617 lines
21 KiB
Python
617 lines
21 KiB
Python
# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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from functools import partial
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from typing import Optional, Tuple, Type
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import numpy as np
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn.initializer import (
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Constant,
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KaimingUniform,
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Normal,
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TruncatedNormal,
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XavierUniform,
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)
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from ppocr.modeling.backbones.rec_donut_swin import DonutSwinModelOutput
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zeros_ = Constant(value=0.0)
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ones_ = Constant(value=1.0)
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kaiming_normal_ = KaimingUniform(nonlinearity="relu")
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trunc_normal_ = TruncatedNormal(std=0.02)
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xavier_uniform_ = XavierUniform()
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class MLPBlock(nn.Layer):
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def __init__(
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self,
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embedding_dim: int,
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mlp_dim: int,
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act: Type[nn.Layer] = nn.GELU,
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) -> None:
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super().__init__()
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self.lin1 = nn.Linear(embedding_dim, mlp_dim)
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self.lin2 = nn.Linear(mlp_dim, embedding_dim)
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self.act = act()
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def forward(self, x):
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return self.lin2(self.act(self.lin1(x)))
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# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
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# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
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class LayerNorm2d(nn.Layer):
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def __init__(self, num_channels: int, epsilon: float = 1e-6) -> None:
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super().__init__()
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self.weight = paddle.create_parameter([num_channels], dtype="float32")
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ones_(self.weight)
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self.bias = paddle.create_parameter([num_channels], dtype="float32")
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zeros_(self.bias)
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self.epsilon = epsilon
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def forward(self, x):
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u = x.mean(1, keepdim=True)
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s = (x - u).pow(2).mean(1, keepdim=True)
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x = (x - u) / paddle.sqrt(s + self.epsilon)
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x = self.weight[:, None, None] * x + self.bias[:, None, None]
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return x
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# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
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class ImageEncoderViT(nn.Layer):
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def __init__(
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self,
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img_size: int = 1024,
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patch_size: int = 16,
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in_chans: int = 3,
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embed_dim: int = 768,
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depth: int = 12,
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num_heads: int = 12,
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mlp_ratio: float = 4.0,
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out_chans: int = 256,
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qkv_bias: bool = True,
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norm_layer: Type[nn.Layer] = nn.LayerNorm,
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act_layer: Type[nn.Layer] = nn.GELU,
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use_abs_pos: bool = True,
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use_rel_pos: bool = False,
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rel_pos_zero_init: bool = True,
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window_size: int = 0,
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global_attn_indexes: Tuple[int, ...] = (),
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is_formula: bool = False,
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) -> None:
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"""
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Args:
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img_size (int): Input image size.
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patch_size (int): Patch size.
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in_chans (int): Number of input image channels.
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embed_dim (int): Patch embedding dimension.
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depth (int): Depth of ViT.
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num_heads (int): Number of attention heads in each ViT block.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool): If True, add a learnable bias to query, key, value.
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norm_layer (nn.Layer): Normalization layer.
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act_layer (nn.Layer): Activation layer.
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use_abs_pos (bool): If True, use absolute positional embeddings.
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use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
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rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
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window_size (int): Window size for window attention blocks.
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global_attn_indexes (list): Indexes for blocks using global attention.
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"""
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super().__init__()
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self.img_size = img_size
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self.patch_embed = PatchEmbed(
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kernel_size=(patch_size, patch_size),
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stride=(patch_size, patch_size),
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in_chans=in_chans,
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embed_dim=embed_dim,
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)
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self.pos_embed = None
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if use_abs_pos:
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# Initialize absolute positional embedding with pretrain image size.
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self.pos_embed = paddle.create_parameter(
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shape=(1, img_size // patch_size, img_size // patch_size, embed_dim),
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dtype="float32",
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)
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zeros_(self.pos_embed)
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self.blocks = nn.LayerList()
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for i in range(depth):
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block = Block(
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dim=embed_dim,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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norm_layer=norm_layer,
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act_layer=act_layer,
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use_rel_pos=use_rel_pos,
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rel_pos_zero_init=rel_pos_zero_init,
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window_size=window_size if i not in global_attn_indexes else 0,
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input_size=(img_size // patch_size, img_size // patch_size),
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)
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self.blocks.append(block)
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self.neck = nn.Sequential(
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nn.Conv2D(
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embed_dim,
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out_chans,
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kernel_size=1,
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bias_attr=False,
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),
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LayerNorm2d(out_chans),
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nn.Conv2D(
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out_chans,
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out_chans,
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kernel_size=3,
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padding=1,
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bias_attr=False,
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),
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LayerNorm2d(out_chans),
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)
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self.net_2 = nn.Conv2D(
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256, 512, kernel_size=3, stride=2, padding=1, bias_attr=False
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)
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self.net_3 = nn.Conv2D(
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512, 1024, kernel_size=3, stride=2, padding=1, bias_attr=False
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)
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self.is_formula = is_formula
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def forward(self, x):
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x = self.patch_embed(x)
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if self.pos_embed is not None:
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x = x + self.pos_embed
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for blk in self.blocks:
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x = blk(x)
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x = self.neck(x.transpose([0, 3, 1, 2]))
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x = self.net_2(x)
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if self.is_formula:
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x = self.net_3(x)
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return x
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class Block(nn.Layer):
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"""Transformer blocks with support of window attention and residual propagation blocks"""
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def __init__(
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self,
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dim: int,
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num_heads: int,
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mlp_ratio: float = 4.0,
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qkv_bias: bool = True,
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norm_layer: Type[nn.Layer] = nn.LayerNorm,
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act_layer: Type[nn.Layer] = nn.GELU,
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use_rel_pos: bool = False,
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rel_pos_zero_init: bool = True,
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window_size: int = 0,
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input_size: Optional[Tuple[int, int]] = None,
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) -> None:
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"""
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Args:
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dim (int): Number of input channels.
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num_heads (int): Number of attention heads in each ViT block.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool): If True, add a learnable bias to query, key, value.
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norm_layer (nn.Layer): Normalization layer.
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act_layer (nn.Layer): Activation layer.
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use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
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rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
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window_size (int): Window size for window attention blocks. If it equals 0, then
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use global attention.
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input_size (tuple(int, int) or None): Input resolution for calculating the relative
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positional parameter size.
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"""
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim,
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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use_rel_pos=use_rel_pos,
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rel_pos_zero_init=rel_pos_zero_init,
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input_size=input_size if window_size == 0 else (window_size, window_size),
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)
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self.norm2 = norm_layer(dim)
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self.mlp = MLPBlock(
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embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer
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)
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self.window_size = window_size
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def forward(self, x):
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shortcut = x
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x = self.norm1(x)
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# Window partition
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if self.window_size > 0:
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H, W = x.shape[1], x.shape[2]
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x, pad_hw = window_partition(x, self.window_size)
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x = self.attn(x)
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# Reverse window partition
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if self.window_size > 0:
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x = window_unpartition(x, self.window_size, pad_hw, (H, W))
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x = shortcut + x
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x = x + self.mlp(self.norm2(x))
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return x
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class Attention(nn.Layer):
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"""Multi-head Attention block with relative position embeddings."""
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def __init__(
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self,
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dim: int,
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num_heads: int = 8,
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qkv_bias: bool = True,
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use_rel_pos: bool = False,
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rel_pos_zero_init: bool = True,
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input_size: Optional[Tuple[int, int]] = None,
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) -> None:
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"""
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Args:
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dim (int): Number of input channels.
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num_heads (int): Number of attention heads.
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qkv_bias (bool): If True, add a learnable bias to query, key, value.
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rel_pos (bool): If True, add relative positional embeddings to the attention map.
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rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
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input_size (tuple(int, int) or None): Input resolution for calculating the relative
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positional parameter size.
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"""
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = head_dim**-0.5
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self.qkv = nn.Linear(dim, dim * 3, bias_attr=qkv_bias)
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self.proj = nn.Linear(dim, dim)
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self.use_rel_pos = use_rel_pos
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if self.use_rel_pos:
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assert (
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input_size is not None
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), "Input size must be provided if using relative positional encoding."
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# initialize relative positional embeddings
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self.rel_pos_h = paddle.create_parameter(
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[2 * input_size[0] - 1, head_dim], dtype="float32"
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)
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zeros_(self.rel_pos_h)
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self.rel_pos_w = paddle.create_parameter(
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[2 * input_size[1] - 1, head_dim], dtype="float32"
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)
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zeros_(self.rel_pos_w)
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def forward(self, x):
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B, H, W, _ = x.shape
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qkv = (
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self.qkv(x)
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.reshape([B, H * W, 3, self.num_heads, -1])
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.transpose([2, 0, 3, 1, 4])
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)
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q, k, v = qkv.reshape([3, B * self.num_heads, H * W, -1]).unbind(0)
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attn = (q * self.scale) @ k.transpose([0, 2, 1])
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if self.use_rel_pos:
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attn = add_decomposed_rel_pos(
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attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)
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)
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attn = F.softmax(attn, axis=-1)
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x = (
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(attn @ v)
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.reshape([B, self.num_heads, H, W, -1])
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.transpose([0, 2, 3, 1, 4])
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.reshape([B, H, W, -1])
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)
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x = self.proj(x)
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return x
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def window_partition(x, window_size: int):
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"""
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Partition into non-overlapping windows with padding if needed.
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Args:
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x (tensor): input tokens with [B, H, W, C].
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window_size (int): window size.
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Returns:
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windows: windows after partition with [B * num_windows, window_size, window_size, C].
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(Hp, Wp): padded height and width before partition
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"""
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B, H, W, C = x.shape
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pad_h = (window_size - H % window_size) % window_size
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pad_w = (window_size - W % window_size) % window_size
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if pad_h > 0 or pad_w > 0:
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x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h, 0, 0))
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Hp, Wp = H + pad_h, W + pad_w
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x = x.reshape(
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[B, Hp // window_size, window_size, Wp // window_size, window_size, C]
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)
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windows = x.transpose([0, 1, 3, 2, 4, 5]).reshape([-1, window_size, window_size, C])
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return windows, (Hp, Wp)
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def window_unpartition(
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windows, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
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):
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"""
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Window unpartition into original sequences and removing padding.
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Args:
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windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
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window_size (int): window size.
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pad_hw (Tuple): padded height and width (Hp, Wp).
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hw (Tuple): original height and width (H, W) before padding.
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Returns:
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x: unpartitioned sequences with [B, H, W, C].
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"""
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Hp, Wp = pad_hw
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H, W = hw
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B = windows.shape[0] // (Hp * Wp // window_size // window_size)
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x = windows.reshape(
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[B, Hp // window_size, Wp // window_size, window_size, window_size, -1]
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)
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x = x.transpose([0, 1, 3, 2, 4, 5]).contiguous().reshape([B, Hp, Wp, -1])
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if Hp > H or Wp > W:
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x = x[:, :H, :W, :].contiguous()
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return x
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def get_rel_pos(q_size: int, k_size: int, rel_pos):
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"""
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Get relative positional embeddings according to the relative positions of
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query and key sizes.
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Args:
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q_size (int): size of query q.
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k_size (int): size of key k.
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rel_pos (Tensor): relative position embeddings (L, C).
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Returns:
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Extracted positional embeddings according to relative positions.
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"""
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max_rel_dist = int(2 * max(q_size, k_size) - 1)
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# Interpolate rel pos if needed.
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if rel_pos.shape[0] != max_rel_dist:
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# Interpolate rel pos.
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rel_pos_resized = F.interpolate(
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rel_pos.reshape(1, rel_pos.shape[0], -1).transpose(0, 2, 1),
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size=max_rel_dist,
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mode="linear",
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)
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rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).transpose(1, 0)
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else:
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rel_pos_resized = rel_pos
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# Scale the coords with short length if shapes for q and k are different.
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q_coords = paddle.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
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k_coords = paddle.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
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relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
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return rel_pos_resized[relative_coords.cast(paddle.int64)]
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def add_decomposed_rel_pos(
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attn,
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q,
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rel_pos_h,
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rel_pos_w,
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q_size: Tuple[int, int],
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k_size: Tuple[int, int],
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):
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"""
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Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
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https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
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Args:
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attn (Tensor): attention map.
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q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
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rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
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rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
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q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
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k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
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Returns:
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attn (Tensor): attention map with added relative positional embeddings.
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"""
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q_h, q_w = q_size
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k_h, k_w = k_size
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Rh = get_rel_pos(q_h, k_h, rel_pos_h)
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Rw = get_rel_pos(q_w, k_w, rel_pos_w)
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B, _, dim = q.shape
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r_q = q.reshape([B, q_h, q_w, dim])
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rel_h = paddle.einsum("bhwc,hkc->bhwk", r_q, Rh)
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rel_w = paddle.einsum("bhwc,wkc->bhwk", r_q, Rw)
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attn = (
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attn.reshape([B, q_h, q_w, k_h, k_w])
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+ rel_h[:, :, :, :, None]
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+ rel_w[:, :, :, None, :]
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).reshape([B, q_h * q_w, k_h * k_w])
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return attn
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class PatchEmbed(nn.Layer):
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"""
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Image to Patch Embedding.
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"""
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def __init__(
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self,
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kernel_size: Tuple[int, int] = (16, 16),
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stride: Tuple[int, int] = (16, 16),
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padding: Tuple[int, int] = (0, 0),
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in_chans: int = 3,
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embed_dim: int = 768,
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) -> None:
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"""
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Args:
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kernel_size (Tuple): kernel size of the projection layer.
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|
stride (Tuple): stride of the projection layer.
|
|
padding (Tuple): padding size of the projection layer.
|
|
in_chans (int): Number of input image channels.
|
|
embed_dim (int): Patch embedding dimension.
|
|
"""
|
|
super().__init__()
|
|
|
|
self.proj = nn.Conv2D(
|
|
in_chans,
|
|
embed_dim,
|
|
kernel_size=kernel_size,
|
|
stride=stride,
|
|
padding=padding,
|
|
weight_attr=True,
|
|
bias_attr=True,
|
|
)
|
|
|
|
def forward(self, x):
|
|
x = self.proj(x)
|
|
# B C H W -> B H W C
|
|
x = x.transpose([0, 2, 3, 1])
|
|
return x
|
|
|
|
|
|
def _build_vary(
|
|
encoder_embed_dim,
|
|
encoder_depth,
|
|
encoder_num_heads,
|
|
encoder_global_attn_indexes,
|
|
image_size,
|
|
is_formula=False,
|
|
):
|
|
prompt_embed_dim = 256
|
|
vit_patch_size = 16
|
|
image_embedding_size = image_size // vit_patch_size
|
|
image_encoder = ImageEncoderViT(
|
|
depth=encoder_depth,
|
|
embed_dim=encoder_embed_dim,
|
|
img_size=image_size,
|
|
mlp_ratio=4,
|
|
norm_layer=partial(paddle.nn.LayerNorm, epsilon=1e-6),
|
|
num_heads=encoder_num_heads,
|
|
patch_size=vit_patch_size,
|
|
qkv_bias=True,
|
|
use_rel_pos=True,
|
|
global_attn_indexes=encoder_global_attn_indexes,
|
|
window_size=14,
|
|
out_chans=prompt_embed_dim,
|
|
is_formula=is_formula,
|
|
)
|
|
return image_encoder
|
|
|
|
|
|
class Vary_VIT_B(nn.Layer):
|
|
def __init__(
|
|
self,
|
|
in_channels=3,
|
|
image_size=768,
|
|
encoder_embed_dim=768,
|
|
encoder_depth=12,
|
|
encoder_num_heads=12,
|
|
encoder_global_attn_indexes=[2, 5, 8, 11],
|
|
):
|
|
super().__init__()
|
|
|
|
self.vision_tower_high = _build_vary(
|
|
encoder_embed_dim=768,
|
|
encoder_depth=12,
|
|
encoder_num_heads=12,
|
|
encoder_global_attn_indexes=[2, 5, 8, 11],
|
|
image_size=image_size,
|
|
)
|
|
|
|
self.out_channels = 1024
|
|
|
|
def forward(self, input_data):
|
|
pixel_values = input_data
|
|
num_channels = pixel_values.shape[1]
|
|
if num_channels == 1:
|
|
pixel_values = paddle.repeat_interleave(pixel_values, repeats=3, axis=1)
|
|
cnn_feature = self.vision_tower_high(pixel_values)
|
|
cnn_feature = cnn_feature.flatten(2).transpose([0, 2, 1])
|
|
return cnn_feature
|
|
|
|
|
|
class Vary_VIT_B_Formula(nn.Layer):
|
|
def __init__(
|
|
self,
|
|
in_channels=3,
|
|
image_size=768,
|
|
encoder_embed_dim=768,
|
|
encoder_depth=12,
|
|
encoder_num_heads=12,
|
|
encoder_global_attn_indexes=[2, 5, 8, 11],
|
|
):
|
|
"""
|
|
Vary_VIT_B_Formula
|
|
Args:
|
|
in_channels (int): Number of input channels. Default is 3 (for RGB images).
|
|
image_size (int): Size of the input image. Default is 768.
|
|
encoder_embed_dim (int): Dimension of the encoder's embedding. Default is 768.
|
|
encoder_depth (int): Number of layers (depth) in the encoder. Default is 12.
|
|
encoder_num_heads (int): Number of attention heads in the encoder. Default is 12.
|
|
encoder_global_attn_indexes (list): List of indices specifying which encoder layers use global attention. Default is [2, 5, 8, 11].
|
|
Returns:
|
|
model: nn.Layer. Specific `Vary_VIT_B_Formula` model with defined architecture.
|
|
"""
|
|
super(Vary_VIT_B_Formula, self).__init__()
|
|
|
|
self.vision_tower_high = _build_vary(
|
|
encoder_embed_dim=encoder_embed_dim,
|
|
encoder_depth=encoder_depth,
|
|
encoder_num_heads=encoder_num_heads,
|
|
encoder_global_attn_indexes=[2, 5, 8, 11],
|
|
image_size=image_size,
|
|
is_formula=True,
|
|
)
|
|
self.mm_projector_vary = nn.Linear(1024, 1024)
|
|
self.out_channels = 1024
|
|
|
|
def forward(self, input_data):
|
|
if self.training:
|
|
pixel_values, label, attention_mask = input_data
|
|
else:
|
|
if isinstance(input_data, list):
|
|
pixel_values = input_data[0]
|
|
else:
|
|
pixel_values = input_data
|
|
num_channels = pixel_values.shape[1]
|
|
if num_channels == 1:
|
|
pixel_values = paddle.repeat_interleave(pixel_values, repeats=3, axis=1)
|
|
|
|
cnn_feature = self.vision_tower_high(pixel_values)
|
|
cnn_feature = cnn_feature.flatten(2).transpose([0, 2, 1])
|
|
|
|
cnn_feature = self.mm_projector_vary(cnn_feature)
|
|
donut_swin_output = DonutSwinModelOutput(
|
|
last_hidden_state=cnn_feature,
|
|
pooler_output=None,
|
|
hidden_states=None,
|
|
attentions=None,
|
|
reshaped_hidden_states=None,
|
|
)
|
|
if self.training:
|
|
return donut_swin_output, label, attention_mask
|
|
else:
|
|
return donut_swin_output
|