Add missing docstring (#1159)

gluoncv/model_zoo/action_recognition/actionrec_inceptionv1.py

@@ -19,26 +19,35 @@ class ActionRecInceptionV1(HybridBlock):
 
     Parameters
     ----------
-    nclass : int, number of classes
-    pretrained_base : bool, load pre-trained weights or not
-    dropout_ratio : float, add a dropout layer to prevent overfitting on small datasets, such as UCF101
-    init_std : float, standard deviation value when initialize the last classification layer
-    feat_dim : int, feature dimension. Default is 4096 for VGG16 network
-    num_segments : int, number of segments used
-    num_crop : int, number of crops used during evaluation. Default choice is 1, 3 or 10
+    nclass : int
+        Number of classes in the training dataset.
+    pretrained_base : bool or str, optional, default is True.
+        Load pretrained base network, the extra layers are randomized. Note that
+        if pretrained is `True`, this has no effect.
+    partial_bn : bool, default False.
+        Freeze all batch normalization layers during training except the first layer.
+    dropout_ratio : float, default is 0.5.
+        The dropout rate of a dropout layer.
+        The larger the value, the more strength to prevent overfitting.
+    init_std : float, default is 0.001.
+        Standard deviation value when initialize the dense layers.
+    num_segments : int, default is 1.
+        Number of segments used to evenly divide a video.
+    num_crop : int, default is 1.
+        Number of crops used during evaluation, choices are 1, 3 or 10.
 
     Input: a single video frame or N images from N segments when num_segments > 1
     Output: a single predicted action label
     """
     def __init__(self, nclass, pretrained_base=True,
-                 partial_bn=True, dropout_ratio=0.8, init_std=0.001,
-                 feat_dim=1024, num_segments=1, num_crop=1, **kwargs):
+                 partial_bn=True, dropout_ratio=0.5, init_std=0.001,
+                 num_segments=1, num_crop=1, **kwargs):
         super(ActionRecInceptionV1, self).__init__()
         self.dropout_ratio = dropout_ratio
         self.init_std = init_std
         self.num_segments = num_segments
         self.num_crop = num_crop
-        self.feat_dim = feat_dim
+        self.feat_dim = 1024
 
         pretrained_model = googlenet(pretrained=pretrained_base, partial_bn=partial_bn, **kwargs)
 
@@ -104,6 +113,29 @@ def hybrid_forward(self, F, x):
 def inceptionv1_ucf101(nclass=101, pretrained=False, pretrained_base=True,
                        use_tsn=False, num_segments=1, num_crop=1, partial_bn=True,
                        ctx=mx.cpu(), root='~/.mxnet/models', **kwargs):
+    r"""InceptionV1 model trained on UCF101 dataset.
+
+    Parameters
+    ----------
+    nclass : int.
+        Number of categories in the dataset.
+    pretrained : bool or str.
+        Boolean value controls whether to load the default pretrained weights for model.
+        String value represents the hashtag for a certain version of pretrained weights.
+    pretrained_base : bool or str, optional, default is True.
+        Load pretrained base network, the extra layers are randomized. Note that
+        if pretrained is `True`, this has no effect.
+    ctx : Context, default CPU.
+        The context in which to load the pretrained weights.
+    root : str, default $MXNET_HOME/models
+        Location for keeping the model parameters.
+    num_segments : int, default is 1.
+        Number of segments used to evenly divide a video.
+    num_crop : int, default is 1.
+        Number of crops used during evaluation, choices are 1, 3 or 10.
+    partial_bn : bool, default False.
+        Freeze all batch normalization layers during training except the first layer.
+    """
     model = ActionRecInceptionV1(nclass=nclass,
                                  partial_bn=partial_bn,
                                  pretrained_base=pretrained_base,
@@ -125,6 +157,29 @@ def inceptionv1_ucf101(nclass=101, pretrained=False, pretrained_base=True,
 def inceptionv1_hmdb51(nclass=51, pretrained=False, pretrained_base=True,
                        use_tsn=False, num_segments=1, num_crop=1, partial_bn=True,
                        ctx=mx.cpu(), root='~/.mxnet/models', **kwargs):
+    r"""InceptionV1 model trained on HMDB51 dataset.
+
+    Parameters
+    ----------
+    nclass : int.
+        Number of categories in the dataset.
+    pretrained : bool or str.
+        Boolean value controls whether to load the default pretrained weights for model.
+        String value represents the hashtag for a certain version of pretrained weights.
+    pretrained_base : bool or str, optional, default is True.
+        Load pretrained base network, the extra layers are randomized. Note that
+        if pretrained is `True`, this has no effect.
+    ctx : Context, default CPU.
+        The context in which to load the pretrained weights.
+    root : str, default $MXNET_HOME/models
+        Location for keeping the model parameters.
+    num_segments : int, default is 1.
+        Number of segments used to evenly divide a video.
+    num_crop : int, default is 1.
+        Number of crops used during evaluation, choices are 1, 3 or 10.
+    partial_bn : bool, default False.
+        Freeze all batch normalization layers during training except the first layer.
+    """
     model = ActionRecInceptionV1(nclass=nclass,
                                  partial_bn=partial_bn,
                                  pretrained_base=pretrained_base,
@@ -146,6 +201,29 @@ def inceptionv1_hmdb51(nclass=51, pretrained=False, pretrained_base=True,
 def inceptionv1_kinetics400(nclass=400, pretrained=False, pretrained_base=True,
                             tsn=False, num_segments=1, num_crop=1, partial_bn=True,
                             ctx=mx.cpu(), root='~/.mxnet/models', **kwargs):
+    r"""InceptionV1 model trained on Kinetics400 dataset.
+
+    Parameters
+    ----------
+    nclass : int.
+        Number of categories in the dataset.
+    pretrained : bool or str.
+        Boolean value controls whether to load the default pretrained weights for model.
+        String value represents the hashtag for a certain version of pretrained weights.
+    pretrained_base : bool or str, optional, default is True.
+        Load pretrained base network, the extra layers are randomized. Note that
+        if pretrained is `True`, this has no effect.
+    ctx : Context, default CPU.
+        The context in which to load the pretrained weights.
+    root : str, default $MXNET_HOME/models
+        Location for keeping the model parameters.
+    num_segments : int, default is 1.
+        Number of segments used to evenly divide a video.
+    num_crop : int, default is 1.
+        Number of crops used during evaluation, choices are 1, 3 or 10.
+    partial_bn : bool, default False.
+        Freeze all batch normalization layers during training except the first layer.
+    """
     model = ActionRecInceptionV1(nclass=nclass,
                                  partial_bn=partial_bn,
                                  pretrained_base=pretrained_base,
@@ -167,6 +245,29 @@ def inceptionv1_kinetics400(nclass=400, pretrained=False, pretrained_base=True,
 def inceptionv1_sthsthv2(nclass=174, pretrained=False, pretrained_base=True,
                          tsn=False, num_segments=1, num_crop=1, partial_bn=True,
                          ctx=mx.cpu(), root='~/.mxnet/models', **kwargs):
+    r"""InceptionV1 model trained on Something-Something-V2 dataset.
+
+    Parameters
+    ----------
+    nclass : int.
+        Number of categories in the dataset.
+    pretrained : bool or str.
+        Boolean value controls whether to load the default pretrained weights for model.
+        String value represents the hashtag for a certain version of pretrained weights.
+    pretrained_base : bool or str, optional, default is True.
+        Load pretrained base network, the extra layers are randomized. Note that
+        if pretrained is `True`, this has no effect.
+    ctx : Context, default CPU.
+        The context in which to load the pretrained weights.
+    root : str, default $MXNET_HOME/models
+        Location for keeping the model parameters.
+    num_segments : int, default is 1.
+        Number of segments used to evenly divide a video.
+    num_crop : int, default is 1.
+        Number of crops used during evaluation, choices are 1, 3 or 10.
+    partial_bn : bool, default False.
+        Freeze all batch normalization layers during training except the first layer.
+    """
     model = ActionRecInceptionV1(nclass=nclass,
                                  partial_bn=partial_bn,
                                  pretrained_base=pretrained_base,

gluoncv/model_zoo/action_recognition/actionrec_inceptionv3.py

@@ -19,26 +19,35 @@ class ActionRecInceptionV3(HybridBlock):
 
     Parameters
     ----------
-    nclass : int, number of classes
-    pretrained_base : bool, load pre-trained weights or not
-    dropout_ratio : float, add a dropout layer to prevent overfitting on small datasets, such as UCF101
-    init_std : float, standard deviation value when initialize the last classification layer
-    feat_dim : int, feature dimension. Default is 4096 for VGG16 network
-    num_segments : int, number of segments used
-    num_crop : int, number of crops used during evaluation. Default choice is 1, 3 or 10
+    nclass : int
+        Number of classes in the training dataset.
+    pretrained_base : bool or str, optional, default is True.
+        Load pretrained base network, the extra layers are randomized. Note that
+        if pretrained is `True`, this has no effect.
+    partial_bn : bool, default False.
+        Freeze all batch normalization layers during training except the first layer.
+    dropout_ratio : float, default is 0.5.
+        The dropout rate of a dropout layer.
+        The larger the value, the more strength to prevent overfitting.
+    init_std : float, default is 0.001.
+        Standard deviation value when initialize the dense layers.
+    num_segments : int, default is 1.
+        Number of segments used to evenly divide a video.
+    num_crop : int, default is 1.
+        Number of crops used during evaluation, choices are 1, 3 or 10.
 
     Input: a single video frame or N images from N segments when num_segments > 1
     Output: a single predicted action label
     """
     def __init__(self, nclass, pretrained_base=True,
-                 partial_bn=True, dropout_ratio=0.8, init_std=0.001,
-                 feat_dim=2048, num_segments=1, num_crop=1, **kwargs):
+                 partial_bn=True, dropout_ratio=0.5, init_std=0.001,
+                 num_segments=1, num_crop=1, **kwargs):
         super(ActionRecInceptionV3, self).__init__()
         self.dropout_ratio = dropout_ratio
         self.init_std = init_std
         self.num_segments = num_segments
         self.num_crop = num_crop
-        self.feat_dim = feat_dim
+        self.feat_dim = 2048
 
         pretrained_model = inception_v3(pretrained=pretrained_base, partial_bn=partial_bn, **kwargs)
         self.features = pretrained_model.features
@@ -63,6 +72,29 @@ def hybrid_forward(self, F, x):
 def inceptionv3_ucf101(nclass=101, pretrained=False, pretrained_base=True,
                        use_tsn=False, num_segments=1, num_crop=1, partial_bn=True,
                        ctx=mx.cpu(), root='~/.mxnet/models', **kwargs):
+    r"""InceptionV3 model trained on UCF101 dataset.
+
+    Parameters
+    ----------
+    nclass : int.
+        Number of categories in the dataset.
+    pretrained : bool or str.
+        Boolean value controls whether to load the default pretrained weights for model.
+        String value represents the hashtag for a certain version of pretrained weights.
+    pretrained_base : bool or str, optional, default is True.
+        Load pretrained base network, the extra layers are randomized. Note that
+        if pretrained is `True`, this has no effect.
+    ctx : Context, default CPU.
+        The context in which to load the pretrained weights.
+    root : str, default $MXNET_HOME/models
+        Location for keeping the model parameters.
+    num_segments : int, default is 1.
+        Number of segments used to evenly divide a video.
+    num_crop : int, default is 1.
+        Number of crops used during evaluation, choices are 1, 3 or 10.
+    partial_bn : bool, default False.
+        Freeze all batch normalization layers during training except the first layer.
+    """
     model = ActionRecInceptionV3(nclass=nclass,
                                  partial_bn=partial_bn,
                                  pretrained_base=pretrained_base,
@@ -84,6 +116,29 @@ def inceptionv3_ucf101(nclass=101, pretrained=False, pretrained_base=True,
 def inceptionv3_hmdb51(nclass=51, pretrained=False, pretrained_base=True,
                        use_tsn=False, num_segments=1, num_crop=1, partial_bn=True,
                        ctx=mx.cpu(), root='~/.mxnet/models', **kwargs):
+    r"""InceptionV3 model trained on HMDB51 dataset.
+
+    Parameters
+    ----------
+    nclass : int.
+        Number of categories in the dataset.
+    pretrained : bool or str.
+        Boolean value controls whether to load the default pretrained weights for model.
+        String value represents the hashtag for a certain version of pretrained weights.
+    pretrained_base : bool or str, optional, default is True.
+        Load pretrained base network, the extra layers are randomized. Note that
+        if pretrained is `True`, this has no effect.
+    ctx : Context, default CPU.
+        The context in which to load the pretrained weights.
+    root : str, default $MXNET_HOME/models
+        Location for keeping the model parameters.
+    num_segments : int, default is 1.
+        Number of segments used to evenly divide a video.
+    num_crop : int, default is 1.
+        Number of crops used during evaluation, choices are 1, 3 or 10.
+    partial_bn : bool, default False.
+        Freeze all batch normalization layers during training except the first layer.
+    """
     model = ActionRecInceptionV3(nclass=nclass,
                                  partial_bn=partial_bn,
                                  pretrained_base=pretrained_base,
@@ -105,6 +160,29 @@ def inceptionv3_hmdb51(nclass=51, pretrained=False, pretrained_base=True,
 def inceptionv3_kinetics400(nclass=400, pretrained=False, pretrained_base=True,
                             tsn=False, num_segments=1, num_crop=1, partial_bn=True,
                             ctx=mx.cpu(), root='~/.mxnet/models', **kwargs):
+    r"""InceptionV3 model trained on Kinetics400 dataset.
+
+    Parameters
+    ----------
+    nclass : int.
+        Number of categories in the dataset.
+    pretrained : bool or str.
+        Boolean value controls whether to load the default pretrained weights for model.
+        String value represents the hashtag for a certain version of pretrained weights.
+    pretrained_base : bool or str, optional, default is True.
+        Load pretrained base network, the extra layers are randomized. Note that
+        if pretrained is `True`, this has no effect.
+    ctx : Context, default CPU.
+        The context in which to load the pretrained weights.
+    root : str, default $MXNET_HOME/models
+        Location for keeping the model parameters.
+    num_segments : int, default is 1.
+        Number of segments used to evenly divide a video.
+    num_crop : int, default is 1.
+        Number of crops used during evaluation, choices are 1, 3 or 10.
+    partial_bn : bool, default False.
+        Freeze all batch normalization layers during training except the first layer.
+    """
     model = ActionRecInceptionV3(nclass=nclass,
                                  partial_bn=partial_bn,
                                  pretrained_base=pretrained_base,
@@ -126,6 +204,29 @@ def inceptionv3_kinetics400(nclass=400, pretrained=False, pretrained_base=True,
 def inceptionv3_sthsthv2(nclass=174, pretrained=False, pretrained_base=True,
                          tsn=False, num_segments=1, num_crop=1, partial_bn=True,
                          ctx=mx.cpu(), root='~/.mxnet/models', **kwargs):
+    r"""InceptionV3 model trained on Something-Something-V2 dataset.
+
+    Parameters
+    ----------
+    nclass : int.
+        Number of categories in the dataset.
+    pretrained : bool or str.
+        Boolean value controls whether to load the default pretrained weights for model.
+        String value represents the hashtag for a certain version of pretrained weights.
+    pretrained_base : bool or str, optional, default is True.
+        Load pretrained base network, the extra layers are randomized. Note that
+        if pretrained is `True`, this has no effect.
+    ctx : Context, default CPU.
+        The context in which to load the pretrained weights.
+    root : str, default $MXNET_HOME/models
+        Location for keeping the model parameters.
+    num_segments : int, default is 1.
+        Number of segments used to evenly divide a video.
+    num_crop : int, default is 1.
+        Number of crops used during evaluation, choices are 1, 3 or 10.
+    partial_bn : bool, default False.
+        Freeze all batch normalization layers during training except the first layer.
+    """
     model = ActionRecInceptionV3(nclass=nclass,
                                  partial_bn=partial_bn,
                                  pretrained_base=pretrained_base,