Elaborate the install instructions more. (pytorch#133)

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Author: ezyang

advanced_source/super_resolution_with_caffe2.py

@@ -10,8 +10,12 @@
 # in PyTorch into the ONNX format and then load it into Caffe2. Once in
 # Caffe2, we can run the model to double-check it was exported correctly,
 # and we then show how to use Caffe2 features such as mobile exporter for
-# executing the model on mobile devices. For this tutorial, you will need to
-# install ONNX by following the instructions `here <https://github.com/onnx/onnx>`__
+# executing the model on mobile devices.
+#
+# For this tutorial, you will need to install `onnx <https://github.com/onnx/onnx>`__,
+# `onnx-caffe2 <https://github.com/onnx/onnx-caffe2>`__ and `Caffe2 <https://caffe2.ai/>`__.
+# You can get binary builds of onnx and onnx-caffe2 with
+# ``conda install -c ezyang onnx onnx-caffe2``.
 #
 
 # Some standard imports
@@ -23,9 +27,6 @@
 import torch.utils.model_zoo as model_zoo
 import torch.onnx
 
-import onnx
-import onnx_caffe2.backend
-
 
 ######################################################################
 # Super-resolution is a way of increasing the resolution of images, videos
@@ -121,6 +122,9 @@ def _initialize_weights(self):
 # PyTorch are computing the same value for the network:
 #
 
+import onnx
+import onnx_caffe2.backend
+
 # Load the ONNX GraphProto object. Graph is a standard Python protobuf object
 graph = onnx.load("super_resolution.onnx")