def _resolve_score_threshold(cls, nms_ops_map, nms_scope, graph_helper):
score_threshold_op_name = '{}/map/while/MultiClassNonMaxSuppression/FilterGreaterThan/Greater/y' \
.format(nms_scope)
score_threshold_op = nms_ops_map.get(score_threshold_op_name, None)
if score_threshold_op is None:
raise ConverterError(get_message('ERROR_TF_SSD_NMS_CAN_NOT_RESOLVE_SCORE_THRESHOLD'))
score_threshold = float(graph_helper.evaluate_tensor_output(score_threshold_op.outputs[0]))
return score_threshold
def _resolve_iou_threshold(cls, nms_ops_map, nms_scope, graph_helper):
nms_op_name = '{}/map/while/MultiClassNonMaxSuppression/non_max_suppression/NonMaxSuppressionV2' \
.format(nms_scope)
nms_op = nms_ops_map.get(nms_op_name, None)
if nms_op is None:
raise ConverterError(get_message('ERROR_TF_SSD_NMS_CAN_NOT_RESOLVE_IOU'))
_, _, _, iou_tensor = graph_helper.get_op_input_tensors(nms_op, ('?', '?', '?', 'Const'))
iou_threshold = float(graph_helper.evaluate_tensor_output(iou_tensor))
return iou_threshold
def build_layer(self, converter_context, descriptor, input_descriptors, output_descriptors):
"""
:type input_descriptors: [converters.tensorflow.common.LayerDescriptor]
:type output_descriptors: [converters.tensorflow.common.LayerDescriptor]
:type converter_context: converters.tensorflow.converter.ConverterContext
:type descriptor: ConcatLayerResolver.Descriptor
:rtype: int
"""
if len(input_descriptors) != 2:
raise ConverterError(get_message('ERROR_TF_SSD_NMS_REQUIRES_2_INPUTS'))
input_names = []
input_shapes = []
for i in input_descriptors:
tensors = converter_context.get_output_tensors_between(i, descriptor)
if len(tensors) != 1:
raise ConverterError(get_message('ERROR_TF_SSD_NMS_REQUIRES_SINGLE_INPUT_TENSOR'))
input_shapes.append(converter_context.graph_helper.get_op_output_shape(tensors[0].op))
for index, shape in enumerate(input_shapes):
output_names = input_descriptors[index].output_names
if len(shape) == 3 and shape[-1] == 4:
input_names = output_names + input_names
else:
input_names.extend(output_names)
classes_shape = converter_context.graph_helper.get_op_output_shape(descriptor.classes_output_op)
return converter_context.model.add_multi_class_nms_layer(name=descriptor.layer_name,
input_names=input_names,
output_names=descriptor.output_names,
scoreThreshold=descriptor.score_threshold,
iouThreshold=descriptor.iou_threshold,
maxDetectionPerClass=classes_shape[-1],
maxTotalDetections=classes_shape[-1])
def build_layer(self, converter_context, descriptor, input_descriptors, output_descriptors):
"""
:type input_descriptors: [converters.tensorflow.common.LayerDescriptor]
:type output_descriptors: [converters.tensorflow.common.LayerDescriptor]
:type converter_context: converters.tensorflow.converter.ConverterContext
:type descriptor: ConcatLayerResolver.Descriptor
:rtype: int
"""
output_name = descriptor.output_names[0]
anchor_input = [d for d in input_descriptors if isinstance(d, ConstantLayerResolver.Descriptor)]
boxes_input = [d for d in input_descriptors if not isinstance(d, ConstantLayerResolver.Descriptor)]
if len(anchor_input) != 1:
raise ConverterError(get_message('ERROR_TF_SSD_ANCHOR_INPUT_MISSING'))
anchors_layer_name = anchor_input[0].output_names[0]
boxes_layer_name = boxes_input[0].output_names[0]
return converter_context.model.add_box_decoder_layer(output_name,
[boxes_layer_name, anchors_layer_name],
[output_name],
scale_y=descriptor.scale_y,
scale_x=descriptor.scale_y,
scale_h=descriptor.scale_h,
scale_w=descriptor.scale_w)