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