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6b882c1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.nn.utils.parametrizations import weight_norm
def get_eps(data_type):
return torch.finfo(data_type).eps
EPS = get_eps(torch.float32)
def nn_wrapper(nn_class, norm_weight=True, init_weight=True):
def nn_builder(*args, **kwargs):
nn_instance = nn_class(*args, **kwargs)
if init_weight:
nn.init.trunc_normal_(nn_instance.weight, std=0.02)
nn.init.constant_(nn_instance.bias, 0)
if norm_weight:
nn_instance = weight_norm(nn_instance)
return nn_instance
return nn_builder
Conv1d = nn_wrapper(nn.Conv1d, norm_weight=True, init_weight=True)
Linear = nn_wrapper(nn.Linear, norm_weight=True, init_weight=True)
class Residual(nn.Module):
def __init__(
self, module: nn.Module, drop_prob: float = 0.0, scale_by_keep: bool = True
):
super().__init__()
assert 0 <= drop_prob < 1
self.module = module
self.drop_prob = drop_prob
self.scale_by_keep = scale_by_keep
def drop_path(self, x_side: Tensor):
if self.drop_prob == 0.0 or not self.training:
return x_side
keep_prob = 1 - self.drop_prob
shape = (x_side.shape[0],) + (1,) * (x_side.ndim - 1)
keep_mask = x_side.new_empty(shape).bernoulli_(keep_prob)
if self.scale_by_keep:
keep_mask.div_(keep_prob)
return x_side * keep_mask
def forward(self, x: Tensor):
x_side = self.module(x)
x_side = self.drop_path(x_side)
return x + x_side
class GRN(nn.Module):
"""GRN (Global Response Normalization) layer
Which supports two data formats: channels_last (default) or channels_first.
Channels_last corresponds to inputs with shape (batch_size, Sequence, channels)
while channels_first corresponds to inputs with shape (batch_size, channels, Sequence).
"""
def __init__(self, n_channels, eps=EPS, data_format="channels_last"):
super().__init__()
self.n_channels = n_channels
self.data_format = data_format
if data_format == "channels_last":
self.gamma = nn.Parameter(torch.zeros(1, n_channels))
self.beta = nn.Parameter(torch.zeros(1, n_channels))
self.channel_dim = -1
elif data_format == "channels_first":
self.gamma = nn.Parameter(torch.zeros(n_channels, 1))
self.beta = nn.Parameter(torch.zeros(n_channels, 1))
self.channel_dim = 1
else:
raise ValueError(f"Unsupported data_format: {data_format}")
self.eps = torch.tensor(eps)
def forward(self, x):
g_x = torch.norm(x, p=2, dim=[1, 2], keepdim=True)
n_x = g_x / (g_x.mean(dim=self.channel_dim, keepdim=True) + self.eps)
return self.gamma * (x * n_x) + self.beta + x
def __repr__(self):
return f"{self.__class__.__name__}(n_channels={self.n_channels}, {self.data_format})"
# Scripting this brings model speed up 1.4x
@torch.jit.script
def snake(x, alpha):
# torch.clamp_(alpha, 0.05, 50.)
eps = 1.1920928955078125e-07
x = x + (alpha + eps).reciprocal() * torch.sin(alpha * x).pow(2)
return x
class Snake1d(nn.Module):
def __init__(self, channels, data_format="channels_first"):
super().__init__()
if data_format == "channels_first":
self.alpha = nn.Parameter(torch.ones(1, channels, 1))
elif data_format == "channels_last":
self.alpha = nn.Parameter(torch.ones(1, 1, channels))
else:
raise NotImplementedError
def forward(self, x):
return snake(x, self.alpha)
@torch.jit.script
def channel_norm(x, weight, bias, eps):
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + eps)
x = weight * x + bias
return x
class ChannelNorm(nn.Module):
"""ChannelNorm that supports two data formats: channels_last (default) or channels_first.
Channels_last corresponds to inputs with shape (batch_size, ..., channels)
while channels_first corresponds to inputs with shape (batch_size, channels, ...).
"""
def __init__(self, n_channels, eps=EPS, data_format="channels_last"):
super().__init__()
self.n_channels = n_channels
self.data_format = data_format
self.weight = nn.Parameter(torch.ones(n_channels))
self.bias = nn.Parameter(torch.zeros(n_channels))
self.eps = torch.tensor(eps)
def forward(self, x):
if self.data_format == "channels_first":
extend_dims = (1,) * len(x.shape[2:])
return channel_norm(
x,
self.weight.view(-1, *extend_dims),
self.bias.view(-1, *extend_dims),
self.eps,
)
elif self.data_format == "channels_last":
return F.layer_norm(
x, (self.n_channels,), self.weight, self.bias, self.eps.item()
)
else:
raise NotImplementedError
def __repr__(self):
return f"{self.__class__.__name__}(n_channels={self.n_channels}, {self.data_format})"
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