Merge pull request pytorch#749 from raghuramank100/jlin27-quant-tutor…

…ials

Fix formatting and clean up tutorial on quantized transfer learning

intermediate_source/quantized_transfer_learning_tutorial.py

@@ -84,9 +84,11 @@
 
 
 ######################################################################
-# Load Data (section not needed as it is covered in the original tutorial)
+# Load Data
 # ------------------------------------------------------------------------
 #
+# ..Note :: This section is identical to the original transfer learning tutorial.
+#
 # We will use ``torchvision`` and ``torch.utils.data`` packages to load
 # the data.
 #
@@ -360,7 +362,7 @@ def visualize_model(model, rows=3, cols=3):
 # **Notice that when isolating the feature extractor from a quantized
 # model, you have to place the quantizer in the beginning and in the end
 # of it.**
-#
+# We write a helper function to create a model with a custom head.
 
 from torch import nn
 
@@ -394,8 +396,6 @@ def create_combined_model(model_fe):
   )
   return new_model
 
-new_model = create_combined_model(model_fe)
-
 
 ######################################################################
 # .. warning:: Currently the quantized models can only be run on CPU.
@@ -404,6 +404,7 @@ def create_combined_model(model_fe):
 #
 
 import torch.optim as optim
+new_model = create_combined_model(model_fe)
 new_model = new_model.to('cpu')
 
 criterion = nn.CrossEntropyLoss()
@@ -431,7 +432,7 @@ def create_combined_model(model_fe):
 
 
 ######################################################################
-# **Part 2. Finetuning the quantizable model**
+# Part 2. Finetuning the quantizable model
 #
 # In this part, we fine tune the feature extractor used for transfer
 # learning, and quantize the feature extractor. Note that in both part 1
@@ -446,18 +447,21 @@ def create_combined_model(model_fe):
 # datasets.
 #
 # The pretrained feature extractor must be quantizable, i.e we need to do
-# the following: 1. Fuse (Conv, BN, ReLU), (Conv, BN) and (Conv, ReLU)
-# using torch.quantization.fuse_modules. 2. Connect the feature extractor
-# with a custom head. This requires dequantizing the output of the feature
-# extractor. 3. Insert fake-quantization modules at appropriate locations
-# in the feature extractor to mimic quantization during training.
+# the following:
+#  1. Fuse (Conv, BN, ReLU), (Conv, BN) and (Conv, ReLU)
+#     using torch.quantization.fuse_modules.
+#  2. Connect the feature extractor
+#     with a custom head. This requires dequantizing the output of the feature
+#     extractor.
+#  3. Insert fake-quantization modules at appropriate locations
+#     in the feature extractor to mimic quantization during training.
 #
 # For step (1), we use models from torchvision/models/quantization, which
 # support a member method fuse_model, which fuses all the conv, bn, and
 # relu modules. In general, this would require calling the
 # torch.quantization.fuse_modules API with the list of modules to fuse.
 #
-# Step (2) is done by the function create_custom_model function that we
+# Step (2) is done by the function create_combined_model function that we
 # used in the previous section.
 #
 # Step (3) is achieved by using torch.quantization.prepare_qat, which
@@ -534,4 +538,3 @@ def create_combined_model(model_fe):
 plt.ioff()
 plt.tight_layout()
 plt.show()
-