# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/13_xresnet1d.ipynb (unless otherwise specified).
__all__ = ['delegates', 'store_attr', 'init_default', 'BatchNorm', 'NormType', 'ConvLayer', 'AdaptiveAvgPool',
'MaxPool', 'AvgPool', 'ResBlock', 'init_cnn', 'XResNet1d', 'xresnet1d18', 'xresnet1d34', 'xresnet1d50',
'xresnet1d101', 'xresnet1d152', 'xresnet1d18_deep', 'xresnet1d34_deep', 'xresnet1d50_deep',
'xresnet1d18_deeper', 'xresnet1d34_deeper', 'xresnet1d50_deeper']
# Cell
import torch
import torch.nn as nn
import torch.nn.functional as F
from .basic_conv1d import create_head1d, Flatten
from enum import Enum
import re
# Cell
import inspect
def delegates(to=None, keep=False):
"Decorator: replace `**kwargs` in signature with params from `to`"
def _f(f):
if to is None: to_f,from_f = f.__base__.__init__,f.__init__
else: to_f,from_f = to,f
sig = inspect.signature(from_f)
sigd = dict(sig.parameters)
k = sigd.pop('kwargs')
s2 = {k:v for k,v in inspect.signature(to_f).parameters.items()
if v.default != inspect.Parameter.empty and k not in sigd}
sigd.update(s2)
if keep: sigd['kwargs'] = k
from_f.__signature__ = sig.replace(parameters=sigd.values())
return f
return _f
def store_attr(self, nms):
"Store params named in comma-separated `nms` from calling context into attrs in `self`"
mod = inspect.currentframe().f_back.f_locals
for n in re.split(', *', nms): setattr(self,n,mod[n])
# Cell
NormType = Enum('NormType', 'Batch BatchZero Weight Spectral Instance InstanceZero')
def _conv_func(ndim=2, transpose=False):
"Return the proper conv `ndim` function, potentially `transposed`."
assert 1 <= ndim <=3
return getattr(nn, f'Conv{"Transpose" if transpose else ""}{ndim}d')
def init_default(m, func=nn.init.kaiming_normal_):
"Initialize `m` weights with `func` and set `bias` to 0."
if func and hasattr(m, 'weight'): func(m.weight)
with torch.no_grad():
if getattr(m, 'bias', None) is not None: m.bias.fill_(0.)
return m
def _get_norm(prefix, nf, ndim=2, zero=False, **kwargs):
"Norm layer with `nf` features and `ndim` initialized depending on `norm_type`."
assert 1 <= ndim <= 3
bn = getattr(nn, f"{prefix}{ndim}d")(nf, **kwargs)
if bn.affine:
bn.bias.data.fill_(1e-3)
bn.weight.data.fill_(0. if zero else 1.)
return bn
def BatchNorm(nf, ndim=2, norm_type=NormType.Batch, **kwargs):
"BatchNorm layer with `nf` features and `ndim` initialized depending on `norm_type`."
return _get_norm('BatchNorm', nf, ndim, zero=norm_type==NormType.BatchZero, **kwargs)
# Cell
class ConvLayer(nn.Sequential):
"Create a sequence of convolutional (`ni` to `nf`), ReLU (if `use_activ`) and `norm_type` layers."
def __init__(self, ni, nf, ks=3, stride=1, padding=None, bias=None, ndim=2, norm_type=NormType.Batch, bn_1st=True,
act_cls=nn.ReLU, transpose=False, init=nn.init.kaiming_normal_, xtra=None, **kwargs):
if padding is None: padding = ((ks-1)//2 if not transpose else 0)
bn = norm_type in (NormType.Batch, NormType.BatchZero)
inn = norm_type in (NormType.Instance, NormType.InstanceZero)
if bias is None: bias = not (bn or inn)
conv_func = _conv_func(ndim, transpose=transpose)
conv = init_default(conv_func(ni, nf, kernel_size=ks, bias=bias, stride=stride, padding=padding, **kwargs), init)
if norm_type==NormType.Weight: conv = weight_norm(conv)
elif norm_type==NormType.Spectral: conv = spectral_norm(conv)
layers = [conv]
act_bn = []
if act_cls is not None: act_bn.append(act_cls())
if bn: act_bn.append(BatchNorm(nf, norm_type=norm_type, ndim=ndim))
if inn: act_bn.append(InstanceNorm(nf, norm_type=norm_type, ndim=ndim))
if bn_1st: act_bn.reverse()
layers += act_bn
if xtra: layers.append(xtra)
super().__init__(*layers)
# Cell
def AdaptiveAvgPool(sz=1, ndim=2):
"nn.AdaptiveAvgPool layer for `ndim`"
assert 1 <= ndim <= 3
return getattr(nn, f"AdaptiveAvgPool{ndim}d")(sz)
def MaxPool(ks=2, stride=None, padding=0, ndim=2, ceil_mode=False):
"nn.MaxPool layer for `ndim`"
assert 1 <= ndim <= 3
return getattr(nn, f"MaxPool{ndim}d")(ks, stride=stride, padding=padding)
def AvgPool(ks=2, stride=None, padding=0, ndim=2, ceil_mode=False):
"nn.AvgPool layer for `ndim`"
assert 1 <= ndim <= 3
return getattr(nn, f"AvgPool{ndim}d")(ks, stride=stride, padding=padding, ceil_mode=ceil_mode)
# Cell
class ResBlock(nn.Module):
"Resnet block from `ni` to `nh` with `stride`"
@delegates(ConvLayer.__init__)
def __init__(self, expansion, ni, nf, stride=1, kernel_size=3, groups=1, reduction=None, nh1=None, nh2=None, dw=False, g2=1,
sa=False, sym=False, norm_type=NormType.Batch, act_cls=nn.ReLU, ndim=2,
pool=AvgPool, pool_first=True, **kwargs):
super().__init__()
norm2 = (NormType.BatchZero if norm_type==NormType.Batch else
NormType.InstanceZero if norm_type==NormType.Instance else norm_type)
if nh2 is None: nh2 = nf
if nh1 is None: nh1 = nh2
nf,ni = nf*expansion,ni*expansion
k0 = dict(norm_type=norm_type, act_cls=act_cls, ndim=ndim, **kwargs)
k1 = dict(norm_type=norm2, act_cls=None, ndim=ndim, **kwargs)
layers = [ConvLayer(ni, nh2, kernel_size, stride=stride, groups=ni if dw else groups, **k0),
ConvLayer(nh2, nf, kernel_size, groups=g2, **k1)
] if expansion == 1 else [
ConvLayer(ni, nh1, 1, **k0),
ConvLayer(nh1, nh2, kernel_size, stride=stride, groups=nh1 if dw else groups, **k0),
ConvLayer(nh2, nf, 1, groups=g2, **k1)]
self.convs = nn.Sequential(*layers)
convpath = [self.convs]
if reduction: convpath.append(SEModule(nf, reduction=reduction, act_cls=act_cls))
if sa: convpath.append(SimpleSelfAttention(nf,ks=1,sym=sym))
self.convpath = nn.Sequential(*convpath)
idpath = []
if ni!=nf: idpath.append(ConvLayer(ni, nf, 1, act_cls=None, ndim=ndim, **kwargs))
if stride!=1: idpath.insert((1,0)[pool_first], pool(2, ndim=ndim, ceil_mode=True))
self.idpath = nn.Sequential(*idpath)
self.act = nn.ReLU(inplace=True) if act_cls is nn.ReLU else act_cls()
def forward(self, x): return self.act(self.convpath(x) + self.idpath(x))
# Cell
def init_cnn(m):
if getattr(m, 'bias', None) is not None: nn.init.constant_(m.bias, 0)
if isinstance(m, (nn.Conv1d, nn.Conv2d,nn.Linear)): nn.init.kaiming_normal_(m.weight)
for l in m.children(): init_cnn(l)
# Cell
class XResNet1d(nn.Sequential):
@delegates(ResBlock)
def __init__(self, block, expansion, layers, p=0.0, input_channels=3, num_classes=1000, stem_szs=(32,32,64),kernel_size=5,kernel_size_stem=5,
widen=1.0, sa=False, act_cls=nn.ReLU, lin_ftrs_head=None, ps_head=0.5, bn_final_head=False, bn_head=True, act_head="relu", concat_pooling=True, **kwargs):
store_attr(self, 'block,expansion,act_cls')
stem_szs = [input_channels, *stem_szs]
stem = [ConvLayer(stem_szs[i], stem_szs[i+1], ks=kernel_size_stem, stride=2 if i==0 else 1, act_cls=act_cls, ndim=1)
for i in range(3)]
#block_szs = [int(o*widen) for o in [64,128,256,512] +[256]*(len(layers)-4)]
block_szs = [int(o*widen) for o in [64,64,64,64] +[32]*(len(layers)-4)]
block_szs = [64//expansion] + block_szs
blocks = [self._make_layer(ni=block_szs[i], nf=block_szs[i+1], blocks=l,
stride=1 if i==0 else 2, kernel_size=kernel_size, sa=sa and i==len(layers)-4, ndim=1, **kwargs)
for i,l in enumerate(layers)]
head = create_head1d(block_szs[-1]*expansion, nc=num_classes, lin_ftrs=lin_ftrs_head, ps=ps_head, bn_final=bn_final_head, bn=bn_head, act=act_head, concat_pooling=concat_pooling)
super().__init__(
*stem, nn.MaxPool1d(kernel_size=3, stride=2, padding=1),
*blocks,
head,
)
init_cnn(self)
def _make_layer(self, ni, nf, blocks, stride, kernel_size, sa, **kwargs):
return nn.Sequential(
*[self.block(self.expansion, ni if i==0 else nf, nf, stride=stride if i==0 else 1,
kernel_size=kernel_size, sa=sa and i==(blocks-1), act_cls=self.act_cls, **kwargs)
for i in range(blocks)])
def get_layer_groups(self):
return (self[3],self[-1])
def get_output_layer(self):
return self[-1][-1]
def set_output_layer(self,x):
self[-1][-1]=x
# Cell
def _xresnet1d(expansion, layers, **kwargs):
return XResNet1d(ResBlock, expansion, layers, **kwargs)
def xresnet1d18 (**kwargs): return _xresnet1d(1, [2, 2, 2, 2], **kwargs)
def xresnet1d34 (**kwargs): return _xresnet1d(1, [3, 4, 6, 3], **kwargs)
def xresnet1d50 (**kwargs): return _xresnet1d(4, [3, 4, 6, 3], **kwargs)
def xresnet1d101(**kwargs): return _xresnet1d(4, [3, 4, 23, 3], **kwargs)
def xresnet1d152(**kwargs): return _xresnet1d(4, [3, 8, 36, 3], **kwargs)
def xresnet1d18_deep (**kwargs): return _xresnet1d(1, [2,2,2,2,1,1], **kwargs)
def xresnet1d34_deep (**kwargs): return _xresnet1d(1, [3,4,6,3,1,1], **kwargs)
def xresnet1d50_deep (**kwargs): return _xresnet1d(4, [3,4,6,3,1,1], **kwargs)
def xresnet1d18_deeper(**kwargs): return _xresnet1d(1, [2,2,1,1,1,1,1,1], **kwargs)
def xresnet1d34_deeper(**kwargs): return _xresnet1d(1, [3,4,6,3,1,1,1,1], **kwargs)
def xresnet1d50_deeper(**kwargs): return _xresnet1d(4, [3,4,6,3,1,1,1,1], **kwargs)
Datasets
Models
