[Auto] dataset: remove bad _metadata (#1672)

* remove meta

* fix split reset index

* fix

* fix test

* fix test; mute warnings

* fix show images

gluoncv/auto/data/dataset.py

@@ -56,7 +56,7 @@ class ImageClassificationDataset(pd.DataFrame):
 
     """
     # preserved properties that will be copied to a new instance
-    _metadata = ['classes', 'to_mxnet', 'show_images', 'random_split', 'IMG_COL', 'LABEL_COL']
+    _metadata = ['classes', 'IMG_COL', 'LABEL_COL']
 
     def __init__(self, data, classes=None, image_column='image', label_column='label', **kwargs):
         root = kwargs.pop('root', None)
@@ -131,27 +131,29 @@ def show_images(self, indices=None, nsample=16, ncol=4, shuffle=True, resize=224
         fontsize : int, optional
             The fontsize for the title
         """
+        df = self.reset_index(drop=True)
         if indices is None:
             if not shuffle:
                 indices = range(nsample)
             else:
-                indices = list(range(len(self)))
+                indices = list(range(len(df)))
                 np.random.shuffle(indices)
                 indices = indices[:min(nsample, len(indices))]
-        images = [cv2.cvtColor(cv2.resize(cv2.imread(self.at[idx, self.IMG_COL]), (resize, resize), \
-            interpolation=cv2.INTER_AREA), cv2.COLOR_BGR2RGB) for idx in indices if idx < len(self)]
+        images = [cv2.cvtColor(cv2.resize(cv2.imread(df.at[idx, df.IMG_COL]), (resize, resize), \
+            interpolation=cv2.INTER_AREA), cv2.COLOR_BGR2RGB) for idx in indices if idx < len(df)]
         titles = None
-        if self.LABEL_COL in self.columns:
-            if self.classes:
-                titles = [self.classes[int(self.at[idx, self.LABEL_COL])] + ': ' + str(self.at[idx, self.LABEL_COL]) \
-                    for idx in indices if idx < len(self)]
+        if df.LABEL_COL in df.columns:
+            if df.classes:
+                titles = [df.classes[int(df.at[idx, df.LABEL_COL])] + ': ' + str(df.at[idx, df.LABEL_COL]) \
+                    for idx in indices if idx < len(df)]
             else:
-                titles = [str(self.at[idx, self.LABEL_COL]) for idx in indices if idx < len(self)]
+                titles = [str(df.at[idx, df.LABEL_COL]) for idx in indices if idx < len(df)]
         _show_images(images, cols=ncol, titles=titles, fontsize=fontsize)
 
     def to_mxnet(self):
         """Return a mxnet based iterator that returns ndarray and labels"""
         df = self.rename(columns={self.IMG_COL: "image", self.LABEL_COL: "label"}, errors='ignore')
+        df = df.reset_index(drop=True)
         return _MXImageClassificationDataset(df)
 
     @classmethod
@@ -398,8 +400,7 @@ class ObjectDetectionDataset(pd.DataFrame):
 
     """
     # preserved properties that will be copied to a new instance
-    _metadata = ['dataset_type', 'classes', 'pack', 'unpack', 'is_packed',
-                 'to_mxnet', 'color_map', 'show_images', 'random_split']
+    _metadata = ['dataset_type', 'classes', 'color_map']
 
     def __init__(self, data, dataset_type=None, classes=None, **kwargs):
         # dataset_type will be used to determine metrics, if None then auto resolve at runtime
@@ -606,7 +607,8 @@ def is_packed(self):
 
     def to_mxnet(self):
         """Return a mxnet based iterator that returns ndarray and labels"""
-        return _MXObjectDetectionDataset(self)
+        df = self.reset_index(drop=True)
+        return _MXObjectDetectionDataset(df)
 
     def random_split(self, test_size=0.1, val_size=0, random_state=None):
         r"""Randomly split the dataset into train/val/test sets.
@@ -661,6 +663,7 @@ def show_images(self, indices=None, nsample=16, ncol=4, shuffle=True, resize=512
             The fontsize for title
         """
         df = self.pack()
+        df = df.reset_index(drop=True)
         if indices is None:
             if not shuffle:
                 indices = range(nsample)

gluoncv/auto/estimators/center_net/center_net.py

@@ -320,7 +320,9 @@ def _init_network(self, **kwargs):
                               self._cfg.center_net.transfer)
             net = get_model(self._cfg.center_net.transfer, pretrained=(not load_only))
             if load_only:
-                net.initialize()
+                with warnings.catch_warnings(record=True) as w:
+                    warnings.simplefilter("always")
+                    net.initialize()
                 net(mx.nd.zeros((1, 3, self._cfg.center_net.data_shape[0], self._cfg.center_net.data_shape[1])))
             net.reset_class(self.classes, reuse_weights=[cname for cname in self.classes if cname in net.classes])
         else:

gluoncv/auto/estimators/faster_rcnn/faster_rcnn.py

@@ -406,7 +406,9 @@ def _init_network(self, **kwargs):
                                      **kwargs)
                 self.net.sampler._max_num_gt = self._cfg.faster_rcnn.max_num_gt
             if load_only:
-                self.net.initialize()
+                with warnings.catch_warnings(record=True) as w:
+                    warnings.simplefilter("always")
+                    self.net.initialize()
                 self.net.set_nms(nms_thresh=0)
                 self.net(mx.nd.zeros((1, 3, 600, 800)))
                 self.net.set_nms(nms_thresh=self._cfg.faster_rcnn.nms_thresh)

gluoncv/auto/estimators/ssd/ssd.py

@@ -361,7 +361,9 @@ def _init_network(self, **kwargs):
                                          norm_kwargs={'num_devices': len(self.ctx)})
                     self.async_net = get_model(self._cfg.ssd.transfer, pretrained=(not load_only))  # used by cpu worker
                 if load_only:
-                    self.net.initialize()
+                    with warnings.catch_warnings(record=True) as w:
+                        warnings.simplefilter("always")
+                        self.net.initialize()
                     self.net.set_nms(nms_thresh=0)
                     self.net(mx.nd.zeros((1, 3, self._cfg.ssd.data_shape, self._cfg.ssd.data_shape)))
                     self.net.set_nms(nms_thresh=self._cfg.ssd.nms_thresh, nms_topk=self._cfg.ssd.nms_topk)
@@ -371,7 +373,9 @@ def _init_network(self, **kwargs):
                 self.net = get_model(self._cfg.ssd.transfer, pretrained=(not load_only), norm_layer=gluon.nn.BatchNorm)
                 self.async_net = get_model(self._cfg.ssd.transfer, pretrained=(not load_only), norm_layer=gluon.nn.BatchNorm)
                 if load_only:
-                    self.net.initialize()
+                    with warnings.catch_warnings(record=True) as w:
+                        warnings.simplefilter("always")
+                        self.net.initialize()
                     self.net.set_nms(nms_thresh=0)
                     self.net(mx.nd.zeros((1, 3, self._cfg.ssd.data_shape, self._cfg.ssd.data_shape)))
                     self.net.set_nms(nms_thresh=self._cfg.ssd.nms_thresh, nms_topk=self._cfg.ssd.nms_topk)

gluoncv/auto/estimators/yolo/yolo.py

@@ -391,7 +391,9 @@ def _init_network(self, **kwargs):
                 self.net = get_model(self._cfg.yolo3.transfer, pretrained=(not load_only))
                 self.async_net = self.net
             if load_only:
-                self.net.initialize()
+                with warnings.catch_warnings(record=True) as w:
+                    warnings.simplefilter("always")
+                    self.net.initialize()
                 self.net.set_nms(nms_thresh=0)
                 self.net(mx.nd.zeros((1, 3, self._cfg.yolo3.data_shape, self._cfg.yolo3.data_shape)))
                 self.net.set_nms(nms_thresh=self._cfg.yolo3.nms_thresh, nms_topk=self._cfg.yolo3.nms_topk)

tests/auto/test_auto_estimators.py

@@ -18,6 +18,7 @@
 # under the License.
 """Test auto estimators"""
 from PIL import Image
+import numpy as np
 from gluoncv.auto.tasks import ImageClassification, ImagePrediction
 from gluoncv.auto.tasks import ObjectDetection
 from autogluon.core.scheduler.resource import get_cpu_count, get_gpu_count
@@ -43,7 +44,7 @@ def test_image_regression_estimator():
     est.predict_feature(IMAGE_REGRESS_TEST.iloc[0]['image'])
     # test save/load
     _save_load_test(est, 'img_regression.pkl')
-    
+
 def test_image_classification_estimator():
     from gluoncv.auto.estimators import ImageClassificationEstimator
     est = ImageClassificationEstimator({'train': {'epochs': 1, 'batch_size': 8}, 'gpus': list(range(get_gpu_count()))})
@@ -104,7 +105,7 @@ def test_ssd_estimator():
     # test save/load
     est2 = _save_load_test(est, 'ssd.pkl')
     evaluate_result2 = est2.evaluate(OBJECT_DETECTION_VAL)
-    assert evaluate_result == evaluate_result2, f'{evaluate_result} != \n {evaluate_result2}'
+    np.testing.assert_array_equal(evaluate_result, evaluate_result2, err_msg=f'{evaluate_result} != \n {evaluate_result2}')
 
 def test_yolo3_estimator():
     from gluoncv.auto.estimators import YOLOv3Estimator
@@ -119,7 +120,7 @@ def test_yolo3_estimator():
     # test save/load
     est2 = _save_load_test(est, 'yolo3.pkl')
     evaluate_result2 = est2.evaluate(OBJECT_DETECTION_VAL)
-    assert evaluate_result == evaluate_result2, f'{evaluate_result} != \n {evaluate_result2}'
+    np.testing.assert_array_equal(evaluate_result, evaluate_result2, err_msg=f'{evaluate_result} != \n {evaluate_result2}')
 
 def test_frcnn_estimator():
     from gluoncv.auto.estimators import FasterRCNNEstimator