https://github.com/tokusumi/keras-flops
FLOPs calculator with tf.profiler for neural network architecture written in tensorflow 2.2+ (tf.keras)
https://github.com/tokusumi/keras-flops
flops profiler tensorflow2
Last synced: about 1 year ago
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FLOPs calculator with tf.profiler for neural network architecture written in tensorflow 2.2+ (tf.keras)
- Host: GitHub
- URL: https://github.com/tokusumi/keras-flops
- Owner: tokusumi
- License: mit
- Created: 2020-08-12T16:47:28.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-10-31T00:03:16.000Z (over 2 years ago)
- Last Synced: 2025-03-27T17:22:58.083Z (over 1 year ago)
- Topics: flops, profiler, tensorflow2
- Language: Python
- Homepage:
- Size: 53.7 KB
- Stars: 54
- Watchers: 1
- Forks: 8
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# keras-flops
[](https://badge.fury.io/py/keras-flops)
FLOPs calculator for neural network architecture written in tensorflow (tf.keras) v2.2+
This stands on the shoulders of giants, [tf.profiler](https://www.tensorflow.org/api_docs/python/tf/compat/v1/profiler/Profiler).
## Requirements
* Python 3.6+
* Tensorflow 2.2+
## Installation
Using pip:
```
pip install keras-flops
```
## Example
See colab examples [here](https://github.com/tokusumi/keras-flops/tree/master/notebooks) in details.
```python
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 |