From 9ccbadc8be2b52add807c421aa766a94e9176e44 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期六, 25 二月 2023 18:27:31 +0800
Subject: [PATCH] onnx
---
funasr/export/models/predictor/cif.py | 102 ---------------------------------------------------
1 files changed, 0 insertions(+), 102 deletions(-)
diff --git a/funasr/export/models/predictor/cif.py b/funasr/export/models/predictor/cif.py
index 034e233..c8df7f3 100644
--- a/funasr/export/models/predictor/cif.py
+++ b/funasr/export/models/predictor/cif.py
@@ -76,108 +76,6 @@
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.round(alphas.sum(-1)).int()
-# max_label_len = len_labels.max().item()
-# # print("type: {}".format(type(max_label_len)))
-# 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)], dtype=l.dtype, device=hidden.device)
-# list_ls.append(torch.cat([l, pad_l], 0))
-# return torch.stack(list_ls, 0), fires
-
-# @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], 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)
-# len_labels = torch.floor(torch.sum(alphas, dim=1)).int()
-# max_label_len = torch.max(len_labels)
-# pad_num = max_label_len - len_labels
-# pad_num_max = torch.max(pad_num).item()
-# frames_pad_tensor = torch.zeros([int(batch_size), int(pad_num_max), int(hidden_size)], dtype=frames.dtype,
-# device=frames.device)
-# fires_pad_tensor = torch.ones([int(batch_size), int(pad_num_max)], dtype=fires.dtype, device=fires.device)
-# fires_pad_tensor_mask = sequence_mask_scripts(pad_num, maxlen=int(pad_num_max))
-# fires_pad_tensor *= fires_pad_tensor_mask
-# frames_pad = torch.cat([frames, frames_pad_tensor], dim=1)
-# fires_pad = torch.cat([fires, fires_pad_tensor], dim=1)
-# index_bool = fires_pad >= threshold
-# frames_fire = frames_pad[index_bool]
-# frames_fire = torch.reshape(frames_fire, (int(batch_size), -1, int(hidden_size)))
-# frames_fire_mask = sequence_mask_scripts(len_labels, maxlen=int(max_label_len))
-# frames_fire *= frames_fire_mask[:, :, None]
-#
-# return frames_fire, fires
-
@torch.jit.script
def cif(hidden, alphas, threshold: float):
--
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