mirror of https://github.com/alibaba/EasyCV.git
840 lines
28 KiB
Python
840 lines
28 KiB
Python
# Copyright 2018-2023 OpenMMLab. All rights reserved.
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# Reference: https://github.com/ViTAE-Transformer/ViTDet/blob/main/mmdet/models/backbones/vit.py
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import math
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from functools import partial
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint as checkpoint
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from mmcv.cnn import build_norm_layer, constant_init, kaiming_init
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from mmcv.runner import get_dist_info
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from timm.models.layers import drop_path, to_2tuple, trunc_normal_
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from torch.nn.modules.batchnorm import _BatchNorm
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from easycv.utils.checkpoint import load_checkpoint
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from easycv.utils.logger import get_root_logger
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from ..registry import BACKBONES
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from ..utils import build_conv_layer
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class DropPath(nn.Module):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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"""
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def __init__(self, drop_prob=None):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training)
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def extra_repr(self):
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return 'p={}'.format(self.drop_prob)
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class Mlp(nn.Module):
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def __init__(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.):
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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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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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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.drop(x)
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# commit this for the orignal BERT implement
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x = self.fc2(x)
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x = self.drop(x)
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return x
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self,
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inplanes,
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planes,
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stride=1,
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dilation=1,
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conv_cfg=None,
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norm_cfg=dict(type='BN')):
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super(BasicBlock, self).__init__()
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self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
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self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
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self.conv1 = build_conv_layer(
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conv_cfg,
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inplanes,
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planes,
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3,
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stride=stride,
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padding=dilation,
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dilation=dilation,
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bias=False)
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self.add_module(self.norm1_name, norm1)
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self.conv2 = build_conv_layer(
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conv_cfg, planes, planes, 3, padding=1, bias=False)
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self.add_module(self.norm2_name, norm2)
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self.relu = nn.ReLU(inplace=True)
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self.stride = stride
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self.dilation = dilation
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@property
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def norm1(self):
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return getattr(self, self.norm1_name)
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@property
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def norm2(self):
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return getattr(self, self.norm2_name)
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def forward(self, x, H, W):
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B, _, C = x.shape
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x = x.permute(0, 2, 1).reshape(B, -1, H, W)
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identity = x
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out = self.conv1(x)
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out = self.norm1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.norm2(out)
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out += identity
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out = self.relu(out)
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out = out.flatten(2).transpose(1, 2)
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return out
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self,
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inplanes,
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planes,
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stride=1,
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dilation=1,
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conv_cfg=None,
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norm_cfg=dict(type='BN')):
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"""Bottleneck block for ResNet.
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If style is "pytorch", the stride-two layer is the 3x3 conv layer,
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if it is "caffe", the stride-two layer is the first 1x1 conv layer.
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"""
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super(Bottleneck, self).__init__()
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self.inplanes = inplanes
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self.planes = planes
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self.stride = stride
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self.dilation = dilation
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.conv1_stride = 1
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self.conv2_stride = stride
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self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
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self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
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self.norm3_name, norm3 = build_norm_layer(
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norm_cfg, planes * self.expansion, postfix=3)
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self.conv1 = build_conv_layer(
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conv_cfg,
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inplanes,
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planes,
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kernel_size=1,
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stride=self.conv1_stride,
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bias=False)
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self.add_module(self.norm1_name, norm1)
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self.conv2 = build_conv_layer(
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conv_cfg,
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planes,
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planes,
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kernel_size=3,
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stride=self.conv2_stride,
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padding=dilation,
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dilation=dilation,
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bias=False)
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self.add_module(self.norm2_name, norm2)
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self.conv3 = build_conv_layer(
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conv_cfg,
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planes,
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planes * self.expansion,
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kernel_size=1,
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bias=False)
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self.add_module(self.norm3_name, norm3)
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self.relu = nn.ReLU(inplace=True)
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@property
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def norm1(self):
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return getattr(self, self.norm1_name)
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@property
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def norm2(self):
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return getattr(self, self.norm2_name)
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@property
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def norm3(self):
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return getattr(self, self.norm3_name)
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def forward(self, x, H, W):
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B, _, C = x.shape
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x = x.permute(0, 2, 1).reshape(B, -1, H, W)
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identity = x
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out = self.conv1(x)
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out = self.norm1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.norm2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.norm3(out)
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out += identity
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out = self.relu(out)
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out = out.flatten(2).transpose(1, 2)
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return out
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class Attention(nn.Module):
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def __init__(self,
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dim,
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num_heads=8,
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qkv_bias=False,
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qk_scale=None,
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attn_drop=0.,
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proj_drop=0.,
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window_size=None,
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attn_head_dim=None):
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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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if attn_head_dim is not None:
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head_dim = attn_head_dim
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all_head_dim = head_dim * self.num_heads
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# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
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self.scale = qk_scale or head_dim**-0.5
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self.qkv = nn.Linear(dim, all_head_dim * 3, bias=qkv_bias)
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self.window_size = window_size
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q_size = window_size[0]
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kv_size = q_size
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rel_sp_dim = 2 * q_size - 1
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self.rel_pos_h = nn.Parameter(torch.zeros(rel_sp_dim, head_dim))
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self.rel_pos_w = nn.Parameter(torch.zeros(rel_sp_dim, head_dim))
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(all_head_dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x, H, W, rel_pos_bias=None):
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B, N, C = x.shape
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# qkv_bias = None
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# if self.q_bias is not None:
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# qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
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# qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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qkv = self.qkv(x)
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qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[
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2] # make torchscript happy (cannot use tensor as tuple)
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q = q * self.scale
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attn = (q @ k.transpose(-2, -1))
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attn = calc_rel_pos_spatial(attn, q, self.window_size,
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self.window_size, self.rel_pos_h,
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self.rel_pos_w)
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# if self.relative_position_bias_table is not None:
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# relative_position_bias = \
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# self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
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# self.window_size[0] * self.window_size[1] + 1,
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# self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
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# relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
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# attn = attn + relative_position_bias.unsqueeze(0)
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# if rel_pos_bias is not None:
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# attn = attn + rel_pos_bias
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
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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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def window_partition(x, window_size):
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"""
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Args:
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x: (B, H, W, C)
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window_size (int): window size
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Returns:
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windows: (num_windows*B, window_size, window_size, C)
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"""
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B, H, W, C = x.shape
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x = x.view(B, H // window_size, window_size, W // window_size, window_size,
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C)
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windows = x.permute(0, 1, 3, 2, 4,
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5).contiguous().view(-1, window_size, window_size, C)
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return windows
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def window_reverse(windows, window_size, H, W):
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"""
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Args:
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windows: (num_windows*B, window_size, window_size, C)
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window_size (int): Window size
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H (int): Height of image
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W (int): Width of image
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Returns:
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x: (B, H, W, C)
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"""
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B = int(windows.shape[0] / (H * W / window_size / window_size))
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x = windows.view(B, H // window_size, W // window_size, window_size,
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window_size, -1)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
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return x
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def calc_rel_pos_spatial(
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attn,
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q,
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q_shape,
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k_shape,
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rel_pos_h,
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rel_pos_w,
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):
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"""
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Spatial Relative Positional Embeddings.
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"""
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sp_idx = 0
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q_h, q_w = q_shape
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k_h, k_w = k_shape
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# Scale up rel pos if shapes for q and k are different.
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q_h_ratio = max(k_h / q_h, 1.0)
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k_h_ratio = max(q_h / k_h, 1.0)
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dist_h = (
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torch.arange(q_h)[:, None] * q_h_ratio -
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torch.arange(k_h)[None, :] * k_h_ratio)
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dist_h += (k_h - 1) * k_h_ratio
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q_w_ratio = max(k_w / q_w, 1.0)
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k_w_ratio = max(q_w / k_w, 1.0)
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dist_w = (
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torch.arange(q_w)[:, None] * q_w_ratio -
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torch.arange(k_w)[None, :] * k_w_ratio)
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dist_w += (k_w - 1) * k_w_ratio
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Rh = rel_pos_h[dist_h.long()]
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Rw = rel_pos_w[dist_w.long()]
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B, n_head, q_N, dim = q.shape
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r_q = q[:, :, sp_idx:].reshape(B, n_head, q_h, q_w, dim)
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rel_h = torch.einsum('byhwc,hkc->byhwk', r_q, Rh)
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rel_w = torch.einsum('byhwc,wkc->byhwk', r_q, Rw)
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attn[:, :, sp_idx:, sp_idx:] = (
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attn[:, :, sp_idx:, sp_idx:].view(B, -1, q_h, q_w, k_h, k_w) +
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rel_h[:, :, :, :, :, None] + rel_w[:, :, :, :, None, :]).view(
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B, -1, q_h * q_w, k_h * k_w)
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return attn
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class WindowAttention(nn.Module):
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""" Window based multi-head self attention (W-MSA) module with relative position bias.
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It supports both of shifted and non-shifted window.
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Args:
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dim (int): Number of input channels.
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window_size (tuple[int]): The height and width of the window.
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num_heads (int): Number of attention heads.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
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attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
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proj_drop (float, optional): Dropout ratio of output. Default: 0.0
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"""
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def __init__(self,
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dim,
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window_size,
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num_heads,
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qkv_bias=True,
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qk_scale=None,
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attn_drop=0.,
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proj_drop=0.,
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attn_head_dim=None):
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super().__init__()
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self.dim = dim
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self.window_size = window_size # Wh, Ww
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim**-0.5
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q_size = window_size[0]
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kv_size = window_size[1]
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rel_sp_dim = 2 * q_size - 1
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self.rel_pos_h = nn.Parameter(torch.zeros(rel_sp_dim, head_dim))
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self.rel_pos_w = nn.Parameter(torch.zeros(rel_sp_dim, head_dim))
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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# trunc_normal_(self.relative_position_bias_table, std=.02)
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self.softmax = nn.Softmax(dim=-1)
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def forward(self, x, H, W):
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""" Forward function.
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Args:
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x: input features with shape of (num_windows*B, N, C)
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mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
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"""
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B_, N, C = x.shape
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x = x.reshape(B_, H, W, C)
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pad_l = pad_t = 0
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pad_r = (self.window_size[1] -
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W % self.window_size[1]) % self.window_size[1]
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pad_b = (self.window_size[0] -
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H % self.window_size[0]) % self.window_size[0]
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x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
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_, Hp, Wp, _ = x.shape
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x = window_partition(
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x, self.window_size[0]) # nW*B, window_size, window_size, C
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x = x.view(-1, self.window_size[1] * self.window_size[0],
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C) # nW*B, window_size*window_size, C
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B_w = x.shape[0]
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N_w = x.shape[1]
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qkv = self.qkv(x).reshape(B_w, N_w, 3, self.num_heads,
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C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[
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2] # make torchscript happy (cannot use tensor as tuple)
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q = q * self.scale
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attn = (q @ k.transpose(-2, -1))
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attn = calc_rel_pos_spatial(attn, q, self.window_size,
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self.window_size, self.rel_pos_h,
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self.rel_pos_w)
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attn = self.softmax(attn)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B_w, N_w, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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x = x.view(-1, self.window_size[1], self.window_size[0], C)
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x = window_reverse(x, self.window_size[0], Hp, Wp) # B H' W' C
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if pad_r > 0 or pad_b > 0:
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x = x[:, :H, :W, :].contiguous()
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x = x.view(B_, H * W, C)
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return x
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class Block(nn.Module):
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def __init__(self,
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dim,
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num_heads,
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mlp_ratio=4.,
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qkv_bias=False,
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qk_scale=None,
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drop=0.,
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attn_drop=0.,
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drop_path=0.,
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init_values=None,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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window_size=None,
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attn_head_dim=None,
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window=False,
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aggregation='attn'):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.aggregation = aggregation
|
|
self.window = window
|
|
if not window:
|
|
if aggregation == 'attn':
|
|
self.attn = Attention(
|
|
dim,
|
|
num_heads=num_heads,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
attn_drop=attn_drop,
|
|
proj_drop=drop,
|
|
window_size=window_size,
|
|
attn_head_dim=attn_head_dim)
|
|
else:
|
|
self.attn = WindowAttention(
|
|
dim,
|
|
num_heads=num_heads,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
attn_drop=attn_drop,
|
|
proj_drop=drop,
|
|
window_size=window_size,
|
|
attn_head_dim=attn_head_dim)
|
|
if aggregation == 'basicblock':
|
|
self.conv_aggregation = BasicBlock(
|
|
inplanes=dim, planes=dim)
|
|
elif aggregation == 'bottleneck':
|
|
self.conv_aggregation = Bottleneck(
|
|
inplanes=dim, planes=dim // 4)
|
|
else:
|
|
self.attn = WindowAttention(
|
|
dim,
|
|
num_heads=num_heads,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
attn_drop=attn_drop,
|
|
proj_drop=drop,
|
|
window_size=window_size,
|
|
attn_head_dim=attn_head_dim)
|
|
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
|
self.drop_path = DropPath(
|
|
drop_path) if drop_path > 0. else nn.Identity()
|
|
self.norm2 = norm_layer(dim)
|
|
mlp_hidden_dim = int(dim * mlp_ratio)
|
|
self.mlp = Mlp(
|
|
in_features=dim,
|
|
hidden_features=mlp_hidden_dim,
|
|
act_layer=act_layer,
|
|
drop=drop)
|
|
|
|
if init_values is not None:
|
|
self.gamma_1 = nn.Parameter(
|
|
init_values * torch.ones((dim)), requires_grad=True)
|
|
self.gamma_2 = nn.Parameter(
|
|
init_values * torch.ones((dim)), requires_grad=True)
|
|
else:
|
|
self.gamma_1, self.gamma_2 = None, None
|
|
|
|
def forward(self, x, H, W):
|
|
if self.gamma_1 is None:
|
|
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
|
|
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
|
else:
|
|
x = x + self.drop_path(
|
|
self.gamma_1 * self.attn(self.norm1(x), H, W))
|
|
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
|
|
if not self.window and self.aggregation != 'attn':
|
|
x = self.conv_aggregation(x, H, W)
|
|
return x
|
|
|
|
|
|
class PatchEmbed(nn.Module):
|
|
""" Image to Patch Embedding
|
|
"""
|
|
|
|
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
|
|
super().__init__()
|
|
img_size = to_2tuple(img_size)
|
|
patch_size = to_2tuple(patch_size)
|
|
num_patches = (img_size[1] // patch_size[1]) * (
|
|
img_size[0] // patch_size[0])
|
|
self.patch_shape = (img_size[0] // patch_size[0],
|
|
img_size[1] // patch_size[1])
|
|
self.img_size = img_size
|
|
self.patch_size = patch_size
|
|
self.num_patches = num_patches
|
|
|
|
self.proj = nn.Conv2d(
|
|
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
|
|
|
def forward(self, x, **kwargs):
|
|
B, C, H, W = x.shape
|
|
# FIXME look at relaxing size constraints
|
|
# assert H == self.img_size[0] and W == self.img_size[1], \
|
|
# f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
|
|
x = self.proj(x)
|
|
Hp, Wp = x.shape[2], x.shape[3]
|
|
|
|
x = x.flatten(2).transpose(1, 2)
|
|
return x, (Hp, Wp)
|
|
|
|
|
|
class HybridEmbed(nn.Module):
|
|
""" CNN Feature Map Embedding
|
|
Extract feature map from CNN, flatten, project to embedding dim.
|
|
"""
|
|
|
|
def __init__(self,
|
|
backbone,
|
|
img_size=224,
|
|
feature_size=None,
|
|
in_chans=3,
|
|
embed_dim=768):
|
|
super().__init__()
|
|
assert isinstance(backbone, nn.Module)
|
|
img_size = to_2tuple(img_size)
|
|
self.img_size = img_size
|
|
self.backbone = backbone
|
|
if feature_size is None:
|
|
with torch.no_grad():
|
|
# FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
|
|
# map for all networks, the feature metadata has reliable channel and stride info, but using
|
|
# stride to calc feature dim requires info about padding of each stage that isn't captured.
|
|
training = backbone.training
|
|
if training:
|
|
backbone.eval()
|
|
o = self.backbone(
|
|
torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]
|
|
feature_size = o.shape[-2:]
|
|
feature_dim = o.shape[1]
|
|
backbone.train(training)
|
|
else:
|
|
feature_size = to_2tuple(feature_size)
|
|
feature_dim = self.backbone.feature_info.channels()[-1]
|
|
self.num_patches = feature_size[0] * feature_size[1]
|
|
self.proj = nn.Linear(feature_dim, embed_dim)
|
|
|
|
def forward(self, x):
|
|
x = self.backbone(x)[-1]
|
|
x = x.flatten(2).transpose(1, 2)
|
|
x = self.proj(x)
|
|
return x
|
|
|
|
|
|
class Norm2d(nn.Module):
|
|
|
|
def __init__(self, embed_dim):
|
|
super().__init__()
|
|
self.ln = nn.LayerNorm(embed_dim, eps=1e-6)
|
|
|
|
def forward(self, x):
|
|
x = x.permute(0, 2, 3, 1)
|
|
x = self.ln(x)
|
|
x = x.permute(0, 3, 1, 2).contiguous()
|
|
return x
|
|
|
|
|
|
# todo: refactor vitdet and vit_transformer_dynamic
|
|
@BACKBONES.register_module()
|
|
class ViTDet(nn.Module):
|
|
""" Vision Transformer with support for patch or hybrid CNN input stage
|
|
"""
|
|
|
|
def __init__(self,
|
|
img_size=224,
|
|
patch_size=16,
|
|
in_chans=3,
|
|
num_classes=80,
|
|
embed_dim=768,
|
|
depth=12,
|
|
num_heads=12,
|
|
mlp_ratio=4.,
|
|
qkv_bias=False,
|
|
qk_scale=None,
|
|
drop_rate=0.,
|
|
attn_drop_rate=0.,
|
|
drop_path_rate=0.,
|
|
hybrid_backbone=None,
|
|
norm_layer=None,
|
|
init_values=None,
|
|
use_checkpoint=False,
|
|
use_abs_pos_emb=False,
|
|
use_rel_pos_bias=False,
|
|
use_shared_rel_pos_bias=False,
|
|
out_indices=[11],
|
|
interval=3,
|
|
pretrained=None,
|
|
aggregation='attn'):
|
|
super().__init__()
|
|
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
|
self.num_classes = num_classes
|
|
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
|
|
|
|
if hybrid_backbone is not None:
|
|
self.patch_embed = HybridEmbed(
|
|
hybrid_backbone,
|
|
img_size=img_size,
|
|
in_chans=in_chans,
|
|
embed_dim=embed_dim)
|
|
else:
|
|
self.patch_embed = PatchEmbed(
|
|
img_size=img_size,
|
|
patch_size=patch_size,
|
|
in_chans=in_chans,
|
|
embed_dim=embed_dim)
|
|
|
|
num_patches = self.patch_embed.num_patches
|
|
|
|
self.out_indices = out_indices
|
|
|
|
if use_abs_pos_emb:
|
|
self.pos_embed = nn.Parameter(
|
|
torch.zeros(1, num_patches, embed_dim))
|
|
else:
|
|
self.pos_embed = None
|
|
|
|
self.pos_drop = nn.Dropout(p=drop_rate)
|
|
|
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)
|
|
] # stochastic depth decay rule
|
|
self.use_rel_pos_bias = use_rel_pos_bias
|
|
self.use_checkpoint = use_checkpoint
|
|
self.blocks = nn.ModuleList([
|
|
Block(
|
|
dim=embed_dim,
|
|
num_heads=num_heads,
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop_rate,
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[i],
|
|
norm_layer=norm_layer,
|
|
init_values=init_values,
|
|
window_size=(14, 14) if
|
|
((i + 1) % interval != 0
|
|
or aggregation != 'attn') else self.patch_embed.patch_shape,
|
|
window=((i + 1) % interval != 0),
|
|
aggregation=aggregation) for i in range(depth)
|
|
])
|
|
|
|
if self.pos_embed is not None:
|
|
trunc_normal_(self.pos_embed, std=.02)
|
|
|
|
self.norm = norm_layer(embed_dim)
|
|
|
|
self.pretrained = pretrained
|
|
self._register_load_state_dict_pre_hook(self._prepare_checkpoint_hook)
|
|
|
|
def fix_init_weight(self):
|
|
|
|
def rescale(param, layer_id):
|
|
param.div_(math.sqrt(2.0 * layer_id))
|
|
|
|
for layer_id, layer in enumerate(self.blocks):
|
|
rescale(layer.attn.proj.weight.data, layer_id + 1)
|
|
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
|
|
|
|
def init_weights(self, pretrained=None):
|
|
"""Initialize the weights in backbone.
|
|
Args:
|
|
pretrained (str, optional): Path to pre-trained weights.
|
|
Defaults to None.
|
|
"""
|
|
self.fix_init_weight()
|
|
pretrained = pretrained or self.pretrained
|
|
|
|
def _init_weights(m):
|
|
if isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight, std=.02)
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.LayerNorm):
|
|
nn.init.constant_(m.bias, 0)
|
|
nn.init.constant_(m.weight, 1.0)
|
|
|
|
if isinstance(m, nn.Conv2d):
|
|
kaiming_init(m, mode='fan_in', nonlinearity='relu')
|
|
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
|
|
constant_init(m, 1)
|
|
|
|
if isinstance(m, Bottleneck):
|
|
constant_init(m.norm3, 0)
|
|
elif isinstance(m, BasicBlock):
|
|
constant_init(m.norm2, 0)
|
|
|
|
if isinstance(pretrained, str):
|
|
self.apply(_init_weights)
|
|
logger = get_root_logger()
|
|
load_checkpoint(self, pretrained, strict=False, logger=logger)
|
|
elif pretrained is None:
|
|
self.apply(_init_weights)
|
|
else:
|
|
raise TypeError('pretrained must be a str or None')
|
|
|
|
def _prepare_checkpoint_hook(self, state_dict, prefix, *args, **kwargs):
|
|
rank, _ = get_dist_info()
|
|
if 'pos_embed' in state_dict:
|
|
pos_embed_checkpoint = state_dict['pos_embed']
|
|
embedding_size = pos_embed_checkpoint.shape[-1]
|
|
H, W = self.patch_embed.patch_shape
|
|
num_patches = self.patch_embed.num_patches
|
|
num_extra_tokens = 1
|
|
# height (== width) for the checkpoint position embedding
|
|
orig_size = int(
|
|
(pos_embed_checkpoint.shape[-2] - num_extra_tokens)**0.5)
|
|
# height (== width) for the new position embedding
|
|
new_size = int(num_patches**0.5)
|
|
# class_token and dist_token are kept unchanged
|
|
if orig_size != new_size:
|
|
if rank == 0:
|
|
print('Position interpolate from %dx%d to %dx%d' %
|
|
(orig_size, orig_size, H, W))
|
|
# extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
|
|
# only the position tokens are interpolated
|
|
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
|
|
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size,
|
|
embedding_size).permute(
|
|
0, 3, 1, 2)
|
|
pos_tokens = torch.nn.functional.interpolate(
|
|
pos_tokens,
|
|
size=(H, W),
|
|
mode='bicubic',
|
|
align_corners=False)
|
|
new_pos_embed = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
|
|
# new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
|
|
state_dict['pos_embed'] = new_pos_embed
|
|
|
|
def get_num_layers(self):
|
|
return len(self.blocks)
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay(self):
|
|
return {'pos_embed', 'cls_token'}
|
|
|
|
def forward_features(self, x):
|
|
B, C, H, W = x.shape
|
|
x, (Hp, Wp) = self.patch_embed(x)
|
|
batch_size, seq_len, _ = x.size()
|
|
|
|
if self.pos_embed is not None:
|
|
x = x + self.pos_embed
|
|
x = self.pos_drop(x)
|
|
|
|
outs = []
|
|
for i, blk in enumerate(self.blocks):
|
|
if self.use_checkpoint:
|
|
x = checkpoint.checkpoint(blk, x)
|
|
else:
|
|
x = blk(x, Hp, Wp)
|
|
|
|
x = self.norm(x)
|
|
xp = x.permute(0, 2, 1).reshape(B, -1, Hp, Wp)
|
|
|
|
outs.append(xp)
|
|
|
|
return tuple(outs)
|
|
|
|
def forward(self, x):
|
|
x = self.forward_features(x)
|
|
return x
|