wehub-resource-sync
223 lines
8.7 KiB
Python
223 lines
8.7 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import unittest
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from functools import partial
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import numpy as np
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from program_config import ProgramConfig, TensorConfig
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from trt_layer_auto_scan_test import TrtLayerAutoScanTest
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import paddle.inference as paddle_infer
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class TrtConvertMulticlassNMSTest(TrtLayerAutoScanTest):
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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return True
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def create_inference_config(self, use_trt=True) -> paddle_infer.Config:
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if use_trt:
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config = paddle_infer.Config()
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config.disable_glog_info()
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config.enable_use_gpu(100, 0)
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config.set_optim_cache_dir(self.cache_dir)
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config.switch_ir_debug()
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config.enable_tensorrt_engine(
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max_batch_size=self.trt_param.max_batch_size,
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workspace_size=self.trt_param.workspace_size,
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min_subgraph_size=self.trt_param.min_subgraph_size,
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precision_mode=self.trt_param.precision,
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use_static=self.trt_param.use_static,
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use_calib_mode=self.trt_param.use_calib_mode,
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)
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if (
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len(self.dynamic_shape.min_input_shape) != 0
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and self.dynamic_shape.min_input_shape.keys()
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== self.dynamic_shape.max_input_shape.keys()
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and self.dynamic_shape.min_input_shape.keys()
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== self.dynamic_shape.opt_input_shape.keys()
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):
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config.set_trt_dynamic_shape_info(
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self.dynamic_shape.min_input_shape,
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self.dynamic_shape.max_input_shape,
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self.dynamic_shape.opt_input_shape,
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self.dynamic_shape.disable_trt_plugin_fp16,
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)
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return config
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else:
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config = paddle_infer.Config()
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config.switch_ir_debug(True)
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config.set_optim_cache_dir(self.cache_dir)
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config.disable_glog_info()
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return config
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def sample_program_configs(self):
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def generate_boxes(batch, num_boxes):
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return np.arange(batch * num_boxes * 4, dtype=np.float32).reshape(
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[batch, num_boxes, 4]
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)
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def generate_scores(batch, num_boxes, num_classes):
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max_value = batch * num_classes * num_boxes
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return (1 / max_value) * np.arange(
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max_value, dtype=np.float32
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).reshape([batch, num_classes, num_boxes])
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for batch in [1, 2]:
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self.batch = batch
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for nms_eta in [0.8, 1.1]:
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for num_boxes, num_classes in [[80, 100], [40, 200], [20, 400]]:
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self.num_boxes, self.num_classes = num_boxes, num_classes
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for score_threshold in [
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0.01,
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]:
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ops_config = [
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{
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"op_type": "multiclass_nms",
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"op_inputs": {
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"BBoxes": ["input_bboxes"],
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"Scores": ["input_scores"],
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},
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"op_outputs": {
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"Out": ["nms_output_boxes"],
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},
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"op_attrs": {
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"background_label": -1,
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"score_threshold": score_threshold,
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"nms_top_k": num_boxes,
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"keep_top_k": num_boxes,
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"nms_threshold": 0.3,
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"normalized": False,
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"nms_eta": nms_eta,
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},
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}
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]
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ops = self.generate_op_config(ops_config)
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program_config = ProgramConfig(
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ops=ops,
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weights={},
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inputs={
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"input_bboxes": TensorConfig(
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data_gen=partial(
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generate_boxes, batch, num_boxes
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)
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),
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"input_scores": TensorConfig(
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data_gen=partial(
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generate_scores,
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batch,
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num_boxes,
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num_classes,
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)
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),
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},
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outputs=["nms_output_boxes"],
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)
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yield program_config
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def sample_predictor_configs(
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self, program_config
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) -> tuple[paddle_infer.Config, list[int], float]:
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def generate_dynamic_shape(attrs):
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# The last dim of input_bboxes should be static.
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self.dynamic_shape.min_input_shape = {
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"input_bboxes": [1, self.num_boxes, 4],
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"input_scores": [1, self.num_classes, self.num_boxes],
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}
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self.dynamic_shape.max_input_shape = {
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"input_bboxes": [8, self.num_boxes, 4],
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"input_scores": [8, self.num_classes, self.num_boxes],
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}
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self.dynamic_shape.opt_input_shape = {
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"input_bboxes": [self.batch, self.num_boxes, 4],
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"input_scores": [self.batch, self.num_classes, self.num_boxes],
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}
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def clear_dynamic_shape():
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self.dynamic_shape.min_input_shape = {}
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self.dynamic_shape.max_input_shape = {}
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self.dynamic_shape.opt_input_shape = {}
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def generate_trt_nodes_num(attrs, dynamic_shape):
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return 1, 2
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attrs = [
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program_config.ops[i].attrs for i in range(len(program_config.ops))
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]
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# for dynamic_shape
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generate_dynamic_shape(attrs)
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self.trt_param.precision = paddle_infer.PrecisionType.Float32
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yield (
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self.create_inference_config(),
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generate_trt_nodes_num(attrs, True),
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1e-5,
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)
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# self.trt_param.precision = paddle_infer.PrecisionType.Half
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# yield self.create_inference_config(), generate_trt_nodes_num(
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# attrs, True), (1e-2, 1e-2)
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def assert_tensors_near(
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self,
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atol: float,
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rtol: float,
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tensor: dict[str, np.array],
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baseline: dict[str, np.array],
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):
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# the order of tensorrt outputs are not consistent with paddle
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for key, arr in tensor.items():
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if key == "nms_output_boxes":
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baseline_arr = np.array(
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sorted(
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baseline[key].reshape((-1, 6)),
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key=lambda i: [i[0], i[1]],
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)
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)
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arr = np.array(
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sorted(arr.reshape((-1, 6)), key=lambda i: [i[0], i[1]])
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)
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else:
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baseline_arr = np.array(baseline[key].reshape((-1, 1)))
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arr = np.array(arr.reshape((-1, 1)))
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self.assertTrue(
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baseline_arr.shape == arr.shape,
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"The output shapes are not equal, the baseline shape is "
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+ str(baseline_arr.shape)
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+ ', but got '
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+ str(arr.shape),
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)
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diff = abs(baseline_arr - arr)
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np.testing.assert_allclose(
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baseline_arr,
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arr,
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rtol=rtol,
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atol=atol,
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err_msg=f'Output has diff, Maximum absolute error: {np.amax(diff)}',
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)
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def assert_op_size(self, trt_engine_num, paddle_op_num):
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# tensorrt op num is not consistent with paddle
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return True
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def test(self):
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self.trt_param.workspace_size = 1 << 25
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self.run_test()
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if __name__ == "__main__":
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unittest.main()
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