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Pad Operator Type Support #12035

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Aug 20, 2018
Merged

Pad Operator Type Support #12035

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539f36a
fix no data type inference for pad
sbodenstein Aug 5, 2018
da93382
add support for int types
sbodenstein Aug 5, 2018
b86917b
add tests for all types
sbodenstein Aug 5, 2018
8f8425b
fix gpu type switch
sbodenstein Aug 5, 2018
738f36e
remove integer support
sbodenstein Aug 9, 2018
e971848
fix python op test style issues
sbodenstein Aug 18, 2018
8082e72
fix type bug in python tests
sbodenstein Aug 20, 2018
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11 changes: 11 additions & 0 deletions src/operator/pad-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -189,6 +189,17 @@ class PadProp : public OperatorProperty {
return param_.__DICT__();
}

bool InferType(std::vector<int> *in_type,
std::vector<int> *out_type,
std::vector<int> *aux_type) const override {
int dtype = (*in_type)[0];
type_assign(&dtype, (*out_type)[0]);

TYPE_ASSIGN_CHECK(*in_type, 0, dtype);
TYPE_ASSIGN_CHECK(*out_type, 0, dtype);
return dtype != -1;
}

bool InferShape(std::vector<TShape> *in_shape, std::vector<TShape> *out_shape,
std::vector<TShape> *aux_shape) const override {
using namespace mshadow;
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24 changes: 14 additions & 10 deletions tests/python/unittest/test_operator.py
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Expand Up @@ -2905,16 +2905,16 @@ def test_roipooling():
numeric_eps=1e-4, rtol=1e-1, atol=1E-4)


def check_pad_with_shape(shape, xpu, pad_width, mode):
def check_pad_with_shape(shape, xpu, pad_width, mode, dtype="float64"):
# bind with label
X = mx.symbol.Variable('X')
X = mx.symbol.Variable('X', dtype=dtype)
Y = mx.symbol.Pad(data=X, mode=mode, pad_width=pad_width)
x = mx.random.uniform(-1, 1, shape, ctx=mx.cpu()).copyto(xpu)
x = mx.random.uniform(-1, 1, shape, ctx=mx.cpu(), dtype=dtype).copyto(xpu)
# numpy result
pad_grouped = list(zip(*[iter(list(pad_width))] * 2))
np_out = np.pad(x.asnumpy(), pad_grouped, mode)
# mxnet result
grad = mx.nd.empty(shape, ctx = xpu)
grad = mx.nd.empty(shape, ctx = xpu, dtype=dtype)
exec1 = Y.bind(xpu, args = [x], args_grad = {'X': grad})
exec1.forward(is_train=True)
out = exec1.outputs[0].asnumpy()
Expand All @@ -2926,16 +2926,20 @@ def check_pad_with_shape(shape, xpu, pad_width, mode):

@with_seed()
def test_pad():
ctx = default_context()
shape1 = (2, 3, 3, 5)
pad1 = (0, 0, 0, 0, 1, 2, 3, 4)
shape2 = (2, 3, 3, 5, 4)
pad2 = (0, 0, 0, 0, 1, 2, 3, 4, 3, 1)
check_pad_with_shape(shape1, default_context(), pad1, 'constant')
check_pad_with_shape(shape1, default_context(), pad1, 'edge')
check_pad_with_shape(shape2, default_context(), pad2, 'constant')
check_pad_with_shape(shape2, default_context(), pad2, 'edge')
check_pad_with_shape(shape1, default_context(), pad1, 'reflect')
check_pad_with_shape(shape2, default_context(), pad2, 'reflect')
# note: this op doesn't support ints yet. Add tests when supported
test_types = ["float16", "float32", "float64"]
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should be

dtypes = [np.float16, np.float32, np.float64]

?

for dtype in dtypes:
check_pad_with_shape(shape1, ctx, pad1, 'constant', dtype)
check_pad_with_shape(shape1, ctx, pad1, 'edge', dtype)
check_pad_with_shape(shape2, ctx, pad2, 'constant', dtype)
check_pad_with_shape(shape2, ctx, pad2, 'edge', dtype)
check_pad_with_shape(shape1, ctx, pad1, 'reflect', dtype)
check_pad_with_shape(shape2, ctx, pad2, 'reflect', dtype)


def np_instance_norm(data, weight, bias, eps):
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