def verify_clip(N, a_min, a_max, dtype):
A = te.placeholder((N, N), dtype=dtype, name="A")
B = topi.clip(A, a_min, a_max)
s = te.create_schedule([B.op])
# use memoize to pickle the test data for next time use
@memoize("topi.tests.test_topi_clip")
def get_ref_data():
a_np = np.random.uniform(a_min * 2, a_max * 2,
size=(N, N)).astype(dtype)
b_np = np.clip(a_np, a_min, a_max)
return a_np, b_np
a_np, b_np = get_ref_data()
def check_device(device, ctx):
print("Running on target: %s" % device)
with tvm.target.Target(device):
s = tvm.topi.testing.get_injective_schedule(device)(B)
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx)
f = tvm.build(s, [A, B], device, name="clip")
f(a, b)
tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device, ctx in tvm.testing.enabled_targets():
check_device(device, ctx)
def multibox_prior(data,
sizes=(1, ),
ratios=(1, ),
steps=(-1, -1),
offsets=(0.5, 0.5),
clip=False):
"""Generate prior(anchor) boxes from data, sizes and ratios.
Parameters
----------
data : tvm.te.Tensor
4-D with shape [batch, c_in, h_in, w_in]]
sizes : tuple of float
Tuple of sizes for anchor boxes.
ratios : tuple of float
Tuple of ratios for anchor boxes.
steps : Tuple of float
Priorbox step across y and x, -1 for auto calculation.
offsets : tuple of int
Priorbox center offsets, y and x respectively.
clip : boolean
Whether to clip out-of-boundary boxes.
Returns
-------
out : tvm.te.Tensor
3-D tensor with shape [1, h_in * w_in * (num_sizes + num_ratios - 1), 4]
"""
num_sizes = len(sizes)
num_ratios = len(ratios)
oshape = (1, data.shape[2] * data.shape[3] * (num_sizes + num_ratios - 1),
4)
out = te.extern(
oshape,
[data],
lambda ins, outs: multibox_prior_ir(ins[0], outs[0], sizes, ratios,
steps, offsets),
tag="multibox_prior",
)
if clip:
out = topi.clip(out, 0, 1)
return out
def multibox_prior(data,
sizes=(1, ),
ratios=(1, ),
steps=(-1, -1),
offsets=(0.5, 0.5),
clip=False):
"""Generate prior(anchor) boxes from data, sizes and ratios.
Parameters
----------
data : tvm.te.Tensor
4-D with shape [batch, c_in, h_in, w_in]]
sizes : tuple of float
Tuple of sizes for anchor boxes.
ratios : tuple of float
Tuple of ratios for anchor boxes.
steps : Tuple of float
Priorbox step across y and x, -1 for auto calculation.
offsets : tuple of int
Priorbox center offsets, y and x respectively.
clip : boolean
Whether to clip out-of-boundary boxes.
Returns
-------
out : tvm.te.Tensor
3-D tensor with shape [1, h_in * w_in * (num_sizes + num_ratios - 1), 4]
"""
out = hybrid_multibox_prior(
data,
tvm.runtime.convert(sizes),
tvm.runtime.convert(ratios),
tvm.runtime.convert(steps),
tvm.runtime.convert(offsets),
)
if clip:
out = topi.clip(out, 0, 1)
return out
def clip_compute(attrs, inputs, output_type):
assert len(inputs) == 1
return [topi.clip(inputs[0], attrs.a_min, attrs.a_max)]
def clip_compute(A, A_min, A_max):
"""
Use topi clip implementation
"""
return topi.clip(A, A_min, A_max)
