Spaces:
Running
Running
| #!/usr/bin/env python3 | |
| # -*- encoding: utf-8 -*- | |
| # Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. | |
| # MIT License (https://opensource.org/licenses/MIT) | |
| import torch | |
| import logging | |
| import numpy as np | |
| from funasr_detach.register import tables | |
| from funasr_detach.train_utils.device_funcs import to_device | |
| from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask | |
| class CifPredictor(torch.nn.Module): | |
| def __init__( | |
| self, | |
| idim, | |
| l_order, | |
| r_order, | |
| threshold=1.0, | |
| dropout=0.1, | |
| smooth_factor=1.0, | |
| noise_threshold=0, | |
| tail_threshold=0.45, | |
| ): | |
| super().__init__() | |
| self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0) | |
| self.cif_conv1d = torch.nn.Conv1d( | |
| idim, idim, l_order + r_order + 1, groups=idim | |
| ) | |
| self.cif_output = torch.nn.Linear(idim, 1) | |
| self.dropout = torch.nn.Dropout(p=dropout) | |
| self.threshold = threshold | |
| self.smooth_factor = smooth_factor | |
| self.noise_threshold = noise_threshold | |
| self.tail_threshold = tail_threshold | |
| def forward( | |
| self, | |
| hidden, | |
| target_label=None, | |
| mask=None, | |
| ignore_id=-1, | |
| mask_chunk_predictor=None, | |
| target_label_length=None, | |
| ): | |
| h = hidden | |
| context = h.transpose(1, 2) | |
| queries = self.pad(context) | |
| memory = self.cif_conv1d(queries) | |
| output = memory + context | |
| output = self.dropout(output) | |
| output = output.transpose(1, 2) | |
| output = torch.relu(output) | |
| output = self.cif_output(output) | |
| alphas = torch.sigmoid(output) | |
| alphas = torch.nn.functional.relu( | |
| alphas * self.smooth_factor - self.noise_threshold | |
| ) | |
| if mask is not None: | |
| mask = mask.transpose(-1, -2).float() | |
| alphas = alphas * mask | |
| if mask_chunk_predictor is not None: | |
| alphas = alphas * mask_chunk_predictor | |
| alphas = alphas.squeeze(-1) | |
| mask = mask.squeeze(-1) | |
| if target_label_length is not None: | |
| target_length = target_label_length | |
| elif target_label is not None: | |
| target_length = (target_label != ignore_id).float().sum(-1) | |
| else: | |
| target_length = None | |
| token_num = alphas.sum(-1) | |
| if target_length is not None: | |
| alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1)) | |
| elif self.tail_threshold > 0.0: | |
| hidden, alphas, token_num = self.tail_process_fn( | |
| hidden, alphas, token_num, mask=mask | |
| ) | |
| acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold) | |
| if target_length is None and self.tail_threshold > 0.0: | |
| token_num_int = torch.max(token_num).type(torch.int32).item() | |
| acoustic_embeds = acoustic_embeds[:, :token_num_int, :] | |
| return acoustic_embeds, token_num, alphas, cif_peak | |
| def tail_process_fn(self, hidden, alphas, token_num=None, mask=None): | |
| b, t, d = hidden.size() | |
| tail_threshold = self.tail_threshold | |
| if mask is not None: | |
| zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device) | |
| ones_t = torch.ones_like(zeros_t) | |
| mask_1 = torch.cat([mask, zeros_t], dim=1) | |
| mask_2 = torch.cat([ones_t, mask], dim=1) | |
| mask = mask_2 - mask_1 | |
| tail_threshold = mask * tail_threshold | |
| alphas = torch.cat([alphas, zeros_t], dim=1) | |
| alphas = torch.add(alphas, tail_threshold) | |
| else: | |
| tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to( | |
| alphas.device | |
| ) | |
| tail_threshold = torch.reshape(tail_threshold, (1, 1)) | |
| alphas = torch.cat([alphas, tail_threshold], dim=1) | |
| zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device) | |
| hidden = torch.cat([hidden, zeros], dim=1) | |
| token_num = alphas.sum(dim=-1) | |
| token_num_floor = torch.floor(token_num) | |
| return hidden, alphas, token_num_floor | |
| def gen_frame_alignments( | |
| self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None | |
| ): | |
| batch_size, maximum_length = alphas.size() | |
| int_type = torch.int32 | |
| is_training = self.training | |
| if is_training: | |
| token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type) | |
| else: | |
| token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type) | |
| max_token_num = torch.max(token_num).item() | |
| alphas_cumsum = torch.cumsum(alphas, dim=1) | |
| alphas_cumsum = torch.floor(alphas_cumsum).type(int_type) | |
| alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1) | |
| index = torch.ones([batch_size, max_token_num], dtype=int_type) | |
| index = torch.cumsum(index, dim=1) | |
| index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device) | |
| index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type) | |
| index_div_bool_zeros = index_div.eq(0) | |
| index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1 | |
| index_div_bool_zeros_count = torch.clamp( | |
| index_div_bool_zeros_count, 0, encoder_sequence_length.max() | |
| ) | |
| token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to( | |
| token_num.device | |
| ) | |
| index_div_bool_zeros_count *= token_num_mask | |
| index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat( | |
| 1, 1, maximum_length | |
| ) | |
| ones = torch.ones_like(index_div_bool_zeros_count_tile) | |
| zeros = torch.zeros_like(index_div_bool_zeros_count_tile) | |
| ones = torch.cumsum(ones, dim=2) | |
| cond = index_div_bool_zeros_count_tile == ones | |
| index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones) | |
| index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type( | |
| torch.bool | |
| ) | |
| index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type( | |
| int_type | |
| ) | |
| index_div_bool_zeros_count_tile_out = torch.sum( | |
| index_div_bool_zeros_count_tile, dim=1 | |
| ) | |
| index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type( | |
| int_type | |
| ) | |
| predictor_mask = ( | |
| ( | |
| ~make_pad_mask( | |
| encoder_sequence_length, maxlen=encoder_sequence_length.max() | |
| ) | |
| ) | |
| .type(int_type) | |
| .to(encoder_sequence_length.device) | |
| ) | |
| index_div_bool_zeros_count_tile_out = ( | |
| index_div_bool_zeros_count_tile_out * predictor_mask | |
| ) | |
| predictor_alignments = index_div_bool_zeros_count_tile_out | |
| predictor_alignments_length = predictor_alignments.sum(-1).type( | |
| encoder_sequence_length.dtype | |
| ) | |
| return predictor_alignments.detach(), predictor_alignments_length.detach() | |
| class CifPredictorV2(torch.nn.Module): | |
| def __init__( | |
| self, | |
| idim, | |
| l_order, | |
| r_order, | |
| threshold=1.0, | |
| dropout=0.1, | |
| smooth_factor=1.0, | |
| noise_threshold=0, | |
| tail_threshold=0.0, | |
| tf2torch_tensor_name_prefix_torch="predictor", | |
| tf2torch_tensor_name_prefix_tf="seq2seq/cif", | |
| tail_mask=True, | |
| ): | |
| super(CifPredictorV2, self).__init__() | |
| self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0) | |
| self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1) | |
| self.cif_output = torch.nn.Linear(idim, 1) | |
| self.dropout = torch.nn.Dropout(p=dropout) | |
| self.threshold = threshold | |
| self.smooth_factor = smooth_factor | |
| self.noise_threshold = noise_threshold | |
| self.tail_threshold = tail_threshold | |
| self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch | |
| self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf | |
| self.tail_mask = tail_mask | |
| def forward( | |
| self, | |
| hidden, | |
| target_label=None, | |
| mask=None, | |
| ignore_id=-1, | |
| mask_chunk_predictor=None, | |
| target_label_length=None, | |
| ): | |
| h = hidden | |
| context = h.transpose(1, 2) | |
| queries = self.pad(context) | |
| output = torch.relu(self.cif_conv1d(queries)) | |
| output = output.transpose(1, 2) | |
| output = self.cif_output(output) | |
| alphas = torch.sigmoid(output) | |
| alphas = torch.nn.functional.relu( | |
| alphas * self.smooth_factor - self.noise_threshold | |
| ) | |
| if mask is not None: | |
| mask = mask.transpose(-1, -2).float() | |
| alphas = alphas * mask | |
| if mask_chunk_predictor is not None: | |
| alphas = alphas * mask_chunk_predictor | |
| alphas = alphas.squeeze(-1) | |
| mask = mask.squeeze(-1) | |
| if target_label_length is not None: | |
| target_length = target_label_length.squeeze(-1) | |
| elif target_label is not None: | |
| target_length = (target_label != ignore_id).float().sum(-1) | |
| else: | |
| target_length = None | |
| token_num = alphas.sum(-1) | |
| if target_length is not None: | |
| alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1)) | |
| elif self.tail_threshold > 0.0: | |
| if self.tail_mask: | |
| hidden, alphas, token_num = self.tail_process_fn( | |
| hidden, alphas, token_num, mask=mask | |
| ) | |
| else: | |
| hidden, alphas, token_num = self.tail_process_fn( | |
| hidden, alphas, token_num, mask=None | |
| ) | |
| acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold) | |
| if target_length is None and self.tail_threshold > 0.0: | |
| token_num_int = torch.max(token_num).type(torch.int32).item() | |
| acoustic_embeds = acoustic_embeds[:, :token_num_int, :] | |
| return acoustic_embeds, token_num, alphas, cif_peak | |
| def forward_chunk(self, hidden, cache=None, **kwargs): | |
| is_final = kwargs.get("is_final", False) | |
| batch_size, len_time, hidden_size = hidden.shape | |
| h = hidden | |
| context = h.transpose(1, 2) | |
| queries = self.pad(context) | |
| output = torch.relu(self.cif_conv1d(queries)) | |
| output = output.transpose(1, 2) | |
| output = self.cif_output(output) | |
| alphas = torch.sigmoid(output) | |
| alphas = torch.nn.functional.relu( | |
| alphas * self.smooth_factor - self.noise_threshold | |
| ) | |
| alphas = alphas.squeeze(-1) | |
| token_length = [] | |
| list_fires = [] | |
| list_frames = [] | |
| cache_alphas = [] | |
| cache_hiddens = [] | |
| if cache is not None and "chunk_size" in cache: | |
| alphas[:, : cache["chunk_size"][0]] = 0.0 | |
| if not is_final: | |
| alphas[:, sum(cache["chunk_size"][:2]) :] = 0.0 | |
| if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache: | |
| cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device) | |
| cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device) | |
| hidden = torch.cat((cache["cif_hidden"], hidden), dim=1) | |
| alphas = torch.cat((cache["cif_alphas"], alphas), dim=1) | |
| if cache is not None and is_final: | |
| tail_hidden = torch.zeros( | |
| (batch_size, 1, hidden_size), device=hidden.device | |
| ) | |
| tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device) | |
| tail_alphas = torch.tile(tail_alphas, (batch_size, 1)) | |
| hidden = torch.cat((hidden, tail_hidden), dim=1) | |
| alphas = torch.cat((alphas, tail_alphas), dim=1) | |
| len_time = alphas.shape[1] | |
| for b in range(batch_size): | |
| integrate = 0.0 | |
| frames = torch.zeros((hidden_size), device=hidden.device) | |
| list_frame = [] | |
| list_fire = [] | |
| for t in range(len_time): | |
| alpha = alphas[b][t] | |
| if alpha + integrate < self.threshold: | |
| integrate += alpha | |
| list_fire.append(integrate) | |
| frames += alpha * hidden[b][t] | |
| else: | |
| frames += (self.threshold - integrate) * hidden[b][t] | |
| list_frame.append(frames) | |
| integrate += alpha | |
| list_fire.append(integrate) | |
| integrate -= self.threshold | |
| frames = integrate * hidden[b][t] | |
| cache_alphas.append(integrate) | |
| if integrate > 0.0: | |
| cache_hiddens.append(frames / integrate) | |
| else: | |
| cache_hiddens.append(frames) | |
| token_length.append(torch.tensor(len(list_frame), device=alphas.device)) | |
| list_fires.append(list_fire) | |
| list_frames.append(list_frame) | |
| cache["cif_alphas"] = torch.stack(cache_alphas, axis=0) | |
| cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0) | |
| cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0) | |
| cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0) | |
| max_token_len = max(token_length) | |
| if max_token_len == 0: | |
| return hidden, torch.stack(token_length, 0), None, None | |
| list_ls = [] | |
| for b in range(batch_size): | |
| pad_frames = torch.zeros( | |
| (max_token_len - token_length[b], hidden_size), device=alphas.device | |
| ) | |
| if token_length[b] == 0: | |
| list_ls.append(pad_frames) | |
| else: | |
| list_frames[b] = torch.stack(list_frames[b]) | |
| list_ls.append(torch.cat((list_frames[b], pad_frames), dim=0)) | |
| cache["cif_alphas"] = torch.stack(cache_alphas, axis=0) | |
| cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0) | |
| cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0) | |
| cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0) | |
| return torch.stack(list_ls, 0), torch.stack(token_length, 0), None, None | |
| def tail_process_fn(self, hidden, alphas, token_num=None, mask=None): | |
| b, t, d = hidden.size() | |
| tail_threshold = self.tail_threshold | |
| if mask is not None: | |
| zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device) | |
| ones_t = torch.ones_like(zeros_t) | |
| mask_1 = torch.cat([mask, zeros_t], dim=1) | |
| mask_2 = torch.cat([ones_t, mask], dim=1) | |
| mask = mask_2 - mask_1 | |
| tail_threshold = mask * tail_threshold | |
| alphas = torch.cat([alphas, zeros_t], dim=1) | |
| alphas = torch.add(alphas, tail_threshold) | |
| else: | |
| tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to( | |
| alphas.device | |
| ) | |
| tail_threshold = torch.reshape(tail_threshold, (1, 1)) | |
| if b > 1: | |
| alphas = torch.cat([alphas, tail_threshold.repeat(b, 1)], dim=1) | |
| else: | |
| alphas = torch.cat([alphas, tail_threshold], dim=1) | |
| zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device) | |
| hidden = torch.cat([hidden, zeros], dim=1) | |
| token_num = alphas.sum(dim=-1) | |
| token_num_floor = torch.floor(token_num) | |
| return hidden, alphas, token_num_floor | |
| def gen_frame_alignments( | |
| self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None | |
| ): | |
| batch_size, maximum_length = alphas.size() | |
| int_type = torch.int32 | |
| is_training = self.training | |
| if is_training: | |
| token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type) | |
| else: | |
| token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type) | |
| max_token_num = torch.max(token_num).item() | |
| alphas_cumsum = torch.cumsum(alphas, dim=1) | |
| alphas_cumsum = torch.floor(alphas_cumsum).type(int_type) | |
| alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1) | |
| index = torch.ones([batch_size, max_token_num], dtype=int_type) | |
| index = torch.cumsum(index, dim=1) | |
| index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device) | |
| index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type) | |
| index_div_bool_zeros = index_div.eq(0) | |
| index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1 | |
| index_div_bool_zeros_count = torch.clamp( | |
| index_div_bool_zeros_count, 0, encoder_sequence_length.max() | |
| ) | |
| token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to( | |
| token_num.device | |
| ) | |
| index_div_bool_zeros_count *= token_num_mask | |
| index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat( | |
| 1, 1, maximum_length | |
| ) | |
| ones = torch.ones_like(index_div_bool_zeros_count_tile) | |
| zeros = torch.zeros_like(index_div_bool_zeros_count_tile) | |
| ones = torch.cumsum(ones, dim=2) | |
| cond = index_div_bool_zeros_count_tile == ones | |
| index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones) | |
| index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type( | |
| torch.bool | |
| ) | |
| index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type( | |
| int_type | |
| ) | |
| index_div_bool_zeros_count_tile_out = torch.sum( | |
| index_div_bool_zeros_count_tile, dim=1 | |
| ) | |
| index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type( | |
| int_type | |
| ) | |
| predictor_mask = ( | |
| ( | |
| ~make_pad_mask( | |
| encoder_sequence_length, maxlen=encoder_sequence_length.max() | |
| ) | |
| ) | |
| .type(int_type) | |
| .to(encoder_sequence_length.device) | |
| ) | |
| index_div_bool_zeros_count_tile_out = ( | |
| index_div_bool_zeros_count_tile_out * predictor_mask | |
| ) | |
| predictor_alignments = index_div_bool_zeros_count_tile_out | |
| predictor_alignments_length = predictor_alignments.sum(-1).type( | |
| encoder_sequence_length.dtype | |
| ) | |
| return predictor_alignments.detach(), predictor_alignments_length.detach() | |
| def gen_tf2torch_map_dict(self): | |
| tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch | |
| tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf | |
| map_dict_local = { | |
| ## predictor | |
| "{}.cif_conv1d.weight".format(tensor_name_prefix_torch): { | |
| "name": "{}/conv1d/kernel".format(tensor_name_prefix_tf), | |
| "squeeze": None, | |
| "transpose": (2, 1, 0), | |
| }, # (256,256,3),(3,256,256) | |
| "{}.cif_conv1d.bias".format(tensor_name_prefix_torch): { | |
| "name": "{}/conv1d/bias".format(tensor_name_prefix_tf), | |
| "squeeze": None, | |
| "transpose": None, | |
| }, # (256,),(256,) | |
| "{}.cif_output.weight".format(tensor_name_prefix_torch): { | |
| "name": "{}/conv1d_1/kernel".format(tensor_name_prefix_tf), | |
| "squeeze": 0, | |
| "transpose": (1, 0), | |
| }, # (1,256),(1,256,1) | |
| "{}.cif_output.bias".format(tensor_name_prefix_torch): { | |
| "name": "{}/conv1d_1/bias".format(tensor_name_prefix_tf), | |
| "squeeze": None, | |
| "transpose": None, | |
| }, # (1,),(1,) | |
| } | |
| return map_dict_local | |
| def convert_tf2torch( | |
| self, | |
| var_dict_tf, | |
| var_dict_torch, | |
| ): | |
| map_dict = self.gen_tf2torch_map_dict() | |
| var_dict_torch_update = dict() | |
| for name in sorted(var_dict_torch.keys(), reverse=False): | |
| names = name.split(".") | |
| if names[0] == self.tf2torch_tensor_name_prefix_torch: | |
| name_tf = map_dict[name]["name"] | |
| data_tf = var_dict_tf[name_tf] | |
| if map_dict[name]["squeeze"] is not None: | |
| data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"]) | |
| if map_dict[name]["transpose"] is not None: | |
| data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) | |
| data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu") | |
| assert ( | |
| var_dict_torch[name].size() == data_tf.size() | |
| ), "{}, {}, {} != {}".format( | |
| name, name_tf, var_dict_torch[name].size(), data_tf.size() | |
| ) | |
| var_dict_torch_update[name] = data_tf | |
| logging.info( | |
| "torch tensor: {}, {}, loading from tf tensor: {}, {}".format( | |
| name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape | |
| ) | |
| ) | |
| return var_dict_torch_update | |
| class mae_loss(torch.nn.Module): | |
| def __init__(self, normalize_length=False): | |
| super(mae_loss, self).__init__() | |
| self.normalize_length = normalize_length | |
| self.criterion = torch.nn.L1Loss(reduction="sum") | |
| def forward(self, token_length, pre_token_length): | |
| loss_token_normalizer = token_length.size(0) | |
| if self.normalize_length: | |
| loss_token_normalizer = token_length.sum().type(torch.float32) | |
| loss = self.criterion(token_length, pre_token_length) | |
| loss = loss / loss_token_normalizer | |
| return loss | |
| def cif(hidden, alphas, threshold): | |
| batch_size, len_time, hidden_size = hidden.size() | |
| # loop varss | |
| integrate = torch.zeros([batch_size], device=hidden.device) | |
| frame = torch.zeros([batch_size, hidden_size], device=hidden.device) | |
| # intermediate vars along time | |
| list_fires = [] | |
| list_frames = [] | |
| for t in range(len_time): | |
| alpha = alphas[:, t] | |
| distribution_completion = ( | |
| torch.ones([batch_size], device=hidden.device) - integrate | |
| ) | |
| integrate += alpha | |
| list_fires.append(integrate) | |
| fire_place = integrate >= threshold | |
| integrate = torch.where( | |
| fire_place, | |
| integrate - torch.ones([batch_size], device=hidden.device), | |
| integrate, | |
| ) | |
| cur = torch.where(fire_place, distribution_completion, alpha) | |
| remainds = alpha - cur | |
| frame += cur[:, None] * hidden[:, t, :] | |
| list_frames.append(frame) | |
| frame = torch.where( | |
| fire_place[:, None].repeat(1, hidden_size), | |
| remainds[:, None] * hidden[:, t, :], | |
| frame, | |
| ) | |
| fires = torch.stack(list_fires, 1) | |
| frames = torch.stack(list_frames, 1) | |
| list_ls = [] | |
| len_labels = torch.round(alphas.sum(-1)).int() | |
| max_label_len = len_labels.max() | |
| for b in range(batch_size): | |
| fire = fires[b, :] | |
| l = torch.index_select( | |
| frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze() | |
| ) | |
| pad_l = torch.zeros( | |
| [max_label_len - l.size(0), hidden_size], device=hidden.device | |
| ) | |
| list_ls.append(torch.cat([l, pad_l], 0)) | |
| return torch.stack(list_ls, 0), fires | |
| def cif_wo_hidden(alphas, threshold): | |
| batch_size, len_time = alphas.size() | |
| # loop varss | |
| integrate = torch.zeros([batch_size], device=alphas.device) | |
| # intermediate vars along time | |
| list_fires = [] | |
| for t in range(len_time): | |
| alpha = alphas[:, t] | |
| integrate += alpha | |
| list_fires.append(integrate) | |
| fire_place = integrate >= threshold | |
| integrate = torch.where( | |
| fire_place, | |
| integrate - torch.ones([batch_size], device=alphas.device) * threshold, | |
| integrate, | |
| ) | |
| fires = torch.stack(list_fires, 1) | |
| return fires | |