capsule networks

labml_nn/capsule_networks/__init__.py

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+"""
+This is an implementation of paper
+"""
+
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.data
+
+from labml import experiment, tracker
+from labml.configs import option
+from labml.utils.pytorch import get_device
+from labml_helpers.datasets.mnist import MNISTConfigs
+from labml_helpers.device import DeviceConfigs
+from labml_helpers.module import Module
+from labml_helpers.train_valid import TrainValidConfigs, BatchStep
+
+
+class Squash(Module):
+    """
+    This is **squashing** function from paper.
+    """
+
+    def __init__(self, epsilon=1e-8):
+        super().__init__()
+        self.epsilon = epsilon
+
+    def __call__(self, s: torch.Tensor):
+        # shape: batch, caps, features
+        s2 = (s ** 2).sum(dim=-1, keepdims=True)
+        return (s2 / (1 + s2)) * (s / torch.sqrt(s2 + self.epsilon))
+
+
+class Router(Module):
+    """
+    The routing mechanism
+    """
+
+    def __init__(self, in_caps: int, out_caps: int, in_d: int, out_d: int,
+                 iterations: int):
+        super().__init__()
+        self.in_caps = in_caps
+        self.out_caps = out_caps
+        self.iterations = iterations
+        self.weight = nn.Parameter(torch.randn(in_caps, out_caps, in_d, out_d))
+        self.softmax = nn.Softmax(dim=1)
+        self.squash = Squash()
+
+    def __call__(self, u: torch.Tensor):
+        # batch, in_caps, in_d
+        u_hat = torch.einsum('ijnm,bin->bijm', self.weight, u)
+        b = u.new_zeros(u.shape[0], self.in_caps, self.out_caps)
+        v = None
+        for i in range(self.iterations):
+            c = self.softmax(b)
+            s = torch.einsum('bij,bijm->bjm', c, u_hat)
+            v = self.squash(s)
+            a = torch.einsum('bjm,bijm->bij', v, u_hat)
+            b = b + a
+
+        return v
+
+
+class MarginLoss(Module):
+    def __init__(self, *, n_labels: int, lambda_: float = 0.5, m_positive: float = 0.9, m_negative: float = 0.1):
+        super().__init__()
+
+        self.m_negative = m_negative
+        self.m_positive = m_positive
+        self.lambda_ = lambda_
+        self.n_labels = n_labels
+
+    def __call__(self, v: torch.Tensor, labels: torch.Tensor):
+        v_norm = torch.sqrt((v ** 2).sum(dim=-1))
+
+        labels = torch.eye(self.n_labels, device=labels.device)[labels]
+        loss = labels * F.relu(self.m_positive - v_norm) + \
+               self.lambda_ * (1.0 - labels) * F.relu(v_norm - self.m_negative)
+        loss = loss.sum(dim=-1).mean()
+
+        return loss
+
+
+class MNISTCapsuleNetworkModel(Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(in_channels=1, out_channels=256, kernel_size=9, stride=1)
+        self.conv2 = nn.Conv2d(in_channels=256, out_channels=32 * 8, kernel_size=9, stride=2, padding=0)
+        self.squash = Squash()
+
+        # self.digit_capsules = DigitCaps()
+        self.digit_capsules = Router(32 * 6 * 6, 10, 8, 16, 3)
+        self.reconstruct = nn.Sequential(
+            nn.Linear(16 * 10, 512),
+            nn.ReLU(),
+            nn.Linear(512, 1024),
+            nn.ReLU(),
+            nn.Linear(1024, 784),
+            nn.Sigmoid()
+        )
+
+        self.mse_loss = nn.MSELoss()
+
+    def forward(self, data):
+        x = F.relu(self.conv1(data))
+        caps = self.conv2(x).view(x.shape[0], 8, 32 * 6 * 6).permute(0, 2, 1)
+        caps = self.squash(caps)
+        caps = self.digit_capsules(caps)
+
+        with torch.no_grad():
+            pred = (caps ** 2).sum(-1).argmax(-1)
+            masked = torch.eye(10, device=x.device)[pred]
+
+        reconstructions = self.reconstruct((caps * masked[:, :, None]).view(x.shape[0], -1))
+        reconstructions = reconstructions.view(-1, 1, 28, 28)
+
+        return caps, reconstructions, pred
+
+
+class CapsuleNetworkBatchStep(BatchStep):
+    def __init__(self, *, model, optimizer):
+        super().__init__(model=model, optimizer=optimizer, loss_func=None, accuracy_func=None)
+        self.reconstruction_loss = nn.MSELoss()
+        self.margin_loss = MarginLoss(n_labels=10)
+
+    def calculate_loss(self, batch: any, state: any):
+        device = get_device(self.model)
+        data, target = batch
+        data, target = data.to(device), target.to(device)
+        stats = {'samples': len(data)}
+
+        caps, reconstructions, pred = self.model(data)
+
+        loss = self.margin_loss(caps, target) + 0.0005 * self.reconstruction_loss(reconstructions, data)
+
+        stats['correct'] = pred.eq(target).sum().item()
+        stats['loss'] = loss.detach().item() * stats['samples']
+        tracker.add("loss.", loss)
+
+        return loss, stats, None
+
+
+class Configs(MNISTConfigs, TrainValidConfigs):
+    batch_step = 'capsule_network_batch_step'
+    device: torch.device = DeviceConfigs()
+    epochs: int = 10
+
+    loss_func = None
+    accuracy_func = None
+
+
+@option(Configs.model)
+def model(c: Configs):
+    return MNISTCapsuleNetworkModel().to(c.device)
+
+
+@option(Configs.batch_step)
+def capsule_network_batch_step(c: TrainValidConfigs):
+    return CapsuleNetworkBatchStep(model=c.model, optimizer=c.optimizer)
+
+
+def main():
+    conf = Configs()
+    experiment.create(name='mnist_latest', writers={})
+    experiment.configs(conf, {'optimizer.optimizer': 'Adam',
+                              'device.cuda_device': 1},
+                       'run')
+    experiment.add_pytorch_models(dict(model=conf.model))
+    with experiment.start():
+        conf.run()
+
+
+if __name__ == '__main__':
+    main()