adds parameter space representation images

docs/index.rst

@@ -33,6 +33,8 @@ In-depth
 
 .. toctree::
    overview.rst
+   install.rst
+   pspace_repr.rst
    :maxdepth: 1
 
    api/index.rst

docs/pspace_repr.rst

@@ -0,0 +1,20 @@
+Parameter space representations
+===============================
+
+.. image:: https://github.com/tfjgeorge/nngeometry/raw/master/examples/repr_img/PMatDense.png
+  :width: 400
+
+.. image:: https://github.com/tfjgeorge/nngeometry/raw/master/examples/repr_img/PMatBlockDiag.png
+  :width: 400
+
+.. image:: https://github.com/tfjgeorge/nngeometry/raw/master/examples/repr_img/PMatKFAC.png
+  :width: 400
+
+.. image:: https://github.com/tfjgeorge/nngeometry/raw/master/examples/repr_img/PMatEKFAC.png
+  :width: 400
+
+.. image:: https://github.com/tfjgeorge/nngeometry/raw/master/examples/repr_img/PMatDiag.png
+  :width: 400
+
+.. image:: https://github.com/tfjgeorge/nngeometry/raw/master/examples/repr_img/PMatQuasiDiag.png
+  :width: 400

examples/resnet.py

@@ -0,0 +1,132 @@
+'''ResNet in PyTorch.
+
+from: https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py
+
+Reference:
+[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+    Deep Residual Learning for Image Recognition. arXiv:1512.03385
+'''
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_planes, planes, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = nn.Conv2d(
+            in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+#         self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
+                               stride=1, padding=1, bias=False)
+#         self.bn2 = nn.BatchNorm2d(planes)
+
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != self.expansion*planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, self.expansion*planes,
+                          kernel_size=1, stride=stride, bias=False),
+#                 nn.BatchNorm2d(self.expansion*planes)
+            )
+
+    def forward(self, x):
+        out = F.relu(self.conv1(x))
+        out = self.conv2(out)
+        out += self.shortcut(x)
+        out = F.relu(out)
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, in_planes, planes, stride=1):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
+#         self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
+                               stride=stride, padding=1, bias=False)
+#         self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, self.expansion *
+                               planes, kernel_size=1, bias=False)
+#         self.bn3 = nn.BatchNorm2d(self.expansion*planes)
+
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != self.expansion*planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, self.expansion*planes,
+                          kernel_size=1, stride=stride, bias=False),
+#                 nn.BatchNorm2d(self.expansion*planes)
+            )
+
+    def forward(self, x):
+        out = F.relu(self.conv1(x))
+        out = F.relu(self.conv2(out))
+        out = self.conv3(out)
+        out += self.shortcut(x)
+        out = F.relu(out)
+        return out
+
+
+class ResNet(nn.Module):
+    def __init__(self, block, num_blocks, num_classes=10):
+        super(ResNet, self).__init__()
+        self.in_planes = 64
+
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
+                               stride=1, padding=1, bias=False)
+#         self.bn1 = nn.BatchNorm2d(64)
+        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
+        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
+        self.linear = nn.Linear(512*block.expansion, num_classes)
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1]*(num_blocks-1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = F.relu(self.conv1(x))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.layer4(out)
+        out = F.avg_pool2d(out, 4)
+        out = out.view(out.size(0), -1)
+        out = self.linear(out)
+        return out
+
+
+def ResNet18():
+    return ResNet(BasicBlock, [2, 2, 2, 2])
+
+
+def ResNet34():
+    return ResNet(BasicBlock, [3, 4, 6, 3])
+
+
+def ResNet50():
+    return ResNet(Bottleneck, [3, 4, 6, 3])
+
+
+def ResNet101():
+    return ResNet(Bottleneck, [3, 4, 23, 3])
+
+
+def ResNet152():
+    return ResNet(Bottleneck, [3, 8, 36, 3])
+
+
+def test():
+    net = ResNet18()
+    y = net(torch.randn(1, 3, 32, 32))
+    print(y.size())
+
+# test()