added CIFAR10 + ResNet example

cifar/main.py

@@ -0,0 +1,108 @@
+import argparse
+import resnet
+import numpy as np
+import mlx.nn as nn
+import mlx.core as mx
+import mlx.optimizers as optim
+from dataset import get_cifar10
+
+
+parser = argparse.ArgumentParser(add_help=True)
+parser.add_argument(
+    "--arch",
+    type=str,
+    default="resnet20",
+    help="model architecture [resnet20, resnet32, resnet44, resnet56, resnet110, resnet1202]",
+)
+parser.add_argument("--batch_size", type=int, default=128, help="batch size")
+parser.add_argument("--epochs", type=int, default=100, help="number of epochs")
+parser.add_argument("--lr", type=float, default=1e-3, help="learning rate")
+parser.add_argument("--seed", type=int, default=0, help="random seed")
+parser.add_argument("--cpu_only", action="store_true", help="use cpu only")
+
+
+def loss_fn(model, inp, tgt):
+    return mx.mean(nn.losses.cross_entropy(model(inp), tgt))
+
+
+def eval_fn(model, inp, tgt):
+    return mx.mean(mx.argmax(model(inp), axis=1) == tgt)
+
+
+def train_epoch(model, train_iter, optimizer, epoch):
+    def train_step(model, inp, tgt):
+        output = model(inp)
+        loss = mx.mean(nn.losses.cross_entropy(output, tgt))
+        acc = mx.mean(mx.argmax(output, axis=1) == tgt)
+        return loss, acc
+
+    train_step_fn = nn.value_and_grad(model, train_step)
+
+    losses = []
+    accs = []
+
+    for batch_counter, batch in enumerate(train_iter):
+        x = mx.array(batch["image"])
+        y = mx.array(batch["label"])
+        (loss, acc), grads = train_step_fn(model, x, y)
+        optimizer.update(model, grads)
+        mx.eval(model.parameters(), optimizer.state)
+
+        loss_value = loss.item()
+        acc_value = acc.item()
+        losses.append(loss_value)
+        accs.append(acc_value)
+
+        if batch_counter % 10 == 0:
+            print(
+                f"Epoch {epoch:02d}[{batch_counter:03d}]: tr_loss {loss_value:.3f}, tr_acc {acc_value:.3f}"
+            )
+
+    mean_tr_loss = np.mean(np.array(losses))
+    mean_tr_acc = np.mean(np.array(accs))
+    return mean_tr_loss, mean_tr_acc
+
+
+def test_epoch(model, test_iter, epoch):
+    accs = []
+    for batch_counter, batch in enumerate(test_iter):
+        x = mx.array(batch["image"])
+        y = mx.array(batch["label"])
+        acc = eval_fn(model, x, y)
+        acc_value = acc.item()
+        accs.append(acc_value)
+    mean_acc = np.mean(np.array(accs))
+
+    return mean_acc
+
+
+def main(args):
+    np.random.seed(args.seed)
+    mx.random.seed(args.seed)
+
+    model = resnet.__dict__[args.arch]()
+
+    print("num_params: {:0.04f} M".format(model.num_params() / 1e6))
+    mx.eval(model.parameters())
+
+    optimizer = optim.Adam(learning_rate=args.lr)
+
+    for epoch in range(args.epochs):
+        # get data every epoch
+        # or set .repeat() on the data stream appropriately
+        train_data, test_data, tr_batches, _ = get_cifar10(args.batch_size)
+
+        epoch_tr_loss, epoch_tr_acc = train_epoch(model, train_data, optimizer, epoch)
+        print(
+            f"Epoch {epoch}: avg. tr_loss {epoch_tr_loss:.3f}, avg. tr_acc {epoch_tr_acc:.3f}"
+        )
+
+        epoch_test_acc = test_epoch(model, test_data, epoch)
+        print(f"Epoch {epoch}: Test_acc {epoch_test_acc:.3f}")
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    if args.cpu_only:
+        mx.set_default_device(mx.cpu)
+    main(args)