Add fine-tuning tutorial

docs/source/tutorial/index.rst

@@ -5,4 +5,5 @@ ChainerCV Tutorial
     :maxdepth: 2
 
     detection
+    link
     sliceable

docs/source/tutorial/link.rst

@@ -0,0 +1,74 @@
+Tips using Links
+================
+
+Fine-tuning
+-----------
+
+Models in ChainerCV support the argument :obj:`pretrained_model` to load pretrained weights.
+This functionality is limited in the case when fine-tuning pretrained weights.
+In that circumstance, the layers specific to the classes of the original dataset may need to be randomly initialized.
+In this section, we give a procedure to cope with this problem.
+
+Copying a subset of weights in a chain can be done in few lines of code.
+Here is a block of code that does this.
+
+.. code-block:: python
+
+    # src is a model with pretrained weights
+    # dst is a model randomly initialized
+    # ignore_names is the name of parameters to skip
+    # For the case of VGG16, this should be ['/fc7/W', '/fc7/b']
+    ignore_names = []
+    src_params = {p[0]: p[1] for p in src.namedparams()}
+    for dst_named_param in dst.namedparams():
+        name = dst_named_param[0]
+        if name not in ignore_names:
+            dst_named_param[1].array[:] = src_params[name].array[:]
+
+
+Fine-tuning to a dataset with a different number of classes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+When the number of classes of the target dataset is different from the source dataset during fine-tuning,
+the names of the weights to skip can be found automatically with the following method.
+
+.. code-block:: python
+
+    def get_shape_mismatch_names(src, dst):
+        # all parameters are assumed to be initialized
+        mismatch_names = []
+        src_params = {p[0]: p[1] for p in src.namedparams()}
+        for dst_named_param in dst.namedparams():
+            name = dst_named_param[0]
+            dst_param = dst_named_param[1]
+            src_param = src_params[name]
+            if src_param.shape != dst_param.shape:
+                mismatch_names.append(name)
+        return mismatch_names
+
+Finally, this is a complete example using SSD300.
+
+.. code-block:: python
+
+    from chainercv.links import SSD300
+    import numpy as np
+
+    src = SSD300(pretrained_model='voc0712')
+    # the number of classes in VOC is different from 50
+    dst = SSD300(n_fg_class=50)
+    # initialized weights
+    dst(np.zeros((1, 3, dst.insize, dst.insize), dtype=np.float32))
+
+    # the method described above
+    ignore_names = get_shape_mismatch_names(src, dst)
+    src_params = {p[0]: p[1] for p in src.namedparams()}
+    for dst_named_param in dst.namedparams():
+        name = dst_named_param[0]
+        if name not in ignore_names:
+            dst_named_param[1].array[:] = src_params[name].array[:]
+
+    # check that weights are transfered
+    np.testing.assert_equal(dst.extractor.conv1_1.W.data,
+                            src.extractor.conv1_1.W.data)
+    # the names of the weights that are skipped
+    print(ignore_names)