| | |
|---|
| | | def __init__(self, normalize_length=False):
|
|---|
| | | super(mae_loss, self).__init__()
|
|---|
| | | self.normalize_length = normalize_length
|
|---|
| | | self.criterion = torch.nn.L1Loss(reduction='sum')
|
|---|
| | | self.criterion = torch.nn.L1Loss(reduction="sum")
|
|---|
| | |
|
|---|
| | | def forward(self, token_length, pre_token_length):
|
|---|
| | | loss_token_normalizer = token_length.size(0)
|
|---|
| | |
|---|
| | | 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)
|
|---|
| | | 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)
|
|---|
| | | 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_fires.append(integrate)
|
|---|
| | |
|
|---|
| | | fire_place = integrate >= threshold
|
|---|
| | | integrate = torch.where(fire_place,
|
|---|
| | | integrate - torch.ones([batch_size], device=alphas.device)*threshold,
|
|---|
| | | integrate)
|
|---|
| | | integrate = torch.where(
|
|---|
| | | fire_place,
|
|---|
| | | integrate - torch.ones([batch_size], device=alphas.device) * threshold,
|
|---|
| | | integrate,
|
|---|
| | | )
|
|---|
| | |
|
|---|
| | | fires = torch.stack(list_fires, 1)
|
|---|
| | | return fires
|
|---|
| | |
|
|---|
| | |
|
|---|
| | | @tables.register("predictor_classes", "CifPredictorV3")
|
|---|
| | | class CifPredictorV3(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",
|
|---|
| | | smooth_factor2=1.0,
|
|---|
| | | noise_threshold2=0,
|
|---|
| | | upsample_times=5,
|
|---|
| | | upsample_type="cnn",
|
|---|
| | | use_cif1_cnn=True,
|
|---|
| | | tail_mask=True,
|
|---|
| | | ):
|
|---|
| | | 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",
|
|---|
| | | smooth_factor2=1.0,
|
|---|
| | | noise_threshold2=0,
|
|---|
| | | upsample_times=5,
|
|---|
| | | upsample_type="cnn",
|
|---|
| | | use_cif1_cnn=True,
|
|---|
| | | tail_mask=True,
|
|---|
| | | ):
|
|---|
| | | super(CifPredictorV3, self).__init__()
|
|---|
| | |
|
|---|
| | | self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0)
|
|---|
| | |
|---|
| | | self.upsample_times = upsample_times
|
|---|
| | | self.upsample_type = upsample_type
|
|---|
| | | self.use_cif1_cnn = use_cif1_cnn
|
|---|
| | | if self.upsample_type == 'cnn':
|
|---|
| | | self.upsample_cnn = torch.nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
|
|---|
| | | if self.upsample_type == "cnn":
|
|---|
| | | self.upsample_cnn = torch.nn.ConvTranspose1d(
|
|---|
| | | idim, idim, self.upsample_times, self.upsample_times
|
|---|
| | | )
|
|---|
| | | self.cif_output2 = torch.nn.Linear(idim, 1)
|
|---|
| | | elif self.upsample_type == 'cnn_blstm':
|
|---|
| | | self.upsample_cnn = torch.nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
|
|---|
| | | self.blstm = torch.nn.LSTM(idim, idim, 1, bias=True, batch_first=True, dropout=0.0, bidirectional=True)
|
|---|
| | | self.cif_output2 = torch.nn.Linear(idim*2, 1)
|
|---|
| | | elif self.upsample_type == 'cnn_attn':
|
|---|
| | | self.upsample_cnn = torch.nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
|
|---|
| | | elif self.upsample_type == "cnn_blstm":
|
|---|
| | | self.upsample_cnn = torch.nn.ConvTranspose1d(
|
|---|
| | | idim, idim, self.upsample_times, self.upsample_times
|
|---|
| | | )
|
|---|
| | | self.blstm = torch.nn.LSTM(
|
|---|
| | | idim, idim, 1, bias=True, batch_first=True, dropout=0.0, bidirectional=True
|
|---|
| | | )
|
|---|
| | | self.cif_output2 = torch.nn.Linear(idim * 2, 1)
|
|---|
| | | elif self.upsample_type == "cnn_attn":
|
|---|
| | | self.upsample_cnn = torch.nn.ConvTranspose1d(
|
|---|
| | | idim, idim, self.upsample_times, self.upsample_times
|
|---|
| | | )
|
|---|
| | | from funasr.models.transformer.encoder import EncoderLayer as TransformerEncoderLayer
|
|---|
| | | from funasr.models.transformer.attention import MultiHeadedAttention
|
|---|
| | | from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward
|
|---|
| | |
|
|---|
| | | positionwise_layer_args = (
|
|---|
| | | idim,
|
|---|
| | | idim*2,
|
|---|
| | | idim * 2,
|
|---|
| | | 0.1,
|
|---|
| | | )
|
|---|
| | | self.self_attn = TransformerEncoderLayer(
|
|---|
| | | idim,
|
|---|
| | | MultiHeadedAttention(
|
|---|
| | | 4, idim, 0.1
|
|---|
| | | ),
|
|---|
| | | MultiHeadedAttention(4, idim, 0.1),
|
|---|
| | | PositionwiseFeedForward(*positionwise_layer_args),
|
|---|
| | | 0.1,
|
|---|
| | | True, #normalize_before,
|
|---|
| | | False, #concat_after,
|
|---|
| | | True, # normalize_before,
|
|---|
| | | False, # concat_after,
|
|---|
| | | )
|
|---|
| | | self.cif_output2 = torch.nn.Linear(idim, 1)
|
|---|
| | | self.smooth_factor2 = smooth_factor2
|
|---|
| | | self.noise_threshold2 = noise_threshold2
|
|---|
| | |
|
|---|
| | | def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,
|
|---|
| | | target_label_length=None):
|
|---|
| | | 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 = context
|
|---|
| | | else:
|
|---|
| | | _output = output
|
|---|
| | | if self.upsample_type == 'cnn':
|
|---|
| | | if self.upsample_type == "cnn":
|
|---|
| | | output2 = self.upsample_cnn(_output)
|
|---|
| | | output2 = output2.transpose(1,2)
|
|---|
| | | elif self.upsample_type == 'cnn_blstm':
|
|---|
| | | output2 = output2.transpose(1, 2)
|
|---|
| | | elif self.upsample_type == "cnn_blstm":
|
|---|
| | | output2 = self.upsample_cnn(_output)
|
|---|
| | | output2 = output2.transpose(1,2)
|
|---|
| | | output2 = output2.transpose(1, 2)
|
|---|
| | | output2, (_, _) = self.blstm(output2)
|
|---|
| | | elif self.upsample_type == 'cnn_attn':
|
|---|
| | | elif self.upsample_type == "cnn_attn":
|
|---|
| | | output2 = self.upsample_cnn(_output)
|
|---|
| | | output2 = output2.transpose(1,2)
|
|---|
| | | output2 = output2.transpose(1, 2)
|
|---|
| | | output2, _ = self.self_attn(output2, mask)
|
|---|
| | | # import pdb; pdb.set_trace()
|
|---|
| | | |
|---|
| | | alphas2 = torch.sigmoid(self.cif_output2(output2))
|
|---|
| | | alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
|
|---|
| | | # repeat the mask in T demension to match the upsampled length
|
|---|
| | | if mask is not None:
|
|---|
| | | mask2 = mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1)
|
|---|
| | | mask2 = (
|
|---|
| | | mask.repeat(1, self.upsample_times, 1)
|
|---|
| | | .transpose(-1, -2)
|
|---|
| | | .reshape(alphas2.shape[0], -1)
|
|---|
| | | )
|
|---|
| | | mask2 = mask2.unsqueeze(-1)
|
|---|
| | | alphas2 = alphas2 * mask2
|
|---|
| | | alphas2 = alphas2.squeeze(-1)
|
|---|
| | |
|---|
| | | _output = context
|
|---|
| | | else:
|
|---|
| | | _output = output
|
|---|
| | | if self.upsample_type == 'cnn':
|
|---|
| | | if self.upsample_type == "cnn":
|
|---|
| | | output2 = self.upsample_cnn(_output)
|
|---|
| | | output2 = output2.transpose(1,2)
|
|---|
| | | elif self.upsample_type == 'cnn_blstm':
|
|---|
| | | output2 = output2.transpose(1, 2)
|
|---|
| | | elif self.upsample_type == "cnn_blstm":
|
|---|
| | | output2 = self.upsample_cnn(_output)
|
|---|
| | | output2 = output2.transpose(1,2)
|
|---|
| | | output2 = output2.transpose(1, 2)
|
|---|
| | | output2, (_, _) = self.blstm(output2)
|
|---|
| | | elif self.upsample_type == 'cnn_attn':
|
|---|
| | | elif self.upsample_type == "cnn_attn":
|
|---|
| | | output2 = self.upsample_cnn(_output)
|
|---|
| | | output2 = output2.transpose(1,2)
|
|---|
| | | output2 = output2.transpose(1, 2)
|
|---|
| | | output2, _ = self.self_attn(output2, mask)
|
|---|
| | | alphas2 = torch.sigmoid(self.cif_output2(output2))
|
|---|
| | | alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
|
|---|
| | | # repeat the mask in T demension to match the upsampled length
|
|---|
| | | if mask is not None:
|
|---|
| | | mask2 = mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1)
|
|---|
| | | mask2 = (
|
|---|
| | | mask.repeat(1, self.upsample_times, 1)
|
|---|
| | | .transpose(-1, -2)
|
|---|
| | | .reshape(alphas2.shape[0], -1)
|
|---|
| | | )
|
|---|
| | | mask2 = mask2.unsqueeze(-1)
|
|---|
| | | alphas2 = alphas2 * mask2
|
|---|
| | | alphas2 = alphas2.squeeze(-1)
|
|---|
| | |
|---|
| | |
|
|---|
| | | return hidden, alphas, token_num_floor
|
|---|
| | |
|
|---|
| | | def gen_frame_alignments(self,
|
|---|
| | | alphas: torch.Tensor = None,
|
|---|
| | | encoder_sequence_length: torch.Tensor = None):
|
|---|
| | | 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
|
|---|
| | |
|
|---|
| | |
|---|
| | | 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())
|
|---|
| | | 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)
|
|---|
| | | 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)
|
|---|
| | |
|---|
| | | 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)
|
|---|
| | | 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)
|
|---|
| | | predictor_alignments_length = predictor_alignments.sum(-1).type(
|
|---|
| | | encoder_sequence_length.dtype
|
|---|
| | | )
|
|---|
| | | return predictor_alignments.detach(), predictor_alignments_length.detach()
|
|---|
| | |
|
|---|
| | |
|
|---|
| | | @tables.register("predictor_classes", "CifPredictorV3Export")
|
|---|
| | | class CifPredictorV3Export(torch.nn.Module):
|
|---|
| | | def __init__(self, model, **kwargs):
|
|---|
| | | super().__init__()
|
|---|
| | |
|
|---|
| | | self.pad = model.pad
|
|---|
| | | self.cif_conv1d = model.cif_conv1d
|
|---|
| | | self.cif_output = model.cif_output
|
|---|
| | | self.threshold = model.threshold
|
|---|
| | | self.smooth_factor = model.smooth_factor
|
|---|
| | | self.noise_threshold = model.noise_threshold
|
|---|
| | | self.tail_threshold = model.tail_threshold
|
|---|
| | |
|
|---|
| | | self.upsample_times = model.upsample_times
|
|---|
| | | self.upsample_cnn = model.upsample_cnn
|
|---|
| | | self.blstm = model.blstm
|
|---|
| | | self.cif_output2 = model.cif_output2
|
|---|
| | | self.smooth_factor2 = model.smooth_factor2
|
|---|
| | | self.noise_threshold2 = model.noise_threshold2
|
|---|
| | |
|
|---|
| | | def forward(
|
|---|
| | | self,
|
|---|
| | | hidden: torch.Tensor,
|
|---|
| | | mask: torch.Tensor,
|
|---|
| | | ):
|
|---|
| | | 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)
|
|---|
| | | mask = mask.transpose(-1, -2).float()
|
|---|
| | | alphas = alphas * mask
|
|---|
| | | alphas = alphas.squeeze(-1)
|
|---|
| | | token_num = alphas.sum(-1)
|
|---|
| | |
|
|---|
| | | mask = mask.squeeze(-1)
|
|---|
| | | hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, mask=mask)
|
|---|
| | | acoustic_embeds, cif_peak = cif_export(hidden, alphas, self.threshold)
|
|---|
| | |
|
|---|
| | | return acoustic_embeds, token_num, alphas, cif_peak
|
|---|
| | |
|
|---|
| | | def get_upsample_timestmap(self, hidden, mask=None, token_num=None):
|
|---|
| | | h = hidden
|
|---|
| | | b = hidden.shape[0]
|
|---|
| | | context = h.transpose(1, 2)
|
|---|
| | |
|
|---|
| | | # generate alphas2
|
|---|
| | | _output = context
|
|---|
| | | output2 = self.upsample_cnn(_output)
|
|---|
| | | output2 = output2.transpose(1, 2)
|
|---|
| | | output2, (_, _) = self.blstm(output2)
|
|---|
| | | alphas2 = torch.sigmoid(self.cif_output2(output2))
|
|---|
| | | alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
|
|---|
| | |
|
|---|
| | | mask = (
|
|---|
| | | mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1)
|
|---|
| | | )
|
|---|
| | | mask = mask.unsqueeze(-1)
|
|---|
| | | alphas2 = alphas2 * mask
|
|---|
| | | alphas2 = alphas2.squeeze(-1)
|
|---|
| | | _token_num = alphas2.sum(-1)
|
|---|
| | | alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1))
|
|---|
| | | # upsampled alphas and cif_peak
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|---|
| | | us_alphas = alphas2
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|---|
| | | us_cif_peak = cif_wo_hidden_export(us_alphas, self.threshold - 1e-4)
|
|---|
| | | return us_alphas, us_cif_peak
|
|---|
| | |
|
|---|
| | | def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
|
|---|
| | | b, t, d = hidden.size()
|
|---|
| | | tail_threshold = self.tail_threshold
|
|---|
| | |
|
|---|
| | | 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)
|
|---|
| | |
|
|---|
| | | 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
|
|---|
| | |
|
|---|
| | |
|
|---|
| | | @torch.jit.script
|
|---|
| | | def cif_export(hidden, alphas, threshold: float):
|
|---|
| | | batch_size, len_time, hidden_size = hidden.size()
|
|---|
| | | threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
|
|---|
| | |
|
|---|
| | | # loop varss
|
|---|
| | | integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
|
|---|
| | | frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, 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], dtype=alphas.dtype, device=hidden.device) - integrate
|
|---|
| | | )
|
|---|
| | |
|
|---|
| | | integrate += alpha
|
|---|
| | | list_fires.append(integrate)
|
|---|
| | |
|
|---|
| | | fire_place = integrate >= threshold
|
|---|
| | | integrate = torch.where(
|
|---|
| | | fire_place,
|
|---|
| | | integrate - torch.ones([batch_size], dtype=alphas.dtype, 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)
|
|---|
| | |
|
|---|
| | | fire_idxs = fires >= threshold
|
|---|
| | | frame_fires = torch.zeros_like(hidden)
|
|---|
| | | max_label_len = frames[0, fire_idxs[0]].size(0)
|
|---|
| | | for b in range(batch_size):
|
|---|
| | | frame_fire = frames[b, fire_idxs[b]]
|
|---|
| | | frame_len = frame_fire.size(0)
|
|---|
| | | frame_fires[b, :frame_len, :] = frame_fire
|
|---|
| | |
|
|---|
| | | if frame_len >= max_label_len:
|
|---|
| | | max_label_len = frame_len
|
|---|
| | | frame_fires = frame_fires[:, :max_label_len, :]
|
|---|
| | | return frame_fires, fires
|
|---|
| | |
|
|---|
| | |
|
|---|
| | | @torch.jit.script
|
|---|
| | | def cif_wo_hidden_export(alphas, threshold: float):
|
|---|
| | | batch_size, len_time = alphas.size()
|
|---|
| | |
|
|---|
| | | # loop varss
|
|---|
| | | integrate = torch.zeros([batch_size], dtype=alphas.dtype, 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
|
|---|
