【动手学PaddlePaddle2.0系列】浅谈混合精度训练

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2020/12/28 19:54
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【动手学Paddle2.0系列】浅谈混合精度训练

大家好,本次教程为大家介绍一下如何在Paddle2.0中开启混合精度训练,并对模型进行测试。

下载安装命令

## CPU版本安装命令
pip install -f https://paddlepaddle.org.cn/pip/oschina/cpu paddlepaddle

## GPU版本安装命令
pip install -f https://paddlepaddle.org.cn/pip/oschina/gpu paddlepaddle-gpu

1 混合精度训练

混合精度训练最初是由百度和英伟达联和提出的,在论文Mixed Precision Training中,对混合精度训练进行了详细的阐述,并对其实现进行了讲解,有兴趣的同学可以看看这篇论文。

1.1 半精度与单精度

半精度(也被称为FP16)对比高精度的FP32与FP64降低了神经网络的显存占用,使得我们可以训练部署更大的网络,并且FP16在数据转换时比FP32或者FP64更节省时间。

单精度(也被称为32-bit)是通用的浮点数格式(在C扩展语言中表示为float),64-bit被称为双精度(double)。

如图所示,我们能够很直观的看到半精度的存储空间是单精度存储空间的一半。

1.2 为什么使用混合精度训练

混合精度训练,指代的是单精度 float和半精度 float16 混合训练。

float16和float相比恰里,总结下来就是两个原因:内存占用更少,计算更快。

内存占用更少:这个是显然可见的,通用的模型 fp16 占用的内存只需原来的一半。memory-bandwidth 减半所带来的好处:

模型占用的内存更小,训练的时候可以用更大的batchsize。

模型训练时,通信量(特别是多卡,或者多机多卡)大幅减少,大幅减少等待时间,加快数据的流通。

计算更快:目前的不少GPU都有针对 fp16 的计算进行优化。论文指出:在近期的GPU中,半精度的计算吞吐量可以是单精度的 2-8 倍;

损失控制原理:

2 实验设计

本次实验主要从两个方面进行测试,分别在精度和速度两个部分进行对比。实验中采用ResNet-18作为测试对象,使用的数据集为美食数据集,共五种类别。

# 解压数据集

!cd data/data64280/ && unzip -q trainset.zip

2.1 数据集预处理

import pandas as pd
import numpy as np
import os


all_file_dir = 'data/data64280/trainset'

img_list = []
label_list = []

label_id = 0

class_list = [c for c in os.listdir(all_file_dir) if os.path.isdir(os.path.join(all_file_dir, c))]

for class_dir in class_list:


    image_path_pre = os.path.join(all_file_dir, class_dir)

    for img in os.listdir(image_path_pre):
        img_list.append(os.path.join(image_path_pre, img))
        label_list.append(label_id)
    label_id += 1

img_df =  pd.DataFrame(img_list)
label_df = pd.DataFrame(label_list)

img_df.columns = ['images']
label_df.columns = ['label']

df = pd.concat([img_df, label_df], axis=1)
df = df.reindex(np.random.permutation(df.index))

df.to_csv('food_data.csv', index=0)
import pandas as pd
# 读取数据
df = pd.read_csv('food_data.csv')
image_path_list = df['images'].values
label_list = df['label'].values

# 划分训练集和校验集
all_size = len(image_path_list)
train_size = int(all_size * 0.8)
train_image_path_list = image_path_list[:train_size]
train_label_list = label_list[:train_size]
val_image_path_list = image_path_list[train_size:]
val_label_list = label_list[train_size:]

2.2 自定义数据集


import numpy as np
from PIL import Image
from paddle.io import Dataset
import paddle.vision.transforms as T
import paddle as pd

class MyDataset(Dataset):
    """
    步骤一:继承paddle.io.Dataset类
    """
    def __init__(self, image, label, transform=None):
        """
        步骤二:实现构造函数,定义数据读取方式,划分训练和测试数据集
        """
        super(MyDataset, self).__init__()
        imgs = image
        labels = label
        
        self.labels = labels
        self.imgs = imgs
        self.transform = transform
            # self.loader = loader
    def __getitem__(self, index):  # 这个方法是必须要有的,用于按照索引读取每个元素的具体内容
        fn = self.imgs
        label = self.labels
        # fn是图片path #fn和label分别获得imgs[index]也即是刚才每行中word[0]和word[1]的信息
        for im,la in zip(fn, label):
            img = Image.open(im)
            img = img.convert("RGB")
            img =  np.array(img).astype('float32') / 255.0
            label = np.array([la]).astype(dtype='int64')
        # 按照路径读取图片
        if self.transform is not None:
            img = self.transform(img)
            # 数据标签转换为Tensor
        return img, label
        # return回哪些内容,那么我们在训练时循环读取每个batch时,就能获得哪些内容
        # **********************************  #使用__len__()初始化一些需要传入的参数及数据集的调用**********************

    def __len__(self):
        # 这个函数也必须要写,它返回的是数据集的长度,也就是多少张图片,要和loader的长度作区分
        return len(self.imgs)

2.3 训练准备

import paddle

from paddle.metric import Accuracy

import warnings
warnings.filterwarnings("ignore")


import paddle.vision.transforms as T



transform = T.Compose([
    T.Resize([224, 224]),
    T.ToTensor(),
    # T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
    # T.Transpose(),
])

train_dataset = MyDataset(image=train_image_path_list, label=train_label_list ,transform=transform)

train_loader = paddle.io.DataLoader(train_dataset, places=paddle.CPUPlace(), batch_size=16, shuffle=True)
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform

import math

__all__ = ["ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"]


class ConvBNLayer(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 groups=1,
                 act=None,
                 name=None,
                 data_format="NCHW"):
        super(ConvBNLayer, self).__init__()

        self._conv = Conv2D(
            in_channels=num_channels,
            out_channels=num_filters,
            kernel_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            weight_attr=ParamAttr(name=name + "_weights"),
            bias_attr=False,
            data_format=data_format)
        if name == "conv1":
            bn_name = "bn_" + name
        else:
            bn_name = "bn" + name[3:]
        self._batch_norm = BatchNorm(
            num_filters,
            act=act,
            param_attr=ParamAttr(name=bn_name + "_scale"),
            bias_attr=ParamAttr(bn_name + "_offset"),
            moving_mean_name=bn_name + "_mean",
            moving_variance_name=bn_name + "_variance",
            data_layout=data_format)

    def forward(self, inputs):
        y = self._conv(inputs)
        y = self._batch_norm(y)
        return y


class BottleneckBlock(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 stride,
                 shortcut=True,
                 name=None,
                 data_format="NCHW"):
        super(BottleneckBlock, self).__init__()

        self.conv0 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=1,
            act="relu",
            name=name + "_branch2a",
            data_format=data_format)
        self.conv1 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act="relu",
            name=name + "_branch2b",
            data_format=data_format)
        self.conv2 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters * 4,
            filter_size=1,
            act=None,
            name=name + "_branch2c",
            data_format=data_format)

        if not shortcut:
            self.short = ConvBNLayer(
                num_channels=num_channels,
                num_filters=num_filters * 4,
                filter_size=1,
                stride=stride,
                name=name + "_branch1",
                data_format=data_format)

        self.shortcut = shortcut

        self._num_channels_out = num_filters * 4

    def forward(self, inputs):
        y = self.conv0(inputs)
        conv1 = self.conv1(y)
        conv2 = self.conv2(conv1)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)

        y = paddle.add(x=short, y=conv2)
        y = F.relu(y)
        return y


class BasicBlock(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 stride,
                 shortcut=True,
                 name=None,
                 data_format="NCHW"):
        super(BasicBlock, self).__init__()
        self.stride = stride
        self.conv0 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act="relu",
            name=name + "_branch2a",
            data_format=data_format)
        self.conv1 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters,
            filter_size=3,
            act=None,
            name=name + "_branch2b",
            data_format=data_format)

        if not shortcut:
            self.short = ConvBNLayer(
                num_channels=num_channels,
                num_filters=num_filters,
                filter_size=1,
                stride=stride,
                name=name + "_branch1",
                data_format=data_format)

        self.shortcut = shortcut

    def forward(self, inputs):
        y = self.conv0(inputs)
        conv1 = self.conv1(y)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)
        y = paddle.add(x=short, y=conv1)
        y = F.relu(y)
        return y


class ResNet(nn.Layer):
    def __init__(self, layers=50, class_dim=1000, input_image_channel=3, data_format="NCHW"):
        super(ResNet, self).__init__()

        self.layers = layers
        self.data_format = data_format
        self.input_image_channel = input_image_channel

        supported_layers = [18, 34, 50, 101, 152]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(
                supported_layers, layers)

        if layers == 18:
            depth = [2, 2, 2, 2]
        elif layers == 34 or layers == 50:
            depth = [3, 4, 6, 3]
        elif layers == 101:
            depth = [3, 4, 23, 3]
        elif layers == 152:
            depth = [3, 8, 36, 3]
        num_channels = [64, 256, 512,
                        1024] if layers >= 50 else [64, 64, 128, 256]
        num_filters = [64, 128, 256, 512]

        self.conv = ConvBNLayer(
            num_channels=self.input_image_channel,
            num_filters=64,
            filter_size=7,
            stride=2,
            act="relu",
            name="conv1",
            data_format=self.data_format)
        self.pool2d_max = MaxPool2D(
            kernel_size=3,
            stride=2, 
            padding=1,
            data_format=self.data_format)

        self.block_list = []
        if layers >= 50:
            for block in range(len(depth)):
                shortcut = False
                for i in range(depth[block]):
                    if layers in [101, 152] and block == 2:
                        if i == 0:
                            conv_name = "res" + str(block + 2) + "a"
                        else:
                            conv_name = "res" + str(block + 2) + "b" + str(i)
                    else:
                        conv_name = "res" + str(block + 2) + chr(97 + i)
                    bottleneck_block = self.add_sublayer(
                        conv_name,
                        BottleneckBlock(
                            num_channels=num_channels[block]
                            if i == 0 else num_filters[block] * 4,
                            num_filters=num_filters[block],
                            stride=2 if i == 0 and block != 0 else 1,
                            shortcut=shortcut,
                            name=conv_name,
                            data_format=self.data_format))
                    self.block_list.append(bottleneck_block)
                    shortcut = True
        else:
            for block in range(len(depth)):
                shortcut = False
                for i in range(depth[block]):
                    conv_name = "res" + str(block + 2) + chr(97 + i)
                    basic_block = self.add_sublayer(
                        conv_name,
                        BasicBlock(
                            num_channels=num_channels[block]
                            if i == 0 else num_filters[block],
                            num_filters=num_filters[block],
                            stride=2 if i == 0 and block != 0 else 1,
                            shortcut=shortcut,
                            name=conv_name,
                            data_format=self.data_format))
                    self.block_list.append(basic_block)
                    shortcut = True

        self.pool2d_avg = AdaptiveAvgPool2D(1, data_format=self.data_format)

        self.pool2d_avg_channels = num_channels[-1] * 2

        stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)

        self.out = Linear(
            self.pool2d_avg_channels,
            class_dim,
            weight_attr=ParamAttr(
                initializer=Uniform(-stdv, stdv), name="fc_0.w_0"),
            bias_attr=ParamAttr(name="fc_0.b_0"))

    def forward(self, inputs):
        y = self.conv(inputs)
        y = self.pool2d_max(y)
        for block in self.block_list:
            y = block(y)
        y = self.pool2d_avg(y)
        y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
        y = self.out(y)
        return y


def ResNet18(**args):
    model = ResNet(layers=18, **args)
    return model

2.4 训练过程定义

import paddle
import numpy
import paddle.nn.functional as F
import time

def train(model):
    model.train()
    epochs = 5
    optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
    # 用Adam作为优化函数
    for epoch in range(epochs):
        for batch_id, data in enumerate(train_loader()):
            x_data = data[0]
            y_data = data[1]
            # print(y_data)
            predicts = model(x_data)
            loss = F.cross_entropy(predicts, y_data)
            # 计算损失
            acc = paddle.metric.accuracy(predicts, y_data, k=2)
            loss.backward()
            if batch_id % 10 == 0:
                print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), acc.numpy()))
            optim.step()
            optim.clear_grad()
import paddle
import numpy
import paddle.nn.functional as F
import time

def train_amp(model):
    model.train()
    epochs = 5
    optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
    # 用Adam作为优化函数
    for epoch in range(epochs):
        for batch_id, data in enumerate(train_loader()):
            x_data = data[0].astype('float16')
            y_data = data[1]
           
            scaler = paddle.amp.GradScaler(init_loss_scaling=1024)

            with paddle.amp.auto_cast():
                predicts = model(x_data)
                loss = F.cross_entropy(predicts, y_data)

            scaled = scaler.scale(loss)  # scale the loss
            scaled.backward()  # do backward
        
            acc = paddle.metric.accuracy(predicts, y_data, k=2)
            if batch_id % 10 == 0:
                print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(),
                                                                                acc.numpy()))
            optim.step()
            optim.clear_grad()

2.5 开启训练

此部分,分别对两种训练方式进行对比,主要关注模型的训练速度

model = ResNet18(class_dim=2)
strat = time.time()
train(model)
end = time.time()

print('no_amp:', end-strat)
epoch: 0, batch_id: 0, loss is: [0.21116894], acc is: [1.]
epoch: 1, batch_id: 0, loss is: [0.00010776], acc is: [1.]
epoch: 2, batch_id: 0, loss is: [2.5868081e-05], acc is: [1.]
epoch: 3, batch_id: 0, loss is: [1.442422e-05], acc is: [1.]
epoch: 4, batch_id: 0, loss is: [1.1086402e-05], acc is: [1.]
no_amp: 740.6813971996307

 

strat1 = time.time()
train_amp(model)

end1 = time.time()

'no_amp:', end-strat)
epoch: 0, batch_id: 0, loss is: [0.21116894], acc is: [1.]
epoch: 1, batch_id: 0, loss is: [0.00010776], acc is: [1.]
epoch: 2, batch_id: 0, loss is: [2.5868081e-05], acc is: [1.]
epoch: 3, batch_id: 0, loss is: [1.442422e-05], acc is: [1.]
epoch: 4, batch_id: 0, loss is: [1.1086402e-05], acc is: [1.]
no_amp: 740.6813971996307

 

strat1 = time.time()
train_amp(model)

end1 = time.time()

print('with amp:', end1-strat1)
epoch: 0, batch_id: 0, loss is: [0.512834], acc is: [1.]
epoch: 1, batch_id: 0, loss is: [0.00025519], acc is: [1.]
epoch: 2, batch_id: 0, loss is: [5.9364465e-05], acc is: [1.]
epoch: 3, batch_id: 0, loss is: [3.2305197e-05], acc is: [1.]
epoch: 4, batch_id: 0, loss is: [2.4556812e-05], acc is: [1.]
with amp: 740.9603228569031

 

总结

对于本次实验,由于迭代轮数较少,只迭代了5次,故时间上的优势没有体现出来,大家有兴趣的可以增加迭代次数,或者换更深的网络进行测试。

从训练的结果来看,使用混合精度训练,其loss值是高于未使用混合精度训练模型的。

对于混合精度训练,介绍得还不够详细,大家有兴趣的可以详细的阅读论文,并且以后我对这些有了更深的认识也会和大家分享。相关资料

下载安装命令

## CPU版本安装命令
pip install -f https://paddlepaddle.org.cn/pip/oschina/cpu paddlepaddle

## GPU版本安装命令
pip install -f https://paddlepaddle.org.cn/pip/oschina/gpu paddlepaddle-gpu

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如有侵权,请联系 support@oschina.cn 删除。
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