keras-flops 0.1.2

FLOPs calculator with tf.profiler for neural network architecture written in tensorflow 2.2+ (tf.keras)

keras-flops

FLOPs calculator for neural network architecture written in tensorflow (tf.keras) v2.2+

This stands on the shoulders of giants, tf.profiler.

Requirements

• Python 3.6+
• Tensorflow 2.2+

Installation

Using pip:

pip install keras-flops

Example

See colab examples here in details.

from tensorflow.keras import Model, Input
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout

from keras_flops import get_flops

# build model
inp = Input((32, 32, 3))
x = Conv2D(32, kernel_size=(3, 3), activation="relu")(inp)
x = Conv2D(64, (3, 3), activation="relu")(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)
x = Flatten()(x)
x = Dense(128, activation="relu")(x)
x = Dropout(0.5)(x)
out = Dense(10, activation="softmax")(x)
model = Model(inp, out)

# Calculae FLOPS
flops = get_flops(model, batch_size=1)
print(f"FLOPS: {flops / 10 ** 9:.03} G")
# >>> FLOPS: 0.0338 G

Support

Support tf.keras.layers as follows,

name	layer
Conv	Conv[1D/2D/3D]
	Conv[1D/2D]Transpose
	DepthwiseConv2D
	SeparableConv[1D/2D]
Pooling	AveragePooling[1D/2D]
	GlobalAveragePooling[1D/2D/3D]
	MaxPooling[1D/2D]
	GlobalMaxPool[1D/2D/3D]
Normalization	BatchNormalization
Activation	Softmax
Attention	Attention
	AdditiveAttention
others	Dense

Not supported

Not support tf.keras.layers as follows. They are calculated as zero or smaller value than correct value.

name	layer
Conv	Conv3DTranspose
Pooling	AveragePooling3D
	MaxPooling3D
	UpSampling[1D/2D/3D]
Normalization	LayerNormalization
RNN	SimpleRNN
	LSTM
	GRU
others	Embedding