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trainer.py
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trainer.py
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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 copy
import time
from tqdm import tqdm
import numpy as np
import typing
from PIL import Image, ImageOps, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
import paddle
import paddle.nn as nn
import paddle.distributed as dist
from paddle.distributed import fleet
from paddle.static import InputSpec
from ppdet.optimizer import ModelEMA
from ppdet.core.workspace import create
from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
from ppdet.utils.visualizer import visualize_results, save_result
from ppdet.metrics import get_infer_results, KeyPointTopDownCOCOEval, KeyPointTopDownCOCOWholeBadyHandEval, KeyPointTopDownMPIIEval, Pose3DEval
from ppdet.metrics import Metric, COCOMetric, VOCMetric, WiderFaceMetric, RBoxMetric, JDEDetMetric, SNIPERCOCOMetric, CULaneMetric
from ppdet.data.source.sniper_coco import SniperCOCODataSet
from ppdet.data.source.category import get_categories
import ppdet.utils.stats as stats
from ppdet.utils.fuse_utils import fuse_conv_bn
from ppdet.utils import profiler
from ppdet.modeling.post_process import multiclass_nms
from ppdet.modeling.lane_utils import imshow_lanes
from .callbacks import Callback, ComposeCallback, LogPrinter, Checkpointer, WiferFaceEval, VisualDLWriter, SniperProposalsGenerator, WandbCallback, SemiCheckpointer, SemiLogPrinter
from .export_utils import _dump_infer_config, _prune_input_spec, apply_to_static
from .naive_sync_bn import convert_syncbn
from paddle.distributed.fleet.utils.hybrid_parallel_util import fused_allreduce_gradients
from ppdet.utils.logger import setup_logger
logger = setup_logger('ppdet.engine')
__all__ = ['Trainer']
MOT_ARCH = ['JDE', 'FairMOT', 'DeepSORT', 'ByteTrack', 'CenterTrack']
class Trainer(object):
def __init__(self, cfg, mode='train'):
self.cfg = cfg.copy()
assert mode.lower() in ['train', 'eval', 'test'], \
"mode should be 'train', 'eval' or 'test'"
self.mode = mode.lower()
self.optimizer = None
self.is_loaded_weights = False
self.use_amp = self.cfg.get('amp', False)
self.amp_level = self.cfg.get('amp_level', 'O1')
self.custom_white_list = self.cfg.get('custom_white_list', None)
self.custom_black_list = self.cfg.get('custom_black_list', None)
self.use_master_grad = self.cfg.get('master_grad', False)
if 'slim' in cfg and cfg['slim_type'] == 'PTQ':
self.cfg['TestDataset'] = create('TestDataset')()
# build data loader
capital_mode = self.mode.capitalize()
if cfg.architecture in MOT_ARCH and self.mode in [
'eval', 'test'
] and cfg.metric not in ['COCO', 'VOC']:
self.dataset = self.cfg['{}MOTDataset'.format(
capital_mode)] = create('{}MOTDataset'.format(capital_mode))()
else:
self.dataset = self.cfg['{}Dataset'.format(capital_mode)] = create(
'{}Dataset'.format(capital_mode))()
if cfg.architecture == 'DeepSORT' and self.mode == 'train':
logger.error('DeepSORT has no need of training on mot dataset.')
sys.exit(1)
if cfg.architecture == 'FairMOT' and self.mode == 'eval':
images = self.parse_mot_images(cfg)
self.dataset.set_images(images)
if self.mode == 'train':
self.loader = create('{}Reader'.format(capital_mode))(
self.dataset, cfg.worker_num)
if cfg.architecture == 'JDE' and self.mode == 'train':
self.cfg['JDEEmbeddingHead'][
'num_identities'] = self.dataset.num_identities_dict[0]
# JDE only support single class MOT now.
if cfg.architecture == 'FairMOT' and self.mode == 'train':
self.cfg['FairMOTEmbeddingHead'][
'num_identities_dict'] = self.dataset.num_identities_dict
# FairMOT support single class and multi-class MOT now.
# build model
if 'model' not in self.cfg:
self.model = create(cfg.architecture)
else:
self.model = self.cfg.model
self.is_loaded_weights = True
if cfg.architecture == 'YOLOX':
for k, m in self.model.named_sublayers():
if isinstance(m, nn.BatchNorm2D):
m._epsilon = 1e-3 # for amp(fp16)
m._momentum = 0.97 # 0.03 in pytorch
#normalize params for deploy
if 'slim' in cfg and cfg['slim_type'] == 'OFA':
self.model.model.load_meanstd(cfg['TestReader'][
'sample_transforms'])
elif 'slim' in cfg and cfg['slim_type'] == 'Distill':
self.model.student_model.load_meanstd(cfg['TestReader'][
'sample_transforms'])
elif 'slim' in cfg and cfg[
'slim_type'] == 'DistillPrune' and self.mode == 'train':
self.model.student_model.load_meanstd(cfg['TestReader'][
'sample_transforms'])
else:
self.model.load_meanstd(cfg['TestReader']['sample_transforms'])
# EvalDataset build with BatchSampler to evaluate in single device
# TODO: multi-device evaluate
if self.mode == 'eval':
if cfg.architecture == 'FairMOT':
self.loader = create('EvalMOTReader')(self.dataset, 0)
elif cfg.architecture == "METRO_Body":
reader_name = '{}Reader'.format(self.mode.capitalize())
self.loader = create(reader_name)(self.dataset, cfg.worker_num)
else:
self._eval_batch_sampler = paddle.io.BatchSampler(
self.dataset, batch_size=self.cfg.EvalReader['batch_size'])
reader_name = '{}Reader'.format(self.mode.capitalize())
# If metric is VOC, need to be set collate_batch=False.
if cfg.metric == 'VOC':
self.cfg[reader_name]['collate_batch'] = False
self.loader = create(reader_name)(self.dataset, cfg.worker_num,
self._eval_batch_sampler)
# TestDataset build after user set images, skip loader creation here
# get Params
print_params = self.cfg.get('print_params', False)
if print_params:
params = sum([
p.numel() for n, p in self.model.named_parameters()
if all([x not in n for x in ['_mean', '_variance', 'aux_']])
]) # exclude BatchNorm running status
logger.info('Model Params : {} M.'.format((params / 1e6).numpy()[
0]))
# build optimizer in train mode
if self.mode == 'train':
steps_per_epoch = len(self.loader)
if steps_per_epoch < 1:
logger.warning(
"Samples in dataset are less than batch_size, please set smaller batch_size in TrainReader."
)
self.lr = create('LearningRate')(steps_per_epoch)
self.optimizer = create('OptimizerBuilder')(self.lr, self.model)
# Unstructured pruner is only enabled in the train mode.
if self.cfg.get('unstructured_prune'):
self.pruner = create('UnstructuredPruner')(self.model,
steps_per_epoch)
if self.use_amp and self.amp_level == 'O2':
paddle_version = paddle.__version__[:3]
# paddle version >= 2.5.0 or develop
if paddle_version in ["2.5", "0.0"]:
self.model, self.optimizer = paddle.amp.decorate(
models=self.model,
optimizers=self.optimizer,
level=self.amp_level,
master_grad=self.use_master_grad)
else:
self.model, self.optimizer = paddle.amp.decorate(
models=self.model,
optimizers=self.optimizer,
level=self.amp_level)
# support sync_bn for npu
if (paddle.get_device()[:3]=='npu'):
use_npu = ('use_npu' in cfg and cfg['use_npu'])
norm_type = ('norm_type' in cfg and cfg['norm_type'])
if norm_type == 'sync_bn' and use_npu and dist.get_world_size() > 1:
convert_syncbn(self.model)
self.use_ema = ('use_ema' in cfg and cfg['use_ema'])
if self.use_ema:
ema_decay = self.cfg.get('ema_decay', 0.9998)
ema_decay_type = self.cfg.get('ema_decay_type', 'threshold')
cycle_epoch = self.cfg.get('cycle_epoch', -1)
ema_black_list = self.cfg.get('ema_black_list', None)
ema_filter_no_grad = self.cfg.get('ema_filter_no_grad', False)
self.ema = ModelEMA(
self.model,
decay=ema_decay,
ema_decay_type=ema_decay_type,
cycle_epoch=cycle_epoch,
ema_black_list=ema_black_list,
ema_filter_no_grad=ema_filter_no_grad)
self._nranks = dist.get_world_size()
self._local_rank = dist.get_rank()
self.status = {}
self.start_epoch = 0
self.end_epoch = 0 if 'epoch' not in cfg else cfg.epoch
# initial default callbacks
self._init_callbacks()
# initial default metrics
self._init_metrics()
self._reset_metrics()
def _init_callbacks(self):
if self.mode == 'train':
if self.cfg.get('ssod_method',
False) and self.cfg['ssod_method'] == 'Semi_RTDETR':
self._callbacks = [SemiLogPrinter(self), SemiCheckpointer(self)]
else:
self._callbacks = [LogPrinter(self), Checkpointer(self)]
if self.cfg.get('use_vdl', False):
self._callbacks.append(VisualDLWriter(self))
if self.cfg.get('save_proposals', False):
self._callbacks.append(SniperProposalsGenerator(self))
if self.cfg.get('use_wandb', False) or 'wandb' in self.cfg:
self._callbacks.append(WandbCallback(self))
self._compose_callback = ComposeCallback(self._callbacks)
elif self.mode == 'eval':
self._callbacks = [LogPrinter(self)]
if self.cfg.metric == 'WiderFace':
self._callbacks.append(WiferFaceEval(self))
self._compose_callback = ComposeCallback(self._callbacks)
elif self.mode == 'test' and self.cfg.get('use_vdl', False):
self._callbacks = [VisualDLWriter(self)]
self._compose_callback = ComposeCallback(self._callbacks)
else:
self._callbacks = []
self._compose_callback = None
def _init_metrics(self, validate=False):
if self.mode == 'test' or (self.mode == 'train' and not validate):
self._metrics = []
return
classwise = self.cfg['classwise'] if 'classwise' in self.cfg else False
if self.cfg.metric == 'COCO' or self.cfg.metric == "SNIPERCOCO":
# TODO: bias should be unified
bias = 1 if self.cfg.get('bias', False) else 0
output_eval = self.cfg['output_eval'] \
if 'output_eval' in self.cfg else None
save_prediction_only = self.cfg.get('save_prediction_only', False)
# pass clsid2catid info to metric instance to avoid multiple loading
# annotation file
clsid2catid = {v: k for k, v in self.dataset.catid2clsid.items()} \
if self.mode == 'eval' else None
save_threshold = self.cfg.get('save_threshold', 0)
# when do validation in train, annotation file should be get from
# EvalReader instead of self.dataset(which is TrainReader)
if self.mode == 'train' and validate:
eval_dataset = self.cfg['EvalDataset']
eval_dataset.check_or_download_dataset()
anno_file = eval_dataset.get_anno()
dataset = eval_dataset
else:
dataset = self.dataset
anno_file = dataset.get_anno()
IouType = self.cfg['IouType'] if 'IouType' in self.cfg else 'bbox'
if self.cfg.metric == "COCO":
self._metrics = [
COCOMetric(
anno_file=anno_file,
clsid2catid=clsid2catid,
classwise=classwise,
output_eval=output_eval,
bias=bias,
IouType=IouType,
save_prediction_only=save_prediction_only,
save_threshold=save_threshold)
]
elif self.cfg.metric == "SNIPERCOCO": # sniper
self._metrics = [
SNIPERCOCOMetric(
anno_file=anno_file,
dataset=dataset,
clsid2catid=clsid2catid,
classwise=classwise,
output_eval=output_eval,
bias=bias,
IouType=IouType,
save_prediction_only=save_prediction_only)
]
elif self.cfg.metric == 'RBOX':
# TODO: bias should be unified
bias = self.cfg['bias'] if 'bias' in self.cfg else 0
output_eval = self.cfg['output_eval'] \
if 'output_eval' in self.cfg else None
save_prediction_only = self.cfg.get('save_prediction_only', False)
imid2path = self.cfg.get('imid2path', None)
# when do validation in train, annotation file should be get from
# EvalReader instead of self.dataset(which is TrainReader)
anno_file = self.dataset.get_anno()
if self.mode == 'train' and validate:
eval_dataset = self.cfg['EvalDataset']
eval_dataset.check_or_download_dataset()
anno_file = eval_dataset.get_anno()
self._metrics = [
RBoxMetric(
anno_file=anno_file,
classwise=classwise,
output_eval=output_eval,
bias=bias,
save_prediction_only=save_prediction_only,
imid2path=imid2path)
]
elif self.cfg.metric == 'VOC':
output_eval = self.cfg['output_eval'] \
if 'output_eval' in self.cfg else None
save_prediction_only = self.cfg.get('save_prediction_only', False)
self._metrics = [
VOCMetric(
label_list=self.dataset.get_label_list(),
class_num=self.cfg.num_classes,
map_type=self.cfg.map_type,
classwise=classwise,
output_eval=output_eval,
save_prediction_only=save_prediction_only)
]
elif self.cfg.metric == 'WiderFace':
multi_scale = self.cfg.multi_scale_eval if 'multi_scale_eval' in self.cfg else True
self._metrics = [
WiderFaceMetric(
image_dir=os.path.join(self.dataset.dataset_dir,
self.dataset.image_dir),
anno_file=self.dataset.get_anno(),
multi_scale=multi_scale)
]
elif self.cfg.metric == 'KeyPointTopDownCOCOEval':
eval_dataset = self.cfg['EvalDataset']
eval_dataset.check_or_download_dataset()
anno_file = eval_dataset.get_anno()
save_prediction_only = self.cfg.get('save_prediction_only', False)
self._metrics = [
KeyPointTopDownCOCOEval(
anno_file,
len(eval_dataset),
self.cfg.num_joints,
self.cfg.save_dir,
save_prediction_only=save_prediction_only)
]
elif self.cfg.metric == 'KeyPointTopDownCOCOWholeBadyHandEval':
eval_dataset = self.cfg['EvalDataset']
eval_dataset.check_or_download_dataset()
anno_file = eval_dataset.get_anno()
save_prediction_only = self.cfg.get('save_prediction_only', False)
self._metrics = [
KeyPointTopDownCOCOWholeBadyHandEval(
anno_file,
len(eval_dataset),
self.cfg.num_joints,
self.cfg.save_dir,
save_prediction_only=save_prediction_only)
]
elif self.cfg.metric == 'KeyPointTopDownMPIIEval':
eval_dataset = self.cfg['EvalDataset']
eval_dataset.check_or_download_dataset()
anno_file = eval_dataset.get_anno()
save_prediction_only = self.cfg.get('save_prediction_only', False)
self._metrics = [
KeyPointTopDownMPIIEval(
anno_file,
len(eval_dataset),
self.cfg.num_joints,
self.cfg.save_dir,
save_prediction_only=save_prediction_only)
]
elif self.cfg.metric == 'Pose3DEval':
save_prediction_only = self.cfg.get('save_prediction_only', False)
self._metrics = [
Pose3DEval(
self.cfg.save_dir,
save_prediction_only=save_prediction_only)
]
elif self.cfg.metric == 'MOTDet':
self._metrics = [JDEDetMetric(), ]
elif self.cfg.metric == 'CULaneMetric':
output_eval = self.cfg.get('output_eval', None)
self._metrics = [
CULaneMetric(
cfg=self.cfg,
output_eval=output_eval,
split=self.dataset.split,
dataset_dir=self.cfg.dataset_dir)
]
else:
logger.warning("Metric not support for metric type {}".format(
self.cfg.metric))
self._metrics = []
def _reset_metrics(self):
for metric in self._metrics:
metric.reset()
def register_callbacks(self, callbacks):
callbacks = [c for c in list(callbacks) if c is not None]
for c in callbacks:
assert isinstance(c, Callback), \
"metrics shoule be instances of subclass of Metric"
self._callbacks.extend(callbacks)
self._compose_callback = ComposeCallback(self._callbacks)
def register_metrics(self, metrics):
metrics = [m for m in list(metrics) if m is not None]
for m in metrics:
assert isinstance(m, Metric), \
"metrics shoule be instances of subclass of Metric"
self._metrics.extend(metrics)
def load_weights(self, weights, ARSL_eval=False):
if self.is_loaded_weights:
return
self.start_epoch = 0
load_pretrain_weight(self.model, weights, ARSL_eval)
logger.debug("Load weights {} to start training".format(weights))
def load_weights_sde(self, det_weights, reid_weights):
if self.model.detector:
load_weight(self.model.detector, det_weights)
if self.model.reid:
load_weight(self.model.reid, reid_weights)
else:
load_weight(self.model.reid, reid_weights)
def resume_weights(self, weights):
# support Distill resume weights
if hasattr(self.model, 'student_model'):
self.start_epoch = load_weight(self.model.student_model, weights,
self.optimizer)
else:
self.start_epoch = load_weight(self.model, weights, self.optimizer,
self.ema if self.use_ema else None)
logger.debug("Resume weights of epoch {}".format(self.start_epoch))
def train(self, validate=False):
assert self.mode == 'train', "Model not in 'train' mode"
Init_mark = False
if validate:
self.cfg['EvalDataset'] = self.cfg.EvalDataset = create(
"EvalDataset")()
model = self.model
if self.cfg.get('to_static', False):
model = apply_to_static(self.cfg, model)
sync_bn = (getattr(self.cfg, 'norm_type', None) == 'sync_bn' and
(self.cfg.use_gpu or self.cfg.use_mlu) and self._nranks > 1)
if sync_bn:
model = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(model)
# enabel auto mixed precision mode
if self.use_amp:
scaler = paddle.amp.GradScaler(
enable=self.cfg.use_gpu or self.cfg.use_npu or self.cfg.use_mlu,
init_loss_scaling=self.cfg.get('init_loss_scaling', 1024))
# get distributed model
if self.cfg.get('fleet', False):
model = fleet.distributed_model(model)
self.optimizer = fleet.distributed_optimizer(self.optimizer)
elif self._nranks > 1:
find_unused_parameters = self.cfg[
'find_unused_parameters'] if 'find_unused_parameters' in self.cfg else False
model = paddle.DataParallel(
model, find_unused_parameters=find_unused_parameters)
self.status.update({
'epoch_id': self.start_epoch,
'step_id': 0,
'steps_per_epoch': len(self.loader)
})
self.status['batch_time'] = stats.SmoothedValue(
self.cfg.log_iter, fmt='{avg:.4f}')
self.status['data_time'] = stats.SmoothedValue(
self.cfg.log_iter, fmt='{avg:.4f}')
self.status['training_staus'] = stats.TrainingStats(self.cfg.log_iter)
if self.cfg.get('print_flops', False):
flops_loader = create('{}Reader'.format(self.mode.capitalize()))(
self.dataset, self.cfg.worker_num)
self._flops(flops_loader)
profiler_options = self.cfg.get('profiler_options', None)
self._compose_callback.on_train_begin(self.status)
use_fused_allreduce_gradients = self.cfg[
'use_fused_allreduce_gradients'] if 'use_fused_allreduce_gradients' in self.cfg else False
for epoch_id in range(self.start_epoch, self.cfg.epoch):
self.status['mode'] = 'train'
self.status['epoch_id'] = epoch_id
self._compose_callback.on_epoch_begin(self.status)
self.loader.dataset.set_epoch(epoch_id)
model.train()
iter_tic = time.time()
for step_id, data in enumerate(self.loader):
def deep_pin(blob, blocking):
if isinstance(blob, paddle.Tensor):
return blob.cuda(blocking=blocking)
elif isinstance(blob, dict):
return {k: deep_pin(v, blocking) for k, v in blob.items()}
elif isinstance(blob, (list, tuple)):
return type(blob)([deep_pin(x, blocking) for x in blob])
else:
return blob
if paddle.base.core.is_compiled_with_cuda():
data = deep_pin(data, False)
self.status['data_time'].update(time.time() - iter_tic)
self.status['step_id'] = step_id
profiler.add_profiler_step(profiler_options)
self._compose_callback.on_step_begin(self.status)
data['epoch_id'] = epoch_id
if self.cfg.get('to_static',
False) and 'image_file' in data.keys():
data.pop('image_file')
if self.use_amp:
if isinstance(
model, paddle.
DataParallel) and use_fused_allreduce_gradients:
with model.no_sync():
with paddle.amp.auto_cast(
enable=self.cfg.use_gpu or
self.cfg.use_npu or self.cfg.use_mlu,
custom_white_list=self.custom_white_list,
custom_black_list=self.custom_black_list,
level=self.amp_level):
# model forward
outputs = model(data)
loss = outputs['loss']
# model backward
scaled_loss = scaler.scale(loss)
scaled_loss.backward()
fused_allreduce_gradients(
list(model.parameters()), None)
else:
with paddle.amp.auto_cast(
enable=self.cfg.use_gpu or self.cfg.use_npu or
self.cfg.use_mlu,
custom_white_list=self.custom_white_list,
custom_black_list=self.custom_black_list,
level=self.amp_level):
# model forward
outputs = model(data)
loss = outputs['loss']
# model backward
scaled_loss = scaler.scale(loss)
scaled_loss.backward()
# in dygraph mode, optimizer.minimize is equal to optimizer.step
scaler.minimize(self.optimizer, scaled_loss)
else:
if isinstance(
model, paddle.
DataParallel) and use_fused_allreduce_gradients:
with model.no_sync():
# model forward
outputs = model(data)
loss = outputs['loss']
# model backward
loss.backward()
fused_allreduce_gradients(
list(model.parameters()), None)
else:
# model forward
outputs = model(data)
loss = outputs['loss']
# model backward
loss.backward()
self.optimizer.step()
curr_lr = self.optimizer.get_lr()
self.lr.step()
if self.cfg.get('unstructured_prune'):
self.pruner.step()
self.optimizer.clear_grad()
self.status['learning_rate'] = curr_lr
if self._nranks < 2 or self._local_rank == 0:
self.status['training_staus'].update(outputs)
self.status['batch_time'].update(time.time() - iter_tic)
self._compose_callback.on_step_end(self.status)
if self.use_ema:
self.ema.update()
iter_tic = time.time()
if self.cfg.get('unstructured_prune'):
self.pruner.update_params()
is_snapshot = (self._nranks < 2 or (self._local_rank == 0 or self.cfg.metric == "Pose3DEval")) \
and ((epoch_id + 1) % self.cfg.snapshot_epoch == 0 or epoch_id == self.end_epoch - 1)
if is_snapshot and self.use_ema:
# apply ema weight on model
weight = copy.deepcopy(self.model.state_dict())
self.model.set_dict(self.ema.apply())
self.status['weight'] = weight
self._compose_callback.on_epoch_end(self.status)
if validate and is_snapshot:
if not hasattr(self, '_eval_loader'):
# build evaluation dataset and loader
self._eval_dataset = self.cfg.EvalDataset
self._eval_batch_sampler = \
paddle.io.BatchSampler(
self._eval_dataset,
batch_size=self.cfg.EvalReader['batch_size'])
# If metric is VOC, need to be set collate_batch=False.
if self.cfg.metric == 'VOC':
self.cfg['EvalReader']['collate_batch'] = False
if self.cfg.metric == "Pose3DEval":
self._eval_loader = create('EvalReader')(
self._eval_dataset, self.cfg.worker_num)
else:
self._eval_loader = create('EvalReader')(
self._eval_dataset,
self.cfg.worker_num,
batch_sampler=self._eval_batch_sampler)
# if validation in training is enabled, metrics should be re-init
# Init_mark makes sure this code will only execute once
if validate and Init_mark == False:
Init_mark = True
self._init_metrics(validate=validate)
self._reset_metrics()
with paddle.no_grad():
self.status['save_best_model'] = True
self._eval_with_loader(self._eval_loader)
if is_snapshot and self.use_ema:
# reset original weight
self.model.set_dict(weight)
self.status.pop('weight')
self._compose_callback.on_train_end(self.status)
def _eval_with_loader(self, loader):
sample_num = 0
tic = time.time()
self._compose_callback.on_epoch_begin(self.status)
self.status['mode'] = 'eval'
self.model.eval()
if self.cfg.get('print_flops', False):
flops_loader = create('{}Reader'.format(self.mode.capitalize()))(
self.dataset, self.cfg.worker_num, self._eval_batch_sampler)
self._flops(flops_loader)
for step_id, data in enumerate(loader):
self.status['step_id'] = step_id
self._compose_callback.on_step_begin(self.status)
# forward
if self.use_amp:
with paddle.amp.auto_cast(
enable=self.cfg.use_gpu or self.cfg.use_npu or
self.cfg.use_mlu,
custom_white_list=self.custom_white_list,
custom_black_list=self.custom_black_list,
level=self.amp_level):
outs = self.model(data)
else:
outs = self.model(data)
# update metrics
for metric in self._metrics:
metric.update(data, outs)
# multi-scale inputs: all inputs have same im_id
if isinstance(data, typing.Sequence):
sample_num += data[0]['im_id'].numpy().shape[0]
else:
sample_num += data['im_id'].numpy().shape[0]
self._compose_callback.on_step_end(self.status)
self.status['sample_num'] = sample_num
self.status['cost_time'] = time.time() - tic
# accumulate metric to log out
for metric in self._metrics:
metric.accumulate()
metric.log()
self._compose_callback.on_epoch_end(self.status)
# reset metric states for metric may performed multiple times
self._reset_metrics()
def evaluate(self):
# get distributed model
if self.cfg.get('fleet', False):
self.model = fleet.distributed_model(self.model)
self.optimizer = fleet.distributed_optimizer(self.optimizer)
elif self._nranks > 1:
find_unused_parameters = self.cfg[
'find_unused_parameters'] if 'find_unused_parameters' in self.cfg else False
self.model = paddle.DataParallel(
self.model, find_unused_parameters=find_unused_parameters)
with paddle.no_grad():
self._eval_with_loader(self.loader)
def _eval_with_loader_slice(self,
loader,
slice_size=[640, 640],
overlap_ratio=[0.25, 0.25],
combine_method='nms',
match_threshold=0.6,
match_metric='iou'):
sample_num = 0
tic = time.time()
self._compose_callback.on_epoch_begin(self.status)
self.status['mode'] = 'eval'
self.model.eval()
if self.cfg.get('print_flops', False):
flops_loader = create('{}Reader'.format(self.mode.capitalize()))(
self.dataset, self.cfg.worker_num, self._eval_batch_sampler)
self._flops(flops_loader)
merged_bboxs = []
for step_id, data in enumerate(loader):
self.status['step_id'] = step_id
self._compose_callback.on_step_begin(self.status)
# forward
if self.use_amp:
with paddle.amp.auto_cast(
enable=self.cfg.use_gpu or self.cfg.use_npu or
self.cfg.use_mlu,
custom_white_list=self.custom_white_list,
custom_black_list=self.custom_black_list,
level=self.amp_level):
outs = self.model(data)
else:
outs = self.model(data)
shift_amount = data['st_pix']
outs['bbox'][:, 2:4] = outs['bbox'][:, 2:4] + shift_amount
outs['bbox'][:, 4:6] = outs['bbox'][:, 4:6] + shift_amount
merged_bboxs.append(outs['bbox'])
if data['is_last'] > 0:
# merge matching predictions
merged_results = {'bbox': []}
if combine_method == 'nms':
final_boxes = multiclass_nms(
np.concatenate(merged_bboxs), self.cfg.num_classes,
match_threshold, match_metric)
merged_results['bbox'] = np.concatenate(final_boxes)
elif combine_method == 'concat':
merged_results['bbox'] = np.concatenate(merged_bboxs)
else:
raise ValueError(
"Now only support 'nms' or 'concat' to fuse detection results."
)
merged_results['im_id'] = np.array([[0]])
merged_results['bbox_num'] = np.array(
[len(merged_results['bbox'])])
merged_bboxs = []
data['im_id'] = data['ori_im_id']
# update metrics
for metric in self._metrics:
metric.update(data, merged_results)
# multi-scale inputs: all inputs have same im_id
if isinstance(data, typing.Sequence):
sample_num += data[0]['im_id'].numpy().shape[0]
else:
sample_num += data['im_id'].numpy().shape[0]
self._compose_callback.on_step_end(self.status)
self.status['sample_num'] = sample_num
self.status['cost_time'] = time.time() - tic
# accumulate metric to log out
for metric in self._metrics:
metric.accumulate()
metric.log()
self._compose_callback.on_epoch_end(self.status)
# reset metric states for metric may performed multiple times
self._reset_metrics()
def evaluate_slice(self,
slice_size=[640, 640],
overlap_ratio=[0.25, 0.25],
combine_method='nms',
match_threshold=0.6,
match_metric='iou'):
with paddle.no_grad():
self._eval_with_loader_slice(self.loader, slice_size, overlap_ratio,
combine_method, match_threshold,
match_metric)
def slice_predict(self,
images,
slice_size=[640, 640],
overlap_ratio=[0.25, 0.25],
combine_method='nms',
match_threshold=0.6,
match_metric='iou',
draw_threshold=0.5,
output_dir='output',
save_results=False,
visualize=True):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
self.dataset.set_slice_images(images, slice_size, overlap_ratio)
loader = create('TestReader')(self.dataset, 0)
imid2path = self.dataset.get_imid2path()
def setup_metrics_for_loader():
# mem
metrics = copy.deepcopy(self._metrics)
mode = self.mode
save_prediction_only = self.cfg[
'save_prediction_only'] if 'save_prediction_only' in self.cfg else None
output_eval = self.cfg[
'output_eval'] if 'output_eval' in self.cfg else None
# modify
self.mode = '_test'
self.cfg['save_prediction_only'] = True
self.cfg['output_eval'] = output_dir
self.cfg['imid2path'] = imid2path
self._init_metrics()
# restore
self.mode = mode
self.cfg.pop('save_prediction_only')
if save_prediction_only is not None:
self.cfg['save_prediction_only'] = save_prediction_only
self.cfg.pop('output_eval')
if output_eval is not None:
self.cfg['output_eval'] = output_eval
self.cfg.pop('imid2path')
_metrics = copy.deepcopy(self._metrics)
self._metrics = metrics
return _metrics
if save_results:
metrics = setup_metrics_for_loader()
else:
metrics = []
anno_file = self.dataset.get_anno()
clsid2catid, catid2name = get_categories(
self.cfg.metric, anno_file=anno_file)
# Run Infer
self.status['mode'] = 'test'
self.model.eval()
if self.cfg.get('print_flops', False):
flops_loader = create('TestReader')(self.dataset, 0)
self._flops(flops_loader)
results = [] # all images
merged_bboxs = [] # single image
for step_id, data in enumerate(tqdm(loader)):
self.status['step_id'] = step_id
# forward
outs = self.model(data)
outs['bbox'] = outs['bbox'].numpy() # only in test mode
shift_amount = data['st_pix']
outs['bbox'][:, 2:4] = outs['bbox'][:, 2:4] + shift_amount.numpy()
outs['bbox'][:, 4:6] = outs['bbox'][:, 4:6] + shift_amount.numpy()
merged_bboxs.append(outs['bbox'])
if data['is_last'] > 0:
# merge matching predictions
merged_results = {'bbox': []}
if combine_method == 'nms':
final_boxes = multiclass_nms(
np.concatenate(merged_bboxs), self.cfg.num_classes,
match_threshold, match_metric)
merged_results['bbox'] = np.concatenate(final_boxes)
elif combine_method == 'concat':
merged_results['bbox'] = np.concatenate(merged_bboxs)
else:
raise ValueError(
"Now only support 'nms' or 'concat' to fuse detection results."
)
merged_results['im_id'] = np.array([[0]])
merged_results['bbox_num'] = np.array(
[len(merged_results['bbox'])])
merged_bboxs = []
data['im_id'] = data['ori_im_id']
for _m in metrics:
_m.update(data, merged_results)
for key in ['im_shape', 'scale_factor', 'im_id']:
if isinstance(data, typing.Sequence):
merged_results[key] = data[0][key]
else:
merged_results[key] = data[key]
for key, value in merged_results.items():
if hasattr(value, 'numpy'):
merged_results[key] = value.numpy()
results.append(merged_results)
for _m in metrics:
_m.accumulate()
_m.reset()
if visualize:
for outs in results:
batch_res = get_infer_results(outs, clsid2catid)
bbox_num = outs['bbox_num']
start = 0
for i, im_id in enumerate(outs['im_id']):
image_path = imid2path[int(im_id)]
image = Image.open(image_path).convert('RGB')
image = ImageOps.exif_transpose(image)
self.status['original_image'] = np.array(image.copy())
end = start + bbox_num[i]
bbox_res = batch_res['bbox'][start:end] \
if 'bbox' in batch_res else None
mask_res = batch_res['mask'][start:end] \
if 'mask' in batch_res else None
segm_res = batch_res['segm'][start:end] \
if 'segm' in batch_res else None
keypoint_res = batch_res['keypoint'][start:end] \
if 'keypoint' in batch_res else None
pose3d_res = batch_res['pose3d'][start:end] \
if 'pose3d' in batch_res else None
image = visualize_results(
image, bbox_res, mask_res, segm_res, keypoint_res,
pose3d_res, int(im_id), catid2name, draw_threshold)
self.status['result_image'] = np.array(image.copy())
if self._compose_callback:
self._compose_callback.on_step_end(self.status)
# save image with detection
save_name = self._get_save_image_name(output_dir,
image_path)
logger.info("Detection bbox results save in {}".format(
save_name))
image.save(save_name, quality=95)
start = end
def predict(self,
images,
draw_threshold=0.5,
output_dir='output',
save_results=False,
visualize=True,
save_threshold=0,
do_eval=False):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if do_eval:
save_threshold = 0.0
self.dataset.set_images(images, do_eval=do_eval)
loader = create('TestReader')(self.dataset, 0)
imid2path = self.dataset.get_imid2path()
def setup_metrics_for_loader():
# mem
metrics = copy.deepcopy(self._metrics)