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Commit 0c78a6e

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qubvelqubvel
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Merge branch 'docstrings'
2 parents 92f3ae4 + 569c238 commit 0c78a6e
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‎segmentation_models_pytorch/fpn/model.py

Copy file name to clipboardExpand all lines: segmentation_models_pytorch/fpn/model.py
+20-3Lines changed: 20 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -4,18 +4,35 @@
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class FPN(EncoderDecoder):
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"""FPN_ is a fully convolution neural network for image semantic segmentation
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Args:
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encoder_name: name of classification model (without last dense layers) used as feature
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extractor to build segmentation model.
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encoder_weights: one of ``None`` (random initialization), ``imagenet`` (pre-training on ImageNet).
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decoder_pyramid_channels: a number of convolution filters in Feature Pyramid of FPN_.
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decoder_segmentation_channels: a number of convolution filters in segmentation head of FPN_.
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classes: a number of classes for output (output shape - ``(batch, classes, h, w)``).
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dropout: spatial dropout rate in range (0, 1).
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activation: one of [``sigmoid``, ``softmax``, None]
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Returns:
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``keras.models.Model``: **FPN**
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.. _FPN:
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http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf
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"""
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def __init__(
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self,
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encoder_name='resnet34',
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encoder_weights='imagenet',
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decoder_pyramid_channels=256,
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decoder_segmenation_channels=128,
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decoder_segmentation_channels=128,
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classes=1,
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dropout=0.2,
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activation='sigmoid',
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):
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encoder = get_encoder(
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encoder_name,
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encoder_weights=encoder_weights
@@ -24,7 +41,7 @@ def __init__(
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decoder = FPNDecoder(
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encoder_channels=encoder.out_shapes,
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pyramid_channels=decoder_pyramid_channels,
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segmentation_channels=decoder_segmenation_channels,
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segmentation_channels=decoder_segmentation_channels,
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final_channels=classes,
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dropout=dropout,
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)

‎segmentation_models_pytorch/linknet/model.py

Copy file name to clipboardExpand all lines: segmentation_models_pytorch/linknet/model.py
+20-1Lines changed: 20 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -4,6 +4,26 @@
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class Linknet(EncoderDecoder):
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"""Linknet_ is a fully convolution neural network for fast image semantic segmentation
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Note:
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This implementation by default has 4 skip connections (original - 3).
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Args:
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encoder_name: name of classification model (without last dense layers) used as feature
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extractor to build segmentation model.
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encoder_weights: one of ``None`` (random initialization), ``imagenet`` (pre-training on ImageNet).
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decoder_use_batchnorm: if ``True``, ``BatchNormalisation`` layer between ``Conv2D`` and ``Activation`` layers
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is used.
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classes: a number of classes for output (output shape - ``(batch, classes, h, w)``).
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activation: one of [``sigmoid``, ``softmax``, None]
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Returns:
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``torch.nn.Module``: **Linknet**
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.. _Linknet:
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https://arxiv.org/pdf/1707.03718.pdf
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"""
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def __init__(
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self,
@@ -13,7 +33,6 @@ def __init__(
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classes=1,
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activation='sigmoid',
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):
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encoder = get_encoder(
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encoder_name,
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encoder_weights=encoder_weights

‎segmentation_models_pytorch/pspnet/model.py

Copy file name to clipboardExpand all lines: segmentation_models_pytorch/pspnet/model.py
+22-1Lines changed: 22 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -4,6 +4,28 @@
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class PSPNet(EncoderDecoder):
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"""PSPNet_ is a fully convolution neural network for image semantic segmentation
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Args:
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encoder_name: name of classification model used as feature
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extractor to build segmentation model.
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encoder_weights: one of ``None`` (random initialization), ``imagenet`` (pre-training on ImageNet).
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psp_in_factor: one of 4, 8 and 16. Downsampling rate or in other words backbone depth
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to construct PSP module on it.
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psp_out_channels: number of filters in PSP block.
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psp_use_batchnorm: if ``True``, ``BatchNormalisation`` layer between ``Conv2D`` and ``Activation`` layers
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is used.
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psp_aux_output: if ``True`` add auxiliary classification output for encoder training
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psp_dropout: spatial dropout rate between 0 and 1.
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classes: a number of classes for output (output shape - ``(batch, classes, h, w)``).
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activation: one of [``sigmoid``, ``softmax``, None]
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Returns:
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``torch.nn.Module``: **PSPNet**
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.. _PSPNet:
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https://arxiv.org/pdf/1612.01105.pdf
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"""
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def __init__(
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self,
@@ -17,7 +39,6 @@ def __init__(
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dropout=0.2,
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activation='softmax',
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):
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encoder = get_encoder(
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encoder_name,
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encoder_weights=encoder_weights

‎segmentation_models_pytorch/unet/model.py

Copy file name to clipboardExpand all lines: segmentation_models_pytorch/unet/model.py
+20Lines changed: 20 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -4,6 +4,26 @@
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class Unet(EncoderDecoder):
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"""Unet_ is a fully convolution neural network for image semantic segmentation
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Args:
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encoder_name: name of classification model (without last dense layers) used as feature
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extractor to build segmentation model.
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encoder_weights: one of ``None`` (random initialization), ``imagenet`` (pre-training on ImageNet).
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decoder_channels: list of numbers of ``Conv2D`` layer filters in decoder blocks
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decoder_use_batchnorm: if ``True``, ``BatchNormalisation`` layer between ``Conv2D`` and ``Activation`` layers
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is used.
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classes: a number of classes for output (output shape - ``(batch, classes, h, w)``).
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activation: one of [``sigmoid``, ``softmax``, None]
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center: if ``True`` add ``Conv2dReLU`` block on encoder head (useful for VGG models)
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Returns:
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``torch.nn.Module``: **Unet**
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.. _Unet:
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https://arxiv.org/pdf/1505.04597
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"""
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def __init__(
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self,

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