def main():
with K.get_session():
""" Used for testing'
"""
train_name = 'with_bridge'
use_bridge = True
dataset = ClopemaLoader()
m = SimpleNet(dataset.image_shape(), dataset.n_garment_cats(),
dataset.n_landmark_cats(), use_bridge)
display = Display(dataset, m.bb_size)
data = dataset.fetch_data('val')
data_x, data_y = data
predictions = m.predict_combined(data_x, train_name)
pred = m.loader.as_output(tuple(predictions))
gt_loss = m.loader.as_input(tuple(data))
display.show_landmark_stats(dataset.landmark_names, predictions,
gt_loss)
display.show_garment_stats(pred, data_y)
display.history_charts(load_losses(train_name))
display.show_multiple_results(
data_x,
pred,
annotations=data_y,
rpn_ground_truths=m.get_rpn_ground_truths(data))
def main():
with K.get_session():
""" Used for testing'
"""
dataset = ClopemaLoader()
plot = Display(dataset)
m = TreeNet((224, 224, 3), dataset.n_garment_cats(),
dataset.n_landmark_cats())
train_data, val_data = dataset.fetch_multiple_data(['train', 'val'])
val_data = (filter_by_n_landmarks(filter_class(val_data, 0)))
# plot.show_results(val_data[0], predictions=m.load_losses(val_data), annotations=val_data[1])
train_data = augment(filter_by_n_landmarks(filter_class(train_data,
0)))
results = m.train_roi_pants('pants_overfit_again_night_1',
train_data,
val_data,
n_epochs=10)
plot.history_charts(results[0])
plot.show_results(val_data[0],
predictions=results[1],
annotations=val_data[1])
def main():
with K.get_session():
""" Used for testing'
"""
dataset = ClopemaLoader()
m = SimpleNet(dataset.image_shape(), dataset.n_garment_cats(),
dataset.n_landmark_cats(), False)
display = Display(dataset, m.bb_size)
train_data, val_data = dataset.fetch_multiple_data(['train', 'val'])
# train_data = augment(train_data)
results = m.train_global('global', train_data, val_data, n_epochs=40)
display.history_charts(results[0])
def main():
with K.get_session():
""" Used for testing'
"""
dataset = ClopemaLoader()
m = TridNet((224, 224, 3), dataset.n_garment_cats(),
dataset.n_landmark_cats())
display = Display(dataset)
data = dataset.fetch_data('train')
pred = m.predict_rpns(data[0], 'train_rpn')
display.history_charts(load_losses('train_rpn'))
display.show_multiple_results(
data[0],
pred,
annotations=data[1],
rpn_ground_truths=m.get_rpn_ground_truths(data))
def main():
with K.get_session():
""" Used for testing'
"""
dataset = ClopemaLoader()
plot = Display(dataset)
m = TridNet((224, 224, 3), dataset.n_garment_cats(),
dataset.n_landmark_cats())
train_data, val_data = dataset.fetch_multiple_data(['train', 'val'])
results = m.train_roi('train_roi', train_data, val_data, n_epochs=10)
plot.history_charts(results[0])
plot.show_results(val_data[0],
results[1],
val_data[1],
ground_truths=results[2])
def main():
with K.get_session():
""" Used for testing'
"""
dataset = ClopemaLoader()
m = SimpleNet(dataset.image_shape(), dataset.n_garment_cats(), dataset.n_landmark_cats())
display = Display(dataset, m.bb_size)
data = dataset.fetch_data('val')
predictions = m.predict_landmarks(data[0], 'landmarks_with_spacial_constraint', ground_truths=data)
pred = m.loader.as_output(tuple(predictions) + (None, None))
gt_loss = m.loader.as_input(tuple(data))
display.show_landmark_stats(dataset.landmark_names, predictions, gt_loss)
display.history_charts(load_losses('landmarks_with_spacial_constraint'))
display.show_multiple_results(data[0], pred,
annotations=data[1],
rpn_ground_truths=m.get_rpn_ground_truths(data))
def main():
with K.get_session():
""" Used for testing'
"""
dataset = ClopemaLoader()
m = SimpleNet(dataset.image_shape(), dataset.n_garment_cats(),
dataset.n_landmark_cats(), False)
display = Display(dataset, m.bb_size)
data = dataset.fetch_data('val')
data_x, data_y = data
predictions = m.predict_global(data_x, 'global')
pred = m.loader.as_output((None, None) + tuple(predictions))
display.show_garment_stats(pred, data_y)
display.history_charts(load_losses('global'))
display.show_multiple_results(data_x, pred, data_y)