52 lines
2.5 KiB
Markdown
52 lines
2.5 KiB
Markdown
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# 调试技巧
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## 设置数据集的长度
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在调试代码的过程中，有时需要训练几个 epoch，例如调试验证过程或者权重的保存是否符合期望。然而如果数据集太大，需要花费较长时间才能训完一个 epoch，这种情况下可以设置数据集的长度。注意，只有继承自 [BaseDataset](mmengine.dataset.BaseDataset) 的 Dataset 才支持这个功能，`BaseDataset` 的用法可阅读 [数据集基类（BASEDATASET）](../advanced_tutorials/basedataset.md)。
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以 `MMClassification` 为例（参考[文档](https://mmclassification.readthedocs.io/zh_CN/dev-1.x/get_started.html#id2)安装 MMClassification）。
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启动训练命令
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```bash
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python tools/train.py configs/resnet/resnet18_8xb16_cifar10.py
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```
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下面是训练的部分日志，其中 `3125` 表示需要迭代的次数。
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```
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02/20 14:43:11 - mmengine - INFO - Epoch(train) [1][ 100/3125] lr: 1.0000e-01 eta: 6:12:01 time: 0.0149 data_time: 0.0003 memory: 214 loss: 2.0611
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02/20 14:43:13 - mmengine - INFO - Epoch(train) [1][ 200/3125] lr: 1.0000e-01 eta: 4:23:08 time: 0.0154 data_time: 0.0003 memory: 214 loss: 2.0963
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02/20 14:43:14 - mmengine - INFO - Epoch(train) [1][ 300/3125] lr: 1.0000e-01 eta: 3:46:27 time: 0.0146 data_time: 0.0003 memory: 214 loss: 1.9858
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```
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关掉训练，然后修改 [configs/_base_/datasets/cifar10_bs16.py](https://github.com/open-mmlab/mmclassification/blob/dev-1.x/configs/_base_/datasets/cifar10_bs16.py) 中的 `dataset` 字段，设置 `indices=5000`。
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```python
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train_dataloader = dict(
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batch_size=16,
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num_workers=2,
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dataset=dict(
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type=dataset_type,
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data_prefix='data/cifar10',
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test_mode=False,
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indices=5000, # 设置 indices=5000，表示每个 epoch 只迭代 5000 个样本
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pipeline=train_pipeline),
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sampler=dict(type='DefaultSampler', shuffle=True),
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)
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```
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重新启动训练
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```bash
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python tools/train.py configs/resnet/resnet18_8xb16_cifar10.py
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```
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可以看到，迭代次数变成了 `313`，相比原先，这样能够更快跑完一个 epoch。
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```
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02/20 14:44:58 - mmengine - INFO - Epoch(train) [1][100/313] lr: 1.0000e-01 eta: 0:31:09 time: 0.0154 data_time: 0.0004 memory: 214 loss: 2.1852
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02/20 14:44:59 - mmengine - INFO - Epoch(train) [1][200/313] lr: 1.0000e-01 eta: 0:23:18 time: 0.0143 data_time: 0.0002 memory: 214 loss: 2.0424
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02/20 14:45:01 - mmengine - INFO - Epoch(train) [1][300/313] lr: 1.0000e-01 eta: 0:20:39 time: 0.0143 data_time: 0.0003 memory: 214 loss: 1.814
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```
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