def write_artifacts(topology,
weights,
output_graph,
tf_version,
quantization_dtype=None):
"""Writes weights and topology to the output_dir.
If `topology` is Falsy (e.g., `None`), only emit weights to output_dir.
Args:
topology: tf.GraphDef TensorFlow GraphDef proto object, which represents
the model topology.
weights: an array of weight groups (as defined in tfjs write_weights).
output_graph: the output file name to hold all the contents.
tf_version: Tensorflow version of the input graph.
quantization_dtype: An optional numpy dtype to quantize weights to for
compression. Only np.uint8 and np.uint16 are supported.
"""
model_json = {
common.FORMAT_KEY: common.TFJS_GRAPH_MODEL_FORMAT,
# TODO(piyu): Add tensorflow version below by using `meta_info_def`.
common.GENERATED_BY_KEY: tf_version,
common.CONVERTED_BY_KEY: common.get_converted_by(),
}
model_json[common.ARTIFACT_MODEL_TOPOLOGY_KEY] = topology or None
weights_manifest = write_weights.write_weights(
weights, os.path.dirname(output_graph), write_manifest=False,
quantization_dtype=quantization_dtype)
assert isinstance(weights_manifest, list)
model_json[common.ARTIFACT_WEIGHTS_MANIFEST_KEY] = weights_manifest
with open(output_graph, 'wt') as f:
json.dump(model_json, f)
def write_artifacts(topology,
weights,
output_graph,
tf_version,
signature_def,
quantization_dtype_map=None,
weight_shard_size_bytes=1024 * 1024 * 4,
initializer_graph_def=None,
metadata=None):
"""Writes weights and topology to the output_dir.
If `topology` is Falsy (e.g., `None`), only emit weights to output_dir.
Args:
topology: tf.GraphDef TensorFlow GraphDef proto object, which represents
the model topology.
weights: an array of weight groups (as defined in tfjs write_weights).
output_graph: the output file name to hold all the contents.
tf_version: Tensorflow version of the input graph.
signature_def: the SignatureDef of the inference graph.
quantization_dtype_map: A mapping from dtype
(`uint8`, `uint16`, `float16`) to weights names. The weight mapping
supports wildcard substitution.
weight_shard_size_bytes: Shard size (in bytes) of the weight files.
The size of each weight file will be <= this value.
initializer_graph_def: tf.GraphDef proto object for initializer graph.
metadata: User defined metadata map.
"""
model_json = {
common.FORMAT_KEY:
common.TFJS_GRAPH_MODEL_FORMAT,
# TODO(piyu): Add tensorflow version below by using `meta_info_def`.
common.GENERATED_BY_KEY:
tf_version,
common.CONVERTED_BY_KEY:
common.get_converted_by(),
common.SIGNATURE_KEY:
MessageToDict(signature_def),
}
model_json[common.ARTIFACT_MODEL_TOPOLOGY_KEY] = topology or None
if metadata:
model_json[common.USER_DEFINED_METADATA_KEY] = metadata
if initializer_graph_def and initializer_graph_def.node:
model_json[common.ARTIFACT_MODEL_INITIALIZER] = MessageToDict(
initializer_graph_def)
weights_manifest = write_weights.write_weights(
weights,
os.path.dirname(output_graph),
write_manifest=False,
quantization_dtype_map=quantization_dtype_map,
shard_size_bytes=weight_shard_size_bytes)
assert isinstance(weights_manifest, list)
model_json[common.ARTIFACT_WEIGHTS_MANIFEST_KEY] = weights_manifest
with tf.io.gfile.GFile(output_graph, 'w') as f:
json.dump(model_json, f)
def write_artifacts(topology,
weights,
output_dir,
quantization_dtype_map=None,
weight_shard_size_bytes=1024 * 1024 * 4,
metadata=None):
"""Writes weights and topology to the output_dir.
If `topology` is Falsy (e.g., `None`), only emit weights to output_dir.
Args:
topology: a JSON dictionary, representing the Keras config.
weights: an array of weight groups (as defined in tfjs write_weights).
output_dir: the directory to hold all the contents.
quantization_dtype_map: (Optional) A mapping from dtype
(`uint8`, `uint16`, `float16`) to weights names. The weight mapping
supports wildcard substitution.
weight_shard_size_bytes: Shard size (in bytes) of the weight files.
The size of each weight file will be <= this value.
metadata: User defined metadata map.
"""
# TODO(cais, nielsene): This method should allow optional arguments of
# `write_weights.write_weights` (e.g., shard size) and forward them.
# We write the topology after since write_weights makes no promises about
# preserving directory contents.
if not (isinstance(weight_shard_size_bytes, int)
and weight_shard_size_bytes > 0):
raise ValueError(
'Expected weight_shard_size_bytes to be a positive integer, '
'but got %s' % weight_shard_size_bytes)
if os.path.isfile(output_dir):
raise ValueError(
'Path "%d" already exists as a file (not a directory).' %
output_dir)
model_json = {
common.FORMAT_KEY: common.TFJS_LAYERS_MODEL_FORMAT,
common.GENERATED_BY_KEY: _get_generated_by(topology),
common.CONVERTED_BY_KEY: common.get_converted_by()
}
if metadata:
model_json[common.USER_DEFINED_METADATA_KEY] = metadata
model_json[common.ARTIFACT_MODEL_TOPOLOGY_KEY] = topology or None
weights_manifest = write_weights.write_weights(
weights,
output_dir,
write_manifest=False,
quantization_dtype_map=quantization_dtype_map,
shard_size_bytes=weight_shard_size_bytes)
assert isinstance(weights_manifest, list)
model_json[common.ARTIFACT_WEIGHTS_MANIFEST_KEY] = weights_manifest
model_json_path = os.path.join(output_dir,
common.ARTIFACT_MODEL_JSON_FILE_NAME)
with open(model_json_path, 'wt') as f:
json.dump(model_json, f)
def write_artifacts(topology,
weights,
output_graph,
tf_version,
signature_def,
quantization_dtype=None,
weight_shard_size_bytes=1024 * 1024 * 4):
"""Writes weights and topology to the output_dir.
If `topology` is Falsy (e.g., `None`), only emit weights to output_dir.
Args:
topology: tf.GraphDef TensorFlow GraphDef proto object, which represents
the model topology.
weights: an array of weight groups (as defined in tfjs write_weights).
output_graph: the output file name to hold all the contents.
tf_version: Tensorflow version of the input graph.
signature_def: the SignatureDef of the inference graph.
quantization_dtype: An optional numpy dtype to quantize weights to for
compression. Only np.uint8 and np.uint16 are supported.
weight_shard_size_bytes: Shard size (in bytes) of the weight files.
The size of each weight file will be <= this value.
"""
model_json = {
common.FORMAT_KEY:
common.TFJS_GRAPH_MODEL_FORMAT,
# TODO(piyu): Add tensorflow version below by using `meta_info_def`.
common.GENERATED_BY_KEY:
tf_version,
common.CONVERTED_BY_KEY:
common.get_converted_by(),
common.USER_DEFINED_METADATA_KEY: {
common.SIGNATURE_KEY: MessageToDict(signature_def)
}
}
model_json[common.ARTIFACT_MODEL_TOPOLOGY_KEY] = topology or None
weights_manifest = write_weights.write_weights(
weights,
os.path.dirname(output_graph),
write_manifest=False,
quantization_dtype=quantization_dtype,
shard_size_bytes=weight_shard_size_bytes)
assert isinstance(weights_manifest, list)
model_json[common.ARTIFACT_WEIGHTS_MANIFEST_KEY] = weights_manifest
with tf.io.gfile.GFile(output_graph, 'w') as f:
json.dump(model_json, f)
def write_artifacts(topology, weights, output_dir, quantization_dtype=None):
"""Writes weights and topology to the output_dir.
If `topology` is Falsy (e.g., `None`), only emit weights to output_dir.
Args:
topology: a JSON dictionary, representing the Keras config.
weights: an array of weight groups (as defined in tfjs write_weights).
output_dir: the directory to hold all the contents.
quantization_dtype: An optional numpy dtype to quantize weights to for
compression. Only np.uint8 and np.uint16 are supported.
"""
# TODO(cais, nielsene): This method should allow optional arguments of
# `write_weights.write_weights` (e.g., shard size) and forward them.
# We write the topology after since write_weights makes no promises about
# preserving directory contents.
if os.path.isfile(output_dir):
raise ValueError(
'Path "%d" already exists as a file (not a directory).' %
output_dir)
model_json = {
common.FORMAT_KEY: common.TFJS_LAYERS_MODEL_FORMAT,
common.GENERATED_BY_KEY: _get_generated_by(topology),
common.CONVERTED_BY_KEY: common.get_converted_by(),
}
model_json[common.ARTIFACT_MODEL_TOPOLOGY_KEY] = topology or None
weights_manifest = write_weights.write_weights(
weights,
output_dir,
write_manifest=False,
quantization_dtype=quantization_dtype)
assert isinstance(weights_manifest, list)
model_json[common.ARTIFACT_WEIGHTS_MANIFEST_KEY] = weights_manifest
model_json_path = os.path.join(output_dir,
common.ARTIFACT_MODEL_JSON_FILE_NAME)
with open(model_json_path, 'wt') as f:
json.dump(model_json, f)
