[Feature] Support model complexity computation #779

tonysy · 2022-11-30T17:26:07Z

Implementation of model complexity analysis.

Similar usage with mmcv.cnn.get_model_complexity_info

from mmengine.analysis import get_model_complexity_info

# return a dict of analysis results, including:
# ['flops', 'flops_str', 'activations', 'activations_str', 'params', 'params_str', 'out_table', 'out_str']
analysis_results = get_model_complexity_info(model, input_shape)

print(analysis_results['flops_str'])
print(analysis_results['params_str'])

Documentation
Unit testing
Cls Example
Det Example

zhouzaida · 2022-12-04T03:28:57Z

Lint failed.

mmengine/analysis/complexity_analysis.py

vansin · 2022-12-12T05:19:23Z

success in one stage detector , but failed in two stage detector in mmdet 3.x @ZwwWayne @RangiLyu

get_flops.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse

import numpy as np
import torch
from mmengine.config import Config, DictAction

from mmdet.registry import MODELS
from mmdet.utils import register_all_modules

try:
    from mmengine.analysis import get_model_complexity_info
except ImportError:
    raise ImportError('Please upgrade mmcv to >0.6.2')


def parse_args():
    parser = argparse.ArgumentParser(description='Train a detector')
    parser.add_argument('config', help='train config file path')
    parser.add_argument(
        '--shape',
        type=int,
        nargs='+',
        default=[1280, 800],
        help='input image size')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        'Note that the quotation marks are necessary and that no white space '
        'is allowed.')
    parser.add_argument(
        '--size-divisor',
        type=int,
        default=32,
        help='Pad the input image, the minimum size that is divisible '
        'by size_divisor, -1 means do not pad the image.')
    args = parser.parse_args()
    return args


def main():
    register_all_modules()
    args = parse_args()

    if len(args.shape) == 1:
        h = w = args.shape[0]
    elif len(args.shape) == 2:
        h, w = args.shape
    else:
        raise ValueError('invalid input shape')
    ori_shape = (3, h, w)
    divisor = args.size_divisor
    if divisor > 0:
        h = int(np.ceil(h / divisor)) * divisor
        w = int(np.ceil(w / divisor)) * divisor

    input_shape = (3, h, w)

    cfg = Config.fromfile(args.config)
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)

    model = MODELS.build(cfg.model)
    # if torch.cuda.is_available():
    #     model.cuda()
    model.eval()

    flops, activations, params, complexity_table, complexity_str = get_model_complexity_info(model, input_shape, show_table=True, show_str=True)
    split_line = '=' * 30

    if divisor > 0 and \
            input_shape != ori_shape:
        print(f'{split_line}\nUse size divisor set input shape '
              f'from {ori_shape} to {input_shape}\n')
    print(f'{split_line}\nInput shape: {input_shape}\n'
          f'Flops: {flops}\nParams: {params}\n{split_line}')

    print(activations)
    print(complexity_table)
    # print(complexity_str)
    print('!!!Please be cautious if you use the results in papers. '
          'You may need to check if all ops are supported and verify that the '
          'flops computation is correct.')


if __name__ == '__main__':
    main()

(mmroate) ➜  mmdetection git:(dev-3.x) ✗ python tools/analysis_tools/get_flops.py configs/yolo/yolov3_d53_8xb8-320-273e_coco.py 
12/12 13:20:29 - mmengine - WARNING - Unsupported operator aten::leaky_relu_ encountered 72 time(s)
12/12 13:20:29 - mmengine - WARNING - Unsupported operator aten::add encountered 23 time(s)
12/12 13:20:29 - mmengine - WARNING - The following submodules of the model were never called during the trace of the graph. They may be unused, or they were accessed by direct calls to .forward() or via other python methods. In the latter case they will have zeros for statistics, though their statistics will still contribute to their parent calling module.
bbox_head.loss_cls, bbox_head.loss_conf, bbox_head.loss_wh, bbox_head.loss_xy, data_preprocessor
12/12 13:20:30 - mmengine - WARNING - Unsupported operator aten::batch_norm encountered 72 time(s)
12/12 13:20:30 - mmengine - WARNING - Unsupported operator aten::leaky_relu_ encountered 72 time(s)
12/12 13:20:30 - mmengine - WARNING - Unsupported operator aten::add encountered 23 time(s)
12/12 13:20:30 - mmengine - WARNING - Unsupported operator aten::upsample_nearest2d encountered 2 time(s)
12/12 13:20:30 - mmengine - WARNING - The following submodules of the model were never called during the trace of the graph. They may be unused, or they were accessed by direct calls to .forward() or via other python methods. In the latter case they will have zeros for statistics, though their statistics will still contribute to their parent calling module.
bbox_head.loss_cls, bbox_head.loss_conf, bbox_head.loss_wh, bbox_head.loss_xy, data_preprocessor
==============================
Input shape: (3, 1280, 800)
Flops: 0.195T
Params: 61.949M
==============================
0.232G

| module                           | #parameters or shape   | #flops     | #activations   |
|:---------------------------------|:-----------------------|:-----------|:---------------|
| model                            | 61.949M                | 0.195T     | 0.232G         |
|  backbone                        |  40.585M               |  0.145T    |  0.194G        |
|   backbone.conv1                 |   0.928K               |   0.95G    |   32.768M      |
|    backbone.conv1.conv           |    0.864K              |    0.885G  |    32.768M     |
|    backbone.conv1.bn             |    64                  |    65.536M |    0           |
|   backbone.conv_res_block1       |   39.232K              |   10.043G  |   40.96M       |
|    backbone.conv_res_block1.conv |    18.56K              |    4.751G  |    16.384M     |
|    backbone.conv_res_block1.res0 |    20.672K             |    5.292G  |    24.576M     |
|   backbone.conv_res_block2       |   0.239M               |   15.27G   |   32.768M      |
|    backbone.conv_res_block2.conv |    73.984K             |    4.735G  |    8.192M      |
|    backbone.conv_res_block2.res0 |    82.304K             |    5.267G  |    12.288M     |
|    backbone.conv_res_block2.res1 |    82.304K             |    5.267G  |    12.288M     |
|   backbone.conv_res_block3       |   2.923M               |   46.768G  |   53.248M      |
|    backbone.conv_res_block3.conv |    0.295M              |    4.727G  |    4.096M      |
|    backbone.conv_res_block3.res0 |    0.328M              |    5.255G  |    6.144M      |
|    backbone.conv_res_block3.res1 |    0.328M              |    5.255G  |    6.144M      |
|    backbone.conv_res_block3.res2 |    0.328M              |    5.255G  |    6.144M      |
|    backbone.conv_res_block3.res3 |    0.328M              |    5.255G  |    6.144M      |
|    backbone.conv_res_block3.res4 |    0.328M              |    5.255G  |    6.144M      |
|    backbone.conv_res_block3.res5 |    0.328M              |    5.255G  |    6.144M      |
|    backbone.conv_res_block3.res6 |    0.328M              |    5.255G  |    6.144M      |
|    backbone.conv_res_block3.res7 |    0.328M              |    5.255G  |    6.144M      |
|   backbone.conv_res_block4       |   11.679M              |   46.715G  |   26.624M      |
|    backbone.conv_res_block4.conv |    1.181M              |    4.723G  |    2.048M      |
|    backbone.conv_res_block4.res0 |    1.312M              |    5.249G  |    3.072M      |
|    backbone.conv_res_block4.res1 |    1.312M              |    5.249G  |    3.072M      |
|    backbone.conv_res_block4.res2 |    1.312M              |    5.249G  |    3.072M      |
|    backbone.conv_res_block4.res3 |    1.312M              |    5.249G  |    3.072M      |
|    backbone.conv_res_block4.res4 |    1.312M              |    5.249G  |    3.072M      |
|    backbone.conv_res_block4.res5 |    1.312M              |    5.249G  |    3.072M      |
|    backbone.conv_res_block4.res6 |    1.312M              |    5.249G  |    3.072M      |
|    backbone.conv_res_block4.res7 |    1.312M              |    5.249G  |    3.072M      |
|   backbone.conv_res_block5       |   25.704M              |   25.704G  |   7.168M       |
|    backbone.conv_res_block5.conv |    4.721M              |    4.721G  |    1.024M      |
|    backbone.conv_res_block5.res0 |    5.246M              |    5.246G  |    1.536M      |
|    backbone.conv_res_block5.res1 |    5.246M              |    5.246G  |    1.536M      |
|    backbone.conv_res_block5.res2 |    5.246M              |    5.246G  |    1.536M      |
|    backbone.conv_res_block5.res3 |    5.246M              |    5.246G  |    1.536M      |
|  neck                            |  14.71M                |  33.871G   |  25.856M       |
|   neck.detect1                   |   11.017M              |   11.017G  |   3.584M       |
|    neck.detect1.conv1            |    0.525M              |    0.525G  |    0.512M      |
|    neck.detect1.conv2            |    4.721M              |    4.721G  |    1.024M      |
|    neck.detect1.conv3            |    0.525M              |    0.525G  |    0.512M      |
|    neck.detect1.conv4            |    4.721M              |    4.721G  |    1.024M      |
|    neck.detect1.conv5            |    0.525M              |    0.525G  |    0.512M      |
|   neck.conv1                     |   0.132M               |   0.132G   |   0.256M       |
|    neck.conv1.conv               |    0.131M              |    0.131G  |    0.256M      |
|    neck.conv1.bn                 |    0.512K              |    0.512M  |    0           |
|   neck.detect2                   |   2.822M               |   11.287G  |   7.168M       |
|    neck.detect2.conv1            |    0.197M              |    0.788G  |    1.024M      |
|    neck.detect2.conv2            |    1.181M              |    4.723G  |    2.048M      |
|    neck.detect2.conv3            |    0.132M              |    0.526G  |    1.024M      |
|    neck.detect2.conv4            |    1.181M              |    4.723G  |    2.048M      |
|    neck.detect2.conv5            |    0.132M              |    0.526G  |    1.024M      |
|   neck.conv2                     |   33.024K              |   0.132G   |   0.512M       |
|    neck.conv2.conv               |    32.768K             |    0.131G  |    0.512M      |
|    neck.conv2.bn                 |    0.256K              |    1.024M  |    0           |
|   neck.detect3                   |   0.706M               |   11.301G  |   14.336M      |
|    neck.detect3.conv1            |    49.408K             |    0.791G  |    2.048M      |
|    neck.detect3.conv2            |    0.295M              |    4.727G  |    4.096M      |
|    neck.detect3.conv3            |    33.024K             |    0.528G  |    2.048M      |
|    neck.detect3.conv4            |    0.295M              |    4.727G  |    4.096M      |
|    neck.detect3.conv5            |    33.024K             |    0.528G  |    2.048M      |
|  bbox_head                       |  6.654M                |  15.998G   |  12.523M       |
|   bbox_head.convs_bridge         |   6.197M               |   14.17G   |   7.168M       |
|    bbox_head.convs_bridge.0      |    4.721M              |    4.721G  |    1.024M      |
|    bbox_head.convs_bridge.1      |    1.181M              |    4.723G  |    2.048M      |
|    bbox_head.convs_bridge.2      |    0.295M              |    4.727G  |    4.096M      |
|   bbox_head.convs_pred           |   0.458M               |   1.828G   |   5.355M       |
|    bbox_head.convs_pred.0        |    0.261M              |    0.261G  |    0.255M      |
|    bbox_head.convs_pred.1        |    0.131M              |    0.522G  |    1.02M       |
|    bbox_head.convs_pred.2        |    65.535K             |    1.044G  |    4.08M       |
!!!Please be cautious if you use the results in papers. You may need to check if all ops are supported and verify that the flops computation is correct.
(mmroate) ➜  mmdetection git:(dev-3.x) ✗

(mmroate) ➜  mmdetection git:(dev-3.x) ✗ python tools/analysis_tools/get_flops.py configs/faster_rcnn/faster-rcnn_r101_fpn_1x_coco.py      
Traceback (most recent call last):
  File "tools/analysis_tools/get_flops.py", line 92, in <module>
    main()
  File "tools/analysis_tools/get_flops.py", line 73, in main
    flops, activations, params, complexity_table, complexity_str = get_model_complexity_info(model, input_shape, show_table=True, show_str=True)
  File "/home/ubuntu/mmroate-1.x/mmengine/mmengine/analysis/print_helper.py", line 668, in get_model_complexity_info
    flops = _format_size(flop_handler.total())
  File "/home/ubuntu/mmroate-1.x/mmengine/mmengine/analysis/jit_analysis.py", line 259, in total
    stats = self._analyze()
  File "/home/ubuntu/mmroate-1.x/mmengine/mmengine/analysis/jit_analysis.py", line 550, in _analyze
    graph = _get_scoped_trace_graph(self._model, self._inputs,
  File "/home/ubuntu/mmroate-1.x/mmengine/mmengine/analysis/jit_analysis.py", line 189, in _get_scoped_trace_graph
    graph, _ = _get_trace_graph(module, inputs)
  File "/home/ubuntu/miniconda3/envs/mmroate/lib/python3.8/site-packages/torch/jit/_trace.py", line 1166, in _get_trace_graph
    outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
  File "/home/ubuntu/miniconda3/envs/mmroate/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/ubuntu/miniconda3/envs/mmroate/lib/python3.8/site-packages/torch/jit/_trace.py", line 127, in forward
    graph, out = torch._C._create_graph_by_tracing(
  File "/home/ubuntu/miniconda3/envs/mmroate/lib/python3.8/site-packages/torch/jit/_trace.py", line 118, in wrapper
    outs.append(self.inner(*trace_inputs))
  File "/home/ubuntu/miniconda3/envs/mmroate/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1120, in _call_impl
    result = forward_call(*input, **kwargs)
  File "/home/ubuntu/miniconda3/envs/mmroate/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1090, in _slow_forward
    result = self.forward(*input, **kwargs)
  File "/home/ubuntu/mmroate-1.x/mmdetection/mmdet/models/detectors/base.py", line 96, in forward
    return self._forward(inputs, data_samples)
  File "/home/ubuntu/mmroate-1.x/mmdetection/mmdet/models/detectors/two_stage.py", line 131, in _forward
    rpn_results_list = self.rpn_head.predict(
  File "/home/ubuntu/mmroate-1.x/mmdetection/mmdet/models/dense_heads/base_dense_head.py", line 191, in predict
    batch_img_metas = [
TypeError: 'NoneType' object is not iterable```

CLAassistant · 2022-12-14T03:21:46Z

All committers have signed the CLA.

mmengine/analysis/jit_handles.py

tonysy · 2023-02-01T17:22:46Z

Usage in mmcls

get_flops.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse

from mmengine.analysis import get_model_complexity_info
from mmengine import Config

from mmcls.models import build_classifier


def parse_args():
    parser = argparse.ArgumentParser(description='Get model flops and params')
    parser.add_argument('config', help='config file path')
    parser.add_argument(
        '--shape',
        type=int,
        nargs='+',
        default=[224, 224],
        help='input image size')
    args = parser.parse_args()
    return args


def main():

    args = parse_args()

    if len(args.shape) == 1:
        input_shape = (3, args.shape[0], args.shape[0])
    elif len(args.shape) == 2:
        input_shape = (3, ) + tuple(args.shape)
    else:
        raise ValueError('invalid input shape')

    cfg = Config.fromfile(args.config)
    model = build_classifier(cfg.model)
    model.eval()

    if hasattr(model, 'extract_feat'):
        model.forward = model.extract_feat
    else:
        raise NotImplementedError(
            'FLOPs counter is currently not currently supported with {}'.
            format(model.__class__.__name__))

    analysis_results = get_model_complexity_info(model,input_shape,)


    flops = analysis_results['flops_str']
    params = analysis_results['params_str']
    activations = analysis_results['activations_str']

    out_table = analysis_results['out_table']
    out_arch = analysis_results['out_arch']
    print(out_table)
    print(out_arch)

    split_line = '=' * 30
    print(f'{split_line}\nInput shape: {input_shape}\n'
          f'Flops: {flops}\nParams: {params}\n'
          f'Activation: {activations}\n{split_line}')
    print('!!!Please be cautious if you use the results in papers. '
          'You may need to check if all ops are supported and verify that the '
          'flops computation is correct.')


if __name__ == '__main__':
    main()

CLI command example
python tools/analysis_tools/get_flops.py configs/deit/deit-small_pt-4xb256_in1k.py

tonysy · 2023-02-01T17:27:00Z

Usage in mmdet

get_flops.py:

# Copyright (c) OpenMMLab. All rights reserved.
import argparse

import numpy as np
import torch
from mmengine.config import Config, DictAction

from mmdet.registry import MODELS
from mmengine import Config
from functools import partial
from mmdet.utils import register_all_modules
from mmengine.runner import Runner
from mmengine.logging import MMLogger

from mmengine.analysis import get_model_complexity_info


def parse_args():
    parser = argparse.ArgumentParser(description='Train a detector')
    parser.add_argument('config', help='train config file path')
    parser.add_argument(
        '--shape',
        type=int,
        nargs='+',
        default=[1280, 800],
        help='input image size')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        'Note that the quotation marks are necessary and that no white space '
        'is allowed.')
    parser.add_argument(
        '--size-divisor',
        type=int,
        default=32,
        help='Pad the input image, the minimum size that is divisible '
        'by size_divisor, -1 means do not pad the image.')
    args = parser.parse_args()
    return args


def main():
    register_all_modules()
    args = parse_args()

    if len(args.shape) == 1:
        h = w = args.shape[0]
    elif len(args.shape) == 2:
        h, w = args.shape
    else:
        raise ValueError('invalid input shape')
    ori_shape = (3, h, w)
    divisor = args.size_divisor
    if divisor > 0:
        h = int(np.ceil(h / divisor)) * divisor
        w = int(np.ceil(w / divisor)) * divisor

    input_shape = (3, h, w)

    try:
        cfg = Config.fromfile(args.config)
        if args.cfg_options is not None:
            cfg.merge_from_dict(args.cfg_options)

        model = MODELS.build(cfg.model)
        if torch.cuda.is_available():
            model.cuda()
        model.eval()
        
        analysis_results = get_model_complexity_info(model, input_shape)
        flops = analysis_results['flops_str']
        activations = analysis_results['activations_str']
        params = analysis_results['params_str']

    except:
        logger = MMLogger.get_instance(name='MMLogger')
        logger.warning('Direct get flops failed, try to get flops with data')
        cfg = Config.fromfile(args.config)
        data_loader = Runner.build_dataloader(cfg.val_dataloader)
        data_batch = next(iter(data_loader))
        model = MODELS.build(cfg.model)
        _forward = model.forward
        data = model.data_preprocessor(data_batch)

        model.forward = partial(_forward, data_samples=data['data_samples'])

        analysis_results = get_model_complexity_info(
            model, input_shape, data['inputs'])

        flops = analysis_results['flops_str']
        activations = analysis_results['activations_str']
        params = analysis_results['params_str']
        
    print(analysis_results['out_table'])
    print(analysis_results['out_arch'])

    split_line = '=' * 30

    if divisor > 0 and \
            input_shape != ori_shape:
        print(f'{split_line}\nUse size divisor set input shape '
              f'from {ori_shape} to {input_shape}\n')
    print(f'{split_line}\nInput shape: {input_shape}\n'
          f'Flops: {flops}\nParams: {params}\n{split_line}')
    print('!!!Please be cautious if you use the results in papers. '
          'You may need to check if all ops are supported and verify that the '
          'flops computation is correct.')


if __name__ == '__main__':
    main()

CLI example:
python tools/analysis_tools/get_flops.py configs/fcos/fcos_r101-caffe_fpn_gn-head-1x_coco.py

mmengine/analysis/complexity_analysis.py

docs/en/advanced_tutorials/model_analysis.md

mmengine/analysis/jit_analysis.py

mmengine/analysis/jit_handles.py

codecov · 2023-02-14T09:46:34Z

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tests/test_analysis/test_flop_count.py

zhouzaida · 2023-02-17T12:23:25Z

mmengine/analysis/print_helper.py imports the tabulate but it is not added to runtime.txt. Suggest using rich to replace tabulate.

The reason why ut did not fail was that tabulate was installed when installing openmim.

mmengine/analysis/print_helper.py

docs/en/advanced_tutorials/model_analysis.md

zhouzaida · 2023-02-20T03:08:08Z

mmengine/analysis/complexity_analysis.py

tonysy requested review from zhouzaida and HAOCHENYE as code owners November 30, 2022 17:26

zhouzaida reviewed Dec 4, 2022

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HAOCHENYE added this to the 0.6.0 milestone Jan 12, 2023

tonysy force-pushed the add_complexity branch from a591787 to 14524f0 Compare February 1, 2023 10:28

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tonysy added 7 commits February 14, 2023 17:57

[Feature] Add support model complexity computation

2bfe5b9

[Fix] fix lint error

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[Feature] update print_helper

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Update docstring

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update api, docs, fix lint

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fix lint

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update doc and add test

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minor refinement

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zhouzaida and others added 2 commits February 19, 2023 11:46

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replace tabulate with existing rich

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zhouzaida merged commit aee2f6a into open-mmlab:main Feb 20, 2023

zhouzaida mentioned this pull request Feb 20, 2023

how to get FLOPs of two stage algorithm in MMDetection 3.x? #589

Closed

zhouzaida mentioned this pull request Mar 6, 2023

[Feature] Add support of flops/activation/parameter calculation #403

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Tau-J mentioned this pull request Mar 12, 2023

[Improve] use fvcore to calculate FLOPs open-mmlab/mmpose#2045

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Zheng-LinXiao mentioned this pull request Mar 15, 2023

Update Calculate Flops Method open-mmlab/mmaction2#2299

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[Feature] Support model complexity computation #779

[Feature] Support model complexity computation #779

tonysy commented Nov 30, 2022 •

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tonysy commented Feb 1, 2023

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codecov bot commented Feb 14, 2023 •

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zhouzaida commented Feb 20, 2023

[Feature] Support model complexity computation #779

[Feature] Support model complexity computation #779

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tonysy commented Nov 30, 2022 • edited

zhouzaida commented Dec 4, 2022

vansin commented Dec 12, 2022 • edited

CLAassistant commented Dec 14, 2022 • edited

tonysy commented Feb 1, 2023

Usage in mmcls

tonysy commented Feb 1, 2023

Usage in mmdet

codecov bot commented Feb 14, 2023 • edited

Codecov Report

zhouzaida commented Feb 17, 2023

zhouzaida commented Feb 20, 2023

tonysy commented Nov 30, 2022 •

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vansin commented Dec 12, 2022 •

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