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Tester_hyper.py
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Tester_hyper.py
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import torch
import dgl
import os
from tools.utils import eval_label
from tools.logger import *
class TestPipLine():
def __init__(self, model, m, test_dir, limited):
"""
:param model: the model
:param m: the number of sentence to select
:param test_dir: for saving decode files
:param limited: for limited Recall evaluation
"""
self.model = model
self.limited = limited
self.m = m
self.test_dir = test_dir
self.extracts = []
self.batch_number = 0
self.running_loss = 0
self.example_num = 0
self.total_sentence_num = 0
self._hyps = []
self._refer = []
def evaluation(self, G, index, valset):
pass
def getMetric(self):
pass
def SaveDecodeFile(self):
import datetime
nowTime = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
log_dir = os.path.join(self.test_dir, nowTime)
with open(log_dir, "wb") as resfile:
for i in range(self.rougePairNum):
resfile.write(b"[Reference]\t")
resfile.write(self._refer[i].encode('utf-8'))
resfile.write(b"\n")
resfile.write(b"[Hypothesis]\t")
resfile.write(self._hyps[i].encode('utf-8'))
resfile.write(b"\n")
resfile.write(b"\n")
resfile.write(b"\n")
@property
def running_avg_loss(self):
return self.running_loss / self.batch_number
@property
def rougePairNum(self):
return len(self._hyps)
@property
def hyps(self):
if self.limited:
hlist = []
for i in range(self.rougePairNum):
k = len(self._refer[i].split(" "))
lh = " ".join(self._hyps[i].split(" ")[:k])
hlist.append(lh)
return hlist
else:
return self._hyps
@property
def refer(self):
return self._refer
@property
def extractLabel(self):
return self.extracts
class SLTester(TestPipLine):
def __init__(self, model, m, test_dir=None, limited=False, blocking_win=3):
super().__init__(model, m, test_dir, limited)
self.pred, self.true, self.match, self.match_true = 0, 0, 0, 0
self._F = 0
self.criterion = torch.nn.CrossEntropyLoss(reduction='none')
self.blocking_win = blocking_win
def evaluation(self, G, index, dataset, hyper_edge, actual_node_list, blocking=False):
"""
:param G: the model
:param index: list, example id
:param dataset: dataset which includes text and summary
:param blocking: bool, for n-gram blocking
"""
self.batch_number += 1
outputs = self.model.forward(G, hyper_edge, actual_node_list)
snode_id = G.filter_nodes(lambda nodes: nodes.data["dtype"] == 1)
label = G.ndata["label"][snode_id].sum(-1)
G.nodes[snode_id].data["loss"] = self.criterion(outputs, label).unsqueeze(-1)
loss = dgl.sum_nodes(G, "loss")
loss = loss.mean()
self.running_loss += float(loss.data)
G.nodes[snode_id].data["p"] = outputs
glist = dgl.unbatch(G)
for j in range(len(glist)):
idx = index[j]
example = dataset.get_example(idx)
original_article_sents = example.original_article_sents
sent_max_number = len(original_article_sents)
refer = example.original_abstract
g = glist[j]
snode_id = g.filter_nodes(lambda nodes: nodes.data["dtype"] == 1)
N = len(snode_id)
p_sent = g.ndata["p"][snode_id]
p_sent = p_sent.view(-1, 2)
label = g.ndata["label"][snode_id].sum(-1).squeeze().cpu()
if self.m == 0:
prediction = p_sent.max(1)[1]
pred_idx = torch.arange(N)[prediction != 0].long()
else:
if blocking:
pred_idx = self.ngram_blocking(original_article_sents, p_sent[:, 1], self.blocking_win,
min(self.m, N))
else:
topk, pred_idx = torch.topk(p_sent[:, 1], min(self.m, N))
prediction = torch.zeros(N).long()
prediction[pred_idx] = 1
self.extracts.append(pred_idx.tolist())
self.pred += prediction.sum()
self.true += label.sum()
self.match_true += ((prediction == label) & (prediction == 1)).sum()
self.match += (prediction == label).sum()
self.total_sentence_num += N
self.example_num += 1
hyps = "\n".join(original_article_sents[id] for id in pred_idx if id < sent_max_number)
self._hyps.append(hyps)
self._refer.append(refer)
def getMetric(self):
logger.info("[INFO] Validset match_true %d, pred %d, true %d, total %d, match %d",
self.match_true, self.pred, self.true, self.total_sentence_num, self.match)
self._accu, self._precision, self._recall, self._F = eval_label(
self.match_true, self.pred, self.true, self.total_sentence_num, self.match)
logger.info(
"[INFO] The size of totalset is %d, sent_number is %d, accu is %f, precision is %f, recall is %f, F is %f",
self.example_num, self.total_sentence_num, self._accu, self._precision, self._recall, self._F)
def ngram_blocking(self, sents, p_sent, n_win, k):
"""
:param p_sent: [sent_num, 1]
:param n_win: int, n_win=2,3,4...
:return:
"""
ngram_list = []
_, sorted_idx = p_sent.sort(descending=True)
S = []
for idx in sorted_idx:
sent = sents[idx]
pieces = sent.split()
overlap_flag = 0
sent_ngram = []
for i in range(len(pieces) - n_win):
ngram = " ".join(pieces[i: (i + n_win)])
if ngram in ngram_list:
overlap_flag = 1
break
else:
sent_ngram.append(ngram)
if overlap_flag == 0:
S.append(idx)
ngram_list.extend(sent_ngram)
if len(S) >= k:
break
S = torch.LongTensor(S)
return S
@property
def labelMetric(self):
return self._F