def merge(tbl):
inp = scn.InputBatch(2, spatial_size)
center = spatial_size.float().view(1, 2) / 2
p = torch.LongTensor(2)
v = torch.FloatTensor([1, 0, 0])
for char in tbl['input']:
inp.addSample()
for stroke in char:
stroke = stroke.float() * (Scale - 0.01) / 255 - 0.5 * (Scale -
0.01)
stroke += center.expand_as(stroke)
###############################################################
# To avoid GIL problems use a helper function:
scn.dim_fn(2, 'drawCurve')(inp.metadata.ffi, inp.features,
stroke)
###############################################################
# Above is equivalent to :
# x1,x2,y1,y2,l=0,stroke[0][0],0,stroke[0][1],0
# for i in range(1,stroke.size(0)):
# x1=x2
# y1=y2
# x2=stroke[i][0]
# y2=stroke[i][1]
# l=1e-10+((x2-x1)**2+(y2-y1)**2)**0.5
# v[1]=(x2-x1)/l
# v[2]=(y2-y1)/l
# l=max(x2-x1,y2-y1,x1-x2,y1-y2,0.9)
# for j in numpy.arange(0,1,1/l):
# p[0]=math.floor(x1*j+x2*(1-j))
# p[1]=math.floor(y1*j+y2*(1-j))
# inp.setLocation(p,v,False)
###############################################################
inp.precomputeMetadata(precomputeStride)
return {'input': inp, 'target': torch.LongTensor(tbl['target'])}
def merge(tbl):
inp = scn.InputBatch(2, spatial_size)
center = spatial_size.float().view(1, 2) / 2
p = torch.LongTensor(2)
v = torch.FloatTensor([1, 0, 0])
for char in tbl['input']:
inp.addSample()
m = torch.eye(2)
r = random.randint(1, 3)
alpha = random.uniform(-0.2, 0.2)
if alpha == 1:
m[0][1] = alpha
elif alpha == 2:
m[1][0] = alpha
else:
m = torch.mm(m, torch.FloatTensor(
[[math.cos(alpha), math.sin(alpha)],
[-math.sin(alpha), math.cos(alpha)]]))
c = center + torch.FloatTensor(1, 2).uniform_(-8, 8)
for stroke in char:
stroke = stroke.float() / 255 - 0.5
stroke = c.expand_as(stroke) + \
torch.mm(stroke, m * (Scale - 0.01))
###############################################################
# To avoid GIL problems use a helper function:
scn.dim_fn(
2,
'drawCurve')(
inp.metadata.ffi,
inp.features,
stroke)
###############################################################
# Above is equivalent to :
# x1,x2,y1,y2,l=0,stroke[0][0],0,stroke[0][1],0
# for i in range(1,stroke.size(0)):
# x1=x2
# y1=y2
# x2=stroke[i][0]
# y2=stroke[i][1]
# l=1e-10+((x2-x1)**2+(y2-y1)**2)**0.5
# v[1]=(x2-x1)/l
# v[2]=(y2-y1)/l
# l=max(x2-x1,y2-y1,x1-x2,y1-y2,0.9)
# for j in numpy.arange(0,1,1/l):
# p[0]=math.floor(x1*j+x2*(1-j))
# p[1]=math.floor(y1*j+y2*(1-j))
# inp.setLocation(p,v,False)
###############################################################
inp.precomputeMetadata(precomputeStride)
return {'input': inp, 'target': torch.LongTensor(tbl['target']) - 1}
def merge(tbl):
inp = scn.InputBatch(2, spatial_size)
center = spatial_size.float().view(1, 2) / 2
p = torch.LongTensor(2)
v = torch.FloatTensor([1, 0, 0])
for char in tbl['input']:
inp.addSample()
m = torch.eye(2)
r = random.randint(1, 3)
alpha = random.uniform(-0.2, 0.2)
if alpha == 1:
m[0][1] = alpha
elif alpha == 2:
m[1][0] = alpha
else:
m = torch.mm(m, torch.FloatTensor(
[[math.cos(alpha), math.sin(alpha)],
[-math.sin(alpha), math.cos(alpha)]]))
c = center + torch.FloatTensor(1, 2).uniform_(-8, 8)
for stroke in char:
stroke = stroke.float() / 255 - 0.5
stroke = c.expand_as(stroke) + \
torch.mm(stroke, m * (Scale - 0.01))
###############################################################
# To avoid GIL problems use a helper function:
scn.dim_fn(
2,
'drawCurve')(
inp.metadata.ffi,
inp.features,
stroke)
###############################################################
# Above is equivalent to :
# x1,x2,y1,y2,l=0,stroke[0][0],0,stroke[0][1],0
# for i in range(1,stroke.size(0)):
# x1=x2
# y1=y2
# x2=stroke[i][0]
# y2=stroke[i][1]
# l=1e-10+((x2-x1)**2+(y2-y1)**2)**0.5
# v[1]=(x2-x1)/l
# v[2]=(y2-y1)/l
# l=max(x2-x1,y2-y1,x1-x2,y1-y2,0.9)
# for j in numpy.arange(0,1,1/l):
# p[0]=math.floor(x1*j+x2*(1-j))
# p[1]=math.floor(y1*j+y2*(1-j))
# inp.setLocation(p,v,False)
###############################################################
inp.precomputeMetadata(precomputeStride)
return {'input': inp, 'target': torch.LongTensor(tbl['target']) - 1}
def merge(tbl):
inp = scn.InputBatch(2, spatial_size)
center = spatial_size.float().view(1, 2) / 2
p = torch.LongTensor(2)
v = torch.FloatTensor([1, 0, 0])
for char in tbl['input']:
inp.addSample()
for stroke in char:
stroke = stroke.float() * (Scale - 0.01) / 255 - 0.5 * (Scale -
0.01)
stroke += center.expand_as(stroke)
scn.dim_fn(2, 'drawCurve')(inp.metadata.ffi, inp.features,
stroke)
inp.precomputeMetadata(precomputeStride)
return {'input': inp, 'target': torch.LongTensor(tbl['target'])}
def merge(tbl):
inp = scn.InputBatch(2, spatial_size)
center = spatial_size.float().view(1, 2) / 2
p = torch.LongTensor(2)
v = torch.FloatTensor([1, 0, 0])
for char in tbl['input']:
inp.addSample()
for stroke in char:
stroke = stroke.float() * (Scale - 0.01) / 255 - 0.5 * (Scale - 0.01)
stroke += center.expand_as(stroke)
scn.dim_fn(
2,
'drawCurve')(
inp.metadata.ffi,
inp.features,
stroke)
inp.precomputeMetadata(precomputeStride)
return {'input': inp, 'target': torch.LongTensor(tbl['target']) - 1}