如何在pytorchlightning中使用TensorBoard记录器转储混淆矩阵？

Question

The official doc仅限国家

>>> from pytorch_lightning.metrics import ConfusionMatrix
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> confmat = ConfusionMatrix(num_classes=2)
>>> confmat(preds, target)

这并没有说明如何在框架中使用度量

我的尝试（方法不完整，仅显示相关部分）：

def __init__(...):
    self.val_confusion = pl.metrics.classification.ConfusionMatrix(num_classes=self._config.n_clusters)

def validation_step(self, batch, batch_index):
    ...
    log_probs = self.forward(orig_batch)
    loss = self._criterion(log_probs, label_batch)
   
    self.val_confusion.update(log_probs, label_batch)
    self.log('validation_confusion_step', self.val_confusion, on_step=True, on_epoch=False)

def validation_step_end(self, outputs):
    return outputs

def validation_epoch_end(self, outs):
    self.log('validation_confusion_epoch', self.val_confusion.compute())

在第0个纪元之后，这将给出

    Traceback (most recent call last):
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 521, in train
        self.train_loop.run_training_epoch()
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\trainer\training_loop.py", line 588, in run_training_epoch
        self.trainer.run_evaluation(test_mode=False)
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 613, in run_evaluation
        self.evaluation_loop.log_evaluation_step_metrics(output, batch_idx)
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\trainer\evaluation_loop.py", line 346, in log_evaluation_step_metrics
        self.__log_result_step_metrics(step_log_metrics, step_pbar_metrics, batch_idx)
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\trainer\evaluation_loop.py", line 350, in __log_result_step_metrics
        cached_batch_pbar_metrics, cached_batch_log_metrics = cached_results.update_logger_connector()
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\trainer\connectors\logger_connector\epoch_result_store.py", line 378, in update_logger_connector
        batch_log_metrics = self.get_latest_batch_log_metrics()
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\trainer\connectors\logger_connector\epoch_result_store.py", line 418, in get_latest_batch_log_metrics
        batch_log_metrics = self.run_batch_from_func_name("get_batch_log_metrics")
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\trainer\connectors\logger_connector\epoch_result_store.py", line 414, in run_batch_from_func_name
        results = [func(include_forked_originals=False) for func in results]
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\trainer\connectors\logger_connector\epoch_result_store.py", line 414, in <listcomp>
        results = [func(include_forked_originals=False) for func in results]
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\trainer\connectors\logger_connector\epoch_result_store.py", line 122, in get_batch_log_metrics
        return self.run_latest_batch_metrics_with_func_name("get_batch_log_metrics",
*args, **kwargs)
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\trainer\connectors\logger_connector\epoch_result_store.py", line 115, in run_latest_batch_metrics_with_func_name
        for dl_idx in range(self.num_dataloaders)
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\trainer\connectors\logger_connector\epoch_result_store.py", line 115, in <listcomp>
        for dl_idx in range(self.num_dataloaders)
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\trainer\connectors\logger_connector\epoch_result_store.py", line 100, in get_latest_from_func_name
        results.update(func(*args, add_dataloader_idx=add_dataloader_idx, **kwargs))
      File "C:\code\EPMD\Kodex\Templates\Testing\venv\lib\site-packages\pytorch_lightning\core\step_result.py", line 298, in get_batch_log_metrics
        result[dl_key] = self[k]._forward_cache.detach()
    AttributeError: 'NoneType' object has no attribute 'detach'

                                                      

培训前，它确实通过了健全性验证检查

失败发生在validation_step_end中的返回上。对我来说没什么意义

使用mertics的完全相同的方法可以精确地工作

如何获得正确的混淆矩阵？

Answer 1

这花了很多时间才找到

这是我能粘贴的最简单的代码，它仍然是可读和可复制的

我不想把整个模型数据集和参数放在这里，因为读者对这个问题不感兴趣，它们只是噪音

---

也就是说，这里是创建每个历元的混淆矩阵并在Tensorboard中显示所需的代码

这是一个单帧，例如：

---

import pytorch_lightning as pl
import seaborn as sn
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

def __init__(self, config, trained_vae, latent_dim):
    self.val_confusion = pl.metrics.classification.ConfusionMatrix(num_classes=self._config.n_clusters)
    self.logger: Optional[TensorBoardLogger] = None

def forward(self, x):
    ...
    return log_probs

def validation_step(self, batch, batch_index):
    if self._config.dataset == "mnist":
        orig_batch, label_batch = batch
        orig_batch = orig_batch.reshape(-1, 28 * 28)

    log_probs = self.forward(orig_batch)
    loss = self._criterion(log_probs, label_batch)

    self.val_confusion.update(log_probs, label_batch)
    return {"loss": loss, "labels": label_batch}

def validation_step_end(self, outputs):
    return outputs

def validation_epoch_end(self, outs):
    tb = self.logger.experiment

    # confusion matrix
    conf_mat = self.val_confusion.compute().detach().cpu().numpy().astype(np.int)
    df_cm = pd.DataFrame(
        conf_mat,
        index=np.arange(self._config.n_clusters),
        columns=np.arange(self._config.n_clusters))
    plt.figure()
    sn.set(font_scale=1.2)
    sn.heatmap(df_cm, annot=True, annot_kws={"size": 16}, fmt='d')
    buf = io.BytesIO()
    
    plt.savefig(buf, format='jpeg')
    buf.seek(0)
    im = Image.open(buf)
    im = torchvision.transforms.ToTensor()(im)
    tb.add_image("val_confusion_matrix", im, global_step=self.current_epoch)

还有给教练的电话

logger = TensorBoardLogger(save_dir=tb_logs_folder, name='Classifier')
trainer = Trainer(deterministic=True,
                  max_epochs=10,
                  default_root_dir=classifier_checkpoints_path,
                  logger=logger,
                  gpus=1
                  )

Answer 2

您可以使用self.logger.experiment.add_figure(*tag*, *figure*)报告图形

变量self.logger.experiment实际上是一个SummaryWriter（来自PyTorch，而不是Lightning）。此类具有方法add_figure（documentation）

您可以按如下方式使用它：（MNIST示例）

    def validation_step(self, batch, batch_idx):
        x, y = batch
        preds = self(x)
        loss = F.nll_loss(preds, y)
        return { 'loss': loss, 'preds': preds, 'target': y}

    def validation_epoch_end(self, outputs):
        preds = torch.cat([tmp['preds'] for tmp in outputs])
        targets = torch.cat([tmp['target'] for tmp in outputs])
        confusion_matrix = pl.metrics.functional.confusion_matrix(preds, targets, num_classes=10)

        df_cm = pd.DataFrame(confusion_matrix.numpy(), index = range(10), columns=range(10))
        plt.figure(figsize = (10,7))
        fig_ = sns.heatmap(df_cm, annot=True, cmap='Spectral').get_figure()
        plt.close(fig_)
        
        self.logger.experiment.add_figure("Confusion matrix", fig_, self.current_epoch)