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PaddleGAN

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未验证 提交 eebf94d9 编写于 作者: B Birdylx 提交者： GitHub
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[TIPC] add tipc benchmark for msvsr (#672) 

* add tipc benchmark for msvsr

* update tipc readme img
上级 91dcc906
隐藏空白更改
内联 并排
Showing with 211 addition and 66 deletion
ppgan/engine/trainer.py
浏览文件 @ eebf94d9
......@@ -32,6 +32,7 @@ from ..utils.profiler import add_profiler_step
class IterLoader:
def __init__(self, dataloader):
self._dataloader = dataloader
self.iter_loader = iter(self._dataloader)
......@@ -79,6 +80,7 @@ class Trainer:
# | ||
# save checkpoint (model.nets) \/
"""
def __init__(self, cfg):
# base config
self.logger = logging.getLogger(__name__)
......@@ -181,6 +183,22 @@ class Trainer:
iter_loader = IterLoader(self.train_dataloader)
# use amp
if self.cfg.amp:
self.logger.info('use AMP to train. AMP level = {}'.format(
self.cfg.amp_level))
assert self.cfg.model.name == 'MultiStageVSRModel', "AMP only support msvsr model"
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
# need to decorate model and optim if amp_level == 'O2'
if self.cfg.amp_level == 'O2':
# msvsr has only one generator and one optimizer
self.model.nets['generator'], self.optimizers[
'optim'] = paddle.amp.decorate(
models=self.model.nets['generator'],
optimizers=self.optimizers['optim'],
level='O2',
save_dtype='float32')
# set model.is_train = True
self.model.setup_train_mode(is_train=True)
while self.current_iter < (self.total_iters + 1):
......@@ -195,7 +213,12 @@ class Trainer:
# unpack data from dataset and apply preprocessing
# data input should be dict
self.model.setup_input(data)
self.model.train_iter(self.optimizers)
if self.cfg.amp:
self.model.train_iter_amp(self.optimizers, scaler,
self.cfg.amp_level) # amp train
else:
self.model.train_iter(self.optimizers) # norm train
batch_cost_averager.record(
time.time() - step_start_time,
......
ppgan/models/msvsr_model.py
浏览文件 @ eebf94d9
......@@ -30,6 +30,7 @@ class MultiStageVSRModel(BaseSRModel):
Paper:
PP-MSVSR: Multi-Stage Video Super-Resolution, 2021
"""
def __init__(self, generator, fix_iter, pixel_criterion=None):
"""Initialize the PP-MSVSR class.
......@@ -96,6 +97,48 @@ class MultiStageVSRModel(BaseSRModel):
self.current_iter += 1
# amp train with brute force implementation, maybe decorator can simplify this
def train_iter_amp(self, optims=None, scaler=None, amp_level='O1'):
optims['optim'].clear_grad()
if self.fix_iter:
if self.current_iter == 1:
print('Train MSVSR with fixed spynet for', self.fix_iter,
'iters.')
for name, param in self.nets['generator'].named_parameters():
if 'spynet' in name:
param.trainable = False
elif self.current_iter >= self.fix_iter + 1 and self.flag:
print('Train all the parameters.')
for name, param in self.nets['generator'].named_parameters():
param.trainable = True
if 'spynet' in name:
param.optimize_attr['learning_rate'] = 0.25
self.flag = False
for net in self.nets.values():
net.find_unused_parameters = False
# put loss computation in amp context
with paddle.amp.auto_cast(enable=True, level=amp_level):
output = self.nets['generator'](self.lq)
if isinstance(output, (list, tuple)):
out_stage2, output = output
loss_pix_stage2 = self.pixel_criterion(out_stage2, self.gt)
self.losses['loss_pix_stage2'] = loss_pix_stage2
self.visual_items['output'] = output[:, 0, :, :, :]
# pixel loss
loss_pix = self.pixel_criterion(output, self.gt)
self.losses['loss_pix'] = loss_pix
self.loss = sum(_value for _key, _value in self.losses.items()
if 'loss_pix' in _key)
scaled_loss = scaler.scale(self.loss)
self.losses['loss'] = scaled_loss
scaled_loss.backward()
scaler.minimize(optims['optim'], scaled_loss)
self.current_iter += 1
def test_iter(self, metrics=None):
self.gt = self.gt.cpu()
self.nets['generator'].eval()
......
ppgan/utils/options.py
浏览文件 @ eebf94d9
......@@ -45,9 +45,9 @@ def parse_args():
default=False,
help='skip validation during training')
# config options
parser.add_argument("-o",
"--opt",
nargs='+',
parser.add_argument("-o",
"--opt",
nargs='+',
help="set configuration options")
#for inference
......@@ -60,19 +60,31 @@ def parse_args():
help="path to reference images")
parser.add_argument("--model_path", default=None, help="model for loading")
# for profiler
parser.add_argument('-p',
'--profiler_options',
type=str,
default=None,
help='The option of profiler, which should be in format \"key1=value1;key2=value2;key3=value3\".'
# for profiler
parser.add_argument(
'-p',
'--profiler_options',
type=str,
default=None,
help=
'The option of profiler, which should be in format \"key1=value1;key2=value2;key3=value3\".'
)
# fix random numbers by setting seed
parser.add_argument('--seed',
type=int,
default=None,
help='fix random numbers by setting seed\".'
)
help='fix random numbers by setting seed\".')
# add for amp training
parser.add_argument('--amp',
action='store_true',
default=False,
help='whether to enable amp training')
parser.add_argument('--amp_level',
type=str,
default='O1',
choices=['O1', 'O2'],
help='level of amp training; O2 represent pure fp16')
args = parser.parse_args()
return args
ppgan/utils/setup.py
浏览文件 @ eebf94d9
......@@ -19,6 +19,7 @@ import numpy as np
import random
from .logger import setup_logger
def setup(args, cfg):
if args.evaluate_only:
cfg.is_train = False
......@@ -44,10 +45,13 @@ def setup(args, cfg):
paddle.set_device('gpu')
else:
paddle.set_device('cpu')
if args.seed:
paddle.seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
np.random.seed(args.seed)
paddle.framework.random._manual_program_seed(args.seed)
# add amp and amp_level args into cfg
cfg['amp'] = args.amp
cfg['amp_level'] = args.amp_level
test_tipc/readme.md → test_tipc/README.md
浏览文件 @ eebf94d9
......@@ -57,9 +57,8 @@ test_tipc/
### 测试流程
使用本工具，可以测试不同功能的支持情况，以及预测结果是否对齐，测试流程如下：
<div align="center">
<img src="docs/test.png" width="800">
</div>
![img](https://user-images.githubusercontent.com/79366697/185377097-a0f852a8-2d78-45ae-84ba-ae71b799d738.png)
1. 运行prepare.sh准备测试所需数据和模型；
2. 运行要测试的功能对应的测试脚本`test_*.sh`，产出log，由log可以看到不同配置是否运行成功；
......@@ -72,4 +71,4 @@ test_tipc/
<a name="more"></a>
#### 更多教程
各功能测试中涉及混合精度、裁剪、量化等训练相关，及mkldnn、Tensorrt等多种预测相关参数配置，请点击下方相应链接了解更多细节和使用教程：
[test_train_inference_python 使用](docs/test_train_inference_python.md)
- [test_train_inference_python 使用](docs/test_train_inference_python.md): 测试基于Python的模型训练、评估、推理等基本功能
test_tipc/benchmark_train.sh
浏览文件 @ eebf94d9
......@@ -4,15 +4,15 @@ source test_tipc/common_func.sh
# set env
python=python
export model_branch=`git symbolic-ref HEAD 2>/dev/null | cut -d"/" -f 3`
export model_commit=$(git log|head -n1|awk '{print $2}')
export model_commit=$(git log|head -n1|awk '{print $2}')
export str_tmp=$(echo `pip list|grep paddlepaddle-gpu|awk -F ' ' '{print $2}'`)
export frame_version=${str_tmp%%.post*}
export frame_commit=$(echo `${python} -c "import paddle;print(paddle.version.commit)"`)
# run benchmark sh
# run benchmark sh
# Usage:
# bash run_benchmark_train.sh config.txt params
# or
# or
# bash run_benchmark_train.sh config.txt
function func_parser_params(){
......@@ -100,6 +100,7 @@ for _flag in ${flags_list[*]}; do
done
# set log_name
BENCHMARK_ROOT=./ # self-test only
repo_name=$(get_repo_name )
SAVE_LOG=${BENCHMARK_LOG_DIR:-$(pwd)} # */benchmark_log
mkdir -p "${SAVE_LOG}/benchmark_log/"
......@@ -149,11 +150,11 @@ else
fi
IFS="|"
for batch_size in ${batch_size_list[*]}; do
for batch_size in ${batch_size_list[*]}; do
for precision in ${fp_items_list[*]}; do
for device_num in ${device_num_list[*]}; do
# sed batchsize and precision
#func_sed_params "$FILENAME" "${line_precision}" "$precision"
func_sed_params "$FILENAME" "${line_precision}" "$precision"
func_sed_params "$FILENAME" "${line_batchsize}" "$MODE=$batch_size"
func_sed_params "$FILENAME" "${line_epoch}" "$MODE=$epoch"
gpu_id=$(set_gpu_id $device_num)
......@@ -162,7 +163,7 @@ for batch_size in ${batch_size_list[*]}; do
log_path="$SAVE_LOG/profiling_log"
mkdir -p $log_path
log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_profiling"
func_sed_params "$FILENAME" "${line_gpuid}" "0" # sed used gpu_id
func_sed_params "$FILENAME" "${line_gpuid}" "0" # sed used gpu_id
# set profile_option params
tmp=`sed -i "${line_profile}s/.*/${profile_option}/" "${FILENAME}"`
......@@ -214,7 +215,7 @@ for batch_size in ${batch_size_list[*]}; do
mkdir -p $speed_log_path
log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_log"
speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_speed"
func_sed_params "$FILENAME" "${line_gpuid}" "$gpu_id" # sed used gpu_id
func_sed_params "$FILENAME" "${line_gpuid}" "$gpu_id" # sed used gpu_id
func_sed_params "$FILENAME" "${line_profile}" "null" # sed --profile_option as null
cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "
echo $cmd
......@@ -244,4 +245,4 @@ for batch_size in ${batch_size_list[*]}; do
fi
done
done
done
\ No newline at end of file
done
test_tipc/configs/msvsr/train_amp_infer_python.txt 0 → 100644
浏览文件 @ eebf94d9
===========================train_params===========================
model_name:msvsr
python:python3.7
gpu_list:0
##
auto_cast:null
total_iters:lite_train_lite_infer=10|lite_train_whole_infer=10|whole_train_whole_infer=200
output_dir:./output/
dataset.train.batch_size:lite_train_lite_infer=1|whole_train_whole_infer=1
pretrained_model:null
train_model_name:msvsr_reds*/*checkpoint.pdparams
train_infer_img_dir:./data/msvsr_reds/test
null:null
##
trainer:amp_train
amp_train:tools/main.py --amp --amp_level O1 -c configs/msvsr_reds.yaml --seed 123 -o dataset.train.num_workers=0 log_config.interval=1 snapshot_config.interval=5 dataset.train.dataset.num_frames=2
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
--output_dir:./output/
load:null
norm_export:tools/export_model.py -c configs/msvsr_reds.yaml --inputs_size="1,2,3,180,320" --model_name inference --load
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
inference_dir:inference
train_model:./inference/msvsr/multistagevsrmodel_generator
infer_export:null
infer_quant:False
inference:tools/inference.py --model_type msvsr -c configs/msvsr_reds.yaml --seed 123 -o dataset.test.num_frames=2 --output_path test_tipc/output/
--device:cpu
null:null
null:null
null:null
null:null
null:null
--model_path:
null:null
null:null
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[2,3,180,320]}]
test_tipc/configs/msvsr/train_infer_python.txt
浏览文件 @ eebf94d9
......@@ -13,22 +13,22 @@ train_infer_img_dir:./data/msvsr_reds/test
null:null
##
trainer:norm_train
norm_train:tools/main.py -c configs/msvsr_reds.yaml --seed 123 -o dataset.train.num_workers=0 log_config.interval=1 snapshot_config.interval=5 dataset.train.dataset.num_frames=2
norm_train:tools/main.py -c configs/msvsr_reds.yaml --seed 123 -o log_config.interval=2 snapshot_config.interval=50 dataset.train.dataset.num_frames=15
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
--output_dir:./output/
load:null
norm_export:tools/export_model.py -c configs/msvsr_reds.yaml --inputs_size="1,2,3,180,320" --model_name inference --load
quant_export:null
norm_export:tools/export_model.py -c configs/msvsr_reds.yaml --inputs_size="1,2,3,180,320" --model_name inference --load
quant_export:null
fpgm_export:null
distill_export:null
export1:null
......@@ -49,5 +49,11 @@ null:null
null:null
--benchmark:True
null:null
===========================train_benchmark_params==========================
batch_size:4
fp_items:fp32
total_iters:60
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
flags:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[2,3,180,320]}]
test_tipc/docs/benchmark_train.md
浏览文件 @ eebf94d9
......@@ -9,7 +9,7 @@
```shell
# 运行格式：bash test_tipc/prepare.sh train_benchmark.txt mode
bash test_tipc/prepare.sh test_tipc/configs/basicvsr/train_benchmark.txt benchmark_train
bash test_tipc/prepare.sh test_tipc/configs/msvsr/train_infer_python.txt benchmark_train
```
## 1.2 功能测试
......@@ -17,13 +17,13 @@ bash test_tipc/prepare.sh test_tipc/configs/basicvsr/train_benchmark.txt benchma
```shell
# 运行格式：bash test_tipc/benchmark_train.sh train_benchmark.txt mode
bash test_tipc/benchmark_train.sh test_tipc/configs/basicvsr/train_infer_python.txt benchmark_train
bash test_tipc/benchmark_train.sh test_tipc/configs/msvsr/train_infer_python.txt benchmark_train
```
`test_tipc/benchmark_train.sh`支持根据传入的第三个参数实现只运行某一个训练配置，如下：
```shell
# 运行格式：bash test_tipc/benchmark_train.sh train_benchmark.txt mode
bash test_tipc/benchmark_train.sh test_tipc/configs/basicvsr/train_infer_python.txt benchmark_train dynamic_bs4_fp32_DP_N1C1
bash test_tipc/benchmark_train.sh test_tipc/configs/msvsr/train_infer_python.txt benchmark_train dynamic_bs4_fp32_DP_N1C1
```
dynamic_bs4_fp32_DP_N1C1为test_tipc/benchmark_train.sh传入的参数，格式如下：
`${modeltype}_${batch_size}_${fp_item}_${run_mode}_${device_num}`
......@@ -42,11 +42,11 @@ dynamic_bs4_fp32_DP_N1C1为test_tipc/benchmark_train.sh传入的参数，格式
```
train_log/
├── index
│ ├── PaddleGAN_basicvsr_bs4_fp32_SingleP_DP_N1C1_speed
│ └── PaddleGAN_basicvsr_bs4_fp32_SingleP_DP_N1C4_speed
│ ├── PaddleGAN_msvsr_bs4_fp32_SingleP_DP_N1C1_speed
│ └── PaddleGAN_msvsr_bs4_fp32_SingleP_DP_N1C4_speed
├── profiling_log
│ └── PaddleGAN_basicvsr_bs4_fp32_SingleP_DP_N1C1_profiling
│ └── PaddleGAN_msvsr_bs4_fp32_SingleP_DP_N1C1_profiling
└── train_log
├── PaddleGAN_basicvsr_bs4_fp32_SingleP_DP_N1C1_log
└── PaddleGAN_basicvsr_bs4_fp32_MultiP_DP_N1C4_log
├── PaddleGAN_msvsr_bs4_fp32_SingleP_DP_N1C1_log
└── PaddleGAN_msvsr_bs4_fp32_MultiP_DP_N1C4_log
```
test_tipc/prepare.sh
浏览文件 @ eebf94d9
......@@ -172,5 +172,10 @@ elif [ ${MODE} = "whole_infer" ];then
mkdir -p ./data/singan
mv ./data/SinGAN-official_images/Images/stone.png ./data/singan
fi
elif [ ${MODE} = "benchmark_train" ];then
if [ ${model_name} = "msvsr" ]; then
rm -rf ./data/reds*
wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/reds_lite.tar --no-check-certificate
cd ./data/ && tar xf reds_lite.tar && cd ../
fi
fi
test_tipc/test_train_inference_python.sh
浏览文件 @ eebf94d9
......@@ -48,11 +48,11 @@ norm_export=$(func_parser_value "${lines[29]}")
inference_dir=$(func_parser_value "${lines[35]}")
# parser inference model
# parser inference model
infer_model_dir_list=$(func_parser_value "${lines[36]}")
infer_export_list=$(func_parser_value "${lines[37]}")
infer_is_quant=$(func_parser_value "${lines[38]}")
# parser inference
# parser inference
inference_py=$(func_parser_value "${lines[39]}")
use_gpu_key=$(func_parser_key "${lines[40]}")
use_gpu_list=$(func_parser_value "${lines[40]}")
......@@ -85,7 +85,7 @@ function func_inference(){
_log_path=$4
_img_dir=$5
_flag_quant=$6
# inference
# inference
for use_gpu in ${use_gpu_list[*]}; do
if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
for use_mkldnn in ${use_mkldnn_list[*]}; do
......@@ -96,7 +96,7 @@ function func_inference(){
for batch_size in ${batch_size_list[*]}; do
for precision in ${precision_list[*]}; do
set_precision=$(func_set_params "${precision_key}" "${precision}")
_save_log_path="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
......@@ -118,7 +118,7 @@ function func_inference(){
for precision in ${precision_list[*]}; do
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
continue
fi
fi
if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
continue
fi
......@@ -139,7 +139,7 @@ function func_inference(){
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${command}" "${status_log}"
done
done
done
......@@ -169,7 +169,7 @@ if [ ${MODE} = "whole_infer" ]; then
set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
set_save_infer_key="${save_infer_key} ${save_infer_dir}"
export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key}"
echo ${infer_run_exports[Count]}
echo ${infer_run_exports[Count]}
echo $export_cmd
eval $export_cmd
status_export=$?
......@@ -207,17 +207,17 @@ else
IFS="|"
env=" "
fi
for autocast in ${autocast_list[*]}; do
if [ ${autocast} = "amp" ]; then
set_amp_config="Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True"
for autocast in ${autocast_list[*]}; do
if [ ${autocast} = "fp16" ]; then
set_amp_config="--amp"
else
set_amp_config=" "
fi
for trainer in ${trainer_list[*]}; do
fi
for trainer in ${trainer_list[*]}; do
flag_quant=False
run_train=${norm_trainer}
run_export=${norm_export}
if [ ${run_train} = "null" ]; then
continue
fi
......@@ -239,11 +239,11 @@ else
fi
set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_train_params1} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_amp_config} "
cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_train_params1} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_amp_config}"
elif [ ${#ips} -le 26 ];then # train with multi-gpu
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_train_params1} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_amp_config}"
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_train_params1} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_amp_config}"
else # train with multi-machine
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_train_params1} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_amp_config}"
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_train_params1} ${set_pretrain} ${set_epoch} ${set_batchsize} ${set_amp_config}"
fi
# run train
eval "unset CUDA_VISIBLE_DEVICES"
......@@ -253,17 +253,17 @@ else
status_check $? "${cmd}" "${status_log}"
set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
# save norm trained models to set pretrain for pact training and fpgm training
# run eval
# save norm trained models to set pretrain for pact training and fpgm training
# run eval
if [ ${eval_py} != "null" ]; then
set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}"
eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}"
eval $eval_cmd
status_check $? "${eval_cmd}" "${status_log}"
fi
# run export model
if [ ${run_export} != "null" ]; then
if [ ${run_export} != "null" ]; then
# run export model
save_infer_path="${save_log}"
set_export_weight="${save_log}/${train_model_name}"
......@@ -272,7 +272,7 @@ else
export_cmd="${python} ${run_export} ${set_export_weight_path} ${set_save_infer_key}"
eval "$export_cmd"
status_check $? "${export_cmd}" "${status_log}"
#run inference
eval $env
save_infer_path="${save_log}"
......@@ -282,11 +282,10 @@ else
infer_model_dir=${save_infer_path}
fi
func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
eval "unset CUDA_VISIBLE_DEVICES"
fi
done # done with: for trainer in ${trainer_list[*]}; do
done # done with: for autocast in ${autocast_list[*]}; do
done # done with: for trainer in ${trainer_list[*]}; do
done # done with: for autocast in ${autocast_list[*]}; do
done # done with: for gpu in ${gpu_list[*]}; do
fi # end if [ ${MODE} = "infer" ]; then
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