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import numpy as np
import hypothesis.strategies as st
import unittest
import caffe2.python.hypothesis_test_util as hu
from caffe2.python import core, workspace
from hypothesis import given, settings
import caffe2.python.ideep_test_util as mu
@st.composite
def _tensor_splits(draw, add_axis=False):
"""Generates (axis, split_info, tensor_splits) tuples."""
tensor = draw(hu.tensor(min_dim=2, min_value=4)) # Each dim has at least 4 elements.
axis = draw(st.integers(-len(tensor.shape), len(tensor.shape) - 1))
if add_axis:
# Simple case: get individual slices along one axis, where each of them
# is (N-1)-dimensional. The axis will be added back upon concatenation.
return (
axis,
np.ones(tensor.shape[axis], dtype=np.int32),
[
np.array(tensor.take(i, axis=axis))
for i in range(tensor.shape[axis])
]
)
else:
# General case: pick some (possibly consecutive, even non-unique)
# indices at which we will split the tensor, along the given axis.
splits = sorted(draw(
st.lists(elements=st.integers(0, tensor.shape[axis]), max_size=4)
) + [0, tensor.shape[axis]])
# Not support empty tensor
splits = list(set(splits))
return (
axis,
np.array(np.diff(splits), dtype=np.int32),
[
tensor.take(range(splits[i], splits[i + 1]), axis=axis)
for i in range(len(splits) - 1)
],
)
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestConcatSplitOps(hu.HypothesisTestCase):
@given(tensor_splits=_tensor_splits(),
**mu.gcs)
@settings(deadline=10000)
def test_concat(self, tensor_splits, gc, dc):
axis, _, splits = tensor_splits
op = core.CreateOperator(
"Concat",
['X_{}'.format(i) for i in range(len(splits))],
['concat_result', 'split_info'],
axis=axis
)
self.assertDeviceChecks(dc, op, splits, [0, 1])
self.assertGradientChecks(gc, op, splits, 0, [0])
@given(tensor_splits=_tensor_splits(),
split_as_arg=st.booleans(),
**mu.gcs)
@settings(deadline=10000)
def test_split(self, tensor_splits, split_as_arg, gc, dc):
axis, split_info, splits = tensor_splits
split_as_arg = True
if split_as_arg:
input_names = ['input']
input_tensors = [np.concatenate(splits, axis=axis)]
kwargs = dict(axis=axis, split=split_info)
else:
input_names = ['input', 'split']
input_tensors = [np.concatenate(splits, axis=axis), split_info]
kwargs = dict(axis=axis)
op = core.CreateOperator(
"Split",
input_names,
['X_{}'.format(i) for i in range(len(split_info))],
**kwargs
)
def split_ref(input, split=split_info):
s = np.cumsum([0] + list(split))
return [
np.array(input.take(np.arange(s[i], s[i + 1]), axis=axis))
for i in range(len(split))
]
outputs_with_grad = range(len(split_info))
self.assertDeviceChecks(dc, op, input_tensors, outputs_with_grad)
self.assertGradientChecks(gc, op, input_tensors, 0, outputs_with_grad)
@given(tensor_splits=_tensor_splits(add_axis=True), **mu.gcs)
@settings(deadline=10000)
def test_concat_add_axis(self, tensor_splits, gc, dc):
axis, _, splits = tensor_splits
op = core.CreateOperator(
"Concat",
['X_{}'.format(i) for i in range(len(splits))],
['concat_result', 'split_info'],
axis=axis,
add_axis=1
)
self.assertDeviceChecks(dc, op, splits, [0, 1])
for i in range(len(splits)):
self.assertGradientChecks(gc, op, splits, i, [0])
@given(tensor_splits=_tensor_splits(add_axis=True), **mu.gcs)
def test_concat_with_TensorCPU(self, tensor_splits, gc, dc):
axis, _, splits = tensor_splits
op0 = core.CreateOperator(
"Concat",
['X_{}'.format(i) for i in range(len(splits))],
['concat_result0', 'split_info0'],
axis=axis,
add_axis=1,
device_option=dc[0]
)
op1 = core.CreateOperator(
"Concat",
['X_{}'.format(i) for i in range(len(splits))],
['concat_result1', 'split_info1'],
axis=axis,
add_axis=1,
device_option=dc[1]
)
for i, X in enumerate(splits):
workspace.FeedBlob('X_{}'.format(i), X, dc[0])
workspace.RunOperatorOnce(op0)
res0 = workspace.FetchBlob('concat_result0')
inf0 = workspace.FetchBlob('split_info0')
workspace.RunOperatorOnce(op1)
res1 = workspace.FetchBlob('concat_result1')
inf1 = workspace.FetchBlob('split_info1')
if not np.allclose(res0, res1, atol=0.0, rtol=0.0):
print(res1.flatten())
print(res0.flatten())
print(np.max(np.abs(res1 - res0)))
self.assertTrue(False)
if not np.allclose(inf0, inf1, atol=0.0, rtol=0.0):
print(inf1.flatten())
print(inf0.flatten())
print(np.max(np.abs(inf1 - inf0)))
self.assertTrue(False)
if __name__ == "__main__":
unittest.main()
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