# 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)