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# Copyright (c) Facebook, Inc. and its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Mostly copy-paste from timm library.
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
import math
from functools import partial
from typing import Callable, Final, Optional, Sequence
import torch
from torch import Tensor, nn
from torch.nn import functional as F
from .common import ensure_tuple, get_act_layer, use_fused_attn
def vit_weights_init(module: nn.Module) -> None:
if isinstance(module, nn.Linear):
nn.init.trunc_normal_(module.weight, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.LayerNorm):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
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: Tensor) -> Tensor:
if self.drop_prob == 0 or not self.training:
return x
keep_prob = 1 - self.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 self.scale_by_keep:
random_tensor.div_(keep_prob)
return x * random_tensor
def extra_repr(self):
return f"drop_prob={self.drop_prob:0.3f}"
class Mlp(nn.Module):
def __init__(
self,
in_features: int,
hidden_features: Optional[int] = None,
out_features: Optional[int] = None,
act_layer: Callable[[], nn.Module] = nn.GELU,
drop: float = 0.0,
):
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) if drop > 0.0 else nn.Identity()
def forward(self, x: Tensor) -> Tensor:
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
fused_attn: Final[bool]
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
qk_scale: Optional[float] = None,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
):
super().__init__()
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = qk_scale or self.head_dim**-0.5
self.fused_attn = use_fused_attn()
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop) if attn_drop > 0.0 else nn.Identity()
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop) if proj_drop > 0.0 else nn.Identity()
def forward(self, x: Tensor) -> Tensor:
B, N, C = x.shape
qkv: Tensor = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
if self.fused_attn:
dropout_p = getattr(self.attn_drop, "p", 0.0) if self.training else 0.0
x = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.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: int,
num_heads: int,
mlp_ratio: float = 4.0,
qkv_bias: bool = False,
drop: float = 0.0,
attn_drop: float = 0.0,
drop_path: float = 0.0,
act_layer: Callable[[], nn.Module] = nn.GELU,
norm_layer: Callable[[], nn.Module] = nn.LayerNorm,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
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)
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: Tensor) -> Tensor:
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: int | tuple[int, int] = 224,
patch_size: int | tuple[int, int] = 16,
in_chans: int = 3,
embed_dim: int = 768,
bias: bool = True,
dynamic_pad: bool = False,
):
super().__init__()
self.img_size = ensure_tuple(img_size, 2)
self.patch_size = ensure_tuple(patch_size, 2)
self.num_patches = (self.img_size[0] // self.patch_size[0]) * (self.img_size[1] // self.patch_size[1])
self.dynamic_pad = dynamic_pad
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
def forward(self, x: Tensor) -> Tensor:
_, _, H, W = x.shape
if self.dynamic_pad:
pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
x = F.pad(x, (0, pad_w, 0, pad_h))
x = self.proj(x)
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
return x
class VisionTransformer(nn.Module):
"""Vision Transformer"""
def __init__(
self,
img_size: int | tuple[int, int] = 224,
patch_size: int | tuple[int, int] = 16,
in_chans: int = 3,
num_classes: int = 0,
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.0,
qkv_bias: bool = False,
pre_norm: bool = False,
drop_rate: float = 0.0,
attn_drop_rate: float = 0.0,
drop_path_rate: float = 0.0,
norm_layer: Callable[[], nn.Module] = nn.LayerNorm,
act_layer: Callable[[], nn.Module] = nn.GELU,
skip_init: bool = False,
dynamic_pad: bool = False,
**kwargs,
):
super().__init__()
self.img_size = img_size
self.patch_size = patch_size
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim
self.depth = depth
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
bias=not pre_norm, # disable bias if pre-norm is used (e.g. CLIP)
dynamic_pad=dynamic_pad,
)
num_patches = self.patch_embed.num_patches
embed_len = num_patches + 1 # num_patches + 1 for the [CLS] token
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = nn.Parameter(torch.zeros(1, embed_len, embed_dim))
self.pos_drop = nn.Dropout(p=drop_rate) if drop_rate > 0.0 else nn.Identity()
self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, self.depth)] # stochastic depth decay rule
self.blocks: list[Block] = nn.ModuleList(
[
Block(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
act_layer=act_layer,
norm_layer=norm_layer,
)
for i in range(self.depth)
]
)
self.norm = norm_layer(embed_dim)
# Classifier head
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
if not skip_init:
self.reset_parameters()
def reset_parameters(self):
nn.init.trunc_normal_(self.cls_token, std=0.02)
nn.init.trunc_normal_(self.pos_embed, std=0.02)
self.apply(vit_weights_init)
def interpolate_pos_encoding(self, x: Tensor, w: Tensor, h: Tensor) -> Tensor:
npatch = x.shape[1] - 1
N = self.pos_embed.shape[1] - 1
if npatch == N and w == h:
return self.pos_embed
class_pos_embed = self.pos_embed[:, 0]
patch_pos_embed = self.pos_embed[:, 1:]
dim = x.shape[-1]
w0 = w // self.patch_embed.patch_size[0]
h0 = h // self.patch_embed.patch_size[0]
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
w0, h0 = w0 + 0.1, h0 + 0.1
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
mode="bicubic",
)
if int(w0) != patch_pos_embed.shape[-2] or int(h0) != patch_pos_embed.shape[-1]:
raise ValueError("Error in positional encoding interpolation.")
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
def prepare_tokens(self, x: Tensor) -> Tensor:
B, _, W, H = x.shape
x = self.patch_embed(x) # patch linear embedding
# add the [CLS] token to the embed patch tokens
cls_tokens = self.cls_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
# add positional encoding to each token
x = x + self.interpolate_pos_encoding(x, W, H)
return self.pos_drop(x)
def forward(self, x: Tensor, norm: bool = True) -> Tensor:
x = self.forward_features(x, norm=norm)
x = self.forward_head(x)
return x
def forward_features(self, x: Tensor, norm: bool = True) -> Tensor:
x = self.prepare_tokens(x)
x = self.norm_pre(x)
for blk in self.blocks:
x = blk(x)
if norm:
x = self.norm(x)
return x[:, 0]
def forward_head(self, x: Tensor) -> Tensor:
x = self.head(x)
return x
def get_intermediate_layers(
self,
x: Tensor,
n: int | Sequence[int] = 1,
norm: bool = True,
) -> list[Tensor]:
# we return the output tokens from the `n` last blocks
outputs = []
layer_indices = set(range(self.depth - n, self.depth) if isinstance(n, int) else n)
x = self.prepare_tokens(x)
x = self.norm_pre(x)
for idx, blk in enumerate(self.blocks):
x = blk(x)
if idx in layer_indices:
outputs.append(x)
if norm:
outputs = [self.norm(x) for x in outputs]
return outputs
def vit_base_dreamsim(
patch_size: int = 16,
layer_norm_eps: float = 1e-6,
num_classes: int = 512,
act_layer: str | Callable[[], nn.Module] = "gelu",
**kwargs,
):
if isinstance(act_layer, str):
act_layer = get_act_layer(act_layer)
model = VisionTransformer(
patch_size=patch_size,
num_classes=num_classes,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=layer_norm_eps),
act_layer=act_layer,
**kwargs,
)
return model
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