jonstreeterandClaude
1c79f4ad10
- Enhanced .gitignore to exclude test scripts and temporary files - Removed post-processor dependency checks from auto_installer - Improved CUDA requirements feedback with clearer formatting - Added graceful handling for corrupted spatial_correlation_sampler - Fixed SyntaxError catching in addition to ImportError - Better user guidance for compilation prerequisites 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
948 lines
34 KiB
Python
948 lines
34 KiB
Python
import math
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import pdb
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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 colormnet_model.basic import DropOutLogit, ScaleOffset, DWConv2d
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def multiply_by_ychunks(x, y, chunks=1):
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if chunks <= 1:
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return x @ y
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else:
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return torch.cat([x @ _y for _y in y.chunk(chunks, dim=-1)], dim=-1)
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def multiply_by_xchunks(x, y, chunks=1):
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if chunks <= 1:
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return x @ y
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else:
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return torch.cat([_x @ y for _x in x.chunk(chunks, dim=-2)], dim=-2)
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# Long-term attention
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class MultiheadAttention(nn.Module):
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def __init__(self,
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d_model,
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num_head=8,
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dropout=0.,
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use_linear=True,
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d_att=None,
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use_dis=False,
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qk_chunks=1,
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max_mem_len_ratio=-1,
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top_k=-1):
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super().__init__()
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self.d_model = d_model
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self.num_head = num_head
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self.use_dis = use_dis
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self.qk_chunks = qk_chunks
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self.max_mem_len_ratio = float(max_mem_len_ratio)
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self.top_k = top_k
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self.hidden_dim = d_model // num_head
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self.d_att = self.hidden_dim if d_att is None else d_att
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self.T = self.d_att**0.5
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self.use_linear = use_linear
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if use_linear:
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self.linear_Q = nn.Linear(d_model, d_model)
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self.linear_K = nn.Linear(d_model, d_model)
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self.linear_V = nn.Linear(d_model, d_model)
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self.dropout = nn.Dropout(dropout)
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self.drop_prob = dropout
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self.projection = nn.Linear(d_model, d_model)
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self._init_weight()
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def forward(self, Q, K, V):
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"""
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:param Q: A 3d tensor with shape of [T_q, bs, C_q]
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:param K: A 3d tensor with shape of [T_k, bs, C_k]
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:param V: A 3d tensor with shape of [T_v, bs, C_v]
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"""
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num_head = self.num_head
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hidden_dim = self.hidden_dim
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bs = Q.size()[1]
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# Linear projections
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if self.use_linear:
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Q = self.linear_Q(Q)
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K = self.linear_K(K)
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V = self.linear_V(V)
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# Scale
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Q = Q / self.T
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if not self.training and self.max_mem_len_ratio > 0:
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mem_len_ratio = float(K.size(0)) / Q.size(0)
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if mem_len_ratio > self.max_mem_len_ratio:
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scaling_ratio = math.log(mem_len_ratio) / math.log(
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self.max_mem_len_ratio)
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Q = Q * scaling_ratio
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# Multi-head
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Q = Q.view(-1, bs, num_head, self.d_att).permute(1, 2, 0, 3)
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K = K.view(-1, bs, num_head, self.d_att).permute(1, 2, 3, 0)
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V = V.view(-1, bs, num_head, hidden_dim).permute(1, 2, 0, 3)
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# Multiplication
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QK = multiply_by_ychunks(Q, K, self.qk_chunks)
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if self.use_dis:
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QK = 2 * QK - K.pow(2).sum(dim=-2, keepdim=True)
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# Activation
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if not self.training and self.top_k > 0 and self.top_k < QK.size()[-1]:
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top_QK, indices = torch.topk(QK, k=self.top_k, dim=-1)
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top_attn = torch.softmax(top_QK, dim=-1)
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attn = torch.zeros_like(QK).scatter_(-1, indices, top_attn)
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else:
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attn = torch.softmax(QK, dim=-1)
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# Dropouts
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attn = self.dropout(attn)
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# Weighted sum
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outputs = multiply_by_xchunks(attn, V,
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self.qk_chunks).permute(2, 0, 1, 3)
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# Restore shape
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outputs = outputs.reshape(-1, bs, self.d_model)
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outputs = self.projection(outputs)
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return outputs, attn
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def _init_weight(self):
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for p in self.parameters():
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if p.dim() > 1:
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nn.init.xavier_uniform_(p)
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# Short-term attention
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class MultiheadLocalAttentionV1(nn.Module):
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def __init__(self,
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d_model,
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num_head,
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dropout=0.,
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max_dis=7,
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dilation=1,
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use_linear=True,
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enable_corr=True):
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super().__init__()
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self.dilation = dilation
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self.window_size = 2 * max_dis + 1
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self.max_dis = max_dis
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self.num_head = num_head
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self.T = ((d_model / num_head)**0.5)
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self.use_linear = use_linear
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if use_linear:
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self.linear_Q = nn.Conv2d(d_model, d_model, kernel_size=1)
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self.linear_K = nn.Conv2d(d_model, d_model, kernel_size=1)
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self.linear_V = nn.Conv2d(d_model, d_model, kernel_size=1)
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self.relative_emb_k = nn.Conv2d(d_model,
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num_head * self.window_size *
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self.window_size,
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kernel_size=1,
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groups=num_head)
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self.relative_emb_v = nn.Parameter(
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torch.zeros([
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self.num_head, d_model // self.num_head,
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self.window_size * self.window_size
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]))
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self.enable_corr = enable_corr
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if enable_corr:
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try:
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from spatial_correlation_sampler import SpatialCorrelationSampler
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self.correlation_sampler = SpatialCorrelationSampler(
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kernel_size=1,
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patch_size=self.window_size,
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stride=1,
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padding=0,
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dilation=1,
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dilation_patch=self.dilation)
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except (ImportError, SyntaxError):
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# Fallback to slower PyTorch-only implementation
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import warnings
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warnings.warn(
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"spatial_correlation_sampler not found. Using fallback implementation (20-40% slower). "
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"For better performance, install: pip install spatial-correlation-sampler",
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UserWarning
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)
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self.enable_corr = False
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self.correlation_sampler = None
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self.projection = nn.Linear(d_model, d_model)
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self.dropout = nn.Dropout(dropout)
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self.drop_prob = dropout
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def forward(self, q, k, v):
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n, c, h, w = v.size()
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if self.use_linear:
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q = self.linear_Q(q)
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k = self.linear_K(k)
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v = self.linear_V(v)
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hidden_dim = c // self.num_head
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relative_emb = self.relative_emb_k(q)
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memory_mask = torch.ones((1, 1, h, w), device=v.device).float()
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# Scale
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q = q / self.T
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q = q.view(-1, hidden_dim, h, w)
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k = k.reshape(-1, hidden_dim, h, w).contiguous()
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unfolded_vu = self.pad_and_unfold(v).view(
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n, self.num_head, hidden_dim, self.window_size * self.window_size,
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h * w) + self.relative_emb_v.unsqueeze(0).unsqueeze(-1)
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relative_emb = relative_emb.view(n, self.num_head,
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self.window_size * self.window_size,
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h * w)
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unfolded_k_mask = self.pad_and_unfold(memory_mask).bool().view(
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1, 1, self.window_size * self.window_size,
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h * w).expand(n, self.num_head, -1, -1)
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if self.enable_corr:
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qk = self.correlation_sampler(q, k).view(
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n, self.num_head, self.window_size * self.window_size,
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h * w) + relative_emb
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else:
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unfolded_k = self.pad_and_unfold(k).view(
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n * self.num_head, hidden_dim,
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self.window_size * self.window_size, h, w)
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qk = (q.unsqueeze(2) * unfolded_k).sum(dim=1).view(
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n, self.num_head, self.window_size * self.window_size,
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h * w) + relative_emb
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qk_mask = 1 - unfolded_k_mask
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qk -= qk_mask * 1e+8 if qk.dtype == torch.float32 else qk_mask * 1e+4
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local_attn = torch.softmax(qk, dim=2)
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local_attn = self.dropout(local_attn)
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output = (local_attn.unsqueeze(2) * unfolded_vu).sum(dim=3).permute(
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3, 0, 1, 2).view(h * w, n, c)
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output = self.projection(output)
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return output, local_attn
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def pad_and_unfold(self, x):
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pad_pixel = self.max_dis * self.dilation
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x = F.pad(x, (pad_pixel, pad_pixel, pad_pixel, pad_pixel),
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mode='constant',
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value=0)
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x = F.unfold(x,
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kernel_size=(self.window_size, self.window_size),
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stride=(1, 1),
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dilation=self.dilation)
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return x
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class MultiheadLocalAttentionV2(nn.Module):
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def __init__(self,
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d_model,
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num_head,
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dropout=0.,
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max_dis=7,
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dilation=1,
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use_linear=True,
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enable_corr=True,
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d_att=None,
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use_dis=False):
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super().__init__()
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self.dilation = dilation
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self.window_size = 2 * max_dis + 1
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self.max_dis = max_dis
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self.num_head = num_head
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self.hidden_dim = d_model // num_head
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self.d_att = self.hidden_dim if d_att is None else d_att
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self.T = self.d_att**0.5
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self.use_dis = use_dis
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self.use_linear = use_linear
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if use_linear:
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self.linear_Q = nn.Conv2d(d_model, d_model, kernel_size=1)
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self.linear_K = nn.Conv2d(d_model, d_model, kernel_size=1)
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self.linear_V = nn.Conv2d(d_model, d_model, kernel_size=1)
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self.relative_emb_k = nn.Conv2d(self.d_att * self.num_head,
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num_head * self.window_size *
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self.window_size,
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kernel_size=1,
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groups=num_head)
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self.relative_emb_v = nn.Parameter(
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torch.zeros([
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self.num_head, d_model // self.num_head,
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self.window_size * self.window_size
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]))
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self.enable_corr = enable_corr
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if enable_corr:
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try:
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from spatial_correlation_sampler import SpatialCorrelationSampler
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self.correlation_sampler = SpatialCorrelationSampler(
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kernel_size=1,
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patch_size=self.window_size,
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stride=1,
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padding=0,
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dilation=1,
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dilation_patch=self.dilation)
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except (ImportError, SyntaxError):
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# Fallback to slower PyTorch-only implementation
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import warnings
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warnings.warn(
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"spatial_correlation_sampler not found. Using fallback implementation (20-40% slower). "
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"For better performance, install: pip install spatial-correlation-sampler",
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UserWarning
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)
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self.projection = nn.Linear(d_model, d_model)
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self.dropout = nn.Dropout(dropout)
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self.drop_prob = dropout
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self.local_mask = None
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self.last_size_2d = None
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self.qk_mask = None
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def forward(self, q, k, v):
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n, c, h, w = v.size()
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if self.use_linear:
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q = self.linear_Q(q)
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k = self.linear_K(k)
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v = self.linear_V(v)
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hidden_dim = self.hidden_dim
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if self.qk_mask is not None and (h, w) == self.last_size_2d:
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qk_mask = self.qk_mask
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else:
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memory_mask = torch.ones((1, 1, h, w), device=v.device).float()
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unfolded_k_mask = self.pad_and_unfold(memory_mask).view(
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1, 1, self.window_size * self.window_size, h * w)
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qk_mask = 1 - unfolded_k_mask
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self.qk_mask = qk_mask
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relative_emb = self.relative_emb_k(q)
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# Scale
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q = q / self.T
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q = q.view(-1, self.d_att, h, w)
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k = k.view(-1, self.d_att, h, w)
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v = v.view(-1, self.num_head, hidden_dim, h * w)
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relative_emb = relative_emb.view(n, self.num_head,
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self.window_size * self.window_size,
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h * w)
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if self.enable_corr:
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qk = self.correlation_sampler(q, k).view(
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n, self.num_head, self.window_size * self.window_size, h * w)
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else:
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unfolded_k = self.pad_and_unfold(k).view(
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n * self.num_head, self.d_att,
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self.window_size * self.window_size, h, w)
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qk = (q.unsqueeze(2) * unfolded_k).sum(dim=1).view(
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n, self.num_head, self.window_size * self.window_size, h * w)
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if self.use_dis:
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qk = 2 * qk - self.pad_and_unfold(
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k.pow(2).sum(dim=1, keepdim=True)).view(
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n, self.num_head, self.window_size * self.window_size,
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h * w)
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qk = qk + relative_emb
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qk -= qk_mask * 1e+8 if qk.dtype == torch.float32 else qk_mask * 1e+4
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local_attn = torch.softmax(qk, dim=2)
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local_attn = self.dropout(local_attn)
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agg_bias = torch.einsum('bhwn,hcw->bhnc', local_attn,
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self.relative_emb_v)
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global_attn = self.local2global(local_attn, h, w)
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agg_value = (global_attn @ v.transpose(-2, -1))
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output = (agg_value + agg_bias).permute(2, 0, 1,
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3).reshape(h * w, n, c)
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output = self.projection(output)
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self.last_size_2d = (h, w)
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return output, local_attn
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def local2global(self, local_attn, height, width):
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batch_size = local_attn.size()[0]
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pad_height = height + 2 * self.max_dis
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pad_width = width + 2 * self.max_dis
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if self.local_mask is not None and (height,
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width) == self.last_size_2d:
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local_mask = self.local_mask
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else:
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ky, kx = torch.meshgrid([
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torch.arange(0, pad_height, device=local_attn.device),
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torch.arange(0, pad_width, device=local_attn.device)
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])
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qy, qx = torch.meshgrid([
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torch.arange(0, height, device=local_attn.device),
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torch.arange(0, width, device=local_attn.device)
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])
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offset_y = qy.reshape(-1, 1) - ky.reshape(1, -1) + self.max_dis
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offset_x = qx.reshape(-1, 1) - kx.reshape(1, -1) + self.max_dis
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local_mask = (offset_y.abs() <= self.max_dis) & (offset_x.abs() <=
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self.max_dis)
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local_mask = local_mask.view(1, 1, height * width, pad_height,
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pad_width)
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self.local_mask = local_mask
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global_attn = torch.zeros(
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(batch_size, self.num_head, height * width, pad_height, pad_width),
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device=local_attn.device)
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global_attn = global_attn.type(torch.HalfTensor).cuda()
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global_attn[local_mask.expand(batch_size, self.num_head,
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-1, -1, -1)] = local_attn.transpose(
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-1, -2).reshape(-1)
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global_attn = global_attn[:, :, :, self.max_dis:-self.max_dis,
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self.max_dis:-self.max_dis].reshape(
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batch_size, self.num_head,
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height * width, height * width)
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return global_attn
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|
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def pad_and_unfold(self, x):
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pad_pixel = self.max_dis * self.dilation
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x = F.pad(x, (pad_pixel, pad_pixel, pad_pixel, pad_pixel),
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mode='constant',
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value=0)
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x = F.unfold(x,
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kernel_size=(self.window_size, self.window_size),
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stride=(1, 1),
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dilation=self.dilation)
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return x
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|
|
|
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class MultiheadLocalAttentionV3(nn.Module):
|
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def __init__(self,
|
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d_model,
|
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num_head,
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dropout=0.,
|
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max_dis=7,
|
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dilation=1,
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use_linear=True):
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super().__init__()
|
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self.dilation = dilation
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self.window_size = 2 * max_dis + 1
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self.max_dis = max_dis
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self.num_head = num_head
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self.T = ((d_model / num_head)**0.5)
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self.use_linear = use_linear
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if use_linear:
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self.linear_Q = nn.Conv2d(d_model, d_model, kernel_size=1)
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self.linear_K = nn.Conv2d(d_model, d_model, kernel_size=1)
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self.linear_V = nn.Conv2d(d_model, d_model, kernel_size=1)
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|
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self.relative_emb_k = nn.Conv2d(d_model,
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num_head * self.window_size *
|
|
self.window_size,
|
|
kernel_size=1,
|
|
groups=num_head)
|
|
self.relative_emb_v = nn.Parameter(
|
|
torch.zeros([
|
|
self.num_head, d_model // self.num_head,
|
|
self.window_size * self.window_size
|
|
]))
|
|
|
|
self.projection = nn.Linear(d_model, d_model)
|
|
self.dropout = DropOutLogit(dropout)
|
|
|
|
self.padded_local_mask = None
|
|
self.local_mask = None
|
|
self.last_size_2d = None
|
|
self.qk_mask = None
|
|
|
|
def forward(self, q, k, v):
|
|
n, c, h, w = q.size()
|
|
|
|
if self.use_linear:
|
|
q = self.linear_Q(q)
|
|
k = self.linear_K(k)
|
|
v = self.linear_V(v)
|
|
|
|
hidden_dim = c // self.num_head
|
|
|
|
relative_emb = self.relative_emb_k(q)
|
|
relative_emb = relative_emb.view(n, self.num_head,
|
|
self.window_size * self.window_size,
|
|
h * w)
|
|
padded_local_mask, local_mask = self.compute_mask(h,
|
|
w,
|
|
device=q.device)
|
|
qk_mask = (~padded_local_mask).float()
|
|
|
|
# Scale
|
|
q = q / self.T
|
|
|
|
q = q.view(-1, self.num_head, hidden_dim, h * w)
|
|
k = k.view(-1, self.num_head, hidden_dim, h * w)
|
|
v = v.view(-1, self.num_head, hidden_dim, h * w)
|
|
|
|
qk = q.transpose(-1, -2) @ k # [B, nH, kL, qL]
|
|
|
|
pad_pixel = self.max_dis * self.dilation
|
|
|
|
padded_qk = F.pad(qk.view(-1, self.num_head, h * w, h, w),
|
|
(pad_pixel, pad_pixel, pad_pixel, pad_pixel),
|
|
mode='constant',
|
|
value=-1e+8 if qk.dtype == torch.float32 else -1e+4)
|
|
|
|
qk_mask = qk_mask * 1e+8 if (padded_qk.dtype
|
|
== torch.float32) else qk_mask * 1e+4
|
|
padded_qk = padded_qk - qk_mask
|
|
|
|
padded_qk[padded_local_mask.expand(n, self.num_head, -1, -1,
|
|
-1)] += relative_emb.transpose(
|
|
-1, -2).reshape(-1)
|
|
padded_qk = self.dropout(padded_qk)
|
|
|
|
local_qk = padded_qk[padded_local_mask.expand(n, self.num_head, -1, -1,
|
|
-1)]
|
|
|
|
global_qk = padded_qk[:, :, :, self.max_dis:-self.max_dis,
|
|
self.max_dis:-self.max_dis].reshape(
|
|
n, self.num_head, h * w, h * w)
|
|
|
|
local_attn = torch.softmax(local_qk.reshape(
|
|
n, self.num_head, h * w, self.window_size * self.window_size),
|
|
dim=3)
|
|
global_attn = torch.softmax(global_qk, dim=3)
|
|
|
|
agg_bias = torch.einsum('bhnw,hcw->nbhc', local_attn,
|
|
self.relative_emb_v).reshape(h * w, n, c)
|
|
|
|
agg_value = (global_attn @ v.transpose(-2, -1))
|
|
|
|
output = agg_value + agg_bias
|
|
|
|
output = self.projection(output)
|
|
|
|
self.last_size_2d = (h, w)
|
|
return output, local_attn
|
|
|
|
def compute_mask(self, height, width, device=None):
|
|
pad_height = height + 2 * self.max_dis
|
|
pad_width = width + 2 * self.max_dis
|
|
|
|
if self.padded_local_mask is not None and (height,
|
|
width) == self.last_size_2d:
|
|
padded_local_mask = self.padded_local_mask
|
|
local_mask = self.local_mask
|
|
|
|
else:
|
|
ky, kx = torch.meshgrid([
|
|
torch.arange(0, pad_height, device=device),
|
|
torch.arange(0, pad_width, device=device)
|
|
])
|
|
qy, qx = torch.meshgrid([
|
|
torch.arange(0, height, device=device),
|
|
torch.arange(0, width, device=device)
|
|
])
|
|
|
|
qy = qy.reshape(-1, 1)
|
|
qx = qx.reshape(-1, 1)
|
|
offset_y = qy - ky.reshape(1, -1) + self.max_dis
|
|
offset_x = qx - kx.reshape(1, -1) + self.max_dis
|
|
padded_local_mask = (offset_y.abs() <= self.max_dis) & (
|
|
offset_x.abs() <= self.max_dis)
|
|
padded_local_mask = padded_local_mask.view(1, 1, height * width,
|
|
pad_height, pad_width)
|
|
local_mask = padded_local_mask[:, :, :, self.max_dis:-self.max_dis,
|
|
self.max_dis:-self.max_dis]
|
|
pad_pixel = self.max_dis * self.dilation
|
|
local_mask = F.pad(local_mask.float(),
|
|
(pad_pixel, pad_pixel, pad_pixel, pad_pixel),
|
|
mode='constant',
|
|
value=0).view(1, 1, height * width, pad_height,
|
|
pad_width)
|
|
self.padded_local_mask = padded_local_mask
|
|
self.local_mask = local_mask
|
|
|
|
return padded_local_mask, local_mask
|
|
|
|
|
|
def linear_gate(x, dim=-1):
|
|
# return F.relu_(x).pow(2.) / x.size()[dim]
|
|
return torch.softmax(x, dim=dim)
|
|
|
|
|
|
def silu(x):
|
|
return x * torch.sigmoid(x)
|
|
|
|
|
|
class GatedPropagation(nn.Module):
|
|
def __init__(self,
|
|
d_qk,
|
|
d_vu,
|
|
num_head=8,
|
|
dropout=0.,
|
|
use_linear=True,
|
|
d_att=None,
|
|
use_dis=False,
|
|
qk_chunks=1,
|
|
max_mem_len_ratio=-1,
|
|
top_k=-1,
|
|
expand_ratio=2.):
|
|
super().__init__()
|
|
expand_ratio = expand_ratio
|
|
self.expand_d_vu = int(d_vu * expand_ratio)
|
|
self.d_vu = d_vu
|
|
self.d_qk = d_qk
|
|
self.num_head = num_head
|
|
self.use_dis = use_dis
|
|
self.qk_chunks = qk_chunks
|
|
self.max_mem_len_ratio = float(max_mem_len_ratio)
|
|
self.top_k = top_k
|
|
|
|
self.hidden_dim = self.expand_d_vu // num_head
|
|
self.d_att = d_qk // num_head if d_att is None else d_att
|
|
self.T = self.d_att**0.5
|
|
self.use_linear = use_linear
|
|
self.d_middle = self.d_att * self.num_head
|
|
|
|
if use_linear:
|
|
self.linear_QK = nn.Linear(d_qk, self.d_middle)
|
|
half_d_vu = self.hidden_dim * num_head // 2
|
|
self.linear_V1 = nn.Linear(d_vu // 2, half_d_vu)
|
|
self.linear_V2 = nn.Linear(d_vu // 2, half_d_vu)
|
|
self.linear_U1 = nn.Linear(d_vu // 2, half_d_vu)
|
|
self.linear_U2 = nn.Linear(d_vu // 2, half_d_vu)
|
|
|
|
self.dropout = nn.Dropout(dropout)
|
|
self.drop_prob = dropout
|
|
|
|
self.dw_conv = DWConv2d(self.expand_d_vu)
|
|
self.projection = nn.Linear(self.expand_d_vu, d_vu)
|
|
|
|
self._init_weight()
|
|
|
|
def forward(self, Q, K, V, U, size_2d):
|
|
"""
|
|
:param Q: A 3d tensor with shape of [T_q, bs, C_q]
|
|
:param K: A 3d tensor with shape of [T_k, bs, C_k]
|
|
:param V: A 3d tensor with shape of [T_v, bs, C_v]
|
|
"""
|
|
num_head = self.num_head
|
|
hidden_dim = self.hidden_dim
|
|
|
|
l, bs, _ = Q.size()
|
|
|
|
# Linear projections
|
|
if self.use_linear:
|
|
Q = K = self.linear_QK(Q)
|
|
|
|
def cat(X1, X2):
|
|
if num_head > 1:
|
|
X1 = X1.view(-1, bs, num_head, hidden_dim // 2)
|
|
X2 = X2.view(-1, bs, num_head, hidden_dim // 2)
|
|
X = torch.cat([X1, X2],
|
|
dim=-1).view(-1, bs, num_head * hidden_dim)
|
|
else:
|
|
X = torch.cat([X1, X2], dim=-1)
|
|
return X
|
|
|
|
V1, V2 = torch.split(V, self.d_vu // 2, dim=-1)
|
|
V1 = self.linear_V1(V1)
|
|
V2 = self.linear_V2(V2)
|
|
V = silu(cat(V1, V2))
|
|
|
|
U1, U2 = torch.split(U, self.d_vu // 2, dim=-1)
|
|
U1 = self.linear_U1(U1)
|
|
U2 = self.linear_U2(U2)
|
|
U = silu(cat(U1, U2))
|
|
|
|
# Scale
|
|
Q = Q / self.T
|
|
|
|
if not self.training and self.max_mem_len_ratio > 0:
|
|
mem_len_ratio = float(K.size(0)) / Q.size(0)
|
|
if mem_len_ratio > self.max_mem_len_ratio:
|
|
scaling_ratio = math.log(mem_len_ratio) / math.log(
|
|
self.max_mem_len_ratio)
|
|
Q = Q * scaling_ratio
|
|
|
|
# Multi-head
|
|
Q = Q.view(-1, bs, num_head, self.d_att).permute(1, 2, 0, 3)
|
|
K = K.view(-1, bs, num_head, self.d_att).permute(1, 2, 3, 0)
|
|
V = V.view(-1, bs, num_head, hidden_dim).permute(1, 2, 0, 3)
|
|
|
|
# Multiplication
|
|
QK = multiply_by_ychunks(Q, K, self.qk_chunks)
|
|
if self.use_dis:
|
|
QK = 2 * QK - K.pow(2).sum(dim=-2, keepdim=True)
|
|
|
|
# Activation
|
|
if not self.training and self.top_k > 0 and self.top_k < QK.size()[-1]:
|
|
top_QK, indices = torch.topk(QK, k=self.top_k, dim=-1)
|
|
top_attn = linear_gate(top_QK, dim=-1)
|
|
attn = torch.zeros_like(QK).scatter_(-1, indices, top_attn)
|
|
else:
|
|
attn = linear_gate(QK, dim=-1)
|
|
|
|
# Dropouts
|
|
attn = self.dropout(attn)
|
|
|
|
# Weighted sum
|
|
outputs = multiply_by_xchunks(attn, V,
|
|
self.qk_chunks).permute(2, 0, 1, 3)
|
|
|
|
# Restore shape
|
|
outputs = outputs.reshape(l, bs, -1) * U
|
|
|
|
outputs = self.dw_conv(outputs, size_2d)
|
|
outputs = self.projection(outputs)
|
|
|
|
return outputs, attn
|
|
|
|
def _init_weight(self):
|
|
for p in self.parameters():
|
|
if p.dim() > 1:
|
|
nn.init.xavier_uniform_(p)
|
|
|
|
|
|
class LocalGatedPropagation(nn.Module):
|
|
def __init__(self,
|
|
d_qk,
|
|
d_vu,
|
|
num_head,
|
|
dropout=0.,
|
|
max_dis=7,
|
|
dilation=1,
|
|
use_linear=True,
|
|
enable_corr=True,
|
|
d_att=None,
|
|
use_dis=False,
|
|
expand_ratio=2.):
|
|
super().__init__()
|
|
expand_ratio = expand_ratio
|
|
self.expand_d_vu = int(d_vu * expand_ratio)
|
|
self.d_qk = d_qk
|
|
self.d_vu = d_vu
|
|
self.dilation = dilation
|
|
self.window_size = 2 * max_dis + 1
|
|
self.max_dis = max_dis
|
|
self.num_head = num_head
|
|
self.hidden_dim = self.expand_d_vu // num_head
|
|
self.d_att = d_qk // num_head if d_att is None else d_att
|
|
self.T = self.d_att**0.5
|
|
self.use_dis = use_dis
|
|
|
|
self.d_middle = self.d_att * self.num_head
|
|
self.use_linear = use_linear
|
|
if use_linear:
|
|
self.linear_QK = nn.Conv2d(d_qk, self.d_middle, kernel_size=1)
|
|
self.linear_V = nn.Conv2d(d_vu,
|
|
self.expand_d_vu,
|
|
kernel_size=1,
|
|
groups=2)
|
|
self.linear_U = nn.Conv2d(d_vu,
|
|
self.expand_d_vu,
|
|
kernel_size=1,
|
|
groups=2)
|
|
|
|
self.relative_emb_k = nn.Conv2d(self.d_middle,
|
|
num_head * self.window_size *
|
|
self.window_size,
|
|
kernel_size=1,
|
|
groups=num_head)
|
|
|
|
self.enable_corr = enable_corr
|
|
|
|
if enable_corr:
|
|
try:
|
|
from spatial_correlation_sampler import SpatialCorrelationSampler
|
|
self.correlation_sampler = SpatialCorrelationSampler(
|
|
kernel_size=1,
|
|
patch_size=self.window_size,
|
|
stride=1,
|
|
padding=0,
|
|
dilation=1,
|
|
dilation_patch=self.dilation)
|
|
except (ImportError, SyntaxError):
|
|
# Fallback to slower PyTorch-only implementation
|
|
import warnings
|
|
warnings.warn(
|
|
"spatial_correlation_sampler not found. Using fallback implementation (20-40% slower). "
|
|
"For better performance, install: pip install spatial-correlation-sampler",
|
|
UserWarning
|
|
)
|
|
self.enable_corr = False
|
|
self.correlation_sampler = None
|
|
|
|
self.dw_conv = DWConv2d(self.expand_d_vu)
|
|
self.projection = nn.Linear(self.expand_d_vu, d_vu)
|
|
|
|
self.dropout = nn.Dropout(dropout)
|
|
|
|
self.drop_prob = dropout
|
|
|
|
self.local_mask = None
|
|
self.last_size_2d = None
|
|
self.qk_mask = None
|
|
|
|
def forward(self, q, k, v, u, size_2d):
|
|
n, c, h, w = v.size()
|
|
hidden_dim = self.hidden_dim
|
|
|
|
if self.use_linear:
|
|
q = k = self.linear_QK(q)
|
|
v = silu(self.linear_V(v))
|
|
# u = silu(self.linear_U(u))
|
|
if self.num_head > 1:
|
|
v = v.view(-1, 2, self.num_head, hidden_dim // 2,
|
|
h * w).permute(0, 2, 1, 3, 4).reshape(n, -1, h, w)
|
|
# u = u.view(-1, 2, self.num_head, hidden_dim // 2,
|
|
# h * w).permute(4, 0, 2, 1, 3).reshape(h * w, n, -1)
|
|
# else:
|
|
# u = u.permute(2, 3, 0, 1).reshape(h * w, n, -1)
|
|
|
|
if self.qk_mask is not None and (h, w) == self.last_size_2d:
|
|
qk_mask = self.qk_mask
|
|
else:
|
|
memory_mask = torch.ones((1, 1, h, w), device=v.device).float()
|
|
unfolded_k_mask = self.pad_and_unfold(memory_mask).view(
|
|
1, 1, self.window_size * self.window_size, h * w)
|
|
qk_mask = 1 - unfolded_k_mask
|
|
self.qk_mask = qk_mask
|
|
|
|
relative_emb = self.relative_emb_k(q)
|
|
|
|
# Scale
|
|
q = q / self.T
|
|
|
|
# print(q.shape)
|
|
# print(self.d_att, h, w)
|
|
# pdb.set_trace()
|
|
q = q.view(-1, self.d_att, h, w)
|
|
k = k.view(-1, self.d_att, h, w).contiguous()
|
|
v = v.view(-1, self.num_head, hidden_dim, h * w)
|
|
# print([n,c,h,w], q.shape, k.shape, v.shape);assert 0
|
|
# [4, 1024, 24, 24] torch.Size([4, 64, 24, 24]) torch.Size([4, 64, 24, 24]) torch.Size([2, 1, 2048, 576])
|
|
|
|
|
|
relative_emb = relative_emb.view(n, self.num_head,
|
|
self.window_size * self.window_size,
|
|
h * w)
|
|
|
|
if self.enable_corr:
|
|
qk = self.correlation_sampler(q, k).view(
|
|
n, self.num_head, self.window_size * self.window_size, h * w)
|
|
else:
|
|
unfolded_k = self.pad_and_unfold(k).view(
|
|
n * self.num_head, self.d_att,
|
|
self.window_size * self.window_size, h, w)
|
|
qk = (q.unsqueeze(2) * unfolded_k).sum(dim=1).view(
|
|
n, self.num_head, self.window_size * self.window_size, h * w)
|
|
if self.use_dis:
|
|
qk = 2 * qk - self.pad_and_unfold(
|
|
k.pow(2).sum(dim=1, keepdim=True)).view(
|
|
n, self.num_head, self.window_size * self.window_size,
|
|
h * w)
|
|
|
|
qk = qk + relative_emb
|
|
|
|
qk -= qk_mask * 1e+8 if qk.dtype == torch.float32 else qk_mask * 1e+4
|
|
|
|
local_attn = linear_gate(qk, dim=2)
|
|
|
|
local_attn = self.dropout(local_attn)
|
|
|
|
global_attn = self.local2global(local_attn, h, w)
|
|
|
|
agg_value = (global_attn @ v.transpose(-2, -1)).permute(
|
|
2, 0, 1, 3).reshape(h * w, n, -1)
|
|
|
|
# output = agg_value * u
|
|
output = agg_value
|
|
|
|
output = self.dw_conv(output, size_2d)
|
|
output = self.projection(output)
|
|
|
|
self.last_size_2d = (h, w)
|
|
return output, local_attn
|
|
|
|
def local2global(self, local_attn, height, width):
|
|
batch_size = local_attn.size()[0]
|
|
|
|
pad_height = height + 2 * self.max_dis
|
|
pad_width = width + 2 * self.max_dis
|
|
|
|
if self.local_mask is not None and (height,
|
|
width) == self.last_size_2d:
|
|
local_mask = self.local_mask
|
|
else:
|
|
ky, kx = torch.meshgrid([
|
|
torch.arange(0, pad_height, device=local_attn.device),
|
|
torch.arange(0, pad_width, device=local_attn.device)
|
|
])
|
|
qy, qx = torch.meshgrid([
|
|
torch.arange(0, height, device=local_attn.device),
|
|
torch.arange(0, width, device=local_attn.device)
|
|
])
|
|
|
|
offset_y = qy.reshape(-1, 1) - ky.reshape(1, -1) + self.max_dis
|
|
offset_x = qx.reshape(-1, 1) - kx.reshape(1, -1) + self.max_dis
|
|
|
|
local_mask = (offset_y.abs() <= self.max_dis) & (offset_x.abs() <=
|
|
self.max_dis)
|
|
local_mask = local_mask.view(1, 1, height * width, pad_height,
|
|
pad_width)
|
|
self.local_mask = local_mask
|
|
|
|
global_attn = torch.zeros(
|
|
(batch_size, self.num_head, height * width, pad_height, pad_width),
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|
device=local_attn.device)
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|
# global_attn = global_attn.type(torch.HalfTensor).cuda()
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|
global_attn[local_mask.expand(batch_size, self.num_head,
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|
-1, -1, -1)] = local_attn.transpose(
|
|
-1, -2).reshape(-1)
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|
global_attn = global_attn[:, :, :, self.max_dis:-self.max_dis,
|
|
self.max_dis:-self.max_dis].reshape(
|
|
batch_size, self.num_head,
|
|
height * width, height * width)
|
|
|
|
return global_attn
|
|
|
|
def pad_and_unfold(self, x):
|
|
pad_pixel = self.max_dis * self.dilation
|
|
x = F.pad(x, (pad_pixel, pad_pixel, pad_pixel, pad_pixel),
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|
mode='constant',
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|
value=0)
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|
x = F.unfold(x,
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|
kernel_size=(self.window_size, self.window_size),
|
|
stride=(1, 1),
|
|
dilation=self.dilation)
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|
return x
|