#!/usr/bin/env python3

import torch
import torch.nn as nn

OPS = {
	'none' : lambda C, stride, affine: Zero(stride),
	'avg_pool_3x3' : lambda C, stride, affine: nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
	'max_pool_3x3' : lambda C, stride, affine: nn.MaxPool2d(3, stride=stride, padding=1),
	'skip_connect' : lambda C, stride, affine: Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine),
	'sep_conv_3x3' : lambda C, stride, affine: SepConv(C, C, 3, stride, 1, affine=affine),
	'sep_conv_5x5' : lambda C, stride, affine: SepConv(C, C, 5, stride, 2, affine=affine),
	'sep_conv_7x7' : lambda C, stride, affine: SepConv(C, C, 7, stride, 3, affine=affine),
	'dil_conv_3x3' : lambda C, stride, affine: DilConv(C, C, 3, stride, 2, 2, affine=affine),
	'dil_conv_5x5' : lambda C, stride, affine: DilConv(C, C, 5, stride, 4, 2, affine=affine),
	'conv_7x1_1x7' : lambda C, stride, affine: nn.Sequential(
		nn.ReLU(inplace=False),
		nn.Conv2d(C, C, (1,7), stride=(1, stride), padding=(0, 3), bias=False),
		nn.Conv2d(C, C, (7,1), stride=(stride, 1), padding=(3, 0), bias=False),
		nn.BatchNorm2d(C, affine=affine)
		),
}

class ReLUConvBN(nn.Module):
	
	def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
		super(ReLUConvBN, self).__init__()
		self.op = nn.Sequential(
			nn.ReLU(inplace=False),
			nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False),
			nn.BatchNorm2d(C_out, affine=affine)
		)
		
	def forward(self, x):
		return self.op(x)
	
class DilConv(nn.Module):
	
	def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
		super(DilConv, self).__init__()
		self.op = nn.Sequential(
			nn.ReLU(inplace=False),
			nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=C_in, bias=False),
			nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
			nn.BatchNorm2d(C_out, affine=affine),
			)
		
	def forward(self, x):
		return self.op(x)
	
	
class SepConv(nn.Module):
	
	def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
		super(SepConv, self).__init__()
		self.op = nn.Sequential(
			nn.ReLU(inplace=False),
			nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, groups=C_in, bias=False),
			nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False),
			nn.BatchNorm2d(C_in, affine=affine),
			nn.ReLU(inplace=False),
			nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=1, padding=padding, groups=C_in, bias=False),
			nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
			nn.BatchNorm2d(C_out, affine=affine),
			)
		
	def forward(self, x):
		return self.op(x)
	
	
class Identity(nn.Module):
	
	def __init__(self):
		super(Identity, self).__init__()
		
	def forward(self, x):
		return x
	
	
class Zero(nn.Module):
	
	def __init__(self, stride):
		super(Zero, self).__init__()
		self.stride = stride
		
	def forward(self, x):
		if self.stride == 1:
			return x.mul(0.)
		return x[:,:,::self.stride,::self.stride].mul(0.)
	
	
class FactorizedReduce(nn.Module):
	
	def __init__(self, C_in, C_out, affine=True):
		super(FactorizedReduce, self).__init__()
		assert C_out % 2 == 0
		self.relu = nn.ReLU(inplace=False)
		self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
		self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False) 
		self.bn = nn.BatchNorm2d(C_out, affine=affine)
		
	def forward(self, x):
		x = self.relu(x)
		out = torch.cat([self.conv_1(x), self.conv_2(x[:,:,1:,1:])], dim=1)
		out = self.bn(out)
		return out