forked from mindspore-Ecosystem/mindspore
add resnext50
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# ResNext50 Example
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## Description
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This is an example of training ResNext50 with ImageNet dataset in Mindspore.
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## Requirements
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- Install [Mindspore](http://www.mindspore.cn/install/en).
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- Downlaod the dataset ImageNet2012.
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## Structure
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```shell
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.
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└─resnext50
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├─README.md
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├─scripts
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├─run_standalone_train.sh # launch standalone training(1p)
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├─run_distribute_train.sh # launch distributed training(8p)
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└─run_eval.sh # launch evaluating
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├─src
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├─backbone
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├─_init_.py # initalize
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├─resnet.py # resnext50 backbone
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├─utils
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├─_init_.py # initalize
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├─cunstom_op.py # network operation
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├─logging.py # print log
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├─optimizers_init_.py # get parameters
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├─sampler.py # distributed sampler
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├─var_init_.py # calculate gain value
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├─_init_.py # initalize
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├─config.py # parameter configuration
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├─crossentropy.py # CrossEntropy loss function
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├─dataset.py # data preprocessing
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├─head.py # commom head
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├─image_classification.py # get resnet
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├─linear_warmup.py # linear warmup learning rate
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├─warmup_cosine_annealing.py # learning rate each step
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├─warmup_step_lr.py # warmup step learning rate
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├─eval.py # eval net
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└─train.py # train net
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```
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## Parameter Configuration
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Parameters for both training and evaluating can be set in config.py
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```
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"image_height": '224,224' # image size
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"num_classes": 1000, # dataset class number
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"per_batch_size": 128, # batch size of input tensor
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"lr": 0.05, # base learning rate
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"lr_scheduler": 'cosine_annealing', # learning rate mode
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"lr_epochs": '30,60,90,120', # epoch of lr changing
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"lr_gamma": 0.1, # decrease lr by a factor of exponential lr_scheduler
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"eta_min": 0, # eta_min in cosine_annealing scheduler
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"T_max": 150, # T-max in cosine_annealing scheduler
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"max_epoch": 150, # max epoch num to train the model
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"backbone": 'resnext50', # backbone metwork
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"warmup_epochs" : 1, # warmup epoch
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"weight_decay": 0.0001, # weight decay
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"momentum": 0.9, # momentum
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"is_dynamic_loss_scale": 0, # dynamic loss scale
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"loss_scale": 1024, # loss scale
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"label_smooth": 1, # label_smooth
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"label_smooth_factor": 0.1, # label_smooth_factor
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"ckpt_interval": 2000, # ckpt_interval
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"ckpt_path": 'outputs/', # checkpoint save location
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"is_save_on_master": 1,
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"rank": 0, # local rank of distributed
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"group_size": 1 # world size of distributed
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```
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## Running the example
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### Train
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#### Usage
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```
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# distribute training example(8p)
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sh run_distribute_train.sh MINDSPORE_HCCL_CONFIG_PATH DATA_PATH
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# standalone training
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sh run_standalone_train.sh DEVICE_ID DATA_PATH
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```
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#### Launch
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```bash
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# distributed training example(8p)
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sh scripts/run_distribute_train.sh MINDSPORE_HCCL_CONFIG_PATH /ImageNet/train
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# standalone training example
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sh scripts/run_standalone_train.sh 0 /ImageNet_Original/train
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```
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#### Result
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You can find checkpoint file together with result in log.
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### Evaluation
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#### Usage
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```
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# Evaluation
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sh run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH
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```
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#### Launch
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```bash
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# Evaluation with checkpoint
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sh scripts/run_eval.sh 0 /opt/npu/datasets/classification/val /resnext50_100.ckpt
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```
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> checkpoint can be produced in training process.
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#### Result
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Evaluation result will be stored in the scripts path. Under this, you can find result like the followings in log.
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```
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acc=78,16%(TOP1)
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acc=93.88%(TOP5)
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```
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""Eval"""
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import os
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import time
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import argparse
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import datetime
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import glob
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, context
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from mindspore.communication.management import init, get_rank, get_group_size, release
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.common import dtype as mstype
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from src.utils.logging import get_logger
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from src.image_classification import get_network
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from src.dataset import classification_dataset
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from src.config import config
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devid = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
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device_target="Ascend", save_graphs=False, device_id=devid)
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class ParameterReduce(nn.Cell):
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"""ParameterReduce"""
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def __init__(self):
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super(ParameterReduce, self).__init__()
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self.cast = P.Cast()
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self.reduce = P.AllReduce()
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def construct(self, x):
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one = self.cast(F.scalar_to_array(1.0), mstype.float32)
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out = x * one
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ret = self.reduce(out)
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return ret
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def parse_args(cloud_args=None):
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"""parse_args"""
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parser = argparse.ArgumentParser('mindspore classification test')
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# dataset related
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parser.add_argument('--data_dir', type=str, default='/opt/npu/datasets/classification/val', help='eval data dir')
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parser.add_argument('--per_batch_size', default=32, type=int, help='batch size for per npu')
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# network related
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parser.add_argument('--graph_ckpt', type=int, default=1, help='graph ckpt or feed ckpt')
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parser.add_argument('--pretrained', default='', type=str, help='fully path of pretrained model to load. '
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'If it is a direction, it will test all ckpt')
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# logging related
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parser.add_argument('--log_path', type=str, default='outputs/', help='path to save log')
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parser.add_argument('--is_distributed', type=int, default=0, help='if multi device')
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# roma obs
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parser.add_argument('--train_url', type=str, default="", help='train url')
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args, _ = parser.parse_known_args()
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args = merge_args(args, cloud_args)
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args.image_size = config.image_size
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args.num_classes = config.num_classes
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args.backbone = config.backbone
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args.rank = config.rank
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args.group_size = config.group_size
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args.image_size = list(map(int, args.image_size.split(',')))
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return args
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def get_top5_acc(top5_arg, gt_class):
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sub_count = 0
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for top5, gt in zip(top5_arg, gt_class):
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if gt in top5:
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sub_count += 1
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return sub_count
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def merge_args(args, cloud_args):
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"""merge_args"""
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args_dict = vars(args)
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if isinstance(cloud_args, dict):
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for key in cloud_args.keys():
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val = cloud_args[key]
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if key in args_dict and val:
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arg_type = type(args_dict[key])
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if arg_type is not type(None):
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val = arg_type(val)
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args_dict[key] = val
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return args
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def test(cloud_args=None):
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"""test"""
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args = parse_args(cloud_args)
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# init distributed
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if args.is_distributed:
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init()
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args.rank = get_rank()
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args.group_size = get_group_size()
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args.outputs_dir = os.path.join(args.log_path,
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datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
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args.logger = get_logger(args.outputs_dir, args.rank)
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args.logger.save_args(args)
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# network
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args.logger.important_info('start create network')
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if os.path.isdir(args.pretrained):
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models = list(glob.glob(os.path.join(args.pretrained, '*.ckpt')))
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print(models)
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if args.graph_ckpt:
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f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split('_')[0])
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else:
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f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('_')[-1])
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args.models = sorted(models, key=f)
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else:
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args.models = [args.pretrained,]
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for model in args.models:
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de_dataset = classification_dataset(args.data_dir, image_size=args.image_size,
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per_batch_size=args.per_batch_size,
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max_epoch=1, rank=args.rank, group_size=args.group_size,
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mode='eval')
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eval_dataloader = de_dataset.create_tuple_iterator()
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network = get_network(args.backbone, args.num_classes)
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if network is None:
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raise NotImplementedError('not implement {}'.format(args.backbone))
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param_dict = load_checkpoint(model)
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param_dict_new = {}
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for key, values in param_dict.items():
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if key.startswith('moments.'):
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continue
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elif key.startswith('network.'):
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param_dict_new[key[8:]] = values
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else:
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param_dict_new[key] = values
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load_param_into_net(network, param_dict_new)
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args.logger.info('load model {} success'.format(model))
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# must add
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network.add_flags_recursive(fp16=True)
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img_tot = 0
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top1_correct = 0
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top5_correct = 0
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network.set_train(False)
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t_end = time.time()
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it = 0
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for data, gt_classes in eval_dataloader:
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output = network(Tensor(data, mstype.float32))
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output = output.asnumpy()
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top1_output = np.argmax(output, (-1))
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top5_output = np.argsort(output)[:, -5:]
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t1_correct = np.equal(top1_output, gt_classes).sum()
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top1_correct += t1_correct
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top5_correct += get_top5_acc(top5_output, gt_classes)
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img_tot += args.per_batch_size
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if args.rank == 0 and it == 0:
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t_end = time.time()
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it = 1
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if args.rank == 0:
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time_used = time.time() - t_end
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fps = (img_tot - args.per_batch_size) * args.group_size / time_used
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args.logger.info('Inference Performance: {:.2f} img/sec'.format(fps))
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results = [[top1_correct], [top5_correct], [img_tot]]
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args.logger.info('before results={}'.format(results))
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if args.is_distributed:
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model_md5 = model.replace('/', '')
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tmp_dir = '/cache'
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if not os.path.exists(tmp_dir):
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os.mkdir(tmp_dir)
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top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(args.rank, model_md5)
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top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(args.rank, model_md5)
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img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(args.rank, model_md5)
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np.save(top1_correct_npy, top1_correct)
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np.save(top5_correct_npy, top5_correct)
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np.save(img_tot_npy, img_tot)
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while True:
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rank_ok = True
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for other_rank in range(args.group_size):
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top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(other_rank, model_md5)
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top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(other_rank, model_md5)
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img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(other_rank, model_md5)
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if not os.path.exists(top1_correct_npy) or not os.path.exists(top5_correct_npy) or \
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not os.path.exists(img_tot_npy):
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rank_ok = False
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if rank_ok:
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break
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top1_correct_all = 0
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top5_correct_all = 0
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img_tot_all = 0
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for other_rank in range(args.group_size):
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top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(other_rank, model_md5)
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top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(other_rank, model_md5)
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img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(other_rank, model_md5)
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top1_correct_all += np.load(top1_correct_npy)
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top5_correct_all += np.load(top5_correct_npy)
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img_tot_all += np.load(img_tot_npy)
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results = [[top1_correct_all], [top5_correct_all], [img_tot_all]]
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results = np.array(results)
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else:
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results = np.array(results)
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args.logger.info('after results={}'.format(results))
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top1_correct = results[0, 0]
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top5_correct = results[1, 0]
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img_tot = results[2, 0]
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acc1 = 100.0 * top1_correct / img_tot
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acc5 = 100.0 * top5_correct / img_tot
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args.logger.info('after allreduce eval: top1_correct={}, tot={},'
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'acc={:.2f}%(TOP1)'.format(top1_correct, img_tot, acc1))
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args.logger.info('after allreduce eval: top5_correct={}, tot={},'
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'acc={:.2f}%(TOP5)'.format(top5_correct, img_tot, acc5))
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if args.is_distributed:
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release()
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if __name__ == "__main__":
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test()
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#!/bin/bash
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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DATA_DIR=$2
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export RANK_TABLE_FILE=$1
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export RANK_SIZE=8
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PATH_CHECKPOINT=""
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if [ $# == 3 ]
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then
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PATH_CHECKPOINT=$3
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fi
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cores=`cat /proc/cpuinfo|grep "processor" |wc -l`
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echo "the number of logical core" $cores
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avg_core_per_rank=`expr $cores \/ $RANK_SIZE`
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core_gap=`expr $avg_core_per_rank \- 1`
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echo "avg_core_per_rank" $avg_core_per_rank
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echo "core_gap" $core_gap
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for((i=0;i<RANK_SIZE;i++))
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do
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start=`expr $i \* $avg_core_per_rank`
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export DEVICE_ID=$i
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export RANK_ID=$i
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export DEPLOY_MODE=0
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export GE_USE_STATIC_MEMORY=1
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end=`expr $start \+ $core_gap`
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cmdopt=$start"-"$end
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rm -rf LOG$i
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mkdir ./LOG$i
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cp *.py ./LOG$i
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cd ./LOG$i || exit
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echo "start training for rank $i, device $DEVICE_ID"
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env > env.log
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taskset -c $cmdopt python ../train.py \
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--is_distribute=1 \
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--device_id=$DEVICE_ID \
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--pretrained=$PATH_CHECKPOINT \
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--data_dir=$DATA_DIR > log.txt 2>&1 &
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cd ../
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done
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@ -0,0 +1,24 @@
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#!/bin/bash
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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DEVICE_ID=$1
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DATA_DIR=$2
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PATH_CHECKPOINT=$3
|
||||
|
||||
python eval.py \
|
||||
--device_id=$DEVICE_ID \
|
||||
--pretrained=$PATH_CHECKPOINT \
|
||||
--data_dir=$DATA_DIR > log.txt 2>&1 &
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
|
||||
DEVICE_ID=$1
|
||||
DATA_DIR=$2
|
||||
PATH_CHECKPOINT=""
|
||||
if [ $# == 3 ]
|
||||
then
|
||||
PATH_CHECKPOINT=$3
|
||||
fi
|
||||
|
||||
python train.py \
|
||||
--is_distribute=0 \
|
||||
--device_id=$DEVICE_ID \
|
||||
--pretrained=$PATH_CHECKPOINT \
|
||||
--data_dir=$DATA_DIR > log.txt 2>&1 &
|
||||
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""resnet"""
|
||||
from .resnet import *
|
||||
|
|
@ -0,0 +1,273 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
ResNet based ResNext
|
||||
"""
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops.operations import TensorAdd, Split, Concat
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.common.initializer import TruncatedNormal
|
||||
|
||||
from src.utils.cunstom_op import SEBlock, GroupConv
|
||||
|
||||
|
||||
__all__ = ['ResNet', 'resnext50']
|
||||
|
||||
|
||||
def weight_variable(shape, factor=0.1):
|
||||
return TruncatedNormal(0.02)
|
||||
|
||||
|
||||
def conv7x7(in_channels, out_channels, stride=1, padding=3, has_bias=False, groups=1):
|
||||
return nn.Conv2d(in_channels, out_channels, kernel_size=7, stride=stride, has_bias=has_bias,
|
||||
padding=padding, pad_mode="pad", group=groups)
|
||||
|
||||
|
||||
def conv3x3(in_channels, out_channels, stride=1, padding=1, has_bias=False, groups=1):
|
||||
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, has_bias=has_bias,
|
||||
padding=padding, pad_mode="pad", group=groups)
|
||||
|
||||
|
||||
def conv1x1(in_channels, out_channels, stride=1, padding=0, has_bias=False, groups=1):
|
||||
return nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, has_bias=has_bias,
|
||||
padding=padding, pad_mode="pad", group=groups)
|
||||
|
||||
|
||||
class _DownSample(nn.Cell):
|
||||
"""
|
||||
Downsample for ResNext-ResNet.
|
||||
|
||||
Args:
|
||||
in_channels (int): Input channels.
|
||||
out_channels (int): Output channels.
|
||||
stride (int): Stride size for the 1*1 convolutional layer.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>>DownSample(32, 64, 2)
|
||||
"""
|
||||
def __init__(self, in_channels, out_channels, stride):
|
||||
super(_DownSample, self).__init__()
|
||||
self.conv = conv1x1(in_channels, out_channels, stride=stride, padding=0)
|
||||
self.bn = nn.BatchNorm2d(out_channels)
|
||||
|
||||
def construct(self, x):
|
||||
out = self.conv(x)
|
||||
out = self.bn(out)
|
||||
return out
|
||||
|
||||
class BasicBlock(nn.Cell):
|
||||
"""
|
||||
ResNet basic block definition.
|
||||
|
||||
Args:
|
||||
in_channels (int): Input channels.
|
||||
out_channels (int): Output channels.
|
||||
stride (int): Stride size for the first convolutional layer. Default: 1.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>>BasicBlock(32, 256, stride=2)
|
||||
"""
|
||||
expansion = 1
|
||||
|
||||
def __init__(self, in_channels, out_channels, stride=1, down_sample=None, use_se=False, **kwargs):
|
||||
super(BasicBlock, self).__init__()
|
||||
self.conv1 = conv3x3(in_channels, out_channels, stride=stride)
|
||||
self.bn1 = nn.BatchNorm2d(out_channels)
|
||||
self.relu = P.ReLU()
|
||||
self.conv2 = conv3x3(out_channels, out_channels, stride=1)
|
||||
self.bn2 = nn.BatchNorm2d(out_channels)
|
||||
|
||||
self.use_se = use_se
|
||||
if self.use_se:
|
||||
self.se = SEBlock(out_channels)
|
||||
|
||||
self.down_sample_flag = False
|
||||
if down_sample is not None:
|
||||
self.down_sample = down_sample
|
||||
self.down_sample_flag = True
|
||||
|
||||
self.add = TensorAdd()
|
||||
|
||||
def construct(self, x):
|
||||
identity = x
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
|
||||
if self.use_se:
|
||||
out = self.se(out)
|
||||
|
||||
if self.down_sample_flag:
|
||||
identity = self.down_sample(x)
|
||||
|
||||
out = self.add(out, identity)
|
||||
out = self.relu(out)
|
||||
return out
|
||||
|
||||
class Bottleneck(nn.Cell):
|
||||
"""
|
||||
ResNet Bottleneck block definition.
|
||||
|
||||
Args:
|
||||
in_channels (int): Input channels.
|
||||
out_channels (int): Output channels.
|
||||
stride (int): Stride size for the initial convolutional layer. Default: 1.
|
||||
|
||||
Returns:
|
||||
Tensor, the ResNet unit's output.
|
||||
|
||||
Examples:
|
||||
>>>Bottleneck(3, 256, stride=2)
|
||||
"""
|
||||
expansion = 4
|
||||
|
||||
def __init__(self, in_channels, out_channels, stride=1, down_sample=None,
|
||||
base_width=64, groups=1, use_se=False, **kwargs):
|
||||
super(Bottleneck, self).__init__()
|
||||
|
||||
width = int(out_channels * (base_width / 64.0)) * groups
|
||||
self.groups = groups
|
||||
self.conv1 = conv1x1(in_channels, width, stride=1)
|
||||
self.bn1 = nn.BatchNorm2d(width)
|
||||
self.relu = P.ReLU()
|
||||
|
||||
self.conv3x3s = nn.CellList()
|
||||
|
||||
self.conv2 = GroupConv(width, width, 3, stride, pad=1, groups=groups)
|
||||
self.op_split = Split(axis=1, output_num=self.groups)
|
||||
self.op_concat = Concat(axis=1)
|
||||
|
||||
self.bn2 = nn.BatchNorm2d(width)
|
||||
self.conv3 = conv1x1(width, out_channels * self.expansion, stride=1)
|
||||
self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
|
||||
|
||||
self.use_se = use_se
|
||||
if self.use_se:
|
||||
self.se = SEBlock(out_channels * self.expansion)
|
||||
|
||||
self.down_sample_flag = False
|
||||
if down_sample is not None:
|
||||
self.down_sample = down_sample
|
||||
self.down_sample_flag = True
|
||||
|
||||
self.cast = P.Cast()
|
||||
self.add = TensorAdd()
|
||||
|
||||
def construct(self, x):
|
||||
identity = x
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
out = self.relu(out)
|
||||
out = self.conv3(out)
|
||||
out = self.bn3(out)
|
||||
|
||||
if self.use_se:
|
||||
out = self.se(out)
|
||||
|
||||
if self.down_sample_flag:
|
||||
identity = self.down_sample(x)
|
||||
|
||||
out = self.add(out, identity)
|
||||
out = self.relu(out)
|
||||
return out
|
||||
|
||||
class ResNet(nn.Cell):
|
||||
"""
|
||||
ResNet architecture.
|
||||
|
||||
Args:
|
||||
block (cell): Block for network.
|
||||
layers (list): Numbers of block in different layers.
|
||||
width_per_group (int): Width of every group.
|
||||
groups (int): Groups number.
|
||||
|
||||
Returns:
|
||||
Tuple, output tensor tuple.
|
||||
|
||||
Examples:
|
||||
>>>ResNet()
|
||||
"""
|
||||
def __init__(self, block, layers, width_per_group=64, groups=1, use_se=False):
|
||||
super(ResNet, self).__init__()
|
||||
self.in_channels = 64
|
||||
self.groups = groups
|
||||
self.base_width = width_per_group
|
||||
|
||||
self.conv = conv7x7(3, self.in_channels, stride=2, padding=3)
|
||||
self.bn = nn.BatchNorm2d(self.in_channels)
|
||||
self.relu = P.ReLU()
|
||||
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
|
||||
|
||||
self.layer1 = self._make_layer(block, 64, layers[0], use_se=use_se)
|
||||
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, use_se=use_se)
|
||||
self.layer3 = self._make_layer(block, 256, layers[2], stride=2, use_se=use_se)
|
||||
self.layer4 = self._make_layer(block, 512, layers[3], stride=2, use_se=use_se)
|
||||
|
||||
self.out_channels = 512 * block.expansion
|
||||
self.cast = P.Cast()
|
||||
|
||||
def construct(self, x):
|
||||
x = self.conv(x)
|
||||
x = self.bn(x)
|
||||
x = self.relu(x)
|
||||
x = self.maxpool(x)
|
||||
x = self.layer1(x)
|
||||
x = self.layer2(x)
|
||||
x = self.layer3(x)
|
||||
x = self.layer4(x)
|
||||
|
||||
return x
|
||||
|
||||
def _make_layer(self, block, out_channels, blocks_num, stride=1, use_se=False):
|
||||
"""_make_layer"""
|
||||
down_sample = None
|
||||
if stride != 1 or self.in_channels != out_channels * block.expansion:
|
||||
down_sample = _DownSample(self.in_channels,
|
||||
out_channels * block.expansion,
|
||||
stride=stride)
|
||||
|
||||
layers = []
|
||||
layers.append(block(self.in_channels,
|
||||
out_channels,
|
||||
stride=stride,
|
||||
down_sample=down_sample,
|
||||
base_width=self.base_width,
|
||||
groups=self.groups,
|
||||
use_se=use_se))
|
||||
self.in_channels = out_channels * block.expansion
|
||||
for _ in range(1, blocks_num):
|
||||
layers.append(block(self.in_channels, out_channels,
|
||||
base_width=self.base_width, groups=self.groups, use_se=use_se))
|
||||
|
||||
return nn.SequentialCell(layers)
|
||||
|
||||
def get_out_channels(self):
|
||||
return self.out_channels
|
||||
|
||||
|
||||
def resnext50():
|
||||
return ResNet(Bottleneck, [3, 4, 6, 3], width_per_group=4, groups=32)
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""config"""
|
||||
from easydict import EasyDict as ed
|
||||
|
||||
config = ed({
|
||||
"image_size": '224,224',
|
||||
"num_classes": 1000,
|
||||
|
||||
"lr": 0.4,
|
||||
"lr_scheduler": 'cosine_annealing',
|
||||
"lr_epochs": '30,60,90,120',
|
||||
"lr_gamma": 0.1,
|
||||
"eta_min": 0,
|
||||
"T_max": 150,
|
||||
"max_epoch": 150,
|
||||
"backbone": 'resnext50',
|
||||
"warmup_epochs": 1,
|
||||
|
||||
"weight_decay": 0.0001,
|
||||
"momentum": 0.9,
|
||||
"is_dynamic_loss_scale": 0,
|
||||
"loss_scale": 1024,
|
||||
"label_smooth": 1,
|
||||
"label_smooth_factor": 0.1,
|
||||
|
||||
"ckpt_interval": 1250,
|
||||
"ckpt_path": 'outputs/',
|
||||
"is_save_on_master": 1,
|
||||
|
||||
"rank": 0,
|
||||
"group_size": 1
|
||||
})
|
||||
|
|
@ -0,0 +1,41 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
define loss function for network.
|
||||
"""
|
||||
from mindspore.nn.loss.loss import _Loss
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.ops import functional as F
|
||||
from mindspore import Tensor
|
||||
from mindspore.common import dtype as mstype
|
||||
import mindspore.nn as nn
|
||||
|
||||
class CrossEntropy(_Loss):
|
||||
"""
|
||||
the redefined loss function with SoftmaxCrossEntropyWithLogits.
|
||||
"""
|
||||
def __init__(self, smooth_factor=0., num_classes=1000):
|
||||
super(CrossEntropy, self).__init__()
|
||||
self.onehot = P.OneHot()
|
||||
self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
|
||||
self.off_value = Tensor(1.0 * smooth_factor / (num_classes -1), mstype.float32)
|
||||
self.ce = nn.SoftmaxCrossEntropyWithLogits()
|
||||
self.mean = P.ReduceMean(False)
|
||||
|
||||
def construct(self, logit, label):
|
||||
one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
|
||||
loss = self.ce(logit, one_hot_label)
|
||||
loss = self.mean(loss, 0)
|
||||
return loss
|
||||
|
|
@ -0,0 +1,155 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
dataset processing.
|
||||
"""
|
||||
import os
|
||||
from mindspore.common import dtype as mstype
|
||||
import mindspore.dataset as de
|
||||
import mindspore.dataset.transforms.c_transforms as C
|
||||
import mindspore.dataset.transforms.vision.c_transforms as V_C
|
||||
from PIL import Image, ImageFile
|
||||
from src.utils.sampler import DistributedSampler
|
||||
|
||||
ImageFile.LOAD_TRUNCATED_IMAGES = True
|
||||
|
||||
class TxtDataset():
|
||||
"""
|
||||
create txt dataset.
|
||||
|
||||
Args:
|
||||
Returns:
|
||||
de_dataset.
|
||||
"""
|
||||
def __init__(self, root, txt_name):
|
||||
super(TxtDataset, self).__init__()
|
||||
self.imgs = []
|
||||
self.labels = []
|
||||
fin = open(txt_name, "r")
|
||||
for line in fin:
|
||||
img_name, label = line.strip().split(' ')
|
||||
self.imgs.append(os.path.join(root, img_name))
|
||||
self.labels.append(int(label))
|
||||
fin.close()
|
||||
|
||||
def __getitem__(self, index):
|
||||
img = Image.open(self.imgs[index]).convert('RGB')
|
||||
return img, self.labels[index]
|
||||
|
||||
def __len__(self):
|
||||
return len(self.imgs)
|
||||
|
||||
|
||||
def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank, group_size,
|
||||
mode='train',
|
||||
input_mode='folder',
|
||||
root='',
|
||||
num_parallel_workers=None,
|
||||
shuffle=None,
|
||||
sampler=None,
|
||||
class_indexing=None,
|
||||
drop_remainder=True,
|
||||
transform=None,
|
||||
target_transform=None):
|
||||
"""
|
||||
A function that returns a dataset for classification. The mode of input dataset could be "folder" or "txt".
|
||||
If it is "folder", all images within one folder have the same label. If it is "txt", all paths of images
|
||||
are written into a textfile.
|
||||
|
||||
Args:
|
||||
data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"".
|
||||
Or path of the textfile that contains every image's path of the dataset.
|
||||
image_size (str): Size of the input images.
|
||||
per_batch_size (int): the batch size of evey step during training.
|
||||
max_epoch (int): the number of epochs.
|
||||
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).
|
||||
mode (str): "train" or others. Default: " train".
|
||||
input_mode (str): The form of the input dataset. "folder" or "txt". Default: "folder".
|
||||
root (str): the images path for "input_mode="txt"". Default: " ".
|
||||
num_parallel_workers (int): Number of workers to read the data. Default: None.
|
||||
shuffle (bool): Whether or not to perform shuffle on the dataset
|
||||
(default=None, performs shuffle).
|
||||
sampler (Sampler): Object used to choose samples from the dataset. Default: None.
|
||||
class_indexing (dict): A str-to-int mapping from folder name to index
|
||||
(default=None, the folder names will be sorted
|
||||
alphabetically and each class will be given a
|
||||
unique index starting from 0).
|
||||
|
||||
Examples:
|
||||
>>> from mindvision.common.datasets.classification import classification_dataset
|
||||
>>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
|
||||
>>> dataset_dir = "/path/to/imagefolder_directory"
|
||||
>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244],
|
||||
>>> per_batch_size=64, max_epoch=100,
|
||||
>>> rank=0, group_size=4)
|
||||
>>> # Path of the textfile that contains every image's path of the dataset.
|
||||
>>> dataset_dir = "/path/to/dataset/images/train.txt"
|
||||
>>> images_dir = "/path/to/dataset/images"
|
||||
>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244],
|
||||
>>> per_batch_size=64, max_epoch=100,
|
||||
>>> rank=0, group_size=4,
|
||||
>>> input_mode="txt", root=images_dir)
|
||||
"""
|
||||
|
||||
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
|
||||
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
|
||||
|
||||
if transform is None:
|
||||
if mode == 'train':
|
||||
transform_img = [
|
||||
V_C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
|
||||
V_C.RandomHorizontalFlip(prob=0.5),
|
||||
V_C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
|
||||
V_C.Normalize(mean=mean, std=std),
|
||||
V_C.HWC2CHW()
|
||||
]
|
||||
else:
|
||||
transform_img = [
|
||||
V_C.Decode(),
|
||||
V_C.Resize((256, 256)),
|
||||
V_C.CenterCrop(image_size),
|
||||
V_C.Normalize(mean=mean, std=std),
|
||||
V_C.HWC2CHW()
|
||||
]
|
||||
else:
|
||||
transform_img = transform
|
||||
|
||||
if target_transform is None:
|
||||
transform_label = [C.TypeCast(mstype.int32)]
|
||||
else:
|
||||
transform_label = target_transform
|
||||
|
||||
if input_mode == 'folder':
|
||||
de_dataset = de.ImageFolderDatasetV2(data_dir, num_parallel_workers=num_parallel_workers,
|
||||
shuffle=shuffle, sampler=sampler, class_indexing=class_indexing,
|
||||
num_shards=group_size, shard_id=rank)
|
||||
else:
|
||||
dataset = TxtDataset(root, data_dir)
|
||||
sampler = DistributedSampler(dataset, rank, group_size, shuffle=shuffle)
|
||||
de_dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=sampler)
|
||||
de_dataset.set_dataset_size(len(sampler))
|
||||
|
||||
de_dataset = de_dataset.map(input_columns="image", num_parallel_workers=8, operations=transform_img)
|
||||
de_dataset = de_dataset.map(input_columns="label", num_parallel_workers=8, operations=transform_label)
|
||||
|
||||
columns_to_project = ["image", "label"]
|
||||
de_dataset = de_dataset.project(columns=columns_to_project)
|
||||
|
||||
de_dataset = de_dataset.batch(per_batch_size, drop_remainder=drop_remainder)
|
||||
de_dataset = de_dataset.repeat(max_epoch)
|
||||
|
||||
return de_dataset
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
common architecture.
|
||||
"""
|
||||
import mindspore.nn as nn
|
||||
from src.utils.cunstom_op import GlobalAvgPooling
|
||||
|
||||
__all__ = ['CommonHead']
|
||||
|
||||
class CommonHead(nn.Cell):
|
||||
"""
|
||||
commom architecture definition.
|
||||
|
||||
Args:
|
||||
num_classes (int): Number of classes.
|
||||
out_channels (int): Output channels.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
"""
|
||||
def __init__(self, num_classes, out_channels):
|
||||
super(CommonHead, self).__init__()
|
||||
self.avgpool = GlobalAvgPooling()
|
||||
self.fc = nn.Dense(out_channels, num_classes, has_bias=True).add_flags_recursive(fp16=True)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.avgpool(x)
|
||||
x = self.fc(x)
|
||||
return x
|
||||
|
|
@ -0,0 +1,85 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
Image classifiation.
|
||||
"""
|
||||
import math
|
||||
import mindspore.nn as nn
|
||||
from mindspore.common import initializer as init
|
||||
import src.backbone as backbones
|
||||
import src.head as heads
|
||||
from src.utils.var_init import default_recurisive_init, KaimingNormal
|
||||
|
||||
|
||||
class ImageClassificationNetwork(nn.Cell):
|
||||
"""
|
||||
architecture of image classification network.
|
||||
|
||||
Args:
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
"""
|
||||
def __init__(self, backbone, head):
|
||||
super(ImageClassificationNetwork, self).__init__()
|
||||
self.backbone = backbone
|
||||
self.head = head
|
||||
|
||||
def construct(self, x):
|
||||
x = self.backbone(x)
|
||||
x = self.head(x)
|
||||
return x
|
||||
|
||||
class Resnet(ImageClassificationNetwork):
|
||||
"""
|
||||
Resnet architecture.
|
||||
Args:
|
||||
backbone_name (string): backbone.
|
||||
num_classes (int): number of classes.
|
||||
Returns:
|
||||
Resnet.
|
||||
"""
|
||||
def __init__(self, backbone_name, num_classes):
|
||||
self.backbone_name = backbone_name
|
||||
backbone = backbones.__dict__[self.backbone_name]()
|
||||
out_channels = backbone.get_out_channels()
|
||||
head = heads.CommonHead(num_classes=num_classes, out_channels=out_channels)
|
||||
super(Resnet, self).__init__(backbone, head)
|
||||
|
||||
default_recurisive_init(self)
|
||||
|
||||
for cell in self.cells_and_names():
|
||||
if isinstance(cell, nn.Conv2d):
|
||||
cell.weight.default_input = init.initializer(
|
||||
KaimingNormal(a=math.sqrt(5), mode='fan_out', nonlinearity='relu'),
|
||||
cell.weight.default_input.shape, cell.weight.default_input.dtype).to_tensor()
|
||||
elif isinstance(cell, nn.BatchNorm2d):
|
||||
cell.gamma.default_input = init.initializer('ones', cell.gamma.default_input.shape).to_tensor()
|
||||
cell.beta.default_input = init.initializer('zeros', cell.beta.default_input.shape).to_tensor()
|
||||
|
||||
# Zero-initialize the last BN in each residual branch,
|
||||
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
|
||||
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
|
||||
for cell in self.cells_and_names():
|
||||
if isinstance(cell, backbones.resnet.Bottleneck):
|
||||
cell.bn3.gamma.default_input = init.initializer('zeros', cell.bn3.gamma.default_input.shape).to_tensor()
|
||||
elif isinstance(cell, backbones.resnet.BasicBlock):
|
||||
cell.bn2.gamma.default_input = init.initializer('zeros', cell.bn2.gamma.default_input.shape).to_tensor()
|
||||
|
||||
|
||||
|
||||
def get_network(backbone_name, num_classes):
|
||||
if backbone_name in ['resnext50']:
|
||||
return Resnet(backbone_name, num_classes)
|
||||
return None
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
linear warm up learning rate.
|
||||
"""
|
||||
def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
|
||||
lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
|
||||
lr = float(init_lr) + lr_inc * current_step
|
||||
return lr
|
||||
|
|
@ -0,0 +1,108 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
network operations
|
||||
"""
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.common import dtype as mstype
|
||||
|
||||
|
||||
class GlobalAvgPooling(nn.Cell):
|
||||
"""
|
||||
global average pooling feature map.
|
||||
|
||||
Args:
|
||||
mean (tuple): means for each channel.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GlobalAvgPooling, self).__init__()
|
||||
self.mean = P.ReduceMean(True)
|
||||
self.shape = P.Shape()
|
||||
self.reshape = P.Reshape()
|
||||
|
||||
def construct(self, x):
|
||||
x = self.mean(x, (2, 3))
|
||||
b, c, _, _ = self.shape(x)
|
||||
x = self.reshape(x, (b, c))
|
||||
return x
|
||||
|
||||
|
||||
class SEBlock(nn.Cell):
|
||||
"""
|
||||
squeeze and excitation block.
|
||||
|
||||
Args:
|
||||
channel (int): number of feature maps.
|
||||
reduction (int): weight.
|
||||
"""
|
||||
def __init__(self, channel, reduction=16):
|
||||
super(SEBlock, self).__init__()
|
||||
|
||||
self.avg_pool = GlobalAvgPooling()
|
||||
self.fc1 = nn.Dense(channel, channel // reduction)
|
||||
self.relu = P.ReLU()
|
||||
self.fc2 = nn.Dense(channel // reduction, channel)
|
||||
self.sigmoid = P.Sigmoid()
|
||||
self.reshape = P.Reshape()
|
||||
self.shape = P.Shape()
|
||||
self.sum = P.Sum()
|
||||
self.cast = P.Cast()
|
||||
|
||||
def construct(self, x):
|
||||
b, c = self.shape(x)
|
||||
y = self.avg_pool(x)
|
||||
|
||||
y = self.reshape(y, (b, c))
|
||||
y = self.fc1(y)
|
||||
y = self.relu(y)
|
||||
y = self.fc2(y)
|
||||
y = self.sigmoid(y)
|
||||
y = self.reshape(y, (b, c, 1, 1))
|
||||
return x * y
|
||||
|
||||
class GroupConv(nn.Cell):
|
||||
"""
|
||||
group convolution operation.
|
||||
|
||||
Args:
|
||||
in_channels (int): Input channels of feature map.
|
||||
out_channels (int): Output channels of feature map.
|
||||
kernel_size (int): Size of convolution kernel.
|
||||
stride (int): Stride size for the group convolution layer.
|
||||
|
||||
Returns:
|
||||
tensor, output tensor.
|
||||
"""
|
||||
def __init__(self, in_channels, out_channels, kernel_size, stride, pad_mode="pad", pad=0, groups=1, has_bias=False):
|
||||
super(GroupConv, self).__init__()
|
||||
assert in_channels % groups == 0 and out_channels % groups == 0
|
||||
self.groups = groups
|
||||
self.convs = nn.CellList()
|
||||
self.op_split = P.Split(axis=1, output_num=self.groups)
|
||||
self.op_concat = P.Concat(axis=1)
|
||||
self.cast = P.Cast()
|
||||
for _ in range(groups):
|
||||
self.convs.append(nn.Conv2d(in_channels//groups, out_channels//groups,
|
||||
kernel_size=kernel_size, stride=stride, has_bias=has_bias,
|
||||
padding=pad, pad_mode=pad_mode, group=1))
|
||||
|
||||
def construct(self, x):
|
||||
features = self.op_split(x)
|
||||
outputs = ()
|
||||
for i in range(self.groups):
|
||||
outputs = outputs + (self.convs[i](self.cast(features[i], mstype.float32)),)
|
||||
out = self.op_concat(outputs)
|
||||
return out
|
||||
|
|
@ -0,0 +1,82 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
get logger.
|
||||
"""
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from datetime import datetime
|
||||
|
||||
class LOGGER(logging.Logger):
|
||||
"""
|
||||
set up logging file.
|
||||
|
||||
Args:
|
||||
logger_name (string): logger name.
|
||||
log_dir (string): path of logger.
|
||||
|
||||
Returns:
|
||||
string, logger path
|
||||
"""
|
||||
def __init__(self, logger_name, rank=0):
|
||||
super(LOGGER, self).__init__(logger_name)
|
||||
if rank % 8 == 0:
|
||||
console = logging.StreamHandler(sys.stdout)
|
||||
console.setLevel(logging.INFO)
|
||||
formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
|
||||
console.setFormatter(formatter)
|
||||
self.addHandler(console)
|
||||
|
||||
def setup_logging_file(self, log_dir, rank=0):
|
||||
"""set up log file"""
|
||||
self.rank = rank
|
||||
if not os.path.exists(log_dir):
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
log_name = datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S') + '_rank_{}.log'.format(rank)
|
||||
self.log_fn = os.path.join(log_dir, log_name)
|
||||
fh = logging.FileHandler(self.log_fn)
|
||||
fh.setLevel(logging.INFO)
|
||||
formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
|
||||
fh.setFormatter(formatter)
|
||||
self.addHandler(fh)
|
||||
|
||||
def info(self, msg, *args, **kwargs):
|
||||
if self.isEnabledFor(logging.INFO):
|
||||
self._log(logging.INFO, msg, args, **kwargs)
|
||||
|
||||
def save_args(self, args):
|
||||
self.info('Args:')
|
||||
args_dict = vars(args)
|
||||
for key in args_dict.keys():
|
||||
self.info('--> %s: %s', key, args_dict[key])
|
||||
self.info('')
|
||||
|
||||
def important_info(self, msg, *args, **kwargs):
|
||||
if self.isEnabledFor(logging.INFO) and self.rank == 0:
|
||||
line_width = 2
|
||||
important_msg = '\n'
|
||||
important_msg += ('*'*70 + '\n')*line_width
|
||||
important_msg += ('*'*line_width + '\n')*2
|
||||
important_msg += '*'*line_width + ' '*8 + msg + '\n'
|
||||
important_msg += ('*'*line_width + '\n')*2
|
||||
important_msg += ('*'*70 + '\n')*line_width
|
||||
self.info(important_msg, *args, **kwargs)
|
||||
|
||||
|
||||
def get_logger(path, rank):
|
||||
logger = LOGGER("mindversion", rank)
|
||||
logger.setup_logging_file(path, rank)
|
||||
return logger
|
||||
|
|
@ -0,0 +1,39 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
optimizer parameters.
|
||||
"""
|
||||
def get_param_groups(network):
|
||||
"""get param groups"""
|
||||
decay_params = []
|
||||
no_decay_params = []
|
||||
for x in network.trainable_params():
|
||||
parameter_name = x.name
|
||||
if parameter_name.endswith('.bias'):
|
||||
# all bias not using weight decay
|
||||
# print('no decay:{}'.format(parameter_name))
|
||||
no_decay_params.append(x)
|
||||
elif parameter_name.endswith('.gamma'):
|
||||
# bn weight bias not using weight decay, be carefully for now x not include BN
|
||||
# print('no decay:{}'.format(parameter_name))
|
||||
no_decay_params.append(x)
|
||||
elif parameter_name.endswith('.beta'):
|
||||
# bn weight bias not using weight decay, be carefully for now x not include BN
|
||||
# print('no decay:{}'.format(parameter_name))
|
||||
no_decay_params.append(x)
|
||||
else:
|
||||
decay_params.append(x)
|
||||
|
||||
return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
choose samples from the dataset
|
||||
"""
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
class DistributedSampler():
|
||||
"""
|
||||
sampling the dataset.
|
||||
|
||||
Args:
|
||||
Returns:
|
||||
num_samples, number of samples.
|
||||
"""
|
||||
def __init__(self, dataset, rank, group_size, shuffle=True, seed=0):
|
||||
self.dataset = dataset
|
||||
self.rank = rank
|
||||
self.group_size = group_size
|
||||
self.dataset_length = len(self.dataset)
|
||||
self.num_samples = int(math.ceil(self.dataset_length * 1.0 / self.group_size))
|
||||
self.total_size = self.num_samples * self.group_size
|
||||
self.shuffle = shuffle
|
||||
self.seed = seed
|
||||
|
||||
def __iter__(self):
|
||||
if self.shuffle:
|
||||
self.seed = (self.seed + 1) & 0xffffffff
|
||||
np.random.seed(self.seed)
|
||||
indices = np.random.permutation(self.dataset_length).tolist()
|
||||
else:
|
||||
indices = list(range(len(self.dataset_length)))
|
||||
|
||||
indices += indices[:(self.total_size - len(indices))]
|
||||
indices = indices[self.rank::self.group_size]
|
||||
return iter(indices)
|
||||
|
||||
def __len__(self):
|
||||
return self.num_samples
|
||||
|
||||
|
|
@ -0,0 +1,213 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
Initialize.
|
||||
"""
|
||||
import math
|
||||
from functools import reduce
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore.common import initializer as init
|
||||
|
||||
def _calculate_gain(nonlinearity, param=None):
|
||||
r"""
|
||||
Return the recommended gain value for the given nonlinearity function.
|
||||
|
||||
The values are as follows:
|
||||
================= ====================================================
|
||||
nonlinearity gain
|
||||
================= ====================================================
|
||||
Linear / Identity :math:`1`
|
||||
Conv{1,2,3}D :math:`1`
|
||||
Sigmoid :math:`1`
|
||||
Tanh :math:`\frac{5}{3}`
|
||||
ReLU :math:`\sqrt{2}`
|
||||
Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
|
||||
================= ====================================================
|
||||
|
||||
Args:
|
||||
nonlinearity: the non-linear function
|
||||
param: optional parameter for the non-linear function
|
||||
|
||||
Examples:
|
||||
>>> gain = calculate_gain('leaky_relu', 0.2) # leaky_relu with negative_slope=0.2
|
||||
"""
|
||||
linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
|
||||
if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
|
||||
return 1
|
||||
if nonlinearity == 'tanh':
|
||||
return 5.0 / 3
|
||||
if nonlinearity == 'relu':
|
||||
return math.sqrt(2.0)
|
||||
if nonlinearity == 'leaky_relu':
|
||||
if param is None:
|
||||
negative_slope = 0.01
|
||||
elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
|
||||
negative_slope = param
|
||||
else:
|
||||
raise ValueError("negative_slope {} not a valid number".format(param))
|
||||
return math.sqrt(2.0 / (1 + negative_slope ** 2))
|
||||
|
||||
raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
|
||||
|
||||
def _assignment(arr, num):
|
||||
"""Assign the value of `num` to `arr`."""
|
||||
if arr.shape == ():
|
||||
arr = arr.reshape((1))
|
||||
arr[:] = num
|
||||
arr = arr.reshape(())
|
||||
else:
|
||||
if isinstance(num, np.ndarray):
|
||||
arr[:] = num[:]
|
||||
else:
|
||||
arr[:] = num
|
||||
return arr
|
||||
|
||||
def _calculate_in_and_out(arr):
|
||||
"""
|
||||
Calculate n_in and n_out.
|
||||
|
||||
Args:
|
||||
arr (Array): Input array.
|
||||
|
||||
Returns:
|
||||
Tuple, a tuple with two elements, the first element is `n_in` and the second element is `n_out`.
|
||||
"""
|
||||
dim = len(arr.shape)
|
||||
if dim < 2:
|
||||
raise ValueError("If initialize data with xavier uniform, the dimension of data must greater than 1.")
|
||||
|
||||
n_in = arr.shape[1]
|
||||
n_out = arr.shape[0]
|
||||
|
||||
if dim > 2:
|
||||
counter = reduce(lambda x, y: x * y, arr.shape[2:])
|
||||
n_in *= counter
|
||||
n_out *= counter
|
||||
return n_in, n_out
|
||||
|
||||
def _select_fan(array, mode):
|
||||
mode = mode.lower()
|
||||
valid_modes = ['fan_in', 'fan_out']
|
||||
if mode not in valid_modes:
|
||||
raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
|
||||
|
||||
fan_in, fan_out = _calculate_in_and_out(array)
|
||||
return fan_in if mode == 'fan_in' else fan_out
|
||||
|
||||
class KaimingInit(init.Initializer):
|
||||
r"""
|
||||
Base Class. Initialize the array with He kaiming algorithm.
|
||||
|
||||
Args:
|
||||
a: the negative slope of the rectifier used after this layer (only
|
||||
used with ``'leaky_relu'``)
|
||||
mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
|
||||
preserves the magnitude of the variance of the weights in the
|
||||
forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
|
||||
backwards pass.
|
||||
nonlinearity: the non-linear function, recommended to use only with
|
||||
``'relu'`` or ``'leaky_relu'`` (default).
|
||||
"""
|
||||
def __init__(self, a=0, mode='fan_in', nonlinearity='leaky_relu'):
|
||||
super(KaimingInit, self).__init__()
|
||||
self.mode = mode
|
||||
self.gain = _calculate_gain(nonlinearity, a)
|
||||
def _initialize(self, arr):
|
||||
pass
|
||||
|
||||
|
||||
class KaimingUniform(KaimingInit):
|
||||
r"""
|
||||
Initialize the array with He kaiming uniform algorithm. The resulting tensor will
|
||||
have values sampled from :math:`\mathcal{U}(-\text{bound}, \text{bound})` where
|
||||
|
||||
.. math::
|
||||
\text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
|
||||
|
||||
Input:
|
||||
arr (Array): The array to be assigned.
|
||||
|
||||
Returns:
|
||||
Array, assigned array.
|
||||
|
||||
Examples:
|
||||
>>> w = np.empty(3, 5)
|
||||
>>> KaimingUniform(w, mode='fan_in', nonlinearity='relu')
|
||||
"""
|
||||
|
||||
def _initialize(self, arr):
|
||||
fan = _select_fan(arr, self.mode)
|
||||
bound = math.sqrt(3.0) * self.gain / math.sqrt(fan)
|
||||
np.random.seed(0)
|
||||
data = np.random.uniform(-bound, bound, arr.shape)
|
||||
|
||||
_assignment(arr, data)
|
||||
|
||||
|
||||
class KaimingNormal(KaimingInit):
|
||||
r"""
|
||||
Initialize the array with He kaiming normal algorithm. The resulting tensor will
|
||||
have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where
|
||||
|
||||
.. math::
|
||||
\text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}
|
||||
|
||||
Input:
|
||||
arr (Array): The array to be assigned.
|
||||
|
||||
Returns:
|
||||
Array, assigned array.
|
||||
|
||||
Examples:
|
||||
>>> w = np.empty(3, 5)
|
||||
>>> KaimingNormal(w, mode='fan_out', nonlinearity='relu')
|
||||
"""
|
||||
|
||||
def _initialize(self, arr):
|
||||
fan = _select_fan(arr, self.mode)
|
||||
std = self.gain / math.sqrt(fan)
|
||||
np.random.seed(0)
|
||||
data = np.random.normal(0, std, arr.shape)
|
||||
|
||||
_assignment(arr, data)
|
||||
|
||||
|
||||
def default_recurisive_init(custom_cell):
|
||||
"""default_recurisive_init"""
|
||||
for _, cell in custom_cell.cells_and_names():
|
||||
if isinstance(cell, nn.Conv2d):
|
||||
cell.weight.default_input = init.initializer(KaimingUniform(a=math.sqrt(5)),
|
||||
cell.weight.default_input.shape,
|
||||
cell.weight.default_input.dtype).to_tensor()
|
||||
if cell.bias is not None:
|
||||
fan_in, _ = _calculate_in_and_out(cell.weight.default_input.asnumpy())
|
||||
bound = 1 / math.sqrt(fan_in)
|
||||
np.random.seed(0)
|
||||
cell.bias.default_input = Tensor(np.random.uniform(-bound, bound, cell.bias.default_input.shape),
|
||||
cell.bias.default_input.dtype)
|
||||
elif isinstance(cell, nn.Dense):
|
||||
cell.weight.default_input = init.initializer(KaimingUniform(a=math.sqrt(5)),
|
||||
cell.weight.default_input.shape,
|
||||
cell.weight.default_input.dtype).to_tensor()
|
||||
if cell.bias is not None:
|
||||
fan_in, _ = _calculate_in_and_out(cell.weight.default_input.asnumpy())
|
||||
bound = 1 / math.sqrt(fan_in)
|
||||
np.random.seed(0)
|
||||
cell.bias.default_input = Tensor(np.random.uniform(-bound, bound, cell.bias.default_input.shape),
|
||||
cell.bias.default_input.dtype)
|
||||
elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
|
||||
pass
|
||||
|
|
@ -0,0 +1,40 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
warm up cosine annealing learning rate.
|
||||
"""
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
from .linear_warmup import linear_warmup_lr
|
||||
|
||||
|
||||
def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):
|
||||
"""warm up cosine annealing learning rate."""
|
||||
base_lr = lr
|
||||
warmup_init_lr = 0
|
||||
total_steps = int(max_epoch * steps_per_epoch)
|
||||
warmup_steps = int(warmup_epochs * steps_per_epoch)
|
||||
|
||||
lr_each_step = []
|
||||
for i in range(total_steps):
|
||||
last_epoch = i // steps_per_epoch
|
||||
if i < warmup_steps:
|
||||
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
|
||||
else:
|
||||
lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi*last_epoch / T_max)) / 2
|
||||
lr_each_step.append(lr)
|
||||
|
||||
return np.array(lr_each_step).astype(np.float32)
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
warm up step learning rate.
|
||||
"""
|
||||
from collections import Counter
|
||||
import numpy as np
|
||||
|
||||
from .linear_warmup import linear_warmup_lr
|
||||
|
||||
|
||||
def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1):
|
||||
"""warmup_step_lr"""
|
||||
base_lr = lr
|
||||
warmup_init_lr = 0
|
||||
total_steps = int(max_epoch * steps_per_epoch)
|
||||
warmup_steps = int(warmup_epochs * steps_per_epoch)
|
||||
milestones = lr_epochs
|
||||
milestones_steps = []
|
||||
for milestone in milestones:
|
||||
milestones_step = milestone * steps_per_epoch
|
||||
milestones_steps.append(milestones_step)
|
||||
|
||||
lr_each_step = []
|
||||
lr = base_lr
|
||||
milestones_steps_counter = Counter(milestones_steps)
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
|
||||
else:
|
||||
lr = lr * gamma**milestones_steps_counter[i]
|
||||
lr_each_step.append(lr)
|
||||
|
||||
return np.array(lr_each_step).astype(np.float32)
|
||||
|
||||
def multi_step_lr(lr, milestones, steps_per_epoch, max_epoch, gamma=0.1):
|
||||
return warmup_step_lr(lr, milestones, steps_per_epoch, 0, max_epoch, gamma=gamma)
|
||||
|
||||
def step_lr(lr, epoch_size, steps_per_epoch, max_epoch, gamma=0.1):
|
||||
lr_epochs = []
|
||||
for i in range(1, max_epoch):
|
||||
if i % epoch_size == 0:
|
||||
lr_epochs.append(i)
|
||||
return multi_step_lr(lr, lr_epochs, steps_per_epoch, max_epoch, gamma=gamma)
|
||||
|
|
@ -0,0 +1,289 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""train ImageNet."""
|
||||
import os
|
||||
import time
|
||||
import argparse
|
||||
import datetime
|
||||
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor, context
|
||||
from mindspore import ParallelMode
|
||||
from mindspore.nn.optim import Momentum
|
||||
from mindspore.communication.management import init, get_rank, get_group_size
|
||||
from mindspore.train.callback import ModelCheckpoint
|
||||
from mindspore.train.callback import CheckpointConfig, Callback
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||
from mindspore.train.model import Model
|
||||
from mindspore.train.loss_scale_manager import DynamicLossScaleManager, FixedLossScaleManager
|
||||
|
||||
from src.dataset import classification_dataset
|
||||
from src.crossentropy import CrossEntropy
|
||||
from src.warmup_step_lr import warmup_step_lr
|
||||
from src.warmup_cosine_annealing_lr import warmup_cosine_annealing_lr
|
||||
from src.utils.logging import get_logger
|
||||
from src.utils.optimizers__init__ import get_param_groups
|
||||
from src.image_classification import get_network
|
||||
from src.config import config
|
||||
|
||||
devid = int(os.getenv('DEVICE_ID'))
|
||||
context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
|
||||
device_target="Ascend", save_graphs=False, device_id=devid)
|
||||
|
||||
class BuildTrainNetwork(nn.Cell):
|
||||
"""build training network"""
|
||||
def __init__(self, network, criterion):
|
||||
super(BuildTrainNetwork, self).__init__()
|
||||
self.network = network
|
||||
self.criterion = criterion
|
||||
|
||||
def construct(self, input_data, label):
|
||||
output = self.network(input_data)
|
||||
loss = self.criterion(output, label)
|
||||
return loss
|
||||
|
||||
class ProgressMonitor(Callback):
|
||||
"""monitor loss and time"""
|
||||
def __init__(self, args):
|
||||
super(ProgressMonitor, self).__init__()
|
||||
self.me_epoch_start_time = 0
|
||||
self.me_epoch_start_step_num = 0
|
||||
self.args = args
|
||||
self.ckpt_history = []
|
||||
|
||||
def begin(self, run_context):
|
||||
self.args.logger.info('start network train...')
|
||||
|
||||
def epoch_begin(self, run_context):
|
||||
pass
|
||||
|
||||
def epoch_end(self, run_context, *me_args):
|
||||
cb_params = run_context.original_args()
|
||||
me_step = cb_params.cur_step_num - 1
|
||||
|
||||
real_epoch = me_step // self.args.steps_per_epoch
|
||||
time_used = time.time() - self.me_epoch_start_time
|
||||
fps_mean = self.args.per_batch_size * (me_step-self.me_epoch_start_step_num) * self.args.group_size / time_used
|
||||
self.args.logger.info('epoch[{}], iter[{}], loss:{}, mean_fps:{:.2f}'
|
||||
'imgs/sec'.format(real_epoch, me_step, cb_params.net_outputs, fps_mean))
|
||||
|
||||
if self.args.rank_save_ckpt_flag:
|
||||
import glob
|
||||
ckpts = glob.glob(os.path.join(self.args.outputs_dir, '*.ckpt'))
|
||||
for ckpt in ckpts:
|
||||
ckpt_fn = os.path.basename(ckpt)
|
||||
if not ckpt_fn.startswith('{}-'.format(self.args.rank)):
|
||||
continue
|
||||
if ckpt in self.ckpt_history:
|
||||
continue
|
||||
self.ckpt_history.append(ckpt)
|
||||
self.args.logger.info('epoch[{}], iter[{}], loss:{}, ckpt:{},'
|
||||
'ckpt_fn:{}'.format(real_epoch, me_step, cb_params.net_outputs, ckpt, ckpt_fn))
|
||||
|
||||
|
||||
self.me_epoch_start_step_num = me_step
|
||||
self.me_epoch_start_time = time.time()
|
||||
|
||||
def step_begin(self, run_context):
|
||||
pass
|
||||
|
||||
def step_end(self, run_context, *me_args):
|
||||
pass
|
||||
|
||||
def end(self, run_context):
|
||||
self.args.logger.info('end network train...')
|
||||
|
||||
|
||||
def parse_args(cloud_args=None):
|
||||
"""parameters"""
|
||||
parser = argparse.ArgumentParser('mindspore classification training')
|
||||
|
||||
# dataset related
|
||||
parser.add_argument('--data_dir', type=str, default='', help='train data dir')
|
||||
parser.add_argument('--per_batch_size', default=128, type=int, help='batch size for per gpu')
|
||||
# network related
|
||||
parser.add_argument('--pretrained', default='', type=str, help='model_path, local pretrained model to load')
|
||||
|
||||
# distributed related
|
||||
parser.add_argument('--is_distributed', type=int, default=1, help='if multi device')
|
||||
# roma obs
|
||||
parser.add_argument('--train_url', type=str, default="", help='train url')
|
||||
|
||||
args, _ = parser.parse_known_args()
|
||||
args = merge_args(args, cloud_args)
|
||||
args.image_size = config.image_size
|
||||
args.num_classes = config.num_classes
|
||||
args.lr = config.lr
|
||||
args.lr_scheduler = config.lr_scheduler
|
||||
args.lr_epochs = config.lr_epochs
|
||||
args.lr_gamma = config.lr_gamma
|
||||
args.eta_min = config.eta_min
|
||||
args.T_max = config.T_max
|
||||
args.max_epoch = config.max_epoch
|
||||
args.backbone = config.backbone
|
||||
args.warmup_epochs = config.warmup_epochs
|
||||
args.weight_decay = config.weight_decay
|
||||
args.momentum = config.momentum
|
||||
args.is_dynamic_loss_scale = config.is_dynamic_loss_scale
|
||||
args.loss_scale = config.loss_scale
|
||||
args.label_smooth = config.label_smooth
|
||||
args.label_smooth_factor = config.label_smooth_factor
|
||||
args.ckpt_interval = config.ckpt_interval
|
||||
args.ckpt_path = config.ckpt_path
|
||||
args.is_save_on_master = config.is_save_on_master
|
||||
args.rank = config.rank
|
||||
args.group_size = config.group_size
|
||||
args.lr_epochs = list(map(int, args.lr_epochs.split(',')))
|
||||
args.image_size = list(map(int, args.image_size.split(',')))
|
||||
|
||||
return args
|
||||
|
||||
def merge_args(args, cloud_args):
|
||||
"""dictionary"""
|
||||
args_dict = vars(args)
|
||||
if isinstance(cloud_args, dict):
|
||||
for key in cloud_args.keys():
|
||||
val = cloud_args[key]
|
||||
if key in args_dict and val:
|
||||
arg_type = type(args_dict[key])
|
||||
if arg_type is not type(None):
|
||||
val = arg_type(val)
|
||||
args_dict[key] = val
|
||||
return args
|
||||
|
||||
def train(cloud_args=None):
|
||||
"""training process"""
|
||||
args = parse_args(cloud_args)
|
||||
|
||||
# init distributed
|
||||
if args.is_distributed:
|
||||
init()
|
||||
args.rank = get_rank()
|
||||
args.group_size = get_group_size()
|
||||
|
||||
if args.is_dynamic_loss_scale == 1:
|
||||
args.loss_scale = 1 # for dynamic loss scale can not set loss scale in momentum opt
|
||||
|
||||
# select for master rank save ckpt or all rank save, compatiable for model parallel
|
||||
args.rank_save_ckpt_flag = 0
|
||||
if args.is_save_on_master:
|
||||
if args.rank == 0:
|
||||
args.rank_save_ckpt_flag = 1
|
||||
else:
|
||||
args.rank_save_ckpt_flag = 1
|
||||
|
||||
# logger
|
||||
args.outputs_dir = os.path.join(args.ckpt_path,
|
||||
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
|
||||
args.logger = get_logger(args.outputs_dir, args.rank)
|
||||
|
||||
# dataloader
|
||||
de_dataset = classification_dataset(args.data_dir, args.image_size,
|
||||
args.per_batch_size, args.max_epoch,
|
||||
args.rank, args.group_size)
|
||||
de_dataset.map_model = 4 # !!!important
|
||||
args.steps_per_epoch = de_dataset.get_dataset_size()
|
||||
|
||||
args.logger.save_args(args)
|
||||
|
||||
# network
|
||||
args.logger.important_info('start create network')
|
||||
# get network and init
|
||||
network = get_network(args.backbone, args.num_classes)
|
||||
if network is None:
|
||||
raise NotImplementedError('not implement {}'.format(args.backbone))
|
||||
network.add_flags_recursive(fp16=True)
|
||||
# loss
|
||||
if not args.label_smooth:
|
||||
args.label_smooth_factor = 0.0
|
||||
criterion = CrossEntropy(smooth_factor=args.label_smooth_factor,
|
||||
num_classes=args.num_classes)
|
||||
|
||||
# load pretrain model
|
||||
if os.path.isfile(args.pretrained):
|
||||
param_dict = load_checkpoint(args.pretrained)
|
||||
param_dict_new = {}
|
||||
for key, values in param_dict.items():
|
||||
if key.startswith('moments.'):
|
||||
continue
|
||||
elif key.startswith('network.'):
|
||||
param_dict_new[key[8:]] = values
|
||||
else:
|
||||
param_dict_new[key] = values
|
||||
load_param_into_net(network, param_dict_new)
|
||||
args.logger.info('load model {} success'.format(args.pretrained))
|
||||
|
||||
# lr scheduler
|
||||
if args.lr_scheduler == 'exponential':
|
||||
lr = warmup_step_lr(args.lr,
|
||||
args.lr_epochs,
|
||||
args.steps_per_epoch,
|
||||
args.warmup_epochs,
|
||||
args.max_epoch,
|
||||
gamma=args.lr_gamma,
|
||||
)
|
||||
elif args.lr_scheduler == 'cosine_annealing':
|
||||
lr = warmup_cosine_annealing_lr(args.lr,
|
||||
args.steps_per_epoch,
|
||||
args.warmup_epochs,
|
||||
args.max_epoch,
|
||||
args.T_max,
|
||||
args.eta_min)
|
||||
else:
|
||||
raise NotImplementedError(args.lr_scheduler)
|
||||
|
||||
# optimizer
|
||||
opt = Momentum(params=get_param_groups(network),
|
||||
learning_rate=Tensor(lr),
|
||||
momentum=args.momentum,
|
||||
weight_decay=args.weight_decay,
|
||||
loss_scale=args.loss_scale)
|
||||
|
||||
|
||||
criterion.add_flags_recursive(fp32=True)
|
||||
|
||||
# package training process, adjust lr + forward + backward + optimizer
|
||||
train_net = BuildTrainNetwork(network, criterion)
|
||||
if args.is_distributed:
|
||||
parallel_mode = ParallelMode.DATA_PARALLEL
|
||||
else:
|
||||
parallel_mode = ParallelMode.STAND_ALONE
|
||||
if args.is_dynamic_loss_scale == 1:
|
||||
loss_scale_manager = DynamicLossScaleManager(init_loss_scale=65536, scale_factor=2, scale_window=2000)
|
||||
else:
|
||||
loss_scale_manager = FixedLossScaleManager(args.loss_scale, drop_overflow_update=False)
|
||||
|
||||
# Model api changed since TR5_branch 2020/03/09
|
||||
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=args.group_size,
|
||||
parameter_broadcast=True, mirror_mean=True)
|
||||
model = Model(train_net, optimizer=opt, metrics=None, loss_scale_manager=loss_scale_manager)
|
||||
|
||||
# checkpoint save
|
||||
progress_cb = ProgressMonitor(args)
|
||||
callbacks = [progress_cb,]
|
||||
if args.rank_save_ckpt_flag:
|
||||
ckpt_max_num = args.max_epoch * args.steps_per_epoch // args.ckpt_interval
|
||||
ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval,
|
||||
keep_checkpoint_max=ckpt_max_num)
|
||||
ckpt_cb = ModelCheckpoint(config=ckpt_config,
|
||||
directory=args.outputs_dir,
|
||||
prefix='{}'.format(args.rank))
|
||||
callbacks.append(ckpt_cb)
|
||||
|
||||
model.train(args.max_epoch, de_dataset, callbacks=callbacks, dataset_sink_mode=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
train()
|
||||
Loading…
Reference in New Issue