v0.4.2

docs/source/_templates/layout.html

@@ -0,0 +1,7 @@
+{% extends "!layout.html" %}
+
+{%- block extrahead %} 
+
+
+  <script type="text/javascript" src="http://zhanghang1989.github.io/files/hidebib.js"></script>    
+{% endblock %}

docs/source/dilated.rst

@@ -50,37 +50,3 @@ ResNet
 ~~~~~~~~~~~~~~~~~~~
 
 .. autofunction:: resnet152
-
-
-DenseNet
---------
-
-:hidden:`DenseNet`
-~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: DenseNet
-    :members:
-
-
-:hidden:`densenet161`
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: densenet161
-
-
-:hidden:`densenet121`
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: densenet121
-
-
-:hidden:`densenet169`
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: densenet169
-
-
-:hidden:`densenet201`
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: densenet201

docs/source/experiments/segmentation.rst

@@ -0,0 +1,114 @@
+Context Encoding for Semantic Segmentation (EncNet)
+===================================================
+
+Install Package
+---------------
+
+- Clone the GitHub repo::
+    
+    git clone git@github.com:zhanghang1989/PyTorch-Encoding.git
+
+- Install PyTorch Encoding (if not yet). Please follow the installation guide `Installing PyTorch Encoding <../notes/compile.html>`_.
+
+Test Pre-trained Model
+----------------------
+
+.. hint::
+    The model names contain the training information. For instance ``FCN_ResNet50_PContext``:
+      - ``FCN`` indicate the algorithm is “Fully Convolutional Network for Semantic Segmentation”
+      - ``ResNet50`` is the name of backbone network.
+      - ``PContext`` means the PASCAL in Context dataset.
+
+    How to get pretrained model, for example ``FCN_ResNet50_PContext``::
+
+        model = encoding.models.get_model('FCN_ResNet50_PContext', pretrained=True)
+
+    The test script is in the ``experiments/segmentation/`` folder. For evaluating the model (using MS),
+    for example ``Encnet_ResNet50_PContext``::
+
+        python test.py --dataset PContext --model-zoo Encnet_ResNet50_PContext --eval
+        # pixAcc: 0.7862, mIoU: 0.4946: 100%|████████████████████████| 319/319 [09:44<00:00,  1.83s/it]
+
+    The command for training the model can be found by clicking ``cmd`` in the table.
+
+.. role:: raw-html(raw)
+   :format: html
+
++----------------------------------+-----------+-----------+---------------------------------------------------------------------------------------------+
+| Model                            | pixAcc    | mIoU      | Command                                                                                     |
++==================================+===========+===========+=============================================================================================+
+| FCN_ResNet50_PContext            | 76.0%     | 45.7      | :raw-html:`<a href="javascript:toggleblock('cmd_fcn50_pcont')" class="toggleblock">cmd</a>` |
++----------------------------------+-----------+-----------+---------------------------------------------------------------------------------------------+
+| Encnet_ResNet50_PContext         | 78.6%     | 49.5      | :raw-html:`<a href="javascript:toggleblock('cmd_enc50_pcont')" class="toggleblock">cmd</a>` |
++----------------------------------+-----------+-----------+---------------------------------------------------------------------------------------------+
+
+.. raw:: html
+
+    <code xml:space="preserve" id="cmd_fcn50_pcont" style="display: none; text-align: left; white-space: pre-wrap">
+    CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset PContext --model FCN
+    </code>
+
+    <code xml:space="preserve" id="cmd_enc50_pcont" style="display: none; text-align: left; white-space: pre-wrap">
+    CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset PContext --model EncNet --aux --se-loss
+    </code>
+
+Quick Demo
+~~~~~~~~~~
+
+.. code-block:: python
+
+    import torch
+    import encoding
+
+    # Get the model
+    model = encoding.models.get_model('Encnet_ResNet50_PContext', pretrained=True).cuda()
+    model.eval()
+
+    # Prepare the image
+    url = 'https://github.com/zhanghang1989/image-data/blob/master/' + \
+          'encoding/segmentation/pcontext/2010_001829_org.jpg?raw=true'
+    filename = 'example.jpg'
+    img = encoding.utils.load_image(
+        encoding.utils.download(url, filename)).cuda().unsqueeze(0)
+
+    # Make prediction
+    output = model.evaluate(img)
+    predict = torch.max(output, 1)[1].cpu().numpy() + 1
+
+    # Get color pallete for visualization
+    mask = encoding.utils.get_mask_pallete(predict, 'pcontext')
+    mask.save('output.png')
+
+
+.. image:: https://raw.githubusercontent.com/zhanghang1989/image-data/master/encoding/segmentation/pcontext/2010_001829_org.jpg
+   :width: 45%
+
+.. image:: https://raw.githubusercontent.com/zhanghang1989/image-data/master/encoding/segmentation/pcontext/2010_001829.png
+   :width: 45%
+
+Train Your Own Model
+--------------------
+
+- Prepare the datasets by runing the scripts in the ``scripts/`` folder, for example preparing ``PASCAL Context`` dataset::
+
+    python scripts/prepare_pcontext.py
+
+- The training script is in the ``experiments/segmentation/`` folder, example training command::
+
+    CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset pcontext --model encnet --aux --se-loss
+
+- Detail training options, please run ``python train.py -h``.
+
+Citation
+--------
+
+.. note::
+    * Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal. "Context Encoding for Semantic Segmentation"  *The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018*::
+
+        @InProceedings{Zhang_2018_CVPR,
+        author = {Zhang, Hang and Dana, Kristin and Shi, Jianping and Zhang, Zhongyue and Wang, Xiaogang and Tyagi, Ambrish and Agrawal, Amit},
+        title = {Context Encoding for Semantic Segmentation},
+        booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+        month = {June},
+        year = {2018}
+        }

docs/source/functions.rst

@@ -9,19 +9,10 @@ encoding.functions
 .. currentmodule:: encoding.functions
 
 
-:hidden:`batchnorm`
+:hidden:`batchnormtrain`
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: batchnorm
-
-:hidden:`batchnormeval`
-~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: batchnormeval
-:hidden:`dilatedavgpool2d`
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: dilatedavgpool2d
+.. autofunction:: batchnormtrain
 
 :hidden:`aggregate`
 ~~~~~~~~~~~~~~~~~~~

docs/source/nn.rst

@@ -4,7 +4,7 @@
 encoding.nn
 ===========
 
-Customized NN modules in Encoding Package. For Synchronized Cross-GPU Batch Normalization, please visit :class:`encoding.nn.SyncBatchNorm2d`.
+Customized NN modules in Encoding Package. For Synchronized Cross-GPU Batch Normalization, please visit :class:`encoding.nn.BatchNorm2d`.
 
 .. currentmodule:: encoding.nn
 
@@ -14,22 +14,22 @@ Customized NN modules in Encoding Package. For Synchronized Cross-GPU Batch Norm
 .. autoclass:: Encoding
     :members:
 
-:hidden:`SyncBatchNorm2d`
+:hidden:`BatchNorm2d`
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: SyncBatchNorm2d
+.. autoclass:: BatchNorm2d
     :members:
 
-:hidden:`SyncBatchNorm1d`
+:hidden:`BatchNorm1d`
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: SyncBatchNorm1d
+.. autoclass:: BatchNorm1d
     :members:
 
-:hidden:`SyncBatchNorm3d`
+:hidden:`BatchNorm3d`
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: SyncBatchNorm3d
+.. autoclass:: BatchNorm3d
     :members:
 
 :hidden:`Inspiration`
@@ -44,12 +44,6 @@ Customized NN modules in Encoding Package. For Synchronized Cross-GPU Batch Norm
 .. autoclass:: UpsampleConv2d
     :members:
 
-:hidden:`DilatedAvgPool2d`
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: DilatedAvgPool2d
-    :members:
-
 :hidden:`GramMatrix`
 ~~~~~~~~~~~~~~~~~~~~