VAE model updated

causal_demon/Models/VAE/Funcs.py

@@ -0,0 +1,53 @@
+from torch.autograd import Variable
+import torch as th
+from collections import OrderedDict
+import torch.nn as nn
+
+def summary(input_size, model):
+
+    def register_hook(module):
+        def hook(module, input, output):
+            class_name = str(module.__class__).split('.')[-1].split("'")[0]
+            module_idx = len(summary)
+
+            m_key = '%s-%i' % (class_name, module_idx+1)
+            summary[m_key] = OrderedDict()
+            summary[m_key]['input_shape'] = list(input[0].size())
+            summary[m_key]['input_shape'][0] = -1
+            summary[m_key]['output_shape'] = list(output.size())
+            summary[m_key]['output_shape'][0] = -1
+
+            params = 0
+            if hasattr(module, 'weight'):
+                params += th.prod(th.LongTensor(list(module.weight.size())))
+                if module.weight.requires_grad:
+                    summary[m_key]['trainable'] = True
+                else:
+                    summary[m_key]['trainable'] = False
+            if hasattr(module, 'bias'):
+                params +=  th.prod(th.LongTensor(list(module.bias.size())))
+            summary[m_key]['nb_params'] = params
+
+        if not isinstance(module, nn.Sequential) and \
+           not isinstance(module, nn.ModuleList) and \
+           not (module == model):
+            hooks.append(module.register_forward_hook(hook))
+
+    # check if there are multiple inputs to the network
+    if isinstance(input_size[0], (list, tuple)):
+        x = [Variable(th.rand(1,*in_size)) for in_size in input_size]
+    else:
+        x = Variable(th.rand(1,*input_size))
+
+    # create properties
+    summary = OrderedDict()
+    hooks = []
+    # register hook
+    model.apply(register_hook)
+    # make a forward pass
+    model(x)
+    # remove these hooks
+    for h in hooks:
+        h.remove()
+
+    return summary

causal_demon/Models/VAE/VAE_MNIST.py

@@ -0,0 +1,135 @@
+from __future__ import print_function
+import argparse
+import torch
+import torch.utils.data
+from torch import nn, optim
+from torch.nn import functional as F
+from torchvision import datasets, transforms
+from torchvision.utils import save_image
+from Funcs import summary
+
+
+parser = argparse.ArgumentParser(description='VAE MNIST Example')
+parser.add_argument('--batch-size', type=int, default=128, metavar='N',
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=10, metavar='N',
+                    help='number of epochs to train (default: 10)')
+parser.add_argument('--no-cuda', action='store_true', default=False,
+                    help='enables CUDA training')
+parser.add_argument('--seed', type=int, default=1, metavar='S',
+                    help='random seed (default: 1)')
+parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+                    help='how many batches to wait before logging training status')
+args = parser.parse_args()
+args.cuda = not args.no_cuda and torch.cuda.is_available()
+
+torch.manual_seed(args.seed)
+
+device = torch.device("cuda" if args.cuda else "cpu")
+
+kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
+train_loader = torch.utils.data.DataLoader(
+    datasets.MNIST('../data', train=True, download=True,
+                   transform=transforms.ToTensor()),
+    batch_size=args.batch_size, shuffle=True, **kwargs)
+test_loader = torch.utils.data.DataLoader(
+    datasets.MNIST('../data', train=False, transform=transforms.ToTensor()),
+    batch_size=args.batch_size, shuffle=True, **kwargs)
+
+
+class VAE(nn.Module):
+    def __init__(self):
+        super(VAE, self).__init__()
+
+        self.fc1 = nn.Linear(784, 400)
+        self.fc21 = nn.Linear(400, 20)
+        self.fc22 = nn.Linear(400, 20)
+        self.fc3 = nn.Linear(20, 400)
+        self.fc4 = nn.Linear(400, 784)
+
+    def encode(self, x):
+        h1 = F.relu(self.fc1(x))
+        return self.fc21(h1), self.fc22(h1)
+
+    def reparameterize(self, mu, logvar):
+        std = torch.exp(0.5*logvar)
+        eps = torch.randn_like(std)
+        return eps.mul(std).add_(mu)
+
+    def decode(self, z):
+        h3 = F.relu(self.fc3(z))
+        return torch.sigmoid(self.fc4(h3))
+
+    def forward(self, x):
+        mu, logvar = self.encode(x.view(-1, 784))
+        z = self.reparameterize(mu, logvar)
+        return self.decode(z), mu, logvar
+
+
+model = VAE().to(device)
+o = summary([784],model)
+print(dict(o))
+optimizer = optim.Adam(model.parameters(), lr=1e-3)
+
+
+# Reconstruction + KL divergence losses summed over all elements and batch
+def loss_function(recon_x, x, mu, logvar):
+    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), reduction='sum')
+
+    # see Appendix B from VAE paper:
+    # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
+    # https://arxiv.org/abs/1312.6114
+    # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
+    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+
+    return BCE + KLD
+
+
+def train(epoch):
+    model.train()
+    train_loss = 0
+    for batch_idx, (data, _) in enumerate(train_loader):
+        data = data.to(device)
+        optimizer.zero_grad()
+        recon_batch, mu, logvar = model(data)
+        loss = loss_function(recon_batch, data, mu, logvar)
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+        if batch_idx % args.log_interval == 0:
+            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader),
+                loss.item() / len(data)))
+
+    print('====> Epoch: {} Average loss: {:.4f}'.format(
+          epoch, train_loss / len(train_loader.dataset)))
+
+
+def test(epoch):
+    model.eval()
+    test_loss = 0
+    with torch.no_grad():
+        for i, (data, _) in enumerate(test_loader):
+            data = data.to(device)
+            recon_batch, mu, logvar = model(data)
+            test_loss += loss_function(recon_batch, data, mu, logvar).item()
+            if i == 0:
+                n = min(data.size(0), 8)
+                comparison = torch.cat([data[:n],
+                                      recon_batch.view(args.batch_size, 1, 28, 28)[:n]])
+                save_image(comparison.cpu(),
+                         'results/reconstruction_' + str(epoch) + '.png', nrow=n)
+
+    test_loss /= len(test_loader.dataset)
+    print('====> Test set loss: {:.4f}'.format(test_loss))
+
+if __name__ == "__main__":
+    for epoch in range(1, args.epochs + 1):
+        train(epoch)
+        test(epoch)
+        with torch.no_grad():
+            sample = torch.randn(64, 20).to(device)
+            sample = model.decode(sample).cpu()
+            save_image(sample.view(64, 1, 28, 28),
+                       'results/sample_' + str(epoch) + '.png')