Merge 5e8ab40d7d into 4b2a0df237

gan/main.py

@@ -0,0 +1,195 @@
+import mnist
+
+import argparse
+
+import mlx.core as mx
+import mlx.nn as nn
+import mlx.optimizers as optim
+
+from tqdm import tqdm
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Generator Block
+def GenBlock(in_dim:int,out_dim:int):
+    return nn.Sequential(
+        nn.Linear(in_dim,out_dim),
+        nn.BatchNorm(out_dim, 0.8),
+        nn.LeakyReLU(0.2)
+    )
+
+# Generator Model
+class Generator(nn.Module):
+
+    def __init__(self, z_dim:int = 32, im_dim:int = 784, hidden_dim: int = 256):
+        super(Generator, self).__init__()
+
+        self.gen = nn.Sequential(
+            GenBlock(z_dim, hidden_dim),
+            GenBlock(hidden_dim, hidden_dim * 2),
+            GenBlock(hidden_dim * 2, hidden_dim * 4),
+
+            nn.Linear(hidden_dim * 4,im_dim),
+        )
+        
+    def __call__(self, noise):
+        x = self.gen(noise)
+        return mx.tanh(x)
+
+# make 2D noise with shape n_samples x z_dim
+def get_noise(n_samples:list[int], z_dim:int)->list[int]:
+    return mx.random.normal(shape=(n_samples, z_dim))
+
+#---------------------------------------------#
+
+# Discriminator Block
+def DisBlock(in_dim:int,out_dim:int):
+    return nn.Sequential(
+        nn.Linear(in_dim,out_dim),
+        nn.LeakyReLU(negative_slope=0.2),
+        nn.Dropout(0.3),
+    )
+
+# Discriminator Model
+class Discriminator(nn.Module):
+
+    def __init__(self,im_dim:int = 784, hidden_dim:int = 256):
+        super(Discriminator, self).__init__()
+
+        self.disc = nn.Sequential(
+            DisBlock(im_dim, hidden_dim * 4),
+            DisBlock(hidden_dim * 4, hidden_dim * 2),
+            DisBlock(hidden_dim * 2, hidden_dim),
+            
+            nn.Linear(hidden_dim,1),
+            nn.Sigmoid()
+        )
+        
+    def __call__(self, noise):
+        return self.disc(noise)
+    
+# Discriminator Loss
+def disc_loss(gen, disc, real, num_images, z_dim):
+    
+    noise =  mx.array(get_noise(num_images, z_dim))
+    fake_images = gen(noise)
+        
+    fake_disc = disc(fake_images)
+    
+    fake_labels = mx.zeros((fake_images.shape[0],1))
+            
+    fake_loss = mx.mean(nn.losses.binary_cross_entropy(fake_disc,fake_labels,with_logits=True))
+        
+    real_disc = mx.array(disc(real))
+    real_labels = mx.ones((real.shape[0],1))
+    
+    real_loss = mx.mean(nn.losses.binary_cross_entropy(real_disc,real_labels,with_logits=True))
+    
+    disc_loss = (fake_loss + real_loss) / 2.0
+
+    return disc_loss
+
+# Genearator Loss
+def gen_loss(gen, disc, num_images, z_dim):
+
+    noise = mx.array(get_noise(num_images, z_dim))
+    
+    fake_images = gen(noise)
+    fake_disc = mx.array(disc(fake_images))
+
+    fake_labels = mx.ones((fake_images.shape[0],1))
+            
+    gen_loss = nn.losses.binary_cross_entropy(fake_disc,fake_labels,with_logits=True)
+    
+    return mx.mean(gen_loss)
+
+# make batch of images
+def batch_iterate(batch_size: int, ipt: list[int])-> list[int]:
+    perm = np.random.permutation(len(ipt))
+    for s in range(0, len(ipt), batch_size):
+        ids = perm[s : s + batch_size]
+        yield ipt[ids]
+
+# plot batch of images at epoch steps
+def show_images(epoch_num:int,imgs:list[int],num_imgs:int = 25):
+    if (imgs.shape[0] > 0): 
+        fig,axes = plt.subplots(5, 5, figsize=(5, 5))
+        
+        for i, ax in enumerate(axes.flat):
+            img = mx.array(imgs[i]).reshape(28,28)
+            ax.imshow(img,cmap='gray')
+            ax.axis('off')
+        plt.tight_layout()
+        plt.savefig('gen_images/img_{}.png'.format(epoch_num))
+        plt.show()
+
+def main(args:dict):
+    seed = 42
+    n_epochs = 500
+    z_dim = 128
+    batch_size = 128
+    lr = 2e-5
+    
+    mx.random.seed(seed)
+
+    # Load the data
+    train_images,*_ = map(np.array, getattr(mnist,'mnist')())
+    
+    # Normalization images => [-1,1]
+    train_images = train_images * 2.0 - 1.0
+    
+    gen = Generator(z_dim)
+    mx.eval(gen.parameters())
+    gen_opt = optim.Adam(learning_rate=lr, betas=[0.5, 0.999])
+
+    disc = Discriminator()
+    mx.eval(disc.parameters())
+    disc_opt = optim.Adam(learning_rate=lr, betas=[0.5, 0.999])
+
+    # TODO training...
+
+    D_loss_grad = nn.value_and_grad(disc, disc_loss)
+    G_loss_grad = nn.value_and_grad(gen, gen_loss)
+
+    for epoch in tqdm(range(n_epochs)):
+
+        for idx,real in enumerate(batch_iterate(batch_size, train_images)):
+                    
+            # TODO Train Discriminator
+            D_loss,D_grads = D_loss_grad(gen, disc,mx.array(real), batch_size, z_dim)
+
+            # Update optimizer
+            disc_opt.update(disc, D_grads)
+            
+            # Update gradients
+            mx.eval(disc.parameters(), disc_opt.state)
+
+            # TODO Train Generator
+            G_loss,G_grads = G_loss_grad(gen, disc, batch_size, z_dim)
+            
+            # Update optimizer
+            gen_opt.update(gen, G_grads)
+            
+            # Update gradients
+            mx.eval(gen.parameters(), gen_opt.state)        
+            
+        if epoch%100==0:
+                print("Epoch: {}, iteration: {}, Discriminator Loss:{}, Generator Loss: {}".format(epoch,idx,D_loss,G_loss))
+                fake_noise = mx.array(get_noise(batch_size, z_dim))
+                fake = gen(fake_noise)
+                show_images(epoch,fake)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Train a simple GAN on MNIST with MLX.")
+    parser.add_argument("--gpu", action="store_true", help="Use the Metal back-end.")
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="mnist",
+        choices=["mnist", "fashion_mnist"],
+        help="The dataset to use.",
+    )
+    args = parser.parse_args()
+    if not args.gpu:
+        mx.set_default_device(mx.cpu)
+    main(args)

gan/mnist.py

@@ -0,0 +1,83 @@
+# Copyright © 2023 Apple Inc.
+
+import gzip
+import os
+import pickle
+from urllib import request
+
+import numpy as np
+
+
+def mnist(
+    save_dir="/tmp",
+    base_url="https://raw.githubusercontent.com/fgnt/mnist/master/",
+    filename="mnist.pkl",
+):
+    """
+    Load the MNIST dataset in 4 tensors: train images, train labels,
+    test images, and test labels.
+
+    Checks `save_dir` for already downloaded data otherwise downloads.
+
+    Download code modified from:
+      https://github.com/hsjeong5/MNIST-for-Numpy
+    """
+
+    def download_and_save(save_file):
+        filename = [
+            ["training_images", "train-images-idx3-ubyte.gz"],
+            ["test_images", "t10k-images-idx3-ubyte.gz"],
+            ["training_labels", "train-labels-idx1-ubyte.gz"],
+            ["test_labels", "t10k-labels-idx1-ubyte.gz"],
+        ]
+
+        mnist = {}
+        for name in filename:
+            out_file = os.path.join("/tmp", name[1])
+            request.urlretrieve(base_url + name[1], out_file)
+        for name in filename[:2]:
+            out_file = os.path.join("/tmp", name[1])
+            with gzip.open(out_file, "rb") as f:
+                mnist[name[0]] = np.frombuffer(f.read(), np.uint8, offset=16).reshape(
+                    -1, 28 * 28
+                )
+        for name in filename[-2:]:
+            out_file = os.path.join("/tmp", name[1])
+            with gzip.open(out_file, "rb") as f:
+                mnist[name[0]] = np.frombuffer(f.read(), np.uint8, offset=8)
+        with open(save_file, "wb") as f:
+            pickle.dump(mnist, f)
+
+    save_file = os.path.join(save_dir, filename)
+    if not os.path.exists(save_file):
+        download_and_save(save_file)
+    with open(save_file, "rb") as f:
+        mnist = pickle.load(f)
+
+    def preproc(x):
+        return x.astype(np.float32) / 255.0
+
+    mnist["training_images"] = preproc(mnist["training_images"])
+    mnist["test_images"] = preproc(mnist["test_images"])
+    return (
+        mnist["training_images"],
+        mnist["training_labels"].astype(np.uint32),
+        mnist["test_images"],
+        mnist["test_labels"].astype(np.uint32),
+    )
+
+
+def fashion_mnist(save_dir="/tmp"):
+    return mnist(
+        save_dir,
+        base_url="http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/",
+        filename="fashion_mnist.pkl",
+    )
+
+
+if __name__ == "__main__":
+    train_x, train_y, test_x, test_y = mnist()
+    assert train_x.shape == (60000, 28 * 28), "Wrong training set size"
+    assert train_y.shape == (60000,), "Wrong training set size"
+    assert test_x.shape == (10000, 28 * 28), "Wrong test set size"
+    assert test_y.shape == (10000,), "Wrong test set size"