def generate(self):
node_output_detail = self.tflite_interpreter._get_tensor_details(self.op.Outputs(0))
node_input_detail = self.tflite_interpreter._get_tensor_details(self.op.Inputs(0))
kernel_shape = [self.tflite_avgpool_parser.FilterHeight(),self.tflite_avgpool_parser.FilterWidth()]
strides_len = [self.tflite_avgpool_parser.StrideH(),self.tflite_avgpool_parser.StrideW()]
padding_stradegy = 'NONE'
if self.tflite_avgpool_parser.Padding() is Padding.SAME:
padding_stradegy = 'SAME'
elif self.tflite_avgpool_parser.Padding() is Padding.VALID:
padding_stradegy = 'VALID'
input_feature_map_shape = node_input_detail['shape']
avg_pool_name = self.node_name
avg_pool_node = helper.make_node(
'AveragePool',
inputs=self.input_nodes_name,
outputs=[avg_pool_name],
kernel_shape=kernel_shape,
strides=strides_len,
pads=tflite_utils.getPadding(input_feature_map_shape, kernel_shape, strides_len, None, padding_stradegy),
name=avg_pool_name
)
out_shape_info = helper.make_tensor_value_info(
avg_pool_name,
TensorProto.FLOAT,
tflite_utils.tflite2onnx_shape_map(node_output_detail['shape'].tolist())
)
# update tables
self.value_infos.append(out_shape_info)
self.node_list.append(avg_pool_node)
#Generate Quantization Info and Reverse Quantization for Weights and Bias
output_quantization_info = node_output_detail.get("quantization_parameters", {})
output_quantization_info["dtype"] = str(node_output_detail["dtype"]).split(".")[1].split("'")[0]
input_quantization_info = node_input_detail.get("quantization_parameters", {})
input_quantization_info["dtype"] = str(node_input_detail["dtype"]).split(".")[1].split("'")[0]
quantization_info = {}
quantization_info[self.input_nodes_name[0]] = input_quantization_info
quantization_info[avg_pool_name] = output_quantization_info
return self.node_list, self.value_infos, self.weight_node_list, quantization_info
def generate(self):
node_output_detail = self.tflite_interpreter._get_tensor_details(
self.op.Outputs(0))
node_input_detail = self.tflite_interpreter._get_tensor_details(
self.op.Inputs(0))
kernel_shape = [
self.tflite_avgpool_parser.FilterHeight(),
self.tflite_avgpool_parser.FilterWidth()
]
strides_len = [
self.tflite_avgpool_parser.StrideH(),
self.tflite_avgpool_parser.StrideW()
]
padding_stradegy = 'NONE'
if self.tflite_avgpool_parser.Padding() is Padding.SAME:
padding_stradegy = 'SAME'
elif self.tflite_avgpool_parser.Padding() is Padding.VALID:
padding_stradegy = 'VALID'
input_feature_map_shape = node_input_detail['shape']
avg_pool_name = self.node_name
avg_pool_node = helper.make_node(
'AveragePool',
inputs=self.input_nodes_name,
outputs=[avg_pool_name],
kernel_shape=kernel_shape,
strides=strides_len,
pads=tflite_utils.getPadding(input_feature_map_shape, kernel_shape,
strides_len, None, padding_stradegy),
name=avg_pool_name)
out_shape_info = helper.make_tensor_value_info(
avg_pool_name, TensorProto.FLOAT,
tflite_utils.tflite2onnx_shape_map(
node_output_detail['shape'].tolist()))
# update tables
self.value_infos.append(out_shape_info)
self.node_list.append(avg_pool_node)
return self.node_list, self.value_infos, self.weight_node_list
def generate(self):
node_output_detail = self.tflite_interpreter._get_tensor_details(
self.op.Outputs(0))
node_input_detail = self.tflite_interpreter._get_tensor_details(
self.op.Inputs(0))
weights_node_info = self.tflite_interpreter._get_tensor_details(
self.op.Inputs(1))
bias_node_info = self.tflite_interpreter._get_tensor_details(
self.op.Inputs(2))
weights_array = self.tflite_interpreter.get_tensor(
weights_node_info['index'])
bias_array = self.tflite_interpreter.get_tensor(
bias_node_info['index'])
kernel_shape = [weights_array.shape[1], weights_array.shape[2]]
strides_len = [
self.tflite_conv_parser.StrideH(),
self.tflite_conv_parser.StrideW()
]
dilation_factor = [
self.tflite_conv_parser.DilationHFactor(),
self.tflite_conv_parser.DilationWFactor()
]
padding_stradegy = 'NONE'
if self.tflite_conv_parser.Padding() is Padding.SAME:
padding_stradegy = 'SAME'
elif self.tflite_conv_parser.Padding() is Padding.VALID:
padding_stradegy = 'VALID'
input_feature_map_shape = node_input_detail['shape']
# transpose because shape define diffent between tflite and onnx
weights_array = np.transpose(weights_array, (0, 3, 1, 2))
# make weight onnx node
weight_onnx_node_name = self.node_name + "_weight"
weight_onnx_node = onnx.helper.make_tensor(
weight_onnx_node_name, TensorProto.FLOAT, weights_array.shape,
weights_array.flatten().tolist())
# make bias onnx node
bias_onnx_node_name = self.node_name + "_bias"
bias_onnx_node = onnx.helper.make_tensor(bias_onnx_node_name,
TensorProto.FLOAT,
bias_array.shape,
bias_array.flatten().tolist())
# make conv onnx node
previous_onnx_node_names = self.input_nodes_name.copy()
previous_onnx_node_names.extend(
[weight_onnx_node_name, bias_onnx_node_name])
conv_onnx_node = onnx.helper.make_node(
'Conv',
inputs=previous_onnx_node_names,
outputs=[self.node_name],
kernel_shape=kernel_shape,
strides=strides_len,
pads=tflite_utils.getPadding(input_feature_map_shape, kernel_shape,
strides_len, dilation_factor,
padding_stradegy),
dilations=dilation_factor,
name=self.node_name,
group=1)
# original layer output
out_shape_info = onnx.helper.make_tensor_value_info(
self.node_name, TensorProto.FLOAT,
tflite_utils.tflite2onnx_shape_map(
node_output_detail['shape'].tolist()))
self.value_infos.append(out_shape_info)
# add weight, bias node
self.weight_node_list.append(weight_onnx_node)
self.weight_node_list.append(bias_onnx_node)
self.node_list.append(conv_onnx_node)
# change output node's input_name
for o_n in self.output_nodes:
for idx, o_n_i_n in enumerate(o_n.input_nodes_name):
if o_n_i_n == self.node_name:
o_n.input_nodes_name[idx] = self.node_list[-1].name
return self.node_list, self.value_infos, self.weight_node_list
def generate(self):
node_output_detail = self.tflite_interpreter._get_tensor_details(
self.op.Outputs(0))
node_input_detail = self.tflite_interpreter._get_tensor_details(
self.op.Inputs(0))
weights_node_info = self.tflite_interpreter._get_tensor_details(
self.op.Inputs(1))
bias_node_info = self.tflite_interpreter._get_tensor_details(
self.op.Inputs(2))
weights_array = self.tflite_interpreter.get_tensor(
weights_node_info['index'])
bias_array = self.tflite_interpreter.get_tensor(
bias_node_info['index'])
kernel_shape = [weights_array.shape[1], weights_array.shape[2]]
strides_len = [
self.tflite_conv_parser.StrideH(),
self.tflite_conv_parser.StrideW()
]
dilation_factor = [
self.tflite_conv_parser.DilationHFactor(),
self.tflite_conv_parser.DilationWFactor()
]
#Generate Quantization Info and Reverse Quantization for Weights and Bias
output_quantization_info = node_output_detail.get(
"quantization_parameters", {})
output_quantization_info["dtype"] = str(
node_output_detail["dtype"]).split(".")[1].split("'")[0]
input_quantization_info = node_input_detail.get(
"quantization_parameters", {})
input_quantization_info["dtype"] = str(
node_input_detail["dtype"]).split(".")[1].split("'")[0]
weight_quantization_info = weights_node_info.get(
"quantization_parameters", {})
weight_quantization_info["dtype"] = str(
weights_node_info["dtype"]).split(".")[1].split("'")[0]
bias_quantization_info = bias_node_info.get("quantization_parameters",
{})
bias_quantization_info["dtype"] = str(
bias_node_info["dtype"]).split(".")[1].split("'")[0]
# input_quantization_info_clean = utils.get_quantization_info_in_array(input_quantization_info)
# output_quantization_info_clean = utils.get_quantization_info_in_array(output_quantization_info)
# weight_quantization_info_clean = utils.get_quantization_info_in_array(weight_quantization_info)
# bias_quantization_info_clean = utils.get_quantization_info_in_array(bias_quantization_info)
#Nested weight and bias into input
input_quantization_info["weight"] = weight_quantization_info
input_quantization_info["bias"] = bias_quantization_info
weights_array = np.array(weights_array, dtype=np.dtype("f4"))
if "scales" in weight_quantization_info and len(
weight_quantization_info["scales"]) > 0:
weights_array = (weights_array -
weight_quantization_info["zero_points"][0]
) * weight_quantization_info["scales"][0]
bias_array = np.array(bias_array, dtype=np.dtype("f4"))
if "scales" in bias_quantization_info and len(
bias_quantization_info["scales"]) > 0:
bias_array = (bias_array - bias_quantization_info["zero_points"][0]
) * bias_quantization_info["scales"][0]
bias_quantization_info["min"] = [float(min(bias_array))]
bias_quantization_info["max"] = [float(max(bias_array))]
padding_stradegy = 'NONE'
if self.tflite_conv_parser.Padding() is Padding.SAME:
padding_stradegy = 'SAME'
elif self.tflite_conv_parser.Padding() is Padding.VALID:
padding_stradegy = 'VALID'
input_feature_map_shape = node_input_detail['shape']
# transpose because shape define diffent between tflite and onnx
weights_array = np.transpose(weights_array, (0, 3, 1, 2))
# make weight onnx node
weight_onnx_node_name = self.node_name + "_weight"
weight_onnx_node = onnx.helper.make_tensor(
weight_onnx_node_name, TensorProto.FLOAT, weights_array.shape,
weights_array.flatten().tolist())
# make bias onnx node
bias_onnx_node_name = self.node_name + "_bias"
bias_onnx_node = onnx.helper.make_tensor(bias_onnx_node_name,
TensorProto.FLOAT,
bias_array.shape,
bias_array.flatten().tolist())
# make conv onnx node
previous_onnx_node_names = self.input_nodes_name.copy()
previous_onnx_node_names.extend(
[weight_onnx_node_name, bias_onnx_node_name])
conv_onnx_node = onnx.helper.make_node(
'Conv',
inputs=previous_onnx_node_names,
outputs=[self.node_name],
kernel_shape=kernel_shape,
strides=strides_len,
pads=tflite_utils.getPadding(input_feature_map_shape, kernel_shape,
strides_len, dilation_factor,
padding_stradegy),
dilations=dilation_factor,
name=self.node_name,
group=1)
# original layer output
out_shape_info = onnx.helper.make_tensor_value_info(
self.node_name, TensorProto.FLOAT,
tflite_utils.tflite2onnx_shape_map(
node_output_detail['shape'].tolist()))
self.value_infos.append(out_shape_info)
# add weight, bias node
self.weight_node_list.append(weight_onnx_node)
self.weight_node_list.append(bias_onnx_node)
self.node_list.append(conv_onnx_node)
# change output node's input_name
for o_n in self.output_nodes:
for idx, o_n_i_n in enumerate(o_n.input_nodes_name):
if o_n_i_n == self.node_name:
o_n.input_nodes_name[idx] = self.node_list[-1].name
quantization_info = {}
quantization_info[weight_onnx_node_name] = weight_quantization_info
quantization_info[bias_onnx_node_name] = bias_quantization_info
quantization_info[
previous_onnx_node_names[0]] = input_quantization_info
quantization_info[self.node_name] = output_quantization_info
return self.node_list, self.value_infos, self.weight_node_list, quantization_info