@ -16,7 +16,7 @@ To manage various configurations in a deep-learning experiment, we use a kind of
these configurations. This config system has a modular and inheritance design, and more details can be found in
these configurations. This config system has a modular and inheritance design, and more details can be found in
{external+mmengine:doc}`the tutorial in MMEngine <advanced_tutorials/config>`.
{external+mmengine:doc}`the tutorial in MMEngine <advanced_tutorials/config>`.
Usually, we use python files as config file. All configuration files are placed under the [`configs`](https://github.com/open-mmlab/mmpretrain/tree/main/configs) folder, and the directory structure is as follows:
Usually, we use python files as config file. All configuration files are placed under the [`configs`](https://github.com/open-mmlab/mmclassification/tree/pretrain/configs) folder, and the directory structure is as follows:
```text
```text
MMPretrain/
MMPretrain/
@ -38,20 +38,20 @@ MMPretrain/
If you wish to inspect the config file, you may run `python tools/misc/print_config.py /PATH/TO/CONFIG` to see the complete config.
If you wish to inspect the config file, you may run `python tools/misc/print_config.py /PATH/TO/CONFIG` to see the complete config.
This article mainly explains the structure of configuration files, and how to modify it based on the existing configuration files. We will take [ResNet50 config file](https://github.com/open-mmlab/mmpretrain/blob/main/configs/resnet/resnet50_8xb32_in1k.py) as an example and explain it line by line.
This article mainly explains the structure of configuration files, and how to modify it based on the existing configuration files. We will take [ResNet50 config file](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/resnet/resnet50_8xb32_in1k.py) as an example and explain it line by line.
## Config Structure
## Config Structure
There are four kinds of basic component files in the `configs/_base_` folders, namely：
There are four kinds of basic component files in the `configs/_base_` folders, namely：
We call all the config files in the `_base_` folder as _primitive_ config files. You can easily build your training config file by inheriting some primitive config files.
We call all the config files in the `_base_` folder as _primitive_ config files. You can easily build your training config file by inheriting some primitive config files.
For easy understanding, we use [ResNet50 config file](https://github.com/open-mmlab/mmpretrain/blob/main/configs/resnet/resnet50_8xb32_in1k.py) as an example and comment on each line.
For easy understanding, we use [ResNet50 config file](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/resnet/resnet50_8xb32_in1k.py) as an example and comment on each line.
```python
```python
_base_ = [ # This config file will inherit all config files in `_base_`.
_base_ = [ # This config file will inherit all config files in `_base_`.
@ -72,29 +72,30 @@ This primitive config file includes a dict variable `model`, which mainly includ
- For image classification tasks, it's usually `ImageClassifier` You can find more details in the [API documentation](mmpretrain.models.classifiers).
- For image classification tasks, it's usually `ImageClassifier` You can find more details in the [API documentation](mmpretrain.models.classifiers).
- For self-supervised leanrning, there are several `SelfSupervisors`, such as `MoCoV2`, `BEiT`, `MAE`, etc. You can find more details in the [API documentation](mmpretrain.models.selfsup).
- For self-supervised leanrning, there are several `SelfSupervisors`, such as `MoCoV2`, `BEiT`, `MAE`, etc. You can find more details in the [API documentation](mmpretrain.models.selfsup).
- For image retrieval tasks, it's usually `ImageToImageRetriever` You can find more details in the [API documentation](mmpretrain.models.retrievers).
- For image retrieval tasks, it's usually `ImageToImageRetriever` You can find more details in the [API documentation](mmpretrain.models.retrievers).
Usually, we use the `type` field to specify the class of the component and use other fields to pass
the initialization arguments of the class. The {external+mmengine:doc}`registry tutorial <advanced_tutorials/registry>` describes it in detail.
Here, we use the config fields of [`ImageClassifier`](mmpretrain.models.ImageClassifier) as an example to
describe the below initialization arguments:
- `backbone`: The settings of the backbone. The backbone is the main network to extract features of the inputs, like `ResNet`, `Swin Transformer`, `Vision Transformer` etc. All available backbones can be found in the [API documentation](mmpretrain.models.backbones).
- `backbone`: The settings of the backbone. The backbone is the main network to extract features of the inputs, like `ResNet`, `Swin Transformer`, `Vision Transformer` etc. All available backbones can be found in the [API documentation](mmpretrain.models.backbones).
- For self-supervised leanrning, some of the backbones are re-implemented, you can find more details in the [API documentation](mmpretrain.models.selfsup).
- For self-supervised leanrning, some of the backbones are re-implemented, you can find more details in the [API documentation](mmpretrain.models.selfsup).
- `neck`: The settings of the neck. The neck is the intermediate module to connect the backbone and the head, like `GlobalAveragePooling`. All available necks can be found in the [API documentation](mmpretrain.models.necks).
- `neck`: The settings of the neck. The neck is the intermediate module to connect the backbone and the head, like `GlobalAveragePooling`. All available necks can be found in the [API documentation](mmpretrain.models.necks).
- `head`: The settings of the task head. The head is the task-related component to do the final
- `head`: The settings of the task head. The head is the task-related component to do a specified task, like image classification or self-supervised training. All available heads can be found in the [API documentation](mmpretrain.models.heads).
classification. All available heads can be found in the [API documentation](mmpretrain.models.heads).
- `loss`: The loss function to optimize, like `CrossEntropyLoss`, `LabelSmoothLoss`, `PixelReconstructionLoss` and etc. All available losses can be found in the [API documentation](mmpretrain.models.losses).
- `loss`: The loss function to optimize, like `CrossEntropyLoss`, `LabelSmoothLoss`, `PixelReconstructionLoss` and etc. All available losses can be found in the [API documentation](mmpretrain.models.losses).
- `data_preprocessor`: The component before the model forwarding to preprocess the inputs. See the [documentation](mmpretrain.models.utils.data_preprocessor) for more details.
- `data_preprocessor`: The component before the model forwarding to preprocess the inputs. See the [documentation](mmpretrain.models.utils.data_preprocessor) for more details.
- `train_cfg`: The extra settings of the model during training. In MMCLS, we mainly use it to specify batch augmentation settings, like `Mixup` and `CutMix`. See the [documentation](mmpretrain.models.utils.batch_augments) for more details.
- `train_cfg`: The extra settings of `ImageClassifier` during training. In `ImageClassifier`, we mainly use it to specify batch augmentation settings, like `Mixup` and `CutMix`. See the [documentation](mmpretrain.models.utils.batch_augments) for more details.
```{note}
Following is the model primitive config of the ResNet50 config file in [`configs/_base_/models/resnet50.py`](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/_base_/models/resnet50.py):
Usually, we use the `type` field to specify the class of the component and use other fields to pass
the initialization arguments of the class. The {external+mmengine:doc}`registry tutorial <advanced_tutorials/registry>` describes it in detail.
```
Following is the model primitive config of the ResNet50 config file in [`configs/_base_/models/resnet50.py`](https://github.com/open-mmlab/mmpretrain/blob/main/configs/_base_/models/resnet50.py):
```python
```python
model = dict(
model = dict(
type='ImageClassifier', # The type of the main model (classifier).
type='ImageClassifier', # The type of the main model (here is for image classification task).
backbone=dict(
backbone=dict(
type='ResNet', # The type of the backbone module.
type='ResNet', # The type of the backbone module.
# All fields except `type` come from the __init__ method of class `ResNet`
# All fields except `type` come from the __init__ method of class `ResNet`
# and you can find them from https://mmpretrain.readthedocs.io/en/main/api/generated/mmpretrain.models.ResNet.html
# and you can find them from https://mmclassification.readthedocs.io/en/pretrain/api/generated/mmpretrain.models.backbones.ResNet.html
depth=50,
depth=50,
num_stages=4,
num_stages=4,
out_indices=(3, ),
out_indices=(3, ),
@ -104,7 +105,7 @@ model = dict(
head=dict(
head=dict(
type='LinearClsHead', # The type of the classification head module.
type='LinearClsHead', # The type of the classification head module.
# All fields except `type` come from the __init__ method of class `LinearClsHead`
# All fields except `type` come from the __init__ method of class `LinearClsHead`
# and you can find them from https://mmpretrain.readthedocs.io/en/main/api/generated/mmpretrain.models.LinearClsHead.html
# and you can find them from https://mmclassification.readthedocs.io/en/pretrain/api/generated/mmpretrain.models.heads.LinearClsHead.html
@ -126,7 +127,7 @@ This primitive config file includes information to construct the dataloader and
- `type`: The type of the dataset, we support `CustomDataset`, `ImageNet` and many other datasets, refer to [documentation](mmpretrain.datasets).
- `type`: The type of the dataset, we support `CustomDataset`, `ImageNet` and many other datasets, refer to [documentation](mmpretrain.datasets).
- `pipeline`: The data transform pipeline. You can find how to design a pipeline in [this tutorial](https://mmpretrain.readthedocs.io/en/1.x/tutorials/data_pipeline.html).
- `pipeline`: The data transform pipeline. You can find how to design a pipeline in [this tutorial](https://mmpretrain.readthedocs.io/en/1.x/tutorials/data_pipeline.html).
Following is the data primitive config of the ResNet50 config in [`configs/_base_/datasets/imagenet_bs32.py`](https://github.com/open-mmlab/mmpretrain/blob/main/configs/_base_/datasets/imagenet_bs32.py)：
Following is the data primitive config of the ResNet50 config in [`configs/_base_/datasets/imagenet_bs32.py`](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/_base_/datasets/imagenet_bs32.py)：
```python
```python
dataset_type = 'ImageNet'
dataset_type = 'ImageNet'
@ -204,7 +205,7 @@ test loops:
- `param_scheduler`: Optimizer parameters policy. You can use it to specify learning rate and momentum curves during training. See the {external+mmengine:doc}`documentation <tutorials/param_scheduler>` in MMEngine for more details.
- `param_scheduler`: Optimizer parameters policy. You can use it to specify learning rate and momentum curves during training. See the {external+mmengine:doc}`documentation <tutorials/param_scheduler>` in MMEngine for more details.
- `train_cfg | val_cfg | test_cfg`: The settings of the training, validation and test loops, refer to the relevant {external+mmengine:doc}`MMEngine documentation <design/runner>`.
- `train_cfg | val_cfg | test_cfg`: The settings of the training, validation and test loops, refer to the relevant {external+mmengine:doc}`MMEngine documentation <design/runner>`.
Following is the schedule primitive config of the ResNet50 config in [`configs/_base_/datasets/imagenet_bs32.py`](https://github.com/open-mmlab/mmpretrain/blob/main/configs/_base_/datasets/imagenet_bs32.py)：
Following is the schedule primitive config of the ResNet50 config in [`configs/_base_/datasets/imagenet_bs32.py`](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/_base_/datasets/imagenet_bs32.py)：
This part mainly includes saving the checkpoint strategy, log configuration, training parameters, breakpoint weight path, working directory, etc.
This part mainly includes saving the checkpoint strategy, log configuration, training parameters, breakpoint weight path, working directory, etc.
Here is the runtime primitive config file ['configs/_base_/default_runtime.py'](https://github.com/open-mmlab/mmpretrain/blob/main/configs/_base_/default_runtime.py) file used by almost all configs:
Here is the runtime primitive config file ['configs/_base_/default_runtime.py'](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/_base_/default_runtime.py) file used by almost all configs:
Sometimes, you may refer to some fields in the `_base_` config, to avoid duplication of definitions. You can refer to {external+mmengine:doc}`MMEngine <advanced_tutorials/config>` for some more instructions.
Sometimes, you may refer to some fields in the `_base_` config, to avoid duplication of definitions. You can refer to {external+mmengine:doc}`MMEngine <advanced_tutorials/config>` for some more instructions.
The following is an example of using auto augment in the training data preprocessing pipeline, refer to [`configs/resnest/resnest50_32xb64_in1k.py`](https://github.com/open-mmlab/mmpretrain/blob/main/configs/resnest/resnest50_32xb64_in1k.py). When defining `train_pipeline`, just add the definition file name of auto augment to `_base_`, and then use `_base_.auto_increasing_policies` to reference the variables in the primitive config:
The following is an example of using auto augment in the training data preprocessing pipeline, refer to [`configs/resnest/resnest50_32xb64_in1k.py`](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/resnest/resnest50_32xb64_in1k.py). When defining `train_pipeline`, just add the definition file name of auto augment to `_base_`, and then use `_base_.auto_increasing_policies` to reference the variables in the primitive config:
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