for i, att_tar in enumerate(att_list):
if i > 0:
att_diff = att_tar - att_org
samples.append(attgan.G(img_real, att_diff))
samples = torch.cat(samples, dim=3)
out_file = '{:06d}.jpg'.format(idx)
vutils.save_image(samples,
join(output_path, out_file),
nrow=1,
normalize=True,
range=(-1., 1.))
print('{:s} done!'.format(out_file))
elif args_.use_model == 'discriminator':
with torch.no_grad():
result = img_unit
abn_att_real, cabn_att_real = attgan.D(img_real, 'abn')
for i in range(len(att_list) - 1):
abn_att = F.interpolate(abn_att_real[i],
size=(mw, mh),
mode='bilinear',
align_corners=True)
cabn_att = F.interpolate(cabn_att_real[i],
size=(mw, mh),
mode='bilinear',
align_corners=True)
abn_att = (abn_att - abn_att.min()) / (abn_att.max() -
abn_att.min())
cabn_att = (cabn_att - cabn_att.min()) / (cabn_att.max() -
cabn_att.min())
abn_att = np.uint8(abn_att.view(mw, mh) * 255)
'Face Attribute Manipulation') / 10
att_b[0][1] = cv2.getTrackbarPos('Bangs',
'Face Attribute Manipulation') / 10
att_b[0][2] = cv2.getTrackbarPos('Black_Hair',
'Face Attribute Manipulation') / 10
att_b[0][3] = cv2.getTrackbarPos('Blond_Hair',
'Face Attribute Manipulation') / 10
att_b[0][4] = cv2.getTrackbarPos('Brown_Hair',
'Face Attribute Manipulation') / 10
att_b[0][5] = cv2.getTrackbarPos('Bushy_Eyebrows',
'Face Attribute Manipulation') / 10
att_b[0][6] = cv2.getTrackbarPos('Eyeglasses',
'Face Attribute Manipulation') / 10
att_b[0][7] = cv2.getTrackbarPos('Male',
'Face Attribute Manipulation') / 10
att_b[0][8] = cv2.getTrackbarPos('Mouth_Slightly_Open',
'Face Attribute Manipulation') / 10
att_b[0][9] = cv2.getTrackbarPos('Mustache',
'Face Attribute Manipulation') / 10
att_b[0][10] = cv2.getTrackbarPos('No_Beard',
'Face Attribute Manipulation') / 10
att_b[0][11] = cv2.getTrackbarPos('Pale_Skin',
'Face Attribute Manipulation') / 10
att_b[0][12] = cv2.getTrackbarPos('Young',
'Face Attribute Manipulation') / 10
with torch.no_grad():
att_b_ = (att_b * 2 - 1) * args.thres_int
img_a = attgan.G(img_ori, att_b_)
_, att_test = attgan.D(img_a)
