vis_samples = [None] * nb_interp
for x in range(nb_interp):
column = np.empty(shape=(nb_interp, 3, 64, 64), dtype=np.float32)
for y in range(nb_interp):
# 3D interpolation
cx = x / (nb_interp)
cy = y / (nb_interp)
cz = z / (nb_interp)
z_mv1 = n1a * (1 - cx) + n1b * (cx)
z_mv2 = n2a * (1 - cy) + n2b * (cy)
z_mv3 = n3a * (1 - cz) + n3b * (cz)
# get sample
f_image, _ = netG(z_list=[z_mv1, z_mv2, z_mv3])
np_fake = to_numpy(f_image)
column[y] = np_fake
vis_samples[x] = column
# save images
png_file = '3Dinterp__text=%dto%d__shape=%dto%d__background=%dto%d-%d.png' % \
(mv1_ida, mv1_idb, mv2_ida, mv2_idb, mv3_ida, mv3_idb, z)
save_images(vis_samples,
save=not sample_folder == '',
save_path=os.path.join(sample_folder, png_file),
dim_ordering='th')
print('Images saved at %s' % sample_folder)
netEb = netEb.cuda()
# # get batch of test samples
images, _, segs, txt_data, txt_len, captions, *_ = next(iter(dataloader))
images = images.cuda()
segs = segs.cuda()
txt_data = txt_data.cuda()
bimages = images
netG.eval()
netEs.eval()
netEb.eval()
# bimages = roll(images, i+1, dim=0) # for text and seg mismatched backgrounds
np_bimages = to_numpy(bimages)
np_segs = np.repeat(to_numpy(segs), 3, 1) * 2 - 1
n = 4
vis_samples = [None for i in range(1 + n)]
vis_samples[0] = np_segs
for i in range(n):
# generate testing results
segs_code = netEs(segs)
bkgs_code = netEb(bimages[i].unsqueeze(0).repeat(batch_size, 1, 1, 1))
*_, f_images, z_list = netG(txt_data, txt_len, segs_code, bkgs_code)
np_fakes = to_numpy(f_images)
# alignment
if align == 'shape':
bimages = roll(images, 2, dim=0) # for text and seg mismatched backgrounds
bsegs = roll(segs, 2, dim=0) # for text and seg mismatched backgrounds
elif align == 'background':
segs = roll(segs, 1, dim=0) # for text mismatched segmentations
elif align == 'all':
bimages = images.clone()
bsegs = segs.clone()
elif align == 'none':
bimages = roll(images, 2, dim=0) # for text and seg mismatched backgrounds
segs = roll(segs, 1, dim=0) # for text mismatched segmentations
bsegs = roll(segs, 2, dim=0) # for text and seg mismatched backgrounds
np_segs = to_numpy(segs)
np_bsegs = to_numpy(bsegs)
np_images = to_numpy(images)
np_bimages = to_numpy(bimages)
segs_code = netEs(segs)
bkgs_code = netEb(bimages)
*_, f_images, z_list = netG(txt_data, txt_len, segs_code, bkgs_code)
np_fakes = to_numpy(f_images)
for x, b, s, sb in zip(np_fakes, np_bimages, np_segs, np_bsegs):
x = (x.transpose(1,2,0) + 1)/2. * 255.
b = (b.transpose(1,2,0) + 1)/2. * 255.
s = s.transpose(1,2,0)