Add basic setup.py

interimit/nets/deepsets.py

@@ -0,0 +1,111 @@
+import torch
+from torch import nn
+
+from interimit.nets.util import parse_functional
+
+class DeepSetsModule(nn.Module):
+    def __init__(self, input_dim, phi_hidden_n, phi_hidden_dim, latent_dim, rho_hidden_n, rho_hidden_dim, output_dim):
+        """
+        Args:
+            input_dim (int): input size of one instance of the set; input size of phi
+            phi_hidden_n (int): number of hidden layers in phi
+            phi_hidden_dim (int): size of hidden layers in phi
+            latent_dim (int): output size of phi network, where sum is taken over instances; input size of rho
+            rho_hidden_n (int): number of hidden layers in rho
+            rho_hidden_dim (int): size of hidden layers in rho
+            output_dim (int): output size of rho
+        """
+        super(DeepSetsModule, self).__init__()
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.phi = Phi(self.input_dim, phi_hidden_n, phi_hidden_dim, latent_dim)
+        self.rho = Phi(latent_dim, rho_hidden_n, rho_hidden_dim, self.output_dim)
+        self.pooling = torch.sum  # torch.max # torch.mean
+
+    @staticmethod
+    def from_config(config):
+        """
+        Args:
+            config (dict): dictionary with network parameters in the form
+                {
+                    "input_dim": 5,
+                    "phi": {
+                        "hidden_n": 1,
+                        "hidden_dim": 10,
+                    },
+                    "latent_dim": 8,
+                    "rho": {
+                        "hidden_n": 1,
+                        "hidden_dim": 10,
+                    },
+                    "output_dim" : 1,
+                }
+        Returns:
+            m (nn.Module): deep sets module
+        """
+        input_dim = config["input_dim"]
+        phi = config["phi"]
+        latent_dim = config["latent_dim"]
+        rho = config["rho"]
+        output_dim = config["output_dim"]
+        m = DeepSetsModule(input_dim, phi["hidden_n"], phi["hidden_dim"], latent_dim, rho["hidden_n"], rho["hidden_dim"], output_dim)
+        return m
+
+    def forward(self, x):
+        """
+        Args:
+            x (torch.tensor): (batch_size, dynamic_size, input_dim)
+        Returns:
+            y (torch.tensor): (batch_size, output_dim)
+        """
+        # use negative dynamic_dim since batch dimensions are inserted at the front
+        dynamic_dim = -2
+        # iterate over dynamic dimension to apply phi to every instance
+        latent = tuple(self.phi(instance) for instance in x.unbind(dynamic_dim))
+        # stack outputs of phi
+        latent = torch.stack(latent, dim=dynamic_dim)
+        # apply pooling function to reduce dynamic dimension
+        latent = self.pooling(latent, dim=dynamic_dim)
+        # apply rho network
+        y = self.rho(latent)
+        return y
+
+
+class Phi(nn.Module):
+    def __init__(self, input_dim, hidden_n, hidden_dim, output_dim, final_activation=None):
+        """
+        Fully connected feedforward network with same size for all hidden layers and ReLU activation
+
+        Args:
+            input_dim (int): input dimension
+            hidden_n (int): number of hidden layers
+            hidden_dim (int): hidden layer dimension
+            output_dim (int): output dimension
+        """
+        super(Phi, self).__init__()
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.layers = [nn.Linear(self.input_dim, hidden_dim)]
+        for _ in range(hidden_n - 1):
+            self.layers.append(nn.Linear(hidden_dim, hidden_dim))
+        self.layers.append(nn.Linear(hidden_dim, self.output_dim))
+        # self.in_layer = nn.Linear(input_dim, hidden_dim)
+        # self.hidden_layers = [nn.Linear(hidden_dim, hidden_dim) for _ in range(hidden_n - 1)]
+        # self.out_layer = nn.Linear(hidden_dim, output_dim)
+        self.activation = nn.functional.relu
+        self.final_activation = final_activation if final_activation else self.activation
+
+    def forward(self, x):
+        for layer in self.layers[:-1]:
+            x = self.activation(layer(x))
+        x = self.final_activation(self.layers[-1](x))
+        return x
+
+    @staticmethod
+    def from_config(config):
+        args = (config["input_dim"], config["hidden_n"], config["hidden_dim"], config["output_dim"])
+        if "final_activation" in config:
+            kwargs = {"final_activation": parse_functional(config["final_activation"])}
+        else:
+            kwargs = {}
+        return Phi(*args, **kwargs)