预先训练的pytorch vgg16模型分类及其类别

问题描述

我用pytorch的预训练vgg16模型编写了一个图像vgg分类模型.

I wrote a image vgg classification model with pytorch's pretrained vgg16 model.

import matplotlib.pyplot as plt
import numpy as np
import torch
from PIL import Image
import urllib
from skimage.transform import resize
from skimage import io
import yaml

# Downloading imagenet 1000 classes list
file = urllib. request. urlopen("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt")
classes = ''
for f in file:
  classes = classes +  f.decode("utf-8")
classes = yaml.load(classes)

# Downloading pretrained vgg16 model
model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg16', pretrained=True)

print(model)

for param in model.parameters():
    param.requires_grad = False


url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/dog.jpg", "dog.jpg")

image=io.imread(url)

plt.imshow(image)
plt.show()

# resize to 224x224x3
img = resize(image,(224,224,3))

plt.imshow(img)
plt.show()
# Normalizing input for vgg16
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
img1 = mean*img+std
img1 = np.clip(img1,0,1)

img1 = torch.from_numpy(img1).unsqueeze(0)
img1 = img1.permute(0,3,2,1) # batch_size x channels x height x width

model.eval()
pred = model(img1.float())
print(classes[torch.argmax(pred).numpy().tolist()])

代码工作正常，但输出错误的类.我不确定我做错了什么，但是如果我不得不猜测可能是imagenet yaml类列表或规范化的输入图像.谁能告诉我我在哪里犯错误?

The code works fine but its outputting wrong classes. I am not sure where I did wrong but If I have to guess it might be the imagenet yaml classes list or at the normalizing input image. Can anyone tell me where I am making the mistakes?

推荐答案

图像预处理存在一些问题.首先，归一化计算为(value-mean)/std)，而不是 value * mean + std .其次，不应将值裁剪为[0，1]，归一化故意将值从[0，1]移开.其次，作为NumPy数组的图像的形状为 [height，width，3] ，当您置换尺寸时，交换高度和宽度尺寸，创建形状为 [batch_size，通道，宽度，高度] .

There are some issues with the image preprocessing. Firstly, the normalisation is calculated as (value - mean) / std), not value * mean + std. Secondly, the values should not be clipped to [0, 1], the normalisation purposely shifts the values away from [0, 1]. Secondly, the image as NumPy array has shape [height, width, 3], when you permute the dimensions you swap the height and width dimension, creating a tensor with shape [batch_size, channels, width, height].

img = resize(image,(224,224,3))


# Normalizing input for vgg16
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
img1 = (img1 - mean) / std

img1 = torch.from_numpy(img1).unsqueeze(0)
img1 = img1.permute(0, 3, 1, 2) # batch_size x channels x height x width

您可以使用 torchvision.transforms

Instead of doing that manually, you can use torchvision.transforms.

from torchvision import transforms

preprocess = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

img = resize(image,(224,224,3))
img1 = preprocess(img)
img1 = img1.unsqueeze(0)

如果使用PIL加载图像，则还可以通过添加 transforms.Resize((224，224)) 到预处理管道，或者甚至可以添加 transforms.ToPILImage() 首先将图像转换为PIL图像( transforms.Resize 需要一个PIL图片).

If you use PIL to load the images, you could also resize the images by adding transforms.Resize((224, 224)) to the preprocessing pipeline, or you could even add transforms.ToPILImage() to first convert the image to a PIL image (transforms.Resize requires a PIL image).

这篇关于预先训练的pytorch vgg16模型分类及其类别的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！