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陈赣
966
rtdetr_paddle/ppdet/engine/trainer.py
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966
rtdetr_paddle/ppdet/engine/trainer.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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import copy
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import time
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from tqdm import tqdm
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import numpy as np
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import typing
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from PIL import Image, ImageOps, ImageFile
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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import paddle
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import paddle.nn as nn
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import paddle.distributed as dist
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from paddle.distributed import fleet
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from paddle.static import InputSpec
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from ppdet.optimizer import ModelEMA
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from ppdet.core.workspace import create
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from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
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from ppdet.utils.visualizer import visualize_results, save_result
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from ppdet.metrics import Metric, COCOMetric, VOCMetric, get_infer_results
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from ppdet.data.source.category import get_categories
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import ppdet.utils.stats as stats
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from ppdet.utils.fuse_utils import fuse_conv_bn
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from ppdet.utils import profiler
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from ppdet.modeling.post_process import multiclass_nms
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from .callbacks import Callback, ComposeCallback, LogPrinter, Checkpointer, VisualDLWriter, WandbCallback
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from .export_utils import _dump_infer_config, _prune_input_spec, apply_to_static
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from paddle.distributed.fleet.utils.hybrid_parallel_util import fused_allreduce_gradients
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from ppdet.utils.logger import setup_logger
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logger = setup_logger('ppdet.engine')
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__all__ = ['Trainer']
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class Trainer(object):
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def __init__(self, cfg, mode='train'):
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self.cfg = cfg.copy()
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assert mode.lower() in ['train', 'eval', 'test'], \
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"mode should be 'train', 'eval' or 'test'"
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self.mode = mode.lower()
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self.optimizer = None
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self.is_loaded_weights = False
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self.use_amp = self.cfg.get('amp', False)
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self.amp_level = self.cfg.get('amp_level', 'O1')
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self.custom_white_list = self.cfg.get('custom_white_list', None)
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self.custom_black_list = self.cfg.get('custom_black_list', None)
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# build data loader
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capital_mode = self.mode.capitalize()
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self.dataset = self.cfg['{}Dataset'.format(capital_mode)] = create(
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'{}Dataset'.format(capital_mode))()
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if self.mode == 'train':
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self.loader = create('{}Reader'.format(capital_mode))(
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self.dataset, cfg.worker_num)
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# build model
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if 'model' not in self.cfg:
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self.model = create(cfg.architecture)
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else:
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self.model = self.cfg.model
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self.is_loaded_weights = True
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# EvalDataset build with BatchSampler to evaluate in single device
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# TODO: multi-device evaluate
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if self.mode == 'eval':
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self._eval_batch_sampler = paddle.io.BatchSampler(
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self.dataset, batch_size=self.cfg.EvalReader['batch_size'])
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reader_name = '{}Reader'.format(self.mode.capitalize())
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# If metric is VOC, need to be set collate_batch=False.
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if cfg.metric == 'VOC':
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self.cfg[reader_name]['collate_batch'] = False
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self.loader = create(reader_name)(self.dataset, cfg.worker_num,
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self._eval_batch_sampler)
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# TestDataset build after user set images, skip loader creation here
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# get Params
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print_params = self.cfg.get('print_params', False)
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if print_params:
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params = sum([
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p.numel() for n, p in self.model.named_parameters()
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if all([x not in n for x in ['_mean', '_variance', 'aux_']])
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]) # exclude BatchNorm running status
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logger.info('Model Params : {} M.'.format((params / 1e6).numpy()[
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0]))
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# build optimizer in train mode
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if self.mode == 'train':
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steps_per_epoch = len(self.loader)
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if steps_per_epoch < 1:
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logger.warning(
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"Samples in dataset are less than batch_size, please set smaller batch_size in TrainReader."
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)
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self.lr = create('LearningRate')(steps_per_epoch)
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self.optimizer = create('OptimizerBuilder')(self.lr, self.model)
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if self.use_amp and self.amp_level == 'O2':
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self.model, self.optimizer = paddle.amp.decorate(
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models=self.model,
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optimizers=self.optimizer,
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level=self.amp_level)
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self.use_ema = ('use_ema' in cfg and cfg['use_ema'])
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if self.use_ema:
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ema_decay = self.cfg.get('ema_decay', 0.9998)
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ema_decay_type = self.cfg.get('ema_decay_type', 'threshold')
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cycle_epoch = self.cfg.get('cycle_epoch', -1)
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ema_black_list = self.cfg.get('ema_black_list', None)
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ema_filter_no_grad = self.cfg.get('ema_filter_no_grad', False)
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self.ema = ModelEMA(
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self.model,
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decay=ema_decay,
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ema_decay_type=ema_decay_type,
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cycle_epoch=cycle_epoch,
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ema_black_list=ema_black_list,
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ema_filter_no_grad=ema_filter_no_grad)
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self._nranks = dist.get_world_size()
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self._local_rank = dist.get_rank()
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self.status = {}
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self.start_epoch = 0
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self.end_epoch = 0 if 'epoch' not in cfg else cfg.epoch
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# initial default callbacks
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self._init_callbacks()
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# initial default metrics
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self._init_metrics()
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self._reset_metrics()
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def _init_callbacks(self):
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if self.mode == 'train':
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self._callbacks = [LogPrinter(self), Checkpointer(self)]
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if self.cfg.get('use_vdl', False):
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self._callbacks.append(VisualDLWriter(self))
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if self.cfg.get('use_wandb', False) or 'wandb' in self.cfg:
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self._callbacks.append(WandbCallback(self))
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self._compose_callback = ComposeCallback(self._callbacks)
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elif self.mode == 'eval':
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self._callbacks = [LogPrinter(self)]
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self._compose_callback = ComposeCallback(self._callbacks)
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elif self.mode == 'test' and self.cfg.get('use_vdl', False):
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self._callbacks = [VisualDLWriter(self)]
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self._compose_callback = ComposeCallback(self._callbacks)
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else:
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self._callbacks = []
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self._compose_callback = None
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def _init_metrics(self, validate=False):
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if self.mode == 'test' or (self.mode == 'train' and not validate):
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self._metrics = []
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return
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classwise = self.cfg['classwise'] if 'classwise' in self.cfg else False
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if self.cfg.metric == 'COCO':
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# TODO: bias should be unified
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bias = 1 if self.cfg.get('bias', False) else 0
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output_eval = self.cfg['output_eval'] \
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if 'output_eval' in self.cfg else None
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save_prediction_only = self.cfg.get('save_prediction_only', False)
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# pass clsid2catid info to metric instance to avoid multiple loading
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# annotation file
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clsid2catid = {v: k for k, v in self.dataset.catid2clsid.items()} \
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if self.mode == 'eval' else None
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# when do validation in train, annotation file should be get from
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# EvalReader instead of self.dataset(which is TrainReader)
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if self.mode == 'train' and validate:
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eval_dataset = self.cfg['EvalDataset']
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eval_dataset.check_or_download_dataset()
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anno_file = eval_dataset.get_anno()
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dataset = eval_dataset
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else:
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dataset = self.dataset
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anno_file = dataset.get_anno()
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IouType = self.cfg['IouType'] if 'IouType' in self.cfg else 'bbox'
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self._metrics = [
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COCOMetric(
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anno_file=anno_file,
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clsid2catid=clsid2catid,
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classwise=classwise,
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output_eval=output_eval,
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bias=bias,
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IouType=IouType,
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save_prediction_only=save_prediction_only)
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]
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elif self.cfg.metric == 'VOC':
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output_eval = self.cfg['output_eval'] \
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if 'output_eval' in self.cfg else None
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save_prediction_only = self.cfg.get('save_prediction_only', False)
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self._metrics = [
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VOCMetric(
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label_list=self.dataset.get_label_list(),
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class_num=self.cfg.num_classes,
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map_type=self.cfg.map_type,
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classwise=classwise,
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output_eval=output_eval,
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save_prediction_only=save_prediction_only)
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]
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else:
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logger.warning("Metric not support for metric type {}".format(
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self.cfg.metric))
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self._metrics = []
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def _reset_metrics(self):
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for metric in self._metrics:
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metric.reset()
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def register_callbacks(self, callbacks):
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callbacks = [c for c in list(callbacks) if c is not None]
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for c in callbacks:
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assert isinstance(c, Callback), \
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"metrics shoule be instances of subclass of Metric"
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self._callbacks.extend(callbacks)
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self._compose_callback = ComposeCallback(self._callbacks)
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def register_metrics(self, metrics):
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metrics = [m for m in list(metrics) if m is not None]
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for m in metrics:
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assert isinstance(m, Metric), \
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"metrics shoule be instances of subclass of Metric"
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self._metrics.extend(metrics)
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def load_weights(self, weights, ARSL_eval=False):
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if self.is_loaded_weights:
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return
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self.start_epoch = 0
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load_pretrain_weight(self.model, weights, ARSL_eval)
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logger.debug("Load weights {} to start training".format(weights))
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def resume_weights(self, weights):
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self.start_epoch = load_weight(self.model, weights, self.optimizer,
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self.ema if self.use_ema else None)
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logger.debug("Resume weights of epoch {}".format(self.start_epoch))
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def train(self, validate=False):
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assert self.mode == 'train', "Model not in 'train' mode"
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Init_mark = False
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if validate:
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self.cfg['EvalDataset'] = self.cfg.EvalDataset = create(
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"EvalDataset")()
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model = self.model
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if self.cfg.get('to_static', False):
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model = apply_to_static(self.cfg, model)
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sync_bn = (getattr(self.cfg, 'norm_type', None) == 'sync_bn' and
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(self.cfg.use_gpu or self.cfg.use_mlu) and self._nranks > 1)
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if sync_bn:
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model = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(model)
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# enabel auto mixed precision mode
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if self.use_amp:
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scaler = paddle.amp.GradScaler(
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enable=self.cfg.use_gpu or self.cfg.use_npu or self.cfg.use_mlu,
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init_loss_scaling=self.cfg.get('init_loss_scaling', 1024))
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# get distributed model
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if self.cfg.get('fleet', False):
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model = fleet.distributed_model(model)
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self.optimizer = fleet.distributed_optimizer(self.optimizer)
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elif self._nranks > 1:
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find_unused_parameters = self.cfg[
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'find_unused_parameters'] if 'find_unused_parameters' in self.cfg else False
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model = paddle.DataParallel(
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model, find_unused_parameters=find_unused_parameters)
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self.status.update({
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'epoch_id': self.start_epoch,
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'step_id': 0,
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'steps_per_epoch': len(self.loader)
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})
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self.status['batch_time'] = stats.SmoothedValue(
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self.cfg.log_iter, fmt='{avg:.4f}')
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self.status['data_time'] = stats.SmoothedValue(
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self.cfg.log_iter, fmt='{avg:.4f}')
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self.status['training_status'] = stats.TrainingStats(self.cfg.log_iter)
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profiler_options = self.cfg.get('profiler_options', None)
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self._compose_callback.on_train_begin(self.status)
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use_fused_allreduce_gradients = self.cfg[
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'use_fused_allreduce_gradients'] if 'use_fused_allreduce_gradients' in self.cfg else False
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for epoch_id in range(self.start_epoch, self.cfg.epoch):
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self.status['mode'] = 'train'
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self.status['epoch_id'] = epoch_id
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self._compose_callback.on_epoch_begin(self.status)
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self.loader.dataset.set_epoch(epoch_id)
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model.train()
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iter_tic = time.time()
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for step_id, data in enumerate(self.loader):
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self.status['data_time'].update(time.time() - iter_tic)
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self.status['step_id'] = step_id
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profiler.add_profiler_step(profiler_options)
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self._compose_callback.on_step_begin(self.status)
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data['epoch_id'] = epoch_id
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if self.cfg.get('to_static',
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False) and 'image_file' in data.keys():
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data.pop('image_file')
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if self.use_amp:
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if isinstance(
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model, paddle.
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DataParallel) and use_fused_allreduce_gradients:
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with model.no_sync():
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with paddle.amp.auto_cast(
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enable=self.cfg.use_gpu or
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self.cfg.use_npu or self.cfg.use_mlu,
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custom_white_list=self.custom_white_list,
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custom_black_list=self.custom_black_list,
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level=self.amp_level):
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# model forward
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outputs = model(data)
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loss = outputs['loss']
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# model backward
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scaled_loss = scaler.scale(loss)
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scaled_loss.backward()
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fused_allreduce_gradients(
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list(model.parameters()), None)
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else:
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with paddle.amp.auto_cast(
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enable=self.cfg.use_gpu or self.cfg.use_npu or
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self.cfg.use_mlu,
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custom_white_list=self.custom_white_list,
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custom_black_list=self.custom_black_list,
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level=self.amp_level):
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# model forward
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outputs = model(data)
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loss = outputs['loss']
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# model backward
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scaled_loss = scaler.scale(loss)
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scaled_loss.backward()
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# in dygraph mode, optimizer.minimize is equal to optimizer.step
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scaler.minimize(self.optimizer, scaled_loss)
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else:
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if isinstance(
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model, paddle.
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DataParallel) and use_fused_allreduce_gradients:
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with model.no_sync():
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# model forward
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outputs = model(data)
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loss = outputs['loss']
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# model backward
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loss.backward()
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fused_allreduce_gradients(
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list(model.parameters()), None)
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else:
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# model forward
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outputs = model(data)
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loss = outputs['loss']
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# model backward
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loss.backward()
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self.optimizer.step()
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curr_lr = self.optimizer.get_lr()
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self.lr.step()
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self.optimizer.clear_grad()
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self.status['learning_rate'] = curr_lr
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if self._nranks < 2 or self._local_rank == 0:
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self.status['training_status'].update(outputs)
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self.status['batch_time'].update(time.time() - iter_tic)
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self._compose_callback.on_step_end(self.status)
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if self.use_ema:
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self.ema.update()
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iter_tic = time.time()
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is_snapshot = (self._nranks < 2 or (self._local_rank == 0 or self.cfg.metric == "Pose3DEval")) \
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and ((epoch_id + 1) % self.cfg.snapshot_epoch == 0 or epoch_id == self.end_epoch - 1)
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if is_snapshot and self.use_ema:
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# apply ema weight on model
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weight = copy.deepcopy(self.model.state_dict())
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self.model.set_dict(self.ema.apply())
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self.status['weight'] = weight
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self._compose_callback.on_epoch_end(self.status)
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if validate and is_snapshot:
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if not hasattr(self, '_eval_loader'):
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# build evaluation dataset and loader
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self._eval_dataset = self.cfg.EvalDataset
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self._eval_batch_sampler = \
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paddle.io.BatchSampler(
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self._eval_dataset,
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batch_size=self.cfg.EvalReader['batch_size'])
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# If metric is VOC, need to be set collate_batch=False.
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if self.cfg.metric == 'VOC':
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self.cfg['EvalReader']['collate_batch'] = False
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else:
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self._eval_loader = create('EvalReader')(
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self._eval_dataset,
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self.cfg.worker_num,
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batch_sampler=self._eval_batch_sampler)
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||||
# if validation in training is enabled, metrics should be re-init
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||||
# Init_mark makes sure this code will only execute once
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if validate and Init_mark == False:
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Init_mark = True
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self._init_metrics(validate=validate)
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||||
self._reset_metrics()
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||||
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with paddle.no_grad():
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self.status['save_best_model'] = True
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self._eval_with_loader(self._eval_loader)
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||||
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if is_snapshot and self.use_ema:
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||||
# reset original weight
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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()
|
||||
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()
|
||||
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()
|
||||
|
||||
results = [] # all images
|
||||
merged_bboxs = [] # single image
|
||||
for step_id, data in enumerate(tqdm(loader)):
|
||||
self.status['step_id'] = step_id
|
||||
# forward
|
||||
with paddle.no_grad():
|
||||
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):
|
||||
if not os.path.exists(output_dir):
|
||||
os.makedirs(output_dir)
|
||||
|
||||
self.dataset.set_images(images)
|
||||
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()
|
||||
|
||||
results = []
|
||||
for step_id, data in enumerate(tqdm(loader)):
|
||||
self.status['step_id'] = step_id
|
||||
# forward
|
||||
with paddle.no_grad():
|
||||
if hasattr(self.model, 'modelTeacher'):
|
||||
outs = self.model.modelTeacher(data)
|
||||
else:
|
||||
outs = self.model(data)
|
||||
|
||||
for _m in metrics:
|
||||
_m.update(data, outs)
|
||||
|
||||
for key in ['im_shape', 'scale_factor', 'im_id']:
|
||||
if isinstance(data, typing.Sequence):
|
||||
outs[key] = data[0][key]
|
||||
else:
|
||||
outs[key] = data[key]
|
||||
for key, value in outs.items():
|
||||
if hasattr(value, 'numpy'):
|
||||
outs[key] = value.numpy()
|
||||
results.append(outs)
|
||||
|
||||
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
|
||||
return results
|
||||
|
||||
def _get_save_image_name(self, output_dir, image_path):
|
||||
"""
|
||||
Get save image name from source image path.
|
||||
"""
|
||||
image_name = os.path.split(image_path)[-1]
|
||||
name, ext = os.path.splitext(image_name)
|
||||
return os.path.join(output_dir, "{}".format(name)) + ext
|
||||
|
||||
def _get_infer_cfg_and_input_spec(self,
|
||||
save_dir,
|
||||
prune_input=True,
|
||||
kl_quant=False):
|
||||
image_shape = None
|
||||
im_shape = [None, 2]
|
||||
scale_factor = [None, 2]
|
||||
test_reader_name = 'TestReader'
|
||||
if 'inputs_def' in self.cfg[test_reader_name]:
|
||||
inputs_def = self.cfg[test_reader_name]['inputs_def']
|
||||
image_shape = inputs_def.get('image_shape', None)
|
||||
# set image_shape=[None, 3, -1, -1] as default
|
||||
if image_shape is None:
|
||||
image_shape = [None, 3, -1, -1]
|
||||
|
||||
if len(image_shape) == 3:
|
||||
image_shape = [None] + image_shape
|
||||
else:
|
||||
im_shape = [image_shape[0], 2]
|
||||
scale_factor = [image_shape[0], 2]
|
||||
|
||||
if hasattr(self.model, 'deploy'):
|
||||
self.model.deploy = True
|
||||
|
||||
for layer in self.model.sublayers():
|
||||
if hasattr(layer, 'convert_to_deploy'):
|
||||
layer.convert_to_deploy()
|
||||
|
||||
if hasattr(self.cfg, 'export') and 'fuse_conv_bn' in self.cfg[
|
||||
'export'] and self.cfg['export']['fuse_conv_bn']:
|
||||
self.model = fuse_conv_bn(self.model)
|
||||
|
||||
export_post_process = self.cfg['export'].get(
|
||||
'post_process', False) if hasattr(self.cfg, 'export') else True
|
||||
export_nms = self.cfg['export'].get('nms', False) if hasattr(
|
||||
self.cfg, 'export') else True
|
||||
export_benchmark = self.cfg['export'].get(
|
||||
'benchmark', False) if hasattr(self.cfg, 'export') else False
|
||||
if hasattr(self.model, 'export_post_process'):
|
||||
self.model.export_post_process = export_post_process if not export_benchmark else False
|
||||
if hasattr(self.model, 'export_nms'):
|
||||
self.model.export_nms = export_nms if not export_benchmark else False
|
||||
if export_post_process and not export_benchmark:
|
||||
image_shape = [None] + image_shape[1:]
|
||||
|
||||
# Save infer cfg
|
||||
_dump_infer_config(self.cfg,
|
||||
os.path.join(save_dir, 'infer_cfg.yml'), image_shape,
|
||||
self.model)
|
||||
|
||||
input_spec = [{
|
||||
"image": InputSpec(
|
||||
shape=image_shape, name='image'),
|
||||
"im_shape": InputSpec(
|
||||
shape=im_shape, name='im_shape'),
|
||||
"scale_factor": InputSpec(
|
||||
shape=scale_factor, name='scale_factor')
|
||||
}]
|
||||
|
||||
if prune_input:
|
||||
static_model = paddle.jit.to_static(
|
||||
self.model, input_spec=input_spec, full_graph=True)
|
||||
# NOTE: dy2st do not pruned program, but jit.save will prune program
|
||||
# input spec, prune input spec here and save with pruned input spec
|
||||
pruned_input_spec = _prune_input_spec(
|
||||
input_spec, static_model.forward.main_program,
|
||||
static_model.forward.outputs)
|
||||
else:
|
||||
static_model = None
|
||||
pruned_input_spec = input_spec
|
||||
|
||||
return static_model, pruned_input_spec
|
||||
|
||||
def export(self, output_dir='output_inference'):
|
||||
if hasattr(self.model, 'aux_neck'):
|
||||
self.model.__delattr__('aux_neck')
|
||||
if hasattr(self.model, 'aux_head'):
|
||||
self.model.__delattr__('aux_head')
|
||||
self.model.eval()
|
||||
|
||||
model_name = os.path.splitext(os.path.split(self.cfg.filename)[-1])[0]
|
||||
save_dir = os.path.join(output_dir, model_name)
|
||||
if not os.path.exists(save_dir):
|
||||
os.makedirs(save_dir)
|
||||
|
||||
static_model, pruned_input_spec = self._get_infer_cfg_and_input_spec(
|
||||
save_dir)
|
||||
|
||||
# dy2st and save model
|
||||
paddle.jit.save(
|
||||
static_model,
|
||||
os.path.join(save_dir, 'model'),
|
||||
input_spec=pruned_input_spec)
|
||||
|
||||
logger.info("Export model and saved in {}".format(save_dir))
|
||||
Reference in New Issue
Block a user