diff --git a/model_zoo/official/cv/inceptionv3/README.md b/model_zoo/official/cv/inceptionv3/README.md index f03b25aebaf..bfba3390aa0 100644 --- a/model_zoo/official/cv/inceptionv3/README.md +++ b/model_zoo/official/cv/inceptionv3/README.md @@ -40,6 +40,14 @@ Dataset used can refer to paper. - Data format: RGB images. - Note: Data will be processed in src/dataset.py +Dataset used: [CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar.html) + +- Dataset size: 175M, 60,000 32\*32 colorful images in 10 classes + - Train: 146M, 50,000 images + - Test: 29M, 10,000 images +- Data format：binary files + - Note：Data will be processed in src/dataset.py + # [Features](#contents) ## [Mixed Precision(Ascend)](#contents) @@ -67,8 +75,13 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil └─Inception-v3 ├─README.md ├─scripts + ├─run_standalone_train_cpu.sh # launch standalone training with cpu platform + ├─run_standalone_train_gpu.sh # launch standalone training with gpu platform(1p) + ├─run_distribute_train_gpu.sh # launch distributed training with gpu platform(8p) ├─run_standalone_train.sh # launch standalone training with ascend platform(1p) ├─run_distribute_train.sh # launch distributed training with ascend platform(8p) + ├─run_eval_cpu.sh # launch evaluation with cpu platform + ├─run_eval_gpu.sh # launch evaluation with gpu platform └─run_eval.sh # launch evaluating with ascend platform ├─src ├─config.py # parameter configuration @@ -93,6 +106,8 @@ Major parameters in train.py and config.py are: 'batch_size' # input batchsize 'epoch_size' # total epoch numbers 'num_classes' # dataset class numbers +'ds_type' # dataset type, such as: imagenet, cifar10 +'ds_sink_mode' # whether enable dataset sink mode 'smooth_factor' # label smoothing factor 'aux_factor' # loss factor of aux logit 'lr_init' # initiate learning rate @@ -127,6 +142,13 @@ sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH ``` +- CPU: + +```shell +# standalone training +sh scripts/run_standalone_train_cpu.sh DATA_PATH +``` + > Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV3, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size. > > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh` @@ -137,6 +159,7 @@ sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH # training example python: Ascend: python train.py --dataset_path DATA_PATH --platform Ascend + CPU: python train.py --dataset_path DATA_PATH --platform CPU shell: Ascend: @@ -144,12 +167,17 @@ sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH # standalone training example sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH + + CPU: + sh script/run_standalone_train_cpu.sh DATA_PATH ``` ### Result Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log will be redirected to `./log.txt` like followings. +#### Ascend + ```python epoch: 0 step: 1251, loss is 5.7787247 epoch time: 360760.985 ms, per step time: 288.378 ms @@ -157,6 +185,18 @@ epoch: 1 step: 1251, loss is 4.392868 epoch time: 160917.911 ms, per step time: 128.631 ms ``` +#### CPU + +```bash +epoch: 1 step: 390, loss is 2.7072601 +epoch time: 6334572.124 ms, per step time: 16242.493 ms +epoch: 2 step: 390, loss is 2.5908582 +epoch time: 6217897.644 ms, per step time: 15943.327 ms +epoch: 3 step: 390, loss is 2.5612416 +epoch time: 6358482.104 ms, per step time: 16303.800 ms +... +``` + ## [Eval process](#contents) ### Usage @@ -169,15 +209,23 @@ You can start training using python or shell scripts. The usage of shell scripts sh scripts/run_eval.sh DEVICE_ID DATA_PATH PATH_CHECKPOINT ``` +- CPU: + +```python + sh scripts/run_eval_cpu.sh DATA_PATH PATH_CHECKPOINT +``` + ### Launch ```python # eval example python: Ascend: python eval.py --dataset_path DATA_PATH --checkpoint PATH_CHECKPOINT --platform Ascend + CPU: python eval.py --dataset_path DATA_PATH --checkpoint PATH_CHECKPOINT --platform CPU shell: Ascend: sh scripts/run_eval.sh DEVICE_ID DATA_PATH PATH_CHECKPOINT + CPU: sh scripts/run_eval_cpu.sh DATA_PATH PATH_CHECKPOINT ``` > checkpoint can be produced in training process. @@ -236,4 +284,4 @@ In dataset.py, we set the seed inside “create_dataset" function. We also use r # [ModelZoo Homepage](#contents) -Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo). \ No newline at end of file +Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo). diff --git a/model_zoo/official/cv/inceptionv3/README_CN.md b/model_zoo/official/cv/inceptionv3/README_CN.md index 199d90349c3..7a295db95b8 100644 --- a/model_zoo/official/cv/inceptionv3/README_CN.md +++ b/model_zoo/official/cv/inceptionv3/README_CN.md @@ -51,6 +51,14 @@ InceptionV3的总体网络架构如下： - 数据格式：RGB - 注：数据将在src/dataset.py中处理。 +使用的数据集：[CIFAR-10]() + +- 数据集大小：175M，共10个类、6万张32*32彩色图像 + - 训练集：146M，共5万张图像 + - 测试集：29M，共1万张图像 +- 数据格式：二进制文件 + - 注：数据将在src/dataset.py中处理。 + # 特性 ## 混合精度（Ascend） @@ -78,9 +86,14 @@ InceptionV3的总体网络架构如下： └─Inception-v3 ├─README.md ├─scripts + ├─run_standalone_train_cpu.sh # 启动CPU训练 + ├─run_standalone_train_gpu.sh # 启动GPU单机训练（单卡） + ├─run_distribute_train_gpu.sh # 启动GPU分布式训练（8卡） ├─run_standalone_train.sh # 启动Ascend单机训练（单卡） ├─run_distribute_train.sh # 启动Ascend分布式训练（8卡） - ├─run_eval.sh # 启动Ascend评估 + ├─run_eval_cpu.sh # 启动CPU评估 + ├─run_eval_gpu.sh # 启动GPU评估 + └─run_eval.sh # 启动Ascend评估 ├─src ├─config.py # 参数配置 ├─dataset.py # 数据预处理 @@ -106,6 +119,8 @@ train.py和config.py中主要参数如下： 'batch_size' # 输入张量的批次大小 'epoch_size' # 总轮次数 'num_classes' # 数据集类数 +'ds_type' # 数据集类型，如：imagenet, cifar10 +'ds_sink_mode' # 使能数据下沉 'smooth_factor' # 标签平滑因子 'aux_factor' # aux logit的损耗因子 'lr_init' # 初始学习率 @@ -149,6 +164,7 @@ train.py和config.py中主要参数如下： # 训练示例 python: Ascend: python train.py --dataset_path /dataset/train --platform Ascend + CPU: python train.py --dataset_path DATA_PATH --platform CPU shell: Ascend: @@ -156,12 +172,17 @@ train.py和config.py中主要参数如下： sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH # 单机训练 sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH + + CPU: + sh script/run_standalone_train_cpu.sh DATA_PATH ``` ### 结果 训练结果保存在示例路径。检查点默认保存在`checkpoint`，训练日志会重定向到`./log.txt`，如下： +#### Ascend + ```log epoch:0 step:1251, loss is 5.7787247 Epoch time:360760.985, per step time:288.378 @@ -169,6 +190,18 @@ epoch:1 step:1251, loss is 4.392868 Epoch time:160917.911, per step time:128.631 ``` +#### CPU + +```bash +epoch: 1 step: 390, loss is 2.7072601 +epoch time: 6334572.124 ms, per step time: 16242.493 ms +epoch: 2 step: 390, loss is 2.5908582 +epoch time: 6217897.644 ms, per step time: 15943.327 ms +epoch: 3 step: 390, loss is 2.5612416 +epoch time: 6358482.104 ms, per step time: 16303.800 ms +... +``` + ## 评估过程 ### 用法 @@ -181,15 +214,23 @@ Epoch time:160917.911, per step time:128.631 sh scripts/run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT ``` +- CPU: + +```python + sh scripts/run_eval_cpu.sh DATA_PATH PATH_CHECKPOINT +``` + ### 启动 ``` launch # 评估示例 python: Ascend: python eval.py --dataset_path DATA_DIR --checkpoint PATH_CHECKPOINT --platform Ascend + CPU: python eval.py --dataset_path DATA_PATH --checkpoint PATH_CHECKPOINT --platform CPU shell: Ascend: sh scripts/run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT + CPU: sh scripts/run_eval_cpu.sh DATA_PATH PATH_CHECKPOINT ``` > 训练过程中可以生成检查点。 diff --git a/model_zoo/official/cv/inceptionv3/eval.py b/model_zoo/official/cv/inceptionv3/eval.py index a2f0ade1d34..cdd13c022aa 100644 --- a/model_zoo/official/cv/inceptionv3/eval.py +++ b/model_zoo/official/cv/inceptionv3/eval.py @@ -21,33 +21,48 @@ from mindspore import context from mindspore.train.model import Model from mindspore.train.serialization import load_checkpoint, load_param_into_net -from src.config import config_gpu as cfg -from src.dataset import create_dataset +from src.config import config_gpu, config_ascend, config_cpu +from src.dataset import create_dataset_imagenet, create_dataset_cifar10 from src.inception_v3 import InceptionV3 from src.loss import CrossEntropy_Val +CFG_DICT = { + "Ascend": config_ascend, + "GPU": config_gpu, + "CPU": config_cpu, +} + +DS_DICT = { + "imagenet": create_dataset_imagenet, + "cifar10": create_dataset_cifar10, +} if __name__ == '__main__': parser = argparse.ArgumentParser(description='image classification evaluation') parser.add_argument('--checkpoint', type=str, default='', help='checkpoint of inception-v3 (Default: None)') parser.add_argument('--dataset_path', type=str, default='', help='Dataset path') - parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU'), help='run platform') + parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU', 'CPU'), help='run platform') args_opt = parser.parse_args() if args_opt.platform == 'Ascend': device_id = int(os.getenv('DEVICE_ID')) context.set_context(device_id=device_id) + cfg = CFG_DICT[args_opt.platform] + create_dataset = DS_DICT[cfg.ds_type] + context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform) net = InceptionV3(num_classes=cfg.num_classes, is_training=False) ckpt = load_checkpoint(args_opt.checkpoint) load_param_into_net(net, ckpt) net.set_train(False) - dataset = create_dataset(args_opt.dataset_path, False, 0, 1) + cfg.rank = 0 + cfg.group_size = 1 + dataset = create_dataset(args_opt.dataset_path, False, cfg) loss = CrossEntropy_Val(smooth_factor=0.1, num_classes=cfg.num_classes) eval_metrics = {'Loss': nn.Loss(), 'Top1-Acc': nn.Top1CategoricalAccuracy(), 'Top5-Acc': nn.Top5CategoricalAccuracy()} model = Model(net, loss, optimizer=None, metrics=eval_metrics) - metrics = model.eval(dataset) + metrics = model.eval(dataset, dataset_sink_mode=cfg.ds_sink_mode) print("metric: ", metrics) diff --git a/model_zoo/official/cv/inceptionv3/scripts/run_eval_cpu.sh b/model_zoo/official/cv/inceptionv3/scripts/run_eval_cpu.sh new file mode 100644 index 00000000000..781fcdf1552 --- /dev/null +++ b/model_zoo/official/cv/inceptionv3/scripts/run_eval_cpu.sh @@ -0,0 +1,18 @@ +#!/bin/bash +# 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. +# ============================================================================ +DATA_DIR=$1 +PATH_CHECKPOINT=$2 +python ./eval.py --platform 'CPU' --dataset_path $DATA_DIR --checkpoint $PATH_CHECKPOINT > eval.log 2>&1 & diff --git a/model_zoo/official/cv/inceptionv3/scripts/run_standalone_train_cpu.sh b/model_zoo/official/cv/inceptionv3/scripts/run_standalone_train_cpu.sh new file mode 100644 index 00000000000..190dac7a9d0 --- /dev/null +++ b/model_zoo/official/cv/inceptionv3/scripts/run_standalone_train_cpu.sh @@ -0,0 +1,18 @@ +#!/bin/bash +# 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. +# ============================================================================ +DATA_DIR=$1 +python ./train.py --platform 'CPU' --dataset_path $DATA_DIR > train.log 2>&1 & + diff --git a/model_zoo/official/cv/inceptionv3/src/config.py b/model_zoo/official/cv/inceptionv3/src/config.py index 13ff78a79d8..48bd6436351 100644 --- a/model_zoo/official/cv/inceptionv3/src/config.py +++ b/model_zoo/official/cv/inceptionv3/src/config.py @@ -26,6 +26,8 @@ config_gpu = edict({ 'batch_size': 128, 'epoch_size': 250, 'num_classes': 1000, + 'ds_type': 'imagenet', + 'ds_sink_mode': True, 'smooth_factor': 0.1, 'aux_factor': 0.2, 'lr_init': 0.00004, @@ -51,6 +53,8 @@ config_ascend = edict({ 'batch_size': 128, 'epoch_size': 250, 'num_classes': 1000, + 'ds_type': 'imagenet', + 'ds_sink_mode': True, 'smooth_factor': 0.1, 'aux_factor': 0.2, 'lr_init': 0.00004, @@ -67,3 +71,30 @@ config_ascend = edict({ 'has_bias': False, 'amp_level': 'O3' }) + +config_cpu = edict({ + 'random_seed': 1, + 'work_nums': 8, + 'decay_method': 'cosine', + "loss_scale": 1024, + 'batch_size': 128, + 'epoch_size': 120, + 'num_classes': 10, + 'ds_type': 'cifar10', + 'ds_sink_mode': False, + 'smooth_factor': 0.1, + 'aux_factor': 0.2, + 'lr_init': 0.00004, + 'lr_max': 0.1, + 'lr_end': 0.000004, + 'warmup_epochs': 1, + 'weight_decay': 0.00004, + 'momentum': 0.9, + 'opt_eps': 1.0, + 'keep_checkpoint_max': 10, + 'ckpt_path': './', + 'is_save_on_master': 0, + 'dropout_keep_prob': 0.8, + 'has_bias': False, + 'amp_level': 'O0', +}) diff --git a/model_zoo/official/cv/inceptionv3/src/dataset.py b/model_zoo/official/cv/inceptionv3/src/dataset.py index 9ffe08373ca..e7fb6076df4 100644 --- a/model_zoo/official/cv/inceptionv3/src/dataset.py +++ b/model_zoo/official/cv/inceptionv3/src/dataset.py @@ -15,32 +15,32 @@ """ Data operations, will be used in train.py and eval.py """ +import os + import mindspore.common.dtype as mstype import mindspore.dataset as ds import mindspore.dataset.transforms.c_transforms as C2 import mindspore.dataset.vision.c_transforms as C -from src.config import config_gpu as cfg -def create_dataset(dataset_path, do_train, rank, group_size, repeat_num=1): +def create_dataset_imagenet(dataset_path, do_train, cfg, repeat_num=1): """ create a train or eval dataset Args: dataset_path(string): the path of dataset. do_train(bool): whether dataset is used for train or eval. - rank (int): The shard ID within num_shards (default=None). - group_size (int): Number of shards that the dataset should be divided into (default=None). + cfg (dict): the config for creating dataset. repeat_num(int): the repeat times of dataset. Default: 1. Returns: dataset """ - if group_size == 1: + if cfg.group_size == 1: data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=cfg.work_nums, shuffle=True) else: data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=cfg.work_nums, shuffle=True, - num_shards=group_size, shard_id=rank) + num_shards=cfg.group_size, shard_id=cfg.rank) # define map operations if do_train: trans = [ @@ -67,3 +67,44 @@ def create_dataset(dataset_path, do_train, rank, group_size, repeat_num=1): # apply dataset repeat operation data_set = data_set.repeat(repeat_num) return data_set + + +def create_dataset_cifar10(dataset_path, do_train, cfg, repeat_num=1): + """ + create a train or eval dataset + + Args: + dataset_path(string): the path of dataset. + do_train(bool): whether dataset is used for train or eval. + cfg (dict): the config for creating dataset. + repeat_num(int): the repeat times of dataset. Default: 1. + + Returns: + dataset + """ + dataset_path = os.path.join(dataset_path, "cifar-10-batches-bin" if do_train else "cifar-10-verify-bin") + if cfg.group_size == 1: + data_set = ds.Cifar10Dataset(dataset_path, num_parallel_workers=cfg.work_nums, shuffle=True) + else: + data_set = ds.Cifar10Dataset(dataset_path, num_parallel_workers=cfg.work_nums, shuffle=True, + num_shards=cfg.group_size, shard_id=cfg.rank) + + # define map operations + trans = [] + if do_train: + trans.append(C.RandomCrop((32, 32), (4, 4, 4, 4))) + trans.append(C.RandomHorizontalFlip(prob=0.5)) + + trans.append(C.Resize((299, 299))) + trans.append(C.Rescale(1.0 / 255.0, 0.0)) + trans.append(C.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010])) + trans.append(C.HWC2CHW()) + + type_cast_op = C2.TypeCast(mstype.int32) + data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=cfg.work_nums) + data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=cfg.work_nums) + # apply batch operations + data_set = data_set.batch(cfg.batch_size, drop_remainder=do_train) + # apply dataset repeat operation + data_set = data_set.repeat(repeat_num) + return data_set diff --git a/model_zoo/official/cv/inceptionv3/train.py b/model_zoo/official/cv/inceptionv3/train.py index 95d9489c26e..4fd48db1bfe 100644 --- a/model_zoo/official/cv/inceptionv3/train.py +++ b/model_zoo/official/cv/inceptionv3/train.py @@ -29,14 +29,24 @@ from mindspore.train.loss_scale_manager import FixedLossScaleManager from mindspore.common.initializer import XavierUniform, initializer from mindspore.common import set_seed -from src.config import config_gpu, config_ascend -from src.dataset import create_dataset +from src.config import config_gpu, config_ascend, config_cpu +from src.dataset import create_dataset_imagenet, create_dataset_cifar10 from src.inception_v3 import InceptionV3 from src.lr_generator import get_lr from src.loss import CrossEntropy set_seed(1) +CFG_DICT = { + "Ascend": config_ascend, + "GPU": config_gpu, + "CPU": config_cpu, +} + +DS_DICT = { + "imagenet": create_dataset_imagenet, + "cifar10": create_dataset_cifar10, +} if __name__ == '__main__': parser = argparse.ArgumentParser(description='image classification training') @@ -44,13 +54,16 @@ if __name__ == '__main__': parser.add_argument('--resume', type=str, default='', help='resume training with existed checkpoint') parser.add_argument('--is_distributed', action='store_true', default=False, help='distributed training') - parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU'), help='run platform') + parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU', 'CPU'), help='run platform') args_opt = parser.parse_args() + cfg = CFG_DICT[args_opt.platform] + create_dataset = DS_DICT[cfg.ds_type] + context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform, save_graphs=False) if os.getenv('DEVICE_ID', "not_set").isdigit(): context.set_context(device_id=int(os.getenv('DEVICE_ID'))) - cfg = config_ascend if args_opt.platform == 'Ascend' else config_gpu + # init distributed if args_opt.is_distributed: init() @@ -64,7 +77,7 @@ if __name__ == '__main__': cfg.group_size = 1 # dataloader - dataset = create_dataset(args_opt.dataset_path, True, cfg.rank, cfg.group_size) + dataset = create_dataset(args_opt.dataset_path, True, cfg) batches_per_epoch = dataset.get_dataset_size() # network @@ -120,8 +133,8 @@ if __name__ == '__main__': if args_opt.is_distributed & cfg.is_save_on_master: if cfg.rank == 0: callbacks.append(ckpoint_cb) - model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True) + model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=cfg.ds_sink_mode) else: callbacks.append(ckpoint_cb) - model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True) + model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=cfg.ds_sink_mode) print("train success")