wehub-resource-sync
194 lines
7.4 KiB
Python
194 lines
7.4 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import annotations
|
|
|
|
import unittest
|
|
from functools import partial
|
|
|
|
import numpy as np
|
|
from program_config import ProgramConfig, TensorConfig
|
|
from trt_layer_auto_scan_test import TrtLayerAutoScanTest
|
|
|
|
import paddle.inference as paddle_infer
|
|
|
|
|
|
class TrtConvertElementwiseAddTransposeTest(TrtLayerAutoScanTest):
|
|
def is_program_valid(self, program_config: ProgramConfig) -> bool:
|
|
return True
|
|
|
|
def sample_program_configs(self):
|
|
def conv_filter_datagen(dics):
|
|
c = dics["c"]
|
|
x = (np.random.randn(c, c, 1, 1)) * np.sqrt(2 / c) * 0.1
|
|
return x.astype(np.float32)
|
|
|
|
def conv_elementwise_bias_datagen(dics):
|
|
c = dics["c"]
|
|
x = np.random.random([dics["c"]]) * 0.01
|
|
return x.astype(np.float32)
|
|
|
|
def ele1_input_datagen(dics):
|
|
x = np.random.random(
|
|
[dics["batch"], dics["h"] * dics["w"], dics["c"]]
|
|
)
|
|
x = (x - np.mean(x)) / (np.std(x))
|
|
return x.astype(np.float32)
|
|
|
|
def ele2_input_datagen(dics):
|
|
x = np.random.random(
|
|
[dics["batch"], dics["h"] * dics["w"], dics["c"]]
|
|
)
|
|
x = (x - np.mean(x)) / (np.std(x))
|
|
return x.astype(np.float32)
|
|
|
|
for batch in [2]:
|
|
for h in [32, 64]:
|
|
for w in [32, 64]:
|
|
for c in [128, 320, 255, 133]:
|
|
dics = {"batch": batch, "h": h, "w": w, "c": c}
|
|
ops_config = [
|
|
{
|
|
"op_type": "elementwise_add",
|
|
"op_inputs": {
|
|
"X": ["ele_input_1"],
|
|
"Y": ["ele_input_2"],
|
|
},
|
|
"op_outputs": {"Out": ["elementwise_out"]},
|
|
"op_attrs": {"axis": -1},
|
|
},
|
|
{
|
|
"op_type": "reshape",
|
|
"op_inputs": {"X": ["elementwise_out"]},
|
|
"op_outputs": {
|
|
"Out": ["reshape_out"],
|
|
},
|
|
"op_attrs": {"shape": [-1, h, w, c]},
|
|
},
|
|
{
|
|
"op_type": "transpose2",
|
|
"op_inputs": {
|
|
"X": ["reshape_out"],
|
|
},
|
|
"op_outputs": {
|
|
"Out": ["transpose2_out"],
|
|
},
|
|
"op_attrs": {"axis": [0, 3, 1, 2]},
|
|
},
|
|
{
|
|
"op_type": "conv2d",
|
|
"op_inputs": {
|
|
"Input": ["transpose2_out"],
|
|
"Filter": ["conv2d_filter"],
|
|
},
|
|
"op_outputs": {
|
|
"Output": ["conv2d_output"],
|
|
},
|
|
"op_attrs": {
|
|
"dilations": [1, 1],
|
|
"padding_algorithm": "EXPLICIT",
|
|
"groups": 1,
|
|
"paddings": [0, 0],
|
|
"strides": [1, 1],
|
|
"data_format": "NCHW",
|
|
},
|
|
},
|
|
]
|
|
ops = self.generate_op_config(ops_config)
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={
|
|
"conv2d_filter": TensorConfig(
|
|
data_gen=partial(conv_filter_datagen, dics)
|
|
),
|
|
"elementwise_bias": TensorConfig(
|
|
data_gen=partial(
|
|
conv_elementwise_bias_datagen, dics
|
|
)
|
|
),
|
|
},
|
|
inputs={
|
|
"ele_input_1": TensorConfig(
|
|
data_gen=partial(ele1_input_datagen, dics)
|
|
),
|
|
"ele_input_2": TensorConfig(
|
|
data_gen=partial(ele2_input_datagen, dics)
|
|
),
|
|
},
|
|
outputs=["conv2d_output"],
|
|
)
|
|
yield program_config
|
|
|
|
def sample_predictor_configs(
|
|
self, program_config
|
|
) -> tuple[paddle_infer.Config, list[int], float]:
|
|
def generate_dynamic_shape(attrs, inputs):
|
|
channel = inputs['ele_input_1'].shape[2]
|
|
|
|
self.dynamic_shape.min_input_shape = {
|
|
"ele_input_1": [1, 32 * 32, channel],
|
|
"ele_input_2": [1, 32 * 32, channel],
|
|
}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"ele_input_1": [4, 64 * 64, channel],
|
|
"ele_input_2": [4, 64 * 64, channel],
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {
|
|
"ele_input_1": [4, 64 * 64, channel],
|
|
"ele_input_2": [4, 64 * 64, channel],
|
|
}
|
|
|
|
def clear_dynamic_shape():
|
|
self.dynamic_shape.min_input_shape = {}
|
|
self.dynamic_shape.max_input_shape = {}
|
|
self.dynamic_shape.opt_input_shape = {}
|
|
|
|
def generate_trt_nodes_num(attrs, dynamic_shape):
|
|
return 1, 3
|
|
|
|
attrs = [
|
|
program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
]
|
|
inputs = program_config.inputs
|
|
# just support dynamic_shape
|
|
generate_dynamic_shape(attrs, inputs)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
program_config.set_input_type(np.float32)
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(attrs, True),
|
|
1e-5,
|
|
)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
program_config.set_input_type(np.float16)
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(attrs, True),
|
|
(
|
|
1e-2,
|
|
1e-2,
|
|
),
|
|
) # tol 1e-2 for half
|
|
|
|
def add_skip_trt_case(self):
|
|
pass
|
|
|
|
def test(self):
|
|
self.add_skip_trt_case()
|
|
self.run_test()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|