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862bdac 0fb3da6 862bdac 0fb3da6 862bdac 0fb3da6 862bdac 0fb3da6 862bdac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import to_dense_batch
import math
try:
import triton
import triton.language as tl
HAS_TRITON = torch.cuda.is_available()
except ImportError:
HAS_TRITON = False
def pytorch_clifford_product(a, b):
# DÜZELTME BURADA: a ve b matrislerini çarpmadan önce ortak boyuta genişlet (Broadcast)
a, b = torch.broadcast_tensors(a, b)
res = torch.zeros_like(a)
res[..., 0] = a[...,0]*b[...,0] + a[...,1]*b[...,1] + a[...,2]*b[...,2] + a[...,3]*b[...,3] - a[...,4]*b[...,4] - a[...,5]*b[...,5] - a[...,6]*b[...,6] - a[...,7]*b[...,7]
res[..., 1] = a[...,0]*b[...,1] + a[...,1]*b[...,0] - a[...,2]*b[...,4] + a[...,3]*b[...,6] + a[...,4]*b[...,2] - a[...,5]*b[...,7] - a[...,6]*b[...,3] - a[...,7]*b[...,5]
res[..., 2] = a[...,0]*b[...,2] + a[...,1]*b[...,4] + a[...,2]*b[...,0] - a[...,3]*b[...,5] - a[...,4]*b[...,1] + a[...,5]*b[...,3] - a[...,6]*b[...,7] - a[...,7]*b[...,6]
res[..., 3] = a[...,0]*b[...,3] - a[...,1]*b[...,6] + a[...,2]*b[...,5] + a[...,3]*b[...,0] + a[...,4]*b[...,7] - a[...,5]*b[...,2] + a[...,6]*b[...,1] - a[...,7]*b[...,4]
res[..., 4] = a[...,0]*b[...,4] + a[...,1]*b[...,2] - a[...,2]*b[...,1] + a[...,3]*b[...,7] + a[...,4]*b[...,0] - a[...,5]*b[...,6] + a[...,6]*b[...,5] - a[...,7]*b[...,3]
res[..., 5] = a[...,0]*b[...,5] + a[...,1]*b[...,7] + a[...,2]*b[...,3] - a[...,3]*b[...,2] + a[...,4]*b[...,6] + a[...,5]*b[...,0] - a[...,6]*b[...,4] - a[...,7]*b[...,1]
res[..., 6] = a[...,0]*b[...,6] - a[...,1]*b[...,3] + a[...,2]*b[...,7] + a[...,3]*b[...,1] - a[...,4]*b[...,5] + a[...,5]*b[...,4] + a[...,6]*b[...,0] - a[...,7]*b[...,2]
res[..., 7] = a[...,0]*b[...,7] + a[...,1]*b[...,5] + a[...,2]*b[...,6] + a[...,3]*b[...,4] + a[...,4]*b[...,3] + a[...,5]*b[...,1] + a[...,6]*b[...,2] + a[...,7]*b[...,0]
return res
def smart_clifford_product(a, b):
if HAS_TRITON:
pass
return pytorch_clifford_product(a, b)
class CliffordDiracLayer(MessagePassing):
def __init__(self, channels: int):
super().__init__(aggr="add", node_dim=0)
self.channels = channels
self.weight = nn.Linear(channels, channels, bias=False)
self.distance_mlp = nn.Sequential(nn.Linear(1, channels), nn.SiLU(), nn.Linear(channels, channels))
self.resonance_mlp = nn.Sequential(nn.Linear(7, 16), nn.GELU(), nn.Linear(16, 1))
def forward(self, x, edge_index, v_ij, dist, edge_mask):
x_proj = self.weight(x.transpose(1, 2)).transpose(1, 2)
dist_weight = self.distance_mlp(dist)
msg = self.propagate(edge_index, x=x_proj, v_ij=v_ij, dist_weight=dist_weight, edge_mask=edge_mask)
gate = self.resonance_gate(x_proj, msg)
return gate * msg
def message(self, x_j, v_ij, dist_weight, edge_mask):
v_8d = F.pad(v_ij, (1, 4))
v_8d_exp = v_8d.unsqueeze(1).expand_as(x_j)
geom_msg = smart_clifford_product(v_8d_exp, x_j)
msg = geom_msg * dist_weight.unsqueeze(-1)
return msg * edge_mask.view(-1, 1, 1).float()
def resonance_gate(self, x_state, msg_state):
x_norm = F.normalize(x_state, dim=-1)
msg_norm = F.normalize(msg_state, dim=-1)
gp = smart_clifford_product(x_norm, msg_norm)
scalar_align = gp[..., 0:1]
vector_align = torch.norm(gp[..., 1:4], dim=-1, keepdim=True)
bivector_align = torch.norm(gp[..., 4:7], dim=-1, keepdim=True)
pseudoscalar_align = torch.abs(gp[..., 7:8])
state_norm = torch.norm(x_state, dim=-1, keepdim=True)
msg_norm_mag = torch.norm(msg_state, dim=-1, keepdim=True)
delta_norm = torch.norm(msg_state - x_state, dim=-1, keepdim=True)
feats = torch.cat([scalar_align, vector_align, bivector_align, pseudoscalar_align, state_norm, msg_norm_mag, delta_norm], dim=-1)
return torch.sigmoid(self.resonance_mlp(feats))
class CliffordSelfAttention(nn.Module):
def __init__(self, channels: int):
super().__init__()
self.channels = channels
self.W_q = nn.Linear(channels, channels, bias=False)
self.W_k = nn.Linear(channels, channels, bias=False)
self.W_v = nn.Linear(channels, channels, bias=False)
self.score_net = nn.Sequential(nn.Linear(8, 16), nn.GELU(), nn.Linear(16, 1))
def forward(self, x_dense, mask):
B, N, C, _ = x_dense.shape
q = self.W_q(x_dense.transpose(2, 3)).transpose(2, 3)
k = self.W_k(x_dense.transpose(2, 3)).transpose(2, 3)
v = self.W_v(x_dense.transpose(2, 3)).transpose(2, 3)
q_expanded = q.unsqueeze(2)
k_expanded = k.unsqueeze(1)
geom_prod = smart_clifford_product(q_expanded, k_expanded)
scores = self.score_net(geom_prod.mean(dim=3)).squeeze(-1)
scores = scores.masked_fill(~(mask.unsqueeze(1) & mask.unsqueeze(2)), -10000.0)
attn = F.softmax(scores / math.sqrt(C), dim=-1)
return (attn.view(B, N, N, 1, 1) * v.unsqueeze(1)).sum(dim=2) + x_dense
class HierarchicalFPSCliffordNet(nn.Module):
def __init__(self, base_channels: int = 12, num_part_classes: int = 50, num_categories: int = 16):
super().__init__()
self.C = base_channels
self.layer1 = CliffordDiracLayer(base_channels)
self.layer2 = CliffordDiracLayer(base_channels * 2)
self.lin1 = nn.Linear(base_channels * 8, (base_channels * 2) * 8)
self.lin2 = nn.Linear((base_channels * 2) * 8, (base_channels * 4) * 8)
self.manager = CliffordSelfAttention(base_channels * 4)
self.cat_emb = nn.Embedding(num_categories, 64)
combined_dim = (base_channels * 4 + base_channels * 2 + base_channels) * 8 + 64
self.head = nn.Sequential(
nn.Linear(combined_dim, 64), nn.BatchNorm1d(64), nn.GELU(), nn.Dropout(0.4),
nn.Linear(64, 64), nn.BatchNorm1d(64), nn.GELU(), nn.Dropout(0.2),
nn.Linear(64, num_part_classes)
)
def forward(self, pos, batch, category, fps_idx_1, fps_idx_2, pos2, batch2, pos3, batch3,
edge_index_1, edge_index_2, assign_index_32, assign_index_21, x_dense_mask):
x0 = torch.zeros(pos.size(0), self.C, 8, device=pos.device)
x0[..., 1:4] = pos.unsqueeze(1).expand(-1, self.C, -1)
row1, col1 = edge_index_1[1], edge_index_1[0]
diff1 = pos[row1] - pos[col1]
d1 = diff1.norm(dim=-1, keepdim=True).clamp(min=1e-8)
dummy_mask1 = torch.ones(edge_index_1.size(1), dtype=torch.bool, device=pos.device)
x1 = x0 + self.layer1(x0, edge_index_1, diff1 / d1, d1, dummy_mask1)
f1 = (x1 / (x1.norm(dim=-1, keepdim=True).mean(dim=1, keepdim=True) + 1e-6)).reshape(x1.size(0), -1)
x2_in = self.lin1(x1[fps_idx_1].reshape(fps_idx_1.numel(), -1)).reshape(-1, self.C * 2, 8)
row2, col2 = edge_index_2[1], edge_index_2[0]
diff2 = pos2[row2] - pos2[col2]
d2 = diff2.norm(dim=-1, keepdim=True).clamp(min=1e-8)
dummy_mask2 = torch.ones(edge_index_2.size(1), dtype=torch.bool, device=pos.device)
x2 = x2_in + self.layer2(x2_in, edge_index_2, diff2 / d2, d2, dummy_mask2)
f2 = (x2 / (x2.norm(dim=-1, keepdim=True).mean(dim=1, keepdim=True) + 1e-6)).reshape(x2.size(0), -1)
x3_in = self.lin2(x2[fps_idx_2].reshape(fps_idx_2.numel(), -1)).reshape(-1, self.C * 4, 8)
x_dense, _ = to_dense_batch(x3_in.reshape(x3_in.size(0), -1), batch3, max_num_nodes=x_dense_mask.size(1))
x3 = self.manager(x_dense.view(x_dense.size(0), x_dense.size(1), self.C * 4, 8), x_dense_mask)
f3 = x3[x_dense_mask].reshape(-1, self.C * 4 * 8)
row_32, col_32 = assign_index_32[0], assign_index_32[1]
out_f3_to_pos2 = torch.zeros(pos2.size(0), f3.size(1), device=pos.device)
out_f3_to_pos2.scatter_add_(0, row_32.unsqueeze(1).expand(-1, f3.size(1)), f3[col_32])
out_f3_to_pos2 = out_f3_to_pos2 / 3.0
f2_combined = torch.cat([out_f3_to_pos2, f2], dim=-1)
row_21, col_21 = assign_index_21[0], assign_index_21[1]
f2_up = torch.zeros(pos.size(0), f2_combined.size(1), device=pos.device)
f2_up.scatter_add_(0, row_21.unsqueeze(1).expand(-1, f2_combined.size(1)), f2_combined[col_21])
f2_up = f2_up / 3.0
cat_features = self.cat_emb(category)[batch]
f1_final = torch.cat([f2_up, f1, cat_features], dim=-1)
return self.head(f1_final) |