From f98c4bf6d2bb5202488cd4243efdbca65288c313 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 27 二月 2023 14:26:32 +0800
Subject: [PATCH] onnx export
---
funasr/export/models/predictor/cif.py | 53 ++++++++++++++++++++++++++++++++++++++++++++++++-----
1 files changed, 48 insertions(+), 5 deletions(-)
diff --git a/funasr/export/models/predictor/cif.py b/funasr/export/models/predictor/cif.py
index c8df7f3..cb26862 100644
--- a/funasr/export/models/predictor/cif.py
+++ b/funasr/export/models/predictor/cif.py
@@ -77,6 +77,53 @@
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
def cif(hidden, alphas, threshold: float):
batch_size, len_time, hidden_size = hidden.size()
@@ -113,15 +160,11 @@
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
--
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