未验证 提交 f070aa5c 编写于 作者: J JingZhuangzhuang 提交者: GitHub

Add clip convert test (#35694)

* add anchor_generator test

* add anchor_generator test

* add clip convert test

* delete wrong file
Co-authored-by: Nxiaoxiaohehe001 <hiteezsf@163.com>
上级 12bf0502
...@@ -903,6 +903,30 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, ...@@ -903,6 +903,30 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
return false; return false;
} }
if (op_type == "clip") {
// Paddle-TRT does not support the input tensors: Min and Max
auto clip_inputs = desc.Inputs();
if (clip_inputs.find("Min") != clip_inputs.end()) {
if (desc.Input("Min").size() >= 1) {
return false;
}
}
if (clip_inputs.find("Max") != clip_inputs.end()) {
if (desc.Input("Max").size() >= 1) {
return false;
}
}
auto* block = desc.Block();
auto x_var_name = desc.Input("X")[0];
auto* x_var_desc = block->FindVar(x_var_name);
const auto x_shape = x_var_desc->GetShape();
if (x_shape.size() == 1) {
VLOG(3) << "clip op does not support input's dim is 1 in tensorrt.";
return false;
}
}
if (op_type == "reduce_sum" || op_type == "reduce_mean") { if (op_type == "reduce_sum" || op_type == "reduce_mean") {
if (!(desc.HasAttr("keep_dim") && desc.HasAttr("dim") && if (!(desc.HasAttr("keep_dim") && desc.HasAttr("dim") &&
desc.HasAttr("reduce_all"))) { desc.HasAttr("reduce_all"))) {
......
# 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 TrtConvertClipTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def sample_program_configs(self):
def generate_input1(dims, batch, attrs: List[Dict[str, Any]]):
if dims == 1:
return np.ones([64]).astype(np.float32)
elif dims == 2:
return np.ones([3, 64]).astype(np.float32)
elif dims == 3:
return np.ones([3, 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.array([np.random.uniform(1, 10)]).astype("float32")
def generate_weight2(attrs: List[Dict[str, Any]]):
return np.array([np.random.uniform(10, 20)]).astype("float32")
for dims in [1, 2, 3, 4]:
for batch in [1, 2, 4]:
for op_inputs in [{
"X": ["input_data"]
}, {
"X": ["input_data"],
"Min": ["Min_"],
"Max": ["Max_"]
}]:
self.input_num = len(op_inputs)
self.dims = dims
dics = [{
"min": np.random.uniform(1, 10),
"max": np.random.uniform(10, 20)
}, {
"op_inputs": op_inputs
}]
ops_config = [{
"op_type": "clip",
"op_inputs": op_inputs,
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"Min_": TensorConfig(data_gen=partial(
generate_weight1, dics)),
"Max_": TensorConfig(data_gen=partial(
generate_weight2, dics))
},
inputs={
"input_data": TensorConfig(data_gen=partial(
generate_input1, dims, 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 self.dims == 1:
self.dynamic_shape.min_input_shape = {"input_data": [1]}
self.dynamic_shape.max_input_shape = {"input_data": [128]}
self.dynamic_shape.opt_input_shape = {"input_data": [64]}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {"input_data": [1, 32]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 64]}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 64]}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]}
self.dynamic_shape.max_input_shape = {
"input_data": [10, 64, 64]
}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 64, 64]}
else:
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.input_num == 3 or self.dims == 1:
return 0, 3
else:
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 test(self):
self.run_test()
if __name__ == "__main__":
unittest.main()
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