# 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. from dataclasses import dataclass, field import json import logging import os import math import pickle from typing import Optional from data.file_dataset import FileDataset import torch from fairseq import metrics from fairseq.tasks import register_task from data.cv_data.image_classify_dataset import ImageClassifyDataset from data import data_utils from tasks.ofa_task import OFAConfig, OFATask from utils.trie import Trie logger = logging.getLogger(__name__) @dataclass class ImageClassifyConfig(OFAConfig): ans2label_dict: Optional[str] = field( default='{"no": 0, "yes":1}', metadata={"help": 'answer to label dict'}, ) ans2label_file: Optional[str] = field( default=None, metadata={"help": "path to load ans2label file"}, ) valid_batch_size: int = field( default=20, metadata={"help": "valid batch size per step"}, ) uses_ema: Optional[bool] = field( default=False, metadata={"help": "whether to use ema"}, ) @register_task("image_classify", dataclass=ImageClassifyConfig) class ImageClassifyTask(OFATask): def __init__(self, cfg: ImageClassifyConfig, src_dict, tgt_dict): super().__init__(cfg, src_dict, tgt_dict) self.ans2label_dict = None if self.cfg.ans2label_file is not None: self.ans2label_dict = pickle.load(open(self.cfg.ans2label_file, "rb")) else: self.ans2label_dict = json.loads(self.cfg.ans2label_dict) self.uses_ema = self.cfg.uses_ema def load_dataset(self, split, epoch=1, combine=False, **kwargs): paths = self.cfg.data.split(',') assert len(paths) > 0 if split == 'train': table_path = paths[(epoch - 1) % (len(paths) - 1)] else: table_path = paths[-1] dataset = FileDataset(table_path, self.cfg.selected_cols) self.datasets[split] = ImageClassifyDataset( split, dataset, self.bpe, self.src_dict, self.tgt_dict, max_src_length=self.cfg.max_src_length, max_tgt_length=self.cfg.max_tgt_length, patch_image_size=self.cfg.patch_image_size, constraint_trie=self.constraint_trie, imagenet_default_mean_and_std=self.cfg.imagenet_default_mean_and_std ) def build_model(self, cfg): model = super().build_model(cfg) tgt_list = [] prev_output_list = [] self.index2ans = {} self.ans2index = {} self.constraint_trie = Trie(self.tgt_dict.eos()) for i, answer in enumerate(self.ans2label_dict.keys()): answer_item = self.tgt_dict.encode_line( line=self.bpe.encode(' ' + answer), add_if_not_exist=False, append_eos=False ).long() tgt_list += [torch.cat([answer_item, torch.LongTensor([self.tgt_dict.eos()])])] prev_output_list += [torch.cat([torch.LongTensor([self.tgt_dict.bos()]), answer_item])] self.index2ans[i] = answer self.ans2index[answer] = i self.constraint_trie.insert([self.tgt_dict.bos()] + answer_item.tolist() + [self.tgt_dict.eos()]) constraint_mask_list = [] for prev_output_item in prev_output_list: constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool() for i in range(len(prev_output_item)): constraint_prefix_token = prev_output_item[:i+1].tolist() constraint_nodes = self.constraint_trie.get_next_layer(constraint_prefix_token) constraint_mask[i][constraint_nodes] = True constraint_mask_list.append(constraint_mask) eos = self.src_dict.eos() pad = self.src_dict.pad() self.valid_tgt_list = [] self.valid_prev_output_list = [] self.valid_constraint_masks_list = [] for i in range(0, len(tgt_list), self.cfg.valid_batch_size): tgt_item = tgt_list[i:i+self.cfg.valid_batch_size] prev_output_item = prev_output_list[i:i+self.cfg.valid_batch_size] constrain_mask = constraint_mask_list[i:i+self.cfg.valid_batch_size] self.valid_tgt_list.append( data_utils.collate_tokens(tgt_item, pad_idx=pad, eos_idx=eos, left_pad=False) ) self.valid_prev_output_list.append( data_utils.collate_tokens(prev_output_item, pad_idx=pad, eos_idx=eos, left_pad=False) ) self.valid_constraint_masks_list.append( data_utils.collate_tokens(constrain_mask, pad_idx=pad, left_pad=False) ) return model def build_generator( self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None, prefix_allowed_tokens_fn=None, ): seq_generator = super().build_generator(models, args, seq_gen_cls, extra_gen_cls_kwargs, prefix_allowed_tokens_fn) seq_generator.constraint_trie = self.constraint_trie return seq_generator def valid_step(self, sample, model, criterion, **extra_kwargs): loss, sample_size, logging_output = super().valid_step(sample, model, criterion) if self.uses_ema: assert 'ema_model' in extra_kwargs and extra_kwargs['ema_model'] is not None if self.uses_ema: eval_model = extra_kwargs['ema_model'] else: eval_model = model eval_model.eval() with torch.no_grad(): batch_size = sample["net_input"]["src_tokens"].size(0) encoder_out = eval_model.encoder( sample["net_input"]["src_tokens"], src_lengths=sample["net_input"]["src_lengths"], patch_images=sample["net_input"]["patch_images"], patch_masks=sample["net_input"]["patch_masks"] ) device = sample["net_input"]["src_tokens"].device valid_result = [] for valid_tgt, valid_prev_output, valid_constraint_masks in zip(self.valid_tgt_list, self.valid_prev_output_list, self.valid_constraint_masks_list): valid_tgt_size = valid_tgt.size(0) valid_tgt = valid_tgt.repeat(batch_size, 1).to(device) valid_prev_output = valid_prev_output.repeat(batch_size, 1).to(device) valid_constraint_masks = valid_constraint_masks.repeat(batch_size, 1, 1).to(device) new_encoder_out = {} new_encoder_out["encoder_out"] = [ encoder_out["encoder_out"][0].repeat_interleave(valid_tgt_size, dim=1) ] new_encoder_out["encoder_padding_mask"] = [ encoder_out["encoder_padding_mask"][0].repeat_interleave(valid_tgt_size, dim=0) ] new_encoder_out["position_embeddings"] = [ encoder_out["position_embeddings"][0].repeat_interleave(valid_tgt_size, dim=0) ] decoder_out = eval_model.decoder(valid_prev_output, encoder_out=new_encoder_out) decoder_out[0].masked_fill_(~valid_constraint_masks, -math.inf) lprobs = eval_model.get_normalized_probs(decoder_out, log_probs=True) scores = lprobs.gather(dim=-1, index=valid_tgt.unsqueeze(-1)).squeeze(-1) scores = scores.masked_fill(valid_tgt.eq(self.tgt_dict.pad()), 0) scores = scores.sum(1) scores = scores.view(-1, valid_tgt_size) valid_result.append(scores) valid_result = torch.cat(valid_result, dim=-1) predicts = valid_result.argmax(1).tolist() hyps = [self.index2ans[predict_index] for predict_index in predicts] scores = [ref_dict.get(hyp, 0) for ref_dict, hyp in zip(sample['ref_dict'], hyps)] logging_output["_score_sum"] = sum(scores) logging_output["_score_cnt"] = len(scores) return loss, sample_size, logging_output def reduce_metrics(self, logging_outputs, criterion): super().reduce_metrics(logging_outputs, criterion) def sum_logs(key): import torch result = sum(log.get(key, 0) for log in logging_outputs) if torch.is_tensor(result): result = result.cpu() return result def compute_score(meters): score = meters["_score_sum"].sum / meters["_score_cnt"].sum score = score if isinstance(score, float) else score.item() return round(score, 3) if sum_logs("_score_cnt") > 0: metrics.log_scalar("_score_sum", sum_logs("_score_sum")) metrics.log_scalar("_score_cnt", sum_logs("_score_cnt")) metrics.log_derived("score", compute_score)