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from caffe2.python import core
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
class TestPackRNNSequenceOperator(serial.SerializedTestCase):
@serial.given(n=st.integers(0, 10), k=st.integers(1, 5),
dim=st.integers(1, 5), **hu.gcs_cpu_only)
def test_pack_rnn_seqence(self, n, k, dim, gc, dc):
lengths = np.random.randint(k, size=n).astype(np.int32) + 1
values = np.random.rand(sum(lengths), dim).astype(np.float32)
def pack_op(values, lengths):
T = max(lengths) if any(lengths) else 0
N = lengths.size
output = np.zeros((T, N) + values.shape[1:]).astype(np.float32)
offset = 0
for c in range(N):
for r in range(lengths[c]):
output[r][c] = values[offset + r]
offset += lengths[c]
return [output]
op = core.CreateOperator(
'PackRNNSequence',
['values', 'lengths'],
'out'
)
# Check against numpy reference
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[values, lengths],
reference=pack_op,
)
# Check over multiple devices
self.assertDeviceChecks(dc, op, [values, lengths], [0])
# Gradient check
self.assertGradientChecks(gc, op, [values, lengths], 0, [0])
@serial.given(n=st.integers(0, 10), k=st.integers(2, 5),
dim=st.integers(1, 5), **hu.gcs_cpu_only)
def test_unpack_rnn_seqence(self, n, k, dim, gc, dc):
lengths = np.random.randint(k, size=n).astype(np.int32) + 1
T = max(lengths) if any(lengths) else 0
N = lengths.size
values = np.random.rand(T, N, dim).astype(np.float32)
def unpack_op(values, lengths):
M = sum(lengths)
output = np.zeros((M,) + values.shape[2:]).astype(np.float32)
N = lengths.size
offset = 0
for c in range(N):
for r in range(lengths[c]):
output[offset + r] = values[r][c]
offset += lengths[c]
return [output]
op = core.CreateOperator(
'UnpackRNNSequence',
['values', 'lengths'],
'out'
)
# Check against numpy reference
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[values, lengths],
reference=unpack_op,
)
# Check over multiple devices
self.assertDeviceChecks(dc, op, [values, lengths], [0])
# Gradient check
self.assertGradientChecks(gc, op, [values, lengths], 0, [0])
if __name__ == "__main__":
import unittest
unittest.main()
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