def train(dataset, gpu_id):
params = param.getGeneralParams()
gpu = '/gpu:' + str(gpu_id)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
set_session(tf.Session(config=config))
with tf.device(gpu):
vgg_model = myVGG.vgg_norm()
networks.make_trainable(vgg_model, False)
response_weights = sio.loadmat('mean_response.mat')
fgbg = networks.network_fgbg(params, vgg_model, response_weights)
#fgbg.load_weights('../results/networks/fgbg_vgg/140000.h5')
disc = networks.discriminator(params)
gan = networks.gan(fgbg, disc, params, vgg_model, response_weights,
0.01, 1e-4)
gan.load_weights('../results/networks/fgbg_gan/2000.h5')
outputs = [fgbg.outputs[0]]
#outputs.append(fgbg.get_layer('mask_src').output)
#outputs.append(fgbg.get_layer('fg_stack').output)
#outputs.append(fgbg.get_layer('bg_src').output)
#outputs.append(fgbg.get_layer('bg_tgt').output)
#outputs.append(fgbg.get_layer('fg_tgt').output)
outputs.append(fgbg.get_layer('fg_mask_tgt').output)
model = Model(fgbg.inputs, outputs)
test = datareader.makeActionExampleList('test_vids.txt', 1)
feed = datageneration.warpExampleGenerator(test,
params,
do_augment=False,
return_pose_vectors=True)
n_frames = len(test)
true_action = np.zeros((256, 256, 3, n_frames))
pred_action = np.zeros((256, 256, 3, n_frames))
mask = np.zeros((256, 256, 1, n_frames))
for i in xrange(n_frames):
print i
X, Y = next(feed)
pred = model.predict(X[:-2])
true_action[:, :, :, i] = convert(np.reshape(Y, (256, 256, 3)))
pred_action[:, :, :, i] = convert(np.reshape(pred[0], (256, 256, 3)))
mask[:, :, :, i] = pred[1]
sio.savemat('results/action/1_gan.mat', {
'true': true_action,
'pred': pred_action,
'mask': mask
})
def train(model_name, gpu_id):
params = param.get_general_params()
network_dir = params['model_save_dir'] + '/' + model_name
if not os.path.isdir(network_dir):
os.mkdir(network_dir)
train_feed = data_generation.create_feed(params, params['data_dir'], 'train')
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
gan_lr = 1e-4
disc_lr = 1e-4
disc_loss = 0.1
generator = networks.network_posewarp(params)
generator.load_weights('../models/vgg_100000.h5')
discriminator = networks.discriminator(params)
discriminator.compile(loss='binary_crossentropy', optimizer=Adam(lr=disc_lr))
vgg_model = truncated_vgg.vgg_norm()
networks.make_trainable(vgg_model, False)
response_weights = sio.loadmat('../data/vgg_activation_distribution_train.mat')
gan = networks.gan(generator, discriminator, params)
gan.compile(optimizer=Adam(lr=gan_lr),
loss=[networks.vgg_loss(vgg_model, response_weights, 12), 'binary_crossentropy'],
loss_weights=[1.0, disc_loss])
n_iters = 10000
batch_size = params['batch_size']
for step in range(n_iters):
x, y = next(train_feed)
gen = generator.predict(x)
# Train discriminator
x_tgt_img_disc = np.concatenate((y, gen))
x_src_pose_disc = np.concatenate((x[1], x[1]))
x_tgt_pose_disc = np.concatenate((x[2], x[2]))
L = np.zeros([2 * batch_size])
L[0:batch_size] = 1
inputs = [x_tgt_img_disc, x_src_pose_disc, x_tgt_pose_disc]
d_loss = discriminator.train_on_batch(inputs, L)
# Train the discriminator a couple of iterations before starting the gan
if step < 5:
util.printProgress(step, 0, [0, d_loss])
step += 1
continue
# TRAIN GAN
L = np.ones([batch_size])
x, y = next(train_feed)
g_loss = gan.train_on_batch(x, [y, L])
util.printProgress(step, 0, [g_loss[1], d_loss])
if step % params['model_save_interval'] == 0 and step > 0:
gan.save(network_dir + '/' + str(step) + '.h5')
def train(model_name, gpu_id):
params = param.getGeneralParams()
gpu = '/gpu:' + str(gpu_id)
network_dir = params['project_dir'] + '/results/networks/' + model_name
if not os.path.isdir(network_dir):
os.mkdir(network_dir)
train_feed = datageneration.createFeed(params, "train_vids.txt")
test_feed = datageneration.createFeed(params, "test_vids.txt")
batch_size = params['batch_size']
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
gan_lr = 1e-4
disc_lr = 1e-4
disc_loss = 0.1
with tf.device(gpu):
vgg_model = myVGG.vgg_norm()
networks.make_trainable(vgg_model, False)
response_weights = sio.loadmat('mean_response.mat')
#generator = networks.network_pix2pix(params,vgg_model,response_weights)
generator = networks.network_fgbg(params)
generator.load_weights('../results/networks/fgbg_vgg/100000.h5')
discriminator = networks.discriminator(params)
discriminator.compile(loss='binary_crossentropy',
optimizer=Adam(lr=disc_lr))
gan = networks.gan(generator, discriminator, params, vgg_model,
response_weights, disc_loss, gan_lr)
gan.compile(optimizer=Adam(lr=gan_lr),
loss=[
networks.vggLoss(vgg_model, response_weights),
'binary_crossentropy'
],
loss_weights=[1.0, disc_loss])
for step in xrange(10001):
X, Y = next(train_feed)
with tf.device(gpu):
gen = generator.predict(X) #[0:3])
#Train discriminator
X_tgt_img_disc = np.concatenate((Y, gen))
X_src_pose_disc = np.concatenate((X[1], X[1]))
X_tgt_pose_disc = np.concatenate((X[2], X[2]))
L = np.zeros([2 * batch_size])
L[0:batch_size] = 1
inputs = [X_tgt_img_disc, X_src_pose_disc, X_tgt_pose_disc]
d_loss = discriminator.train_on_batch(inputs, L)
#Train the discriminator a couple of iterations before starting the gan
if (step < 5):
util.printProgress(step, 0, [0, d_loss])
step += 1
continue
#TRAIN GAN
L = np.ones([batch_size])
X, Y = next(train_feed)
g_loss = gan.train_on_batch(X, [Y, L])
util.printProgress(step, 0, [g_loss[1], d_loss])
'''
#Test
if(step % params['test_interval'] == 0):
n_batches = 8
test_loss = np.zeros(2)
for j in xrange(n_batches):
X,Y = next(warp_test_feed)
#test_loss += np.array(generator.test_on_batch(X_warp,Y_warp))
L = np.zeros([batch_size,2])
L[:,1] = 1 #Fake images
test_loss_j = gan_warp.test_on_batch(X_warp, [Y_warp,L])
test_loss += np.array(test_loss_j[1:3])
test_loss /= (n_batches)
util.printProgress(step,1,test_loss)
'''
if (step % params['model_save_interval'] == 0 and step > 0):
gan.save(network_dir + '/' + str(step) + '.h5')
def train(dataset, gpu_id):
params = param.getGeneralParams()
gpu = '/gpu:' + str(gpu_id)
lift_params = param.getDatasetParams('weightlifting')
golf_params = param.getDatasetParams('golfswinghd')
workout_params = param.getDatasetParams('workout')
tennis_params = param.getDatasetParams('tennis')
aux_params = param.getDatasetParams('test-aux')
_, lift_test = datareader.makeWarpExampleList(lift_params, 0, 2000, 2, 1)
_, golf_test = datareader.makeWarpExampleList(golf_params, 0, 5000, 2, 2)
_, workout_test = datareader.makeWarpExampleList(workout_params, 0, 2000,
2, 3)
_, tennis_test = datareader.makeWarpExampleList(tennis_params, 0, 2000, 2,
4)
_, aux_test = datareader.makeWarpExampleList(aux_params, 0, 2000, 2, 5)
test = lift_test + golf_test + workout_test + tennis_test + aux_test
feed = datageneration.warpExampleGenerator(test,
params,
do_augment=False,
draw_skeleton=False,
skel_color=(0, 0, 255),
return_pose_vectors=True)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
with tf.device(gpu):
vgg_model = myVGG.vgg_norm()
networks.make_trainable(vgg_model, False)
response_weights = sio.loadmat('mean_response.mat')
gen = networks.network_fgbg(params,
vgg_model,
response_weights,
True,
loss='vgg')
disc = networks.discriminator(params)
gan = networks.gan(gen, disc, params, vgg_model, response_weights,
0.01, 1e-4)
gan.load_weights('../results/networks/gan/10000.h5')
np.random.seed(17)
n_batches = 25
for j in xrange(n_batches):
print j
X, Y = next(feed)
loss = gen.evaluate(X[0:-2], Y)
pred = gen.predict(X[0:-2])
sio.savemat(
'results/outputs/' + str(j) + '.mat', {
'X': X[0],
'Y': Y,
'pred': pred,
'loss': loss,
'src_pose': X[-2],
'tgt_pose': X[-1]
})
def train(model_name, gpu_id):
params = param.getGeneralParams()
gpu = '/gpu:' + str(gpu_id)
network_dir = params['project_dir'] + '/results/networks/' + model_name
if not os.path.isdir(network_dir):
os.mkdir(network_dir)
train_feed = datageneration.createFeed(params, "train_vids.txt", 50000)
test_feed = datageneration.createFeed(params, "test_vids.txt", 5000)
batch_size = params['batch_size']
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
set_session(tf.Session(config=config))
gan_lr = 5e-5
disc_lr = 5e-5
disc_loss = 0.1
vgg_model_num = 184000
with tf.device(gpu):
vgg_model = myVGG.vgg_norm()
networks.make_trainable(vgg_model, False)
response_weights = sio.loadmat('mean_response.mat')
generator = networks.network_fgbg(params, vgg_model, response_weights)
generator.load_weights('../results/networks/fgbg_vgg_new/' +
str(vgg_model_num) + '.h5')
discriminator = networks.discriminator(params)
discriminator.compile(loss=networks.wass, optimizer=RMSprop(disc_lr))
gan = networks.gan(generator, discriminator, params, vgg_model,
response_weights, disc_loss, gan_lr)
for step in xrange(vgg_model_num + 1, vgg_model_num + 5001):
for j in xrange(2):
for l in discriminator.layers:
weights = l.get_weights()
weights = [np.clip(w, -0.01, 0.01) for w in weights]
l.set_weights(weights)
X, Y = next(train_feed)
with tf.device(gpu):
gen = generator.predict(X)
#Train discriminator
networks.make_trainable(discriminator, True)
X_tgt_img_disc = np.concatenate((Y, gen))
X_src_pose_disc = np.concatenate((X[1], X[1]))
X_tgt_pose_disc = np.concatenate((X[2], X[2]))
L = np.ones(2 * batch_size)
L[0:batch_size] = -1
inputs = [X_tgt_img_disc, X_src_pose_disc, X_tgt_pose_disc]
d_loss = discriminator.train_on_batch(inputs, L)
networks.make_trainable(discriminator, False)
#TRAIN GAN
L = -1 * np.ones(batch_size)
X, Y = next(train_feed)
g_loss = gan.train_on_batch(X, [Y, L])
util.printProgress(step, 0, [g_loss[1], d_loss])
if (step % params['model_save_interval'] == 0):
gan.save(network_dir + '/' + str(step) + '.h5')
'''
def train(dataset,gpu_id):
params = param.getGeneralParams()
gpu = '/gpu:' + str(gpu_id)
np.random.seed(17)
feed = datageneration.createFeed(params,'test_vids.txt',5000,False,True,True)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
set_session(tf.Session(config=config))
with tf.device(gpu):
vgg_model = myVGG.vgg_norm()
networks.make_trainable(vgg_model,False)
response_weights = sio.loadmat('mean_response.mat')
#fgbg_vgg = networks.network_fgbg(params,vgg_model,response_weights)
#fgbg_vgg.load_weights('../results/networks/fgbg_vgg/184000.h5')
#gen = networks.network_fgbg(params,vgg_model,response_weights)
#disc = networks.discriminator(params)
#gan = networks.gan(gen,disc,params,vgg_model,response_weights,0.1,1e-4)
#gan.load_weights('../results/networks/fgbg_gan/7000.h5')
#fgbg_l1 = networks.network_fgbg(params,vgg_model,response_weights,loss='l1')
#fgbg_l1.load_weights('../results/networks/fgbg_l1/100000.h5')
#mask_model = Model(fgbg_vgg.inputs,fgbg_vgg.get_layer('fg_mask_tgt').output)
ed_vgg = networks.network_pix2pix(params,vgg_model,response_weights)
ed_vgg.load_weights('../results/networks/ed_vgg/135000.h5')
gen = networks.network_pix2pix(params,vgg_model,response_weights)
disc = networks.discriminator(params)
gan = networks.gan(gen,disc,params,vgg_model,response_weights,0.1,1e-4)
gan.load_weights('../results/networks/ed_gan/2000.h5')
ed_l1 = networks.network_pix2pix(params,vgg_model,response_weights,loss='l1')
ed_l1.load_weights('../results/networks/ed_l1/80000.h5')
n_examples = 500
metrics = np.zeros((n_examples,9))
poses = np.zeros((n_examples,28*2))
classes = np.zeros(n_examples)
for j in xrange(n_examples):
print j
X,Y = next(feed)
pred_l1 = ed_l1.predict(X[:3]) #X[:-3])
pred_vgg = ed_vgg.predict(X[:3]) #X[:-3])
pred_gan = gen.predict(X[:3]) #[:-3])
'''
#mask = mask_model.predict(X[:-3])
pred_l1_fg = pred_l1 * mask
pred_vgg_fg = pred_vgg * mask
pred_gan_fg = pred_gan * mask
pred_l1_bg = pred_l1 * (1-mask)
pred_vgg_bg = pred_vgg * (1-mask)
pred_gan_bg = pred_gan * (1-mask)
Y_fg = Y * mask
Y_bg = Y * (1-mask)
'''
#,pred_l1_fg,pred_vgg_fg,pred_gan_fg,pred_l1_bg,pred_vgg_bg,pred_gan_bg]
#,Y_fg,Y_fg,Y_fg,Y_bg,Y_bg,Y_bg]
preds = [pred_l1,pred_vgg,pred_gan]
targets = [Y,Y,Y]
metrics[j,0:3] = [l1Error(preds[i],targets[i]) for i in xrange(len(preds))]
metrics[j,3:6] = [vggError(vgg_model.predict(util.vgg_preprocess(preds[i])),
vgg_model.predict(util.vgg_preprocess(targets[i])),response_weights) for i in xrange(len(preds))]
metrics[j,6:] = [ssimError(preds[i],targets[i]) for i in xrange(len(preds))]
poses[j,0:28] = X[-3]
poses[j,28:] = X[-2]
classes[j] = int(X[-1])
sio.savemat('results/comparison/pix2pix/' + str(j) + '.mat', {'X': X[0], 'Y': Y, 'pred_l1': pred_l1, 'pred_vgg': pred_vgg, 'pred_gan': pred_gan})
sio.savemat('results/comparison_pix2pix.mat',{'metrics': metrics, 'poses': poses, 'classes': classes})
def train(model_name, gpu_id):
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
tf_config.allow_soft_placement = True
with tf.Session(config=tf_config) as sess:
params = param.get_general_params()
network_dir = params['model_save_dir'] + '/' + model_name
if not os.path.isdir(network_dir):
os.mkdir(network_dir)
train_feed = data_generation.create_feed(params, params['data_dir'], "train")
# test_feed = data_generation.create_feed(params, params['data_dir'], "test")
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
gan_lr = 1e-3
disc_lr = 1e-3
disc_loss = 0.1
generator = networks.network_posewarp(params)
# generator.load_weights('../models/posewarp_vgg/100000.h5')
discriminator = networks.discriminator(params)
discriminator.compile(loss='binary_crossentropy', optimizer=Adam(lr=disc_lr))
vgg_model = truncated_vgg.vgg_norm()
networks.make_trainable(vgg_model, False)
response_weights = sio.loadmat('../Models/vgg_activation_distribution_train.mat')
gan = networks.gan(generator, discriminator, params)
gan.compile(optimizer=Adam(lr=gan_lr),
loss=[networks.vgg_loss(vgg_model, response_weights, 12), 'binary_crossentropy'],
loss_weights=[1.0, disc_loss])
n_iters = params['n_training_iter']
batch_size = params['batch_size']
summary_writer = tf.summary.FileWriter("./logs", graph=sess.graph)
tr_x, tr_y = next(train_feed)
# te_x, te_y = next(test_feed)
# Prepare output directories if they don't exist.
output_dir = '../Output/' + model_name + '/'
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
scipy.misc.imsave(output_dir + 'tr_orig_image.png', tr_x[0][0, :, :, :])
scipy.misc.imsave(output_dir + 'tr_targ_image.png', tr_y[0, :, :, :])
# scipy.misc.imsave(output_dir + 'te_orig_image.png', te_x[0][0, :, :, :])
# scipy.misc.imsave(output_dir + 'te_targ_image.png', te_y[0, :, :, :])
print("Batch size: " + str(batch_size))
for step in range(n_iters):
x, y = next(train_feed)
gen = generator.predict(x)
# Train discriminator
x_tgt_img_disc = np.concatenate((y, gen))
x_src_pose_disc = np.concatenate((x[1], x[1]))
x_tgt_pose_disc = np.concatenate((x[2], x[2]))
L = np.zeros([2 * batch_size])
L[0:batch_size] = 1
inputs = [x_tgt_img_disc, x_src_pose_disc, x_tgt_pose_disc]
d_loss = discriminator.train_on_batch(inputs, L)
# Train the discriminator a couple of iterations before starting the gan
if step < 5:
util.printProgress(step, 0, [0, d_loss])
step += 1
continue
# TRAIN GAN
L = np.ones([batch_size])
x, y = next(train_feed)
g_loss = gan.train_on_batch(x, [y, L])
util.printProgress(step, 0, [g_loss[1], d_loss])
if step % params['test_interval'] == 0:
print(gen[0])
gen = tf.get_default_graph().get_tensor_by_name("model_1/add_2_1/add:0")
inp = tf.get_default_graph().get_tensor_by_name("in_img0:0")
out = tf.get_default_graph().get_tensor_by_name("in_img1:0")
p_s = tf.get_default_graph().get_tensor_by_name("mask_src/truediv:0")
# p_t = tf.get_default_graph().get_tensor_by_name("in_pose1:0")
image_summary_1 = tf.summary.image('images', [inp[0, :, :, :], out[0, :, :, :], gen[0, :, :, :]], max_outputs=100)
# image_summary_2 = tf.summary.image('pose', [tf.reduce_sum(p_s[0, :, :, :], 2, keepdims=True)], max_outputs=100)
image_summary_1 = sess.run(image_summary_1,feed_dict={"in_img0:0": x[0], "in_pose0:0": x[1], "in_pose1:0": x[2],
"mask_prior:0": x[3], "trans_in:0": x[4], "in_img1:0": y,
"input_3:0": x[0], "input_4:0": x[1], "input_5:0": x[2],
"input_6:0": x[3], "input_7:0": x[4]})
#
# img_gen = sess.run(image_summary_1,feed_dict={"in_img0:0": x[0], "in_pose0:0": x[1], "in_pose1:0": x[2],
# "mask_prior:0": x[3], "trans_in:0": x[4], "in_img1:0": y,
# "input_3:0": x[0], "input_4:0": x[1], "input_5:0": x[2],
# "input_6:0": x[3], "input_7:0": x[4]})
# image_summary_2 = sess.run(image_summary_2, feed_dict={"in_img0:0" : x[0], "in_pose0:0" : x[1], "in_pose1:0" : x[2],
# "mask_prior:0" : x[3], "trans_in:0" : x[4], "in_img1:0" : y})
summary_writer.add_summary(image_summary_1)
# summary_writer.add_summary(image_summary_2)
train_image = sess.run(gen, feed_dict={"in_img0:0": tr_x[0], "in_pose0:0": tr_x[1], "in_pose1:0": tr_x[2],
"mask_prior:0": tr_x[3], "trans_in:0": tr_x[4], "in_img1:0": tr_y,
"input_3:0": tr_x[0], "input_4:0": tr_x[1], "input_5:0": tr_x[2],
"input_6:0": tr_x[3], "input_7:0": tr_x[4]})
#
# test_image = sess.run(gen, feed_dict={"in_img0:0": te_x[0], "in_pose0:0": te_x[1], "in_pose1:0": te_x[2],
# "mask_prior:0": te_x[3], "trans_in:0": te_x[4], "in_img1:0": te_y,
# "input_3:0": te_x[0], "input_4:0": te_x[1], "input_5:0": te_x[2],
# "input_6:0": te_x[3], "input_7:0": te_x[4]})
scipy.misc.imsave(output_dir + 'tr' + str(step) + ".png", train_image[0, :, :, :])
# scipy.misc.imsave(output_dir + 'te' + str(step) + ".png", test_image[0, :, :, :])
if step % params['model_save_interval'] == 0 and step > 0:
gan.save(network_dir + '/' + str(step) + '.h5')
def test(model_name, gpu_id):
params = param.get_general_params()
network_dir = params['model_save_dir'] + '/' + model_name
# if not os.path.isdir(network_dir):
# os.mkdir(network_dir)
train_feed = data_generation.create_feed(params,
params['data_dir'],
'test',
do_augment=False)
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
gan_lr = 1e-4
disc_lr = 1e-4
disc_loss = 0.1
generator = networks.network_posewarp(params)
# generator.load_weights('../models/vgg_100000.h5')
generator.load_weights(
'/versa/kangliwei/motion_transfer/posewarp-cvpr2018/models/0301_fullfinetune/9000.h5'
)
mask_delta_model = Model(input=generator.input,
output=generator.get_layer('mask_delta').output)
src_mask_model = Model(input=generator.input,
output=generator.get_layer('mask_src').output)
discriminator = networks.discriminator(params)
discriminator.compile(loss='binary_crossentropy',
optimizer=Adam(lr=disc_lr))
vgg_model = truncated_vgg.vgg_norm()
networks.make_trainable(vgg_model, False)
response_weights = sio.loadmat(
'../data/vgg_activation_distribution_train.mat')
gan = networks.gan(generator, discriminator, params)
gan.compile(optimizer=Adam(lr=gan_lr),
loss=[
networks.vgg_loss(vgg_model, response_weights, 12),
'binary_crossentropy'
],
loss_weights=[1.0, disc_loss])
n_iters = 10000
batch_size = params['batch_size']
for step in range(n_iters):
x, y = next(train_feed)
gen = generator.predict(x)
src_mask_delta = mask_delta_model.predict(x)
print('delta_max', src_mask_delta.max())
src_mask_delta = src_mask_delta * 255
src_mask = src_mask_model.predict(x)
print('mask_max', src_mask.max())
src_mask = src_mask * 255
# print('src_mask_delta', type(src_mask_delta), src_mask_delta.shape)
y = (y / 2 + 0.5) * 255.0
gen = (gen / 2 + 0.5) * 255.0
for i in range(gen.shape[0]): # iterate in batch
cv2.imwrite('pics/src' + str(i) + '.jpg', x[0][i] * 255)
cv2.imwrite('pics/gen' + str(i) + '.jpg', gen[i])
cv2.imwrite('pics/y' + str(i) + '.jpg', y[i])
for j in range(11):
cv2.imwrite('pics/seg_delta_' + str(i) + '_' + str(j) + '.jpg',
src_mask_delta[i][:, :, j])
for j in range(11):
cv2.imwrite('pics/seg_' + str(i) + '_' + str(j) + '.jpg',
src_mask[i][:, :, j])
break
# Train discriminator
x_tgt_img_disc = np.concatenate((y, gen))
x_src_pose_disc = np.concatenate((x[1], x[1]))
x_tgt_pose_disc = np.concatenate((x[2], x[2]))
L = np.zeros([2 * batch_size])
L[0:batch_size] = 1
inputs = [x_tgt_img_disc, x_src_pose_disc, x_tgt_pose_disc]
d_loss = discriminator.train_on_batch(inputs, L)
# Train the discriminator a couple of iterations before starting the gan
if step < 5:
util.printProgress(step, 0, [0, d_loss])
step += 1
continue
# TRAIN GAN
L = np.ones([batch_size])
x, y = next(train_feed)
g_loss = gan.train_on_batch(x, [y, L])
util.printProgress(step, 0, [g_loss[1], d_loss])
if step % params['model_save_interval'] == 0 and step > 0:
generator.save(network_dir + '/' + str(step) + '.h5')