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test.py
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import os
import sys
import traceback
import numpy as np
import argparse
import threading
import codecs
import logging
from tqdm import tqdm
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO, format="%(message)s")
import torch
import models, configs, data_loader
from modules import get_cosine_schedule_with_warmup
from utils import similarity, normalize
from data_loader import *
import matplotlib.pyplot as plt
def test(config, model, device):
logger.info('Test Begin...')
args.seed = 0
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
model.eval()
model.to(device)
# load data
data_path = args.data_path+args.dataset+'/'
test_set = eval(config['dataset_name'])(config, data_path,
config['test_ir'], config['n_node'],
config['test_desc'], config['desc_len'])
data_loader = torch.utils.data.DataLoader(dataset=test_set, batch_size=32,
shuffle=False, drop_last=False, num_workers=1)
# encode tokens and descs
code_reprs, desc_reprs = [], []
n_processed = 0
for batch in data_loader:
# batch[0:3]: init_input, adjmat, node_mask
code_batch = [tensor.to(device) for tensor in batch[:4]]
# batch[3:5]: good_desc, good_desc_len
desc_batch = [tensor.to(device) for tensor in batch[4:6]]
with torch.no_grad():
code_repr = model.code_encoding(*code_batch).data.cpu().numpy().astype(np.float32)
desc_repr = model.desc_encoding(*desc_batch).data.cpu().numpy().astype(np.float32) # [poolsize x hid_size]
# normalize when sim_measure=='cos'
code_repr = normalize(code_repr)
desc_repr = normalize(desc_repr)
code_reprs.append(code_repr)
desc_reprs.append(desc_repr)
n_processed += batch[0].size(0) # +batch_size
# code_reprs: [n_processed x n_hidden]
code_reprs, desc_reprs = np.vstack(code_reprs), np.vstack(desc_reprs)
# calculate similarity
sum_1, sum_5, sum_10, sum_mrr = [], [], [], []
test_sim_result, test_rank_result = [], []
for i in tqdm(range(0, n_processed)):
desc_vec = np.expand_dims(desc_reprs[i], axis=0) # [1 x n_hidden]
sims = np.dot(code_reprs, desc_vec.T)[:,0] # [n_processed]
negsims = np.negative(sims)
predict = np.argsort(negsims)
# SuccessRate@k
predict_1, predict_5, predict_10 = [int(predict[0])], [int(k) for k in predict[0:5]], [int(k) for k in predict[0:10]]
sum_1.append(1.0) if i in predict_1 else sum_1.append(0.0)
sum_5.append(1.0) if i in predict_5 else sum_5.append(0.0)
sum_10.append(1.0) if i in predict_10 else sum_10.append(0.0)
# MRR
predict_list = predict.tolist()
rank = predict_list.index(i)
sum_mrr.append(1/float(rank+1))
# results need to be saved
predict_20 = [int(k) for k in predict[0:20]]
sim_20 = [sims[k] for k in predict_20]
test_sim_result.append(zip(predict_20, sim_20))
test_rank_result.append(rank+1)
logger.info(f'R@1={np.mean(sum_1)}, R@5={np.mean(sum_5)}, R@10={np.mean(sum_10)}, MRR={np.mean(sum_mrr)}')
save_path = args.data_path + 'result/'
sim_result_filename, rank_result_filename = 'sim.npy', 'rank.npy'
np.save(save_path+sim_result_filename, test_sim_result)
np.save(save_path+rank_result_filename, test_rank_result)
def test_batch(config, model, device):
logger.info('Test Begin...')
args.seed = 0 # 45
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
model.eval()
model.to(device)
# load data
data_path = args.data_path + args.dataset + '/'
test_set = eval(config['dataset_name'])(config, data_path,
config['test_ir'], config['n_node'],
config['test_desc'], config['desc_len'])
# test_set = eval(config['dataset_name'])(config, data_path,
# "reduced10." + config['test_ir'], config['n_node'],
# "reduced10." + config['test_desc'], config['desc_len'])
data_loader = torch.utils.data.DataLoader(dataset=test_set, batch_size=config['batch_size'],
shuffle=False, drop_last=False, num_workers=1)
# encode tokens and descs
code_reprs, desc_reprs = [], []
n_processed = 0
final_r1 = 0
final_r5 = 0
final_r10 = 0
final_mrr = 0
cnt = 0
print("ok")
for batch in data_loader:
# batch[0:3]: init_input, adjmat, node_mask
code_batch = [tensor.to(device) for tensor in batch[:4]]
# batch[3:5]: good_desc, good_desc_len
desc_batch = [tensor.to(device) for tensor in batch[4:6]]
with torch.no_grad():
code_repr = model.code_encoding(*code_batch).data.cpu().numpy().astype(np.float32)
desc_repr = model.desc_encoding(*desc_batch).data.cpu().numpy().astype(np.float32) # [poolsize x hid_size]
# normalize when sim_measure=='cos'
code_repr = normalize(code_repr)
desc_repr = normalize(desc_repr)
code_reprs.append(code_repr)
desc_reprs.append(desc_repr)
n_processed += batch[0].size(0) # +batch_size
if n_processed >= 2000:
# code_reprs: [n_processed x n_hidden]
code_reprs, desc_reprs = np.vstack(code_reprs), np.vstack(desc_reprs)
# calculate similarity
sum_1, sum_5, sum_10, sum_mrr = [], [], [], []
sum_idx = []
test_sim_result, test_rank_result = [], []
for i in range(0, n_processed):
desc_vec = np.expand_dims(desc_reprs[i], axis=0) # [1 x n_hidden]
sims = np.dot(code_reprs, desc_vec.T)[:, 0] # [n_processed]
negsims = np.negative(sims)
predict = np.argsort(negsims)
# SuccessRate@k
predict_1, predict_5, predict_10 = [int(predict[0])], [int(k) for k in predict[0:5]], [int(k) for k in
predict[0:10]]
sum_1.append(1.0) if i in predict_1 else sum_1.append(0.0)
sum_5.append(1.0) if i in predict_5 else sum_5.append(0.0)
sum_10.append(1.0) if i in predict_10 else sum_10.append(0.0)
# MRR
predict_list = predict.tolist()
rank = predict_list.index(i)
sum_idx.append(rank)
sum_mrr.append(1 / float(rank + 1))
# results need to be saved
predict_20 = [int(k) for k in predict[0:20]]
sim_20 = [sims[k] for k in predict_20]
test_sim_result.append(zip(predict_20, sim_20))
test_rank_result.append(rank + 1)
# print(sum_1)
# print(sum_5)
# print(sum_10)
# print(sum_mrr)
print(sum_idx)
R1 = np.mean(sum_1)
R5 = np.mean(sum_5)
R10 = np.mean(sum_10)
MRR = np.mean(sum_mrr)
final_r1 += R1
final_r5 += R5
final_r10 += R10
final_mrr += MRR
cnt += 1
logger.info(f'R@1={np.mean(sum_1)}, R@5={np.mean(sum_5)}, R@10={np.mean(sum_10)}, MRR={np.mean(sum_mrr)}')
code_reprs, desc_reprs = [], []
n_processed = 0
logger.info(f'ave result')
logger.info(f'R@1={final_r1/cnt}, R@5={final_r5/cnt}, R@10={final_r10/cnt}, MRR={final_mrr/cnt}')
return final_r1/cnt, final_r10/cnt, final_mrr/cnt
#save_path = args.data_path + 'result/'
#sim_result_filename, rank_result_filename = 'sim.npy', 'rank.npy'
#np.save(save_path + sim_result_filename, test_sim_result)
#np.save(save_path + rank_result_filename, test_rank_result)
def parse_args():
parser = argparse.ArgumentParser("Train and Test Code Search(Embedding) Model")
parser.add_argument('--data_path', type=str, default='./data/', help='location of the data corpus')
parser.add_argument('--model', type=str, default='IREmbeder', help='model name')
parser.add_argument('-d', '--dataset', type=str, default='c_python_best', help='name of dataset.java, python')
parser.add_argument('--reload_from', type=int, default=100, help='epoch to reload from')
parser.add_argument('-g', '--gpu_id', type=int, default=0, help='GPU ID')
parser.add_argument('-v', "--visual",action="store_true", default=False, help="Visualize training status in tensorboard")
parser.add_argument('--trainset_num', type=int, default=39000)
parser.add_argument('--testset_num', type=int, default=2000)
parser.add_argument('--testset_start_ind', type=int, default=39000)
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
device = torch.device(f"cuda:{args.gpu_id}" if torch.cuda.is_available() else "cpu")
config = getattr(configs, 'config_'+args.model)()
##### Define model ######
logger.info('Constructing Model..')
final_mrr_list = []
final_r1_list = []
final_r10_list = []
# for epo_id in range(20, args.reload_from, 10):
# print(epo_id)
model = getattr(models, args.model)(config) # initialize the model
ckpt=f'./output/{args.model}/{args.dataset}/models/epo{args.reload_from}.h5'
model.load_state_dict(torch.load(ckpt, map_location=device))
# #test(config, model, device)
final_r1, final_r10, final_mrr = test_batch(config, model, device)
# final_r1_list.append(final_r1)
# final_r10_list.append(final_r10)
# final_mrr_list.append(final_mrr)
# plt.plot(final_r1_list)
# plt.plot(final_r10_list)
# plt.plot(final_mrr_list)
# plt.show()