From e821caabd98744d5662fd9b51f5e91bbf1966e90 Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Wed, 29 Sep 2021 10:40:35 +0800 Subject: [PATCH] add 1st reprod doc (#5352) --- docs/ThesisReproduction_CV.md | 862 ++++++++++++++++++++++++++++++++++ docs/images/framework.png | Bin 0 -> 897897 bytes 2 files changed, 862 insertions(+) create mode 100644 docs/ThesisReproduction_CV.md create mode 100644 docs/images/framework.png diff --git a/docs/ThesisReproduction_CV.md b/docs/ThesisReproduction_CV.md new file mode 100644 index 00000000..faab1aaf --- /dev/null +++ b/docs/ThesisReproduction_CV.md @@ -0,0 +1,862 @@ +# 论文复现赛指南 + +## 1. 总览 + +### 1.1 背景 + +* 以深度学习为核心的人工智能技术仍在高速的渗透(AI+行业)和创新(AI+科学计算),复现论文是为了更好的掌握第二步能力。 +* 在论文复现的过程中,开发者也可以获得 + * 自我能力提升 + * 成果被千万开发者使用的成就 + * 对科研或工作有所帮助和启发 + * 奖金 + +### 1.2 前序工作 + +基于本文规范复现论文过程中,建议开发者准备以下内容。 + +* 了解该模型输入输出格式。以AlexNet图像分类任务为例,通过阅读论文与参考代码,了解到模型输入为`[batch_size, 3, 224, 244]`的tensor,类型为`float32`或者`float16`,label为`[batch, ]`的label,类型为`int64`。 +* 准备好训练/验证数据集,用于模型训练与评估 +* 准备好fake input data以及label,与模型输入shape、type等保持一致,用于后续模型前向对齐。 + * 在对齐模型前向过程中,我们不需要考虑数据集模块等其他模块,此时使用fake data是将模型结构和数据部分解耦非常合适的一种方式。 + * 将fake data以文件的形式存储下来,也可以保证PaddlePaddle与参考代码的模型结构输入是完全一致的,更便于排查问题。 + * 在该步骤中,以AlexNet为例,生成fake data的脚本可以参考:[gen_fake_data.py](https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/fake_data/gen_fake_data.py)。 +* 在特定设备(CPU/GPU)上,跑通参考代码的预测过程(前向)以及至少2轮(iteration)迭代过程,保证后续基于PaddlePaddle复现论文过程中可对比。 +* 本文档基于 `AlexNet-Prod` 代码以及`reprod_log` whl包进行说明与测试。如果希望体验,建议参考[AlexNet-Reprod文档](https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/README.md)进行安装与测试。 + + +## 2. 整体框图 + +### 2.1 流程概览 + +基于通用的计算机视觉任务,论文复现整体流程图如下所示。 + +![图片](images/framework.png) + + +总共包含11个步骤,为了高效复现论文,设置了5个验收节点。如上图中黄色框所示。对应地,第3章到第5章的内容安排如下: + +* 第3章:介绍11个复现步骤的理论知识以及操作方法 +* 第4章:介绍5个验收节点的自查与验收方法 +* 第5章:针对第3章在复现流程过程中可能出现的问题,在第5章会进行详细介绍。如果还是不能解决问题的话,可以进交流群讨论 + + +### 2.2 reprod_log whl包使用说明 + +`reprod_log`是用于论文复现赛自动化测试和验收的工具,源代码地址在:[https://github.com/WenmuZhou/reprod_log](https://github.com/WenmuZhou/reprod_log)。主要功能包括: + +* 存取指定节点的输入输出tensor +* 基于文件的tensor读写 +* 2个字典的对比验证 +* 对比结果的输出与记录 + +在论文复现赛中,主要用到的reprod_log类如下所示。 + +* ReprodLogger + * 功能:记录和保存复现过程中的中间变量,用于后续的diff排查 + * 初始化参数:无 + * 方法 + * add(key, val) + * 功能:向logger中添加key-val pair + * 输入 + * key (str) : PaddlePaddle中的key与参考代码中保存的key应该完全相同,否则会提示报错 + * value (numpy.ndarray) : key对应的值 + * 返回: None + * remove(key) + * 功能:移除logger中的关键字段key及其value + * 输入 + * key (str) : 关键字段 + * value (numpy.ndarray) : key对应的值 + * 返回: None + * clear() + * 功能:清空logger中的关键字段key及其value + * 输入: None + * 返回: None + * save(path) + * 功能:将logger中的所有的key-value信息保存到文件中 + * 输入: + * path (str): 路径 + * 返回: None +* ReprodDiffHelper + * 功能:对`ReprodLogger`保存的日志文件进行解析,打印与记录diff + * 初始化参数:无 + * 方法 + * load_info(path) + * 功能:加载 + * 输入: + * path (str): 日志文件路径 + * 返回: dict信息,key为str,value为numpy.ndarray + * compare_info(info1, info2) + * 功能:计算两个字典对于相同key的value的diff,具体计算方法为`diff = np.abs(info1[key] - np.abs(info[key]))` + * 输入: + * info1/info2 (dict): PaddlePaddle与参考代码保存的文件信息 + * 返回: diff的dict信息 + * report(diff_method="mean", diff_threshold=1e-6, path="./diff.txt") + * 功能:可视化diff,保存到文件或者到屏幕 + * 参数 + * diff_method (str): diff计算方法,包括`mean`、`min`、`max`、`all`,默认为`mean` + * diff_threshold (float): 阈值,如果diff大于该阈值,则核验失败,默认为`1e-6` + * path (str): 日志保存的路径,默认为`./diff.txt` + +### 2.3 使用demo + +下面基于代码:[https://github.com/littletomatodonkey/AlexNet-Prod/tree/master/pipeline/reprod_log_demo](https://github.com/littletomatodonkey/AlexNet-Prod/tree/master/pipeline/reprod_log_demo),给出详细的工具包API使用功能。 + +文件夹中包含`write_log.py`和`check_log_diff.py`文件,其中`write_log.py`中给出了`ReprodLogger`类的使用方法,`check_log_diff.py`给出了`ReprodDiffHelper`类的使用方法,依次运行两个python文件,使用下面的方式运行代码。 + +```shell +# 进入文件夹 +cd pipeline/reprod_log_demo +# 随机生成矩阵,写入文件中 +python3.7 write_log.py +# 进行文件对比,输出日志 +python3.7 check_log_diff.py +``` + +最终会输出以下内容 + +``` +2021-09-28 01:07:44,832 - reprod_log.utils - INFO - demo_test_1: +2021-09-28 01:07:44,832 - reprod_log.utils - INFO - mean diff: check passed: True, value: 0.0 +2021-09-28 01:07:44,832 - reprod_log.utils - INFO - demo_test_2: +2021-09-28 01:07:44,832 - reprod_log.utils - INFO - mean diff: check passed: False, value: 0.3336232304573059 +2021-09-28 01:07:44,832 - reprod_log.utils - INFO - diff check failed +``` + +可以看出:对于key为`demo_test_1`的矩阵,由于diff为0,小于设置的阈值`1e-6`,核验成功;对于key为`demo_test_2`的矩阵,由于diff为0.33,大于设置的阈值`1e-6`,核验失败。 + +### 2.4 论文复现赛自动化测试 + +假设基于上述工具的结果记录模块,产出下面若干文件 +``` +log_reprod +├── forward_paddle.npy +├── forward_torch.npy # 与forward_paddle.npy作为一并核查的文件对 +├── metric_paddle.npy +├── metric_torch.npy # 与metric_paddle.npy作为一并核查的文件对 +├── loss_paddle.npy +├── loss_torch.npy # 与loss_paddle.npy作为一并核查的文件对 +├── bp_align_paddle.npy +├── bp_align_torch.npy # 与bp_align_paddle.npy作为一并核查的文件对 +├── train_align_paddle.npy +├── train_align_benchmark.npy # PaddlePaddle提供的参考评估指标 +``` + +使用该工具中的`ReprodDiffHelper`模块,产出下面的日志文件。 + +``` +├── forward_diff.log # forward_paddle.npy与forward_torch.npy生成的diff结果文件 +├── metric_diff.log # metric_paddle.npy与metric_torch.npy生成的diff结果文件 +├── loss_diff.log # loss_paddle.npy与loss_torch.npy生成的diff结果文件 +├── bp_align_diff.log # bp_align_paddle.npy与bp_align_torch.npy生成的diff结果文件 +├── train_align_diff.log # train_align_paddle.npy与train_align_benchmark.npy生成的diff结果文件 +``` + +5个结果文件中,会显示基于`repod_log`的检查结果。 + +下面以后续的前向对齐为例,介绍下基于`repord_log`工具对齐的检查流程。其中与`reprod_log`工具有关的部分都是需要开发者需要去添加的部分。 + +具体代码地址为:[https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step1/AlexNet_paddle/forward_alexnet.py](https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step1/AlexNet_paddle/forward_alexnet.py)。 + +下载代码后,按照下面的步骤运行脚本。 + +```shell +# 进入文件夹 +cd pipeline/Step1/ +# 下载预训练模型 +wget -P ../weights https://paddle-model-ecology.bj.bcebos.com/model/alexnet_reprod/alexnet_paddle.pdparams +wget -P ../weights https://paddle-model-ecology.bj.bcebos.com/model/alexnet_reprod/alexnet-owt-7be5be79.pth + +# 生成paddle的前向数据 +cd AlexNet_paddle/ && python3.7 forward_alexnet.py +# 生成torch的前向数据 +cd ../AlexNet_torch && python3.7 forward_alexnet.py +# 对比生成log +cd .. +python3.7 check_step1.py +``` + +以PaddlePaddle为例,`forward_alexnet.py`的具体代码如下所示。 + +```python +import numpy as np +import paddle +# 导入模型 +from paddlevision.models.alexnet import alexnet +# 导入reprod_log中的ReprodLogger类 +from reprod_log import ReprodLogger + +reprod_logger = ReprodLogger() +# 组网并初始化 +model = alexnet(pretrained="../../weights/alexnet_paddle.pdparams" num_classes=1000) +model.eval() +# 读入fake data并转换为tensor,这里也可以固定seed在线生成fake data +fake_data = np.load("../../fake_data/fake_data.npy") +fake_data = paddle.to_tensor(fake_data) +# 模型前向 +out = model(fake_data) +# 保存前向结果,对于不同的任务,需要开发者添加。 +reprod_logger.add("logits", fake_data.cpu().detach().numpy()) +reprod_logger.save("forward_paddle.npy") +``` + +diff检查的代码可以参考:[check_step1.py](https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step1/check_step1.py),具体代码如下所示。 + +```python +# https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step1/check_step1.py +# 使用reprod_log排查diff +from reprod_log import ReprodDiffHelper +if __name__ == "__main__": + diff_helper = ReprodDiffHelper() + torch_info = diff_helper.load_info("AlexNet_torch/forward_torch.npy") + paddle_info = diff_helper.load_info("AlexNet_paddle/forward_paddle.npy") + diff_helper.compare_info(torch_info, paddle_info) + diff_helper.report(path="forward_diff.log") +``` + +产出日志如下。 + +``` +2021-09-27 10:35:46,172 - reprod_log.utils - INFO - logits: +2021-09-27 10:35:46,173 - reprod_log.utils - INFO - mean diff: check passed: True, value: 0.0 +2021-09-27 10:35:46,173 - reprod_log.utils - INFO - diff check passed +``` + +平均绝对误差为0,测试通过。 + +## 3. 论文复现赛操作流程规范 + +### 3.1 模型结构对齐 + +对齐模型结构时,一般有3个主要步骤: + +* 网络结构代码转换 +* 权重转换 +* 给定完全相同输入,生成tensor,验证模型的正确性 + +下面详细介绍这3个部分。 + +#### 3.1.1 网络结构代码转换 + +由于PyTorch的API和PaddlePaddle的API非常相似,所以组网部分代码直接手动转换即可。如果是PaddlePaddle没有的API,可以尝试用多种API来组合,高优的API如果希望获得支持,也可以提给PaddlePaddle团队提[ISSUE](https://github.com/PaddlePaddle/Paddle/issues)。API对应的列表也可以参考:[PyTorch-PaddlePaddle API映射表](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/08_api_mapping/pytorch_api_mapping_cn.html)。 + +对于AlexNet,PyTorch实现为: [alexnet-pytorch](https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/AlexNet-torch/torchvision/models/alexnet.py), 复现的PaddlePaddle实现为: [alexnet-paddle](https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/AlexNet-paddle/paddlevision/models/alexnet.py)。 + +#### 3.1.2 权重转换 + +组网代码转换完成之后,需要对模型权重进行转换,如果PyTorch repo中已经提供权重,那么可以直接下载并进行后续的转换;如果没有提供,则可以基于PyTorch代码,随机生成一个初始化权重(定义完model以后,使用`torch.save()` API保存模型权重),然后进行权重转换。 + +AlexNet的代码转换脚本可以在这里查看:[https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/weights/torch2paddle.py](https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/weights/torch2paddle.py),核心函数如下所示。 + +注:运行该代码需要首先下载PyTorch的AlexNet预训练模型到该目录下,下载地址为:[https://download.pytorch.org/models/alexnet-owt-7be5be79.pth](https://download.pytorch.org/models/alexnet-owt-7be5be79.pth) + +```python +# https://github.com/littletomatodonkey/AlexNet-Prod/blob/d7b1977c2043a346b3fee0039949d5334fb990a3/pipeline/weights/torch2paddle.py#L6 + +import numpy as np +import torch +import paddle + +def transfer(): + input_fp = "alexnet-owt-7be5be79.pth" + output_fp = "alexnet_paddle.pdparams" + torch_dict = torch.load(input_fp) + paddle_dict = {} + fc_names = [ + "classifier.1.weight", "classifier.4.weight", "classifier.6.weight" + ] + for key in torch_dict: + weight = torch_dict[key].cpu().detach().numpy() + flag = [i in key for i in fc_names] + if any(flag): + print("weight {} need to be trans".format(key)) + weight = weight.transpose() + paddle_dict[key] = weight + paddle.save(paddle_dict, output_fp) + +transfer() +``` + +运行完成之后,会在这里生成`alexnet_paddle.pdparams`文件,为PaddlePaddle的预训练模型。 + +在权重转换的时候,需要注意`paddle.nn.Linear`以及`paddle.nn.BatchNorm2D`等API的权重保存格式和名称等与PyTorch稍有diff,具体内容可以参考`5.1章节`。 + + +#### 3.1.3 模型组网正确性验证 + +* 操作流程 + * 定义PyTorch模型,加载权重,固定seed,基于numpy生成随机数,转换为PyTorch可以处理的tensor,送入网络,获取输出,使用reprod_log保存结果。 + * 定义PaddlePaddle模型,加载权重,固定seed,基于numpy生成随机数,转换为PaddlePaddle可以处理的tensor,送入网络,获取输出,使用reprod_log保存结果。 + * 使用reprod_log排查diff,小于阈值,即可完成自测。 +* 注意事项 + * 模型在前向对齐验证时,需要调用`model.eval()`方法,保证组网中的随机量被关闭,比如BatchNorm、Dropout等。 + * 给定相同的输入数据,为保证可复现性,建议随机数生成时,固定seed进行生成。 + * 输出diff可以使用`np.mean(np.abs(o1 - o2))`进行计算,一般小于1e-6的话,可以认为前向没有问题。 + * 如果最终输出结果diff较大,可以使用二分的方法进行排查,比如说ResNet50,包含1个stem、4个res-stage、global avg-pooling以及最后的fc层,那么完成模型组网和权重转换之后,如果模型输出没有对齐,可以尝试输出中间某一个res-stage的tensor进行对比,如果相同,则向后进行排查;如果不同,则继续向前进行排查,以此类推,直到找到导致没有对齐的操作。 + + +### 3.2 验证/测试集数据读取对齐 + + +#### 3.2.1 数据集类Dataset复现方法 + +对于一个数据集,一般有以下一些信息需要重点关注 + +* 数据集名称、下载地址 +* 训练集/验证集/测试集图像数量、类别数量、分辨率等 +* 数据集标注格式、标注信息 +* 数据集通用的预处理方法 + +PaddlePaddle中数据集类为`paddle.io.Dataset`,PyTorch中对应为`torch.utils.data.Dataset`,二者功能一致,在绝大多数情况下,可以使用该类构建数据集。它是描述Dataset方法和行为的抽象类,在具体实现的时候,需要继承这个基类,实现其中的`__getitem__`和`__len__`方法。更多使用细节可以参考论文中的说明。 + +论文中一般会提供数据集的名称以及基本信息。复现过程中,我们在下载完数据之后,建议先检查下是否和论文中描述一致,否则可能存在的问题有: + +* 数据集年份不同,比如论文中使用了MS-COCO2014数据集,但是我们下载的是MS-COCO2017数据集,如果不对其进行检查,可能会导致我们最终训练的数据量等与论文中有diff +* 数据集使用方式不同,有些论文中,可能只是抽取了该数据集的子集进行方法验证,此时需要注意抽取方法,需要保证抽取出的子集完全相同 + +构建数据集时,也会涉及到一些预处理方法,以CV领域为例,PaddlePaddle提供了一些现成的视觉类操作api,具体可以参考:[paddle.vision类API](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html)。对应地,PyTorch中的数据处理api可以参考:[torchvision.transforms类API](https://pytorch.org/vision/stable/transforms.html)。对于其中之一,可以找到另一个平台的实现。 + +在这里也需要注意: +* 有些自定义的数据处理方法,如果不涉及到深度学习框架的部分,可以直接复用。 +* 对于特定任务中的数据预处理方法,比如说图像分类、检测、分割等,如果没有现成的API可以调用,可以参考官方模型套件中的一些实现方法,比如PaddleClas、PaddleDetection、PaddleSeg等。 + +#### 3.2.2 数据加载器Dataloader复现方法 + +复现完Dataset之后,可以构建Dataloader,对数据进行组batch、批处理,送进网络进行计算。 + +`paddle.io.DataLoader`可以进行数据加载,将数据分成批数据,并提供加载过程中的采样。PyTorch对应的实现为`torch.utils.data.DataLoader`,二者在功能上一致,只是在参数方面稍有diff:(1)PaddlePaddle缺少对`pin_memory`等参数的支持;(2)PaddlePaddle增加了`use_shared_memory`参数来选择是否使用共享内存加速数据加载过程。 + +在评估指标对齐时,我们可以固定batch size,关闭Dataloader的shuffle操作。 + +#### 3.2.3 demo + +关于dataset与dataloader的检查demo可以参考代码: [https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step2/test_data.py](https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step2/test_data.py) + +核心代码如下,构建dataset与dataloader,并使用reprod_log工具进行检查。 + +```python +def test_data_pipeline(): + diff_helper = ReprodDiffHelper() + paddle_dataset, paddle_dataloader = build_paddle_data_pipeline() + torch_dataset, torch_dataloader = build_torch_data_pipeline() + + logger_paddle_data = ReprodLogger() + logger_torch_data = ReprodLogger() + + logger_paddle_data.add("length", np.array(len(paddle_dataset))) + logger_torch_data.add("length", np.array(len(torch_dataset))) + + # random choose 5 images and check + for idx in range(5): + rnd_idx = np.random.randint(0, len(paddle_dataset)) + logger_paddle_data.add(f"dataset_{idx}", + paddle_dataset[rnd_idx][0].numpy()) + logger_torch_data.add(f"dataset_{idx}", + torch_dataset[rnd_idx][0].detach().cpu().numpy()) + + for idx, (paddle_batch, torch_batch + ) in enumerate(zip(paddle_dataloader, torch_dataloader)): + if idx >= 5: + break + logger_paddle_data.add(f"dataloader_{idx}", paddle_batch[0].numpy()) + logger_torch_data.add(f"dataloader_{idx}", + torch_batch[0].detach().cpu().numpy()) + + diff_helper.compare_info(logger_paddle_data.data, logger_torch_data.data) + diff_helper.report() +``` + +最后产出日志如下,检查成功。 + +``` +INFO:reprod_log.utils:length: +INFO:reprod_log.utils: mean diff: check passed: True, value: 0.0 +INFO:reprod_log.utils:dataset_0: +INFO:reprod_log.utils: mean diff: check passed: True, value: 0.0 +INFO:reprod_log.utils:dataset_1: +INFO:reprod_log.utils: mean diff: check passed: True, value: 0.0 +INFO:reprod_log.utils:dataset_2: +INFO:reprod_log.utils: mean diff: check passed: True, value: 0.0 +INFO:reprod_log.utils:dataset_3: +INFO:reprod_log.utils: mean diff: check passed: True, value: 0.0 +INFO:reprod_log.utils:dataset_4: +INFO:reprod_log.utils: mean diff: check passed: True, value: 0.0 +INFO:reprod_log.utils:dataloader_0: +INFO:reprod_log.utils: mean diff: check passed: True, value: 0.0 +INFO:reprod_log.utils:dataloader_1: +INFO:reprod_log.utils: mean diff: check passed: True, value: 0.0 +INFO:reprod_log.utils:dataloader_2: +INFO:reprod_log.utils: mean diff: check passed: True, value: 0.0 +INFO:reprod_log.utils:dataloader_3: +INFO:reprod_log.utils: mean diff: check passed: True, value: 0.0 +INFO:reprod_log.utils:dataloader_4: +INFO:reprod_log.utils: mean diff: check passed: True, value: 0.0 +INFO:reprod_log.utils:diff check passed +``` + + + + +### 3.3 评估指标对齐 + +#### 3.3.1 复现方法 + +PaddlePaddle提供了一系列Metric计算类,比如说`Accuracy`, `Auc`, `Precision`, `Recall`等,而PyTorch中,目前可以通过组合的方式实现metric计算,或者调用[torchmetrics](https://torchmetrics.readthedocs.io/en/latest/),在论文复现的过程中,需要注意保证对于该模块,给定相同的输入,二者输出完全一致。 + +* 操作流程 + * 定义PyTorch模型,加载训练好的权重(需要是官网repo提供好的),获取评估结果,使用reprod_log保存结果。 + * 定义PaddlePaddle模型,加载训练好的权重(需要是从PyTorch转换得到),获取评估结果,使用reprod_log保存结果。 + * 使用reprod_log排查diff,小于阈值,即可完成自测。 + +#### 3.3.2 准确率评估指标代码 + +Pytorch准确率评估指标代码如下。 + +```python +# https://github.com/littletomatodonkey/AlexNet-Prod/blob/ea49142949e891e2523d5c44e01539900d5b6e70/pipeline/Step2/AlexNet_torch/utils.py#L162 +def accuracy(output, target, topk=(1, )): + """Computes the accuracy over the k top predictions for the specified values of k""" + with torch.no_grad(): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target[None]) + + res = [] + for k in topk: + correct_k = correct[:k].flatten().sum(dtype=torch.float32) + res.append(correct_k * (100.0 / batch_size)) + return res +``` + +对应地,PaddlePaddle评估指标代码如下 + +```python +# https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step2/AlexNet_paddle/utils.py#L145 +def accuracy(output, target, topk=(1, )): + """Computes the accuracy over the k top predictions for the specified values of k""" + with paddle.no_grad(): + maxk = max(topk) + batch_size = target.shape[0] + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.equal(target) + + res = [] + for k in topk: + correct_k = correct.astype(paddle.int32)[:k].flatten().sum( + dtype='float32') + res.append(correct_k * (100.0 / batch_size)) + return res +``` + +#### 3.3.3 demo + +* 测试demo地址:[https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step2/README.md](https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step2/README.md) + +具体地,对于AlexNet复现,找到其中的预测评估逻辑,在评估完成之后获取返回值,记录在`metric_paddle.npy`文件中。 + +```python +... +def main(args): + if args.test_only: + top1 = evaluate(model, criterion, data_loader_test, device=device) + return top1 +... +# 打开main test-only选项,仅测试评估流程 +if __name__ == "__main__": + args = get_args_parser().parse_args() + top1 = main(args) + reprod_logger = ReprodLogger() + reprod_logger.add("top1", np.array([top1])) + reprod_logger.save("metric_paddle.npy") +``` + +PyTorch操作同理。获取评估指标之后,使用`reprod_log`工具进行diff自查。本部分检查方法可以参考文档:[评估指标对齐检查方法文档](https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step2/README.md) + +```python +# https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step2/check_step2.py +from reprod_log import ReprodDiffHelper + +if __name__ == "__main__": + diff_helper = ReprodDiffHelper() + torch_info = diff_helper.load_info("AlexNet_torch/metric_torch.npy") + paddle_info = diff_helper.load_info("AlexNet_paddle/metric_paddle.npy") + + diff_helper.compare_info(torch_info, paddle_info) + + diff_helper.report(path="metric_diff.log") +``` + +输出如下 + +``` +2021-09-27 11:19:48,955 - reprod_log.utils - INFO - top1: +2021-09-27 11:19:48,955 - reprod_log.utils - INFO - mean diff: check passed: True, value: 0.0 +2021-09-27 11:19:48,955 - reprod_log.utils - INFO - diff check passed +``` + +check通过。 + +### 3.4 损失函数对齐 + +#### 3.4.1 复现方法 + +PaddlePaddle与PyTorch均提供了很多loss function,用于模型训练,具体的API映射表可以参考:[Loss类API映射列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/08_api_mapping/pytorch_api_mapping_cn.html#lossapi)。以CrossEntropyLoss为例,主要区别为: +* PaddlePaddle提供了对软标签、指定softmax计算纬度的支持。 + +如果论文中使用的loss function没有指定的API,则可以尝试通过组合API的方式,实现自定义的loss function。 + +* 操作流程 + * 定义PyTorch模型,加载权重,固定seed,基于numpy生成随机数,转换为PyTorch可以处理的tensor,送入网络,获取loss结果,使用reprod_log保存结果。 + * 定义PaddlePaddle模型,加载权重,固定seed,基于numpy生成随机数,转换为PaddlePaddle可以处理的tensor,送入网络,获取loss结果,使用reprod_log保存结果。 + * 使用reprod_log排查diff,小于阈值,即可完成自测。 + +#### 3.4.2 demo + +* 测试demo地址:[https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step3/README.md](https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step3/README.md) + +按照README文档中操作,记录loss并返回。 + +``` +2021-09-27 11:29:40,692 - reprod_log.utils - INFO - loss: +2021-09-27 11:29:40,692 - reprod_log.utils - INFO - mean diff: check passed: True, value: 9.5367431640625e-07 +2021-09-27 11:29:40,692 - reprod_log.utils - INFO - diff check passed +``` + +### 3.5 优化器对齐 + +PaddlePaddle中的optimizer有`paddle.optimizer`等一系列实现,PyTorch中则有`torch.Optim`等一系列实现。具体地,以SGD为例,区别主要如下。 + +* PaddlePaddle在优化器中增加了对梯度裁剪的支持,在训练GAN或者一些NLP、多模态任务中,这个用到的比较多。 +* PaddlePaddle的SGD不支持动量更新、动量衰减和Nesterov动量,这里需要使用`paddle.optimizer.Momentum` API实现这些功能。 + + +### 3.6 学习率对齐 + +* 学习率策略主要用于指定训练过程中的学习率变化曲线,这里可以将定义好的学习率策略,不断step,即可得到对应的学习率值,绘制二者的变化曲线即可进行排查是否对齐。下面给出lr与optimizer的使用方式。 +* 注意 + * PaddlePaddle中,需要首先构建学习率策略,再传入优化器对象中;对于PyTorch,如果希望使用更丰富的学习率策略,需要先构建优化器,再传入学习率策略类API。下面给出了一个示例demo。 + + +```python +linear_paddle = paddle.nn.Linear(10, 10) +lr_sch_paddle = paddle.optimizer.lr.StepDecay( + 0.1, + step_size=1, + gamma=0.1) +opt_paddle = paddle.optimizer.Momentum( + learning_rate=lr_sch_paddle, + parameters=linear_paddle.parameters(), + weight_decay=0.01) +linear_torch = torch.nn.Linear(10, 10) +opt_torch = torch.optim.SGD( + linear_torch.parameters(), + lr=0.1, + momentum=0.9, + weight_decay=0.1) +lr_sch_torch = torch.optim.lr_scheduler.StepLR( + opt_torch, + step_size=1, gamma=0.1) +for idx in range(1, 4): + lr_sch_paddle.step() + lr_sch_torch.step() + print("step {}, paddle lr: {:.6f}, torch lr: {:.6f}".format( + idx, + lr_sch_paddle.get_lr(), + lr_sch_torch.get_lr()[0])) +``` + +如果希望基于`reprod_log`工具测试学习率设置是否正确,可以参考代码:[https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step4/test_lr.py](https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step4/test_lr.py)。 + +### 3.7 正则化策略对齐 + +L2正则化策略用于模型训练,可以防止模型对训练数据过拟合,L1正则化可以用于得到稀疏化的权重矩阵,PaddlePaddle中有`paddle.regularizer.L1Decay`与`paddle.regularizer.L2Decay` API。PyTorch中,torch.optim集成的优化器只有L2正则化方法,直接在构建optimizer的时候,传入`weight_decay`参数即可。 + +优化器、学习率和正则化策略是模型训练中比较重要的部分,有以下一些需要注意的地方 + +* PaddlePaddle的optimizer中支持weight_decay +* PyTorch的optimizer支持不同参数列表的学习率分别设置,params传入字典即可,而PaddlePaddle目前尚未支持这种行为,可以通过设置`ParamAttr`的`learning_rate`参数,来确定相对学习率倍数,使用链接可以参考:[PaddleClas-ResNet model](https://github.com/PaddlePaddle/PaddleClas/blob/d67a352fcacc49ae6bbc7d1c7158e2c65f8e06d9/ppcls/arch/backbone/legendary_models/resnet.py#L121)。 +* 本部分内容建议结合反向对齐一并排查。 + +### 3.8 反向对齐 + +#### 3.8.1 基本步骤 + +模型前向对齐之后,复现完优化器、损失函数等模块之后,需要进行模型反向对齐。 + +此处可以通过numpy生成假的数据和label(推荐),也可以准备固定的真实数据。 + +* 操作流程: + * 检查两个代码的训练超参数全部一致,如优化器及其超参数、学习率、BatchNorm/LayerNorm中的eps等。 + * 将PaddlePaddle与PyTorch网络中涉及的所有随机操作全部关闭,如dropout、drop_path等,推荐将模型设置为eval模式(`model.eval()`) + * 加载相同的weight dict(可以通过PyTorch来存储随机的权重),将准备好的数据分别传入网络并迭代,观察二者loss是否一致(此处batch-size要一致,如果使用多个真实数据,要保证传入网络的顺序一致) + * 如果经过2轮以上,loss均可以对齐,则基本可以认为反向对齐。 +* 注意: + * 如果第一轮loss就没有对齐,则需要仔细排查一下模型前向部分。 + * 如果第二轮开始,loss开始无法对齐,则首先需要排查下超参数的差异,没问题的话,在`model.backward()`方法之后,使用`tensor.grad`获取梯度值,二分的方法查找diff,定位出PaddlePaddle与PyTorch梯度无法对齐的API或者操作,然后进一步验证并反馈。 + +#### 3.8.2 demo + +* 测试demo地址:[https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step4/README.md](https://github.com/littletomatodonkey/AlexNet-Prod/blob/master/pipeline/Step4/README.md)。 + +按照READM文档进行操作,记录多轮的loss并返回,使用`reprod_log`进行diff比较,日志如下所示。 + +``` +2021-09-27 11:32:53,460 - reprod_log.utils - INFO - loss_0: +2021-09-27 11:32:53,460 - reprod_log.utils - INFO - mean diff: check passed: True, value: 9.5367431640625e-07 +2021-09-27 11:32:53,460 - reprod_log.utils - INFO - loss_1: +2021-09-27 11:32:53,460 - reprod_log.utils - INFO - mean diff: check passed: True, value: 4.76837158203125e-07 +2021-09-27 11:32:53,460 - reprod_log.utils - INFO - loss_2: +2021-09-27 11:32:53,460 - reprod_log.utils - INFO - mean diff: check passed: True, value: 0.0 +2021-09-27 11:32:53,461 - reprod_log.utils - INFO - loss_3: +2021-09-27 11:32:53,461 - reprod_log.utils - INFO - mean diff: check passed: True, value: 1.1548399925231934e-07 +2021-09-27 11:32:53,461 - reprod_log.utils - INFO - loss_4: +2021-09-27 11:32:53,461 - reprod_log.utils - INFO - mean diff: check passed: True, value: 3.7834979593753815e-10 +2021-09-27 11:32:53,461 - reprod_log.utils - INFO - diff check passed +``` + +在基于假数据的5次迭代中,PaddlePaddle与参考代码的loss diff使用在期望范围内,check通过。 + +### 3.9 训练集数据读取对齐 + +该部分内容与3.2节内容基本一致,参考PyTorch的代码,实现训练集数据读取与预处理模块即可。 + +该部分内容,可以参考3.8节的自测方法,将输入的`fake data & label`替换为训练的dataloader,但是需要注意的是: +* 在使用train dataloader的时候,建议设置random seed,对于PyTorch来说 + +```python +#initialize random seed +torch.manual_seed(config.SEED) +torch.cuda.manual_seed_all(config.SEED) +np.random.seed(config.SEED) +random.seed(config.SEED) +``` + +对于PaddlePaddle来说 + +```python +paddle.seed(config.SEED) +np.random.seed(config.SEED) +random.seed(config.SEED) +``` + +### 3.10 网络初始化对齐 + +* 下面给出了部分初始化API的映射表。 + +|PaddlePaddle API | PyTorch API | +|---|---| +| paddle.nn.initializer.KaimingNormal | torch.nn.init.kaiming_normal_ | +| paddle.nn.initializer.KaimingUniform | torch.nn.init.kaiming_uniform_ | +| paddle.nn.initializer.XavierNormal | torch.nn.init.xavier_normal_ | +| paddle.nn.initializer.XavierUniform | torch.nn.init.xavier_uniform_ | + + +* 更多初始化API可以参考[PyTorch初始化API文档](https://pytorch.org/docs/stable/nn.init.html)以及[PaddlePaddle初始化API文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Overview_cn.html#chushihuaxiangguan) + + +### 3.11 模型训练对齐 + +完成前面的步骤之后,就可以开始全量数据的训练对齐任务了。 + +* 操作流程 + * 准备train/eval data, loader, model + * 对model按照论文所述进行初始化(如果论文中提到加载pretrain,则按需加载pretrained model) + * 加载配置,开始训练,迭代得到最终模型与评估指标,将评估指标使用reprod_log保存到文件中。 + * 将PaddlePaddle提供的参考指标使用reprod_log提交到另一个文件中。 + * 使用reprod_log排查diff,小于阈值,即可完成自测。 + +### 3.12 单机多卡训练 + +如果希望使用单机多卡提升训练效率,可以从以下几个过程对代码进行修改。 + +#### 3.12.1 数据读取 + +对于PaddlePaddle来说,多卡数据读取这块主要的变化在sampler + +对于单机单卡,sampler实现方式如下所示。 + +```python +train_sampler = paddle.io.RandomSampler(dataset) +train_batch_sampler = paddle.io.BatchSampler( + sampler=train_sampler, batch_size=args.batch_size) +``` + +对于单机多卡任务,sampler实现方式如下所示。 + +```python +train_batch_sampler = paddle.io.DistributedBatchSampler( + dataset=dataset, + batch_size=args.batch_size, + shuffle=True, + drop_last=False + ) +``` + +注意:在这种情况下,单机多卡的代码仍然能够以单机单卡的方式运行,因此建议以这种sampler方式进行论文复现。 + + +#### 3.12.2 多卡模型初始化 + +如果以多卡的方式运行,需要初始化并行训练环境,代码如下所示。 + +```python +if paddle.distributed.get_world_size() > 1: + paddle.distributed.init_parallel_env() +``` + +在模型组网并初始化参数之后,需要使用`paddle.DataParallel()`对模型进行封装,使得模型可以通过数据并行的模式被执行。代码如下所示。 + +```python +if paddle.distributed.get_world_size() > 1: + model = paddle.DataParallel(model) +``` + + +#### 3.12.3 模型保存、日志保存等其他模块 + +以模型保存为例,我们只需要在0号卡上保存即可,否则多个trainer同时保存的话,可能会造成写冲突,导致最终保存的模型不可用。 + + +#### 3.12.4 程序启动方式 + +对于单机单卡,启动脚本如下所示。 + +```shell +export CUDA_VISIBLE_DEVICES=0 +python3.7 train.py \ + --data-path /paddle/data/ILSVRC2012_torch \ + --lr 0.00125 \ + --batch-size 32 \ + --output-dir "./output/" +``` + + +对于单机多卡(示例中为8卡训练),启动脚本如下所示。 + +```shell +export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 + +python3.7 -m paddle.distributed.launch \ + --gpus="0,1,2,3,4,5,6,7" \ + train.py \ + --data-path /paddle/data/ILSVRC2012_torch \ + --lr 0.01 \ + --batch-size 32 \ + --output-dir "./output/" +``` + +注意:这里8卡训练时,虽然单卡的batch size没有变化(32),但是总卡的batch size相当于是单卡的8倍,因此学习率也设置为了单卡时的8倍。 + + +## 4. 验收点与验收方法 + +### 4.1 模型结构对齐验收方法 + +* 输入:fake data + * 固定种子,生成numpy随机矩阵,转化tensor + * 使用参考代码的dataloader,生成一个batch的数据,保存下来,在前向对齐时,直接从文件中读入。 +* 输出: + * PaddlePaddle/PyTorch:dict,key为tensor的name(自定义),value为tensor的值。最后将dict保存到文件中。建议命名为`forward_paddle.npy`和`forward_pytorch.npy`。 +* 自测:使用reprod_log加载2个文件,使用report功能,记录结果到日志文件中,建议命名为`forward_diff_log.txt`,观察diff,二者diff小于特定的阈值即可。 +* 注意 + * PaddlePaddle与PyTorch保存的dict的key需要保持相同,否则report过程可能会提示key无法对应,从而导致report失败,下同。 + * 建议将fake data保存到dict中,方便check 参考代码和PaddlePaddle的输入是否一致。 + + +### 4.2 评估指标对齐验收方法 + +* 输入:dataloader, model +* 输出: + * PaddlePaddle/PyTorch:dict,key为tensor的name(自定义),value为具体评估指标的值。最后将dict使用reprod_log保存到各自的文件中,建议命名为`metric_paddle.npy`和`metric_pytorch.npy`。 + * 自测:使用reprod_log加载2个文件,使用report功能,记录结果到日志文件中,建议命名为`metric_diff_log.txt`,观察diff,二者diff小于特定的阈值即可。 +* 注意: + * 数据需要是真实数据 + * 需要检查论文是否只是抽取了验证集/测试集中的部分文件,如果是的话,则需要保证PaddlePaddle和参考代码中dataset使用的数据集一致。 + +### 4.3 损失函数对齐验收方法 + +* 输入:fake data & label +* 输出: + * PaddlePaddle/PyTorch:dict,key为tensor的name(自定义),value为具体评估指标的值。最后将dict使用reprod_log保存到各自的文件中,建议命名为`loss_paddle.npy`和`loss_pytorch.npy`。 +* 自测:使用reprod_log加载2个文件,使用report功能,记录结果到日志文件中,建议命名为`loss_diff_log.txt`,观察diff,二者diff小于特定的阈值即可。 +* 注意: + * 这里需要将fake_data与fake_label也同时保存到文件中,方便check参考代码与PaddlePaddle的输入数据是否完全相同 + + +### 4.4 反向对齐验收方法 + +构建符合该模型的假数据,使用假数据进行训练,连续**2轮或者以上loss**相同,即可认为模型反向没有问题。 + +* 输入:fake data & label +* 输出: + * PaddlePaddle/PyTorch:dict,key为tensor的name(自定义),value为具体loss的值。最后将dict使用reprod_log保存到各自的文件中,建议命名为`bp_align_paddle.npy`和`bp_align_pytorch.npy`。 +* 自测:使用reprod_log加载2个文件,使用report功能,记录结果到日志文件中,建议命名为`bp_align_diff_log.txt`,观察diff,二者diff小于特定的阈值即可。 +* 注意: + * 这里需要将`fake_data`与`fake_label`也同时保存到文件中,方便确认输入数据是否完全相同 + * loss需要保存至少2轮以上 + * 在迭代的过程中,需要保证模型的batch size等超参数完全相同 + * 在迭代的过程中,需要设置`model.eval()`,使用固定的假数据,同时加载相同权重的预训练模型,保证二者完全相同 + + +### 4.5 训练对齐验收方法 + +* 输入:train/eval dataloader, model +* 输出: + * PaddlePaddle:dict,key为保存值的name(自定义),value为具体评估指标的值。最后将dict使用reprod_log保存到文件中,建议命名为`train_align_paddle.npy`。 + * benchmark:dict,key为保存值的name(自定义),value为论文复现赛的评估指标要求的值。最后将dict使用reprod_log保存到文件中,建议命名为`train_align_benchmark.npy`。 +* 自测:使用reprod_log加载2个文件,使用report功能,记录结果到日志文件中,建议命名为`train_align_diff_log.txt`,观察diff,二者diff小于特定的阈值即可。 +* 注意: + * 全量数据训练时,需要打开网络(Dropout, Droppath)和数据加载(shuffle, augmentation)的随机量,防止模型过拟合 + * 如果有条件,可以跑一下PyTorch的全量数据训练任务,对比二者的收敛曲线,最初由于随机抖动,可能有些波动,在一段时间之后,如果相差较大,则需要进一步排查优化器、学习率等超参。 + * 不同数据集以及任务对于指标的波动范围要求不一样,比如说ImageNet1k分类任务上0.15%的diff可以接受,在COCO2017检测数据集上0.2%以内的mAP diff可以接受。需要具体情况具体分析。 + * 如果前面的步骤全部对齐,这一步最终没有能够对齐,那么需要重点关注train模式下,数据集的预处理方法、网络初始化、网络的dropout、droppath、batchnorm等与eval模式不同的操作。 + + +## 5. 论文复现注意事项与FAQ + +本部分主要总结大家在论文复现赛过程中遇到的问题,如果本章内容没有能够解决你的问题,欢迎在群里提问讨论。 + +### 5.1 模型结构对齐 + +* 对于`nn.Linear`层的weight参数,PaddlePaddle与PyTorch的保存方式不同,在转换时需要进行转置 +* paddle.nn.BatchNorm2D包含4个参数`weight`, `bias`, `_mean`, `_variance`,torch.nn.BatchNorm2d包含4个参数`weight`, `bias`, `running_mean`, `running_var`, `num_batches_tracked`,`num_batches_tracked`在PaddlePaddle中没有用到,剩下4个的对应关系为 + * `weight` -> `weight` + * `weight` -> `weight` + * `_variance` -> `running_var` + * `_mean` -> `running_mean` + + +### 5.2 验证/测试集数据读取对齐 + +* 如果使用PaddlePaddle提供的数据集API,比如说`paddle.vision.datasets.Cifar10`等,可能无法完全与参考代码在数据顺序上保持一致,但是这些数据集的实现都是经过广泛验证的,可以使用。此时对数据预处理和后处理进行排查就好。`数据集+数据处理`的部分可以通过评估指标对齐完成自查。 + +### 5.3 评估指标对齐 + + +### 5.4 损失函数对齐 + + +### 5.5 优化器对齐 + +* 在某些任务中,比如说深度学习可视化、可解释性等任务中,一般只要求模型前向过程,不需要训练,此时优化器、学习率等用于模型训练的模块对于该类论文复现是不需要的。 + +### 5.6 学习率对齐 + +### 5.7 正则化策略对齐 + +### 5.8 反向对齐 + +### 5.9 训练集数据读取对齐 + +### 5.10 网络初始化对齐 + +* 对于不同的深度学习框架,网络初始化在大多情况下,即使值的分布完全一致,也无法保证值完全一致,这里也是论文复现中不确定性比较大的地方。 +* CNN对于模型初始化相对来说没有那么敏感,在迭代轮数与数据集足够的情况下,最终精度指标基本接近;而transformer系列模型对于初始化比较敏感,在transformer系列模型训练对齐过程中,建议对这一块进行重点检查。 + +### 5.11 模型训练对齐 +* 小数据上指标波动可能比较大,时间允许的话,可以跑多次实验,取平均值。 diff --git a/docs/images/framework.png b/docs/images/framework.png new file mode 100644 index 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