如何使用 a.pickle 文件来预测图像？

Question

我在 PyTorch 中训练了一个 CNN model 来检测 6 个不同类别的皮肤病。 我的 model 的准确率为 92%，我将其保存在一个 .pickle 文件中。 我希望使用此 model 进行预测，但我不知道该怎么做。 如果有人可以在必要的步骤中帮助我，我将不胜感激。 我曾尝试使用 Streamlit，但显然 Streamlit 不再起作用，因此我选择了离线解决方案，我可以只上传图像，model 会给我这样的预测。

这是我的 model 的代码。 我使用了预训练的 ResNet18 model 并在 Kaggle 的 Skin Cancer MNIST: HAM10000 数据集上对其进行了训练。

def set_parameter_requires_grad(model, feature_extracting):
    if feature_extracting:
        for param in model.parameters():
            param.requires_grad = False

def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
    # Initialize these variables which will be set in this if statement. Each of these
    #   variables is model specific.
    model_ft = None
    input_size = 0

    if model_name == "resnet":
        """ Resnet18, resnet34, resnet50, resnet101
        """
        model_ft = models.resnet18(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.fc.in_features
        model_ft.fc = nn.Linear(num_ftrs, num_classes)
        input_size = 224


    elif model_name == "vgg":
        """ VGG11_bn
        """
        model_ft = models.vgg11_bn(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier[6].in_features
        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
        input_size = 224


    elif model_name == "densenet":
        """ Densenet121
        """
        model_ft = models.densenet121(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier.in_features
        model_ft.classifier = nn.Linear(num_ftrs, num_classes)
        input_size = 224

    elif model_name == "inception":
        """ Inception v3
        """
        model_ft = models.inception_v3(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        # Handle the auxilary net
        num_ftrs = model_ft.AuxLogits.fc.in_features
        model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
        # Handle the primary net
        num_ftrs = model_ft.fc.in_features
        model_ft.fc = nn.Linear(num_ftrs,num_classes)
        input_size = 299

    else:
        print("Invalid model name, exiting...")
        exit()
    return model_ft, input_size

# resnet,vgg,densenet,inception
model_name = 'resnet'
num_classes = 7
feature_extract = False
# Initialize the model for this run
model_ft, input_size = initialize_model(model_name, num_classes, feature_extract, use_pretrained=True)
# Define the device:
device = torch.device('cuda:0')
# Put the model on the device:
model = model_ft.to(device)

# norm_mean = (0.49139968, 0.48215827, 0.44653124)
# norm_std = (0.24703233, 0.24348505, 0.26158768)
# define the transformation of the train images.
train_transform = transforms.Compose([transforms.Resize((input_size,input_size)),transforms.RandomHorizontalFlip(),
                                      transforms.RandomVerticalFlip(),transforms.RandomRotation(20),
                                      transforms.ColorJitter(brightness=0.1, contrast=0.1, hue=0.1),
                                        transforms.ToTensor(), transforms.Normalize(norm_mean, norm_std)])
# define the transformation of the val images.
val_transform = transforms.Compose([transforms.Resize((input_size,input_size)), transforms.ToTensor(),
                                    transforms.Normalize(norm_mean, norm_std)])

# Define a pytorch dataloader for this dataset
class HAM10000(Dataset):
    def __init__(self, df, transform=None):
        self.df = df
        self.transform = transform

    def __len__(self):
        return len(self.df)

    def __getitem__(self, index):
        # Load data and get label
        X = Image.open(self.df['path'][index])
        y = torch.tensor(int(self.df['cell_type_idx'][index]))

        if self.transform:
            X = self.transform(X)

        return X, y

# Define the training set using the table train_df and using our defined transitions (train_transform)
training_set = HAM10000(df_train, transform=train_transform)
train_loader = DataLoader(training_set, batch_size=64, shuffle=True, num_workers=4)
# Same for the validation set:
validation_set = HAM10000(df_val, transform=train_transform)
val_loader = DataLoader(validation_set, batch_size=64, shuffle=False, num_workers=4)

# we use Adam optimizer, use cross entropy loss as our loss function
optimizer = optim.Adam(model.parameters(), lr=1e-5)
criterion = nn.CrossEntropyLoss().to(device)

这是训练过程和保存文件。

class AverageMeter(object):
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

total_loss_train, total_acc_train = [],[]
def train(train_loader, model, criterion, optimizer, epoch):
    model.train()
    train_loss = AverageMeter()
    train_acc = AverageMeter()
    curr_iter = (epoch - 1) * len(train_loader)
    for i, data in enumerate(train_loader):
        images, labels = data
        N = images.size(0)
        # print('image shape:',images.size(0), 'label shape',labels.size(0))
        images = Variable(images).to(device)
        labels = Variable(labels).to(device)

        optimizer.zero_grad()
        outputs = model(images)

        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        prediction = outputs.max(1, keepdim=True)[1]
        train_acc.update(prediction.eq(labels.view_as(prediction)).sum().item()/N)
        train_loss.update(loss.item())
        curr_iter += 1
        if (i + 1) % 100 == 0:
            print('[epoch %d], [iter %d / %d], [train loss %.5f], [train acc %.5f]' % (
                epoch, i + 1, len(train_loader), train_loss.avg, train_acc.avg))
            total_loss_train.append(train_loss.avg)
            total_acc_train.append(train_acc.avg)
    return train_loss.avg, train_acc.avg

def validate(val_loader, model, criterion, optimizer, epoch):
    model.eval()
    val_loss = AverageMeter()
    val_acc = AverageMeter()
    with torch.no_grad():
        for i, data in enumerate(val_loader):
            images, labels = data
            N = images.size(0)
            images = Variable(images).to(device)
            labels = Variable(labels).to(device)

            outputs = model(images)
            prediction = outputs.max(1, keepdim=True)[1]

            val_acc.update(prediction.eq(labels.view_as(prediction)).sum().item()/N)

            val_loss.update(criterion(outputs, labels).item())

    print('------------------------------------------------------------')
    print('[epoch %d], [val loss %.5f], [val acc %.5f]' % (epoch, val_loss.avg, val_acc.avg))
    print('------------------------------------------------------------')
    return val_loss.avg, val_acc.avg



if os.path.exists("Tested_model2.pickle"):
  print("Loading Trained Model")
  model = pickle.load(open("Tested_model2.pickle", "rb"))
  print(model)
else:
  print("Training New Model.")
  print("Training begins.")
  print("********************************************************")
  epoch_num = 25
  load_model = True
  best_val_acc = 0
  total_loss_val, total_acc_val = [],[]
  for epoch in range(1, epoch_num+1):
    loss_train, acc_train = train(train_loader, model, criterion, optimizer, epoch)
    loss_val, acc_val = validate(val_loader, model, criterion, optimizer, epoch)
    total_loss_val.append(loss_val)
    total_acc_val.append(acc_val)
    if acc_val > best_val_acc:
        best_val_acc = acc_val
        print('*****************************************************')
        print('best record: [epoch %d], [val loss %.5f], [val acc %.5f]' % (epoch, loss_val, acc_val))
        print('*****************************************************')

with open ("Tested_model2.pickle", "wb") as file:
  pickle.dump(model, file)

简单地说，我想知道如何使用 pickle 文件进行预测。

编辑：我在下面添加了评估部分，请帮助我了解如何进一步处理此代码。

model.eval()
y_label = []
y_predict = []
with torch.no_grad():
    for i, data in enumerate(val_loader):
        images, labels = data
        N = images.size(0)
        images = Variable(images).to(device)
        outputs = model(images)
        prediction = outputs.max(1, keepdim=True)[1]
        y_label.extend(labels.cpu().numpy())
        y_predict.extend(np.squeeze(prediction.cpu().numpy().T))

另外，这是我之前用来加载和预测的代码，但是我不知道代码是否正确或方法是否正确。

%%writefile app.py
import streamlit as st
import torch 

st.set_option('deprecation.showfileUploaderEncoding', False)
@st.cache(allow_output_mutation=True)

def load_model():
  model = pickle.load(open("Trained_Model_part2.pickle", "rb"))
  return mdoel

model = load_model()
st.write("""
         #Classification of skin disease
         """)

file = st.file_uploader("Please upload the image of the affected area.", type = ["jpg", "png"])

import cv2
from PIL import Image, ImageOps
import numpy as np

def import_and_predict(image_data, model):
  size = (224, 224)
  image = ImageOps.fit(image_data, size, Image.ANTIALIAS)
  img = np.asarray(image)
  image_reshape = img[np.newaxis,...]
  prediction = model.predict(img_reshape)

  return prediction

if file is None:
  st.text("Please upload an image file.")
else:
  image = Image.open(file)
  st.image(image, use_column_width = True)
  predictions = import_and_predict(image, model)
  class_names = ["Melanocytic nevi", "Melanoma", "Benign keratosis-like lesions", "Basal cell carcinoma", "Actinic keratoses", "Vascular lesions", "Dermatofibroma"]
  string = "It is: " + class_names[np.argmax(predictions)]
  st.success(string)

这使用 streamlit 并且加载器是以前的 pickle 文件加载器，它将被 a.pth 加载器替换。 我想知道我必须进行哪些更改，以便代码将要求输入图像或在特定文件夹中查找图像并提供预测。 谢谢你。

Answer 1

我将向您展示如何正确保存和加载 pytorch model 参数（您应该使用.pt扩展名）：

要保存 model，请执行以下操作（每个 epoch 或训练后一次）：

torch.save(model.state_dict(), "your/path/model_file.pt")

所有 model 参数现在都加载到“your/path/model_file.pt”中。

现在，要加载 model，您将需要 model class YourModel(nn.Module): ...

model = YourModel()
model.load_state_dict(torch.load("your/path/model_file.pt"))

model 现在已使用经过优化的参数进行初始化并可以使用。 例如像这样：

model = YourModel()
model.load_state_dict(torch.load("your/path/model_file.pt"))

# set to evaluation mode
model.eval()

# load an image
sample = get_sample()

# reshape sample to (batch-size x width x height) but batch-size is 1 because you probably want to predict just one image at a time in real-life usage
sample = torch.reshape(1, sample.size(0), sample.size(1))

prediction = model(sample)

编辑以回答评论中的问题：要加载经过训练的 pytorch model 您需要保存模型参数的文件和 model 结构本身。 model 结构只是 pytorch 模块 class 的 python 代码。 您还没有自己构建 model 因此您没有直接的 model 代码，但在您的情况下它应该是model_ft 。 只是 python class 持有所有层。 所以model class就像骨架，参数像肉什么的。

当您完全自己创建 model class 并将训练的权重加载到其中时，它看起来像这样：

import torch
import torch.nn as nn
import torch.nn.functional as F

# the model (skeleton class)
class YourModel(nn.Module):
    def __init__(self):
        super(YourModel, self).__init__()

        self.dense1 = nn.Linear(128, 64)
        self.dense2 = nn.Linear(64, 2)

    def forward(self, x):
        x = F.relu(self.dense1(x))
        x = torch.sigmoid(self.dense2(x))

        return x

# . . .
# train model and save it to model.pt
# . . .

# load "empty" model
model = YourModel()

# load trained paramters/weights into the model
model.load_state_dict(torch.load("/path/model.pt"))

正如我所说，在您的情况下， model class 应该是model_ft 。