def merge(tbl):
v=torch.Tensor([[1,0,0]])
targets=[x['target'] for x in tbl]
locations=[]
features=[]
for idx,char in enumerate(tbl):
m = torch.eye(2)
r = torch.randint(0,3,[1]).int().item()
alpha = torch.rand(1).item()*0.4-0.2
if r == 1:
m[0][1] = alpha
elif r == 2:
m[1][0] = alpha
else:
m = torch.mm(m, torch.FloatTensor(
[[math.cos(alpha), math.sin(alpha)],
[-math.sin(alpha), math.cos(alpha)]]))
coords=char['coords']
coords = torch.mm(coords, m) + torch.FloatTensor(1, 2).uniform_(center-jitter, center+jitter)
coords = torch.cat([coords.long(),torch.LongTensor([idx]).expand([coords.size(0),1])],1)
locations.append(coords)
f=char['features']
f=torch.mm(f, m)
f /= (f**2).sum(1,keepdim=True)**0.5
f = torch.cat([f,torch.ones([f.size(0),1])],1)
features.append(f)
return {'input': scn.InputLayerInput(torch.cat(locations,0), torch.cat(features,0)), 'target': torch.LongTensor(targets)}
def merge(tbl):
v=torch.Tensor([[1,0,0]])
targets=[x['target'] for x in tbl]
locations=[]
features=[]
for idx,char in enumerate(tbl):
coords=char['coords']+center
coords = torch.cat([coords.long(),torch.LongTensor([idx]).expand([coords.size(0),1])],1)
locations.append(coords)
f=char['features']
f = torch.cat([f,torch.ones([f.size(0),1])],1)
features.append(f)
return {'input': scn.InputLayerInput(torch.cat(locations,0), torch.cat(features,0)), 'target': torch.LongTensor(targets)}
def merge(tbl):
targets=[x[1] for x in tbl]
locations=[]
features=[]
for idx, (img, tar) in enumerate(tbl):
#print(np.array(img).shape) (256, 256)
coords = np.nonzero(np.array(img)[0:spatial_size, 0:spatial_size])
coords = torch.LongTensor(np.vstack(coords).T)
coords = torch.cat([coords.long(), torch.LongTensor([idx]).expand([coords.size(0), 1])], 1)
locations.append(coords)
f = torch.ones([coords.size(0), 1])
f = torch.cat([f, torch.ones([coords.size(0), 1])], 1)
features.append(f)
return {'input': scn.InputLayerInput(torch.cat(locations,0), torch.cat(features,0)),
'target': torch.LongTensor(targets)}
def merge(tbl):
#targets=[x['target'] for x in tbl]
locations = []
features = []
targets = []
mask = torch.FloatTensor(32, 32).uniform_() <= density
#print(mask)
for idx, item in enumerate(tbl):
l = []
f = []
for r in range(0, item[0].shape[1]):
for c in range(0, item[0].shape[2]):
if mask[r, c] == True:
#print(str(r) + " " + str(c))
l.append([r, c])
f.append(item[0][:, r, c].tolist())
l = torch.LongTensor(l)
#print("l raw")
#print(l)
l = torch.cat(
[l, torch.LongTensor([idx]).expand([l.size(0), 1])], 1)
#print("l padded")
#print(l)
locations.append(l)
#print("f raw")
#print(f)
f = torch.FloatTensor(f)
#print("f padded")
#print(f)
features.append(f)
targets.append(item[1])
return {
'input':
scn.InputLayerInput(torch.cat(locations, 0),
torch.cat(features, 0)),
'target':
torch.LongTensor(targets)
}
