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# Copyright (c) 2020 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
import datetime
import six
import copy
import json
import paddle
import paddle.distributed as dist
from ppdet.utils.checkpoint import save_model
from ppdet.metrics import get_infer_results
from ppdet.utils.logger import setup_logger
logger = setup_logger('ppdet.engine')
__all__ = [
'Callback', 'ComposeCallback', 'LogPrinter', 'Checkpointer',
'VisualDLWriter', 'SniperProposalsGenerator'
]
class Callback(object):
def __init__(self, model):
self.model = model
def on_step_begin(self, status):
pass
def on_step_end(self, status):
pass
def on_epoch_begin(self, status):
pass
def on_epoch_end(self, status):
pass
def on_train_begin(self, status):
pass
def on_train_end(self, status):
pass
class ComposeCallback(object):
def __init__(self, callbacks):
callbacks = [c for c in list(callbacks) if c is not None]
for c in callbacks:
assert isinstance(
c, Callback), "callback should be subclass of Callback"
self._callbacks = callbacks
def on_step_begin(self, status):
for c in self._callbacks:
c.on_step_begin(status)
def on_step_end(self, status):
for c in self._callbacks:
c.on_step_end(status)
def on_epoch_begin(self, status):
for c in self._callbacks:
c.on_epoch_begin(status)
def on_epoch_end(self, status):
for c in self._callbacks:
c.on_epoch_end(status)
def on_train_begin(self, status):
for c in self._callbacks:
c.on_train_begin(status)
def on_train_end(self, status):
for c in self._callbacks:
c.on_train_end(status)
class LogPrinter(Callback):
def __init__(self, model):
super(LogPrinter, self).__init__(model)
def on_step_end(self, status):
if dist.get_world_size() < 2 or dist.get_rank() == 0:
mode = status['mode']
if mode == 'train':
epoch_id = status['epoch_id']
step_id = status['step_id']
steps_per_epoch = status['steps_per_epoch']
training_staus = status['training_staus']
batch_time = status['batch_time']
data_time = status['data_time']
epoches = self.model.cfg.epoch
batch_size = self.model.cfg['{}Reader'.format(mode.capitalize(
))]['batch_size']
logs = training_staus.log()
space_fmt = ':' + str(len(str(steps_per_epoch))) + 'd'
if step_id % self.model.cfg.log_iter == 0:
eta_steps = (epoches - epoch_id) * steps_per_epoch - step_id
eta_sec = eta_steps * batch_time.global_avg
eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
ips = float(batch_size) / batch_time.avg
fmt = ' '.join([
'Epoch: [{}]',
'[{' + space_fmt + '}/{}]',
'learning_rate: {lr:.6f}',
'{meters}',
'eta: {eta}',
'batch_cost: {btime}',
'data_cost: {dtime}',
'ips: {ips:.4f} images/s',
])
fmt = fmt.format(
epoch_id,
step_id,
steps_per_epoch,
lr=status['learning_rate'],
meters=logs,
eta=eta_str,
btime=str(batch_time),
dtime=str(data_time),
ips=ips)
logger.info(fmt)
if mode == 'eval':
step_id = status['step_id']
if step_id % 100 == 0:
logger.info("Eval iter: {}".format(step_id))
def on_epoch_end(self, status):
if dist.get_world_size() < 2 or dist.get_rank() == 0:
mode = status['mode']
if mode == 'eval':
sample_num = status['sample_num']
cost_time = status['cost_time']
logger.info('Total sample number: {}, average FPS: {}'.format(
sample_num, sample_num / cost_time))
class Checkpointer(Callback):
def __init__(self, model):
super(Checkpointer, self).__init__(model)
self.best_ap = -1000.
self.save_dir = os.path.join(self.model.cfg.save_dir,
self.model.cfg.filename)
if hasattr(self.model.model, 'student_model'):
self.weight = self.model.model.student_model
else:
self.weight = self.model.model
def on_epoch_end(self, status):
# Checkpointer only performed during training
mode = status['mode']
epoch_id = status['epoch_id']
weight = None
save_name = None
if dist.get_world_size() < 2 or dist.get_rank() == 0:
if mode == 'train':
end_epoch = self.model.cfg.epoch
if (
epoch_id + 1
) % self.model.cfg.snapshot_epoch == 0 or epoch_id == end_epoch - 1:
save_name = str(
epoch_id) if epoch_id != end_epoch - 1 else "model_final"
weight = self.weight.state_dict()
elif mode == 'eval':
if 'save_best_model' in status and status['save_best_model']:
for metric in self.model._metrics:
map_res = metric.get_results()
eval_func = "ap"
if 'pose3d' in map_res:
key = 'pose3d'
eval_func = "mpjpe"
elif 'bbox' in map_res:
key = 'bbox'
elif 'keypoint' in map_res:
key = 'keypoint'
else:
key = 'mask'
if key not in map_res:
logger.warning("Evaluation results empty, this may be due to " \
"training iterations being too few or not " \
"loading the correct weights.")
return
if map_res[key][0] >= self.best_ap:
self.best_ap = map_res[key][0]
save_name = 'best_model'
weight = self.weight.state_dict()
logger.info("Best test {} {} is {:0.3f}.".format(
key, eval_func, abs(self.best_ap)))
if weight:
if self.model.use_ema:
exchange_save_model = status.get('exchange_save_model',
False)
if not exchange_save_model:
# save model and ema_model
save_model(
status['weight'],
self.model.optimizer,
self.save_dir,
save_name,
epoch_id + 1,
ema_model=weight)
else:
# save model(student model) and ema_model(teacher model)
# in DenseTeacher SSOD, the teacher model will be higher,
# so exchange when saving pdparams
student_model = status['weight'] # model
teacher_model = weight # ema_model
save_model(
teacher_model,
self.model.optimizer,
self.save_dir,
save_name,
epoch_id + 1,
ema_model=student_model)
del teacher_model
del student_model
else:
save_model(weight, self.model.optimizer, self.save_dir,
save_name, epoch_id + 1)
class WiferFaceEval(Callback):
def __init__(self, model):
super(WiferFaceEval, self).__init__(model)
def on_epoch_begin(self, status):
assert self.model.mode == 'eval', \
"WiferFaceEval can only be set during evaluation"
for metric in self.model._metrics:
metric.update(self.model.model)
sys.exit()
class VisualDLWriter(Callback):
"""
Use VisualDL to log data or image
"""
def __init__(self, model):
super(VisualDLWriter, self).__init__(model)
assert six.PY3, "VisualDL requires Python >= 3.5"
try:
from visualdl import LogWriter
except Exception as e:
logger.error('visualdl not found, plaese install visualdl. '
'for example: `pip install visualdl`.')
raise e
self.vdl_writer = LogWriter(
model.cfg.get('vdl_log_dir', 'vdl_log_dir/scalar'))
self.vdl_loss_step = 0
self.vdl_mAP_step = 0
self.vdl_image_step = 0
self.vdl_image_frame = 0
def on_step_end(self, status):
mode = status['mode']
if dist.get_world_size() < 2 or dist.get_rank() == 0:
if mode == 'train':
training_staus = status['training_staus']
for loss_name, loss_value in training_staus.get().items():
self.vdl_writer.add_scalar(loss_name, loss_value,
self.vdl_loss_step)
self.vdl_loss_step += 1
elif mode == 'test':
ori_image = status['original_image']
result_image = status['result_image']
self.vdl_writer.add_image(
"original/frame_{}".format(self.vdl_image_frame), ori_image,
self.vdl_image_step)
self.vdl_writer.add_image(
"result/frame_{}".format(self.vdl_image_frame),
result_image, self.vdl_image_step)
self.vdl_image_step += 1
# each frame can display ten pictures at most.
if self.vdl_image_step % 10 == 0:
self.vdl_image_step = 0
self.vdl_image_frame += 1
def on_epoch_end(self, status):
mode = status['mode']
if dist.get_world_size() < 2 or dist.get_rank() == 0:
if mode == 'eval':
for metric in self.model._metrics:
for key, map_value in metric.get_results().items():
self.vdl_writer.add_scalar("{}-mAP".format(key),
map_value[0],
self.vdl_mAP_step)
self.vdl_mAP_step += 1
class WandbCallback(Callback):
def __init__(self, model):
super(WandbCallback, self).__init__(model)
try:
import wandb
self.wandb = wandb
except Exception as e:
logger.error('wandb not found, please install wandb. '
'Use: `pip install wandb`.')
raise e
self.wandb_params = model.cfg.get('wandb', None)
self.save_dir = os.path.join(self.model.cfg.save_dir,
self.model.cfg.filename)
if self.wandb_params is None:
self.wandb_params = {}
for k, v in model.cfg.items():
if k.startswith("wandb_"):
self.wandb_params.update({k.lstrip("wandb_"): v})
self._run = None
if dist.get_world_size() < 2 or dist.get_rank() == 0:
_ = self.run
self.run.config.update(self.model.cfg)
self.run.define_metric("epoch")
self.run.define_metric("eval/*", step_metric="epoch")
self.best_ap = -1000.
self.fps = []
@property
def run(self):
if self._run is None:
if self.wandb.run is not None:
logger.info(
"There is an ongoing wandb run which will be used"
"for logging. Please use `wandb.finish()` to end that"
"if the behaviour is not intended")
self._run = self.wandb.run
else:
self._run = self.wandb.init(**self.wandb_params)
return self._run
def save_model(self,
optimizer,
save_dir,
save_name,
last_epoch,
ema_model=None,
ap=None,
fps=None,
tags=None):
if dist.get_world_size() < 2 or dist.get_rank() == 0:
model_path = os.path.join(save_dir, save_name)
metadata = {}
metadata["last_epoch"] = last_epoch
if ap:
metadata["ap"] = ap
if fps:
metadata["fps"] = fps
if ema_model is None:
ema_artifact = self.wandb.Artifact(
name="ema_model-{}".format(self.run.id),
type="model",
metadata=metadata)
model_artifact = self.wandb.Artifact(
name="model-{}".format(self.run.id),
type="model",
metadata=metadata)
ema_artifact.add_file(model_path + ".pdema", name="model_ema")
model_artifact.add_file(model_path + ".pdparams", name="model")
self.run.log_artifact(ema_artifact, aliases=tags)
self.run.log_artfact(model_artifact, aliases=tags)
else:
model_artifact = self.wandb.Artifact(
name="model-{}".format(self.run.id),
type="model",
metadata=metadata)
model_artifact.add_file(model_path + ".pdparams", name="model")
self.run.log_artifact(model_artifact, aliases=tags)
def on_step_end(self, status):
mode = status['mode']
if dist.get_world_size() < 2 or dist.get_rank() == 0:
if mode == 'train':
training_status = status['training_staus'].get()
for k, v in training_status.items():
training_status[k] = float(v)
# calculate ips, data_cost, batch_cost
batch_time = status['batch_time']
data_time = status['data_time']
batch_size = self.model.cfg['{}Reader'.format(mode.capitalize(
))]['batch_size']
ips = float(batch_size) / float(batch_time.avg)
data_cost = float(data_time.avg)
batch_cost = float(batch_time.avg)
metrics = {"train/" + k: v for k, v in training_status.items()}
metrics["train/ips"] = ips
metrics["train/data_cost"] = data_cost
metrics["train/batch_cost"] = batch_cost
self.fps.append(ips)
self.run.log(metrics)
def on_epoch_end(self, status):
mode = status['mode']
epoch_id = status['epoch_id']
save_name = None
if dist.get_world_size() < 2 or dist.get_rank() == 0:
if mode == 'train':
fps = sum(self.fps) / len(self.fps)
self.fps = []
end_epoch = self.model.cfg.epoch
if (
epoch_id + 1
) % self.model.cfg.snapshot_epoch == 0 or epoch_id == end_epoch - 1:
save_name = str(
epoch_id) if epoch_id != end_epoch - 1 else "model_final"
tags = ["latest", "epoch_{}".format(epoch_id)]
self.save_model(
self.model.optimizer,
self.save_dir,
save_name,
epoch_id + 1,
self.model.use_ema,
fps=fps,
tags=tags)
if mode == 'eval':
sample_num = status['sample_num']
cost_time = status['cost_time']
fps = sample_num / cost_time
merged_dict = {}
for metric in self.model._metrics:
for key, map_value in metric.get_results().items():
merged_dict["eval/{}-mAP".format(key)] = map_value[0]
merged_dict["epoch"] = status["epoch_id"]
merged_dict["eval/fps"] = sample_num / cost_time
self.run.log(merged_dict)
if 'save_best_model' in status and status['save_best_model']:
for metric in self.model._metrics:
map_res = metric.get_results()
if 'pose3d' in map_res:
key = 'pose3d'
elif 'bbox' in map_res:
key = 'bbox'
elif 'keypoint' in map_res:
key = 'keypoint'
else:
key = 'mask'
if key not in map_res:
logger.warning("Evaluation results empty, this may be due to " \
"training iterations being too few or not " \
"loading the correct weights.")
return
if map_res[key][0] >= self.best_ap:
self.best_ap = map_res[key][0]
save_name = 'best_model'
tags = ["best", "epoch_{}".format(epoch_id)]
self.save_model(
self.model.optimizer,
self.save_dir,
save_name,
last_epoch=epoch_id + 1,
ema_model=self.model.use_ema,
ap=abs(self.best_ap),
fps=fps,
tags=tags)
def on_train_end(self, status):
self.run.finish()
class SniperProposalsGenerator(Callback):
def __init__(self, model):
super(SniperProposalsGenerator, self).__init__(model)
ori_dataset = self.model.dataset
self.dataset = self._create_new_dataset(ori_dataset)
self.loader = self.model.loader
self.cfg = self.model.cfg
self.infer_model = self.model.model
def _create_new_dataset(self, ori_dataset):
dataset = copy.deepcopy(ori_dataset)
# init anno_cropper
dataset.init_anno_cropper()
# generate infer roidbs
ori_roidbs = dataset.get_ori_roidbs()
roidbs = dataset.anno_cropper.crop_infer_anno_records(ori_roidbs)
# set new roidbs
dataset.set_roidbs(roidbs)
return dataset
def _eval_with_loader(self, loader):
results = []
with paddle.no_grad():
self.infer_model.eval()
for step_id, data in enumerate(loader):
outs = self.infer_model(data)
for key in ['im_shape', 'scale_factor', 'im_id']:
outs[key] = data[key]
for key, value in outs.items():
if hasattr(value, 'numpy'):
outs[key] = value.numpy()
results.append(outs)
return results
def on_train_end(self, status):
self.loader.dataset = self.dataset
results = self._eval_with_loader(self.loader)
results = self.dataset.anno_cropper.aggregate_chips_detections(results)
# sniper
proposals = []
clsid2catid = {v: k for k, v in self.dataset.catid2clsid.items()}
for outs in results:
batch_res = get_infer_results(outs, clsid2catid)
start = 0
for i, im_id in enumerate(outs['im_id']):
bbox_num = outs['bbox_num']
end = start + bbox_num[i]
bbox_res = batch_res['bbox'][start:end] \
if 'bbox' in batch_res else None
if bbox_res:
proposals += bbox_res
logger.info("save proposals in {}".format(self.cfg.proposals_path))
with open(self.cfg.proposals_path, 'w') as f:
json.dump(proposals, f)
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