ImageClassificationTorchDemo

ImageClassificationTorchDemo.py

from __future__ import print_function, division
import os
import torch
from torchvision import transforms, datasets
import pandas as pd
from skimage import io, transform
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision   import transforms, utils
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.utils.data as data
import torchvision
from torchvision import transforms

import warnings
warnings.filterwarnings("ignore")






train_data_path= '/home/donvex/Projects/DLCNN/hymenoptera/train/'
test_data_path= '/home/donvex/Projects/DLCNN/hymenoptera/val/'


mean =[.4363,.4328, .3292]
std =[.2129,.2075, .2038]


train_transforms = transforms.Compose([
    transforms.Resize([224,224]),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.ToTensor(),
    transforms.Normalize(torch.Tensor(mean),torch.Tensor(std))
])

test_transforms = transforms.Compose([
    transforms.Resize([224,224]),
    transforms.ToTensor(),
    transforms.Normalize(torch.Tensor(mean), torch.Tensor(std))
])

train_dataset = torchvision.datasets.ImageFolder(root=train_data_path,transform=train_transforms)
test_dataset = torchvision.datasets.ImageFolder(root=test_data_path,transform=test_transforms)

def show_transformed_image(dataset):
    loder = torch.utils.data.DataLoader(dataset,batch_size=8,shuffle=True)
    batch = next(iter(loder))
    images,labels = batch

    grid= torchvision.utils.make_grid(images,nrow=4)
    fig=plt.figure(figsize=(12,10))
    plt.imshow(np.transpose(grid,(1,2,0)))
    print('labes: ',labels)
    fig.tight_layout()
    plt.show()
    plt.close(fig)

show_transformed_image(train_dataset)

train_loader = torch.utils.data.DataLoader(train_dataset,batch_size=32,shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset,batch_size=32,shuffle=False)