04de4e34bc
* Added additional telemetry events. * Separated sending tf1 and tf2. * Small correction. * Unit test fix. * Added op_names_statistic field in graph. Added op names saving in loop ext, while ext. * Optimize imports. * Added debug print. * Added comments, removed debug print. * Added comment. * Renamed dynamic shapes event label to partially defined, added unit tests. * Added attribute checks, moved telemetry methods to separate file. * Small corrections. * Updated BOM file.
312 lines
17 KiB
Python
312 lines
17 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import logging as log
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import os
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import re
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from distutils.version import LooseVersion
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from mo.graph.graph import Node
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from mo.utils.error import Error, FrameworkError
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from mo.utils.utils import refer_to_faq_msg
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from mo.utils.versions_checker import get_environment_setup
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try:
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import tensorflow.compat.v1 as tf_v1
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# disable eager execution of TensorFlow 2 environment immediately
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tf_v1.disable_eager_execution()
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import tensorflow as tf
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from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
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except ImportError:
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import tensorflow as tf_v1
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from google.protobuf import text_format
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from mo.graph.graph import fill_graph_with_nodes, Graph
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from mo.utils.summarize_graph import summarize_graph
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def freeze_checkpoints(graph_def: tf_v1.GraphDef, checkpoint_dir: str, output_node_names: list):
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"""
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Loads all the variables in a graph and stores them in a separate dictionary. Freezes output nodes in the graph
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:param graph_def: GraphDef object holding the network.
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:param checkpoint_dir: path to directory with checkpoint files with values of graph variables.
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:param output_node_names: list of output node names.
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:return: GraphDef containing a simplified version of the original.
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"""
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log.debug("Loading checkpoint files from directory: {}".format(checkpoint_dir))
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checkpoint_files = []
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for checkpoint_name in sorted(os.listdir(checkpoint_dir)):
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checkpoint_path = os.path.join(checkpoint_dir, checkpoint_name)
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if os.path.isfile(checkpoint_path):
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checkpoint_files.append(checkpoint_path)
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log.debug("File {} will be loaded".format(checkpoint_path))
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else:
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log.debug("Path {} is not a file. Skipping")
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if len(checkpoint_files) == 0:
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raise Error("There are no checkpoint files in directory: {}".format(checkpoint_dir))
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tf_v1.import_graph_def(graph_def, name='')
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with tf_v1.Session() as sess:
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uninitialized_variables = [str(v, 'utf-8') for v in set(sess.run(tf_v1.report_uninitialized_variables()))]
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all_variables = [n.name for n in sess.graph.as_graph_def().node if n.op in ['Variable', 'VariableV2']]
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white_list = [v for v in all_variables if v not in uninitialized_variables]
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black_list = [v for v in all_variables if v in uninitialized_variables]
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output_graph_def = tf_v1.graph_util.convert_variables_to_constants(sess, graph_def, output_node_names,
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variable_names_whitelist=white_list,
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variable_names_blacklist=black_list)
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variable_values = {}
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for checkpoint_file in checkpoint_files:
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log.debug("Loading {}".format(checkpoint_file))
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with tf_v1.Session() as sess:
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var_list = {}
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var_to_shape_map = tf_v1.train.load_checkpoint(checkpoint_file).get_variable_to_shape_map()
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for key in var_to_shape_map:
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try:
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tensor = sess.graph.get_operation_by_name(key).outputs[0]
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except KeyError:
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continue
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var_list[key] = tensor
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tf_v1.train.Saver(var_list=var_list).restore(sess, checkpoint_file)
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for name, tensor in var_list.items():
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variable_values[name] = sess.run(tensor)
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return output_graph_def, variable_values
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def freeze_checkpoint(graph_def, checkpoint, output_node_names):
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"""
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Replaces all the variables in a graph with constants of the same values.
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:param graph_def: GraphDef object holding the network.
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:param checkpoint: path to checkpoint file with values of variables.
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:param output_node_names: list of output node names
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:return: GraphDef containing a simplified version of the original.
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"""
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tf_v1.import_graph_def(graph_def, name="")
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with tf_v1.Session() as sess:
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var_list = {}
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var_to_shape_map = tf_v1.train.NewCheckpointReader(checkpoint).get_variable_to_shape_map()
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for key in var_to_shape_map:
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try:
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tensor = sess.graph.get_operation_by_name(key).outputs[0]
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except KeyError:
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continue
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var_list[key] = tensor
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tf_v1.train.Saver(var_list=var_list).restore(sess, checkpoint)
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output_graph_def = tf_v1.graph_util.convert_variables_to_constants(sess, graph_def, output_node_names)
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return output_graph_def
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def read_file_to_graph_def(graph_def: [tf_v1.GraphDef, tf_v1.MetaGraphDef], graph_file_name: str = "",
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is_binary: bool = True):
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"""
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Reads file to protobuf
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:param graph_def: GraphDef orr MetaGraphDef object to store the network
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:param graph_file_name: path to file with graph
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:param is_binary: flag to switch between binary and test protobuf format of graph file
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:return: GraphDef or MetaGaphDef containing the network with cleared device info.
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"""
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try:
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if is_binary:
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with open(graph_file_name, "rb") as f:
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graph_def.ParseFromString(f.read())
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else:
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with open(graph_file_name, "r") as f:
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text_format.Merge(f.read(), graph_def)
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nodes_to_clear_device = graph_def.node if isinstance(graph_def, tf_v1.GraphDef) else graph_def.graph_def.node
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for node in nodes_to_clear_device:
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node.device = ""
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except Exception as e:
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raise FrameworkError(
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'TensorFlow cannot read the model file: "{}" is incorrect TensorFlow model file. '
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'\nThe file should contain one of the following TensorFlow graphs:'
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'\n1. frozen graph in text or binary format'
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'\n2. inference graph for freezing with checkpoint (--input_checkpoint) in text or binary format'
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'\n3. meta graph'
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'\n\nMake sure that --input_model_is_text is provided for a model in text format. '
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'By default, a model is interpreted in binary format. Framework error details: {}. ' +
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refer_to_faq_msg(43),
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graph_file_name,
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str(e)
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) from e
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return graph_def
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def get_output_node_names_list(graph_def, user_defined_output_node_names_list: list):
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return summarize_graph(graph_def)['outputs'] \
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if user_defined_output_node_names_list is None or len(user_defined_output_node_names_list) == 0 \
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else user_defined_output_node_names_list
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def deducing_metagraph_path(meta_graph_file: str):
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match = re.search('^(.*)\.(data-\d*-of-\d*|index|meta)$', meta_graph_file)
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if match is not None:
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deduced_meta_graph_file = match.group(1) + '.meta'
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if not os.path.isfile(deduced_meta_graph_file):
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raise Error('\n\nMetaGraph freezing mechanism was enabled. '
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'\n{} file does not represent MetaGraph. '
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'\n{} path to MetaGraph was deduced, but it does not exist'
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'\n\nModel with MetaGraph consists of 3-4 files:'
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'\n1. model_name.meta'
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'\n2. model_name.index'
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'\n3. model_name.data-00000-of-00001 (digit part may vary)'
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'\n4. checkpoint (optional)'.format(meta_graph_file, deduced_meta_graph_file))
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else:
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meta_graph_file = deduced_meta_graph_file
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else:
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raise Error('\n\nMetaGraph freezing mechanism was enabled. '
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'\n{} file does not represent MetaGraph. '
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'\n\nModel with MetaGraph consists of 3-4 files:'
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'\n1. model_name.meta'
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'\n2. model_name.index'
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'\n3. model_name.data-00000-of-00001 (digit part may vary)'
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'\n4. checkpoint (optional)'
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'\n\nTo load this model, simply run:'
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'\npython3 mo_tf.py --input_meta_graph model_name.meta'
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''.format(meta_graph_file))
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return meta_graph_file
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def load_tf_graph_def(graph_file_name: str = "", is_binary: bool = True, checkpoint: str = "",
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model_dir: str = "", saved_model_tags: list = [], meta_graph_file: str = "",
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user_output_node_names_list: list = []):
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# As a provisional solution, use a native TF methods to load a model protobuf
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graph_def = tf_v1.GraphDef()
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if isinstance(graph_file_name, str) and (re.match('.*\.(ckpt|meta)$', graph_file_name)):
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print('[ WARNING ] The value for the --input_model command line parameter ends with ".ckpt" or ".meta" '
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'extension.\n'
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'It means that the model is not frozen.\n'
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'To load non frozen model to Model Optimizer run:'
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'\n\n1. For "*.ckpt" file:'
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'\n- if inference graph is in binary format'
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'\npython3 mo_tf.py --input_model "path/to/inference_graph.pb" --input_checkpoint "path/to/*.ckpt"'
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'\n- if inference graph is in text format'
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'\npython3 mo_tf.py --input_model "path/to/inference_graph.pbtxt" --input_model_is_text '
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'--input_checkpoint "path/to/*.ckpt"'
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'\n\n2. For "*.meta" file:'
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'\npython3 mo_tf.py --input_meta_graph "path/to/*.meta"')
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variables_values = {}
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try:
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if graph_file_name and not meta_graph_file and not checkpoint:
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# frozen graph
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return read_file_to_graph_def(graph_def, graph_file_name, is_binary), variables_values, 'tf'
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if graph_file_name and not meta_graph_file and checkpoint:
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# inference graph and checkpoint
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graph_def = read_file_to_graph_def(graph_def, graph_file_name, is_binary)
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outputs = get_output_node_names_list(graph_def, user_output_node_names_list)
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if os.path.isfile(checkpoint):
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graph_def = freeze_checkpoint(graph_def=graph_def, checkpoint=checkpoint, output_node_names=outputs)
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elif os.path.isdir(checkpoint):
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graph_def, variables_values = freeze_checkpoints(graph_def=graph_def, checkpoint_dir=checkpoint,
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output_node_names=outputs)
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# we are sure that checkpoint is existing file or directory due to cli_parser configuration
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return graph_def, variables_values, 'tf'
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if not graph_file_name and meta_graph_file:
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meta_graph_file = deducing_metagraph_path(meta_graph_file)
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input_meta_graph_def = read_file_to_graph_def(tf_v1.MetaGraphDef(), meta_graph_file, is_binary)
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# pylint: disable=no-member
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with tf_v1.Session() as sess:
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restorer = tf_v1.train.import_meta_graph(input_meta_graph_def)
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restorer.restore(sess, re.sub('\.meta$', '', meta_graph_file))
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outputs = get_output_node_names_list(input_meta_graph_def.graph_def, user_output_node_names_list)
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graph_def = tf_v1.graph_util.convert_variables_to_constants(sess, input_meta_graph_def.graph_def,
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outputs)
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return graph_def, variables_values, 'tf'
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if model_dir:
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# saved model directory
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try:
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env_setup = get_environment_setup("tf")
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# enable eager execution temporarily while TensorFlow 2 model is being loaded
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tf_v1.enable_eager_execution()
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# code to extract GraphDef for TF 2.0 SavedModel format
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# tf.saved_model.load function throws TypeError for TF 1.x SavedModel format in case TF 1.x installed
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imported = tf.saved_model.load(model_dir, saved_model_tags) # pylint: disable=E1120
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# to get a signature by key throws KeyError for TF 1.x SavedModel format in case TF 2.x installed
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concrete_func = imported.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
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# the aggressive inlining parameter needs to freeze a table of embeddings for Keras Embedding operation
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# and a model with Embedding operation cannot properly converted to IR without this function parameter
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if "tensorflow" in env_setup and env_setup["tensorflow"] >= LooseVersion("2.2.0"):
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frozen_func = convert_variables_to_constants_v2(concrete_func,
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lower_control_flow=False,
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aggressive_inlining=True) # pylint: disable=E1123
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else:
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frozen_func = convert_variables_to_constants_v2(concrete_func,
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lower_control_flow=False) # pylint: disable=E1123
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graph_def = frozen_func.graph.as_graph_def(add_shapes=True)
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# disable eager execution since next steps are executed with a graph in non-eager mode
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tf_v1.disable_eager_execution()
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return graph_def, variables_values, 'tf2'
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except (TypeError, KeyError):
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# disable eager execution since TensorFlow 1 model is handled
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tf_v1.disable_eager_execution()
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# code to extract GraphDef for TF 1.0 SavedModel format
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tags = saved_model_tags if saved_model_tags is not None else [tf_v1.saved_model.tag_constants.SERVING]
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with tf_v1.Session() as sess:
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meta_graph_def = tf_v1.saved_model.loader.load(sess, tags, model_dir)
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outputs = get_output_node_names_list(meta_graph_def.graph_def, user_output_node_names_list)
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graph_def = tf_v1.graph_util.convert_variables_to_constants(sess, meta_graph_def.graph_def, outputs)
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return graph_def, variables_values, 'tf'
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except Exception as e:
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raise FrameworkError('SavedModel format load failure: {}', e) from e
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except Exception as e:
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raise FrameworkError('Cannot load input model: {}', e) from e
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raise Error("Unknown configuration of input model parameters")
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def protobuf_attrs(pb:tf_v1.NodeDef):
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return {'pb': pb}
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def protobuf2nx(graph, pb: tf_v1.GraphDef):
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fill_graph_with_nodes(graph, pb.node, get_id=lambda pb: pb.name, get_attrs=protobuf_attrs)
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if hasattr(graph, 'op_names_statistic'):
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for node_name in graph.nodes:
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node = Node(graph, node_name)
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node_pb = node.soft_get('pb', None)
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if node_pb is not None:
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if hasattr(node_pb, 'op'):
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graph.op_names_statistic[node_pb.op] += 1
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# Create a library with auxiliary functions used in TensorFlow 2 operations
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if hasattr(pb, 'library') and hasattr(pb.library, 'function'):
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graph.graph['library'] = {}
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for library_function in pb.library.function:
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function_name = library_function.signature.name
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graph.graph['library'][function_name] = {}
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graph.graph['library'][function_name]['input_arg'] = library_function.signature.input_arg
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graph.graph['library'][function_name]['output_arg'] = library_function.signature.output_arg
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graph.graph['library'][function_name]['node_def'] = library_function.node_def
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graph.graph['library'][function_name]['ret'] = library_function.ret
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# initial order of nodes in the GraphDef. It is used to specify order in
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# which merged nodes are added to the generated sub-graph GraphDef for the TensorFlow offload feature.
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graph.graph['initial_nodes_order'] = [node.name for node in pb.node]
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# Remove data dependency edges. This is needed for the TF offload case
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for _, attrs in list(graph.nodes(data=True)):
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pb = attrs['pb']
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if '_class' in pb.attr:
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index = 0
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while index < len(pb.attr['_class'].list.s):
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if re.match('^loc:@.*', pb.attr['_class'].list.s[index].decode('utf-8')):
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del pb.attr['_class'].list.s[index]
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else:
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index = index + 1
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def variables_to_constants(graph: Graph, variables_values: dict):
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"""
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Converts `Variable<V2>` operations to FakeConst operations with `value` from `variables_values` dictionary
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:param graph: graph to operate on
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:param variables_values: dictionary with variable names as keys and np.array data as values
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"""
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for node in graph.get_op_nodes(op='FakeConst'):
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node_name = node.name
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if node_name not in variables_values:
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log.debug("There is no value for '{}': {} in checkpoint variable values".format(node.op, node_name))
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continue
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node['value'] = variables_values[node_name]
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