update cif onnx

shixian.shi

2023-02-27 508e518b12f52eb84843e9cad4b3e51165bb52fb

 funasr/export/models/predictor/cif.py

@@ -48,11 +48,11 @@
      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(hidden, alphas, self.threshold)

      

      return acoustic_embeds, token_num, alphas, cif_peak

@@ -63,18 +63,67 @@
      

      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, tail_threshold], dim=1)

		
      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(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], 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.floor(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([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)

#       list_ls.append(torch.cat([l, pad_l], 0))

#    return torch.stack(list_ls, 0), fires





@torch.jit.script

@@ -113,15 +162,11 @@
   

   fires = torch.stack(list_fires, 1)

   frames = torch.stack(list_frames, 1)

   # list_ls = []

   len_labels = torch.round(alphas.sum(-1)).type(torch.int32)

   # max_label_len = int(torch.max(len_labels).item())

   # print("type: {}".format(type(max_label_len)))



   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):

      # fire = fires[b, :]

      frame_fire = frames[b, fire_idxs[b]]

      frame_len = frame_fire.size(0)

      frame_fires[b, :frame_len, :] = frame_fire