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,False,False,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 = networks.network_fgbg(params,vgg_model,response_weights)
fgbg.load_weights('../results/networks/fgbg_vgg/184000.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/7000.h5')
n_batches = 200
for j in xrange(n_batches):
print j
X,Y = next(feed)
pred = fgbg.predict(X)
sio.savemat('results/transfer_vgg/' + str(j) + '.mat',{'X': X[0],'Y': Y, 'pred': pred})
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")
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')
model = networks.network_fgbg(params)
#model.load_weights('../results/networks/fgbg_vgg/60000.h5')
model.compile(optimizer=Adam(lr=1e-4),
loss=[networks.vggLoss(vgg_model, response_weights)])
#model.summary()
for step in xrange(0, 250000):
start = time.time()
#X,Y = next(test_feed)
#sio.savemat('data/data' + str(step) + '.mat',{'X':X[0],'Y':Y, 'ps': X[1], 'pt': X[2], 'mask': X[3]})
#return
X, Y = next(train_feed)
with tf.device(gpu):
train_loss = model.train_on_batch(X, Y)
end = time.time()
util.printProgress(step, 0, train_loss, end - start)
'''
if(step % params['test_interval'] == 0):
n_batches = 8
test_loss = 0
for j in xrange(n_batches):
X,Y = next(test_feed)
test_loss += np.array(model.test_on_batch(X,Y))
test_loss /= (n_batches)
util.printProgress(step,1,test_loss,0)
'''
if (step > 0 and step % params['model_save_interval'] == 0):
model.save(network_dir + '/' + str(step) + '.h5')
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(dataset, gpu_id):
params = param.getGeneralParams()
gpu = '/gpu:' + str(gpu_id)
np.random.seed(17)
feed = datageneration.createFeed(params, 'test_vids.txt', False, 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 = networks.network_fgbg(params)
fgbg.load_weights('../results/networks/fgbg_vgg/45000.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/7000.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)
n_batches = 10
for j in xrange(n_batches):
print j
X, Y = next(feed)
pred = model.predict(X[:-2])
sio.savemat(
'results/fgbg_vgg/' + str(j) + '.mat', {
'X': X[0],
'Y': Y,
'pred': pred[0],
'mask_src': pred[1],
'fg_stack': pred[2],
'bg_src': pred[3],
'bg_tgt': pred[4],
'fg_tgt': pred[5],
'fg_mask_tgt': pred[6],
'prior': X[3],
'pose_src': X[-2],
'pose_tgt': X[-1]
})
def train(gpu_id):
params = param.getGeneralParams()
gpu = '/gpu:' + str(gpu_id)
test_params = param.getDatasetParams('test-aux')
_, test = datareader.makeWarpExampleList(test_params, 0, 200, 2, 0)
feed = datageneration.warpExampleGenerator(test,
params,
do_augment=False,
draw_skeleton=False,
skel_color=(0, 0, 255))
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')
fgbg = networks.network_fgbg(params, vgg_model, response_weights,
True)
np.random.seed(17)
n_batches = 50
for i in xrange(146000, 1452000, 2000):
fgbg.load_weights('../results/networks/fgbg_boundary/' + str(i) +
'.h5')
loss = 0
for j in xrange(n_batches):
X, Y = next(feed)
loss += fgbg.test_on_batch(X, Y)
loss /= n_batches
print loss
sys.stdout.flush()
def main(gpu_id):
params = param.getGeneralParams()
gpu = '/gpu:' + str(gpu_id)
n_steps = 2000
train = datareader.makeWarpExampleList('train_vids.txt', n_steps)
train_feed = datageneration.warpExampleGenerator(train,
params,
return_pose_vectors=False)
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()
mean_response = []
num_elements = []
for step in xrange(n_steps):
print step
X, Y = next(train_feed)
pred_step = vgg_model.predict(util.vgg_preprocess(X[0]))
for i in xrange(len(pred_step)):
sum_i = np.sum(pred_step[i], axis=(0, 1, 2))
n_elt = np.prod(pred_step[i].shape[0:3])
if (step == 0):
mean_response.append(sum_i)
num_elements.append(n_elt)
else:
mean_response[i] += sum_i
num_elements[i] += n_elt
for i in xrange(len(mean_response)):
mean_response[i] /= (1.0 * num_elements[i])
std_response = []
for step in xrange(n_steps):
print step
X, Y = next(train_feed)
pred_step = vgg_model.predict(util.vgg_preprocess(X[0]))
for i in xrange(len(pred_step)):
mean_response_i = np.reshape(mean_response[i], (1, 1, 1, -1))
mean_response_i = np.tile(mean_response_i,
(pred_step[i].shape[0:3]) + (1, ))
d = np.sum((pred_step[i] - mean_response_i)**2, axis=(0, 1, 2))
if (step == 0):
std_response.append(d)
else:
std_response[i] += d
for i in xrange(12):
std_response[i] = np.sqrt(std_response[i] / (num_elements[i] - 1.0))
responses = {}
for i in xrange(12):
responses[str(i)] = (mean_response[i], std_response[i])
sio.savemat('mean_response_new.mat', responses)
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})
