codingutf8 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 orglicenseslicense2 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 all paths are set with the intent you should run this script from the root of the repo with the command python utilscheckcopies py internal tensorflow ops that can be safely ignored mostly specific to a saved model iterate over every metagraph in case there is more than one a saved model can contain multiple graphs add operations in the graph definition go through the functions in the graph definition add operations in each function convert to list sorted if you want coding utf 8 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 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 internal tensorflow ops that can be safely ignored mostly specific to a saved model iterate over every metagraph in case there is more than one a saved model can contain multiple graphs add operations in the graph definition go through the functions in the graph definition add operations in each function convert to list sorted if you want
import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pb2 import SavedModel




REPO_PATH = "."


INTERNAL_OPS = [
    "Assert",
    "AssignVariableOp",
    "EmptyTensorList",
    "MergeV2Checkpoints",
    "ReadVariableOp",
    "ResourceGather",
    "RestoreV2",
    "SaveV2",
    "ShardedFilename",
    "StatefulPartitionedCall",
    "StaticRegexFullMatch",
    "VarHandleOp",
]


def onnx_compliancy(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()

    
    for meta_graph in saved_model.meta_graphs:
        
        model_op_names.update(node.op for node in meta_graph.graph_def.node)

        
        for func in meta_graph.graph_def.library.function:
            
            model_op_names.update(node.op for node in func.node_def)

    
    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_compliancy(args.saved_model_path, args.strict, args.opset)