Description:
kerasflops 0.1.2
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
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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