Benchmark conv block and depthwise conv block.

bench.py

import time

import numpy as np
from keras import Input
from keras.applications.mobilenet import DepthwiseConv2D
from keras.engine import Model
from keras.layers import Conv2D

batch_num = 16


def conv_net(inputs):
    x = Conv2D(128, (3, 3), padding='same', strides=(2, 2))(inputs)
    return Model(inputs, x)


def depthwise_conv_net(inputs):
    x = DepthwiseConv2D((3, 3), padding='same', strides=(2, 2))(inputs)
    x = Conv2D(128, (1, 1))(x)
    return Model(inputs, x)


def main():
    input_tensor = Input(shape=(64, 64, 64))

    experiments = [
        {'name': 'conv', 'model': conv_net(input_tensor)},
        {'name': 'depthwise_conv', 'model': depthwise_conv_net(input_tensor)},
    ]

    inputs = np.random.randn(batch_num, 64, 64, 64)

    for e in experiments:
        time_per_batch = []

        for i in range(10):
            start = time.time()
            e['model'].predict(inputs, batch_size=batch_num)
            elapsed = time.time() - start
            time_per_batch.append(elapsed)
            print(elapsed, e['name'])

        time_per_batch = np.array(time_per_batch)
        print(e['name'])
        print(time_per_batch[1:].mean())
        print(time_per_batch[1:].std())


if __name__ == '__main__':
    main()