def verify_multibox_prior(dshape,
sizes=(1, ),
ratios=(1, ),
steps=(-1, -1),
offsets=(0.5, 0.5),
clip=False):
data = sym.Variable("data")
out = sym.multibox_prior(data=data,
sizes=sizes,
ratios=ratios,
steps=steps,
offsets=offsets,
clip=clip)
in_height = dshape[2]
in_width = dshape[3]
num_sizes = len(sizes)
num_ratios = len(ratios)
size_ratio_concat = sizes + ratios
steps_h = steps[0] if steps[0] > 0 else 1.0 / in_height
steps_w = steps[1] if steps[1] > 0 else 1.0 / in_width
offset_h = offsets[0]
offset_w = offsets[1]
oshape = (1, in_height * in_width * (num_sizes + num_ratios - 1), 4)
dtype = "float32"
np_out = np.zeros(oshape).astype(dtype)
for i in range(in_height):
center_h = (i + offset_h) * steps_h
for j in range(in_width):
center_w = (j + offset_w) * steps_w
for k in range(num_sizes + num_ratios - 1):
w = size_ratio_concat[k] * in_height / in_width / 2.0 if k < num_sizes else \
size_ratio_concat[0] * in_height / in_width * math.sqrt(size_ratio_concat[k + 1]) / 2.0
h = size_ratio_concat[k] / 2.0 if k < num_sizes else \
size_ratio_concat[0] / math.sqrt(size_ratio_concat[k + 1]) / 2.0
count = i * in_width * (num_sizes + num_ratios - 1) + j * (
num_sizes + num_ratios - 1) + k
np_out[0][count][0] = center_w - w
np_out[0][count][1] = center_h - h
np_out[0][count][2] = center_w + w
np_out[0][count][3] = center_h + h
if clip:
np_out = np.clip(np_out, 0, 1)
for target, ctx in ctx_list():
graph, lib, _ = nnvm.compiler.build(out, target, {"data": dshape})
m = graph_runtime.create(graph, lib, ctx)
m.set_input("data", np.random.uniform(size=dshape).astype(dtype))
m.run()
tvm_out = m.get_output(0, tvm.nd.empty(np_out.shape, dtype))
tvm.testing.assert_allclose(tvm_out.asnumpy(),
np_out,
atol=1e-5,
rtol=1e-5)
def verify_multibox_prior(dshape, sizes=(1,), ratios=(1,), steps=(-1, -1),
offsets=(0.5, 0.5), clip=False):
data = sym.Variable("data")
out = sym.multibox_prior(data=data, sizes=sizes, ratios=ratios, steps=steps,
offsets=offsets, clip=clip)
in_height = dshape[2]
in_width = dshape[3]
num_sizes = len(sizes)
num_ratios = len(ratios)
size_ratio_concat = sizes + ratios
steps_h = steps[0] if steps[0] > 0 else 1.0 / in_height
steps_w = steps[1] if steps[1] > 0 else 1.0 / in_width
offset_h = offsets[0]
offset_w = offsets[1]
oshape = (1, in_height * in_width * (num_sizes + num_ratios - 1), 4)
dtype = "float32"
np_out = np.zeros(oshape).astype(dtype)
for i in range(in_height):
center_h = (i + offset_h) * steps_h
for j in range(in_width):
center_w = (j + offset_w) * steps_w
for k in range(num_sizes + num_ratios - 1):
w = size_ratio_concat[k] * in_height / in_width / 2.0 if k < num_sizes else \
size_ratio_concat[0] * in_height / in_width * math.sqrt(size_ratio_concat[k + 1]) / 2.0
h = size_ratio_concat[k] / 2.0 if k < num_sizes else \
size_ratio_concat[0] / math.sqrt(size_ratio_concat[k + 1]) / 2.0
count = i * in_width * (num_sizes + num_ratios - 1) + j * (num_sizes + num_ratios - 1) + k
np_out[0][count][0] = center_w - w
np_out[0][count][1] = center_h - h
np_out[0][count][2] = center_w + w
np_out[0][count][3] = center_h + h
if clip:
np_out = np.clip(np_out, 0, 1)
target = "llvm"
ctx = tvm.cpu()
graph, lib, _ = nnvm.compiler.build(out, target, {"data": dshape})
m = graph_runtime.create(graph, lib, ctx)
m.set_input("data", np.random.uniform(size=dshape).astype(dtype))
m.run()
out = m.get_output(0, tvm.nd.empty(np_out.shape, dtype))
tvm.testing.assert_allclose(out.asnumpy(), np_out, atol=1e-5, rtol=1e-5)