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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import List, Optional, Tuple, Type
import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNorm2d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
class PatchEmbed(nn.Module):
"""2D Image to Patch Embedding"""
def __init__(
self,
img_size,
patch_size,
in_chans,
embed_dim,
):
super().__init__()
self.proj = nn.Conv2d(
in_chans,
embed_dim,
kernel_size=(patch_size, patch_size),
stride=(patch_size, patch_size),
bias=True,
)
def forward(self, x):
B, C, H, W = x.shape
x = self.proj(x)
return x
class Attention(nn.Module):
def __init__(
self,
dim,
num_heads,
qkv_bias,
qk_scale=None,
):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim**-0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.proj = nn.Linear(dim, dim)
def forward(self, x):
B, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B, N, 3, self.num_heads, C // self.num_heads)
.permute(2, 0, 3, 1, 4)
)
q, k, v = (
qkv[0],
qkv[1],
qkv[2],
)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
return x
class Mlp(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
):
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)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.fc2(x)
return x
class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.0,
qkv_bias=False,
qk_scale=None,
act_layer=nn.GELU,
):
super().__init__()
self.norm1 = nn.LayerNorm(dim, eps=1e-6)
self.attn = Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
)
self.norm2 = nn.LayerNorm(dim, eps=1e-6)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
)
def forward(self, x):
x = x + self.attn(self.norm1(x))
x = x + self.mlp(self.norm2(x))
return x
@torch.jit.export
def get_abs_pos(
abs_pos: torch.Tensor, has_cls_token: bool, hw: List[int]
) -> torch.Tensor:
"""
Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token
dimension for the original embeddings.
Args:
abs_pos (Tensor): absolute positional embeddings with (1, num_position, C).
has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token.
hw (Tuple): size of input image tokens.
Returns:
Absolute positional embeddings after processing with shape (1, H, W, C)
"""
h = hw[0]
w = hw[1]
if has_cls_token:
abs_pos = abs_pos[:, 1:]
xy_num = abs_pos.shape[1]
size = int(math.sqrt(xy_num))
assert size * size == xy_num
if size != h or size != w:
new_abs_pos = F.interpolate(
abs_pos.reshape(1, size, size, -1).permute(0, 3, 1, 2),
size=(h, w),
mode="bicubic",
align_corners=False,
)
return new_abs_pos.permute(0, 2, 3, 1)
else:
return abs_pos.reshape(1, h, w, -1)
# Image encoder for efficient SAM.
class ImageEncoderViT(nn.Module):
def __init__(
self,
img_size: int,
patch_size: int,
in_chans: int,
patch_embed_dim: int,
normalization_type: str,
depth: int,
num_heads: int,
mlp_ratio: float,
neck_dims: List[int],
act_layer: Type[nn.Module],
) -> None:
"""
Args:
img_size (int): Input image size.
patch_size (int): Patch size.
in_chans (int): Number of input image channels.
patch_embed_dim (int): Patch embedding dimension.
depth (int): Depth of ViT.
num_heads (int): Number of attention heads in each ViT block.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
act_layer (nn.Module): Activation layer.
"""
super().__init__()
self.img_size = img_size
self.image_embedding_size = img_size // ((patch_size if patch_size > 0 else 1))
self.transformer_output_dim = ([patch_embed_dim] + neck_dims)[-1]
self.pretrain_use_cls_token = True
pretrain_img_size = 224
self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, patch_embed_dim)
# Initialize absolute positional embedding with pretrain image size.
num_patches = (pretrain_img_size // patch_size) * (
pretrain_img_size // patch_size
)
num_positions = num_patches + 1
self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, patch_embed_dim))
self.blocks = nn.ModuleList()
for i in range(depth):
vit_block = Block(patch_embed_dim, num_heads, mlp_ratio, True)
self.blocks.append(vit_block)
self.neck = nn.Sequential(
nn.Conv2d(
patch_embed_dim,
neck_dims[0],
kernel_size=1,
bias=False,
),
LayerNorm2d(neck_dims[0]),
nn.Conv2d(
neck_dims[0],
neck_dims[0],
kernel_size=3,
padding=1,
bias=False,
),
LayerNorm2d(neck_dims[0]),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
assert (
x.shape[2] == self.img_size and x.shape[3] == self.img_size
), "input image size must match self.img_size"
x = self.patch_embed(x)
# B C H W -> B H W C
x = x.permute(0, 2, 3, 1)
x = x + get_abs_pos(
self.pos_embed, self.pretrain_use_cls_token, [x.shape[1], x.shape[2]]
)
num_patches = x.shape[1]
assert x.shape[2] == num_patches
x = x.reshape(x.shape[0], num_patches * num_patches, x.shape[3])
for blk in self.blocks:
x = blk(x)
x = x.reshape(x.shape[0], num_patches, num_patches, x.shape[2])
x = self.neck(x.permute(0, 3, 1, 2))
return x
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