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[Paddle Inference]Add BN op TRT converter unittest (#35527)
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* add_bn_

* add_bn_teller

* add_bn_teller

* add_bn_teller

* add_bn_teller
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@xiaoxiaohehe001
xiaoxiaohehe001 authored Sep 14, 2021
1 parent e93228e commit 39bc7ea
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7 changes: 6 additions & 1 deletion paddle/fluid/inference/tensorrt/op_teller.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -513,7 +513,12 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
return false;
}
}

auto batch_norm_inputs = desc.Inputs();
if (batch_norm_inputs.find("MomentumTensor") != batch_norm_inputs.end()) {
if (desc.Input("MomentumTensor").size() >= 1) {
return false;
}
}
if (desc.Output("Y").size() != 1) {
VLOG(3) << "Invalid output Y's size of batch_norm TRT "
"converter. Expected 1, received "
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220 changes: 220 additions & 0 deletions python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_batch_norm.py
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@@ -0,0 +1,220 @@
# 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 trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
from program_config import TensorConfig, ProgramConfig
import numpy as np
import paddle.inference as paddle_infer
from functools import partial
from typing import Optional, List, Callable, Dict, Any, Set


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 in [2, 3, 4]:
for num_input in [0, 1]:
for batch in [1, 2, 4]:
for epsilon in [1e-6, 1e-5, 1e-4]:
for data_layout in ["NCHW"]:
for momentum in [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_intput = [{
"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_intputs = [{
"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_intput[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_intputs[num_input],
inputs={
"batch_norm_input": TensorConfig(
data_gen=partial(generate_input1,
dics, batch))
},
outputs=["batch_norm_out"])

yield program_config

def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
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, 24, 24]
}
self.dynamic_shape.max_input_shape = {
"batch_norm_input": [4, 3, 48, 48]
}
self.dynamic_shape.opt_input_shape = {
"batch_norm_input": [1, 3, 24, 48]
}
elif attrs[0]['data_layout'] == "NHWC":
self.dynamic_shape.min_input_shape = {
"batch_norm_input": [1, 24, 24, 3]
}
self.dynamic_shape.max_input_shape = {
"batch_norm_input": [4, 48, 48, 3]
}
self.dynamic_shape.opt_input_shape = {
"batch_norm_input": [1, 24, 48, 3]
}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {
"batch_norm_input": [1, 3, 24]
}
self.dynamic_shape.max_input_shape = {
"batch_norm_input": [4, 3, 48]
}
self.dynamic_shape.opt_input_shape = {
"batch_norm_input": [1, 3, 48]
}
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
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False), 1e-5

# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(attrs,
True), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(attrs,
True), 1e-5

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()

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