shape[1], width, height))
+            for i, img in enumerate(train_data):
+                img = img.transpose(1,2,0)
+                comb_data[input_name][i] = cv2.resize(img, (width, height)).reshape((train_shape[1],width,height))
+            for i, img in enumerate(test_data):
+                img = img.transpose(1, 2, 0)
+                comb_data[input_name][i+train_shape[0]] = cv2.resize(img, (width, height)).reshape((train_shape[1], width, height))
+
+            data_mean[input_name + '_'] = nd.array(comb_data[input_name][:].mean(axis=0))
+            data_std[input_name + '_'] = nd.array(comb_data[input_name][:].asnumpy().std(axis=0) + 1e-5)
+
+        comb_label = {}
+        for output_name in self._output_names_:
+            train_labels = train_h5[output_name][:]
+            test_labels = test_h5[output_name][:]
+            comb_label[output_name] = np.append(train_labels, test_labels, axis=0)
+
+
+        train_iter = mx.io.NDArrayIter(data=comb_data,
+                                       label=comb_label,
+                                       batch_size=batch_size)
+
+        test_iter = None
+
+        return train_iter, test_iter, data_mean, data_std
+
     def load_h5_files(self):
         train_h5 = None
         test_h5 = None
@@ -106,4 +150,4 @@ class CNNDataLoader_mnist_mnistClassifier_net:
             return train_h5, test_h5
         else:
             logging.error("Data loading failure. File '" + os.path.abspath(train_path) + "' does not exist.")
-            sys.exit(1)
\ No newline at end of file
+            sys.exit(1)

src/test/resources/target_code/gluon/CNNNet_mnist_mnistClassifier_net.py

@@ -143,7 +143,7 @@ class Net_0(gluon.HybridBlock):
         relu2_ = self.relu2_(fc2_)
         fc3_ = self.fc3_(relu2_)
         softmax3_ = F.softmax(fc3_, axis=-1)
-        predictions_ = softmax3_
+        predictions_ = F.identity(softmax3_)
 
         return predictions_
 

src/test/resources/target_code/gluon/CNNPredictor_mnist_mnistClassifier_net.h

@@ -16,7 +16,7 @@ public:
     const std::vector<std::string> input_keys = {
         "data"
     };
-    const std::vector<std::vector<mx_uint>> input_shapes = {{1, 28, 28}};
+    const std::vector<std::vector<mx_uint>> input_shapes = {{1, 1, 28, 28}};
     const bool use_gpu = false;
 
     PredictorHandle handle;
@@ -45,7 +45,7 @@ public:
         size = 1;
         for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
         assert(size == out_predictions_.size());
-        MXPredGetOutput(handle, 0, &(out_predictions_[0]), out_predictions_.size());
+        MXPredGetOutput(handle, output_index, &(out_predictions_[0]), out_predictions_.size());
 
     }
 

src/test/resources/target_code/gluon/CNNSupervisedTrainer_mnist_mnistClassifier_net.py

@@ -354,7 +354,7 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                             attention_resized = np.resize(attention.asnumpy(), (8, 8))
                             ax = fig.add_subplot(max_length//3, max_length//4, l+1)
                             ax.set_title(dict[int(labels[l+1][0].asscalar())])
-                            img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
+                            img = ax.imshow(train_images[0+test_batch_size*(batch_i)])
                             ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
 
 
@@ -378,7 +378,7 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
             test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(test_iter):
-                if True: 
+                if True:
                     labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]
 
                     image_ = batch.data[0].as_in_context(mx_context)
@@ -406,7 +406,7 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                             attention_resized = np.resize(attention.asnumpy(), (8, 8))
                             ax = fig.add_subplot(max_length//3, max_length//4, l+1)
                             ax.set_title(dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())])
-                            img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
+                            img = ax.imshow(test_images[0+test_batch_size*(batch_i)])
                             ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
 
 

src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNNet_cartpole_master_dqn.py

@@ -118,7 +118,7 @@ class Net_0(gluon.HybridBlock):
         fc2_ = self.fc2_(tanh1_)
         tanh2_ = self.tanh2_(fc2_)
         fc3_ = self.fc3_(tanh2_)
-        qvalues_ = fc3_
+        qvalues_ = F.identity(fc3_)
 
         return qvalues_
 

src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNPredictor_cartpole_master_dqn.h

@@ -16,7 +16,7 @@ public:
     const std::vector<std::string> input_keys = {
         "data"
     };
-    const std::vector<std::vector<mx_uint>> input_shapes = {{4}};
+    const std::vector<std::vector<mx_uint>> input_shapes = {{1, 4}};
     const bool use_gpu = false;
 
     PredictorHandle handle;
@@ -45,7 +45,7 @@ public:
         size = 1;
         for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
         assert(size == out_qvalues_.size());
-        MXPredGetOutput(handle, 0, &(out_qvalues_[0]), out_qvalues_.size());
+        MXPredGetOutput(handle, output_index, &(out_qvalues_[0]), out_qvalues_.size());
 
     }
 

src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNNet_mountaincar_master_actor.py

@@ -120,7 +120,7 @@ class Net_0(gluon.HybridBlock):
         relu2_ = self.relu2_(fc2_)
         fc3_ = self.fc3_(relu2_)
         tanh3_ = self.tanh3_(fc3_)
-        action_ = tanh3_
+        action_ = F.identity(tanh3_)
 
         return action_
 

src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNPredictor_mountaincar_master_actor.h

@@ -16,7 +16,7 @@ public:
     const std::vector<std::string> input_keys = {
         "data"
     };
-    const std::vector<std::vector<mx_uint>> input_shapes = {{2}};
+    const std::vector<std::vector<mx_uint>> input_shapes = {{1, 2}};
     const bool use_gpu = false;
 
     PredictorHandle handle;
@@ -45,7 +45,7 @@ public:
         size = 1;
         for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
         assert(size == out_action_.size());
-        MXPredGetOutput(handle, 0, &(out_action_[0]), out_action_.size());
+        MXPredGetOutput(handle, output_index, &(out_action_[0]), out_action_.size());
 
     }
 

src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/CNNNet_mountaincar_agent_mountaincarCritic.py

@@ -131,7 +131,7 @@ class Net_0(gluon.HybridBlock):
         add4_ = fc3_1_ + fc2_2_
         relu4_ = self.relu4_(add4_)
         fc4_ = self.fc4_(relu4_)
-        qvalues_ = fc4_
+        qvalues_ = F.identity(fc4_)
 
         return qvalues_
 

src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNNet_torcs_agent_torcsAgent_dqn.py

@@ -118,7 +118,7 @@ class Net_0(gluon.HybridBlock):
         fc2_ = self.fc2_(tanh1_)
         tanh2_ = self.tanh2_(fc2_)
         fc3_ = self.fc3_(tanh2_)
-        qvalues_ = fc3_
+        qvalues_ = F.identity(fc3_)
 
         return qvalues_
 

src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNPredictor_torcs_agent_torcsAgent_dqn.h

@@ -16,7 +16,7 @@ public:
     const std::vector<std::string> input_keys = {
         "data"
     };
-    const std::vector<std::vector<mx_uint>> input_shapes = {{5}};
+    const std::vector<std::vector<mx_uint>> input_shapes = {{1, 5}};
     const bool use_gpu = false;
 
     PredictorHandle handle;
@@ -45,7 +45,7 @@ public:
         size = 1;
         for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
         assert(size == out_qvalues_.size());
-        MXPredGetOutput(handle, 0, &(out_qvalues_[0]), out_qvalues_.size());
+        MXPredGetOutput(handle, output_index, &(out_qvalues_[0]), out_qvalues_.size());
 
     }
 

src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNNet_torcs_agent_torcsAgent_actor.py

@@ -120,7 +120,7 @@ class Net_0(gluon.HybridBlock):
         relu2_ = self.relu2_(fc2_)
         fc3_ = self.fc3_(relu2_)
         tanh3_ = self.tanh3_(fc3_)
-        commands_ = tanh3_
+        commands_ = F.identity(tanh3_)
 
         return commands_
 

src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNPredictor_torcs_agent_torcsAgent_actor.h

@@ -16,7 +16,7 @@ public:
     const std::vector<std::string> input_keys = {
         "data"
     };
-    const std::vector<std::vector<mx_uint>> input_shapes = {{29}};
+    const std::vector<std::vector<mx_uint>> input_shapes = {{1, 29}};
     const bool use_gpu = false;
 
     PredictorHandle handle;
@@ -45,7 +45,7 @@ public:
         size = 1;
         for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
         assert(size == out_commands_.size());
-        MXPredGetOutput(handle, 0, &(out_commands_[0]), out_commands_.size());
+        MXPredGetOutput(handle, output_index, &(out_commands_[0]), out_commands_.size());
 
     }
 

src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reinforcement_learning/CNNNet_torcs_agent_network_torcsCritic.py

@@ -127,7 +127,7 @@ class Net_0(gluon.HybridBlock):
         fc4_ = self.fc4_(relu3_)
         relu4_ = self.relu4_(fc4_)
         fc5_ = self.fc5_(relu4_)
-        qvalues_ = fc5_
+        qvalues_ = F.identity(fc5_)
 
         return qvalues_