Updated docstrings, FPN docstring

Author: qubvel

segmentation_models_pytorch/fpn/model.py

@@ -4,18 +4,35 @@
 
 
 class FPN(EncoderDecoder):
+    """FPN_ is a fully convolution neural network for image semantic segmentation
+    Args:
+        encoder_name: name of classification model (without last dense layers) used as feature
+                extractor to build segmentation model.
+        encoder_weights: one of ``None`` (random initialization), ``imagenet`` (pre-training on ImageNet).
+        decoder_pyramid_channels: a number of convolution filters in Feature Pyramid of FPN_.
+        decoder_segmentation_channels: a number of convolution filters in segmentation head of FPN_.
+        classes: a number of classes for output (output shape - ``(batch, classes, h, w)``).
+        dropout: spatial dropout rate in range (0, 1).
+        activation: one of [``sigmoid``, ``softmax``, None]
+
+    Returns:
+        ``keras.models.Model``: **FPN**
+
+    .. _FPN:
+        http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf
+
+    """
 
     def __init__(
             self,
             encoder_name='resnet34',
             encoder_weights='imagenet',
             decoder_pyramid_channels=256,
-            decoder_segmenation_channels=128,
+            decoder_segmentation_channels=128,
             classes=1,
             dropout=0.2,
             activation='sigmoid',
     ):
-
         encoder = get_encoder(
             encoder_name,
             encoder_weights=encoder_weights
@@ -24,7 +41,7 @@ def __init__(
         decoder = FPNDecoder(
             encoder_channels=encoder.out_shapes,
             pyramid_channels=decoder_pyramid_channels,
-            segmentation_channels=decoder_segmenation_channels,
+            segmentation_channels=decoder_segmentation_channels,
             final_channels=classes,
             dropout=dropout,
         )

segmentation_models_pytorch/linknet/model.py

@@ -15,7 +15,7 @@ class Linknet(EncoderDecoder):
         encoder_weights: one of ``None`` (random initialization), ``imagenet`` (pre-training on ImageNet).
         decoder_use_batchnorm: if ``True``, ``BatchNormalisation`` layer between ``Conv2D`` and ``Activation`` layers
             is used.
-        classes: a number of classes for output (output shape - ``(h, w, classes)``).
+        classes: a number of classes for output (output shape - ``(batch, classes, h, w)``).
         activation: one of [``sigmoid``, ``softmax``, None]
 
     Returns:

segmentation_models_pytorch/unet/model.py

@@ -13,7 +13,7 @@ class Unet(EncoderDecoder):
         decoder_channels: list of numbers of ``Conv2D`` layer filters in decoder blocks
         decoder_use_batchnorm: if ``True``, ``BatchNormalisation`` layer between ``Conv2D`` and ``Activation`` layers
             is used.
-        classes: a number of classes for output (output shape - ``(h, w, classes)``).
+        classes: a number of classes for output (output shape - ``(batch, classes, h, w)``).
         activation: one of [``sigmoid``, ``softmax``, None]
         center: if ``True`` add ``Conv2dReLU`` block on encoder head (useful for VGG models)