def evaluate_finegan(self):
if cfg.TRAIN.NET_G == '':
print('Error: the path for model not found!')
else:
# Build and load the generator
netG = G_NET()
netG.apply(weights_init)
netG = torch.nn.DataParallel(netG, device_ids=self.gpus)
model_dict = netG.state_dict()
state_dict = \
torch.load(cfg.TRAIN.NET_G,
map_location=lambda storage, loc: storage)['birds128']
state_dict = {k: v for k, v in state_dict.items() if k in model_dict}
model_dict.update(state_dict)
netG.load_state_dict(model_dict)
print('Load ', cfg.TRAIN.NET_G)
# Uncomment this to print Generator layers
print(netG)
sys
nz = cfg.GAN.Z_DIM
noise = torch.FloatTensor(self.batch_size, nz)
noise.data.normal_(0, 1)
if cfg.CUDA:
netG.cuda()
noise = noise.cuda()
netG.eval()
background_class = cfg.TEST_BACKGROUND_CLASS
parent_class = cfg.TEST_PARENT_CLASS
child_class = cfg.TEST_CHILD_CLASS
bg_code = torch.zeros([self.batch_size, cfg.FINE_GRAINED_CATEGORIES])
p_code = torch.zeros([self.batch_size, cfg.SUPER_CATEGORIES])
c_code = torch.zeros([self.batch_size, cfg.FINE_GRAINED_CATEGORIES])
for j in range(self.batch_size):
bg_code[j][background_class] = 1
p_code[j][parent_class] = 1
c_code[j][child_class] = 1
fake_imgs, fg_imgs, mk_imgs, fgmk_imgs = netG(noise, c_code, p_code, bg_code) # Forward pass through the generator
self.save_image(fake_imgs[0][0], self.save_dir, 'background')
self.save_image(fake_imgs[1][0], self.save_dir, 'parent_final')
self.save_image(fake_imgs[2][0], self.save_dir, 'child_final')
self.save_image(fg_imgs[0][0], self.save_dir, 'parent_foreground')
self.save_image(fg_imgs[1][0], self.save_dir, 'child_foreground')
self.save_image(mk_imgs[0][0], self.save_dir, 'parent_mask')
self.save_image(mk_imgs[1][0], self.save_dir, 'child_mask')
self.save_image(fgmk_imgs[0][0], self.save_dir, 'parent_foreground_masked')
self.save_image(fgmk_imgs[1][0], self.save_dir, 'child_foreground_masked')
def sample_images(self):
sample_size = 24
save_dir = '../sample_images/'
save_final = '../sample_finals/'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if not os.path.exists(save_final):
os.makedirs(save_final)
random.seed(datetime.now())
depth = cfg.TEST_DEPTH
res = 32 * 2**depth
if cfg.TRAIN.NET_G == '':
print('Error: the path for model not found!')
else:
# Build and load the generator
netG = G_NET(depth)
netG.apply(weights_init)
netG = torch.nn.DataParallel(netG, device_ids=self.gpus)
model_dict = netG.state_dict()
state_dict = \
torch.load(cfg.TRAIN.NET_G,
map_location=lambda storage, loc: storage)
state_dict = {
k: v
for k, v in state_dict.items() if k in model_dict
}
model_dict.update(state_dict)
netG.load_state_dict(model_dict)
print('Load ', cfg.TRAIN.NET_G)
# Uncomment this to print Generator layers
# print(netG)
nz = cfg.GAN.Z_DIM
noise = torch.FloatTensor(1, nz)
# noise.data.normal_(0, 1)
# noise = noise.repeat(1, 1)
if cfg.CUDA:
netG.cuda()
noise = noise.cuda()
netG.eval()
for i in tqdm(range(sample_size)):
noise.data.normal_(0, 1)
bg_code = torch.zeros([1, cfg.FINE_GRAINED_CATEGORIES]).cuda()
p_code = torch.zeros([1, cfg.SUPER_CATEGORIES]).cuda()
c_code = torch.zeros([1, cfg.FINE_GRAINED_CATEGORIES]).cuda()
b = random.randint(0, cfg.FINE_GRAINED_CATEGORIES - 1)
p = random.randint(0, cfg.SUPER_CATEGORIES - 1)
c = random.randint(0, cfg.FINE_GRAINED_CATEGORIES - 1)
bg_code[0][b] = 1
p_code[0][p] = 1
c_code[0][c] = 1
fake_imgs, fg_imgs, mk_imgs, fgmk_imgs = netG(
noise, c_code, 1, p_code,
bg_code) # Forward pass through the generator
self.save_image(fake_imgs[3 * depth + 0][0], save_dir,
'%d_bg' % i)
self.save_image(fake_imgs[3 * depth + 1][0], save_dir,
'%d_pf' % i)
self.save_image(fake_imgs[3 * depth + 2][0], save_dir,
'%d_cf' % i)
self.save_image(fake_imgs[3 * depth + 2][0], save_final,
'%d' % i)
# self.save_image(fg_imgs[2 * depth + 0][0], save_dir, 'parent_foreground')
# self.save_image(fg_imgs[2 * depth + 1][0], save_dir, 'child_foreground')
self.save_image(mk_imgs[2 * depth + 0][0], save_dir,
'%d_pmk' % i)
self.save_image(mk_imgs[2 * depth + 1][0], save_dir,
'%d_cmk' % i)
def evaluate_finegan(self):
self.save_dir = os.path.join(cfg.SAVE_DIR, 'images')
mkdir_p(self.save_dir)
random.seed(datetime.now())
depth = cfg.TEST_DEPTH
res = 32 * 2**depth
if cfg.TRAIN.NET_G == '':
print('Error: the path for model not found!')
else:
# Build and load the generator
netG = G_NET(depth)
netG.apply(weights_init)
netG = torch.nn.DataParallel(netG, device_ids=self.gpus)
model_dict = netG.state_dict()
state_dict = \
torch.load(cfg.TRAIN.NET_G,
map_location=lambda storage, loc: storage)
state_dict = {
k: v
for k, v in state_dict.items() if k in model_dict
}
model_dict.update(state_dict)
netG.load_state_dict(model_dict)
print('Load ', cfg.TRAIN.NET_G)
# Uncomment this to print Generator layers
# print(netG)
nrow = 6
ncol = 4
z_std = 0.1
p_vs_c = False
reprod = False
if not reprod:
torch.manual_seed(random.randint(-9999, 9999))
bg_li = []
pf_li = []
cf_li = []
pk_li = []
ck_li = []
pfg_li = []
cfg_li = []
pfgmk_li = []
cfgmk_li = []
b = random.randint(0, cfg.FINE_GRAINED_CATEGORIES - 1)
nz = cfg.GAN.Z_DIM
noise = torch.FloatTensor(1, nz)
noise.data.normal_(0, z_std)
# noise = noise.repeat(self.batch_size, 1)
if cfg.CUDA:
netG.cuda()
noise = noise.cuda()
netG.eval()
c_li = np.random.randint(0,
cfg.FINE_GRAINED_CATEGORIES - 1,
size=nrow)
p_li = np.random.randint(0, cfg.SUPER_CATEGORIES - 1, size=nrow)
for k in range(ncol):
p = p_li[k]
# p = random.randint(0, cfg.SUPER_CATEGORIES-1)
for i in range(nrow):
bg_code = torch.zeros(
[self.batch_size, cfg.FINE_GRAINED_CATEGORIES])
p_code = torch.zeros(
[self.batch_size, cfg.SUPER_CATEGORIES])
c_code = torch.zeros(
[self.batch_size, cfg.FINE_GRAINED_CATEGORIES])
c = c_li[i]
for j in range(self.batch_size):
bg_code[j][b] = 1
p_code[j][p] = 1
c_code[j][c] = 1
fake_imgs, fg_imgs, mk_imgs, fgmk_imgs = netG(
noise, c_code, None, p_code,
bg_code) # Forward pass through the generator
bg_li.append(fake_imgs[3 * depth][0])
pf_li.append(fake_imgs[3 * depth + 1][0])
cf_li.append(fake_imgs[3 * depth + 2][0])
pk_li.append(mk_imgs[2 * depth][0])
ck_li.append(mk_imgs[2 * depth + 1][0])
pfg_li.append(fg_imgs[2 * depth][0])
cfg_li.append(fg_imgs[2 * depth + 1][0])
pfgmk_li.append(fgmk_imgs[2 * depth][0])
cfgmk_li.append(fgmk_imgs[2 * depth + 1][0])
save_image(bg_li, self.save_dir, 'background_pvc', nrow, res)
save_image(pf_li, self.save_dir, 'parent_final_pvc', nrow, res)
save_image(cf_li, self.save_dir, 'child_final_pvc', nrow, res)
save_image(pfg_li, self.save_dir, 'parent_foreground_pvc', nrow,
res)
save_image(cfg_li, self.save_dir, 'child_foreground_pvc', nrow,
res)
save_image(pk_li, self.save_dir, 'parent_mask_pvc', nrow, res)
save_image(ck_li, self.save_dir, 'child_mask_pvc', nrow, res)
save_image(pfgmk_li, self.save_dir, 'parent_foreground_masked_pvc',
nrow, res)
save_image(cfgmk_li, self.save_dir, 'child_foreground_masked_pvc',
nrow, res)
bg_li = []
pf_li = []
cf_li = []
pk_li = []
ck_li = []
pfg_li = []
cfg_li = []
pfgmk_li = []
cfgmk_li = []
for _ in range(ncol):
noise.data.normal_(0, z_std)
for i in range(nrow):
bg_code = torch.zeros(
[self.batch_size, cfg.FINE_GRAINED_CATEGORIES])
p_code = torch.zeros(
[self.batch_size, cfg.SUPER_CATEGORIES])
c_code = torch.zeros(
[self.batch_size, cfg.FINE_GRAINED_CATEGORIES])
c = c_li[i]
p = p_li[i]
for j in range(self.batch_size):
bg_code[j][b] = 1
p_code[j][p] = 1
c_code[j][c] = 1
fake_imgs, fg_imgs, mk_imgs, fgmk_imgs = netG(
noise, c_code, None, p_code,
bg_code) # Forward pass through the generator
bg_li.append(fake_imgs[3 * depth][0])
pf_li.append(fake_imgs[3 * depth + 1][0])
cf_li.append(fake_imgs[3 * depth + 2][0])
pk_li.append(mk_imgs[2 * depth][0])
ck_li.append(mk_imgs[2 * depth + 1][0])
pfg_li.append(fg_imgs[2 * depth][0])
cfg_li.append(fg_imgs[2 * depth + 1][0])
pfgmk_li.append(fgmk_imgs[2 * depth][0])
cfgmk_li.append(fgmk_imgs[2 * depth + 1][0])
save_image(bg_li, self.save_dir, 'background_zvpc', nrow, res)
save_image(pf_li, self.save_dir, 'parent_final_zvpc', nrow, res)
save_image(cf_li, self.save_dir, 'child_final_zvpc', nrow, res)
save_image(pfg_li, self.save_dir, 'parent_foreground_zvpc', nrow,
res)
save_image(cfg_li, self.save_dir, 'child_foreground_zvpc', nrow,
res)
save_image(pk_li, self.save_dir, 'parent_mask_zvpc', nrow, res)
save_image(ck_li, self.save_dir, 'child_mask_zvpc', nrow, res)
save_image(pfgmk_li, self.save_dir,
'parent_foreground_masked_zvpc', nrow, res)
save_image(cfgmk_li, self.save_dir, 'child_foreground_masked_zvpc',
nrow, res)
