mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
147 lines
4.3 KiB
Python
147 lines
4.3 KiB
Python
import matplotlib
|
|
from torch.nn import functional as F
|
|
|
|
import torch
|
|
matplotlib.use('Agg')
|
|
import time
|
|
|
|
class Timer():
|
|
''' Timer for recording training time distribution. '''
|
|
def __init__(self):
|
|
self.prev_t = time.time()
|
|
self.clear()
|
|
|
|
def set(self):
|
|
self.prev_t = time.time()
|
|
|
|
def cnt(self, mode):
|
|
self.time_table[mode] += time.time()-self.prev_t
|
|
self.set()
|
|
if mode == 'bw':
|
|
self.click += 1
|
|
|
|
def show(self):
|
|
total_time = sum(self.time_table.values())
|
|
self.time_table['avg'] = total_time/self.click
|
|
self.time_table['rd'] = 100*self.time_table['rd']/total_time
|
|
self.time_table['fw'] = 100*self.time_table['fw']/total_time
|
|
self.time_table['bw'] = 100*self.time_table['bw']/total_time
|
|
msg = '{avg:.3f} sec/step (rd {rd:.1f}% | fw {fw:.1f}% | bw {bw:.1f}%)'.format(
|
|
**self.time_table)
|
|
self.clear()
|
|
return msg
|
|
|
|
def clear(self):
|
|
self.time_table = {'rd': 0, 'fw': 0, 'bw': 0}
|
|
self.click = 0
|
|
|
|
# Reference : https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/e2e_asr.py#L168
|
|
|
|
def human_format(num):
|
|
magnitude = 0
|
|
while num >= 1000:
|
|
magnitude += 1
|
|
num /= 1000.0
|
|
# add more suffixes if you need them
|
|
return '{:3.1f}{}'.format(num, [' ', 'K', 'M', 'G', 'T', 'P'][magnitude])
|
|
|
|
|
|
# provide easy access of attribute from dict, such abc.key
|
|
class AttrDict(dict):
|
|
def __init__(self, *args, **kwargs):
|
|
super(AttrDict, self).__init__(*args, **kwargs)
|
|
self.__dict__ = self
|
|
|
|
def init_weights(m, mean=0.0, std=0.01):
|
|
classname = m.__class__.__name__
|
|
if classname.find("Conv") != -1:
|
|
m.weight.data.normal_(mean, std)
|
|
|
|
def get_padding(kernel_size, dilation=1):
|
|
return int((kernel_size*dilation - dilation)/2)
|
|
|
|
|
|
def sequence_mask(length, max_length=None):
|
|
if max_length is None:
|
|
max_length = length.max()
|
|
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
|
return x.unsqueeze(0) < length.unsqueeze(1)
|
|
|
|
def slice_segments(x, ids_str, segment_size=4):
|
|
ret = torch.zeros_like(x[:, :, :segment_size])
|
|
for i in range(x.size(0)):
|
|
idx_str = ids_str[i]
|
|
idx_end = idx_str + segment_size
|
|
ret[i] = x[i, :, idx_str:idx_end]
|
|
return ret
|
|
|
|
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
|
b, d, t = x.size()
|
|
if x_lengths is None:
|
|
x_lengths = t
|
|
ids_str_max = x_lengths - segment_size + 1
|
|
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
|
ret = slice_segments(x, ids_str, segment_size)
|
|
return ret, ids_str
|
|
|
|
|
|
def convert_pad_shape(pad_shape):
|
|
l = pad_shape[::-1]
|
|
pad_shape = [item for sublist in l for item in sublist]
|
|
return pad_shape
|
|
|
|
def generate_path(duration, mask):
|
|
"""
|
|
duration: [b, 1, t_x]
|
|
mask: [b, 1, t_y, t_x]
|
|
"""
|
|
device = duration.device
|
|
|
|
b, _, t_y, t_x = mask.shape
|
|
cum_duration = torch.cumsum(duration, -1)
|
|
|
|
cum_duration_flat = cum_duration.view(b * t_x)
|
|
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
|
path = path.view(b, t_x, t_y)
|
|
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
|
path = path.unsqueeze(1).transpose(2,3) * mask
|
|
return path
|
|
|
|
@torch.jit.script
|
|
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
|
n_channels_int = n_channels[0]
|
|
in_act = input_a + input_b
|
|
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
|
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
|
acts = t_act * s_act
|
|
return acts
|
|
|
|
|
|
def subsequent_mask(length):
|
|
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
|
return mask
|
|
|
|
|
|
def intersperse(lst, item):
|
|
result = [item] * (len(lst) * 2 + 1)
|
|
result[1::2] = lst
|
|
return result
|
|
|
|
|
|
def clip_grad_value_(parameters, clip_value, norm_type=2):
|
|
if isinstance(parameters, torch.Tensor):
|
|
parameters = [parameters]
|
|
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
|
norm_type = float(norm_type)
|
|
if clip_value is not None:
|
|
clip_value = float(clip_value)
|
|
|
|
total_norm = 0
|
|
for p in parameters:
|
|
param_norm = p.grad.data.norm(norm_type)
|
|
total_norm += param_norm.item() ** norm_type
|
|
if clip_value is not None:
|
|
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
|
total_norm = total_norm ** (1. / norm_type)
|
|
return total_norm
|