baseline_model/data_utils/train.py [167:288]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - trg_seq, gold = map(lambda x: x.to(device), patch_trg(batch.trg, trg_pad_idx)) output_dim = output.shape[-1] output = output.contiguous().view(-1, output_dim) trg = trg[:,1:].contiguous().view(-1) loss, n_correct, n_word = cal_performance( output, gold, trg_pad_idx, smoothing=False) # loss = criterion(output, trg) n_word_total += n_word n_word_correct += n_correct epoch_loss += loss.item() loss_per_word = epoch_loss/n_word_total accuracy = n_word_correct/n_word_total return loss_per_word, accuracy # return epoch_loss / len(iterator) # def evaluate(model, iterator, criterion,dump_file=False): # # model.eval() # epoch_loss = 0 # trg_all = [] # output_all = [] # collect_hidden_all = [] # with torch.no_grad(): # for i, batch in enumerate(iterator): # # src = batch.src # trg_raw = batch.trg # output_raw, collect_hidden,_ = model(src, trg_raw, 0) #turn off teacher forcing # # output_raw = model(src, trg_raw, 0) #turn off teacher forcing # # #trg = [trg sent len, batch size] # #output = [trg sent len, batch size, output dim] # # output = output_raw[1:].view(-1, output_raw.shape[-1]) # trg = trg_raw[1:].view(-1) # # #trg = [(trg sent len - 1) * batch size] # #output = [(trg sent len - 1) * batch size, output dim] # # loss = criterion(output, trg) # # epoch_loss += loss.item() # if(dump_file is True): # collect_hidden_all.append(collect_hidden) # trg_all.append(trg_raw) # output_all.append(torch.max(output_raw,2)[1]) # return trg_all, output_all, collect_hidden_all, epoch_loss / len(iterator) def evaluate_att(model, iterator, criterion,dump_file=False): model.eval() epoch_loss = 0 trg_all = [] output_all = [] with torch.no_grad(): for i, batch in enumerate(iterator): src = batch.src.transpose(1,0) trg_raw = batch.trg.transpose(1,0) output_raw = model(src, trg_raw[:,:-1]) #output = [batch size, trg sent len - 1, output dim] #trg = [batch size, trg sent len] output = output_raw.contiguous().view(-1, output_raw.shape[-1]) trg = trg_raw[:,1:].contiguous().view(-1) #output = [batch size * trg sent len - 1, output dim] #trg = [batch size * trg sent len - 1] loss = criterion(output, trg) epoch_loss += loss.item() if(dump_file is True): trg_all.append(batch.trg) output_all.append(torch.max(output_raw,2)[1]) return trg_all, output_all, epoch_loss / len(iterator) def train_att(model, iterator, optimizer, criterion, clip): model.train() epoch_loss = 0 for i, batch in enumerate(iterator): src = batch.src.transpose(1,0) trg = batch.trg.transpose(1,0) # src = src.transpose(0,1) # src = src.transpose(0,1) optimizer.optimizer.zero_grad() # print(i) # if i == 40 : # pdb.set_trace() output = model(src, trg[:,:-1]) #output = [batch size, trg sent len - 1, output dim] #trg = [batch size, trg sent len] output = output.contiguous().view(-1, output.shape[-1]) trg = trg[:,1:].contiguous().view(-1) #output = [batch size * trg sent len - 1, output dim] #trg = [batch size * trg sent len - 1] loss = criterion(output, trg) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), clip) optimizer.step() epoch_loss += loss.item() return epoch_loss / len(iterator) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - baseline_model/data_utils/train_gnn.py [281:403]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - trg_seq, gold = map(lambda x: x.to(device), patch_trg(batch.trg, trg_pad_idx)) output_dim = output.shape[-1] output = output.contiguous().view(-1, output_dim) trg = trg[:,1:].contiguous().view(-1) loss, n_correct, n_word = cal_performance( output, gold, trg_pad_idx, smoothing=False) # loss = criterion(output, trg) n_word_total += n_word n_word_correct += n_correct epoch_loss += loss.item() loss_per_word = epoch_loss/n_word_total accuracy = n_word_correct/n_word_total return loss_per_word, accuracy # return epoch_loss / len(iterator) # def evaluate(model, iterator, criterion,dump_file=False): # # model.eval() # epoch_loss = 0 # trg_all = [] # output_all = [] # collect_hidden_all = [] # with torch.no_grad(): # for i, batch in enumerate(iterator): # # src = batch.src # trg_raw = batch.trg # output_raw, collect_hidden,_ = model(src, trg_raw, 0) #turn off teacher forcing # # output_raw = model(src, trg_raw, 0) #turn off teacher forcing # # #trg = [trg sent len, batch size] # #output = [trg sent len, batch size, output dim] # # output = output_raw[1:].view(-1, output_raw.shape[-1]) # trg = trg_raw[1:].view(-1) # # #trg = [(trg sent len - 1) * batch size] # #output = [(trg sent len - 1) * batch size, output dim] # # loss = criterion(output, trg) # # epoch_loss += loss.item() # if(dump_file is True): # collect_hidden_all.append(collect_hidden) # trg_all.append(trg_raw) # output_all.append(torch.max(output_raw,2)[1]) # return trg_all, output_all, collect_hidden_all, epoch_loss / len(iterator) def evaluate_att(model, iterator, criterion,dump_file=False): model.eval() epoch_loss = 0 trg_all = [] output_all = [] with torch.no_grad(): for i, batch in enumerate(iterator): src = batch.src.transpose(1,0) trg_raw = batch.trg.transpose(1,0) output_raw = model(src, trg_raw[:,:-1]) # pdb.set_trace() #output = [batch size, trg sent len - 1, output dim] #trg = [batch size, trg sent len] output = output_raw.contiguous().view(-1, output_raw.shape[-1]) trg = trg_raw[:,1:].contiguous().view(-1) #output = [batch size * trg sent len - 1, output dim] #trg = [batch size * trg sent len - 1] loss = criterion(output, trg) epoch_loss += loss.item() if(dump_file is True): trg_all.append(batch.trg) output_all.append(torch.max(output_raw,2)[1]) return trg_all, output_all, epoch_loss / len(iterator) def train_att(model, iterator, optimizer, criterion, clip): model.train() epoch_loss = 0 for i, batch in enumerate(iterator): src = batch.src.transpose(1,0) trg = batch.trg.transpose(1,0) # src = src.transpose(0,1) # src = src.transpose(0,1) optimizer.optimizer.zero_grad() # print(i) # if i == 40 : # pdb.set_trace() output = model(src, trg[:,:-1]) #output = [batch size, trg sent len - 1, output dim] #trg = [batch size, trg sent len] output = output.contiguous().view(-1, output.shape[-1]) trg = trg[:,1:].contiguous().view(-1) #output = [batch size * trg sent len - 1, output dim] #trg = [batch size * trg sent len - 1] loss = criterion(output, trg) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), clip) optimizer.step() epoch_loss += loss.item() return epoch_loss / len(iterator) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -