def test_topo_sort_constant(self):
test_model_path = "onnx_model_topo_sort_constant.onnx"
self.construct_model_Constant(test_model_path)
onnx_model = ONNXModel(onnx.load(test_model_path))
check_op_type_order(self, onnx_model.model, ["Add", "Constant"])
onnx_model.topological_sort()
check_op_type_order(self, onnx_model.model, ["Constant", "Add"])
def verify(self, per_channel):
np.random.seed(1)
model_fp32_path = 'conv_clip_fp32.{}.onnx'.format(per_channel)
model_int8_qdq_path = 'conv_clip_quant_qdq.{}.onnx'.format(per_channel)
model_int8_qop_path = 'conv_clip_quant_qop.{}.onnx'.format(per_channel)
data_reader = self.input_feeds(1, {'input': [1, 8, 33, 33]})
self.construct_model_conv_clip(model_fp32_path,
[1, 8, 33, 33],
[16, 8, 3, 3],
[15376])
quantize_static(model_fp32_path,
model_int8_qdq_path,
data_reader,
quant_format=QuantFormat.QDQ,
per_channel = per_channel,
reduce_range = per_channel
)
data_reader.rewind()
#topo sort check
check_op_type_order(self, model_int8_qdq_path, ['DequantizeLinear', 'QuantizeLinear', 'DequantizeLinear', 'Conv', 'QuantizeLinear', 'DequantizeLinear', 'Reshape', 'QuantizeLinear', 'DequantizeLinear'])
check_model_correctness(self, model_fp32_path, model_int8_qdq_path, data_reader.get_next())
data_reader.rewind()
quantize_static(model_fp32_path,
model_int8_qop_path,
data_reader,
quant_format=QuantFormat.QOperator,
per_channel = per_channel,
reduce_range = per_channel
)
data_reader.rewind()
qop_nodes = {'QLinearConv': 1, 'QuantizeLinear': 1, 'DequantizeLinear': 1}
check_op_type_count(self, model_int8_qop_path, **qop_nodes)
check_model_correctness(self, model_fp32_path, model_int8_qop_path, data_reader.get_next())
def test_topo_sort_constant(self):
test_model_path = 'onnx_model_topo_sort_constant.onnx'
self.construct_model_Constant(test_model_path)
onnx_model = ONNXModel(onnx.load(test_model_path))
check_op_type_order(self, onnx_model.model, ['Add', 'Constant'])
onnx_model.topological_sort()
check_op_type_order(self, onnx_model.model, ['Constant', 'Add'])
def verify(self, per_channel, is_quant_type_int8):
np.random.seed(1)
model_fp32_path = "conv_relu_fp32.{}.onnx".format(per_channel)
model_int8_qdq_path = "conv_relu_quant_qdq.{}.onnx".format(per_channel)
model_int8_qop_path = "conv_relu_quant_qop.{}.onnx".format(per_channel)
data_reader = self.input_feeds(1, {"input": [1, 8, 33, 33]})
self.construct_model_conv_relu(model_fp32_path, [1, 8, 33, 33],
[16, 8, 3, 3], [1, 16, 31, 31])
quantize_static(
model_fp32_path,
model_int8_qdq_path,
data_reader,
quant_format=QuantFormat.QDQ,
per_channel=per_channel,
reduce_range=per_channel,
activation_type=QuantType.QInt8
if is_quant_type_int8 else QuantType.QUInt8,
weight_type=QuantType.QInt8
if is_quant_type_int8 else QuantType.QUInt8,
)
data_reader.rewind()
# topo sort check
check_op_type_order(
self,
model_int8_qdq_path,
[
"DequantizeLinear",
"QuantizeLinear",
"DequantizeLinear",
"Conv",
"QuantizeLinear",
"DequantizeLinear",
],
)
check_model_correctness(self, model_fp32_path, model_int8_qdq_path,
data_reader.get_next())
data_reader.rewind()
quantize_static(
model_fp32_path,
model_int8_qop_path,
data_reader,
quant_format=QuantFormat.QOperator,
per_channel=per_channel,
reduce_range=per_channel,
activation_type=QuantType.QInt8
if is_quant_type_int8 else QuantType.QUInt8,
weight_type=QuantType.QInt8
if is_quant_type_int8 else QuantType.QUInt8,
)
data_reader.rewind()
qop_nodes = {
"QLinearConv": 1,
"QuantizeLinear": 1,
"DequantizeLinear": 1
}
check_op_type_count(self, model_int8_qop_path, **qop_nodes)
check_model_correctness(self, model_fp32_path, model_int8_qop_path,
data_reader.get_next())