add pcontext 269 (zhanghang1989#268)

docs/source/model_zoo/segmentation.rst

@@ -78,12 +78,23 @@ Pascal Context Dataset
 ==============================================================================  ====================    ====================    =========================================================================================================
 Model                                                                           pixAcc                  mIoU                    Command                                                                                      
 ==============================================================================  ====================    ====================    =========================================================================================================
+FCN_ResNeSt50_PContext                                                          79.19%                  51.98%                  :raw-html:`<a href="javascript:toggleblock('cmd_fcn_nest50_pcont')" class="toggleblock">cmd</a>`
+DeepLab_ResNeSt50_PContext                                                      80.41%                  53.19%                  :raw-html:`<a href="javascript:toggleblock('cmd_deeplab_nest50_pcont')" class="toggleblock">cmd</a>`
 DeepLab_ResNeSt101_PContext                                                     81.91%                  56.49%                  :raw-html:`<a href="javascript:toggleblock('cmd_deeplab_nest101_pcont')" class="toggleblock">cmd</a>`
 DeepLab_ResNeSt200_PContext                                                     82.50%                  58.37%                  :raw-html:`<a href="javascript:toggleblock('cmd_deeplab_nest200_pcont')" class="toggleblock">cmd</a>`
+DeepLab_ResNeSt269_PContext                                                     83.06%                  58.92%                  :raw-html:`<a href="javascript:toggleblock('cmd_deeplab_nest269_pcont')" class="toggleblock">cmd</a>`
 ==============================================================================  ====================    ====================    =========================================================================================================
 
 .. raw:: html
 
+    <code xml:space="preserve" id="cmd_fcn_nest50_pcont" style="display: none; text-align: left; white-space: pre-wrap">
+    python train.py --dataset pcontext --model fcn --aux --backbone resnest50
+    </code>
+
+    <code xml:space="preserve" id="cmd_deeplab_nest50_pcont" style="display: none; text-align: left; white-space: pre-wrap">
+    python train.py --dataset pcontext --model deeplab --aux --backbone resnest50
+    </code>
+
     <code xml:space="preserve" id="cmd_deeplab_nest101_pcont" style="display: none; text-align: left; white-space: pre-wrap">
     python train.py --dataset pcontext --model deeplab --aux --backbone resnest101
     </code>
@@ -92,6 +103,9 @@ DeepLab_ResNeSt200_PContext
     python train.py --dataset pcontext --model deeplab --aux --backbone resnest200
     </code>
 
+    <code xml:space="preserve" id="cmd_deeplab_nest269_pcont" style="display: none; text-align: left; white-space: pre-wrap">
+    python train.py --dataset pcontext --model deeplab --aux --backbone resnest269
+    </code>
 
 
 ResNet Backbone Models

encoding/models/model_store.py

@@ -36,8 +36,11 @@
     ('06ca799c8cc148fe0fafb5b6d052052935aa3cc8', 'deeplab_resnest101_ade'),
     ('7b9e7d3e6f0e2c763c7d77cad14d306c0a31fe05', 'deeplab_resnest200_ade'),
     ('0074dd10a6e6696f6f521653fb98224e75955496', 'deeplab_resnest269_ade'),
+    ('77a2161deeb1564e8b9c41a4bb7a3f33998b00ad', 'fcn_resnest50_pcontext'),
+    ('08dccbc4f4694baab631e037a374d76d8108c61f', 'deeplab_resnest50_pcontext'),
     ('faf5841853aae64bd965a7bdc2cdc6e7a2b5d898', 'deeplab_resnest101_pcontext'),
     ('fe76a26551dd5dcf2d474fd37cba99d43f6e984e', 'deeplab_resnest200_pcontext'),
+    ('b661fd26c49656e01e9487cd9245babb12f37449', 'deeplab_resnest269_pcontext'),
     ]}
 
 encoding_repo_url = 'https://hangzh.s3.amazonaws.com/'

encoding/models/model_zoo.py

@@ -44,8 +44,11 @@
     'deeplab_resnest101_ade': get_deeplab_resnest101_ade,
     'deeplab_resnest200_ade': get_deeplab_resnest200_ade,
     'deeplab_resnest269_ade': get_deeplab_resnest269_ade,
+    'fcn_resnest50_pcontext': get_fcn_resnest50_pcontext,
+    'deeplab_resnest50_pcontext': get_deeplab_resnest50_pcontext,
     'deeplab_resnest101_pcontext': get_deeplab_resnest101_pcontext,
     'deeplab_resnest200_pcontext': get_deeplab_resnest200_pcontext,
+    'deeplab_resnest269_pcontext': get_deeplab_resnest269_pcontext,
 }
 
 model_list = list(models.keys())

encoding/models/sseg/deeplab.py

@@ -233,6 +233,24 @@ def get_deeplab_resnest269_ade(pretrained=False, root='~/.encoding/models', **kw
     """
     return get_deeplab('ade20k', 'resnest269', pretrained, aux=True, root=root, **kwargs)
 
+def get_deeplab_resnest50_pcontext(pretrained=False, root='~/.encoding/models', **kwargs):
+    r"""DeepLabV3 model from the paper `"Context Encoding for Semantic Segmentation"
+    <https://arxiv.org/pdf/1803.08904.pdf>`_
+
+    Parameters
+    ----------
+    pretrained : bool, default False
+        Whether to load the pretrained weights for model.
+    root : str, default '~/.encoding/models'
+        Location for keeping the model parameters.
+
+    Examples
+    --------
+    >>> model = get_deeplab_resnest101_pcontext(pretrained=True)
+    >>> print(model)
+    """
+    return get_deeplab('pcontext', 'resnest50', pretrained, aux=True, root=root, **kwargs)
+
 def get_deeplab_resnest101_pcontext(pretrained=False, root='~/.encoding/models', **kwargs):
     r"""DeepLabV3 model from the paper `"Context Encoding for Semantic Segmentation"
     <https://arxiv.org/pdf/1803.08904.pdf>`_
@@ -272,3 +290,21 @@ def get_deeplab_resnest200_pcontext(pretrained=False, root='~/.encoding/models',
     return get_deeplab('pcontext', 'resnest200', pretrained, aux=True, root=root, **kwargs)
 
 
+def get_deeplab_resnest269_pcontext(pretrained=False, root='~/.encoding/models', **kwargs):
+    r"""DeepLabV3 model from the paper `"Context Encoding for Semantic Segmentation"
+    <https://arxiv.org/pdf/1803.08904.pdf>`_
+
+    Parameters
+    ----------
+    pretrained : bool, default False
+        Whether to load the pretrained weights for model.
+    root : str, default '~/.encoding/models'
+        Location for keeping the model parameters.
+
+
+    Examples
+    --------
+    >>> model = get_deeplab_resnest269_pcontext(pretrained=True)
+    >>> print(model)
+    """
+    return get_deeplab('pcontext', 'resnest269', pretrained, aux=True, root=root, **kwargs)

encoding/models/sseg/fcn.py

@@ -14,7 +14,7 @@
 from .base import BaseNet
 
 __all__ = ['FCN', 'get_fcn', 'get_fcn_resnet50_pcontext', 'get_fcn_resnet50_ade',
-           'get_fcn_resnest50_ade']
+           'get_fcn_resnest50_ade', 'get_fcn_resnest50_pcontext']
 
 class FCN(BaseNet):
     r"""Fully Convolutional Networks for Semantic Segmentation
@@ -194,3 +194,23 @@ def get_fcn_resnest50_ade(pretrained=False, root='~/.encoding/models', **kwargs)
     """
     kwargs['aux'] = True
     return get_fcn('ade20k', 'resnest50', pretrained, root=root, **kwargs)
+
+def get_fcn_resnest50_pcontext(pretrained=False, root='~/.encoding/models', **kwargs):
+    r"""EncNet-PSP model from the paper `"Context Encoding for Semantic Segmentation"
+    <https://arxiv.org/pdf/1803.08904.pdf>`_
+
+    Parameters
+    ----------
+    pretrained : bool, default False
+        Whether to load the pretrained weights for model.
+    root : str, default '~/.encoding/models'
+        Location for keeping the model parameters.
+
+
+    Examples
+    --------
+    >>> model = get_fcn_resnet50_ade(pretrained=True)
+    >>> print(model)
+    """
+    kwargs['aux'] = True
+    return get_fcn('pcontext', 'resnest50', pretrained, root=root, **kwargs)