Update model.py

803143d about 1 year ago

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	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import torchvision.models as models
	from torch.jit import script


	class WSConv2d(nn.Conv2d):
	def __init___(self, in_channels, out_channels, kernel_size, stride=1,
	padding=0, dilation=1, groups=1, bias=True):
	super(WSConv2d, self).__init__(in_channels, out_channels, kernel_size, stride,
	padding, dilation, groups, bias)

	def forward(self, x):
	weight = self.weight
	weight_mean = weight.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True).mean(dim=3, keepdim=True)
	weight = weight - weight_mean
	std = weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1) + 1e-5
	# std = torch.sqrt(torch.var(weight.view(weight.size(0),-1),dim=1)+1e-12).view(-1,1,1,1)+1e-5
	weight = weight / std.expand_as(weight)
	return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)


	def conv_ws(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
	return WSConv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
	groups=groups, bias=bias)


	'''
	class Mish(nn.Module):
	def __init__(self):
	super(Mish, self).__init__()
	def forward(self, x):
	return x*torch.tanh(F.softplus(x))
	'''


	@script
	def _mish_jit_fwd(x): return x.mul(torch.tanh(F.softplus(x)))


	@script
	def _mish_jit_bwd(x, grad_output):
	x_sigmoid = torch.sigmoid(x)
	x_tanh_sp = F.softplus(x).tanh()
	return grad_output.mul(x_tanh_sp + x * x_sigmoid * (1 - x_tanh_sp * x_tanh_sp))


	class MishJitAutoFn(torch.autograd.Function):
	@staticmethod
	def forward(ctx, x):
	ctx.save_for_backward(x)
	return _mish_jit_fwd(x)

	@staticmethod
	def backward(ctx, grad_output):
	x = ctx.saved_variables[0]
	return _mish_jit_bwd(x, grad_output)


	# Cell
	def mish(x): return MishJitAutoFn.apply(x)


	class Mish(nn.Module):
	def __init__(self, inplace: bool = False):
	super(Mish, self).__init__()

	def forward(self, x):
	return MishJitAutoFn.apply(x)


	######################################################################################################################
	######################################################################################################################

	# pre-activation based upsampling conv block
	class upConvLayer(nn.Module):
	def __init__(self, in_channels, out_channels, scale_factor, norm, act, num_groups):
	super(upConvLayer, self).__init__()
	conv = conv_ws
	if act == 'ELU':
	act = nn.ELU()
	elif act == 'Mish':
	act = Mish()
	else:
	act = nn.ReLU(True)
	self.conv = conv(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1,
	bias=False)
	if norm == 'GN':
	self.norm = nn.GroupNorm(num_groups=num_groups, num_channels=in_channels)
	else:
	self.norm = nn.BatchNorm2d(in_channels, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
	self.act = act
	self.scale_factor = scale_factor

	def forward(self, x):
	x = self.norm(x)
	x = self.act(x) # pre-activation
	x = F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear')
	x = self.conv(x)
	return x


	# pre-activation based conv block
	class myConv(nn.Module):
	def __init__(self, in_ch, out_ch, kSize, stride=1,
	padding=0, dilation=1, bias=True, norm='GN', act='ELU', num_groups=32):
	super(myConv, self).__init__()
	conv = conv_ws
	if act == 'ELU':
	act = nn.ELU()
	elif act == 'Mish':
	act = Mish()
	else:
	act = nn.ReLU(True)
	module = []
	if norm == 'GN':
	module.append(nn.GroupNorm(num_groups=num_groups, num_channels=in_ch))
	else:
	module.append(nn.BatchNorm2d(in_ch, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))
	module.append(act)
	module.append(conv(in_ch, out_ch, kernel_size=kSize, stride=stride,
	padding=padding, dilation=dilation, groups=1, bias=bias))
	self.module = nn.Sequential(*module)

	def forward(self, x):
	out = self.module(x)
	return out


	# Deep Feature Fxtractor
	class deepFeatureExtractor_ResNext101(nn.Module):
	def __init__(self, lv6=False):
	super(deepFeatureExtractor_ResNext101, self).__init__()

	# after passing ReLU : H/2 x W/2
	# after passing Layer1 : H/4 x W/4
	# after passing Layer2 : H/8 x W/8
	# after passing Layer3 : H/16 x W/16
	self.encoder = models.resnext101_32x8d(weights=models.ResNeXt101_32X8D_Weights.DEFAULT)
	self.fixList = ['layer1.0', 'layer1.1', '.bn']
	self.lv6 = lv6

	if lv6 is True:
	self.layerList = ['relu', 'layer1', 'layer2', 'layer3', 'layer4']
	self.dimList = [64, 256, 512, 1024, 2048]
	else:
	del self.encoder.layer4
	del self.encoder.fc
	self.layerList = ['relu', 'layer1', 'layer2', 'layer3']
	self.dimList = [64, 256, 512, 1024]

	for name, parameters in self.encoder.named_parameters():
	if name == 'conv1.weight':
	parameters.requires_grad = False
	if any(x in name for x in self.fixList):
	parameters.requires_grad = False

	def forward(self, x):
	out_featList = []
	feature = x
	for k, v in self.encoder._modules.items():
	if k == 'avgpool':
	break
	feature = v(feature)
	# feature = v(features[-1])
	# features.append(feature)
	if any(x in k for x in self.layerList):
	out_featList.append(feature)
	return out_featList

	def freeze_bn(self, enable=False):
	""" Adapted from https://discuss.pytorch.org/t/how-to-train-with-frozen-batchnorm/12106/8 """
	for module in self.modules():
	if isinstance(module, nn.BatchNorm2d):
	module.train() if enable else module.eval()

	module.weight.requires_grad = enable
	module.bias.requires_grad = enable


	# ASPP Module
	class Dilated_bottleNeck(nn.Module):
	def __init__(self, norm, act, in_feat):
	super(Dilated_bottleNeck, self).__init__()
	conv = conv_ws
	# in feat = 1024 in ResNext101 and ResNet101
	self.reduction1 = conv(in_feat, in_feat // 2, kernel_size=1, stride=1, bias=False, padding=0)
	self.aspp_d3 = nn.Sequential(
	myConv(in_feat // 2, in_feat // 4, kSize=1, stride=1, padding=0, dilation=1, bias=False, norm=norm, act=act,
	num_groups=(in_feat // 2) // 16),
	myConv(in_feat // 4, in_feat // 4, kSize=3, stride=1, padding=3, dilation=3, bias=False, norm=norm, act=act,
	num_groups=(in_feat // 4) // 16))
	self.aspp_d6 = nn.Sequential(
	myConv(in_feat // 2 + in_feat // 4, in_feat // 4, kSize=1, stride=1, padding=0, dilation=1, bias=False,
	norm=norm, act=act, num_groups=(in_feat // 2 + in_feat // 4) // 16),
	myConv(in_feat // 4, in_feat // 4, kSize=3, stride=1, padding=6, dilation=6, bias=False, norm=norm, act=act,
	num_groups=(in_feat // 4) // 16))
	self.aspp_d12 = nn.Sequential(
	myConv(in_feat, in_feat // 4, kSize=1, stride=1, padding=0, dilation=1, bias=False, norm=norm, act=act,
	num_groups=(in_feat) // 16),
	myConv(in_feat // 4, in_feat // 4, kSize=3, stride=1, padding=12, dilation=12, bias=False, norm=norm,
	act=act, num_groups=(in_feat // 4) // 16))
	self.aspp_d18 = nn.Sequential(
	myConv(in_feat + in_feat // 4, in_feat // 4, kSize=1, stride=1, padding=0, dilation=1, bias=False,
	norm=norm, act=act, num_groups=(in_feat + in_feat // 4) // 16),
	myConv(in_feat // 4, in_feat // 4, kSize=3, stride=1, padding=18, dilation=18, bias=False, norm=norm,
	act=act, num_groups=(in_feat // 4) // 16))
	self.reduction2 = myConv(((in_feat // 4) * 4) + (in_feat // 2), in_feat // 2, kSize=3, stride=1, padding=1,
	bias=False, norm=norm, act=act, num_groups=((in_feat // 4) * 4 + (in_feat // 2)) // 16)

	def forward(self, x):
	x = self.reduction1(x)
	d3 = self.aspp_d3(x)
	cat1 = torch.cat([x, d3], dim=1)
	d6 = self.aspp_d6(cat1)
	cat2 = torch.cat([cat1, d6], dim=1)
	d12 = self.aspp_d12(cat2)
	cat3 = torch.cat([cat2, d12], dim=1)
	d18 = self.aspp_d18(cat3)
	out = self.reduction2(torch.cat([x, d3, d6, d12, d18], dim=1))
	return out # 512 x H/16 x W/16


	class Dilated_bottleNeck2(nn.Module):
	def __init__(self, norm, act, in_feat):
	super(Dilated_bottleNeck2, self).__init__()
	conv = conv_ws
	# in feat = 1024 in ResNext101 and ResNet101
	# self.reduction1 = conv(in_feat, in_feat//2, kernel_size=1, stride = 1, bias=False, padding=0)
	self.reduction1 = conv(in_feat, in_feat // 2, kernel_size=3, stride=1, padding=1, bias=False)
	self.aspp_d3 = nn.Sequential(
	myConv(in_feat // 2, in_feat // 4, kSize=1, stride=1, padding=0, dilation=1, bias=False, norm=norm, act=act,
	num_groups=(in_feat // 2) // 16),
	myConv(in_feat // 4, in_feat // 4, kSize=3, stride=1, padding=3, dilation=3, bias=False, norm=norm, act=act,
	num_groups=(in_feat // 4) // 16))
	self.aspp_d6 = nn.Sequential(
	myConv(in_feat // 2 + in_feat // 4, in_feat // 4, kSize=1, stride=1, padding=0, dilation=1, bias=False,
	norm=norm, act=act, num_groups=(in_feat // 2 + in_feat // 4) // 16),
	myConv(in_feat // 4, in_feat // 4, kSize=3, stride=1, padding=6, dilation=6, bias=False, norm=norm, act=act,
	num_groups=(in_feat // 4) // 16))
	self.aspp_d12 = nn.Sequential(
	myConv(in_feat, in_feat // 4, kSize=1, stride=1, padding=0, dilation=1, bias=False, norm=norm, act=act,
	num_groups=(in_feat) // 16),
	myConv(in_feat // 4, in_feat // 4, kSize=3, stride=1, padding=12, dilation=12, bias=False, norm=norm,
	act=act, num_groups=(in_feat // 4) // 16))
	self.aspp_d18 = nn.Sequential(
	myConv(in_feat + in_feat // 4, in_feat // 4, kSize=1, stride=1, padding=0, dilation=1, bias=False,
	norm=norm, act=act, num_groups=(in_feat + in_feat // 4) // 16),
	myConv(in_feat // 4, in_feat // 4, kSize=3, stride=1, padding=18, dilation=18, bias=False, norm=norm,
	act=act, num_groups=(in_feat // 4) // 16))
	self.aspp_d24 = nn.Sequential(
	myConv(in_feat + in_feat // 2, in_feat // 4, kSize=1, stride=1, padding=0, dilation=1, bias=False,
	norm=norm, act=act, num_groups=(in_feat + in_feat // 2) // 16),
	myConv(in_feat // 4, in_feat // 4, kSize=3, stride=1, padding=24, dilation=24, bias=False, norm=norm,
	act=act, num_groups=(in_feat // 4) // 16))
	self.reduction2 = myConv(((in_feat // 4) * 5) + (in_feat // 2), in_feat // 2, kSize=3, stride=1, padding=1,
	bias=False, norm=norm, act=act, num_groups=((in_feat // 4) * 5 + (in_feat // 2)) // 16)

	def forward(self, x):
	x = self.reduction1(x)
	d3 = self.aspp_d3(x)
	cat1 = torch.cat([x, d3], dim=1)
	d6 = self.aspp_d6(cat1)
	cat2 = torch.cat([cat1, d6], dim=1)
	d12 = self.aspp_d12(cat2)
	cat3 = torch.cat([cat2, d12], dim=1)
	d18 = self.aspp_d18(cat3)
	cat4 = torch.cat([cat3, d18], dim=1)
	d24 = self.aspp_d24(cat4)
	out = self.reduction2(torch.cat([x, d3, d6, d12, d18, d24], dim=1))
	return out # 512 x H/16 x W/16


	class Dilated_bottleNeck_lv6(nn.Module):
	def __init__(self, norm, act, in_feat):
	super(Dilated_bottleNeck_lv6, self).__init__()
	conv = conv_ws
	in_feat = in_feat // 2
	self.reduction1 = myConv(in_feat * 2, in_feat // 2, kSize=3, stride=1, padding=1, bias=False, norm=norm,
	act=act, num_groups=(in_feat) // 16)
	self.aspp_d3 = nn.Sequential(
	myConv(in_feat // 2, in_feat // 4, kSize=1, stride=1, padding=0, dilation=1, bias=False, norm=norm, act=act,
	num_groups=(in_feat // 2) // 16),
	myConv(in_feat // 4, in_feat // 4, kSize=3, stride=1, padding=3, dilation=3, bias=False, norm=norm, act=act,
	num_groups=(in_feat // 4) // 16))
	self.aspp_d6 = nn.Sequential(
	myConv(in_feat // 2 + in_feat // 4, in_feat // 4, kSize=1, stride=1, padding=0, dilation=1, bias=False,
	norm=norm, act=act, num_groups=(in_feat // 2 + in_feat // 4) // 16),
	myConv(in_feat // 4, in_feat // 4, kSize=3, stride=1, padding=6, dilation=6, bias=False, norm=norm, act=act,
	num_groups=(in_feat // 4) // 16))
	self.aspp_d12 = nn.Sequential(
	myConv(in_feat, in_feat // 4, kSize=1, stride=1, padding=0, dilation=1, bias=False, norm=norm, act=act,
	num_groups=(in_feat) // 16),
	myConv(in_feat // 4, in_feat // 4, kSize=3, stride=1, padding=12, dilation=12, bias=False, norm=norm,
	act=act, num_groups=(in_feat // 4) // 16))
	self.aspp_d18 = nn.Sequential(
	myConv(in_feat + in_feat // 4, in_feat // 4, kSize=1, stride=1, padding=0, dilation=1, bias=False,
	norm=norm, act=act, num_groups=(in_feat + in_feat // 4) // 16),
	myConv(in_feat // 4, in_feat // 4, kSize=3, stride=1, padding=18, dilation=18, bias=False, norm=norm,
	act=act, num_groups=(in_feat // 4) // 16))
	self.reduction2 = myConv(((in_feat // 4) * 4) + (in_feat // 2), in_feat, kSize=3, stride=1, padding=1,
	bias=False, norm=norm, act=act, num_groups=((in_feat // 4) * 4 + (in_feat // 2)) // 16)

	def forward(self, x):
	x = self.reduction1(x)
	d3 = self.aspp_d3(x)
	cat1 = torch.cat([x, d3], dim=1)
	d6 = self.aspp_d6(cat1)
	cat2 = torch.cat([cat1, d6], dim=1)
	d12 = self.aspp_d12(cat2)
	cat3 = torch.cat([cat2, d12], dim=1)
	d18 = self.aspp_d18(cat3)
	out = self.reduction2(torch.cat([x, d3, d6, d12, d18], dim=1))
	return out # 512 x H/16 x W/16


	# Laplacian Decoder Network
	class Lap_decoder_lv5(nn.Module):
	def __init__(self, dimList, norm="BN", rank=0, act='ReLU', max_depth=80):
	super(Lap_decoder_lv5, self).__init__()

	conv = conv_ws
	if norm == 'GN':
	if rank == 0:
	print("==> Norm: GN")
	else:
	if rank == 0:
	print("==> Norm: BN")

	if act == 'ELU':
	act = 'ELU'
	elif act == 'Mish':
	act = 'Mish'
	else:
	act = 'ReLU'
	kSize = 3
	self.max_depth = max_depth
	self.ASPP = Dilated_bottleNeck(norm, act, dimList[3])
	self.dimList = dimList
	############################################ Pyramid Level 5 ###################################################
	# decoder1 out : 1 x H/16 x W/16 (Level 5)
	self.decoder1 = nn.Sequential(
	myConv(dimList[3] // 2, dimList[3] // 4, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 2) // 16),
	myConv(dimList[3] // 4, dimList[3] // 8, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 4) // 16),
	myConv(dimList[3] // 8, dimList[3] // 16, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 8) // 16),
	myConv(dimList[3] // 16, dimList[3] // 32, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 16) // 16),
	myConv(dimList[3] // 32, 1, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 32) // 16)
	)
	########################################################################################################################

	############################################ Pyramid Level 4 ###################################################
	# decoder2 out : 1 x H/8 x W/8 (Level 4)
	# decoder2_up : (H/16,W/16)->(H/8,W/8)
	self.decoder2_up1 = upConvLayer(dimList[3] // 2, dimList[3] // 4, 2, norm, act, (dimList[3] // 2) // 16)
	self.decoder2_reduc1 = myConv(dimList[3] // 4 + dimList[2], dimList[3] // 4 - 4, kSize=1, stride=1, padding=0,
	bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 4 + dimList[2]) // 16)
	self.decoder2_1 = myConv(dimList[3] // 4, dimList[3] // 4, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 4) // 16)

	self.decoder2_2 = myConv(dimList[3] // 4, dimList[3] // 8, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 4) // 16)
	self.decoder2_3 = myConv(dimList[3] // 8, dimList[3] // 16, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 8) // 16)

	self.decoder2_4 = myConv(dimList[3] // 16, 1, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 16) // 16)
	########################################################################################################################

	############################################ Pyramid Level 3 ###################################################
	# decoder2 out2 : 1 x H/4 x W/4 (Level 3)
	# decoder2_1_up2 : (H/8,W/8)->(H/4,W/4)
	self.decoder2_1_up2 = upConvLayer(dimList[3] // 4, dimList[3] // 8, 2, norm, act, (dimList[3] // 4) // 16)
	self.decoder2_1_reduc2 = myConv(dimList[3] // 8 + dimList[1], dimList[3] // 8 - 4, kSize=1, stride=1, padding=0,
	bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 8 + dimList[1]) // 16)
	self.decoder2_1_1 = myConv(dimList[3] // 8, dimList[3] // 8, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 8) // 16)

	self.decoder2_1_2 = myConv(dimList[3] // 8, dimList[3] // 16, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 8) // 16)

	self.decoder2_1_3 = myConv(dimList[3] // 16, 1, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 16) // 16)
	########################################################################################################################

	############################################ Pyramid Level 2 ###################################################
	# decoder2 out3 : 1 x H/2 x W/2 (Level 2)
	# decoder2_1_1_up3 : (H/4,W/4)->(H/2,W/2)
	self.decoder2_1_1_up3 = upConvLayer(dimList[3] // 8, dimList[3] // 16, 2, norm, act, (dimList[3] // 8) // 16)
	self.decoder2_1_1_reduc3 = myConv(dimList[3] // 16 + dimList[0], dimList[3] // 16 - 4, kSize=1, stride=1,
	padding=0, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 16 + dimList[0]) // 16)
	self.decoder2_1_1_1 = myConv(dimList[3] // 16, dimList[3] // 16, kSize, stride=1, padding=kSize // 2,
	bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 16) // 16)

	self.decoder2_1_1_2 = myConv(dimList[3] // 16, 1, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 16) // 16)
	########################################################################################################################

	############################################ Pyramid Level 1 ###################################################
	# decoder5 out : 1 x H x W (Level 1)
	# decoder2_1_1_1_up4 : (H/2,W/2)->(H,W)
	self.decoder2_1_1_1_up4 = upConvLayer(dimList[3] // 16, dimList[3] // 16 - 4, 2, norm, act,
	(dimList[3] // 16) // 16)
	self.decoder2_1_1_1_1 = myConv(dimList[3] // 16, dimList[3] // 16, kSize, stride=1, padding=kSize // 2,
	bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 16) // 16)

	self.decoder2_1_1_1_2 = myConv(dimList[3] // 16, dimList[3] // 32, kSize, stride=1, padding=kSize // 2,
	bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 16) // 16)
	self.decoder2_1_1_1_3 = myConv(dimList[3] // 32, 1, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[3] // 32) // 16)
	########################################################################################################################
	self.upscale = F.interpolate

	def forward(self, x, rgb):
	cat1, cat2, cat3, dense_feat = x[0], x[1], x[2], x[3]
	rgb_lv6, rgb_lv5, rgb_lv4, rgb_lv3, rgb_lv2, rgb_lv1 = rgb[0], rgb[1], rgb[2], rgb[3], rgb[4], rgb[5]
	dense_feat = self.ASPP(dense_feat) # Dense feature for lev 5
	# decoder 1 - Pyramid level 5
	lap_lv5 = torch.sigmoid(self.decoder1(dense_feat))
	lap_lv5_up = self.upscale(lap_lv5, scale_factor=2, mode='bilinear')

	# decoder 2 - Pyramid level 4
	dec2 = self.decoder2_up1(dense_feat)
	dec2 = self.decoder2_reduc1(torch.cat([dec2, cat3], dim=1))
	dec2_up = self.decoder2_1(torch.cat([dec2, lap_lv5_up, rgb_lv4], dim=1))
	dec2 = self.decoder2_2(dec2_up)
	dec2 = self.decoder2_3(dec2)
	lap_lv4 = torch.tanh(self.decoder2_4(dec2) + (0.1 * rgb_lv4.mean(dim=1, keepdim=True)))
	# if depth range is (0,1), laplacian of image range is (-1,1)
	lap_lv4_up = self.upscale(lap_lv4, scale_factor=2, mode='bilinear')
	# decoder 2 - Pyramid level 3
	dec3 = self.decoder2_1_up2(dec2_up)
	dec3 = self.decoder2_1_reduc2(torch.cat([dec3, cat2], dim=1))
	dec3_up = self.decoder2_1_1(torch.cat([dec3, lap_lv4_up, rgb_lv3], dim=1))
	dec3 = self.decoder2_1_2(dec3_up)
	lap_lv3 = torch.tanh(self.decoder2_1_3(dec3) + (0.1 * rgb_lv3.mean(dim=1, keepdim=True)))
	# if depth range is (0,1), laplacian of image range is (-1,1)
	lap_lv3_up = self.upscale(lap_lv3, scale_factor=2, mode='bilinear')
	# decoder 2 - Pyramid level 2
	dec4 = self.decoder2_1_1_up3(dec3_up)
	dec4 = self.decoder2_1_1_reduc3(torch.cat([dec4, cat1], dim=1))
	dec4_up = self.decoder2_1_1_1(torch.cat([dec4, lap_lv3_up, rgb_lv2], dim=1))

	lap_lv2 = torch.tanh(self.decoder2_1_1_2(dec4_up) + (0.1 * rgb_lv2.mean(dim=1, keepdim=True)))
	# if depth range is (0,1), laplacian of image range is (-1,1)
	lap_lv2_up = self.upscale(lap_lv2, scale_factor=2, mode='bilinear')
	# decoder 2 - Pyramid level 1
	dec5 = self.decoder2_1_1_1_up4(dec4_up)
	dec5 = self.decoder2_1_1_1_1(torch.cat([dec5, lap_lv2_up, rgb_lv1], dim=1))
	dec5 = self.decoder2_1_1_1_2(dec5)
	lap_lv1 = torch.tanh(self.decoder2_1_1_1_3(dec5) + (0.1 * rgb_lv1.mean(dim=1, keepdim=True)))
	# if depth range is (0,1), laplacian of image range is (-1,1)

	# Laplacian restoration
	lap_lv4_img = lap_lv4 + lap_lv5_up
	lap_lv3_img = lap_lv3 + self.upscale(lap_lv4_img, scale_factor=2, mode='bilinear')
	lap_lv2_img = lap_lv2 + self.upscale(lap_lv3_img, scale_factor=2, mode='bilinear')
	final_depth = lap_lv1 + self.upscale(lap_lv2_img, scale_factor=2, mode='bilinear')
	final_depth = torch.sigmoid(final_depth)
	return [(lap_lv5) * self.max_depth, (lap_lv4) * self.max_depth, (lap_lv3) * self.max_depth,
	(lap_lv2) * self.max_depth, (lap_lv1) * self.max_depth], final_depth * self.max_depth
	# fit laplacian image range (-80,80), depth image range(0,80)


	class Lap_decoder_lv6(nn.Module):
	def __init__(self, dimList, norm="BN", rank=0, act='ReLU', max_depth=80):
	super(Lap_decoder_lv6, self).__init__()
	norm = norm
	conv = conv_ws
	if norm == 'GN':
	if rank == 0:
	print("==> Norm: GN")
	else:
	if rank == 0:
	print("==> Norm: BN")

	if act == 'ELU':
	act = 'ELU'
	elif act == 'Mish':
	act = 'Mish'
	else:
	act = 'ReLU'
	kSize = 3
	self.max_depth = max_depth
	self.ASPP = Dilated_bottleNeck_lv6(norm, act, dimList[4])
	dimList[4] = dimList[4] // 2
	self.dimList = dimList
	############################################ Pyramid Level 6 ###################################################
	# decoder1 out : 1 x H/32 x W/32 (Level 6)
	self.decoder1 = nn.Sequential(
	myConv(dimList[4] // 2, dimList[4] // 4, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 2) // 16),
	myConv(dimList[4] // 4, dimList[4] // 8, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 4) // 16),
	myConv(dimList[4] // 8, dimList[4] // 16, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 8) // 16),
	myConv(dimList[4] // 16, dimList[4] // 32, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 16) // 16),
	myConv(dimList[4] // 32, 1, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 32) // 8)
	)
	########################################################################################################################

	############################################ Pyramid Level 5 ###################################################
	# decoder2 out : 1 x H/16 x W/16 (Level 5)
	# decoder2_up : (H/32,W/32)->(H/16,W/16)
	self.decoder2_up1 = upConvLayer(dimList[4] // 2, dimList[4] // 4, 2, norm, act, (dimList[4] // 2) // 16)
	self.decoder2_reduc1 = myConv(dimList[4] // 4 + dimList[3], dimList[4] // 4 - 4, kSize=1, stride=1, padding=0,
	bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 4 + dimList[3]) // 16)
	self.decoder2_1 = myConv(dimList[4] // 4, dimList[4] // 4, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 4) // 16)

	self.decoder2_2 = myConv(dimList[4] // 4, dimList[4] // 8, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 4) // 16)
	self.decoder2_3 = myConv(dimList[4] // 8, dimList[4] // 16, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 8) // 16)

	self.decoder2_4 = myConv(dimList[4] // 16, 1, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 16) // 16)
	########################################################################################################################

	############################################ Pyramid Level 4 ###################################################
	# decoder2 out2 : 1 x H/8 x W/8 (Level 4)
	# decoder2_1_up2 : (H/16,W/16)->(H/8,W/8)
	self.decoder2_1_up2 = upConvLayer(dimList[4] // 4, dimList[4] // 8, 2, norm, act, (dimList[4] // 4) // 16)
	self.decoder2_1_reduc2 = myConv(dimList[4] // 8 + dimList[2], dimList[4] // 8 - 4, kSize=1, stride=1, padding=0,
	bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 8 + dimList[2]) // 16)
	self.decoder2_1_1 = myConv(dimList[4] // 8, dimList[4] // 8, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 8) // 16)

	self.decoder2_1_2 = myConv(dimList[4] // 8, dimList[4] // 16, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 8) // 16)

	self.decoder2_1_3 = myConv(dimList[4] // 16, 1, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 16) // 16)
	########################################################################################################################

	############################################ Pyramid Level 3 ###################################################
	# decoder2 out3 : 1 x H/4 x W/4 (Level 3)
	# decoder2_1_1_up3 : (H/8,W/8)->(H/4,W/4)
	self.decoder2_1_1_up3 = upConvLayer(dimList[4] // 8, dimList[4] // 16, 2, norm, act, (dimList[4] // 8) // 16)
	self.decoder2_1_1_reduc3 = myConv(dimList[4] // 16 + dimList[1], dimList[4] // 16 - 4, kSize=1, stride=1,
	padding=0, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 16 + dimList[1]) // 8)
	self.decoder2_1_1_1 = myConv(dimList[4] // 16, dimList[4] // 16, kSize, stride=1, padding=kSize // 2,
	bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 16) // 16)

	self.decoder2_1_1_2 = myConv(dimList[4] // 16, 1, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 16) // 16)
	########################################################################################################################

	############################################ Pyramid Level 2 ###################################################
	# decoder2 out4 : 1 x H/2 x W/2 (Level 2)
	# decoder2_1_1_1_up4 : (H/4,W/4)->(H/2,W/2)
	self.decoder2_1_1_1_up4 = upConvLayer(dimList[4] // 16, dimList[4] // 32, 2, norm, act,
	(dimList[4] // 16) // 16)
	self.decoder2_1_1_1_reduc4 = myConv(dimList[4] // 32 + dimList[0], dimList[4] // 32 - 4, kSize=1, stride=1,
	padding=0, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 32 + dimList[0]) // 8)
	self.decoder2_1_1_1_1 = myConv(dimList[4] // 32, dimList[4] // 32, kSize, stride=1, padding=kSize // 2,
	bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 32) // 8)

	self.decoder2_1_1_1_2 = myConv(dimList[4] // 32, 1, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 32) // 8)
	########################################################################################################################

	############################################ Pyramid Level 1 ###################################################
	# decoder5 out : 1 x H x W (Level 1)
	# decoder2_1_1_1_1_up5 : (H/2,W/2)->(H,W)
	self.decoder2_1_1_1_1_up5 = upConvLayer(dimList[4] // 32, dimList[4] // 32 - 4, 2, norm, act,
	(dimList[4] // 32) // 8) # H x W (64 -> 60)
	self.decoder2_1_1_1_1_1 = myConv(dimList[4] // 32, dimList[4] // 32, kSize, stride=1, padding=kSize // 2,
	bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 32) // 8)

	self.decoder2_1_1_1_1_2 = myConv(dimList[4] // 32, dimList[4] // 64, kSize, stride=1, padding=kSize // 2,
	bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 32) // 8)
	self.decoder2_1_1_1_1_3 = myConv(dimList[4] // 64, 1, kSize, stride=1, padding=kSize // 2, bias=False,
	norm=norm, act=act, num_groups=(dimList[4] // 64) // 4)
	########################################################################################################################
	self.upscale = F.interpolate

	def forward(self, x, rgb):
	cat1, cat2, cat3, cat4, dense_feat = x[0], x[1], x[2], x[3], x[4]
	rgb_lv6, rgb_lv5, rgb_lv4, rgb_lv3, rgb_lv2, rgb_lv1 = rgb[0], rgb[1], rgb[2], rgb[3], rgb[4], rgb[5]
	dense_feat = self.ASPP(dense_feat) # Dense feature for lev 6
	# decoder 1 - Pyramid level 6
	lap_lv6 = torch.sigmoid(self.decoder1(dense_feat))
	lap_lv6_up = self.upscale(lap_lv6, scale_factor=2, mode='bilinear')

	# decoder 2 - Pyramid level 5
	dec2 = self.decoder2_up1(dense_feat)
	dec2 = self.decoder2_reduc1(torch.cat([dec2, cat4], dim=1))
	dec2_up = self.decoder2_1(torch.cat([dec2, lap_lv6_up, rgb_lv5], dim=1))
	dec2 = self.decoder2_2(dec2_up)
	dec2 = self.decoder2_3(dec2)
	lap_lv5 = torch.tanh(self.decoder2_4(dec2) + (0.1 * rgb_lv5.mean(dim=1, keepdim=True)))
	# if depth range is (0,1), laplacian image range is (-1,1)
	lap_lv5_up = self.upscale(lap_lv5, scale_factor=2, mode='bilinear')
	# decoder 2 - Pyramid level 4
	dec3 = self.decoder2_1_up2(dec2_up)
	dec3 = self.decoder2_1_reduc2(torch.cat([dec3, cat3], dim=1))
	dec3_up = self.decoder2_1_1(torch.cat([dec3, lap_lv5_up, rgb_lv4], dim=1))
	dec3 = self.decoder2_1_2(dec3_up)
	lap_lv4 = torch.tanh(self.decoder2_1_3(dec3) + (0.1 * rgb_lv4.mean(dim=1, keepdim=True)))
	# if depth range is (0,1), laplacian image range is (-1,1)
	lap_lv4_up = self.upscale(lap_lv4, scale_factor=2, mode='bilinear')
	# decoder 2 - Pyramid level 3
	dec4 = self.decoder2_1_1_up3(dec3_up)
	dec4 = self.decoder2_1_1_reduc3(torch.cat([dec4, cat2], dim=1))
	dec4_up = self.decoder2_1_1_1(torch.cat([dec4, lap_lv4_up, rgb_lv3], dim=1))

	lap_lv3 = torch.tanh(self.decoder2_1_1_2(dec4_up) + (0.1 * rgb_lv3.mean(dim=1, keepdim=True)))
	# if depth range is (0,1), laplacian image range is (-1,1)
	lap_lv3_up = self.upscale(lap_lv3, scale_factor=2, mode='bilinear')
	# decoder 2 - Pyramid level 2
	dec5 = self.decoder2_1_1_1_up4(dec4_up)
	dec5 = self.decoder2_1_1_1_reduc4(torch.cat([dec5, cat1], dim=1))
	dec5_up = self.decoder2_1_1_1_1(torch.cat([dec5, lap_lv3_up, rgb_lv2], dim=1))

	lap_lv2 = torch.tanh(self.decoder2_1_1_1_2(dec5_up) + (0.1 * rgb_lv2.mean(dim=1, keepdim=True)))
	# if depth range is (0,1), laplacian image range is (-1,1)
	lap_lv2_up = self.upscale(lap_lv2, scale_factor=2, mode='bilinear')
	# decoder 2 - Pyramid level 1
	dec6 = self.decoder2_1_1_1_1_up5(dec5_up)
	dec6 = self.decoder2_1_1_1_1_1(torch.cat([dec6, lap_lv2_up, rgb_lv1], dim=1))
	dec6 = self.decoder2_1_1_1_1_2(dec6)
	lap_lv1 = torch.tanh(self.decoder2_1_1_1_1_3(dec6) + (0.1 * rgb_lv1.mean(dim=1, keepdim=True)))
	# if depth range is (0,1), laplacian image range is (-1,1)

	# Laplacian restoration
	lap_lv5_img = lap_lv5 + lap_lv6_up
	lap_lv4_img = lap_lv4 + self.upscale(lap_lv5_img, scale_factor=2, mode='bilinear')
	lap_lv3_img = lap_lv3 + self.upscale(lap_lv4_img, scale_factor=2, mode='bilinear')
	lap_lv2_img = lap_lv2 + self.upscale(lap_lv3_img, scale_factor=2, mode='bilinear')
	final_depth = lap_lv1 + self.upscale(lap_lv2_img, scale_factor=2, mode='bilinear')
	final_depth = torch.sigmoid(final_depth)
	return [(lap_lv6) * self.max_depth, (lap_lv5) * self.max_depth, (lap_lv4) * self.max_depth,
	(lap_lv3) * self.max_depth, (lap_lv2) * self.max_depth,
	(lap_lv1) * self.max_depth], final_depth * self.max_depth
	# fit laplacian image range (-80,80), depth image range(0,80)


	# Laplacian Depth Residual Network
	class LDRN(nn.Module):
	def __init__(self, lv6=False, norm="BN", rank=0, act='ReLU', max_depth=80):
	super(LDRN, self).__init__()

	self.encoder = deepFeatureExtractor_ResNext101(lv6)

	if lv6 is True:
	self.decoder = Lap_decoder_lv6(self.encoder.dimList, norm, rank, act, max_depth)
	else:
	self.decoder = Lap_decoder_lv5(self.encoder.dimList, norm, rank, act, max_depth)

	def forward(self, x):
	out_featList = self.encoder(x)
	rgb_down2 = F.interpolate(x, scale_factor=0.5, mode='bilinear')
	rgb_down4 = F.interpolate(rgb_down2, scale_factor=0.5, mode='bilinear')
	rgb_down8 = F.interpolate(rgb_down4, scale_factor=0.5, mode='bilinear')
	rgb_down16 = F.interpolate(rgb_down8, scale_factor=0.5, mode='bilinear')
	rgb_down32 = F.interpolate(rgb_down16, scale_factor=0.5, mode='bilinear')
	rgb_up16 = F.interpolate(rgb_down32, rgb_down16.shape[2:], mode='bilinear')
	rgb_up8 = F.interpolate(rgb_down16, rgb_down8.shape[2:], mode='bilinear')
	rgb_up4 = F.interpolate(rgb_down8, rgb_down4.shape[2:], mode='bilinear')
	rgb_up2 = F.interpolate(rgb_down4, rgb_down2.shape[2:], mode='bilinear')
	rgb_up = F.interpolate(rgb_down2, x.shape[2:], mode='bilinear')
	lap1 = x - rgb_up
	lap2 = rgb_down2 - rgb_up2
	lap3 = rgb_down4 - rgb_up4
	lap4 = rgb_down8 - rgb_up8
	lap5 = rgb_down16 - rgb_up16
	rgb_list = [rgb_down32, lap5, lap4, lap3, lap2, lap1]

	d_res_list, depth = self.decoder(out_featList, rgb_list)
	return d_res_list, depth

	def train(self, mode=True):
	super().train(mode)
	self.encoder.freeze_bn()