未验证 提交 82f255d0 编写于 作者: J JingZhuangzhuang 提交者: GitHub

add pool2d convert test (#35923)

* add pool2d convert test

* modify error

* modify error

* modify error

* modify error

* modify error

* modify error
上级 485b387d
...@@ -87,6 +87,10 @@ class Pool2dOpConverter : public OpConverter { ...@@ -87,6 +87,10 @@ class Pool2dOpConverter : public OpConverter {
bool adaptive = false; bool adaptive = false;
if (op_desc.HasAttr("adaptive")) if (op_desc.HasAttr("adaptive"))
adaptive = BOOST_GET_CONST(bool, op_desc.GetAttr("adaptive")); adaptive = BOOST_GET_CONST(bool, op_desc.GetAttr("adaptive"));
std::string padding_algorithm = "EXPLICIT";
if (op_desc.HasAttr("padding_algorithm"))
padding_algorithm =
BOOST_GET_CONST(std::string, op_desc.GetAttr("padding_algorithm"));
nvinfer1::PoolingType nv_pool_type = nvinfer1::PoolingType::kMAX; nvinfer1::PoolingType nv_pool_type = nvinfer1::PoolingType::kMAX;
nvinfer1::ReduceOperation reduce_operation = nvinfer1::ReduceOperation reduce_operation =
...@@ -124,6 +128,9 @@ class Pool2dOpConverter : public OpConverter { ...@@ -124,6 +128,9 @@ class Pool2dOpConverter : public OpConverter {
pool_layer->setStride(nv_strides); pool_layer->setStride(nv_strides);
pool_layer->setPadding(nv_paddings); pool_layer->setPadding(nv_paddings);
pool_layer->setAverageCountExcludesPadding(exclusive); pool_layer->setAverageCountExcludesPadding(exclusive);
if (padding_algorithm == "SAME") {
pool_layer->setPaddingMode(nvinfer1::PaddingMode::kSAME_UPPER);
}
layer = pool_layer; layer = pool_layer;
} else if (global_pooling) { } else if (global_pooling) {
auto *reduce_layer = TRT_ENGINE_ADD_LAYER(engine_, Reduce, *input1, auto *reduce_layer = TRT_ENGINE_ADD_LAYER(engine_, Reduce, *input1,
...@@ -159,6 +166,9 @@ class Pool2dOpConverter : public OpConverter { ...@@ -159,6 +166,9 @@ class Pool2dOpConverter : public OpConverter {
auto output_name = op_desc.Output("Out")[0]; auto output_name = op_desc.Output("Out")[0];
pool_layer->setStride(nv_strides); pool_layer->setStride(nv_strides);
pool_layer->setPadding(nv_paddings); pool_layer->setPadding(nv_paddings);
if (padding_algorithm == "SAME") {
pool_layer->setPaddingMode(nvinfer1::PaddingMode::kSAME_UPPER);
}
pool_layer->setAverageCountExcludesPadding(exclusive); pool_layer->setAverageCountExcludesPadding(exclusive);
pool_layer->setName(("pool2d (Output: " + output_name + ")").c_str()); pool_layer->setName(("pool2d (Output: " + output_name + ")").c_str());
pool_layer->getOutput(0)->setName(output_name.c_str()); pool_layer->getOutput(0)->setName(output_name.c_str());
...@@ -198,6 +208,9 @@ class Pool2dOpConverter : public OpConverter { ...@@ -198,6 +208,9 @@ class Pool2dOpConverter : public OpConverter {
"trt pool layer in converter could not be created.")); "trt pool layer in converter could not be created."));
pool_layer->setStride(nv_strides); pool_layer->setStride(nv_strides);
pool_layer->setPadding(nv_paddings); pool_layer->setPadding(nv_paddings);
if (padding_algorithm == "SAME") {
pool_layer->setPaddingMode(nvinfer1::PaddingMode::kSAME_UPPER);
}
pool_layer->setAverageCountExcludesPadding(exclusive); pool_layer->setAverageCountExcludesPadding(exclusive);
layer = pool_layer; layer = pool_layer;
} else { } else {
......
...@@ -172,6 +172,22 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, ...@@ -172,6 +172,22 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
std::vector<int> paddings = std::vector<int> paddings =
BOOST_GET_CONST(std::vector<int>, desc.GetAttr("paddings")); BOOST_GET_CONST(std::vector<int>, desc.GetAttr("paddings"));
if (paddings.size() > 2) return false; if (paddings.size() > 2) return false;
if (desc.HasAttr("exclusive")) {
if (BOOST_GET_CONST(bool, desc.GetAttr("exclusive"))) {
std::vector<int> ksize =
BOOST_GET_CONST(std::vector<int>, desc.GetAttr("ksize"));
for (size_t i = 0; i < ksize.size(); i++) {
if (ksize[i] <= paddings[i]) {
VLOG(3) << "the padding size should be less than the filter size "
"for exclusive-counting pooling.";
return false;
}
}
}
}
if (desc.HasAttr("ceil_mode")) {
if (BOOST_GET_CONST(bool, desc.GetAttr("ceil_mode"))) return false;
}
if (desc.Input("X").size() != 1) { if (desc.Input("X").size() != 1) {
VLOG(3) << "TRT Pool2d expect 1 input, but got " VLOG(3) << "TRT Pool2d expect 1 input, but got "
<< desc.Input("X").size(); << desc.Input("X").size();
...@@ -440,6 +456,10 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, ...@@ -440,6 +456,10 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
} }
} }
if (op_type == "anchor_generator") {
if (!with_dynamic_shape) return false;
}
if (op_type == "yolo_box") { if (op_type == "yolo_box") {
if (with_dynamic_shape) return false; if (with_dynamic_shape) return false;
bool has_attrs = bool has_attrs =
......
# 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 TrtConvertAnchorGeneratorTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def sample_program_configs(self):
def generate_input1(batch, attrs: List[Dict[str, Any]]):
return np.random.random([batch, 3, 64, 64]).astype(np.float32)
for batch in [1, 2, 4]:
for anchor_sizes in [[64.0, 128.0, 256.0, 512.0]]:
for aspect_ratios in [[0.5, 1, 2], [0.4, 1.2, 3]]:
for variances in [[1.0, 1.0, 1.0, 1.0],
[0.5, 1.0, 0.5, 1.0]]:
for stride in [[16.0, 16.0], [16.0, 32.0]]:
for offset in [0.5, 0.8]:
dics = [{
"anchor_sizes": anchor_sizes,
"aspect_ratios": aspect_ratios,
"variances": variances,
"stride": stride,
"offset": offset
}]
ops_config = [{
"op_type": "anchor_generator",
"op_inputs": {
"Input": ["input_data"]
},
"op_outputs": {
"Anchors": ["output_anchors"],
"Variances": ["output_variances"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data": TensorConfig(
data_gen=partial(generate_input1,
batch, dics))
},
outputs=[
"output_anchors", "output_variances"
])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]}
self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}
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))
]
# 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 test(self):
self.run_test()
if __name__ == "__main__":
unittest.main()
# 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 TrtConvertConv2dTransposeTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
weights = program_config.weights
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
]
if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[
1] * attrs[0]['groups']:
return False
if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[0]:
return False
return True
def sample_program_configs(self):
self.trt_param.workspace_size = 1073741824
def generate_input1(batch, num_channels, attrs: List[Dict[str, Any]]):
return np.ones([batch, num_channels, 64, 64]).astype(np.float32)
def generate_weight1(num_channels, attrs: List[Dict[str, Any]]):
if attrs[0]['groups'] == 1:
return np.random.random(
[num_channels, num_channels, 3, 3]).astype(np.float32)
else:
return np.random.random(
[num_channels, int(num_channels / 2), 3, 3]).astype(
np.float32)
for num_channels in [2, 4, 6]:
for batch in [1, 2, 4]:
for strides in [[1, 1], [2, 2], [1, 2]]:
for paddings in [[0, 3], [1, 2, 3, 4]]:
for groups in [2]:
for padding_algorithm in [
'EXPLICIT', 'SAME', 'VALID'
]:
for dilations in [[1, 1], [2, 2], [1, 2]]:
for data_format in ['NCHW']:
self.num_channels = num_channels
dics = [{
"data_fromat": data_format,
"dilations": dilations,
"padding_algorithm":
padding_algorithm,
"groups": groups,
"paddings": paddings,
"strides": strides,
"data_format": data_format,
"output_size": [],
"output_padding": []
}]
ops_config = [{
"op_type": "conv2d_transpose",
"op_inputs": {
"Input": ["input_data"],
"Filter": ["conv2d_weight"]
},
"op_outputs": {
"Output": ["output_data"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(
ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(
generate_weight1,
num_channels, dics))
},
inputs={
"input_data":
TensorConfig(data_gen=partial(
generate_input1, batch,
num_channels, dics))
},
outputs=["output_data"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
if self.num_channels == 2:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 2, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 2, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 2, 64, 64],
"output_data": [1, 24, 64, 64]
}
elif self.num_channels == 4:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 4, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 4, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 4, 64, 64],
"output_data": [1, 24, 64, 64]
}
else:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 6, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 6, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 6, 64, 64],
"output_data": [1, 24, 64, 64]
}
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, 1e-5)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False), (1e-5, 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, 1e-5)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True), (1e-5, 1e-5)
def add_skip_trt_case(self):
def teller1(program_config, predictor_config):
if program_config.ops[0].attrs[
'padding_algorithm'] == "SAME" or program_config.ops[
0].attrs['padding_algorithm'] == "VALID":
return True
return False
self.add_skip_case(
teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
"When padding_algorithm is 'SAME' or 'VALID', Trt dose not support. In this case, trt build error is caused by scale op."
)
def teller2(program_config, predictor_config):
if program_config.ops[0].attrs['dilations'][
0] != 1 or program_config.ops[0].attrs['dilations'][1] != 1:
return True
return False
self.add_skip_case(
teller2, SkipReasons.TRT_NOT_IMPLEMENTED,
"When dilations's element is not equal 1, there are different behaviors between Trt and Paddle."
)
def test(self):
self.add_skip_trt_case()
self.run_test()
def test_quant(self):
self.add_skip_trt_case()
self.run_test(quant=True)
if __name__ == "__main__":
unittest.main()
# 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 TrtConvertDepthwiseConv2dTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
weights = program_config.weights
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
]
if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[
1] * attrs[0]['groups']:
return False
return True
def sample_program_configs(self):
self.trt_param.workspace_size = 1073741824
def generate_input1(batch, attrs: List[Dict[str, Any]]):
if attrs[0]['groups'] == 1:
return np.ones([batch, 1, 64, 64]).astype(np.float32)
elif attrs[0]['groups'] == 2:
return np.ones([batch, 2, 64, 64]).astype(np.float32)
else:
return np.ones([batch, 3, 64, 64]).astype(np.float32)
def generate_weight1(attrs: List[Dict[str, Any]]):
return np.random.random([24, 1, 3, 3]).astype(np.float32)
for batch in [1, 2, 4]:
for strides in [[1, 1], [2, 2], [1, 2]]:
for paddings in [[0, 3], [1, 2, 3, 4]]:
for groups in [1, 2, 3]:
for padding_algorithm in ['EXPLICIT', 'SAME', 'VALID']:
for dilations in [[1, 1], [2, 2], [1, 2]]:
for data_format in ['NCHW']:
dics = [{
"data_fromat": data_format,
"dilations": dilations,
"padding_algorithm": padding_algorithm,
"groups": groups,
"paddings": paddings,
"strides": strides,
"data_format": data_format
}]
ops_config = [{
"op_type": "depthwise_conv2d",
"op_inputs": {
"Input": ["input_data"],
"Filter": ["conv2d_weight"]
},
"op_outputs": {
"Output": ["output_data"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(
generate_weight1, dics))
},
inputs={
"input_data":
TensorConfig(data_gen=partial(
generate_input1, batch, dics))
},
outputs=["output_data"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
if attrs[0]['groups'] == 1:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 1, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 1, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 1, 64, 64],
"output_data": [1, 24, 64, 64]
}
elif attrs[0]['groups'] == 2:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 2, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 2, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 2, 64, 64],
"output_data": [1, 24, 64, 64]
}
else:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 3, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 3, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 3, 64, 64],
"output_data": [1, 24, 64, 64]
}
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, 1e-5)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False), (1e-5, 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, 1e-5)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True), (1e-5, 1e-5)
def add_skip_trt_case(self):
def teller1(program_config, predictor_config):
if program_config.ops[0].attrs[
'padding_algorithm'] == "SAME" or program_config.ops[
0].attrs['padding_algorithm'] == "VALID":
return True
return False
self.add_skip_case(
teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
"When padding_algorithm is 'SAME' or 'VALID', Trt dose not support. In this case, trt build error is caused by scale op."
)
def test(self):
self.add_skip_trt_case()
self.run_test()
def test_quant(self):
self.add_skip_trt_case()
self.run_test(quant=True)
if __name__ == "__main__":
unittest.main()
# 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 TrtConvertDepthwiseConv2dTransposeTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
weights = program_config.weights
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
]
if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[
1] * attrs[0]['groups']:
return False
if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[1]:
return False
if inputs['input_data'].shape[1] != attrs[0]['groups']:
return False
return True
def sample_program_configs(self):
self.trt_param.workspace_size = 1073741824
def generate_input1(batch, attrs: List[Dict[str, Any]]):
return np.ones(
[batch, attrs[0]['groups'], 64, 64]).astype(np.float32)
def generate_weight1(attrs: List[Dict[str, Any]]):
return np.random.random(
[attrs[0]['groups'], 1, 3, 3]).astype(np.float32)
for batch in [1, 2, 4]:
for strides in [[1, 1], [2, 2], [1, 2]]:
for paddings in [[0, 3], [1, 2, 3, 4]]:
for groups in [1, 2, 3]:
for padding_algorithm in ['EXPLICIT', 'SAME', 'VALID']:
for dilations in [[1, 1], [2, 2], [1, 2]]:
for data_format in ['NCHW']:
dics = [{
"data_fromat": data_format,
"dilations": dilations,
"padding_algorithm": padding_algorithm,
"groups": groups,
"paddings": paddings,
"strides": strides,
"data_format": data_format,
"output_size": [],
"output_padding": []
}]
ops_config = [{
"op_type": "conv2d_transpose",
"op_inputs": {
"Input": ["input_data"],
"Filter": ["conv2d_weight"]
},
"op_outputs": {
"Output": ["output_data"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(
generate_weight1, dics))
},
inputs={
"input_data":
TensorConfig(data_gen=partial(
generate_input1, batch, dics))
},
outputs=["output_data"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
self.dynamic_shape.min_input_shape = {
"input_data": [1, attrs[0]['groups'], 32, 32],
"output_data": [1, attrs[0]['groups'], 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, attrs[0]['groups'], 64, 64],
"output_data": [4, attrs[0]['groups'], 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, attrs[0]['groups'], 64, 64],
"output_data": [1, attrs[0]['groups'], 64, 64]
}
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, 1e-5)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False), (1e-5, 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, 1e-5)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True), (1e-5, 1e-5)
def add_skip_trt_case(self):
def teller1(program_config, predictor_config):
if program_config.ops[0].attrs[
'padding_algorithm'] == "SAME" or program_config.ops[
0].attrs['padding_algorithm'] == "VALID":
return True
return False
self.add_skip_case(
teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
"When padding_algorithm is 'SAME' or 'VALID', Trt dose not support. In this case, trt build error is caused by scale op."
)
def teller2(program_config, predictor_config):
if program_config.ops[0].attrs['dilations'][
0] != 1 or program_config.ops[0].attrs['dilations'][1] != 1:
return True
return False
self.add_skip_case(
teller2, SkipReasons.TRT_NOT_IMPLEMENTED,
"When dilations's element is not equal 1, there are different behaviors between Trt and Paddle."
)
def test(self):
self.add_skip_trt_case()
self.run_test()
def test_quant(self):
self.add_skip_trt_case()
self.run_test(quant=True)
if __name__ == "__main__":
unittest.main()
# 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 TrtConvertPool2dTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def sample_program_configs(self):
self.trt_param.workspace_size = 1073741824
def generate_input1(attrs: List[Dict[str, Any]]):
return np.ones([1, 3, 64, 64]).astype(np.float32)
def generate_weight1(attrs: List[Dict[str, Any]]):
return np.random.random([24, 3, 3, 3]).astype(np.float32)
for strides in [[1, 1], [2, 2], [1, 2]]:
for paddings in [[0, 2], [0, 3], [1, 2, 3, 4]]:
for pooling_type in ['max', 'avg']:
for padding_algotithm in ['EXPLICIT', 'SAME', 'VAILD']:
for ksize in [[2, 3], [3, 3]]:
for data_format in ['NCHW']:
for global_pooling in [True, False]:
for exclusive in [True, False]:
for adaptive in [True, False]:
for ceil_mode in [True, False]:
self.paddings = paddings
dics = [{
"pooling_type":
pooling_type,
"ksize": ksize,
"data_fromat": data_format,
"padding_algorithm":
padding_algotithm,
"paddings": paddings,
"strides": strides,
"data_format": data_format,
"global_pooling":
global_pooling,
"exclusive": exclusive,
"adaptive": adaptive,
"ceil_mode": ceil_mode
}]
ops_config = [{
"op_type": "pool2d",
"op_inputs": {
"X": ["input_data"],
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(
ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data":
TensorConfig(
data_gen=partial(
generate_input1,
dics))
},
outputs=["output_data"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]}
self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}
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):
if self.paddings == [0, 3] or attrs[0][
'global_pooling'] == True or attrs[0]['ceil_mode'] == True:
return 0, 3
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.ops[0].attrs['paddings']) == 4:
return True
return False
self.add_skip_case(teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
"4-dims paddings are not support for trt now.")
def test(self):
self.add_skip_trt_case()
self.run_test()
if __name__ == "__main__":
unittest.main()
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