mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
512 lines
24 KiB
Python
512 lines
24 KiB
Python
import torch
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from torch import nn
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from torch import Tensor
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import logging
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import numpy as np
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from funasr.train_utils.device_funcs import to_device
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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from funasr.models.scama.utils import sequence_mask
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from typing import Optional, Tuple
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from funasr.register import tables
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@tables.register("predictor_classes", "CifPredictor")
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class CifPredictor(nn.Module):
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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):
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super().__init__()
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self.pad = nn.ConstantPad1d((l_order, r_order), 0)
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self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)
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self.cif_output = nn.Linear(idim, 1)
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self.dropout = torch.nn.Dropout(p=dropout)
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self.threshold = threshold
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self.smooth_factor = smooth_factor
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self.noise_threshold = noise_threshold
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self.tail_threshold = tail_threshold
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def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,
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target_label_length=None):
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h = hidden
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context = h.transpose(1, 2)
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queries = self.pad(context)
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memory = self.cif_conv1d(queries)
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output = memory + context
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output = self.dropout(output)
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output = output.transpose(1, 2)
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output = torch.relu(output)
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output = self.cif_output(output)
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alphas = torch.sigmoid(output)
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alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
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if mask is not None:
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mask = mask.transpose(-1, -2).float()
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alphas = alphas * mask
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if mask_chunk_predictor is not None:
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alphas = alphas * mask_chunk_predictor
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alphas = alphas.squeeze(-1)
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mask = mask.squeeze(-1)
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if target_label_length is not None:
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target_length = target_label_length
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elif target_label is not None:
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target_length = (target_label != ignore_id).float().sum(-1)
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else:
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target_length = None
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token_num = alphas.sum(-1)
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if target_length is not None:
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alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
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elif self.tail_threshold > 0.0:
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hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)
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acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
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if target_length is None and self.tail_threshold > 0.0:
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token_num_int = torch.max(token_num).type(torch.int32).item()
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acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
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return acoustic_embeds, token_num, alphas, cif_peak
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def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
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b, t, d = hidden.size()
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tail_threshold = self.tail_threshold
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if mask is not None:
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zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
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ones_t = torch.ones_like(zeros_t)
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mask_1 = torch.cat([mask, zeros_t], dim=1)
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mask_2 = torch.cat([ones_t, mask], dim=1)
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mask = mask_2 - mask_1
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tail_threshold = mask * tail_threshold
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alphas = torch.cat([alphas, zeros_t], dim=1)
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alphas = torch.add(alphas, tail_threshold)
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else:
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tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
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tail_threshold = torch.reshape(tail_threshold, (1, 1))
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alphas = torch.cat([alphas, tail_threshold], dim=1)
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zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
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hidden = torch.cat([hidden, zeros], dim=1)
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token_num = alphas.sum(dim=-1)
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token_num_floor = torch.floor(token_num)
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return hidden, alphas, token_num_floor
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def gen_frame_alignments(self,
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alphas: torch.Tensor = None,
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encoder_sequence_length: torch.Tensor = None):
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batch_size, maximum_length = alphas.size()
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int_type = torch.int32
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is_training = self.training
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if is_training:
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token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type)
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else:
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token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type)
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max_token_num = torch.max(token_num).item()
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alphas_cumsum = torch.cumsum(alphas, dim=1)
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alphas_cumsum = torch.floor(alphas_cumsum).type(int_type)
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alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1)
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index = torch.ones([batch_size, max_token_num], dtype=int_type)
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index = torch.cumsum(index, dim=1)
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index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device)
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index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type)
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index_div_bool_zeros = index_div.eq(0)
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index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1
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index_div_bool_zeros_count = torch.clamp(index_div_bool_zeros_count, 0, encoder_sequence_length.max())
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token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to(token_num.device)
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index_div_bool_zeros_count *= token_num_mask
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index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(1, 1, maximum_length)
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ones = torch.ones_like(index_div_bool_zeros_count_tile)
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zeros = torch.zeros_like(index_div_bool_zeros_count_tile)
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ones = torch.cumsum(ones, dim=2)
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cond = index_div_bool_zeros_count_tile == ones
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index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones)
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index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(torch.bool)
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index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(int_type)
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index_div_bool_zeros_count_tile_out = torch.sum(index_div_bool_zeros_count_tile, dim=1)
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index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(int_type)
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predictor_mask = (~make_pad_mask(encoder_sequence_length, maxlen=encoder_sequence_length.max())).type(
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int_type).to(encoder_sequence_length.device)
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index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out * predictor_mask
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predictor_alignments = index_div_bool_zeros_count_tile_out
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predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype)
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return predictor_alignments.detach(), predictor_alignments_length.detach()
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@tables.register("predictor_classes", "CifPredictorV2")
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class CifPredictorV2(nn.Module):
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def __init__(self,
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idim,
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l_order,
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r_order,
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threshold=1.0,
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dropout=0.1,
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smooth_factor=1.0,
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noise_threshold=0,
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tail_threshold=0.0,
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tf2torch_tensor_name_prefix_torch="predictor",
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tf2torch_tensor_name_prefix_tf="seq2seq/cif",
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tail_mask=True,
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):
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super(CifPredictorV2, self).__init__()
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self.pad = nn.ConstantPad1d((l_order, r_order), 0)
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self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1)
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self.cif_output = nn.Linear(idim, 1)
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self.dropout = torch.nn.Dropout(p=dropout)
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self.threshold = threshold
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self.smooth_factor = smooth_factor
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self.noise_threshold = noise_threshold
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self.tail_threshold = tail_threshold
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self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
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self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
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self.tail_mask = tail_mask
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def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,
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target_label_length=None):
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h = hidden
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context = h.transpose(1, 2)
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queries = self.pad(context)
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output = torch.relu(self.cif_conv1d(queries))
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output = output.transpose(1, 2)
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output = self.cif_output(output)
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alphas = torch.sigmoid(output)
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alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
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if mask is not None:
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mask = mask.transpose(-1, -2).float()
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alphas = alphas * mask
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if mask_chunk_predictor is not None:
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alphas = alphas * mask_chunk_predictor
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alphas = alphas.squeeze(-1)
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mask = mask.squeeze(-1)
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if target_label_length is not None:
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target_length = target_label_length
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elif target_label is not None:
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target_length = (target_label != ignore_id).float().sum(-1)
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else:
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target_length = None
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token_num = alphas.sum(-1)
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if target_length is not None:
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alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
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elif self.tail_threshold > 0.0:
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if self.tail_mask:
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hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)
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else:
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hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=None)
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acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
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if target_length is None and self.tail_threshold > 0.0:
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token_num_int = torch.max(token_num).type(torch.int32).item()
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acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
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return acoustic_embeds, token_num, alphas, cif_peak
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def forward_chunk(self, hidden, cache=None, **kwargs):
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is_final = kwargs.get("is_final", False)
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batch_size, len_time, hidden_size = hidden.shape
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h = hidden
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context = h.transpose(1, 2)
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queries = self.pad(context)
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output = torch.relu(self.cif_conv1d(queries))
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output = output.transpose(1, 2)
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output = self.cif_output(output)
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alphas = torch.sigmoid(output)
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alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
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alphas = alphas.squeeze(-1)
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token_length = []
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list_fires = []
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list_frames = []
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cache_alphas = []
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cache_hiddens = []
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if cache is not None and "chunk_size" in cache:
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alphas[:, :cache["chunk_size"][0]] = 0.0
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if not is_final:
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alphas[:, sum(cache["chunk_size"][:2]):] = 0.0
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if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache:
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cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device)
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cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device)
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hidden = torch.cat((cache["cif_hidden"], hidden), dim=1)
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alphas = torch.cat((cache["cif_alphas"], alphas), dim=1)
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if cache is not None and is_final:
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tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device)
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tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device)
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tail_alphas = torch.tile(tail_alphas, (batch_size, 1))
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hidden = torch.cat((hidden, tail_hidden), dim=1)
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alphas = torch.cat((alphas, tail_alphas), dim=1)
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len_time = alphas.shape[1]
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for b in range(batch_size):
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integrate = 0.0
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frames = torch.zeros((hidden_size), device=hidden.device)
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list_frame = []
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list_fire = []
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for t in range(len_time):
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alpha = alphas[b][t]
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if alpha + integrate < self.threshold:
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integrate += alpha
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list_fire.append(integrate)
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frames += alpha * hidden[b][t]
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else:
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frames += (self.threshold - integrate) * hidden[b][t]
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list_frame.append(frames)
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integrate += alpha
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list_fire.append(integrate)
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integrate -= self.threshold
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frames = integrate * hidden[b][t]
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cache_alphas.append(integrate)
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if integrate > 0.0:
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cache_hiddens.append(frames / integrate)
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else:
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cache_hiddens.append(frames)
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token_length.append(torch.tensor(len(list_frame), device=alphas.device))
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list_fires.append(list_fire)
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list_frames.append(list_frame)
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cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
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cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
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cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
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cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
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max_token_len = max(token_length)
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if max_token_len == 0:
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return hidden, torch.stack(token_length, 0), None, None
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list_ls = []
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for b in range(batch_size):
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pad_frames = torch.zeros((max_token_len - token_length[b], hidden_size), device=alphas.device)
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if token_length[b] == 0:
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list_ls.append(pad_frames)
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else:
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list_frames[b] = torch.stack(list_frames[b])
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list_ls.append(torch.cat((list_frames[b], pad_frames), dim=0))
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cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
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cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
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cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
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cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
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return torch.stack(list_ls, 0), torch.stack(token_length, 0), None, None
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def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
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b, t, d = hidden.size()
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tail_threshold = self.tail_threshold
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if mask is not None:
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zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
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ones_t = torch.ones_like(zeros_t)
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mask_1 = torch.cat([mask, zeros_t], dim=1)
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mask_2 = torch.cat([ones_t, mask], dim=1)
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mask = mask_2 - mask_1
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tail_threshold = mask * tail_threshold
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alphas = torch.cat([alphas, zeros_t], dim=1)
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alphas = torch.add(alphas, tail_threshold)
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else:
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tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
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tail_threshold = torch.reshape(tail_threshold, (1, 1))
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if b > 1:
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alphas = torch.cat([alphas, tail_threshold.repeat(b, 1)], dim=1)
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else:
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alphas = torch.cat([alphas, tail_threshold], dim=1)
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zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
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hidden = torch.cat([hidden, zeros], dim=1)
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token_num = alphas.sum(dim=-1)
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token_num_floor = torch.floor(token_num)
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return hidden, alphas, token_num_floor
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def gen_frame_alignments(self,
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alphas: torch.Tensor = None,
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encoder_sequence_length: torch.Tensor = None):
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batch_size, maximum_length = alphas.size()
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int_type = torch.int32
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is_training = self.training
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if is_training:
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token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type)
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else:
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token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type)
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max_token_num = torch.max(token_num).item()
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alphas_cumsum = torch.cumsum(alphas, dim=1)
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alphas_cumsum = torch.floor(alphas_cumsum).type(int_type)
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alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1)
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index = torch.ones([batch_size, max_token_num], dtype=int_type)
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index = torch.cumsum(index, dim=1)
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index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device)
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index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type)
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index_div_bool_zeros = index_div.eq(0)
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index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1
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index_div_bool_zeros_count = torch.clamp(index_div_bool_zeros_count, 0, encoder_sequence_length.max())
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token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to(token_num.device)
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index_div_bool_zeros_count *= token_num_mask
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index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(1, 1, maximum_length)
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ones = torch.ones_like(index_div_bool_zeros_count_tile)
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zeros = torch.zeros_like(index_div_bool_zeros_count_tile)
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ones = torch.cumsum(ones, dim=2)
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cond = index_div_bool_zeros_count_tile == ones
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index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones)
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index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(torch.bool)
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index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(int_type)
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index_div_bool_zeros_count_tile_out = torch.sum(index_div_bool_zeros_count_tile, dim=1)
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index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(int_type)
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predictor_mask = (~make_pad_mask(encoder_sequence_length, maxlen=encoder_sequence_length.max())).type(
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int_type).to(encoder_sequence_length.device)
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index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out * predictor_mask
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predictor_alignments = index_div_bool_zeros_count_tile_out
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predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype)
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return predictor_alignments.detach(), predictor_alignments_length.detach()
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def gen_tf2torch_map_dict(self):
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tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
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tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
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map_dict_local = {
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## predictor
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"{}.cif_conv1d.weight".format(tensor_name_prefix_torch):
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{"name": "{}/conv1d/kernel".format(tensor_name_prefix_tf),
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"squeeze": None,
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"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(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
|
|
|