You may well use the result for simple comparisons, but double check it before you adopt it in technical reports or papers.
(1) FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 340, 256) for 2D recognizer, (1, 3, 32, 340, 256) for 3D recognizer.
(2) Some custom operators are not counted into FLOPs. You can add support for new operators by modifying [`mmaction/utils/flops_counter.py`](/mmaction/utils/file_client.py).
(2) Some custom operators are not counted into FLOPs. You can add support for new operators by modifying [`mmaction/utils/flops_counter.py`](../mmaction/utils/file_client.py).
### Publish a model
...
...
@@ -543,9 +543,9 @@ There are two ways to work with custom datasets.
- online conversion
You can write a new Dataset class inherited from [BaseDataset](/mmaction/datasets/base.py), and overwrite two methods
You can write a new Dataset class inherited from [BaseDataset](../mmaction/datasets/base.py), and overwrite two methods
`load_annotations(self)` and `evaluate(self, results, metrics, logger)`,
like [RawframeDataset](/mmaction/datasets/rawframe_dataset.py), [VideoDataset](/mmaction/datasets/video_dataset.py) or [ActivityNetDataset](/mmaction/datasets/activitynet_dataset.py).
like [RawframeDataset](../mmaction/datasets/rawframe_dataset.py), [VideoDataset](../mmaction/datasets/video_dataset.py) or [ActivityNetDataset](../mmaction/datasets/activitynet_dataset.py).
- offline conversion
...
...
@@ -554,7 +554,7 @@ There are two ways to work with custom datasets.
### Customize optimizer
An example of customized optimizer is [CopyOfSGD](/mmaction/core/optimizer/copy_of_sgd.py).
An example of customized optimizer is [CopyOfSGD](../mmaction/core/optimizer/copy_of_sgd.py).
More generally, a customized optimizer could be defined as following.
In `mmaction/core/optimizer/my_optimizer.py`:
...
...
@@ -580,7 +580,7 @@ Especially, If you want to construct a optimizer based on a specified model and
You can write a new optimizer constructor inherit from [DefaultOptimizerConstructor](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py)
and overwrite the `add_params(self, params, module)` method.
An example of customized optimizer constructor is [TSMOptimizerConstructor](/mmaction/core/optimizer/tsm_optimizer_constructor.py).
An example of customized optimizer constructor is [TSMOptimizerConstructor](../mmaction/core/optimizer/tsm_optimizer_constructor.py).
More generally, a customized optimizer constructor could be defined as following.
In `mmaction/core/optimizer/my_optimizer_constructor.py`:
...
...
@@ -641,7 +641,7 @@ Here we show how to develop new components with an example of TSN.
3. Create a new file `mmaction/models/heads/tsn_head.py`.
You can write a new classification head inherit from [BaseHead](/mmaction/models/heads/base.py),
You can write a new classification head inherit from [BaseHead](../mmaction/models/heads/base.py),
and overwrite `init_weights(self)` and `forward(self, x)` method.
```python
...
...
@@ -687,5 +687,5 @@ Here we show how to develop new components with an example of TSN.
## Tutorials
Currently, we provide some tutorials for users to [finetune model](/docs/tutorials/finetune.md),
[add new dataset](/docs/tutorials/new_dataset.md), [add new modules](/docs/tutorials/new_modules.md).
Currently, we provide some tutorials for users to [finetune model](tutorials/finetune.md),
[add new dataset](tutorials/new_dataset.md), [add new modules](tutorials/new_modules.md).
@@ -84,9 +84,9 @@ There are two ways to work with custom datasets.
- online conversion
You can write a new Dataset class inherited from [BaseDataset](/mmaction/datasets/base.py), and overwrite three methods
You can write a new Dataset class inherited from [BaseDataset](../../mmaction/datasets/base.py), and overwrite three methods
`load_annotations(self)`, `evaluate(self, results, metrics, logger)` and `dump_results(self, results, out)`,
like [RawframeDataset](/mmaction/datasets/rawframe_dataset.py), [VideoDataset](/mmaction/datasets/video_dataset.py) or [ActivityNetDataset](/mmaction/datasets/activitynet_dataset.py).
like [RawframeDataset](../../mmaction/datasets/rawframe_dataset.py), [VideoDataset](../../mmaction/datasets/video_dataset.py) or [ActivityNetDataset](../../mmaction/datasets/activitynet_dataset.py).
An example of customized optimizer is [CopyOfSGD](/mmaction/core/optimizer/copy_of_sgd.py) is defined in `mmaction/core/optimizer/copy_of_sgd.py`.
An example of customized optimizer is [CopyOfSGD](../../mmaction/core/optimizer/copy_of_sgd.py) is defined in `mmaction/core/optimizer/copy_of_sgd.py`.
More generally, a customized optimizer could be defined as following.
Assume you want to add an optimizer named as `MyOptimizer`, which has arguments `a`, `b` and `c`.
...
...
@@ -54,7 +54,7 @@ The users can do those fine-grained parameter tuning through customizing optimiz
You can write a new optimizer constructor inherit from [DefaultOptimizerConstructor](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py)
and overwrite the `add_params(self, params, module)` method.
An example of customized optimizer constructor is [TSMOptimizerConstructor](/mmaction/core/optimizer/tsm_optimizer_constructor.py).
An example of customized optimizer constructor is [TSMOptimizerConstructor](../../mmaction/core/optimizer/tsm_optimizer_constructor.py).
More generally, a customized optimizer constructor could be defined as following.
In `mmaction/core/optimizer/my_optimizer_constructor.py`:
...
...
@@ -144,7 +144,7 @@ Here we show how to develop a new head with the example of TSNHead as the follow
1. Create a new file `mmaction/models/heads/tsn_head.py`.
You can write a new classification head inheriting from [BaseHead](/mmaction/models/heads/base.py),
You can write a new classification head inheriting from [BaseHead](../../mmaction/models/heads/base.py),
and overwrite `init_weights(self)` and `forward(self, x)` method.
