def test_dynamic_get_attribute_value(int_dtype, fp_dtype):
attributes = {
"attrs.num_classes": int_dtype(85),
"attrs.background_label_id": int_dtype(13),
"attrs.top_k": int_dtype(16),
"attrs.variance_encoded_in_target": True,
"attrs.keep_top_k": np.array([64, 32, 16, 8], dtype=int_dtype),
"attrs.code_type": "pytorch.some_parameter_name",
"attrs.share_location": False,
"attrs.nms_threshold": fp_dtype(0.645),
"attrs.confidence_threshold": fp_dtype(0.111),
"attrs.clip_after_nms": True,
"attrs.clip_before_nms": False,
"attrs.decrease_label_id": True,
"attrs.normalized": True,
"attrs.input_height": int_dtype(86),
"attrs.input_width": int_dtype(79),
"attrs.objectness_score": fp_dtype(0.77),
}
box_logits = ng.parameter([4, 1, 5, 5], fp_dtype, "box_logits")
class_preds = ng.parameter([2, 1, 4, 5], fp_dtype, "class_preds")
proposals = ng.parameter([2, 1, 4, 5], fp_dtype, "proposals")
aux_class_preds = ng.parameter([2, 1, 4, 5], fp_dtype, "aux_class_preds")
aux_box_preds = ng.parameter([2, 1, 4, 5], fp_dtype, "aux_box_preds")
node = ng.detection_output(box_logits, class_preds, proposals, attributes,
aux_class_preds, aux_box_preds)
assert node.get_num_classes() == int_dtype(85)
assert node.get_background_label_id() == int_dtype(13)
assert node.get_top_k() == int_dtype(16)
assert node.get_variance_encoded_in_target()
assert np.all(
np.equal(node.get_keep_top_k(),
np.array([64, 32, 16, 8], dtype=int_dtype)))
assert node.get_code_type() == "pytorch.some_parameter_name"
assert not node.get_share_location()
assert np.isclose(node.get_nms_threshold(), fp_dtype(0.645))
assert np.isclose(node.get_confidence_threshold(), fp_dtype(0.111))
assert node.get_clip_after_nms()
assert not node.get_clip_before_nms()
assert node.get_decrease_label_id()
assert node.get_normalized()
assert node.get_input_height() == int_dtype(86)
assert node.get_input_width() == int_dtype(79)
assert np.isclose(node.get_objectness_score(), fp_dtype(0.77))
assert node.get_num_classes() == int_dtype(85)
def test_dynamic_get_attribute_value(int_dtype, fp_dtype):
attributes = {
"num_classes": int_dtype(85),
"background_label_id": int_dtype(13),
"top_k": int_dtype(16),
"variance_encoded_in_target": True,
"keep_top_k": np.array([64, 32, 16, 8], dtype=int_dtype),
"code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
"share_location": False,
"nms_threshold": fp_dtype(0.645),
"confidence_threshold": fp_dtype(0.111),
"clip_after_nms": True,
"clip_before_nms": False,
"decrease_label_id": True,
"normalized": True,
"input_height": int_dtype(86),
"input_width": int_dtype(79),
"objectness_score": fp_dtype(0.77),
}
box_logits = ng.parameter([4, 680], fp_dtype, "box_logits")
class_preds = ng.parameter([4, 170], fp_dtype, "class_preds")
proposals = ng.parameter([4, 1, 8], fp_dtype, "proposals")
aux_class_preds = ng.parameter([4, 4], fp_dtype, "aux_class_preds")
aux_box_preds = ng.parameter([4, 680], fp_dtype, "aux_box_preds")
node = ng.detection_output(box_logits, class_preds, proposals, attributes,
aux_class_preds, aux_box_preds)
assert node.get_num_classes() == int_dtype(85)
assert node.get_background_label_id() == int_dtype(13)
assert node.get_top_k() == int_dtype(16)
assert node.get_variance_encoded_in_target()
assert np.all(
np.equal(node.get_keep_top_k(),
np.array([64, 32, 16, 8], dtype=int_dtype)))
assert node.get_code_type() == "caffe.PriorBoxParameter.CENTER_SIZE"
assert not node.get_share_location()
assert np.isclose(node.get_nms_threshold(), fp_dtype(0.645))
assert np.isclose(node.get_confidence_threshold(), fp_dtype(0.111))
assert node.get_clip_after_nms()
assert not node.get_clip_before_nms()
assert node.get_decrease_label_id()
assert node.get_normalized()
assert node.get_input_height() == int_dtype(86)
assert node.get_input_width() == int_dtype(79)
assert np.isclose(node.get_objectness_score(), fp_dtype(0.77))
assert node.get_num_classes() == int_dtype(85)
def test_detection_output(int_dtype, fp_dtype):
attributes = {
"keep_top_k": np.array([64], dtype=int_dtype),
"nms_threshold": fp_dtype(0.645),
}
box_logits = ng.parameter([4, 8], fp_dtype, "box_logits")
class_preds = ng.parameter([4, 170], fp_dtype, "class_preds")
proposals = ng.parameter([4, 2, 10], fp_dtype, "proposals")
aux_class_preds = ng.parameter([4, 4], fp_dtype, "aux_class_preds")
aux_box_preds = ng.parameter([4, 8], fp_dtype, "aux_box_preds")
node = ng.detection_output(box_logits, class_preds, proposals, attributes, aux_class_preds, aux_box_preds)
assert node.get_type_name() == "DetectionOutput"
assert node.get_output_size() == 1
assert list(node.get_output_shape(0)) == [1, 1, 256, 7]