def train(n_channels=3,
resolution=32,
z_dim=128,
n_labels=0,
lr=1e-3,
e_drift=1e-3,
wgp_target=750,
initial_resolution=4,
total_kimg=25000,
training_kimg=500,
transition_kimg=500,
iters_per_checkpoint=500,
n_checkpoint_images=16,
glob_str='cifar10',
out_dir='cifar10'):
# instantiate logger
logger = SummaryWriter(out_dir)
# load data
batch_size = MINIBATCH_OVERWRITES[0]
train_iterator = iterate_minibatches(glob_str, batch_size, resolution)
# build models
G = Generator(n_channels, resolution, z_dim, n_labels)
D = Discriminator(n_channels, resolution, n_labels)
G_train, D_train = GAN(G, D, z_dim, n_labels, resolution, n_channels)
D_opt = Adam(lr=lr, beta_1=0.0, beta_2=0.99, epsilon=1e-8)
G_opt = Adam(lr=lr, beta_1=0.0, beta_2=0.99, epsilon=1e-8)
# define loss functions
D_loss = [loss_mean, loss_gradient_penalty, 'mse']
G_loss = [loss_wasserstein]
# compile graphs used during training
G.compile(G_opt, loss=loss_wasserstein)
D.trainable = False
G_train.compile(G_opt, loss=G_loss)
D.trainable = True
D_train.compile(D_opt, loss=D_loss, loss_weights=[1, GP_WEIGHT, e_drift])
# for computing the loss
ones = np.ones((batch_size, 1), dtype=np.float32)
zeros = ones * 0.0
# fix a z vector for training evaluation
z_fixed = np.random.normal(0, 1, size=(n_checkpoint_images, z_dim))
# vars
resolution_log2 = int(np.log2(resolution))
starting_block = resolution_log2
starting_block -= np.floor(np.log2(initial_resolution))
cur_block = starting_block
cur_nimg = 0
# compute duration of each phase and use proxy to update minibatch size
phase_kdur = training_kimg + transition_kimg
phase_idx_prev = 0
# offset variable for transitioning between blocks
offset = 0
i = 0
while cur_nimg < total_kimg * 1000:
# block processing
kimg = cur_nimg / 1000.0
phase_idx = int(np.floor((kimg + transition_kimg) / phase_kdur))
phase_idx = max(phase_idx, 0.0)
phase_kimg = phase_idx * phase_kdur
# update batch size and ones vector if we switched phases
if phase_idx_prev < phase_idx:
batch_size = MINIBATCH_OVERWRITES[phase_idx]
train_iterator = iterate_minibatches(glob_str, batch_size)
ones = np.ones((batch_size, 1), dtype=np.float32)
zeros = ones * 0.0
phase_idx_prev = phase_idx
# possibly gradually update current level of detail
if transition_kimg > 0 and phase_idx > 0:
offset = (kimg + transition_kimg - phase_kimg) / transition_kimg
offset = min(offset, 1.0)
offset = offset + phase_idx - 1
cur_block = max(starting_block - offset, 0.0)
# update level of detail
K.set_value(G_train.cur_block, np.float32(cur_block))
K.set_value(D_train.cur_block, np.float32(cur_block))
# train D
for j in range(N_CRITIC_ITERS):
z = np.random.normal(0, 1, size=(batch_size, z_dim))
real_batch = next(train_iterator)
fake_batch = G.predict_on_batch([z])
interpolated_batch = get_interpolated_images(
real_batch, fake_batch)
losses_d = D_train.train_on_batch(
[real_batch, fake_batch, interpolated_batch],
[ones, ones * wgp_target, zeros])
cur_nimg += batch_size
# train G
z = np.random.normal(0, 1, size=(batch_size, z_dim))
loss_g = G_train.train_on_batch(z, -1 * ones)
logger.add_scalar("cur_block", cur_block, i)
logger.add_scalar("learning_rate", lr, i)
logger.add_scalar("batch_size", z.shape[0], i)
print("iter", i, "cur_block", cur_block, "lr", lr, "kimg", kimg,
"losses_d", losses_d, "loss_g", loss_g)
if (i % iters_per_checkpoint) == 0:
G.trainable = False
fake_images = G.predict(z_fixed)
# log fake images
log_images(fake_images, 'fake', i, logger, fake_images.shape[1],
fake_images.shape[2], int(np.sqrt(n_checkpoint_images)))
# plot real images for reference
log_images(real_batch[:n_checkpoint_images], 'real', i, logger,
real_batch.shape[1], real_batch.shape[2],
int(np.sqrt(n_checkpoint_images)))
# save the model to eventually resume training or do inference
save_model(G, out_dir + "/model.json", out_dir + "/model.h5")
log_losses(losses_d, loss_g, i, logger)
i += 1
def train():
parser = argparse.ArgumentParser(description="keras pix2pix")
parser.add_argument('--batchsize', '-b', type=int, default=1)
parser.add_argument('--patchsize', '-p', type=int, default=64)
parser.add_argument('--epoch', '-e', type=int, default=500)
parser.add_argument('--out', '-o', default='result')
parser.add_argument('--lmd', '-l', type=int, default=100)
parser.add_argument('--dark', '-d', type=float, default=0.01)
parser.add_argument('--gpu', '-g', type=int, default=2)
args = parser.parse_args()
args = parser.parse_args()
PATCH_SIZE = args.patchsize
BATCH_SIZE = args.batchsize
epoch = args.epoch
lmd = args.lmd
# set gpu environment
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
K.set_session(sess)
# make directory to save results
if not os.path.exists("./result"):
os.mkdir("./result")
resultDir = "./result/" + args.out
modelDir = resultDir + "/model/"
if not os.path.exists(resultDir):
os.mkdir(resultDir)
if not os.path.exists(modelDir):
os.mkdir(modelDir)
# make a logfile and add colnames
o = open(resultDir + "/log.txt", "w")
o.write("batch:" + str(BATCH_SIZE) + " lambda:" + str(lmd) + "\n")
o.write(
"epoch,dis_loss,gan_mae,gan_entropy,vdis_loss,vgan_mae,vgan_entropy" +
"\n")
o.close()
# load data
ds1_first, ds1_last, num_ds1 = 1, 1145, 1145
ds2_first, ds2_last, num_ds2 = 2000, 6749, 4750
# ds1_first, ds1_last, num_ds1 = 1, 100, 100
# ds2_first, ds2_last, num_ds2 = 101, 200, 100
train_data_i = np.concatenate([
np.arange(ds1_first, ds1_last + 1)[:int(num_ds1 * 0.7)],
np.arange(ds2_first, ds2_last + 1)[:int(num_ds2 * 0.7)]
])
test_data_i = np.concatenate([
np.arange(ds1_first, ds1_last + 1)[int(num_ds1 * 0.7):],
np.arange(ds2_first, ds2_last + 1)[int(num_ds2 * 0.7):]
])
train_gt, _, train_night = load_dataset(data_range=train_data_i,
dark=args.dark)
test_gt, _, test_night = load_dataset(data_range=test_data_i,
dark=args.dark)
# Create optimizers
opt_Gan = Adam(lr=1E-3)
opt_Discriminator = Adam(lr=1E-3)
opt_Generator = Adam(lr=1E-3)
# set the loss of gan
def dis_entropy(y_true, y_pred):
return -K.log(K.abs((y_pred - y_true)) + 1e-07)
gan_loss = ['mae', dis_entropy]
gan_loss_weights = [lmd, 1]
# make models
Generator = generator()
Generator.compile(loss='mae', optimizer=opt_Generator)
Discriminator = discriminator()
Discriminator.trainable = False
Gan = GAN(Generator, Discriminator)
Gan.compile(loss=gan_loss,
loss_weights=gan_loss_weights,
optimizer=opt_Gan)
Discriminator.trainable = True
Discriminator.compile(loss=dis_entropy, optimizer=opt_Discriminator)
# start training
n_train = train_gt.shape[0]
n_test = test_gt.shape[0]
print(n_train, n_test)
p = ProgressBar()
for epoch in p(range(epoch)):
p.update(epoch + 1)
out_file = open(resultDir + "/log.txt", "a")
train_ind = np.random.permutation(n_train)
test_ind = np.random.permutation(n_test)
dis_losses = []
gan_losses = []
test_dis_losses = []
test_gan_losses = []
y_real = np.array([1] * BATCH_SIZE)
y_fake = np.array([0] * BATCH_SIZE)
y_gan = np.array([1] * BATCH_SIZE)
# training
for batch_i in range(int(n_train / BATCH_SIZE)):
gt_batch = train_gt[train_ind[(batch_i *
BATCH_SIZE):((batch_i + 1) *
BATCH_SIZE)], :, :, :]
night_batch = train_night[train_ind[(
batch_i * BATCH_SIZE):((batch_i + 1) * BATCH_SIZE)], :, :, :]
generated_batch = Generator.predict(night_batch)
# train Discriminator
dis_real_loss = np.array(
Discriminator.train_on_batch([night_batch, gt_batch], y_real))
dis_fake_loss = np.array(
Discriminator.train_on_batch([night_batch, generated_batch],
y_fake))
dis_loss_batch = (dis_real_loss + dis_fake_loss) / 2
dis_losses.append(dis_loss_batch)
gan_loss_batch = np.array(
Gan.train_on_batch(night_batch, [gt_batch, y_gan]))
gan_losses.append(gan_loss_batch)
dis_loss = np.mean(np.array(dis_losses))
gan_loss = np.mean(np.array(gan_losses), axis=0)
# validation
for batch_i in range(int(n_test / BATCH_SIZE)):
gt_batch = test_gt[test_ind[(batch_i *
BATCH_SIZE):((batch_i + 1) *
BATCH_SIZE)], :, :, :]
night_batch = test_night[test_ind[(
batch_i * BATCH_SIZE):((batch_i + 1) * BATCH_SIZE)], :, :, :]
generated_batch = Generator.predict(night_batch)
# train Discriminator
dis_real_loss = np.array(
Discriminator.test_on_batch([night_batch, gt_batch], y_real))
dis_fake_loss = np.array(
Discriminator.test_on_batch([night_batch, generated_batch],
y_fake))
test_dis_loss_batch = (dis_real_loss + dis_fake_loss) / 2
test_dis_losses.append(test_dis_loss_batch)
test_gan_loss_batch = np.array(
Gan.test_on_batch(night_batch, [gt_batch, y_gan]))
test_gan_losses.append(test_gan_loss_batch)
test_dis_loss = np.mean(np.array(test_dis_losses))
test_gan_loss = np.mean(np.array(gan_losses), axis=0)
# write log of leaning
out_file.write(
str(epoch) + "," + str(dis_loss) + "," + str(gan_loss[1]) + "," +
str(gan_loss[2]) + "," + str(test_dis_loss) + "," +
str(test_gan_loss[1]) + "," + str(test_gan_loss[2]) + "\n")
# visualize
if epoch % 50 == 0:
# for training data
gt_batch = train_gt[train_ind[0:9], :, :, :]
night_batch = train_night[train_ind[0:9], :, :, :]
generated_batch = Generator.predict(night_batch)
save_images(night_batch,
resultDir + "/label_" + str(epoch) + "epoch.png")
save_images(gt_batch,
resultDir + "/gt_" + str(epoch) + "epoch.png")
save_images(generated_batch,
resultDir + "/generated_" + str(epoch) + "epoch.png")
# for validation data
gt_batch = test_gt[test_ind[0:9], :, :, :]
night_batch = test_night[test_ind[0:9], :, :, :]
generated_batch = Generator.predict(night_batch)
save_images(night_batch,
resultDir + "/vlabel_" + str(epoch) + "epoch.png")
save_images(gt_batch,
resultDir + "/vgt_" + str(epoch) + "epoch.png")
save_images(generated_batch,
resultDir + "/vgenerated_" + str(epoch) + "epoch.png")
Gan.save_weights(modelDir + 'gan_weights' + "_lambda" + str(lmd) +
"_epoch" + str(epoch) + '.h5')
out_file.close()
out_file.close()
