def test_plot_sample(verbose=False):
conf = loader.default_conf.copy()
myloader = loader.LocalSegmentationLoader()
myvis = visualizer.LocalSegVisualizer(class_file=class_file, conf=conf)
sample = myloader[1]
if verbose:
myvis.plot_sample(sample)
def test_plot_sample_2d():
conf = loader.default_conf.copy()
conf['label_encoding'] = 'spatial_2d'
conf['grid_dims'] = 2
conf['grid_size'] = 10
myloader = loader.LocalSegmentationLoader(conf=conf)
myvis = visualizer.LocalSegVisualizer(class_file=class_file, conf=conf)
sample = myloader[1]
return
myvis.plot_sample(sample)
def test_plot_sample(verbose=False):
return
conf = loader.default_conf.copy()
myloader = loader.WarpingSegmentationLoader(lst_file='val')
label_coder = LabelCoding(conf=conf)
myvis = visualizer.LocalSegVisualizer(class_file=class_file,
conf=conf,
label_coder=label_coder)
sample = myloader[1]
if verbose:
myvis.plot_sample(sample)
def test_scatter_plot_2d():
conf = loader.default_conf.copy()
conf['label_encoding'] = 'spatial_2d'
conf['grid_dims'] = 2
conf['grid_size'] = 10
myloader = loader.get_data_loader(conf=conf,
batch_size=6,
pin_memory=False,
split='val')
batch = next(myloader.__iter__())
myvis = visualizer.LocalSegVisualizer(class_file=class_file, conf=conf)
label = batch['label'][0].numpy()
prediction = np.random.random((label.shape)) - 0.5 + label
myvis.scatter_plot(label=label, prediction=prediction)
def test_plot_batch_2d():
conf = loader.default_conf.copy()
conf['label_encoding'] = 'spatial_2d'
conf['grid_dims'] = 2
conf['grid_size'] = 10
myloader = loader.get_data_loader(conf=conf,
batch_size=6,
pin_memory=False,
split='val')
batch = next(myloader.__iter__())
myvis = visualizer.LocalSegVisualizer(class_file=class_file, conf=conf)
start_time = time.time()
return
myvis.plot_batch(batch)
duration = time.time() - start_time
logging.info("Visualizing one batch took {} seconds".format(duration))
def test_plot_batch(verbose=False):
conf = loader.default_conf.copy()
conf['dataset'] = 'blender_mini'
return
myloader = loader.get_data_loader(conf=conf,
batch_size=6,
pin_memory=False,
split='train')
batch = next(myloader.__iter__())
myvis = visualizer.LocalSegVisualizer(class_file=class_file, conf=conf)
if verbose:
start_time = time.time()
myvis.plot_batch(batch)
duration = time.time() - start_time
logging.info("Visualizing one batch took {} seconds".format(duration))
def test_plot_batch(verbose=False):
return
conf = loader.default_conf.copy()
myloader = loader.get_data_loader(conf=conf,
batch_size=6,
pin_memory=False,
split='train',
lst_file='val')
batch = next(myloader.__iter__())
label_coder = LabelCoding(conf=conf)
myvis = visualizer.LocalSegVisualizer(class_file=class_file,
conf=conf,
label_coder=label_coder)
if verbose:
start_time = time.time()
myvis.plot_batch(batch)
duration = time.time() - start_time
logging.info("Visualizing one batch took {} seconds".format(duration))
def test_loading_blender(verbose=False):
conf = loader.default_conf.copy()
conf["dataset"] = "blender_mini"
conf['num_worker'] = 8
# conf['transform'] = loader.mytransform
myloader = loader.get_data_loader(
conf=conf, batch_size=8, pin_memory=False)
for step, sample in enumerate(myloader):
myvis = visualizer.LocalSegVisualizer(
class_file=conf["vis_file"], conf=conf)
start_time = time.time()
myvis.plot_batch(sample)
duration = time.time() - start_time # NOQA
if step == 5:
break
if verbose:
plt.show()