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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import string
from dataclasses import dataclass, field
from collections import OrderedDict
from typing import Optional
import torch
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
from omegaconf import II
from data import data_utils
from utils.cider.pyciderevalcap.ciderD.ciderD import CiderD
def scst_loss(lprobs, target, reward, ignore_index=None, reduce=True):
loss = -lprobs.gather(dim=-1, index=target.unsqueeze(-1)).squeeze() * reward.unsqueeze(-1)
if ignore_index is not None:
pad_mask = target.eq(ignore_index)
loss.masked_fill_(pad_mask, 0.0)
ntokens = (~pad_mask).sum()
else:
loss = loss.squeeze(-1)
ntokens = target.numel()
if reduce:
loss = loss.sum()
return loss, ntokens
@dataclass
class ScstRewardCriterionConfig(FairseqDataclass):
scst_cider_cached_tokens: str = field(
default="coco-train-words.p",
metadata={"help": "path to cached cPickle file used to calculate CIDEr scores"},
)
ignore_prefix_size: int = field(
default=0,
metadata={"help": "Ignore first N tokens"},
)
sentence_avg: bool = II("optimization.sentence_avg")
constraint_range: Optional[str] = field(
default=None,
metadata={"help": "constraint range"}
)
@register_criterion(
"scst_reward_criterion", dataclass=ScstRewardCriterionConfig
)
class ScstRewardCriterion(FairseqCriterion):
CIDER_REWARD_WEIGHT = 1
def __init__(
self,
task,
scst_cider_cached_tokens,
sentence_avg,
ignore_prefix_size=0,
constraint_range=None
):
super().__init__(task)
self.scst_cider_scorer = CiderD(df=scst_cider_cached_tokens)
self.sentence_avg = sentence_avg
self.ignore_prefix_size = ignore_prefix_size
self.transtab = str.maketrans({key: None for key in string.punctuation})
self.constraint_start = None
self.constraint_end = None
if constraint_range is not None:
constraint_start, constraint_end = constraint_range.split(',')
self.constraint_start = int(constraint_start)
self.constraint_end = int(constraint_end)
def forward(self, model, sample, update_num=0, reduce=True):
"""Compute the loss for the given sample.
Returns a tuple with three elements:
1) the loss
2) the sample size, which is used as the denominator for the gradient
3) logging outputs to display while training
"""
loss, score, ntokens, nsentences = self.compute_loss(model, sample, reduce=reduce)
sample_size = (
nsentences if self.sentence_avg else ntokens
)
logging_output = {
"loss": loss.data,
"score": score,
"ntokens": ntokens,
"nsentences": nsentences,
"sample_size": sample_size,
}
return loss, sample_size, logging_output
def _calculate_eval_scores(self, gen_res, gt_idx, gt_res):
'''
gen_res: generated captions, list of str
gt_idx: list of int, of the same length as gen_res
gt_res: ground truth captions, list of list of str.
gen_res[i] corresponds to gt_res[gt_idx[i]]
Each image can have multiple ground truth captions
'''
gen_res_size = len(gen_res)
res = OrderedDict()
for i in range(gen_res_size):
res[i] = [self._wrap_sentence(gen_res[i].strip().translate(self.transtab))]
gts = OrderedDict()
gt_res_ = [
[self._wrap_sentence(gt_res[i][j].strip().translate(self.transtab)) for j in range(len(gt_res[i]))]
for i in range(len(gt_res))
]
for i in range(gen_res_size):
gts[i] = gt_res_[gt_idx[i]]
res_ = [{'image_id':i, 'caption': res[i]} for i in range(len(res))]
_, batch_cider_scores = self.scst_cider_scorer.compute_score(gts, res_)
scores = self.CIDER_REWARD_WEIGHT * batch_cider_scores
return scores
@classmethod
def _wrap_sentence(self, s):
# ensure the sentence ends with <eos> token
# in order to keep consisitent with cider_cached_tokens
r = s.strip()
if r.endswith('.'):
r = r[:-1]
r += ' <eos>'
return r
def get_generator_out(self, model, sample):
def decode(toks):
hypo = toks.int().cpu()
hypo_str = self.task.tgt_dict.string(hypo)
hypo_str = self.task.bpe.decode(hypo_str).strip()
return hypo, hypo_str
model.eval()
with torch.no_grad():
self.task.scst_generator.model.eval()
gen_out = self.task.scst_generator.generate([model], sample)
gen_target = []
gen_res = []
gt_res = []
for i in range(len(gen_out)):
for j in range(len(gen_out[i])):
hypo, hypo_str = decode(gen_out[i][j]["tokens"])
gen_target.append(hypo)
gen_res.append(hypo_str)
gt_res.append(
decode(utils.strip_pad(sample["target"][i], self.padding_idx))[1].split('&&')
)
return gen_target, gen_res, gt_res
def get_reward_and_scores(self, gen_res, gt_res, device):
batch_size = len(gt_res)
gen_res_size = len(gen_res)
seq_per_img = gen_res_size // batch_size
gt_idx = [i // seq_per_img for i in range(gen_res_size)]
scores = self._calculate_eval_scores(gen_res, gt_idx, gt_res)
sc_ = scores.reshape(batch_size, seq_per_img)
baseline = (sc_.sum(1, keepdims=True) - sc_) / (sc_.shape[1] - 1)
# sample - baseline
reward = scores.reshape(batch_size, seq_per_img)
reward = reward - baseline
reward = reward.reshape(gen_res_size)
reward = torch.as_tensor(reward, device=device, dtype=torch.float64)
return reward, scores
def get_net_output(self, model, sample, gen_target):
def merge(sample_list, eos=self.task.tgt_dict.eos(), move_eos_to_beginning=False):
return data_utils.collate_tokens(
sample_list,
pad_idx=self.padding_idx,
eos_idx=eos,
left_pad=False,
move_eos_to_beginning=move_eos_to_beginning,
)
batch_size = len(sample["target"])
gen_target_size = len(gen_target)
seq_per_img = gen_target_size // batch_size
model.train()
sample_src_tokens = torch.repeat_interleave(
sample['net_input']['src_tokens'], seq_per_img, dim=0
)
sample_src_lengths = torch.repeat_interleave(
sample['net_input']['src_lengths'], seq_per_img, dim=0
)
sample_patch_images = torch.repeat_interleave(
sample['net_input']['patch_images'], seq_per_img, dim=0
)
sample_patch_masks = torch.repeat_interleave(
sample['net_input']['patch_masks'], seq_per_img, dim=0
)
gen_prev_output_tokens = torch.as_tensor(
merge(gen_target, eos=self.task.tgt_dict.bos(), move_eos_to_beginning=True),
device=sample["target"].device, dtype=torch.int64
)
gen_target_tokens = torch.as_tensor(
merge(gen_target), device=sample["target"].device, dtype=torch.int64
)
net_output = model(
src_tokens=sample_src_tokens, src_lengths=sample_src_lengths,
patch_images=sample_patch_images, patch_masks=sample_patch_masks,
prev_output_tokens=gen_prev_output_tokens
)
return net_output, gen_target_tokens
def get_lprobs_and_target(self, model, net_output, gen_target):
if self.constraint_start is not None and self.constraint_end is not None:
net_output[0][:, :, 4:self.constraint_start] = -math.inf
net_output[0][:, :, self.constraint_end:] = -math.inf
lprobs = model.get_normalized_probs(net_output, log_probs=True)
if self.ignore_prefix_size > 0:
if getattr(lprobs, "batch_first", False):
lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
gen_target = gen_target[:, self.ignore_prefix_size :].contiguous()
else:
lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
gen_target = gen_target[self.ignore_prefix_size :, :].contiguous()
return lprobs, gen_target
def compute_loss(self, model, sample, reduce=True):
gen_target, gen_res, gt_res = self.get_generator_out(model, sample)
reward, scores = self.get_reward_and_scores(gen_res, gt_res, device=sample["target"].device)
net_output, gen_target_tokens = self.get_net_output(model, sample, gen_target)
gen_lprobs, gen_target_tokens = self.get_lprobs_and_target(model, net_output, gen_target_tokens)
loss, ntokens = scst_loss(gen_lprobs, gen_target_tokens, reward, ignore_index=self.padding_idx, reduce=reduce)
nsentences = gen_target_tokens.size(0)
return loss, scores.sum(), ntokens, nsentences
@classmethod
def reduce_metrics(cls, logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training."""
loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
score_sum = sum(log.get("score", 0) for log in logging_outputs)
ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
metrics.log_scalar(
"loss", loss_sum / sample_size, sample_size, round=3
)
metrics.log_scalar(
"score", score_sum / nsentences, nsentences, round=3
)
metrics.log_scalar(
"ntokens", ntokens, 1, round=3
)
metrics.log_scalar(
"nsentences", nsentences, 1, round=3
)
metrics.log_scalar(
"sample_size", sample_size, 1, round=3
)
@staticmethod
def logging_outputs_can_be_summed() -> bool:
"""
Whether the logging outputs returned by `forward` can be summed
across workers prior to calling `reduce_metrics`. Setting this
to True will improves distributed training speed.
"""
return True
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