netd = NetD(nc=1)
neta = NetA(nc=1)
device = th.device("cuda:0")
netg = netg.to(device)
netd = netd.to(device)
neta = neta.to(device)
fineSize = 64
checkpoint = '/home/mg/code/my_GAN_dataSet/snapshots/snapshot_449.t7'
checkpoint = th.load(checkpoint)
neta.load_state_dict(checkpoint['netA'])
netg.load_state_dict(checkpoint['netG'])
netd.load_state_dict(checkpoint['netD'])
neta.eval()
netg.eval()
netd.eval()
angles = [
'000', '018', '036', '054', '072', '090', '108', '126', '144', '162', '180'
]
for cond in ['nm-01', 'nm-02', 'nm-03', 'nm-04', 'cl-01', 'cl-02']:
dataset = CASIABDatasetGenerate(
data_dir='/home/mg/code/data/GEI_CASIA_B/gei/', cond=cond)
for i in range(1, 125):
ass_label, img = dataset.getbatch(i, 11)
img = img.to(device).to(th.float32)
with th.no_grad():
fake = netg(img)
def main(args):
# manualSeed to control the noise
manualSeed = 100
random.seed(manualSeed)
np.random.seed(manualSeed)
torch.manual_seed(manualSeed)
with open(args.json_file, 'r') as f:
dataset_json = json.load(f)
# load rnn encoder
text_encoder = RNN_ENCODER(dataset_json['n_words'], nhidden=dataset_json['text_embed_dim'])
text_encoder_dir = args.rnn_encoder
state_dict = torch.load(text_encoder_dir, map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
# load netG
state_dict = torch.load(args.model_path, map_location=torch.device('cpu'))
# netG = NetG(int(dataset_json['n_channels']), int(dataset_json['cond_dim']))
netG = NetG(64, int(dataset_json['cond_dim']))
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`nvidia
new_state_dict[name] = v
model_dict = netG.state_dict()
pretrained_dict = {k: v for k, v in new_state_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
netG.load_state_dict(model_dict)
# use gpu or not, change model to evaluation mode
if args.use_gpu:
text_encoder.cuda()
netG.cuda()
caption_idx.cuda()
caption_len.cuda()
noise.cuda()
text_encoder.eval()
netG.eval()
# generate noise
num_noise = 100
noise = torch.FloatTensor(num_noise, 100)
# cub bird captions
# caption = 'this small bird has a light yellow breast and brown wings'
# caption = 'this small bird has a short beak a light gray breast a darker gray crown and black wing tips'
# caption = 'this small bird has wings that are gray and has a white belly'
# caption = 'this bird has a yellow throat belly abdomen and sides with lots of brown streaks on them'
# caption = 'this little bird has a yellow belly and breast with a gray wing with white wingbars'
# caption = 'this bird has a white belly and breast wit ha blue crown and nape'
# caption = 'a bird with brown and black wings red crown and throat and the bill is short and pointed'
# caption = 'this small bird has a yellow crown and a white belly'
# caption = 'this bird has a blue crown with white throat and brown secondaries'
# caption = 'this bird has wings that are black and has a white belly'
# caption = 'a yellow bird has wings with dark stripes and small eyes'
# caption = 'a black bird has wings with dark stripes and small eyes'
# caption = 'a red bird has wings with dark stripes and small eyes'
# caption = 'a white bird has wings with dark stripes and small eyes'
# caption = 'a blue bird has wings with dark stripes and small eyes'
# caption = 'a pink bird has wings with dark stripes and small eyes'
# caption = 'this is a white and grey bird with black wings and a black stripe by its eyes'
# caption = 'a small bird with an orange bill and grey crown and breast'
# caption = 'a small bird with black gray and white wingbars'
# caption = 'this bird is white and light orange in color with a black beak'
# caption = 'a small sized bird that has tones of brown and a short pointed bill' # beak?
# MS coco captions
# caption = 'two men skiing down a snow covered mountain in the evening'
# caption = 'a man walking down a grass covered mountain'
# caption = 'a close up of a boat on a field under a sunset'
# caption = 'a close up of a boat on a field with a clear sky'
# caption = 'a herd of black and white cattle standing on a field'
# caption = 'a herd of black and white sheep standing on a field'
# caption = 'a herd of black and white dogs standing on a field'
# caption = 'a herd of brown cattle standing on a field'
# caption = 'a herd of black and white cattle standing in a river'
# caption = 'some horses in a field of green grass with a sky in the background'
# caption = 'some horses in a field of yellow grass with a sky in the background'
caption = 'some horses in a field of green grass with a sunset in the background'
# convert caption to index
caption_idx, caption_len = get_caption_idx(dataset_json, caption)
caption_idx = torch.LongTensor(caption_idx)
caption_len = torch.LongTensor([caption_len])
caption_idx = caption_idx.view(1, -1)
caption_len = caption_len.view(-1)
# use rnn encoder to get caption embedding
hidden = text_encoder.init_hidden(1)
words_embs, sent_emb = text_encoder(caption_idx, caption_len, hidden)
# generate fake image
noise.data.normal_(0, 1)
sent_emb = sent_emb.repeat(num_noise, 1)
words_embs = words_embs.repeat(num_noise, 1, 1)
with torch.no_grad():
fake_imgs, fusion_mask = netG(noise, sent_emb)
# create path to save image, caption and mask
cap_number = 10000
main_path = 'result/mani/cap_%s_0_coco_ch64' % (str(cap_number))
img_save_path = '%s/image' % main_path
mask_save_path = '%s/mask_' % main_path
mkdir_p(img_save_path)
for i in range(7):
mkdir_p(mask_save_path + str(i))
# save caption as image
ixtoword = {v: k for k, v in dataset_json['word2idx'].items()}
cap_img = cap2img(ixtoword, caption_idx, caption_len)
im = cap_img[0].data.cpu().numpy()
im = (im + 1.0) * 127.5
im = im.astype(np.uint8)
im = np.transpose(im, (1, 2, 0))
im = Image.fromarray(im)
full_path = '%s/caption.png' % main_path
im.save(full_path)
# save generated images and masks
for i in tqdm(range(num_noise)):
full_path = '%s/image_%d.png' % (img_save_path, i)
im = fake_imgs[i].data.cpu().numpy()
im = (im + 1.0) * 127.5
im = im.astype(np.uint8)
im = np.transpose(im, (1, 2, 0))
im = Image.fromarray(im)
im.save(full_path)
for j in range(7):
full_path = '%s%1d/mask_%d.png' % (mask_save_path, j, i)
im = fusion_mask[j][i][0].data.cpu().numpy()
im = im * 255
im = im.astype(np.uint8)
im = Image.fromarray(im)
im.save(full_path)
def train_network():
init_epoch = 0
best_f1 = 0
total_steps = 0
train_dir = ct.TRAIN_TXT
val_dir = ct.VAL_TXT
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.backends.cudnn.benchmark = True
train_data = OSCD_TRAIN(train_dir)
train_dataloader = DataLoader(train_data,
batch_size=ct.BATCH_SIZE,
shuffle=True)
val_data = OSCD_TEST(val_dir)
val_dataloader = DataLoader(val_data, batch_size=1, shuffle=False)
netg = NetG(ct.ISIZE, ct.NC * 2, ct.NZ, ct.NDF,
ct.EXTRALAYERS).to(device=device)
netd = NetD(ct.ISIZE, ct.GT_C, 1, ct.NGF, ct.EXTRALAYERS).to(device=device)
netg.apply(weights_init)
netd.apply(weights_init)
if ct.RESUME:
assert os.path.exists(os.path.join(ct.WEIGHTS_SAVE_DIR, 'current_netG.pth')) \
and os.path.exists(os.path.join(ct.WEIGHTS_SAVE_DIR, 'current_netG.pth')), \
'There is not found any saved weights'
print("\nLoading pre-trained networks.")
init_epoch = torch.load(
os.path.join(ct.WEIGHTS_SAVE_DIR, 'current_netG.pth'))['epoch']
netg.load_state_dict(
torch.load(os.path.join(ct.WEIGHTS_SAVE_DIR,
'current_netG.pth'))['model_state_dict'])
netd.load_state_dict(
torch.load(os.path.join(ct.WEIGHTS_SAVE_DIR,
'current_netD.pth'))['model_state_dict'])
with open(os.path.join(ct.OUTPUTS_DIR, 'f1_score.txt')) as f:
lines = f.readlines()
best_f1 = float(lines[-2].strip().split(':')[-1])
print("\tDone.\n")
l_adv = l2_loss
l_con = nn.L1Loss()
l_enc = l2_loss
l_bce = nn.BCELoss()
l_cos = cos_loss
dice = DiceLoss()
optimizer_d = optim.Adam(netd.parameters(), lr=ct.LR, betas=(0.5, 0.999))
optimizer_g = optim.Adam(netg.parameters(), lr=ct.LR, betas=(0.5, 0.999))
start_time = time.time()
for epoch in range(init_epoch + 1, ct.EPOCH):
loss_g = []
loss_d = []
netg.train()
netd.train()
epoch_iter = 0
for i, data in enumerate(train_dataloader):
INPUT_SIZE = [ct.ISIZE, ct.ISIZE]
x1, x2, gt = data
x1 = x1.to(device, dtype=torch.float)
x2 = x2.to(device, dtype=torch.float)
gt = gt.to(device, dtype=torch.float)
gt = gt[:, 0, :, :].unsqueeze(1)
x = torch.cat((x1, x2), 1)
epoch_iter += ct.BATCH_SIZE
total_steps += ct.BATCH_SIZE
real_label = torch.ones(size=(x1.shape[0], ),
dtype=torch.float32,
device=device)
fake_label = torch.zeros(size=(x1.shape[0], ),
dtype=torch.float32,
device=device)
#forward
fake = netg(x)
pred_real = netd(gt)
pred_fake = netd(fake).detach()
err_d_fake = l_bce(pred_fake, fake_label)
err_g = l_con(fake, gt)
err_g_total = ct.G_WEIGHT * err_g + ct.D_WEIGHT * err_d_fake
pred_fake_ = netd(fake.detach())
err_d_real = l_bce(pred_real, real_label)
err_d_fake_ = l_bce(pred_fake_, fake_label)
err_d_total = (err_d_real + err_d_fake_) * 0.5
#backward
optimizer_g.zero_grad()
err_g_total.backward(retain_graph=True)
optimizer_g.step()
optimizer_d.zero_grad()
err_d_total.backward()
optimizer_d.step()
errors = utils.get_errors(err_d_total, err_g_total)
loss_g.append(err_g_total.item())
loss_d.append(err_d_total.item())
counter_ratio = float(epoch_iter) / len(train_dataloader.dataset)
if (i % ct.DISPOLAY_STEP == 0 and i > 0):
print(
'epoch:', epoch, 'iteration:', i,
' G|D loss is {}|{}'.format(np.mean(loss_g[-51:]),
np.mean(loss_d[-51:])))
if ct.DISPLAY:
utils.plot_current_errors(epoch, counter_ratio, errors,
vis)
utils.display_current_images(gt.data, fake.data, vis)
utils.save_current_images(epoch, gt.data, fake.data, ct.IM_SAVE_DIR,
'training_output_images')
with open(os.path.join(ct.OUTPUTS_DIR, 'train_loss.txt'), 'a') as f:
f.write(
'after %s epoch, loss is %g,loss1 is %g,loss2 is %g,loss3 is %g'
% (epoch, np.mean(loss_g), np.mean(loss_d), np.mean(loss_g),
np.mean(loss_d)))
f.write('\n')
if not os.path.exists(ct.WEIGHTS_SAVE_DIR):
os.makedirs(ct.WEIGHTS_SAVE_DIR)
utils.save_weights(epoch, netg, optimizer_g, ct.WEIGHTS_SAVE_DIR,
'netG')
utils.save_weights(epoch, netd, optimizer_d, ct.WEIGHTS_SAVE_DIR,
'netD')
duration = time.time() - start_time
print('training duration is %g' % duration)
#val phase
print('Validating.................')
pretrained_dict = torch.load(
os.path.join(ct.WEIGHTS_SAVE_DIR,
'current_netG.pth'))['model_state_dict']
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net = NetG(ct.ISIZE, ct.NC * 2, ct.NZ, ct.NDF,
ct.EXTRALAYERS).to(device=device)
net.load_state_dict(pretrained_dict, False)
with net.eval() and torch.no_grad():
TP = 0
FN = 0
FP = 0
TN = 0
for k, data in enumerate(val_dataloader):
x1, x2, label = data
x1 = x1.to(device, dtype=torch.float)
x2 = x2.to(device, dtype=torch.float)
label = label.to(device, dtype=torch.float)
label = label[:, 0, :, :].unsqueeze(1)
x = torch.cat((x1, x2), 1)
time_i = time.time()
v_fake = net(x)
tp, fp, tn, fn = eva.f1(v_fake, label)
TP += tp
FN += fn
TN += tn
FP += fp
precision = TP / (TP + FP + 1e-8)
oa = (TP + TN) / (TP + FN + TN + FP + 1e-8)
recall = TP / (TP + FN + 1e-8)
f1 = 2 * precision * recall / (precision + recall + 1e-8)
if not os.path.exists(ct.BEST_WEIGHT_SAVE_DIR):
os.makedirs(ct.BEST_WEIGHT_SAVE_DIR)
if f1 > best_f1:
best_f1 = f1
shutil.copy(
os.path.join(ct.WEIGHTS_SAVE_DIR, 'current_netG.pth'),
os.path.join(ct.BEST_WEIGHT_SAVE_DIR, 'netG.pth'))
print('current F1: {}'.format(f1))
print('best f1: {}'.format(best_f1))
with open(os.path.join(ct.OUTPUTS_DIR, 'f1_score.txt'), 'a') as f:
f.write('current epoch:{},current f1:{},best f1:{}'.format(
epoch, f1, best_f1))
f.write('\n')
class AnoGAN:
"""AnoGAN Class
"""
def __init__(self, opt):
# super(AnoGAN, self).__init__(opt, dataloader)
# Initalize variables.
self.opt = opt
self.niter = self.opt.niter
self.start_iter = 0
self.netd_niter = 5
self.test_iter = 100
self.lr = self.opt.lr
self.batchsize = {'train': self.opt.batchsize, 'test': 1}
self.pretrained = False
self.phase = 'train'
self.outf = self.opt.experiment_group
self.algorithm = 'wgan'
# LOAD DATA SET
self.dataloader = {
'train':
provider('train',
opt.category,
batch_size=self.batchsize['train'],
num_workers=4),
'test':
provider('test',
opt.category,
batch_size=self.batchsize['test'],
num_workers=4)
}
self.trn_dir = os.path.join(self.outf, self.opt.experiment_name,
'train')
self.tst_dir = os.path.join(self.outf, self.opt.experiment_name,
'test')
self.test_img_dir = os.path.join(self.outf, self.opt.experiment_name,
'test', 'images')
if not os.path.isdir(self.test_img_dir):
os.makedirs(self.test_img_dir)
self.best_test_dir = os.path.join(self.outf, self.opt.experiment_name,
'test', 'best_images')
if not os.path.isdir(self.best_test_dir):
os.makedirs(self.best_test_dir)
self.weight_dir = os.path.join(self.trn_dir, 'weights')
if not os.path.exists(self.weight_dir): os.makedirs(self.weight_dir)
# -- Misc attributes
self.epoch = 0
self.l_con = l1_loss
self.l_enc = l2_loss
##
# Create and initialize networks.
self.netg = NetG().cuda()
self.netd = NetD().cuda()
# Setup optimizer
self.optimizer_d = optim.RMSprop(self.netd.parameters(), lr=self.lr)
self.optimizer_g = optim.Adam(self.netg.parameters(), lr=self.lr)
##
self.weight_path = os.path.join(self.outf, self.opt.experiment_name,
'train', 'weights')
if os.path.exists(self.weight_path) and len(
os.listdir(self.weight_path)) == 2:
print("Loading pre-trained networks...\n")
self.netg.load_state_dict(
torch.load(os.path.join(self.weight_path,
'netG.pth'))['state_dict'])
self.netd.load_state_dict(
torch.load(os.path.join(self.weight_path,
'netD.pth'))['state_dict'])
self.optimizer_g.load_state_dict(
torch.load(os.path.join(self.weight_path,
'netG.pth'))['optimizer'])
self.optimizer_d.load_state_dict(
torch.load(os.path.join(self.weight_path,
'netD.pth'))['optimizer'])
self.start_iter = torch.load(
os.path.join(self.weight_path, 'netG.pth'))['epoch']
##
def start(self):
""" Train the model
"""
##
# TRAIN
# self.total_steps = 0
best_criterion = -1 #float('inf')
best_auc = -1
# Train for niter epochs.
# print(">> Training model %s." % self.name)
for self.epoch in range(self.start_iter, self.niter):
# Train for one epoch
mean_wass = self.train()
(auc, res, best_rec, best_threshold), res_total = self.test()
message = ''
# message += 'criterion: (%.3f+%.3f)/2=%.3f ' % (best_rec[0], best_rec[1], res)
# message += 'best threshold: %.3f ' % best_threshold
message += 'Wasserstein Distance:%.3d ' % mean_wass
message += 'AUC: %.3f ' % auc
print(message)
torch.save(
{
'epoch': self.epoch + 1,
'state_dict': self.netg.state_dict(),
'optimizer': self.optimizer_g.state_dict()
}, '%s/netG.pth' % (self.weight_dir))
torch.save(
{
'epoch': self.epoch + 1,
'state_dict': self.netd.state_dict(),
'optimizer': self.optimizer_d.state_dict()
}, '%s/netD.pth' % (self.weight_dir))
if auc > best_auc:
best_auc = auc
new_message = "******** New optimal found, saving state ********"
message = message + '\n' + new_message
print(new_message)
for img in os.listdir(self.best_test_dir):
os.remove(os.path.join(self.best_test_dir, img))
for img in os.listdir(self.test_img_dir):
shutil.copyfile(os.path.join(self.test_img_dir, img),
os.path.join(self.best_test_dir, img))
shutil.copyfile('%s/netG.pth' % (self.weight_dir),
'%s/netg_best.pth' % (self.weight_dir))
log_name = os.path.join(self.outf, self.opt.experiment_name,
'loss_log.txt')
message = 'Epoch%3d:' % self.epoch + ' ' + message
with open(log_name, "a") as log_file:
if self.epoch == 0:
log_file.write('\n\n')
log_file.write('%s\n' % message)
print(">> Training %s Done..." % self.opt.experiment_name)
##
def train(self):
""" Train the model for one epoch.
"""
print("\n>>> Epoch %d/%d, Running " % (self.epoch + 1, self.niter) +
self.opt.experiment_name)
self.netg.train()
self.netd.train()
# for p in self.netg.parameters(): p.requires_grad = True
mean_wass = 0
tk0 = tqdm(self.dataloader['train'],
total=len(self.dataloader['train']))
for i, itr in enumerate(tk0):
input, _ = itr
input = input.cuda()
wasserstein_d = None
# if self.algorithm == 'wgan':
# train NetD
for _ in range(self.netd_niter):
# for p in self.netd.parameters(): p.requires_grad = True
self.optimizer_d.zero_grad()
# forward_g
latent_i = torch.rand(self.batchsize['train'], 64, 1, 1).cuda()
fake = self.netg(latent_i)
# forward_d
_, pred_real = self.netd(input)
_, pred_fake = self.netd(fake) # .detach() TODO
# Backward-pass
wasserstein_d = (pred_fake.mean() - pred_real.mean()) * 1
wasserstein_d.backward()
self.optimizer_d.step()
for p in self.netd.parameters():
p.data.clamp_(-0.01, 0.01) #<<<<<<<
# train netg
# for p in self.netd.parameters(): p.requires_grad = False
self.optimizer_g.zero_grad()
noise = torch.rand(self.batchsize['train'], 64, 1, 1).cuda()
fake = self.netg(noise)
_, pred_fake = self.netd(fake)
err_g_d = -pred_fake.mean() # negative
err_g_d.backward()
self.optimizer_g.step()
errors = {
'loss_netD': wasserstein_d.item(),
'loss_netG': round(err_g_d.item(), 3),
}
mean_wass += wasserstein_d.item()
tk0.set_postfix(errors)
if i % 50 == 0:
img_dir = os.path.join(self.outf, self.opt.experiment_name,
'train', 'images')
if not os.path.isdir(img_dir):
os.makedirs(img_dir)
self.save_image_cv2(input.data, '%s/reals.png' % img_dir)
self.save_image_cv2(fake.data,
'%s/fakes%03d.png' % (img_dir, i))
mean_wass /= len(self.dataloader['train'])
return mean_wass
##
def test(self):
""" Test AnoGAN model.
Args:
dataloader ([type]): Dataloader for the test set
Raises:
IOError: Model weights not found.
"""
self.netg.eval()
self.netd.eval()
# for p in self.netg.parameters(): p.requires_grad = False
# for p in self.netd.parameters(): p.requires_grad = False
for img in os.listdir(self.test_img_dir):
os.remove(os.path.join(self.test_img_dir, img))
self.phase = 'test'
meter = Meter_AnoGAN()
tk1 = tqdm(self.dataloader['test'], total=len(self.dataloader['test']))
for i, itr in enumerate(tk1):
input, target = itr
input = input.cuda()
latent_i = torch.rand(self.batchsize['test'], 64, 1, 1).cuda()
latent_i.requires_grad = True
optimizer_latent = optim.Adam([latent_i], lr=self.lr)
test_loss = None
for _ in range(self.test_iter):
optimizer_latent.zero_grad()
fake = self.netg(latent_i)
residual_loss = self.l_con(input, fake)
latent_o, _ = self.netd(fake)
discrimination_loss = self.l_enc(latent_i, latent_o)
alpha = 0.1
test_loss = (
1 - alpha) * residual_loss + alpha * discrimination_loss
test_loss.backward()
optimizer_latent.step()
abnormal_score = test_loss
meter.update(abnormal_score, target) #<<<TODO
# Save test images.
combine = torch.cat([input.cpu(), fake.cpu()], dim=0)
self.save_image_cv2(combine,
'%s/%05d.jpg' % (self.test_img_dir, i + 1))
criterion, res_total = meter.get_metrics()
# rename images
for i, res in enumerate(res_total):
os.rename('%s/%05d.jpg' % (self.test_img_dir, i + 1),
'%s/%05d_%s.jpg' % (self.test_img_dir, i + 1, res))
return criterion, res_total
@staticmethod
def save_image_cv2(tensor, filename):
# return
from torchvision.utils import make_grid
# tensor = (tensor + 1) / 2
grid = make_grid(tensor, 8, 2, 0, False, None, False)
ndarray = grid.mul_(255).clamp_(0, 255).permute(1, 2, 0).to(
'cpu', torch.uint8).numpy()
cv2.imwrite(filename, ndarray)
def test_network():
threshold = ct.THRESHOLD
test_dir = ct.TEST_TXT
path = os.path.join(ct.BEST_WEIGHT_SAVE_DIR, 'netG.pth')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
pretrained_dict = torch.load(
path, map_location=torch.device(device))['model_state_dict']
test_data = OSCD_TEST(test_dir)
test_dataloader = DataLoader(test_data, batch_size=1, shuffle=False)
net = NetG(ct.ISIZE, ct.NC * 2, ct.NZ, ct.NDF, ct.EXTRALAYERS).to(device)
# net = nn.DataParallel(net)
net.load_state_dict(pretrained_dict, False)
torch.no_grad()
net.eval()
i = 0
TP = 0
FN = 0
FP = 0
TN = 0
for i, data in enumerate(test_dataloader):
INPUT_SIZE = [ct.ISIZE, ct.ISIZE]
x1, x2, gt = data
x1 = x1.to(device, dtype=torch.float)
x2 = x2.to(device, dtype=torch.float)
gt = gt.to(device, dtype=torch.float)
gt = gt[:, 0, :, :].unsqueeze(1)
x = torch.cat((x1, x2), 1)
fake = net(x)
save_path = os.path.join(ct.IM_SAVE_DIR, 'test_output_images')
if not os.path.isdir(save_path):
os.makedirs(save_path)
if ct.SAVE_TEST_IAMGES:
vutils.save_image(x1.data,
os.path.join(save_path, '%d_x1.png' % i),
normalize=True)
vutils.save_image(x2.data,
os.path.join(save_path, '%d_x2.png' % i),
normalize=True)
vutils.save_image(fake.data,
os.path.join(save_path, '%d_gt_fake.png' % i),
normalize=True)
vutils.save_image(gt,
os.path.join(save_path, '%d_gt.png' % i),
normalize=True)
tp, fp, tn, fn = eva.f1(fake, gt)
TP += tp
FN += fn
TN += tn
FP += fp
i += 1
print('testing {}th images'.format(i))
iou = TP / (FN + TP + FP + 1e-8)
precision = TP / (TP + FP + 1e-8)
oa = (TP + TN) / (TP + FN + TN + FP + 1e-8)
recall = TP / (TP + FN + 1e-8)
f1 = 2 * precision * recall / (precision + recall + 1e-8)
P = ((TP + FP) * (TP + FN) + (FN + TN) *
(FP + TN)) / ((TP + TN + FP + FN)**2 + 1e-8)
Kappa = (oa - P) / (1 - P + 1e-8)
results = {
'iou': iou,
'precision': precision,
'oa': oa,
'recall': recall,
'f1': f1,
'kappa': Kappa
}
with open(os.path.join(ct.OUTPUTS_DIR, 'test_score.txt'), 'a') as f:
f.write('-----test results on the best model {}-----'.format(
time.strftime('%Y-%m-%d %H:%M:%S')))
f.write('\n')
for key, value in results.items():
print(key, value)
f.write('{}: {}'.format(key, value))
f.write('\n')
