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Fix the TF tutorial to run against TF2.0 and TF1.x #4104

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merged 15 commits into from
Nov 12, 2019
Merged

Fix the TF tutorial to run against TF2.0 and TF1.x #4104

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e63e80e
WIP Run the TF tutorial on TF2
ic Oct 11, 2019
6ec2b28
Remove debugger statement.
ic Oct 11, 2019
bbf927a
Complete the support for TF2.0's `resize`.
ic Oct 11, 2019
eeb8650
Support TF2.0 in the tutorial by using the compat API.
ic Oct 16, 2019
63ce005
Use the TF compat API, so as to support TF2.0.
ic Oct 16, 2019
2ad38bf
Partial support for EXPLICIT padding introduced in TF2.0.
ic Oct 16, 2019
f00982f
Merge branch 'master' into tf2
ic Oct 17, 2019
3fa86a6
Guard on checking for optional TF2.0 attribute.
ic Oct 17, 2019
2ebc516
Do not expect Relay to implement TF-specific attributes.
ic Oct 17, 2019
5f63530
Remove unclear comment.
ic Oct 25, 2019
8018983
Merge branch 'master' of github.com:dmlc/tvm into tf2
ic Oct 25, 2019
92d5677
Merge branch 'master' into tf2
ic Oct 31, 2019
f2fca5a
Changes after review.
ic Oct 31, 2019
84702fe
Fix the arguments set mistakenly.
ic Oct 31, 2019
92cfcb5
Merge branch 'master' of github.com:dmlc/tvm into tf2
ic Nov 12, 2019
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5 changes: 4 additions & 1 deletion python/tvm/relay/frontend/tensorflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -306,8 +306,10 @@ def _impl(inputs, attr, params):
use_bias = len(inputs) == 3
channel_axis = 1 if attr['data_format'] == "NCHW" else 3

# Ignore the new attributes from TF2.0, for now.
out = AttrCvt(
op_name=_dimension_picker('conv'),
ignores=['explicit_paddings'],
transforms={
'kernel_shape': 'kernel_size',
'data_format': 'data_layout',
Expand Down Expand Up @@ -404,8 +406,9 @@ def _impl(inputs, attr, params):
# NHWC
attr['layout'] = 'NHWC'

# Ignore the new attributes from TF2.0, for now.
return AttrCvt(op_name="resize",
ignores=['Tdim'],
ignores=['Tdim', 'half_pixel_centers'],
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it would be nice if add half_pixel_centers in tvm resize op. If the result is different from tensorflow, we might need to raise error instead of ignoring

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I think half_pixel_centers is the new name of align_corners (used in TF 1.X) that we do support in tvm resize.

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then add transforms={'half_pixel_centers':'align_corners'} here should be working

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@ic ic Oct 25, 2019 •
edited
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It seems the two attributes serve different purposes. The doc says nothing really. The source code does half_pixel_centers processing is applied optionally on top of align_corners processing. E.g.

Eigen::Index in_y = std::min(
            (align_corners)
                ? static_cast<Eigen::Index>(roundf(scaler(y, height_scale)))
                : static_cast<Eigen::Index>(floorf(scaler(y, height_scale))),
            in_height - 1);
if (half_pixel_centers) {
  in_y = std::max(static_cast<Eigen::Index>(0), in_y);
}

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Idea: How about a unit test that compares a TF model using that attribute to Relay ignoring the attribute? I was reading the test suite to add to this PR, when I saw little coverage on such kind of issue, and fixing the tutorial seemed good to release early.

I'll try a test today, but if you have some guidance/advice/preference, I'd be happy to hear it. It may just be a different PR.

extras={'method': "bilinear"})(inputs, attr)
return _impl

Expand Down
14 changes: 7 additions & 7 deletions python/tvm/relay/testing/tf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,7 @@ def AddShapesToGraphDef(session, out_node):

"""

graph_def = tf.graph_util.convert_variables_to_constants(
graph_def = tf.compat.v1.graph_util.convert_variables_to_constants(
session,
session.graph.as_graph_def(add_shapes=True),
[out_node],
Expand Down Expand Up @@ -112,13 +112,13 @@ def load(self, label_lookup_path, uid_lookup_path):
dict from integer node ID to human-readable string.

"""
if not tf.gfile.Exists(uid_lookup_path):
if not tf.compat.v1.io.gfile.exists(uid_lookup_path):
tf.logging.fatal('File does not exist %s', uid_lookup_path)
if not tf.gfile.Exists(label_lookup_path):
if not tf.compat.v1.io.gfile.exists(label_lookup_path):
tf.logging.fatal('File does not exist %s', label_lookup_path)

# Loads mapping from string UID to human-readable string
proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
proto_as_ascii_lines = tf.compat.v1.gfile.GFile(uid_lookup_path).readlines()
uid_to_human = {}
p = re.compile(r'[n\d]*[ \S,]*')
for line in proto_as_ascii_lines:
Expand All @@ -129,7 +129,7 @@ def load(self, label_lookup_path, uid_lookup_path):

# Loads mapping from string UID to integer node ID.
node_id_to_uid = {}
proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
proto_as_ascii = tf.compat.v1.gfile.GFile(label_lookup_path).readlines()
for line in proto_as_ascii:
if line.startswith(' target_class:'):
target_class = int(line.split(': ')[1])
Expand Down Expand Up @@ -209,7 +209,7 @@ def get_workload(model_path, model_sub_path=None):
path_model = download_testdata(model_url, model_path, module='tf')

# Creates graph from saved graph_def.pb.
with tf.gfile.FastGFile(path_model, 'rb') as f:
with tf.compat.v1.gfile.FastGFile(path_model, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
graph = tf.import_graph_def(graph_def, name='')
Expand Down Expand Up @@ -299,7 +299,7 @@ def _create_ptb_vocabulary(data_dir):
file_name = 'ptb.train.txt'
def _read_words(filename):
"""Read the data for creating vocabulary"""
with tf.gfile.GFile(filename, "r") as f:
with tf.compat.v1.gfile.GFile(filename, "r") as f:
return f.read().encode("utf-8").decode("utf-8").replace("\n", "<eos>").split()

def _build_vocab(filename):
Expand Down
16 changes: 8 additions & 8 deletions tutorials/frontend/from_tensorflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -89,14 +89,14 @@
# ------------
# Creates tensorflow graph definition from protobuf file.

with tf.gfile.FastGFile(model_path, 'rb') as f:
graph_def = tf.GraphDef()
with tf.compat.v1.gfile.GFile(model_path, 'rb') as f:
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
graph = tf.import_graph_def(graph_def, name='')
# Call the utility to import the graph definition into default graph.
graph_def = tf_testing.ProcessGraphDefParam(graph_def)
# Add shapes to the graph.
with tf.Session() as sess:
with tf.compat.v1.Session() as sess:
graph_def = tf_testing.AddShapesToGraphDef(sess, 'softmax')

######################################################################
Expand Down Expand Up @@ -187,8 +187,8 @@
def create_graph():
"""Creates a graph from saved GraphDef file and returns a saver."""
# Creates graph from saved graph_def.pb.
with tf.gfile.FastGFile(model_path, 'rb') as f:
graph_def = tf.GraphDef()
with tf.compat.v1.gfile.GFile(model_path, 'rb') as f:
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
graph = tf.import_graph_def(graph_def, name='')
# Call the utility to import the graph definition into default graph.
Expand All @@ -206,14 +206,14 @@ def run_inference_on_image(image):
-------
Nothing
"""
if not tf.gfile.Exists(image):
if not tf.compat.v1.io.gfile.exists(image):
tf.logging.fatal('File does not exist %s', image)
image_data = tf.gfile.FastGFile(image, 'rb').read()
image_data = tf.compat.v1.gfile.GFile(image, 'rb').read()

# Creates graph from saved GraphDef.
create_graph()

with tf.Session() as sess:
with tf.compat.v1.Session() as sess:
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
Expand Down