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Unverified Commit 9fe5092c authored by Hongzhi (Steve), Chen's avatar Hongzhi (Steve), Chen Committed by GitHub
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Co-authored-by: default avatarUbuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>
parent 9836f78e
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examples/pytorch/capsule/DGLRoutingLayer.py
View file @ 9fe5092c
import dgl
import torch as th
import torch.nn as nn
import torch.nn.functional as F
class DGLRoutingLayer(nn.Module):
def __init__(self, in_nodes, out_nodes, f_size, batch_size=0, device="cpu"):
super(DGLRoutingLayer, self).__init__()
self.batch_size = batch_size
self.g = init_graph(in_nodes, out_nodes, f_size, device=device)
self.in_nodes = in_nodes
self.out_nodes = out_nodes
self.in_indx = list(range(in_nodes))
self.out_indx = list(range(in_nodes, in_nodes + out_nodes))
self.device = device
def forward(self, u_hat, routing_num=1):
self.g.edata["u_hat"] = u_hat
batch_size = self.batch_size
# step 2 (line 5)
def cap_message(edges):
if batch_size:
return {"m": edges.data["c"].unsqueeze(1) * edges.data["u_hat"]}
else:
return {"m": edges.data["c"] * edges.data["u_hat"]}
def cap_reduce(nodes):
return {"s": th.sum(nodes.mailbox["m"], dim=1)}
for r in range(routing_num):
# step 1 (line 4): normalize over out edges
edges_b = self.g.edata["b"].view(self.in_nodes, self.out_nodes)
self.g.edata["c"] = F.softmax(edges_b, dim=1).view(-1, 1)
# Execute step 1 & 2
self.g.update_all(message_func=cap_message, reduce_func=cap_reduce)
# step 3 (line 6)
if self.batch_size:
self.g.nodes[self.out_indx].data["v"] = squash(
self.g.nodes[self.out_indx].data["s"], dim=2
)
else:
self.g.nodes[self.out_indx].data["v"] = squash(
self.g.nodes[self.out_indx].data["s"], dim=1
)
# step 4 (line 7)
v = th.cat(
[self.g.nodes[self.out_indx].data["v"]] * self.in_nodes, dim=0
)
if self.batch_size:
self.g.edata["b"] = self.g.edata["b"] + (
self.g.edata["u_hat"] * v
).mean(dim=1).sum(dim=1, keepdim=True)
else:
self.g.edata["b"] = self.g.edata["b"] + (
self.g.edata["u_hat"] * v
).sum(dim=1, keepdim=True)
def squash(s, dim=1):
sq = th.sum(s**2, dim=dim, keepdim=True)
s_norm = th.sqrt(sq)
s = (sq / (1.0 + sq)) * (s / s_norm)
return s
def init_graph(in_nodes, out_nodes, f_size, device="cpu"):
g = dgl.DGLGraph()
g.set_n_initializer(dgl.frame.zero_initializer)
all_nodes = in_nodes + out_nodes
g.add_nodes(all_nodes)
in_indx = list(range(in_nodes))
out_indx = list(range(in_nodes, in_nodes + out_nodes))
# add edges use edge broadcasting
for u in in_indx:
g.add_edges(u, out_indx)
g = g.to(device)
g.edata["b"] = th.zeros(in_nodes * out_nodes, 1).to(device)
return g
import dgl
import torch as th
import torch.nn as nn
import torch.nn.functional as F
class DGLRoutingLayer(nn.Module):
def __init__(self, in_nodes, out_nodes, f_size, batch_size=0, device="cpu"):
super(DGLRoutingLayer, self).__init__()
self.batch_size = batch_size
self.g = init_graph(in_nodes, out_nodes, f_size, device=device)
self.in_nodes = in_nodes
self.out_nodes = out_nodes
self.in_indx = list(range(in_nodes))
self.out_indx = list(range(in_nodes, in_nodes + out_nodes))
self.device = device
def forward(self, u_hat, routing_num=1):
self.g.edata["u_hat"] = u_hat
batch_size = self.batch_size
# step 2 (line 5)
def cap_message(edges):
if batch_size:
return {"m": edges.data["c"].unsqueeze(1) * edges.data["u_hat"]}
else:
return {"m": edges.data["c"] * edges.data["u_hat"]}
def cap_reduce(nodes):
return {"s": th.sum(nodes.mailbox["m"], dim=1)}
for r in range(routing_num):
# step 1 (line 4): normalize over out edges
edges_b = self.g.edata["b"].view(self.in_nodes, self.out_nodes)
self.g.edata["c"] = F.softmax(edges_b, dim=1).view(-1, 1)
# Execute step 1 & 2
self.g.update_all(message_func=cap_message, reduce_func=cap_reduce)
# step 3 (line 6)
if self.batch_size:
self.g.nodes[self.out_indx].data["v"] = squash(
self.g.nodes[self.out_indx].data["s"], dim=2
)
else:
self.g.nodes[self.out_indx].data["v"] = squash(
self.g.nodes[self.out_indx].data["s"], dim=1
)
# step 4 (line 7)
v = th.cat(
[self.g.nodes[self.out_indx].data["v"]] * self.in_nodes, dim=0
)
if self.batch_size:
self.g.edata["b"] = self.g.edata["b"] + (
self.g.edata["u_hat"] * v
).mean(dim=1).sum(dim=1, keepdim=True)
else:
self.g.edata["b"] = self.g.edata["b"] + (
self.g.edata["u_hat"] * v
).sum(dim=1, keepdim=True)
def squash(s, dim=1):
sq = th.sum(s**2, dim=dim, keepdim=True)
s_norm = th.sqrt(sq)
s = (sq / (1.0 + sq)) * (s / s_norm)
return s
def init_graph(in_nodes, out_nodes, f_size, device="cpu"):
g = dgl.DGLGraph()
g.set_n_initializer(dgl.frame.zero_initializer)
all_nodes = in_nodes + out_nodes
g.add_nodes(all_nodes)
in_indx = list(range(in_nodes))
out_indx = list(range(in_nodes, in_nodes + out_nodes))
# add edges use edge broadcasting
for u in in_indx:
g.add_edges(u, out_indx)
g = g.to(device)
g.edata["b"] = th.zeros(in_nodes * out_nodes, 1).to(device)
return g
examples/pytorch/capsule/simple_routing.py
View file @ 9fe5092c
import dgl
import torch as th
import torch.nn as nn
from DGLRoutingLayer import DGLRoutingLayer
from torch.nn import functional as F
g = dgl.DGLGraph()
g.graph_data = {}
in_nodes = 20
out_nodes = 10
g.graph_data["in_nodes"] = in_nodes
g.graph_data["out_nodes"] = out_nodes
all_nodes = in_nodes + out_nodes
g.add_nodes(all_nodes)
in_indx = list(range(in_nodes))
out_indx = list(range(in_nodes, in_nodes + out_nodes))
g.graph_data["in_indx"] = in_indx
g.graph_data["out_indx"] = out_indx
# add edges use edge broadcasting
for u in out_indx:
g.add_edges(in_indx, u)
# init states
f_size = 4
g.ndata["v"] = th.zeros(all_nodes, f_size)
g.edata["u_hat"] = th.randn(in_nodes * out_nodes, f_size)
g.edata["b"] = th.randn(in_nodes * out_nodes, 1)
routing_layer = DGLRoutingLayer(g)
entropy_list = []
for i in range(15):
routing_layer()
dist_matrix = g.edata["c"].view(in_nodes, out_nodes)
entropy = (-dist_matrix * th.log(dist_matrix)).sum(dim=0)
entropy_list.append(entropy.data.numpy())
std = dist_matrix.std(dim=0)
import dgl
import torch as th
import torch.nn as nn
from DGLRoutingLayer import DGLRoutingLayer
from torch.nn import functional as F
g = dgl.DGLGraph()
g.graph_data = {}
in_nodes = 20
out_nodes = 10
g.graph_data["in_nodes"] = in_nodes
g.graph_data["out_nodes"] = out_nodes
all_nodes = in_nodes + out_nodes
g.add_nodes(all_nodes)
in_indx = list(range(in_nodes))
out_indx = list(range(in_nodes, in_nodes + out_nodes))
g.graph_data["in_indx"] = in_indx
g.graph_data["out_indx"] = out_indx
# add edges use edge broadcasting
for u in out_indx:
g.add_edges(in_indx, u)
# init states
f_size = 4
g.ndata["v"] = th.zeros(all_nodes, f_size)
g.edata["u_hat"] = th.randn(in_nodes * out_nodes, f_size)
g.edata["b"] = th.randn(in_nodes * out_nodes, 1)
routing_layer = DGLRoutingLayer(g)
entropy_list = []
for i in range(15):
routing_layer()
dist_matrix = g.edata["c"].view(in_nodes, out_nodes)
entropy = (-dist_matrix * th.log(dist_matrix)).sum(dim=0)
entropy_list.append(entropy.data.numpy())
std = dist_matrix.std(dim=0)
examples/pytorch/rect/utils.py
View file @ 9fe5092c
import dgl
import torch
from dgl.data import CiteseerGraphDataset, CoraGraphDataset
def load_data(args):
if args.dataset == "cora":
data = CoraGraphDataset()
elif args.dataset == "citeseer":
data = CiteseerGraphDataset()
else:
raise ValueError("Unknown dataset: {}".format(args.dataset))
g = data[0]
if args.gpu < 0:
cuda = False
else:
cuda = True
g = g.int().to(args.gpu)
features = g.ndata["feat"]
labels = g.ndata["label"]
train_mask = g.ndata["train_mask"]
test_mask = g.ndata["test_mask"]
g = dgl.add_self_loop(g)
return g, features, labels, train_mask, test_mask, data.num_classes, cuda
def svd_feature(features, d=200):
"""Get 200-dimensional node features, to avoid curse of dimensionality"""
if features.shape[1] <= d:
return features
U, S, VT = torch.svd(features)
res = torch.mm(U[:, 0:d], torch.diag(S[0:d]))
return res
def process_classids(labels_temp):
"""Reorder the remaining classes with unseen classes removed.
Input: the label only removing unseen classes
Output: the label with reordered classes
"""
labeldict = {}
num = 0
for i in labels_temp:
labeldict[int(i)] = 1
labellist = sorted(labeldict)
for label in labellist:
labeldict[int(label)] = num
num = num + 1
for i in range(labels_temp.numel()):
labels_temp[i] = labeldict[int(labels_temp[i])]
return labels_temp
import dgl
import torch
from dgl.data import CiteseerGraphDataset, CoraGraphDataset
def load_data(args):
if args.dataset == "cora":
data = CoraGraphDataset()
elif args.dataset == "citeseer":
data = CiteseerGraphDataset()
else:
raise ValueError("Unknown dataset: {}".format(args.dataset))
g = data[0]
if args.gpu < 0:
cuda = False
else:
cuda = True
g = g.int().to(args.gpu)
features = g.ndata["feat"]
labels = g.ndata["label"]
train_mask = g.ndata["train_mask"]
test_mask = g.ndata["test_mask"]
g = dgl.add_self_loop(g)
return g, features, labels, train_mask, test_mask, data.num_classes, cuda
def svd_feature(features, d=200):
"""Get 200-dimensional node features, to avoid curse of dimensionality"""
if features.shape[1] <= d:
return features
U, S, VT = torch.svd(features)
res = torch.mm(U[:, 0:d], torch.diag(S[0:d]))
return res
def process_classids(labels_temp):
"""Reorder the remaining classes with unseen classes removed.
Input: the label only removing unseen classes
Output: the label with reordered classes
"""
labeldict = {}
num = 0
for i in labels_temp:
labeldict[int(i)] = 1
labellist = sorted(labeldict)
for label in labellist:
labeldict[int(label)] = num
num = num + 1
for i in range(labels_temp.numel()):
labels_temp[i] = labeldict[int(labels_temp[i])]
return labels_temp
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