PaddleSlim 开源项目教程

项目介绍

PaddleSlim 是一个专注于深度学习模型压缩的工具库，提供低比特量化、知识蒸馏、稀疏化和模型结构搜索等模型压缩策略。它帮助开发者快速实现模型的小型化，适用于图像领域模型，并计划扩展对NLP领域模型的支持。

项目快速启动

以下是快速启动 PaddleSlim 的步骤和示例代码：

安装 PaddleSlim

首先，确保已安装 PaddlePaddle 1.6 或更新版本。然后安装 PaddleSlim：

pip install paddleslim -i https://pypi.org/simple

示例代码

以下是一个简单的量化训练示例：

import paddle
import paddle.fluid as fluid
from paddleslim.quant import quant_aware, convert

# 定义网络结构
def conv_net(img, label):
    conv_pool_1 = fluid.nets.simple_img_conv_pool(
        input=img,
        filter_size=5,
        num_filters=20,
        pool_size=2,
        pool_stride=2,
        act="relu")
    conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
    conv_pool_2 = fluid.nets.simple_img_conv_pool(
        input=conv_pool_1,
        filter_size=5,
        num_filters=50,
        pool_size=2,
        pool_stride=2,
        act="relu")
    prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
    loss = fluid.layers.cross_entropy(input=prediction, label=label)
    avg_loss = fluid.layers.mean(loss)
    return avg_loss

# 数据准备
train_reader = paddle.batch(
    paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=500),
    batch_size=64)

# 定义输入输出
image = fluid.layers.data(name='image', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')

# 获取损失函数
loss = conv_net(image, label)

# 定义优化器
optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.001)
opts = optimizer.minimize(loss)

# 定义执行器
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

# 量化配置
quant_config = {
    'weight_quantize_type': 'channel_wise_abs_max',
    'activation_quantize_type': 'moving_average_abs_max',
    'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul']
}

# 量化训练
quant_train_program = quant_aware(fluid.default_main_program(), place, quant_config, for_test=False)

# 训练循环
for pass_id in range(10):
    for batch_id, data in enumerate(train_reader()):
        img_data = np.array([x[0].reshape((1, 28, 28)) for x in data]).astype('float32')
        label_data = np.array([x[1] for x in data]).astype('int64').reshape((-1, 1))
        loss_val, = exe.run(quant_train_program,
                            feed={'image': img_data, 'label': label_data},
                            fetch_list=[loss])
        print("Pass {}, Batch {}, Loss {}".format(pass_id, batch_id, loss_val))

# 转换量化模型
quant_infer_program = quant_aware(fluid.default_main_program(), place, quant_config, for_test=True)
quant_infer_program = convert(quant_infer_program, place, quant_config)

应用案例和最佳实践

PaddleSlim 在多个领域有广泛的应用，例如图像分类、