如何获得 PyTorch 图像分类器的预测？

Question

我已经为我的 Keras 模型实现了这个代码，它可以正常工作，但现在我想为我的 PyTorch 模型实现这个代码，但是我无法配置它来进行预测，下面是我的完整代码，我需要有关分类功能的帮助：

# importing libraries

import tkinter as tk
from tkinter import *
# import PIL
from tkinter import filedialog

import numpy
from PIL import Image, ImageTk
import torch

# importing model

model=torch.load('corelK_model_0.pt')

classes = {
    0:'africa',
    1: 'beach',
    2: 'tallbuilding', 
    3: 'buses',
    4: 'dinosaurs',
    5: 'elephants',
    6: 'Roses', 
    7: 'horses',
    8: 'mountains',
    9: 'food'
}


def upload_image():
    file_path = filedialog.askopenfilename()
    uploaded = Image.open(file_path)
    uploaded.thumbnail(((top.winfo_width() / 2.25, (top.winfo_height() / 2.25))))
    im = ImageTk.PhotoImage(uploaded)
    sign_image.configure(image=im)
    sign_image.image = im
    label.configure(text=' ')
    show_classify_button(file_path)


def show_classify_button(file_path):
    classify_btn = Button(top, text="Classify Image", command=lambda: classify(file_path), padx=10, pady=5)
    classify_btn.configure(background="#364156", foreground="white", font=('arial', 10, 'bold'))
    classify_btn.place(relx=0.79, rely=0.46)


def classify(file_path):
    image = Image.open(file_path)
    image = image.resize((32, 32))
    image = numpy.expand_dims(image, axis=0)
    image = numpy.array(image)
    pred = model.predict_classes([image])[0]
    sign = classes[pred]
    
    print(sign)
    label.configure(foreground='#011638', text=sign)


# initialize GUI
top = tk.Tk()  # calling the constructor or creating the object of tk class
top.geometry('800x600')  # set height and width
top.title("Image Classification CIFAR10")
top.configure(background="#CDCDCD")

# set Heading

heading = Label(top, text="Image Classifier", pady=20, font=('arial', 20, 'bold'))
heading.configure(background="#CDCDCD", foreground='#364156')
heading.pack()

upload = Button(top, text="Upload an image", command=upload_image, padx=10, pady=5)
heading.configure(background="#364156", foreground='white', font=('arial', 10, 'bold'))
upload.pack(side=BOTTOM, pady=50)

# upload image
sign_image = Label(top)
sign_image.pack(side=BOTTOM, expand=True)

# predicted class

label = Label(top, background="#CDCDCD", font=('arial', 15, 'bold'))
label.pack(side=BOTTOM, expand=True)

top.mainloop()

我使用的模型是迁移学习后的VGG16，我使用torch.save保存了它。

Answer 1

您可以尝试以下方法吗？

1. 加载模型后，使用以下语句将其设置为评估模式：

model=torch.load('corelK_model_0.pt')
model.eval()

1. 您没有应用模型训练时使用的相同图像转换（在测试图像上）。您的分类函数应如下所示：

def classify(file_path):
    image = Image.open(file_path)
    image = image.resize((32, 32))
    image = numpy.expand_dims(image, axis=0)
    image = numpy.array(image)

    # Start of transformations
    # .......................
    # End of transformations

    pred = model.predict_classes([image])[0]
    sign = classes[pred]

    print(sign)
    label.configure(foreground='#011638', text=sign)