def testCanBeCalledMultipleTimes(self):
batch_size = 20
val_input_batch = [array_ops.zeros([2, 3, 4])]
lbl_input_batch = array_ops.ones([], dtype=dtypes.int32)
probs = np.array([0, 1, 0, 0, 0])
batches = sampling_ops.stratified_sample(val_input_batch,
lbl_input_batch,
probs,
batch_size,
init_probs=probs)
batches += sampling_ops.stratified_sample(val_input_batch,
lbl_input_batch,
probs,
batch_size,
init_probs=probs)
summary_op = logging_ops.merge_summary(
ops.get_collection(ops.GraphKeys.SUMMARIES))
with self.cached_session() as sess:
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(coord=coord)
sess.run(batches + (summary_op, ))
coord.request_stop()
coord.join(threads)
def curried_sampler(val, lbls, probs, batch, enqueue_many=False):
return sampling_ops.stratified_sample(val,
lbls,
probs,
batch,
init_probs=None,
enqueue_many=enqueue_many)
def testRejectionDataListInput(self):
batch_size = 20
val_input_batch = [
array_ops.zeros([2, 3, 4]),
array_ops.ones([2, 4]),
array_ops.ones(2) * 3
]
lbl_input_batch = array_ops.ones([], dtype=dtypes.int32)
probs = np.array([0, 1, 0, 0, 0])
val_list, lbls = sampling_ops.stratified_sample(
val_input_batch,
lbl_input_batch,
probs,
batch_size,
init_probs=[0, 1, 0, 0, 0])
# Check output shapes.
self.assertTrue(isinstance(val_list, list))
self.assertEqual(len(val_list), len(val_input_batch))
self.assertTrue(isinstance(lbls, ops.Tensor))
with self.cached_session() as sess:
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(coord=coord)
out = sess.run(val_list + [lbls])
coord.request_stop()
coord.join(threads)
# Check output shapes.
self.assertEqual(len(out), len(val_input_batch) + 1)
def testRejectionDataListInput(self):
batch_size = 20
val_input_batch = [
array_ops.zeros([2, 3, 4]), array_ops.ones([2, 4]), array_ops.ones(2) *
3
]
lbl_input_batch = array_ops.ones([], dtype=dtypes.int32)
probs = np.array([0, 1, 0, 0, 0])
val_list, lbls = sampling_ops.stratified_sample(
val_input_batch,
lbl_input_batch,
probs,
batch_size,
init_probs=[0, 1, 0, 0, 0])
# Check output shapes.
self.assertTrue(isinstance(val_list, list))
self.assertEqual(len(val_list), len(val_input_batch))
self.assertTrue(isinstance(lbls, ops.Tensor))
with self.test_session() as sess:
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(coord=coord)
out = sess.run(val_list + [lbls])
coord.request_stop()
coord.join(threads)
# Check output shapes.
self.assertEqual(len(out), len(val_input_batch) + 1)
def curried_sampler(val, lbls, probs, batch, enqueue_many=False):
return sampling_ops.stratified_sample(
val,
lbls,
probs,
batch,
init_probs=initial_p,
enqueue_many=enqueue_many)
def testCanBeCalledMultipleTimes(self):
batch_size = 20
val_input_batch = [array_ops.zeros([2, 3, 4])]
lbl_input_batch = array_ops.ones([], dtype=dtypes.int32)
probs = np.array([0, 1, 0, 0, 0])
batches = sampling_ops.stratified_sample(
val_input_batch, lbl_input_batch, probs, batch_size, init_probs=probs)
batches += sampling_ops.stratified_sample(
val_input_batch, lbl_input_batch, probs, batch_size, init_probs=probs)
summary_op = logging_ops.merge_summary(
ops.get_collection(ops.GraphKeys.SUMMARIES))
with self.test_session() as sess:
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(coord=coord)
sess.run(batches + (summary_op,))
coord.request_stop()
coord.join(threads)
def testRejectionBatchingBehavior(self):
batch_size = 20
input_batch_size = 11
val_input_batch = [array_ops.zeros([input_batch_size, 2, 3, 4])]
lbl_input_batch = control_flow_ops.cond(
math_ops.greater(.5, random_ops.random_uniform([])),
lambda: array_ops.ones([input_batch_size], dtype=dtypes.int32) * 1,
lambda: array_ops.ones([input_batch_size], dtype=dtypes.int32) * 3)
probs = np.array([0, .2, 0, .8, 0])
data_batch, labels = sampling_ops.stratified_sample(
val_input_batch,
lbl_input_batch,
probs,
batch_size,
init_probs=[0, .3, 0, .7, 0],
enqueue_many=True)
with self.cached_session() as sess:
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(coord=coord)
sess.run([data_batch, labels])
coord.request_stop()
coord.join(threads)
def testRejectionBatchingBehavior(self):
batch_size = 20
input_batch_size = 11
val_input_batch = [array_ops.zeros([input_batch_size, 2, 3, 4])]
lbl_input_batch = control_flow_ops.cond(
math_ops.greater(.5, random_ops.random_uniform([])),
lambda: array_ops.ones([input_batch_size], dtype=dtypes.int32) * 1,
lambda: array_ops.ones([input_batch_size], dtype=dtypes.int32) * 3)
probs = np.array([0, .2, 0, .8, 0])
data_batch, labels = sampling_ops.stratified_sample(
val_input_batch,
lbl_input_batch,
probs,
batch_size,
init_probs=[0, .3, 0, .7, 0],
enqueue_many=True)
with self.test_session() as sess:
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(coord=coord)
sess.run([data_batch, labels])
coord.request_stop()
coord.join(threads)
def testGraphBuildAssertionFailures(self):
val = [array_ops.zeros([1, 3]), array_ops.ones([1, 5])]
label = constant_op.constant([1],
shape=[1]) # must have batch dimension
probs = [.2] * 5
init_probs = [.1, .3, .1, .3, .2]
batch_size = 16
# Label must have only batch dimension if enqueue_many is True.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(val,
array_ops.zeros([]),
probs,
batch_size,
init_probs,
enqueue_many=True)
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(val,
array_ops.zeros([1, 1]),
probs,
batch_size,
init_probs,
enqueue_many=True)
# Label must not be one-hot.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(
val, constant_op.constant([0, 1, 0, 0, 0]), probs, batch_size,
init_probs)
# Data must be list, not singleton tensor.
with self.assertRaises(TypeError):
sampling_ops.stratified_sample(array_ops.zeros([1, 3]), label,
probs, batch_size, init_probs)
# Data must have batch dimension if enqueue_many is True.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(val,
constant_op.constant(1),
probs,
batch_size,
init_probs,
enqueue_many=True)
# Batch dimensions on data and labels should be equal.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample([array_ops.zeros([2, 1])],
label,
probs,
batch_size,
init_probs,
enqueue_many=True)
# Probabilities must be numpy array, python list, or tensor.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(val, label, 1, batch_size,
init_probs)
# Probabilities shape must be fully defined.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(
val, label, array_ops.placeholder(dtypes.float32,
shape=[None]), batch_size,
init_probs)
# In the rejection sampling case, make sure that probability lengths are
# the same.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(val,
label, [.1] * 10,
batch_size,
init_probs=[.2] * 5)
# In the rejection sampling case, make sure that zero initial probability
# classes also have zero target probability.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(val,
label, [.2, .4, .4],
batch_size,
init_probs=[0, .5, .5])
def testGraphBuildAssertionFailures(self):
val = [array_ops.zeros([1, 3]), array_ops.ones([1, 5])]
label = constant_op.constant([1], shape=[1]) # must have batch dimension
probs = [.2] * 5
init_probs = [.1, .3, .1, .3, .2]
batch_size = 16
# Label must have only batch dimension if enqueue_many is True.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(
val,
array_ops.zeros([]),
probs,
batch_size,
init_probs,
enqueue_many=True)
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(
val,
array_ops.zeros([1, 1]),
probs,
batch_size,
init_probs,
enqueue_many=True)
# Label must not be one-hot.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(val,
constant_op.constant([0, 1, 0, 0, 0]),
probs, batch_size, init_probs)
# Data must be list, not singleton tensor.
with self.assertRaises(TypeError):
sampling_ops.stratified_sample(
array_ops.zeros([1, 3]), label, probs, batch_size, init_probs)
# Data must have batch dimension if enqueue_many is True.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(
val,
constant_op.constant(1),
probs,
batch_size,
init_probs,
enqueue_many=True)
# Batch dimensions on data and labels should be equal.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(
[array_ops.zeros([2, 1])],
label,
probs,
batch_size,
init_probs,
enqueue_many=True)
# Probabilities must be numpy array, python list, or tensor.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(val, label, 1, batch_size, init_probs)
# Probabilities shape must be fully defined.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(
val,
label,
array_ops.placeholder(
dtypes.float32, shape=[None]),
batch_size,
init_probs)
# In the rejection sampling case, make sure that probability lengths are
# the same.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(
val, label, [.1] * 10, batch_size, init_probs=[.2] * 5)
# In the rejection sampling case, make sure that zero initial probability
# classes also have zero target probability.
with self.assertRaises(ValueError):
sampling_ops.stratified_sample(
val, label, [.2, .4, .4], batch_size, init_probs=[0, .5, .5])
