tvayer / sgw Goto Github PK

s_samples, t_samples = 100, 200
n_projections = 5
dimensionality = 8
Xs=np.random.rand(s_samples, dimensionality)
Xt=np.random.rand(t_samples, dimensionality)
P=np.random.randn(dimensionality, n_projections)

%%time
sgw_cpu(Xs,Xt,P=P)


---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<timed eval> in <module>

~/hax/SGW/lib/sgw_numpy.py in sgw_cpu(xs, xt, nproj, tolog, P)
     66         log['gw_1d_details']=log_gw1d
     67     else:
---> 68         d=gromov_1d(xsp,xtp,tolog=False)
     69 
     70     if tolog:

~/hax/SGW/lib/sgw_numpy.py in gromov_1d(xs, xt, tolog, fast)
    170         xt_asc=np.sort(xt,axis=0)
    171         xt_desc=np.sort(xt,axis=0)[::-1]
--> 172         l1=_cost(xs2,xt_asc,tolog=tolog)
    173         l2=_cost(xs2,xt_desc,tolog=tolog)
    174     toreturn=np.mean(np.minimum(l1,l2))

~/hax/SGW/lib/sgw_numpy.py in _cost(xsp, xtp, tolog)
    109         Y4=np.sum(xt**4)
    110 
--> 111         xxyy_=np.sum((xs**2)*(xt**2))
    112         xxy_=np.sum((xs**2)*(xt))
    113         xyy_=np.sum((xs)*(xt**2))

ValueError: operands could not be broadcast together with shapes (100,) (200,) 

xs=torch.from_numpy(Xs).to(torch.float32).to('cuda')
xt=torch.from_numpy(Xt).to(torch.float32).to('cuda')
device = 'cuda'

%%time
sgw_gpu(xs,xt,device,P=torch.from_numpy(P).to(torch.float32))


----------------------------------------
xs origi dim : torch.Size([100, 8])
xt origi dim : torch.Size([200, 8])
dim_p : 8
dim_d : 8
random_projection_dim :  8
projector dimension :  torch.Size([2, 5])
xs2 dim : torch.Size([100, 8])
xt2 dim : torch.Size([200, 8])
xs_tmp dim : torch.Size([100, 8])
xt_tmp dim : torch.Size([200, 8])
----------------------------------------
size mismatch, m1: [100 x 8], m2: [2 x 5] at /opt/conda/conda-bld/pytorch_1587428266983/work/aten/src/THC/generic/THCTensorMathBlas.cu:283
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
~/hax/SGW/lib/sgw_pytorch.py in sink_(xs, xt, device, nproj, P)
    242     try:
--> 243 
    244         xsp = torch.matmul(xs2, p.to(device))

RuntimeError: size mismatch, m1: [100 x 8], m2: [2 x 5] at /opt/conda/conda-bld/pytorch_1587428266983/work/aten/src/THC/generic/THCTensorMathBlas.cu:283

During handling of the above exception, another exception occurred:

BadShapeError                             Traceback (most recent call last)
<timed eval> in <module>

~/hax/SGW/lib/sgw_pytorch.py in sgw_gpu(xs, xt, device, nproj, tolog, P)
     61         xsp, xtp = sink_(xs, xt, device, nproj, P)
     62         ed = time.time()
---> 63         log['time_sink_'] = ed-st
     64     else:
     65         xsp, xtp = sink_(xs, xt, device, nproj, P)

~/hax/SGW/lib/sgw_pytorch.py in sink_(xs, xt, device, nproj, P)
    257         print('xt_tmp dim :', xt2.shape)
    258         print('----------------------------------------')
--> 259         print(error)
    260         raise BadShapeError
    261 

BadShapeError: