vgg19_pytorch.py

import torch
import torch.nn.functional as F
import argparse
import cv2
import numpy as np
from glob import glob

num_classes = 2
img_height, img_width = 224, 224
channel = 3
GPU = False
torch.manual_seed(0)

class VGG19(torch.nn.Module):
    def __init__(self):
        super(VGG19, self).__init__()
        self.conv1_1 = torch.nn.Conv2d(channel, 64, kernel_size=3, padding=1, stride=1)
        self.conv1_2 = torch.nn.Conv2d(64, 64, kernel_size=3, padding=1, stride=1)
        self.conv2_1 = torch.nn.Conv2d(64, 128, kernel_size=3, padding=1, stride=1)
        self.conv2_2 = torch.nn.Conv2d(128, 128, kernel_size=3, padding=1, stride=1)
        self.conv3_1 = torch.nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=1)
        self.conv3_2 = torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1)
        self.conv3_3 = torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1)
        self.conv3_4 = torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1)
        self.conv4_1 = torch.nn.Conv2d(256, 512, kernel_size=3, padding=1, stride=1)
        self.conv4_2 = torch.nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1)
        self.conv4_3 = torch.nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1)
        self.conv4_4 = torch.nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1)
        self.conv5_1 = torch.nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1)
        self.conv5_2 = torch.nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1)
        self.conv5_3 = torch.nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1)
        self.conv5_4 = torch.nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1)
        
        self.fc1 = torch.nn.Linear(25088, 4096)
        self.fc2 = torch.nn.Linear(4096, 4096)
        self.fc_out = torch.nn.Linear(4096, num_classes)
        
    def forward(self, x):
        x = F.relu(self.conv1_1(x))
        x = F.relu(self.conv1_2(x))
        x = F.max_pool2d(x, 2, stride=2, padding=0)
        x = F.relu(self.conv2_1(x))
        x = F.relu(self.conv2_2(x))
        x = F.max_pool2d(x, 2, stride=2, padding=0)
        x = F.relu(self.conv3_1(x))
        x = F.relu(self.conv3_2(x))
        x = F.relu(self.conv3_3(x))
        x = F.relu(self.conv3_4(x))
        x = F.max_pool2d(x, 2, stride=2, padding=0)
        x = F.relu(self.conv4_1(x))
        x = F.relu(self.conv4_2(x))
        x = F.relu(self.conv4_3(x))
        x = F.relu(self.conv4_4(x))
        x = F.max_pool2d(x, 2, stride=2, padding=0)
        x = F.relu(self.conv5_1(x))
        x = F.relu(self.conv5_2(x))
        x = F.relu(self.conv5_3(x))
        x = F.relu(self.conv5_4(x))
        x = F.max_pool2d(x, 2, stride=2, padding=0)
    
        x = x.view(x.shape[0], -1)
        x = F.relu(self.fc1(x))
        x = torch.nn.Dropout()(x)
        x = F.relu(self.fc2(x))
        x = torch.nn.Dropout()(x)
        x = self.fc_out(x)
        x = F.softmax(x, dim=1)
        
        return x


CLS = ['akahara', 'madara']

# get train data
def data_load(path, hf=False, vf=False):
    xs = []
    ts = []
    paths = []
    
    for dir_path in glob(path + '/*'):
        for path in glob(dir_path + '/*'):
            x = cv2.imread(path)
            x = cv2.resize(x, (img_width, img_height)).astype(np.float32)
            x /= 255.
            xs.append(x)

            for i, cls in enumerate(CLS):
                if cls in path:
                    t = i
            
            ts.append(t)

            paths.append(path)

            if hf:
                xs.append(x[:, ::-1])
                ts.append(t)
                paths.append(path)

            if vf:
                xs.append(x[::-1])
                ts.append(t)
                paths.append(path)

            if hf and vf:
                xs.append(x[::-1, ::-1])
                ts.append(t)
                paths.append(path)

    xs = np.array(xs, dtype=np.float32)
    ts = np.array(ts, dtype=np.int)
    
    xs = xs.transpose(0,3,1,2)

    return xs, ts, paths


# train
def train():
    # GPU
    device = torch.device("cuda" if GPU else "cpu")

    # model
    model = VGG19().to(device)
    opt = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
    model.train()

    xs, ts, paths = data_load('../Dataset/train/images/', hf=True, vf=True)

    # training
    mb = 16
    mbi = 0
    train_ind = np.arange(len(xs))
    np.random.seed(0)
    np.random.shuffle(train_ind)

    loss_fn = torch.nn.NLLLoss() #torch.nn.CrossEntropyLoss()
    
    for i in range(500):
        if mbi + mb > len(xs):
            mb_ind = train_ind[mbi:]
            np.random.shuffle(train_ind)
            mb_ind = np.hstack((mb_ind, train_ind[:(mb-(len(xs)-mbi))]))
        else:
            mb_ind = train_ind[mbi: mbi+mb]
            mbi += mb

        x = torch.tensor(xs[mb_ind], dtype=torch.float).to(device)
        t = torch.tensor(ts[mb_ind], dtype=torch.long).to(device)

        opt.zero_grad()
        y = model(x)
        loss = loss_fn(torch.log(y), t)
        loss.backward()
        opt.step()
    
        pred = y.argmax(dim=1, keepdim=True)
        acc = pred.eq(t.view_as(pred)).sum().item() / mb
        
        print("iter >>", i+1, ',loss >>', loss.item(), ',accuracy >>', acc)

    torch.save(model.state_dict(), 'cnn.pt')

# test
def test():
    device = torch.device("cuda" if GPU else "cpu")
    model = VGG19().to(device)
    model.eval()
    model.load_state_dict(torch.load('cnn.pt'))

    xs, ts, paths = data_load('../Dataset/test/images/')

    for i in range(len(paths)):
        x = xs[i]
        t = ts[i]
        path = paths[i]
        
        x = np.expand_dims(x, axis=0)
        x = torch.tensor(x, dtype=torch.float).to(device)
        
        pred = model(x)
        pred = pred.detach().cpu().numpy()[0]
    
        print("in {}, predicted probabilities >> {}".format(path, pred))
    

def arg_parse():
    parser = argparse.ArgumentParser(description='CNN implemented with Keras')
    parser.add_argument('--train', dest='train', action='store_true')
    parser.add_argument('--test', dest='test', action='store_true')
    args = parser.parse_args()
    return args

# main
if __name__ == '__main__':
    args = arg_parse()

    if args.train:
        train()
    if args.test:
        test()

    if not (args.train or args.test):
        print("please select train or test flag")
        print("train: python main.py --train")
        print("test:  python main.py --test")
        print("both:  python main.py --train --test")