bug fixes

Author: ckxz105

.travis/generate-pipelines-json.py

@@ -22,16 +22,16 @@
 
 # add templates here
 # TEMPLATE_LIST.append(UU3TestTemplate()) # no enough memory for travis ci
+TEMPLATE_LIST.append(DefaultObjectDetectionTemplate())
+TEMPLATE_LIST.append(ARIMATemplate())
 TEMPLATE_LIST.append(TA1ImageProcessingRegressionTemplate())
 TEMPLATE_LIST.append(TA1ImageProcessingRegressionTemplate2())
-TEMPLATE_LIST.append(ARIMATemplate())
 TEMPLATE_LIST.append(DefaultClassificationTemplate())
 TEMPLATE_LIST.append(DefaultClassificationTemplate2())
 TEMPLATE_LIST.append(DefaultTimeseriesCollectionTemplate())
 TEMPLATE_LIST.append(DefaultRegressionTemplate())
 TEMPLATE_LIST.append(DefaultRegressionTemplate2())
 TEMPLATE_LIST.append(VotingTemplate())
-TEMPLATE_LIST.append(DefaultObjectDetectionTemplate())
 TEMPLATE_LIST.append(HorizontalVotingTemplate())
 # ends
 

.travis/library.py

@@ -822,8 +822,9 @@ def __init__(self):
                         {
                             "primitive": "d3m.primitives.feature_extraction.yolo.DSBOX",
                             "hyperparameters": {
-                            # "use_fitted_weight": [(False)],
-                            # "epochs": [(30)]
+                            "use_fitted_weight": [(True)],
+                            # "confidences_threshold": [(0.8)],
+                            # "nms_threshold": [0.6],
                             }
                         }
                     ],

.travis/template.py

@@ -416,13 +416,13 @@ def __init__(self, config={}, pipeline_type="classification", test_dataset_id="3
 
 
 DATASET_MAPPER = {
-            'classification': "38_sick",
-            'voting_classification': '38_sick',
-            'regression': "196_autoMpg",
-            'timeseries': "uu1_datasmash",
-            'image_regression': '22_handgeometry',
-            'time_series_forecasting': 'LL1_736_stock_market',
-            'object_detection': 'LL1_penn_fudan_pedestrian',
+            'classification': "38_sick_MIN_METADATA",
+            'voting_classification': '38_sick_MIN_METADATA',
+            'regression': "196_autoMpg_MIN_METADATA",
+            'timeseries': "66_chlorineConcentration_MIN_METADATA",
+            'image_regression': '22_handgeometry_MIN_METADATA',
+            'time_series_forecasting': 'LL1_736_population_spawn_MIN_METADATA',
+            'object_detection': 'LL1_penn_fudan_pedestrian_MIN_METADATA',
             'multitable_dataset': 'uu3_world_development_indicators'
         }
 

dsbox/datapreprocessing/featurizer/image/object_detection.py

@@ -125,7 +125,7 @@ class Params(params.Params):
     output_layer: typing.List[str]
     target_column_name: str
     input_image_column_name: str
-    dump_model_path: str
+    dump_model_path: typing.Optional[str]
 
 
 class Yolo(SupervisedLearnerPrimitiveBase[Inputs, Outputs, Params, YoloHyperparams]):
@@ -191,7 +191,7 @@ def __init__(self, *, hyperparams: YoloHyperparams, volumes: typing.Union[typing
         self.warmup_steps = None
         self.total_steps = None
         self._loaded_dataset = None
-        self._dump_model_path = None
+        self._dump_model_path = ""
         self._current_phase = None
 
     def fit(self, *, timeout: float = None, iterations: int = None) -> CallResult[None]:
@@ -392,9 +392,9 @@ def _produce_for_fitted_weight(self, input_df):
             image_path = os.path.join(self._location_base_uris, each_image_name)
             each_image = cv2.imread(image_path)
             if each_image is None:
-                logger.error("loading image from " + image_path + " failed!")
+                logger.error("loading image from {} failed!".format(str(image_path)))
                 continue
-            logger.debug("Now detecting objects in", each_image_name)
+            logger.debug("Now detecting objects in {}".format(each_image_name))
             # Creates 4-dimensional blob from image.
             # swapRB has to be True, otherwise the channel is not R,G,B style
             blob = cv2.dnn.blobFromImage(each_image, self.hyperparams["blob_scale_factor"],