saymrwulf/onnxruntime
1
0
Fork 0
mirror of https://github.com/saymrwulf/onnxruntime.git synced 2026-07-30 20:18:08 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Fix 'SyntaxWarning: "is" with a literal' issues in Python transformers (#8658)

Browse source
This commit is contained in:
Mike Roberts 2021-08-09 23:03:52 +01:00 committed by GitHub
parent 20f006c580
commit cadb43a715
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
2 changed files with 5 additions and 5 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

6
onnxruntime/python/tools/transformers/convert_tf_models_to_pytorch.py
View file

@ -94,9 +94,9 @@ def init_pytorch_model(model_name, tf_checkpoint_path):
parent_path = tf_checkpoint_path.rpartition('/')[0]
config_path = glob.glob(parent_path + "/*config.json")
config = model_config() if len(config_path) is 0 else model_config.from_json_file(str(config_path[0]))
config = model_config() if len(config_path) == 0 else model_config.from_json_file(str(config_path[0]))
if TFMODELS[model_name][2] is "":
if TFMODELS[model_name][2] == "":
from transformers import AutoModelForPreTraining
init_model = AutoModelForPreTraining.from_config(config)
else:
@ -115,7 +115,7 @@ def convert_tf_checkpoint_to_pytorch(model_name, config, init_model, tf_checkpoi
if is_tf2 is False:
load_tf_weight_func = getattr(module, load_tf_weight_func_name)
else:
if TFMODELS[model_name][0] is not "bert":
if TFMODELS[model_name][0] != "bert":
raise NotImplementedError("Only support tf2 ckeckpoint for Bert model")
from transformers import convert_bert_original_tf2_checkpoint_to_pytorch
load_tf_weight_func = convert_bert_original_tf2_checkpoint_to_pytorch.load_tf2_weights_in_bert

4
onnxruntime/python/tools/transformers/onnx_model_bert.py
View file

@ -193,8 +193,8 @@ class BertOnnxModel(OnnxModel):
slice_node = reshape_path[-1]
if expand_shape_value is not None and shape_value is not None and len(
expand_shape_value) is 2 and len(
shape_value) is 1 and expand_shape_value[1] == shape_value[0]:
expand_shape_value) == 2 and len(
shape_value) == 1 and expand_shape_value[1] == shape_value[0]:
node.input[0] = slice_node.output[0]
if nodes_to_remove: