From 5ea8d0e35a1778d4e416891b587ac7e56b84d4a2 Mon Sep 17 00:00:00 2001 From: tianxi-yi Date: Thu, 4 Jul 2024 15:09:53 +0800 Subject: [PATCH 1/2] Add SwinL in IxRT link #IA9VI6 Add SwinL in IxRT Signed-off-by: tianxi-yi --- .../swin_transformer_Large/ixrt/README.md | 87 +++++ .../ixrt/perf_engine.py | 349 ++++++++++++++++++ .../scripts/infer_swinl_fp16_performance.sh | 44 +++ .../ixrt/scripts/prepare_model_and_dataset.sh | 34 ++ .../swin_transformer_Large/ixrt/torch2onnx.py | 73 ++++ 5 files changed, 587 insertions(+) create mode 100644 models/cv/classification/swin_transformer_Large/ixrt/README.md create mode 100644 models/cv/classification/swin_transformer_Large/ixrt/perf_engine.py create mode 100644 models/cv/classification/swin_transformer_Large/ixrt/scripts/infer_swinl_fp16_performance.sh create mode 100644 models/cv/classification/swin_transformer_Large/ixrt/scripts/prepare_model_and_dataset.sh create mode 100644 models/cv/classification/swin_transformer_Large/ixrt/torch2onnx.py diff --git a/models/cv/classification/swin_transformer_Large/ixrt/README.md b/models/cv/classification/swin_transformer_Large/ixrt/README.md new file mode 100644 index 00000000..1b429206 --- /dev/null +++ b/models/cv/classification/swin_transformer_Large/ixrt/README.md @@ -0,0 +1,87 @@ +# Swin-L + +## Description + +Swin Transformer-Large is a variant of the Swin Transformer, an architecture designed for computer vision tasks, particularly within the realms of image classification, object detection, and segmentation. The Swin Transformer-Large model represents an expanded version with more layers and parameters compared to its base configuration, aiming for improved performance and deeper processing of visual data. + +## Setup + +### Install + +```bash +pip3 install onnxsim +pip3 install onnx_graphsurgeon +pip3 install scikit-learn +pip3 install tqdm +pip3 install pycuda +pip3 install onnx +pip3 install tabulate +pip3 install cv2 +pip3 install pycocotools +pip3 install opencv-python==4.6.0.66 +``` + +### Download + +Pretrained model: + +Dataset: to download the open_imagenet dataset. + +or you can : +```bash +bash /scripts/prepare_model_and_dataset.sh + +``` + +### Model Conversion +Please correct the paths in the following commands or files. +```bash +tar -xvf open-swin-large.tar +wget < https://github.com/bytedance/ByteMLPerf/blob/main/byte_infer_perf/general_perf/model_zoo/swin-large-torch-fp32.json > +python3 torch2onnx.py --model_path swin-transformer-large.pt --output_path swin-large-torch-fp32.onnx + +``` + +## Inference + + +```bash +export ORIGIN_ONNX_NAME=/Path/swin-large-torch-fp32.onnx +export OPTIMIER_FILE=/Path/ixrt/oss/tools/optimizer/optimizer.py +export PROJ_PATH=./ +``` + +### Performance + +```bash + +bash scripts/infer_swinl_fp16_performance.sh +``` + +### Accuracy + +If you want to evaluate the accuracy of this model, please visit the website: < https://github.com/yudefu/ByteMLPerf/tree/iluvatar_general_infer >, which integrates inference and training of many models under this framework, supporting the ILUVATAR backend + +```bash + +git clone https://github.com/yudefu/ByteMLPerf.git -b iluvatar_general_infer +``` + +For detailed steps regarding this model, please refer to this document: < https://github.com/yudefu/ByteMLPerf/blob/iluvatar_general_infer/byte_infer_perf/general_perf/backends/ILUVATAR/README.zh_CN.md > Note: You need to modify the relevant paths in the code to your own correct paths. + +```bash + +pip3 install -r https://github.com/yudefu/ByteMLPerf/blob/iluvatar_general_infer/byte_infer_perf/general_perf/requirements.txt +mv /ixrt/perf_engine.py /ByteMLPerf/byte_infer_perf/general_perf/core/perf_engine.py +cd /ByteMLPerf/byte_infer_perf/ +mv /general_perf/general_perf/model_zoo/popular/swin-large /general_perf/model_zoo/popular/swin-large +cd /ByteMLPerf/byte_infer_perf/general_perf +python3 core/perf_engine.py --hardware_type ILUVATAR --task swin-large-torch-fp32 +``` + + +## Results + +Model |BatchSize |Precision |QPS |Top-1 Acc | +--------|-----------|----------|----------|-----------| +Swin-L | 16 | FP16 | 5.746 | 85.62 | \ No newline at end of file diff --git a/models/cv/classification/swin_transformer_Large/ixrt/perf_engine.py b/models/cv/classification/swin_transformer_Large/ixrt/perf_engine.py new file mode 100644 index 00000000..089d9860 --- /dev/null +++ b/models/cv/classification/swin_transformer_Large/ixrt/perf_engine.py @@ -0,0 +1,349 @@ +# Copyright 2023 ByteDance and/or its affiliates. +# +# 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. +import sys +import os +import logging +import importlib +import json +import subprocess +import time + +from typing import Any, Dict, Tuple +from prompt_toolkit.shortcuts import radiolist_dialog, input_dialog, yes_no_dialog +from prompt_toolkit.styles import Style + +BYTE_MLPERF_ROOT = os.path.dirname( + os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +os.chdir(BYTE_MLPERF_ROOT) +sys.path.insert(0, BYTE_MLPERF_ROOT) + +import argparse +from general_perf.core.configs.workload_store import load_workload +from general_perf.core.configs.dataset_store import load_dataset +from general_perf.core.configs.backend_store import init_compile_backend, init_runtime_backend + +logging.basicConfig(level=logging.INFO) +log = logging.getLogger("PerfEngine") +os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3' + + +def get_args(): + """Parse commandline.""" + parser = argparse.ArgumentParser() + parser.add_argument( + "--task", + default="resnet50-tf-fp32", + help="The task going to be evaluted, refs to workloads/") + parser.add_argument( + "--hardware_type", + default="GPU", + help="The backend going to be evaluted, refs to backends/") + parser.add_argument("--compile_only", + action='store_true', + help="Run compilation only") + + args = parser.parse_args() + return args + + +class PerfEngine: + def __init__(self) -> None: + super().__init__() + self.args = get_args() + self.workload = load_workload(self.args.task) + self.backend_type = self.args.hardware_type + self.compile_backend = None + self.old_os_path = os.environ['PATH'] + self.prev_sys_path = list(sys.path) + self.real_prefix = sys.prefix + self.compile_only_mode = False + + def start_engine(self) -> None: + ''' + Byte MlPerf will create an virtual env for each backend to avoid dependance conflict + ''' + success, total = 0, len(self.workload) + if total == 0: + return + log.info("******************* Backend Env Initization *******************") + status = self.activate_venv(self.backend_type) + if not status: + log.warning("Activate virtualenv Failed, Please Check...") + + self.compile_backend = init_compile_backend(self.backend_type) + self.runtime_backend = init_runtime_backend(self.backend_type) + + output_dir = os.path.abspath('general_perf/reports/' + + self.backend_type) + os.makedirs(output_dir, exist_ok=True) + + status = self.single_workload_perf(self.workload) + + def single_workload_perf( + self, workload: Dict[str, Any]) -> bool: + log.info("******************************************* Start to test model: {}. *******************************************".format(workload['model'])) + + # Check Compile Only Mode + self.compile_only_mode = False + if self.args.compile_only or workload['compile_only']: + self.compile_only_mode = True + + base_report = { + "Model": workload['model'].upper(), + "Backend": self.backend_type, + "Host Info": self.get_cpu_name() + } + + # Initalize Model Config Info + model_info = self.get_model_info(workload['model']) + pre_compile_config = {"workload": workload, 'model_info': model_info} + interact_info = self.check_interact_info(pre_compile_config) + pre_compile_config['interact_info'] = interact_info + if not model_info['dataset_name']: + model_info['dataset_name'] = 'fake_dataset' + + + ''' + Compile Backend could do some optimization like convert model format here + ''' + log.info("******************************************* Running Backend Compilation... *******************************************") + log.info("Running Backend Preoptimization...") + pre_compile_config = self.compile_backend.pre_optimize(pre_compile_config) + + + # Initalize dataset + dataset = load_dataset(model_info) + dataset.preprocess() + base_report['Dataset'] = model_info['dataset_name'].upper( + ) if model_info['dataset_name'] else None + + #Placeholder Only + segment_info = self.compile_backend.segment(pre_compile_config) + + best_batch_sizes = self.compile_backend.get_best_batch_size() + if isinstance(best_batch_sizes, list): + pre_compile_config['workload'][ + 'batch_sizes'] = best_batch_sizes + + log.info("Start to compile the model...") + start = time.time() + compile_info = self.compile_backend.compile(pre_compile_config, + dataset) + end = time.time() + + graph_compile_report = {} + graph_compile_report["Compile Duration"] = round(end - start, 5) + graph_compile_report["Compile Precision"] = compile_info[ + 'compile_precision'] + graph_compile_report["Subgraph Coverage"] = compile_info['sg_percent'] + if 'optimizations' in compile_info: + graph_compile_report['Optimizations'] = compile_info['optimizations'] + if 'instance_count' in compile_info: + base_report['Instance Count'] = compile_info['instance_count'] + if 'device_count' in compile_info: + base_report['Device Count'] = compile_info['device_count'] + base_report['Graph Compile'] = graph_compile_report + + # Initalize Output Dir and Reports + output_dir = os.path.abspath('general_perf/reports/' + + self.backend_type + '/' + + workload['model']) + os.makedirs(output_dir, exist_ok=True) + + # Compile only mode will stop here + if self.compile_only_mode: + base_report.pop("Backend") + return compile_info["compile_status"], base_report + + # load runtime backend + """ + Start Here + """ + batch_sizes = pre_compile_config['workload']['batch_sizes'] + self.runtime_backend.configs = compile_info + self.runtime_backend.workload = workload + self.runtime_backend.model_info = model_info + + self.runtime_backend.load(workload['batch_sizes'][0]) + # test accuracy + accuracy_report = {} + AccuracyChecker = self.get_accuracy_checker( + model_info['dataset_name'] + if model_info['dataset_name'] else 'fake_dataset') + AccuracyChecker.runtime_backend = self.runtime_backend + AccuracyChecker.dataloader = dataset + AccuracyChecker.output_dir = output_dir + AccuracyChecker.configs = compile_info + + if workload['test_accuracy']: + log.info("******************************************* Running Accuracy Checker... *******************************************") + + dataset.rebatch(self.runtime_backend.get_loaded_batch_size()) + accuracy_results = AccuracyChecker.calculate_acc( + workload['data_percent']) + + accuracy_report['Data Percent'] = workload['data_percent'] + accuracy_report.update(accuracy_results) + + # test numeric + if workload['test_numeric']: + log.info("******************************************* Running Numeric Checker... *******************************************") + + dataset.rebatch(self.runtime_backend.get_loaded_batch_size()) + if not workload['test_accuracy']: + accuracy_results = AccuracyChecker.calculate_acc( + workload['data_percent']) + diff_results = AccuracyChecker.calculate_diff() + accuracy_report.update(diff_results) + # accuracy_report['Diff Dist'] = compile_info['model'] + '-to-' + compile_info['compile_precision'].lower() + ".png" + + if accuracy_report: + base_report['Accuracy'] = accuracy_report + + # function to test qps and latency + if workload['test_perf']: + log.info("******************************************* Runing QPS Checker... *******************************************") + performance_reports = [] + qs_status = self.runtime_backend.is_qs_mode_supported() + if qs_status: + qs_config = self.runtime_backend.generate_qs_config() + performance_reports = self.qs_benchmark(qs_config) + else: + for bs in batch_sizes: + self.runtime_backend.load(bs) + batch_reports = self.runtime_backend.benchmark(dataset) + performance_reports.append(batch_reports) + base_report['Performance'] = performance_reports + + if "Instance Count" not in base_report: + log.warning("Vendors need to Add # of instances") + if "Device Count" not in base_report: + log.warning("Vendors need to Add # of devices") + + # write output to json file + output_report_path = output_dir + "/result-" + compile_info['compile_precision'].lower() + ".json" + with open(output_report_path, 'w') as file: + json.dump(base_report, file, indent=4) + + base_report.pop("Backend") + log.info("Testing Finish. Report is saved in path: [ {}/{} ]". + format(output_dir[output_dir.rfind('general_perf'):], + os.path.basename(output_report_path))) + + return compile_info["compile_status"] + + #WIP + def qs_benchmark(self, qs_config: Dict[str, Any]) -> list: + return [] + + def get_accuracy_checker(self, dataset_name: str): + AccuracyChecker = importlib.import_module('general_perf.datasets.' + + dataset_name + + ".test_accuracy") + AccuracyChecker = getattr(AccuracyChecker, 'AccuracyChecker') + return AccuracyChecker() + + def get_model_info(self, model_name: str) -> Dict[str, Any]: + with open("general_perf/model_zoo/" + model_name + '.json', + 'r') as file: + model_info = json.load(file) + return model_info + + def get_cpu_name(self): + command = "lscpu | grep 'Model name' | awk -F: '{print $2}'" + cpu_name = subprocess.check_output(command, shell=True) + return cpu_name.decode().strip() + + def check_interact_info( + self, pre_compile_config: Dict[str, Dict]) -> Dict[str, Any]: + interact_info = self.compile_backend.get_interact_profile( + pre_compile_config) + + answer = {} + if len(interact_info) == 0: + return answer + + dialog_style = Style.from_dict({ + 'dialog': 'bg:#88b8ff', + 'dialog frame.label': 'bg:#ffffff #000000', + 'dialog.body': 'bg:#000000 #a0acde', + 'dialog shadow': 'bg:#004aaa', + }) + + input_style = Style.from_dict({ + 'dialog': 'bg:#88b8ff', + 'dialog frame.label': 'bg:#ffffff #000000', + 'dialog.body': 'bg:#000000 #a0acde', + 'dialog shadow': 'bg:#004aaa', + 'text-area.prompt': 'bg:#ffffff', + 'text-area': '#000000', + }) + + option = yes_no_dialog(title=self.backend_type + '编译配置', + text='[请选择]：是否进行编译后端配置:', + style=dialog_style).run() + if option: + sum_question = len(interact_info) + for i, question in enumerate(interact_info): + if question['depends']: + state = 0 + for title in question['depends'].split(','): + if not answer[title]: + state = 1 + if state: + continue + if question['dialog_type'] == 'Yes/No Dialog': + option = yes_no_dialog( + title=self.backend_type + '编译配置进度(' + str(i + 1) + + '/' + str(sum_question) + ')', + text="[Backend " + self.backend_type + "]: " + + question['note'], + style=dialog_style).run() + elif question['dialog_type'] == "Input Dialog": + option = input_dialog( + title=self.backend_type + '编译配置进度(' + str(i + 1) + + '/' + str(sum_question) + ')', + text="[Backend " + self.backend_type + "]: " + + question['note'], + style=input_style).run() + elif question['dialog_type'] == "Radiolist Dialog": + choice = [(i, text) + for i, text in enumerate(question['options'])] + num = radiolist_dialog( + title=self.backend_type + '编译配置进度(' + str(i + 1) + + '/' + str(sum_question) + ')', + text="[Backend " + self.backend_type + "]: " + + question['note'], + values=choice, + style=dialog_style).run() + option = question['options'][num] if num is not None else question[ + 'default'] + answer[question['name']] = option + + return answer + + def activate_venv(self, hardware_type: str) -> bool: + + return True + + def deactivate_venv(self): + sys.path[: + 0] = self.prev_sys_path #will also revert the added site-packages + sys.prefix = self.real_prefix + os.environ['PATH'] = self.old_os_path + + +if __name__ == "__main__": + engine = PerfEngine() + engine.start_engine() diff --git a/models/cv/classification/swin_transformer_Large/ixrt/scripts/infer_swinl_fp16_performance.sh b/models/cv/classification/swin_transformer_Large/ixrt/scripts/infer_swinl_fp16_performance.sh new file mode 100644 index 00000000..eb9350ff --- /dev/null +++ b/models/cv/classification/swin_transformer_Large/ixrt/scripts/infer_swinl_fp16_performance.sh @@ -0,0 +1,44 @@ +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# 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. + + +# Start to test +set -x +ORIGIN_ONNX=${ORIGIN_ONNX_NAME}.onnx +cd ${PROJ_PATH} + +run(){ + BS=16 + TARGET_ONNX=${ORIGIN_ONNX_NAME}_end.onnx + TARGET_ENGINE=${ORIGIN_ONNX_NAME}_end.engine + if [[ ! -f "${ORIGIN_ONNX}" ]];then + echo "${ORIGIN_ONNX} not exists!" + exit 1 + fi + + # Graph optimize + python3 ${OPTIMIER_FILE} --onnx ${ORIGIN_ONNX} --model_type swint --dump_onnx + OPTMIZE_STATUS=$? + + # Build Engine + ixrtexec --onnx ${TARGET_ONNX} --save_engine ${TARGET_ENGINE} --log_level error --plugins ixrt_plugin\ + --min_shape pixel_values.1:${BS}x3x384x384 --opt_shape pixel_values.1:${BS}x3x384x384 --max_shape pixel_values.1:${BS}x3x384x384 + + # Test Performance + ixrtexec --load_engine ${TARGET_ENGINE} --plugins ixrt_plugin --shapes pixel_values.1:${BS}x3x384x384 --shapes pixel_values.1:${BS}x3x384x384 + PERFORMANCE_STATUS=$? + +} +run 1 \ No newline at end of file diff --git a/models/cv/classification/swin_transformer_Large/ixrt/scripts/prepare_model_and_dataset.sh b/models/cv/classification/swin_transformer_Large/ixrt/scripts/prepare_model_and_dataset.sh new file mode 100644 index 00000000..8154d350 --- /dev/null +++ b/models/cv/classification/swin_transformer_Large/ixrt/scripts/prepare_model_and_dataset.sh @@ -0,0 +1,34 @@ +# Copyright (c) 2024, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# 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. + +# #!/bin/bash +echo "******************* Downloading Model.... *******************" + +mkdir -p general_perf/model_zoo/regular +mkdir -p general_perf/model_zoo/popular +mkdir -p general_perf/model_zoo/sota +mkdir -p general_perf/download +mkdir -p datasets/open_imagenet/ + +wget -O general_perf/download/open-swin-large.tar https://lf-bytemlperf.17mh.cn/obj/bytemlperf-zoo/open-swin-large.tar +tar xf general_perf/download/open-swin-large.tar -C general_perf/model_zoo/popular/ + + +# # Download Datasets +wget -O general_perf/download/open_imagenet.tar https://lf-bytemlperf.17mh.cn/obj/bytemlperf-zoo/open_imagenet.tar +tar xf general_perf/download/open_imagenet.tar -C datasets/ + + +echo "Extract Done." diff --git a/models/cv/classification/swin_transformer_Large/ixrt/torch2onnx.py b/models/cv/classification/swin_transformer_Large/ixrt/torch2onnx.py new file mode 100644 index 00000000..9f115b73 --- /dev/null +++ b/models/cv/classification/swin_transformer_Large/ixrt/torch2onnx.py @@ -0,0 +1,73 @@ +# Copyright 2023 ByteDance and/or its affiliates. +# +# 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. + +import argparse +import json + +import numpy as np +import torch + + +def torch_to_onnx(model_path, output_path): + model_name = output_path.split("/")[-1][:-4] + with open(model_name + "json", "r") as f: + model_info = json.load(f) + model_inputs = model_info["inputs"].split(",") + input_shapes = model_info["input_shape"] + input_type = model_info["input_type"].split(",") + example_inputs = _get_fake_samples(input_shapes, input_type) + + model = torch.jit.load(model_path, map_location=torch.device("cpu")) + model.eval() + + names = model_inputs + dynamic_inputs = {} + for i in range(len(names)): + dynamic_inputs[names[i]] = {0: "batch_size"} + outputs = model_info["outputs"].split(",") + for output in outputs: + dynamic_inputs[output] = {0: "batch_size"} + torch.onnx.export( + model, + example_inputs, + output_path, + opset_version=11, + input_names=names, + output_names=outputs, + dynamic_axes=dynamic_inputs, + ) + + +def _get_fake_samples(shape, type): + data = [] + idx = 0 + for key, val in shape.items(): + val = [val[0] * 1] + val[1:] + data.append(torch.from_numpy(np.random.random(val).astype(type[idx].lower()))) + idx += 1 + return data + + +def get_args(): + """Parse commandline.""" + parser = argparse.ArgumentParser() + parser.add_argument("--model_path", default="") + parser.add_argument("--output_path", default="") + args = parser.parse_args() + return args + + +if __name__ == "__main__": + args = get_args() + torch_to_onnx(args.model_path, args.output_path) \ No newline at end of file -- Gitee From 26143f6474d71102d4be5a09de9fd22636664f2a Mon Sep 17 00:00:00 2001 From: tianxi-yi Date: Thu, 18 Jul 2024 09:41:05 +0800 Subject: [PATCH 2/2] update model.json path --- .../swin_transformer_Large/ixrt/README.md | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/models/cv/classification/swin_transformer_Large/ixrt/README.md b/models/cv/classification/swin_transformer_Large/ixrt/README.md index 1b429206..9cb811bf 100644 --- a/models/cv/classification/swin_transformer_Large/ixrt/README.md +++ b/models/cv/classification/swin_transformer_Large/ixrt/README.md @@ -37,7 +37,7 @@ bash /scripts/prepare_model_and_dataset.sh Please correct the paths in the following commands or files. ```bash tar -xvf open-swin-large.tar -wget < https://github.com/bytedance/ByteMLPerf/blob/main/byte_infer_perf/general_perf/model_zoo/swin-large-torch-fp32.json > +wget python3 torch2onnx.py --model_path swin-transformer-large.pt --output_path swin-large-torch-fp32.onnx ``` @@ -60,22 +60,18 @@ bash scripts/infer_swinl_fp16_performance.sh ### Accuracy -If you want to evaluate the accuracy of this model, please visit the website: < https://github.com/yudefu/ByteMLPerf/tree/iluvatar_general_infer >, which integrates inference and training of many models under this framework, supporting the ILUVATAR backend +If you want to evaluate the accuracy of this model, please visit here: , which integrates inference and training of many models under this framework, supporting the ILUVATAR backend -```bash - -git clone https://github.com/yudefu/ByteMLPerf.git -b iluvatar_general_infer -``` -For detailed steps regarding this model, please refer to this document: < https://github.com/yudefu/ByteMLPerf/blob/iluvatar_general_infer/byte_infer_perf/general_perf/backends/ILUVATAR/README.zh_CN.md > Note: You need to modify the relevant paths in the code to your own correct paths. +For detailed steps regarding this model, please refer to this document: Note: You need to modify the relevant paths in the code to your own correct paths. ```bash -pip3 install -r https://github.com/yudefu/ByteMLPerf/blob/iluvatar_general_infer/byte_infer_perf/general_perf/requirements.txt -mv /ixrt/perf_engine.py /ByteMLPerf/byte_infer_perf/general_perf/core/perf_engine.py -cd /ByteMLPerf/byte_infer_perf/ +pip3 install -r toolbox/ByteMLPerf/blob/iluvatar_general_infer/byte_infer_perf/general_perf/requirements.txt +mv /ixrt/perf_engine.py toolbox/ByteMLPerf/byte_infer_perf/general_perf/core/perf_engine.py +cd toolbox/ByteMLPerf/byte_infer_perf/ mv /general_perf/general_perf/model_zoo/popular/swin-large /general_perf/model_zoo/popular/swin-large -cd /ByteMLPerf/byte_infer_perf/general_perf +cd toolbox/ByteMLPerf/byte_infer_perf/general_perf python3 core/perf_engine.py --hardware_type ILUVATAR --task swin-large-torch-fp32 ``` -- Gitee