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How to use prepare_source method in avocado

Best Python code snippet using avocado_python
Learning.py
Source:Learning.py
...75        ### forward76        ###77        if self.model.type() == 's_sc':78          batch_src, batch_tgt = batch_idxs[0], batch_idxs[1]79          src, msk_src = prepare_source(batch_src, self.idx_pad, device)80          tgt, ref, msk_tgt = prepare_target(batch_tgt, self.idx_pad, device)81          pred = self.model.forward(src, tgt, msk_src, msk_tgt) #no log_softmax is applied82        elif self.model.type() == 's_s_scc_scc': 83          batch_src, batch_tgt, batch_xsrc, batch_xtgt = batch_idxs[0], batch_idxs[1], batch_idxs[2], batch_idxs[3]84          src, msk_src = prepare_source(batch_src, self.idx_pad, device)85          tgt, ref, msk_tgt = prepare_target(batch_tgt, self.idx_pad, device)86          xsrc, msk_xsrc = prepare_source(batch_xsrc, self.idx_pad, device)87          xtgt, msk_xtgt = prepare_source(batch_xtgt, self.idx_pad, device)88          pred_msk, pred = self.model.forward(src, xsrc, xtgt, tgt, msk_src, msk_xsrc, msk_xtgt, msk_tgt) #no log_softmax is applied89        elif self.model.type() == '2nmt_2c': 90          batch_src, batch_tgt, batch_xsrc, batch_xtgt, batch_hide_xtgt = batch_idxs[0], batch_idxs[1], batch_idxs[2], batch_idxs[3], batch_idxs[4]91          src, msk_src = prepare_source(batch_src, self.idx_pad, device)92#          logging.info('src = {}'.format(src.shape))93#          logging.info('msk_src = {}'.format(msk_src.shape))94          tgt, ref, msk_tgt = prepare_target(batch_tgt, self.idx_pad, device)95#          logging.info('tgt = {}'.format(tgt.shape))96#          logging.info('ref = {}'.format(ref.shape))97#          logging.info('msk_tgt = {}'.format(msk_tgt.shape))98          _, msk_tgt_cross = prepare_source_cross(batch_tgt, self.idx_pad, device)99#          logging.info('msk_tgt_cross = {}'.format(msk_tgt_cross.shape))100          hide_xtgt, msk_hide_xtgt = prepare_source(batch_hide_xtgt, self.idx_pad, device)101#          logging.info('hide_xtgt = {}'.format(hide_xtgt.shape))102#          logging.info('msk_hide_xtgt = {}'.format(msk_hide_xtgt.shape))103          xsrc, msk_xsrc = prepare_source(batch_xsrc, self.idx_pad, device)104#          logging.info('xsrc = {}'.format(xsrc.shape))105#          logging.info('msk_xsrc = {}'.format(msk_xsrc.shape))106          xtgt, msk_xtgt = prepare_source(batch_xtgt, self.idx_pad, device)107#          logging.info('xtgt = {}'.format(xtgt.shape))108#          logging.info('msk_xtgt = {}'.format(msk_xtgt.shape))109          pred_hide, pred = self.model.forward(src, xsrc, xtgt, tgt, msk_src, msk_xsrc, msk_xtgt, msk_tgt, msk_tgt_cross) #no log_softmax is applied110        elif self.model.type() == 'sxs_sc':111          batch_src, batch_tgt, batch_xtgt = batch_idxs[0], batch_idxs[1], batch_idxs[2]112          src, msk_src = prepare_source(batch_src, self.idx_pad, device)113          tgt, ref, msk_tgt = prepare_target(batch_tgt, self.idx_pad, device)114          xtgt, msk_xtgt = prepare_source(batch_xtgt, self.idx_pad, device)115          pred = self.model.forward(src, xtgt, tgt, msk_src, msk_xtgt, msk_tgt) #no log_softmax is applied116        elif self.model.type() == 'sxsc_sc':117          batch_src, batch_tgt, batch_xtgt = batch_idxs[0], batch_idxs[1], batch_idxs[2]118          src, msk_src = prepare_source(batch_src, self.idx_pad, device)119          tgt, ref, msk_tgt = prepare_target(batch_tgt, self.idx_pad, device)120          xtgt, msk_xtgt = prepare_source(batch_xtgt, self.idx_pad, device)121          pred = self.model.forward(src, xtgt, tgt, msk_src, msk_xtgt, msk_tgt) #no log_softmax is applied122        elif self.model.type() == 's_s_scc':123          batch_src, batch_tgt, batch_xtgt = batch_idxs[0], batch_idxs[1], batch_idxs[2]124          src, msk_src = prepare_source(batch_src, self.idx_pad, device)125          tgt, ref, msk_tgt = prepare_target(batch_tgt, self.idx_pad, device)126          xtgt, msk_xtgt = prepare_source(batch_xtgt, self.idx_pad, device)127          pred = self.model.forward(src, xtgt, tgt, msk_src, msk_xtgt, msk_tgt) #no log_softmax is applied128        ###129        ### compute loss130        ###131        ntok_in_batch = torch.sum(ref != self.idx_pad)132        ntok_in_step += ntok_in_batch133        loss_trns = self.criter(pred, ref) / ntok_in_batch / self.accum_n_batchs #sum of losses in batch (normalized by tokens in batch) (n batchs will be accumulated before model update, so i normalize by n batchs)134        if self.model.type() == '2nmt_2c':135 #         logging.info('pred_hide = {}'.format(pred_hide.shape))136 #         logging.info('hide_xtgt = {}'.format(hide_xtgt.shape))137          alpha = 0.5138          ntokhide_in_batch = torch.sum(hide_xtgt != self.idx_pad)139#          logging.info('ntokhide_in_batch = {}'.format(ntokhide_in_batch))140          loss_hide = self.criter(pred_hide, hide_xtgt) / ntokhide_in_batch / self.accum_n_batchs141          loss = (alpha * loss_trns) + ((1.0-alpha) * loss_hide)142#          logging.info('loss_trns = {:.6f} loss_trns = {:.6f}'.format(loss_trns.item(), loss_hide.item()))143        else:144          loss = loss_trns145        loss_accum += loss.item()146        ###147        ### compute/accumulate gradients (accumulate gradients until step() is called)148        ###149        loss.backward()150        if n_batch % self.accum_n_batchs == 0: 151          ###152          ### optimize (update model)153          ###154          if self.clip > 0.0: ### clip gradients norm155            torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip)156          self.optScheduler.step() ### updates model parameters after incrementing step and updating lr157          self.optScheduler.optimizer.zero_grad() ### sets gradients to zero for next update158          ### add score159          score.step(loss_accum, ntok_in_step)160          ntok_in_step = 0161          loss_accum = 0.0162          ###163          ### report164          ###165          if self.report_every and self.optScheduler._step and self.optScheduler._step % self.report_every == 0:  ### first _step is 0166            loss_per_tok, steps_per_sec = score.report()167            logging.info('Learning step: {} epoch: {} batch: {} steps/sec: {:.2f} lr: {:.6f} Loss: {:.3f}'.format(self.optScheduler._step, n_epoch, n_batch, steps_per_sec, self.optScheduler._rate, loss_per_tok))168            score = Score()169            if tensorboard:170              self.writer.add_scalar('Loss/train', loss_accum, self.optScheduler._step)171              self.writer.add_scalar('LearningRate', self.optScheduler._rate, self.optScheduler._step)172          ###173          ### validate`174          ###175          if self.validate_every and self.optScheduler._step and self.optScheduler._step % self.validate_every == 0: 176            if validset is not None:177              vloss = self.validate(validset, device)178          ###179          ### save180          ###181          if self.save_every and self.optScheduler._step and self.optScheduler._step % self.save_every == 0: 182            save_checkpoint(self.suffix, self.model, self.optScheduler.optimizer, self.optScheduler._step, self.keep_last_n)183          ###184          ### stop by max_steps185          ###186          if self.max_steps and self.optScheduler._step and self.optScheduler._step >= self.max_steps: 187            if validset is not None:188              vloss = self.validate(validset, device)189            save_checkpoint(self.suffix, self.model, self.optScheduler.optimizer, self.optScheduler._step, self.keep_last_n)190            logging.info('Learning STOP by [steps={}]'.format(self.optScheduler._step))191            return192      ###193      ### stop by max_epochs194      ###195      if self.max_epochs and n_epoch >= self.max_epochs: ### stop by max_epochs196        if validset is not None:197          vloss = self.validate(validset, device)198        save_checkpoint(self.suffix, self.model, self.optScheduler.optimizer, self.optScheduler._step, self.keep_last_n)199        logging.info('Learning STOP by [epochs={}]'.format(n_epoch))200        return201  def validate(self, validset, device):202    tic = time.time()203    valid_loss = 0.0204    n_batch = 0205    with torch.no_grad():206      self.model.eval()207      for batch_pos, batch_idxs in validset:208        if self.model.type() == 's_sc':209          batch_src, batch_tgt = batch_idxs[0], batch_idxs[1]210          src, msk_src = prepare_source(batch_src, self.idx_pad, device)211          tgt, ref, msk_tgt = prepare_target(batch_tgt, self.idx_pad, device)212          pred = self.model.forward(src, tgt, msk_src, msk_tgt) #no log_softmax is applied213        elif self.model.type() == 's_s_scc_scc':214          batch_src, batch_tgt, batch_xsrc, batch_xtgt = batch_idxs[0], batch_idxs[1], batch_idxs[2], batch_idxs[3]215          src, msk_src = prepare_source(batch_src, self.idx_pad, device)216          tgt, ref, msk_tgt = prepare_target(batch_tgt, self.idx_pad, device)217          xsrc, msk_xsrc = prepare_source(batch_xsrc, self.idx_pad, device)218          xtgt, msk_xtgt = prepare_source(batch_xtgt, self.idx_pad, device)219          pred_msk, pred = self.model.forward(src, xsrc, xtgt, tgt, msk_src, msk_xsrc, msk_xtgt, msk_tgt) #no log_softmax is applied220        elif self.model.type() == '2nmt_2c': 221          batch_src, batch_tgt, batch_xsrc, batch_xtgt = batch_idxs[0], batch_idxs[1], batch_idxs[2], batch_idxs[3]222          src, msk_src = prepare_source(batch_src, self.idx_pad, device)223          tgt, ref, msk_tgt = prepare_target(batch_tgt, self.idx_pad, device)224          _, msk_tgt_cross = prepare_source_cross(batch_tgt, self.idx_pad, device)225          xsrc, msk_xsrc = prepare_source(batch_xsrc, self.idx_pad, device)226          xtgt, msk_xtgt = prepare_source(batch_xtgt, self.idx_pad, device)227          pred_hide, pred = self.model.forward(src, xsrc, xtgt, tgt, msk_src, msk_xsrc, msk_xtgt, msk_tgt, msk_tgt_cross) #no log_softmax is applied228        elif self.model.type() == 'sxs_sc':229          batch_src, batch_tgt, batch_xtgt = batch_idxs[0], batch_idxs[1], batch_idxs[2]230          src, msk_src = prepare_source(batch_src, self.idx_pad, device)231          tgt, ref, msk_tgt = prepare_target(batch_tgt, self.idx_pad, device)232          xtgt, msk_xtgt = prepare_source(batch_xtgt, self.idx_pad, device)233          pred = self.model.forward(src, xtgt, tgt, msk_src, msk_xtgt, msk_tgt) #no log_softmax is applied234        elif self.model.type() == 'sxsc_sc':235          batch_src, batch_tgt, batch_xtgt = batch_idxs[0], batch_idxs[1], batch_idxs[2]236          src, msk_src = prepare_source(batch_src, self.idx_pad, device)237          tgt, ref, msk_tgt = prepare_target(batch_tgt, self.idx_pad, device)238          xtgt, msk_xtgt = prepare_source(batch_xtgt, self.idx_pad, device)239          pred = self.model.forward(src, xtgt, tgt, msk_src, msk_xtgt, msk_tgt) #no log_softmax is applied240        elif self.model.type() == 's_s_scc':241          batch_src, batch_tgt, batch_xtgt = batch_idxs[0], batch_idxs[1], batch_idxs[2]242          src, msk_src = prepare_source(batch_src, self.idx_pad, device)243          tgt, ref, msk_tgt = prepare_target(batch_tgt, self.idx_pad, device)244          xtgt, msk_xtgt = prepare_source(batch_xtgt, self.idx_pad, device)245          pred = self.model.forward(src, xtgt, tgt, msk_src, msk_xtgt, msk_tgt) #no log_softmax is applied246        n_batch += 1247        loss = self.criter(pred, ref) ### batch loss248        valid_loss += loss.item() / torch.sum(ref != self.idx_pad)249        if n_batch == 1:250          print_pos_src_tgt_hyp_ref(pred[0], batch_pos[0], src[0], tgt[0], ref[0])251    loss = 1.0*valid_loss/n_batch if n_batch else 0.0252    fref = validset.files[1]253    bleu = 0.0 #self.translate_valid(validset, fref)254    toc = time.time()255    logging.info('Validation step: {} #batchs: {} sec: {:.2f} bleu: {:.2f} loss: {:.3f}'.format(self.optScheduler._step, n_batch, toc-tic, bleu, loss))256    if tensorboard:257      self.writer.add_scalar('Loss/valid', loss, self.optScheduler._step)258    return loss...
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