yongwww commented on a change in pull request #4543: [FRONTEND][TFLITE] Add
support for TFLite_Detection_PostProcess
URL: https://github.com/apache/incubator-tvm/pull/4543#discussion_r363440816
##########
File path: python/tvm/relay/frontend/tflite.py
##########
@@ -1494,6 +1499,112 @@ def convert_transpose_conv(self, op):
return out
+ def _convert_detection_postprocess(self, op):
+ """Convert TFLite_Detection_PostProcess"""
+ _option_names = [
+ "w_scale",
+ "max_detections",
+ "_output_quantized",
+ "detections_per_class",
+ "x_scale",
+ "nms_score_threshold",
+ "num_classes",
+ "max_classes_per_detection",
+ "use_regular_nms",
+ "y_scale",
+ "h_scale",
+ "_support_output_type_float_in_quantized_op",
+ "nms_iou_threshold"
+ ]
+
+ custom_options = get_custom_options(op, _option_names)
+ if custom_options["use_regular_nms"]:
+ raise tvm.error.OpAttributeUnImplemented(
+ "use_regular_nms=True is not yet supported for operator {}."
+ .format("TFLite_Detection_PostProcess")
+ )
+
+ inputs = self.get_input_tensors(op)
+ cls_pred = self.get_expr(inputs[1].tensor_idx)
+ loc_prob = self.get_expr(inputs[0].tensor_idx)
+ anchor_values = self.get_tensor_value(inputs[2])
+ anchor_boxes = len(anchor_values)
+ anchor_type = self.get_tensor_type_str(inputs[2].tensor.Type())
+ anchor_expr = self.exp_tab.new_const(anchor_values, dtype=anchor_type)
+
+ if inputs[0].qnn_params:
+ loc_prob = _qnn.op.dequantize(data=loc_prob,
+
input_scale=inputs[0].qnn_params['scale'],
+
input_zero_point=inputs[0].qnn_params['zero_point'])
+ if inputs[1].qnn_params:
+ cls_pred = _qnn.op.dequantize(data=cls_pred,
+
input_scale=inputs[1].qnn_params['scale'],
+
input_zero_point=inputs[1].qnn_params['zero_point'])
+ if inputs[2].qnn_params:
+ anchor_expr = _qnn.op.dequantize(data=anchor_expr,
+
input_scale=inputs[2].qnn_params['scale'],
+
input_zero_point=inputs[2].qnn_params['zero_point'])
+
+ # reshape the cls_pred and loc_prob tensors so
+ # they can be consumed by multibox_transform_loc
+ cls_pred = _op.transpose(cls_pred, [0, 2, 1])
+ # loc_prob coords are in yxhw format
+ # need to convert to xywh
+ loc_coords = _op.split(loc_prob, 4, axis=2)
+ loc_prob = _op.concatenate(
+ [loc_coords[1], loc_coords[0], loc_coords[3], loc_coords[2]],
axis=2
+ )
+ loc_prob = _op.reshape(loc_prob, [1, anchor_boxes*4])
+
+ # anchor coords are in yxhw format
+ # need to convert to ltrb
+ anchor_coords = _op.split(anchor_expr, 4, axis=1)
+ anchor_y = anchor_coords[0]
+ anchor_x = anchor_coords[1]
+ anchor_h = anchor_coords[2]
+ anchor_w = anchor_coords[3]
+ plus_half = _expr.const(0.5, dtype='float32')
+ minus_half = _expr.const(-0.5, dtype='float32')
+ anchor_l = _op.add(anchor_x, _op.multiply(anchor_w, minus_half))
+ anchor_r = _op.add(anchor_x, _op.multiply(anchor_w, plus_half))
+ anchor_t = _op.add(anchor_y, _op.multiply(anchor_h, minus_half))
+ anchor_b = _op.add(anchor_y, _op.multiply(anchor_h, plus_half))
+ anchor_expr = _op.concatenate([anchor_l, anchor_t, anchor_r,
anchor_b], axis=1)
+ anchor_expr = _op.expand_dims(anchor_expr, 0)
+
+ # attributes for multibox_transform_loc
+ new_attrs0 = {}
+ new_attrs0["clip"] = False
+ new_attrs0["threshold"] = custom_options["nms_score_threshold"]
+ new_attrs0["variances"] = (
+ 1/custom_options["x_scale"],
+ 1/custom_options["y_scale"],
+ 1/custom_options["w_scale"],
+ 1/custom_options["h_scale"],
+ )
+
+ # attributes for non_max_suppression
+ new_attrs1 = {}
+ new_attrs1["return_indices"] = False
Review comment:
Thanks. cool, then relay VM could be used to support dynamism.
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