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hourglass_2.py
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'''
Hourglass network inserted in the pre-activated Resnet
Use lr=0.01 for current version
(c) YANG, Wei
'''
import torch.nn as nn
import torch.nn.functional as F
import torch
from torch.autograd import Variable
# from .preresnet import BasicBlock, Bottleneck
__all__ = ['HourglassNet', 'hg']
class linear_model(nn.Module):
def __init__(self,linear_size,predict_14):
super(linear_model, self).__init__()
self.HUMAN_2D_SIZE = 16 * 2
self.HUMAN_3D_SIZE = 16 * 3 if predict_14 else 16 * 3
self.input_size = self.HUMAN_2D_SIZE
self.output_size = self.HUMAN_3D_SIZE
self.linear_size = linear_size
self.l_initial = nn.Linear(self.input_size,linear_size)
self.relu = nn.ReLU(inplace = True)
self.dropout = nn.Dropout(p=0.5)
def linear_block(self,x, input_size,linear_size):
l = nn.Linear(input_size,linear_size)
y = l(x)
y = self.relu(y)
y = self.dropout(y)
l = nn.Linear(linear_size,linear_size)
y = l(x)
y = self.relu(y)
y = self.dropout(y)
return x + y
def forward(self,x):
x = self.l_initial(x)
x = self.relu(x)
x = self.dropout(x)
input_l1_size = x.size()
for i in range(2):
x = self.linear_block(x,int(input_l1_size[1]),self.linear_size)
x = self.relu(x)
x = self.dropout(x)
l2 = nn.Linear(self.linear_size, self.HUMAN_3D_SIZE)
x = l2(x)
return x
class Bottleneck(nn.Module):
expansion = 2
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(inplanes)
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=True)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=True)
self.bn3 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1, bias=True)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.bn1(x)
out = self.relu(out)
out = self.conv1(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn3(out)
out = self.relu(out)
out = self.conv3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
return out
class Hourglass(nn.Module):
def __init__(self, block, num_blocks, planes, depth):
super(Hourglass, self).__init__()
self.depth = depth
self.block = block
self.upsample = nn.Upsample(scale_factor=2)
self.hg = self._make_hour_glass(block, num_blocks, planes, depth)
def _make_residual(self, block, num_blocks, planes):
layers = []
for i in range(0, num_blocks):
layers.append(block(planes*block.expansion, planes))
return nn.Sequential(*layers)
def _make_hour_glass(self, block, num_blocks, planes, depth):
hg = []
for i in range(depth):
res = []
for j in range(3):
res.append(self._make_residual(block, num_blocks, planes))
if i == 0:
res.append(self._make_residual(block, num_blocks, planes))
hg.append(nn.ModuleList(res))
return nn.ModuleList(hg)
def _hour_glass_forward(self, n, x):
up1 = self.hg[n-1][0](x)
low1 = F.max_pool2d(x, 2, stride=2)
low1 = self.hg[n-1][1](low1)
if n > 1:
low2 = self._hour_glass_forward(n-1, low1)
else:
low2 = self.hg[n-1][3](low1)
low3 = self.hg[n-1][2](low2)
up2 = self.upsample(low3)
out = up1 + up2
return out
def forward(self, x):
return self._hour_glass_forward(self.depth, x)
class HourglassNet(nn.Module):
'''Hourglass model from Newell et al ECCV 2016'''
def __init__(self, block, num_stacks=2, num_blocks=4, num_classes=16):
super(HourglassNet, self).__init__()
self.inplanes = 64
self.num_feats = 128
self.num_stacks = num_stacks
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
bias=True)
self.bn1 = nn.BatchNorm2d(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_residual(block, self.inplanes, 1)
self.layer2 = self._make_residual(block, self.inplanes, 1)
self.layer3 = self._make_residual(block, self.num_feats, 1)
self.maxpool = nn.MaxPool2d(2, stride=2)
# build hourglass modules
ch = self.num_feats*block.expansion
hg, res, fc, score, fc_, score_ = [], [], [], [], [], []
for i in range(num_stacks):
hg.append(Hourglass(block, num_blocks, self.num_feats, 4))
res.append(self._make_residual(block, self.num_feats, num_blocks))
fc.append(self._make_fc(ch, ch))
score.append(nn.Conv2d(ch, num_classes, kernel_size=1, bias=True))
if i < num_stacks-1:
fc_.append(nn.Conv2d(ch, ch, kernel_size=1, bias=True))
score_.append(nn.Conv2d(num_classes, ch, kernel_size=1, bias=True))
self.hg = nn.ModuleList(hg)
self.res = nn.ModuleList(res)
self.fc = nn.ModuleList(fc)
self.score = nn.ModuleList(score)
self.fc_ = nn.ModuleList(fc_)
self.score_ = nn.ModuleList(score_)
self.soft = nn.Softmax()
self.linear = linear_model(1024,True)
def _make_residual(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=True),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def _make_fc(self, inplanes, outplanes):
bn = nn.BatchNorm2d(inplanes)
conv = nn.Conv2d(inplanes, outplanes, kernel_size=1, bias=True)
return nn.Sequential(
conv,
bn,
self.relu,
)
def get_preds(self,scores):
''' get predictions from score maps in torch Tensor
return type: torch.LongTensor
'''
assert scores.dim() == 4, 'Score maps should be 4-dim'
maxval, idx = torch.max(scores.view(scores.size(0), scores.size(1), -1), 2)
maxval = maxval.view(scores.size(0), scores.size(1), 1)
idx = idx.view(scores.size(0), scores.size(1), 1) + 1
preds = idx.repeat(1, 1, 2).float()
preds[:,:,0] = (preds[:,:,0] - 1) % scores.size(3) + 1
preds[:,:,1] = torch.floor((preds[:,:,1] - 1) / scores.size(3)) + 1
pred_mask = maxval.gt(0).repeat(1, 1, 2).float()
preds *= pred_mask
return preds
def forward(self, x):
out = []
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x)
x = self.maxpool(x)
x = self.layer2(x)
x = self.layer3(x)
for i in range(self.num_stacks):
y = self.hg[i](x)
y = self.res[i](y)
y = self.fc[i](y)
score = self.score[i](y)
out.append(score)
if i < self.num_stacks-1:
fc_ = self.fc_[i](y)
score_ = self.score_[i](score)
x = x + fc_ + score_
last_heatmap = out[-1]
print(last_heatmap.size())
preds = self.get_preds(last_heatmap)
batch = preds.size()[0]
temp = Variable(torch.FloatTensor(batch,32))
for i in range(batch):
temp[i,:] = torch.cat((preds[i,:,0],preds[i,:,1]))
print(temp.size())
final_out = self.linear(temp)
print(final_out.size())
return final_out
def hg(**kwargs):
model = HourglassNet(Bottleneck, num_stacks=kwargs['num_stacks'], num_blocks=kwargs['num_blocks'],
num_classes=kwargs['num_classes'])
return model