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 time
import random
import datetime
import numpy as np
from PIL import Image

import paddle
import paddle.distributed as dist
from paddle.distributed import fleet
from paddle import amp
from paddle.static import InputSpec

from ppdet.core.workspace import create
from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
from ppdet.utils.visualizer import visualize_results
from ppdet.metrics import Metric, COCOMetric, VOCMetric, WiderFaceMetric, get_infer_results
from ppdet.data.source.category import get_categories
import ppdet.utils.stats as stats

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

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

__all__ = ['Trainer']


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.slim = None

        # build model
        self.model = create(cfg.architecture)

        # model slim build
        if 'slim' in cfg and cfg.slim:
            if self.mode == 'train':
                self.load_weights(cfg.pretrain_weights)
            self.slim = create(cfg.slim)
            self.slim(self.model)

        # build data loader
        self.dataset = cfg['{}Dataset'.format(self.mode.capitalize())]
        if self.mode == 'train':
            self.loader = create('{}Reader'.format(self.mode.capitalize()))(
                self.dataset, cfg.worker_num)
        # 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.parameters())

        self._nranks = dist.get_world_size()
        self._local_rank = dist.get_rank()

        self.status = {}

        self.start_epoch = 0
        self.end_epoch = 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 'use_vdl' in self.cfg and self.cfg.use_vdl:
                self._callbacks.append(VisualDLWriter(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 'use_vdl' in self.cfg and self.cfg.use_vdl:
            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':
            # 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

            # 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 = [
                COCOMetric(
                    anno_file=anno_file,
                    clsid2catid=clsid2catid,
                    classwise=classwise,
                    output_eval=output_eval,
                    bias=bias)
            ]
        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)
            ]
        else:
            logger.warn("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):
        self.start_epoch = 0
        load_pretrain_weight(self.model, weights)
        logger.debug("Load weights {} to start training".format(weights))

    def resume_weights(self, weights):
        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"

        # if validation in training is enabled, metrics should be re-init
        if validate:
            self._init_metrics(validate=validate)
            self._reset_metrics()

        model = self.model
        if self.cfg.fleet:
            model = fleet.distributed_model(model)
            self.optimizer = fleet.distributed_optimizer(
                self.optimizer).user_defined_optimizer
        elif self._nranks > 1:
            model = paddle.DataParallel(self.model)

        # initial fp16
        if self.cfg.fp16:
            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)

        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
                self._compose_callback.on_step_begin(self.status)

                if self.cfg.fp16:
                    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()
                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)
                iter_tic = time.time()

            self._compose_callback.on_epoch_end(self.status)

            if validate and (self._nranks < 2 or self._local_rank == 0) \
                    and (epoch_id % 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)
                with paddle.no_grad():
                    self.status['save_best_model'] = True
                    self._eval_with_loader(self._eval_loader)

    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
            outs = self.model(data)

            # update metrics
            for metric in self._metrics:
                metric.update(data, outs)

            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):
        self._eval_with_loader(self.loader)

    def predict(self, images, draw_threshold=0.5, output_dir='output'):
        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)

        # Run Infer 
        self.status['mode'] = 'test'
        self.model.eval()
        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']:
                outs[key] = data[key]
            for key, value in outs.items():
                outs[key] = value.numpy()

            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')
                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
                image = visualize_results(image, bbox_res, mask_res, segm_res,
                                          int(outs['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 _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 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)
        image_shape = None
        if 'inputs_def' in self.cfg['TestReader']:
            inputs_def = self.cfg['TestReader']['inputs_def']
            image_shape = inputs_def.get('image_shape', None)
        # set image_shape=[3, -1, -1] as default
        if image_shape is None:
            image_shape = [3, -1, -1]

        self.model.eval()

        # 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=[None] + image_shape, name='image'),
            "im_shape": InputSpec(
                shape=[None, 2], name='im_shape'),
            "scale_factor": InputSpec(
                shape=[None, 2], name='scale_factor')
        }]

        # dy2st and save model
        if self.slim is None or self.cfg['slim'] != 'QAT':
            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 = self._prune_input_spec(
                input_spec, static_model.forward.main_program,
                static_model.forward.outputs)
            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))
        else:
            self.slim.save_quantized_model(
                self.model,
                os.path.join(save_dir, 'model'),
                input_spec=input_spec)

    def _prune_input_spec(self, input_spec, program, targets):
        # try to prune static program to figure out pruned input spec
        # so we perform following operations in static mode
        paddle.enable_static()
        pruned_input_spec = [{}]
        program = program.clone()
        program = program._prune(targets=targets)
        global_block = program.global_block()
        for name, spec in input_spec[0].items():
            try:
                v = global_block.var(name)
                pruned_input_spec[0][name] = spec
            except Exception:
                pass
        paddle.disable_static()
        return pruned_input_spec