string_int_label_map_pb2.py
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: object_detection/protos/string_int_label_map.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='object_detection/protos/string_int_label_map.proto',
package='object_detection.protos',
syntax='proto2',
serialized_options=None,
serialized_pb=_b('\n2object_detection/protos/string_int_label_map.proto\x12\x17object_detection.protos\"G\n\x15StringIntLabelMapItem\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\n\n\x02id\x18\x02 \x01(\x05\x12\x14\n\x0c\x64isplay_name\x18\x03 \x01(\t\"Q\n\x11StringIntLabelMap\x12<\n\x04item\x18\x01 \x03(\x0b\x32..object_detection.protos.StringIntLabelMapItem')
)
_STRINGINTLABELMAPITEM = _descriptor.Descriptor(
name='StringIntLabelMapItem',
full_name='object_detection.protos.StringIntLabelMapItem',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='object_detection.protos.StringIntLabelMapItem.name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='id', full_name='object_detection.protos.StringIntLabelMapItem.id', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='display_name', full_name='object_detection.protos.StringIntLabelMapItem.display_name', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=79,
serialized_end=150,
)
_STRINGINTLABELMAP = _descriptor.Descriptor(
name='StringIntLabelMap',
full_name='object_detection.protos.StringIntLabelMap',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='item', full_name='object_detection.protos.StringIntLabelMap.item', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=152,
serialized_end=233,
)
_STRINGINTLABELMAP.fields_by_name['item'].message_type = _STRINGINTLABELMAPITEM
DESCRIPTOR.message_types_by_name['StringIntLabelMapItem'] = _STRINGINTLABELMAPITEM
DESCRIPTOR.message_types_by_name['StringIntLabelMap'] = _STRINGINTLABELMAP
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
StringIntLabelMapItem = _reflection.GeneratedProtocolMessageType('StringIntLabelMapItem', (_message.Message,), dict(
DESCRIPTOR = _STRINGINTLABELMAPITEM,
__module__ = 'object_detection.protos.string_int_label_map_pb2'
# @@protoc_insertion_point(class_scope:object_detection.protos.StringIntLabelMapItem)
))
_sym_db.RegisterMessage(StringIntLabelMapItem)
StringIntLabelMap = _reflection.GeneratedProtocolMessageType('StringIntLabelMap', (_message.Message,), dict(
DESCRIPTOR = _STRINGINTLABELMAP,
__module__ = 'object_detection.protos.string_int_label_map_pb2'
# @@protoc_insertion_point(class_scope:object_detection.protos.StringIntLabelMap)
))
_sym_db.RegisterMessage(StringIntLabelMap)
# @@protoc_insertion_point(module_scope)
read_pbtxt_file.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 26 13:42:50 2018
@author: shirhe-lyh
"""
"""A tool to read .pbtxt file.
See Details at:
TensorFlow models/research/object_detetion/protos/string_int_label_pb2.py
TensorFlow models/research/object_detection/utils/label_map_util.py
"""
import tensorflow as tf
from google.protobuf import text_format
import string_int_label_map_pb2
def load_pbtxt_file(path):
"""Read .pbtxt file.
Args:
path: Path to StringIntLabelMap proto text file (.pbtxt file).
Returns:
A StringIntLabelMapProto.
Raises:
ValueError: If path is not exist.
"""
if not tf.gfile.Exists(path):
raise ValueError('`path` is not exist.')
with tf.gfile.GFile(path, 'r') as fid:
pbtxt_string = fid.read()
pbtxt = string_int_label_map_pb2.StringIntLabelMap()
try:
text_format.Merge(pbtxt_string, pbtxt)
except text_format.ParseError:
pbtxt.ParseFromString(pbtxt_string)
return pbtxt
def get_label_map_dict(path):
"""Reads a .pbtxt file and returns a dictionary.
Args:
path: Path to StringIntLabelMap proto text file.
Returns:
A dictionary mapping class names to indices.
"""
pbtxt = load_pbtxt_file(path)
result_dict = {}
for item in pbtxt.item:
result_dict[item.display_name] = item.id
return result_dict
export_inference_graph.py
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
r"""Tool to export an object detection model for inference.
Prepares an object detection tensorflow graph for inference using model
configuration and a trained checkpoint. Outputs inference
graph, associated checkpoint files, a frozen inference graph and a
SavedModel (https://tensorflow.github.io/serving/serving_basic.html).
The inference graph contains one of three input nodes depending on the user
specified option.
* `image_tensor`: Accepts a uint8 4-D tensor of shape [None, None, None, 3]
* `encoded_image_string_tensor`: Accepts a 1-D string tensor of shape [None]
containing encoded PNG or JPEG images. Image resolutions are expected to be
the same if more than 1 image is provided.
* `tf_example`: Accepts a 1-D string tensor of shape [None] containing
serialized TFExample protos. Image resolutions are expected to be the same
if more than 1 image is provided.
and the following output nodes returned by the model.postprocess(..):
* `num_detections`: Outputs float32 tensors of the form [batch]
that specifies the number of valid boxes per image in the batch.
* `detection_boxes`: Outputs float32 tensors of the form
[batch, num_boxes, 4] containing detected boxes.
* `detection_scores`: Outputs float32 tensors of the form
[batch, num_boxes] containing class scores for the detections.
* `detection_classes`: Outputs float32 tensors of the form
[batch, num_boxes] containing classes for the detections.
* `detection_masks`: Outputs float32 tensors of the form
[batch, num_boxes, mask_height, mask_width] containing predicted instance
masks for each box if its present in the dictionary of postprocessed
tensors returned by the model.
Notes:
* This tool uses `use_moving_averages` from eval_config to decide which
weights to freeze.
Example Usage:
--------------
python export_inference_graph \
--input_type image_tensor \
--pipeline_config_path path/to/ssd_inception_v2.config \
--trained_checkpoint_prefix path/to/model.ckpt \
--output_directory path/to/exported_model_directory
The expected output would be in the directory
path/to/exported_model_directory (which is created if it does not exist)
with contents:
- inference_graph.pbtxt
- model.ckpt.data-00000-of-00001
- model.ckpt.info
- model.ckpt.meta
- frozen_inference_graph.pb
+ saved_model (a directory)
Config overrides (see the `config_override` flag) are text protobufs
(also of type pipeline_pb2.TrainEvalPipelineConfig) which are used to override
certain fields in the provided pipeline_config_path. These are useful for
making small changes to the inference graph that differ from the training or
eval config.
Example Usage (in which we change the second stage post-processing score
threshold to be 0.5):
python export_inference_graph \
--input_type image_tensor \
--pipeline_config_path path/to/ssd_inception_v2.config \
--trained_checkpoint_prefix path/to/model.ckpt \
--output_directory path/to/exported_model_directory \
--config_override " \
model{ \
faster_rcnn { \
second_stage_post_processing { \
batch_non_max_suppression { \
score_threshold: 0.5 \
} \
} \
} \
}"
"""
import tensorflow as tf
from google.protobuf import text_format
from object_detection import exporter
from object_detection.protos import pipeline_pb2
slim = tf.contrib.slim
flags = tf.app.flags
flags.DEFINE_string('input_type', 'image_tensor', 'Type of input node. Can be '
'one of [`image_tensor`, `encoded_image_string_tensor`, '
'`tf_example`]')
flags.DEFINE_string('input_shape', None,
'If input_type is `image_tensor`, this can explicitly set '
'the shape of this input tensor to a fixed size. The '
'dimensions are to be provided as a comma-separated list '
'of integers. A value of -1 can be used for unknown '
'dimensions. If not specified, for an `image_tensor, the '
'default shape will be partially specified as '
'`[None, None, None, 3]`.')
flags.DEFINE_string('pipeline_config_path', None,
'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
'file.')
flags.DEFINE_string('trained_checkpoint_prefix', None,
'Path to trained checkpoint, typically of the form '
'path/to/model.ckpt')
flags.DEFINE_string('output_directory', None, 'Path to write outputs.')
flags.DEFINE_string('config_override', '',
'pipeline_pb2.TrainEvalPipelineConfig '
'text proto to override pipeline_config_path.')
flags.DEFINE_boolean('write_inference_graph', False,
'If true, writes inference graph to disk.')
tf.app.flags.mark_flag_as_required('pipeline_config_path')
tf.app.flags.mark_flag_as_required('trained_checkpoint_prefix')
tf.app.flags.mark_flag_as_required('output_directory')
FLAGS = flags.FLAGS
def main(_):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
text_format.Merge(f.read(), pipeline_config)
text_format.Merge(FLAGS.config_override, pipeline_config)
if FLAGS.input_shape:
input_shape = [
int(dim) if dim != '-1' else None
for dim in FLAGS.input_shape.split(',')
]
else:
input_shape = None
exporter.export_inference_graph(
FLAGS.input_type, pipeline_config, FLAGS.trained_checkpoint_prefix,
FLAGS.output_directory, input_shape=input_shape,
write_inference_graph=FLAGS.write_inference_graph)
if __name__ == '__main__':
tf.app.run()
批处理
python export_inference_graph.py ^
--input_type image_tensor ^
--pipeline_config_path mask_rcnn_inception_v2_coco.config ^
--trained_checkpoint_prefix train_dir/model.ckpt-1000 ^
--output_directory models
pause
train.py
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
r"""Training executable for detection models.
This executable is used to train DetectionModels. There are two ways of
configuring the training job:
1) A single pipeline_pb2.TrainEvalPipelineConfig configuration file
can be specified by --pipeline_config_path.
Example usage:
./train \
--logtostderr \
--train_dir=path/to/train_dir \
--pipeline_config_path=pipeline_config.pbtxt
2) Three configuration files can be provided: a model_pb2.DetectionModel
configuration file to define what type of DetectionModel is being trained, an
input_reader_pb2.InputReader file to specify what training data will be used and
a train_pb2.TrainConfig file to configure training parameters.
Example usage:
./train \
--logtostderr \
--train_dir=path/to/train_dir \
--model_config_path=model_config.pbtxt \
--train_config_path=train_config.pbtxt \
--input_config_path=train_input_config.pbtxt
"""
import functools
import json
import os
import tensorflow as tf
from object_detection.builders import dataset_builder
from object_detection.builders import graph_rewriter_builder
from object_detection.builders import model_builder
from object_detection.legacy import trainer
from object_detection.utils import config_util
tf.logging.set_verbosity(tf.logging.INFO)
flags = tf.app.flags
flags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.')
flags.DEFINE_integer('task', 0, 'task id')
flags.DEFINE_integer('num_clones', 1, 'Number of clones to deploy per worker.')
flags.DEFINE_boolean('clone_on_cpu', False,
'Force clones to be deployed on CPU. Note that even if '
'set to False (allowing ops to run on gpu), some ops may '
'still be run on the CPU if they have no GPU kernel.')
flags.DEFINE_integer('worker_replicas', 1, 'Number of worker+trainer '
'replicas.')
flags.DEFINE_integer('ps_tasks', 0,
'Number of parameter server tasks. If None, does not use '
'a parameter server.')
flags.DEFINE_string('train_dir', '',
'Directory to save the checkpoints and training summaries.')
flags.DEFINE_string('pipeline_config_path', '',
'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
'file. If provided, other configs are ignored')
flags.DEFINE_string('train_config_path', '',
'Path to a train_pb2.TrainConfig config file.')
flags.DEFINE_string('input_config_path', '',
'Path to an input_reader_pb2.InputReader config file.')
flags.DEFINE_string('model_config_path', '',
'Path to a model_pb2.DetectionModel config file.')
FLAGS = flags.FLAGS
@tf.contrib.framework.deprecated(None, 'Use object_detection/model_main.py.')
def main(_):
assert FLAGS.train_dir, '`train_dir` is missing.'
if FLAGS.task == 0: tf.gfile.MakeDirs(FLAGS.train_dir)
if FLAGS.pipeline_config_path:
configs = config_util.get_configs_from_pipeline_file(
FLAGS.pipeline_config_path)
if FLAGS.task == 0:
tf.gfile.Copy(FLAGS.pipeline_config_path,
os.path.join(FLAGS.train_dir, 'pipeline.config'),
overwrite=True)
else:
configs = config_util.get_configs_from_multiple_files(
model_config_path=FLAGS.model_config_path,
train_config_path=FLAGS.train_config_path,
train_input_config_path=FLAGS.input_config_path)
if FLAGS.task == 0:
for name, config in [('model.config', FLAGS.model_config_path),
('train.config', FLAGS.train_config_path),
('input.config', FLAGS.input_config_path)]:
tf.gfile.Copy(config, os.path.join(FLAGS.train_dir, name),
overwrite=True)
model_config = configs['model']
train_config = configs['train_config']
input_config = configs['train_input_config']
model_fn = functools.partial(
model_builder.build,
model_config=model_config,
is_training=True)
def get_next(config):
return dataset_builder.make_initializable_iterator(
dataset_builder.build(config)).get_next()
create_input_dict_fn = functools.partial(get_next, input_config)
env = json.loads(os.environ.get('TF_CONFIG', '{}'))
cluster_data = env.get('cluster', None)
cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None
task_data = env.get('task', None) or {'type': 'master', 'index': 0}
task_info = type('TaskSpec', (object,), task_data)
# Parameters for a single worker.
ps_tasks = 0
worker_replicas = 1
worker_job_name = 'lonely_worker'
task = 0
is_chief = True
master = ''
if cluster_data and 'worker' in cluster_data:
# Number of total worker replicas include "worker"s and the "master".
worker_replicas = len(cluster_data['worker']) + 1
if cluster_data and 'ps' in cluster_data:
ps_tasks = len(cluster_data['ps'])
if worker_replicas > 1 and ps_tasks < 1:
raise ValueError('At least 1 ps task is needed for distributed training.')
if worker_replicas >= 1 and ps_tasks > 0:
# Set up distributed training.
server = tf.train.Server(tf.train.ClusterSpec(cluster), protocol='grpc',
job_name=task_info.type,
task_index=task_info.index)
if task_info.type == 'ps':
server.join()
return
worker_job_name = '%s/task:%d' % (task_info.type, task_info.index)
task = task_info.index
is_chief = (task_info.type == 'master')
master = server.target
graph_rewriter_fn = None
if 'graph_rewriter_config' in configs:
graph_rewriter_fn = graph_rewriter_builder.build(
configs['graph_rewriter_config'], is_training=True)
trainer.train(
create_input_dict_fn,
model_fn,
train_config,
master,
task,
FLAGS.num_clones,
worker_replicas,
FLAGS.clone_on_cpu,
ps_tasks,
worker_job_name,
is_chief,
FLAGS.train_dir,
graph_hook_fn=graph_rewriter_fn)
if __name__ == '__main__':
tf.app.run()
批处理
python train.py ^
--logtostderr ^
--pipeline_config_path=mask_rcnn_inception_v2_coco.config ^
--train_dir=train_dir ^
pause