convlab2.e2e.damd.multiwoz package

Submodules

convlab2.e2e.damd.multiwoz.clean_dataset module

convlab2.e2e.damd.multiwoz.clean_dataset.clean_slot_values(domain, slot, value)
convlab2.e2e.damd.multiwoz.clean_dataset.clean_text(text)
convlab2.e2e.damd.multiwoz.clean_dataset.clean_time(utter)

convlab2.e2e.damd.multiwoz.config module

convlab2.e2e.damd.multiwoz.damd module

Created on Mon Mar 23 21:03:36 2020

@author: truthless

class convlab2.e2e.damd.multiwoz.damd.Damd(model_file='https://convlab.blob.core.windows.net/convlab-2/damd_multiwoz.zip', name='DAMD')

Bases: convlab2.dialog_agent.agent.Agent

add_torch_input(inputs, first_turn=False)
init_session()

Reset the class variables to prepare for a new session.

load_model(path=None)
response(usr)

Generate agent response given user input.

Args:
observation (str):

The input to the agent.

Returns:
response (str):

The response generated by the agent.

convlab2.e2e.damd.multiwoz.damd_net module

class convlab2.e2e.damd.multiwoz.damd_net.ActSelectionModel(hidden_size, length, nbest)

Bases: torch.nn.modules.module.Module

forward(hiddens_batch)

[summary] :param hiddens_batch: [B, nbest, T, H] :param decoded_batch: [B, nbest, T]

class convlab2.e2e.damd.multiwoz.damd_net.ActSpanDecoder(embedding, vocab_size_oov, Wgen=None, dropout=0.0)

Bases: torch.nn.modules.module.Module

forward(inputs, hidden_states, dec_last_w, dec_last_h, first_turn, first_step, bidx=None, mode='train')

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

get_probs(inputs, hidden_states, dec_hs, first_turn=False, bidx=None)

[summary] :param dec_hs: [B, Tdec, H] :param dec_ws: word index [B, Tdec] :param dec_hs: decoder hidden states [B, Tdec, H] :returns: [description]

class convlab2.e2e.damd.multiwoz.damd_net.Attn(hidden_size)

Bases: torch.nn.modules.module.Module

forward(hidden, encoder_outputs, mask=None)
Parameters

  • hidden – tensor of size [n_layer, B, H]

  • encoder_outputs – tensor of size [B,T, H]

score(hidden, encoder_outputs)
class convlab2.e2e.damd.multiwoz.damd_net.BeamSearchNode(hiddenstate, previousNode, wordId, logProb, length, rank=None)

Bases: object

eval(alpha=0)
print_node()
class convlab2.e2e.damd.multiwoz.damd_net.BeliefSpanDecoder(embedding, vocab_size_oov, bspn_mode, Wgen=None, dropout=0.0)

Bases: torch.nn.modules.module.Module

forward(inputs, hidden_states, dec_last_w, dec_last_h, first_turn, first_step, mode='train')

[summary] :param inputs: inputs dict :param hidden_states: hidden states dict, size [B, T, H] :param dec_last_w: word index of last decoding step :param dec_last_h: hidden state of last decoding step :param first_turn: [description], defaults to False :returns: [description]

get_probs(inputs, hidden_states, dec_hs, first_turn=False)
class convlab2.e2e.damd.multiwoz.damd_net.Copy(hidden_size, copy_weight=1.0)

Bases: torch.nn.modules.module.Module

forward(enc_out_hs, dec_hs)

get unnormalized copy score :param enc_out_hs: [B, Tenc, H] :param dec_hs: [B, Tdec, H] testing: Tdec=1 :return: raw_cp_score of each position, size [B, Tdec, Tenc]

class convlab2.e2e.damd.multiwoz.damd_net.DAMD(reader)

Bases: torch.nn.modules.module.Module

RL_forward(inputs, decoded, hiddens_batch, decoded_batch)

[summary] :param hiddens_batch: [B, nbest, T, H] :param decoded_batch: [B, nbest, T]

RL_train(inputs, hs, hiddens_batch, decoded_batch, first_turn)

[summary] :param hiddens_batch: [B, nbest, T, H] :param decoded_batch: [B, nbest, T]

addActSelection()
aspn_selection(inputs, decoded, hiddens_batch, decoded_batch)

[summary] :param hiddens_batch: [B, nbest, T, H] :param decoded_batch: [B, nbest, T]

beam_decode(name, init_hidden, first_turn, inputs, hidden_states, decoded)
forward(inputs, hidden_states, first_turn, mode)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

greedy_decode(name, init_hidden, first_turn, inputs, hidden_states, decoded)
sampling_decode(name, init_hidden, first_turn, inputs, hidden_states, decoded)
supervised_loss(inputs, probs)
test_forward(inputs, hs, first_turn)
train_forward(inputs, hidden_states, first_turn)

compute required outputs for a single dialogue turn. Turn state{Dict} will be updated in each call.

class convlab2.e2e.damd.multiwoz.damd_net.DomainSpanDecoder(embedding, vocab_size_oov, Wgen=None, dropout=0.0)

Bases: torch.nn.modules.module.Module

forward(inputs, hidden_states, dec_last_w, dec_last_h, first_turn, first_step, mode='train')

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

get_probs(inputs, hidden_states, dec_hs, first_turn=False)
class convlab2.e2e.damd.multiwoz.damd_net.LayerNormalization(hidden_size, eps=0.001)

Bases: torch.nn.modules.module.Module

Layer normalization module

forward(z)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class convlab2.e2e.damd.multiwoz.damd_net.MultiLayerGRUwithLN(input_size, hidden_size, layer_num=1, bidirec=False, layer_norm=False, skip_connect=False, dropout=0.0)

Bases: torch.nn.modules.module.Module

multi-layer GRU with layer normalization

forward(inputs, hidden=None)

[summary]

Parameters

  • inputs – tensor of size [B, T, H]

  • hidden – tensor of size [n_layer*bi-direc,B,H]

Returns

in_l: tensor of size [B, T, H * bi-direc] hs: tensor of size [n_layer * bi-direc,B,H]

class convlab2.e2e.damd.multiwoz.damd_net.ResponseDecoder(embedding, vocab_size_oov, Wgen=None, dropout=0.0)

Bases: torch.nn.modules.module.Module

forward(inputs, hidden_states, dec_last_w, dec_last_h, first_turn, first_step, mode='train')

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

get_probs(inputs, hidden_states, dec_hs, first_turn=False)

[summary] :param dec_hs: [B, Tdec, H] :param dec_ws: word index [B, Tdec] :param dec_hs: decoder hidden states [B, Tdec, H] :returns: [description]

class convlab2.e2e.damd.multiwoz.damd_net.biGRUencoder(embedding)

Bases: torch.nn.modules.module.Module

forward(input_seqs, hidden=None)

forward procedure. No need for inputs to be sorted :param input_seqs: Variable of [B,T] :param hidden: :return: outputs [B,T,H], hidden [n_layer*bi-direc,B,H]

convlab2.e2e.damd.multiwoz.damd_net.cuda_(var)
convlab2.e2e.damd.multiwoz.damd_net.get_final_scores(raw_scores, word_onehot_input, input_idx_oov, vocab_size_oov)
Parameters

  • raw_scores – list of tensor of size [B, Tdec, V], [B, Tdec, Tenc1], [B, Tdec, Tenc1] …

  • word_onehot_input – list of nparray of size [B, Tenci, V+Tenci]

  • input_idx_oov – list of nparray of size [B, Tenc]

  • vocab_size_oov –

Returns

tensor of size [B, Tdec, vocab_size_oov]

convlab2.e2e.damd.multiwoz.damd_net.get_one_hot_input(x_input_np)

sparse input of :param x_input_np: [B, Tenc] :return: tensor: [B,Tenc, V+Tenc]

convlab2.e2e.damd.multiwoz.damd_net.init_gru(gru)
convlab2.e2e.damd.multiwoz.damd_net.label_smoothing(labels, smoothing_rate, vocab_size_oov)
convlab2.e2e.damd.multiwoz.damd_net.update_input(name, inputs)

convlab2.e2e.damd.multiwoz.db_ops module

class convlab2.e2e.damd.multiwoz.db_ops.MultiWozDB(dir, db_paths)

Bases: object

addBookingPointer(constraint, domain, book_state)

Add information about availability of the booking option.

addDBPointer(domain, match_num, return_num=False)

Create database pointer for all related domains.

get_match_num(constraints, return_entry=False)

Create database pointer for all related domains.

oneHotVector(domain, num)

Return number of available entities for particular domain.

pointerBack(vector, domain)
queryJsons(domain, constraints, exactly_match=True, return_name=False)

Returns the list of entities for a given domain based on the annotation of the belief state constraints: dict e.g. {‘pricerange’: ‘cheap’, ‘area’: ‘west’}

convlab2.e2e.damd.multiwoz.ontology module

convlab2.e2e.damd.multiwoz.reader module

class convlab2.e2e.damd.multiwoz.reader.MultiWozReader

Bases: object

aspan_to_act_list(aspan)
bspan_to_DBpointer(bspan, turn_domain)
bspan_to_constraint_dict(bspan, bspn_mode='bspn')
delex_by_valdict(text)
dspan_to_domain(dspan)
prepare_input_np(u, u_delex)
preprocess_utterance(user)
reset()
restore(resp, domain, constraint_dict)
wrap_result(result_dict, eos_syntax=None)

convlab2.e2e.damd.multiwoz.utils module

class convlab2.e2e.damd.multiwoz.utils.Vocab(vocab_size=0)

Bases: object

add_word(word)
construct()
decode(idx, indicate_oov=False)
encode(word, include_oov=True)
has_word(word)
load_vocab(vocab_path)
nl_decode(l, eos=None)
oov_idx_map(idx)
save_vocab(vocab_path)
sentence_decode(index_list, eos=None, indicate_oov=False)
sentence_encode(word_list)
sentence_oov_map(index_list)
convlab2.e2e.damd.multiwoz.utils.f1_score(label_list, pred_list)
convlab2.e2e.damd.multiwoz.utils.get_glove_matrix(glove_path, vocab, initial_embedding_np)

return a glove embedding matrix :param self: :param glove_file: :param initial_embedding_np: :return: np array of [V,E]

convlab2.e2e.damd.multiwoz.utils.padSeqs(sequences, maxlen=None, truncated=False, pad_method='post', trunc_method='pre', dtype='int32', value=0.0)
convlab2.e2e.damd.multiwoz.utils.position_encoding_init(self, n_position, d_pos_vec)
convlab2.e2e.damd.multiwoz.utils.py2np(list)
convlab2.e2e.damd.multiwoz.utils.write_dict(fn, dic)

Module contents