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from caffe2.python import core, workspace
from caffe2.proto import caffe2_pb2
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
def _one_hots():
index_size = st.integers(min_value=1, max_value=5)
lengths = st.lists(
elements=st.integers(min_value=0, max_value=5))
return st.tuples(index_size, lengths).flatmap(
lambda x: st.tuples(
st.just(x[0]),
st.just(x[1]),
st.lists(
elements=st.integers(min_value=0, max_value=x[0] - 1),
min_size=sum(x[1]),
max_size=sum(x[1]))))
class TestOneHotOps(serial.SerializedTestCase):
@serial.given(
x=hu.tensor(
min_dim=2, max_dim=2, dtype=np.int32,
elements=st.integers(min_value=0, max_value=10)),
**hu.gcs_cpu_only)
def test_batch_one_hot(self, x, gc, dc):
d = x.shape[1]
lens = []
vals = []
for i in range(0, d):
val = np.unique(x[:, i])
vals.extend(val)
lens.append(len(val))
lens = np.array(lens, dtype=np.int32)
vals = np.array(vals, dtype=np.int32)
def ref(x, lens, vals):
output_dim = vals.size
ret = np.zeros((x.shape[0], output_dim)).astype(x.dtype)
p = 0
for i, l in enumerate(lens):
for j in range(0, l):
v = vals[p + j]
ret[x[:, i] == v, p + j] = 1
p += lens[i]
return (ret, )
op = core.CreateOperator('BatchOneHot', ["X", "LENS", "VALS"], ["Y"])
self.assertReferenceChecks(gc, op, [x, lens, vals], ref)
@given(
x=hu.tensor(
min_dim=2, max_dim=2, dtype=np.float32,
elements=st.integers(min_value=-5, max_value=5)),
seed=st.integers(min_value=0, max_value=1000),
**hu.gcs_cpu_only)
@settings(deadline=10000)
def test_batch_bucketized_one_hot(self, x, seed, gc, dc):
np.random.seed(seed)
d = x.shape[1]
lens = np.random.randint(low=1, high=5, size=d)
boundaries = []
for i in range(d):
# add [0, 0] as duplicated boundary for duplicated bucketization
if lens[i] > 2:
cur_boundary = np.append(
np.random.randn(lens[i] - 2) * 5, [0, 0])
else:
cur_boundary = np.random.randn(lens[i]) * 5
cur_boundary.sort()
boundaries += cur_boundary.tolist()
lens = np.array(lens, dtype=np.int32)
boundaries = np.array(boundaries, dtype=np.float32)
def ref(x, lens, boundaries):
output_dim = lens.size + boundaries.size
ret = np.zeros((x.shape[0], output_dim)).astype(x.dtype)
boundary_offset = 0
output_offset = 0
for i, l in enumerate(lens):
bucket_idx_right = np.digitize(
x[:, i],
boundaries[boundary_offset:boundary_offset + l],
right=True
)
bucket_idx_left = np.digitize(
x[:, i],
boundaries[boundary_offset:boundary_offset + l],
right=False
)
bucket_idx = np.floor_divide(
np.add(bucket_idx_right, bucket_idx_left), 2)
for j in range(x.shape[0]):
ret[j, output_offset + bucket_idx[j]] = 1.0
boundary_offset += lens[i]
output_offset += (lens[i] + 1)
return (ret, )
op = core.CreateOperator('BatchBucketOneHot',
["X", "LENS", "BOUNDARIES"], ["Y"])
self.assertReferenceChecks(gc, op, [x, lens, boundaries], ref)
@serial.given(
hot_indices=hu.tensor(
min_dim=1, max_dim=1, dtype=np.int64,
elements=st.integers(min_value=0, max_value=42)),
end_padding=st.integers(min_value=0, max_value=2),
**hu.gcs)
def test_one_hot(self, hot_indices, end_padding, gc, dc):
def one_hot_ref(hot_indices, size):
out = np.zeros([len(hot_indices), size], dtype=float)
x = enumerate(hot_indices)
for i, x in enumerate(hot_indices):
out[i, x] = 1.
return (out, )
size = np.array(max(hot_indices) + end_padding + 1, dtype=np.int64)
if size == 0:
size = 1
op = core.CreateOperator('OneHot', ['hot_indices', 'size'], ['output'])
self.assertReferenceChecks(
gc,
op,
[hot_indices, size],
one_hot_ref,
input_device_options={'size': core.DeviceOption(caffe2_pb2.CPU)})
@serial.given(hot_indices=_one_hots())
def test_segment_one_hot(self, hot_indices):
index_size, lengths, indices = hot_indices
index_size = np.array(index_size, dtype=np.int64)
lengths = np.array(lengths, dtype=np.int32)
indices = np.array(indices, dtype=np.int64)
def segment_one_hot_ref(lengths, hot_indices, size):
offset = 0
out = np.zeros([len(lengths), size], dtype=float)
for i, length in enumerate(lengths):
for idx in hot_indices[offset:offset + length]:
out[i, idx] = 1.
offset += length
return (out, )
op = core.CreateOperator(
'SegmentOneHot',
['lengths', 'hot_indices', 'size'],
['output'])
self.assertReferenceChecks(
hu.cpu_do,
op,
[lengths, indices, index_size],
segment_one_hot_ref)
@given(
x=hu.tensor(
min_dim=2, max_dim=2, dtype=np.float32,
elements=st.integers(min_value=-5, max_value=5)),
seed=st.integers(min_value=0, max_value=1000),
**hu.gcs_cpu_only)
def test_batch_bucket_one_hot_shape_inference(self, x, seed, gc, dc):
np.random.seed(seed)
d = x.shape[1]
lens = np.random.randint(low=1, high=5, size=d)
boundaries = []
for i in range(d):
# add [0, 0] as duplicated boundary for duplicated bucketization
if lens[i] > 2:
cur_boundary = np.append(
np.random.randn(lens[i] - 2) * 5, [0, 0])
else:
cur_boundary = np.random.randn(lens[i]) * 5
cur_boundary.sort()
boundaries += cur_boundary.tolist()
lens = np.array(lens, dtype=np.int32)
boundaries = np.array(boundaries, dtype=np.float32)
workspace.FeedBlob('lens', lens)
workspace.FeedBlob('boundaries', boundaries)
workspace.FeedBlob('x', x)
net = core.Net("batch_bucket_one_hot_test")
result = net.BatchBucketOneHot(["x", "lens", "boundaries"], 1)
(shapes, types) = workspace.InferShapesAndTypes([net])
workspace.RunNetOnce(net)
self.assertEqual(shapes[result], list(workspace.blobs[result].shape))
self.assertEqual(
shapes[result], [x.shape[0], lens.shape[0] + boundaries.shape[0]])
self.assertEqual(types[result], core.DataType.FLOAT)
if __name__ == "__main__":
import unittest
unittest.main()
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