mirror of
https://github.com/open-mmlab/mmsegmentation.git
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764 lines
28 KiB
Python
764 lines
28 KiB
Python
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# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from mmcv.cnn import build_norm_layer
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from mmcv.cnn.bricks.transformer import FFN, build_dropout
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from mmengine.model import BaseModule
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from mmengine.utils import to_2tuple
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from mmseg.registry import MODELS
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from ..utils import FullAttention, LinearAttention
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class AGWindowMSA(BaseModule):
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"""Appearance Guidance Window based multi-head self-attention (W-MSA)
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module with relative position bias.
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Args:
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embed_dims (int): Number of input channels.
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appearance_dims (int): Number of appearance guidance feature channels.
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num_heads (int): Number of attention heads.
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window_size (tuple[int]): The height and width of the window.
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qkv_bias (bool, optional): If True, add a learnable bias to q, k, v.
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Default: True.
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qk_scale (float | None, optional): Override default qk scale of
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head_dim ** -0.5 if set. Default: None.
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attn_drop_rate (float, optional): Dropout ratio of attention weight.
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Default: 0.0
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proj_drop_rate (float, optional): Dropout ratio of output. Default: 0.
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init_cfg (dict | None, optional): The Config for initialization.
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Default: None.
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"""
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def __init__(self,
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embed_dims,
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appearance_dims,
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num_heads,
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window_size,
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qkv_bias=True,
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qk_scale=None,
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attn_drop_rate=0.,
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proj_drop_rate=0.,
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init_cfg=None):
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super().__init__(init_cfg=init_cfg)
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self.embed_dims = embed_dims
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self.appearance_dims = appearance_dims
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self.window_size = window_size # Wh, Ww
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self.num_heads = num_heads
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head_embed_dims = embed_dims // num_heads
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self.scale = qk_scale or head_embed_dims**-0.5
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# About 2x faster than original impl
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Wh, Ww = self.window_size
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rel_index_coords = self.double_step_seq(2 * Ww - 1, Wh, 1, Ww)
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rel_position_index = rel_index_coords + rel_index_coords.T
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rel_position_index = rel_position_index.flip(1).contiguous()
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self.register_buffer('relative_position_index', rel_position_index)
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self.qk = nn.Linear(
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embed_dims + appearance_dims, embed_dims * 2, bias=qkv_bias)
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self.v = nn.Linear(embed_dims, embed_dims, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop_rate)
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self.proj = nn.Linear(embed_dims, embed_dims)
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self.proj_drop = nn.Dropout(proj_drop_rate)
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self.softmax = nn.Softmax(dim=-1)
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def forward(self, x, mask=None):
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"""
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Args:
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x (tensor): input features with shape of (num_windows*B, N, C),
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C = embed_dims + appearance_dims.
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mask (tensor | None, Optional): mask with shape of (num_windows,
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Wh*Ww, Wh*Ww), value should be between (-inf, 0].
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"""
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B, N, _ = x.shape
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qk = self.qk(x).reshape(B, N, 2, self.num_heads,
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self.embed_dims // self.num_heads).permute(
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2, 0, 3, 1,
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4) # 2 B NUM_HEADS N embed_dims//NUM_HEADS
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v = self.v(x[:, :, :self.embed_dims]).reshape(
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B, N, self.num_heads, self.embed_dims // self.num_heads).permute(
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0, 2, 1, 3) # B NUM_HEADS N embed_dims//NUM_HEADS
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# make torchscript happy (cannot use tensor as tuple)
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q, k = qk[0], qk[1]
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q = q * self.scale
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attn = (q @ k.transpose(-2, -1))
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if mask is not None:
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nW = mask.shape[0]
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attn = attn.view(B // nW, nW, self.num_heads, N,
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N) + mask.unsqueeze(1).unsqueeze(0)
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attn = attn.view(-1, self.num_heads, N, N)
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attn = self.softmax(attn)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, self.embed_dims)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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@staticmethod
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def double_step_seq(step1, len1, step2, len2):
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"""Double step sequence."""
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seq1 = torch.arange(0, step1 * len1, step1)
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seq2 = torch.arange(0, step2 * len2, step2)
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return (seq1[:, None] + seq2[None, :]).reshape(1, -1)
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class AGShiftWindowMSA(BaseModule):
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"""Appearance Guidance Shifted Window Multihead Self-Attention Module.
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Args:
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embed_dims (int): Number of input channels.
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appearance_dims (int): Number of appearance guidance channels
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num_heads (int): Number of attention heads.
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window_size (int): The height and width of the window.
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shift_size (int, optional): The shift step of each window towards
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right-bottom. If zero, act as regular window-msa. Defaults to 0.
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qkv_bias (bool, optional): If True, add a learnable bias to q, k, v.
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Default: True
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qk_scale (float | None, optional): Override default qk scale of
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head_dim ** -0.5 if set. Defaults: None.
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attn_drop_rate (float, optional): Dropout ratio of attention weight.
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Defaults: 0.
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proj_drop_rate (float, optional): Dropout ratio of output.
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Defaults: 0.
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dropout_layer (dict, optional): The dropout_layer used before output.
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Defaults: dict(type='DropPath', drop_prob=0.).
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init_cfg (dict, optional): The extra config for initialization.
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Default: None.
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"""
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def __init__(self,
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embed_dims,
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appearance_dims,
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num_heads,
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window_size,
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shift_size=0,
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qkv_bias=True,
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qk_scale=None,
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attn_drop_rate=0,
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proj_drop_rate=0,
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dropout_layer=dict(type='DropPath', drop_prob=0.),
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init_cfg=None):
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super().__init__(init_cfg=init_cfg)
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self.window_size = window_size
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self.shift_size = shift_size
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assert 0 <= self.shift_size < self.window_size
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self.w_msa = AGWindowMSA(
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embed_dims=embed_dims,
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appearance_dims=appearance_dims,
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num_heads=num_heads,
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window_size=to_2tuple(window_size),
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop_rate=attn_drop_rate,
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proj_drop_rate=proj_drop_rate,
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init_cfg=None)
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self.drop = build_dropout(dropout_layer)
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def forward(self, query, hw_shape):
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"""
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Args:
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query: The input query.
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hw_shape: The shape of the feature height and width.
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"""
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B, L, C = query.shape
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H, W = hw_shape
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assert L == H * W, 'input feature has wrong size'
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query = query.view(B, H, W, C)
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# pad feature maps to multiples of window size
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pad_r = (self.window_size - W % self.window_size) % self.window_size
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pad_b = (self.window_size - H % self.window_size) % self.window_size
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query = F.pad(query, (0, 0, 0, pad_r, 0, pad_b))
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H_pad, W_pad = query.shape[1], query.shape[2]
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# cyclic shift
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if self.shift_size > 0:
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shifted_query = torch.roll(
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query,
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shifts=(-self.shift_size, -self.shift_size),
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dims=(1, 2))
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# calculate attention mask for SW-MSA
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img_mask = torch.zeros((1, H_pad, W_pad, 1), device=query.device)
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h_slices = (slice(0, -self.window_size),
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slice(-self.window_size,
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-self.shift_size), slice(-self.shift_size, None))
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w_slices = (slice(0, -self.window_size),
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slice(-self.window_size,
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-self.shift_size), slice(-self.shift_size, None))
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cnt = 0
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for h in h_slices:
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for w in w_slices:
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img_mask[:, h, w, :] = cnt
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cnt += 1
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# nW, window_size, window_size, 1
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mask_windows = self.window_partition(img_mask)
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mask_windows = mask_windows.view(
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-1, self.window_size * self.window_size)
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attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
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attn_mask = attn_mask.masked_fill(attn_mask != 0,
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float(-100.0)).masked_fill(
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attn_mask == 0, float(0.0))
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else:
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shifted_query = query
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attn_mask = None
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# nW*B, window_size, window_size, C
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query_windows = self.window_partition(shifted_query)
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# nW*B, window_size*window_size, C
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query_windows = query_windows.view(-1, self.window_size**2, C)
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# W-MSA/SW-MSA (nW*B, window_size*window_size, C)
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attn_windows = self.w_msa(query_windows, mask=attn_mask)
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# merge windows
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attn_windows = attn_windows.view(-1, self.window_size,
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self.window_size,
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self.w_msa.embed_dims)
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# B H' W' self.w_msa.embed_dims
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shifted_x = self.window_reverse(attn_windows, H_pad, W_pad)
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# reverse cyclic shift
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if self.shift_size > 0:
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x = torch.roll(
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shifted_x,
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shifts=(self.shift_size, self.shift_size),
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dims=(1, 2))
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else:
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x = shifted_x
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if pad_r > 0 or pad_b:
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x = x[:, :H, :W, :].contiguous()
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x = x.view(B, H * W, self.w_msa.embed_dims)
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x = self.drop(x)
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return x
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def window_reverse(self, windows, H, W):
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"""
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Args:
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windows: (num_windows*B, window_size, window_size, C)
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H (int): Height of image
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W (int): Width of image
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Returns:
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x: (B, H, W, C)
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"""
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window_size = self.window_size
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B = int(windows.shape[0] / (H * W / window_size / window_size))
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x = windows.view(B, H // window_size, W // window_size, window_size,
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window_size, -1)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
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return x
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def window_partition(self, x):
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"""
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Args:
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x: (B, H, W, C)
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Returns:
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windows: (num_windows*B, window_size, window_size, C)
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"""
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B, H, W, C = x.shape
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window_size = self.window_size
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x = x.view(B, H // window_size, window_size, W // window_size,
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window_size, C)
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous()
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windows = windows.view(-1, window_size, window_size, C)
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return windows
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class AGSwinBlock(BaseModule):
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"""Appearance Guidance Swin Transformer Block.
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Args:
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embed_dims (int): The feature dimension.
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appearance_dims (int): The appearance guidance dimension.
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num_heads (int): Parallel attention heads.
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mlp_ratios (int): The hidden dimension ratio w.r.t. embed_dims
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for FFNs.
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window_size (int, optional): The local window scale.
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Default: 7.
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shift (bool, optional): whether to shift window or not.
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Default False.
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qkv_bias (bool, optional): enable bias for qkv if True.
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Default: True.
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qk_scale (float | None, optional): Override default qk scale of
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head_dim ** -0.5 if set. Default: None.
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drop_rate (float, optional): Dropout rate. Default: 0.
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attn_drop_rate (float, optional): Attention dropout rate.
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Default: 0.
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drop_path_rate (float, optional): Stochastic depth rate.
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Default: 0.
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act_cfg (dict, optional): The config dict of activation function.
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Default: dict(type='GELU').
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norm_cfg (dict, optional): The config dict of normalization.
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Default: dict(type='LN').
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init_cfg (dict | list | None, optional): The init config.
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Default: None.
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"""
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def __init__(self,
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embed_dims,
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appearance_dims,
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num_heads,
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mlp_ratios=4,
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window_size=7,
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shift=False,
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qkv_bias=True,
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qk_scale=None,
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drop_rate=0.,
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attn_drop_rate=0.,
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drop_path_rate=0.,
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act_cfg=dict(type='GELU'),
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norm_cfg=dict(type='LN'),
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init_cfg=None):
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super().__init__(init_cfg=init_cfg)
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self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
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self.attn = AGShiftWindowMSA(
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embed_dims=embed_dims,
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appearance_dims=appearance_dims,
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num_heads=num_heads,
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window_size=window_size,
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shift_size=window_size // 2 if shift else 0,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop_rate=attn_drop_rate,
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proj_drop_rate=drop_rate,
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dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
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|
init_cfg=None)
|
||
|
|
|
||
|
|
self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
|
||
|
|
self.ffn = FFN(
|
||
|
|
embed_dims=embed_dims,
|
||
|
|
feedforward_channels=embed_dims * mlp_ratios,
|
||
|
|
num_fcs=2,
|
||
|
|
ffn_drop=drop_rate,
|
||
|
|
dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
|
||
|
|
act_cfg=act_cfg,
|
||
|
|
add_identity=True,
|
||
|
|
init_cfg=None)
|
||
|
|
|
||
|
|
def forward(self, inputs, hw_shape):
|
||
|
|
"""
|
||
|
|
Args:
|
||
|
|
inputs (list[Tensor]): appearance_guidance (B, H, W, C);
|
||
|
|
x (B, L, C)
|
||
|
|
hw_shape (tuple[int]): shape of feature.
|
||
|
|
"""
|
||
|
|
x, appearance_guidance = inputs
|
||
|
|
B, L, C = x.shape
|
||
|
|
H, W = hw_shape
|
||
|
|
assert L == H * W, 'input feature has wrong size'
|
||
|
|
|
||
|
|
identity = x
|
||
|
|
x = self.norm1(x)
|
||
|
|
|
||
|
|
# appearance guidance
|
||
|
|
x = x.view(B, H, W, C)
|
||
|
|
if appearance_guidance is not None:
|
||
|
|
x = torch.cat([x, appearance_guidance], dim=-1).flatten(1, 2)
|
||
|
|
|
||
|
|
x = self.attn(x, hw_shape)
|
||
|
|
|
||
|
|
x = x + identity
|
||
|
|
|
||
|
|
identity = x
|
||
|
|
x = self.norm2(x)
|
||
|
|
x = self.ffn(x, identity=identity)
|
||
|
|
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
@MODELS.register_module()
|
||
|
|
class SpatialAggregateLayer(BaseModule):
|
||
|
|
"""Spatial aggregation layer of CAT-Seg.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
embed_dims (int): The feature dimension.
|
||
|
|
appearance_dims (int): The appearance guidance dimension.
|
||
|
|
num_heads (int): Parallel attention heads.
|
||
|
|
mlp_ratios (int): The hidden dimension ratio w.r.t. embed_dims
|
||
|
|
for FFNs.
|
||
|
|
window_size (int, optional): The local window scale. Default: 7.
|
||
|
|
qk_scale (float | None, optional): Override default qk scale of
|
||
|
|
head_dim ** -0.5 if set. Default: None.
|
||
|
|
init_cfg (dict | list | None, optional): The init config.
|
||
|
|
Default: None.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self,
|
||
|
|
embed_dims,
|
||
|
|
appearance_dims,
|
||
|
|
num_heads,
|
||
|
|
mlp_ratios,
|
||
|
|
window_size=7,
|
||
|
|
qk_scale=None,
|
||
|
|
init_cfg=None):
|
||
|
|
super().__init__(init_cfg=init_cfg)
|
||
|
|
self.block_1 = AGSwinBlock(
|
||
|
|
embed_dims,
|
||
|
|
appearance_dims,
|
||
|
|
num_heads,
|
||
|
|
mlp_ratios,
|
||
|
|
window_size=window_size,
|
||
|
|
shift=False,
|
||
|
|
qk_scale=qk_scale)
|
||
|
|
self.block_2 = AGSwinBlock(
|
||
|
|
embed_dims,
|
||
|
|
appearance_dims,
|
||
|
|
num_heads,
|
||
|
|
mlp_ratios,
|
||
|
|
window_size=window_size,
|
||
|
|
shift=True,
|
||
|
|
qk_scale=qk_scale)
|
||
|
|
self.guidance_norm = nn.LayerNorm(
|
||
|
|
appearance_dims) if appearance_dims > 0 else None
|
||
|
|
|
||
|
|
def forward(self, x, appearance_guidance):
|
||
|
|
"""
|
||
|
|
Args:
|
||
|
|
x (torch.Tensor): B C T H W.
|
||
|
|
appearance_guidance (torch.Tensor): B C H W.
|
||
|
|
"""
|
||
|
|
B, C, T, H, W = x.shape
|
||
|
|
x = x.permute(0, 2, 3, 4, 1).flatten(0, 1).flatten(1, 2) # BT, HW, C
|
||
|
|
if appearance_guidance is not None:
|
||
|
|
appearance_guidance = appearance_guidance.repeat(
|
||
|
|
T, 1, 1, 1).permute(0, 2, 3, 1) # BT, HW, C
|
||
|
|
appearance_guidance = self.guidance_norm(appearance_guidance)
|
||
|
|
else:
|
||
|
|
assert self.appearance_dims == 0
|
||
|
|
x = self.block_1((x, appearance_guidance), (H, W))
|
||
|
|
x = self.block_2((x, appearance_guidance), (H, W))
|
||
|
|
x = x.transpose(1, 2).reshape(B, T, C, -1)
|
||
|
|
x = x.transpose(1, 2).reshape(B, C, T, H, W)
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
class AttentionLayer(nn.Module):
|
||
|
|
"""Attention layer for ClassAggregration of CAT-Seg.
|
||
|
|
|
||
|
|
Source: https://github.com/KU-CVLAB/CAT-Seg/blob/main/cat_seg/modeling/transformer/model.py#L310 # noqa
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self,
|
||
|
|
hidden_dim,
|
||
|
|
guidance_dim,
|
||
|
|
nheads=8,
|
||
|
|
attention_type='linear'):
|
||
|
|
super().__init__()
|
||
|
|
self.nheads = nheads
|
||
|
|
self.q = nn.Linear(hidden_dim + guidance_dim, hidden_dim)
|
||
|
|
self.k = nn.Linear(hidden_dim + guidance_dim, hidden_dim)
|
||
|
|
self.v = nn.Linear(hidden_dim, hidden_dim)
|
||
|
|
|
||
|
|
if attention_type == 'linear':
|
||
|
|
self.attention = LinearAttention()
|
||
|
|
elif attention_type == 'full':
|
||
|
|
self.attention = FullAttention()
|
||
|
|
else:
|
||
|
|
raise NotImplementedError
|
||
|
|
|
||
|
|
def forward(self, x, guidance=None):
|
||
|
|
"""
|
||
|
|
Args:
|
||
|
|
x: B*H_p*W_p, T, C
|
||
|
|
guidance: B*H_p*W_p, T, C
|
||
|
|
"""
|
||
|
|
B, L, _ = x.shape
|
||
|
|
q = self.q(torch.cat([x, guidance],
|
||
|
|
dim=-1)) if guidance is not None else self.q(x)
|
||
|
|
k = self.k(torch.cat([x, guidance],
|
||
|
|
dim=-1)) if guidance is not None else self.k(x)
|
||
|
|
v = self.v(x)
|
||
|
|
|
||
|
|
q = q.reshape(B, L, self.nheads, -1)
|
||
|
|
k = k.reshape(B, L, self.nheads, -1)
|
||
|
|
v = v.reshape(B, L, self.nheads, -1)
|
||
|
|
|
||
|
|
out = self.attention(q, k, v)
|
||
|
|
out = out.reshape(B, L, -1)
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
@MODELS.register_module()
|
||
|
|
class ClassAggregateLayer(BaseModule):
|
||
|
|
"""Class aggregation layer of CAT-Seg.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
hidden_dims (int): The feature dimension.
|
||
|
|
guidance_dims (int): The appearance guidance dimension.
|
||
|
|
num_heads (int): Parallel attention heads.
|
||
|
|
attention_type (str): Type of attention layer. Default: 'linear'.
|
||
|
|
pooling_size (tuple[int] | list[int]): Pooling size.
|
||
|
|
init_cfg (dict | list | None, optional): The init config.
|
||
|
|
Default: None.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
hidden_dims=64,
|
||
|
|
guidance_dims=64,
|
||
|
|
num_heads=8,
|
||
|
|
attention_type='linear',
|
||
|
|
pooling_size=(4, 4),
|
||
|
|
init_cfg=None,
|
||
|
|
):
|
||
|
|
super().__init__(init_cfg=init_cfg)
|
||
|
|
self.pool = nn.AvgPool2d(pooling_size)
|
||
|
|
self.attention = AttentionLayer(
|
||
|
|
hidden_dims,
|
||
|
|
guidance_dims,
|
||
|
|
nheads=num_heads,
|
||
|
|
attention_type=attention_type)
|
||
|
|
self.MLP = FFN(
|
||
|
|
embed_dims=hidden_dims,
|
||
|
|
feedforward_channels=hidden_dims * 4,
|
||
|
|
num_fcs=2)
|
||
|
|
self.norm1 = nn.LayerNorm(hidden_dims)
|
||
|
|
self.norm2 = nn.LayerNorm(hidden_dims)
|
||
|
|
|
||
|
|
def pool_features(self, x):
|
||
|
|
"""Intermediate pooling layer for computational efficiency.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: B, C, T, H, W
|
||
|
|
"""
|
||
|
|
B, C, T, H, W = x.shape
|
||
|
|
x = x.transpose(1, 2).reshape(-1, C, H, W)
|
||
|
|
x = self.pool(x)
|
||
|
|
*_, H_, W_ = x.shape
|
||
|
|
x = x.reshape(B, T, C, H_, W_).transpose(1, 2)
|
||
|
|
return x
|
||
|
|
|
||
|
|
def forward(self, x, guidance):
|
||
|
|
"""
|
||
|
|
Args:
|
||
|
|
x: B, C, T, H, W
|
||
|
|
guidance: B, T, C
|
||
|
|
"""
|
||
|
|
B, C, T, H, W = x.size()
|
||
|
|
x_pool = self.pool_features(x)
|
||
|
|
*_, H_pool, W_pool = x_pool.size()
|
||
|
|
|
||
|
|
x_pool = x_pool.permute(0, 3, 4, 2, 1).reshape(-1, T, C)
|
||
|
|
# B*H_p*W_p T C
|
||
|
|
if guidance is not None:
|
||
|
|
guidance = guidance.repeat(H_pool * W_pool, 1, 1)
|
||
|
|
|
||
|
|
x_pool = x_pool + self.attention(self.norm1(x_pool),
|
||
|
|
guidance) # Attention
|
||
|
|
x_pool = x_pool + self.MLP(self.norm2(x_pool)) # MLP
|
||
|
|
|
||
|
|
x_pool = x_pool.reshape(B, H_pool * W_pool, T,
|
||
|
|
C).permute(0, 2, 3, 1).reshape(
|
||
|
|
B, T, C, H_pool,
|
||
|
|
W_pool).flatten(0, 1) # BT C H_p W_p
|
||
|
|
x_pool = F.interpolate(
|
||
|
|
x_pool, size=(H, W), mode='bilinear', align_corners=True)
|
||
|
|
x_pool = x_pool.reshape(B, T, C, H, W).transpose(1, 2) # B C T H W
|
||
|
|
x = x + x_pool # Residual
|
||
|
|
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
@MODELS.register_module()
|
||
|
|
class AggregatorLayer(BaseModule):
|
||
|
|
"""Single Aggregator Layer of CAT-Seg."""
|
||
|
|
|
||
|
|
def __init__(self,
|
||
|
|
embed_dims=64,
|
||
|
|
text_guidance_dims=512,
|
||
|
|
appearance_guidance_dims=512,
|
||
|
|
num_heads=4,
|
||
|
|
mlp_ratios=4,
|
||
|
|
window_size=7,
|
||
|
|
attention_type='linear',
|
||
|
|
pooling_size=(2, 2),
|
||
|
|
init_cfg=None) -> None:
|
||
|
|
super().__init__(init_cfg=init_cfg)
|
||
|
|
self.spatial_agg = SpatialAggregateLayer(
|
||
|
|
embed_dims,
|
||
|
|
appearance_guidance_dims,
|
||
|
|
num_heads=num_heads,
|
||
|
|
mlp_ratios=mlp_ratios,
|
||
|
|
window_size=window_size)
|
||
|
|
self.class_agg = ClassAggregateLayer(
|
||
|
|
embed_dims,
|
||
|
|
text_guidance_dims,
|
||
|
|
num_heads=num_heads,
|
||
|
|
attention_type=attention_type,
|
||
|
|
pooling_size=pooling_size)
|
||
|
|
|
||
|
|
def forward(self, x, appearance_guidance, text_guidance):
|
||
|
|
"""
|
||
|
|
Args:
|
||
|
|
x: B C T H W
|
||
|
|
"""
|
||
|
|
x = self.spatial_agg(x, appearance_guidance)
|
||
|
|
x = self.class_agg(x, text_guidance)
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
@MODELS.register_module()
|
||
|
|
class CATSegAggregator(BaseModule):
|
||
|
|
"""CATSeg Aggregator.
|
||
|
|
|
||
|
|
This Aggregator is the mmseg implementation of
|
||
|
|
`CAT-Seg <https://arxiv.org/abs/2303.11797>`_.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
text_guidance_dim (int): Text guidance dimensions. Default: 512.
|
||
|
|
text_guidance_proj_dim (int): Text guidance projection dimensions.
|
||
|
|
Default: 128.
|
||
|
|
appearance_guidance_dim (int): Appearance guidance dimensions.
|
||
|
|
Default: 512.
|
||
|
|
appearance_guidance_proj_dim (int): Appearance guidance projection
|
||
|
|
dimensions. Default: 128.
|
||
|
|
num_layers (int): Aggregator layer number. Default: 4.
|
||
|
|
num_heads (int): Attention layer head number. Default: 4.
|
||
|
|
embed_dims (int): Input feature dimensions. Default: 128.
|
||
|
|
pooling_size (tuple | list): Pooling size of the class aggregator
|
||
|
|
layer. Default: (6, 6).
|
||
|
|
mlp_ratios (int): The hidden dimension ratio w.r.t. input dimension.
|
||
|
|
Default: 4.
|
||
|
|
window_size (int): Swin block window size. Default:12.
|
||
|
|
attention_type (str): Attention type of class aggregator layer.
|
||
|
|
Default:'linear'.
|
||
|
|
prompt_channel (int): Prompt channels. Default: 80.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self,
|
||
|
|
text_guidance_dim=512,
|
||
|
|
text_guidance_proj_dim=128,
|
||
|
|
appearance_guidance_dim=512,
|
||
|
|
appearance_guidance_proj_dim=128,
|
||
|
|
num_layers=4,
|
||
|
|
num_heads=4,
|
||
|
|
embed_dims=128,
|
||
|
|
pooling_size=(6, 6),
|
||
|
|
mlp_ratios=4,
|
||
|
|
window_size=12,
|
||
|
|
attention_type='linear',
|
||
|
|
prompt_channel=80,
|
||
|
|
**kwargs):
|
||
|
|
super().__init__(**kwargs)
|
||
|
|
self.num_layers = num_layers
|
||
|
|
self.embed_dims = embed_dims
|
||
|
|
|
||
|
|
self.layers = nn.ModuleList([
|
||
|
|
AggregatorLayer(
|
||
|
|
embed_dims=embed_dims,
|
||
|
|
text_guidance_dims=text_guidance_proj_dim,
|
||
|
|
appearance_guidance_dims=appearance_guidance_proj_dim,
|
||
|
|
num_heads=num_heads,
|
||
|
|
mlp_ratios=mlp_ratios,
|
||
|
|
window_size=window_size,
|
||
|
|
attention_type=attention_type,
|
||
|
|
pooling_size=pooling_size) for _ in range(num_layers)
|
||
|
|
])
|
||
|
|
|
||
|
|
self.conv1 = nn.Conv2d(
|
||
|
|
prompt_channel, embed_dims, kernel_size=7, stride=1, padding=3)
|
||
|
|
|
||
|
|
self.guidance_projection = nn.Sequential(
|
||
|
|
nn.Conv2d(
|
||
|
|
appearance_guidance_dim,
|
||
|
|
appearance_guidance_proj_dim,
|
||
|
|
kernel_size=3,
|
||
|
|
stride=1,
|
||
|
|
padding=1),
|
||
|
|
nn.ReLU(),
|
||
|
|
) if appearance_guidance_dim > 0 else None
|
||
|
|
|
||
|
|
self.text_guidance_projection = nn.Sequential(
|
||
|
|
nn.Linear(text_guidance_dim, text_guidance_proj_dim),
|
||
|
|
nn.ReLU(),
|
||
|
|
) if text_guidance_dim > 0 else None
|
||
|
|
|
||
|
|
def feature_map(self, img_feats, text_feats):
|
||
|
|
"""Concatenation type cost volume.
|
||
|
|
|
||
|
|
For ablation study of cost volume type.
|
||
|
|
"""
|
||
|
|
img_feats = F.normalize(img_feats, dim=1) # B C H W
|
||
|
|
img_feats = img_feats.unsqueeze(2).repeat(1, 1, text_feats.shape[1], 1,
|
||
|
|
1)
|
||
|
|
text_feats = F.normalize(text_feats, dim=-1) # B T P C
|
||
|
|
text_feats = text_feats.mean(dim=-2)
|
||
|
|
text_feats = F.normalize(text_feats, dim=-1) # B T C
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||
|
|
text_feats = text_feats.unsqueeze(-1).unsqueeze(-1).repeat(
|
||
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|
1, 1, 1, img_feats.shape[-2], img_feats.shape[-1]).transpose(1, 2)
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||
|
|
return torch.cat((img_feats, text_feats), dim=1) # B 2C T H W
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||
|
|
|
||
|
|
def correlation(self, img_feats, text_feats):
|
||
|
|
"""Correlation of image features and text features."""
|
||
|
|
img_feats = F.normalize(img_feats, dim=1) # B C H W
|
||
|
|
text_feats = F.normalize(text_feats, dim=-1) # B T P C
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||
|
|
corr = torch.einsum('bchw, btpc -> bpthw', img_feats, text_feats)
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||
|
|
return corr
|
||
|
|
|
||
|
|
def corr_embed(self, x):
|
||
|
|
"""Correlation embeddings encoding."""
|
||
|
|
B = x.shape[0]
|
||
|
|
corr_embed = x.permute(0, 2, 1, 3, 4).flatten(0, 1)
|
||
|
|
corr_embed = self.conv1(corr_embed)
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||
|
|
corr_embed = corr_embed.reshape(B, -1, self.embed_dims, x.shape[-2],
|
||
|
|
x.shape[-1]).transpose(1, 2)
|
||
|
|
return corr_embed
|
||
|
|
|
||
|
|
def forward(self, inputs):
|
||
|
|
"""
|
||
|
|
Args:
|
||
|
|
inputs (dict): including the following keys,
|
||
|
|
'appearance_feat': list[torch.Tensor], w.r.t. out_indices of
|
||
|
|
`self.feature_extractor`.
|
||
|
|
'clip_text_feat': the text feature extracted by clip text
|
||
|
|
encoder.
|
||
|
|
'clip_text_feat_test': the text feature extracted by clip text
|
||
|
|
encoder for testing.
|
||
|
|
'clip_img_feat': the image feature extracted clip image
|
||
|
|
encoder.
|
||
|
|
"""
|
||
|
|
img_feats = inputs['clip_img_feat']
|
||
|
|
B = img_feats.size(0)
|
||
|
|
appearance_guidance = inputs[
|
||
|
|
'appearance_feat'][::-1] # order (out_indices) 2, 1, 0
|
||
|
|
text_feats = inputs['clip_text_feat'] if self.training else inputs[
|
||
|
|
'clip_text_feat_test']
|
||
|
|
text_feats = text_feats.repeat(B, 1, 1, 1)
|
||
|
|
|
||
|
|
corr = self.correlation(img_feats, text_feats)
|
||
|
|
# corr = self.feature_map(img_feats, text_feats)
|
||
|
|
corr_embed = self.corr_embed(corr)
|
||
|
|
|
||
|
|
projected_guidance, projected_text_guidance = None, None
|
||
|
|
|
||
|
|
if self.guidance_projection is not None:
|
||
|
|
projected_guidance = self.guidance_projection(
|
||
|
|
appearance_guidance[0])
|
||
|
|
|
||
|
|
if self.text_guidance_projection is not None:
|
||
|
|
text_feats = text_feats.mean(dim=-2)
|
||
|
|
text_feats = text_feats / text_feats.norm(dim=-1, keepdim=True)
|
||
|
|
projected_text_guidance = self.text_guidance_projection(text_feats)
|
||
|
|
|
||
|
|
for layer in self.layers:
|
||
|
|
corr_embed = layer(corr_embed, projected_guidance,
|
||
|
|
projected_text_guidance)
|
||
|
|
|
||
|
|
return dict(
|
||
|
|
corr_embed=corr_embed, appearance_feats=appearance_guidance[1:])
|