Remove check_tf_utils to avoid an unnecessary TF import for now (#566)

utils/check_tf_ops.py

-# coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import json
-import os
-
-from tensorflow.core.protobuf.saved_model_pb2 import SavedModel
-
-
-# All paths are set with the intent you should run this script from the root of the repo with the command
-# python utils/check_copies.py
-REPO_PATH = "."
-
-# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
-INTERNAL_OPS = [
-    "Assert",
-    "AssignVariableOp",
-    "EmptyTensorList",
-    "MergeV2Checkpoints",
-    "ReadVariableOp",
-    "ResourceGather",
-    "RestoreV2",
-    "SaveV2",
-    "ShardedFilename",
-    "StatefulPartitionedCall",
-    "StaticRegexFullMatch",
-    "VarHandleOp",
-]
-
-
-def onnx_compliance(saved_model_path, strict, opset):
-    saved_model = SavedModel()
-    onnx_ops = []
-
-    with open(os.path.join(REPO_PATH, "utils", "tf_ops", "onnx.json")) as f:
-        onnx_opsets = json.load(f)["opsets"]
-
-    for i in range(1, opset + 1):
-        onnx_ops.extend(onnx_opsets[str(i)])
-
-    with open(saved_model_path, "rb") as f:
-        saved_model.ParseFromString(f.read())
-
-    model_op_names = set()
-
-    # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
-    for meta_graph in saved_model.meta_graphs:
-        # Add operations in the graph definition
-        model_op_names.update(node.op for node in meta_graph.graph_def.node)
-
-        # Go through the functions in the graph definition
-        for func in meta_graph.graph_def.library.function:
-            # Add operations in each function
-            model_op_names.update(node.op for node in func.node_def)
-
-    # Convert to list, sorted if you want
-    model_op_names = sorted(model_op_names)
-    incompatible_ops = []
-
-    for op in model_op_names:
-        if op not in onnx_ops and op not in INTERNAL_OPS:
-            incompatible_ops.append(op)
-
-    if strict and len(incompatible_ops) > 0:
-        raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops)
-    elif len(incompatible_ops) > 0:
-        print(f"Found the following incompatible ops for the opset {opset}:")
-        print(*incompatible_ops, sep="\n")
-    else:
-        print(f"The saved model {saved_model_path} can properly be converted with ONNX.")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
-    parser.add_argument(
-        "--opset", default=12, type=int, help="The ONNX opset against which the model has to be tested."
-    )
-    parser.add_argument(
-        "--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
-    )
-    parser.add_argument(
-        "--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
-    )
-    args = parser.parse_args()
-
-    if args.framework == "onnx":
-        onnx_compliance(args.saved_model_path, args.strict, args.opset)