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test.py
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from random import random
from sklearn.metrics import top_k_accuracy_score
import torch
import torch.nn.functional as F
import argparse
import os
import time
import random
import numpy as np
from tqdm import tqdm
from torch.utils.data import Subset
import pdb
import sys
#import swats
import src.hyperdataset as hdatasets
import src.hypermodel as hmodels
from src.logger import Logger
from torch_geometric.loader import DataLoader
from src.util import InversePairs, kendall, mykendall
#from torch.utils.tensorboard import writer
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=777, help='seed')
parser.add_argument('--device', type=str, default='cuda:0',help='device')
parser.add_argument('--model', type=str, default='GClassifier',help='which mdoel to use')
parser.add_argument('--batch_size', type=int, default=8,help='train batch size')
parser.add_argument('--batch_step', type=int, default=1,help='how many batches per update')
parser.add_argument('--test_batch_size', type=int, default=8,help='test batch size')
parser.add_argument('--lr', type=float, default=0.001, help='learning rate')
parser.add_argument('--step_size', type=int, default=50, help='learning rate decay step')
parser.add_argument('--lr_decay', type=float, default=1., help='learning rate decay ratio')
parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay')
parser.add_argument('--nhid', type=int, default=16, help='hidden size')
parser.add_argument('--layers',type=int,default=2,help='conv layers')
parser.add_argument('--egnn_layers',type=int,default=3,help='egnn layers')
parser.add_argument('--egnn_nhid',type=int,default=16,help='egnn layers hidden dim')
#parser.add_argument('--pooling_ratio', type=float, default=0.1,help='pooling ratio')
parser.add_argument('--dropout_ratio', type=float, default=0.1,help='dropout ratio')
parser.add_argument('--group', type=int, default=0, help='which data group to use')
parser.add_argument('--tests', type=str, nargs='+',
default=['mgc_des_perf_a', 'mgc_fft_a', 'mgc_matrix_mult_a', 'mgc_matrix_mult_c', 'mgc_superblue14', 'mgc_superblue19'],help='test data')
parser.add_argument('--trains', type=str, nargs='+',
default=['mgc_edit_dist_a', 'mgc_fft_b', 'mgc_matrix_mult_b', 'mgc_pci_bridge32_b', 'mgc_superblue11_a', 'mgc_superblue16_a'],help='train data')
parser.add_argument('--dataset_path', type=str, default='data')
parser.add_argument('--dataset', type=str, default='PlainClusterSet')
parser.add_argument('--epochs', type=int, default=400,help='maximum number of epochs')
parser.add_argument('--patience', type=int, default=400,help='patience for earlystopping')
parser.add_argument('--save_dir', type=str, default='save')
parser.add_argument('--goon', action='store_true',help='continue training')
parser.add_argument('--con', action='store_true',help='continue training')
parser.add_argument('--checkp', type=str, default='test.pth')
parser.add_argument('--pos_encode', type=int, default=4, help='whether use pos encoding on position')
parser.add_argument('--size_encode', type=int, default=0, help='whether use pos encoding on size')
parser.add_argument('--offset_encode', type=int, default=0, help='whether use pos encoding on offset')
parser.add_argument('--design', type=str, default='all',help='whitch design to train')
parser.add_argument('--loss', type=str, default='MAE',help='loss func')
parser.add_argument('--acc', type=str, default='rel',help='loss func')
parser.add_argument('--skip_cnt', action='store_true', default=True ,help='use skip cnt ?')
parser.add_argument('--regresion', action='store_true', help='regression')
parser.add_argument('--classifier', action='store_true', help='classification')
parser.add_argument('--base_model', type=str, default='EGNN',help='which base mdoel to use in classifier')
parser.add_argument('--metric', type=str, default='lambdda',help='which metric to use as lambda, [lambdda (top1 prob), ndcg]')
parser.add_argument('--label', type=list[int],default=[1],help='which label to use, [0~5] = [hpwl, rwl, via, short, score]')
parser.add_argument('--train_ratio', type=float, default=0.8,help='train ratio')
parser.add_argument('--optimizer',type=str,default='Adam')
args = parser.parse_args()
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.cuda.manual_seed(seed)
def build_test_loader():
MySet = getattr(hdatasets,args.dataset)
dataset = MySet(args.dataset_path, mode=args.model, test_files=args.tests, train_files=args.trains, args=args)
if args.model != 'CNN' and args.model != 'Classifier':
args.num_node_features = dataset.num_node_features
args.num_edge_features = dataset.num_edge_features
args.num_pin_features = dataset.num_pin_features
if args.model == 'EHGNN':
args.num_pos_features = dataset.num_pos_features
loader = {}
for design in dataset.raw_file_names:
design_set = Subset(dataset,range(dataset.ptr[design],
dataset.ptr[design] + dataset.file_num[design]))
loader[design] = DataLoader(design_set, batch_size= 1)
return dataset, loader
def build_model():
Model = getattr(hmodels,args.model)
model = Model(args).to(args.device)
#print(model)
return model
def build_log():
# make save dir
st = time.strftime("%b:%d:%X",time.localtime())
args.save_dir = os.path.join(args.save_dir,'{}_{}_{}_{}'.format(args.model,args.label,args.group,st))
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
# rederict to save dir
sys.stdout = Logger(path=args.save_dir)
# print args
print(args)
# save paths
best_model_path = os.path.join(args.save_dir,'best.pth'.format(st))
last_model_path = os.path.join(args.save_dir,'last.pth'.format(st))
return best_model_path, last_model_path
# preparing
torch.set_num_threads(16)
# choose data group
if args.group == 1:
tmp = args.tests
args.tests = args.trains
args.trains = tmp
label = [int(i) for i in args.label][0]
set_seed(args.seed)
# build up
# print('loading dataset ...')
dataset, loader = build_test_loader()
model = build_model()
# os.makedirs('log/{}'.format(args.checkp), exist_ok=True)
# logger = writer.SummaryWriter('log/{}'.format(args.checkp))
checkp = torch.load(args.checkp, map_location='cuda')
model.load_state_dict(checkp['model'])
model = model.to(args.device)
#print('load model from {}, loss = {}, err = {}'.format(args.checkp,checkp['val_loss'], checkp['rank_err']))
# golds = {}
# for design in args.tests:
# test_loader = loader[design]
# preds = []
# reals = []
# origins = []
# label_p = 'data/raw/{}/labels.txt'.format(design)
# idx_p = 'data/raw/{}/names.txt'.format(design)
# golds[design] = np.loadtxt(label_p)[np.loadtxt(idx_p,dtype=int)]
# meann = np.mean(labels, 0 )
# maxx = np.max(labels, 0 )
# minn = np.min(labels, 0 )
# pdb.set_trace()
dataset.mode = 'CNN'
mres = 0
taut = 0
score = 0
print('model =', args.model, ", test group = ", args.group + 1)
print("{:20}\t{:10}\t{:10}\t{}\t{}".format('design', 'mean_score', 'top30_score', 'mre', 'tau'))
with torch.no_grad():
designs = []
embdds = []
model.eval()
for design in args.tests:
test_loader = loader[design]
preds = []
reals = []
origins = []
#print(design, end='\t\t')
label_p = 'data/raw/{}/labels.txt'.format(design)
idx_p = 'data/raw/{}/names.txt'.format(design)
labels_this = np.loadtxt(label_p)[np.loadtxt(idx_p,dtype=int)]
#pdb.set_trace()
for i, data in enumerate(test_loader):
data = data.to(args.device)
if args.model == 'HGNN' or args.model == 'EHGNN':
out = model(data)
else:
out = model.predict(data)
#out = out * (maxx[label - 1] - minn[label - 1]) + meann[label - 1]
#data.y[:, 1 : ] = data.y[:, 1 : ].cuda() * (maxx - minn) + meann
reals.append(data.y[:, label].view(-1).item())
origins.append(dataset.origin[design][i][:, label].item())
preds.append(out.view(-1).item())
reals = np.array(reals)
preds = np.array(preds)
origins = np.array(origins)
mre = np.mean(np.abs(reals - preds)/np.abs(reals))
tau = mykendall(reals, preds)
taut += tau/len(args.tests)
mres += mre/len(args.tests)
top30 = np.argsort(preds)[:30]
top30_score = origins[top30].mean()
mean_score = np.mean(origins)
score += top30_score/mean_score/len(args.tests)
print("{:20}\t{:>10.4f}\t{:>.4f}\t{:>.3f}\t{:>.3f}".format(design, mean_score, top30_score, mre, tau))
print("{:20}\t{:>10.4f}\t{:>10.4f}\t{:>.3f}\t{:>.3f}".format('average', 1., score, mres, taut))
#print('average mre = {:.3f}'.format(mres/len(args.tests)), ', tau = {:.3f}'.format(taut/len(args.tests)))