PaddleX/paddlex_restful/restful/project/train/detection.py

# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.

import os.path as osp
import numpy as np
import paddle
from paddleslim import L1NormFilterPruner


def build_yolo_transforms(params):
    from paddlex import transforms as T
    target_size = params.image_shape[0]
    use_mixup = params.use_mixup
    dt_list = []
    if use_mixup:
        dt_list.append(
            T.MixupImage(
                alpha=params.mixup_alpha,
                beta=params.mixup_beta,
                mixup_epoch=int(params.num_epochs * 25. / 27)))
    dt_list.extend([
        T.RandomDistort(
            brightness_range=params.brightness_range,
            brightness_prob=params.brightness_prob,
            contrast_range=params.contrast_range,
            contrast_prob=params.contrast_prob,
            saturation_range=params.saturation_range,
            saturation_prob=params.saturation_prob,
            hue_range=params.hue_range,
            hue_prob=params.hue_prob),
        T.RandomExpand(
            prob=params.expand_prob,
            im_padding_value=[float(int(x * 255)) for x in params.image_mean])
    ])
    crop_image = params.crop_image
    if crop_image:
        dt_list.append(T.RandomCrop())
    dt_list.extend([
        T.Resize(
            target_size=target_size, interp='RANDOM'),
        T.RandomHorizontalFlip(prob=params.horizontal_flip_prob), T.Normalize(
            mean=params.image_mean, std=params.image_std)
    ])
    train_transforms = T.Compose(dt_list)
    eval_transforms = T.Compose([
        T.Resize(
            target_size=target_size, interp='CUBIC'),
        T.Normalize(
            mean=params.image_mean, std=params.image_std),
    ])
    return train_transforms, eval_transforms


def build_rcnn_transforms(params):
    from paddlex import transforms as T
    short_size = min(params.image_shape)
    max_size = max(params.image_shape)
    train_transforms = T.Compose([
        T.RandomDistort(
            brightness_range=params.brightness_range,
            brightness_prob=params.brightness_prob,
            contrast_range=params.contrast_range,
            contrast_prob=params.contrast_prob,
            saturation_range=params.saturation_range,
            saturation_prob=params.saturation_prob,
            hue_range=params.hue_range,
            hue_prob=params.hue_prob),
        T.RandomHorizontalFlip(prob=params.horizontal_flip_prob),
        T.Normalize(
            mean=params.image_mean, std=params.image_std),
        T.ResizeByShort(
            short_size=short_size, max_size=max_size),
    ])
    eval_transforms = T.Compose([
        T.Normalize(),
        T.ResizeByShort(
            short_size=short_size, max_size=max_size),
    ])
    return train_transforms, eval_transforms


def build_voc_datasets(dataset_path, train_transforms, eval_transforms):
    import paddlex as pdx
    train_file_list = osp.join(dataset_path, 'train_list.txt')
    eval_file_list = osp.join(dataset_path, 'val_list.txt')
    label_list = osp.join(dataset_path, 'labels.txt')
    train_dataset = pdx.datasets.VOCDetection(
        data_dir=dataset_path,
        file_list=train_file_list,
        label_list=label_list,
        transforms=train_transforms,
        shuffle=True)
    eval_dataset = pdx.datasets.VOCDetection(
        data_dir=dataset_path,
        file_list=eval_file_list,
        label_list=label_list,
        transforms=eval_transforms)
    return train_dataset, eval_dataset


def build_coco_datasets(dataset_path, train_transforms, eval_transforms):
    import paddlex as pdx
    data_dir = osp.join(dataset_path, 'JPEGImages')
    train_ann_file = osp.join(dataset_path, 'train.json')
    eval_ann_file = osp.join(dataset_path, 'val.json')
    train_dataset = pdx.datasets.CocoDetection(
        data_dir=data_dir,
        ann_file=train_ann_file,
        transforms=train_transforms,
        shuffle=True)
    eval_dataset = pdx.datasets.CocoDetection(
        data_dir=data_dir, ann_file=eval_ann_file, transforms=eval_transforms)
    return train_dataset, eval_dataset


def build_optimizer(parameters, step_each_epoch, params):
    import paddle
    from paddle.regularizer import L2Decay
    learning_rate = params.learning_rate
    lr_decay_epochs = params.lr_decay_epochs
    warmup_steps = params.warmup_steps
    warmup_start_lr = params.warmup_start_lr

    boundaries = [b * step_each_epoch for b in lr_decay_epochs]
    values = [
        learning_rate * (0.1**i) for i in range(len(lr_decay_epochs) + 1)
    ]
    lr = paddle.optimizer.lr.PiecewiseDecay(
        boundaries=boundaries, values=values)
    lr = paddle.optimizer.lr.LinearWarmup(
        learning_rate=lr,
        warmup_steps=warmup_steps,
        start_lr=warmup_start_lr,
        end_lr=learning_rate)
    factor = 1e-04 if params.model in ['FasterRCNN', 'MaskRCNN'] else 5e-04
    optimizer = paddle.optimizer.Momentum(
        learning_rate=lr,
        momentum=0.9,
        weight_decay=L2Decay(factor),
        parameters=parameters)
    return optimizer


def train(task_path, dataset_path, params):
    import paddlex as pdx
    pdx.log_level = 3
    if params.model in ['YOLOv3', 'PPYOLO', 'PPYOLOTiny', 'PPYOLOv2']:
        train_transforms, eval_transforms = build_yolo_transforms(params)
    elif params.model in ['FasterRCNN', 'MaskRCNN']:
        train_transforms, eval_transforms = build_rcnn_transforms(params)
    if osp.exists(osp.join(dataset_path, 'JPEGImages')) and \
        osp.exists(osp.join(dataset_path, 'train.json')) and \
        osp.exists(osp.join(dataset_path, 'val.json')):
        train_dataset, eval_dataset = build_coco_datasets(
            dataset_path=dataset_path,
            train_transforms=train_transforms,
            eval_transforms=eval_transforms)
    elif osp.exists(osp.join(dataset_path, 'train_list.txt')) and \
        osp.exists(osp.join(dataset_path, 'val_list.txt')) and \
        osp.exists(osp.join(dataset_path, 'labels.txt')):
        train_dataset, eval_dataset = build_voc_datasets(
            dataset_path=dataset_path,
            train_transforms=train_transforms,
            eval_transforms=eval_transforms)
    step_each_epoch = train_dataset.num_samples // params.batch_size
    train_batch_size = params.batch_size
    save_interval_epochs = params.save_interval_epochs
    save_dir = osp.join(task_path, 'output')
    pretrain_weights = params.pretrain_weights
    if pretrain_weights is not None and osp.exists(pretrain_weights):
        pretrain_weights = osp.join(pretrain_weights, 'model.pdparams')

    detector = getattr(pdx.det, params.model)
    num_classes = len(train_dataset.labels)
    sensitivities_path = params.sensitivities_path
    pruned_flops = params.pruned_flops
    model = detector(num_classes=num_classes, backbone=params.backbone)

    if sensitivities_path is not None:
        # load weights
        model.net_initialize(pretrain_weights=pretrain_weights)
        pretrain_weights = None
        # prune
        dataset = eval_dataset or train_dataset
        im_shape = dataset[0]['image'].shape[:2]
        if getattr(model, 'with_fpn', False):
            im_shape[0] = int(np.ceil(im_shape[0] / 32) * 32)
            im_shape[1] = int(np.ceil(im_shape[1] / 32) * 32)
        inputs = [{
            "image": paddle.ones(
                shape=[1, 3] + list(im_shape), dtype='float32'),
            "im_shape": paddle.full(
                [1, 2], 640, dtype='float32'),
            "scale_factor": paddle.ones(
                shape=[1, 2], dtype='float32')
        }]
        model.net.eval()
        model.pruner = L1NormFilterPruner(
            model.net, inputs=inputs, sen_file=sensitivities_path)
        model.prune(pruned_flops=pruned_flops)

    optimizer = build_optimizer(model.net.parameters(), step_each_epoch,
                                params)
    model.train(
        num_epochs=params.num_epochs,
        train_dataset=train_dataset,
        train_batch_size=train_batch_size,
        eval_dataset=eval_dataset,
        save_interval_epochs=save_interval_epochs,
        log_interval_steps=2,
        save_dir=save_dir,
        pretrain_weights=pretrain_weights,
        optimizer=optimizer,
        use_vdl=True,
        resume_checkpoint=params.resume_checkpoint)