def rl_train_step(self, batch):
maxlen = self.question_truncate or 30
preds, text, nll = self.sample(batch, latest_turn_only=True)
if self.rl_baseline_method == "self_critic":
g_preds, g_text, g_scores = self.predict(batch,
latest_turn_only=True,
no_grad=True)
retval = Output(text[:1],
log_probs=nll[:1],
episode_end=[batch['episode_end']],
ques_len=[len(preds[0]) - 1],
diverged_outputs=[[(t, nll[i], len(preds[i]) - 1)
for i, t in enumerate(text[1:])]
],
greedy_master_output=g_text[:1],
greedy_output=[[t for t in g_text[1:]]])
else:
retval = Output(text[:1],
log_probs=nll[:1],
episode_end=[batch['episode_end']],
ques_len=[len(preds[0]) - 1],
diverged_outputs=[[(t, nll[i], len(preds[i]) - 1)
for i, t in enumerate(text[1:])]
])
return retval
def eval_step(self, batch):
"""
Evaluate a single batch of examples.
"""
if batch.text_vec is None:
return
self.model.eval()
scores = self.score(batch)
probs = F.softmax(scores, dim=1)
_, prediction_id = torch.max(probs.float().cpu(), 1)
preds = [self.class_list[idx] for idx in prediction_id]
if batch.labels is None or self.opt['ignore_labels']:
# interactive mode
if self.opt.get('print_scores', False):
preds = self._format_interactive_output(probs, prediction_id)
else:
labels = self._get_label_tensor(batch)
loss = self.criterion(scores, labels)
self.record_local_metric('loss', AverageMetric.many(loss))
preds = [self.class_list[idx] for idx in prediction_id]
labels = batch.labels
if preds is not None and labels is not None:
self._update_confusion_matrix(preds, labels)
if self.opt.get('print_scores', False):
return Output(preds, probs=probs.cpu())
else:
return Output(preds)
def train_step(self, batch):
"""Train on a single batch of examples."""
if batch.text_vec is None:
return
batchsize = batch.text_vec.size(0)
self.model.train()
self.zero_grad()
cands, cand_vecs, label_inds = self._build_candidates(
batch, source=self.opt['candidates'], mode='train')
scores = self.score_candidates(batch, cand_vecs)
loss = self.rank_loss(scores, label_inds)
# Update loss
self.metrics['loss'] += loss.item()
self.metrics['examples'] += batchsize
loss.backward()
self.update_params()
# Get train predictions
if self.opt['candidates'] == 'batch':
self.get_batch_train_metrics(scores)
return Output()
if not self.opt.get('train_predict', False):
warn_once(
"Some training metrics are omitted for speed. Set the flag "
"`--train-predict` to calculate train metrics.")
return Output()
return self.get_train_preds(scores, label_inds, cands, cand_vecs)
def train_step(self, batch):
"""
Train on a single batch of examples.
"""
if batch.text_vec is None:
return Output()
self.model.train()
self.zero_grad()
# Calculate loss
labels = self._get_label_tensor(batch)
scores = self.score(batch)
loss = self.criterion(scores, labels)
self.record_local_metric('loss', AverageMetric.many(loss))
loss = loss.mean()
self.backward(loss)
self.update_params()
# Get predictions
_, prediction_id = torch.max(scores.float().cpu(), 1)
preds = [self.class_list[idx] for idx in prediction_id]
labels_field = self.get_labels_field(batch['observations'])
labels_lst = self._get_labels(batch['observations'], labels_field)
self._update_confusion_matrix(preds, labels_lst)
return Output(preds)
def eval_step(self, batch):
if batch.text_vec is None:
return
self.model.eval()
batchsize = batch.text_vec.size(0)
self.metrics['examples'] += batchsize
if self.subtask == 'dialog':
_, preds, cand_ranked = self.dialog_step(batch)
if self.opt['interactive']:
if self.opt['prev_response_filter']:
preds = self.check_prev_response(preds, cand_ranked)
return Output(preds)
else:
return Output(preds, cand_ranked)
elif self.subtask == 'feedback':
_, preds, cand_ranked = self.feedback_step(batch)
return Output(preds, cand_ranked)
elif self.subtask == 'satisfaction':
if self.opt['uncertainty_predictor']:
# Use uncertainty of dialog model to classify bot's previous utterance
preds = self.predict_satisfaction_by_uncertainty(batch)
else:
# Use satisfaction of user response to classify bot's previous response
_, preds = self.satisfaction_step(batch)
preds = [str(p) for p in preds]
return Output(preds)
def eval_step(self, batch):
"""
Train on a single batch of examples.
"""
if batch.text_vec is None:
return
self.model.eval()
scores = self.score(batch)
probs = F.softmax(scores, dim=1)
if self.threshold is None:
_, prediction_id = torch.max(probs.cpu(), 1)
else:
ref_prob = probs.cpu()[:, 0]
# choose ref class if Prob(ref class) > threshold
prediction_id = (ref_prob <= self.threshold).to(torch.int64)
preds = [self.class_list[idx] for idx in prediction_id]
if batch.labels is None or self.opt['ignore_labels']:
# interactive mode
if self.opt.get('print_scores', False):
preds = self._format_interactive_output(probs, prediction_id)
else:
labels = self._get_labels(batch)
loss = self.criterion(scores, labels)
self.record_local_metric('loss', AverageMetric.many(loss))
loss = loss.mean()
self._update_confusion_matrix(batch, preds)
if self.opt.get('print_scores', False):
return Output(preds, probs=probs.cpu())
else:
return Output(preds)
def train_step(self, batch):
"""
Train on a single batch of examples.
"""
if batch.text_vec is None:
return Output()
self.model.train()
self.optimizer.zero_grad()
# calculate loss
labels = self._get_labels(batch)
scores = self.score(batch)
loss = self.criterion(scores, labels)
loss.backward()
self.update_params()
# update metrics
self.metrics['loss'] += loss.item()
self.metrics['examples'] += len(batch.text_vec)
# get predictions
_, prediction_id = torch.max(scores.cpu(), 1)
preds = [self.class_list[idx] for idx in prediction_id]
self._update_confusion_matrix(batch, preds)
return Output(preds)
def eval_step(self, batch):
"""
Evaluate a single batch of examples.
"""
#print(self.model._encoder_input(batch))
if batch.text_vec is None and batch.image is None:
return Output('N')
if batch.text_vec is not None:
bsz = batch.text_vec.size(0)
else:
bsz = len(batch.image)
self.model.eval()
cand_scores = None
token_losses = None
if batch.label_vec is not None:
# calculate loss on targets with teacher forcing
loss, model_output = self.compute_loss(batch, return_output=True)
if self.output_token_losses:
token_losses = self._construct_token_losses(
batch.label_vec, model_output)
preds = None
if self.skip_generation:
warn_once(
"--skip-generation does not produce accurate metrics beyond ppl",
RuntimeWarning,
)
else:
maxlen = self.label_truncate or 20
n_best_beam_preds_scores, _ = self._generate(
batch, self.beam_size, maxlen)
preds = []
scores = []
for n_best_list in n_best_beam_preds_scores:
p, s = zip(*n_best_list)
preds.append(p)
scores.append(s)
cand_choices = None
self.rank_candidates = True
if self.rank_candidates:
# compute MMI to rank candidates
bestpreds = []
for i in range(bsz):
cands, _ = self._pad_tensor(preds[i])
cand_scores = self.computeMMI(batch.text_vec[i], cands,
list(scores[i]))
_, ordering = cand_scores.sort()
bestpreds.append(preds[i][ordering[0]])
text = [self._v2t(p)
for p in bestpreds] if bestpreds is not None else None
if text and self.compute_tokenized_bleu:
# compute additional bleu scores
self._compute_fairseq_bleu(batch, preds)
self._compute_nltk_bleu(batch, text)
return Output(text, cand_choices, token_losses=token_losses)
def train_step(self, batch):
"""
Train on a single batch of examples.
"""
self._maybe_invalidate_fixed_encs_cache()
if batch.text_vec is None and batch.image is None:
return
batchsize = (
batch.text_vec.size(0)
if batch.text_vec is not None
else batch.image.size(0)
)
self.model.train()
self.zero_grad()
cands, cand_vecs, label_inds = self._build_candidates(
batch, source=self.candidates, mode='train'
)
try:
scores = self.score_candidates(batch, cand_vecs)
loss = self.criterion(scores, label_inds)
self.backward(loss)
self.update_params()
except RuntimeError as e:
# catch out of memory exceptions during fwd/bck (skip batch)
if 'out of memory' in str(e):
print(
'| WARNING: ran out of memory, skipping batch. '
'if this happens frequently, decrease batchsize or '
'truncate the inputs to the model.'
)
return Output()
else:
raise e
# Update loss
self.metrics['loss'] += loss.item()
self.metrics['examples'] += batchsize
# Get train predictions
if self.candidates == 'batch':
self._get_batch_train_metrics(scores)
return Output()
if not self.opt.get('train_predict', False):
warn_once(
"Some training metrics are omitted for speed. Set the flag "
"`--train-predict` to calculate train metrics."
)
return Output()
return self._get_train_preds(scores, label_inds, cands, cand_vecs)
def eval_step(self, batch):
questions = batch.text_vec
contexts = padded_3d(batch.memory_vecs)
if contexts.shape[0] != self.batch_size:
return Output(
self.dict.vec2txt(
np.random.choice(self.dictionnary_size,
size=contexts.shape[0])).split(" "))
output = self.recurrent_entity_network(questions, contexts)
pred = output.argmax(dim=1)
return Output(self.dict.vec2txt(pred).split(" "))
def eval_step(self, batch):
"""
Evaluate a single batch of examples.
"""
if batch.text_vec is None:
return
self.model.eval()
scores = self.score(batch)
probs = F.softmax(scores, dim=1)
if self.calc_auc:
self._update_aucs(batch, probs)
if self.threshold is None:
_, prediction_id = torch.max(probs.cpu(), 1)
else:
ref_prob = probs.cpu()[:, 0]
# choose ref class if Prob(ref class) > threshold
prediction_id = (ref_prob <= self.threshold).to(torch.int64)
preds = [self.class_list[idx] for idx in prediction_id]
if batch.labels is None or self.opt['ignore_labels']:
# interactive mode
if self.opt.get('print_scores', False):
preds = self._format_interactive_output(probs, prediction_id)
else:
labels = self._get_labels(batch)
loss = self.criterion(scores, labels)
self.record_local_metric('loss', AverageMetric.many(loss))
loss = loss.mean()
self._update_confusion_matrix(batch, preds)
if self.opt.get('print_scores', False):
return Output(preds,
class_list=[self.class_list],
probs=probs.cpu())
if self.opt.get('return_cand_scores', False):
sorted_scores, ranks = probs.sort(1, descending=True)
sorted_scores = sorted_scores.cpu()
text_cands = []
for i in range(0, ranks.size(0)):
ordered_list = [self.class_list[i] for i in ranks[i]]
text_cands.append(ordered_list)
return Output(preds,
text_candidates=text_cands,
sorted_scores=sorted_scores)
else:
return Output(preds)
def eval_step(self, batch):
"""Evaluate a single batch of examples."""
self.model.eval()
cand_params = self._build_cands(batch)
out = self.model(batch.text_vec, ys=None, cand_params=cand_params)
scores, cand_scores = out[0], out[1]
_, preds = scores.max(2)
if batch.label_vec is not None:
# calculate loss on targets with teacher forcing
out = self.model(batch.text_vec, batch.label_vec)
f_scores = out[0] # forced scores
_, f_preds = f_scores.max(2) # forced preds
score_view = f_scores.view(-1, f_scores.size(-1))
loss = self.criterion(score_view, batch.label_vec.view(-1))
# save loss to metrics
notnull = batch.label_vec.ne(self.NULL_IDX)
target_tokens = notnull.long().sum().item()
correct = ((batch.label_vec == f_preds) * notnull).sum().item()
self.metrics['correct_tokens'] += correct
self.metrics['loss'] += loss.item()
self.metrics['num_tokens'] += target_tokens
cand_choices = None
if cand_scores is not None:
cand_preds = cand_scores.sort(1, True)[1]
# now select the text of the cands based on their scores
cand_choices = self._pick_cands(cand_preds, cand_params[1],
batch.candidates)
text = [self._v2t(p) for p in preds.cpu()]
return Output(text, cand_choices)
def rl_eval_step(self, batch):
div_batch = batch.get('diverged_batch', None)
if not div_batch:
div_batch = batch
token_losses = None
if batch.label_vec is not None:
# calculate loss on targets with teacher forcing
loss, model_output = self.compute_loss(batch, return_output=True)
if self.output_token_losses:
token_losses = self._construct_token_losses(
batch.label_vec, model_output)
preds = None
maxlen = self.question_truncate or 30
if self.eva_sample:
preds, text, scores = self.sample(div_batch, latest_turn_only=True)
else:
preds, text, scores = self.predict(div_batch,
latest_turn_only=True)
retval = Output(text[:1],
log_probs=scores[:1],
episode_end=[batch.episode_end],
ques_len=[len(preds[0]) - 1],
diverged_outputs=[[(t, scores[i], len(preds[i]) - 1)
for i, t in enumerate(text[1:])]])
return retval
def _eval_dnli_step(self, batch):
"""Evaluate a single batch of examples."""
assert self.alpha >= 0
self.model.eval()
ranked_cands, ordering = self.rank(batch)
bsz = len(ranked_cands)
dnli_metrics = []
for batch_idx in range(bsz):
dnli_score = {'contradict@1': 0, 'entail@1': 0, 'neutral@1': 0}
top1_idx = ordering[batch_idx][0].item()
if top1_idx == 0:
pass
# dnli_metrics['dnli_hit@1'] += 1
elif top1_idx > 0 and top1_idx < 11:
dnli_score['contradict@1'] += 1
elif top1_idx >= 11 and top1_idx < 21:
dnli_score['entail@1'] += 1
else:
dnli_score['neutral@1'] += 1
dnli_metrics.append(dnli_score)
return Output(text_candidates=ranked_cands, metrics=dnli_metrics)
def probe_step(self, batch):
"""Probe a single batch of examples."""
if batch.text_vec is None:
return
bsz = batch.text_vec.size(0)
self.model.eval()
cand_scores = None
if self.opt['probe'] == 'word_embeddings':
embeddings = self.probe_word_embeddings(batch)
elif self.opt['probe'] == 'encoder_state':
embeddings = self.probe_encoder_state(batch)
elif self.opt['probe'] == 'combined':
embeddings = self.probe_combined(batch)
else:
raise Exception(f"Input type {self.opt['probe']} not understood.")
try:
self.probing_outputs = np.vstack(
(self.probing_outputs, embeddings))
except:
# In case probing_outputs empty array
self.probing_outputs = embeddings
cand_choices = None
text = None
return Output(text, cand_choices)
def train_step(self, batch):
"""
Train on a single batch of examples.
"""
if batch.text_vec is None:
return
self.model.train()
self.optimizer.zero_grad()
batchsize = batch.text_vec.size(0)
self.metrics['examples'] += batchsize
if self.subtask == 'dialog':
loss, preds, _ = self.dialog_step(batch)
elif self.subtask == 'feedback':
loss, preds, _ = self.feedback_step(batch)
elif self.subtask == 'satisfaction':
loss, preds = self.satisfaction_step(batch)
preds = [str(p) for p in preds]
# Weight loss by task-weight
loss *= self.task_weight[self.subtask]
loss.backward()
self.update_params()
return Output(preds)
def eval_step(self, batch):
"""Generate a response to the input tokens.
:param batch: parlai.core.torch_agent.Batch, contains tensorized
version of observations.
Return predicted responses (list of strings of length batchsize).
"""
item1 = {
"text": "Do you like playing, or watching sports?",
"labels": ["I like watching sports."],
"label_candidates": list(batch.observations[0]["label_candidates"])
}
item1["label_candidates"].append(item1["labels"][0])
item2 = {
"text": "Do you think chess counts as a sport?",
"labels": ["Yes, I think it does."],
"label_candidates": list(batch.observations[0]["label_candidates"])
}
item2["label_candidates"].append(item2["labels"][0])
batch = Batch(observations=[item1, item2])
inputs, candidates = self._tokenize_observation(batch)
# just predict
self.model.eval()
output = self.model(inputs.cuda())
pred_text = self._get_predictions(output, candidates, 2)
print(pred_text)
print("EVALUATING")
return Output(pred_text)
def eval_step(self, batch):
"""Return confirmation of evaluation."""
return Output([
'Evaluating {} (responding to {})!'.format(
i, batch.observations[i]['text'])
for i in range(len(batch.text_vec))
])
def make_preds_for_inject(self, batch):
assert self.inject == True
assert len(batch.labels
) == 1, 'only single batch is assumed here to keep order'
# human
human = batch.labels
# greedy
out = self.model(batch.text_vec, ys=None)
scores, _ = out[0], out[1]
_, preds = scores.max(2)
greedy = [self._v2t(p) for p in preds]
encoder_states = out[2]
# beam
out = SteroidSeq2seqAgent.beam_search(
self.model,
batch,
encoder_states,
self.beam_size,
start=self.START_IDX,
end=self.END_IDX,
pad=self.NULL_IDX,
min_length=self.beam_min_length,
min_n_best=self.beam_min_n_best,
beam_block_hypos=[],
block_ngram=self.beam_block_ngram)
beam10 = [self._v2t(out[0][0][0][1:])]
output = Output(beam10, cand_choices, human, greedy, beam10)
return output
def train_step(self, batch):
"""Train on a single batch of examples."""
if batch.text_vec is None:
return
batchsize = batch.text_vec.size(0)
self.model.train()
self.optimizer.zero_grad()
mems = self._build_mems(batch.memory_vecs)
cands, label_inds = self._build_train_cands(batch.label_vec,
batch.candidate_vecs)
scores = self.model(batch.text_vec, mems, cands)
loss = self.rank_loss(scores, label_inds)
self.metrics['loss'] += loss.item()
self.metrics['batches'] += batchsize
_, ranks = scores.sort(1, descending=True)
for b in range(batchsize):
rank = (ranks[b] == label_inds[b]).nonzero().item()
self.metrics['rank'] += 1 + rank
loss.backward()
self.update_params()
# get predictions but not full rankings--too slow to get hits@1 score
preds = [self._v2t(cands[row[0]]) for row in ranks]
return Output(preds)
def train_step(self, batch):
"""Train on a single batch of examples."""
if batch.text_vec is None:
return
batchsize = batch.text_vec.size(0)
self.model.train()
self.optimizer.zero_grad()
cands, cand_vecs, label_inds = self._build_candidates(
batch, source=self.opt['candidates'], mode='train')
scores = self.score_candidates(batch, cand_vecs)
loss = self.rank_loss(scores, label_inds)
# Update metrics
self.metrics['loss'] += loss.item()
self.metrics['examples'] += batchsize
_, ranks = scores.sort(1, descending=True)
for b in range(batchsize):
rank = (ranks[b] == label_inds[b]).nonzero().item()
self.metrics['rank'] += 1 + rank
loss.backward()
self.update_params()
# Get predictions but not full rankings for the sake of speed
if cand_vecs.dim() == 2:
preds = [cands[ordering[0]] for ordering in ranks]
elif cand_vecs.dim() == 3:
preds = [cands[i][ordering[0]] for i, ordering in enumerate(ranks)]
return Output(preds)
def eval_step(self, batch):
images = torch.stack([self.transform(img) for img in batch.image])
if self.use_cuda:
images = images.cuda(async=True)
# Need to collate then sort the captions by length
cands = [
self.candidate_helper(label_cands_vec, label_cands,
self.mode == 'test') for label_cands_vec,
label_cands in zip(batch.candidate_vecs, batch.candidates)
]
self.model.eval()
# Obtain the image embeddings
img_embs, _ = self.model(images, None, None)
ranks = []
top1 = []
# Each image has their own caption candidates, so we need to
# iteratively create the embeddings and rank
for i, (cap, _, lens, truth_idx) in enumerate(cands):
_, embs = self.model(None, cap, lens)
# Hack to pass through the truth label's index to compute the
# rank and top metrics
offset = truth_idx if truth_idx is not None else 0
_, rank, top = self.criterion(img_embs[i, :].unsqueeze(0), embs,
offset)
ranks += rank
top1.append(top[0])
self.metrics['r@'] += ranks
predictions = []
for i, score_idx in enumerate(top1):
predictions.append(cands[i][1][score_idx])
return Output(predictions, None)
def eval_step(self, batch):
"""Train on a single batch of examples."""
if batch.text_vec is None:
return
self.model.eval()
scores = self.score(batch)
probs = F.softmax(scores, dim=1)
if self.threshold is None:
_, prediction_id = torch.max(probs.cpu(), 1)
else:
ref_prob = probs.cpu()[:, 0]
# choose ref class if Prob(ref class) > threshold
prediction_id = ref_prob <= self.threshold
preds = [self.class_list[idx] for idx in prediction_id]
if batch.labels is None:
# interactive mode
if self.opt.get('print_scores', False):
preds = self._format_interactive_output(probs, prediction_id)
else:
labels = self._get_labels(batch)
loss = self.criterion(scores, labels)
self.metrics['loss'] += loss.item()
self.metrics['examples'] += len(batch.text_vec)
self._update_confusion_matrix(batch, preds)
return Output(preds)
def eval_step(self, batch):
"""Evaluate a single batch of examples."""
if batch.text_vec is None:
return
batchsize = batch.text_vec.size(0)
self.model.eval()
cands, cand_vecs, label_inds = self._build_candidates(
batch, source=self.opt['eval_candidates'], mode='eval')
scores = self.score_candidates(batch, cand_vecs)
_, ranks = scores.sort(1, descending=True)
# Update metrics
if label_inds is not None:
loss = self.rank_loss(scores, label_inds)
self.metrics['loss'] += loss.item()
self.metrics['examples'] += batchsize
for b in range(batchsize):
rank = (ranks[b] == label_inds[b]).nonzero().item()
self.metrics['rank'] += 1 + rank
cand_preds = []
for i, ordering in enumerate(ranks):
if cand_vecs.dim() == 2:
cand_list = cands
elif cand_vecs.dim() == 3:
cand_list = cands[i]
cand_preds.append([cand_list[rank] for rank in ordering])
preds = [cand_preds[i][0] for i in range(batchsize)]
return Output(preds, cand_preds)
def eval_step(self, batch):
"""Process batch of inputs.
If the batch includes labels, calculate validation metrics as well.
:param batch: parlai.core.torch_agent.Batch, contains tensorized
version of observations.
"""
if batch.text_vec is None:
return
self.is_training = False
self.model.eval()
output = self.model(batch.text_vec, batch.text_mask)
if batch.label_vec is not None:
# Interactive mode won't have a gold label
missed = self.criterion(batch.label_vec, output[0],
batch.text_mask)
self.metrics['error.sum'] += float(missed.sum())
self.metrics['eval_exs'] += int(batch.text_lengths.sum())
pred = predict(output[0], batch.text_lengths, batch.text_vec,
batch.text_mask)
text = self._v2t(batch.text_vec[0])
self.vars = (text, pred[0], batch.text_vec[0],
int(batch.text_lengths[0]), *tuple(v[0]
for v in output[2:]))
return Output(text=pred)
def eval_step(self, batch):
"""
Evaluate a single batch of examples.
"""
if batch.text_vec is None:
return
bsz = batch.text_vec.size(0)
self.model.eval()
cand_scores = None
token_losses = None
if batch.label_vec is not None:
# calculate loss on targets with teacher forcing
loss, model_output = self.compute_loss(batch, return_output=True)
self.metrics['loss'] += loss.item()
if self.output_token_losses:
token_losses = self._construct_token_losses(
batch.label_vec, model_output)
preds = None
if self.skip_generation:
warn_once(
"--skip-generation does not produce accurate metrics beyond ppl",
RuntimeWarning,
)
else:
maxlen = self.label_truncate or 256
beam_preds_scores, _ = self._generate(batch, self.beam_size,
maxlen)
preds, scores = zip(*beam_preds_scores)
cand_choices = None
# TODO: abstract out the scoring here
if self.rank_candidates:
# compute roughly ppl to rank candidates
cand_choices = []
encoder_states = self.model.encoder(*self._model_input(batch))
for i in range(bsz):
num_cands = len(batch.candidate_vecs[i])
enc = self.model.reorder_encoder_states(
encoder_states, [i] * num_cands)
cands, _ = padded_tensor(batch.candidate_vecs[i],
self.NULL_IDX, self.use_cuda)
scores, _ = self.model.decode_forced(enc, cands)
cand_losses = F.cross_entropy(
scores.view(num_cands * cands.size(1), -1),
cands.view(-1),
reduction='none',
).view(num_cands, cands.size(1))
# now cand_losses is cands x seqlen size, but we still need to
# check padding and such
mask = (cands != self.NULL_IDX).float()
cand_scores = (cand_losses *
mask).sum(dim=1) / (mask.sum(dim=1) + 1e-9)
_, ordering = cand_scores.sort()
cand_choices.append([batch.candidates[i][o] for o in ordering])
text = [self._v2t(p) for p in preds] if preds is not None else None
return Output(text, cand_choices, token_losses=token_losses)
def eval_step(self, batch):
"""Evaluate a single batch of examples."""
if batch.text_vec is None and batch.image is None:
return
batchsize = (batch.text_vec.size(0)
if batch.text_vec is not None else batch.image.size(0))
self.model.eval()
cands, cand_vecs, label_inds = self._build_candidates(
batch, source=self.eval_candidates, mode='eval')
cand_encs = None
if self.encode_candidate_vecs:
# if we cached candidate encodings for a fixed list of candidates,
# pass those into the score_candidates function
if self.eval_candidates == 'fixed':
cand_encs = self.fixed_candidate_encs
elif self.eval_candidates == 'vocab':
cand_encs = self.vocab_candidate_encs
scores = self.score_candidates(batch, cand_vecs, cand_encs=cand_encs)
if self.rank_top_k > 0:
_, ranks = scores.topk(min(self.rank_top_k, scores.size(1)),
1,
largest=True)
else:
_, ranks = scores.sort(1, descending=True)
# Update metrics
if label_inds is not None:
loss = self.rank_loss(scores, label_inds)
self.metrics['loss'] += loss.item()
self.metrics['examples'] += batchsize
for b in range(batchsize):
rank = (ranks[b] == label_inds[b]).nonzero()
rank = rank.item() if len(rank) == 1 else scores.size(1)
self.metrics['rank'] += 1 + rank
self.metrics['mrr'] += 1.0 / (1 + rank)
ranks = ranks.cpu()
max_preds = self.opt['cap_num_predictions']
cand_preds = []
for i, ordering in enumerate(ranks):
if cand_vecs.dim() == 2:
cand_list = cands
elif cand_vecs.dim() == 3:
cand_list = cands[i]
# using a generator instead of a list comprehension allows
# to cap the number of elements.
cand_preds_generator = (cand_list[rank] for rank in ordering
if rank < len(cand_list))
cand_preds.append(list(islice(cand_preds_generator, max_preds)))
if (self.opt.get('repeat_blocking_heuristic', True)
and self.eval_candidates == 'fixed'):
cand_preds = self.block_repeats(cand_preds)
preds = [cand_preds[i][0] for i in range(batchsize)]
return Output(preds, cand_preds)
def eval_step(self, batch):
"""
Return confirmation of evaluation.
"""
return Output([
f'Evaluating {i} (responding to {batch.text_vec.tolist()})!'
for i in range(batch.batchsize)
])
def eval_step(self, batch):
if batch.text_vec is None:
return
self.model.eval()
bs = (batch.label_vec == 1).sum().item()
labels = torch.zeros(bs, dtype=torch.long)
# create subgraph for propagation
seed_sets = []
turns = []
for i, (b, movieIdx) in enumerate(batch.label_vec.nonzero().tolist()):
# seed set (i.e. mentioned movies + entitites)
seed_set = batch.text_vec[b].nonzero().view(-1).tolist()
labels[i] = movieIdx
seed_sets.append(seed_set)
turns.append(batch.turn[b])
if self.use_cuda:
labels = labels.cuda()
return_dict = self.model(seed_sets, labels)
loss = return_dict["loss"]
self.metrics["base_loss"] += return_dict["base_loss"].item()
self.metrics["loss"] += loss.item()
self.counts["num_tokens"] += bs
self.counts["num_batches"] += 1
outputs = return_dict["scores"].cpu()
outputs = outputs[:, torch.LongTensor(self.movie_ids)]
_, pred_idx = torch.topk(outputs, k=100, dim=1)
for b in range(bs):
target_idx = self.movie_ids.index(labels[b].item())
self.metrics["recall@1"] += int(
target_idx in pred_idx[b][:1].tolist())
self.metrics["recall@10"] += int(
target_idx in pred_idx[b][:10].tolist())
self.metrics["recall@50"] += int(
target_idx in pred_idx[b][:50].tolist())
self.metrics[f"recall@1@turn{turns[b]}"] += int(
target_idx in pred_idx[b][:1].tolist())
self.metrics[f"recall@10@turn{turns[b]}"] += int(
target_idx in pred_idx[b][:10].tolist())
self.metrics[f"recall@50@turn{turns[b]}"] += int(
target_idx in pred_idx[b][:50].tolist())
self.counts[f"recall@1@turn{turns[b]}"] += 1
self.counts[f"recall@10@turn{turns[b]}"] += 1
self.counts[f"recall@50@turn{turns[b]}"] += 1
self.counts[f"recall@1"] += 1
self.counts[f"recall@10"] += 1
self.counts[f"recall@50"] += 1
return Output(
list(
map(lambda x: str(self.movie_ids[x]),
outputs.argmax(dim=1).tolist())))
def eval_step(self, batch):
if batch.batchsize <= 0:
return
else:
bsz = batch.batchsize
self.model.eval()
loss, outputs = self.compute_loss(batch, return_output=True)
batch_best_preds = outputs['pred']['outputs']
return Output(batch_best_preds)