From 386d2c4d1bb47610be298170f7b1cfec45f0c846 Mon Sep 17 00:00:00 2001 From: pengyanjun Date: Wed, 6 Jan 2021 17:34:17 +0800 Subject: [PATCH] fix dpn --- model_zoo/official/cv/dpn/README.md | 96 +++++------------------------ model_zoo/official/cv/dpn/eval.py | 4 +- model_zoo/official/cv/dpn/export.py | 52 ++++++++++++++++ 3 files changed, 70 insertions(+), 82 deletions(-) create mode 100644 model_zoo/official/cv/dpn/export.py diff --git a/model_zoo/official/cv/dpn/README.md b/model_zoo/official/cv/dpn/README.md index d8ccd71ca24..29c9107dad0 100644 --- a/model_zoo/official/cv/dpn/README.md +++ b/model_zoo/official/cv/dpn/README.md @@ -22,10 +22,6 @@ - [Model Description](#model-description) - [Performance](#performance) - [Accuracy](#accuracy) - - [DPN92 (Pretrain)](#dpn92-pretrain) - - [DPN98 (Pretrain)](#dpn98-pretrain) - - [DPN131 (Pretrain)](#dpn131-pretrain) - - [DPN92 (Fine tune)](#dpn92-fine-tune) - [DPN92 (Training)](#dpn92-training) - [Efficiency](#efficiency) - [DPN92](#dpn92) @@ -77,9 +73,7 @@ To run the python scripts in the repository, you need to prepare the environment - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to [ascend@huawei.com](mailto:ascend@huawei.com). Once approved, you can get the resources. - Python and dependencies - Python3.7 - - Mindspore 1.0.0 - - Easydict - - MXNet 1.6.0 if running the script `param_convert.py` + - Mindspore 1.1.0 - For more information, please check the resources below： - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html) @@ -124,6 +118,7 @@ The structure of the files in this repository is shown below. │ └─ lr_scheduler.py // dpn learning rate scheduler ├─ eval.py // evaluation script ├─ train.py // training script + ├─ export.py // export model └─ README.md // descriptions about this repository ``` @@ -196,7 +191,9 @@ The script will run training in the background, you can view the results through ```text epoch: 1 step: 40036, loss is 3.6232593 -Epoch time: 10048893.336, per step time: 250.996 +epoch time: 10048893.336 ms, per step time: 250.996 ms +epoch: 2 step: 40036, loss is 3.200775 +epoch time: 9306154.456 ms, per step time: 232.445 ms ... ``` @@ -204,7 +201,7 @@ or as follows (eval_each_epoch = 1): ```text epoch: 1 step: 40036, loss is 3.6232593 -Epoch time: 10048893.336, per step time: 250.996 +epoch time: 10048893.336 ms, per step time: 250.996 ms Save the maximum accuracy checkpoint,the accuracy is 0.2629158669225848 ... ``` @@ -230,20 +227,10 @@ sh scripts/train_distributed.sh /home/rank_table.json /data/dataset/imagenet/ .. The above shell script will run distribute training in the background. You can view the results through the file `train_parallel[X]/log.txt` as follows: ```text -train_parallel0/log: -epoch: 1 step 20018, loss is 5.74429988861084 -Epoch time: 7908183.789, per step time: 395.054, avg loss: 5.744 -train_parallel0/log: -epoch: 2 step 20018, loss is 4.53381872177124 -Epoch time: 5036189.547, per step time: 251.583, avg loss: 4.534 -... -train_parallel1/log: -poch: 1 step 20018, loss is 5.751555442810059 -Epoch time: 7895946.079, per step time: 394.442, avg loss: 5.752 -train_parallel1/log: -epoch: 2 step 20018, loss is 4.875896453857422 -Epoch time: 5036190.008, per step time: 251.583, avg loss: 4.876 -... +epoch: 1 step 5004, loss is 4.5680037 +epoch time: 2312519.441 ms, per step time: 462.134 ms +epoch: 2 step 5004, loss is 2.964888 +Epoch time: 1350398.913 ms, per step time: 369.864 ms ... ``` @@ -262,7 +249,7 @@ sh scripts/eval.sh [device_id] [dataset_dir] [pretrained_ckpt] For example, you can run the shell command below to launch the validation procedure. ```text -sh scripts/eval.sh 0 /data/dataset/imageNet/ pretrain/dpn92.ckpt +sh scripts/eval.sh 0 /data/dataset/imagenet/ pretrain/dpn-180_5004.ckpt ``` The above shell script will run evaluation in the background. You can view the results through the file `eval_log.txt`. The result will be achieved as follows: @@ -282,65 +269,14 @@ All results are validated at image size of 224x224. The dataset preprocessing an ### [Accuracy](#contents) -The `Pretrain` tag in the table above means that the model's weights are converted from MXNet directly without further training. Relatively, the `Fine tune` tag means that the model is fine tuned after converted from MXNet. - -#### DPN92 (Pretrain) - -| Parameters | Ascend | -| ----------------- | --------------------------- | -| Model Version | DPN92 (Pretrain) | -| Resource | Ascend 910 | -| Uploaded Date | 09/19/2020 (month/day/year) | -| MindSpore Version | 0.5.0 | -| Dataset | ImageNet-1K | -| outputs | probability | -| train performance | Top1:79.12%; Top5:94.49% | - -#### DPN98 (Pretrain) - -| Parameters | Ascend | -| ----------------- | --------------------------- | -| Model Version | DPN98 (Pretrain) | -| Resource | Ascend 910 | -| Uploaded Date | 09/19/2020 (month/day/year) | -| MindSpore Version | 0.5.0 | -| Dataset | ImageNet-1K | -| outputs | probability | -| train performance | Top1:79.90%; Top5:94.81% | - -#### DPN131 (Pretrain) - -| Parameters | Ascend | -| ----------------- | --------------------------- | -| Model Version | DPN131 (Pretrain) | -| Resource | Ascend 910 | -| Uploaded Date | 09/19/2020 (month/day/year) | -| MindSpore Version | 0.5.0 | -| Dataset | ImageNet-1K | -| outputs | probability | -| train performance | Top1:79.96%; Top5:94.81% | - -#### DPN92 (Fine tune) - -| Parameters | Ascend | -| ----------------- | --------------------------- | -| Model Version | DPN92 (Pretrain) | -| Resource | Ascend 910 | -| Uploaded Date | 09/19/2020 (month/day/year) | -| MindSpore Version | 0.5.0 | -| Dataset | ImageNet-1K | -| epochs | 30 | -| outputs | probability | -| train performance | Top1:79.30%; Top5:94.58% | - #### DPN92 (Training) | Parameters | Ascend | | ----------------- | --------------------------- | | Model Version | DPN92 (Train) | | Resource | Ascend 910 | -| Uploaded Date | 11/13/2020 (month/day/year) | -| MindSpore Version | 1.0.0 | +| Uploaded Date | 12/20/2020 (month/day/year) | +| MindSpore Version | 1.1.0 | | Dataset | ImageNet-1K | | epochs | 180 | | outputs | probability | @@ -354,12 +290,12 @@ The `Pretrain` tag in the table above means that the model's weights are convert | ----------------- | --------------------------------- | | Model Version | DPN92 | | Resource | Ascend 910 | -| Uploaded Date | 09/19/2020 (month/day/year) | -| MindSpore Version | 0.5.0 | +| Uploaded Date | 12/20/2020 (month/day/year) | +| MindSpore Version | 1.1.0 | | Dataset | ImageNet-1K | | batch_size | 32 | | outputs | probability | -| speed | 1pc:127.90 img/s;8pc:1023.2 img/s | +| speed | 1pc:233 ms/step;8pc:240 ms/step | # [Description of Random Situation](#contents) diff --git a/model_zoo/official/cv/dpn/eval.py b/model_zoo/official/cv/dpn/eval.py index 963b23621c9..83593d2b553 100644 --- a/model_zoo/official/cv/dpn/eval.py +++ b/model_zoo/official/cv/dpn/eval.py @@ -68,8 +68,8 @@ def dpn_evaluate(args): # create network net = dpns[args.backbone](num_classes=args.num_classes) # load checkpoint - if os.path.isfile(args.pretrained): - load_param_into_net(net, load_checkpoint(args.pretrained)) + load_param_into_net(net, load_checkpoint(args.pretrained)) + print("load checkpoint from [{}].".format(args.pretrained)) # loss if args.dataset == "imagenet-1K": loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') diff --git a/model_zoo/official/cv/dpn/export.py b/model_zoo/official/cv/dpn/export.py new file mode 100644 index 00000000000..be8a1d96fda --- /dev/null +++ b/model_zoo/official/cv/dpn/export.py @@ -0,0 +1,52 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# 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. +# ============================================================================ +"""Export DPN""" +import argparse +import numpy as np + +from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export + +from src.dpn import dpns +from src.config import config + +parser = argparse.ArgumentParser(description="export dpn") +parser.add_argument("--device_id", type=int, default=0, help="device id") +parser.add_argument("--ckpt_file", type=str, required=True, help="dpn ckpt file.") +parser.add_argument("--width", type=int, default=224, help="input width") +parser.add_argument("--height", type=int, default=224, help="input height") +parser.add_argument("--file_name", type=str, default="dpn", help="dpn output file name.") +parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], + default="MINDIR", help="file format") +parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend", + help="device target") +args = parser.parse_args() + +context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) +if args.device_target == "Ascend": + context.set_context(device_id=args.device_id) + +if __name__ == "__main__": + # define net + backbone = config.backbone + num_classes = config.num_classes + net = dpns[backbone](num_classes=num_classes) + + # load checkpoint + param_dict = load_checkpoint(args.ckpt_file) + load_param_into_net(net, param_dict) + net.set_train(False) + + image = Tensor(np.zeros([config.batch_size, 3, args.height, args.width], np.float32)) + export(net, image, file_name=args.file_name, file_format=args.file_format)