SavedModel 形式の使用

import os
import tempfile

from matplotlib import pyplot as plt
import numpy as np
import tensorflow as tf

tmpdir = tempfile.mkdtemp()

2024-01-11 18:43:07.320116: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-01-11 18:43:07.320163: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-01-11 18:43:07.321721: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

physical_devices = tf.config.list_physical_devices('GPU')
for device in physical_devices:
  tf.config.experimental.set_memory_growth(device, True)

file = tf.keras.utils.get_file(
    "grace_hopper.jpg",
    "https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg")
img = tf.keras.utils.load_img(file, target_size=[224, 224])
plt.imshow(img)
plt.axis('off')
x = tf.keras.utils.img_to_array(img)
x = tf.keras.applications.mobilenet.preprocess_input(
    x[tf.newaxis,...])

Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg
61306/61306 [==============================] - 0s 0us/step

labels_path = tf.keras.utils.get_file(
    'ImageNetLabels.txt',
    'https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')
imagenet_labels = np.array(open(labels_path).read().splitlines())

Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt
10484/10484 [==============================] - 0s 0us/step

pretrained_model = tf.keras.applications.MobileNet()
result_before_save = pretrained_model(x)

decoded = imagenet_labels[np.argsort(result_before_save)[0,::-1][:5]+1]

print("Result before saving:\n", decoded)

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf.h5
17225924/17225924 [==============================] - 0s 0us/step
Result before saving:
 ['military uniform' 'bow tie' 'suit' 'bearskin' 'pickelhaube']

mobilenet_save_path = os.path.join(tmpdir, "mobilenet/1/")
tf.saved_model.save(pretrained_model, mobilenet_save_path)

INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpwpnio7cy/mobilenet/1/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpwpnio7cy/mobilenet/1/assets

loaded = tf.saved_model.load(mobilenet_save_path)
print(list(loaded.signatures.keys()))  # ["serving_default"]

['serving_default']

infer = loaded.signatures["serving_default"]
print(infer.structured_outputs)

{'predictions': TensorSpec(shape=(None, 1000), dtype=tf.float32, name='predictions')}

labeling = infer(tf.constant(x))[pretrained_model.output_names[0]]

decoded = imagenet_labels[np.argsort(labeling)[0,::-1][:5]+1]

print("Result after saving and loading:\n", decoded)

Result after saving and loading:
 ['military uniform' 'bow tie' 'suit' 'bearskin' 'pickelhaube']

ls {mobilenet_save_path}

assets  fingerprint.pb  saved_model.pb  variables

saved_model_cli show --dir {mobilenet_save_path} --tag_set serve

2024-01-11 18:43:23.804359: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-01-11 18:43:23.804415: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-01-11 18:43:23.805873: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
The given SavedModel MetaGraphDef contains SignatureDefs with the following keys:
SignatureDef key: "__saved_model_init_op"
SignatureDef key: "serving_default"

ls {mobilenet_save_path}/variables

variables.data-00000-of-00001  variables.index

class CustomModule(tf.Module):

  def __init__(self):
    super(CustomModule, self).__init__()
    self.v = tf.Variable(1.)

  @tf.function
  def __call__(self, x):
    print('Tracing with', x)
    return x * self.v

  @tf.function(input_signature=[tf.TensorSpec([], tf.float32)])
  def mutate(self, new_v):
    self.v.assign(new_v)

module = CustomModule()

module_no_signatures_path = os.path.join(tmpdir, 'module_no_signatures')
module(tf.constant(0.))
print('Saving model...')
tf.saved_model.save(module, module_no_signatures_path)

Tracing with Tensor("x:0", shape=(), dtype=float32)
Saving model...
Tracing with Tensor("x:0", shape=(), dtype=float32)
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpwpnio7cy/module_no_signatures/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpwpnio7cy/module_no_signatures/assets

imported = tf.saved_model.load(module_no_signatures_path)
assert imported(tf.constant(3.)).numpy() == 3
imported.mutate(tf.constant(2.))
assert imported(tf.constant(3.)).numpy() == 6

imported(tf.constant([3.]))

ValueError: Could not find matching function to call for canonicalized inputs ((<tf.Tensor 'args_0:0' shape=(1,) dtype=float32>,), {}). Only existing signatures are [((TensorSpec(shape=(), dtype=tf.float32, name=u'x'),), {})].

optimizer = tf.keras.optimizers.SGD(0.05)

def train_step():
  with tf.GradientTape() as tape:
    loss = (10. - imported(tf.constant(2.))) ** 2
  variables = tape.watched_variables()
  grads = tape.gradient(loss, variables)
  optimizer.apply_gradients(zip(grads, variables))
  return loss

for _ in range(10):
  # "v" approaches 5, "loss" approaches 0
  print("loss={:.2f} v={:.2f}".format(train_step(), imported.v.numpy()))

loss=36.00 v=3.20
loss=12.96 v=3.92
loss=4.67 v=4.35
loss=1.68 v=4.61
loss=0.60 v=4.77
loss=0.22 v=4.86
loss=0.08 v=4.92
loss=0.03 v=4.95
loss=0.01 v=4.97
loss=0.00 v=4.98
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1704998607.284908  100396 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

loaded = tf.saved_model.load(mobilenet_save_path)
print("MobileNet has {} trainable variables: {}, ...".format(
          len(loaded.trainable_variables),
          ", ".join([v.name for v in loaded.trainable_variables[:5]])))

MobileNet has 83 trainable variables: conv1/kernel:0, conv1_bn/gamma:0, conv1_bn/beta:0, conv_dw_1/depthwise_kernel:0, conv_dw_1_bn/gamma:0, ...

trainable_variable_ids = {id(v) for v in loaded.trainable_variables}
non_trainable_variables = [v for v in loaded.variables
                           if id(v) not in trainable_variable_ids]
print("MobileNet also has {} non-trainable variables: {}, ...".format(
          len(non_trainable_variables),
          ", ".join([v.name for v in non_trainable_variables[:3]])))

MobileNet also has 54 non-trainable variables: conv1_bn/moving_mean:0, conv1_bn/moving_variance:0, conv_dw_1_bn/moving_mean:0, ...

assert len(imported.signatures) == 0

module_with_signature_path = os.path.join(tmpdir, 'module_with_signature')
call = module.__call__.get_concrete_function(tf.TensorSpec(None, tf.float32))
tf.saved_model.save(module, module_with_signature_path, signatures=call)

Tracing with Tensor("x:0", dtype=float32)
Tracing with Tensor("x:0", dtype=float32)
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpwpnio7cy/module_with_signature/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpwpnio7cy/module_with_signature/assets

imported_with_signatures = tf.saved_model.load(module_with_signature_path)
list(imported_with_signatures.signatures.keys())

['serving_default']

module_multiple_signatures_path = os.path.join(tmpdir, 'module_with_multiple_signatures')
signatures = {"serving_default": call,
              "array_input": module.__call__.get_concrete_function(tf.TensorSpec([None], tf.float32))}

tf.saved_model.save(module, module_multiple_signatures_path, signatures=signatures)

INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpwpnio7cy/module_with_multiple_signatures/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpwpnio7cy/module_with_multiple_signatures/assets

imported_with_multiple_signatures = tf.saved_model.load(module_multiple_signatures_path)
list(imported_with_multiple_signatures.signatures.keys())

['serving_default', 'array_input']

class CustomModuleWithOutputName(tf.Module):
  def __init__(self):
    super(CustomModuleWithOutputName, self).__init__()
    self.v = tf.Variable(1.)

  @tf.function(input_signature=[tf.TensorSpec(None, tf.float32)])
  def __call__(self, x):
    return {'custom_output_name': x * self.v}

module_output = CustomModuleWithOutputName()
call_output = module_output.__call__.get_concrete_function(tf.TensorSpec(None, tf.float32))
module_output_path = os.path.join(tmpdir, 'module_with_output_name')
tf.saved_model.save(module_output, module_output_path,
                    signatures={'serving_default': call_output})

INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpwpnio7cy/module_with_output_name/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpwpnio7cy/module_with_output_name/assets

imported_with_output_name = tf.saved_model.load(module_output_path)
imported_with_output_name.signatures['serving_default'].structured_outputs

{'custom_output_name': TensorSpec(shape=<unknown>, dtype=tf.float32, name='custom_output_name')}

ValueError: Message tensorflow.SavedModel exceeds maximum protobuf size of 2GB: ...

google.protobuf.message.DecodeError: Error parsing message as the message exceeded the protobuf limit with type 'tensorflow.GraphDef'

tf.saved_model.save(
  ...,
  options=tf.saved_model.SaveOptions(experimental_image_format=True)
)

const string export_dir = ...
SavedModelBundle bundle;
...
LoadSavedModel(session_options, run_options, export_dir, {kSavedModelTagTrain},
               &bundle);

$ bazel build //tensorflow/python/tools:saved_model_cli

usage: saved_model_cli show [-h] --dir DIR [--all]
[--tag_set TAG_SET] [--signature_def SIGNATURE_DEF_KEY]

$ saved_model_cli show --dir /tmp/saved_model_dir
The given SavedModel contains the following tag-sets:
serve
serve, gpu

$ saved_model_cli show --dir /tmp/saved_model_dir --tag_set serve The given SavedModel `MetaGraphDef` contains `SignatureDefs` with the following keys: SignatureDef key: "classify_x2_to_y3" SignatureDef key: "classify_x_to_y" SignatureDef key: "regress_x2_to_y3" SignatureDef key: "regress_x_to_y" SignatureDef key: "regress_x_to_y2" SignatureDef key: "serving_default"

$ saved_model_cli show --dir /tmp/saved_model_dir --tag_set serve,gpu

$ saved_model_cli show --dir \
/tmp/saved_model_dir --tag_set serve --signature_def serving_default
The given SavedModel SignatureDef contains the following input(s):
  inputs['x'] tensor_info:
      dtype: DT_FLOAT
      shape: (-1, 1)
      name: x:0
The given SavedModel SignatureDef contains the following output(s):
  outputs['y'] tensor_info:
      dtype: DT_FLOAT
      shape: (-1, 1)
      name: y:0
Method name is: tensorflow/serving/predict

$ saved_model_cli show --dir /tmp/saved_model_dir --all
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['classify_x2_to_y3']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['inputs'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: x2:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: y3:0
  Method name is: tensorflow/serving/classify

...

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['x'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: x:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['y'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: y:0
  Method name is: tensorflow/serving/predict

usage: saved_model_cli run [-h] --dir DIR --tag_set TAG_SET --signature_def
                           SIGNATURE_DEF_KEY [--inputs INPUTS]
                           [--input_exprs INPUT_EXPRS]
                           [--input_examples INPUT_EXAMPLES] [--outdir OUTDIR]
                           [--overwrite] [--tf_debug]

--inputs <INPUTS>

`<input_key>=[[1],[2],[3]]`

`<input_key>=np.ones((32,32,3))`

`<input_key>=[{"age":[22,24],"education":["BS","MS"]}]`