125 lines
3.5 KiB
Python
125 lines
3.5 KiB
Python
import math
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from typing import Optional
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import paddle
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import paddle.nn as nn
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import paddle.nn.initializer as init
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class NestedTensor(object):
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def __init__(self, tensors, mask: Optional[paddle.Tensor]):
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self.tensors = tensors
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self.mask = mask
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def decompose(self):
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return self.tensors, self.mask
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def __repr__(self):
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return str(self.tensors)
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class PositionEmbeddingSine(nn.Layer):
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"""
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This is a more standard version of the position embedding, very similar to the one
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used by the Attention is all you need paper, generalized to work on images.
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"""
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def __init__(
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self, num_pos_feats=64, temperature=10000, normalize=False, scale=None
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):
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super().__init__()
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self.num_pos_feats = num_pos_feats
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self.temperature = temperature
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self.normalize = normalize
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if scale is not None and normalize is False:
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raise ValueError("normalize should be True if scale is passed")
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if scale is None:
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scale = 2 * math.pi
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self.scale = scale
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def forward(self, mask):
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assert mask is not None
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y_embed = mask.cumsum(axis=1, dtype="float32")
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x_embed = mask.cumsum(axis=2, dtype="float32")
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if self.normalize:
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eps = 1e-06
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y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
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x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
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dim_t = paddle.arange(end=self.num_pos_feats, dtype="float32")
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dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
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pos_x = x_embed[:, :, :, None] / dim_t
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pos_y = y_embed[:, :, :, None] / dim_t
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pos_x = paddle.stack(
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(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), axis=4
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).flatten(3)
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pos_y = paddle.stack(
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(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), axis=4
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).flatten(3)
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pos = paddle.concat((pos_y, pos_x), axis=3).transpose(perm=[0, 3, 1, 2])
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return pos
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class PositionEmbeddingLearned(nn.Layer):
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"""
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Absolute pos embedding, learned.
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"""
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def __init__(self, num_pos_feats=256):
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super().__init__()
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self.row_embed = nn.Embedding(50, num_pos_feats)
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self.col_embed = nn.Embedding(50, num_pos_feats)
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self.reset_parameters()
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def reset_parameters(self):
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init_Constant = init.Uniform()
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init_Constant(self.row_embed.weight)
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init_Constant(self.col_embed.weight)
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def forward(self, tensor_list: NestedTensor):
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x = tensor_list.tensors
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h, w = x.shape[-2:]
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i = paddle.arange(end=w)
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j = paddle.arange(end=h)
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x_emb = self.col_embed(i)
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y_emb = self.row_embed(j)
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pos = (
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paddle.concat(
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[
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x_emb.unsqueeze(0).tile([h, 1, 1]),
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y_emb.unsqueeze(1).tile([1, w, 1]),
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],
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axis=-1,
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)
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.transpose([2, 0, 1])
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.unsqueeze(0)
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.tile([x.shape[0], 1, 1, 1])
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)
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return pos
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def build_position_encoding(hidden_dim=512, position_embedding="sine"):
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N_steps = hidden_dim // 2
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if position_embedding in ("v2", "sine"):
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position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
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elif position_embedding in ("v3", "learned"):
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position_embedding = PositionEmbeddingLearned(N_steps)
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else:
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raise ValueError(f"not supported {position_embedding}")
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return position_embedding
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