Upload graph_decoder/conditions.py with huggingface_hub

graph_decoder/conditions.py

@@ -0,0 +1,84 @@
+import torch
+import torch.nn as nn
+import math
+import torch.nn.functional as F
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+    def __init__(self, hidden_size, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            nn.SiLU(),
+            nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+        :param t: a 1-D Tensor of N indices, one per batch element.
+                          These may be fractional.
+        :param dim: the dimension of the output.
+        :param max_period: controls the minimum frequency of the embeddings.
+        :return: an (N, D) Tensor of positional embeddings.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+        ).to(device=t.device)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t):
+        t = t.view(-1)
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+class ConditionEmbedder(nn.Module):
+    def __init__(self, input_size, hidden_size, dropout_prob, max_weight=1.0, sigma_factor=0.25):
+        super().__init__()
+        self.embedding_drop = nn.Embedding(input_size, hidden_size)
+        
+        self.mlps = nn.ModuleList([
+            nn.Sequential(
+                nn.Linear(1, hidden_size, bias=True),
+                nn.Softmax(dim=1),
+                nn.Linear(hidden_size, hidden_size, bias=False)
+            ) for _ in range(input_size)
+        ])
+
+        self.hidden_size = hidden_size
+        self.dropout_prob = dropout_prob
+
+    def forward(self, labels, train, unconditioned):
+        embeddings = 0
+
+        for dim in range(labels.shape[1]):
+            label = labels[:, dim]
+            if unconditioned: 
+                drop_ids = torch.ones_like(label).bool()
+            else:
+                drop_ids = torch.isnan(label)
+                if train:
+                    random_tensor = torch.rand(label.shape).type_as(labels)
+                    probability_mask = random_tensor < self.dropout_prob
+                    drop_ids = drop_ids | probability_mask
+
+            label = label.unsqueeze(1)
+            embedding = torch.zeros((label.shape[0], self.hidden_size)).type_as(labels)
+            mlp_out = self.mlps[dim](label[~drop_ids])
+            embedding[~drop_ids] = mlp_out.type_as(embedding)
+            embedding[drop_ids] += self.embedding_drop.weight[dim]
+
+            embeddings += embedding
+            
+        return embeddings