train_cifar100_kaggle_model.py

# model from https://www.kaggle.com/pankajj/fashion-mnist-with-pytorch-93-accuracy
from torchvision import datasets, transforms, models
from torch.utils.data import Dataset, TensorDataset
import torch.nn.functional as F
from torch import nn, optim
import torchvision
import numpy as np
import sys
import torch


class FashionCNN(nn.Module):
    def __init__(self):
        super(FashionCNN, self).__init__()
        
        self.layer1 = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        
        self.layer2 = nn.Sequential(
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        
        self.fc1 = nn.Linear(in_features=3136, out_features=600)
        #self.drop = nn.Dropout2d(0.25)
        self.fc2 = nn.Linear(in_features=600, out_features=120)
        self.fc3 = nn.Linear(in_features=120, out_features=100)
        
    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.view(out.size(0), -1)
        #print(out.size())
        out = self.fc1(out)
        #out = self.drop(out)
        out = self.fc2(out)
        out = self.fc3(out)
        
        return out

assert(len(sys.argv) > 1)
save_path = sys.argv[1]

n_epochs = 50
log_interval = 100
use_cuda = torch.cuda.is_available()
if use_cuda:
    torch.cuda.empty_cache()
    #torch.cuda.set_device(0)

preprocess_train = transforms.Compose([
    # transforms.RandomRotation(30),
    # transforms.RandomResizedCrop(28),
    # transforms.RandomHorizontalFlip(),
    transforms.ToTensor()
])

preprocess_test = transforms.Compose([
    transforms.ToTensor(),
])

trainset = torchvision.datasets.CIFAR100(root="cifar100", train=True, download=True, transform=preprocess_train)
testset = torchvision.datasets.CIFAR100(root="cifar100", train=False, download=True, transform=preprocess_test)

train_loader = torch.utils.data.DataLoader(trainset, batch_size=1280, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False, num_workers=2)

net = FashionCNN()
if use_cuda:
    net.cuda()
    net = nn.DataParallel(net, device_ids=[0, 1])
    
optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min',
    factor=0.1, patience=15, threshold=0.0001, threshold_mode='abs')
criterion = nn.CrossEntropyLoss().cuda()

def train(epoch):
  net.train()
  for batch_idx, (data, target) in enumerate(train_loader):
    if use_cuda:
        data, target = data.cuda(), target.cuda()
    optimizer.zero_grad()
    output = net(data)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()
    if batch_idx % log_interval == 0:
      scheduler.step(epoch)
      print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
        epoch, batch_idx * len(data), len(train_loader.dataset),
        100. * batch_idx / len(train_loader), loss.item()))


def test():
  net.eval()
  test_loss = 0
  correct = 0
  with torch.no_grad():
    for data, target in test_loader:
      if use_cuda:
        data, target = data.cuda(), target.cuda()
      output = net(data)
      test_loss += criterion(output, target).item()
      pred = output.data.max(1, keepdim=True)[1]
      correct += pred.eq(target.data.view_as(pred)).sum()
  test_loss /= len(test_loader.dataset)
  print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
    test_loss, correct, len(test_loader.dataset),
    100. * correct / len(test_loader.dataset)))

def save_model(net, path):
    torch.save(net.state_dict(), path)
    #torch.save(optimizer.state_dict(), 'results/_optimizer_.pth')

# save_model(net, "results/init_mobilev2_2.pth")
for epoch in range(1, n_epochs + 1):
  train(epoch)
  test()

save_model(net, "models/" + save_path + ".pth")