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[Improve] Update benchmark scripts (#1028) 

* Update train benchmark scripts

* Add `--cfg-options` for dev scripts and enhance `--range`.

* Fix bug of regex expression.

* Fix minor bugs

* Update ShuffleNet configs

* Update rsb-a1 configs and label smooth loss mode.

* Update inference dev scripts

* From `mmengine` instead of `mmcv` import fileio.

* Fix lint

* Update pre-commit hook

* Use `use_sigmoid` option instead of "bce" mode in label smooth loss.
pull/1037/head
Ma Zerun 2022-09-20 15:50:21 +08:00 committed by GitHub
parent e4e8047563
commit e9e2d48cb2
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9 changed files with 272 additions and 175 deletions
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42
.dev_scripts/benchmark_regression/1-benchmark_valid.py
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@ -9,8 +9,10 @@ from typing import OrderedDict
import mmcv
import numpy as np
import torch
from mmengine import Config, MMLogger, Runner
from mmengine.dataset import Compose
from mmengine import Config, DictAction, MMLogger
from mmengine.dataset import Compose, default_collate
from mmengine.fileio import FileClient
from mmengine.runner import Runner
from modelindex.load_model_index import load
from rich.console import Console
from rich.table import Table
@ -52,6 +54,16 @@ def parse_args():
'--flops-str',
action='store_true',
help='Output FLOPs and params counts in a string form.')
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.')
args = parser.parse_args()
return args
@ -62,6 +74,8 @@ def inference(config_file, checkpoint, work_dir, args, exp_name):
cfg.load_from = checkpoint
cfg.log_level = 'WARN'
cfg.experiment_name = exp_name
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# build the data pipeline
test_dataset = cfg.test_dataloader.dataset
@ -72,7 +86,8 @@ def inference(config_file, checkpoint, work_dir, args, exp_name):
test_dataset.pipeline.insert(1, dict(type='Resize', scale=32))
data = Compose(test_dataset.pipeline)({'img_path': args.img})
resolution = tuple(data['inputs'].shape[1:])
data = default_collate([data])
resolution = tuple(data['inputs'].shape[-2:])
runner: Runner = Runner.from_cfg(cfg)
model = runner.model
@ -83,26 +98,30 @@ def inference(config_file, checkpoint, work_dir, args, exp_name):
if args.inference_time:
time_record = []
for _ in range(10):
model.val_step(data) # warmup before profiling
torch.cuda.synchronize()
start = time()
model.val_step([data])
model.val_step(data)
torch.cuda.synchronize()
time_record.append((time() - start) * 1000)
result['time_mean'] = np.mean(time_record[1:-1])
result['time_std'] = np.std(time_record[1:-1])
else:
model.val_step([data])
model.val_step(data)
result['model'] = config_file.stem
if args.flops:
from mmcv.cnn.utils import get_model_complexity_info
from fvcore.nn import FlopCountAnalysis, parameter_count
from fvcore.nn.print_model_statistics import _format_size
_format_size = _format_size if args.flops_str else lambda x: x
with torch.no_grad():
if hasattr(model, 'extract_feat'):
model.forward = model.extract_feat
flops, params = get_model_complexity_info(
model,
input_shape=(3, ) + resolution,
print_per_layer_stat=False,
as_strings=args.flops_str)
model.to('cpu')
inputs = (torch.randn((1, 3, *resolution)), )
flops = _format_size(FlopCountAnalysis(model, inputs).total())
params = _format_size(parameter_count(model)[''])
result['flops'] = flops if args.flops_str else int(flops)
result['params'] = params if args.flops_str else int(params)
else:
@ -184,7 +203,6 @@ def main(args):
if args.checkpoint_root is not None:
root = args.checkpoint_root
if 's3://' in args.checkpoint_root:
from mmcv.fileio import FileClient
from petrel_client.common.exception import AccessDeniedError
file_client = FileClient.infer_client(uri=root)
checkpoint = file_client.join_path(

9
.dev_scripts/benchmark_regression/2-benchmark_test.py
View File

@ -62,6 +62,12 @@ def parse_args():
action='store_true',
help='Summarize benchmark test results.')
parser.add_argument('--save', action='store_true', help='Save the summary')
parser.add_argument(
'--cfg-options',
nargs='+',
type=str,
default=[],
help='Config options for all config files.')
args = parser.parse_args()
return args
@ -76,7 +82,7 @@ def create_test_job_batch(commands, model_info, args, port, script_name):
http_prefix = 'https://download.openmmlab.com/mmclassification/'
if 's3://' in args.checkpoint_root:
from mmcv.fileio import FileClient
from mmengine.fileio import FileClient
from petrel_client.common.exception import AccessDeniedError
file_client = FileClient.infer_client(uri=args.checkpoint_root)
checkpoint = file_client.join_path(
@ -125,6 +131,7 @@ def create_test_job_batch(commands, model_info, args, port, script_name):
f'--work-dir={work_dir} '
f'--out={result_file} '
f'--cfg-option dist_params.port={port} '
f'{" ".join(args.cfg_options)} '
f'--launcher={launcher}\n')
with open(work_dir / 'job.sh', 'w') as f:

162
.dev_scripts/benchmark_regression/3-benchmark_train.py
View File

@ -3,22 +3,48 @@ import json
import os
import os.path as osp
import re
from collections import OrderedDict
from datetime import datetime
from pathlib import Path
from zipfile import ZipFile
import yaml
from modelindex.load_model_index import load
from rich.console import Console
from rich.syntax import Syntax
from rich.table import Table
console = Console()
MMCLS_ROOT = Path(__file__).absolute().parents[2]
CYCLE_LEVELS = ['month', 'quarter', 'half-year', 'no-training']
METRICS_MAP = {
'Top 1 Accuracy': 'accuracy/top1',
'Top 5 Accuracy': 'accuracy/top5'
}
class RangeAction(argparse.Action):
def __call__(self, parser, namespace, values: str, option_string):
matches = re.match(r'([><=]*)([-\w]+)', values)
if matches is None:
raise ValueError(f'Unavailable range option {values}')
symbol, range_str = matches.groups()
assert range_str in CYCLE_LEVELS, \
f'{range_str} are not in {CYCLE_LEVELS}.'
level = CYCLE_LEVELS.index(range_str)
symbol = symbol or '<='
ranges = set()
if '=' in symbol:
ranges.add(level)
if '>' in symbol:
ranges.update(range(level + 1, len(CYCLE_LEVELS)))
if '<' in symbol:
ranges.update(range(level))
assert len(ranges) > 0, 'No range are selected.'
setattr(namespace, self.dest, ranges)
def parse_args():
parser = argparse.ArgumentParser(
description='Train models (in bench_train.yml) and compare accuracy.')
@ -32,6 +58,14 @@ def parse_args():
parser.add_argument('--port', type=int, default=29666, help='dist port')
parser.add_argument(
'--models', nargs='+', type=str, help='Specify model names to run.')
parser.add_argument(
'--range',
type=str,
default={0},
action=RangeAction,
metavar='{month,quarter,half-year,no-training}',
help='The training benchmark range, "no-training" means all models '
"including those we haven't trained.")
parser.add_argument(
'--work-dir',
default='work_dirs/benchmark_train',
@ -63,18 +97,33 @@ def parse_args():
'--save',
action='store_true',
help='Save the summary and archive log files.')
parser.add_argument(
'--cfg-options',
nargs='+',
type=str,
default=[],
help='Config options for all config files.')
args = parser.parse_args()
return args
def get_gpu_number(model_info):
config = osp.basename(model_info.config)
matches = re.match(r'.*[-_](\d+)xb(\d+).*', config)
if matches is None:
raise ValueError(
'Cannot get gpu numbers from the config name {config}')
gpus = int(matches.groups()[0])
return gpus
def create_train_job_batch(commands, model_info, args, port, script_name):
fname = model_info.name
assert 'Gpus' in model_info.data, \
f"Haven't specify gpu numbers for {fname}"
gpus = model_info.data['Gpus']
gpus = get_gpu_number(model_info)
gpus_per_node = min(gpus, 8)
config = Path(model_info.config)
assert config.exists(), f'"{fname}": {config} not found.'
@ -101,15 +150,17 @@ def create_train_job_batch(commands, model_info, args, port, script_name):
f'#SBATCH --output {work_dir}/job.%j.out\n'
f'#SBATCH --partition={args.partition}\n'
f'#SBATCH --job-name {job_name}\n'
f'#SBATCH --gres=gpu:8\n'
f'#SBATCH --gres=gpu:{gpus_per_node}\n'
f'{mail_cfg}{quota_cfg}'
f'#SBATCH --ntasks-per-node=8\n'
f'#SBATCH --ntasks-per-node={gpus_per_node}\n'
f'#SBATCH --ntasks={gpus}\n'
f'#SBATCH --cpus-per-task=5\n\n'
f'{runner} -u {script_name} {config} '
f'--work-dir={work_dir} --cfg-option '
f'dist_params.port={port} '
f'checkpoint_config.max_keep_ckpts=10 '
f'env_cfg.dist_cfg.port={port} '
f'{" ".join(args.cfg_options)} '
f'default_hooks.checkpoint.max_keep_ckpts=2 '
f'default_hooks.checkpoint.save_best="auto" '
f'--launcher={launcher}\n')
with open(work_dir / 'job.sh', 'w') as f:
@ -124,33 +175,16 @@ def create_train_job_batch(commands, model_info, args, port, script_name):
return work_dir / 'job.sh'
def train(args):
models_cfg = load(str(Path(__file__).parent / 'bench_train.yml'))
models_cfg.build_models_with_collections()
models = {model.name: model for model in models_cfg.models}
def train(models, args):
script_name = osp.join('tools', 'train.py')
port = args.port
commands = []
if args.models:
patterns = [re.compile(pattern) for pattern in args.models]
filter_models = {}
for k, v in models.items():
if any([re.match(pattern, k) for pattern in patterns]):
filter_models[k] = v
if len(filter_models) == 0:
print('No model found, please specify models in:')
print('\n'.join(models.keys()))
return
models = filter_models
for model_info in models.values():
months = model_info.data.get('Months', range(1, 13))
if datetime.now().month in months:
script_path = create_train_job_batch(commands, model_info, args,
port, script_name)
port += 1
script_path = create_train_job_batch(commands, model_info, args, port,
script_name)
port += 1
command_str = '\n'.join(commands)
@ -245,12 +279,14 @@ def show_summary(summary_data):
metric = summary[metric_key]
expect = metric['expect']
last = metric['last']
last_epoch = metric['last_epoch']
last_color = set_color(last, expect)
best = metric['best']
best_color = set_color(best, expect)
best_epoch = metric['best_epoch']
row.append(f'{expect:.2f}')
row.append(f'[{last_color}]{last:.2f}[/{last_color}]')
row.append(
f'[{last_color}]{last:.2f}[/{last_color}] ({last_epoch})')
row.append(
f'[{best_color}]{best:.2f}[/{best_color}] ({best_epoch})')
table.add_row(*row)
@ -258,25 +294,11 @@ def show_summary(summary_data):
console.print(table)
def summary(args):
models_cfg = load(str(Path(__file__).parent / 'bench_train.yml'))
models = {model.name: model for model in models_cfg.models}
def summary(models, args):
work_dir = Path(args.work_dir)
dir_map = {p.name: p for p in work_dir.iterdir() if p.is_dir()}
if args.models:
patterns = [re.compile(pattern) for pattern in args.models]
filter_models = {}
for k, v in models.items():
if any([re.match(pattern, k) for pattern in patterns]):
filter_models[k] = v
if len(filter_models) == 0:
print('No model found, please specify models in:')
print('\n'.join(models.keys()))
return
models = filter_models
summary_data = {}
for model_name, model_info in models.items():
@ -287,17 +309,19 @@ def summary(args):
# Skip if not found any vis_data folder.
sub_dir = dir_map[model_name]
vis_folders = [d for d in sub_dir.iterdir() if d.is_dir()]
if len(vis_folders) == 0:
continue
log_file = sorted(vis_folders)[-1] / 'vis_data' / 'scalars.json'
if not log_file.exists():
log_files = [f for f in sub_dir.glob('*/vis_data/scalars.json')]
if len(log_files) == 0:
continue
log_file = sorted(log_files)[-1]
# parse train log
with open(log_file) as f:
json_logs = [json.loads(s) for s in f.readlines()]
val_logs = [log for log in json_logs if 'loss' not in log]
val_logs = [
log for log in json_logs
# TODO: need a better method to extract validate log
if 'loss' not in log and 'accuracy/top1' in log
]
if len(val_logs) == 0:
continue
@ -320,9 +344,10 @@ def summary(args):
summary[key_yml] = dict(
expect=expect_result,
last=last,
last_epoch=len(val_logs),
best=best,
best_epoch=best_epoch)
summary_data[model_name] = summary
best_epoch=best_epoch + 1)
summary_data[model_name].update(summary)
show_summary(summary_data)
if args.save:
@ -332,10 +357,39 @@ def summary(args):
def main():
args = parse_args()
model_index_file = MMCLS_ROOT / 'model-index.yml'
model_index = load(str(model_index_file))
model_index.build_models_with_collections()
all_models = {model.name: model for model in model_index.models}
with open(Path(__file__).parent / 'bench_train.yml', 'r') as f:
train_items = yaml.safe_load(f)
models = OrderedDict()
for item in train_items:
name = item['Name']
model_info = all_models[name]
model_info.cycle = item.get('Cycle', None)
cycle = getattr(model_info, 'cycle', 'month')
cycle_level = CYCLE_LEVELS.index(cycle)
if cycle_level in args.range:
models[name] = model_info
if args.models:
patterns = [re.compile(pattern) for pattern in args.models]
filter_models = {}
for k, v in models.items():
if any([re.match(pattern, k) for pattern in patterns]):
filter_models[k] = v
if len(filter_models) == 0:
print('No model found, please specify models in:')
print('\n'.join(models.keys()))
return
models = filter_models
if args.summary:
summary(args)
summary(models, args)
else:
train(args)
train(models, args)
if __name__ == '__main__':

160
.dev_scripts/benchmark_regression/bench_train.yml
View File

@ -1,88 +1,86 @@
Models:
- Name: resnet50
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 76.55
Top 5 Accuracy: 93.06
Config: configs/resnet/resnet50_8xb32_in1k.py
Gpus: 8
- Name: mobilenet-v2_8xb32_in1k
Cycle: month
- Name: seresnet50
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 77.74
Top 5 Accuracy: 93.84
Config: configs/seresnet/seresnet50_8xb32_in1k.py
Gpus: 8
- Name: resnet50_8xb32_in1k
Cycle: month
- Name: vit-base
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 82.37
Top 5 Accuracy: 96.15
Config: configs/vision_transformer/vit-base-p16_pt-32xb128-mae_in1k-224.py
Gpus: 32
- Name: seresnet50_8xb32_in1k
Cycle: month
- Name: mobilenetv2
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 71.86
Top 5 Accuracy: 90.42
Config: configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py
Gpus: 8
- Name: swin-small_16xb64_in1k
Cycle: month
- Name: swin_tiny
Results:
- Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.18
Top 5 Accuracy: 95.61
Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth
Config: configs/swin_transformer/swin-tiny_16xb64_in1k.py
Gpus: 16
- Name: vit-base-p16_pt-32xb128-mae_in1k
Cycle: month
- Name: vgg16
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 71.62
Top 5 Accuracy: 90.49
Config: configs/vgg/vgg16_8xb32_in1k.py
Gpus: 8
Months:
- 1
- 4
- 7
- 10
- Name: resnet50_8xb256-rsb-a1-600e_in1k
Cycle: quarter
- Name: shufflenet_v2
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 69.55
Top 5 Accuracy: 88.92
Config: configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py
Gpus: 16
Months:
- 2
- 5
- 8
- 11
- Name: resnext50-32x4d_8xb32_in1k
Cycle: quarter
- Name: resnet-rsb
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 80.12
Top 5 Accuracy: 94.78
Config: configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py
Gpus: 8
Months:
- 3
- 6
- 9
- 12
- Name: shufflenet-v2-1x_16xb64_in1k
Cycle: quarter
- Name: vgg16_8xb32_in1k
Cycle: quarter
- Name: shufflenet-v1-1x_16xb64_in1k
Cycle: half-year
- Name: t2t-vit-t-14_8xb64_in1k
Cycle: half-year
- Name: regnetx-1.6gf_8xb128_in1k
Cycle: half-year
- Name: van-small_8xb128_in1k
Cycle: no-training
- Name: res2net50-w14-s8_3rdparty_8xb32_in1k
Cycle: no-training
- Name: repvgg-A2_3rdparty_4xb64-coslr-120e_in1k
Cycle: no-training
- Name: tnt-small-p16_3rdparty_in1k
Cycle: no-training
- Name: mlp-mixer-base-p16_3rdparty_64xb64_in1k
Cycle: no-training
- Name: conformer-small-p16_3rdparty_8xb128_in1k
Cycle: no-training
- Name: twins-pcpvt-base_3rdparty_8xb128_in1k
Cycle: no-training
- Name: efficientnet-b0_3rdparty_8xb32_in1k
Cycle: no-training
- Name: convnext-small_3rdparty_32xb128_in1k
Cycle: no-training
- Name: hrnet-w18_3rdparty_8xb32_in1k
Cycle: no-training
- Name: repmlp-base_3rdparty_8xb64_in1k
Cycle: no-training
- Name: wide-resnet50_3rdparty_8xb32_in1k
Cycle: no-training
- Name: cspresnet50_3rdparty_8xb32_in1k
Cycle: no-training
- Name: convmixer-768-32_10xb64_in1k
Cycle: no-training
- Name: densenet169_4xb256_in1k
Cycle: no-training
- Name: poolformer-s24_3rdparty_32xb128_in1k
Cycle: no-training
- Name: inception-v3_3rdparty_8xb32_in1k
Cycle: no-training

4
.pre-commit-config.yaml
View File

@ -44,10 +44,10 @@ repos:
- id: docformatter
args: ["--in-place", "--wrap-descriptions", "79"]
- repo: https://github.com/open-mmlab/pre-commit-hooks
rev: v0.2.0
rev: v0.4.0
hooks:
- id: check-copyright
args: ["mmcls", "tests", "demo", "tools"]
args: ["mmcls", "tests", "demo", "tools", "--excludes", "mmcls/.mim/", "--ignore-file-not-found-error"]
# - repo: local
# hooks:
# - id: clang-format

10
configs/_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py
View File

@ -6,14 +6,8 @@ optim_wrapper = dict(
# learning policy
param_scheduler = [
dict(
type='ConstantLR',
factor=0.1,
by_epoch=True,
begin=0,
end=5,
convert_to_iter_based=True),
dict(type='PolyLR', eta_min=0, by_epoch=True, begin=5, end=300)
dict(type='ConstantLR', factor=0.1, by_epoch=False, begin=0, end=5000),
dict(type='PolyLR', eta_min=0, by_epoch=False, begin=5000)
]
# train, val, test setting

1
configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py
View File

@ -16,6 +16,7 @@ model = dict(
type='LabelSmoothLoss',
label_smooth_val=0.1,
mode='original',
use_sigmoid=True,
)),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.2, num_classes=1000),

48
mmcls/models/losses/label_smooth_loss.py
View File

@ -24,37 +24,42 @@ class LabelSmoothLoss(nn.Module):
label_smooth_val (float): The degree of label smoothing.
num_classes (int, optional): Number of classes. Defaults to None.
mode (str): Refers to notes, Options are 'original', 'classy_vision',
'multi_label'. Defaults to 'original'
'multi_label'. Defaults to 'original'.
use_sigmoid (bool, optional): Whether the prediction uses sigmoid of
softmax. Defaults to None, which means to use sigmoid in
"multi_label" mode and not use in other modes.
reduction (str): The method used to reduce the loss.
Options are "none", "mean" and "sum". Defaults to 'mean'.
loss_weight (float): Weight of the loss. Defaults to 1.0.
Notes:
if the mode is "original", this will use the same label smooth method
as the original paper as:
- if the mode is **"original"**, this will use the same label smooth
method as the original paper as:
.. math::
(1-\epsilon)\delta_{k, y} + \frac{\epsilon}{K}
.. math::
(1-\epsilon)\delta_{k, y} + \frac{\epsilon}{K}
where epsilon is the `label_smooth_val`, K is the num_classes and
delta(k,y) is Dirac delta, which equals 1 for k=y and 0 otherwise.
where :math:`\epsilon` is the ``label_smooth_val``, :math:`K` is the
``num_classes`` and :math:`\delta_{k, y}` is Dirac delta, which
equals 1 for :math:`k=y` and 0 otherwise.
if the mode is "classy_vision", this will use the same label smooth
method as the facebookresearch/ClassyVision repo as:
- if the mode is **"classy_vision"**, this will use the same label
smooth method as the facebookresearch/ClassyVision repo as:
.. math::
\frac{\delta_{k, y} + \epsilon/K}{1+\epsilon}
.. math::
\frac{\delta_{k, y} + \epsilon/K}{1+\epsilon}
if the mode is "multi_label", this will accept labels from multi-label
task and smoothing them as:
- if the mode is **"multi_label"**, this will accept labels from
multi-label task and smoothing them as:
.. math::
(1-2\epsilon)\delta_{k, y} + \epsilon
.. math::
(1-2\epsilon)\delta_{k, y} + \epsilon
"""
def __init__(self,
label_smooth_val,
num_classes=None,
use_sigmoid=None,
mode='original',
reduction='mean',
loss_weight=1.0):
@ -82,12 +87,21 @@ class LabelSmoothLoss(nn.Module):
self._eps = label_smooth_val
if mode == 'classy_vision':
self._eps = label_smooth_val / (1 + label_smooth_val)
if mode == 'multi_label':
self.ce = CrossEntropyLoss(use_sigmoid=True)
if not use_sigmoid:
from mmengine.logging import MMLogger
MMLogger.get_current_instance().warning(
'For multi-label tasks, please set `use_sigmoid=True` '
'to use binary cross entropy.')
self.smooth_label = self.multilabel_smooth_label
use_sigmoid = True if use_sigmoid is None else use_sigmoid
else:
self.ce = CrossEntropyLoss(use_soft=True)
self.smooth_label = self.original_smooth_label
use_sigmoid = False if use_sigmoid is None else use_sigmoid
self.ce = CrossEntropyLoss(
use_sigmoid=use_sigmoid, use_soft=not use_sigmoid)
def generate_one_hot_like_label(self, label):
"""This function takes one-hot or index label vectors and computes one-

11
tests/test_models/test_losses.py
View File

@ -247,6 +247,17 @@ def test_label_smooth_loss():
correct = 0.2269 # from timm
assert loss(cls_score, label) - correct <= 0.0001
loss_cfg = dict(
type='LabelSmoothLoss',
label_smooth_val=0.1,
mode='original',
use_sigmoid=True,
reduction='mean',
loss_weight=1.0)
loss = build_loss(loss_cfg)
correct = 0.3633 # from timm
assert loss(cls_score, label) - correct <= 0.0001
# test classy_vision mode label smooth loss
loss_cfg = dict(
type='LabelSmoothLoss',