参考 在ubuntu 18.04(GeForce GTX 760 4GB显存)编译/测试MaskTextSpotter(CUDA-10.1) 建立能运行的测试环境。
由于测试集使用的是 icdar2013 ,因此，务必保证已经可以在 icdar2013 数据集中进行测试。
接下来就是进行数据训练：
1. 修改训练脚本，默认情况下，训练脚本中使用了 8 张卡进行训练，我们只有一张卡，因此要调整训练参数
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$ cd MaskTextSpotter $ export ROOT_PATH=`pwd` $ sed -i 's/nproc_per_node=8/nproc_per_node=1/g' train.sh |
2. 下载训练集 MaskTextSpotter 默认使用的是 SynthText 数据集进行训练，需要先下载这个数据集，大约 40GB
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$ mkdir datasets $ cd datasets $ sudo apt-get install aria2 $ aria2c -c -j16 -s16 -x16 --follow-torrent=mem -o 'hyperai.torrent' 'https://hyper.ai/tracker/download?torrent=7783' # 也可下载种子文件 wget https://www.mobibrw.com/wp-content/uploads/2019/11/SynthText.zip |
3. 解压缩 SynthText 数据集到指定目录
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$ mkdir synthtext $ unzip SynthText/data/SynthText.zip -d synthtext # 目录改名 $ mv synthtext/SynthText synthtext/train_images |
4. 下载转换后的 SynthText 数据集索引文件，上面解压缩出来的索引是 .mat 扩展名的文件，我们需要转换成 MaskTextSpotter 需要的数据索引文件，作者提供了一份已经转换好的文件，我们直接下载并使用这个文件即可,这个文件大概要 1.6GB 的样子。
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$ aria2 -c https://1drv.ms/u/s!ArsnjfK83FbXgb5vgOOVPYywgCWuQw?e=UPuNTa # 解压缩到指定目录 $ tar -xvf SynthText_GT_E2E.tar.gz -C synthtext # 目录改名 $ mv synthtext/SynthText_GT_E2E synthtext/train_gts |
5. 生成训练文件 train_list.txt
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import os path = 'train_images' train_list = 'train_list.txt' tf = open(train_list, 'w') for root, dirs, files in os.walk(path): files = [f for f in files if not f[0] == '.'] dirs[:] = [d for d in dirs if not d[0] == '.'] # use files and dirs for file_name in files: fn = os.path.join(root, file_name) fn = fn.replace('./', '') fn = fn.replace(path + '/', '') ext = os.path.splitext(fn)[1] if '.jpg' == ext : tf.write(fn + '\n') tf.close() |
执行脚本，生成文件
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$ cd synthtext $ python gen_train.py |
执行测试
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$ cd $ROOT_PATH # 减少一次性加载图片数量，解决“OSError: [Errno 24] Too many open files” # 参数设置为 0 代表从主进程加载图片资源 $ sed -i "s/NUM_WORKERS: 4/NUM_WORKERS: 0/g" configs/pretrain.yaml # 调整训练参数，对于单个GPU来说，默认参数太大了，会导致GPU内存不足 # 解决 “RuntimeError: CUDA out of memory.” $ sed -i "s/IMS_PER_BATCH: 8/IMS_PER_BATCH: 1/g" configs/pretrain.yaml # 修正错误 “AttributeError: module 'torch' has no attribute 'bool'” # 从Pytorch 1.2开始，torch.uint8被修改为torch.bool,如果是低于 Pytorch 1.2的版本 # 需要修改为torch.uint8 $ sed -i "s/torch.bool/torch.uint8/g" maskrcnn_benchmark/modeling/rpn/inference.py $ sed -i "s/torch.bool/torch.uint8/g" maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py # 修改SOLVER设置上的GPU相关参数 # https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14 # 官方参考建议单个GPU的学习速率是0.0025但是实际运行中会报错，调整为0.0015可以正常运行 $ sed -i "s/BASE_LR: 0.01/BASE_LR: 0.0015/g" configs/pretrain.yaml # 4GB 显存设置为 8 ，8GB显存可以设置为64/128 $ sed -i "s/MASK_BATCH_SIZE_PER_IM: 512/MASK_BATCH_SIZE_PER_IM: 8/g" configs/pretrain.yaml # 目前在RTX 2070 Super 8GB显存版本上测试来看，使用 # “WEIGHT: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl” # 的配置情况下，BASE_LR可以设置为 0.0025 , MASK_BATCH_SIZE_PER_IM 可以设置为 128 # 进入运行环境 $ source activate MaskTextSpotter $ bash train.sh |
注意，我们在 configs/pretrain.yaml 加载的权重文件是 "WEIGHT: "./outputs/finetune/model_finetune.pth" ,这个权重文件是从 SynthText 训练得来的，那么这个"model_finetune.pth"是怎么生成的呢？
作者没有详细介绍，我们从 masktextspotter.caffe2 项目的配置文件中可以知道，这个文件其实是从 " WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl" 开始生成的。这个文件也可以从本站下载 R-50.pkl
R-50.pkl: converted copy of MSRA’s original ResNet-50 model
具体配置文件内容参考如下：
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MODEL: TYPE: generalized_rcnn CONV_BODY: FPN.add_fpn_ResNet50_conv5_body NUM_CLASSES: 2 FASTER_RCNN: True MASK_ON: True NAME: shrink++ NUM_GPUS: 1 SOLVER: WEIGHT_DECAY: 0.0001 LR_POLICY: steps_with_decay BASE_LR: 0.005 #synth GAMMA: 0.1 MAX_ITER: 200000 STEPS: [0, 120000] FPN: FPN_ON: True MULTILEVEL_ROIS: True MULTILEVEL_RPN: True USE_DEFORMABLE: False RPN_ASPECT_RATIOS: (0.5, 1, 2) FAST_RCNN: ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head ROI_XFORM_METHOD: RoIAlign ROI_XFORM_RESOLUTION: 7 ROI_XFORM_SAMPLING_RATIO: 2 MRCNN: ROI_MASK_HEAD: text_mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs RESOLUTION: 28 # (output mask resolution) default 14 RESOLUTION_H: 32 RESOLUTION_W: 128 ROI_XFORM_METHOD: RoIAlign ROI_XFORM_RESOLUTION: 14 # default 7 ROI_XFORM_RESOLUTION_H: 16 ROI_XFORM_RESOLUTION_W: 64 ROI_XFORM_SAMPLING_RATIO: 2 # default 0 DILATION: 1 # default 2 CONV_INIT: MSRAFill # default GaussianFill IS_E2E: True MASK_BATCH_SIZE_PER_IM: 16 WEIGHT_LOSS_MASK: 1.0 WEIGHT_LOSS_CHAR_BOX: 1.0 WEIGHT_WH: True ## default is false TRAIN: BATCH_SIZE_PER_IM: 512 RPN_PRE_NMS_TOP_N: 2000 # Per FPN level AUTO_RESUME: True SNAPSHOT_ITERS: 10000 ##################### pre-train on synth ########################## WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl DATASETS: ('synth_train', ) SCALES: (800,) MAX_SIZE: 1333 IMS_PER_BATCH: 2 ASPECT_GROUPING: True MIX_TRAIN: False USE_CHARANNS: [True] ###################### Fine tune ################################# # MIX_TRAIN: True # WEIGHTS: ./train/synth_train/pretrain_model/model_iter159999.pkl # DATASETS: ('totaltext_train', 'scut-eng-char_train', 'synth_train', 'icdar2013_train', 'icdar2015_train') # USE_CHARANNS: [False, True, True, True, False] # # the ratios of synth, icdar2013, icdar2015 is 2:1:1, defaultly # # MIX_RATIOS: [0.125, 0.125, 0.5, 0.125, 0.125] # MIX_RATIOS: [1.0 / 6, 1.0 / 6, 1.0 / 3, 1.0 / 6, 1.0 / 6] # SCALES: (600, 800, 1000) # MAX_SIZE: 1333 # # # SCALES: (800,) # # # MAX_SIZE: 1333 # IMS_PER_BATCH: 1 # ASPECT_GROUPING: False IMAGE: aug: False saturation_prob: 0.5 saturation_lower: 0.5 saturation_upper: 1.5 hue_prob: 0.5 hue_delta: 18 lighting_noise_prob: 0.5 contrast_prob: 0.5 contrast_lower: 0.5 contrast_upper: 1.5 brightness_prob: 0.5 brightness_delta: 32 rotate_prob: 0.5 rotate_delta: 15 TEST: OUTPUT_POLYGON: False # only set to True for totaltext WEIGHTS: ./train/shrink++_finetune/model_iter79999.pkl DATASETS: ('icdar2015_test',) SCALES: (1000,) MAX_SIZE: 3333 NMS: 0.5 RPN_PRE_NMS_TOP_N: 1000 # Per FPN level RPN_POST_NMS_TOP_N: 1000 VIS: False SCORE_THRESH: 0.2 BBOX_AUG: ENABLED: False SCORE_HEUR: UNION # AVG NOTE: cannot use AVG for e2e model COORD_HEUR: UNION # AVG NOTE: cannot use AVG for e2e model H_FLIP: False SCALES: (800,) MAX_SIZE: 2000 SCALE_H_FLIP: False SCALE_SIZE_DEP: False AREA_TH_LO: 2500 # 50^2 AREA_TH_HI: 32400 # 180^2 ASPECT_RATIOS: () ASPECT_RATIO_H_FLIP: False MASK_AUG: ENABLED: False HEUR: SOFT_AVG H_FLIP: False SCALES: (1600,) MAX_SIZE: 3333 SCALE_H_FLIP: False SCALE_SIZE_DEP: False AREA_TH: 32400 # 180^2 ASPECT_RATIOS: () ASPECT_RATIO_H_FLIP: False BBOX_VOTE: ENABLED: True VOTE_TH: 0.9 SOFT_NMS: ENABLED: False OUTPUT_DIR: . |
其实我们直接删除或者注释掉权重文件加载部分也是可以的。只是，如果想要复现原作者的测试成果的话，我们最好使用相同的配置信息。
对于 4GB 显存的机器来说，由于显存非常有限，导致非常可能在运行的途中出现 "RuntimeError: CUDA out of memory." ,目前测试来看，继续执行命令即可。
训练结果存储在 outputs/pretrain 目录下，训练结果会在训练到一定阶段之后，存储到这个目录下。
如果出现类似如下错误，请适当减少学习速率 BASE_LR
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Traceback (most recent call last): File "tools/train_net.py", line 173, in <module> main() File "tools/train_net.py", line 166, in main model = train(cfg, args.local_rank, args.distributed) File "tools/train_net.py", line 76, in train cfg, File "~/MaskTextSpotter/maskrcnn_benchmark/engine/trainer.py", line 66, in do_train loss_dict = model(images, targets) File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 50, in forward proposals, proposal_losses = self.rpn(images, features, targets) File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/rpn.py", line 94, in forward return self._forward_train(anchors, objectness, rpn_box_regression, targets) File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/rpn.py", line 110, in _forward_train anchors, objectness, rpn_box_regression, targets File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/inference.py", line 138, in forward sampled_boxes.append(self.forward_for_single_feature_map(a, o, b)) File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/inference.py", line 113, in forward_for_single_feature_map boxlist = remove_small_boxes(boxlist, self.min_size) File "~/MaskTextSpotter/maskrcnn_benchmark/structures/boxlist_ops.py", line 46, in remove_small_boxes (ws >= min_size) & (hs >= min_size) RuntimeError: copy_if failed to synchronize: cudaErrorAssert: device-side assert triggered |
参考链接
- SynthText 自然场景图像数据集
- 「CV」 SynthText 数据集
- Pytorch 1.2.0 来了！
- tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml
- RuntimeError: copy_if failed to synchronize: device-side assert triggered #658
- pytorch报RuntimeError: copy_if failed to synchronize: device-side assert triggered
- masktextspotter.caffe2/configs/text/mask_textspotter.yaml
- Caffe2 - Detectron 初步使用
你好，我是华科的学生最近也是在研究这个masktextspotter，想请教您一些问题方便和您用邮箱交流吗。
jokiwhite@163.com
如果可以，十分感谢。
我不是学生，只是来研究一下，自己训练了一下，能正常使用即可了。
我在训练的时候有一些问题。想请教您一下。方便给您发邮件吗
40GB的是要通过BT文件下载的，文章提供了这个BT文件的下载地址的。