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ppocr/modeling/backbones/rec_hybridvit.py
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529
ppocr/modeling/backbones/rec_hybridvit.py
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# 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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"""
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This code is refer from:
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https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer_hybrid.py
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from itertools import repeat
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import collections
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import math
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from functools import partial
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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 ppocr.modeling.backbones.rec_resnetv2 import (
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ResNetV2,
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StdConv2dSame,
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DropPath,
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get_padding,
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)
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from paddle.nn.initializer import (
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TruncatedNormal,
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Constant,
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Normal,
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KaimingUniform,
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XavierUniform,
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)
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normal_ = Normal(mean=0.0, std=1e-6)
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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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def _ntuple(n):
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def parse(x):
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if isinstance(x, collections.abc.Iterable):
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return x
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return tuple(repeat(x, n))
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return parse
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to_1tuple = _ntuple(1)
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to_2tuple = _ntuple(2)
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to_3tuple = _ntuple(3)
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to_4tuple = _ntuple(4)
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to_ntuple = _ntuple
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class Conv2dAlign(nn.Conv2D):
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"""Conv2d with Weight Standardization. Used for BiT ResNet-V2 models.
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Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` -
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https://arxiv.org/abs/1903.10520v2
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"""
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def __init__(
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self,
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in_channel,
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out_channels,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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bias=True,
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eps=1e-6,
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):
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super().__init__(
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in_channel,
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out_channels,
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kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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bias_attr=bias,
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weight_attr=True,
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)
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self.eps = eps
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def forward(self, x):
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x = F.conv2d(
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x,
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self.weight,
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self.bias,
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self._stride,
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self._padding,
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self._dilation,
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self._groups,
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)
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return x
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class HybridEmbed(nn.Layer):
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"""CNN Feature Map Embedding
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Extract feature map from CNN, flatten, project to embedding dim.
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"""
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def __init__(
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self,
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backbone,
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img_size=224,
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patch_size=1,
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feature_size=None,
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in_chans=3,
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embed_dim=768,
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):
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super().__init__()
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assert isinstance(backbone, nn.Layer)
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img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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self.img_size = img_size
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self.patch_size = patch_size
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self.backbone = backbone
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feature_dim = 1024
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feature_size = (42, 12)
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patch_size = (1, 1)
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assert (
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feature_size[0] % patch_size[0] == 0
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and feature_size[1] % patch_size[1] == 0
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)
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self.grid_size = (
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feature_size[0] // patch_size[0],
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feature_size[1] // patch_size[1],
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)
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self.num_patches = self.grid_size[0] * self.grid_size[1]
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self.proj = nn.Conv2D(
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feature_dim,
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embed_dim,
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kernel_size=patch_size,
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stride=patch_size,
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weight_attr=True,
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bias_attr=True,
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)
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def forward(self, x):
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x = self.backbone(x)
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if isinstance(x, (list, tuple)):
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x = x[-1] # last feature if backbone outputs list/tuple of features
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x = self.proj(x).flatten(2).transpose([0, 2, 1])
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return x
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class myLinear(nn.Linear):
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def __init__(self, in_channel, out_channels, weight_attr=True, bias_attr=True):
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super().__init__(
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in_channel, out_channels, weight_attr=weight_attr, bias_attr=bias_attr
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)
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def forward(self, x):
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return paddle.matmul(x, self.weight, transpose_y=True) + self.bias
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class Attention(nn.Layer):
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def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0):
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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.attn_drop = nn.Dropout(attn_drop)
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self.proj = myLinear(dim, dim, weight_attr=True, bias_attr=True)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x):
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B, N, C = x.shape
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qkv = (
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self.qkv(x)
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.reshape([B, N, 3, self.num_heads, C // self.num_heads])
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.transpose([2, 0, 3, 1, 4])
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)
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q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
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attn = (q @ k.transpose([0, 1, 3, 2])) * self.scale
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attn = F.softmax(attn, axis=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose([0, 2, 1, 3]).reshape([B, N, C])
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class Mlp(nn.Layer):
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"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
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def __init__(
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self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.GELU,
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drop=0.0,
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):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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drop_probs = to_2tuple(drop)
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.drop1 = nn.Dropout(drop_probs[0])
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop2 = nn.Dropout(drop_probs[1])
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop1(x)
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x = self.fc2(x)
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x = self.drop2(x)
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return x
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class Block(nn.Layer):
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def __init__(
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self,
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dim,
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num_heads,
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mlp_ratio=4.0,
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qkv_bias=False,
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drop=0.0,
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attn_drop=0.0,
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drop_path=0.0,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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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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attn_drop=attn_drop,
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proj_drop=drop,
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)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(
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in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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drop=drop,
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)
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def forward(self, x):
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x = x + self.drop_path(self.attn(self.norm1(x)))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class HybridTransformer(nn.Layer):
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"""Implementation of HybridTransformer.
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Args:
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x: input images with shape [N, 1, H, W]
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label: LaTeX-OCR labels with shape [N, L] , L is the max sequence length
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attention_mask: LaTeX-OCR attention mask with shape [N, L] , L is the max sequence length
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Returns:
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The encoded features with shape [N, 1, H//16, W//16]
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"""
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def __init__(
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self,
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backbone_layers=[2, 3, 7],
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input_channel=1,
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is_predict=False,
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is_export=False,
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img_size=(224, 224),
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patch_size=16,
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num_classes=1000,
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embed_dim=768,
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depth=12,
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num_heads=12,
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mlp_ratio=4.0,
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qkv_bias=True,
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representation_size=None,
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distilled=False,
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drop_rate=0.0,
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attn_drop_rate=0.0,
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drop_path_rate=0.0,
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embed_layer=None,
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norm_layer=None,
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act_layer=None,
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weight_init="",
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**kwargs,
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):
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super(HybridTransformer, self).__init__()
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self.num_classes = num_classes
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self.num_features = self.embed_dim = (
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embed_dim # num_features for consistency with other models
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)
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self.num_tokens = 2 if distilled else 1
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norm_layer = norm_layer or partial(nn.LayerNorm, epsilon=1e-6)
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act_layer = act_layer or nn.GELU
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self.height, self.width = img_size
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self.patch_size = patch_size
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backbone = ResNetV2(
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layers=backbone_layers,
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num_classes=0,
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global_pool="",
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in_chans=input_channel,
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preact=False,
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stem_type="same",
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conv_layer=StdConv2dSame,
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is_export=is_export,
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)
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min_patch_size = 2 ** (len(backbone_layers) + 1)
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self.patch_embed = HybridEmbed(
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img_size=img_size,
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patch_size=patch_size // min_patch_size,
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in_chans=input_channel,
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embed_dim=embed_dim,
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backbone=backbone,
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)
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num_patches = self.patch_embed.num_patches
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self.cls_token = paddle.create_parameter([1, 1, embed_dim], dtype="float32")
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self.dist_token = (
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paddle.create_parameter(
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[1, 1, embed_dim],
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dtype="float32",
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)
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if distilled
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else None
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)
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self.pos_embed = paddle.create_parameter(
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[1, num_patches + self.num_tokens, embed_dim], dtype="float32"
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)
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self.pos_drop = nn.Dropout(p=drop_rate)
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zeros_(self.cls_token)
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if self.dist_token is not None:
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zeros_(self.dist_token)
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zeros_(self.pos_embed)
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dpr = [
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x.item() for x in paddle.linspace(0, drop_path_rate, depth)
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] # stochastic depth decay rule
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self.blocks = nn.Sequential(
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*[
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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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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i],
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norm_layer=norm_layer,
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act_layer=act_layer,
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)
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for i in range(depth)
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]
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)
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self.norm = norm_layer(embed_dim)
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# Representation layer
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if representation_size and not distilled:
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self.num_features = representation_size
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self.pre_logits = nn.Sequential(
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("fc", nn.Linear(embed_dim, representation_size)), ("act", nn.Tanh())
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)
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else:
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self.pre_logits = nn.Identity()
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# Classifier head(s)
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self.head = (
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nn.Linear(self.num_features, num_classes)
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if num_classes > 0
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else nn.Identity()
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)
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self.head_dist = None
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if distilled:
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self.head_dist = (
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nn.Linear(self.embed_dim, self.num_classes)
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if num_classes > 0
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else nn.Identity()
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)
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self.init_weights(weight_init)
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self.out_channels = embed_dim
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self.is_predict = is_predict
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self.is_export = is_export
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def init_weights(self, mode=""):
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assert mode in ("jax", "jax_nlhb", "nlhb", "")
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head_bias = -math.log(self.num_classes) if "nlhb" in mode else 0.0
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trunc_normal_(self.pos_embed)
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trunc_normal_(self.cls_token)
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self.apply(_init_vit_weights)
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def _init_weights(self, m):
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# this fn left here for compat with downstream users
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_init_vit_weights(m)
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def load_pretrained(self, checkpoint_path, prefix=""):
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raise NotImplementedError
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||||
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||||
def no_weight_decay(self):
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return {"pos_embed", "cls_token", "dist_token"}
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||||
def get_classifier(self):
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if self.dist_token is None:
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return self.head
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else:
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return self.head, self.head_dist
|
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||||
def reset_classifier(self, num_classes, global_pool=""):
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self.num_classes = num_classes
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self.head = (
|
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nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
||||
)
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if self.num_tokens == 2:
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self.head_dist = (
|
||||
nn.Linear(self.embed_dim, self.num_classes)
|
||||
if num_classes > 0
|
||||
else nn.Identity()
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||||
)
|
||||
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def forward_features(self, x):
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B, c, h, w = x.shape
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x = self.patch_embed(x)
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cls_tokens = self.cls_token.expand(
|
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[B, -1, -1]
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) # stole cls_tokens impl from Phil Wang, thanks
|
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x = paddle.concat((cls_tokens, x), axis=1)
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h, w = h // self.patch_size, w // self.patch_size
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repeat_tensor = (
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paddle.arange(h) * (self.width // self.patch_size - w)
|
||||
).reshape([-1, 1])
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||||
repeat_tensor = paddle.repeat_interleave(
|
||||
repeat_tensor, paddle.to_tensor(w), axis=1
|
||||
).reshape([-1])
|
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pos_emb_ind = repeat_tensor + paddle.arange(h * w)
|
||||
pos_emb_ind = paddle.concat(
|
||||
(paddle.zeros([1], dtype="int64"), pos_emb_ind + 1), axis=0
|
||||
).cast(paddle.int64)
|
||||
x += self.pos_embed[:, pos_emb_ind]
|
||||
x = self.pos_drop(x)
|
||||
|
||||
for blk in self.blocks:
|
||||
x = blk(x)
|
||||
|
||||
x = self.norm(x)
|
||||
return x
|
||||
|
||||
def forward(self, input_data):
|
||||
|
||||
if self.training:
|
||||
x, label, attention_mask = input_data
|
||||
else:
|
||||
if isinstance(input_data, list):
|
||||
x = input_data[0]
|
||||
else:
|
||||
x = input_data
|
||||
x = self.forward_features(x)
|
||||
x = self.head(x)
|
||||
if self.training:
|
||||
return x, label, attention_mask
|
||||
else:
|
||||
return x
|
||||
|
||||
|
||||
def _init_vit_weights(
|
||||
module: nn.Layer, name: str = "", head_bias: float = 0.0, jax_impl: bool = False
|
||||
):
|
||||
"""ViT weight initialization
|
||||
* When called without n, head_bias, jax_impl args it will behave exactly the same
|
||||
as my original init for compatibility with prev hparam / downstream use cases (ie DeiT).
|
||||
* When called w/ valid n (module name) and jax_impl=True, will (hopefully) match JAX impl
|
||||
"""
|
||||
if isinstance(module, nn.Linear):
|
||||
if name.startswith("head"):
|
||||
zeros_(module.weight)
|
||||
constant_ = Constant(value=head_bias)
|
||||
constant_(module.bias, head_bias)
|
||||
elif name.startswith("pre_logits"):
|
||||
zeros_(module.bias)
|
||||
else:
|
||||
if jax_impl:
|
||||
xavier_uniform_(module.weight)
|
||||
if module.bias is not None:
|
||||
if "mlp" in name:
|
||||
normal_(module.bias)
|
||||
else:
|
||||
zeros_(module.bias)
|
||||
else:
|
||||
trunc_normal_(module.weight)
|
||||
if module.bias is not None:
|
||||
zeros_(module.bias)
|
||||
elif jax_impl and isinstance(module, nn.Conv2D):
|
||||
# NOTE conv was left to pytorch default in my original init
|
||||
if module.bias is not None:
|
||||
zeros_(module.bias)
|
||||
elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2D)):
|
||||
zeros_(module.bias)
|
||||
ones_(module.weight)
|
||||
Reference in New Issue
Block a user