Channel Pruning for Image Classification¶

In this tutorial, you will learn how to use channel pruning API of PaddleSlim by a demo of MobileNetV1 model on MNIST dataset. This tutorial following workflow:

Import dependency
Build model
Prune model
Train pruned model

1. Import dependency¶

PaddleSlim dependents on Paddle1.7. Please ensure that you have installed paddle correctly. Import Paddle and PaddleSlim as below:

import paddle
import paddle.fluid as fluid
import paddleslim as slim

2. Build Model¶

This section will build a classsification model based MobileNetV1 for MNIST task. The shape of the input is [1, 28, 28] and the output number is 10.

To make the code simple, we define a function in package paddleslim.models to build classification model. Excute following code to build a model,

exe, train_program, val_program, inputs, outputs =
    slim.models.image_classification("MobileNet", [1, 28, 28], 10, use_gpu=False)

Note：The functions in paddleslim.models is just used in tutorials or demos.

3. Prune model¶

3.1 Compute FLOPs bofore pruning¶

FLOPs = slim.analysis.flops(train_program)
print("FLOPs: {}".format(FLOPs))

3.2 Pruning¶

The section will prune the parameters named conv2_1_sep_weights and conv2_2_sep_weights by 20% and 30%.

pruner = slim.prune.Pruner()
pruned_program, _, _ = pruner.prune(
        train_program,
        fluid.global_scope(),
        params=["conv2_1_sep_weights", "conv2_2_sep_weights"],
        ratios=[0.33] * 2,
        place=fluid.CPUPlace())

It will change the shapes of parameters defined in train_program. And the parametersvalues stored influid.global_scope()` will be pruned.

3.3 Compute FLOPs after pruning¶

FLOPs = paddleslim.analysis.flops(train_program)
print("FLOPs: {}".format(FLOPs))

4. Train pruned model¶

4.1 Define dataset¶

To make you easily run this demo, it will training on MNIST dataset. The package paddle.dataset.mnist of Paddle defines the downloading and reading of MNIST dataset. Define training data reader and test data reader as below：

import paddle.dataset.mnist as reader
train_reader = paddle.batch(
        reader.train(), batch_size=128, drop_last=True)
train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())

4.2 Training¶

Excute following code to run an epoch training:

for data in train_reader():
    acc1, acc5, loss = exe.run(pruned_program, feed=train_feeder.feed(data), fetch_list=outputs)
    print(acc1, acc5, loss)