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import torch
import torch.nn.functional as F
from torch import nn
class GroupNorm1D(nn.Module):
def __init__(self, indim, groups=8):
super().__init__()
self.gn = nn.GroupNorm(groups, indim)
def forward(self, x):
return self.gn(x.permute(1, 2, 0)).permute(2, 0, 1)
class GNActDWConv2d(nn.Module):
def __init__(self, indim, gn_groups=32):
super().__init__()
self.gn = nn.GroupNorm(gn_groups, indim)
self.conv = nn.Conv2d(indim,
indim,
5,
dilation=1,
padding=2,
groups=indim,
bias=False)
def forward(self, x, size_2d):
h, w = size_2d
_, bs, c = x.size()
x = x.view(h, w, bs, c).permute(2, 3, 0, 1)
x = self.gn(x)
x = F.gelu(x)
x = self.conv(x)
x = x.view(bs, c, h * w).permute(2, 0, 1)
return x
class DWConv2d(nn.Module):
def __init__(self, indim, dropout=0.1):
super().__init__()
self.conv = nn.Conv2d(indim,
indim,
5,
dilation=1,
padding=2,
groups=indim,
bias=False)
self.dropout = nn.Dropout2d(p=dropout, inplace=True)
def forward(self, x, size_2d):
h, w = size_2d
_, bs, c = x.size()
x = x.view(h, w, bs, c).permute(2, 3, 0, 1)
x = self.conv(x)
x = self.dropout(x)
x = x.view(bs, c, h * w).permute(2, 0, 1)
return x
class ScaleOffset(nn.Module):
def __init__(self, indim):
super().__init__()
self.gamma = nn.Parameter(torch.ones(indim))
# torch.nn.init.normal_(self.gamma, std=0.02)
self.beta = nn.Parameter(torch.zeros(indim))
def forward(self, x):
if len(x.size()) == 3:
return x * self.gamma + self.beta
else:
return x * self.gamma.view(1, -1, 1, 1) + self.beta.view(
1, -1, 1, 1)
class ConvGN(nn.Module):
def __init__(self, indim, outdim, kernel_size, gn_groups=8):
super().__init__()
self.conv = nn.Conv2d(indim,
outdim,
kernel_size,
padding=kernel_size // 2)
self.gn = nn.GroupNorm(gn_groups, outdim)
def forward(self, x):
return self.gn(self.conv(x))
def seq_to_2d(tensor, size_2d):
h, w = size_2d
_, n, c = tensor.size()
tensor = tensor.view(h, w, n, c).permute(2, 3, 0, 1).contiguous()
return tensor
def drop_path(x, drop_prob: float = 0., training: bool = False):
if drop_prob == 0. or not training:
return x
keep_prob = 1 - drop_prob
shape = (
x.shape[0],
x.shape[1],
) + (1, ) * (x.ndim - 2
) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(
shape, dtype=x.dtype, device=x.device)
random_tensor.floor_() # binarize
output = x.div(keep_prob) * random_tensor
return output
def mask_out(x, y, mask_rate=0.15, training=False):
if mask_rate == 0. or not training:
return x
keep_prob = 1 - mask_rate
shape = (
x.shape[0],
x.shape[1],
) + (1, ) * (x.ndim - 2
) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(
shape, dtype=x.dtype, device=x.device)
random_tensor.floor_() # binarize
output = x * random_tensor + y * (1 - random_tensor)
return output
class DropPath(nn.Module):
def __init__(self, drop_prob=None, batch_dim=0):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
self.batch_dim = batch_dim
def forward(self, x):
return self.drop_path(x, self.drop_prob)
def drop_path(self, x, drop_prob):
if drop_prob == 0. or not self.training:
return x
keep_prob = 1 - drop_prob
shape = [1 for _ in range(x.ndim)]
shape[self.batch_dim] = x.shape[self.batch_dim]
random_tensor = keep_prob + torch.rand(
shape, dtype=x.dtype, device=x.device)
random_tensor.floor_() # binarize
output = x.div(keep_prob) * random_tensor
return output
class DropOutLogit(nn.Module):
def __init__(self, drop_prob=None):
super(DropOutLogit, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return self.drop_logit(x, self.drop_prob)
def drop_logit(self, x, drop_prob):
if drop_prob == 0. or not self.training:
return x
random_tensor = drop_prob + torch.rand(
x.shape, dtype=x.dtype, device=x.device)
random_tensor.floor_() # binarize
mask = random_tensor * 1e+8 if (
x.dtype == torch.float32) else random_tensor * 1e+4
output = x - mask
return output
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