mirrors/icefall
1
0
Fork 0
mirror of https://github.com/k2-fsa/icefall.git synced 2025-09-19 05:54:20 +00:00
Code Issues Packages Projects Releases Wiki Activity

Change first1k to decay1k

Browse Source
This commit is contained in:
Daniel Povey 2022-06-10 23:33:16 +08:00
parent 41cdbae753
commit 2bbc63a2f5
1 changed files with 8 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py
View File

@ -841,7 +841,8 @@ class Decorrelate(torch.nn.Module):
Args: Args:
num_channels: The number of channels, e.g. 256. num_channels: The number of channels, e.g. 256.
apply_steps: The number of steps for which we apply this penalty. apply_prob_decay: The probability with which we apply this each time, in
training mode, will decay as apply_prob_decay/(apply_prob_decay + step).
scale: This number determines the scale of the gradient contribution from scale: This number determines the scale of the gradient contribution from
this module, relative to whatever the gradient was before; this module, relative to whatever the gradient was before;
this is applied per frame or pixel, by scaling gradients. this is applied per frame or pixel, by scaling gradients.
@ -854,13 +855,13 @@ class Decorrelate(torch.nn.Module):
def __init__(self, def __init__(self,
num_channels: int, num_channels: int,
scale: float = 0.1, scale: float = 0.1,
apply_steps: int = 1000, apply_prob_decay: int = 1000,
eps: float = 1.0e-05, eps: float = 1.0e-05,
beta: float = 0.95, beta: float = 0.95,
channel_dim: int = -1): channel_dim: int = -1):
super(Decorrelate, self).__init__() super(Decorrelate, self).__init__()
self.scale = scale self.scale = scale
self.apply_steps = apply_steps self.apply_prob_decay = apply_prob_decay
self.eps = eps self.eps = eps
self.beta = beta self.beta = beta
self.channel_dim = channel_dim self.channel_dim = channel_dim
@ -878,11 +879,14 @@ class Decorrelate(torch.nn.Module):
def forward(self, x: Tensor) -> Tensor: def forward(self, x: Tensor) -> Tensor:
if not self.training or self.step >= self.apply_steps: if not self.training:
return x return x
else: else:
apply_prob = self.apply_prob_decay / (self.step + self.apply_prob_decay)
self.step += 1 self.step += 1
self.step_buf.fill_(float(self.step)) self.step_buf.fill_(float(self.step))
if random.random() > apply_prob:
return x
with torch.cuda.amp.autocast(enabled=False): with torch.cuda.amp.autocast(enabled=False):
x = x.to(torch.float32) x = x.to(torch.float32)
# the function updates self.cov in its backward pass (it needs the gradient # the function updates self.cov in its backward pass (it needs the gradient