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Validation_ResNet18.py
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Fri, Oct 18, 08:18

Validation_ResNet18.py
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#https://missinglink.ai/guides/pytorch/pytorch-resnet-building-training-scaling-residual-networks-pytorch/
#https://www.kaggle.com/gxkok21/resnet50-with-pytorch
#https://www.kaggle.com/pintu161/transfer-learning-in-pytorch-using-resnet18
import torchvision.transforms as transforms
import torchvision
import torch
from torchsummary import summary
import matplotlib.pyplot as plt
import numpy as np
from torch.optim.lr_scheduler import StepLR
from mlxtend.plotting import plot_confusion_matrix
import seaborn as sns
from torchvision import datasets
from Heatmap import heatmap , annotate_heatmap
#torch.cuda.empty_cache()
from torch import optim, cuda
import os
from PIL import Image
import pandas as pd
import torchvision.models as models
from torch import nn
from collections import OrderedDict
# Whether to train on a gpu
train_on_gpu = cuda.is_available()
print(f'Train on gpu: {train_on_gpu}')
from Utils import *
#%%
PATH = './ResNet18-Pytorch.pth'
Trained_model = torch.load(PATH)
datadir = 'Bronze_dataset/'
traindir = datadir + 'Train/'
testdir = datadir + 'Test/'
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
Trained_model.to(device)
summary(Trained_model, (3 ,32 ,32))
#%%
def get_lr(optimizer):
for param_group in optimizer.param_groups:
print('Learning rate =')
print(param_group['lr'])
return param_group['lr']
transform = transforms.Compose([transforms.Resize((512,512)),
transforms.ToTensor()])
#transform = transforms.Compose([transforms.ToTensor()])
trainload = datasets.ImageFolder(root=traindir, transform=transform)
trainset = torch.utils.data.DataLoader(trainload, batch_size=40,
shuffle=True, num_workers=0)
testload = datasets.ImageFolder(root=testdir, transform=transform)
testset = torch.utils.data.DataLoader(testload, batch_size=40,
shuffle=True, num_workers=0)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#device = torch.device("cpu")
print(device)
net=Trained_model
net.to(device)
y_pred = []
y_true = []
correctHits=0
total=0
for batches in testset:
data,output = batches
data,output = data.to(device),output.to(device)
prediction = net(data)
# _,prediction = torch.max(prediction.data,1) #returns max as well as its index
prediction = torch.argmax(prediction, dim=1)
total += output.size(0)
correctHits += (prediction==output).sum().item()
prediction=prediction.data.cpu().numpy()
output=output.data.cpu().numpy()
y_true.extend(output) # Save Truth
y_pred.extend(prediction)
print('Accuracy = '+str((correctHits/total)*100))
#%%
classes = ('Balling', 'LoF', 'Nopores','Keyhole')
count_parameters(net)
plotname= 'ResNet18'+'_Cross_Bronze'+'_confusion_matrix'+'.png'
plot_confusion_matrix(y_true, y_pred,classes,plotname)

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