未验证 提交 4cf1255c 编写于 作者: 提交者: GitHub

add test (#35405)

上级 09a48965
......@@ -59,15 +59,16 @@ class NearestInterpolateOpConverter : public OpConverter {
float scale_w = 1.f;
std::vector<float> scales;
if (scale > 0.f && (out_h <= 0 && out_w <= 0)) {
if (scale > 0.f) {
scale_h = scale;
scale_w = scale;
} else {
// axis are different in static/dynamic mode
bool with_dynamic = engine_->with_dynamic_shape();
int h_axis = (data_layout == framework::DataLayout::kNCHW) + with_dynamic;
if (!with_dynamic) {
int h_axis =
(data_layout == framework::DataLayout::kNCHW) + with_dynamic;
int w_axis =
(data_layout == framework::DataLayout::kNCHW) + 1 + with_dynamic;
......@@ -76,6 +77,7 @@ class NearestInterpolateOpConverter : public OpConverter {
scale_w =
static_cast<float>(out_w) / static_cast<float>(in_dim.d[w_axis]);
}
}
if (engine_->with_dynamic_shape()) {
scales.push_back(1.f);
......
......@@ -476,6 +476,10 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
return false;
}
}
if ((scale <= 0.f) && with_dynamic_shape) {
VLOG(3) << "dynamic shape not support scale not set.";
return false;
}
}
}
......
# 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
import unittest
class TrtConvertNearestInterpTest(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 attrs[0]['scale'] <= 0 and (attrs[0]['out_h'] <= 0 or
attrs[0]['out_w'] <= 0):
return False
if (attrs[0]['out_h'] <= 0) ^ (attrs[0]['out_w'] <= 0):
return False
return True
def sample_program_configs(self):
def generate_input1(attrs: List[Dict[str, Any]]):
return np.ones([1, 3, 64, 64]).astype(np.float32)
for data_layout in ["NCHW", "NHWC"]:
for interp_method in ["nearest"]:
for align_corners in [True, False]:
for scale in [2.0, -1.0, 0.0]:
for out_h in [32, 64, 128 - 32]:
for out_w in [32, -32]:
dics = [{
"data_layout": data_layout,
"interp_method": interp_method,
"align_corners": align_corners,
"scale": scale,
"out_h": out_h,
"out_w": out_w
}]
ops_config = [{
"op_type": "nearest_interp",
"op_inputs": {
"X": ["input_data"]
},
"op_outputs": {
"Out": ["nearest_interp_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=["nearest_interp_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):
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-2
# 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-2
def add_skip_trt_case(self):
def teller1(program_config, predictor_config):
if program_config.ops[0].attrs[
'scale'] <= 0 and self.dynamic_shape.min_input_shape:
return True
return False
self.add_skip_case(
teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
"NOT Implemented: we need to add support scale <= 0 in dynamic shape in the future"
)
pass
def test(self):
self.add_skip_trt_case()
self.run_test()
if __name__ == "__main__":
unittest.main()
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