feat: train on single GPU

train/dataset.py

@@ -0,0 +1,37 @@
+import os
+import numpy as np
+from torch.utils.data import Dataset, DataLoader
+
+class Cifar10Dataset(Dataset):
+    def __init__(self, data_dir):
+        self.imgs = []
+        self.labels = []
+        for file in os.listdir(data_dir):
+            if 'data_batch' in file:
+                batch = self.unpickle(f'{data_dir}/{file}')
+
+                length = len(batch[b'data'])
+                self.labels += batch[b'labels']
+
+                # read image data
+                values = np.array(batch[b'data']) / 255.0
+                imgs = np.zeros((length, 3, 32, 32))
+                for index in range(length):
+                    for channel in range(3):
+                        imgs[index][channel] = values[index][32*32*channel : 32*32*(channel+1)].reshape((32, 32))
+                self.imgs.append(imgs)
+        self.imgs = np.concatenate(self.imgs)
+        print(f"load images : {self.imgs.shape}")
+        print(f"load labels : {len(self.labels)}")
+
+    def unpickle(self, file):
+        import pickle
+        with open(file, 'rb') as fo:
+            dict = pickle.load(fo, encoding='bytes')
+        return dict
+
+    def __getitem__(self, index):
+        return self.imgs[index], self.labels[index]
+
+    def __len__(self):
+        return len(self.imgs)

train/model.py

@@ -0,0 +1,34 @@
+from torch import nn
+import torch.nn.functional as F
+
+class Network(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding='same')
+        self.conv2 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, stride=1, padding='same')
+        self.conv3 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding='same')
+        self.conv4 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding='same')
+        self.fc1 = nn.Linear(2048, 1024)
+        self.fc2 = nn.Linear(1024, 128)
+        self.fc3 = nn.Linear(128, 10)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(0.3)
+
+    def forward(self, x):
+        x = self.relu(self.conv1(x))
+        x = self.relu(self.conv2(x))
+        x = self.pool(x)
+        x = self.dropout(x)
+        x = self.relu(self.conv3(x))
+        x = self.relu(self.conv4(x))
+        x = self.pool(x)
+        x = self.dropout(x)
+        x = x.reshape((x.shape[0], -1))
+        x = self.relu(self.fc1(x))
+        x = self.dropout(x)
+        x = self.relu(self.fc2(x))
+        x = self.dropout(x)
+        x = self.fc3(x)
+        return x
+

train/train.py

@@ -0,0 +1,39 @@
+import torch
+from torch import optim
+from torch import nn
+from dataset import Cifar10Dataset
+from model import Network
+from torch.utils.data import DataLoader
+import matplotlib.pyplot as plt
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model = Network().to(device)
+dataset = Cifar10Dataset('./dataset_dir/cifar-10-batches-py')
+loader = DataLoader(dataset, batch_size=32, shuffle=True)
+
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+criterion = nn.CrossEntropyLoss()
+
+for epoch in range(50):
+    model.train()
+    train_loss_sum = 0
+    train_correct_sum = 0
+    for x, y in loader:
+        x = x.float()
+        x, y = x.to(device), y.to(device)
+
+        predict = model(x)
+        loss = criterion(predict, y)
+        loss.backward()
+
+        # evaluate
+        train_loss_sum += loss.item()
+        predicted_classes = torch.argmax(predict, dim=1)
+        train_correct_sum += (predicted_classes == y).sum()
+
+        optimizer.step()
+        optimizer.zero_grad()
+    print(train_loss_sum / len(loader))
+    print((train_correct_sum / len(dataset)).item(),'%')
+    print()