PyTorch Transfer Learning教程的混淆矩阵和测试准确性

Question

在Pytorch Transfer学习教程之后，我感兴趣的是仅报告训练和测试的准确性以及混淆矩阵（例如使用sklearn confusionmatrix）。我怎样才能做到这一点？当前的教程仅报告火车/ Val的准确性，我很难确定如何在其中合并sklearn混淆矩阵代码。链接到此处的原始教程：https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html

%matplotlib inline
from graphviz import Digraph
import torch
from torch.autograd import Variable
# Author: Sasank Chilamkurthy

from __future__ import print_function, division

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

plt.ion()
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}


data_dir = "images"
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
                                             shuffle=True, num_workers=4)
              for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

def imshow(inp, title=None):
    """Imshow for Tensor."""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    plt.imshow(inp)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)  # pause a bit so that plots are updated


# Get a batch of training data
inputs, classes = next(iter(dataloaders['train']))

# Make a grid from batch
out = torchvision.utils.make_grid(inputs)

imshow(out, title=[class_names[x] for x in classes])

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                scheduler.step()
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model

def visualize_model(model, num_images=6):
    was_training = model.training
    model.eval()
    images_so_far = 0
    fig = plt.figure()

    with torch.no_grad():
        for i, (inputs, labels) in enumerate(dataloaders['val']):
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)

            for j in range(inputs.size()[0]):
                images_so_far += 1
                ax = plt.subplot(num_images//2, 2, images_so_far)
                ax.axis('off')
                ax.set_title('predicted: {}'.format(class_names[preds[j]]))
                imshow(inputs.cpu().data[j])

                if images_so_far == num_images:
                    model.train(mode=was_training)
                    return
        model.train(mode=was_training)

model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 9)

model_ft = model_ft.to(device)

criterion = nn.CrossEntropyLoss()

# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
                       num_epochs=25)

visualize_model(model_ft)

Answer 1

PyTorch社区的ptrblck提供的答案。非常感谢！

nb_classes = 9

confusion_matrix = torch.zeros(nb_classes, nb_classes)
with torch.no_grad():
    for i, (inputs, classes) in enumerate(dataloaders['val']):
        inputs = inputs.to(device)
        classes = classes.to(device)
        outputs = model_ft(inputs)
        _, preds = torch.max(outputs, 1)
        for t, p in zip(classes.view(-1), preds.view(-1)):
                confusion_matrix[t.long(), p.long()] += 1

print(confusion_matrix)

要获得每个班级的准确度：

print(confusion_matrix.diag()/confusion_matrix.sum(1))

Answer 2

我使用以下代码将火炬张量转换为定义预测类的 int。

services = new List<Services>
{
    new Services()
    {
        description = "Service1",
        shortname = "S1",
        orderdates = new string[] { "2020-01-01","2020-02-01"},
    },
    new Services()
    {
        description = "Service2",
        shortname = "S2",
        orderdates = new string[] { "2020-01-01", "2020-02-01" },
    }
}

我希望这会有所帮助！我还是个菜鸟，所以请温柔...

Answer 3

这是使用sklearn的confusion_matrix进行的稍微修改（直接）的方法：-

from sklearn.metrics import confusion_matrix

nb_classes = 9

# Initialize the prediction and label lists(tensors)
predlist=torch.zeros(0,dtype=torch.long, device='cpu')
lbllist=torch.zeros(0,dtype=torch.long, device='cpu')

with torch.no_grad():
    for i, (inputs, classes) in enumerate(dataloaders['val']):
        inputs = inputs.to(device)
        classes = classes.to(device)
        outputs = model_ft(inputs)
        _, preds = torch.max(outputs, 1)

        # Append batch prediction results
        predlist=torch.cat([predlist,preds.view(-1).cpu()])
        lbllist=torch.cat([lbllist,classes.view(-1).cpu()])

# Confusion matrix
conf_mat=confusion_matrix(lbllist.numpy(), predlist.numpy())
print(conf_mat)

# Per-class accuracy
class_accuracy=100*conf_mat.diagonal()/conf_mat.sum(1)
print(class_accuracy)

Answer 4

按照上面的答案...这是一些可视化的答案

return A.at(A.size()) + 1;

Answer 5

另一种简单的方式来获得精确度是使用sklearns“accuracy_score”。 这是一个例子：

from sklearn.metrics import accuracy_score
y_pred = y_pred.data.numpy()
accuracy = accuracy_score(labels, np.argmax(y_pred, axis=1))

首先，你需要从变量获取数据。 “ y_pred”是模型的预测，标签当然是您的标签。

所述阵列内的最大值的np.argmax返回的索引。我们希望最大的价值，因为它使用了添加Softmax多类分类时，对应的概率最高的类。准确性得分将返回标签和y_pred之间匹配的百分比。