diff --git a/model_zoo/official/cv/resnext101/README_CN.md b/model_zoo/official/cv/resnext101/README_CN.md new file mode 100644 index 00000000000..e88c873e24e --- /dev/null +++ b/model_zoo/official/cv/resnext101/README_CN.md @@ -0,0 +1,224 @@ +# ResNext101-64x4d for MindSpore + +本仓库提供了ResNeXt101-64x4d模型的训练脚本和超参配置，以达到论文中的准确性。 + +## 模型概述 + +模型名称：ResNeXt101 + +论文：`"Aggregated Residual Transformations for Deep Neural Networks" ` + +这里提供的版本是ResNeXt101-64x4d + +### 模型架构 + +ResNeXt是ResNet网络的改进版本，比ResNet的网络多了块多了cardinality设置。ResNeXt101-64x4d的网络结构如下： + +| 网络层 | 输出 | 参数 | +| ---------- | ------- | ------------------------------------------- | +| conv1 | 112x112 | 7x7,64,stride 2 | +| maxpooline | 56x56 | 3x3,stride 2 | +| conv2 | 56x56 | [(1x1,64)->(3x3,64)->(1x1,256) C=64]x3 | +| conv3 | 28x28 | [(1x1,256)->(3x3,256)->(1x1,512) C=64]x4 | +| conv4 | 14x14 | [(1x1,512)->(3x3,512)->(1x1,1024) C=64]x23 | +| conv5 | 7x7 | [(1x1,1024)->(3x3,1024)->(1x1,2048) C=64]x3 | +| | 1x1 | average pool；1000-d fc；softmax | + +### 默认设置 + +以下各节介绍ResNext50模型的默认配置和超参数。 + +#### 优化器 + +本模型使用Mindspore框架提供的Momentum优化器，超参设置如下： + +- Momentum : 0.9 +- Learning rate (LR) : 0.05 +- LR schedule: cosine_annealing +- LR epochs: [30, 60, 90, 120] +- LR gamma: 0.1 +- Batch size : 64 +- Weight decay : 0.0001. +- Label smoothing = 0.1 +- Eta_min: 0 +- Warmup_epochs: 1 +- Loss_scale: 1024 +- 训练轮次: + - 150 epochs + +#### 数据增强 + +本模型使用了以下数据增强： + +- 对于训练脚本: + - RandomResizeCrop, scale=(0.08, 1.0), ratio=(0.75, 1.333) + - RandomHorizontalFlip, prob=0.5 + - Normalize, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225) +- 对于验证（前向推理）: + - Resize to (256, 256) + - CenterCrop to (224, 224) + - Normalize, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225) + +## 设定 + +以下各节列出了开始训练ResNext101-64x4d模型的要求。 + +## 快速入门指南 + +目录说明，代码参考了Modelzoo上的[ResNext50_for_MindSpore](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnext50) + +```path +. +└─resnext101-64x4d-mindspore + ├─README.md + ├─scripts + ├─run_standalone_train.sh # 启动Ascend单机训练（单卡） + ├─run_distribute_train.sh # 启动Ascend分布式训练（8卡） + ├─run_standalone_train_for_gpu.sh # 启动GPU单机训练（单卡） + ├─run_distribute_train_for_gpu.sh # 启动GPU分布式训练（8卡） + └─run_eval.sh # 启动评估 + ├─src + ├─backbone + ├─_init_.py # 初始化 + ├─resnext.py # ResNeXt101骨干 + ├─utils + ├─_init_.py # 初始化 + ├─cunstom_op.py # 网络操作 + ├─logging.py # 打印日志 + ├─optimizers_init_.py # 获取参数 + ├─sampler.py # 分布式采样器 + ├─var_init_.py # 计算增益值 + ├─_init_.py # 初始化 + ├─config.py # 参数配置 + ├─crossentropy.py # 交叉熵损失函数 + ├─dataset.py # 数据预处理 + ├─head.py # 常见头 + ├─image_classification.py # 获取ResNet + ├─linear_warmup.py # 线性热身学习率 + ├─warmup_cosine_annealing.py # 每次迭代的学习率 + ├─warmup_step_lr.py # 热身迭代学习率 + ├─eval.py # 评估网络 + ├──train.py # 训练网络 + ├──mindspore_hub_conf.py # MindSpore Hub接口 +``` + +### 1. 仓库克隆 + +```shell +git clone https://gitee.com/neoming/resnext101-64x4d-mindspore.git +cd resnext101-64x4d-mindspore/ +``` + +### 2. 数据下载和预处理 + +1. 下载ImageNet数据集 +2. 解压训练数据集和验证数据 +3. 训练和验证图像分别位于train /和val /目录下。 一个文件夹中的所有图像都具有相同的标签。 + +### 3. 训练（单卡） + +可以通过python脚本开始训练： + +```shell +python train.py --data_dir ~/imagenet/train/ --platform Ascend --is_distributed +``` + +或通过shell脚本开始训练： + +```shell +Ascend: + # 分布式训练示例（8卡） + bash scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH + # 单机训练 + bash scripts/run_standalone_train.sh DEVICE_ID DATA_PATH +GPU: + # 分布式训练示例（8卡） + bash scripts/run_distribute_train_for_gpu.sh DATA_PATH + # 单机训练 + bash scripts/run_standalone_train_for_gpu.sh DEVICE_ID DATA_PATH +``` + +### 4. 测试 + +您可以通过python脚本开始验证： + +```shell +python eval.py --data_dir ~/imagenet/val/ --platform Ascend --pretrained resnext.ckpt +``` + +或通过shell脚本开始训练： + +```shell +# 评估 +bash scripts/run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH PLATFORM +``` + +## 模型导出 + +```shell +python export.py --device_target [PLATFORM] --ckpt_file [CKPT_PATH] --file_format [EXPORT_FORMAT] +``` + +`EXPORT_FORMAT` 可选 ["AIR", "ONNX", "MINDIR"]. + +## 高级设置 + +### 超参设置 + +通过`src/config.py`文件进行设置，下面是ImageNet单卡实验的设置 + +```python +"image_size": '224,224', +"num_classes": 1000, + +"lr": 0.05, +"lr_scheduler": 'cosine_annealing', +"lr_epochs": '30,60,90,120', +"lr_gamma": 0.1, +"eta_min": 0, +"T_max": 150, +"max_epoch": 150, +"backbone": 'resnext101', +"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 +``` + +### 训练过程 + +训练的所有结果将存储在用--ckpt_path参数指定的目录中。 +训练脚本将会存储： + +- checkpoints. +- log. + +## 性能 + +### 结果 + +通过运行训练脚本获得了以下结果。 要获得相同的结果，请遵循快速入门指南中的步骤。 + +#### 准确度 + +| **epochs** | Top1/Top5 | +| :--------: | :-----------: | +| 150 | 79.56%(TOP1)/94.68%(TOP5) | + +#### 训练性能结果 + +| **NPUs** | train performance | +| :------: | :---------------: | +| 1 | 196.33image/sec | + diff --git a/model_zoo/official/cv/resnext101/eval.py b/model_zoo/official/cv/resnext101/eval.py new file mode 100644 index 00000000000..111748a1ecf --- /dev/null +++ b/model_zoo/official/cv/resnext101/eval.py @@ -0,0 +1,251 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +"""Eval""" +import os +import time +import argparse +import datetime +import glob +import numpy as np +import mindspore.nn as nn + +from mindspore import Tensor, context +from mindspore.context import ParallelMode +from mindspore.communication.management import init, get_rank, get_group_size, release +from mindspore.ops import operations as P +from mindspore.ops import functional as F +from mindspore.common import dtype as mstype + +from src.utils.logging import get_logger +from src.utils.auto_mixed_precision import auto_mixed_precision +from src.utils.var_init import load_pretrain_model +from src.image_classification import get_network +from src.dataset import classification_dataset +from src.config import config + + +class ParameterReduce(nn.Cell): + """ParameterReduce""" + def __init__(self): + super(ParameterReduce, self).__init__() + self.cast = P.Cast() + self.reduce = P.AllReduce() + + def construct(self, x): + one = self.cast(F.scalar_to_array(1.0), mstype.float32) + out = x * one + ret = self.reduce(out) + return ret + + +def parse_args(cloud_args=None): + """parse_args""" + parser = argparse.ArgumentParser('mindspore classification test') + parser.add_argument('--platform', type=str, default='Ascend', choices=('Ascend', 'GPU'), help='run platform') + + # dataset related + parser.add_argument('--data_dir', type=str, default='/opt/npu/datasets/classification/val', help='eval data dir') + parser.add_argument('--per_batch_size', default=32, type=int, help='batch size for per npu') + # network related + parser.add_argument('--graph_ckpt', action='store_true', default=True, help='graph ckpt or feed ckpt') + parser.add_argument('--pretrained', default='', type=str, help='fully path of pretrained model to load. ' + 'If it is a direction, it will test all ckpt') + + # logging related + parser.add_argument('--log_path', type=str, default='outputs/', help='path to save log') + parser.add_argument('--is_distributed', action='store_true', default=False, 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.rank = config.rank + args.group_size = config.group_size + + args.image_size = list(map(int, args.image_size.split(','))) + + # init distributed + if args.is_distributed: + if args.platform == "Ascend": + init() + elif args.platform == "GPU": + init("nccl") + args.rank = get_rank() + args.group_size = get_group_size() + else: + args.rank = 0 + args.group_size = 1 + + args.outputs_dir = os.path.join(args.log_path, + datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S')) + + args.logger = get_logger(args.outputs_dir, args.rank) + return args + + +def get_top5_acc(top5_arg, gt_class): + sub_count = 0 + for top5, gt in zip(top5_arg, gt_class): + if gt in top5: + sub_count += 1 + return sub_count + +def merge_args(args, cloud_args): + """merge_args""" + 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 get_result(args, model, top1_correct, top5_correct, img_tot): + """calculate top1 and top5 value.""" + results = [[top1_correct], [top5_correct], [img_tot]] + args.logger.info('before results={}'.format(results)) + if args.is_distributed: + model_md5 = model.replace('/', '') + tmp_dir = '/cache' + if not os.path.exists(tmp_dir): + os.mkdir(tmp_dir) + top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(args.rank, model_md5) + top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(args.rank, model_md5) + img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(args.rank, model_md5) + np.save(top1_correct_npy, top1_correct) + np.save(top5_correct_npy, top5_correct) + np.save(img_tot_npy, img_tot) + while True: + rank_ok = True + for other_rank in range(args.group_size): + top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(other_rank, model_md5) + top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(other_rank, model_md5) + img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(other_rank, model_md5) + if not os.path.exists(top1_correct_npy) or not os.path.exists(top5_correct_npy) or \ + not os.path.exists(img_tot_npy): + rank_ok = False + if rank_ok: + break + + top1_correct_all = 0 + top5_correct_all = 0 + img_tot_all = 0 + for other_rank in range(args.group_size): + top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(other_rank, model_md5) + top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(other_rank, model_md5) + img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(other_rank, model_md5) + top1_correct_all += np.load(top1_correct_npy) + top5_correct_all += np.load(top5_correct_npy) + img_tot_all += np.load(img_tot_npy) + results = [[top1_correct_all], [top5_correct_all], [img_tot_all]] + results = np.array(results) + else: + results = np.array(results) + + args.logger.info('after results={}'.format(results)) + return results + + +def test(cloud_args=None): + """test""" + args = parse_args(cloud_args) + context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, + device_target=args.platform, save_graphs=False) + if os.getenv('DEVICE_ID', "not_set").isdigit(): + context.set_context(device_id=int(os.getenv('DEVICE_ID'))) + + # init distributed + if args.is_distributed: + parallel_mode = ParallelMode.DATA_PARALLEL + context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=args.group_size, + gradients_mean=True) + + args.logger.save_args(args) + + # network + args.logger.important_info('start create network') + if os.path.isdir(args.pretrained): + models = list(glob.glob(os.path.join(args.pretrained, '*.ckpt'))) + print(models) + if args.graph_ckpt: + f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split('_')[0]) + else: + f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('_')[-1]) + args.models = sorted(models, key=f) + else: + args.models = [args.pretrained,] + + for model in args.models: + de_dataset = classification_dataset(args.data_dir, image_size=args.image_size, + per_batch_size=args.per_batch_size, + max_epoch=1, rank=args.rank, group_size=args.group_size, + mode='eval') + eval_dataloader = de_dataset.create_tuple_iterator(output_numpy=True, num_epochs=1) + network = get_network(num_classes=args.num_classes, platform=args.platform) + + load_pretrain_model(model, network, args) + + img_tot = 0 + top1_correct = 0 + top5_correct = 0 + if args.platform == "Ascend": + network.to_float(mstype.float16) + else: + auto_mixed_precision(network) + network.set_train(False) + t_end = time.time() + it = 0 + for data, gt_classes in eval_dataloader: + output = network(Tensor(data, mstype.float32)) + output = output.asnumpy() + + top1_output = np.argmax(output, (-1)) + top5_output = np.argsort(output)[:, -5:] + + t1_correct = np.equal(top1_output, gt_classes).sum() + top1_correct += t1_correct + top5_correct += get_top5_acc(top5_output, gt_classes) + img_tot += args.per_batch_size + + if args.rank == 0 and it == 0: + t_end = time.time() + it = 1 + if args.rank == 0: + time_used = time.time() - t_end + fps = (img_tot - args.per_batch_size) * args.group_size / time_used + args.logger.info('Inference Performance: {:.2f} img/sec'.format(fps)) + results = get_result(args, model, top1_correct, top5_correct, img_tot) + top1_correct = results[0, 0] + top5_correct = results[1, 0] + img_tot = results[2, 0] + acc1 = 100.0 * top1_correct / img_tot + acc5 = 100.0 * top5_correct / img_tot + args.logger.info('after allreduce eval: top1_correct={}, tot={},' + 'acc={:.2f}%(TOP1)'.format(top1_correct, img_tot, acc1)) + args.logger.info('after allreduce eval: top5_correct={}, tot={},' + 'acc={:.2f}%(TOP5)'.format(top5_correct, img_tot, acc5)) + if args.is_distributed: + release() + + +if __name__ == "__main__": + test() diff --git a/model_zoo/official/cv/resnext101/export.py b/model_zoo/official/cv/resnext101/export.py new file mode 100644 index 00000000000..917cbb5883c --- /dev/null +++ b/model_zoo/official/cv/resnext101/export.py @@ -0,0 +1,47 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +resnext export mindir. +""" +import argparse +import numpy as np +from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export +from src.config import config +from src.image_classification import get_network + +parser = argparse.ArgumentParser(description='checkpoint export') +parser.add_argument("--device_id", type=int, default=0, help="Device id") +parser.add_argument("--batch_size", type=int, default=1, help="batch size") +parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.") +parser.add_argument('--width', type=int, default=224, help='input width') +parser.add_argument('--height', type=int, default=224, help='input height') +parser.add_argument("--file_name", type=str, default="resnext50", help="output file name.") +parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format") +parser.add_argument("--device_target", type=str, default="Ascend", + choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)") +args = parser.parse_args() + +context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) +if args.device_target == "Ascend": + context.set_context(device_id=args.device_id) + +if __name__ == '__main__': + net = get_network(num_classes=config.num_classes, platform=args.device_target) + + param_dict = load_checkpoint(args.ckpt_file) + load_param_into_net(net, param_dict) + input_shp = [args.batch_size, 3, args.height, args.width] + input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32)) + export(net, input_array, file_name=args.file_name, file_format=args.file_format) diff --git a/model_zoo/official/cv/resnext101/scripts/run_distribute_train.sh b/model_zoo/official/cv/resnext101/scripts/run_distribute_train.sh new file mode 100644 index 00000000000..51afa397977 --- /dev/null +++ b/model_zoo/official/cv/resnext101/scripts/run_distribute_train.sh @@ -0,0 +1,57 @@ +#!/bin/bash +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +DATA_DIR=$2 +export RANK_TABLE_FILE=$1 +export RANK_SIZE=8 +export HCCL_CONNECT_TIMEOUT=600 +echo "hccl connect time out has changed to 600 second" +PATH_CHECKPOINT="" +if [ $# == 3 ] +then + PATH_CHECKPOINT=$3 +fi + +cores=`cat /proc/cpuinfo|grep "processor" |wc -l` +echo "the number of logical core" $cores +avg_core_per_rank=`expr $cores \/ $RANK_SIZE` +core_gap=`expr $avg_core_per_rank \- 1` +echo "avg_core_per_rank" $avg_core_per_rank +echo "core_gap" $core_gap +for((i=0;i env.log + taskset -c $cmdopt python ../train.py \ + --is_distribute \ + --device_id=$DEVICE_ID \ + --pretrained=$PATH_CHECKPOINT \ + --data_dir=$DATA_DIR > log.txt 2>&1 & + cd ../ +done diff --git a/model_zoo/official/cv/resnext101/scripts/run_distribute_train_for_gpu.sh b/model_zoo/official/cv/resnext101/scripts/run_distribute_train_for_gpu.sh new file mode 100644 index 00000000000..4edb871b626 --- /dev/null +++ b/model_zoo/official/cv/resnext101/scripts/run_distribute_train_for_gpu.sh @@ -0,0 +1,30 @@ +#!/bin/bash +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +DATA_DIR=$1 +export RANK_SIZE=8 +PATH_CHECKPOINT="" +if [ $# == 2 ] +then + PATH_CHECKPOINT=$2 +fi + +mpirun --allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \ + python train.py \ + --is_distribute \ + --platform="GPU" \ + --pretrained=$PATH_CHECKPOINT \ + --data_dir=$DATA_DIR > log.txt 2>&1 & diff --git a/model_zoo/official/cv/resnext101/scripts/run_eval.sh b/model_zoo/official/cv/resnext101/scripts/run_eval.sh new file mode 100644 index 00000000000..31624f1d015 --- /dev/null +++ b/model_zoo/official/cv/resnext101/scripts/run_eval.sh @@ -0,0 +1,29 @@ +#!/bin/bash +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +export DEVICE_ID=$1 +DATA_DIR=$2 +PATH_CHECKPOINT=$3 +PLATFORM=Ascend +if [ $# == 4 ] +then + PLATFORM=$4 +fi + +python eval.py \ + --pretrained=$PATH_CHECKPOINT \ + --platform=$PLATFORM \ + --data_dir=$DATA_DIR > log.txt 2>&1 & diff --git a/model_zoo/official/cv/resnext101/scripts/run_standalone_train.sh b/model_zoo/official/cv/resnext101/scripts/run_standalone_train.sh new file mode 100644 index 00000000000..97241c4e95c --- /dev/null +++ b/model_zoo/official/cv/resnext101/scripts/run_standalone_train.sh @@ -0,0 +1,29 @@ +#!/bin/bash +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +export DEVICE_ID=$1 +DATA_DIR=$2 +PATH_CHECKPOINT="" +if [ $# == 3 ] +then + PATH_CHECKPOINT=$3 +fi + +python train.py \ + --device_id=$DEVICE_ID \ + --pretrained=$PATH_CHECKPOINT \ + --data_dir=$DATA_DIR > log.txt 2>&1 & + diff --git a/model_zoo/official/cv/resnext101/scripts/run_standalone_train_for_gpu.sh b/model_zoo/official/cv/resnext101/scripts/run_standalone_train_for_gpu.sh new file mode 100644 index 00000000000..b8a4bac77ed --- /dev/null +++ b/model_zoo/official/cv/resnext101/scripts/run_standalone_train_for_gpu.sh @@ -0,0 +1,29 @@ +#!/bin/bash +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +export DEVICE_ID=$1 +DATA_DIR=$2 +PATH_CHECKPOINT="" +if [ $# == 3 ] +then + PATH_CHECKPOINT=$3 +fi + +python train.py \ + --pretrained=$PATH_CHECKPOINT \ + --platform="GPU" \ + --data_dir=$DATA_DIR > log.txt 2>&1 & + diff --git a/model_zoo/official/cv/resnext101/src/__init__.py b/model_zoo/official/cv/resnext101/src/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/model_zoo/official/cv/resnext101/src/backbone/__init__.py b/model_zoo/official/cv/resnext101/src/backbone/__init__.py new file mode 100644 index 00000000000..4cee88027f1 --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/backbone/__init__.py @@ -0,0 +1,16 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +"""resnext""" +from .resnext import * diff --git a/model_zoo/official/cv/resnext101/src/backbone/resnext.py b/model_zoo/official/cv/resnext101/src/backbone/resnext.py new file mode 100644 index 00000000000..ee6c6053583 --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/backbone/resnext.py @@ -0,0 +1,279 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +ResNext +""" +import mindspore.nn as nn +from mindspore.ops.operations import TensorAdd +from mindspore.ops import operations as P +from mindspore.common.initializer import TruncatedNormal + +from src.utils.cunstom_op import SEBlock, GroupConv + + +__all__ = ['resnext50', 'resnext101'] + + +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): + """ + ResNeXt 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, + platform="Ascend", **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): + """ + ResNeXt 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, platform="Ascend", **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() + + if platform == "GPU": + self.conv2 = nn.Conv2d(width, width, 3, stride, pad_mode='pad', padding=1, group=groups) + else: + self.conv2 = GroupConv(width, width, 3, stride, pad=1, groups=groups) + + 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 ResNeXt(nn.Cell): + """ + ResNeXt 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: + >>>ResNeXt() + """ + def __init__(self, block, layers, width_per_group=64, groups=1, use_se=False, platform="Ascend"): + 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, platform=platform) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2, use_se=use_se, platform=platform) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2, use_se=use_se, platform=platform) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2, use_se=use_se, platform=platform) + + 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, platform="Ascend"): + """_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, + platform=platform)) + 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, platform=platform)) + + return nn.SequentialCell(layers) + + def get_out_channels(self): + return self.out_channels + + +def resnext50(platform="Ascend"): + return ResNeXt(Bottleneck, [3, 4, 6, 3], width_per_group=4, groups=32, platform=platform) + +def resnext101(platform="Ascend"): + return ResNeXt(Bottleneck, [3, 4, 23, 3], width_per_group=4, groups=64, platform=platform) diff --git a/model_zoo/official/cv/resnext101/src/config.py b/model_zoo/official/cv/resnext101/src/config.py new file mode 100644 index 00000000000..2e25861873b --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/config.py @@ -0,0 +1,46 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +"""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, + "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": 5, + "ckpt_save_max": 5, + "ckpt_path": 'outputs/', + "is_save_on_master": 1, + + # this two parameter is used for mindspore distributed configuration + "rank": 0, + "group_size": 1 +}) diff --git a/model_zoo/official/cv/resnext101/src/crossentropy.py b/model_zoo/official/cv/resnext101/src/crossentropy.py new file mode 100644 index 00000000000..a5abe602196 --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/crossentropy.py @@ -0,0 +1,41 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +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 diff --git a/model_zoo/official/cv/resnext101/src/dataset.py b/model_zoo/official/cv/resnext101/src/dataset.py new file mode 100644 index 00000000000..b577fdccef5 --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/dataset.py @@ -0,0 +1,158 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +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.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 (Union(int, sequence)): 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 src.dataset import classification_dataset + >>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images + >>> data_dir = "/path/to/imagefolder_directory" + >>> de_dataset = classification_dataset(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. + >>> data_dir = "/path/to/dataset/images/train.txt" + >>> images_dir = "/path/to/dataset/images" + >>> de_dataset = classification_dataset(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.ImageFolderDataset(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 = de_dataset.map(operations=transform_img, input_columns="image", + num_parallel_workers=num_parallel_workers) + de_dataset = de_dataset.map(operations=transform_label, input_columns="label", + num_parallel_workers=num_parallel_workers) + + 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 diff --git a/model_zoo/official/cv/resnext101/src/head.py b/model_zoo/official/cv/resnext101/src/head.py new file mode 100644 index 00000000000..bfc63befc31 --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/head.py @@ -0,0 +1,42 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +common architecture. +""" +import mindspore.nn as nn +from src.utils.cunstom_op import GlobalAvgPooling + +__all__ = ['CommonHead'] + +class CommonHead(nn.Cell): + """ + common 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 diff --git a/model_zoo/official/cv/resnext101/src/image_classification.py b/model_zoo/official/cv/resnext101/src/image_classification.py new file mode 100644 index 00000000000..3fd5d2516eb --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/image_classification.py @@ -0,0 +1,98 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +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, include_top=True, activation="None"): + super(ImageClassificationNetwork, self).__init__() + self.backbone = backbone + self.include_top = include_top + self.need_activation = False + if self.include_top: + self.head = head + if activation != "None": + self.need_activation = True + if activation == "Sigmoid": + self.activation = P.Sigmoid() + elif activation == "Softmax": + self.activation = P.Softmax() + else: + raise NotImplementedError(f"The activation {activation} not in [Sigmoid, Softmax].") + + def construct(self, x): + x = self.backbone(x) + if self.include_top: + x = self.head(x) + if self.need_activation: + x = self.activation(x) + return x + + +class ResNeXt(ImageClassificationNetwork): + """ + ResNeXt architecture. + Args: + backbone_name (string): backbone. + num_classes (int): number of classes, Default is 1000. + Returns: + Resnet. + """ + def __init__(self, backbone_name, num_classes=1000, platform="Ascend", include_top=True, activation="None"): + self.backbone_name = backbone_name + backbone = backbones.__dict__[self.backbone_name](platform=platform) + out_channels = backbone.get_out_channels() + head = heads.CommonHead(num_classes=num_classes, out_channels=out_channels) + super(ResNeXt, self).__init__(backbone, head, include_top, activation) + + default_recurisive_init(self) + + for cell in self.cells_and_names(): + if isinstance(cell, nn.Conv2d): + cell.weight.set_data(init.initializer( + KaimingNormal(a=math.sqrt(5), mode='fan_out', nonlinearity='relu'), + cell.weight.shape, cell.weight.dtype)) + elif isinstance(cell, nn.BatchNorm2d): + cell.gamma.set_data(init.initializer('ones', cell.gamma.shape)) + cell.beta.set_data(init.initializer('zeros', cell.beta.shape)) + + # 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.set_data(init.initializer('zeros', cell.bn3.gamma.shape)) + elif isinstance(cell, backbones.resnet.BasicBlock): + cell.bn2.gamma.set_data(init.initializer('zeros', cell.bn2.gamma.shape)) + + + +def get_network(**kwargs): + return ResNeXt('resnext101', **kwargs) diff --git a/model_zoo/official/cv/resnext101/src/lr_generator.py b/model_zoo/official/cv/resnext101/src/lr_generator.py new file mode 100644 index 00000000000..0fcca9ad252 --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/lr_generator.py @@ -0,0 +1,142 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +learning rate generator. +""" +import math +from collections import Counter +import numpy as np + + +def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr): + """ + Applies liner Increasing to generate learning rate array in warmup stage. + + Args: + current_step(int): current step in warmup stage. + warmup_steps(int): all steps in warmup stage. + base_lr(float): init learning rate. + init_lr(float): end learning rate + + Returns: + float, learning rate. + """ + lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps) + lr = float(init_lr) + lr_inc * current_step + return lr + + +def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0): + """ + Applies cosine decay to generate learning rate array with warmup. + + Args: + lr(float): init learning rate + steps_per_epoch(int): steps of one epoch + warmup_epochs(int): number of warmup epochs + max_epoch(int): total epoch of training + T_max(int): max epoch in decay. + eta_min(float): end learning rate + + Returns: + np.array, learning rate array. + """ + 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) + + +def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1): + """ + Applies step decay to generate learning rate array with warmup. + + Args: + lr(float): init learning rate + lr_epochs(list): learning rate decay epoches list + steps_per_epoch(int): steps of one epoch + warmup_epochs(int): number of warmup epochs + max_epoch(int): total epoch of training + gamma(float): attenuation constants. + + Returns: + np.array, learning rate array. + """ + 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) + + +def get_lr(args): + """generate learning rate array.""" + 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) + return lr diff --git a/model_zoo/official/cv/resnext101/src/utils/__init__.py b/model_zoo/official/cv/resnext101/src/utils/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/model_zoo/official/cv/resnext101/src/utils/auto_mixed_precision.py b/model_zoo/official/cv/resnext101/src/utils/auto_mixed_precision.py new file mode 100644 index 00000000000..6be124658a2 --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/utils/auto_mixed_precision.py @@ -0,0 +1,53 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +"""Auto mixed precision.""" +import mindspore.nn as nn +from mindspore.ops import functional as F +from mindspore._checkparam import Validator as validator +from mindspore.common import dtype as mstype + + +class OutputTo(nn.Cell): + "Cast cell output back to float16 or float32" + + def __init__(self, op, to_type=mstype.float16): + super(OutputTo, self).__init__(auto_prefix=False) + self._op = op + validator.check_type_name('to_type', to_type, [mstype.float16, mstype.float32], None) + self.to_type = to_type + + def construct(self, x): + return F.cast(self._op(x), self.to_type) + + +def auto_mixed_precision(network): + """Do keep batchnorm fp32.""" + cells = network.name_cells() + change = False + network.to_float(mstype.float16) + for name in cells: + subcell = cells[name] + if subcell == network: + continue + elif name == 'fc': + network.insert_child_to_cell(name, OutputTo(subcell, mstype.float32)) + change = True + elif isinstance(subcell, (nn.BatchNorm2d, nn.BatchNorm1d)): + network.insert_child_to_cell(name, OutputTo(subcell.to_float(mstype.float32), mstype.float16)) + change = True + else: + auto_mixed_precision(subcell) + if isinstance(network, nn.SequentialCell) and change: + network.cell_list = list(network.cells()) diff --git a/model_zoo/official/cv/resnext101/src/utils/cunstom_op.py b/model_zoo/official/cv/resnext101/src/utils/cunstom_op.py new file mode 100644 index 00000000000..d0c6b60a176 --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/utils/cunstom_op.py @@ -0,0 +1,104 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +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(False) + + def construct(self, x): + x = self.mean(x, (2, 3)) + 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 diff --git a/model_zoo/official/cv/resnext101/src/utils/logging.py b/model_zoo/official/cv/resnext101/src/utils/logging.py new file mode 100644 index 00000000000..c17befd265b --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/utils/logging.py @@ -0,0 +1,82 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +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 diff --git a/model_zoo/official/cv/resnext101/src/utils/optimizers__init__.py b/model_zoo/official/cv/resnext101/src/utils/optimizers__init__.py new file mode 100644 index 00000000000..c70a530f2c7 --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/utils/optimizers__init__.py @@ -0,0 +1,36 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +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 + 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 + 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 + no_decay_params.append(x) + else: + decay_params.append(x) + + return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}] diff --git a/model_zoo/official/cv/resnext101/src/utils/sampler.py b/model_zoo/official/cv/resnext101/src/utils/sampler.py new file mode 100644 index 00000000000..1c83d43d917 --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/utils/sampler.py @@ -0,0 +1,53 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +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 + \ No newline at end of file diff --git a/model_zoo/official/cv/resnext101/src/utils/var_init.py b/model_zoo/official/cv/resnext101/src/utils/var_init.py new file mode 100644 index 00000000000..d2954978269 --- /dev/null +++ b/model_zoo/official/cv/resnext101/src/utils/var_init.py @@ -0,0 +1,228 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" +Initialize. +""" +import os +import math +from functools import reduce +import numpy as np +import mindspore.nn as nn +from mindspore.common import initializer as init +from mindspore.train.serialization import load_checkpoint, load_param_into_net + +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) + 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) + 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.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)), + cell.weight.shape, + cell.weight.dtype)) + if cell.bias is not None: + fan_in, _ = _calculate_in_and_out(cell.weight) + bound = 1 / math.sqrt(fan_in) + cell.bias.set_data(init.initializer(init.Uniform(bound), + cell.bias.shape, + cell.bias.dtype)) + elif isinstance(cell, nn.Dense): + cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)), + cell.weight.shape, + cell.weight.dtype)) + if cell.bias is not None: + fan_in, _ = _calculate_in_and_out(cell.weight) + bound = 1 / math.sqrt(fan_in) + cell.bias.set_data(init.initializer(init.Uniform(bound), + cell.bias.shape, + cell.bias.dtype)) + elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)): + pass + + +def load_pretrain_model(ckpt_file, network, args): + """load pretrain model.""" + if os.path.isfile(ckpt_file): + param_dict = load_checkpoint(ckpt_file) + 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(ckpt_file)) diff --git a/model_zoo/official/cv/resnext101/train.py b/model_zoo/official/cv/resnext101/train.py new file mode 100644 index 00000000000..fae1c1998a1 --- /dev/null +++ b/model_zoo/official/cv/resnext101/train.py @@ -0,0 +1,259 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +"""train ImageNet.""" +import os +import time +import argparse +import datetime + +import mindspore.nn as nn +from mindspore import Tensor, context +from mindspore.context 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.model import Model +from mindspore.train.loss_scale_manager import DynamicLossScaleManager, FixedLossScaleManager +from mindspore.common import set_seed + +from src.dataset import classification_dataset +from src.crossentropy import CrossEntropy +from src.lr_generator import get_lr +from src.utils.logging import get_logger +from src.utils.optimizers__init__ import get_param_groups +from src.utils.var_init import load_pretrain_model +from src.image_classification import get_network +from src.config import config + +set_seed(1) + +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') + parser.add_argument('--platform', type=str, default='Ascend', choices=('Ascend', 'GPU'), help='run platform') + + # 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', action="store_true", default=False, 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.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_save_max = config.ckpt_save_max + 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(','))) + + context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, + device_target=args.platform, save_graphs=False) + # init distributed + if args.is_distributed: + init() + args.rank = get_rank() + args.group_size = get_group_size() + else: + args.rank = 0 + args.group_size = 1 + + 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, compatible 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) + 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) + if os.getenv('DEVICE_ID', "not_set").isdigit(): + context.set_context(device_id=int(os.getenv('DEVICE_ID'))) + + # init distributed + if args.is_distributed: + parallel_mode = ParallelMode.DATA_PARALLEL + context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=args.group_size, + gradients_mean=True) + # dataloader + de_dataset = classification_dataset(args.data_dir, args.image_size, + args.per_batch_size, 1, + args.rank, args.group_size, num_parallel_workers=8) + 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(num_classes=args.num_classes, platform=args.platform) + + load_pretrain_model(args.pretrained, network, args) + + # lr scheduler + lr = get_lr(args) + + # 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) + + + # loss + if not args.label_smooth: + args.label_smooth_factor = 0.0 + loss = CrossEntropy(smooth_factor=args.label_smooth_factor, num_classes=args.num_classes) + + 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 = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale_manager, + metrics={'acc'}, amp_level="O3") + + # checkpoint save + progress_cb = ProgressMonitor(args) + callbacks = [progress_cb,] + if args.rank_save_ckpt_flag: + ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval * args.steps_per_epoch, + keep_checkpoint_max=args.ckpt_save_max) + save_ckpt_path = os.path.join(args.outputs_dir, 'ckpt_' + str(args.rank) + '/') + ckpt_cb = ModelCheckpoint(config=ckpt_config, + directory=save_ckpt_path, + 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()