* WIP Run the TF tutorial on TF2
* Remove debugger statement.
* Complete the support for TF2.0's `resize`.
TF2.0 adds a `half_pixel_centers` attribute to the `resize` function in
the image API. This commit completes the hooks in Relay's TF frontend.
At the point of this commit, no new test yet. Also, this commit
addresses solely the `resize` change. Other commits address other
changes in TF2.0.
* Support TF2.0 in the tutorial by using the compat API.
This looks cleaner than trying to detect the TF version.
* Use the TF compat API, so as to support TF2.0.
This is a direct change, relying on the compat API provided by the TF
team.
This code will last as long as the compat API exists, so a
"proper" support for TF1.x and 2.x will require more work in some
future.
* Partial support for EXPLICIT padding introduced in TF2.0.
Explicit padding is a special case in TF2.0 (see reference linked
below). Some models are serialized with that mode, and break TF support
in TVM.
Support is *partial* as EXPLICIT falls back to set padding on the
Relay op, which only supports 2 values. At some point, padding may need
to be extended to support 4 values, but that is out of scope of this
support commit.
Reference on EXPLICIT padding: https://github.com/tensorflow/tensorflow/commit/
ec81825aaf7e848d9f8ddffdf1e0d20aebe9172c#diff-
1d1c0bb0a880f85b6164f71dbb2f446e
* Guard on checking for optional TF2.0 attribute.
* Do not expect Relay to implement TF-specific attributes.
The `half_pixel_centers` attribute is a new feature in TF2.0. Earlier
commits of mine mistakenly introduce them in the Relay API. This is
probably not what Relay is expected to support, and the semantics of
`half_pixel_centers` is unclear (to me, at least) at this point.
* Remove unclear comment.
CR https://github.com/dmlc/tvm/pull/4104#discussion_r338705742
Addresses #4104
* Changes after review.
Complying without understanding the rationale for now.
* Fix the arguments set mistakenly.
An argument ignored for the wrong operation.
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',
# 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'],
extras={'method': "bilinear"})(inputs, attr)
return _impl
"""
- 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],
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:
# 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])
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='')
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):
# ------------
# 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')
######################################################################
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.
-------
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})
projects / platform / upstream / tvm.git / commitdiff