forked from PulseFocusPlatform/PulseFocusPlatform
349 lines
12 KiB
Python
349 lines
12 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import os
|
|
import sys
|
|
|
|
# add python path of PadleDetection to sys.path
|
|
parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 3)))
|
|
if parent_path not in sys.path:
|
|
sys.path.append(parent_path)
|
|
|
|
import time
|
|
import numpy as np
|
|
import datetime
|
|
from collections import deque
|
|
import shutil
|
|
|
|
from paddle import fluid
|
|
|
|
import logging
|
|
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
|
|
logging.basicConfig(level=logging.INFO, format=FORMAT)
|
|
logger = logging.getLogger(__name__)
|
|
|
|
try:
|
|
from ppdet.core.workspace import load_config, merge_config, create
|
|
from ppdet.data.reader import create_reader
|
|
from ppdet.utils import dist_utils
|
|
from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results
|
|
from ppdet.utils.stats import TrainingStats
|
|
from ppdet.utils.cli import ArgsParser
|
|
from ppdet.utils.check import check_gpu, check_version, check_config, enable_static_mode
|
|
import ppdet.utils.checkpoint as checkpoint
|
|
except ImportError as e:
|
|
if sys.argv[0].find('static') >= 0:
|
|
logger.error("Importing ppdet failed when running static model "
|
|
"with error: {}\n"
|
|
"please try:\n"
|
|
"\t1. run static model under PaddleDetection/static "
|
|
"directory\n"
|
|
"\t2. run 'pip uninstall ppdet' to uninstall ppdet "
|
|
"dynamic version firstly.".format(e))
|
|
sys.exit(-1)
|
|
else:
|
|
raise e
|
|
|
|
from paddleslim.quant import quant_aware
|
|
from pact import pact, get_optimizer
|
|
|
|
|
|
def save_checkpoint(exe, prog, path, train_prog):
|
|
if os.path.isdir(path):
|
|
shutil.rmtree(path)
|
|
logger.info('Save model to {}.'.format(path))
|
|
fluid.io.save_persistables(exe, path, main_program=prog)
|
|
|
|
|
|
def main():
|
|
if FLAGS.eval is False:
|
|
raise ValueError(
|
|
"Currently only supports `--eval==True` while training in `quantization`."
|
|
)
|
|
env = os.environ
|
|
FLAGS.dist = 'PADDLE_TRAINER_ID' in env \
|
|
and 'PADDLE_TRAINERS_NUM' in env \
|
|
and int(env['PADDLE_TRAINERS_NUM']) > 1
|
|
num_trainers = int(env.get('PADDLE_TRAINERS_NUM', 1))
|
|
if FLAGS.dist:
|
|
trainer_id = int(env['PADDLE_TRAINER_ID'])
|
|
import random
|
|
local_seed = (99 + trainer_id)
|
|
random.seed(local_seed)
|
|
np.random.seed(local_seed)
|
|
|
|
cfg = load_config(FLAGS.config)
|
|
merge_config(FLAGS.opt)
|
|
check_config(cfg)
|
|
# check if set use_gpu=True in paddlepaddle cpu version
|
|
check_gpu(cfg.use_gpu)
|
|
# check if paddlepaddle version is satisfied
|
|
check_version()
|
|
|
|
main_arch = cfg.architecture
|
|
|
|
if cfg.use_gpu:
|
|
devices_num = fluid.core.get_cuda_device_count()
|
|
else:
|
|
devices_num = int(os.environ.get('CPU_NUM', 1))
|
|
|
|
if 'FLAGS_selected_gpus' in env:
|
|
device_id = int(env['FLAGS_selected_gpus'])
|
|
else:
|
|
device_id = 0
|
|
place = fluid.CUDAPlace(device_id) if cfg.use_gpu else fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
|
|
lr_builder = create('LearningRate')
|
|
optim_builder = create('OptimizerBuilder')
|
|
|
|
# build program
|
|
startup_prog = fluid.Program()
|
|
train_prog = fluid.Program()
|
|
with fluid.program_guard(train_prog, startup_prog):
|
|
with fluid.unique_name.guard():
|
|
model = create(main_arch)
|
|
inputs_def = cfg['TrainReader']['inputs_def']
|
|
feed_vars, train_loader = model.build_inputs(**inputs_def)
|
|
if FLAGS.use_pact:
|
|
feed_vars['image'].stop_gradient = False
|
|
train_fetches = model.train(feed_vars)
|
|
loss = train_fetches['loss']
|
|
lr = lr_builder()
|
|
optimizer = optim_builder(lr)
|
|
optimizer.minimize(loss)
|
|
|
|
# parse train fetches
|
|
train_keys, train_values, _ = parse_fetches(train_fetches)
|
|
train_values.append(lr)
|
|
|
|
if FLAGS.eval:
|
|
eval_prog = fluid.Program()
|
|
with fluid.program_guard(eval_prog, startup_prog):
|
|
with fluid.unique_name.guard():
|
|
model = create(main_arch)
|
|
inputs_def = cfg['EvalReader']['inputs_def']
|
|
feed_vars, eval_loader = model.build_inputs(**inputs_def)
|
|
fetches = model.eval(feed_vars)
|
|
eval_prog = eval_prog.clone(True)
|
|
|
|
eval_reader = create_reader(cfg.EvalReader)
|
|
# When iterable mode, set set_sample_list_generator(eval_reader, place)
|
|
eval_loader.set_sample_list_generator(eval_reader)
|
|
|
|
# parse eval fetches
|
|
extra_keys = []
|
|
if cfg.metric == 'COCO':
|
|
extra_keys = ['im_info', 'im_id', 'im_shape']
|
|
if cfg.metric == 'VOC':
|
|
extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
|
|
if cfg.metric == 'WIDERFACE':
|
|
extra_keys = ['im_id', 'im_shape', 'gt_bbox']
|
|
eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
|
|
extra_keys)
|
|
|
|
# compile program for multi-devices
|
|
build_strategy = fluid.BuildStrategy()
|
|
build_strategy.fuse_all_optimizer_ops = False
|
|
build_strategy.fuse_elewise_add_act_ops = True
|
|
build_strategy.fuse_all_reduce_ops = False
|
|
|
|
# only enable sync_bn in multi GPU devices
|
|
sync_bn = getattr(model.backbone, 'norm_type', None) == 'sync_bn'
|
|
sync_bn = False
|
|
build_strategy.sync_batch_norm = sync_bn and devices_num > 1 \
|
|
and cfg.use_gpu
|
|
|
|
exec_strategy = fluid.ExecutionStrategy()
|
|
# iteration number when CompiledProgram tries to drop local execution scopes.
|
|
# Set it to be 1 to save memory usages, so that unused variables in
|
|
# local execution scopes can be deleted after each iteration.
|
|
exec_strategy.num_iteration_per_drop_scope = 1
|
|
if FLAGS.dist:
|
|
dist_utils.prepare_for_multi_process(exe, build_strategy, startup_prog,
|
|
train_prog)
|
|
exec_strategy.num_threads = 1
|
|
|
|
exe.run(startup_prog)
|
|
not_quant_pattern = []
|
|
if FLAGS.not_quant_pattern:
|
|
not_quant_pattern = FLAGS.not_quant_pattern
|
|
config = {
|
|
'weight_quantize_type': 'channel_wise_abs_max',
|
|
'activation_quantize_type': 'moving_average_abs_max',
|
|
'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'],
|
|
'not_quant_pattern': not_quant_pattern
|
|
}
|
|
|
|
ignore_params = cfg.finetune_exclude_pretrained_params \
|
|
if 'finetune_exclude_pretrained_params' in cfg else []
|
|
|
|
fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
|
|
|
|
if cfg.pretrain_weights and fuse_bn and not ignore_params:
|
|
checkpoint.load_and_fusebn(exe, train_prog, cfg.pretrain_weights)
|
|
elif cfg.pretrain_weights:
|
|
checkpoint.load_params(
|
|
exe, train_prog, cfg.pretrain_weights, ignore_params=ignore_params)
|
|
|
|
if FLAGS.use_pact:
|
|
act_preprocess_func = pact
|
|
optimizer_func = get_optimizer
|
|
executor = exe
|
|
else:
|
|
act_preprocess_func = None
|
|
optimizer_func = None
|
|
executor = None
|
|
# insert quantize op in train_prog, return type is CompiledProgram
|
|
train_prog_quant = quant_aware(
|
|
train_prog,
|
|
place,
|
|
config,
|
|
scope=None,
|
|
act_preprocess_func=act_preprocess_func,
|
|
optimizer_func=optimizer_func,
|
|
executor=executor,
|
|
for_test=False)
|
|
|
|
compiled_train_prog = train_prog_quant.with_data_parallel(
|
|
loss_name=loss.name,
|
|
build_strategy=build_strategy,
|
|
exec_strategy=exec_strategy)
|
|
|
|
if FLAGS.eval:
|
|
# insert quantize op in eval_prog
|
|
eval_prog = quant_aware(
|
|
eval_prog,
|
|
place,
|
|
config,
|
|
scope=None,
|
|
act_preprocess_func=act_preprocess_func,
|
|
optimizer_func=optimizer_func,
|
|
executor=executor,
|
|
for_test=True)
|
|
compiled_eval_prog = fluid.CompiledProgram(eval_prog)
|
|
|
|
start_iter = 0
|
|
|
|
train_reader = create_reader(
|
|
cfg.TrainReader, (cfg.max_iters - start_iter) * devices_num,
|
|
cfg,
|
|
devices_num=devices_num,
|
|
num_trainers=num_trainers)
|
|
# When iterable mode, set set_sample_list_generator(train_reader, place)
|
|
train_loader.set_sample_list_generator(train_reader)
|
|
|
|
# whether output bbox is normalized in model output layer
|
|
is_bbox_normalized = False
|
|
if hasattr(model, 'is_bbox_normalized') and \
|
|
callable(model.is_bbox_normalized):
|
|
is_bbox_normalized = model.is_bbox_normalized()
|
|
|
|
# if map_type not set, use default 11point, only use in VOC eval
|
|
map_type = cfg.map_type if 'map_type' in cfg else '11point'
|
|
|
|
train_stats = TrainingStats(cfg.log_iter, train_keys)
|
|
train_loader.start()
|
|
start_time = time.time()
|
|
end_time = time.time()
|
|
|
|
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
|
|
save_dir = os.path.join(cfg.save_dir, cfg_name)
|
|
time_stat = deque(maxlen=cfg.log_iter)
|
|
best_box_ap_list = [0.0, 0] #[map, iter]
|
|
|
|
for it in range(start_iter, cfg.max_iters):
|
|
start_time = end_time
|
|
end_time = time.time()
|
|
time_stat.append(end_time - start_time)
|
|
time_cost = np.mean(time_stat)
|
|
eta_sec = (cfg.max_iters - it) * time_cost
|
|
eta = str(datetime.timedelta(seconds=int(eta_sec)))
|
|
outs = exe.run(compiled_train_prog, fetch_list=train_values)
|
|
stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
|
|
|
|
train_stats.update(stats)
|
|
logs = train_stats.log()
|
|
if it % cfg.log_iter == 0 and (not FLAGS.dist or trainer_id == 0):
|
|
strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
|
|
it, np.mean(outs[-1]), logs, time_cost, eta)
|
|
logger.info(strs)
|
|
|
|
if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1) \
|
|
and (not FLAGS.dist or trainer_id == 0):
|
|
save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
|
|
|
|
if FLAGS.eval:
|
|
# evaluation
|
|
results = eval_run(
|
|
exe,
|
|
compiled_eval_prog,
|
|
eval_loader,
|
|
eval_keys,
|
|
eval_values,
|
|
eval_cls,
|
|
cfg=cfg)
|
|
resolution = None
|
|
if 'mask' in results[0]:
|
|
resolution = model.mask_head.resolution
|
|
box_ap_stats = eval_results(
|
|
results, cfg.metric, cfg.num_classes, resolution,
|
|
is_bbox_normalized, FLAGS.output_eval, map_type,
|
|
cfg['EvalReader']['dataset'])
|
|
|
|
if box_ap_stats[0] > best_box_ap_list[0]:
|
|
best_box_ap_list[0] = box_ap_stats[0]
|
|
best_box_ap_list[1] = it
|
|
save_checkpoint(exe, eval_prog,
|
|
os.path.join(save_dir, "best_model"),
|
|
train_prog)
|
|
logger.info("Best test box ap: {}, in iter: {}".format(
|
|
best_box_ap_list[0], best_box_ap_list[1]))
|
|
|
|
train_loader.reset()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
enable_static_mode()
|
|
parser = ArgsParser()
|
|
parser.add_argument(
|
|
"--loss_scale",
|
|
default=8.,
|
|
type=float,
|
|
help="Mixed precision training loss scale.")
|
|
parser.add_argument(
|
|
"--eval",
|
|
action='store_true',
|
|
default=False,
|
|
help="Whether to perform evaluation in train")
|
|
parser.add_argument(
|
|
"--output_eval",
|
|
default=None,
|
|
type=str,
|
|
help="Evaluation directory, default is current directory.")
|
|
parser.add_argument(
|
|
"--not_quant_pattern",
|
|
nargs='+',
|
|
type=str,
|
|
help="Layers which name_scope contains string in not_quant_pattern will not be quantized"
|
|
)
|
|
parser.add_argument(
|
|
"--use_pact", nargs='+', type=bool, help="Whether to use PACT or not.")
|
|
FLAGS = parser.parse_args()
|
|
main()
|