pil example Project Eval (snippet)

Python pil example 'Project Eval'

Functions in program:

def get_accuracy(net , test_dataloader, threshHold):
def Eval(net, data):

Modules used in program:

import os
import torch.nn.functional as F
import torchvision.utils as vutils
import torch.nn as nn
import pandas as pd
import PIL.ImageOps
import torch
import random
import numpy as np
import torchvision.utils
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import torchvision.datasets as dset
import torchvision

python Project Eval

Python pil example: Project Eval

import torchvision
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torch.utils.data import DataLoader,Dataset
import matplotlib.pyplot as plt
import torchvision.utils
from torchvision.models import vgg16_bn

from dataReader.DataReader import get_triples, save_checkpoint, load_checkpoint
from dataReader.Relabeler import write_list, train_test_split, update_list
from logger import Logger
from nets.SiamNetwork import ContrastiveLoss, SiameseNetwork, SiameseNetworkRGB, SiameseNetwork2
from dataReader.dataset import SiameseNetworkDataset, get_siam_set
from nets.alexnet import get_alexSet
from nets.autoEncoder import siamAutoencoder
from nets.resnet import ResNet18, ResNetSiam
from util.config import DATA_DIR, IMAGE_DIR, train_batch_size, NUMBER_OF_EPOCH
from util.plot_image import show_plot, imshow
import numpy as np
import random
from PIL import Image
import torch
from torch.autograd import Variable
import PIL.ImageOps
import pandas as pd
import torch.nn as nn
from torch import optim
import torchvision.utils as vutils
import torch.nn.functional as F
import os




def Eval(net, data):
    img0, img1, label1 = data
    output1, output2 = net(Variable(img0).cuda(), Variable(img1).cuda())
    prediction = F.pairwise_distance(output1, output2).cpu().data.numpy()[0][0]
    return label1, prediction


def get_accuracy(net , test_dataloader, threshHold):
    dataiter = iter(test_dataloader)
    correct = 0
    incorrect = 0
    for i in range(100):
        data = next(dataiter)
        label, prediction = Eval(net,data)
        if (label > 0.5).numpy() & (prediction < threshHold):
            correct = correct+1
        elif (label < 0.5).numpy() & (prediction > threshHold):
            correct = correct + 1
        else:
            incorrect = incorrect + 1
    return correct/(correct+incorrect)

Python links

Learn Python: https://pythonbasics.org/
Python Tutorial: https://pythonprogramminglanguage.com