def save_clustering_results(self, i, kernel_stack, centroids, idx):
_, kh, kw = kernel_stack.size()
k_kernels = centroids.size(0)
savedir = '../experiment/{}/results'.format(args.save)
clustering.save_kernels(
centroids,
'{}/iter000_c{:0>2}.png'.format(savedir, i)
)
clustering.save_distribution(
idx,
'{}/distribution_{:0>2}.pdf'.format(savedir, i)
)
torch.save(
idx,
'{}/labels_{:0>2}.pt'.format(savedir, i)
)
# visualize full kernels and their centroids
if args.debug:
for t in range(k_kernels):
mask = (idx == t)
mask_idx = torch.arange(len(kernel_stack))
mask_idx = mask_idx.masked_select(mask).long()
ref = centroids[t].view(1, kh, kw)
collect = torch.cat((ref, kernel_stack[mask_idx]), dim=0)
clustering.save_kernels(
collect,
'{}/vis_c{:0>2}_{:0>4}.png'.format(savedir, i, t),
highlight=True
)
def save_clustering_results(self, i, kernel_stack, centroids, idx):
_, kh, kw = kernel_stack.size()
k_kernels = centroids.size(0)
savedir = os.path.join(args.dir_save, args.save,
'clustering_results')
# savedir = '/scratch_net/ofsoundof/yawli/srbasis/{}/clustering_results'.format(args.save)
if not os.path.exists(savedir):
os.makedirs(savedir)
clustering.save_kernels(
centroids, '{}/iter000_c{:0>2}.png'.format(savedir, i))
clustering.save_distribution(
idx, '{}/distribution_{:0>2}.pdf'.format(savedir, i))
torch.save(idx, '{}/labels_{:0>2}.pt'.format(savedir, i))
# visualize full kernels and their centroids
if args.debug:
for t in range(k_kernels):
mask = (idx == t)
mask_idx = torch.arange(len(kernel_stack))
# print('222222222222222222')
# embed()
mask_idx = mask_idx.masked_select(mask).long()
ref = centroids[t].view(1, kh, kw)
collect = torch.cat((ref, kernel_stack[mask_idx]), dim=0)
clustering.save_kernels(collect,
'{}/vis_c{:0>2}_{:0>4}.png'.format(
savedir, i, t),
highlight=True)
def save_results(self, epoch, m):
m = m.get_model()
if hasattr(m, 'split'):
from model import clustering
for i in set(v for v in m.split['map'].values()):
centroids = getattr(m, 'centroids_{}'.format(i), None)
if centroids is not None:
clustering.save_kernels(
centroids, '{}/results/iter{:0>3}_c{:0>2}.png'.format(
self.dir, epoch, i))
