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import torch.nn as nn
from .layers.PRM import Residual as ResidualPyramid
from .layers.Residual import Residual as Residual
from torch.autograd import Variable
import torch
from opt import opt
import math
class Hourglass(nn.Module):
def __init__(self, n, nFeats, nModules, inputResH, inputResW, net_type, B, C):
super(Hourglass, self).__init__()
self.ResidualUp = ResidualPyramid if n >= 2 else Residual
self.ResidualDown = ResidualPyramid if n >= 3 else Residual
self.depth = n
self.nModules = nModules
self.nFeats = nFeats
self.net_type = net_type
self.B = B
self.C = C
self.inputResH = inputResH
self.inputResW = inputResW
up1 = self._make_residual(self.ResidualUp, False, inputResH, inputResW)
low1 = nn.Sequential(
nn.MaxPool2d(2),
self._make_residual(self.ResidualDown, False, inputResH / 2, inputResW / 2)
)
if n > 1:
low2 = Hourglass(n - 1, nFeats, nModules, inputResH / 2, inputResW / 2, net_type, B, C)
else:
low2 = self._make_residual(self.ResidualDown, False, inputResH / 2, inputResW / 2)
low3 = self._make_residual(self.ResidualDown, True, inputResH / 2, inputResW / 2)
up2 = nn.UpsamplingNearest2d(scale_factor=2)
self.upperBranch = up1
self.lowerBranch = nn.Sequential(
low1,
low2,
low3,
up2
)
def _make_residual(self, resBlock, useConv, inputResH, inputResW):
layer_list = []
for i in range(self.nModules):
layer_list.append(resBlock(self.nFeats, self.nFeats, inputResH, inputResW,
stride=1, net_type=self.net_type, useConv=useConv,
baseWidth=self.B, cardinality=self.C))
return nn.Sequential(*layer_list)
def forward(self, x: Variable):
up1 = self.upperBranch(x)
up2 = self.lowerBranch(x)
# out = up1 + up2
out = torch.add(up1, up2)
return out
class PyraNet(nn.Module):
def __init__(self):
super(PyraNet, self).__init__()
B, C = opt.baseWidth, opt.cardinality
self.inputResH = opt.inputResH / 4
self.inputResW = opt.inputResW / 4
self.nStack = opt.nStack
conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
if opt.init:
nn.init.xavier_normal(conv1.weight, gain=math.sqrt(1 / 3))
cnv1 = nn.Sequential(
conv1,
nn.BatchNorm2d(64),
nn.ReLU(True)
)
r1 = nn.Sequential(
ResidualPyramid(64, 128, opt.inputResH / 2, opt.inputResW / 2,
stride=1, net_type='no_preact', useConv=False, baseWidth=B, cardinality=C),
nn.MaxPool2d(2)
)
r4 = ResidualPyramid(128, 128, self.inputResH, self.inputResW,
stride=1, net_type='preact', useConv=False, baseWidth=B, cardinality=C)
r5 = ResidualPyramid(128, opt.nFeats, self.inputResH, self.inputResW,
stride=1, net_type='preact', useConv=False, baseWidth=B, cardinality=C)
self.preact = nn.Sequential(
cnv1,
r1,
r4,
r5
)
self.stack_lin = nn.ModuleList()
self.stack_out = nn.ModuleList()
self.stack_lin_ = nn.ModuleList()
self.stack_out_ = nn.ModuleList()
for i in range(self.nStack):
hg = Hourglass(4, opt.nFeats, opt.nResidual, self.inputResH, self.inputResW, 'preact', B, C)
conv1 = nn.Conv2d(opt.nFeats, opt.nFeats, kernel_size=1, stride=1, padding=0)
if opt.init:
nn.init.xavier_normal(conv1.weight, gain=math.sqrt(1 / 2))
lin = nn.Sequential(
hg,
nn.BatchNorm2d(opt.nFeats),
nn.ReLU(True),
conv1,
nn.BatchNorm2d(opt.nFeats),
nn.ReLU(True)
)
tmpOut = nn.Conv2d(opt.nFeats, opt.nClasses, kernel_size=1, stride=1, padding=0)
if opt.init:
nn.init.xavier_normal(tmpOut.weight)
self.stack_lin.append(lin)
self.stack_out.append(tmpOut)
if i < self.nStack - 1:
lin_ = nn.Conv2d(opt.nFeats, opt.nFeats, kernel_size=1, stride=1, padding=0)
tmpOut_ = nn.Conv2d(opt.nClasses, opt.nFeats, kernel_size=1, stride=1, padding=0)
if opt.init:
nn.init.xavier_normal(lin_.weight)
nn.init.xavier_normal(tmpOut_.weight)
self.stack_lin_.append(lin_)
self.stack_out_.append(tmpOut_)
def forward(self, x: Variable):
out = []
inter = self.preact(x)
for i in range(self.nStack):
lin = self.stack_lin[i](inter)
tmpOut = self.stack_out[i](lin)
out.append(tmpOut)
if i < self.nStack - 1:
lin_ = self.stack_lin_[i](lin)
tmpOut_ = self.stack_out_[i](tmpOut)
inter = inter + lin_ + tmpOut_
return out
class PyraNet_Inference(nn.Module):
def __init__(self):
super(PyraNet_Inference, self).__init__()
B, C = opt.baseWidth, opt.cardinality
self.inputResH = opt.inputResH / 4
self.inputResW = opt.inputResW / 4
self.nStack = opt.nStack
conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
if opt.init:
nn.init.xavier_normal(conv1.weight, gain=math.sqrt(1 / 3))
cnv1 = nn.Sequential(
conv1,
nn.BatchNorm2d(64),
nn.ReLU(True)
)
r1 = nn.Sequential(
ResidualPyramid(64, 128, opt.inputResH / 2, opt.inputResW / 2,
stride=1, net_type='no_preact', useConv=False, baseWidth=B, cardinality=C),
nn.MaxPool2d(2)
)
r4 = ResidualPyramid(128, 128, self.inputResH, self.inputResW,
stride=1, net_type='preact', useConv=False, baseWidth=B, cardinality=C)
r5 = ResidualPyramid(128, opt.nFeats, self.inputResH, self.inputResW,
stride=1, net_type='preact', useConv=False, baseWidth=B, cardinality=C)
self.preact = nn.Sequential(
cnv1,
r1,
r4,
r5
)
self.stack_lin = nn.ModuleList()
self.stack_out = nn.ModuleList()
self.stack_lin_ = nn.ModuleList()
self.stack_out_ = nn.ModuleList()
for i in range(self.nStack):
hg = Hourglass(4, opt.nFeats, opt.nResidual,
self.inputResH, self.inputResW, 'preact', B, C)
conv1 = nn.Conv2d(opt.nFeats, opt.nFeats,
kernel_size=1, stride=1, padding=0)
if opt.init:
nn.init.xavier_normal(conv1.weight, gain=math.sqrt(1 / 2))
lin = nn.Sequential(
hg,
nn.BatchNorm2d(opt.nFeats),
nn.ReLU(True),
conv1,
nn.BatchNorm2d(opt.nFeats),
nn.ReLU(True)
)
tmpOut = nn.Conv2d(opt.nFeats, opt.nClasses,
kernel_size=1, stride=1, padding=0)
if opt.init:
nn.init.xavier_normal(tmpOut.weight)
self.stack_lin.append(lin)
self.stack_out.append(tmpOut)
if i < self.nStack - 1:
lin_ = nn.Conv2d(opt.nFeats, opt.nFeats,
kernel_size=1, stride=1, padding=0)
tmpOut_ = nn.Conv2d(opt.nClasses, opt.nFeats,
kernel_size=1, stride=1, padding=0)
if opt.init:
nn.init.xavier_normal(lin_.weight)
nn.init.xavier_normal(tmpOut_.weight)
self.stack_lin_.append(lin_)
self.stack_out_.append(tmpOut_)
def forward(self, x: Variable):
inter = self.preact(x)
for i in range(self.nStack):
lin = self.stack_lin[i](inter)
tmpOut = self.stack_out[i](lin)
out = tmpOut
if i < self.nStack - 1:
lin_ = self.stack_lin_[i](lin)
tmpOut_ = self.stack_out_[i](tmpOut)
inter = inter + lin_ + tmpOut_
return out
def createModel(**kw):
model = PyraNet()
return model
def createModel_Inference(**kw):
model = PyraNet_Inference()
return model
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