import time import os import glog as log import numpy as np import json import torch import torch.nn.functional as F from runners.runner import Runner from copy import deepcopy from optim_utils import init_optim from transformers.models.bart.configuration_bart import BartConfig from models.nextqa_bart import AVSDBart from time import time def tokenize(obj, tokenizer): if isinstance(obj, str): return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(obj)) if isinstance(obj, dict): return dict((n, tokenize(o)) for n, o in obj.items()) return list(tokenize(o) for o in obj) class NEXTQARunner(Runner): def __init__(self, config, tokenizer, vocab_size): super(NEXTQARunner, self).__init__(config) bart_config = BartConfig.from_json_file(self.config['bart_config']) self.model = AVSDBart.from_pretrained( 'facebook/bart-{}'.format(self.config['bart_size']), config=bart_config) # Resize the embedding to match the vocab with additional special toks # This takes care of resizing weights of related parts of the network if vocab_size != bart_config.vocab_size: self.model.resize_token_embeddings(vocab_size) self.model.to(self.config['device']) if not self.config['generating']: self.optimizer, self.scheduler = init_optim(self.model, self.config) self.tokenizer = tokenizer def forward(self, batch): for key in batch: if isinstance(batch[key], torch.Tensor): batch[key] = batch[key].cuda() ######################################################## input_ids = batch['input_ids'] video_place_holder_ids = batch['video_place_holder_ids'] app_feats = batch['app_feats'] mot_feats = batch['mot_feats'] lm_labels = batch['lm_labels'] input_mask = batch['input_mask'] app_interval = batch['app_interval'] mot_interval = batch['mot_interval'] question_intervals = batch['question_intervals'] vis_state_vector_idx = batch['vis_state_vector_idx'] question_state_vector_idx = batch['question_state_vector_idx'] ######################################################## bart_output = self.model( input_ids=input_ids, video_place_holder_ids=video_place_holder_ids, i3d_rgb=app_feats, i3d_flow=mot_feats, attention_mask=input_mask, labels=lm_labels, i3d_rgb_interval=app_interval, i3d_flow_interval=mot_interval, question_intervals=question_intervals, vis_state_vector_idx=vis_state_vector_idx, question_state_vector_idx=question_state_vector_idx, output_attentions=True, return_dict=True ) output = {} if self.config['print_output']: logits = bart_output['logits'] probs = F.softmax(logits, dim=-1) preds = torch.topk(probs, 1)[1].squeeze(-1) preds = preds.tolist() lm_labels_list = lm_labels[:, 1:].tolist() lm_labels_list = [[s for s in label if s != -1] for label in lm_labels_list] reponses = '' labels = '' for pred, label in zip(preds, lm_labels_list): reponses += self.tokenizer.decode(pred) + '\n' labels += self.tokenizer.decode(label) + '\n' output['reponses'] = reponses output['gt'] = labels gen_key = 'gen_loss (x{})'.format(self.config['gen_coeff']) gen_loss = bart_output['gen_loss'] gen_loss = self.config['gen_coeff'] * gen_loss elbo_global_key = 'elbo_loss_global (x{})'.format(self.config['elbo_global_coeff']) if bart_output['elbo_loss_global'] is not None: elbo_global_loss = bart_output['elbo_loss_global'] elbo_global_loss = self.config['elbo_global_coeff'] * elbo_global_loss else: elbo_global_loss = torch.tensor(0.0) elbo_local_key = 'elbo_loss_local (x{})'.format(self.config['elbo_local_coeff']) if bart_output['elbo_loss_local'] is not None: elbo_local_loss = bart_output['elbo_loss_local'] elbo_local_loss = self.config['elbo_local_coeff'] * elbo_local_loss else: elbo_local_loss = torch.tensor(0.0) total_loss = gen_loss + elbo_global_loss + elbo_local_loss output['losses'] = { gen_key: gen_loss, elbo_local_key: elbo_local_loss, elbo_global_key: elbo_global_loss, 'tot_loss': total_loss } del bart_output return output def generate(self, dataset, app_feats, mot_feats, tag, tokenizer, start_idx_gen, end_idx_gen, gen_subset_num=None): self.model.eval() results = {} app_sep, mot_sep, ph_token = tokenizer.convert_tokens_to_ids( ['', '', '']) # Generate the repsonse for each round log.info('[INFO] Generating responses for {} samples'.format(len(dataset))) with torch.no_grad(): counter = 0 for idx in range(start_idx_gen, end_idx_gen): start_time = time() cur_sample = dataset.loc[idx] video_name, ques, ans, qid = str(cur_sample['video']), str(cur_sample['question']),\ str(cur_sample['answer']), str(cur_sample['qid']) if video_name not in results: results[video_name] = {} input_ids = tokenize(ques, tokenizer) app_feat = app_feats[video_name] app_feat = torch.from_numpy(app_feat).type(torch.float32) mot_feat = mot_feats[video_name] mot_feat = torch.from_numpy(mot_feat).type(torch.float32) bos, eos, ques_state = self.tokenizer.convert_tokens_to_ids(['', '', '']) # Add state tokens input_ids.insert(0, ques_state) input_ids = torch.Tensor(input_ids).long() dummy = torch.ones((1, 16)) * ph_token video_place_holder_ids = torch.cat( [torch.ones((1, 1)) * app_sep, dummy, torch.ones((1, 1)) * mot_sep, dummy, ], dim=-1).long() # Now we get the intervals of the visual input tokens # Here the interval do not change across the batch dimension app_interval = [0, 16 + 1] # the last token is not part of this modality mot_interval = [16 + 1, 2 * 16 + 2] vis_state_vector_idx = [app_interval[0], mot_interval[0]] response = self.beam_search_generation( input_ids, app_feat, mot_feat, app_interval, mot_interval, vis_state_vector_idx, video_place_holder_ids, tokenizer) # Decode the response response = self.tokenizer.decode(response) results[video_name][qid] = response time_elapsed = int(time() - start_time) print('Generating resonse {} / {} -- took {}s'.format(counter + 1, len(dataset), time_elapsed)) counter += 1 # Create a file with all responses file_name = 'results_nextqa_beam_depth_{}'.format(self.config['beam_depth']) if gen_subset_num is not None: file_name += f'-part_{gen_subset_num}' file_name = f'{tag}_' + file_name output_path = os.path.join(self.config['output_dir_nextqa'], file_name + '.json') with open(output_path, 'w') as f: json.dump(results, f, indent=4) log.info('Results logged to {}'.format(output_path)) print(os.getcwd()) # Switch back to training mode self.model.train() def beam_search_generation( self, input_ids, app_feat, mot_feat, app_interval, mot_interval, vis_state_vector_idx, video_place_holder_ids, tokenizer): eos_token = tokenizer.eos_token_id unk_token = tokenizer.unk_token_id question_sep = tokenizer.convert_tokens_to_ids('') gen_ans = [eos_token] hyplist = [([], 0.0, [eos_token])] best_state = None comp_hyplist = [] app_feat = app_feat.unsqueeze(0).cuda() mot_feat = mot_feat.unsqueeze(0).cuda() video_place_holder_ids = video_place_holder_ids.cuda() text_shift_len = video_place_holder_ids.size(-1) question_intervals = [[0 + text_shift_len, input_ids.size(0) + text_shift_len]] # The last token is the question state token (not part of the history) question_state_vector_idx = [x[0] for x in question_intervals] input_ids = input_ids.long().cuda().unsqueeze(0) encoder_outputs = None for i in range(self.config['max_generation_length']): new_hyplist = [] argmin = 0 for out, lp, st in hyplist: decoder_input_ids = torch.tensor(st).long().cuda().unsqueeze(0) bart_output = self.model( input_ids=input_ids, video_place_holder_ids=video_place_holder_ids, i3d_rgb=app_feat, i3d_flow=mot_feat, encoder_outputs=encoder_outputs, decoder_input_ids=decoder_input_ids, i3d_rgb_interval=app_interval, i3d_flow_interval=mot_interval, question_intervals=question_intervals, vis_state_vector_idx=vis_state_vector_idx, question_state_vector_idx=question_state_vector_idx, output_attentions=True, generate=True, return_dict=True ) if encoder_outputs is None: encoder_outputs = [ bart_output['encoder_last_hidden_state'], bart_output['encoder_hidden_states'], bart_output['encoder_attentions'], bart_output['encoder_QAs_local'], bart_output['encoder_PAs_local'], bart_output['encoder_QA_global'], bart_output['encoder_PA_global'], bart_output['encoder_state_vectors'] ] logits = bart_output['logits'][:,-1,:].squeeze() # get the logits of the last token logp = F.log_softmax(logits, dim=0) lp_vec = logp.cpu().data.numpy() + lp if i >= self.config['min_generation_length']: new_lp = lp_vec[eos_token] + self.config['length_penalty'] * (len(out) + 1) comp_hyplist.append((out, new_lp)) if best_state is None or best_state < new_lp: best_state = new_lp count = 1 for o in np.argsort(lp_vec)[::-1]: # reverse the order if o in [eos_token, unk_token]: continue new_lp = lp_vec[o] if len(new_hyplist) == self.config['beam_depth']: if new_hyplist[argmin][1] < new_lp: new_st = deepcopy(st) new_st.append(int(o)) new_hyplist[argmin] = (out + [o], new_lp, new_st) argmin = min(enumerate(new_hyplist), key=lambda h: h[1][1])[0] else: break else: new_st = deepcopy(st) new_st.append(int(o)) new_hyplist.append((out + [o], new_lp, new_st)) if len(new_hyplist) == self.config['beam_depth']: argmin = min(enumerate(new_hyplist), key=lambda h: h[1][1])[0] count += 1 hyplist = new_hyplist if len(comp_hyplist) > 0: maxhyps = sorted(comp_hyplist, key=lambda h: -h[1])[:1] return maxhyps[0][0] else: return []