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train_rdreg.py
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train_rdreg.py
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#!/usr/bin/env python
"""
train.py
"""
from __future__ import division
from __future__ import print_function
import os
from functools import partial
import sys
import argparse
import ujson as json
import numpy as np
from time import time
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import torch
from torch.autograd import Variable
from torch.nn import functional as F
from models import HINGCN_GS, MyDataParallel,HINGCN_Dense
from problem import NodeProblem, ReadCosSim
from helpers import set_seeds, to_numpy
from nn_modules import aggregator_lookup, prep_lookup, sampler_lookup, edge_aggregator_lookup, \
metapath_aggregator_lookup
from lr import LRSchedule
from line_graph_models import LineGraphGCN
from LG_sage import LineGraphSage
from model.models import conch_rd
import math
# --
# Helpers
def set_progress(optimizer, lr_scheduler, progress):
lr = lr_scheduler(progress)
LRSchedule.set_lr(optimizer, lr)
def rampup(epoch, scaled_unsup_weight_max, exp=5.0, rampup_length=80):
if epoch < rampup_length:
p = max(0.0, float(epoch)) / float(rampup_length)
p = 1.0 - p
return math.exp(-p * p * exp) * scaled_unsup_weight_max
else:
return 1.0 * scaled_unsup_weight_max
# def train_step(model, optimizer, ids, targets, loss_fn, coff):
# optimizer.zero_grad()
# preds,weights,reg_loss = model(ids, train=True)
# if weights is not None:
# weights=weights.cpu().detach().numpy()
# if len(weights.shape)>1 and weights.shape[0] != 1:
# weights=np.sum(weights,axis=0)/weights.shape[0]
# # print(weights)
# loss = loss_fn(preds, targets.squeeze())+reg_loss*coff
# loss.backward()
# # torch.nn.utils.clip_grad_norm_(model.parameters(), 5)
# optimizer.step()
# return loss, preds
def evaluate(model, problem, batch_size, loss_fn, coff, mode='val'):
assert mode in ['test', 'val']
preds, acts = [], []
loss=0
for (ids, targets, _) in problem.iterate(mode=mode, shuffle=False, batch_size=batch_size):
# print(ids.shape,targets.shape)
pred = model(problem.feats, feat2=None, msk=None, samp_bias1=None, samp_bias2=None, get_embed=True)
loss += loss_fn(pred[ids], targets.squeeze()).item()
preds.append(to_numpy(pred[ids]))
acts.append(to_numpy(targets))
#
return loss, problem.metric_fn(np.vstack(acts), np.vstack(preds))
# def evaluate(model, problem, batch_size, mode='val'):
# assert mode in ['test', 'val']
# preds, acts = [], []
# for (ids, targets, _) in problem.iterate(mode=mode, shuffle=False, batch_size=batch_size):
# # print(ids.shape,targets.shape)
# preds.append(to_numpy(model(ids, train=False)))
# acts.append(to_numpy(targets))
#
# return problem.metric_fn(np.vstack(acts), np.vstack(preds))
# # --
# Args
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--problem-path', type=str, default='data/dblp2/')
parser.add_argument('--problem', type=str, default='dblp')
parser.add_argument('--no-cuda', action="store_true",default=False)
# Optimization params
parser.add_argument('--batch-size', type=int, default=99999)
parser.add_argument('--epochs', type=int, default=10000)
parser.add_argument('--lr-init', type=float, default=0.001)
parser.add_argument('--lr-schedule', type=str, default='constant')
parser.add_argument('--weight-decay', type=float, default=1e-4)
parser.add_argument('--dropout', type=float, default=0.5)
parser.add_argument('--batchnorm', action="store_true")
parser.add_argument('--tolerance', type=int, default=100)
parser.add_argument('--attn-dropout',type=float,default=0)
# Architecture params
parser.add_argument('--sampler-class', type=str, default='sparse_uniform_neighbor_sampler')
parser.add_argument('--prep-class', type=str, default='linear') # linear,identity,node_embedding
parser.add_argument('--prep-len', type=int, default=128)
parser.add_argument('--in-edge-len', type=int, default=16)
parser.add_argument('--aggregator-class', type=str, default='sum')
parser.add_argument('--edge-aggr-class', type=str, default='sum')
parser.add_argument('--mpaggr-class', type=str, default='attention')
parser.add_argument('--concat-node', action="store_true",default=False)
parser.add_argument('--concat-edge', action="store_true")
parser.add_argument('--n-head', type=int, default=4)
parser.add_argument('--k', type=int, default=4044)
# parser.add_argument('--n-train-samples', type=str, default='600,600')
# parser.add_argument('--n-val-samples', type=str, default='600,600')
parser.add_argument('--output-dims', type=str, default='64,32,32,32')
parser.add_argument('--n-layer', type=int, default='2')
parser.add_argument('--train-per', type=float, default=0.1)
parser.add_argument('--coff-scheme', type=str, default='constant')
parser.add_argument('--max-coff', type=float, default=1)
parser.add_argument('--max-epoch', type=float, default=100)
parser.add_argument('--coff-exp', type=float, default=5)
# Logging
parser.add_argument('--log-interval', default=1, type=int)
parser.add_argument('--verbose', default=1, type=int)
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--show-test', action="store_true")
# --
# Validate args
args = parser.parse_args()
args.cuda = not args.no_cuda
assert args.prep_class in prep_lookup.keys(), 'parse_args: prep_class not in %s' % str(prep_lookup.keys())
assert args.aggregator_class in aggregator_lookup.keys(), 'parse_args: aggregator_class not in %s' % str(
aggregator_lookup.keys())
assert args.batch_size > 1, 'parse_args: batch_size must be > 1'
return args
if __name__ == "__main__":
args = parse_args()
set_seeds(args.seed)
verbose = args.verbose
torch.backends.cudnn.enabled = True
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
# --
# Load problem
mp_index = {'dblp':['APA','APAPA','APCPA'],#
'yelp': [ 'BRKRB','BRURB'], #'BRURB',
'yago': ['MAM','MDM','MWM'],
'cora': ['PAP','PPP','PP']
}
schemes = mp_index[args.problem]
device = torch.device("cuda:0" if torch.cuda.is_available() and args.cuda else "cpu")
problem = NodeProblem(problem_path=args.problem_path, problem=args.problem, device=device, schemes=schemes, K=args.k, input_edge_dims =args.in_edge_len,train_per=args.train_per)
# cos_problem = ReadCosSim(problem_path=args.problem_path, problem=args.problem, device=device, schemes=schemes, K=args.k, input_edge_dims =args.in_edge_len,train_per=args.train_per)
# --
# Define model
# n_train_samples = list(map(int, args.n_train_samples.split(',')))
# n_val_samples = list(map(int, args.n_val_samples.split(',')))
output_dims = list(map(int, args.output_dims.split(',')))
model = conch_rd(**{
"problem": problem,
# "cos_problem": cos_problem,
"n_mp": len(schemes),
"sampler_class": sampler_lookup[args.sampler_class],
"K":10,
"prep_class": prep_lookup[args.prep_class],
"prep_len": args.prep_len,
"aggregator_class": aggregator_lookup[args.aggregator_class],
"mpaggr_class": metapath_aggregator_lookup[args.mpaggr_class],
"edge_aggr_class": aggregator_lookup[args.edge_aggr_class],
"n_head": args.n_head,
"node_layer_specs": [
{
# "n_train_samples": n_train_samples[0],
# "n_val_samples": n_val_samples[0],
"output_dim": output_dims[0],
"activation": F.relu,
"concat_node": args.concat_node,
"concat_edge": args.concat_edge,
},
{
# "n_train_samples": n_train_samples[1],
# "n_val_samples": n_val_samples[1],
"output_dim": output_dims[1],
"activation": F.relu, # lambda x: x
"concat_node": args.concat_node,
"concat_edge": args.concat_edge,
},
{
# "n_train_samples": n_train_samples[1],
# "n_val_samples": n_val_samples[1],
"output_dim": output_dims[2],
"activation": F.relu, # lambda x: x
"concat_node": args.concat_node,
"concat_edge": args.concat_edge,
},
{
# "n_train_samples": n_train_samples[2],
# "n_val_samples": n_val_samples[2],
"output_dim": output_dims[3],
"activation": F.relu,
"concat_node": args.concat_node,
"concat_edge": args.concat_edge,
},
][:args.n_layer],
"edge_layer_specs": [
{
# "n_train_samples": n_train_samples[0],
# "n_val_samples": n_val_samples[0],
"output_dim": output_dims[0],
"activation": F.relu,
"concat_node": args.concat_node,
"concat_edge": args.concat_edge,
},
{
# "n_train_samples": n_train_samples[1],
# "n_val_samples": n_val_samples[1],
"output_dim": output_dims[1],
"activation": F.relu, # lambda x: x
"concat_node": args.concat_node,
"concat_edge": args.concat_edge,
},
{
# "n_train_samples": n_train_samples[1],
# "n_val_samples": n_val_samples[1],
"output_dim": output_dims[2],
"activation": F.relu, # lambda x: x
"concat_node": args.concat_node,
"concat_edge": args.concat_edge,
},
{
# "n_train_samples": n_train_samples[2],
# "n_val_samples": n_val_samples[2],
"output_dim": output_dims[3],
"activation": F.relu,
"concat_node": args.concat_node,
"concat_edge": args.concat_edge,
},
][:args.n_layer],
"dropout": args.dropout,
"batchnorm": args.batchnorm,
"attn_dropout":args.attn_dropout,
})
if args.cuda:
# print("Let's use", torch.cuda.device_count(), "GPUs!")
model = torch.nn.DataParallel(model)
# model = model.half()
model = model.to(device)
# --
# Define optimizer
lr_scheduler = partial(getattr(LRSchedule, args.lr_schedule), lr_init=args.lr_init)
lr = lr_scheduler(0.0)
optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=args.weight_decay)
#optimizer = torch.optim.SGD(model.parameters(), lr=lr, weight_decay=args.weight_decay,momentum=0.9)
# print(model, file=sys.stdout)
# if args.cuda:
# print('GPU memory allocated: ', torch.cuda.memory_allocated() / 1000 / 1000 / 1000)
# --
# Train
set_seeds(args.seed)
start_time = time()
val_metric = None
tolerance = 0
best_val_loss=100000
best_val_acc=0
best_result = None
coff = 0
max_coff = args.max_coff
if args.lr_schedule=='cosine':
Ti=1
mult=2
Tcur=0
for epoch in range(args.epochs):
if args.coff_scheme=='linear':
coff=max_coff/args.epochs*(epoch+1)
elif args.coff_scheme=='exp':
coff = rampup(epoch, args.max_coff, exp=args.coff_exp, rampup_length=args.max_epoch)
elif args.coff_scheme=='constant':
coff = max_coff
# early stopping
if tolerance > args.tolerance:
break
train_loss = 0
X = problem.feats
idx = np.random.permutation(X.shape[0])
X_tilda = X[idx, :]
# n_mp = len(schemes)
n_mp = 1
lbl_1 = torch.ones(n_mp, X.shape[0])
lbl_2 = torch.zeros(n_mp, X.shape[0])
lbl = torch.cat((lbl_1, lbl_2), 1)
if torch.cuda.is_available():
X_tilda = X_tilda.cuda()
lbl = lbl.cuda()
bce = torch.nn.BCEWithLogitsLoss()
# Train
_ = model.train()
for ids, targets, epoch_progress in problem.iterate(mode='train', shuffle=True, batch_size=args.batch_size):
if args.lr_schedule=='cosine':
lr = lr_scheduler(Tcur + epoch_progress, epochs=Ti)
LRSchedule.set_lr(optimizer, lr)
print('learning rate:{}'.format(lr))
else:
# set_progress(optimizer, lr_scheduler, (epoch + epoch_progress) / args.epochs)
pass
optimizer.zero_grad()
preds,weights,reg = model(X, X_tilda, None, None, None, get_embed=False)
if weights is not None:
weights=weights.cpu().detach().numpy()
if len(weights.shape)>1 and weights.shape[0] != 1:
weights=np.sum(weights,axis=0)/weights.shape[0]
# print(weights)
loss = problem.loss_fn(preds[ids], targets.squeeze())+bce(reg,lbl)*coff
loss.backward()
# torch.nn.utils.clip_grad_norm_(model.parameters(), 5)
optimizer.step()
train_loss += loss.item()
# print(targets.shape)
# print(preds.shape)
train_metric = problem.metric_fn(to_numpy(targets), to_numpy(preds[ids]))
#print(json.dumps({
# "epoch": epoch,
# "epoch_progress": epoch_progress,
# "train_metric": train_metric,
# "time": time() - start_time,
#}, double_precision=5))
#sys.stdout.flush()
if verbose >=2:
print(json.dumps({
"epoch": epoch,
"time": time() - start_time,
"train_loss": train_loss,
}, double_precision=5))
sys.stdout.flush()
#update learning rate for cosine annealing
if args.lr_schedule=='cosine':
if Tcur%Ti==0 and Tcur>0:
Ti*=mult
Tcur=0
else:
Tcur+=1
# Evaluate
if epoch % args.log_interval == 0:
_ = model.eval()
loss, val_metric = evaluate(model, problem, batch_size=args.batch_size, mode='val',loss_fn=problem.loss_fn,coff=coff)
_, test_metric =evaluate(model, problem, batch_size=args.batch_size, mode='test',loss_fn=problem.loss_fn,coff=coff)
if val_metric['accuracy']>best_val_acc or (val_metric['accuracy']==best_val_acc and loss < best_val_loss):
tolerance = 0
best_val_loss = loss
best_val_acc = val_metric['accuracy']
best_result = json.dumps({
"epoch": epoch,
"val_loss": loss,
"val_metric": val_metric,
"test_metric": test_metric,
}, double_precision=5)
else:
tolerance+=1
if verbose >=2:
print(json.dumps({
"epoch": epoch,
"val_loss": loss,
"val_metric": val_metric,
"test_metric": test_metric,
"tolerance:": tolerance,
}, double_precision=5))
sys.stdout.flush()
print('-- done --')
print(best_result)
print(best_result, file=sys.stderr)
sys.stdout.flush()
# if args.show_test:
# _ = model.eval()
# print(json.dumps({
# "test_metric": evaluate(model, problem, batch_size=args.batch_size, mode='test',loss_fn=problem.loss_fn,)
# }, double_precision=5))