def test_dynamic_init(self):
sampler = WeightedSampler(reader=get_dynamic_window_reader(),
window_sizes=DYNAMIC_MOD_DATA,
batch_size=2,
windows_per_image=10,
queue_length=10)
with self.cached_session() as sess:
sampler.set_num_threads(2)
out = sess.run(sampler.pop_batch_op())
self.assertAllClose(out['image'].shape[1:], (8, 2, 256, 2))
def test_2d_init(self):
sampler = WeightedSampler(reader=get_2d_reader(),
window_sizes=MOD_2D_DATA,
batch_size=2,
windows_per_image=10,
queue_length=10)
with self.cached_session() as sess:
sampler.set_num_threads(2)
out = sess.run(sampler.pop_batch_op())
self.assertAllClose(out['image'].shape, (2, 10, 9, 1))
sampler.close_all()
border=(20, 20, 20),
mode='constant'))
_, img, _ = reader(idx=0)
#Create samplers with window_size
weighted_sampler = WeightedSampler(reader, window_sizes=(48, 48, 48))
balanced_sampler = BalancedSampler(reader, window_sizes=(48, 48, 48))
uniform_sampler = UniformSampler(reader, window_sizes=(48, 48, 48))
#Generate N samples for each type
N = 30
import tensorflow as tf
# adding the tensorflow tensors
next_window = weighted_sampler.pop_batch_op()
# run the tensors
with tf.Session() as sess:
weighted_sampler.run_threads(sess) #initialise the iterator
w_coords = []
for _ in range(N):
windows = sess.run(next_window)
#print(windows.keys(), windows['MR_location'], windows['MR'].shape)
w_coords.append(windows['MR_location'])
next_window = balanced_sampler.pop_batch_op()
# run the tensors
with tf.Session() as sess:
balanced_sampler.run_threads(sess) #initialise the iterator
b_coords = []
for _ in range(N):