wehub-resource-sync
273 lines
9.8 KiB
Python
273 lines
9.8 KiB
Python
# Copyright (c) 2021 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
|
|
from itertools import product
|
|
from typing import TYPE_CHECKING, Any
|
|
|
|
import numpy as np
|
|
from program_config import ProgramConfig, TensorConfig
|
|
from trt_layer_auto_scan_test import SkipReasons, TrtLayerAutoScanTest
|
|
|
|
import paddle.inference as paddle_infer
|
|
|
|
if TYPE_CHECKING:
|
|
from collections.abc import Generator
|
|
|
|
|
|
class TrtConvertBatchNormTest(TrtLayerAutoScanTest):
|
|
def is_program_valid(self, program_config: ProgramConfig) -> bool:
|
|
return True
|
|
|
|
def sample_program_configs(self):
|
|
def generate_input1(attrs: list[dict[str, Any]], batch):
|
|
if self.dims == 4:
|
|
if attrs[0]['data_layout'] == "NCHW":
|
|
return np.ones([batch, 3, 24, 24]).astype(np.float32)
|
|
elif attrs[0]['data_layout'] == "NHWC":
|
|
return np.ones([batch, 24, 24, 3]).astype(np.float32)
|
|
elif self.dims == 3:
|
|
return np.ones([batch, 3, 24]).astype(np.float32)
|
|
elif self.dims == 2:
|
|
return np.ones([batch, 3]).astype(np.float32)
|
|
|
|
def generate_bias(attrs: list[dict[str, Any]], batch):
|
|
return np.full((3), 0.9).astype("float32")
|
|
|
|
def generate_mean(attrs: list[dict[str, Any]], batch):
|
|
return np.full((3), 0.9).astype("float32")
|
|
|
|
def generate_scale(attrs: list[dict[str, Any]], batch):
|
|
return np.full((3), 1.1).astype("float32")
|
|
|
|
def generate_variance(attrs: list[dict[str, Any]], batch):
|
|
return np.full((3), 1.2).astype("float32")
|
|
|
|
def generate_MomentumTensor(attrs: list[dict[str, Any]], batch):
|
|
return np.full((3), 0.9).astype("float32")
|
|
|
|
for dims, num_input, batch, epsilon, data_layout, momentum in product(
|
|
[2, 3, 4], [0, 1], [1, 4], [1e-6, 1e-5, 1e-4], ["NCHW"], [0.9, 0.8]
|
|
):
|
|
self.num_input = num_input
|
|
self.dims = dims
|
|
dics = [
|
|
{
|
|
"epsilon": epsilon,
|
|
"data_layout": data_layout,
|
|
"momentum": momentum,
|
|
"is_test": True,
|
|
"trainable_statistics": False,
|
|
},
|
|
{},
|
|
]
|
|
dics_input = [
|
|
{
|
|
"X": ["batch_norm_input"],
|
|
"Bias": ["Bias"],
|
|
"Mean": ["Mean"],
|
|
"Scale": ["Scale"],
|
|
"Variance": ["Variance"],
|
|
"MomentumTensor": ["MomentumTensor"],
|
|
},
|
|
{
|
|
"X": ["batch_norm_input"],
|
|
"Bias": ["Bias"],
|
|
"Mean": ["Mean"],
|
|
"Scale": ["Scale"],
|
|
"Variance": ["Variance"],
|
|
},
|
|
]
|
|
dics_inputs = [
|
|
{
|
|
"Bias": TensorConfig(
|
|
data_gen=partial(generate_bias, dics, batch)
|
|
),
|
|
"Mean": TensorConfig(
|
|
data_gen=partial(generate_mean, dics, batch)
|
|
),
|
|
"Scale": TensorConfig(
|
|
data_gen=partial(generate_scale, dics, batch)
|
|
),
|
|
"Variance": TensorConfig(
|
|
data_gen=partial(generate_variance, dics, batch)
|
|
),
|
|
"MomentumTensor": TensorConfig(
|
|
data_gen=partial(
|
|
generate_MomentumTensor,
|
|
dics,
|
|
batch,
|
|
)
|
|
),
|
|
},
|
|
{
|
|
"Bias": TensorConfig(
|
|
data_gen=partial(generate_bias, dics, batch)
|
|
),
|
|
"Mean": TensorConfig(
|
|
data_gen=partial(generate_mean, dics, batch)
|
|
),
|
|
"Scale": TensorConfig(
|
|
data_gen=partial(generate_scale, dics, batch)
|
|
),
|
|
"Variance": TensorConfig(
|
|
data_gen=partial(generate_variance, dics, batch)
|
|
),
|
|
},
|
|
]
|
|
ops_config = [
|
|
{
|
|
"op_type": "batch_norm",
|
|
"op_inputs": dics_input[num_input],
|
|
"op_outputs": {
|
|
"Y": ["batch_norm_out"],
|
|
"MeanOut": ["Mean"],
|
|
"VarianceOut": ["Variance"],
|
|
"SavedMean": ["SavedMean"],
|
|
"SavedVariance": ["SavedVariance"],
|
|
},
|
|
"op_attrs": dics[0],
|
|
}
|
|
]
|
|
ops = self.generate_op_config(ops_config)
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights=dics_inputs[num_input],
|
|
inputs={
|
|
"batch_norm_input": TensorConfig(
|
|
data_gen=partial(generate_input1, dics, batch)
|
|
)
|
|
},
|
|
outputs=["batch_norm_out"],
|
|
no_cast_list=list(dics_inputs[num_input].keys()),
|
|
)
|
|
|
|
yield program_config
|
|
|
|
def sample_predictor_configs(
|
|
self, program_config
|
|
) -> Generator[
|
|
tuple[paddle_infer.Config, list[int], float] | None, Any, Any
|
|
]:
|
|
def generate_dynamic_shape(attrs):
|
|
if self.dims == 4:
|
|
if attrs[0]['data_layout'] == "NCHW":
|
|
self.dynamic_shape.min_input_shape = {
|
|
"batch_norm_input": [1, 3, 12, 12]
|
|
}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"batch_norm_input": [4, 3, 24, 24]
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {
|
|
"batch_norm_input": [1, 3, 24, 24]
|
|
}
|
|
elif attrs[0]['data_layout'] == "NHWC":
|
|
self.dynamic_shape.min_input_shape = {
|
|
"batch_norm_input": [1, 12, 12, 3]
|
|
}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"batch_norm_input": [4, 24, 24, 3]
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {
|
|
"batch_norm_input": [1, 24, 24, 3]
|
|
}
|
|
elif self.dims == 3:
|
|
self.dynamic_shape.min_input_shape = {
|
|
"batch_norm_input": [1, 3, 12]
|
|
}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"batch_norm_input": [4, 3, 24]
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {
|
|
"batch_norm_input": [1, 3, 24]
|
|
}
|
|
elif self.dims == 2:
|
|
self.dynamic_shape.min_input_shape = {
|
|
"batch_norm_input": [1, 3]
|
|
}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"batch_norm_input": [4, 3]
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {
|
|
"batch_norm_input": [1, 3]
|
|
}
|
|
|
|
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, 2
|
|
|
|
attrs = [
|
|
program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
]
|
|
# for static_shape
|
|
clear_dynamic_shape()
|
|
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, False),
|
|
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, False),
|
|
(1e-3, 1e-3),
|
|
)
|
|
|
|
# for dynamic_shape
|
|
generate_dynamic_shape(attrs)
|
|
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-3, 1e-3),
|
|
)
|
|
|
|
def add_skip_trt_case(self):
|
|
def teller1(program_config, predictor_config):
|
|
if len(program_config.weights) == 5:
|
|
return True
|
|
return False
|
|
|
|
self.add_skip_case(
|
|
teller1,
|
|
SkipReasons.TRT_NOT_SUPPORT,
|
|
"INPUT MomentumTensor NOT SUPPORT",
|
|
)
|
|
|
|
def test(self):
|
|
self.add_skip_trt_case()
|
|
self.run_test()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|