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Minor type issues in Squeeze #16448

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Oct 13, 2019
Merged

Minor type issues in Squeeze #16448

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b1bd878
minor
junrushao Oct 11, 2019
a6cad6d
test
junrushao Oct 12, 2019
1221a0e
Retrigger
junrushao Oct 12, 2019
ae91f5a
Retrigger
junrushao Oct 12, 2019
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2 changes: 1 addition & 1 deletion src/operator/numpy/np_matrix_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -248,7 +248,7 @@ NNVM_REGISTER_OP(_np_squeeze)
.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
.set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_squeeze"})
.add_argument("a", "NDArray-or-Symbol[]", "data to squeeze")
.add_argument("a", "NDArray-or-Symbol", "data to squeeze")
.add_arguments(SqueezeParam::__FIELDS__());

bool ConcatShape(const nnvm::NodeAttrs& attrs,
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2 changes: 1 addition & 1 deletion src/operator/tensor/matrix_op.cc
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Expand Up @@ -1019,7 +1019,7 @@ Examples::
.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
.set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_squeeze"})
.add_argument("data", "NDArray-or-Symbol[]", "data to squeeze")
.add_argument("data", "NDArray-or-Symbol", "data to squeeze")
.add_arguments(SqueezeParam::__FIELDS__());

NNVM_REGISTER_OP(_backward_squeeze)
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16 changes: 8 additions & 8 deletions tests/python/unittest/test_numpy_op.py
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Expand Up @@ -228,11 +228,11 @@ def __init__(self):

def hybrid_forward(self, F, x1, x2):
return F.np.ldexp(x1, x2)

def _np_ldexp(x1, x2):
return x1 * _np.power(2.0, x2)

def dldx(x1, x2):
def dldx(x1, x2):
grad_a = _np.power(2.0, x2)
grad_b = _np_ldexp(x1, x2) * _np.log(2.0)
if len(x1) == 1:
Expand All @@ -241,7 +241,7 @@ def dldx(x1, x2):
grad_b = _np.sum(grad_b)
return [grad_a, grad_b]

shapes = [
shapes = [
((3, 1), (3, 1)),
((3, 1, 2), (3, 1, 2)),
((1, ),(1, )),
Expand All @@ -250,15 +250,15 @@ def dldx(x1, x2):
((3, 0), (3, 0)), # zero-size shape
((0, 1), (0, 1)), # zero-size shape
((2, 0, 2), (2, 0, 2)), # zero-size shape
]
]

for hybridize in [True, False]:
for shape1, shape2 in shapes:
for dtype in [_np.float16, _np.float32, _np.float64]:
test_ldexp = TestLdexp()
if hybridize:
test_ldexp.hybridize()
x1 = rand_ndarray(shape=shape1, dtype=dtype).as_np_ndarray()
x1 = rand_ndarray(shape=shape1, dtype=dtype).as_np_ndarray()
x1.attach_grad()
x2 = rand_ndarray(shape=shape2, dtype=dtype).as_np_ndarray()
x2.attach_grad()
Expand Down Expand Up @@ -997,13 +997,13 @@ def __init__(self, axis):
self._axis = axis

def hybrid_forward(self, F, x):
return F.np.squeeze(x, axis=self._axis)
return F.np.squeeze(x, self._axis)

for shape, axis in config:
data_np = _np.random.uniform(size=shape)
data_mx = np.array(data_np, dtype=data_np.dtype)
ret_np = _np.squeeze(data_np, axis=axis)
ret_mx = np.squeeze(data_mx, axis=axis)
ret_np = _np.squeeze(data_np, axis)
ret_mx = np.squeeze(data_mx, axis)
assert_almost_equal(ret_mx.asnumpy(), ret_np, rtol=1e-5, atol=1e-6, use_broadcast=False)

net = TestSqueeze(axis)
Expand Down