delete op for detection
@@ -359,7 +359,7 @@ class ONNXOpMapper(OpMapper):
inputs, outputs)
shape_dtype = _np.dtype('int32')
if shape is None:
- shape = [1, -1] # who knows
+ shape = [1, -1]
_logger.warning(
'in %s(%s -> Reshape -> %s): '
'input "shape" not inferred, use [1, -1] as dummy value, '
@@ -419,55 +419,6 @@ class ONNXOpMapper(OpMapper):
output=node,
param_attr=attr)
- def _roi_pool(self, node, fluid_op=None):
-
- val_x = self.graph.get_node(node.layer.input[0], copy=True)
- val_rois = self.graph.get_node(node.layer.input[1], copy=True)
- val_y = self.graph.get_node(node.layer.output[0], copy=True)
-
- spatial_scale = node.get_attr('spatial_scale') # required
- pooled_height, pooled_width = node.get_attr('pooled_shape') # required
-
- attr = {'pooled_height': pooled_height, 'spatial_scale': spatial_scale}
- feature_attr = ''
- is_max_pool = fluid_op == 'roi_pool'
- if 'sampling_ratio' in node.attr_map: #
- sampling_ratio = node.get_attr['sampling_ratio']
- attr['sampling_ratio'] = sampling_ratio
- if 'output_channels' in node.attr_map: #
- output_channels = node.get_attr['output_channels']
- attr['output_channels'] = output_channels
-
- node.fluid_code.add_layer(fluid_op,
- inputs=','.join([valx, val_rois]),
- output=node,
- param_attr=attr)
-
- def RoiAlign(self, node):
- self._roi_pool(node, fluid_op='roi_align')
-
- def NonMaxSuppression(self, node):
- (val_boxes, val_scores, val_max_output_boxes_per_class,
- val_iou_threshold,
- val_score_threshold) = self.graph.get_nodes(node.layer.input,
- copy=True)
-
- center_point_box = node.get_attr('center_point_box', 0)
-
- scores = _const_weight_or_none(val_scores)
- max_output_boxes_per_class = _const_weight_or_none(
- val_max_output_boxes_per_class)
- iou_threshold = _const_weight_or_none(val_iou_threshold)
- score_threshold = _const_weight_or_none(val_score_threshold)
- if center_point_box == 1:
- pass
- attr = {
- 'scores': scores,
- 'score_threshold': score_threshold,
- 'nms_threshold': iou_threshold,
- 'nms_top_k': max_output_boxes_per_class,
- }
-
def Concat(self, node):
inputs = []
for i in range(len(node.layer.input)):
