forked from mindspore-Ecosystem/mindspore
!16956 maskrcnn test
From: @huchunmei Reviewed-by: @c_34,@wuxuejian Signed-off-by: @c_34
This commit is contained in:
Gitee
commit
ee6d47c35a
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@ -155,17 +155,17 @@ bash scripts/docker_start.sh maskrcnn:20.1.0 [DATA_DIR] [MODEL_DIR]
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```shell
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# standalone training
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bash run_standalone_train.sh [PRETRAINED_CKPT]
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bash run_standalone_train.sh [PRETRAINED_CKPT] [DATA_PATH]
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# distributed training
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bash run_distribute_train.sh [RANK_TABLE_FILE] [PRETRAINED_CKPT]
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bash run_distribute_train.sh [RANK_TABLE_FILE] [PRETRAINED_CKPT] [DATA_PATH]
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```
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4. Eval
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```shell
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# Evaluation
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bash run_eval.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
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bash run_eval.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH] [DATA_PATH]
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```
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5. Inference.
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@ -203,12 +203,17 @@ bash run_eval.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
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├─resnet50.py # backbone network
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├─roi_align.py # roi align network
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└─rpn.py # reagion proposal network
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├─config.py # network configuration
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├─convert_checkpoint.py # convert resnet50 backbone checkpoint
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├─dataset.py # dataset utils
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├─lr_schedule.py # leanring rate geneatore
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├─network_define.py # network define for maskrcnn
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└─util.py # routine operation
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├─util.py # routine operation
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└─model_utils
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├─config.py # Processing configuration parameters
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├─device_adapter.py # Get cloud ID
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├─local_adapter.py # Get local ID
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└─moxing_adapter.py # Parameter processing
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├─default_config.yaml # Training parameter profile
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├─mindspore_hub_conf.py # mindspore hub interface
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├─export.py #script to export AIR,MINDIR,ONNX model
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├─eval.py # evaluation scripts
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@ -197,12 +197,17 @@ bash run_eval.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
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├─resnet50.py # 骨干网
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├─roi_align.py # 兴趣点对齐网络
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└─rpn.py # 区域候选网络
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├─config.py # 网络配置
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├─convert_checkpoint.py # 转换预训练checkpoint文件
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├─dataset.py # 数据集工具
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├─lr_schedule.py # 学习率生成器
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├─network_define.py # MaskRCNN的网络定义
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└─util.py # 例行操作
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├─util.py # 例行操作
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└─model_utils
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├─config.py # 训练配置
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├─device_adapter.py # 获取云上id
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├─local_adapter.py # 获取本地id
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└─moxing_adapter.py # 参数处理
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├─default_config.yaml # 训练参数配置文件
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├─mindspore_hub_conf.py # MindSpore hub接口
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├─export.py #导出 AIR,MINDIR,ONNX模型的脚本
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├─eval.py # 评估脚本
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@ -28,9 +28,9 @@
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#include "include/api/model.h"
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#include "include/api/types.h"
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#include "include/api/serialization.h"
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#include "include/dataset/vision.h"
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#include "include/dataset/transforms.h"
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#include "include/dataset/execute.h"
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#include "include/minddata/dataset/include/vision.h"
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#include "include/minddata/dataset/include/transforms.h"
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#include "include/minddata/dataset/include/execute.h"
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#include "../inc/utils.h"
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using mindspore::Context;
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@ -0,0 +1,217 @@
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# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
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enable_modelarts: False
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data_url: ""
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train_url: ""
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checkpoint_url: ""
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data_path: "/cache/data"
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output_path: "/cache/train"
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load_path: "/cache/checkpoint_path"
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checkpoint_path: './checkpoint/'
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checkpoint_file: './checkpoint/mask_rcnn-12_7393.ckpt'
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device_target: Ascend
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enable_profiling: False
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pre_trained: "/cache/data"
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coco_root: "/cache/data"
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ckpt_path: './ckpt_maskrcnn/mask_rcnn-12_7393.ckpt'
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ckpt_file: '/cache/data/cocodataset/ckpt_maskrcnn/mask_rcnn-12_7393.ckpt'
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ann_file: "./annotations/instances_val2017.json"
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# ==============================================================================
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modelarts_dataset_unzip_name: 'cocodataset'
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need_modelarts_dataset_unzip: True
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img_path: '' # "image file path."
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result_path: '' # "result file path."
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# Training options
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img_width: 1280
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img_height: 768
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keep_ratio: True
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flip_ratio: 0.5
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expand_ratio: 1.0
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max_instance_count: 128
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mask_shape: (28, 28)
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# anchor
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feature_shapes: [(192, 320), (96, 160), (48, 80), (24, 40), (12, 20)]
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anchor_scales: [8]
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anchor_ratios: [0.5, 1.0, 2.0]
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anchor_strides: [4, 8, 16, 32, 64]
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num_anchors: 3
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# resnet
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resnet_block: [3, 4, 6, 3]
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resnet_in_channels: [64, 256, 512, 1024]
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resnet_out_channels: [256, 512, 1024, 2048]
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# fpn
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fpn_in_channels: [256, 512, 1024, 2048]
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fpn_out_channels: 256
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fpn_num_outs: 5
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# rpn
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rpn_in_channels: 256
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rpn_feat_channels: 256
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rpn_loss_cls_weight: 1.0
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rpn_loss_reg_weight: 1.0
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rpn_cls_out_channels: 1
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rpn_target_means: [0., 0., 0., 0.]
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rpn_target_stds: [1.0, 1.0, 1.0, 1.0]
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# bbox_assign_sampler
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neg_iou_thr: 0.3
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pos_iou_thr: 0.7
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min_pos_iou: 0.3
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num_bboxes: 245520
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num_gts: 128
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num_expected_neg: 256
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num_expected_pos: 128
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# proposal
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activate_num_classes: 2
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use_sigmoid_cls: True
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# roi_align
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roi_layer: dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2)
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roi_align_out_channels: 256
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roi_align_featmap_strides: [4, 8, 16, 32]
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roi_align_finest_scale: 56
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roi_sample_num: 640
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# bbox_assign_sampler_stage2
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neg_iou_thr_stage2: 0.5
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pos_iou_thr_stage2: 0.5
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min_pos_iou_stage2: 0.5
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num_bboxes_stage2: 2000
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num_expected_pos_stage2: 128
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num_expected_neg_stage2: 512
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num_expected_total_stage2: 512
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# rcnn
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rcnn_num_layers: 2
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rcnn_in_channels: 256
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rcnn_fc_out_channels: 1024
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rcnn_mask_out_channels: 256
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rcnn_loss_cls_weight: 1
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rcnn_loss_reg_weight: 1
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rcnn_loss_mask_fb_weight: 1
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rcnn_target_means: [0., 0., 0., 0.]
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rcnn_target_stds: [0.1, 0.1, 0.2, 0.2]
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# train proposal
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rpn_proposal_nms_across_levels: False
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rpn_proposal_nms_pre: 2000
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rpn_proposal_nms_post: 2000
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rpn_proposal_max_num: 2000
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rpn_proposal_nms_thr: 0.7
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rpn_proposal_min_bbox_size: 0
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# test proposal
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rpn_nms_across_levels: False
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rpn_nms_pre: 1000
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rpn_nms_post: 1000
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rpn_max_num: 1000
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rpn_nms_thr: 0.7
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rpn_min_bbox_min_size: 0
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test_score_thr: 0.05
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test_iou_thr: 0.5
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test_max_per_img: 100
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test_batch_size: 2
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rpn_head_use_sigmoid: True
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rpn_head_weight: 1.0
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mask_thr_binary: 0.5
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# LR
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base_lr: 0.02
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base_step: 58633
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total_epoch: 13
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warmup_step: 500
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warmup_ratio: 1/3.0
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sgd_momentum: 0.9
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# train
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batch_size: 2
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loss_scale: 1
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momentum: 0.91
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weight_decay: 0.0001 # 1e-4
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pretrain_epoch_size: 0
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epoch_size: 12
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save_checkpoint: True
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save_checkpoint_epochs: 1
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keep_checkpoint_max: 12
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save_checkpoint_path: "./"
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mindrecord_dir: "./MindRecord_COCO" # "/home/mask_rcnn/MindRecord_COCO2017_Train"
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train_data_type: "train2017"
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val_data_type: "val2017"
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instance_set: "annotations/instances_{}.json"
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coco_classes: ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
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'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
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'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
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'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
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'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
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'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
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'kite', 'baseball bat', 'baseball glove', 'skateboard',
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'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
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'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
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'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
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'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
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'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
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'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
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'refrigerator', 'book', 'clock', 'vase', 'scissors',
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'teddy bear', 'hair drier', 'toothbrush')
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num_classes: 81
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only_create_dataset: False
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run_distribute: False
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do_train: True
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do_eval: False
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dataset: "coco"
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device_id: 0
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device_num: 1
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rank_id: 0
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# batch_size_export: 1
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file_name: "maskrcnn"
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file_format: "AIR"
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# other
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learning_rate: 0.002
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buffer_size: 1000
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save_checkpoint_steps: 1562
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sink_size: -1
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dataset_sink_mode: True
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lr: 0.01
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# Model Description
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model_name: maskrcnn
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---
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# Config description for each option
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enable_modelarts: 'Whether training on modelarts, default: False'
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data_url: 'Dataset url for obs'
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train_url: 'Training output url for obs'
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data_path: 'Dataset path for local'
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output_path: 'Training output path for local'
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ann_file: 'Ann file, default is val.json.'
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device_target: 'Target device type'
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enable_profiling: 'Whether enable profiling while training, default: False'
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only_create_dataset: 'If set it true, only create Mindrecord, default is false.'
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run_distribute: 'Run distribute, default is false.'
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do_train: 'Do train or not, default is true.'
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do_eval: 'Do eval or not, default is false.'
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dataset: 'Dataset, default is coco.'
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pre_trained: 'Pretrain file path.'
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device_id: 'Device id, default is 0.'
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device_num: 'Use device nums, default is 1.'
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rank_id: 'Rank id, default is 0.'
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file_format: 'file format'
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img_path: "image file path."
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result_path: "result file path."
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---
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device_target: ['Ascend', 'GPU', 'CPU']
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file_format: ["AIR", "ONNX", "MINDIR"]
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@ -15,29 +15,31 @@
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"""Evaluation for MaskRcnn"""
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import os
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import argparse
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import time
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import re
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import numpy as np
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from src.model_utils.config import config
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from src.model_utils.moxing_adapter import moxing_wrapper
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from src.model_utils.device_adapter import get_device_id, get_device_num
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from src.maskrcnn.mask_rcnn_r50 import Mask_Rcnn_Resnet50
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from src.dataset import data_to_mindrecord_byte_image, create_maskrcnn_dataset
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from src.util import coco_eval, bbox2result_1image, results2json, get_seg_masks
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from pycocotools.coco import COCO
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from mindspore import context, Tensor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.common import set_seed
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from src.maskrcnn.mask_rcnn_r50 import Mask_Rcnn_Resnet50
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from src.config import config
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from src.dataset import data_to_mindrecord_byte_image, create_maskrcnn_dataset
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from src.util import coco_eval, bbox2result_1image, results2json, get_seg_masks
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set_seed(1)
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parser = argparse.ArgumentParser(description="MaskRcnn evaluation")
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parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
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parser.add_argument("--ann_file", type=str, default="val.json", help="Ann file, default is val.json.")
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parser.add_argument("--checkpoint_path", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
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args_opt = parser.parse_args()
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lss = [int(re.findall(r'[0-9]+', i)[0]) for i in config.feature_shapes]
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config.feature_shapes = [(lss[2*i], lss[2*i+1]) for i in range(int(len(lss)/2))]
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config.roi_layer = dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2)
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config.warmup_ratio = 1/3.0
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config.mask_shape = (28, 28)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
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def maskrcnn_eval(dataset_path, ckpt_path, ann_file):
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"""MaskRcnn evaluation."""
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@ -106,14 +108,77 @@ def maskrcnn_eval(dataset_path, ckpt_path, ann_file):
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result_files = results2json(dataset_coco, outputs, "./results.pkl")
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coco_eval(result_files, eval_types, dataset_coco, single_result=False)
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if __name__ == '__main__':
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def modelarts_process():
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""" modelarts process """
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def unzip(zip_file, save_dir):
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import zipfile
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s_time = time.time()
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if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)):
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zip_isexist = zipfile.is_zipfile(zip_file)
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if zip_isexist:
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fz = zipfile.ZipFile(zip_file, 'r')
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data_num = len(fz.namelist())
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print("Extract Start...")
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print("unzip file num: {}".format(data_num))
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data_print = int(data_num / 100) if data_num > 100 else 1
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i = 0
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for file in fz.namelist():
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if i % data_print == 0:
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print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True)
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i += 1
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fz.extract(file, save_dir)
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print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),\
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int(int(time.time() - s_time) % 60)))
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print("Extract Done")
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else:
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print("This is not zip.")
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else:
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print("Zip has been extracted.")
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if config.need_modelarts_dataset_unzip:
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zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip")
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save_dir_1 = os.path.join(config.data_path)
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sync_lock = "/tmp/unzip_sync.lock"
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# Each server contains 8 devices as most
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if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
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print("Zip file path: ", zip_file_1)
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print("Unzip file save dir: ", save_dir_1)
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unzip(zip_file_1, save_dir_1)
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print("===Finish extract data synchronization===")
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try:
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os.mknod(sync_lock)
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except IOError:
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pass
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while True:
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if os.path.exists(sync_lock):
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break
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time.sleep(1)
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print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1))
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print("#" * 200, os.listdir(save_dir_1))
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print("#" * 200, os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name)))
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config.dataset_path = os.path.join(config.data_path, config.modelarts_dataset_unzip_name)
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config.coco_root = config.dataset_path
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config.checkpoint_path = os.path.join(config.dataset_path, config.ckpt_path)
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config.ann_file = os.path.join(config.dataset_path, config.ann_file)
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config.mindrecord_dir = os.path.join(config.dataset_path, config.mindrecord_dir)
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||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=get_device_id())
|
||||
|
||||
@moxing_wrapper(pre_process=modelarts_process)
|
||||
def eval_():
|
||||
prefix = "MaskRcnn_eval.mindrecord"
|
||||
mindrecord_dir = config.mindrecord_dir
|
||||
mindrecord_file = os.path.join(mindrecord_dir, prefix)
|
||||
if not os.path.exists(mindrecord_file):
|
||||
if not os.path.isdir(mindrecord_dir):
|
||||
os.makedirs(mindrecord_dir)
|
||||
if args_opt.dataset == "coco":
|
||||
if config.dataset == "coco":
|
||||
if os.path.isdir(config.coco_root):
|
||||
print("Create Mindrecord.")
|
||||
data_to_mindrecord_byte_image("coco", False, prefix, file_num=1)
|
||||
|
|
@ -129,5 +194,9 @@ if __name__ == '__main__':
|
|||
print("IMAGE_DIR or ANNO_PATH not exits.")
|
||||
|
||||
print("Start Eval!")
|
||||
maskrcnn_eval(mindrecord_file, args_opt.checkpoint_path, args_opt.ann_file)
|
||||
print("ckpt_path=", args_opt.checkpoint_path)
|
||||
maskrcnn_eval(mindrecord_file, config.checkpoint_path, config.ann_file)
|
||||
print("ckpt_path=", config.checkpoint_path)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
eval_()
|
||||
|
|
|
|||
|
|
@ -13,31 +13,28 @@
|
|||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""export checkpoint file into air, onnx, mindir models"""
|
||||
import argparse
|
||||
|
||||
import re
|
||||
import numpy as np
|
||||
from src.model_utils.config import config
|
||||
from src.model_utils.device_adapter import get_device_id
|
||||
from src.maskrcnn.mask_rcnn_r50 import MaskRcnn_Infer
|
||||
|
||||
from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
|
||||
|
||||
from src.maskrcnn.mask_rcnn_r50 import MaskRcnn_Infer
|
||||
from src.config import config
|
||||
lss = [int(re.findall(r'[0-9]+', i)[0]) for i in config.feature_shapes]
|
||||
config.feature_shapes = [(lss[2*i], lss[2*i+1]) for i in range(int(len(lss)/2))]
|
||||
config.roi_layer = dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2)
|
||||
config.warmup_ratio = 1/3.0
|
||||
config.mask_shape = (28, 28)
|
||||
|
||||
parser = argparse.ArgumentParser(description='maskrcnn 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("--file_name", type=str, default="maskrcnn", 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)
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
|
||||
if config.device_target == "Ascend":
|
||||
context.set_context(device_id=get_device_id())
|
||||
|
||||
if __name__ == '__main__':
|
||||
net = MaskRcnn_Infer(config=config)
|
||||
param_dict = load_checkpoint(args.ckpt_file)
|
||||
param_dict = load_checkpoint(config.ckpt_file)
|
||||
|
||||
param_dict_new = {}
|
||||
for key, value in param_dict.items():
|
||||
|
|
@ -48,8 +45,8 @@ if __name__ == '__main__':
|
|||
|
||||
bs = config.test_batch_size
|
||||
|
||||
img = Tensor(np.zeros([args.batch_size, 3, config.img_height, config.img_width], np.float16))
|
||||
img_metas = Tensor(np.zeros([args.batch_size, 4], np.float16))
|
||||
img = Tensor(np.zeros([config.batch_size, 3, config.img_height, config.img_width], np.float16))
|
||||
img_metas = Tensor(np.zeros([config.batch_size, 4], np.float16))
|
||||
|
||||
input_data = [img, img_metas]
|
||||
export(net, *input_data, file_name=args.file_name, file_format=args.file_format)
|
||||
export(net, *input_data, file_name=config.file_name, file_format=config.file_format)
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@
|
|||
# ============================================================================
|
||||
"""hub config."""
|
||||
from src.maskrcnn.mask_rcnn_r50 import Mask_Rcnn_Resnet50
|
||||
from src.config import config
|
||||
from src.model_utils.config import config
|
||||
|
||||
def create_network(name, *args, **kwargs):
|
||||
if name == "maskrcnn":
|
||||
|
|
|
|||
|
|
@ -14,23 +14,16 @@
|
|||
# ============================================================================
|
||||
"""post process for 310 inference"""
|
||||
import os
|
||||
import argparse
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from pycocotools.coco import COCO
|
||||
|
||||
from src.config import config
|
||||
from src.model_utils.config import config
|
||||
from src.util import coco_eval, bbox2result_1image, results2json, get_seg_masks
|
||||
|
||||
dst_width = 1280
|
||||
dst_height = 768
|
||||
|
||||
parser = argparse.ArgumentParser(description="maskrcnn inference")
|
||||
parser.add_argument("--ann_file", type=str, required=True, help="ann file.")
|
||||
parser.add_argument("--img_path", type=str, required=True, help="image file path.")
|
||||
parser.add_argument("--result_path", type=str, required=True, help="result file path.")
|
||||
args = parser.parse_args()
|
||||
|
||||
def get_img_size(file_name):
|
||||
img = Image.open(file_name)
|
||||
return img.size
|
||||
|
|
@ -96,4 +89,4 @@ def get_eval_result(ann_file, img_path, result_path):
|
|||
coco_eval(result_files, eval_types, dataset_coco, single_result=False)
|
||||
|
||||
if __name__ == '__main__':
|
||||
get_eval_result(args.ann_file, args.img_path, args.result_path)
|
||||
get_eval_result(config.ann_file, config.img_path, config.result_path)
|
||||
|
|
|
|||
|
|
@ -14,9 +14,9 @@
|
|||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
if [ $# != 2 ]
|
||||
if [ $# != 3 ]
|
||||
then
|
||||
echo "Usage: bash run_train.sh [RANK_TABLE_FILE] [PRETRAINED_PATH]"
|
||||
echo "Usage: bash run_train.sh [RANK_TABLE_FILE] [PRETRAINED_PATH] [DATA_PATH]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
|
|
@ -29,9 +29,11 @@ get_real_path(){
|
|||
}
|
||||
PATH1=$(get_real_path $1)
|
||||
PATH2=$(get_real_path $2)
|
||||
PATH3=$(get_real_path $3)
|
||||
|
||||
echo $PATH1
|
||||
echo $PATH2
|
||||
echo $PATH3
|
||||
|
||||
if [ ! -f $PATH1 ]
|
||||
then
|
||||
|
|
@ -68,12 +70,13 @@ do
|
|||
rm -rf ./train_parallel$i
|
||||
mkdir ./train_parallel$i
|
||||
cp ../*.py ./train_parallel$i
|
||||
cp ../*.yaml ./train_parallel$i
|
||||
cp *.sh ./train_parallel$i
|
||||
cp -r ../src ./train_parallel$i
|
||||
cd ./train_parallel$i || exit
|
||||
echo "start training for rank $RANK_ID, device $DEVICE_ID"
|
||||
env > env.log
|
||||
taskset -c $cmdopt python train.py --do_train=True --device_id=$i --rank_id=$i --run_distribute=True --device_num=$DEVICE_NUM \
|
||||
--pre_trained=$PATH2 &> log &
|
||||
--pre_trained=$PATH2 --data_path=$PATH3 &> log &
|
||||
cd ..
|
||||
done
|
||||
|
|
|
|||
|
|
@ -14,9 +14,9 @@
|
|||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
if [ $# != 2 ]
|
||||
if [ $# != 3 ]
|
||||
then
|
||||
echo "Usage: bash run_eval.sh [ANN_FILE] [CHECKPOINT_PATH]"
|
||||
echo "Usage: bash run_eval.sh [ANN_FILE] [CHECKPOINT_PATH] [DATA_PATH]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
|
|
@ -29,8 +29,10 @@ get_real_path(){
|
|||
}
|
||||
PATH1=$(get_real_path $1)
|
||||
PATH2=$(get_real_path $2)
|
||||
PATH3=$(get_real_path $3)
|
||||
echo $PATH1
|
||||
echo $PATH2
|
||||
echo $PATH3
|
||||
|
||||
if [ ! -f $PATH1 ]
|
||||
then
|
||||
|
|
@ -56,10 +58,12 @@ then
|
|||
fi
|
||||
mkdir ./eval
|
||||
cp ../*.py ./eval
|
||||
cp ../*.yaml ./eval
|
||||
cp *.sh ./eval
|
||||
cp -r ../src ./eval
|
||||
cd ./eval || exit
|
||||
env > env.log
|
||||
env > env.log_eval
|
||||
echo "start eval for device $DEVICE_ID"
|
||||
python eval.py --device_id=$DEVICE_ID --ann_file=$PATH1 --checkpoint_path=$PATH2 &> log &
|
||||
python ./eval.py --device_id=$DEVICE_ID --ann_file=$PATH1 --checkpoint_path=$PATH2 \
|
||||
--data_path=$PATH3 &> log_eval.txt &
|
||||
cd ..
|
||||
|
|
|
|||
|
|
@ -14,9 +14,9 @@
|
|||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
if [ $# != 1 ]
|
||||
if [ $# != 2 ]
|
||||
then
|
||||
echo "Usage: bash run_standalone_train.sh [PRETRAINED_PATH]"
|
||||
echo "Usage: bash run_standalone_train.sh [PRETRAINED_PATH] [DATA_PATH]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
|
|
@ -28,7 +28,9 @@ get_real_path(){
|
|||
fi
|
||||
}
|
||||
PATH1=$(get_real_path $1)
|
||||
PATH2=$(get_real_path $2)
|
||||
echo $PATH1
|
||||
echo $PATH2
|
||||
|
||||
if [ ! -f $PATH1 ]
|
||||
then
|
||||
|
|
@ -48,10 +50,11 @@ then
|
|||
fi
|
||||
mkdir ./train
|
||||
cp ../*.py ./train
|
||||
cp ../*.yaml ./train
|
||||
cp *.sh ./train
|
||||
cp -r ../src ./train
|
||||
cd ./train || exit
|
||||
echo "start training for device $DEVICE_ID"
|
||||
env > env.log
|
||||
python train.py --do_train=True --device_id=$DEVICE_ID --pre_trained=$PATH1 &> log &
|
||||
python ./train.py --do_train=True --device_id=$DEVICE_ID --pre_trained=$PATH1 --data_path=$PATH2 &> log.txt &
|
||||
cd ..
|
||||
|
|
|
|||
|
|
@ -1,161 +0,0 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#" :===========================================================================
|
||||
"""
|
||||
network config setting, will be used in train.py and eval.py
|
||||
"""
|
||||
from easydict import EasyDict as ed
|
||||
|
||||
config = ed({
|
||||
"img_width": 1280,
|
||||
"img_height": 768,
|
||||
"keep_ratio": True,
|
||||
"flip_ratio": 0.5,
|
||||
"expand_ratio": 1.0,
|
||||
|
||||
"max_instance_count": 128,
|
||||
"mask_shape": (28, 28),
|
||||
|
||||
# anchor
|
||||
"feature_shapes": [(192, 320), (96, 160), (48, 80), (24, 40), (12, 20)],
|
||||
"anchor_scales": [8],
|
||||
"anchor_ratios": [0.5, 1.0, 2.0],
|
||||
"anchor_strides": [4, 8, 16, 32, 64],
|
||||
"num_anchors": 3,
|
||||
|
||||
# resnet
|
||||
"resnet_block": [3, 4, 6, 3],
|
||||
"resnet_in_channels": [64, 256, 512, 1024],
|
||||
"resnet_out_channels": [256, 512, 1024, 2048],
|
||||
|
||||
# fpn
|
||||
"fpn_in_channels": [256, 512, 1024, 2048],
|
||||
"fpn_out_channels": 256,
|
||||
"fpn_num_outs": 5,
|
||||
|
||||
# rpn
|
||||
"rpn_in_channels": 256,
|
||||
"rpn_feat_channels": 256,
|
||||
"rpn_loss_cls_weight": 1.0,
|
||||
"rpn_loss_reg_weight": 1.0,
|
||||
"rpn_cls_out_channels": 1,
|
||||
"rpn_target_means": [0., 0., 0., 0.],
|
||||
"rpn_target_stds": [1.0, 1.0, 1.0, 1.0],
|
||||
|
||||
# bbox_assign_sampler
|
||||
"neg_iou_thr": 0.3,
|
||||
"pos_iou_thr": 0.7,
|
||||
"min_pos_iou": 0.3,
|
||||
"num_bboxes": 245520,
|
||||
"num_gts": 128,
|
||||
"num_expected_neg": 256,
|
||||
"num_expected_pos": 128,
|
||||
|
||||
# proposal
|
||||
"activate_num_classes": 2,
|
||||
"use_sigmoid_cls": True,
|
||||
|
||||
# roi_align
|
||||
"roi_layer": dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2),
|
||||
"roi_align_out_channels": 256,
|
||||
"roi_align_featmap_strides": [4, 8, 16, 32],
|
||||
"roi_align_finest_scale": 56,
|
||||
"roi_sample_num": 640,
|
||||
|
||||
# bbox_assign_sampler_stage2
|
||||
"neg_iou_thr_stage2": 0.5,
|
||||
"pos_iou_thr_stage2": 0.5,
|
||||
"min_pos_iou_stage2": 0.5,
|
||||
"num_bboxes_stage2": 2000,
|
||||
"num_expected_pos_stage2": 128,
|
||||
"num_expected_neg_stage2": 512,
|
||||
"num_expected_total_stage2": 512,
|
||||
|
||||
# rcnn
|
||||
"rcnn_num_layers": 2,
|
||||
"rcnn_in_channels": 256,
|
||||
"rcnn_fc_out_channels": 1024,
|
||||
"rcnn_mask_out_channels": 256,
|
||||
"rcnn_loss_cls_weight": 1,
|
||||
"rcnn_loss_reg_weight": 1,
|
||||
"rcnn_loss_mask_fb_weight": 1,
|
||||
"rcnn_target_means": [0., 0., 0., 0.],
|
||||
"rcnn_target_stds": [0.1, 0.1, 0.2, 0.2],
|
||||
|
||||
# train proposal
|
||||
"rpn_proposal_nms_across_levels": False,
|
||||
"rpn_proposal_nms_pre": 2000,
|
||||
"rpn_proposal_nms_post": 2000,
|
||||
"rpn_proposal_max_num": 2000,
|
||||
"rpn_proposal_nms_thr": 0.7,
|
||||
"rpn_proposal_min_bbox_size": 0,
|
||||
|
||||
# test proposal
|
||||
"rpn_nms_across_levels": False,
|
||||
"rpn_nms_pre": 1000,
|
||||
"rpn_nms_post": 1000,
|
||||
"rpn_max_num": 1000,
|
||||
"rpn_nms_thr": 0.7,
|
||||
"rpn_min_bbox_min_size": 0,
|
||||
"test_score_thr": 0.05,
|
||||
"test_iou_thr": 0.5,
|
||||
"test_max_per_img": 100,
|
||||
"test_batch_size": 2,
|
||||
|
||||
"rpn_head_use_sigmoid": True,
|
||||
"rpn_head_weight": 1.0,
|
||||
"mask_thr_binary": 0.5,
|
||||
|
||||
# LR
|
||||
"base_lr": 0.02,
|
||||
"base_step": 58633,
|
||||
"total_epoch": 13,
|
||||
"warmup_step": 500,
|
||||
"warmup_ratio": 1/3.0,
|
||||
"sgd_momentum": 0.9,
|
||||
|
||||
# train
|
||||
"batch_size": 2,
|
||||
"loss_scale": 1,
|
||||
"momentum": 0.91,
|
||||
"weight_decay": 1e-4,
|
||||
"pretrain_epoch_size": 0,
|
||||
"epoch_size": 12,
|
||||
"save_checkpoint": True,
|
||||
"save_checkpoint_epochs": 1,
|
||||
"keep_checkpoint_max": 12,
|
||||
"save_checkpoint_path": "./",
|
||||
|
||||
"mindrecord_dir": "/home/mask_rcnn/MindRecord_COCO2017_Train",
|
||||
"coco_root": "/home/mask_rcnn/coco2017/",
|
||||
"train_data_type": "train2017",
|
||||
"val_data_type": "val2017",
|
||||
"instance_set": "annotations/instances_{}.json",
|
||||
"coco_classes": ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
|
||||
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
|
||||
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
|
||||
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
|
||||
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
|
||||
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
|
||||
'kite', 'baseball bat', 'baseball glove', 'skateboard',
|
||||
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
|
||||
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
||||
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
|
||||
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
|
||||
'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
|
||||
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
|
||||
'refrigerator', 'book', 'clock', 'vase', 'scissors',
|
||||
'teddy bear', 'hair drier', 'toothbrush'),
|
||||
"num_classes": 81
|
||||
})
|
||||
|
|
@ -15,15 +15,13 @@
|
|||
"""
|
||||
convert resnet50 pretrain model to faster_rcnn backbone pretrain model
|
||||
"""
|
||||
import argparse
|
||||
from mindspore.train.serialization import load_checkpoint, save_checkpoint
|
||||
from mindspore.common.parameter import Parameter
|
||||
from mindspore.common.tensor import Tensor
|
||||
import mindspore.common.dtype as mstype
|
||||
from model_utils.config import config
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser(description='load_ckpt')
|
||||
parser.add_argument('--ckpt_file', type=str, default='', help='ckpt file path')
|
||||
args_opt = parser.parse_args()
|
||||
def load_weights(model_path, use_fp16_weight):
|
||||
"""
|
||||
load resnet50 pretrain checkpoint file.
|
||||
|
|
@ -60,5 +58,5 @@ def load_weights(model_path, use_fp16_weight):
|
|||
return param_list
|
||||
|
||||
if __name__ == "__main__":
|
||||
parameter_list = load_weights(args_opt.ckpt_file, use_fp16_weight=False)
|
||||
parameter_list = load_weights(config.ckpt_file, use_fp16_weight=False)
|
||||
save_checkpoint(parameter_list, "resnet50_backbone.ckpt")
|
||||
|
|
|
|||
|
|
@ -17,15 +17,25 @@
|
|||
from __future__ import division
|
||||
|
||||
import os
|
||||
import re
|
||||
import numpy as np
|
||||
from numpy import random
|
||||
import cv2
|
||||
import mmcv
|
||||
|
||||
import mindspore.dataset as de
|
||||
import mindspore.dataset.vision.c_transforms as C
|
||||
from mindspore.mindrecord import FileWriter
|
||||
from src.config import config
|
||||
|
||||
from model_utils.config import config
|
||||
|
||||
|
||||
config.mask_shape = (28, 28)
|
||||
|
||||
if config.enable_modelarts and config.need_modelarts_dataset_unzip:
|
||||
config.coco_root = os.path.join(config.data_path, config.modelarts_dataset_unzip_name)
|
||||
else:
|
||||
config.coco_root = config.data_path
|
||||
config.mindrecord_dir = os.path.join(config.coco_root, config.mindrecord_dir)
|
||||
|
||||
def bbox_overlaps(bboxes1, bboxes2, mode='iou'):
|
||||
"""Calculate the ious between each bbox of bboxes1 and bboxes2.
|
||||
|
|
@ -385,8 +395,12 @@ def create_coco_label(is_training):
|
|||
if is_training:
|
||||
data_type = config.train_data_type
|
||||
|
||||
#Classes need to train or test.
|
||||
train_cls = config.coco_classes
|
||||
# Classes need to train or test.
|
||||
# train_cls = config.coco_classes
|
||||
train_cls = [i for i in re.findall(r'[a-zA-Z\s]+', config.coco_classes) if i != ' ']
|
||||
train_cls = np.array(train_cls)
|
||||
print(train_cls)
|
||||
|
||||
train_cls_dict = {}
|
||||
for i, cls in enumerate(train_cls):
|
||||
train_cls_dict[cls] = i
|
||||
|
|
|
|||
|
|
@ -0,0 +1,127 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
"""Parse arguments"""
|
||||
|
||||
import os
|
||||
import ast
|
||||
import argparse
|
||||
from pprint import pprint, pformat
|
||||
import yaml
|
||||
|
||||
class Config:
|
||||
"""
|
||||
Configuration namespace. Convert dictionary to members.
|
||||
"""
|
||||
def __init__(self, cfg_dict):
|
||||
for k, v in cfg_dict.items():
|
||||
if isinstance(v, (list, tuple)):
|
||||
setattr(self, k, [Config(x) if isinstance(x, dict) else x for x in v])
|
||||
else:
|
||||
setattr(self, k, Config(v) if isinstance(v, dict) else v)
|
||||
|
||||
def __str__(self):
|
||||
return pformat(self.__dict__)
|
||||
|
||||
def __repr__(self):
|
||||
return self.__str__()
|
||||
|
||||
|
||||
def parse_cli_to_yaml(parser, cfg, helper=None, choices=None, cfg_path="default_config.yaml"):
|
||||
"""
|
||||
Parse command line arguments to the configuration according to the default yaml.
|
||||
|
||||
Args:
|
||||
parser: Parent parser.
|
||||
cfg: Base configuration.
|
||||
helper: Helper description.
|
||||
cfg_path: Path to the default yaml config.
|
||||
"""
|
||||
parser = argparse.ArgumentParser(description="[REPLACE THIS at config.py]",
|
||||
parents=[parser])
|
||||
helper = {} if helper is None else helper
|
||||
choices = {} if choices is None else choices
|
||||
for item in cfg:
|
||||
if not isinstance(cfg[item], list) and not isinstance(cfg[item], dict):
|
||||
help_description = helper[item] if item in helper else "Please reference to {}".format(cfg_path)
|
||||
choice = choices[item] if item in choices else None
|
||||
if isinstance(cfg[item], bool):
|
||||
parser.add_argument("--" + item, type=ast.literal_eval, default=cfg[item], choices=choice,
|
||||
help=help_description)
|
||||
else:
|
||||
parser.add_argument("--" + item, type=type(cfg[item]), default=cfg[item], choices=choice,
|
||||
help=help_description)
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def parse_yaml(yaml_path):
|
||||
"""
|
||||
Parse the yaml config file.
|
||||
|
||||
Args:
|
||||
yaml_path: Path to the yaml config.
|
||||
"""
|
||||
with open(yaml_path, 'r') as fin:
|
||||
try:
|
||||
cfgs = yaml.load_all(fin.read(), Loader=yaml.FullLoader)
|
||||
cfgs = [x for x in cfgs]
|
||||
if len(cfgs) == 1:
|
||||
cfg_helper = {}
|
||||
cfg = cfgs[0]
|
||||
cfg_choices = {}
|
||||
elif len(cfgs) == 2:
|
||||
cfg, cfg_helper = cfgs
|
||||
cfg_choices = {}
|
||||
elif len(cfgs) == 3:
|
||||
cfg, cfg_helper, cfg_choices = cfgs
|
||||
else:
|
||||
raise ValueError("At most 3 docs (config, description for help, choices) are supported in config yaml")
|
||||
print(cfg_helper)
|
||||
except:
|
||||
raise ValueError("Failed to parse yaml")
|
||||
return cfg, cfg_helper, cfg_choices
|
||||
|
||||
|
||||
def merge(args, cfg):
|
||||
"""
|
||||
Merge the base config from yaml file and command line arguments.
|
||||
|
||||
Args:
|
||||
args: Command line arguments.
|
||||
cfg: Base configuration.
|
||||
"""
|
||||
args_var = vars(args)
|
||||
for item in args_var:
|
||||
cfg[item] = args_var[item]
|
||||
return cfg
|
||||
|
||||
|
||||
def get_config():
|
||||
"""
|
||||
Get Config according to the yaml file and cli arguments.
|
||||
"""
|
||||
parser = argparse.ArgumentParser(description="default name", add_help=False)
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
parser.add_argument("--config_path", type=str, default=os.path.join(current_dir, "../../default_config.yaml"),
|
||||
help="Config file path")
|
||||
path_args, _ = parser.parse_known_args()
|
||||
default, helper, choices = parse_yaml(path_args.config_path)
|
||||
pprint(default)
|
||||
args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path)
|
||||
final_config = merge(args, default)
|
||||
return Config(final_config)
|
||||
|
||||
config = get_config()
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
"""Device adapter for ModelArts"""
|
||||
|
||||
from .config import config
|
||||
|
||||
if config.enable_modelarts:
|
||||
from .moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
|
||||
else:
|
||||
from .local_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
|
||||
|
||||
__all__ = [
|
||||
"get_device_id", "get_device_num", "get_rank_id", "get_job_id"
|
||||
]
|
||||
|
|
@ -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.
|
||||
# ============================================================================
|
||||
|
||||
"""Local adapter"""
|
||||
|
||||
import os
|
||||
|
||||
def get_device_id():
|
||||
device_id = os.getenv('DEVICE_ID', '0')
|
||||
return int(device_id)
|
||||
|
||||
|
||||
def get_device_num():
|
||||
device_num = os.getenv('RANK_SIZE', '1')
|
||||
return int(device_num)
|
||||
|
||||
|
||||
def get_rank_id():
|
||||
global_rank_id = os.getenv('RANK_ID', '0')
|
||||
return int(global_rank_id)
|
||||
|
||||
|
||||
def get_job_id():
|
||||
return "Local Job"
|
||||
|
|
@ -0,0 +1,122 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
"""Moxing adapter for ModelArts"""
|
||||
|
||||
import os
|
||||
import functools
|
||||
from mindspore import context
|
||||
from mindspore.profiler import Profiler
|
||||
from .config import config
|
||||
|
||||
_global_sync_count = 0
|
||||
|
||||
def get_device_id():
|
||||
device_id = os.getenv('DEVICE_ID', '0')
|
||||
return int(device_id)
|
||||
|
||||
|
||||
def get_device_num():
|
||||
device_num = os.getenv('RANK_SIZE', '1')
|
||||
return int(device_num)
|
||||
|
||||
|
||||
def get_rank_id():
|
||||
global_rank_id = os.getenv('RANK_ID', '0')
|
||||
return int(global_rank_id)
|
||||
|
||||
|
||||
def get_job_id():
|
||||
job_id = os.getenv('JOB_ID')
|
||||
job_id = job_id if job_id != "" else "default"
|
||||
return job_id
|
||||
|
||||
def sync_data(from_path, to_path):
|
||||
"""
|
||||
Download data from remote obs to local directory if the first url is remote url and the second one is local path
|
||||
Upload data from local directory to remote obs in contrast.
|
||||
"""
|
||||
import moxing as mox
|
||||
import time
|
||||
global _global_sync_count
|
||||
sync_lock = "/tmp/copy_sync.lock" + str(_global_sync_count)
|
||||
_global_sync_count += 1
|
||||
|
||||
# Each server contains 8 devices as most.
|
||||
if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
|
||||
print("from path: ", from_path)
|
||||
print("to path: ", to_path)
|
||||
mox.file.copy_parallel(from_path, to_path)
|
||||
print("===finish data synchronization===")
|
||||
try:
|
||||
os.mknod(sync_lock)
|
||||
except IOError:
|
||||
pass
|
||||
print("===save flag===")
|
||||
|
||||
while True:
|
||||
if os.path.exists(sync_lock):
|
||||
break
|
||||
time.sleep(1)
|
||||
|
||||
print("Finish sync data from {} to {}.".format(from_path, to_path))
|
||||
|
||||
|
||||
def moxing_wrapper(pre_process=None, post_process=None):
|
||||
"""
|
||||
Moxing wrapper to download dataset and upload outputs.
|
||||
"""
|
||||
def wrapper(run_func):
|
||||
@functools.wraps(run_func)
|
||||
def wrapped_func(*args, **kwargs):
|
||||
# Download data from data_url
|
||||
if config.enable_modelarts:
|
||||
if config.data_url:
|
||||
sync_data(config.data_url, config.data_path)
|
||||
print("Dataset downloaded: ", os.listdir(config.data_path))
|
||||
if config.checkpoint_url:
|
||||
sync_data(config.checkpoint_url, config.load_path)
|
||||
print("Preload downloaded: ", os.listdir(config.load_path))
|
||||
if config.train_url:
|
||||
sync_data(config.train_url, config.output_path)
|
||||
print("Workspace downloaded: ", os.listdir(config.output_path))
|
||||
|
||||
context.set_context(save_graphs_path=os.path.join(config.output_path, str(get_rank_id())))
|
||||
config.device_num = get_device_num()
|
||||
config.device_id = get_device_id()
|
||||
if not os.path.exists(config.output_path):
|
||||
os.makedirs(config.output_path)
|
||||
|
||||
if pre_process:
|
||||
pre_process()
|
||||
|
||||
if config.enable_profiling:
|
||||
profiler = Profiler()
|
||||
|
||||
run_func(*args, **kwargs)
|
||||
|
||||
if config.enable_profiling:
|
||||
profiler.analyse()
|
||||
|
||||
# Upload data to train_url
|
||||
if config.enable_modelarts:
|
||||
if post_process:
|
||||
post_process()
|
||||
|
||||
if config.train_url:
|
||||
print("Start to copy output directory")
|
||||
sync_data(config.output_path, config.train_url)
|
||||
return wrapped_func
|
||||
return wrapper
|
||||
|
|
@ -20,7 +20,7 @@ from pycocotools.coco import COCO
|
|||
from pycocotools.cocoeval import COCOeval
|
||||
from pycocotools import mask as maskUtils
|
||||
|
||||
from src.config import config
|
||||
from model_utils.config import config
|
||||
|
||||
_init_value = np.array(0.0)
|
||||
summary_init = {
|
||||
|
|
|
|||
|
|
@ -15,10 +15,17 @@
|
|||
|
||||
"""train MaskRcnn and get checkpoint files."""
|
||||
|
||||
import os
|
||||
import time
|
||||
import argparse
|
||||
import ast
|
||||
import os
|
||||
import re
|
||||
|
||||
from src.model_utils.config import config
|
||||
from src.model_utils.moxing_adapter import moxing_wrapper
|
||||
from src.model_utils.device_adapter import get_device_id, get_device_num
|
||||
from src.maskrcnn.mask_rcnn_r50 import Mask_Rcnn_Resnet50
|
||||
from src.network_define import LossCallBack, WithLossCell, TrainOneStepCell, LossNet
|
||||
from src.dataset import data_to_mindrecord_byte_image, create_maskrcnn_dataset
|
||||
from src.lr_schedule import dynamic_lr
|
||||
|
||||
import mindspore.common.dtype as mstype
|
||||
from mindspore import context, Tensor
|
||||
|
|
@ -31,32 +38,78 @@ from mindspore.nn import Momentum
|
|||
from mindspore.common import set_seed
|
||||
from mindspore.communication.management import get_rank, get_group_size
|
||||
|
||||
from src.maskrcnn.mask_rcnn_r50 import Mask_Rcnn_Resnet50
|
||||
from src.network_define import LossCallBack, WithLossCell, TrainOneStepCell, LossNet
|
||||
from src.config import config
|
||||
from src.dataset import data_to_mindrecord_byte_image, create_maskrcnn_dataset
|
||||
from src.lr_schedule import dynamic_lr
|
||||
|
||||
set_seed(1)
|
||||
|
||||
parser = argparse.ArgumentParser(description="MaskRcnn training")
|
||||
parser.add_argument("--only_create_dataset", type=ast.literal_eval, default=False, help="If set it true, only create "
|
||||
"Mindrecord, default is false.")
|
||||
parser.add_argument("--run_distribute", type=ast.literal_eval, default=False, help="Run distribute, default is false.")
|
||||
parser.add_argument("--do_train", type=ast.literal_eval, default=True, help="Do train or not, default is true.")
|
||||
parser.add_argument("--do_eval", type=ast.literal_eval, default=False, help="Do eval or not, default is false.")
|
||||
parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
|
||||
parser.add_argument("--pre_trained", type=str, default="", help="Pretrain file path.")
|
||||
parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
|
||||
parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
|
||||
parser.add_argument("--rank_id", type=int, default=0, help="Rank id, default is 0.")
|
||||
args_opt = parser.parse_args()
|
||||
lss = [int(re.findall(r'[0-9]+', i)[0]) for i in config.feature_shapes]
|
||||
config.feature_shapes = [(lss[2*i], lss[2*i+1]) for i in range(int(len(lss)/2))]
|
||||
config.roi_layer = dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2)
|
||||
config.warmup_ratio = 1/3.0
|
||||
config.mask_shape = (28, 28)
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
|
||||
def modelarts_pre_process():
|
||||
def unzip(zip_file, save_dir):
|
||||
import zipfile
|
||||
s_time = time.time()
|
||||
if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)):
|
||||
zip_isexist = zipfile.is_zipfile(zip_file)
|
||||
if zip_isexist:
|
||||
fz = zipfile.ZipFile(zip_file, 'r')
|
||||
data_num = len(fz.namelist())
|
||||
print("Extract Start...")
|
||||
print("unzip file num: {}".format(data_num))
|
||||
data_print = int(data_num / 100) if data_num > 100 else 1
|
||||
i = 0
|
||||
for file in fz.namelist():
|
||||
if i % data_print == 0:
|
||||
print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True)
|
||||
i += 1
|
||||
fz.extract(file, save_dir)
|
||||
print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),\
|
||||
int(int(time.time() - s_time) % 60)))
|
||||
print("Extract Done")
|
||||
else:
|
||||
print("This is not zip.")
|
||||
else:
|
||||
print("Zip has been extracted.")
|
||||
|
||||
if __name__ == '__main__':
|
||||
if config.need_modelarts_dataset_unzip:
|
||||
zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip")
|
||||
save_dir_1 = os.path.join(config.data_path)
|
||||
|
||||
sync_lock = "/tmp/unzip_sync.lock"
|
||||
|
||||
# Each server contains 8 devices as most
|
||||
if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
|
||||
print("Zip file path: ", zip_file_1)
|
||||
print("Unzip file save dir: ", save_dir_1)
|
||||
unzip(zip_file_1, save_dir_1)
|
||||
print("===Finish extract data synchronization===")
|
||||
try:
|
||||
os.mknod(sync_lock)
|
||||
except IOError:
|
||||
pass
|
||||
|
||||
while True:
|
||||
if os.path.exists(sync_lock):
|
||||
break
|
||||
time.sleep(1)
|
||||
|
||||
print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1))
|
||||
print("#" * 200, os.listdir(save_dir_1))
|
||||
print("#" * 200, os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name)))
|
||||
|
||||
config.dataset_path = os.path.join(config.data_path, config.modelarts_dataset_unzip_name)
|
||||
config.pre_trained = os.path.join(config.dataset_path, config.ckpt_path)
|
||||
config.save_checkpoint_path = config.output_path
|
||||
config.mindrecord_dir = os.path.join(config.dataset_path, config.mindrecord_dir)
|
||||
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=get_device_id())
|
||||
|
||||
@moxing_wrapper(pre_process=modelarts_pre_process)
|
||||
def train_maskrcnn():
|
||||
print("Start train for maskrcnn!")
|
||||
if not args_opt.do_eval and args_opt.run_distribute:
|
||||
if not config.do_eval and config.run_distribute:
|
||||
init()
|
||||
rank = get_rank()
|
||||
device_num = get_group_size()
|
||||
|
|
@ -68,7 +121,7 @@ if __name__ == '__main__':
|
|||
|
||||
print("Start create dataset!")
|
||||
|
||||
# It will generate mindrecord file in args_opt.mindrecord_dir,
|
||||
# It will generate mindrecord file in config.mindrecord_dir,
|
||||
# and the file name is MaskRcnn.mindrecord0, 1, ... file_num.
|
||||
prefix = "MaskRcnn.mindrecord"
|
||||
mindrecord_dir = config.mindrecord_dir
|
||||
|
|
@ -76,13 +129,13 @@ if __name__ == '__main__':
|
|||
if rank == 0 and not os.path.exists(mindrecord_file):
|
||||
if not os.path.isdir(mindrecord_dir):
|
||||
os.makedirs(mindrecord_dir)
|
||||
if args_opt.dataset == "coco":
|
||||
if os.path.isdir(config.coco_root):
|
||||
if config.dataset == "coco":
|
||||
if os.path.isdir(config.data_path):
|
||||
print("Create Mindrecord.")
|
||||
data_to_mindrecord_byte_image("coco", True, prefix)
|
||||
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
|
||||
else:
|
||||
raise Exception("coco_root not exits.")
|
||||
raise Exception("data_path not exits.")
|
||||
else:
|
||||
if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
|
||||
print("Create Mindrecord.")
|
||||
|
|
@ -93,9 +146,8 @@ if __name__ == '__main__':
|
|||
while not os.path.exists(mindrecord_file+".db"):
|
||||
time.sleep(5)
|
||||
|
||||
if not args_opt.only_create_dataset:
|
||||
loss_scale = float(config.loss_scale)
|
||||
|
||||
if not config.only_create_dataset:
|
||||
# loss_scale = float(config.loss_scale)
|
||||
# When create MindDataset, using the fitst mindrecord file, such as MaskRcnn.mindrecord0.
|
||||
dataset = create_maskrcnn_dataset(mindrecord_file, batch_size=config.batch_size,
|
||||
device_num=device_num, rank_id=rank)
|
||||
|
|
@ -107,7 +159,7 @@ if __name__ == '__main__':
|
|||
net = Mask_Rcnn_Resnet50(config=config)
|
||||
net = net.set_train()
|
||||
|
||||
load_path = args_opt.pre_trained
|
||||
load_path = config.pre_trained
|
||||
if load_path != "":
|
||||
param_dict = load_checkpoint(load_path)
|
||||
if config.pretrain_epoch_size == 0:
|
||||
|
|
@ -123,7 +175,7 @@ if __name__ == '__main__':
|
|||
weight_decay=config.weight_decay, loss_scale=config.loss_scale)
|
||||
|
||||
net_with_loss = WithLossCell(net, loss)
|
||||
if args_opt.run_distribute:
|
||||
if config.run_distribute:
|
||||
net = TrainOneStepCell(net_with_loss, opt, sens=config.loss_scale, reduce_flag=True,
|
||||
mean=True, degree=device_num)
|
||||
else:
|
||||
|
|
@ -141,3 +193,7 @@ if __name__ == '__main__':
|
|||
|
||||
model = Model(net)
|
||||
model.train(config.epoch_size, dataset, callbacks=cb)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
train_maskrcnn()
|
||||
|
|
|
|||
|
|
@ -16,13 +16,8 @@
|
|||
|
||||
import os
|
||||
import pytest
|
||||
import numpy as np
|
||||
from model_zoo.official.cv.maskrcnn.src.maskrcnn.mask_rcnn_r50 import Mask_Rcnn_Resnet50
|
||||
from model_zoo.official.cv.maskrcnn.src.config import config
|
||||
from tests.st.model_zoo_tests import utils
|
||||
|
||||
from mindspore import Tensor, context, export
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
|
||||
@pytest.mark.level1
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
|
|
@ -32,24 +27,20 @@ def test_maskrcnn_export():
|
|||
"""
|
||||
export maskrcnn air.
|
||||
"""
|
||||
net = Mask_Rcnn_Resnet50(config=config)
|
||||
net.set_train(False)
|
||||
old_list = ["(config=config)", "(net, param_dict_new)"]
|
||||
new_list = ["(config=config\\n) '''", "(net, param_dict_new)\\n '''"]
|
||||
|
||||
bs = config.test_batch_size
|
||||
|
||||
img = Tensor(np.zeros([bs, 3, 768, 1280], np.float16))
|
||||
img_metas = Tensor(np.zeros([bs, 4], np.float16))
|
||||
gt_bboxes = Tensor(np.zeros([bs, 128, 4], np.float16))
|
||||
gt_labels = Tensor(np.zeros([bs, 128], np.int32))
|
||||
gt_num = Tensor(np.zeros([bs, 128], np.bool))
|
||||
gt_mask = Tensor(np.zeros([bs, 128], np.bool))
|
||||
|
||||
input_data = [img, img_metas, gt_bboxes, gt_labels, gt_num, gt_mask]
|
||||
export(net, *input_data, file_name="maskrcnn", file_format="AIR")
|
||||
file_name = "maskrcnn.air"
|
||||
assert os.path.exists(file_name)
|
||||
os.remove(file_name)
|
||||
cur_path = os.getcwd()
|
||||
model_path = "{}/../../../../model_zoo/official/cv".format(cur_path)
|
||||
model_name = "maskrcnn"
|
||||
utils.copy_files(model_path, cur_path, model_name)
|
||||
cur_model_path = os.path.join(cur_path, model_name)
|
||||
|
||||
utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "export.py"))
|
||||
# ckpt_path = os.path.join(utils.ckpt_root, "bgcf/bgcf_trained.ckpt")
|
||||
exec_export_shell = "cd {}; python export.py --config_path default_config.yaml".format(model_name)
|
||||
os.system(exec_export_shell)
|
||||
assert os.path.exists(os.path.join(cur_model_path, "{}.air".format(model_name)))
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_maskrcnn_export()
|
||||
|
|
|
|||
Loading…
Reference in New Issue