Tutorial enhancement to keep it clean on docs.tvm.ai

nnvm/python/nnvm/frontend/tensorflow.py

@@ -620,7 +620,7 @@ def from_tensorflow(self, graph):
                     attr['_input_shapes'] = input_shapes
                 except KeyError:
                     # TODO: Need to find clean way to handle '^CheckNumerics'
-                    print("Some Exception while inputs list:", node.input, " ignoring...")
+                    pass
 
                 inputs = self._fix_extranodes(node.op, attr, inputs)
 

tutorials/nnvm/from_tensorflow.py

@@ -1,17 +1,20 @@
 """
 Compile Tensorflow Models
 =========================
-This article is an introductory tutorial to deploy tensorflow models with NNVM.
+This article is an introductory tutorial to deploy tensorflow models with TVM.
 
-For us to begin with, tensorflow module is required to be installed.
+For us to begin with, tensorflow python module is required to be installed.
 
 A quick solution is to install tensorlfow from
 
-https://www.tensorflow.org/install/install_sources
+https://www.tensorflow.org/install
 """
 
+# tvm and nnvm
 import nnvm
 import tvm
+
+# os and numpy
 import numpy as np
 import os.path
 
@@ -21,23 +24,39 @@
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import tensor_util
 
+# Tensorflow utility functions
 import nnvm.testing.tf
 
+# Base location for model related files.
 repo_base = 'https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV1/'
+
+# Test image
 img_name = 'elephant-299.jpg'
 image_url = os.path.join(repo_base, img_name)
+
+# InceptionV1 model protobuf
+# .. note::
+#
+#   protobuf should be exported with :any:`add_shapes=True` option.
+#   Could use https://github.com/dmlc/web-data/tree/master/tensorflow/scripts/tf-to-nnvm.py
+#   to add shapes for existing models.
+#
 model_name = 'classify_image_graph_def-with_shapes.pb'
 model_url = os.path.join(repo_base, model_name)
+
+# Image label map
 map_proto = 'imagenet_2012_challenge_label_map_proto.pbtxt'
 map_proto_url = os.path.join(repo_base, map_proto)
+
+# Human readable text for labels
 lable_map = 'imagenet_synset_to_human_label_map.txt'
 lable_map_url = os.path.join(repo_base, lable_map)
 
 
 ######################################################################
-# Download processed tensorflow model
-# -----------------------------------
-# In this section, we download a pretrained Tensorflow model and classify an image.
+# Download required files
+# -----------------------
+# Download files listed above.
 from mxnet.gluon.utils import download
 
 download(image_url, img_name)
@@ -47,8 +66,9 @@
 
 
 ######################################################################
-# Creates graph from saved graph_def.pb.
-# --------------------------------------
+# Import model
+# ------------
+# Creates tensorflow graph definition from protobuf file.
 
 with tf.gfile.FastGFile(os.path.join(
         "./", model_name), 'rb') as f:
@@ -62,6 +82,13 @@
 ######################################################################
 # Decode image
 # ------------
+# .. note::
+#
+#   tensorflow frontend import doesn't support preprocessing ops like JpegDecode
+#   JpegDecode is a bypass (copy source) here.
+#   Hence we supply decoded frame to TVM instead.
+#
+
 from PIL import Image
 image = Image.open(img_name).resize((299, 299))
 
@@ -74,10 +101,24 @@ def transform_image(image):
 ######################################################################
 # Import the graph to NNVM
 # ------------------------
+# Import tensorflow graph definition to nnvm.
+#
+# Results:
+#   sym: nnvm graph for given tensorflow protobuf.
+#   params: params converted from tensorflow params (tensor protobuf).
 sym, params = nnvm.frontend.from_tensorflow(graph_def)
 
+print ("Tensorflow protobuf imported as nnvm graph")
 ######################################################################
-# Now compile the graph through NNVM
+# NNVM Compilation
+# ----------------
+# Compile the graph to llvm target with given input specification.
+#
+# Results:
+#   graph: Final graph after compilation.
+#   params: final params after compilation.
+#   lib: target library which can be deployed on target with tvm runtime.
+
 import nnvm.compiler
 target = 'llvm'
 shape_dict = {'DecodeJpeg/contents': x.shape}
@@ -87,7 +128,8 @@ def transform_image(image):
 ######################################################################
 # Execute the portable graph on TVM
 # ---------------------------------
-# Now, we would like to reproduce the same forward computation using TVM.
+# Now we can try deploying the NNVM compiled model on cpu target.
+
 from tvm.contrib import graph_runtime
 ctx = tvm.cpu(0)
 dtype = 'uint8'
@@ -100,26 +142,28 @@ def transform_image(image):
 # get outputs
 tvm_output = m.get_output(0, tvm.nd.empty(((1, 1008)), 'float32'))
 
-
 ######################################################################
-# Process the output to human readable
-# ------------------------------------
+# Process the output
+# ------------------
+# Process the model output to human readable text for InceptionV1.
 predictions = tvm_output.asnumpy()
 predictions = np.squeeze(predictions)
 
 # Creates node ID --> English string lookup.
 node_lookup = nnvm.testing.tf.NodeLookup(label_lookup_path=os.path.join("./", map_proto),
                                          uid_lookup_path=os.path.join("./", lable_map))
 
+# Print top 5 predictions from TVM output.
 top_k = predictions.argsort()[-5:][::-1]
 for node_id in top_k:
     human_string = node_lookup.id_to_string(node_id)
     score = predictions[node_id]
     print('%s (score = %.5f)' % (human_string, score))
 
 ######################################################################
-# Run the same graph with tensorflow and dump output.
-# ---------------------------------------------------
+# Inference on tensorflow
+# -----------------------
+# Now lets run the same on tensorflow
 
 def create_graph():
     """Creates a graph from saved GraphDef file and returns a saver."""
@@ -161,6 +205,7 @@ def run_inference_on_image(image):
         node_lookup = nnvm.testing.tf.NodeLookup(label_lookup_path=os.path.join("./", map_proto),
                                                  uid_lookup_path=os.path.join("./", lable_map))
 
+        # Print top 5 predictions from tensorflow.
         top_k = predictions.argsort()[-5:][::-1]
         print ("===== TENSORFLOW RESULTS =======")
         for node_id in top_k: