Useful tools

We provide lots of useful tools under tools/ directory.

Get the FLOPs and params (experimental)

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

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

For example,

python 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 into 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 (1) convert model weights to CPU tensors, (2) delete the optimizer states and (3) compute the hash of the checkpoint file and append the hash id to the filename.

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

E.g.,

python tools/publish_model.py work_dirs/example_exp/latest.pth example_model_20200202.pth

The final output filename will be example_model_20200202-{hash id}.pth.

Convert to ONNX (experimental)

We provide a script to convert model to ONNX format. The converted model could be visualized by tools like Netron. Besides, we also support comparing the output results between Pytorch and ONNX model.

python tools/pytorch2onnx.py
    ${CFG_PATH} \
    ${CHECKPOINT_PATH} \
    ${MODEL_TYPE} \
    ${IMAGE_PATH} \
    --trimap-path ${TRIMAP_PATH} \
    --output-file ${OUTPUT_ONNX} \
    --show \
    --verify \
    --dynamic-export

Description of arguments:

• config : The path of a model config file.

• checkpoint : The path of a model checkpoint file.

• model_type :The model type of the config file, options: inpainting, mattor, restorer, synthesizer.

• image_path : path to input image file.

• --trimap-path : path to input trimap file, used in mattor model.

• --output-file: The path of output ONNX model. If not specified, it will be set to tmp.onnx.

• --opset-version : ONNX opset version, default to 11.

• --show: Determines whether to print the architecture of the exported model. If not specified, it will be set to False.

• --verify: Determines whether to verify the correctness of an exported model. If not specified, it will be set to False.

• --dynamic-export: Determines whether to export ONNX model with dynamic input and output shapes. If not specified, it will be set to False.

Note: This tool is still experimental. Some customized operators are not supported for now. And we only support mattor and restorer for now.

List of supported models exportable to ONNX

The table below lists the models that are guaranteed to be exportable to ONNX and runnable in ONNX Runtime.

Model	Config	Dynamic Shape	Batch Inference	Note
ESRGAN	esrgan_x4c64b23g32_g1_400k_div2k.py	Y	Y
ESRGAN	esrgan_psnr_x4c64b23g32_g1_1000k_div2k.py	Y	Y
SRCNN	srcnn_x4k915_g1_1000k_div2k.py	Y	Y
DIM	dim_stage3_v16_pln_1x1_1000k_comp1k.py	Y	Y
GCA	gca_r34_4x10_200k_comp1k.py	N	Y
IndexNet	indexnet_mobv2_1x16_78k_comp1k.py	Y	Y

Notes:

• All models above are tested with Pytorch==1.6.0 and onnxruntime==1.5.1

• If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, please try to solve them by yourself.

• Because this feature is experimental and may change fast, please always try with the latest mmcv and mmedit.

Convert ONNX to TensorRT (experimental)

We also provide a script to convert ONNX model to TensorRT format. Besides, we support comparing the output results between ONNX and TensorRT model.

python tools/onnx2tensorrt.py
    ${CFG_PATH} \
    ${MODEL_TYPE} \
    ${IMAGE_PATH} \
    ${INPUT_ONNX} \
    --trt-file ${OUT_TENSORRT} \
    --max-shape INT INT INT INT \
    --min-shape INT INT INT INT \
    --workspace-size INT \
    --fp16 \
    --show \
    --verify \
    --verbose

Description of arguments:

• config : The path of a model config file.

• model_type :The model type of the config file, options: inpainting, mattor, restorer, synthesizer.

• img_path : The path to input image file.

• onnx_file : The path to input ONNX file.

• --trt-file : The path of output TensorRT model. If not specified, it will be set to tmp.trt.

• --max-shape : Maximum shape of model input.

• --min-shape : Minimum shape of model input.

• --workspace-size: Max workspace size in GiB. If not specified, it will be set to 1 GiB.

• --fp16: Determines whether to export TensorRT with fp16 mode. If not specified, it will be set to False.

• --show: Determines whether to show the output of ONNX and TensorRT. If not specified, it will be set to False.

• --verify: Determines whether to verify the correctness of an exported model. If not specified, it will be set to False.

• --verbose: Determines whether to verbose logging messages while creating TensorRT engine. If not specified, it will be set to False.

Note: This tool is still experimental. Some customized operators are not supported for now. We only support restorer for now. While generating ONNX file of SRCNN, replace ‘bicubic’ with ‘bilinear’ in SCRNN model here. For TensorRT does not support bicubic interpolation by now and final performance will be weaken by about 4%.

List of supported models exportable to TensorRT

The table below lists the models that are guaranteed to be exportable to TensorRT engine and runnable in TensorRT.

Model	Config	Dynamic Shape	Batch Inference	Note
ESRGAN	esrgan_x4c64b23g32_g1_400k_div2k.py	Y	Y
ESRGAN	esrgan_psnr_x4c64b23g32_g1_1000k_div2k.py	Y	Y
SRCNN	srcnn_x4k915_g1_1000k_div2k.py	Y	Y	'bicubic' upsampling must be replaced with 'bilinear'

Notes:

• All models above are tested with Pytorch==1.8.1, onnxruntime==1.7.0 and tensorrt==7.2.3.4

• If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, please try to solve them by yourself.

• Because this feature is experimental and may change fast, please always try with the latest mmcv and mmedit.

Evaluate ONNX and TensorRT Models (experimental)

We provide methods to evaluate TensorRT and ONNX models in tools/deploy_test.py.

Prerequisite

To evaluate ONNX and TensorRT models, onnx, onnxruntime and TensorRT should be installed first. Install mmcv-full with ONNXRuntime custom ops and TensorRT plugins follow ONNXRuntime in mmcv and TensorRT plugin in mmcv.

Usage

python tools/deploy_test.py \
    ${CONFIG_FILE} \
    ${MODEL_PATH} \
    ${BACKEND} \
    --out ${OUTPUT_FILE} \
    --save-path ${SAVE_PATH} \
    ----cfg-options ${CFG_OPTIONS} \

Description of all arguments

• config: The path of a model config file.

• model: The path of a TensorRT or an ONNX model file.

• backend: The backend for testing, choose tensorrt or onnxruntime.

• --out: The path of output result file in pickle format.

• --save-path: The path to store images and if not given, it will not save image.

• --cfg-options: Override some settings in the used config file, the key-value pair in xxx=yyy format will be merged into config file.

Results and Models

Model	Config	Dataset	Metric	PyTorch	ONNX Runtime	TensorRT FP32	TensorRT FP16
ESRGAN	esrgan_x4c64b23g32_g1_400k_div2k.py	Set5	PSNR	28.2700	28.2619	28.2619	28.2616
			SSIM	0.7778	0.7784	0.7784	0.7783
		Set14	PSNR	24.6328	24.6290	24.6290	24.6274
			SSIM	0.6491	0.6494	0.6494	0.6494
		DIV2K	PSNR	26.6531	26.6532	26.6532	26.6532
			SSIM	0.7340	0.7340	0.7340	0.7340
ESRGAN	esrgan_psnr_x4c64b23g32_g1_1000k_div2k.py	Set5	PSNR	30.6428	30.6307	30.6307	30.6305
			SSIM	0.8559	0.8565	0.8565	0.8566
		Set14	PSNR	27.0543	27.0422	27.0422	27.0411
			SSIM	0.7447	0.7450	0.7450	0.7449
		DIV2K	PSNR	29.3354	29.3354	29.3354	29.3339
			SSIM	0.8263	0.8263	0.8263	0.8263
SRCNN	srcnn_x4k915_g1_1000k_div2k.py	Set5	PSNR	28.4316	28.4120	27.2144	27.2127
			SSIM	0.8099	0.8106	0.7782	0.7781
		Set14	PSNR	25.6486	25.6367	24.8613	24.8599
			SSIM	0.7014	0.7015	0.6674	0.6673
		DIV2K	PSNR	27.7460	27.7460	26.9891	26.9862
			SSIM	0.7854	0.78543	0.7605	0.7604

Notes:

• All ONNX and TensorRT models are evaluated with dynamic shape on the datasets and images are preprocessed according to the original config file.

• This tool is still experimental, and we only support restorer for now.