PaddleX/paddlex/ppdet/engine/trainer.py

# 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

import numpy as np
import typing
from PIL import Image, ImageOps

import paddle
import paddle.distributed as dist
from paddle.distributed import fleet
from paddle import amp
from paddle.static import InputSpec
from paddlex.ppdet.optimizer import ModelEMA

from paddlex.ppdet.core.workspace import create
from paddlex.ppdet.utils.checkpoint import load_weight, load_pretrain_weight
from paddlex.ppdet.utils.visualizer import visualize_results, save_result
from paddlex.ppdet.metrics import Metric, COCOMetric, VOCMetric, WiderFaceMetric, get_infer_results, KeyPointTopDownCOCOEval, KeyPointTopDownMPIIEval
from paddlex.ppdet.metrics import RBoxMetric, JDEDetMetric, SNIPERCOCOMetric
from paddlex.ppdet.data.source.sniper_coco import SniperCOCODataSet
from paddlex.ppdet.data.source.category import get_categories
from paddlex.ppdet.utils import stats
from paddlex.ppdet.utils import profiler

from .callbacks import Callback, ComposeCallback, LogPrinter, Checkpointer, WiferFaceEval, VisualDLWriter, SniperProposalsGenerator
from .export_utils import _dump_infer_config, _prune_input_spec

from paddlex.ppdet.utils.logger import setup_logger
logger = setup_logger('ppdet.engine')

__all__ = ['Trainer']

MOT_ARCH = ['DeepSORT', 'JDE', 'FairMOT']


class Trainer(object):
    def __init__(self, cfg, mode='train'):
        self.cfg = cfg
        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

        # build data loader
        if cfg.architecture in MOT_ARCH and self.mode in ['eval', 'test']:
            self.dataset = cfg['{}MOTDataset'.format(self.mode.capitalize())]
        else:
            self.dataset = cfg['{}Dataset'.format(self.mode.capitalize())]

        if cfg.architecture == 'DeepSORT' and self.mode == 'train':
            logger.error('DeepSORT has no need of training on mot dataset.')
            sys.exit(1)

        if self.mode == 'train':
            self.loader = create('{}Reader'.format(self.mode.capitalize()))(
                self.dataset, cfg.worker_num)

        if cfg.architecture == 'JDE' and self.mode == 'train':
            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':
            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

        #normalize params for deploy
        self.model.load_meanstd(cfg['TestReader']['sample_transforms'])

        self.use_ema = ('use_ema' in cfg and cfg['use_ema'])
        if self.use_ema:
            ema_decay = self.cfg.get('ema_decay', 0.9998)
            cycle_epoch = self.cfg.get('cycle_epoch', -1)
            self.ema = ModelEMA(
                self.model,
                decay=ema_decay,
                use_thres_step=True,
                cycle_epoch=cycle_epoch)

        # EvalDataset build with BatchSampler to evaluate in single device
        # TODO: multi-device evaluate
        if self.mode == 'eval':
            self._eval_batch_sampler = paddle.io.BatchSampler(
                self.dataset, batch_size=self.cfg.EvalReader['batch_size'])
            self.loader = create('{}Reader'.format(self.mode.capitalize()))(
                self.dataset, cfg.worker_num, self._eval_batch_sampler)
        # TestDataset build after user set images, skip loader creation here

        # build optimizer in train mode
        if self.mode == 'train':
            steps_per_epoch = len(self.loader)
            self.lr = create('LearningRate')(steps_per_epoch)
            self.optimizer = create('OptimizerBuilder')(self.lr, self.model)

        if self.cfg.get('unstructured_prune'):
            self.pruner = create('UnstructuredPruner')(self.model,
                                                       steps_per_epoch)

        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':
            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))
            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 = 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)

            # 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

            # 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()
            dataset = self.dataset
            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

            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)
                ]
            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)

            # 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

            # 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,
                    clsid2catid=clsid2catid,
                    classwise=classwise,
                    output_eval=output_eval,
                    bias=bias,
                    save_prediction_only=save_prediction_only)
            ]
        elif self.cfg.metric == 'VOC':
            self._metrics = [
                VOCMetric(
                    label_list=self.dataset.get_label_list(),
                    class_num=self.cfg.num_classes,
                    map_type=self.cfg.map_type,
                    classwise=classwise)
            ]
        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 == '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 == 'MOTDet':
            self._metrics = [JDEDetMetric(), ]
        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):
        if self.is_loaded_weights:
            return
        self.start_epoch = 0
        load_pretrain_weight(self.model, weights)
        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)
            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)
        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

        model = self.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(
                self.model, find_unused_parameters=find_unused_parameters)

        # initial fp16
        if self.cfg.get('fp16', False):
            scaler = amp.GradScaler(
                enable=self.cfg.use_gpu, init_loss_scaling=1024)

        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):
            self._flops(self.loader)
        profiler_options = self.cfg.get('profiler_options', None)

        self._compose_callback.on_train_begin(self.status)

        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):
                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('fp16', False):
                    with amp.auto_cast(enable=self.cfg.use_gpu):
                        # 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:
                    # 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(self.model)
                iter_tic = time.time()

            # apply ema weight on model
            if self.use_ema:
                weight = copy.deepcopy(self.model.state_dict())
                self.model.set_dict(self.ema.apply())
            if self.cfg.get('unstructured_prune'):
                self.pruner.update_params()

            self._compose_callback.on_epoch_end(self.status)

            if validate and (self._nranks < 2 or self._local_rank == 0) \
                    and ((epoch_id + 1) % self.cfg.snapshot_epoch == 0 \
                             or epoch_id == self.end_epoch - 1):
                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'])
                    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)

            # restore origin weight on model
            if self.use_ema:
                self.model.set_dict(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):
            self._flops(loader)
        for step_id, data in enumerate(loader):
            self.status['step_id'] = step_id
            self._compose_callback.on_step_begin(self.status)
            # forward
            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):
        with paddle.no_grad():
            self._eval_with_loader(self.loader)

    def predict(self,
                images,
                draw_threshold=0.5,
                output_dir='output',
                save_txt=False):
        self.dataset.set_images(images)
        loader = create('TestReader')(self.dataset, 0)

        imid2path = self.dataset.get_imid2path()

        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):
            self._flops(loader)
        results = []
        for step_id, data in enumerate(loader):
            self.status['step_id'] = step_id
            # forward
            outs = self.model(data)

            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)
        # sniper
        if type(self.dataset) == SniperCOCODataSet:
            results = self.dataset.anno_cropper.aggregate_chips_detections(
                results)

        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
                image = visualize_results(
                    image, bbox_res, mask_res, segm_res, keypoint_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)
                if save_txt:
                    save_path = os.path.splitext(save_name)[0] + '.txt'
                    results = {}
                    results["im_id"] = im_id
                    if bbox_res:
                        results["bbox_res"] = bbox_res
                    if keypoint_res:
                        results["keypoint_res"] = keypoint_res
                    save_result(save_path, results, catid2name, draw_threshold)
                start = end

    def _get_save_image_name(self, output_dir, image_path):
        """
        Get save image name from source image path.
        """
        if not os.path.exists(output_dir):
            os.makedirs(output_dir)
        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):
        image_shape = None
        im_shape = [None, 2]
        scale_factor = [None, 2]
        if self.cfg.architecture in MOT_ARCH:
            test_reader_name = 'TestMOTReader'
        else:
            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
        if hasattr(self.model, 'fuse_norm'):
            self.model.fuse_norm = self.cfg['TestReader'].get('fuse_normalize',
                                                              False)

        # 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 self.cfg.architecture == 'DeepSORT':
            input_spec[0].update({
                "crops": InputSpec(
                    shape=[None, 3, 192, 64], name='crops')
            })
        if prune_input:
            static_model = paddle.jit.to_static(
                self.model, input_spec=input_spec)
            # 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

        # TODO: Hard code, delete it when support prune input_spec.
        if self.cfg.architecture == 'PicoDet':
            pruned_input_spec = [{
                "image": InputSpec(
                    shape=image_shape, name='image')
            }]

        return static_model, pruned_input_spec

    def export(self, output_dir='output_inference'):
        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
        if 'slim' not in self.cfg or self.cfg['slim_type'] != 'QAT':
            paddle.jit.save(
                static_model,
                os.path.join(save_dir, 'model'),
                input_spec=pruned_input_spec)
        else:
            self.cfg.slim.save_quantized_model(
                self.model,
                os.path.join(save_dir, 'model'),
                input_spec=pruned_input_spec)
        logger.info("Export model and saved in {}".format(save_dir))

    def post_quant(self, output_dir='output_inference'):
        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)

        for idx, data in enumerate(self.loader):
            self.model(data)
            if idx == int(self.cfg.get('quant_batch_num', 10)):
                break

        # TODO: support prune input_spec
        _, pruned_input_spec = self._get_infer_cfg_and_input_spec(
            save_dir, prune_input=False)

        self.cfg.slim.save_quantized_model(
            self.model,
            os.path.join(save_dir, 'model'),
            input_spec=pruned_input_spec)
        logger.info("Export Post-Quant model and saved in {}".format(save_dir))

    def _flops(self, loader):
        self.model.eval()
        try:
            import paddleslim
        except Exception as e:
            logger.warning(
                'Unable to calculate flops, please install paddleslim, for example: `pip install paddleslim`'
            )
            return

        from paddleslim.analysis import dygraph_flops as flops
        input_data = None
        for data in loader:
            input_data = data
            break

        input_spec = [{
            "image": input_data['image'][0].unsqueeze(0),
            "im_shape": input_data['im_shape'][0].unsqueeze(0),
            "scale_factor": input_data['scale_factor'][0].unsqueeze(0)
        }]
        flops = flops(self.model, input_spec) / (1000**3)
        logger.info(" Model FLOPs : {:.6f}G. (image shape is {})".format(
            flops, input_data['image'][0].unsqueeze(0).shape))