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GETTING_STARTED.md

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Getting Started

This page provides basic tutorials about the usage of mmdetection. For installation instructions, please see INSTALL.md.

Training

The following script will start training a mcan_small model on the VQA-v2 dataset:

$ python3 run.py --RUN='train' --MODEL='mcan_small' --DATASET='vqa'

  • --RUN={'train','val','test'} to set the mode to be executed.

  • --MODEL=str, e.g., to assign the model to be executed.

  • --DATASET={'vqa','gqa','clevr'} to choose the dataset to be executed.

All checkpoint files will be saved to:

ckpts/ckpt_<VERSION>/epoch<EPOCH_NUMBER>.pkl

and the training log file will be placed at:

results/log/log_run_<VERSION>.txt

To add：

  • --VERSION=str, e.g., --VERSION='v1' to assign a name for your this model.

  • --GPU=str, e.g., --GPU='2' to train the model on specified GPU device.

  • --SEED=int, e.g., --SEED=123 to use a fixed seed to initialize the model, which obtains exactly the same model. Unset it results in random seeds.

  • --NW=int, e.g., --NW=8 to accelerate I/O speed.

  • --SPLIT=str to set the training sets as you want. Setting --SPLIT='train' will trigger the evaluation script to run the validation score after every epoch automatically.

  • --RESUME=True to start training with saved checkpoint parameters. In this stage, you should assign the checkpoint version--CKPT_V=str and the resumed epoch number CKPT_E=int.

  • --MAX_EPOCH=int to stop training at a specified epoch number.

If you want to resume training from an existing checkpoint, you can use the following script:

$ python3 run.py --RUN='train' --MODEL='mcan_small' --DATASET='vqa' --CKPT_V=str --CKPT_E=int

where the args CKPT_V and CKPT_E must be specified, corresponding to the version and epoch number of the loaded model.

Multi-GPU Training and Gradient Accumulation

We recommend to use the GPU with at least 8 GB memory, but if you don't have such device, we provide two solutions to deal with it:

  • Multi-GPU Training:

    If you want to accelerate training or train the model on a device with limited GPU memory, you can use more than one GPUs:

    Add --GPU='0, 1, 2, 3...'

    The batch size on each GPU will be adjusted to BATCH_SIZE/#GPUs automatically.

  • Gradient Accumulation:

    If you only have one GPU less than 8GB, an alternative strategy is provided to use the gradient accumulation during training:

    Add --ACCU=n

    This makes the optimizer accumulate gradients forn small batches and update the model weights at once. It is worth noting that BATCH_SIZE must be divided by n to run this mode correctly.

Validation and Testing

Warning: The args --MODEL and --DATASET should be set to the same values as those in the training stage.

Validation on Local Machine

Offline evaluation on local machine only support the evaluations on the val split. If you want to evaluate the test split, please see [Evaluation on online server](#Evaluation on online server).

There are two ways to start:

(Recommend)

$ python3 run.py --RUN='val' --MODEL=str --DATASET='{vqa,gqa,clecr}' --CKPT_V=str --CKPT_E=int

or use the absolute path instead:

$ python3 run.py --RUN='val' --MODEL=str --DATASET='{vqa,gqa,clecr}' --CKPT_PATH=str
  • For VQA-v2, the results on val split

Testing on Online Server

All the evaluations on the test split of VQA-v2, GQA and CLEVR benchmarks can be achieved by using

$ python3 run.py --RUN='test' --MODEL=str --DATASET='{vqa,gqa,clecr}' --CKPT_V=str --CKPT_E=int

Result file are saved at: results/result_test/result_run_<CKPT_V>_<CKPT_E>.json

  • For VQA-v2, the result file is uploaded the VQA challenge website to evaluate the scores on test-dev or test-std split.

  • For GQA, the result file is uploaded to the GQA Challenge website to evaluate the scores on test or test-dev split.

  • For CLEVR, the result file can be evaluated via sending an email to the author Justin Johnson with attaching this file, and he will reply the scores via email too.