def forward(self, x):
x0 = self.wR2.module.features[0](x)
_x1 = self.wR2.module.features[1](x0)
x2 = self.wR2.module.features[2](_x1)
_x3 = self.wR2.module.features[3](x2)
x4 = self.wR2.module.features[4](_x3)
_x5 = self.wR2.module.features[5](x4)
x6 = self.wR2.module.features[6](_x5)
x7 = self.wR2.module.features[7](x6)
x8 = self.wR2.module.features[8](x7)
x9 = self.wR2.module.features[9](x8)
x9 = x9.view(x9.size(0), -1)
boxLoc = self.wR2.module.classifier(x9)
h1, w1 = _x1.data.size()[2], _x1.data.size()[3]
p1 = Variable(
torch.FloatTensor([[w1, 0, 0, 0], [0, h1, 0, 0], [0, 0, w1, 0],
[0, 0, 0, h1]]).cuda(),
requires_grad=False,
)
h2, w2 = _x3.data.size()[2], _x3.data.size()[3]
p2 = Variable(
torch.FloatTensor([[w2, 0, 0, 0], [0, h2, 0, 0], [0, 0, w2, 0],
[0, 0, 0, h2]]).cuda(),
requires_grad=False,
)
h3, w3 = _x5.data.size()[2], _x5.data.size()[3]
p3 = Variable(
torch.FloatTensor([[w3, 0, 0, 0], [0, h3, 0, 0], [0, 0, w3, 0],
[0, 0, 0, h3]]).cuda(),
requires_grad=False,
)
# x, y, w, h --> x1, y1, x2, y2
assert boxLoc.data.size()[1] == 4
postfix = Variable(
torch.FloatTensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0],
[0, -0.5, 0, 0.5]]).cuda(),
requires_grad=False,
)
boxNew = boxLoc.mm(postfix).clamp(min=0, max=1)
# input = Variable(torch.rand(2, 1, 10, 10), requires_grad=True)
# rois = Variable(torch.LongTensor([[0, 1, 2, 7, 8], [0, 3, 3, 8, 8], [1, 3, 3, 8, 8]]), requires_grad=False)
roi1 = roi_pooling_ims(_x1, boxNew.mm(p1), size=(16, 8))
roi2 = roi_pooling_ims(_x3, boxNew.mm(p2), size=(16, 8))
roi3 = roi_pooling_ims(_x5, boxNew.mm(p3), size=(16, 8))
rois = torch.cat((roi1, roi2, roi3), 1)
_rois = rois.view(rois.size(0), -1)
y0 = self.classifier1(_rois)
y1 = self.classifier2(_rois)
y2 = self.classifier3(_rois)
y3 = self.classifier4(_rois)
y4 = self.classifier5(_rois)
y5 = self.classifier6(_rois)
y6 = self.classifier7(_rois)
return boxLoc, [y0, y1, y2, y3, y4, y5, y6]
def forward(self, x):
x0 = self.wR2.module.features[0](x)
_x1 = self.wR2.module.features[1](x0)
x2 = self.wR2.module.features[2](_x1)
_x3 = self.wR2.module.features[3](x2)
x4 = self.wR2.module.features[4](_x3)
_x5 = self.wR2.module.features[5](x4)
x6 = self.wR2.module.features[6](_x5)
x7 = self.wR2.module.features[7](x6)
x8 = self.wR2.module.features[8](x7)
x9 = self.wR2.module.features[9](x8)
x9 = x9.view(x9.size(0), -1)
#print(x9.shape)
boxLoc = self.wR2.module.classifier(x9)
h1, w1 = _x1.data.size()[2], _x1.data.size()[3]
if use_gpu:
p1 = Variable(torch.FloatTensor([[w1,0,0,0],[0,h1,0,0],[0,0,w1,0],[0,0,0,h1]]).cuda(), requires_grad=False)
else:
p1 = Variable(torch.FloatTensor([[w1,0,0,0],[0,h1,0,0],[0,0,w1,0],[0,0,0,h1]]), requires_grad=False)
h2, w2 = _x3.data.size()[2], _x3.data.size()[3]
if use_gpu:
p2 = Variable(torch.FloatTensor([[w2,0,0,0],[0,h2,0,0],[0,0,w2,0],[0,0,0,h2]]).cuda(), requires_grad=False)
else:
p2 = Variable(torch.FloatTensor([[w2,0,0,0],[0,h2,0,0],[0,0,w2,0],[0,0,0,h2]]), requires_grad=False)
h3, w3 = _x5.data.size()[2], _x5.data.size()[3]
if use_gpu:
p3 = Variable(torch.FloatTensor([[w3,0,0,0],[0,h3,0,0],[0,0,w3,0],[0,0,0,h3]]).cuda(), requires_grad=False)
else:
p3 = Variable(torch.FloatTensor([[w3,0,0,0],[0,h3,0,0],[0,0,w3,0],[0,0,0,h3]]), requires_grad=False)
# x, y, w, h --> x1, y1, x2, y2
assert boxLoc.data.size()[1] == 4
if use_gpu:
postfix = Variable(torch.FloatTensor([[1,0,1,0],[0,1,0,1],[-0.5,0,0.5,0],[0,-0.5,0,0.5]]).cuda(), requires_grad=False)
else:
postfix = Variable(torch.FloatTensor([[1,0,1,0],[0,1,0,1],[-0.5,0,0.5,0],[0,-0.5,0,0.5]]), requires_grad=False)
boxNew = boxLoc.mm(postfix).clamp(min=0, max=1)
roi1 = roi_pooling_ims(_x1, boxNew.mm(p1), size=(16, 8))
roi2 = roi_pooling_ims(_x3, boxNew.mm(p2), size=(16, 8))
roi3 = roi_pooling_ims(_x5, boxNew.mm(p3), size=(16, 8))
rois = torch.cat((roi1, roi2, roi3), 1)#(N,416,8,16)
rois_r = rois.view(rois.shape[0],rois.shape[1],-1) #(N,416,1,128)
rois_r = rois_r.permute(0,2,1) #(N,128,416)
#Pass to sequence modeller
output = self.rnn(rois_r) #(N,128,nout)
return boxLoc, output
def forward(self, x):
x0 = self.wR2.module.features[0](x) # [1,3,480,480]--->[1,48,121,121]
_x1 = self.wR2.module.features[1](x0) # [1,48,121,121]--->[1,64,122,122]
x2 = self.wR2.module.features[2](_x1) # [1,64,122,122]--->[1,128,62,62]
_x3 = self.wR2.module.features[3](x2) # [1,128,62,62]--->[1,160,63,63]
x4 = self.wR2.module.features[4](_x3) # [1,160,63,63]--->[1,192,32,32]
_x5 = self.wR2.module.features[5](x4) # [1,192,32,32]--->[1,192,33,33]
x6 = self.wR2.module.features[6](_x5) # [1,192,33,33]--->[1,192,17,17]
x7 = self.wR2.module.features[7](x6) # [1,192,17,17]--->[1,192,18,18]
x8 = self.wR2.module.features[8](x7) # [1,192,18,18]--->[1,192,10,10]
x9 = self.wR2.module.features[9](x8) # [1,192,10,10]--->[1,192,11,11]
x9 = x9.view(x9.size(0), -1) # [1,192,11,11]--->[1,23232]
boxLoc = self.wR2.module.classifier(x9) # [1,23232]--->[1,4] #预测车牌位置
# 多尺度卷积：在不同层数的feature map在通道维数上进行拼接，然后预测
with torch.no_grad():
h1, w1 = _x1.data.size()[2], _x1.data.size()[3]
p1 = torch.FloatTensor([[w1, 0, 0, 0], [0, h1, 0, 0], [0, 0, w1, 0], [0, 0, 0, h1]]).cuda()
h2, w2 = _x3.data.size()[2], _x3.data.size()[3]
p2 = torch.FloatTensor([[w2, 0, 0, 0], [0, h2, 0, 0], [0, 0, w2, 0], [0, 0, 0, h2]]).cuda()
h3, w3 = _x5.data.size()[2], _x5.data.size()[3]
p3 = torch.FloatTensor([[w3, 0, 0, 0], [0, h3, 0, 0], [0, 0, w3, 0], [0, 0, 0, h3]]).cuda()
# x, y, w, h --> x1, y1, x2, y2
assert boxLoc.data.size()[1] == 4
postfix = torch.FloatTensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]]).cuda()
boxNew = boxLoc.mm(postfix).clamp(min=0, max=1)
# input = torch.rand(2, 1, 10, 10)
# rois = torch.LongTensor([[0, 1, 2, 7, 8], [0, 3, 3, 8, 8], [1, 3, 3, 8, 8]])
roi1 = roi_pooling_ims(_x1, boxNew.mm(p1), size=(16, 8)) # [1,64,16,8]
roi2 = roi_pooling_ims(_x3, boxNew.mm(p2), size=(16, 8)) # [1,160,16,8]
roi3 = roi_pooling_ims(_x5, boxNew.mm(p3), size=(16, 8)) # [1,192,16,8]
rois = torch.cat((roi1, roi2, roi3), 1) # [1,416,16,8]
_rois = rois.view(rois.size(0), -1) # [1,53248]
y0 = self.classifier1(_rois) # [1,38] 预测车牌的第一个字符：省份
y1 = self.classifier2(_rois) # [1,25] 预测车牌的第二个字符：市
y2 = self.classifier3(_rois) # [1,35] 预测车牌的第三个字符
y3 = self.classifier4(_rois) # [1,35] 预测车牌的第四个字符
y4 = self.classifier5(_rois) # [1,35] 预测车牌的第五个字符
y5 = self.classifier6(_rois) # [1,35] 预测车牌的第六个字符
y6 = self.classifier7(_rois) # [1,35] 预测车牌的第七个字符
return boxLoc, [y0, y1, y2, y3, y4, y5, y6]
def forward(self, x):
x0 = self.wR2.module.features[0](x)
_x1 = self.wR2.module.features[1](x0)
x2 = self.wR2.module.features[2](_x1)
_x3 = self.wR2.module.features[3](x2)
x4 = self.wR2.module.features[4](_x3)
_x5 = self.wR2.module.features[5](x4)
x6 = self.wR2.module.features[6](_x5)
x7 = self.wR2.module.features[7](x6)
x8 = self.wR2.module.features[8](x7)
x9 = self.wR2.module.features[9](x8)
x9 = x9.view(x9.size(0), -1)
boxLoc = self.wR2.module.classifier(x9)
#print('BoxLox is {} and shape is {}'.format(boxLoc, boxLoc.shape))
h1, w1 = _x1.data.size()[2], _x1.data.size()[3]
p1 = Variable(torch.FloatTensor([[w1, 0, 0, 0], [0, h1, 0, 0],
[0, 0, w1, 0], [0, 0, 0, h1]]).cuda(),
requires_grad=False)
h2, w2 = _x3.data.size()[2], _x3.data.size()[3]
p2 = Variable(torch.FloatTensor([[w2, 0, 0, 0], [0, h2, 0, 0],
[0, 0, w2, 0], [0, 0, 0, h2]]).cuda(),
requires_grad=False)
h3, w3 = _x5.data.size()[2], _x5.data.size()[3]
p3 = Variable(torch.FloatTensor([[w3, 0, 0, 0], [0, h3, 0, 0],
[0, 0, w3, 0], [0, 0, 0, h3]]).cuda(),
requires_grad=False)
# x, y, w, h --> x1, y1, x2, y2
assert boxLoc.data.size()[1] == 4
postfix = Variable(torch.FloatTensor([[1, 0, 1, 0], [0, 1, 0, 1],
[-0.5, 0, 0.5, 0],
[0, -0.5, 0, 0.5]]).cuda(),
requires_grad=False)
boxNew = boxLoc.mm(postfix).clamp(min=0, max=1)
#print('boxNew is {} and shape is {}'.format(boxNew , boxNew .shape))
# input = Variable(torch.rand(2, 1, 10, 10), requires_grad=True)
# rois = Variable(torch.LongTensor([[0, 1, 2, 7, 8], [0, 3, 3, 8, 8], [1, 3, 3, 8, 8]]), requires_grad=False)
#print(' boxNew.mm(p1) shape: {}'.format( torch.mm(boxNew,p1)))
roi1 = roi_pooling_ims(_x1, boxNew.mm(p1), size=(16, 8)) #(N,64,8,16)
roi2 = roi_pooling_ims(_x3, boxNew.mm(p2), size=(16, 8)) #(N,160,8,16)
roi3 = roi_pooling_ims(_x5, boxNew.mm(p3), size=(16, 8)) #(N,192,8,16)
rois = torch.cat((roi1, roi2, roi3), 1) #(N,416,8,16)
#_rois = rois.view(rois.size(0), -1)
rois_r = rois.view(rois.shape[0], rois.shape[1], -1) #(N,416,1,128)
#rois_r = rois_r.squeeze(2) #(N,416,128)
rois_r = rois_r.permute(0, 2, 1) #(N,128,416)
#print('Input to RNN: {}'.format(rois_r.shape))
output = self.rnn(rois_r) #(128,N,nout)
# y0 = self.classifier1(_rois)
# y1 = self.classifier2(_rois)
# y2 = self.classifier3(_rois)
# y3 = self.classifier4(_rois)
# y4 = self.classifier5(_rois)
# y5 = self.classifier6(_rois)
# y6 = self.classifier7(_rois)
#return boxLoc, [y0, y1, y2, y3, y4, y5, y6]
#print('output.shape: {}'.format(output.shape))
return boxLoc, output
