def eigDecompose(laplacian, cuda, device=None):
if TORCH_EIGN:
if cuda:
if device is None:
device = torch.cuda.current_device()
laplacian = torch.from_numpy(laplacian).float().to(device)
else:
laplacian = torch.from_numpy(laplacian).float()
lambdas, diffusion_map = eigh(laplacian)
lambdas = lambdas.cpu().numpy()
diffusion_map = diffusion_map.cpu().numpy()
else:
lambdas, diffusion_map = eigh(laplacian)
return lambdas, diffusion_map
def eigDecompose(laplacian: torch.Tensor,
cuda: bool,
device: torch.device = torch.device('cpu')):
"""
Calculate eigenvalues and eigenvectors from the Laplacian matrix.
"""
if cuda:
if device is None:
device = torch.cuda.current_device()
laplacian = laplacian.float().to(device)
else:
laplacian = laplacian.float()
lambdas, diffusion_map = eigh(laplacian)
return lambdas, diffusion_map
def forward(self, input, image_sizes=None, targets=None):
features = self.backbone(input)
#print(features.shape)
features = features.view(features.size(0), -1)
matrix = (self.fc1(features.relu()))
matrix = self.fc2(matrix.relu())
A, B = self.fc3(matrix.relu()), self.fc4(matrix.relu())
A = A.view(A.size(0), self.size, self.size)
B = B.view(B.size(0), self.limit, self.limit)
#print(matrix)
A = torch.einsum('bcd,bde->bce', A, A)
B = torch.einsum('bcd,bde->bce', B, B)
#print([t.box.shape for t in targets])
if self.training:
boxes = [t.box/500 for t in targets if t.box.numel() > 0]
#print(boxes[0], boxes[0].shape)
labels = [F.one_hot(t.fields['labels'] - 1, self.config.n_class - 1).float()*3 for t in targets if t.box.numel() > 0]
#print(labels[0], labels[0].shape)
vectors = [torch.cat([b, l], -1)[:self.limit] for b, l in zip(boxes, labels)]
old_vectors = vectors
vectors = [self.enc2(self.enc1(v).relu()) for v in vectors]
rec_vectors = [self.dec2(self.dec1(v.relu()).relu()) for v in vectors]
loss_rec = torch.stack([self.crit(o,r) for o, r in zip(old_vectors, rec_vectors)], 0).mean()
#print(vectors[0], vectors[.shape)
del boxes, labels
svd = [LA.svd(m, full_matrices=False) for m in vectors]
d = A.device
#print([(u.shape, s.shape, vh.shape) for u, s, vh in svd])
#svd = [(u.to(d), s.to(d), vh.to(d), max(u.size(0), vh.size(0))) for u,s,vh in svd]
svd = [(pad(u, (self.limit, self.limit)), pad(torch.diag(s), (self.limit, self.limit)), pad(vh, (self.limit, self.limit))) for (u, s, vh) in svd]
#print([(vh.shape) for u, s, vh in svd])
U, P = zip(*[(u @ pad(vh, (self.limit, self.limit)), vh.transpose(0, 1) @ s @ vh) for u, s, vh in svd])
#print(vectors[0], '\n\n')
#print(U[0] @ P[0])
P = torch.stack(P, 0)
loss_herm = self.crit(A, P)
D = [U[i] @ B[i] @ U[i].transpose(0,1) for i in range(len(U))]
#print(D[0])
loss_unit = torch.stack([self.crit(d, torch.zeros_like(d)) / torch.diag(d).square().mean() for d in D], 0).mean()
#print(D[0])
#print(matrix[0, 0, 0, 0], P[0, 0, 0])
losses = {
'loss_herm': loss_herm,
'loss_unit': loss_unit,
'loss_rec': loss_rec
}
return None, losses
else:
#print(matrix.shape)
#print(P.shape, A.shape)
w, v = LA.eigh(B)
v = torch.einsum('bcd,bde->bce', v.transpose(-1, 2), A).abs()
v = v.view(-1, self.limit)
v = self.dec2(self.dec1(v.relu()).relu())
v = v.view(-1, self.limit, self.limit)
#print(v)
#print(w.sort(-1))
#print(v, '\n\n')
b = v[:, :, :4] * 500
l = v[:, :, :(self.config.n_class - 1)]
l = l.argmax(-1)
print(b.shape, l.shape)
print(b[0], l[0]+1) #print(b.shape, l.shape, w.shape)
boxes = []
for i in range(w.size(0)):
mybox = b[i][w[i] > 0]
mylabel = l[i][w[i] > 0]
myscores = w[i][w[i] > 0]
#print(mybox, mylabel)
#print(targets[i].box, targets[i].fields['labels'])
box = BoxList(mybox, image_sizes[i])
box.fields['labels'] = mylabel + 1
box.fields['scores'] = myscores
boxes.append(box)
return boxes, None