Python utils.visualize() Examples
The following are 8
code examples of utils.visualize().
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Example #1
| Source File: demo_graph.py From yolo2-pytorch with GNU Lesser General Public License v3.0 | 5 votes |
|
def main():
args = make_args()
config = configparser.ConfigParser()
utils.load_config(config, args.config)
for cmd in args.modify:
utils.modify_config(config, cmd)
with open(os.path.expanduser(os.path.expandvars(args.logging)), 'r') as f:
logging.config.dictConfig(yaml.load(f))
model_dir = utils.get_model_dir(config)
category = utils.get_category(config)
anchors = torch.from_numpy(utils.get_anchors(config)).contiguous()
try:
path, step, epoch = utils.train.load_model(model_dir)
state_dict = torch.load(path, map_location=lambda storage, loc: storage)
except (FileNotFoundError, ValueError):
logging.warning('model cannot be loaded')
state_dict = None
dnn = utils.parse_attr(config.get('model', 'dnn'))(model.ConfigChannels(config, state_dict), anchors, len(category))
logging.info(humanize.naturalsize(sum(var.cpu().numpy().nbytes for var in dnn.state_dict().values())))
if state_dict is not None:
dnn.load_state_dict(state_dict)
height, width = tuple(map(int, config.get('image', 'size').split()))
image = torch.autograd.Variable(torch.randn(args.batch_size, 3, height, width))
output = dnn(image)
state_dict = dnn.state_dict()
graph = utils.visualize.Graph(config, state_dict)
graph(output.grad_fn)
diff = [key for key in state_dict if key not in graph.drawn]
if diff:
logging.warning('variables not shown: ' + str(diff))
path = graph.dot.view(os.path.basename(model_dir) + '.gv', os.path.dirname(model_dir))
logging.info(path)
Example #2
| Source File: main_mmd.py From opt-mmd with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def main(_):
pp.pprint(flags.FLAGS.__flags)
sample_dir_ = os.path.join(FLAGS.sample_dir, FLAGS.name)
checkpoint_dir_ = os.path.join(FLAGS.checkpoint_dir, FLAGS.name)
log_dir_ = os.path.join(FLAGS.log_dir, FLAGS.name)
if not os.path.exists(checkpoint_dir_):
os.makedirs(checkpoint_dir_)
if not os.path.exists(sample_dir_):
os.makedirs(sample_dir_)
if not os.path.exists(log_dir_):
os.makedirs(log_dir_)
with tf.Session() as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess, config=FLAGS, batch_size=FLAGS.batch_size, output_size=28, c_dim=1,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=checkpoint_dir_, sample_dir=sample_dir_, log_dir=log_dir_)
else:
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, output_size=FLAGS.output_size, c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.sampling(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)
Example #3
| Source File: main_tmmd.py From opt-mmd with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def main(_):
pp.pprint(flags.FLAGS.__flags)
sample_dir_ = os.path.join(FLAGS.sample_dir, FLAGS.name)
checkpoint_dir_ = os.path.join(FLAGS.checkpoint_dir, FLAGS.name)
log_dir_ = os.path.join(FLAGS.log_dir, FLAGS.name)
if not os.path.exists(checkpoint_dir_):
os.makedirs(checkpoint_dir_)
if not os.path.exists(sample_dir_):
os.makedirs(sample_dir_)
if not os.path.exists(log_dir_):
os.makedirs(log_dir_)
with tf.Session() as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess, config=FLAGS, batch_size=FLAGS.batch_size, output_size=28, c_dim=1,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=checkpoint_dir_, sample_dir=sample_dir_, log_dir=log_dir_)
else:
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, output_size=FLAGS.output_size, c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.sampling(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)
Example #4
| Source File: main_mmd_fm.py From opt-mmd with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def main(_):
pp.pprint(flags.FLAGS.__flags)
sample_dir_ = os.path.join(FLAGS.sample_dir, FLAGS.name)
checkpoint_dir_ = os.path.join(FLAGS.checkpoint_dir, FLAGS.name)
log_dir_ = os.path.join(FLAGS.log_dir, FLAGS.name)
if not os.path.exists(checkpoint_dir_):
os.makedirs(checkpoint_dir_)
if not os.path.exists(sample_dir_):
os.makedirs(sample_dir_)
if not os.path.exists(log_dir_):
os.makedirs(log_dir_)
with tf.Session() as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess, config=FLAGS, batch_size=FLAGS.batch_size, output_size=28, c_dim=1,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=checkpoint_dir_, sample_dir=sample_dir_, log_dir=log_dir_)
else:
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, output_size=FLAGS.output_size, c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.sampling(FLAGS)
#dcgan.load(FLAGS.checkpoint_dir)
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)
Example #5
| Source File: main_non_linear_3DMM.py From Nonlinear_Face_3DMM with Apache License 2.0 | 5 votes |
|
def main(_):
#pp.pprint(FLAGS.__flags)
pp.pprint(tf.app.flags.FLAGS.flag_values_dict())
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.samples_dir):
os.makedirs(FLAGS.samples_dir)
gpu_options = tf.GPUOptions(visible_device_list =FLAGS.gpu, per_process_gpu_memory_fraction = 0.8, allow_growth = True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, gpu_options=gpu_options)) as sess:
dcgan = DCGAN(sess, FLAGS)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
dcgan.test(FLAGS, True)
'''
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)'''
Example #6
| Source File: mainmhl.py From WaterGAN with MIT License | 4 votes |
|
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:
wgan = WGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
input_water_width=FLAGS.input_water_width,
input_water_height=FLAGS.input_water_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
c_dim=FLAGS.c_dim,
max_depth = FLAGS.max_depth,
save_epoch=FLAGS.save_epoch,
water_dataset_name=FLAGS.water_dataset,
air_dataset_name = FLAGS.air_dataset,
depth_dataset_name = FLAGS.depth_dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
results_dir = FLAGS.results_dir,
sample_dir=FLAGS.sample_dir,
num_samples = FLAGS.num_samples)
if FLAGS.is_train:
wgan.train(FLAGS)
else:
if not wgan.load(FLAGS.checkpoint_dir):
raise Exception("[!] Train a model first, then run test mode")
wgan.test(FLAGS)
# 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)
Example #7
| Source File: mainjamaica.py From WaterGAN with MIT License | 4 votes |
|
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:
wgan = WGAN(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
input_water_width=FLAGS.input_water_width,
input_water_height=FLAGS.input_water_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
c_dim=FLAGS.c_dim,
max_depth = FLAGS.max_depth,
save_epoch=FLAGS.save_epoch,
water_dataset_name=FLAGS.water_dataset,
air_dataset_name = FLAGS.air_dataset,
depth_dataset_name = FLAGS.depth_dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir,
results_dir = FLAGS.results_dir,
sample_dir=FLAGS.sample_dir,
num_samples = FLAGS.num_samples)
if FLAGS.is_train:
wgan.train(FLAGS)
else:
if not wgan.load(FLAGS.checkpoint_dir):
raise Exception("[!] Train a model first, then run test mode")
wgan.test(FLAGS)
# 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)
Example #8
| Source File: pacgan_task.py From PacGAN with MIT License | 4 votes |
|
def main(self):
FLAGS = Struct(**self._config)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
FLAGS.checkpoint_dir = os.path.join(self._work_dir, "checkpoint")
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
FLAGS.sample_dir = os.path.join(self._work_dir, "samples")
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
FLAGS.work_dir = self._work_dir
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth=True
if FLAGS.random:
seed = random.randint(1, 100000)
np.random.seed(seed)
with open(os.path.join(self._work_dir, "seed.txt"), "w") as f:
f.write("{}".format(seed))
t_num_test_samples = int(ceil(float(FLAGS.num_test_sample) / float(FLAGS.batch_size))) * FLAGS.batch_size
test_samples = np.random.uniform(-1, 1, size = (t_num_test_samples, FLAGS.z_dim))
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,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
packing_num=FLAGS.packing_num,
num_training_sample=FLAGS.num_training_sample,
num_test_sample=FLAGS.num_test_sample,
z_dim=FLAGS.z_dim,
test_samples=test_samples)
show_all_variables()
dcgan.train(FLAGS)
#OPTION = 0
#visualize(sess, dcgan, FLAGS, OPTION)
