def get_evaluator(args, model, loss_fn):
def _inference(evaluator, batch):
model.eval()
with torch.no_grad():
qids = batch["qid"]
net_inputs, _ = prepare_batch(args, batch, model.vocab)
y_pred = model(**net_inputs)
y_pred = y_pred.argmax(dim=-1) # + 1 # 0~4 -> 1~5
for qid, ans_idx in zip(qids, y_pred):
engine.answers[qid] = ans_idx.item()
return
engine = Engine(_inference)
engine.answers = {}
return engine
def get_evaluator(args, model, loss_fn):
def _inference(evaluator, batch):
model.eval()
with torch.no_grad():
qids = batch["qid"]
net_inputs, _ = prepare_batch(args, batch, model.module.vocab)
y_pred, char_pred, mask_pred = model(**net_inputs)
print("Before argmax:", y_pred.size())
y_pred = y_pred.argmax(dim=-1) # + 1 # 0~4 -> 1~5
print("After argmax:", y_pred.size())
for qid, ans in zip(qids, y_pred):
engine.answers[qid] = ans.item()
return
engine = Engine(_inference)
engine.answers = {}
return engine
def get_evaluator(args, model, loss_fn):
def _inference(evaluator, batch):
model.eval()
with torch.no_grad():
new_qids = batch["new_qid"]
real_ans = batch["correct_idx"]
net_inputs, _ = prepare_batch(args, batch, model.vocab)
y_pred = model(**net_inputs)
y_pred = y_pred.argmax(dim=-1) # + 1 # 0~4 -> 1~5
for new_qid, ans, r_ans in zip(new_qids, y_pred, real_ans):
engine.answers[new_qid] = ans.item()
if ans.item() == r_ans:
engine.count += 1
return
engine = Engine(_inference)
engine.answers = {}
engine.count = 0
return engine
