# Modified from OFA code. # Copyright 2022 The OFA-Sys Team. # All rights reserved. # This source code is licensed under the Apache 2.0 license # found in the LICENSE file in the root directory. 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, ce=False): if ce: loss = -lprobs.gather(dim=-1, index=target.unsqueeze(-1)).squeeze(-1) elif isinstance(reward, float): loss = -lprobs.gather(dim=-1, index=target.unsqueeze(-1)).squeeze() * reward else: 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 RefCOCOScstRewardCriterionConfig(FairseqDataclass): scst_cider_cached_tokens: Optional[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"} ) acc_thresh: Optional[float] = field( default=None, metadata={"help": "acc thresh for refcoco"} ) metric: Optional[str] = field( default='acc', metadata={"help": "metric"} ) max_area_size: Optional[float] = field( default=None, metadata={"help": "max_area_size"} ) min_area_size: Optional[float] = field( default=None, metadata={"help": "min_area_size"} ) logprob: Optional[bool] = field( default=False, metadata={"help": "maximise log prob"} ) pos_reward: Optional[float] = field( default=None, metadata={"help": "pos_reward"} ) neg_reward: Optional[float] = field( default=None, metadata={"help": "neg_reward"} ) reinforce: Optional[bool] = field( default=False, metadata={"help": "reinforce"} ) lambda_reinforce: Optional[float] = field( default=0, metadata={"help": "lambda_reinforce"} ) medium_area: Optional[bool] = field( default=False, metadata={"help": "reinforce"} ) @register_criterion( "refcoco_scst_reward_criterion", dataclass=RefCOCOScstRewardCriterionConfig ) class RefCOCOScstRewardCriterion(FairseqCriterion): CIDER_REWARD_WEIGHT = 1 def __init__( self, task, scst_cider_cached_tokens, sentence_avg, ignore_prefix_size=0, constraint_range=None, acc_thresh=None, metric='acc', max_area_size=None, min_area_size=None, logprob=False, pos_reward=None, neg_reward=None, reinforce=False, lambda_reinforce=0, medium_area=False, ): super().__init__(task) 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) self.metric = metric print("metric", metric) self.acc_thresh = acc_thresh self.metric = metric self.min_area_size = min_area_size self.max_area_size = max_area_size self.logprob = logprob self.pos_reward = pos_reward self.neg_reward = neg_reward self.reinforce = reinforce self.lambda_reinforce = lambda_reinforce self.medium_area = medium_area 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))] # replace with other metrics if self.metric != 'cider': predicts = [res[i][0] if isinstance(res[i], list) else res[i] for i in range(len(res))] answers = [gts[i] for i in range(gen_res_size)] results = self.evaluator.run_evaluation(predicts, answers) batch_cider_scores = results[self.metric] batch_cider_scores = torch.tensor(batch_cider_scores).repeat(gen_res_size) else: _, 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 token # in order to keep consisitent with cider_cached_tokens r = s.strip() if r.endswith('.'): r = r[:-1] r += ' ' return r def get_generator_out(self, model, sample): 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)): gen_res.append(gen_out[i][0]["tokens"][:-1] - len(self.task.src_dict) + self.task.cfg.num_bins) gt_res.append(sample["target"][i][:-1] - len(self.task.src_dict) + self.task.cfg.num_bins) gen_target.append(gen_out[i][0]["tokens"][:-1].int().cpu()) return gen_target, gen_res, gt_res def _calculate_ap_score(self, hyps, refs, thresh=0.5, min_area_size=None, max_area_size=None, medium_area=False): interacts = torch.cat( [torch.where(hyps[:, :2] < refs[:, :2], refs[:, :2], hyps[:, :2]), torch.where(hyps[:, 2:] < refs[:, 2:], hyps[:, 2:], refs[:, 2:])], dim=1 ) area_predictions = (hyps[:, 2] - hyps[:, 0]) * (hyps[:, 3] - hyps[:, 1]) ## x1, y1, x2, y2, x1 < x2 area_targets = (refs[:, 2] - refs[:, 0]) * (refs[:, 3] - refs[:, 1]) interacts_w = interacts[:, 2] - interacts[:, 0] interacts_h = interacts[:, 3] - interacts[:, 1] area_interacts = interacts_w * interacts_h ious = area_interacts / (area_predictions + area_targets - area_interacts + 1e-6) if max_area_size is not None and min_area_size is not None: if medium_area: ious = ious * (torch.logical_and(area_targets > max_area_size, area_targets < min_area_size).float()) else: ious = ious * (torch.logical_or(area_targets < max_area_size, area_targets > min_area_size).float()) elif min_area_size is not None: if medium_area: ious = ious * (area_targets < min_area_size).float() # as max areas else: ious = ious * (area_targets > min_area_size).float() elif max_area_size is not None: if medium_area: ious = ious * (area_targets > max_area_size).float() else: ious = ious * (area_targets < max_area_size).float() if thresh is None: return ious else: return ((ious >= thresh) & (interacts_w > 0) & (interacts_h > 0)).float() def get_reward_and_scores(self, gen_res, gt_res, device, sample): hyps_, refs_ = torch.stack(gen_res, dim=0), torch.stack(gt_res, dim=0) hyps = hyps_ / (self.task.cfg.num_bins - 1) * self.task.cfg.max_image_size refs = refs_ / (self.task.cfg.num_bins - 1) * self.task.cfg.max_image_size hyps[:, ::2] /= sample['w_resize_ratios'].unsqueeze(1) hyps[:, 1::2] /= sample['h_resize_ratios'].unsqueeze(1) refs[:, ::2] /= sample['w_resize_ratios'].unsqueeze(1) refs[:, 1::2] /= sample['h_resize_ratios'].unsqueeze(1) if self.metric == 'acc': scores = self._calculate_ap_score(hyps, sample['region_coords'].float(), thresh=self.acc_thresh, min_area_size=self.min_area_size, max_area_size=self.max_area_size, medium_area=self.medium_area) else: raise NotImplemented if self.pos_reward: scores = torch.where(scores > 0, self.pos_reward, scores) if self.neg_reward: scores = torch.where(scores == 0, self.neg_reward, scores) return scores, 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, sample=sample) 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) if self.lambda_reinforce > 0: target = model.get_targets(sample, net_output)[:, :-1] # ignore eos token if self.ignore_prefix_size > 0: target = target[:, self.ignore_prefix_size :].contiguous() loss_ce, ntokens_ = scst_loss(gen_lprobs, target, reward=1, ignore_index=self.padding_idx, reduce=reduce, ce=True) loss = loss_ce + self.lambda_reinforce*loss 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