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# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
from functools import partial
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
from ..builder import BACKBONES
from .base_backbone import BaseBackbone
from einops import repeat
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
def extra_repr(self):
return 'p={}'.format(self.drop_prob)
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
def __init__(
self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
proj_drop=0., attn_head_dim=None, ):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.dim = dim
if attn_head_dim is not None:
head_dim = attn_head_dim
all_head_dim = head_dim * self.num_heads
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, all_head_dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(all_head_dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x)
qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None,
drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU,
norm_layer=nn.LayerNorm, attn_head_dim=None
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
attn_drop=attn_drop, proj_drop=drop, 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)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
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, ratio=1):
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]) * (ratio ** 2)
self.patch_shape = (int(img_size[0] // patch_size[0] * ratio), int(img_size[1] // patch_size[1] * ratio))
self.origin_patch_shape = (int(img_size[0] // patch_size[0]), int(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[0] // ratio),
padding=4 + 2 * (ratio // 2 - 1))
def forward(self, x, **kwargs):
B, C, H, W = x.shape
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():
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
@BACKBONES.register_module()
class ViT(BaseBackbone):
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, use_checkpoint=False,
frozen_stages=-1, ratio=1, last_norm=True,
patch_padding='pad', freeze_attn=False, freeze_ffn=False, task_tokens_num=1+1+2+2+25
):
# Protect mutable default arguments
super(ViT, self).__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
self.frozen_stages = frozen_stages
self.use_checkpoint = use_checkpoint
self.patch_padding = patch_padding
self.freeze_attn = freeze_attn
self.freeze_ffn = freeze_ffn
self.depth = depth
self.task_tokens_num = task_tokens_num
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, ratio=ratio)
num_patches = self.patch_embed.num_patches
# task tokens for HPS estimation
self.task_tokens = nn.Parameter(torch.zeros(1, task_tokens_num, embed_dim))
trunc_normal_(self.task_tokens, std=.02)
# since the pretraining model has class token
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
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,
)
for i in range(depth)])
self.last_norm = norm_layer(embed_dim) if last_norm else nn.Identity()
if self.pos_embed is not None:
trunc_normal_(self.pos_embed, std=.02)
self._freeze_stages()
def _freeze_stages(self):
"""Freeze parameters."""
if self.frozen_stages >= 0:
self.patch_embed.eval()
for param in self.patch_embed.parameters():
param.requires_grad = False
for i in range(1, self.frozen_stages + 1):
m = self.blocks[i]
m.eval()
for param in m.parameters():
param.requires_grad = False
if self.freeze_attn:
for i in range(0, self.depth):
m = self.blocks[i]
m.attn.eval()
m.norm1.eval()
for param in m.attn.parameters():
param.requires_grad = False
for param in m.norm1.parameters():
param.requires_grad = False
if self.freeze_ffn:
self.pos_embed.requires_grad = False
self.patch_embed.eval()
for param in self.patch_embed.parameters():
param.requires_grad = False
for i in range(0, self.depth):
m = self.blocks[i]
m.mlp.eval()
m.norm2.eval()
for param in m.mlp.parameters():
param.requires_grad = False
for param in m.norm2.parameters():
param.requires_grad = False
def init_weights(self, pretrained=None):
"""Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
super().init_weights(pretrained, patch_padding=self.patch_padding)
if pretrained is None:
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)
self.apply(_init_weights)
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)
task_tokens = repeat(self.task_tokens, '() n d -> b n d', b=B)
if self.pos_embed is not None:
# fit for multiple GPU training
# since the first element for pos embed (sin-cos manner) is zero, it will cause no difference
x = x + self.pos_embed[:, 1:] + self.pos_embed[:, :1]
x = torch.cat((task_tokens, x), dim=1)
for blk in self.blocks:
if self.use_checkpoint:
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
x = self.last_norm(x)
task_tokens = x[:, :self.task_tokens_num] # [N,J,C]
# task_tokens = torch.cat(task_tokens_, dim=-1)
xp = x[:, self.task_tokens_num:] # [N,Hp*Wp,C]
xp = xp.permute(0, 2, 1).reshape(B, -1, Hp, Wp).contiguous()
return xp, task_tokens
def forward(self, x):
x = self.forward_features(x)
return x
def train(self, mode=True):
"""Convert the model into training mode."""
super().train(mode)
self._freeze_stages()