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# Copyright (C) 2022-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
# --------------------------------------------------------
# Main encoder/decoder blocks
# --------------------------------------------------------
# References:
# timm
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/helpers.py
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/mlp.py
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/patch_embed.py
import collections.abc
from itertools import repeat
import torch
import torch.nn as nn
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
return x
return tuple(repeat(x, n))
return parse
to_2tuple = _ntuple(2)
def drop_path(
x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True
):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (
x.ndim - 1
) # work with diff dim tensors, not just 2D ConvNets
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
if keep_prob > 0.0 and scale_by_keep:
random_tensor.div_(keep_prob)
return x * random_tensor
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
self.scale_by_keep = scale_by_keep
def forward(self, x):
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
def extra_repr(self):
return f"drop_prob={round(self.drop_prob,3):0.3f}"
class Mlp(nn.Module):
"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
bias=True,
drop=0.0,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class Attention(nn.Module):
def __init__(
self, dim, rope=None, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0
):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim**-0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.rope = rope
def forward(self, x, xpos):
B, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B, N, 3, self.num_heads, C // self.num_heads)
.transpose(1, 3)
)
q, k, v = [qkv[:, :, i] for i in range(3)]
# q,k,v = qkv.unbind(2) # make torchscript happy (cannot use tensor as tuple)
if self.rope is not None:
q = self.rope(q, xpos)
k = self.rope(k, xpos)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.0,
qkv_bias=False,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
rope=None,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
rope=rope,
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=drop,
)
# 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 > 0.0 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, xpos):
x = x + self.drop_path(self.attn(self.norm1(x), xpos))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class CrossAttention(nn.Module):
def __init__(
self, dim, rope=None, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0
):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim**-0.5
self.projq = nn.Linear(dim, dim, bias=qkv_bias)
self.projk = nn.Linear(dim, dim, bias=qkv_bias)
self.projv = nn.Linear(dim, dim, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.rope = rope
def forward(self, query, key, value, qpos, kpos):
B, Nq, C = query.shape
Nk = key.shape[1]
Nv = value.shape[1]
q = (
self.projq(query)
.reshape(B, Nq, self.num_heads, C // self.num_heads)
.permute(0, 2, 1, 3)
)
k = (
self.projk(key)
.reshape(B, Nk, self.num_heads, C // self.num_heads)
.permute(0, 2, 1, 3)
)
v = (
self.projv(value)
.reshape(B, Nv, self.num_heads, C // self.num_heads)
.permute(0, 2, 1, 3)
)
if self.rope is not None:
q = self.rope(q, qpos)
k = self.rope(k, kpos)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, Nq, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class DecoderBlock(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.0,
qkv_bias=False,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
norm_mem=True,
rope=None,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
rope=rope,
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=drop,
)
self.cross_attn = CrossAttention(
dim,
rope=rope,
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=drop,
)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.norm2 = norm_layer(dim)
self.norm3 = 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,
)
self.norm_y = norm_layer(dim) if norm_mem else nn.Identity()
def forward(self, x, y, xpos, ypos):
x = x + self.drop_path(self.attn(self.norm1(x), xpos))
y_ = self.norm_y(y)
x = x + self.drop_path(self.cross_attn(self.norm2(x), y_, y_, xpos, ypos))
x = x + self.drop_path(self.mlp(self.norm3(x)))
return x, y
# patch embedding
class PositionGetter(object):
"""return positions of patches"""
def __init__(self):
self.cache_positions = {}
def __call__(self, b, h, w, device):
if not (h, w) in self.cache_positions:
x = torch.arange(w, device=device)
y = torch.arange(h, device=device)
self.cache_positions[h, w] = torch.cartesian_prod(y, x) # (h, w, 2)
pos = self.cache_positions[h, w].view(1, h * w, 2).expand(b, -1, 2).clone()
return pos
class PatchEmbed(nn.Module):
"""just adding _init_weights + position getter compared to timm.models.layers.patch_embed.PatchEmbed"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
embed_dim=768,
norm_layer=None,
flatten=True,
):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.flatten = flatten
self.proj = nn.Conv2d(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
self.position_getter = PositionGetter()
def forward(self, x):
B, C, H, W = x.shape
torch._assert(
H == self.img_size[0],
f"Input image height ({H}) doesn't match model ({self.img_size[0]}).",
)
torch._assert(
W == self.img_size[1],
f"Input image width ({W}) doesn't match model ({self.img_size[1]}).",
)
x = self.proj(x)
pos = self.position_getter(B, x.size(2), x.size(3), x.device)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
x = self.norm(x)
return x, pos
def _init_weights(self):
w = self.proj.weight.data
torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
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