def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session() as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, y_dim=10,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir)
else:
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
to_json("./web/js/gen_layers.js", dcgan.h0_w, dcgan.h1_w, dcgan.h2_w, dcgan.h3_w, dcgan.h4_w)
z_sample = np.random.uniform(-1, 1, size=(FLAGS.batch_size, dcgan.z_dim))
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
save_images(samples, [8, 8], './samples/test_%s.png' % strftime("%Y-%m-%d %H:%M:%S", gmtime()))
def copy(ntm, seq_length, sess, max_length=50, print_=True):
start_symbol = np.zeros([ntm.cell.input_dim], dtype=np.float32)
start_symbol[0] = 1
end_symbol = np.zeros([ntm.cell.input_dim], dtype=np.float32)
end_symbol[1] = 1
seq = generate_copy_sequence(seq_length, ntm.cell.input_dim - 2)
feed_dict = {input_:vec for vec, input_ in zip(seq, ntm.inputs)}
feed_dict.update(
{true_output:vec for vec, true_output in zip(seq, ntm.true_outputs)}
)
feed_dict.update({
ntm.start_symbol: start_symbol,
ntm.end_symbol: end_symbol
})
result = sess.run(ntm.get_outputs(seq_length) + [ntm.get_loss(seq_length)], feed_dict=feed_dict)
outputs = result[:-1]
loss = result[-1]
if print_:
np.set_printoptions(suppress=True)
print(" true output : ")
pp.pprint(seq)
print(" predicted output :")
pp.pprint(np.round(outputs))
print(" Loss : %f" % loss)
np.set_printoptions(suppress=False)
else:
return seq, outputs, loss
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
print(" [*] Creating checkpoint directory...")
os.makedirs(FLAGS.checkpoint_dir)
if FLAGS.game_name == "home":
game = HomeGame(game_dir=FLAGS.game_dir, seq_length=FLAGS.seq_length)
else:
raise Exception(" [!] %s not implemented yet" % self.game_name)
with tf.device('/cpu:0'):
model = LSTMDQN(game, checkpoint_dir=FLAGS.checkpoint_dir,
seq_length=FLAGS.seq_length,
embed_dim=FLAGS.embed_dim,
layer_depth=FLAGS.layer_depth,
batch_size=FLAGS.batch_size,
start_epsilon=FLAGS.start_epsilon,
forward_only=FLAGS.forward_only)
if not FLAGS.forward_only:
model.train()
else:
test_loss = model.test(2)
print(" [*] Test loss: %2.6f, perplexity: %2.6f" % (test_loss, np.exp(test_loss)))
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
config = tf.ConfigProto(
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9),
device_count = {'GPU': 1},
allow_soft_placement=True
#log_device_placement=True,
)
config.device_filters.append('/gpu:0')
config.device_filters.append('/cpu:0')
with tf.Session(config=config) as sess:
#with tf.device('/gpu:0'):
autoencoder = Autoencoder(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size,
dataset_name=FLAGS.dataset, noise = FLAGS.noise, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir)
if FLAGS.is_train:
autoencoder.train(FLAGS)
elif FLAGS.is_run:
autoencoder.run(FLAGS)
else:
autoencoder.load(FLAGS.checkpoint_dir)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
print(" [*] Creating checkpoint directory...")
os.makedirs(FLAGS.checkpoint_dir)
with tf.Session() as sess:
model = model_dict[FLAGS.model](sess, checkpoint_dir=FLAGS.checkpoint_dir,
seq_length=FLAGS.seq_length,
word_embed_dim=FLAGS.word_embed_dim,
char_embed_dim=FLAGS.char_embed_dim,
feature_maps=eval(FLAGS.feature_maps),
kernels=eval(FLAGS.kernels),
batch_size=FLAGS.batch_size,
dropout_prob=FLAGS.dropout_prob,
max_word_length=FLAGS.max_word_length,
forward_only=FLAGS.forward_only,
dataset_name=FLAGS.dataset,
use_char=FLAGS.use_char,
use_word=FLAGS.use_word,
data_dir=FLAGS.data_dir)
if not FLAGS.forward_only:
model.run(FLAGS.epoch, FLAGS.learning_rate, FLAGS.decay)
else:
test_loss = model.test(2)
print(" [*] Test loss: %2.6f, perplexity: %2.6f" % (test_loss, np.exp(test_loss)))
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session() as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, y_dim=10,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir)
else:
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
[dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
[dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
[dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
[dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 2
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
with tf.device('/cpu:0'), tf.Session() as sess:
FLAGS.sess = sess
dcgan = DCGAN(y_dim=10)
def main(_):
pp.pprint(flags.FLAGS.__flags)
with tf.device('/cpu:0'), tf.Session() as sess:
if FLAGS.task == 'copy':
if FLAGS.is_train:
cell, ntm = copy_train(FLAGS, sess)
else:
cell = NTMCell(input_dim=FLAGS.input_dim,
output_dim=FLAGS.output_dim,
controller_layer_size=FLAGS.controller_layer_size,
write_head_size=FLAGS.write_head_size,
read_head_size=FLAGS.read_head_size)
ntm = NTM(cell, sess, 1, FLAGS.max_length,
test_max_length=FLAGS.test_max_length, forward_only=True)
ntm.load(FLAGS.checkpoint_dir, 'copy')
copy(ntm, FLAGS.test_max_length*1/3, sess)
print
copy(ntm, FLAGS.test_max_length*2/3, sess)
print
copy(ntm, FLAGS.test_max_length*3/3, sess)
elif FLAGS.task == 'recall':
pass
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
random.seed(31241)
np.random.seed(41982)
tf.set_random_seed(1327634)
color = True # Must change this and the dataset Flags to the correct path to use color
if FLAGS.is_debug:
reader = Bouncing_Balls_Data_Reader(FLAGS.dataset, FLAGS.batch_size, color=color, train_size=160*5, validation_size=8*5, test_size=8*5, num_partitions=5)
else:
reader = Bouncing_Balls_Data_Reader(FLAGS.dataset, FLAGS.batch_size, color=color)
data_fn = lambda epoch, batch_index: reader.read_data(batch_index, reader.TRAIN)
frame_shape = reader.read_data(0, reader.TRAIN).shape[2:]
print("Frame shape: ", frame_shape)
num_batches = reader.num_batches(reader.TRAIN)
print("Num batches: %d" % num_batches)
input_sequence_range = range(5, 16)
print("Input sequence range min: %d, max: %d" % (min(input_sequence_range), max(input_sequence_range)))
save_sample_fn = utils.gen_save_sample_fn(FLAGS.sample_dir, image_prefix="train")
with tf.Session() as sess:
pgn = PGN(sess, FLAGS.dataset_name, FLAGS.epoch, num_batches, FLAGS.batch_size, input_sequence_range,
data_fn, frame_shape=frame_shape, save_sample_fn=save_sample_fn, checkpoint_dir=FLAGS.checkpoint_dir,
lambda_adv_loss= FLAGS.lambda_adv_loss)
if FLAGS.is_train:
pgn.train()
else:
print("Loading from: %s" %(FLAGS.checkpoint_dir,))
if pgn.load(FLAGS.checkpoint_dir) :
print(" [*] Successfully loaded")
else:
print(" [!] Load failed")
if FLAGS.is_test:
result = test.test(pgn, reader)
result_str = pp.pformat(result)
fid = open(os.path.join(FLAGS.sample_dir, 'test_out.txt'), mode='w')
fid.write(unicode(result_str))
fid.close()
if FLAGS.is_visualize:
for i in range(3):
vid_seq = reader.read_data(i, data_set_type=reader.TEST, batch_size=1)[:, 0, :, :, :]
utils.make_prediction_gif(pgn, os.path.join(FLAGS.sample_dir, 'vis_%d.gif' % i), video_sequence=vid_seq)
utils.plot_convergence(pgn.get_MSE_history(), "MSE Convergence",
path=os.path.join(FLAGS.sample_dir, "vis_MSE_convergence.png"))
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
print(" [*] Creating checkpoint directory...")
os.makedirs(FLAGS.checkpoint_dir)
with tf.device('/cpu:0'), tf.Session() as sess:
model = model_dict[FLAGS.model](batch_size=FLAGS.batch_size,
checkpoint_dir=FLAGS.checkpoint_dir, forward_only=FLAGS.forward_only)
if not FLAGS.forward_only:
model.train(sess, FLAGS.vocab_size, FLAGS.epoch,
FLAGS.learning_rate, FLAGS.momentum, FLAGS.decay,
FLAGS.data_dir, FLAGS.dataset)
else:
model.load(FLAGS.checkpoint_dir)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
Analogy = model_dict[FLAGS.dataset]
with tf.Session() as sess:
analogy = Analogy(sess, image_size=FLAGS.image_size, model_type=FLAGS.model_type,
batch_size=FLAGS.batch_size, dataset=FLAGS.dataset)
if FLAGS.is_train:
analogy.train(max_iter=FLAGS.max_iter, alpha=FLAGS.alpha,
learning_rate=FLAGS.learning_rate, checkpoint_dir=FLAGS.checkpoint_dir)
else:
analogy.load(FLAGS.checkpoint_dir)
analogy.test()
def main(_):
pp.pprint(flags.FLAGS.__flags)
data_path = "./data/%s" % FLAGS.dataset
reader = TextReader(data_path)
with tf.Session() as sess:
m = MODELS[FLAGS.model]
model = m(sess, reader, dataset=FLAGS.dataset,
embed_dim=FLAGS.embed_dim, h_dim=FLAGS.h_dim,
learning_rate=FLAGS.learning_rate, max_iter=FLAGS.max_iter,
checkpoint_dir=FLAGS.checkpoint_dir)
if FLAGS.forward_only:
model.load(FLAGS.checkpoint_dir)
else:
model.train(FLAGS)
while True:
text = raw_input(" [*] Enter text to test: ")
model.sample(5, text)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
print(" [*] Creating checkpoint directory...")
os.makedirs(FLAGS.checkpoint_dir)
with tf.Session() as sess:
model = LSTMDQN(checkpoint_dir=FLAGS.checkpoint_dir,
seq_length=FLAGS.seq_length,
embed_dim=FLAGS.embed_dim,
layer_depth=FLAGS.layer_depth,
batch_size=FLAGS.batch_size,
forward_only=FLAGS.forward_only,
game_name=FLAGS.game_name,
game_dir=FLAGS.game_dir)
if not FLAGS.forward_only:
model.run()
else:
test_loss = model.test(2)
print(" [*] Test loss: %2.6f, perplexity: %2.6f" % (test_loss, np.exp(test_loss)))
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session() as sess:
dcgan = DCGAN(sess,
dataset=FLAGS.dataset,
batch_size=FLAGS.batch_size,
output_size=FLAGS.output_size,
c_dim=FLAGS.c_dim)
if FLAGS.is_train:
if FLAGS.preload_data == True:
data = get_data_arr(FLAGS)
else:
data = glob(os.path.join('./data', FLAGS.dataset, '*.jpg'))
train.train_wasserstein(sess, dcgan, data, FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
def main(_):
pp.pprint(flags.FLAGS.__flags)
with tf.device('/cpu:0'), tf.Session() as sess:
FLAGS.sess = sess
if FLAGS.task == 'copy':
if FLAGS.is_train:
copy_train(FLAGS)
cell = NTMCell(input_dim=FLAGS.input_dim, output_dim=FLAGS.output_dim)
ntm = NTM(cell, sess, 1, FLAGS.max_length,
test_max_length=FLAGS.test_max_length, forward_only=True)
ntm.load(FLAGS.checkpoint_dir, 'copy')
copy(ntm, FLAGS.test_max_length*1/3, sess)
print
copy(ntm, FLAGS.test_max_length*2/3, sess)
print
copy(ntm, FLAGS.test_max_length*3/3, sess)
elif FLAGS.task == 'recall':
pass
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session(config=tf.ConfigProto(
allow_soft_placement=True, log_device_placement=False)) as sess:
if FLAGS.dataset == 'mnist':
assert False
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size,
sample_size = 64,
z_dim = 8192,
d_label_smooth = .25,
generator_target_prob = .75 / 2.,
out_stddev = .075,
out_init_b = - .45,
image_shape=[FLAGS.image_width, FLAGS.image_width, 3],
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
generator=Generator(),
train_func=train, discriminator_func=discriminator,
build_model_func=build_model, config=FLAGS,
devices=["gpu:0", "gpu:1", "gpu:2", "gpu:3"] #, "gpu:4"]
)
if FLAGS.is_train:
print "TRAINING"
dcgan.train(FLAGS)
print "DONE TRAINING"
else:
dcgan.load(FLAGS.checkpoint_dir)
OPTION = 2
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth=True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
c_dim=1,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
else:
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
show_all_variables()
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir):
raise Exception("[!] Train a model first, then run test mode")
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
c_dim=1,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
else:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
show_all_variables()
if FLAGS.is_train:
dcgan.train(FLAGS)
# Below is codes for visualization
OPTION = 1 # generate 100 test image
visualize(sess, dcgan, FLAGS, OPTION)
else:
# if not dcgan.load(FLAGS.checkpoint_dir):
# raise Exception("[!] Train a model first, then run test mode")
# else:
# iteration = 10000
# project_x_to_z(dcgan, iteration, sess, FLAGS)
projector = Sketch2Image(dcgan, FLAGS)
projector.build_model()
projector.train(iteration=100)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
##########AYAD###################################
data, labels = readMiasDATA()
#data = readFaces()
options = {
"cropping": "seq",
"crop_step": 8,
"crop_skip": 0.031,
"crop_size": (FLAGS.input_width, FLAGS.input_width)
}
mias = MIAS(data, None, [1, 0, 0], False, ["NORM"], options)
GANoptions = {
'noise size': 100,
'noise type': 'normal',
'label smooth': True
}
##########AYAD###################################
print("entering sesh")
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
else:
print("going into else")
dcgan = DCGAN(sess,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
input_class=mias,
GANoptions=GANoptions) #AYAD
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
#memory allocation
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
#run_config.gpu_options.per_process_gpu_memory_fraction = 0.4
#tf.Session: A class for running TensorFlow operations.
#config=~ : tells to use specific configuration(setting)
with tf.Session(config=run_config) as sess:
#initialization
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
test_batch_size=FLAGS.test_batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
test_dir=FLAGS.test_dir)
else:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
test_batch_size=FLAGS.test_batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
test_dir=FLAGS.test_dir)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
if FLAGS.anomaly_test:
dcgan.anomaly_detector()
assert len(dcgan.test_data_names) > 0
fp = open('anomaly_score_record.txt', 'a+')
#for idx in range(2):
for idx in range(len(dcgan.test_data_names)):
test_input = np.expand_dims(dcgan.test_data[idx],
axis=0) ###################
test_name = dcgan.test_data_names[idx]
dcgan.train_anomaly_detector(FLAGS, test_input, test_name) ##
#assert: Python evealuates the statement, if false, exception error will raise
#image = np.expand_dims(image, <your desired dimension>)
# Below is codes for visualization
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION)
def train(self,config,prin):
"""Train netmodel"""
print ("Initializing all variable...")
init = tf.global_variables_initializer()
self.sess.run(init)
self.sess.graph.finalize()
counter = 1
start_time = time.time()
pp.pprint(prin)
if self.load(self.checkpoint_dir, config.model_dir):
print("[*] load success...")
else:
print("[*] load failed....")
# if self.load1('some', config.model_dir):
# print("[*] load success...")
# else:
# print("[*] load failed....")
############################################## load Train data ############################################
print("[prepare loading train data...")
train_Inputs1, train_Labels1, train_Inputs2, train_Labels2 , train_Inputs3, train_Labels3, train_Inputs4, train_Labels4 = self.prepare_data('train')#train Set10
train_size = train_Inputs1.shape[2]
image_size = train_Inputs1.shape[1]
batch_index = range(train_size)
print("[INFO] the train dataset is: %s, image_size is %d * %d" % (str(train_size*config.num_gpus), image_size, image_size))
############################################# load validate data ############################################
print("[prepare loading validate data...")
self.val_Inputs1, self.val_Labels1, self.val_Inputs2, self.val_Labels2 , self.val_Inputs3, self.val_Labels3, self.val_Inputs4, self.val_Labels4 = self.prepare_data('validate')#validate Set10
validate_size = self.val_Inputs1.shape[2]
val_image_size = self.val_Inputs1.shape[1]
val_batch_size = config.batch_size
validate_index = range(validate_size)
random.shuffle(validate_index)
validate_idxs = validate_size // val_batch_size
print("[INFO] the validate dataset is: %s, image_size is %d * %d" % (str(validate_size*config.num_gpus), val_image_size, val_image_size))
trn_counter = 0
LOSS_trn = 0
learning_rate = config.learning_rate
###############################################################################################################################################################################################################
for epoch in xrange(config.epoch):
random.shuffle(batch_index)
batch_idxs = min(train_size, config.train_size) // config.batch_size
save_step = np.array(batch_idxs// 4).astype(int)
if epoch % 3 == 0:
learning_rate = config.learning_rate * 0.5 ** (epoch // 3)
# if epoch ==3:
# learning_rate = config.learning_rate * 0.5
# elif epoch ==7:
# learning_rate = config.learning_rate * 0.5**2
#
# elif epoch ==11:
# learning_rate = config.learning_rate * 0.5**3
#
# elif epoch == 16:
# learning_rate = config.learning_rate * 0.5 ** 3
for idx in xrange(0, batch_idxs): #batch_idxs
############################################## Prepare Train data ############################################
Train_batch_input1 = np.transpose(train_Inputs1[:, :, batch_index[idx*config.batch_size:(idx+1)*config.batch_size]],[2, 1, 0])
Train_batch_input1 = np.reshape(Train_batch_input1, list(Train_batch_input1.shape) + [1])
Train_batch_label1 = np.transpose(train_Labels1[:, :, batch_index[idx*config.batch_size:(idx+1)*config.batch_size]],[2, 1, 0])
Train_batch_label1 = np.reshape(Train_batch_label1, list(Train_batch_label1.shape) + [1])
Train_batch_input2 = np.transpose(train_Inputs2[:, :, batch_index[idx*config.batch_size:(idx+1)*config.batch_size]],[2, 1, 0])
Train_batch_input2 = np.reshape(Train_batch_input2, list(Train_batch_input2.shape) + [1])
Train_batch_label2 = np.transpose(train_Labels2[:, :, batch_index[idx*config.batch_size:(idx+1)*config.batch_size]],[2, 1, 0])
Train_batch_label2 = np.reshape(Train_batch_label2, list(Train_batch_label2.shape) + [1])
Train_batch_input3 = np.transpose(train_Inputs3[:, :, batch_index[idx*config.batch_size:(idx+1)*config.batch_size]],[2, 1, 0])
Train_batch_input3 = np.reshape(Train_batch_input3, list(Train_batch_input3.shape) + [1])
Train_batch_label3 = np.transpose(train_Labels3[:, :, batch_index[idx*config.batch_size:(idx+1)*config.batch_size]],[2, 1, 0])
Train_batch_label3 = np.reshape(Train_batch_label3, list(Train_batch_label3.shape) + [1])
Train_batch_input4 = np.transpose(train_Inputs4[:, :, batch_index[idx*config.batch_size:(idx+1)*config.batch_size]],[2, 1, 0])
Train_batch_input4 = np.reshape(Train_batch_input4, list(Train_batch_input4.shape) + [1])
Train_batch_label4 = np.transpose(train_Labels4[:, :, batch_index[idx*config.batch_size:(idx+1)*config.batch_size]],[2, 1, 0])
Train_batch_label4 = np.reshape(Train_batch_label4, list(Train_batch_label4.shape) + [1])
fd_trn = {self.lr: learning_rate,
self.input1: Train_batch_input1, self.label1: Train_batch_label1,
self.input2: Train_batch_input2, self.label2: Train_batch_label2,
self.input3: Train_batch_input3, self.label3: Train_batch_label3,
self.input4: Train_batch_input4, self.label4: Train_batch_label4}
_ = self.sess.run(self.train_op, feed_dict=fd_trn)
nmse_train = self.sess.run(self.avg_loss, feed_dict=fd_trn)
rate = self.sess.run(self.learning_rate, feed_dict=fd_trn)
# loss_train_all = self.sess.run(self.merged, feed_dict=fd_trn)
# self.train_all_writer.add_summary(loss_train_all, counter)
LOSS_trn = LOSS_trn + nmse_train
trn_counter = trn_counter +1
counter += 1
########################## The validate running ####################33
# if counter % 5000 == 0: # 5000
if counter % (save_step) == 0: # save_step
avg_LOSS_validate = 0
for val_idx in xrange(0, validate_idxs):#validate_idxs
Validate_batch_input1 = np.transpose(self.val_Inputs1[:, :,validate_index[ val_idx*val_batch_size:(val_idx+1) * val_batch_size]],[2, 1, 0])
Validate_batch_input1 = np.reshape(Validate_batch_input1,list(Validate_batch_input1.shape) + [1])
Validate_batch_label1 = np.transpose(self.val_Labels1[:, :,validate_index[ val_idx*val_batch_size:(val_idx+1) * val_batch_size]],[2, 1, 0])
Validate_batch_label1 = np.reshape(Validate_batch_label1,list(Validate_batch_label1.shape) + [1])
Validate_batch_input2 = np.transpose(self.val_Inputs2[:, :,validate_index[ val_idx*val_batch_size:(val_idx+1) * val_batch_size]],[2, 1, 0])
Validate_batch_input2 = np.reshape(Validate_batch_input2,list(Validate_batch_input2.shape) + [1])
Validate_batch_label2 = np.transpose(self.val_Labels2[:, :,validate_index[ val_idx*val_batch_size:(val_idx+1) * val_batch_size]],[2, 1, 0])
Validate_batch_label2 = np.reshape(Validate_batch_label2,list(Validate_batch_label2.shape) + [1])
Validate_batch_input3 = np.transpose(self.val_Inputs3[:, :,validate_index[ val_idx*val_batch_size:(val_idx+1) * val_batch_size]],[2, 1, 0])
Validate_batch_input3 = np.reshape(Validate_batch_input3,list(Validate_batch_input3.shape) + [1])
Validate_batch_label3 = np.transpose(self.val_Labels3[:, :,validate_index[ val_idx*val_batch_size:(val_idx+1) * val_batch_size]],[2, 1, 0])
Validate_batch_label3 = np.reshape(Validate_batch_label3,list(Validate_batch_label3.shape) + [1])
Validate_batch_input4 = np.transpose(self.val_Inputs4[:, :,validate_index[ val_idx*val_batch_size:(val_idx+1) * val_batch_size]],[2, 1, 0])
Validate_batch_input4 = np.reshape(Validate_batch_input4,list(Validate_batch_input4.shape) + [1])
Validate_batch_label4 = np.transpose(self.val_Labels4[:, :,validate_index[ val_idx*val_batch_size:(val_idx+1) * val_batch_size]],[2, 1, 0])
Validate_batch_label4 = np.reshape(Validate_batch_label4,list(Validate_batch_label4.shape) + [1])
fd_val = {self.lr: learning_rate,
self.input1: Validate_batch_input1, self.label1: Validate_batch_label1,
self.input2: Validate_batch_input2, self.label2: Validate_batch_label2,
self.input3: Validate_batch_input3, self.label3: Validate_batch_label3,
self.input4: Validate_batch_input4, self.label4: Validate_batch_label4}
# _ = self.sess.run(self.train_op, feed_dict=fd_val) # if train, can run the step
nmse_validate = self.sess.run(self.avg_loss, feed_dict=fd_val)
avg_LOSS_validate = avg_LOSS_validate + nmse_validate
# loss_train = self.sess.run(self.merged, feed_dict=fd_trn)
# self.train_writer.add_summary(loss_train, counter)
# loss_val = self.sess.run(self.merged, feed_dict=fd_val)
# self.val_writer.add_summary(loss_val, counter)
avg_LOSS_validate = avg_LOSS_validate / validate_idxs
avg_MSE_validate = avg_LOSS_validate* np.square(255.0)
avg_PSNR_validate = 20.0 * np.log10(255.0 / np.sqrt(avg_MSE_validate))
avg_loss_trn = LOSS_trn /trn_counter
avg_MSE_trn = avg_loss_trn* np.square(255.0)
avg_PSNR_trn = 20.0 * np.log10(255.0 / np.sqrt(avg_MSE_trn))
trn_counter = 0
LOSS_trn =0
print("Epoch: [%3d] [%4d/%4d][%7d] time: %10.4f, lr: %1.8f PSNR_trn: %2.4f, PSNR_val: %2.4f, loss_trn: %.8f, loss_val: %.8f" % (epoch + 1, idx + 1, batch_idxs,counter,
time.time() - start_time, rate,avg_PSNR_trn, avg_PSNR_validate, avg_loss_trn, avg_LOSS_validate))
# if counter % 5000 == 0:
self.save(config.checkpoint_dir, counter, config.model_dir)
def main(_):
width_size = 905
height_size = 565
#width_size = 1104
#height_size = 764
#width_size = 1123
#height_size = 900
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
if not os.path.exists('./logs_multifreq_skip3'):
os.makedirs('./logs_multifreq_skip3')
gpu_config = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu)
#with tf.Session() as sess:
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_config)) as sess:
if FLAGS.is_train:
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size,\
num_block = FLAGS.num_block,dataset_name=FLAGS.dataset,is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir)
else:
dcgan = EVAL(sess, batch_size=1,num_block=FLAGS.num_block,ir_image_shape=[None,None,1],dataset_name=FLAGS.dataset,\
is_crop=False, checkpoint_dir=FLAGS.checkpoint_dir)
print('deep model test \n')
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
list_val = [11, 16, 21, 22, 33, 36, 38, 53, 59, 92]
print '1: Estimating Normal maps from arbitary obejcts \n'
print '2: EStimating Normal maps according to only object tilt angles(Light direction is fixed(EX:3) \n'
print '3: Estimating Normal maps according to Light directions and object tilt angles \n'
x = input('Selecting a Evaluation mode:')
VAL_OPTION = int(x)
if VAL_OPTION == 1: # arbitary dataset
print("Computing arbitary dataset ")
trained_models = glob.glob(
os.path.join(FLAGS.checkpoint_dir, FLAGS.dataset,
'DCGAN.model*'))
trained_models = natsorted(trained_models)
model = trained_models[6]
model = model.split('/')
model = model[-1]
print('Load trained network: %s\n' % model)
dcgan.load(FLAGS.checkpoint_dir, model)
datapath = '/research3/datain/gmchoe_normal/0403/IR_0.25'
#datapath = '/research3/dataout/ECCV_2018/2130/centerview'
#datapath = '/research2/proposal_linux/dataset/coin'
savepath = datapath
mean_nir = -0.3313
img_files = glob.glob(os.path.join(datapath, '*.png'))
img_files = natsorted(img_files)
pdb.set_trace()
#listdir = natsorted(os.listdir(datapath))
#fulldatapath = natsorted(fulldatapath)
for idx in xrange(0, len(img_files)):
print('Processing %d/%d \n' % (len(img_files), idx))
#img_file = glob.glob(os.path.join(datapath,'nir.jpg'))
#img_file = glob.glob(os.path.join(datapath,listdir[idx]))
input_ = scipy.misc.imread(img_files[idx],
'F').astype(float)
height_size = input_.shape[0]
width_size = input_.shape[1]
#input_ = scipy.misc.imresize(input_,[565,905])
input_ = np.reshape(
input_,
(height_size, width_size, 1)) # LF size:383 x 552
#input_ = np.power(input_,0.6)
nondetail_input_ = ndimage.gaussian_filter(input_,
sigma=(1, 1, 0),
order=0)
input_ = input_ / 127.5 - 1.0
nondetail_input_ = nondetail_input_ / 127.5 - 1.0 # normalize -1 ~1
detail_input_ = input_ - nondetail_input_
nondetail_input_ = np.reshape(
nondetail_input_,
(1, height_size, width_size, 1)) # LF size:383 x 552
detail_input_ = np.reshape(detail_input_,
(1, height_size, width_size, 1))
#detail_input_ = detail_input_/127.5 -1.0 # normalize -1 ~1
start_time = time.time()
sample = sess.run(dcgan.G,
feed_dict={
dcgan.nondetail_images:
nondetail_input_,
dcgan.detail_images: detail_input_
})
print('time: %.8f' % (time.time() - start_time))
sample = np.squeeze(sample).astype(np.float32)
# normalization #
output = np.sqrt(np.sum(np.power(sample, 2), axis=2))
output = np.expand_dims(output, axis=-1)
output = sample / output
output = (output + 1.) / 2.
"""
if not os.path.exists(os.path.join(savepath,'%s/%s/%s' %(FLAGS.dataset,model,listdir[idx]))):
os.makedirs(os.path.join(savepath,'%s/%s/%s' %(FLAGS.dataset,model,listdir[idx])))
"""
savename = os.path.join(
savepath, 'result/%s.bmp' % (img_files[idx][-10:]))
#savename = os.path.join(savepath,'single_normal_%02d.bmp' % (idx+1))
#savename = os.path.join(savepath,'%s/%s/%s/single_normal.bmp' % (FLAGS.dataset,model,listdir[idx]))
scipy.misc.imsave(savename, output)
elif VAL_OPTION == 2: # light source fixed
list_val = [11, 16, 21, 22, 33, 36, 38, 53, 59, 92]
#save_files = glob.glob(os.path.join(FLAGS.checkpoint_dir,FLAGS.dataset,'DCGAN.model*'))
#save_files = natsorted(save_files)
load, iteratoin = dcgan.load(FLAGS.checkpoint_dir)
savepath = './result/skip3_result/Light3/L2ang/%06d' % iteration
if load:
for idx in range(len(list_val)):
if not os.path.exists(
os.path.join(savepath,
'%03d' % list_val[idx])):
os.makedirs(
os.path.join(savepath, '%03d' % list_val[idx]))
for idx2 in range(1, 10):
print("Selected material %03d/%d" %
(list_val[idx], idx2))
img = '/research2/IR_normal_small/save%03d/%d' % (
list_val[idx], idx2)
input_ = scipy.misc.imread(img +
'/3.bmp').astype(float)
#gt_ = scipy.misc.imread('/research2/IR_normal_small/save016/1/12_Normal.bmp').astype(float)
input_ = scipy.misc.imresize(input_, [600, 800])
input_ = np.reshape(input_, (600, 800, 1))
nondetail_input = ndimage.median_filter(input_,
size=(3, 3,
3))
#nondetail_input_ = ndimage.gaussian_filter(input_,sigma=(1,1,0),order=0)
input_ = input_ / 127.5 - 1.0
nondetail_input_ = nondetail_input_ / 127.5 - 1.0 # normalize -1 ~1
detail_input_ = input_ - nondetail_input_
nondetail_input_ = np.reshape(
nondetail_input_, (1, 600, 800, 1))
detail_input_ = np.reshape(detail_input_,
(1, 600, 800, 1))
start_time = time.time()
sample = sess.run(
[dcgan.G],
feed_dict={
dcgan.nondetail_images: nondetail_input_,
dcgan.detail_images: detail_input_
})
print('time: %.8f' % (time.time() - start_time))
# normalization #
sample = np.squeeze(sample).astype(np.float32)
output = np.zeros((600, 800, 3)).astype(np.float32)
output[:, :, 0] = sample[:, :, 0] / (np.sqrt(
np.power(sample[:, :, 0], 2) +
np.power(sample[:, :, 1], 2) +
np.power(sample[:, :, 2], 2)))
output[:, :, 1] = sample[:, :, 1] / (np.sqrt(
np.power(sample[:, :, 0], 2) +
np.power(sample[:, :, 1], 2) +
np.power(sample[:, :, 2], 2)))
output[:, :, 2] = sample[:, :, 2] / (np.sqrt(
np.power(sample[:, :, 0], 2) +
np.power(sample[:, :, 1], 2) +
np.power(sample[:, :, 2], 2)))
output[output == inf] = 0.0
sample = (output + 1.) / 2.
if not os.path.exists(
os.path.join(
savepath, '%03d/%d' %
(list_val[idx], idx2))):
os.makedirs(
os.path.join(
savepath,
'%03d/%d' % (list_val[idx], idx2)))
savename = os.path.join(
savepath, '%03d/%d/single_normal.bmp' %
(list_val[idx], idx2))
scipy.misc.imsave(savename, sample)
else:
print("Failed to load network")
elif VAL_OPTION == 3: # depends on light sources
list_val = [11, 16, 21, 22, 33, 36, 38, 53, 59, 92]
mean_nir = -0.3313 #-1~1
load, iteratoin = dcgan.load(FLAGS.checkpoint_dir)
savepath = './result/skip3_result/allviews/L2ang/%06d' % iteration
if not os.path.exists(os.path.join(savepath)):
os.makedirs(os.path.join(savepath))
if load:
print(" Load Success")
for idx in range(len(list_val)):
if not os.path.exists(
os.path.join(savepath,
'%03d' % list_val[idx])):
os.makedirs(
os.path.join(savepath, '%03d' % list_val[idx]))
for idx2 in range(1, 10): #tilt angles 1~9
for idx3 in range(1, 13): # light source
print("Selected material %03d/%d" %
(list_val[idx], idx2))
img = '/research2/IR_normal_small/save%03d/%d' % (
list_val[idx], idx2)
input_ = scipy.misc.imread(
img + '/%d.bmp' % idx3).astype(
np.float32) #input NIR image
input_ = scipy.misc.imresize(
input_, [600, 800], 'nearest')
input_ = np.reshape(input_, (600, 800, 1))
nondetail_input_ = ndimage.gaussian_filter(
input_, sigma=(1, 1, 0), order=0)
input_ = input_ / 127.5 - 1.0
nondetail_input_ = nondetail_input_ / 127.5 - 1.0 # normalize -1 ~1
detail_input_ = input_ - nondetail_input_
nondetail_input_ = np.reshape(
nondetail_input_, (1, 600, 800, 1))
detail_input_ = np.reshape(
detail_input_, (1, 600, 800, 1))
start_time = time.time()
sample = sess.run(
[dcgan.G],
feed_dict={
dcgan.nondetail_images:
nondetail_input_,
dcgan.detail_images: detail_input_
})
sample = np.squeeze(sample[-1]).astype(
np.float32)
print('time: %.8f' %
(time.time() - start_time))
# normalization #
output = np.sqrt(
np.sum(np.power(sample, 2), axis=2))
output = np.expand_dims(output, axis=-1)
output = sample / output
output = (output + 1.) / 2.
if not os.path.exists(
os.path.join(
savepath, '%s/%03d/%d' %
(FLAGS.dataset, list_val[idx],
idx2))):
os.makedirs(
os.path.join(
savepath, '%s/%03d/%d' %
(FLAGS.dataset, list_val[idx],
idx2)))
savename = os.path.join(
savepath,
'%s/%03d/%d/single_normal_%03d.bmp' %
(FLAGS.dataset, list_val[idx], idx2, idx3))
scipy.misc.imsave(savename, output)
else:
print("Failed to laod network")
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(SAMPLE_DIR):
os.makedirs(SAMPLE_DIR)
# If checkpoint_dir specified exists, copy its contents to the checkpoint dir used internally
if os.path.exists(FLAGS.checkpoint_dir):
shutil.copytree(FLAGS.checkpoint_dir, INTERNAL_CHECKPOINT_DIR)
elif not os.path.exists(INTERNAL_CHECKPOINT_DIR):
os.makedirs(INTERNAL_CHECKPOINT_DIR)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=INTERNAL_CHECKPOINT_DIR,
sample_dir=SAMPLE_DIR,
data_dir=DATA_DIR,
)
else:
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=INTERNAL_CHECKPOINT_DIR,
sample_dir=SAMPLE_DIR,
data_dir=DATA_DIR,
)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(INTERNAL_CHECKPOINT_DIR)[0]:
raise Exception("[!] Train a model first, then run test mode")
# Visualisation
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION)
# Copy checkpoints to floydhub output for potential reuse
shutil.copytree(INTERNAL_CHECKPOINT_DIR,
'{}/checkpoint'.format(SAMPLE_DIR))
def main(_):
pp.pprint(flags.FLAGS.__flags)
FLAGS.data_dir = expand_path(FLAGS.data_dir)
FLAGS.out_dir = expand_path(FLAGS.out_dir)
FLAGS.out_name = expand_path(FLAGS.out_name)
FLAGS.checkpoint_dir = expand_path(FLAGS.checkpoint_dir)
FLAGS.sample_dir = expand_path(FLAGS.sample_dir)
if FLAGS.output_height is None: FLAGS.output_height = FLAGS.input_height
if FLAGS.input_width is None: FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None: FLAGS.output_width = FLAGS.output_height
if FLAGS.out_name == "":
FLAGS.out_name = '{} - {} - {}'.format(timestamp(),
FLAGS.data_dir.split('/')[-1],
FLAGS.dataset)
if FLAGS.train:
FLAGS.out_name += ' - x{}.z{}.{}.y{}.b{}'.format(
FLAGS.input_width, FLAGS.z_dim, FLAGS.z_dist,
FLAGS.output_width, FLAGS.batch_size)
#FLAGS.out_dir = os.path.join(FLAGS.out_dir, FLAGS.out_name)
#FLAGS.checkpoint_dir = os.path.join(FLAGS.out_dir, FLAGS.checkpoint_dir)
#FLAGS.sample_dir = os.path.join(FLAGS.out_dir, FLAGS.sample_dir)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir)
with open(os.path.join(FLAGS.out_dir, 'FLAGS.json'), 'w') as f:
flags_dict = {k: FLAGS[k].value for k in FLAGS}
json.dump(flags_dict, f, indent=4, sort_keys=True, ensure_ascii=False)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.z_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir,
out_dir=FLAGS.out_dir,
max_to_keep=FLAGS.max_to_keep)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
load_success, load_counter = dcgan.load(FLAGS.checkpoint_dir)
if not load_success:
raise Exception("checkpoint not found in " +
FLAGS.checkpoint_dir)
if FLAGS.export:
export_dir = os.path.join(FLAGS.checkpoint_dir,
'export_b' + str(FLAGS.batch_size))
dcgan.save(export_dir, load_counter, ckpt=True, frozen=False)
if FLAGS.freeze:
export_dir = os.path.join(FLAGS.checkpoint_dir,
'frozen_b' + str(FLAGS.batch_size))
dcgan.save(export_dir, load_counter, ckpt=False, frozen=True)
if FLAGS.visualize:
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION, FLAGS.sample_dir)
sess.close()
def main(_):
"""
The main function for training steps
"""
pp.pprint(flags.FLAGS.__flags)
n_per_itr_print_results = 100
kb_work_on_patch = True
# ---------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------
# Manual Switchs ------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------
# DATASET PARAMETER : UCSD
#FLAGS.dataset = 'UCSD'
#FLAGS.dataset_address = './dataset/UCSD_Anomaly_Dataset.v1p2/UCSDped2/Train'
nd_input_frame_size = (240, 360)
nd_slice_size = (45, 45)
n_stride = 25
n_fetch_data = 600
# ---------------------------------------------------------------------------------------------
# # DATASET PARAMETER : MNIST
# FLAGS.dataset = 'mnist'
# FLAGS.dataset_address = './dataset/mnist'
# nd_input_frame_size = (28, 28)
# nd_slice_size = (28, 28)
FLAGS.train = True
FLAGS.input_width = nd_slice_size[0]
FLAGS.input_height = nd_slice_size[1]
FLAGS.output_width = nd_slice_size[0]
FLAGS.output_height = nd_slice_size[1]
FLAGS.sample_dir = 'export/' + FLAGS.dataset + '_%d.%d' % (
nd_slice_size[0], nd_slice_size[1])
FLAGS.input_fname_pattern = '*'
check_some_assertions()
# manual handling of GPU
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
run_config = tf.ConfigProto(gpu_options=gpu_options)
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
tmp_model = ALOCC_Model(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
attention_label=FLAGS.attention_label,
r_alpha=FLAGS.r_alpha,
dataset_name=FLAGS.dataset,
dataset_address=FLAGS.dataset_address,
input_fname_pattern=FLAGS.input_fname_pattern,
checkpoint_dir=FLAGS.checkpoint_dir,
is_training=FLAGS.train,
log_dir=FLAGS.log_dir,
sample_dir=FLAGS.sample_dir,
nd_patch_size=nd_slice_size,
n_stride=n_stride,
n_per_itr_print_results=n_per_itr_print_results,
kb_work_on_patch=kb_work_on_patch,
nd_input_frame_size=nd_input_frame_size,
n_fetch_data=n_fetch_data)
#show_all_variables()
if FLAGS.train:
print('Program is on Train Mode')
tmp_model.train(FLAGS)
else:
if not tmp_model.load(FLAGS.checkpoint_dir)[0]:
print('Program is on Test Mode')
raise Exception(
"[!] Train a model first, then run test mode from file test.py"
)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
if 'cond' in FLAGS.dataset:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=128,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
else:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
data = np.load('bird_mappings.npy')
# coco = COCO('../coco/annotations/captions_train2014.json')
# print('Preprocessing COCO data...')
# imgIds = [x[1] for x in data]
# caps = coco.loadAnns(ids=coco.getAnnIds(imgIds=imgIds))
# true_img_ids = []
# for cap in caps:
# lst = cap['caption'].split(' ')
# if 'car' in lst or 'truck' in lst:
# true_img_ids.append(cap['image_id'])
#
# true_img_ids = np.array(true_img_ids)
# true_img_ids = np.unique(true_img_ids)
## all_imgs = {img_id: get_image(img_id,
## input_height=self.input_height,
## input_width=self.input_width,
## resize_height=self.output_height,
## resize_width=self.output_width,
## crop=self.crop,
## grayscale=self.grayscale,
## coco=coco) for img_id in true_img_ids}
# #print('Saving Stuff, brace yourself..')
# #np.save('vehicle_imgs.npy', all_imgs)
# #print('Saved the stuff, exit now dude!.....')
# #time.sleep(10)
# true_img_ids = {x:1 for x in true_img_ids}
# data = data[[(data[i][1] in true_img_ids) for i in range(len(data))]]
#data = np.array([(data[i][0], [data[i][1]]) for i in range(len(data))])
b_size = 64
tot = 20000
print(data.shape)
ret = np.empty((0, 64, 64, 3), dtype=np.float32)
#ret = np.array(birds_gt)
data = np.array([(x[0], x[1][19:-4]) for x in data])
for i in range(tot // b_size):
test_idx = np.random.randint(0, len(data))
print('Loading text embedding - ', data[test_idx][1])
inp = np.random.uniform(-1.0, 1.0, size=[b_size, 50])
samples = sess.run(dcgan.sampler,
feed_dict={
dcgan.z:
inp,
dcgan.y:
np.array([data[test_idx][0]] * b_size)
})
print('Done with ', i, ' ...')
print(samples.shape)
save_images(
samples, image_manifold_size(samples.shape[0]),
'./{}/train_{}_{:04d}.png'.format('output_cond_cub',
data[test_idx][1], 64))
ret = np.vstack((ret, samples))
print(ret.shape)
ret = (ret + 1.) / 2
np.save('imgs_cond_CUB_bird.npy', ret)
def main(_):
# 打印上面的配置参数
pp.pprint(flags.FLAGS.__flags)
# 定义输入和输出的宽度
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
# 创建检查点和存放输出样本的文件夹，没有就创建
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
# 目测是使配置参数生效
run_config = tf.ConfigProto()
# 使用gpu
run_config.gpu_options.allow_growth = True
# 在会话中训练
with tf.Session(config=run_config) as sess:
# 如果数据集是mnist的操作
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir)
else:
dcgan = DCGAN(
sess,
# 获取输入宽度，高
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
# 获取输出宽度，高
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
# batch_size 和 样本数量的值
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
# 噪声的维度
z_dim=FLAGS.generate_test_images,
# 获取数据集名字
dataset_name=FLAGS.dataset,
# 输入的文件格式(.jpg)
input_fname_pattern=FLAGS.input_fname_pattern,
# 是否使用中心裁剪
crop=FLAGS.crop,
# 检查点,样本储存,数据集的文件夹路径
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir)
# 输出所有参数??
show_all_variables()
# 训练模式为True
if FLAGS.train:
# 调用函数执行训练
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 1
# 可视化操作
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
print('Before processing flags')
pp.pprint(flags.FLAGS.__flags)
if FLAGS.use_s3:
import aws
if FLAGS.s3_bucket is None:
raise ValueError('use_s3 flag set, but no bucket set. ')
# check to see if s3 bucket exists:
elif not aws.bucket_exists(FLAGS.s3_bucket):
raise ValueError(
'`use_s3` flag set, but bucket "%s" doesn\'t exist. Not using s3'
% FLAGS.s3_bucket)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
# configure the log_dir to match the params
log_dir = os.path.join(
FLAGS.log_dir,
"dataset={},isCan={},lr={},imsize={},hasStyleNet={},batch_size={}".
format(FLAGS.dataset, FLAGS.can, FLAGS.learning_rate,
FLAGS.input_height, FLAGS.style_net_checkpoint is not None,
FLAGS.batch_size))
if not glob(log_dir + "*"):
log_dir = os.path.join(log_dir, "000")
else:
containing_dir = os.path.join(log_dir, "[0-9][0-9][0-9]")
nums = [int(x[-3:])
for x in glob(containing_dir)] # TODO FIX THESE HACKS
if nums == []:
num = 0
else:
num = max(nums) + 1
log_dir = os.path.join(log_dir, "{:03d}".format(num))
FLAGS.log_dir = log_dir
if FLAGS.checkpoint_dir is None:
FLAGS.checkpoint_dir = os.path.join(FLAGS.log_dir, 'checkpoint')
FLAGS.use_default_checkpoint = True
elif FLAGS.use_default_checkpoint:
raise ValueError(
'`use_default_checkpoint` flag only works if you keep checkpoint_dir as None'
)
if FLAGS.sample_dir is None:
FLAGS.sample_dir = os.path.join(FLAGS.log_dir, 'samples')
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
print('After processing flags')
pp.pprint(flags.FLAGS.__flags)
if FLAGS.style_net_checkpoint:
from slim.nets import nets_factory
network_fn = nets_factory
sess = None
if FLAGS.dataset == 'mnist':
y_dim = 10
elif FLAGS.dataset == 'wikiart':
y_dim = 27
else:
y_dim = None
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.sample_size,
use_resize=FLAGS.use_resize,
replay=FLAGS.replay,
y_dim=y_dim,
smoothing=FLAGS.smoothing,
lamb=FLAGS.lambda_val,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
wgan=FLAGS.wgan,
learning_rate=FLAGS.learning_rate,
style_net_checkpoint=FLAGS.style_net_checkpoint,
can=FLAGS.can)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = FLAGS.allow_gpu_growth
with tf.Session(config=run_config) as sess:
dcgan.set_sess(sess)
# show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
OPTION = 0
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth=True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
wgan = WGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
dataset=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
clip_up=FLAGS.clip_up,
clip_down=FLAGS.clip_down,
critic_num=FLAGS.critic_num,
mode=FLAGS.mode,
LAMBDA=FLAGS.LAMBDA)
else:
wgan = WGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
dataset=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
clip_up=FLAGS.clip_up,
clip_down=FLAGS.clip_down,
critic_num=FLAGS.critic_num,
mode=FLAGS.mode,
LAMBDA=FLAGS.LAMBDA)
show_all_variables()
if FLAGS.train:
wgan.train(FLAGS)
else:
if not wgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
# to_json("./web/js/layers.js", [wgan.h0_w, wgan.h0_b, wgan.g_bn0],
# [wgan.h1_w, wgan.h1_b, wgan.g_bn1],
# [wgan.h2_w, wgan.h2_b, wgan.g_bn2],
# [wgan.h3_w, wgan.h3_b, wgan.g_bn3],
# [wgan.h4_w, wgan.h4_b, None])
# Below is codes for visualization
OPTION = 1
visualize(sess, wgan, FLAGS, OPTION)
def main(_):
print('Program is started at', time.clock())
pp.pprint(flags.FLAGS.__flags)
n_per_itr_print_results = 100
n_fetch_data = 10
kb_work_on_patch = False
nd_input_frame_size = (240, 360)
#nd_patch_size = (45, 45)
n_stride = 10
#FLAGS.checkpoint_dir = "./checkpoint/UCSD_128_45_45/"
#FLAGS.dataset = 'UCSD'
#FLAGS.dataset_address = './dataset/UCSD_Anomaly_Dataset.v1p2/UCSDped2/Test'
lst_test_dirs = ['Test004', 'Test005', 'Test006']
#DATASET PARAMETER : MNIST
#FLAGS.dataset = 'mnist'
#FLAGS.dataset_address = './dataset/mnist'
#nd_input_frame_size = (28, 28)
#nd_patch_size = (28, 28)
#FLAGS.checkpoint_dir = "./checkpoint/mnist_128_28_28/"
#FLAGS.input_width = nd_patch_size[0]
#FLAGS.input_height = nd_patch_size[1]
#FLAGS.output_width = nd_patch_size[0]
#FLAGS.output_height = nd_patch_size[1]
check_some_assertions()
nd_patch_size = (FLAGS.input_width, FLAGS.input_height)
# FLAGS.nStride = n_stride
#FLAGS.input_fname_pattern = '*'
FLAGS.train = False
FLAGS.epoch = 1
FLAGS.batch_size = 504
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
run_config = tf.ConfigProto(gpu_options=gpu_options)
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
tmp_ALOCC_model = ALOCC_Model(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
attention_label=FLAGS.attention_label,
r_alpha=FLAGS.r_alpha,
is_training=FLAGS.train,
dataset_name=FLAGS.dataset,
dataset_address=FLAGS.dataset_address,
input_fname_pattern=FLAGS.input_fname_pattern,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
nd_patch_size=nd_patch_size,
n_stride=n_stride,
n_per_itr_print_results=n_per_itr_print_results,
kb_work_on_patch=kb_work_on_patch,
nd_input_frame_size=nd_input_frame_size,
n_fetch_data=n_fetch_data)
show_all_variables()
print('--------------------------------------------------')
print('Load Pretrained Model...')
tmp_ALOCC_model.f_check_checkpoint()
if FLAGS.dataset == 'mnist':
mnist = input_data.read_data_sets(FLAGS.dataset_address)
specific_idx_anomaly = np.where(mnist.train.labels != 6)[0]
specific_idx = np.where(mnist.train.labels == 6)[0]
ten_precent_anomaly = [
specific_idx_anomaly[x]
for x in random.sample(range(0, len(specific_idx_anomaly)),
len(specific_idx) // 40)
]
data = mnist.train.images[specific_idx].reshape(-1, 28, 28, 1)
tmp_data = mnist.train.images[ten_precent_anomaly].reshape(
-1, 28, 28, 1)
data = np.append(data, tmp_data).reshape(-1, 28, 28, 1)
lst_prob = tmp_ALOCC_model.f_test_frozen_model(
data[0:FLAGS.batch_size])
print('check is ok')
exit()
#generated_data = tmp_ALOCC_model.feed2generator(data[0:FLAGS.batch_size])
else:
data = read_data.test_data(1)
np.random.shuffle(data)
lst_prob = tmp_ALOCC_model.f_test_frozen_model(
data[0:FLAGS.batch_size])
print('check is ok')
exit()
def main(_):
date = time.strftime('%d%m')
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.output):
os.makedirs(FLAGS.output)
if not os.path.exists(os.path.join('./logs', date)):
os.makedirs(os.path.join('./logs', date))
gpu_config = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_config)) as sess:
if FLAGS.is_train:
LFSR = Model(sess,date, image_wid=FLAGS.image_wid,image_hei =FLAGS.image_hei ,batch_size=FLAGS.batch_size,\
dataset_name=FLAGS.dataset, checkpoint_dir=FLAGS.checkpoint_dir)
LFSR.train(FLAGS)
else:
LFSR = Model(sess,date, image_wid=FLAGS.image_wid ,image_hei = FLAGS.image_hei ,batch_size=FLAGS.batch_size,\
dataset_name=FLAGS.dataset, checkpoint_dir=FLAGS.checkpoint_dir)
if LFSR.loadnet(FLAGS.checkpoint_dir):
print('Load pretrained network \n')
else:
print('Fail to Load network \n')
ssim_val = 0.0
psnr_val = 0.0
#test_batch_idxs = test_input_vertical.shape[-1]/FLAGS.batch_size
view = 2
count = 0
if view == 0:
for tt in [0, 5, 10, 15]:
for ii in range(5):
inputdata = sio.loadmat(
'/research2/SPL/spaSR/buddha/sr_%04d.mat' %
(tt + ii))
sr1 = inputdata['Predict']
inputdata = sio.loadmat(
'/research2/SPL/spaSR/buddha/sr_%04d.mat' %
(ii + tt + 5))
sr2 = inputdata['Predict']
final_output = np.zeros(
(sr1.shape[0], sr2.shape[1], 3)).astype(np.float32)
for ch in range(3):
tmp1 = np.expand_dims(sr1[:, :, ch], axis=-1)
tmp2 = np.expand_dims(sr2[:, :, ch], axis=-1)
tmp = np.concatenate([tmp1, tmp2], axis=-1)
input_ = np.expand_dims(tmp, axis=0)
output = LFSR.sess.run(
LFSR.output_ver,
feed_dict={LFSR.train_input_vertical: input_})
output = np.squeeze(output)
final_output[:, :, ch] = output
sio.savemat(
os.path.join('buddha', 'ang_ver_%04d.mat' % count),
{'Predict': final_output})
count += 1
if view == 1:
for tt in [0, 5, 10, 15, 20]:
for ii in range(4):
inputdata = sio.loadmat(
'/research2/SPL/spaSR/buddha/sr_%04d.mat' %
(tt + ii))
sr1 = inputdata['Predict']
inputdata = sio.loadmat(
'/research2/SPL/spaSR/buddha/sr_%04d.mat' %
(ii + tt + 1))
sr2 = inputdata['Predict']
final_output = np.zeros(
(sr1.shape[0], sr2.shape[1], 3)).astype(np.float32)
for ch in range(3):
tmp1 = np.expand_dims(sr1[:, :, ch], axis=-1)
tmp2 = np.expand_dims(sr2[:, :, ch], axis=-1)
tmp = np.concatenate([tmp1, tmp2], axis=-1)
input_ = np.expand_dims(tmp, axis=0)
output = LFSR.sess.run(
LFSR.output_hor,
feed_dict={
LFSR.train_input_horizontal: input_
})
output = np.squeeze(output)
final_output[:, :, ch] = output
sio.savemat(
os.path.join('buddha', 'ang_hor_%04d.mat' % count),
{'Predict': final_output})
count += 1
if view == 2:
for tt in [0, 5, 10, 15]:
for ii in range(4):
inputdata = sio.loadmat(
'/research2/SPL/spaSR/buddha/sr_%04d.mat' %
(tt + ii))
sr1 = inputdata['Predict']
inputdata = sio.loadmat(
'/research2/SPL/spaSR/buddha/sr_%04d.mat' %
(tt + ii + 1))
sr2 = inputdata['Predict']
inputdata = sio.loadmat(
'/research2/SPL/spaSR/buddha/sr_%04d.mat' %
(tt + ii + 5))
sr3 = inputdata['Predict']
inputdata = sio.loadmat(
'/research2/SPL/spaSR/buddha/sr_%04d.mat' %
(ii + tt + 6))
sr4 = inputdata['Predict']
final_output = np.zeros(
(sr1.shape[0], sr2.shape[1], 3)).astype(np.float32)
for ch in range(3):
tmp1 = np.expand_dims(sr1[:, :, ch], axis=-1)
tmp2 = np.expand_dims(sr2[:, :, ch], axis=-1)
tmp3 = np.expand_dims(sr3[:, :, ch], axis=-1)
tmp4 = np.expand_dims(sr4[:, :, ch], axis=-1)
tmp = np.concatenate([tmp1, tmp2, tmp3, tmp4],
axis=-1)
input_ = np.expand_dims(tmp, axis=0)
output = LFSR.sess.run(
LFSR.output_views,
feed_dict={LFSR.train_input_4views: input_})
output = np.squeeze(output)
final_output[:, :, ch] = output
sio.savemat(
os.path.join('buddha',
'ang_views_%04d.mat' % count),
{'Predict': final_output})
count += 1
def main(_):
width_size = 905
height_size = 565
#width_size = 1104
#height_size = 764
#width_size = 1123
#height_size = 900
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
gpu_config = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu)
#with tf.Session() as sess:
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_config)) as sess:
if FLAGS.is_train:
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size,\
num_block = FLAGS.num_block,dataset_name=FLAGS.dataset,is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir)
else:
dcgan = EVAL(sess, batch_size=1,num_block=FLAGS.num_block,ir_image_shape=[None,None,1],dataset_name=FLAGS.dataset,\
is_crop=False, checkpoint_dir=FLAGS.checkpoint_dir)
print('deep model test \n')
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
list_val = [11, 16, 21, 22, 33, 36, 38, 53, 59, 92]
print '1: Estimating Normal maps from arbitary obejcts \n'
print '2: EStimating Normal maps of NIR dataset \n'
x = input('Selecting a Evaluation mode:')
VAL_OPTION = int(x)
if VAL_OPTION == 1: # arbitary dataset
print("Computing arbitary dataset ")
trained_models = glob.glob(
os.path.join(FLAGS.checkpoint_dir, FLAGS.dataset,
'DCGAN.model*'))
trained_models = natsorted(trained_models)
model = trained_models[6]
model = model.split('/')
model = model[-1]
print('Load trained network: %s\n' % model)
dcgan.load(FLAGS.checkpoint_dir, model)
datapath = '/research3/datain/gmchoe_normal/0403/IR_0.25'
savepath = datapath
mean_nir = -0.3313
img_files = glob.glob(os.path.join(datapath, '*.png'))
img_files = natsorted(img_files)
pdb.set_trace()
for idx in xrange(0, len(img_files)):
print('Processing %d/%d \n' % (len(img_files), idx))
input_ = scipy.misc.imread(img_files[idx],
'F').astype(float)
height_size = input_.shape[0]
width_size = input_.shape[1]
input_ = np.reshape(
input_,
(height_size, width_size, 1)) # LF size:383 x 552
nondetail_input_ = ndimage.gaussian_filter(input_,
sigma=(1, 1, 0),
order=0)
input_ = input_ / 127.5 - 1.0
nondetail_input_ = nondetail_input_ / 127.5 - 1.0 # normalize -1 ~1
detail_input_ = input_ - nondetail_input_
nondetail_input_ = np.reshape(
nondetail_input_,
(1, height_size, width_size, 1)) # LF size:383 x 552
detail_input_ = np.reshape(detail_input_,
(1, height_size, width_size, 1))
#detail_input_ = detail_input_/127.5 -1.0 # normalize -1 ~1
start_time = time.time()
sample = sess.run(dcgan.G,
feed_dict={
dcgan.nondetail_images:
nondetail_input_,
dcgan.detail_images: detail_input_
})
print('time: %.8f' % (time.time() - start_time))
sample = np.squeeze(sample).astype(np.float32)
# normalization #
output = np.sqrt(np.sum(np.power(sample, 2), axis=2))
output = np.expand_dims(output, axis=-1)
output = sample / output
output = (output + 1.) / 2.
"""
if not os.path.exists(os.path.join(savepath,'%s/%s/%s' %(FLAGS.dataset,model,listdir[idx]))):
os.makedirs(os.path.join(savepath,'%s/%s/%s' %(FLAGS.dataset,model,listdir[idx])))
"""
savename = os.path.join(
savepath, 'result/%s.bmp' % (img_files[idx][-10:]))
#savename = os.path.join(savepath,'single_normal_%02d.bmp' % (idx+1))
#savename = os.path.join(savepath,'%s/%s/%s/single_normal.bmp' % (FLAGS.dataset,model,listdir[idx]))
scipy.misc.imsave(savename, output)
elif VAL_OPTION == 2: # light source fixed
list_val = [11, 16, 21, 22, 33, 36, 38, 53, 59, 92]
load, iteration = dcgan.load(FLAGS.checkpoint_dir)
pdb.set_trace()
savepath = './singleview_nir/L2ang/%d' % iteration
obj = 1
count = 1
if load:
for idx in range(len(list_val)):
if not os.path.exists(
os.path.join(savepath,
'%03d' % list_val[idx])):
os.makedirs(
os.path.join(savepath, '%03d' % list_val[idx]))
for idx2 in range(1, 10): #tilt angles
print("Selected material %03d/%d" %
(list_val[idx], idx2))
img = './dataset/multi-view/testdata_3579/%03d/%03d/patch_%06d.mat' % (
obj, idx2, count)
input_ = scipy.io.loadmat(img)
input_ = input_['input_']
input_ = input_.astype(np.float)
#input_ = input_[:,:,0:3]
input_ = np.reshape(input_, (1, 600, 800, 4))
input_ = input_ / 127.5 - 1.0
start_time = time.time()
sample = sess.run([dcgan.G],
feed_dict={dcgan.images: input_})
print('time: %.8f' % (time.time() - start_time))
# normalization #
sample = np.squeeze(sample).astype(np.float32)
output = np.sqrt(
np.sum(np.power(sample, 2), axis=2))
output = np.expand_dims(output, axis=-1)
output = sample / output
output = (output + 1.) / 2.
if not os.path.exists(
os.path.join(
savepath, '%03d/%03d' %
(list_val[idx], idx2))):
os.makedirs(
os.path.join(
savepath,
'%03d/%03d' % (list_val[idx], idx2)))
savename = os.path.join(
savepath, '%03d/%03d/multiview_normal.bmp' %
(list_val[idx], idx2))
scipy.misc.imsave(savename, output)
count = count + 1
obj = obj + 1
else:
print("Failed to load network")
def main(_):
pp.pprint(flags.FLAGS.__flags)
FLAGS.train = True
alpha_max_str = str(FLAGS.alpha_max)
# if FLAGS.steer:
# print('Training with steerable G -> loading model_argminGW2_{} ...'.format(FLAGS.transform_type))
# DCGAN = getattr(importlib.import_module('model_argminGW2_{}'.format(FLAGS.transform_type)), 'DCGAN')
# else:
# print('Training vanilla G -> loading model_vanilla_{} ...'.format(FLAGS.transform_type))
# DCGAN = getattr(importlib.import_module('model_vanilla_{}'.format(FLAGS.transform_type)), 'DCGAN')
print('Training with steerable G for {} transformation ...'.format(FLAGS.transform_type))
if FLAGS.transform_type == 'zoom':
if FLAGS.steer:
from model_argminGW2_zoom import DCGAN
else:
from model_vanilla_zoom import DCGAN
if FLAGS.transform_type == 'shiftx':
alpha_max_str = str(np.uint8(FLAGS.alpha_max))
if FLAGS.steer:
from model_argminGW2_shiftx import DCGAN
else:
from model_vanilla_shiftx import DCGAN
if FLAGS.transform_type == 'shifty':
alpha_max_str = str(np.uint8(FLAGS.alpha_max))
if FLAGS.steer:
from model_argminGW2_shifty import DCGAN
else:
from model_vanilla_shifty import DCGAN
if FLAGS.transform_type == 'rot2d':
alpha_max_str = str(np.uint8(FLAGS.alpha_max))
if FLAGS.steer:
from model_argminGW2_rot2d import DCGAN
else:
from model_vanilla_rot2d import DCGAN
augment_flag_str = 'NoAug'
if FLAGS.aug:
augment_flag_str = 'aug'
steer_flag_str = 'vanilla'
if FLAGS.steer:
steer_flag_str = 'argminGW'
else:
if FLAGS.aug:
steer_flag_str = 'argminW'
if FLAGS.out_name:
FLAGS.out_name = expand_path(FLAGS.out_name)
else:
FLAGS.out_name = FLAGS.transform_type+'_'+augment_flag_str+'_'+steer_flag_str+\
'_alphamax'+alpha_max_str+'_lr'+ str(FLAGS.learning_rate)
print('Results will be saved in {}'.format(FLAGS.out_name))
# expand user name and environment variables
FLAGS.data_dir = expand_path(FLAGS.data_dir)
FLAGS.out_dir = expand_path(FLAGS.out_dir)
# FLAGS.out_name = expand_path(FLAGS.out_name)
FLAGS.checkpoint_dir = expand_path(FLAGS.checkpoint_dir)
FLAGS.sample_dir = expand_path(FLAGS.sample_dir)
if FLAGS.output_height is None: FLAGS.output_height = FLAGS.input_height
if FLAGS.input_width is None: FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None: FLAGS.output_width = FLAGS.output_height
# output folders
if FLAGS.out_name == "":
FLAGS.out_name = '{} - {} - {}'.format(timestamp(), FLAGS.data_dir.split('/')[-1], FLAGS.dataset) # penultimate folder of path
if FLAGS.train:
FLAGS.out_name += ' - x{}.z{}.{}.y{}.b{}'.format(FLAGS.input_width, FLAGS.z_dim, FLAGS.z_dist, FLAGS.output_width, FLAGS.batch_size)
FLAGS.out_dir = os.path.join(FLAGS.out_dir, FLAGS.out_name)
FLAGS.checkpoint_dir = os.path.join(FLAGS.out_dir, FLAGS.checkpoint_dir)
FLAGS.sample_dir = os.path.join(FLAGS.out_dir, FLAGS.sample_dir)
if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir)
with open(os.path.join(FLAGS.out_dir, 'FLAGS.json'), 'w') as f:
flags_dict = {k:FLAGS[k].value for k in FLAGS}
json.dump(flags_dict, f, indent=4, sort_keys=True, ensure_ascii=False)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth=True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
z_dim=FLAGS.z_dim,
dataset_name=FLAGS.dataset,
aug=FLAGS.aug,
alpha_max=FLAGS.alpha_max,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir,
out_dir=FLAGS.out_dir,
max_to_keep=FLAGS.max_to_keep)
else:
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.z_dim,
dataset_name=FLAGS.dataset,
aug=FLAGS.aug,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir,
out_dir=FLAGS.out_dir,
max_to_keep=FLAGS.max_to_keep)
show_all_variables()
if FLAGS.train:
print('>>>---Traning mode is set to {}---<<<'.format(FLAGS.train))
time.sleep(10)
dcgan.train(FLAGS)
else:
print('<<<---Testing mode--->>>')
time.sleep(10)
load_success, load_counter = dcgan.load(FLAGS.checkpoint_dir)
if not load_success:
raise Exception("Checkpoint not found in " + FLAGS.checkpoint_dir)
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
if FLAGS.export:
export_dir = os.path.join(FLAGS.checkpoint_dir, 'export_b'+str(FLAGS.batch_size))
dcgan.save(export_dir, load_counter, ckpt=True, frozen=False)
if FLAGS.freeze:
export_dir = os.path.join(FLAGS.checkpoint_dir, 'frozen_b'+str(FLAGS.batch_size))
dcgan.save(export_dir, load_counter, ckpt=False, frozen=True)
if FLAGS.visualize:
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION, FLAGS.sample_dir)
def main(_):
pp.pprint(flags.FLAGS.__flags)
# expand user name and environment variables
FLAGS.data_dir = expand_path(FLAGS.data_dir)
FLAGS.out_dir = expand_path(FLAGS.out_dir)
FLAGS.out_name = expand_path(FLAGS.out_name)
FLAGS.checkpoint_dir = expand_path(FLAGS.checkpoint_dir)
FLAGS.sample_dir = expand_path(FLAGS.sample_dir)
if FLAGS.output_height is None: FLAGS.output_height = FLAGS.input_height
if FLAGS.input_width is None: FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None: FLAGS.output_width = FLAGS.output_height
# output folders
if FLAGS.out_name == "":
FLAGS.out_name = '{} - {} - {}'.format(timestamp(), FLAGS.data_dir.split('/')[-1],
FLAGS.dataset) # penultimate folder of path
if FLAGS.train:
FLAGS.out_name += ' - x{}.z{}.{}.y{}.b{}'.format(FLAGS.input_width, FLAGS.z_dim, FLAGS.z_dist,
FLAGS.output_width, FLAGS.batch_size)
FLAGS.out_dir = os.path.join(FLAGS.out_dir, FLAGS.out_name)
FLAGS.checkpoint_dir = os.path.join(FLAGS.out_dir, FLAGS.checkpoint_dir)
FLAGS.sample_dir = os.path.join(FLAGS.out_dir, FLAGS.sample_dir)
if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir)
with open(os.path.join(FLAGS.out_dir, 'FLAGS.json'), 'w') as f:
flags_dict = {k: FLAGS[k].value for k in FLAGS}
json.dump(flags_dict, f, indent=4, sort_keys=True, ensure_ascii=False)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
z_dim=FLAGS.z_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir,
out_dir=FLAGS.out_dir,
max_to_keep=FLAGS.max_to_keep)
else:
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.z_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir,
out_dir=FLAGS.out_dir,
max_to_keep=FLAGS.max_to_keep)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
# load_success, load_counter = dcgan.load(
# ' ./out\\20190807.100629 - data - mnist - x28.z100.uniform_signed.y28.b64\checkpoint')
load_success, load_counter = dcgan.load(FLAGS.checkpoint_dir)
if not load_success:
raise Exception("Checkpoint not found in " + FLAGS.checkpoint_dir)
if FLAGS.export:
export_dir = os.path.join(FLAGS.checkpoint_dir, 'export_b' + str(FLAGS.batch_size))
dcgan.save(export_dir, load_counter, ckpt=True, frozen=False)
if FLAGS.freeze:
export_dir = os.path.join(FLAGS.checkpoint_dir, 'frozen_b' + str(FLAGS.batch_size))
dcgan.save(export_dir, load_counter, ckpt=False, frozen=True)
if FLAGS.visualize:
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION, FLAGS.sample_dir)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1)
gpu_options.allow_growth = True
run_config = tf.ConfigProto(allow_soft_placement=False,
gpu_options=gpu_options,
log_device_placement=False)
run_config.gpu_options.allow_growth = True
tf.reset_default_graph()
input_shape = [FLAGS.batch_size, 182, 218, 182, 1]
# input_shape = [FLAGS.batch_size, 145, 174, 145, 1]
modal = FLAGS.modal
seqs = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
# seqs=[0,3]
t = len(seqs)
start_t = time.time()
for iter in range(1):
acc = [0] * t
acc_val = [0] * t
for acc_id, dataset_seq in enumerate(seqs):
tf.reset_default_graph()
if FLAGS.train:
with tf.Session(config=run_config) as sess:
next_batch, next_batch_v, next_batch_test = load_data_with_val(
sess,
batch_size=FLAGS.batch_size,
zoom_rate=FLAGS.zoom_rate,
cross=dataset_seq,
modal='flirt')
cnn_mri = CNN_MRI(sess,
input_shape=input_shape,
config=FLAGS,
batch_size=FLAGS.batch_size,
zoom_rate=FLAGS.zoom_rate,
y_dim1=2,
y_dim2=4,
stride=[1, 1, 1, 1, 1],
padding='SAME',
checkpoint_dir=FLAGS.checkpoint_dir,
model_name='CNN_{}_{}'.format(
modal, dataset_seq),
isTrain=True)
cnn_mri.train(next_batch, next_batch_v, FLAGS, dataset_seq)
n1 = 0
r1 = 0
for i in range(1):
data, label = sess.run(next_batch_v)
label = np.squeeze(label)
label = np.squeeze(label)
g1 = cnn_mri.cnn_correct(data, label, FLAGS)
n1 = n1 + FLAGS.batch_size
r1 = r1 + g1
a_t_g = r1 / n1
print('validation set 100 elements accuracy:{} '.format(
a_t_g))
tf.reset_default_graph()
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
_, next_batch_v, next_batch_test = load_data_with_val(
sess,
batch_size=FLAGS.batch_size,
zoom_rate=FLAGS.zoom_rate,
cross=dataset_seq)
cnn_mri = CNN_MRI(sess,
config=FLAGS,
input_shape=input_shape,
batch_size=FLAGS.batch_size,
zoom_rate=FLAGS.zoom_rate,
y_dim1=2,
y_dim2=4,
stride=[1, 1, 1, 1, 1],
padding='SAME',
checkpoint_dir=FLAGS.checkpoint_dir,
model_name='CNN_{}_{}'.format(
modal, dataset_seq),
isTrain=False)
cnn_mri.load(FLAGS.checkpoint_dir)
n1 = 0
r1 = 0
for i in range(100):
data, label = sess.run(next_batch_v)
label = np.squeeze(label)
g1 = cnn_mri.cnn_correct(data, label, FLAGS)
n1 = n1 + FLAGS.batch_size
r1 = r1 + g1
a_v_g = r1 / n1
acc_val[acc_id] = a_v_g
print('validation set 100 elements accuracy:{} '.format(a_v_g))
n1 = 0
r1 = 0
for i in range(100):
data, label = sess.run(next_batch_test)
label = np.squeeze(label)
label = np.squeeze(label)
g1 = cnn_mri.cnn_correct(data, label, FLAGS)
n1 = n1 + FLAGS.batch_size
r1 = r1 + g1
a_v_g = r1 / n1
acc[acc_id] = a_v_g
print('test set 100 elements accuracy:{} '.format(a_v_g))
print('acc_val:{}'.format(acc_val))
print('acc:{}'.format(acc))
print('avg_acc:{}'.format(sum(acc) / t))
print('time cost:{}'.format(time.time() - start_t))
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth=True
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
c_dim=1,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
else:
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
show_all_variables()
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir):
raise Exception("[!] Train a model first, then run test mode")
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
STARTED_DATESTRING = "{0:%Y-%m-%dT%H:%M:%S}".format(
datetime.now()).replace(":", "-")
pp.pprint(flags.FLAGS.__flags)
assert FLAGS.mode.lower() in (
'train', 'generate'), "mode must be 'train' or 'generate'!"
FLAGS.mode = FLAGS.mode.lower()
if FLAGS.mode == 'train':
if FLAGS.out_dir is None:
FLAGS.out_dir = 'out/train_' + STARTED_DATESTRING
print('Using default out_dir {0}'.format(FLAGS.out_dir))
else:
if FLAGS.out_dir.endswith('/'): FLAGS.out_dir = FLAGS.out_dir[:-1]
if FLAGS.checkpoint_dir is None:
FLAGS.checkpoint_dir = FLAGS.out_dir + '/checkpoint'
else:
if FLAGS.checkpoint_dir is None:
raise Exception(
'Cannot generate: checkpoint {0} does not exist!'.format(
FLAGS.checkpoint_dir))
else:
if FLAGS.checkpoint_dir.endswith('/'):
FLAGS.checkpoint_dir = FLAGS.checkpoint_dir[:-1]
if FLAGS.out_dir is None:
FLAGS.out_dir = 'out/gene_' + STARTED_DATESTRING
if not os.path.exists(FLAGS.out_dir):
os.makedirs(FLAGS.out_dir)
#import IPython; IPython.embed()
if FLAGS.mode == 'train':
os.makedirs(FLAGS.out_dir + '/samples')
os.makedirs(FLAGS.out_dir + '/checkpoint')
os.makedirs(FLAGS.out_dir + '/logs')
if FLAGS.audio_params is None:
if FLAGS.mode == 'train':
FLAGS.audio_params = './audio_params.json'
copyfile(FLAGS.audio_params,
FLAGS.checkpoint_dir + '/audio_params.json')
else:
print('Using json file from {0}'.format(FLAGS.checkpoint_dir))
FLAGS.audio_params = FLAGS.checkpoint_dir + '/audio_params.json'
with tf.Session(config=tf.ConfigProto(log_device_placement=False)) as sess:
#G
if FLAGS.dataset == 'wav':
with open('audio_params.json', 'r') as f:
audio_params = json.load(f)
FLAGS.epoch = audio_params['epoch']
FLAGS.learning_rate = audio_params['learning_rate']
FLAGS.beta1 = audio_params['beta1']
FLAGS.sample_length = audio_params['sample_length']
dcgan = DCGAN(sess,
batch_size=FLAGS.batch_size,
z_dim=audio_params['z_dim'],
sample_length=FLAGS.sample_length,
c_dim=1,
dataset_name=FLAGS.dataset,
audio_params=FLAGS.audio_params,
data_dir=FLAGS.data_dir,
use_disc=FLAGS.use_disc,
use_fourier=FLAGS.use_fourier,
run_g=FLAGS.run_g,
checkpoint_dir=FLAGS.checkpoint_dir,
out_dir=FLAGS.out_dir,
mode=FLAGS.mode)
else:
raise Exception('dataset not understood')
if FLAGS.mode == 'train':
dcgan.train(FLAGS)
else:
print('Generating {0} batches of size {1} from checkpoint {2}'.
format(FLAGS.gen_size, FLAGS.batch_size,
FLAGS.checkpoint_dir))
dcgan.load(FLAGS.checkpoint_dir)
dcgan.generate(FLAGS)
if FLAGS.visualize:
to_json("./web/js/layers.js",
[dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
[dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
[dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
[dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
[dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 2
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
if FLAGS.is_train:
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, input_size=FLAGS.input_size,
dataset_name=FLAGS.dataset,
is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir)
else:
dcgan = EVAL(sess, input_size = 600, batch_size=1,ir_image_shape=[600,800,1],normal_image_shape=[600,800,3],dataset_name=FLAGS.dataset,\
is_crop=False, checkpoint_dir=FLAGS.checkpoint_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
OPTION = 2 # for validation
list_val = [11,16,21,22,33,36,38,53,59,92]
VAL_OPTION =2
"""
if OPTION == 1:
data = json.load(open("/research2/IR_normal_small/json/traininput_single_224_ori_small.json"))
data_label = json.load(open("/research2/IR_normal_small/json/traingt_single_224_ori_small.json"))
elif OPTION == 2:
data = json.load(open("/research2/IR_normal_small/json/testinput_single_224_ori_small.json"))
data_label = json.load(open("/research2/IR_normal_small/json/testgt_single_224_ori_small.json"))
"""
if VAL_OPTION ==1:
list_val = [11,16,21,22,33,36,38,53,59,92]
for idx in range(len(list_val)):
for idx2 in range(1,10):
print("Selected material %03d/%d" % (list_val[idx],idx2))
img = '/research2/IR_normal_small/save%03d/%d' % (list_val[idx],idx2)
input_ = scipy.misc.imread(img+'/3.bmp').astype(float)
gt_ = scipy.misc.imread('/research2/IR_normal_small/save016/1/12_Normal.bmp').astype(float)
input_ = scipy.misc.imresize(input_,[600,800])
gt_ = scipy.misc.imresize(gt_,[600,800])
#input_ = input_[240:840,515:1315]
#gt_ = gt_[240:840,515:1315]
input_ = np.reshape(input_,(1,600,800,1))
gt_ = np.reshape(gt_,(1,600,800,3))
input_ = np.array(input_).astype(np.float32)
gt_ = np.array(gt_).astype(np.float32)
start_time = time.time()
sample = sess.run(dcgan.sampler, feed_dict={dcgan.ir_images: input_})
print('time: %.8f' %(time.time()-start_time))
# normalization #
sample = np.squeeze(sample).astype(np.float32)
gt_ = np.squeeze(gt_).astype(np.float32)
output = np.zeros((600,800,3)).astype(np.float32)
output[:,:,0] = sample[:,:,0]/(np.sqrt(np.power(sample[:,:,0],2) + np.power(sample[:,:,1],2) + np.power(sample[:,:,2],2)))
output[:,:,1] = sample[:,:,1]/(np.sqrt(np.power(sample[:,:,0],2) + np.power(sample[:,:,1],2) + np.power(sample[:,:,2],2)))
output[:,:,2] = sample[:,:,2]/(np.sqrt(np.power(sample[:,:,0],2) + np.power(sample[:,:,1],2) + np.power(sample[:,:,2],2)))
output[output ==inf] = 0.0
sample = (output+1.)/2.
savename = '/home/yjyoon/Dropbox/ECCV16_IRNormal/single_result/%03d/%d/single_normal_L2ang.bmp' % (list_val[idx],idx2)
scipy.misc.imsave(savename, sample)
elif VAL_OPTION ==2:
print("Computing all validation set ")
ErrG =0.0
num_img =13
for idx in xrange(5, num_img+1):
print("[Computing Validation Error %d/%d]" % (idx, num_img))
img = '/home/yjyoon/Dropbox/ECCV16_IRNormal/extra/extra_%d.bmp' % (idx)
input_ = scipy.misc.imread(img).astype(float)
input_ = input_[:,:,0]
gt_ = scipy.misc.imread('/research2/IR_normal_small/save016/1/12_Normal.bmp').astype(float)
input_ = scipy.misc.imresize(input_,[600,800])
gt_ = scipy.misc.imresize(gt_,[600,800])
input_ = np.reshape(input_,(1,600,800,1))
gt_ = np.reshape(gt_,(1,600,800,3))
input_ = np.array(input_).astype(np.float32)
gt_ = np.array(gt_).astype(np.float32)
start_time = time.time()
sample = sess.run(dcgan.sampler, feed_dict={dcgan.ir_images: input_})
print('time: %.8f' %(time.time()-start_time))
# normalization #
sample = np.squeeze(sample).astype(np.float32)
gt_ = np.squeeze(gt_).astype(np.float32)
output = np.zeros((600,800,3)).astype(np.float32)
output[:,:,0] = sample[:,:,0]/(np.sqrt(np.power(sample[:,:,0],2) + np.power(sample[:,:,1],2) + np.power(sample[:,:,2],2)))
output[:,:,1] = sample[:,:,1]/(np.sqrt(np.power(sample[:,:,0],2) + np.power(sample[:,:,1],2) + np.power(sample[:,:,2],2)))
output[:,:,2] = sample[:,:,2]/(np.sqrt(np.power(sample[:,:,0],2) + np.power(sample[:,:,1],2) + np.power(sample[:,:,2],2)))
output[output ==inf] = 0.0
sample = (output+1.)/2.
savename = '/home/yjyoon/Dropbox/ECCV16_IRNormal/extra/extra_result%d.bmp' % (idx)
scipy.misc.imsave(savename, sample)
def main(_):
global INTERMEDIATE_PATH, OUTPUT_PATH
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
print(FLAGS.base_dir)
if not os.path.exists(os.path.join(FLAGS.base_dir, FLAGS.checkpoint_dir)):
os.makedirs(os.path.join(FLAGS.base_dir, FLAGS.checkpoint_dir))
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
# print('Training: ',FLAGS.train)
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=FLAGS.num_classes,
z_dim=FLAGS.noise_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
base_dir=FLAGS.base_dir,
num_test_images=FLAGS.num_test_images)
elif FLAGS.dataset == 'images5':
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=FLAGS.num_classes,
z_dim=FLAGS.noise_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
dataset_dir=FLAGS.dataset_dir,
base_dir=FLAGS.base_dir,
num_test_images=FLAGS.num_test_images)
else:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.noise_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
dataset_dir=FLAGS.dataset_dir,
base_dir=FLAGS.base_dir,
num_test_images=FLAGS.num_test_images)
INTERMEDIATE_PATH = FLAGS.intermediate_path
OUTPUT_PATH = FLAGS.output_path
show_all_variables()
if FLAGS.train:
print('going to train')
dcgan.train(FLAGS)
else:
print('Saving Images..')
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 1
num_gen_images, images_list = visualize(
sess, dcgan, FLAGS, OPTION, generate_output_path=OUTPUT_PATH)
with open(os.path.join(INTERMEDIATE_PATH, 'num_images_gen.txt'),
'w+') as ffile:
ffile.write(str(num_gen_images) + '\n')
# for i in images_list:
# print(i)
with open(os.path.join(INTERMEDIATE_PATH, 'gen_images_list.txt'),
'w+') as ffile:
for i in images_list:
ffile.write(str(i) + '\n')
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session() as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, y_dim=10,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir)
else:
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
[dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
[dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
[dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
[dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 2
if OPTION == 0:
z_sample = np.random.uniform(-0.5, 0.5, size=(FLAGS.batch_size, dcgan.z_dim))
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
save_images(samples, [8, 8], './samples/test_%s.png' % strftime("%Y-%m-%d %H:%M:%S", gmtime()))
elif OPTION == 1:
values = np.arange(0, 1, 1./FLAGS.batch_size)
for idx in xrange(100):
print(" [*] %d" % idx)
z_sample = np.zeros([FLAGS.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
save_images(samples, [8, 8], './samples/test_arange_%s.png' % (idx))
elif OPTION == 2:
values = np.arange(0, 1, 1./FLAGS.batch_size)
for idx in [random.randint(0, 99) for _ in xrange(100)]:
print(" [*] %d" % idx)
z = np.random.uniform(-0.2, 0.2, size=(dcgan.z_dim))
z_sample = np.tile(z, (FLAGS.batch_size, 1))
#z_sample = np.zeros([FLAGS.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
make_gif(samples, './samples/test_gif_%s.gif' % (idx))
elif OPTION == 3:
values = np.arange(0, 1, 1./FLAGS.batch_size)
for idx in xrange(100):
print(" [*] %d" % idx)
z_sample = np.zeros([FLAGS.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
make_gif(samples, './samples/test_gif_%s.gif' % (idx))
elif OPTION == 4:
image_set = []
values = np.arange(0, 1, 1./FLAGS.batch_size)
for idx in xrange(100):
print(" [*] %d" % idx)
z_sample = np.zeros([FLAGS.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample): z[idx] = values[kdx]
image_set.append(sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample}))
make_gif(image_set[-1], './samples/test_gif_%s.gif' % (idx))
new_image_set = [merge(np.array([images[idx] for images in image_set]), [10, 10]) for idx in range(64) + range(63, -1, -1)]
make_gif(new_image_set, './samples/test_gif_merged.gif', duration=8)
elif OPTION == 5:
image_set = []
values = np.arange(0, 1, 1./FLAGS.batch_size)
z_idx = [[random.randint(0,99) for _ in xrange(5)] for _ in xrange(200)]
for idx in xrange(200):
print(" [*] %d" % idx)
#z_sample = np.zeros([FLAGS.batch_size, dcgan.z_dim])
z = np.random.uniform(-1e-1, 1e-1, size=(dcgan.z_dim))
z_sample = np.tile(z, (FLAGS.batch_size, 1))
for kdx, z in enumerate(z_sample):
for jdx in xrange(5):
z_sample[kdx][z_idx[idx][jdx]] = values[kdx]
image_set.append(sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample}))
make_gif(image_set[-1], './samples/test_gif_%s.gif' % (idx))
new_image_set = [merge(np.array([images[idx] for images in image_set]), [10, 20]) for idx in range(64) + range(63, -1, -1)]
make_gif(new_image_set, './samples/test_gif_random_merged.gif', duration=4)
elif OPTION == 6:
image_set = []
values = np.arange(0, 1, 1.0/FLAGS.batch_size).tolist()
z_idx = [[random.randint(0,99) for _ in xrange(10)] for _ in xrange(100)]
for idx in xrange(100):
print(" [*] %d" % idx)
z = np.random.uniform(-0.2, 0.2, size=(dcgan.z_dim))
z_sample = np.tile(z, (FLAGS.batch_size, 1))
for kdx, z in enumerate(z_sample):
for jdx in xrange(10):
z_sample[kdx][z_idx[idx][jdx]] = values[kdx]
image_set.append(sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample}))
save_images(image_set[-1], [8, 8], './samples/test_random_arange_%s.png' % (idx))
new_image_set = [merge(np.array([images[idx] for images in image_set]), [10, 10]) for idx in range(64) + range(63, -1, -1)]
make_gif(new_image_set, './samples/test_gif_merged_random.gif', duration=4)
elif OPTION == 7:
for _ in xrange(50):
z_idx = [[random.randint(0,99) for _ in xrange(10)] for _ in xrange(8)]
zs = []
for idx in xrange(8):
z = np.random.uniform(-0.2, 0.2, size=(dcgan.z_dim))
zs.append(np.tile(z, (8, 1)))
z_sample = np.concatenate(zs)
values = np.arange(0, 1, 1/8.)
for idx in xrange(FLAGS.batch_size):
for jdx in xrange(8):
z_sample[idx][z_idx[idx/8][jdx]] = values[idx%8]
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
save_images(samples, [8, 8], './samples/multiple_testt_%s.png' % strftime("%Y-%m-%d %H:%M:%S", gmtime()))
elif OPTION == 8:
counter = 0
for _ in xrange(50):
import scipy.misc
z_idx = [[random.randint(0,99) for _ in xrange(10)] for _ in xrange(8)]
zs = []
for idx in xrange(8):
z = np.random.uniform(-0.2, 0.2, size=(dcgan.z_dim))
zs.append(np.tile(z, (8, 1)))
z_sample = np.concatenate(zs)
values = np.arange(0, 1, 1/8.)
for idx in xrange(FLAGS.batch_size):
for jdx in xrange(8):
z_sample[idx][z_idx[idx/8][jdx]] = values[idx%8]
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
for sample in samples:
scipy.misc.imsave('./samples/turing/%s.png' % counter, sample)
counter += 1
else:
import scipy.misc
from glob import glob
samples = []
fnames = glob("/Users/carpedm20/Downloads/x/1/*.png")
fnames = sorted(fnames, key = lambda x: int(x.split("_")[1]) * 10000 + int(x.split('_')[2].split(".")[0]))
for f in fnames:
samples.append(scipy.misc.imread(f))
make_gif(samples, './samples/training.gif', duration=8, true_image=True)
def main(_):
pp.pprint(flags.FLAGS.__flags)
# only width needed for square images
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
# if no sample dir, set to subdir with dataset name
if FLAGS.sample_dir is None:
FLAGS.sample_dir = os.path.join('samples', FLAGS.dataset)
# make sure set paths exist
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
# during training it makes no sense to have interpolation active
if FLAGS.is_train:
FLAGS.interpolate = False
if FLAGS.interpolate:
FLAGS.visualize = False
if FLAGS.ipython:
FLAGS.visualize = False
# allows specifying additional options
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
# initialise session with chosen flags
with tf.Session(config=run_config) as sess:
dcgan = DCGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
num_g_updates=FLAGS.num_g_updates,
z_dim=FLAGS.z_dim,
gf_dim=FLAGS.gf_dim,
gf_size=FLAGS.gf_size,
df_dim=FLAGS.df_dim,
df_size=FLAGS.df_size,
gfc_dim=FLAGS.gfc_dim,
dfc_dim=FLAGS.dfc_dim,
data_dir=FLAGS.data_dir,
is_train=FLAGS.is_train,
label_path=FLAGS.labels,
gauss_sigma=FLAGS.gauss_sigma,
gauss_trunc=FLAGS.gauss_trunc,
blur_fade=FLAGS.blur_fade,
y_dim=FLAGS.y_dim)
show_all_variables()
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir):
raise Exception("[!] Train a model first, then run test mode")
# visualisation
if FLAGS.visualize:
option = 2
visualize(sess, dcgan, FLAGS, option)
if FLAGS.interpolate:
interactive_interp(sess, dcgan, FLAGS, sampling='uniform')
if FLAGS.ipython:
from vector import Vector
from IPython import embed
vec = Vector(sess, dcgan, FLAGS)
embed()
def main(_):
print('begin')
pp.pprint(flags.FLAGS.__flags)
print('flags.FLAGS[epoch]=', flags.FLAGS.__flags['epoch'])
# expand user name and environment variables
FLAGS.data_dir = expand_path(FLAGS.data_dir)
FLAGS.out_dir = expand_path(FLAGS.out_dir)
FLAGS.out_name = expand_path(FLAGS.out_name)
#FLAGS.checkpoint_dir = expand_path(FLAGS.checkpoint_dir)
FLAGS.sample_dir = expand_path(FLAGS.sample_dir)
if FLAGS.output_height is None: FLAGS.output_height = FLAGS.input_height
if FLAGS.input_width is None: FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None: FLAGS.output_width = FLAGS.output_height
# output folders
if FLAGS.out_name == "":
FLAGS.out_name = '{} - {} - {}'.format(
timestamp(),
FLAGS.data_dir.split('/')[-1],
FLAGS.dataset) # penultimate folder of path
if FLAGS.train:
FLAGS.out_name += ' - x{}.z{}.{}.y{}.b{}'.format(
FLAGS.input_width, FLAGS.z_dim, FLAGS.z_dist,
FLAGS.output_width, FLAGS.batch_size)
FLAGS.out_dir = os.path.join(FLAGS.out_dir, FLAGS.out_name)
#FLAGS.checkpoint_dir = os.path.join(FLAGS.out_dir, FLAGS.checkpoint_dir)
FLAGS.sample_dir = os.path.join(FLAGS.out_dir, FLAGS.sample_dir)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir)
with open(os.path.join(FLAGS.out_dir, 'FLAGS.json'), 'w') as f:
flags_dict = {k: flags.FLAGS.__flags[k] for k in flags.FLAGS.__flags}
#flags_dict = {k:FLAGS[k].value for k in FLAGS.__flags}
json.dump(flags_dict, f, indent=4, sort_keys=True, ensure_ascii=False)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
z_dim=FLAGS.z_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir,
out_dir=FLAGS.out_dir,
max_to_keep=FLAGS.max_to_keep)
else:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.z_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir,
out_dir=FLAGS.out_dir,
max_to_keep=FLAGS.max_to_keep)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
load_success, load_counter = dcgan.load(FLAGS.checkpoint_dir)
if not load_success:
raise Exception("Checkpoint not found in " +
FLAGS.checkpoint_dir)
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
if FLAGS.export:
export_dir = os.path.join(FLAGS.checkpoint_dir,
'export_b' + str(FLAGS.batch_size))
dcgan.save(export_dir, load_counter, ckpt=True, frozen=False)
if FLAGS.freeze:
export_dir = os.path.join(FLAGS.checkpoint_dir,
'frozen_b' + str(FLAGS.batch_size))
dcgan.save(export_dir, load_counter, ckpt=False, frozen=True)
if FLAGS.visualize:
OPTION = 2
visualize(sess, dcgan, FLAGS, OPTION, FLAGS.sample_dir)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
#run_config.gpu_options.per_process_gpu_memory_fraction = 0.4
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
test_batch_size=FLAGS.test_batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
test_dir=FLAGS.test_dir)
else:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
test_batch_size=FLAGS.test_batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
test_dir=FLAGS.test_dir)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
if FLAGS.anomaly_test:
dcgan.anomaly_detector()
assert len(dcgan.test_data_names) > 0
for idx in range(len(dcgan.test_data_names)):
test_input = np.expand_dims(dcgan.test_data[idx], axis=0)
test_name = dcgan.test_data_names[idx]
dcgan.train_anomaly_detector(FLAGS, test_input, test_name)
def main(_):
if FLAGS.auto_anneal:
FLAGS.anneal_rate = 'auto'
FLAGS.sample_dir = FLAGS.sample_dir + "/" + FLAGS.dataset + "/" + FLAGS.architecture + "_bluff-" + str(
FLAGS.br_initial) + "_anneal-" + str(FLAGS.anneal_rate)
FLAGS.checkpoint_dir = FLAGS.checkpoint_dir + "/" + FLAGS.dataset + "/" + FLAGS.architecture + "_bluff-" + str(
FLAGS.br_initial) + "_anneal-" + str(FLAGS.anneal_rate)
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
dataset_name=FLAGS.dataset,
architecture=FLAGS.architecture,
br_initial=FLAGS.br_initial,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
anneal_rate=FLAGS.anneal_rate,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
else:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
dataset_name=FLAGS.dataset,
architecture=FLAGS.architecture,
br_initial=FLAGS.br_initial,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
anneal_rate=FLAGS.anneal_rate,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session() as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
image_size=FLAGS.image_size,
batch_size=FLAGS.batch_size,
y_dim=10,
dataset_name=FLAGS.dataset,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir)
else:
dcgan = DCGAN(sess,
image_size=FLAGS.image_size,
batch_size=FLAGS.batch_size,
dataset_name=FLAGS.dataset,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
[dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
[dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
[dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
[dcgan.h4_w, dcgan.h4_b, None])
OPTION = 6
if OPTION == 0:
z_sample = np.random.uniform(-0.5,
0.5,
size=(FLAGS.batch_size, dcgan.z_dim))
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
save_images(
samples, [8, 8], './samples/test_%s.png' %
strftime("%Y-%m-%d %H:%M:%S", gmtime()))
elif OPTION == 1:
values = np.arange(0, 1, 1. / FLAGS.batch_size)
for idx in xrange(100):
print(" [*] %d" % idx)
z_sample = np.zeros([FLAGS.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
samples = sess.run(dcgan.sampler,
feed_dict={dcgan.z: z_sample})
save_images(samples, [8, 8],
'./samples/test_arange_%s.png' % (idx))
elif OPTION == 2:
values = np.arange(0, 1, 1. / FLAGS.batch_size)
for idx in [random.randint(100) for _ in xrange(5)]:
print(" [*] %d" % idx)
z_sample = np.zeros([FLAGS.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
samples = sess.run(dcgan.sampler,
feed_dict={dcgan.z: z_sample})
make_gif(samples, './samples/test_gif_%s.gif' % (idx))
elif OPTION == 3:
values = np.arange(0, 1, 1. / FLAGS.batch_size)
for idx in xrange(100):
print(" [*] %d" % idx)
z_sample = np.zeros([FLAGS.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
samples = sess.run(dcgan.sampler,
feed_dict={dcgan.z: z_sample})
make_gif(samples, './samples/test_gif_%s.gif' % (idx))
elif OPTION == 4:
image_set = []
values = np.arange(0, 1, 1. / FLAGS.batch_size)
for idx in xrange(100):
print(" [*] %d" % idx)
z_sample = np.zeros([FLAGS.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
image_set.append(
sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample}))
make_gif(image_set[-1], './samples/test_gif_%s.gif' % (idx))
new_image_set = [
merge(np.array([images[idx]
for images in image_set]), [10, 10])
for idx in range(64) + range(63, -1, -1)
]
make_gif(new_image_set,
'./samples/test_gif_merged.gif',
duration=8)
elif OPTION == 5:
image_set = []
values = np.arange(0, 1, 1. / FLAGS.batch_size)
z_idx = [[random.randint(0, 99) for _ in xrange(5)]
for _ in xrange(200)]
for idx in xrange(200):
print(" [*] %d" % idx)
#z_sample = np.zeros([FLAGS.batch_size, dcgan.z_dim])
z = np.random.uniform(-1e-1, 1e-1, size=(dcgan.z_dim))
z_sample = np.tile(z, (FLAGS.batch_size, 1))
for kdx, z in enumerate(z_sample):
for jdx in xrange(5):
z_sample[kdx][z_idx[idx][jdx]] = values[kdx]
image_set.append(
sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample}))
make_gif(image_set[-1], './samples/test_gif_%s.gif' % (idx))
new_image_set = [
merge(np.array([images[idx]
for images in image_set]), [10, 20])
for idx in range(64) + range(63, -1, -1)
]
make_gif(new_image_set,
'./samples/test_gif_random_merged.gif',
duration=4)
elif OPTION == 6:
image_set = []
values = np.arange(0, 1, 1.0 / FLAGS.batch_size).tolist()
z_idx = [[random.randint(0, 99) for _ in xrange(10)]
for _ in xrange(100)]
for idx in xrange(100):
print(" [*] %d" % idx)
z = np.random.uniform(-0.2, 0.2, size=(dcgan.z_dim))
z_sample = np.tile(z, (FLAGS.batch_size, 1))
for kdx, z in enumerate(z_sample):
for jdx in xrange(10):
z_sample[kdx][z_idx[idx][jdx]] = values[kdx]
image_set.append(
sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample}))
save_images(image_set[-1], [8, 8],
'./samples/test_random_arange_%s.png' % (idx))
new_image_set = [
merge(np.array([images[idx]
for images in image_set]), [10, 10])
for idx in range(64) + range(63, -1, -1)
]
make_gif(new_image_set,
'./samples/test_gif_merged_random.gif',
duration=4)
elif OPTION == 7:
for _ in xrange(50):
z_idx = [[random.randint(0, 99) for _ in xrange(10)]
for _ in xrange(8)]
zs = []
for idx in xrange(8):
z = np.random.uniform(-0.2, 0.2, size=(dcgan.z_dim))
zs.append(np.tile(z, (8, 1)))
z_sample = np.concatenate(zs)
values = np.arange(0, 1, 1 / 8.)
for idx in xrange(FLAGS.batch_size):
for jdx in xrange(8):
z_sample[idx][z_idx[idx / 8][jdx]] = values[idx % 8]
samples = sess.run(dcgan.sampler,
feed_dict={dcgan.z: z_sample})
save_images(
samples, [8, 8], './samples/multiple_testt_%s.png' %
strftime("%Y-%m-%d %H:%M:%S", gmtime()))
elif OPTION == 8:
counter = 0
for _ in xrange(50):
import scipy.misc
z_idx = [[random.randint(0, 99) for _ in xrange(10)]
for _ in xrange(8)]
zs = []
for idx in xrange(8):
z = np.random.uniform(-0.2, 0.2, size=(dcgan.z_dim))
zs.append(np.tile(z, (8, 1)))
z_sample = np.concatenate(zs)
values = np.arange(0, 1, 1 / 8.)
for idx in xrange(FLAGS.batch_size):
for jdx in xrange(8):
z_sample[idx][z_idx[idx / 8][jdx]] = values[idx % 8]
samples = sess.run(dcgan.sampler,
feed_dict={dcgan.z: z_sample})
for sample in samples:
scipy.misc.imsave('./samples/turing/%s.png' % counter,
sample)
counter += 1
else:
import scipy.misc
from glob import glob
samples = []
fnames = glob("/Users/carpedm20/Downloads/x/1/*.png")
fnames = sorted(fnames,
key=lambda x: int(x.split("_")[1]) * 10000 + int(
x.split('_')[2].split(".")[0]))
for f in fnames:
samples.append(scipy.misc.imread(f))
make_gif(samples,
'./samples/training.gif',
duration=8,
true_image=True)
def main(_):
print('Program is started at', time.clock())
pp.pprint(flags.FLAGS.__flags)
n_per_itr_print_results = 100
n_fetch_data = 10
kb_work_on_patch = False
nd_input_frame_size = (240, 360)
#nd_patch_size = (45, 45)
n_stride = 10
#FLAGS.checkpoint_dir = "./checkpoint/UCSD_128_45_45/"
#FLAGS.dataset = 'UCSD'
#FLAGS.dataset_address = './dataset/UCSD_Anomaly_Dataset.v1p2/UCSDped2/Test'
lst_test_dirs = ['Test004', 'Test005', 'Test006']
#DATASET PARAMETER : MNIST
#FLAGS.dataset = 'mnist'
#FLAGS.dataset_address = './dataset/mnist'
#nd_input_frame_size = (28, 28)
#nd_patch_size = (28, 28)
#FLAGS.checkpoint_dir = "./checkpoint/mnist_128_28_28/"
#FLAGS.input_width = nd_patch_size[0]
#FLAGS.input_height = nd_patch_size[1]
#FLAGS.output_width = nd_patch_size[0]
#FLAGS.output_height = nd_patch_size[1]
check_some_assertions()
nd_patch_size = (FLAGS.input_width, FLAGS.input_height)
nd_patch_step = (n_stride, n_stride)
FLAGS.nStride = n_stride
#FLAGS.input_fname_pattern = '*'
FLAGS.train = False
FLAGS.epoch = 1
FLAGS.batch_size = 504
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
run_config = tf.ConfigProto(gpu_options=gpu_options)
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
tmp_ALOCC_model = ALOCC_Model(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
attention_label=FLAGS.attention_label,
r_alpha=FLAGS.r_alpha,
is_training=FLAGS.train,
dataset_name=FLAGS.dataset,
dataset_address=FLAGS.dataset_address,
input_fname_pattern=FLAGS.input_fname_pattern,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
nd_patch_size=nd_patch_size,
n_stride=n_stride,
n_per_itr_print_results=n_per_itr_print_results,
kb_work_on_patch=kb_work_on_patch,
nd_input_frame_size=nd_input_frame_size,
n_fetch_data=n_fetch_data)
show_all_variables()
print('--------------------------------------------------')
print('Load Pretrained Model...')
tmp_ALOCC_model.f_check_checkpoint()
if FLAGS.dataset == 'mnist':
mnist = input_data.read_data_sets(FLAGS.dataset_address)
specific_idx_anomaly = np.where(mnist.train.labels != 6)[0]
specific_idx = np.where(mnist.train.labels == 6)[0]
ten_precent_anomaly = [
specific_idx_anomaly[x]
for x in random.sample(range(0, len(specific_idx_anomaly)),
len(specific_idx) // 40)
]
data = mnist.train.images[specific_idx].reshape(-1, 28, 28, 1)
tmp_data = mnist.train.images[ten_precent_anomaly].reshape(
-1, 28, 28, 1)
data = np.append(data, tmp_data).reshape(-1, 28, 28, 1)
lst_prob = tmp_ALOCC_model.f_test_frozen_model(
data[0:FLAGS.batch_size])
print('check is ok')
exit()
#generated_data = tmp_ALOCC_model.feed2generator(data[0:FLAGS.batch_size])
# else in UCDS (depends on infrustructure)
for s_image_dirs in sorted(
glob(os.path.join(FLAGS.dataset_address,
'Test[0-9][0-9][0-9]'))):
tmp_lst_image_paths = []
if os.path.basename(s_image_dirs) not in ['Test004']:
print('Skip ', os.path.basename(s_image_dirs))
continue
for s_image_dir_files in sorted(
glob(os.path.join(s_image_dirs + '/*'))):
if os.path.basename(s_image_dir_files) not in ['068.tif']:
print('Skip ', os.path.basename(s_image_dir_files))
continue
tmp_lst_image_paths.append(s_image_dir_files)
#random
#lst_image_paths = [tmp_lst_image_paths[x] for x in random.sample(range(0, len(tmp_lst_image_paths)), n_fetch_data)]
lst_image_paths = tmp_lst_image_paths
#images =read_lst_images(lst_image_paths,nd_patch_size,nd_patch_step,b_work_on_patch=False)
images = read_lst_images_w_noise2(lst_image_paths, nd_patch_size,
nd_patch_step)
lst_prob = process_frame(os.path.basename(s_image_dirs), images,
tmp_ALOCC_model)
print('pseudocode test is finished')
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
if not os.path.exists(os.path.join('./logs',time.strftime('%d%m'))):
os.makedirs(os.path.join('./logs',time.strftime('%d%m')))
gpu_config = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_config)) as sess:
if FLAGS.is_train:
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size,\
dataset_name=FLAGS.dataset,is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir)
else:
dcgan = EVAL(sess, batch_size=1,num_block=FLAGS.num_block,ir_image_shape=[600,800,1],normal_image_shape=[600,800,3],dataset_name=FLAGS.dataset,\
is_crop=False, checkpoint_dir=FLAGS.checkpoint_dir)
print('deep model test \n')
if FLAGS.is_train:
pdb.set_trace()
dcgan.train(FLAGS)
else:
list_val = [11,16,21,22,33,36,38,53,59,92]
print '1: Estimating Normal maps from arbitary obejcts \n'
print '2: Estimating Normal maps according to Light directions and object tilt angles \n'
x = input('Selecting a Evaluation mode:')
VAL_OPTION = int(x)
if VAL_OPTION ==1: # arbitary dataset
print("Computing arbitary dataset ")
trained_models = glob.glob(os.path.join(FLAGS.checkpoint_dir,FLAGS.dataset,'DCGAN.model*'))
trained_models = natsorted(trained_models)
datapath = '/research2/Ammonight/*.bmp'
savepath = '/research2/Ammonight/output'
mean_nir = -0.3313
fulldatapath = os.path.join(glob.glob(datapath))
model = trained_models[4]
model = model.split('/')
model = model[-1]
dcgan.load(FLAGS.checkpoint_dir,model)
for idx in xrange(len(fulldatapath)):
input_= scipy.misc.imread(fulldatapath[idx]).astype(float)
input_ = scipy.misc.imresize(input_,[600,800])
input_ = (input_/127.5)-1. # normalize -1 ~1
input_ = np.reshape(input_,(1,input_.shape[0],input_.shape[1],1))
input_ = np.array(input_).astype(np.float32)
mask = [input_>-1.0][0]*1.0
mean_mask = mask * mean_nir
#input_ = input_ - mean_mask
start_time = time.time()
sample = sess.run(dcgan.sampler, feed_dict={dcgan.ir_images: input_})
print('time: %.8f' %(time.time()-start_time))
# normalization #
sample = np.squeeze(sample).astype(np.float32)
output = np.sqrt(np.sum(np.power(sample,2),axis=2))
output = np.expand_dims(output,axis=-1)
output = sample/output
output[output ==inf] = 0.0
sample = (output+1.0)/2.0
name = fulldatapath[idx].split('/')
name = name[-1].split('.')
name = name[0]
savename = savepath + '/normal_' + name +'.bmp'
scipy.misc.imsave(savename, sample)
elif VAL_OPTION ==2: # depends on light sources
list_val = [11,16,21,22,33,36,38,53,59,92]
mean_nir = -0.3313 #-1~1
save_files = glob.glob(os.path.join(FLAGS.checkpoint_dir,FLAGS.dataset,'DCGAN.model*'))
save_files = natsorted(save_files)
savepath ='./Deconv_L1_result'
if not os.path.exists(os.path.join(savepath)):
os.makedirs(os.path.join(savepath))
selec_model=[-2]
#[selec_model.append(ii) for ii in range(0,len(save_files),2)]
for m in range(len(selec_model)):
model = save_files[selec_model[m]]
model = model.split('/')
model = model[-1]
dcgan.load(FLAGS.checkpoint_dir,model)
for idx in range(len(list_val)):
if not os.path.exists(os.path.join(savepath,'%03d' %list_val[idx])):
os.makedirs(os.path.join(savepath,'%03d' %list_val[idx]))
for idx2 in range(1,10): #tilt angles 1~9
for idx3 in range(5,7): # light source
print("Selected material %03d/%d" % (list_val[idx],idx2))
img = '/research2/IR_normal_small/save%03d/%d' % (list_val[idx],idx2)
noise = np.random.rand(1,600,800,1)
#noise = np.random.uniform(-1,1,size=(1,600,800,1))
input_ = scipy.misc.imread(img+'/%d.bmp' %idx3).astype(float) #input NIR image
input_ = scipy.misc.imresize(input_,[600,800])
input_ = input_/127.5 -1.0 # normalize -1 ~1
input_ = np.reshape(input_,(1,600,800,1))
input_ = np.array(input_).astype(np.float32)
gt_ = scipy.misc.imread(img+'/12_Normal.bmp').astype(float)
gt_ = np.sum(gt_,axis=2)
gt_ = scipy.misc.imresize(gt_,[600,800])
gt_ = np.reshape(gt_,[1,600,800,1])
mask =[gt_ >0.0][0]*1.0
mean_mask = mean_nir * mask
#input_ = input_ - mean_mask
start_time = time.time()
sample = sess.run(dcgan.G, feed_dict={dcgan.ir_images: input_,dcgan.noise:noise})
#sample = sess.run(dcgan.sampler, feed_dict={dcgan.ir_images: input_})
print('time: %.8f' %(time.time()-start_time))
# normalization #
sample = np.squeeze(sample).astype(np.float32)
output = np.sqrt(np.sum(np.power(sample,2),axis=2))
output = np.expand_dims(output,axis=-1)
output = sample/output
output = (output+1.)/2.
if not os.path.exists(os.path.join(savepath,'%03d/%d/%s' %(list_val[idx],idx2,model))):
os.makedirs(os.path.join(savepath,'%03d/%d/%s' %(list_val[idx],idx2,model)))
savename = os.path.join(savepath,'%03d/%d/%s/single_normal_%03d.bmp' % (list_val[idx],idx2,model,idx3))
scipy.misc.imsave(savename, output)
def main(_):
pp.pprint(flags.FLAGS.__flags)
with tf.device('/cpu:0'), tf.Session(config=tf.ConfigProto(
intra_op_parallelism_threads=16)) as sess:
if FLAGS.task == 'copy':
if FLAGS.is_train:
cell, ntm = copy_train(FLAGS, sess)
else:
cell = NTMCell(input_dim=FLAGS.input_dim,
output_dim=FLAGS.output_dim,
controller_layer_size=FLAGS.controller_layer_size,
write_head_size=FLAGS.write_head_size,
read_head_size=FLAGS.read_head_size)
ntm = NTM(cell, sess, 1, FLAGS.max_length,
test_max_length=FLAGS.test_max_length, forward_only=True)
ntm.load(FLAGS.checkpoint_dir, 'copy')
copy(ntm, FLAGS.test_max_length*1/3, sess)
print
copy(ntm, FLAGS.test_max_length*2/3, sess)
print
copy(ntm, FLAGS.test_max_length*3/3, sess)
elif FLAGS.task == 'recall':
if FLAGS.is_train:
cell, ntm = recall_train(FLAGS, sess)
else:
cell = NTMCell(input_dim=FLAGS.input_dim,
output_dim=FLAGS.output_dim,
controller_layer_size=FLAGS.controller_layer_size,
write_head_size=FLAGS.write_head_size,
read_head_size=FLAGS.read_head_size)
ntm = NTM(cell, sess, 1, FLAGS.max_length,
test_max_length=FLAGS.test_max_length, forward_only=True)
ntm.load(FLAGS.checkpoint_dir, 'recall')
recall(ntm, FLAGS.test_max_length*1/3, sess)
print
recall(ntm, FLAGS.test_max_length*2/3, sess)
print
recall(ntm, FLAGS.test_max_length*3/3, sess)
elif FLAGS.task == 'predict':
if FLAGS.is_train:
cell, ntm = predict_train(FLAGS, sess)
else:
cell = NTMCell(input_dim=FLAGS.input_dim,
output_dim=FLAGS.output_dim,
controller_layer_size=FLAGS.controller_layer_size,
write_head_size=FLAGS.write_head_size,
read_head_size=FLAGS.read_head_size)
ntm = NTM(cell, sess, 1, FLAGS.max_length,
test_max_length=FLAGS.test_max_length, forward_only=True)
ntm.load(FLAGS.checkpoint_dir, 'predict')
predict(ntm, FLAGS.test_max_length*1/3, sess)
print
predict(ntm, FLAGS.test_max_length*2/3, sess)
print
predict(ntm, FLAGS.test_max_length*3/3, sess)
def main(_):
"""
The main function for training steps
"""
n_per_itr_print_results = 100
kb_work_on_patch = True
# ---------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------
# Manual Switchs ------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------
# DATASET PARAMETER : data-alocc
FLAGS.dataset = "data-alocc"
FLAGS.dataset_address = "./dataset/data-alocc/train"
kb_work_on_patch = False
nd_input_frame_size = (180, 270)
nd_slice_size = (180, 270)
n_stride = 1
FLAGS.train = True
FLAGS.input_width = nd_slice_size[0]
FLAGS.input_height = nd_slice_size[1]
FLAGS.output_width = nd_slice_size[0]
FLAGS.output_height = nd_slice_size[1]
FLAGS.sample_dir = ("export/" + FLAGS.dataset + "_%d.%d.%f_" %
(nd_slice_size[0], nd_slice_size[1], FLAGS.r_alpha))
FLAGS.input_fname_pattern = "*"
pp.pprint(flags.FLAGS.__flags)
check_some_assertions()
# manual handling of GPU
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
run_config = tf.ConfigProto(gpu_options=gpu_options)
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
tmp_model = ALOCC_Model(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
attention_label=FLAGS.attention_label,
r_alpha=FLAGS.r_alpha,
dataset_name=FLAGS.dataset,
dataset_address=FLAGS.dataset_address,
input_fname_pattern=FLAGS.input_fname_pattern,
checkpoint_dir=FLAGS.checkpoint_dir,
is_training=FLAGS.train,
log_dir=FLAGS.log_dir,
sample_dir=FLAGS.sample_dir,
nd_patch_size=nd_slice_size,
n_stride=n_stride,
n_per_itr_print_results=n_per_itr_print_results,
kb_work_on_patch=kb_work_on_patch,
nd_input_frame_size=nd_input_frame_size,
)
# show_all_variables()
if FLAGS.train:
print("Program is on Train Mode")
tmp_model.train(FLAGS)
else:
if not tmp_model.load(FLAGS.checkpoint_dir)[0]:
print("Program is on Test Mode")
raise Exception(
"[!] Train a model first, then run test mode from file test.py"
)