forked from mindspore-Ecosystem/mindspore
!7951 Modelzoo retinaface, improvement of learning rate descent function.
Merge pull request !7951 from zhanghuiyao/4p_retinaface
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3b1694a762
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@ -80,7 +80,7 @@ After installing MindSpore via the official website and download the dataset, yo
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python train.py > train.log 2>&1 &
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# run distributed training example
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bash scripts/run_distribute_gpu_train.sh 3 0,1,2
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bash scripts/run_distribute_gpu_train.sh 4 0,1,2,3
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# run evaluation example
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export CUDA_VISIBLE_DEVICES=0
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@ -109,7 +109,8 @@ After installing MindSpore via the official website and download the dataset, yo
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│ ├──config.py // parameter configuration
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│ ├──augmentation.py // data augment method
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│ ├──loss.py // loss function
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│ ├──utils.py // data preprocessing
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│ ├──utils.py // data preprocessing
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│ ├──lr_schedule.py // learning rate schedule
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├── data
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│ ├──widerface // dataset data
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│ ├──resnet50_pretrain.ckpt // resnet50 imagenet pretrain model
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@ -136,7 +137,7 @@ Parameters for both training and evaluation can be set in config.py
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'batch_size': 8, # Batch size of train
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'num_workers': 8, # Num worker of dataset load data
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'num_anchor': 29126, # Num of anchor boxes, it depends on the image size
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'ngpu': 3, # Num gpu of train
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'ngpu': 4, # Num gpu of train
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'epoch': 100, # Training epoch number
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'decay1': 70, # Epoch number of the first weight attenuation
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'decay2': 90, # Epoch number of the second weight attenuation
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@ -146,29 +147,31 @@ Parameters for both training and evaluation can be set in config.py
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'out_channel': 256, # Output channel of DetectionHead
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'match_thresh': 0.35, # Threshold for match box
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'optim': 'sgd', # Optimizer type
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'warmup_epoch': -1, # Warmup size, -1 means no warm-up
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'initial_lr': 0.001, # Learning rate
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'warmup_epoch': 5, # Warmup size, 0 means no warm-up
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'initial_lr': 0.01, # Learning rate
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'network': 'resnet50', # Backbone name
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'momentum': 0.9, # Momentum for Optimizer
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'weight_decay': 5e-4, # Weight decay for Optimizer
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'gamma': 0.1, # Attenuation ratio of learning rate
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'ckpt_path': './checkpoint/', # Model save path
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'save_checkpoint_steps': 1000, # Save checkpoint steps
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'save_checkpoint_steps': 2000, # Save checkpoint steps
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'keep_checkpoint_max': 1, # Number of reserved checkpoints
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'resume_net': None, # Network for restart, default is None
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'training_dataset': '', # Training dataset label path, like 'data/widerface/train/label.txt'
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'pretrain': True, # whether training based on the pre-trained backbone
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'pretrain_path': './data/res50_pretrain.ckpt', # Pre-trained backbone checkpoint path
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# val
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'val_model': './checkpoint/ckpt_0/RetinaFace-100_536.ckpt', # Validation model path
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'val_dataset_folder': './data/widerface/val/', # Validation dataset path
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'val_origin_size': False, # Is full size verification used
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'val_confidence_threshold': 0.02, # Threshold for val confidence
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'val_nms_threshold': 0.4, # Threshold for val NMS
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'val_iou_threshold': 0.5, # Threshold for val IOU
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'val_save_result': False, # Whether save the resultss
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'val_predict_save_folder': './widerface_result', # Result save path
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'val_gt_dir': './data/ground_truth/', # Path of val set ground_truth
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'seed': 1, # setup train seed
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'lr_type': 'dynamic_lr',
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# val
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'val_model': './checkpoint/ckpt_0/RetinaFace-100_536.ckpt', # Validation model path
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'val_dataset_folder': './data/widerface/val/', # Validation dataset path
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'val_origin_size': False, # Is full size verification used
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'val_confidence_threshold': 0.02, # Threshold for val confidence
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'val_nms_threshold': 0.4, # Threshold for val NMS
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'val_iou_threshold': 0.5, # Threshold for val IOU
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'val_save_result': False, # Whether save the resultss
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'val_predict_save_folder': './widerface_result', # Result save path
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'val_gt_dir': './data/ground_truth/', # Path of val set ground_truth
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```
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@ -193,7 +196,7 @@ Parameters for both training and evaluation can be set in config.py
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- running on GPU
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```
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bash scripts/run_distribute_gpu_train.sh 3 0,1,2
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bash scripts/run_distribute_gpu_train.sh 4 0,1,2,3
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```
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The above shell script will run distribute training in the background. You can view the results through the file `train/train.log`.
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@ -207,7 +210,7 @@ Parameters for both training and evaluation can be set in config.py
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- evaluation on WIDERFACE dataset when running on GPU
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Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path in src/config.py, e.g., "username/retinaface/checkpoint/ckpt_0/RetinaFace-100_536.ckpt".
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Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path in src/config.py, e.g., "username/retinaface/checkpoint/ckpt_0/RetinaFace-100_402.ckpt".
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```
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export CUDA_VISIBLE_DEVICES=0
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@ -218,7 +221,7 @@ Parameters for both training and evaluation can be set in config.py
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```
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# grep "Val AP" eval.log
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Easy Val AP : 0.9413
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Easy Val AP : 0.9422
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Medium Val AP : 0.9325
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Hard Val AP : 0.8900
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```
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@ -233,7 +236,7 @@ Parameters for both training and evaluation can be set in config.py
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```
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# grep "Val AP" eval.log
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Easy Val AP : 0.9413
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Easy Val AP : 0.9422
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Medium Val AP : 0.9325
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Hard Val AP : 0.8900
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```
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@ -253,14 +256,14 @@ Parameters for both training and evaluation can be set in config.py
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| uploaded Date | 10/16/2020 (month/day/year) |
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| MindSpore Version | 1.0.0 |
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| Dataset | WIDERFACE |
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| Training Parameters | epoch=100, steps=536, batch_size=8, lr=0.001 |
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| Training Parameters | epoch=100, steps=402, batch_size=8, lr=0.01 |
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| Optimizer | SGD |
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| Loss Function | MultiBoxLoss + Softmax Cross Entropy |
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| outputs | bounding box + confidence + landmark |
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| Loss | 1.200 |
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| Speed | 3pcs: 566 ms/step |
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| Total time | 3pcs: 8.43 hours |
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| Parameters (M) | 27.29M |
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| Speed | 4pcs: 560 ms/step |
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| Total time | 4pcs: 6.4 hours |
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| Parameters (M) | 27.29M |
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| Checkpoint for Fine tuning | 336.3M (.ckpt file) |
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| Scripts | [retinaface script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/retinaface) |
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@ -17,7 +17,7 @@
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echo "=============================================================================================================="
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echo "Please run the script as: "
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echo "bash run_distribute_gpu_train.sh DEVICE_NUM CUDA_VISIBLE_DEVICES"
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echo "for example: bash run_distribute_gpu_train.sh 3 0,1,2"
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echo "for example: bash run_distribute_gpu_train.sh 4 0,1,2,3"
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echo "=============================================================================================================="
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RANK_SIZE=$1
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@ -25,30 +25,34 @@ cfg_res50 = {
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'batch_size': 8,
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'num_workers': 8,
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'num_anchor': 29126,
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'ngpu': 3,
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'epoch': 100,
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'decay1': 70,
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'decay2': 90,
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'ngpu': 4,
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'image_size': 840,
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'return_layers': {'layer2': 1, 'layer3': 2, 'layer4': 3},
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'in_channel': 256,
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'out_channel': 256,
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'match_thresh': 0.35,
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'optim': 'sgd',
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'warmup_epoch': -1,
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'initial_lr': 0.001,
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'network': 'resnet50',
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# opt
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'optim': 'sgd',
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'momentum': 0.9,
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'weight_decay': 5e-4,
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# seed
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'seed': 1,
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# lr
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'epoch': 100,
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'decay1': 70,
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'decay2': 90,
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'lr_type': 'dynamic_lr',
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'initial_lr': 0.01,
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'warmup_epoch': 5,
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'gamma': 0.1,
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# checkpoint
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'ckpt_path': './checkpoint/',
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'save_checkpoint_steps': 1000,
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'save_checkpoint_steps': 2000,
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'keep_checkpoint_max': 1,
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'resume_net': None,
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@ -0,0 +1,61 @@
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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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# less 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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"""learning rate schedule."""
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import math
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from .config import cfg_res50
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def _linear_warmup_learning_rate(current_step, warmup_steps, base_lr, init_lr):
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lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
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learning_rate = float(init_lr) + lr_inc * current_step
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return learning_rate
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def _a_cosine_learning_rate(current_step, base_lr, warmup_steps, decay_steps):
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base = float(current_step - warmup_steps) / float(decay_steps)
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learning_rate = (1 + math.cos(base * math.pi)) / 2 * base_lr
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return learning_rate
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def _dynamic_lr(base_lr, total_steps, warmup_steps, warmup_ratio=1 / 3):
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lr = []
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for i in range(total_steps):
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if i < warmup_steps:
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lr.append(_linear_warmup_learning_rate(i, warmup_steps, base_lr, base_lr * warmup_ratio))
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else:
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lr.append(_a_cosine_learning_rate(i, base_lr, warmup_steps, total_steps))
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return lr
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def adjust_learning_rate(initial_lr, gamma, stepvalues, steps_pre_epoch, total_epochs, warmup_epoch=5):
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if cfg_res50['lr_type'] == 'dynamic_lr':
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return _dynamic_lr(initial_lr, total_epochs * steps_pre_epoch, warmup_epoch * steps_pre_epoch,
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warmup_ratio=1 / 3)
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lr_each_step = []
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for epoch in range(1, total_epochs + 1):
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for _ in range(steps_pre_epoch):
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if epoch <= warmup_epoch:
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lr = 0.1 * initial_lr * (1.5849 ** (epoch - 1))
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else:
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if stepvalues[0] <= epoch <= stepvalues[1]:
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lr = initial_lr * (gamma ** (1))
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elif epoch > stepvalues[1]:
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lr = initial_lr * (gamma ** (2))
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else:
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lr = initial_lr
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lr_each_step.append(lr)
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return lr_each_step
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@ -14,12 +14,9 @@
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# ============================================================================
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"""Train Retinaface_resnet50."""
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from __future__ import print_function
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import random
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import math
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import numpy as np
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import mindspore
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import mindspore.nn as nn
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import mindspore.dataset as de
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from mindspore import context
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from mindspore.context import ParallelMode
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from mindspore.train import Model
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@ -31,28 +28,7 @@ from src.config import cfg_res50
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from src.network import RetinaFace, RetinaFaceWithLossCell, TrainingWrapper, resnet50
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from src.loss import MultiBoxLoss
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from src.dataset import create_dataset
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def setup_seed(seed):
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random.seed(seed)
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np.random.seed(seed)
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de.config.set_seed(seed)
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def adjust_learning_rate(initial_lr, gamma, stepvalues, steps_per_epoch, total_epochs, warmup_epoch=5):
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lr_each_step = []
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for epoch in range(1, total_epochs+1):
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for step in range(steps_per_epoch):
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if epoch <= warmup_epoch:
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lr = 1e-6 + (initial_lr - 1e-6) * ((epoch - 1) * steps_per_epoch + step) / \
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(steps_per_epoch * warmup_epoch)
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else:
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if stepvalues[0] <= epoch <= stepvalues[1]:
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lr = initial_lr * (gamma ** (1))
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elif epoch > stepvalues[1]:
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lr = initial_lr * (gamma ** (2))
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else:
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lr = initial_lr
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lr_each_step.append(lr)
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return lr_each_step
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from src.lr_schedule import adjust_learning_rate
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def train(cfg):
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@ -107,10 +83,10 @@ def train(cfg):
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warmup_epoch=cfg['warmup_epoch'])
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if cfg['optim'] == 'momentum':
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opt = nn.Momentum(net.trainable_params(), lr, momentum)
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opt = mindspore.nn.Momentum(net.trainable_params(), lr, momentum)
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elif cfg['optim'] == 'sgd':
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opt = nn.SGD(params=net.trainable_params(), learning_rate=lr, momentum=momentum,
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weight_decay=weight_decay, loss_scale=1)
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opt = mindspore.nn.SGD(params=net.trainable_params(), learning_rate=lr, momentum=momentum,
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weight_decay=weight_decay, loss_scale=1)
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else:
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raise ValueError('optim is not define.')
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@ -127,14 +103,13 @@ def train(cfg):
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print("============== Starting Training ==============")
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model.train(max_epoch, ds_train, callbacks=callback_list,
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dataset_sink_mode=False)
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dataset_sink_mode=True)
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if __name__ == '__main__':
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setup_seed(1)
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config = cfg_res50
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mindspore.common.seed.set_seed(config['seed'])
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print('train config:\n', config)
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train(cfg=config)
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