移动doc_dewarp

services/paddle_services/doc_dewarp/position_encoding.py

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+import math
+from typing import Optional
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.initializer as init
+
+
+class NestedTensor(object):
+    def __init__(self, tensors, mask: Optional[paddle.Tensor]):
+        self.tensors = tensors
+        self.mask = mask
+
+    def decompose(self):
+        return self.tensors, self.mask
+
+    def __repr__(self):
+        return str(self.tensors)
+
+
+class PositionEmbeddingSine(nn.Layer):
+    """
+    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, mask):
+        assert mask is not None
+
+        y_embed = mask.cumsum(axis=1, dtype="float32")
+        x_embed = mask.cumsum(axis=2, dtype="float32")
+
+        if self.normalize:
+            eps = 1e-06
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = paddle.arange(end=self.num_pos_feats, dtype="float32")
+        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 = paddle.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), axis=4
+        ).flatten(3)
+        pos_y = paddle.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), axis=4
+        ).flatten(3)
+
+        pos = paddle.concat((pos_y, pos_x), axis=3).transpose(perm=[0, 3, 1, 2])
+
+        return pos
+
+
+class PositionEmbeddingLearned(nn.Layer):
+    """
+    Absolute pos embedding, learned.
+    """
+
+    def __init__(self, num_pos_feats=256):
+        super().__init__()
+        self.row_embed = nn.Embedding(50, num_pos_feats)
+        self.col_embed = nn.Embedding(50, num_pos_feats)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        init_Constant = init.Uniform()
+        init_Constant(self.row_embed.weight)
+        init_Constant(self.col_embed.weight)
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+
+        h, w = x.shape[-2:]
+
+        i = paddle.arange(end=w)
+        j = paddle.arange(end=h)
+
+        x_emb = self.col_embed(i)
+        y_emb = self.row_embed(j)
+
+        pos = (
+            paddle.concat(
+                [
+                    x_emb.unsqueeze(0).tile([h, 1, 1]),
+                    y_emb.unsqueeze(1).tile([1, w, 1]),
+                ],
+                axis=-1,
+            )
+            .transpose([2, 0, 1])
+            .unsqueeze(0)
+            .tile([x.shape[0], 1, 1, 1])
+        )
+
+        return pos
+
+
+def build_position_encoding(hidden_dim=512, position_embedding="sine"):
+    N_steps = hidden_dim // 2
+
+    if position_embedding in ("v2", "sine"):
+        position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
+    elif position_embedding in ("v3", "learned"):
+        position_embedding = PositionEmbeddingLearned(N_steps)
+    else:
+        raise ValueError(f"not supported {position_embedding}")
+    return position_embedding