Unable to run my Inference code for Image classification

vision
1

I was trying to do Inference from my saved model i got this error can anyone help me with it.

import torch
import torch.nn as nn
from torchvision.transforms import transforms
import numpy as np
from torch.autograd import Variable
import torch.functional as F
from io import open
import os
from PIL import Image
import pathlib
import glob
import cv2


train_path='D:/Anaconda Environment/Dataset/final/train'
pred_path='D:/Anaconda Environment/Dataset/Infence Images'


root=pathlib.Path(train_path)
classes=sorted([j.name.split('/')[-1] for j in root.iterdir()])

model=torch.load('Inference model/mobilenet_v2_pre(8).pth')
model.eval()

#Transforms
transformer=transforms.Compose([
    transforms.Resize((224,224)),
    transforms.ToTensor(),  #0-255 to 0-1, numpy to tensors
    transforms.Normalize([0.5,0.5,0.5], # 0-1 to [-1,1] , formula (x-mean)/std
                        [0.5,0.5,0.5])
])

#prediction function
def prediction(img_path,transformer):
    
    image=Image.open(img_path)
    
    image_tensor=transformer(image).float()
    
    
    image_tensor=image_tensor.unsqueeze_(0)
    
    if torch.cuda.is_available():
        image_tensor.cuda()
        
    input=Variable(image_tensor)
    
    
    output=model(input)
    
    index=output.data.numpy().argmax()
    
    pred=classes[index]
    
    return pred

images_path=glob.glob(pred_path+'/*.jpg')

pred_dict={}

for i in images_path:
    pred_dict[i[i.rfind('/')+1:]]=prediction(i,transformer)

RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor

Can anyone suggest me a solution

2

This won’t work:

    if torch.cuda.is_available():
        image_tensor.cuda()

as you need to re-assign the tensor:

    if torch.cuda.is_available():
        image_tensor = image_tensor.cuda()
1 Like
3

Thank you man
This is Working