def visualizeSingleBatch(fp_loader_test, opt):
with torch.no_grad():
# Unpack batch
mks, nds, eds, nd_to_sample, ed_to_sample = next(iter(fp_loader_test))
real_mks = Variable(mks.type(Tensor))
given_nds = Variable(nds.type(Tensor))
given_eds = eds
# Generate a batch of images
z_shape = [real_mks.shape[0], opt.latent_dim]
z = Variable(Tensor(np.random.normal(0, 1, tuple(z_shape))))
gen_mks = generator(z, given_nds, given_eds)
# Generate image tensors
real_imgs_tensor = combine_images_maps(real_mks, given_nds, given_eds, \
nd_to_sample, ed_to_sample)
fake_imgs_tensor = combine_images_maps(gen_mks, given_nds, given_eds, \
nd_to_sample, ed_to_sample)
# Save images
save_image(real_imgs_tensor, "./exps/{}/{}_real.png".format(exp_folder, batches_done), \
nrow=12, normalize=False)
save_image(fake_imgs_tensor, "./exps/{}/{}_fake.png".format(exp_folder, batches_done), \
nrow=12, normalize=False)
return
def visualizeSingleBatch(fp_loader_test, opt,):
with torch.no_grad():
# Unpack batch
mks, nds, eds, nd_to_sample, ed_to_sample = next(iter(fp_loader_test))
real_mks = Variable(mks.type(Tensor))
given_nds = Variable(nds.type(Tensor))
given_eds = eds
# Generate a batch of images
z_shape = [real_mks.shape[0], opt.latent_dim]
z = Variable(Tensor(np.random.normal(0, 1, tuple(z_shape))))
gen_mks = generator(z, given_nds, given_eds)
# Generate image tensors
real_imgs_tensor = combine_images_maps(real_mks, given_nds, given_eds, \
nd_to_sample, ed_to_sample)
fake_imgs_tensor = combine_images_maps(gen_mks, given_nds, given_eds, \
nd_to_sample, ed_to_sample)
#iou_norm,giou_norm = compute_iou_list(real_mks,given_y,given_w,nd_to_sample,ed_to_sample,'valid')
# Save images
#np.save()
save_image(real_imgs_tensor, "./exps/{}/{}_real.png".format(exp_folder, batches_done), \
nrow=12, normalize=False)
save_image(fake_imgs_tensor, "./exps/{}/{}_fake.png".format(exp_folder, batches_done), \
nrow=12, normalize=False)
extracted_room_stats = [gen_mks.detach().cpu().numpy(),real_mks.detach().cpu().numpy(), given_nds.detach().cpu().numpy(), given_eds.detach().cpu().numpy(), \
nd_to_sample.detach().cpu().numpy(), ed_to_sample.detach().cpu().numpy()]
np.save('./tracking/vaild_stats_'+exp_folder+str(batches_done)+'.npy',extracted_room_stats)
def visualizeSingleBatch(fp_loader_test,
opt,
exp_folder,
batches_done,
batch_size=8):
print('Loading saved model ... \n{}'.format(
'./checkpoints/{}_{}.pth'.format(exp_folder, batches_done)))
generatorTest = Generator()
generatorTest.load_state_dict(
torch.load('./checkpoints/{}_{}.pth'.format(exp_folder, batches_done)))
generatorTest = generatorTest.eval()
generatorTest.cuda()
with torch.no_grad():
# Unpack batch
mks, nds, eds, nd_to_sample, ed_to_sample = next(iter(fp_loader_test))
real_mks = Variable(mks.type(Tensor))
given_nds = Variable(nds.type(Tensor))
given_eds = eds
# Generate a batch of images
z_shape = [real_mks.shape[0], opt.latent_dim]
z = Variable(Tensor(np.random.normal(0, 1, tuple(z_shape))))
# Select random nodes
ind_fixed_nodes, bin_fixed_nodes = selectNodesTypes(
nd_to_sample, batch_size, nds)
# Generate a batch of images
given_masks = torch.tensor(real_mks)
ind_not_fixed_nodes = torch.tensor([
k for k in range(given_masks.shape[0]) if k not in ind_fixed_nodes
])
## Set non fixed masks to -1.0
given_masks[ind_not_fixed_nodes.long()] = -1.0
given_masks = given_masks.unsqueeze(1)
## Add channel to indicate given nodes
inds_masks = torch.zeros_like(given_masks)
inds_masks[ind_not_fixed_nodes.long()] = 0.0
inds_masks[ind_fixed_nodes.long()] = 1.0
given_masks = torch.cat([given_masks, inds_masks], 1)
gen_mks = generatorTest(z, None, given_masks, given_nds, given_eds)
# Generate image tensors
real_imgs_tensor = combine_images_maps(real_mks, given_nds, given_eds, \
nd_to_sample, ed_to_sample)
fake_imgs_tensor = combine_images_maps(gen_mks, given_nds, given_eds, \
nd_to_sample, ed_to_sample)
# Save images
save_image(real_imgs_tensor, "./exps/{}/{}_real.png".format(exp_folder, batches_done), \
nrow=12, normalize=False)
save_image(fake_imgs_tensor, "./exps/{}/{}_fake.png".format(exp_folder, batches_done), \
nrow=12, normalize=False)
return
def visualizeBatch(real_mks,gen_mks,given_nds,given_eds,nd_to_sample,ed_to_sample):
with torch.no_grad():
imgs_tensor = combine_images_maps(gen_mks, given_nds, given_eds, \
nd_to_sample, ed_to_sample)
save_image(imgs_tensor,"./exps/{}/{}_train_gen.png".format(exp_folder, batches_done), \
nrow=12, normalize=False)
imgs_tensor = combine_images_maps(real_mks, given_nds, given_eds, \
nd_to_sample, ed_to_sample)
save_image(imgs_tensor,"./exps/{}/{}_train_real.png".format(exp_folder, batches_done), \
nrow=12, normalize=False)
extracted_room_stats = [gen_mks.detach().cpu().numpy(),real_mks.detach().cpu().numpy(), given_nds.detach().cpu().numpy(), given_eds.detach().cpu().numpy(), \
nd_to_sample.detach().cpu().numpy(), ed_to_sample.detach().cpu().numpy()]
np.save('./tracking/train_stats_'+exp_folder+str(batches_done)+'.npy',extracted_room_stats)
return
num_workers=0,
collate_fn=floorplan_collate_fn)
fp_iter = tqdm(fp_loader, total=len(fp_dataset) // opt.batch_size + 1)
# Generate samples
cuda = False #True if torch.cuda.is_available() else False
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
graphs = []
for i, batch in enumerate(fp_loader): #len(fp_iter) = all_samples/batch_size
# Unpack batch
mks, nds, eds, nd_to_sample, ed_to_sample = batch #mks is all single batch sampls combined, 39 = sampl_1.len[=2] + sampl_2.len[=2]+...+sampl_25.len
real_mks = Variable(mks.type(Tensor))
given_nds = Variable(nds.type(Tensor))
given_eds = eds
real_imgs_tensor = combine_images_maps(real_mks, given_nds, given_eds, \
nd_to_sample, ed_to_sample)
save_image(real_imgs_tensor, "/Users/home/Dissertation/Code/housegan/{}/{}_real.png".format("stats/", i), \
nrow=12, normalize=False)
exit()
# real_nodes = np.where(nds.detach().cpu()==1)[-1]
# graphs.append(len(real_nodes))
# samples_per_len = defaultdict(int)
# for g_len in graphs:
# samples_per_len[g_len] += 1
# print(samples_per_len)