Tutorial 7: Useful tools¶

We provide lots of useful tools under tools/ directory.

The structure of this guide is as follows:

Tutorial 7: Useful tools

Get the FLOPs and params¶

We provide a script adapted from flops-counter.pytorch to compute the FLOPs and params of a given model.

python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]

For example,

python tools/analysis_tools/get_flops.py configs/resotorer/srresnet.py --shape 40 40

You will get the result like this.

==============================
Input shape: (3, 40, 40)
Flops: 4.07 GMac
Params: 1.52 M
==============================

Note: This tool is still experimental and we do not guarantee that the number is correct. 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, 250, 250). (2) Some operators are not counted in FLOPs like GN and custom operators. You can add support for new operators by modifying mmcv/cnn/utils/flops_counter.py.

Publish a model¶

Before you upload a model to AWS, you may want to

convert model weights to CPU tensors
delete the optimizer states and
compute the hash of the checkpoint file and append time and the hash id to the filename.

python tools/model_converters/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}

E.g.,

python tools/model_converters/publish_model.py work_dirs/stylegan2/latest.pth stylegan2_c2_8xb4_ffhq-1024x1024.pth

The final output filename will be stylegan2_c2_8xb4_ffhq-1024x1024_{time}-{hash id}.pth.

Print full config¶

MMGeneration incorporates config mechanism to set parameters used for training and testing models. With our config mechanism, users can easily conduct extensive experiments without hard coding. 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.

An Example:

python tools/misc/print_config.py configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py