def test_multibox_transform_loc():
batch_size = 1
num_anchors = 3
num_classes = 3
cls_prob = sym.Variable("cls_prob")
loc_preds = sym.Variable("loc_preds")
anchors = sym.Variable("anchors")
transform_loc_data, valid_count = sym.multibox_transform_loc(cls_prob=cls_prob, loc_pred=loc_preds,
anchor=anchors)
out = sym.nms(data=transform_loc_data, valid_count=valid_count)
# Manually create test case
np_cls_prob = np.array([[[0.2, 0.5, 0.3], [0.25, 0.3, 0.45], [0.7, 0.1, 0.2]]])
np_loc_preds = np.array([[0.1, -0.2, 0.3, 0.2, 0.2, 0.4, 0.5, -0.3, 0.7, -0.2, -0.4, -0.8]])
np_anchors = np.array([[[-0.1, -0.1, 0.1, 0.1], [-0.2, -0.2, 0.2, 0.2], [1.2, 1.2, 1.5, 1.5]]])
expected_np_out = np.array([[[1, 0.69999999, 0, 0, 0.10818365, 0.10008108],
[0, 0.44999999, 1, 1, 1, 1],
[0, 0.30000001, 0, 0, 0.22903419, 0.20435292]]])
target = "llvm"
dtype = "float32"
ctx = tvm.cpu()
graph, lib, _ = nnvm.compiler.build(out, target, {"cls_prob": (batch_size, num_anchors, num_classes),
"loc_preds": (batch_size, num_anchors * 4),
"anchors": (1, num_anchors, 4)})
m = graph_runtime.create(graph, lib, ctx)
m.set_input(**{"cls_prob": np_cls_prob.astype(dtype), "loc_preds": np_loc_preds.astype(dtype), "anchors": np_anchors.astype(dtype)})
m.run()
out = m.get_output(0, tvm.nd.empty(expected_np_out.shape, dtype))
np.testing.assert_allclose(out.asnumpy(), expected_np_out, atol=1e-5, rtol=1e-5)
def get_multibox_detection_output(np_cls_prob, np_loc_preds, np_anchors,
batch_size, num_anchors, num_classes):
import nnvm.symbol as sym
cls_prob = sym.Variable("cls_prob")
loc_preds = sym.Variable("loc_preds")
anchors = sym.Variable("anchors")
transform_loc_data, valid_count = sym.multibox_transform_loc(
cls_prob=cls_prob, loc_pred=loc_preds, anchor=anchors)
out = sym.nms(data=transform_loc_data, valid_count=valid_count)
target = "llvm"
dtype = "float32"
ctx = tvm.cpu()
graph, lib, _ = nnvm.compiler.build(
out, target, {
"cls_prob": (batch_size, num_classes, num_anchors),
"loc_preds": (batch_size, num_anchors * 4),
"anchors": (1, num_anchors, 4)
})
m = graph_runtime.create(graph, lib, ctx)
m.set_input(**{
"cls_prob": np_cls_prob,
"loc_preds": np_loc_preds,
"anchors": np_anchors
})
m.run()
_, out_shape = nnvm.compiler.graph_util.infer_shape(graph,
shape={"data": dshape})
out = m.get_output(0, tvm.nd.empty(
tuple(out_shape[0]),
dtype)) # output of "mbox_conf_softmax", shape: (1, 21, 8732)
return out
def test_multibox_transform_loc():
batch_size = 1
num_anchors = 3
num_classes = 3
cls_prob = sym.Variable("cls_prob")
loc_preds = sym.Variable("loc_preds")
anchors = sym.Variable("anchors")
transform_loc_data, valid_count = sym.multibox_transform_loc(cls_prob=cls_prob, loc_pred=loc_preds,
anchor=anchors)
out = sym.non_max_suppression(data=transform_loc_data, valid_count=valid_count, return_indices=False)
# Manually create test case
np_cls_prob = np.array([[[0.2, 0.5, 0.3], [0.25, 0.3, 0.45], [0.7, 0.1, 0.2]]])
np_loc_preds = np.array([[0.1, -0.2, 0.3, 0.2, 0.2, 0.4, 0.5, -0.3, 0.7, -0.2, -0.4, -0.8]])
np_anchors = np.array([[[-0.1, -0.1, 0.1, 0.1], [-0.2, -0.2, 0.2, 0.2], [1.2, 1.2, 1.5, 1.5]]])
expected_np_out = np.array([[[1, 0.69999999, 0, 0, 0.10818365, 0.10008108],
[0, 0.44999999, 1, 1, 1, 1],
[0, 0.30000001, 0, 0, 0.22903419, 0.20435292]]])
dtype = "float32"
for target, ctx in ctx_list():
graph, lib, _ = nnvm.compiler.build(out, target, {"cls_prob": (batch_size, num_anchors, num_classes),
"loc_preds": (batch_size, num_anchors * 4),
"anchors": (1, num_anchors, 4)})
m = graph_runtime.create(graph, lib, ctx)
m.set_input(**{"cls_prob": np_cls_prob.astype(dtype), "loc_preds": np_loc_preds.astype(dtype), "anchors": np_anchors.astype(dtype)})
m.run()
tvm_out = m.get_output(0, tvm.nd.empty(expected_np_out.shape, dtype))
tvm.testing.assert_allclose(tvm_out.asnumpy(), expected_np_out, atol=1e-5, rtol=1e-5)