How to save/restore large model in tensorflow 2.0 w/ keras?

Question

I have a large custom model made with the new tensorflow 2.0 and mixing keras and tensorflow. I want to save it (architecture and weights). Exact command to reproduce:

import tensorflow as tf


OUTPUT_CHANNELS = 3

def downsample(filters, size, apply_batchnorm=True):
  initializer = tf.random_normal_initializer(0., 0.02)

  result = tf.keras.Sequential()
  result.add(
      tf.keras.layers.Conv2D(filters, size, strides=2, padding='same',
                             kernel_initializer=initializer, use_bias=False))

  if apply_batchnorm:
    result.add(tf.keras.layers.BatchNormalization())

  result.add(tf.keras.layers.LeakyReLU())

  return result

def upsample(filters, size, apply_dropout=False):
  initializer = tf.random_normal_initializer(0., 0.02)

  result = tf.keras.Sequential()
  result.add(
    tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
                                    padding='same',
                                    kernel_initializer=initializer,
                                    use_bias=False))

  result.add(tf.keras.layers.BatchNormalization())

  if apply_dropout:
      result.add(tf.keras.layers.Dropout(0.5))

  result.add(tf.keras.layers.ReLU())

  return result


def Generator():
  down_stack = [
    downsample(64, 4, apply_batchnorm=False), # (bs, 128, 128, 64)
    downsample(128, 4), # (bs, 64, 64, 128)
    downsample(256, 4), # (bs, 32, 32, 256)
    downsample(512, 4), # (bs, 16, 16, 512)
    downsample(512, 4), # (bs, 8, 8, 512)
    downsample(512, 4), # (bs, 4, 4, 512)
    downsample(512, 4), # (bs, 2, 2, 512)
    downsample(512, 4), # (bs, 1, 1, 512)
  ]

  up_stack = [
    upsample(512, 4, apply_dropout=True), # (bs, 2, 2, 1024)
    upsample(512, 4, apply_dropout=True), # (bs, 4, 4, 1024)
    upsample(512, 4, apply_dropout=True), # (bs, 8, 8, 1024)
    upsample(512, 4), # (bs, 16, 16, 1024)
    upsample(256, 4), # (bs, 32, 32, 512)
    upsample(128, 4), # (bs, 64, 64, 256)
    upsample(64, 4), # (bs, 128, 128, 128)
  ]

  initializer = tf.random_normal_initializer(0., 0.02)
  last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 4,
                                         strides=2,
                                         padding='same',
                                         kernel_initializer=initializer,
                                         activation='tanh') # (bs, 256, 256, 3)

  concat = tf.keras.layers.Concatenate()

  inputs = tf.keras.layers.Input(shape=[None,None,3])
  x = inputs

  # Downsampling through the model
  skips = []
  for down in down_stack:
    x = down(x)
    skips.append(x)

  skips = reversed(skips[:-1])

  # Upsampling and establishing the skip connections
  for up, skip in zip(up_stack, skips):
    x = up(x)
    x = concat([x, skip])

  x = last(x)

  return tf.keras.Model(inputs=inputs, outputs=x)

generator = Generator()
generator.summary()

generator.save('generator.h5')
generator_loaded = tf.keras.models.load_model('generator.h5')

I manage to save the model with:

generator.save('generator.h5')

But when I try to load it with:

generator_loaded = tf.keras.models.load_model('generator.h5')

It never ends (no error message). Maybe the model is too large? I tried to save as JSON with model.to_json() as well as the full API tf.keras.models.save_model(), but same problem, impossible to load it (or at least far too long).

Same problem on Windows/Linux and with/without GPU.

The save and restore work well with full Keras and simple model.

Edit

• Saving weights and then loading them works well, but it's impossible to load the model structure.
• I put the model I use to reproduce the bug, it comes from Pix2Pix example (https://www.tensorflow.org/alpha/tutorials/generative/pix2pix)
• I also wrote an issue on tensorflow github : https://github.com/tensorflow/tensorflow/issues/28281

Answer 1

As of tensorflow release 2.0.0 there is now a keras / tf agnostic way of saving models using tf.saved_model

        ....

        model.fit(images, labels , epochs=30, validation_data=(images_val, labels_val), verbose=1)

        tf.saved_model.save( model, "path/to/model_dir" )

You can then load with

        loaded_model = tf.saved_model.load("path/to/model_dir")

Answer 2

Try instead to save the model as:

model.save('model_name.model')

Then Load it with:

model = tf.keras.models.load_model('model_name.model')

Answer 3

I found a temporary solution. It seems that the issue occurs with the sequential API tf.keras.Sequential, by using the functional API, tf.keras.models.load_model manages to load the saved model. I hope they will fixed this issue in the final release, have a look to the issue I raised in github https://github.com/tensorflow/tensorflow/issues/28281.

Cheers,

Answer 4

I managed to save and load custom models by implementing similar functions to the Sequential model in Keras.

The key functions are CustomModel.get_config() CustomModel.from_config(), which also should exist on any of your custom layers (similar to the functions below, but see keras layers if you want a better understanding):

# In the CustomModel class    
def get_config(self):
    layer_configs = []
    for layer in self.layers:
        layer_configs.append({
            'class_name': layer.__class__.__name__,
            'config': layer.get_config()
        })
    config = {
        'name': self.name,
        'layers': copy.deepcopy(layer_configs),
        "arg1": self.arg1,
        ...
    }
    if self._build_input_shape:
        config['build_input_shape'] = self._build_input_shape
    return config

@classmethod
def from_config(cls, config, custom_objects=None):
    from tensorflow.python.keras import layers as layer_module
    if custom_objects is None:
        custom_objects = {'CustomLayer1Class': CustomLayer1Class, ...}
    else:
        custom_objects = dict(custom_objects, **{'CustomLayer1Class': CustomLayer1Class, ...})

    if 'name' in config:
        name = config['name']
        build_input_shape = config.get('build_input_shape')
        layer_configs = config['layers']
    else:
        name = None
        build_input_shape = None
        layer_configs = config
    model = cls(name=name,
                arg1=config['arg1'],
                should_build_graph=False,
                ...)
    for layer_config in tqdm(layer_configs, 'Loading Layers'):
        layer = layer_module.deserialize(layer_config,
                                         custom_objects=custom_objects)
        model.add(layer) # This function looks at the name of the layers to place them in the right order
    if not model.inputs and build_input_shape:
        model.build(build_input_shape)
    if not model._is_graph_network:
        # Still needs to be built when passed input data.
        model.built = False
    return model

I also added a CustomModel.add() function that adds layers one by one from their config. Also a parameter should_build_graph=False that makes sure you do not build the graph in the __init__() when calling cls().

Then the CustomModel.save() function looks like this:

    def save(self, filepath, overwrite=True, include_optimizer=True, **kwargs):
        from tensorflow.python.keras.models import save_model  
        save_model(self, filepath, overwrite, include_optimizer)

After that you can save using:

model.save("model.h5")
new_model = keras.models.load_model('model.h5',
                                        custom_objects={
                                        'CustomModel': CustomModel,                                                     
                                        'CustomLayer1Class': CustomLayer1Class,
                                        ...
                                        })

But somehow this approach seem to be quite slow... This approach on the other hand is almost 30x faster. Not sure why:

    model.save_weights("weights.h5")
    config = model.get_config()
    reinitialized_model = CustomModel.from_config(config)
    reinitialized_model.load_weights("weights.h5")

I works, but it seems quite hacky. Maybe future versions of TF2 will make the process clearer.

Answer 5

One other method of saving a trained model is to use the pickle module in python.

import pickle
pickle.dump(model, open(filename, 'wb'))

In order to load the pickled model,

loaded_model = pickle.load(open(filename, 'rb'))

The extension of the pickle file is usually .sav