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Grounded-Segment-Anything / segment_anything /modeling /image_encoder.py

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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 torch
	import torch.nn as nn
	import torch.nn.functional as F

	from typing import Optional, Tuple, Type

	from .common import LayerNorm2d, MLPBlock


	# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
	class ImageEncoderViT(nn.Module):
	def __init__(
	self,
	img_size: int = 1024,
	patch_size: int = 16,
	in_chans: int = 3,
	embed_dim: int = 768,
	depth: int = 12,
	num_heads: int = 12,
	mlp_ratio: float = 4.0,
	out_chans: int = 256,
	qkv_bias: bool = True,
	norm_layer: Type[nn.Module] = nn.LayerNorm,
	act_layer: Type[nn.Module] = nn.GELU,
	use_abs_pos: bool = True,
	use_rel_pos: bool = False,
	rel_pos_zero_init: bool = True,
	window_size: int = 0,
	global_attn_indexes: Tuple[int, ...] = (),
	) -> None:
	"""
	Args:
	img_size (int): Input image size.
	patch_size (int): Patch size.
	in_chans (int): Number of input image channels.
	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.
	qkv_bias (bool): If True, add a learnable bias to query, key, value.
	norm_layer (nn.Module): Normalization layer.
	act_layer (nn.Module): Activation layer.
	use_abs_pos (bool): If True, use absolute positional embeddings.
	use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
	rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
	window_size (int): Window size for window attention blocks.
	global_attn_indexes (list): Indexes for blocks using global attention.
	"""
	super().__init__()
	self.img_size = img_size

	self.patch_embed = PatchEmbed(
	kernel_size=(patch_size, patch_size),
	stride=(patch_size, patch_size),
	in_chans=in_chans,
	embed_dim=embed_dim,
	)

	self.pos_embed: Optional[nn.Parameter] = None
	if use_abs_pos:
	# Initialize absolute positional embedding with pretrain image size.
	self.pos_embed = nn.Parameter(
	torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
	)

	self.blocks = nn.ModuleList()
	for i in range(depth):
	block = Block(
	dim=embed_dim,
	num_heads=num_heads,
	mlp_ratio=mlp_ratio,
	qkv_bias=qkv_bias,
	norm_layer=norm_layer,
	act_layer=act_layer,
	use_rel_pos=use_rel_pos,
	rel_pos_zero_init=rel_pos_zero_init,
	window_size=window_size if i not in global_attn_indexes else 0,
	input_size=(img_size // patch_size, img_size // patch_size),
	)
	self.blocks.append(block)

	self.neck = nn.Sequential(
	nn.Conv2d(
	embed_dim,
	out_chans,
	kernel_size=1,
	bias=False,
	),
	LayerNorm2d(out_chans),
	nn.Conv2d(
	out_chans,
	out_chans,
	kernel_size=3,
	padding=1,
	bias=False,
	),
	LayerNorm2d(out_chans),
	)

	def forward(self, x: torch.Tensor) -> torch.Tensor:
	x = self.patch_embed(x)
	if self.pos_embed is not None:
	x = x + self.pos_embed

	for blk in self.blocks:
	x = blk(x)

	x = self.neck(x.permute(0, 3, 1, 2))

	return x


	class Block(nn.Module):
	"""Transformer blocks with support of window attention and residual propagation blocks"""

	def __init__(
	self,
	dim: int,
	num_heads: int,
	mlp_ratio: float = 4.0,
	qkv_bias: bool = True,
	norm_layer: Type[nn.Module] = nn.LayerNorm,
	act_layer: Type[nn.Module] = nn.GELU,
	use_rel_pos: bool = False,
	rel_pos_zero_init: bool = True,
	window_size: int = 0,
	input_size: Optional[Tuple[int, int]] = None,
	) -> None:
	"""
	Args:
	dim (int): Number of input channels.
	num_heads (int): Number of attention heads in each ViT block.
	mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
	qkv_bias (bool): If True, add a learnable bias to query, key, value.
	norm_layer (nn.Module): Normalization layer.
	act_layer (nn.Module): Activation layer.
	use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
	rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
	window_size (int): Window size for window attention blocks. If it equals 0, then
	use global attention.
	input_size (int or None): Input resolution for calculating the relative positional
	parameter size.
	"""
	super().__init__()
	self.norm1 = norm_layer(dim)
	self.attn = Attention(
	dim,
	num_heads=num_heads,
	qkv_bias=qkv_bias,
	use_rel_pos=use_rel_pos,
	rel_pos_zero_init=rel_pos_zero_init,
	input_size=input_size if window_size == 0 else (window_size, window_size),
	)

	self.norm2 = norm_layer(dim)
	self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)

	self.window_size = window_size

	def forward(self, x: torch.Tensor) -> torch.Tensor:
	shortcut = x
	x = self.norm1(x)
	# Window partition
	if self.window_size > 0:
	H, W = x.shape[1], x.shape[2]
	x, pad_hw = window_partition(x, self.window_size)

	x = self.attn(x)
	# Reverse window partition
	if self.window_size > 0:
	x = window_unpartition(x, self.window_size, pad_hw, (H, W))

	x = shortcut + x
	x = x + self.mlp(self.norm2(x))

	return x


	class Attention(nn.Module):
	"""Multi-head Attention block with relative position embeddings."""

	def __init__(
	self,
	dim: int,
	num_heads: int = 8,
	qkv_bias: bool = True,
	use_rel_pos: bool = False,
	rel_pos_zero_init: bool = True,
	input_size: Optional[Tuple[int, int]] = None,
	) -> None:
	"""
	Args:
	dim (int): Number of input channels.
	num_heads (int): Number of attention heads.
	qkv_bias (bool: If True, add a learnable bias to query, key, value.
	rel_pos (bool): If True, add relative positional embeddings to the attention map.
	rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
	input_size (int or None): Input resolution for calculating the relative positional
	parameter size.
	"""
	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.proj = nn.Linear(dim, dim)

	self.use_rel_pos = use_rel_pos
	if self.use_rel_pos:
	assert (
	input_size is not None
	), "Input size must be provided if using relative positional encoding."
	# initialize relative positional embeddings
	self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
	self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))

	def forward(self, x: torch.Tensor) -> torch.Tensor:
	B, H, W, _ = x.shape
	# qkv with shape (3, B, nHead, H * W, C)
	qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
	# q, k, v with shape (B * nHead, H * W, C)
	q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)

	attn = (q * self.scale) @ k.transpose(-2, -1)

	if self.use_rel_pos:
	attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))

	attn = attn.softmax(dim=-1)
	x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
	x = self.proj(x)

	return x


	def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
	"""
	Partition into non-overlapping windows with padding if needed.
	Args:
	x (tensor): input tokens with [B, H, W, C].
	window_size (int): window size.

	Returns:
	windows: windows after partition with [B * num_windows, window_size, window_size, C].
	(Hp, Wp): padded height and width before partition
	"""
	B, H, W, C = x.shape

	pad_h = (window_size - H % window_size) % window_size
	pad_w = (window_size - W % window_size) % window_size
	if pad_h > 0 or pad_w > 0:
	x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
	Hp, Wp = H + pad_h, W + pad_w

	x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
	windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
	return windows, (Hp, Wp)


	def window_unpartition(
	windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
	) -> torch.Tensor:
	"""
	Window unpartition into original sequences and removing padding.
	Args:
	x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
	window_size (int): window size.
	pad_hw (Tuple): padded height and width (Hp, Wp).
	hw (Tuple): original height and width (H, W) before padding.

	Returns:
	x: unpartitioned sequences with [B, H, W, C].
	"""
	Hp, Wp = pad_hw
	H, W = hw
	B = windows.shape[0] // (Hp * Wp // window_size // window_size)
	x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
	x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)

	if Hp > H or Wp > W:
	x = x[:, :H, :W, :].contiguous()
	return x


	def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
	"""
	Get relative positional embeddings according to the relative positions of
	query and key sizes.
	Args:
	q_size (int): size of query q.
	k_size (int): size of key k.
	rel_pos (Tensor): relative position embeddings (L, C).

	Returns:
	Extracted positional embeddings according to relative positions.
	"""
	max_rel_dist = int(2 * max(q_size, k_size) - 1)
	# Interpolate rel pos if needed.
	if rel_pos.shape[0] != max_rel_dist:
	# Interpolate rel pos.
	rel_pos_resized = F.interpolate(
	rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
	size=max_rel_dist,
	mode="linear",
	)
	rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
	else:
	rel_pos_resized = rel_pos

	# Scale the coords with short length if shapes for q and k are different.
	q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
	k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
	relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)

	return rel_pos_resized[relative_coords.long()]


	def add_decomposed_rel_pos(
	attn: torch.Tensor,
	q: torch.Tensor,
	rel_pos_h: torch.Tensor,
	rel_pos_w: torch.Tensor,
	q_size: Tuple[int, int],
	k_size: Tuple[int, int],
	) -> torch.Tensor:
	"""
	Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
	https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
	Args:
	attn (Tensor): attention map.
	q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
	rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
	rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
	q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
	k_size (Tuple): spatial sequence size of key k with (k_h, k_w).

	Returns:
	attn (Tensor): attention map with added relative positional embeddings.
	"""
	q_h, q_w = q_size
	k_h, k_w = k_size
	Rh = get_rel_pos(q_h, k_h, rel_pos_h)
	Rw = get_rel_pos(q_w, k_w, rel_pos_w)

	B, _, dim = q.shape
	r_q = q.reshape(B, q_h, q_w, dim)
	rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
	rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)

	attn = (
	attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
	).view(B, q_h * q_w, k_h * k_w)

	return attn


	class PatchEmbed(nn.Module):
	"""
	Image to Patch Embedding.
	"""

	def __init__(
	self,
	kernel_size: Tuple[int, int] = (16, 16),
	stride: Tuple[int, int] = (16, 16),
	padding: Tuple[int, int] = (0, 0),
	in_chans: int = 3,
	embed_dim: int = 768,
	) -> None:
	"""
	Args:
	kernel_size (Tuple): kernel size of the projection layer.
	stride (Tuple): stride of the projection layer.
	padding (Tuple): padding size of the projection layer.
	in_chans (int): Number of input image channels.
	embed_dim (int): embed_dim (int): Patch embedding dimension.
	"""
	super().__init__()

	self.proj = nn.Conv2d(
	in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
	)

	def forward(self, x: torch.Tensor) -> torch.Tensor:
	x = self.proj(x)
	# B C H W -> B H W C
	x = x.permute(0, 2, 3, 1)
	return x