Spaces:
Runtime error
Runtime error
File size: 5,758 Bytes
5672777 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 |
# Copyright 2023 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"""Vision models export binary for serving/inference.
To export a trained checkpoint in saved_model format (shell script):
EXPERIMENT_TYPE = XX
CHECKPOINT_PATH = XX
EXPORT_DIR_PATH = XX
export_saved_model --experiment=${EXPERIMENT_TYPE} \
--export_dir=${EXPORT_DIR_PATH}/ \
--checkpoint_path=${CHECKPOINT_PATH} \
--batch_size=2 \
--input_image_size=224,224
To serve (python):
export_dir_path = XX
input_type = XX
input_images = XX
imported = tf.saved_model.load(export_dir_path)
model_fn = imported.signatures['serving_default']
output = model_fn(input_images)
"""
from absl import app
from absl import flags
from official.core import exp_factory
from official.modeling import hyperparams
from official.vision import registry_imports # pylint: disable=unused-import
from official.vision.serving import export_saved_model_lib
FLAGS = flags.FLAGS
_EXPERIMENT = flags.DEFINE_string(
'experiment', None, 'experiment type, e.g. retinanet_resnetfpn_coco')
_EXPORT_DIR = flags.DEFINE_string('export_dir', None, 'The export directory.')
_CHECKPOINT_PATH = flags.DEFINE_string('checkpoint_path', None,
'Checkpoint path.')
_CONFIG_FILE = flags.DEFINE_multi_string(
'config_file',
default=None,
help='YAML/JSON files which specifies overrides. The override order '
'follows the order of args. Note that each file '
'can be used as an override template to override the default parameters '
'specified in Python. If the same parameter is specified in both '
'`--config_file` and `--params_override`, `config_file` will be used '
'first, followed by params_override.')
_PARAMS_OVERRIDE = flags.DEFINE_string(
'params_override', '',
'The JSON/YAML file or string which specifies the parameter to be overriden'
' on top of `config_file` template.')
_BATCH_SIZE = flags.DEFINE_integer('batch_size', None, 'The batch size.')
_IMAGE_TYPE = flags.DEFINE_string(
'input_type', 'image_tensor',
'One of `image_tensor`, `image_bytes`, `tf_example` and `tflite`.')
_INPUT_IMAGE_SIZE = flags.DEFINE_string(
'input_image_size', '224,224',
'The comma-separated string of two integers representing the height,width '
'of the input to the model.')
_EXPORT_CHECKPOINT_SUBDIR = flags.DEFINE_string(
'export_checkpoint_subdir', 'checkpoint',
'The subdirectory for checkpoints.')
_EXPORT_SAVED_MODEL_SUBDIR = flags.DEFINE_string(
'export_saved_model_subdir', 'saved_model',
'The subdirectory for saved model.')
_LOG_MODEL_FLOPS_AND_PARAMS = flags.DEFINE_bool(
'log_model_flops_and_params', False,
'If true, logs model flops and parameters.')
_INPUT_NAME = flags.DEFINE_string(
'input_name', None,
'Input tensor name in signature def. Default at None which'
'produces input tensor name `inputs`.')
_FUNCTION_KEYS = flags.DEFINE_string(
'function_keys',
'',
(
'An optional comma-separated string of one or more key:value pair'
' indicating the serving function key and corresponding signature_def'
' name. For example,'
' `tf_example:serving_default,image_tensor:serving_image_tensor` means'
' two serving functions are defined for `tf_example` and `image_tensor`'
' input types.'
),
)
_ADD_TPU_FUNCTION_ALIAS = flags.DEFINE_bool(
'add_tpu_function_alias',
False,
(
'Whether to add TPU function alias so later it can be converted to a'
' TPU SavedModel for inference.'
),
)
def main(_):
params = exp_factory.get_exp_config(_EXPERIMENT.value)
for config_file in _CONFIG_FILE.value or []:
try:
params = hyperparams.override_params_dict(
params, config_file, is_strict=True
)
except KeyError:
params = hyperparams.override_params_dict(
params, config_file, is_strict=False
)
if _PARAMS_OVERRIDE.value:
try:
params = hyperparams.override_params_dict(
params, _PARAMS_OVERRIDE.value, is_strict=True
)
except KeyError:
params = hyperparams.override_params_dict(
params, _PARAMS_OVERRIDE.value, is_strict=False
)
params.validate()
params.lock()
function_keys = None
if _FUNCTION_KEYS.value:
function_keys = {}
for key_val in _FUNCTION_KEYS.value.split(','):
key_val_split = key_val.split(':')
function_keys[key_val_split[0]] = key_val_split[1]
export_saved_model_lib.export_inference_graph(
input_type=_IMAGE_TYPE.value,
batch_size=_BATCH_SIZE.value,
input_image_size=[int(x) for x in _INPUT_IMAGE_SIZE.value.split(',')],
params=params,
checkpoint_path=_CHECKPOINT_PATH.value,
export_dir=_EXPORT_DIR.value,
function_keys=function_keys,
export_checkpoint_subdir=_EXPORT_CHECKPOINT_SUBDIR.value,
export_saved_model_subdir=_EXPORT_SAVED_MODEL_SUBDIR.value,
log_model_flops_and_params=_LOG_MODEL_FLOPS_AND_PARAMS.value,
input_name=_INPUT_NAME.value,
add_tpu_function_alias=_ADD_TPU_FUNCTION_ALIAS.value,
)
if __name__ == '__main__':
app.run(main)
|