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() 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}