updated tests, pom.xml

pom.xml

@@ -18,11 +18,11 @@
     <!-- .. SE-Libraries .................................................. -->
 
       <CNNArch.version>0.3.4-SNAPSHOT</CNNArch.version>
-      <CNNTrain.version>0.3.9-SNAPSHOT</CNNTrain.version>
-      <CNNArch2X.version>0.0.5-SNAPSHOT</CNNArch2X.version>
+      <CNNTrain.version>0.3.10-SNAPSHOT</CNNTrain.version>
+      <CNNArch2X.version>0.0.6-SNAPSHOT</CNNArch2X.version>
       <embedded-montiarc-math-opt-generator>0.1.6</embedded-montiarc-math-opt-generator>
       <EMADL2PythonWrapper.version>0.0.2-SNAPSHOT</EMADL2PythonWrapper.version>
-    
+
         <!-- .. Libraries .................................................. -->
         <guava.version>18.0</guava.version>
         <junit.version>4.12</junit.version>
@@ -144,7 +144,7 @@
         </dependency>
 
     </dependencies>
-  
+
 
   <!-- == PROJECT BUILD SETTINGS =========================================== -->
 

src/main/resources/templates/gluon/CNNCreator.ftl

@@ -36,6 +36,7 @@ class ${tc.fileNameWithoutEnding}:
                 os.remove(self._model_dir_ + self._model_prefix_ + "_" + str(i) + "_newest-symbol.json")
             except OSError:
                 pass
+
             if os.path.isdir(self._model_dir_):
                 for file in os.listdir(self._model_dir_):
                     if ".params" in file and self._model_prefix_ + "_" + str(i) in file:

src/main/resources/templates/gluon/elements/Convolution.ftl

@@ -7,7 +7,6 @@
             self.${element.name} = gluon.nn.Conv2D(channels=${element.channels?c},
                 kernel_size=(${tc.join(element.kernel, ",")}),
                 strides=(${tc.join(element.stride, ",")}),
-                groups=${element.groups?c},
                 use_bias=${element.noBias?string("False", "True")})
 <#include "OutputShape.ftl">
 <#elseif mode == "FORWARD_FUNCTION">

src/main/resources/templates/gluon/elements/UpConvolution.ftl

@@ -8,7 +8,6 @@
                 kernel_size=(${tc.join(element.kernel, ",")}),
                 strides=(${tc.join(element.stride, ",")}),
                 padding=self.${element.name}padding,
-                groups=${element.groups?c},
                 use_bias=${element.noBias?string("False", "True")})
 <#include "OutputShape.ftl">
 <#elseif mode == "FORWARD_FUNCTION">

src/test/resources/target_code/CNNCreator_Alexnet.py

 import mxnet as mx
 import logging
 import os
+import shutil
 
 from CNNNet_Alexnet import Net_0
 
@@ -11,6 +12,7 @@ class CNNCreator_Alexnet:
     def __init__(self):
         self.weight_initializer = mx.init.Normal()
         self.networks = {}
+        self._weights_dir_ = None
 
     def load(self, context):
         earliestLastEpoch = None
@@ -47,6 +49,29 @@ class CNNCreator_Alexnet:
 
         return earliestLastEpoch
 
+    def load_pretrained_weights(self, context):
+        if os.path.isdir(self._model_dir_):
+            shutil.rmtree(self._model_dir_)
+        if self._weights_dir_ is not None:
+            for i, network in self.networks.items():
+                # param_file = self._model_prefix_ + "_" + str(i) + "_newest-0000.params"
+                param_file = None
+                if os.path.isdir(self._weights_dir_):
+                    lastEpoch = 0
+
+                    for file in os.listdir(self._weights_dir_):
+
+                        if ".params" in file and self._model_prefix_ + "_" + str(i) in file:
+                            epochStr = file.replace(".params","").replace(self._model_prefix_ + "_" + str(i) + "-","")
+                            epoch = int(epochStr)
+                            if epoch > lastEpoch:
+                                lastEpoch = epoch
+                                param_file = file
+                    logging.info("Loading pretrained weights: " + self._weights_dir_ + param_file)
+                    network.load_parameters(self._weights_dir_ + param_file, allow_missing=True, ignore_extra=True)
+                else:
+                    logging.info("No pretrained weights available at: " + self._weights_dir_ + param_file)
+
     def construct(self, context, data_mean=None, data_std=None):
         self.networks[0] = Net_0(data_mean=data_mean, data_std=data_std)
         self.networks[0].collect_params().initialize(self.weight_initializer, ctx=context)

src/test/resources/target_code/CNNCreator_CifarClassifierNetwork.py

 import mxnet as mx
 import logging
 import os
+import shutil
 
 from CNNNet_CifarClassifierNetwork import Net_0
 
@@ -11,6 +12,7 @@ class CNNCreator_CifarClassifierNetwork:
     def __init__(self):
         self.weight_initializer = mx.init.Normal()
         self.networks = {}
+        self._weights_dir_ = None
 
     def load(self, context):
         earliestLastEpoch = None
@@ -47,6 +49,29 @@ class CNNCreator_CifarClassifierNetwork:
 
         return earliestLastEpoch
 
+    def load_pretrained_weights(self, context):
+        if os.path.isdir(self._model_dir_):
+            shutil.rmtree(self._model_dir_)
+        if self._weights_dir_ is not None:
+            for i, network in self.networks.items():
+                # param_file = self._model_prefix_ + "_" + str(i) + "_newest-0000.params"
+                param_file = None
+                if os.path.isdir(self._weights_dir_):
+                    lastEpoch = 0
+
+                    for file in os.listdir(self._weights_dir_):
+
+                        if ".params" in file and self._model_prefix_ + "_" + str(i) in file:
+                            epochStr = file.replace(".params","").replace(self._model_prefix_ + "_" + str(i) + "-","")
+                            epoch = int(epochStr)
+                            if epoch > lastEpoch:
+                                lastEpoch = epoch
+                                param_file = file
+                    logging.info("Loading pretrained weights: " + self._weights_dir_ + param_file)
+                    network.load_parameters(self._weights_dir_ + param_file, allow_missing=True, ignore_extra=True)
+                else:
+                    logging.info("No pretrained weights available at: " + self._weights_dir_ + param_file)
+
     def construct(self, context, data_mean=None, data_std=None):
         self.networks[0] = Net_0(data_mean=data_mean, data_std=data_std)
         self.networks[0].collect_params().initialize(self.weight_initializer, ctx=context)

src/test/resources/target_code/CNNCreator_VGG16.py

 import mxnet as mx
 import logging
 import os
+import shutil
 
 from CNNNet_VGG16 import Net_0
 
@@ -11,6 +12,7 @@ class CNNCreator_VGG16:
     def __init__(self):
         self.weight_initializer = mx.init.Normal()
         self.networks = {}
+        self._weights_dir_ = None
 
     def load(self, context):
         earliestLastEpoch = None
@@ -47,6 +49,29 @@ class CNNCreator_VGG16:
 
         return earliestLastEpoch
 
+    def load_pretrained_weights(self, context):
+        if os.path.isdir(self._model_dir_):
+            shutil.rmtree(self._model_dir_)
+        if self._weights_dir_ is not None:
+            for i, network in self.networks.items():
+                # param_file = self._model_prefix_ + "_" + str(i) + "_newest-0000.params"
+                param_file = None
+                if os.path.isdir(self._weights_dir_):
+                    lastEpoch = 0
+
+                    for file in os.listdir(self._weights_dir_):
+
+                        if ".params" in file and self._model_prefix_ + "_" + str(i) in file:
+                            epochStr = file.replace(".params","").replace(self._model_prefix_ + "_" + str(i) + "-","")
+                            epoch = int(epochStr)
+                            if epoch > lastEpoch:
+                                lastEpoch = epoch
+                                param_file = file
+                    logging.info("Loading pretrained weights: " + self._weights_dir_ + param_file)
+                    network.load_parameters(self._weights_dir_ + param_file, allow_missing=True, ignore_extra=True)
+                else:
+                    logging.info("No pretrained weights available at: " + self._weights_dir_ + param_file)
+
     def construct(self, context, data_mean=None, data_std=None):
         self.networks[0] = Net_0(data_mean=data_mean, data_std=data_std)
         self.networks[0].collect_params().initialize(self.weight_initializer, ctx=context)

src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py

@@ -86,6 +86,53 @@ class DiceLoss(gluon.loss.Loss):
         diceloss = self.dice_loss(F, pred, label)
         return F.mean(loss, axis=self._batch_axis, exclude=True) + diceloss
 
+class SoftmaxCrossEntropyLossIgnoreLabel(gluon.loss.Loss):
+    def __init__(self, axis=-1, from_logits=False, weight=None,
+                 batch_axis=0, ignore_label=255, **kwargs):
+        super(SoftmaxCrossEntropyLossIgnoreLabel, self).__init__(weight, batch_axis, **kwargs)
+        self._axis = axis
+        self._from_logits = from_logits
+        self._ignore_label = ignore_label
+
+    def hybrid_forward(self, F, output, label, sample_weight=None):
+        if not self._from_logits:
+            output = F.log_softmax(output, axis=self._axis)
+
+        valid_label_map = (label != self._ignore_label)
+        loss = -(F.pick(output, label, axis=self._axis, keepdims=True) * valid_label_map )
+
+        loss = gluon.loss._apply_weighting(F, loss, self._weight, sample_weight)
+        return F.sum(loss) / F.sum(valid_label_map)
+
+@mx.metric.register
+class ACCURACY_IGNORE_LABEL(mx.metric.EvalMetric):
+    """Ignores a label when computing accuracy.
+    """
+    def __init__(self, axis=1, metric_ignore_label=255, name='accuracy',
+                 output_names=None, label_names=None):
+        super(ACCURACY_IGNORE_LABEL, self).__init__(
+            name, axis=axis,
+            output_names=output_names, label_names=label_names)
+        self.axis = axis
+        self.ignore_label = metric_ignore_label
+
+    def update(self, labels, preds):
+        mx.metric.check_label_shapes(labels, preds)
+
+        for label, pred_label in zip(labels, preds):
+            if pred_label.shape != label.shape:
+                pred_label = mx.nd.argmax(pred_label, axis=self.axis, keepdims=True)
+            label = label.astype('int32')
+            pred_label = pred_label.astype('int32').as_in_context(label.context)
+
+            mx.metric.check_label_shapes(label, pred_label)
+
+            correct = mx.nd.sum( (label == pred_label) * (label != self.ignore_label) ).asscalar()
+            total = mx.nd.sum( (label != self.ignore_label) ).asscalar()
+
+            self.sum_metric += correct
+            self.num_inst += total
+
 @mx.metric.register
 class BLEU(mx.metric.EvalMetric):
     N = 4
@@ -221,6 +268,7 @@ class CNNSupervisedTrainer_Alexnet:
               optimizer_params=(('learning_rate', 0.001),),
               load_checkpoint=True,
               checkpoint_period=5,
+              load_pretrained=False,
               log_period=50,
               context='gpu',
               save_attention_image=False,
@@ -265,6 +313,8 @@ class CNNSupervisedTrainer_Alexnet:
         begin_epoch = 0
         if load_checkpoint:
             begin_epoch = self._net_creator.load(mx_context)
+        elif load_pretrained:
+            self._net_creator.load_pretrained_weights(mx_context)
         else:
             if os.path.isdir(self._net_creator._model_dir_):
                 shutil.rmtree(self._net_creator._model_dir_)
@@ -297,6 +347,10 @@ class CNNSupervisedTrainer_Alexnet:
         elif loss == 'dice_loss':
             loss_weight = loss_params['loss_weight'] if 'loss_weight' in loss_params else None
             loss_function = DiceLoss(axis=loss_axis, weight=loss_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+        elif loss == 'softmax_cross_entropy_ignore_label':
+            loss_weight = loss_params['loss_weight'] if 'loss_weight' in loss_params else None
+            loss_ignore_label = loss_params['loss_ignore_label'] if 'loss_ignore_label' in loss_params else None
+            loss_function = SoftmaxCrossEntropyLossIgnoreLabel(axis=loss_axis, ignore_label=loss_ignore_label, weight=loss_weight, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':
@@ -475,7 +529,7 @@ class CNNSupervisedTrainer_Alexnet:
             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)]
 
                     data_ = batch.data[0].as_in_context(mx_context)

src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py

@@ -86,6 +86,53 @@ class DiceLoss(gluon.loss.Loss):
         diceloss = self.dice_loss(F, pred, label)
         return F.mean(loss, axis=self._batch_axis, exclude=True) + diceloss
 
+class SoftmaxCrossEntropyLossIgnoreLabel(gluon.loss.Loss):
+    def __init__(self, axis=-1, from_logits=False, weight=None,
+                 batch_axis=0, ignore_label=255, **kwargs):
+        super(SoftmaxCrossEntropyLossIgnoreLabel, self).__init__(weight, batch_axis, **kwargs)
+        self._axis = axis
+        self._from_logits = from_logits
+        self._ignore_label = ignore_label
+
+    def hybrid_forward(self, F, output, label, sample_weight=None):
+        if not self._from_logits:
+            output = F.log_softmax(output, axis=self._axis)
+
+        valid_label_map = (label != self._ignore_label)
+        loss = -(F.pick(output, label, axis=self._axis, keepdims=True) * valid_label_map )
+
+        loss = gluon.loss._apply_weighting(F, loss, self._weight, sample_weight)
+        return F.sum(loss) / F.sum(valid_label_map)
+
+@mx.metric.register
+class ACCURACY_IGNORE_LABEL(mx.metric.EvalMetric):
+    """Ignores a label when computing accuracy.
+    """
+    def __init__(self, axis=1, metric_ignore_label=255, name='accuracy',
+                 output_names=None, label_names=None):
+        super(ACCURACY_IGNORE_LABEL, self).__init__(
+            name, axis=axis,
+            output_names=output_names, label_names=label_names)
+        self.axis = axis
+        self.ignore_label = metric_ignore_label
+
+    def update(self, labels, preds):
+        mx.metric.check_label_shapes(labels, preds)
+
+        for label, pred_label in zip(labels, preds):
+            if pred_label.shape != label.shape:
+                pred_label = mx.nd.argmax(pred_label, axis=self.axis, keepdims=True)
+            label = label.astype('int32')
+            pred_label = pred_label.astype('int32').as_in_context(label.context)
+
+            mx.metric.check_label_shapes(label, pred_label)
+
+            correct = mx.nd.sum( (label == pred_label) * (label != self.ignore_label) ).asscalar()
+            total = mx.nd.sum( (label != self.ignore_label) ).asscalar()
+
+            self.sum_metric += correct
+            self.num_inst += total
+
 @mx.metric.register
 class BLEU(mx.metric.EvalMetric):
     N = 4
@@ -221,6 +268,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
               optimizer_params=(('learning_rate', 0.001),),
               load_checkpoint=True,
               checkpoint_period=5,
+              load_pretrained=False,
               log_period=50,
               context='gpu',
               save_attention_image=False,
@@ -265,6 +313,8 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
         begin_epoch = 0
         if load_checkpoint:
             begin_epoch = self._net_creator.load(mx_context)
+        elif load_pretrained:
+            self._net_creator.load_pretrained_weights(mx_context)
         else:
             if os.path.isdir(self._net_creator._model_dir_):
                 shutil.rmtree(self._net_creator._model_dir_)
@@ -297,6 +347,10 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
         elif loss == 'dice_loss':
             loss_weight = loss_params['loss_weight'] if 'loss_weight' in loss_params else None
             loss_function = DiceLoss(axis=loss_axis, weight=loss_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+        elif loss == 'softmax_cross_entropy_ignore_label':
+            loss_weight = loss_params['loss_weight'] if 'loss_weight' in loss_params else None
+            loss_ignore_label = loss_params['loss_ignore_label'] if 'loss_ignore_label' in loss_params else None
+            loss_function = SoftmaxCrossEntropyLossIgnoreLabel(axis=loss_axis, ignore_label=loss_ignore_label, weight=loss_weight, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':
@@ -475,7 +529,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
             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)]
 
                     data_ = batch.data[0].as_in_context(mx_context)

src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py

@@ -86,6 +86,53 @@ class DiceLoss(gluon.loss.Loss):
         diceloss = self.dice_loss(F, pred, label)
         return F.mean(loss, axis=self._batch_axis, exclude=True) + diceloss
 
+class SoftmaxCrossEntropyLossIgnoreLabel(gluon.loss.Loss):
+    def __init__(self, axis=-1, from_logits=False, weight=None,
+                 batch_axis=0, ignore_label=255, **kwargs):
+        super(SoftmaxCrossEntropyLossIgnoreLabel, self).__init__(weight, batch_axis, **kwargs)
+        self._axis = axis
+        self._from_logits = from_logits
+        self._ignore_label = ignore_label
+
+    def hybrid_forward(self, F, output, label, sample_weight=None):
+        if not self._from_logits:
+            output = F.log_softmax(output, axis=self._axis)
+
+        valid_label_map = (label != self._ignore_label)
+        loss = -(F.pick(output, label, axis=self._axis, keepdims=True) * valid_label_map )
+
+        loss = gluon.loss._apply_weighting(F, loss, self._weight, sample_weight)
+        return F.sum(loss) / F.sum(valid_label_map)
+
+@mx.metric.register
+class ACCURACY_IGNORE_LABEL(mx.metric.EvalMetric):
+    """Ignores a label when computing accuracy.
+    """
+    def __init__(self, axis=1, metric_ignore_label=255, name='accuracy',
+                 output_names=None, label_names=None):
+        super(ACCURACY_IGNORE_LABEL, self).__init__(
+            name, axis=axis,
+            output_names=output_names, label_names=label_names)
+        self.axis = axis
+        self.ignore_label = metric_ignore_label
+
+    def update(self, labels, preds):
+        mx.metric.check_label_shapes(labels, preds)
+
+        for label, pred_label in zip(labels, preds):
+            if pred_label.shape != label.shape:
+                pred_label = mx.nd.argmax(pred_label, axis=self.axis, keepdims=True)
+            label = label.astype('int32')
+            pred_label = pred_label.astype('int32').as_in_context(label.context)
+
+            mx.metric.check_label_shapes(label, pred_label)
+
+            correct = mx.nd.sum( (label == pred_label) * (label != self.ignore_label) ).asscalar()
+            total = mx.nd.sum( (label != self.ignore_label) ).asscalar()
+
+            self.sum_metric += correct
+            self.num_inst += total
+
 @mx.metric.register
 class BLEU(mx.metric.EvalMetric):
     N = 4
@@ -221,6 +268,7 @@ class CNNSupervisedTrainer_VGG16:
               optimizer_params=(('learning_rate', 0.001),),
               load_checkpoint=True,
               checkpoint_period=5,
+              load_pretrained=False,
               log_period=50,
               context='gpu',
               save_attention_image=False,
@@ -265,6 +313,8 @@ class CNNSupervisedTrainer_VGG16:
         begin_epoch = 0
         if load_checkpoint:
             begin_epoch = self._net_creator.load(mx_context)
+        elif load_pretrained:
+            self._net_creator.load_pretrained_weights(mx_context)
         else:
             if os.path.isdir(self._net_creator._model_dir_):
                 shutil.rmtree(self._net_creator._model_dir_)
@@ -297,6 +347,10 @@ class CNNSupervisedTrainer_VGG16:
         elif loss == 'dice_loss':
             loss_weight = loss_params['loss_weight'] if 'loss_weight' in loss_params else None
             loss_function = DiceLoss(axis=loss_axis, weight=loss_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+        elif loss == 'softmax_cross_entropy_ignore_label':
+            loss_weight = loss_params['loss_weight'] if 'loss_weight' in loss_params else None
+            loss_ignore_label = loss_params['loss_ignore_label'] if 'loss_ignore_label' in loss_params else None
+            loss_function = SoftmaxCrossEntropyLossIgnoreLabel(axis=loss_axis, ignore_label=loss_ignore_label, weight=loss_weight, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':
@@ -475,7 +529,7 @@ class CNNSupervisedTrainer_VGG16:
             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)]
 
                     data_ = batch.data[0].as_in_context(mx_context)

src/test/resources/target_code/ddpg/reinforcement_learning/CNNCreator_CriticNetwork.py

 import mxnet as mx
 import logging
 import os
+import shutil
 
 from CNNNet_CriticNetwork import Net_0
 
@@ -11,6 +12,7 @@ class CNNCreator_CriticNetwork:
     def __init__(self):
         self.weight_initializer = mx.init.Normal()
         self.networks = {}
+        self._weights_dir_ = None
 
     def load(self, context):
         earliestLastEpoch = None
@@ -47,6 +49,29 @@ class CNNCreator_CriticNetwork:
 
         return earliestLastEpoch
 
+    def load_pretrained_weights(self, context):
+        if os.path.isdir(self._model_dir_):
+            shutil.rmtree(self._model_dir_)
+        if self._weights_dir_ is not None:
+            for i, network in self.networks.items():
+                # param_file = self._model_prefix_ + "_" + str(i) + "_newest-0000.params"
+                param_file = None
+                if os.path.isdir(self._weights_dir_):
+                    lastEpoch = 0
+
+                    for file in os.listdir(self._weights_dir_):
+
+                        if ".params" in file and self._model_prefix_ + "_" + str(i) in file:
+                            epochStr = file.replace(".params","").replace(self._model_prefix_ + "_" + str(i) + "-","")
+                            epoch = int(epochStr)
+                            if epoch > lastEpoch:
+                                lastEpoch = epoch
+                                param_file = file
+                    logging.info("Loading pretrained weights: " + self._weights_dir_ + param_file)
+                    network.load_parameters(self._weights_dir_ + param_file, allow_missing=True, ignore_extra=True)
+                else:
+                    logging.info("No pretrained weights available at: " + self._weights_dir_ + param_file)
+
     def construct(self, context, data_mean=None, data_std=None):
         self.networks[0] = Net_0(data_mean=data_mean, data_std=data_std)
         self.networks[0].collect_params().initialize(self.weight_initializer, ctx=context)

src/test/resources/target_code/ros-ddpg/reinforcement_learning/CNNCreator_RosCriticNetwork.py

 import mxnet as mx
 import logging
 import os
+import shutil
 
 from CNNNet_RosCriticNetwork import Net_0
 
@@ -11,6 +12,7 @@ class CNNCreator_RosCriticNetwork:
     def __init__(self):
         self.weight_initializer = mx.init.Normal()
         self.networks = {}
+        self._weights_dir_ = None
 
     def load(self, context):
         earliestLastEpoch = None
@@ -47,6 +49,29 @@ class CNNCreator_RosCriticNetwork:
 
         return earliestLastEpoch
 
+    def load_pretrained_weights(self, context):
+        if os.path.isdir(self._model_dir_):
+            shutil.rmtree(self._model_dir_)
+        if self._weights_dir_ is not None:
+            for i, network in self.networks.items():
+                # param_file = self._model_prefix_ + "_" + str(i) + "_newest-0000.params"
+                param_file = None
+                if os.path.isdir(self._weights_dir_):
+                    lastEpoch = 0
+
+                    for file in os.listdir(self._weights_dir_):
+
+                        if ".params" in file and self._model_prefix_ + "_" + str(i) in file:
+                            epochStr = file.replace(".params","").replace(self._model_prefix_ + "_" + str(i) + "-","")
+                            epoch = int(epochStr)
+                            if epoch > lastEpoch:
+                                lastEpoch = epoch
+                                param_file = file
+                    logging.info("Loading pretrained weights: " + self._weights_dir_ + param_file)
+                    network.load_parameters(self._weights_dir_ + param_file, allow_missing=True, ignore_extra=True)
+                else:
+                    logging.info("No pretrained weights available at: " + self._weights_dir_ + param_file)
+
     def construct(self, context, data_mean=None, data_std=None):
         self.networks[0] = Net_0(data_mean=data_mean, data_std=data_std)
         self.networks[0].collect_params().initialize(self.weight_initializer, ctx=context)

src/test/resources/target_code/td3/reinforcement_learning/CNNCreator_CriticNetwork.py

 import mxnet as mx
 import logging
 import os
+import shutil
 
 from CNNNet_CriticNetwork import Net_0
 
@@ -11,6 +12,7 @@ class CNNCreator_CriticNetwork:
     def __init__(self):
         self.weight_initializer = mx.init.Normal()
         self.networks = {}
+        self._weights_dir_ = None