Upload models/position_encoding.py

models/position_encoding.py

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+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Various positional encodings for the transformer.
+"""
+import math
+import torch
+from torch import nn
+
+
+class PositionEmbeddingSine(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, token_tensors):
+        ## input: (B,C,H,W)
+        x = token_tensors
+        h, w = x.shape[-2:]
+        identity_map= torch.ones((h,w), device=x.device)
+        y_embed = identity_map.cumsum(0, dtype=torch.float32)
+        x_embed = identity_map.cumsum(1, dtype=torch.float32)
+        if self.normalize:
+            eps = 1e-6
+            y_embed = y_embed / (y_embed[-1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, None] / dim_t
+        pos_y = y_embed[:, :, None] / dim_t
+        pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
+        pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
+        pos = torch.cat((pos_y, pos_x), dim=2).permute(2, 0, 1)
+        batch_pos = pos.unsqueeze(0).repeat(x.shape[0], 1, 1, 1)
+        return batch_pos
+
+
+class PositionEmbeddingLearned(nn.Module):
+    """
+    Absolute pos embedding, learned.
+    """
+    def __init__(self, n_pos_x=16, n_pos_y=16, num_pos_feats=64):
+        super().__init__()
+        self.row_embed = nn.Embedding(n_pos_y, num_pos_feats)
+        self.col_embed = nn.Embedding(n_pos_x, num_pos_feats)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.uniform_(self.row_embed.weight)
+        nn.init.uniform_(self.col_embed.weight)
+
+    def forward(self, token_tensors):
+        ## input: (B,C,H,W)
+        x = token_tensors
+        h, w = x.shape[-2:]
+        i = torch.arange(w, device=x.device)
+        j = torch.arange(h, device=x.device)
+        x_emb = self.col_embed(i)
+        y_emb = self.row_embed(j)
+        pos = torch.cat([
+            x_emb.unsqueeze(0).repeat(h, 1, 1),
+            y_emb.unsqueeze(1).repeat(1, w, 1),
+        ], dim=-1).permute(2, 0, 1)
+        batch_pos = pos.unsqueeze(0).repeat(x.shape[0], 1, 1, 1)
+        return batch_pos
+
+
+def build_position_encoding(num_pos_feats=64, n_pos_x=16, n_pos_y=16, is_learned=False):
+    if is_learned:
+        position_embedding = PositionEmbeddingLearned(n_pos_x, n_pos_y, num_pos_feats)
+    else:
+        position_embedding = PositionEmbeddingSine(num_pos_feats, normalize=True)
+
+    return position_embedding