Zheng-Bicheng commented on PR #16651:
URL: https://github.com/apache/tvm/pull/16651#issuecomment-1970274975
I integrated the inference code for TVM, PaddlePaddle, and ONNX. The code is
as follows:
```python
import paddle
import tvm
from tvm import relay
from tvm.contrib import graph_executor
import numpy as np
import onnx
import onnxruntime as rt
# Model Attr
input_shape = [1, 3, 224, 224]
input_name = "inputs"
def infer_by_paddlepaddle(temp_prefix, temp_input_data):
paddle.enable_static()
exe = paddle.static.Executor(paddle.CPUPlace())
temp_prog, feed_target_names, fetch_targets =
paddle.static.load_inference_model(temp_prefix, exe)
temp_output, = exe.run(temp_prog, feed={feed_target_names[0]:
temp_input_data}, fetch_list=fetch_targets)
return temp_prog, temp_output
def infer_by_onnx(temp_model_path, temp_input_data):
sess = rt.InferenceSession(temp_model_path, None)
temp_input_name = sess.get_inputs()[0].name
out_name = sess.get_outputs()[0].name
temp_onnx_output = sess.run([out_name], {temp_input_name:
temp_input_data})[0]
temp_onnx_model = onnx.load_model(temp_model_path)
return temp_onnx_model, temp_onnx_output
def infer_by_tvm(temp_model, temp_input_data):
if isinstance(temp_model, paddle.static.Program):
# model is loaded by `paddle.static.load_inference_model`
mod, params = relay.frontend.from_paddle(temp_model,
shape_dict={input_name: input_shape})
else:
mod, params = relay.frontend.from_onnx(temp_model,
shape={input_name: input_shape})
with tvm.transform.PassContext(opt_level=5):
lib = relay.build(mod, target="llvm", params=params)
# tvm inference
ctx = tvm.cpu()
tvm_model = graph_executor.GraphModule(lib['default'](ctx))
tvm_model.set_input(input_name, temp_input_data)
tvm_model.run()
tvm_output = tvm_model.get_output(0).asnumpy()
return tvm_output
log_file = "tune.json"
if __name__ == "__main__":
np.random.seed(520)
# create input data
input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)
paddle_prefix = "MobileNetV1_QAT/inference"
paddle_model, paddle_output = infer_by_paddlepaddle(paddle_prefix,
input_data)
onnx_model_path = "MobileNetV1_QAT/inference.onnx"
onnx_model, onnx_output = infer_by_paddlepaddle(paddle_prefix,
input_data)
# 对比测试Paddle模型和ONNX模型的输出(通过测试)
np.testing.assert_allclose(paddle_output[0], onnx_output[0], rtol=1e-5,
atol=1e-5)
# 测试TVM_Paddle模型和TVM_ONNX模型的输出(通过测试)
tvm_paddle_result = infer_by_tvm(paddle_model, input_data)
tvm_onnx_result = infer_by_tvm(onnx_model, input_data)
np.testing.assert_allclose(tvm_paddle_result[0], tvm_onnx_result[0],
rtol=1e-5, atol=1e-5)
# 测试Paddle模型和TVM_Paddle模型的输出
# np.testing.assert_allclose(tvm_paddle_result[0], paddle_output[0],
rtol=1e-5, atol=1e-5)
# 测试ONNX模型和TVM_ONNX模型的输出
np.testing.assert_allclose(tvm_onnx_result[0], onnx_output[0],
rtol=1e-5, atol=1e-5)
```
I found that when inputting the same data, the output data of the Paddle
model and the ONNX model are consistent.
The differences between TVM and Paddle are as follows:
```text
Mismatched elements: 4 / 1000 (0.4%)
Max absolute difference: 0.01572984
Max relative difference: 1.
x: array([0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. ,...
y: array([0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. ,...
```
The differences between TVM and ONNX are as follows:
```text
Mismatched elements: 4 / 1000 (0.4%)
Max absolute difference: 0.01572984
Max relative difference: 1.
x: array([0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. ,...
y: array([0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. ,...
```
Therefore, my initial statement should be considered incorrect; under the
same data conditions, both the Paddle model and the ONNX model exhibit the same
symptoms.
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