TensorFlow
SOL’s TensorFlow integration supports to translate tf.Function, tf.Module,
Keras and tf.saved_model models into SOL models. If your tf.saved_model has
multiple signatures, you need to select the preferred one using
sol.optimize(my_saved_model.signatures['my_signature']). By default SOL uses
the tf.saved_model.__call__ function.
import tensorflow as tf
import sol
import tensorflow.keras as keras
def AlexNet(input_shape=(224, 224, 3), format="channels_last"):
inputs = keras.Input(shape=(input_shape))
x = inputs
x = keras.layers.Conv2D (input_shape=input_shape, filters=64, kernel_size=(11,11), strides=(4,4), padding='same', activation='relu', data_format=format)(x)
x = keras.layers.MaxPooling2D (pool_size=3, strides=2, padding='valid', data_format=format)(x)
x = keras.layers.Conv2D (filters=192, kernel_size=5, strides=1, padding='same', activation='relu', data_format=format)(x)
x = keras.layers.MaxPooling2D (pool_size=3, strides=2, padding="valid", data_format=format)(x)
x = keras.layers.Conv2D (filters=384, kernel_size=3, strides=1, padding="same", activation='relu', data_format=format)(x)
x = keras.layers.Conv2D (filters=256, kernel_size=3, strides=1, padding="same", activation='relu', data_format=format)(x)
x = keras.layers.Conv2D (filters=256, kernel_size=3, strides=1, padding="same", activation='relu', data_format=format)(x)
x = keras.layers.MaxPooling2D (pool_size=3, strides=2, padding="valid", data_format=format)(x)
x = keras.layers.Flatten (data_format=format)(x)
x = keras.layers.Dropout (rate=0.5)(x)
x = keras.layers.Dense (4096, input_shape=(256*6*6,), activation='relu')(x)
x = keras.layers.Dropout (rate=0.5)(x)
x = keras.layers.Dense (4096, activation="relu")(x)
x = keras.layers.Dense (1000)(x)
return keras.models.Model (inputs=inputs, outputs=x)
@tf.function(input_signature=[tf.TensorSpec([None, 224, 224, 3], dtype=tf.float32)])
def tf_function(input):
return ...
class TFModule(tf.Module):
def init(self):
super().__init__()
self.var = tf.Variable(...)
@tf.function(input_signature=[tf.TensorSpec([None, 224, 224, 3], dtype=tf.float32)])
def __call__(self, input):
return ...
with tf.device('/CPU:0'):
sol_model = sol.optimize(AlexNet(), batch_size=1)
# or
sol_model = sol.optimize(tf_function)
# or
sol_model = sol.optimize(TFModule())
# or
sol_model = sol.optimize(tf.saved_model.load("/path/to/saved/model"))
# Inference
output = sol_model(inputs)
# Training for Keras Models
sol_model.compile(...)
sol_model.fit(inputs, targets)
# Training for tf.Function and tf.Module
# TODO:
Since SOL v0.5.3 we integrated SOL tighter into Keras’s Model.compile(...)
function. It’s still experimental and might not work in all situations.
import sol.tensorflow # required to enable the modifications to Keras
model = init_your_model()
model.compile(optimizer, loss, sol_compile=True) # use sol_vdims=[...] to modify the sol.optimize(..., vdims=sol_vdims) attribute.
model(input_data) # runs using SOL
F.A.Q.
| What are the best configurations for using SOL with TensorFlow? |
|---|
If you are using SOL with TensorFlow on X86 you should set the following env vars:
OMP_NUM_THREADS=$(lscpu -b -p=Core,Socket | grep -v '^#' | sort -u | wc -l)
OMP_PROC_BIND=TRUE
TF_NUM_INTEROP_THREADS=1
|
| How can I define that the model input shall return a gradient? |
|---|
By default all inputs get no gradients assigned. If you want to override this behavior, use
sol_model = sol.optimize(model, requires_grad={"input_1", "whatever"})
|
| How can I override the model input shapes? |
|---|
By default all inputs use the input shapes defined in the model.
If you want to override this behavior, use
sol_model = sol.optimize(model, shapes={"input_1": [1, 2, 3], "whatever": [77, 3, 5]})
|
| How can I update/downgrade to another TensorFlow version? |
|---|
Before switching version, please have a look at the compatibility list if your TensorFlow
version is supported by SOL. If yes then you can just use pip3 install
tensorflow~={VERSION}.
|
| How do I store/load a Tensorflow Keras model? |
|---|
|
SOL model's cannot be stored directly. For storing/loading a SOL Keras model, use
model.save_weights(...) and model.load_weights(...) methods.
# Storing
sol_model = sol.optimize(keras_model)
sol_model.save_weights(checkpoint_path)
# Loading
sol_model = sol.optimize(keras_model)
sol_model.load_weights(checkpoint_path)
More information on loading/storing the weights can be found
here
|
| Which activations/recurrent_activations are supported by RNN layers? |
|---|
SOL currently supports [None, 'linear', 'tanh', 'sigmoid', 'relu'].
If you need another RNN activation function, please get in contact with us.
|
Tested Models
keras.applications (v2.15.1)
- ConvNeXtBase
- ConvNeXtLarge
- ConvNeXtSmall
- ConvNeXtTiny
- ConvNeXtXLarge
- DenseNet121
- DenseNet169
- DenseNet201
- EfficientNetB0
- EfficientNetB1
- EfficientNetB2
- EfficientNetB3
- EfficientNetB4
- EfficientNetB5
- EfficientNetB6
- EfficientNetB7
- EfficientNetV2B0
- EfficientNetV2B1
- EfficientNetV2B2
- EfficientNetV2B3
- EfficientNetV2L
- EfficientNetV2M
- EfficientNetV2S
- InceptionResNetV2
- InceptionV3
- MobileNet
- MobileNetV2
- MobileNetV3Large
- MobileNetV3Small
- NASNetLarge
- NASNetMobile
- RegNetX002
- RegNetX004
- RegNetX006
- RegNetX008
- RegNetX016
- RegNetX032
- RegNetX040
- RegNetX064
- RegNetX080
- RegNetX120
- RegNetX160
- RegNetX320
- RegNetY002
- RegNetY004
- RegNetY006
- RegNetY008
- RegNetY016
- RegNetY032
- RegNetY040
- RegNetY064
- RegNetY080
- RegNetY120
- RegNetY160
- RegNetY320
- ResNet101
- ResNet101V2
- ResNet152
- ResNet152V2
- ResNet50
- ResNet50V2
- ResNetRS101
- ResNetRS152
- ResNetRS200
- ResNetRS270
- ResNetRS350
- ResNetRS420
- ResNetRS50
- VGG16
- VGG19
- Xception
Supported Layers
Please refer to https://www.tensorflow.org/api/stable for how these functions are used. This documentation only contains which layers, functions and tensor functionality is currently implemented within SOL.
Layers