# Copyright (c) 2019 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 print_function from __future__ import division import os import inspect from ppdet.core.workspace import register, serializable from ppdet.utils.download import get_dataset_path from ppdet.data.reader import Reader # XXX these are for triggering the decorator from ppdet.data.transform.operators import ( DecodeImage, MixupImage, NormalizeBox, NormalizeImage, RandomDistort, RandomFlipImage, RandomInterpImage, ResizeImage, ExpandImage, CropImage, Permute, MultiscaleTestResize) from ppdet.data.transform.arrange_sample import ( ArrangeRCNN, ArrangeEvalRCNN, ArrangeTestRCNN, ArrangeSSD, ArrangeEvalSSD, ArrangeTestSSD, ArrangeYOLO, ArrangeEvalYOLO, ArrangeTestYOLO) __all__ = [ 'PadBatch', 'MultiScale', 'RandomShape', 'PadMSTest', 'DataSet', 'CocoDataSet', 'DataFeed', 'TrainFeed', 'EvalFeed', 'FasterRCNNTrainFeed', 'MaskRCNNTrainFeed', 'FasterRCNNEvalFeed', 'MaskRCNNEvalFeed', 'FasterRCNNTestFeed', 'MaskRCNNTestFeed', 'SSDTrainFeed', 'SSDEvalFeed', 'SSDTestFeed', 'YoloTrainFeed', 'YoloEvalFeed', 'YoloTestFeed', 'create_reader' ] def _prepare_data_config(feed, args_path): # if `DATASET_DIR` does not exists, search ~/.paddle/dataset for a directory # named `DATASET_DIR` (e.g., coco, pascal), if not present either, download dataset_home = args_path if args_path else feed.dataset.dataset_dir if dataset_home: annotation = getattr(feed.dataset, 'annotation', None) image_dir = getattr(feed.dataset, 'image_dir', None) dataset_dir = get_dataset_path(dataset_home, annotation, image_dir) if annotation: feed.dataset.annotation = os.path.join(dataset_dir, annotation) if image_dir: feed.dataset.image_dir = os.path.join(dataset_dir, image_dir) mixup_epoch = -1 if getattr(feed, 'mixup_epoch', None) is not None: mixup_epoch = feed.mixup_epoch data_config = { 'ANNO_FILE': feed.dataset.annotation, 'IMAGE_DIR': feed.dataset.image_dir, 'USE_DEFAULT_LABEL': feed.dataset.use_default_label, 'IS_SHUFFLE': feed.shuffle, 'SAMPLES': feed.samples, 'WITH_BACKGROUND': feed.with_background, 'MIXUP_EPOCH': mixup_epoch, 'TYPE': type(feed.dataset).__source__ } if feed.mode == 'TRAIN': data_config['CLASS_AWARE_SAMPLING'] = getattr( feed, 'class_aware_sampling', False) if len(getattr(feed.dataset, 'images', [])) > 0: data_config['IMAGES'] = feed.dataset.images return data_config def create_reader(feed, max_iter=0, args_path=None, my_source=None): """ Return iterable data reader. Args: max_iter (int): number of iterations. my_source (callable): callable function to create a source iterator which is used to provide source data in 'ppdet.data.reader' """ # if `DATASET_DIR` does not exists, search ~/.paddle/dataset for a directory # named `DATASET_DIR` (e.g., coco, pascal), if not present either, download data_config = _prepare_data_config(feed, args_path) bufsize = getattr(feed, 'bufsize', 10) use_process = getattr(feed, 'use_process', False) memsize = getattr(feed, 'memsize', '3G') transform_config = { 'WORKER_CONF': { 'bufsize': bufsize, 'worker_num': feed.num_workers, 'use_process': use_process, 'memsize': memsize }, 'BATCH_SIZE': feed.batch_size, 'DROP_LAST': feed.drop_last, 'USE_PADDED_IM_INFO': feed.use_padded_im_info, } batch_transforms = feed.batch_transforms pad = [t for t in batch_transforms if isinstance(t, PadBatch)] rand_shape = [t for t in batch_transforms if isinstance(t, RandomShape)] multi_scale = [t for t in batch_transforms if isinstance(t, MultiScale)] pad_ms_test = [t for t in batch_transforms if isinstance(t, PadMSTest)] if any(pad): transform_config['IS_PADDING'] = True if pad[0].pad_to_stride != 0: transform_config['COARSEST_STRIDE'] = pad[0].pad_to_stride if any(rand_shape): transform_config['RANDOM_SHAPES'] = rand_shape[0].sizes if any(multi_scale): transform_config['MULTI_SCALES'] = multi_scale[0].scales if any(pad_ms_test): transform_config['ENABLE_MULTISCALE_TEST'] = True transform_config['NUM_SCALE'] = feed.num_scale transform_config['COARSEST_STRIDE'] = pad_ms_test[0].pad_to_stride if hasattr(inspect, 'getfullargspec'): argspec = inspect.getfullargspec else: argspec = inspect.getargspec ops = [] for op in feed.sample_transforms: op_dict = op.__dict__.copy() argnames = [ arg for arg in argspec(type(op).__init__).args if arg != 'self' ] op_dict = {k: v for k, v in op_dict.items() if k in argnames} op_dict['op'] = op.__class__.__name__ ops.append(op_dict) transform_config['OPS'] = ops return Reader.create(feed.mode, data_config, transform_config, max_iter, my_source) # XXX batch transforms are only stubs for now, actually handled by `post_map` @serializable class PadBatch(object): """ Pad a batch of samples to same dimensions Args: pad_to_stride (int): pad to multiple of strides, e.g., 32 """ def __init__(self, pad_to_stride=0): super(PadBatch, self).__init__() self.pad_to_stride = pad_to_stride @serializable class MultiScale(object): """ Randomly resize image by scale Args: scales (list): list of int, randomly resize to one of these scales """ def __init__(self, scales=[]): super(MultiScale, self).__init__() self.scales = scales @serializable class RandomShape(object): """ Randomly reshape a batch Args: sizes (list): list of int, random choose a size from these """ def __init__(self, sizes=[]): super(RandomShape, self).__init__() self.sizes = sizes @serializable class PadMSTest(object): """ Padding for multi-scale test Args: pad_to_stride (int): pad to multiple of strides, e.g., 32 """ def __init__(self, pad_to_stride=0): super(PadMSTest, self).__init__() self.pad_to_stride = pad_to_stride @serializable class DataSet(object): """ Dataset, e.g., coco, pascal voc Args: annotation (str): annotation file path image_dir (str): directory where image files are stored shuffle (bool): shuffle samples """ __source__ = 'RoiDbSource' def __init__(self, annotation, image_dir=None, dataset_dir=None, use_default_label=None): super(DataSet, self).__init__() self.dataset_dir = dataset_dir self.annotation = annotation self.image_dir = image_dir self.use_default_label = use_default_label COCO_DATASET_DIR = 'dataset/coco' COCO_TRAIN_ANNOTATION = 'annotations/instances_train2017.json' COCO_TRAIN_IMAGE_DIR = 'train2017' COCO_VAL_ANNOTATION = 'annotations/instances_val2017.json' COCO_VAL_IMAGE_DIR = 'val2017' @serializable class CocoDataSet(DataSet): def __init__(self, dataset_dir=COCO_DATASET_DIR, annotation=COCO_TRAIN_ANNOTATION, image_dir=COCO_TRAIN_IMAGE_DIR): super(CocoDataSet, self).__init__( dataset_dir=dataset_dir, annotation=annotation, image_dir=image_dir) VOC_DATASET_DIR = 'dataset/voc' VOC_TRAIN_ANNOTATION = 'train.txt' VOC_VAL_ANNOTATION = 'val.txt' VOC_IMAGE_DIR = None VOC_USE_DEFAULT_LABEL = True @serializable class VocDataSet(DataSet): __source__ = 'VOCSource' def __init__(self, dataset_dir=VOC_DATASET_DIR, annotation=VOC_TRAIN_ANNOTATION, image_dir=VOC_IMAGE_DIR, use_default_label=VOC_USE_DEFAULT_LABEL): super(VocDataSet, self).__init__( dataset_dir=dataset_dir, annotation=annotation, image_dir=image_dir, use_default_label=use_default_label) @serializable class SimpleDataSet(DataSet): __source__ = 'SimpleSource' def __init__(self, dataset_dir=None, annotation=None, image_dir=None, use_default_label=None): super(SimpleDataSet, self).__init__( dataset_dir=dataset_dir, annotation=annotation, image_dir=image_dir) self.images = [] def add_images(self, images): self.images.extend(images) @serializable class DataFeed(object): """ DataFeed encompasses all data loading related settings Args: dataset (object): a `Dataset` instance fields (list): list of data fields needed image_shape (list): list of image dims (C, MAX_DIM, MIN_DIM) sample_transforms (list): list of sample transformations to use batch_transforms (list): list of batch transformations to use batch_size (int): number of images per device shuffle (bool): if samples should be shuffled drop_last (bool): drop last batch if size is uneven num_workers (int): number of workers processes (or threads) bufsize (int): size of queue used to buffer results from workers use_process (bool): use process or thread as workers memsize (str): size of shared memory used in result queue when 'use_process' is True, default to '3G' """ __category__ = 'data' def __init__(self, dataset, fields, image_shape, sample_transforms=None, batch_transforms=None, batch_size=1, shuffle=False, samples=-1, drop_last=False, with_background=True, num_workers=2, bufsize=10, use_process=False, memsize=None, use_padded_im_info=False, class_aware_sampling=False): super(DataFeed, self).__init__() self.fields = fields self.image_shape = image_shape self.sample_transforms = sample_transforms self.batch_transforms = batch_transforms self.batch_size = batch_size self.shuffle = shuffle self.samples = samples self.drop_last = drop_last self.with_background = with_background self.num_workers = num_workers self.bufsize = bufsize self.use_process = use_process self.memsize = memsize self.dataset = dataset self.use_padded_im_info = use_padded_im_info self.class_aware_sampling = class_aware_sampling if isinstance(dataset, dict): self.dataset = DataSet(**dataset) # for custom (i.e., Non-preset) datasets @register class TrainFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset, fields, image_shape, sample_transforms=[], batch_transforms=[], batch_size=1, shuffle=True, samples=-1, drop_last=False, with_background=True, num_workers=2, bufsize=10, use_process=True, memsize=None): super(TrainFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, with_background=with_background, num_workers=num_workers, bufsize=bufsize, use_process=use_process, memsize=memsize) @register class EvalFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset, fields, image_shape, sample_transforms=[], batch_transforms=[], batch_size=1, shuffle=False, samples=-1, drop_last=False, with_background=True, num_workers=2): super(EvalFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, with_background=with_background, num_workers=num_workers) @register class TestFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset, fields, image_shape, sample_transforms=[], batch_transforms=[], batch_size=1, shuffle=False, drop_last=False, with_background=True, num_workers=2): super(TestFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, with_background=with_background, num_workers=num_workers) # yapf: disable @register class FasterRCNNTrainFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset=CocoDataSet().__dict__, fields=[ 'image', 'im_info', 'im_id', 'gt_box', 'gt_label', 'is_crowd' ], image_shape=[None, 3, None, None], sample_transforms=[ DecodeImage(to_rgb=True), RandomFlipImage(prob=0.5), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_scale=True, is_channel_first=False), ResizeImage(target_size=800, max_size=1333, interp=1), Permute(to_bgr=False) ], batch_transforms=[PadBatch()], batch_size=1, shuffle=True, samples=-1, drop_last=False, bufsize=10, num_workers=2, use_process=False, memsize=None, class_aware_sampling=False): # XXX this should be handled by the data loader, since `fields` is # given, just collect them sample_transforms.append(ArrangeRCNN()) super(FasterRCNNTrainFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, bufsize=bufsize, num_workers=num_workers, use_process=use_process, memsize=memsize, class_aware_sampling=class_aware_sampling) # XXX these modes should be unified self.mode = 'TRAIN' @register class FasterRCNNEvalFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset=CocoDataSet(COCO_VAL_ANNOTATION, COCO_VAL_IMAGE_DIR).__dict__, fields=['image', 'im_info', 'im_id', 'im_shape', 'gt_box', 'gt_label', 'is_difficult'], image_shape=[None, 3, None, None], sample_transforms=[ DecodeImage(to_rgb=True), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_scale=True, is_channel_first=False), ResizeImage(target_size=800, max_size=1333, interp=1), Permute(to_bgr=False) ], batch_transforms=[PadBatch()], batch_size=1, shuffle=False, samples=-1, drop_last=False, num_workers=2, use_padded_im_info=True, enable_multiscale=False, num_scale=1, enable_aug_flip=False): sample_transforms.append(ArrangeEvalRCNN()) super(FasterRCNNEvalFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, num_workers=num_workers, use_padded_im_info=use_padded_im_info) self.mode = 'VAL' self.enable_multiscale = enable_multiscale self.num_scale = num_scale self.enable_aug_flip = enable_aug_flip @register class FasterRCNNTestFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset=SimpleDataSet(COCO_VAL_ANNOTATION, COCO_VAL_IMAGE_DIR).__dict__, fields=['image', 'im_info', 'im_id', 'im_shape'], image_shape=[None, 3, None, None], sample_transforms=[ DecodeImage(to_rgb=True), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_scale=True, is_channel_first=False), Permute(to_bgr=False) ], batch_transforms=[PadBatch()], batch_size=1, shuffle=False, samples=-1, drop_last=False, num_workers=2, use_padded_im_info=True): sample_transforms.append(ArrangeTestRCNN()) if isinstance(dataset, dict): dataset = SimpleDataSet(**dataset) super(FasterRCNNTestFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, num_workers=num_workers, use_padded_im_info=use_padded_im_info) self.mode = 'TEST' # XXX currently use two presets, in the future, these should be combined into a # single `RCNNTrainFeed`. Mask (and keypoint) should be processed # automatically if `gt_mask` (or `gt_keypoints`) is in the required fields @register class MaskRCNNTrainFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset=CocoDataSet().__dict__, fields=[ 'image', 'im_info', 'im_id', 'gt_box', 'gt_label', 'is_crowd', 'gt_mask' ], image_shape=[None, 3, None, None], sample_transforms=[ DecodeImage(to_rgb=True), RandomFlipImage(prob=0.5, is_mask_flip=True), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_scale=True, is_channel_first=False), ResizeImage(target_size=800, max_size=1333, interp=1, use_cv2=True), Permute(to_bgr=False, channel_first=True) ], batch_transforms=[PadBatch()], batch_size=1, shuffle=True, samples=-1, drop_last=False, num_workers=2, use_process=False, use_padded_im_info=False): sample_transforms.append(ArrangeRCNN(is_mask=True)) super(MaskRCNNTrainFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, num_workers=num_workers, use_process=use_process) self.mode = 'TRAIN' @register class MaskRCNNEvalFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset=CocoDataSet(COCO_VAL_ANNOTATION, COCO_VAL_IMAGE_DIR).__dict__, fields=['image', 'im_info', 'im_id', 'im_shape'], image_shape=[None, 3, None, None], sample_transforms=[ DecodeImage(to_rgb=True), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_scale=True, is_channel_first=False), ResizeImage(target_size=800, max_size=1333, interp=1, use_cv2=True), Permute(to_bgr=False, channel_first=True) ], batch_transforms=[PadBatch()], batch_size=1, shuffle=False, samples=-1, drop_last=False, num_workers=2, use_process=False, use_padded_im_info=True, enable_multiscale=False, num_scale=1, enable_aug_flip=False): sample_transforms.append(ArrangeTestRCNN()) super(MaskRCNNEvalFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, num_workers=num_workers, use_process=use_process, use_padded_im_info=use_padded_im_info) self.mode = 'VAL' self.enable_multiscale = enable_multiscale self.num_scale = num_scale self.enable_aug_flip = enable_aug_flip @register class MaskRCNNTestFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset=SimpleDataSet(COCO_VAL_ANNOTATION, COCO_VAL_IMAGE_DIR).__dict__, fields=['image', 'im_info', 'im_id', 'im_shape'], image_shape=[None, 3, None, None], sample_transforms=[ DecodeImage(to_rgb=True), NormalizeImage( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_scale=True, is_channel_first=False), Permute(to_bgr=False, channel_first=True) ], batch_transforms=[PadBatch()], batch_size=1, shuffle=False, samples=-1, drop_last=False, num_workers=2, use_process=False, use_padded_im_info=True): sample_transforms.append(ArrangeTestRCNN()) if isinstance(dataset, dict): dataset = SimpleDataSet(**dataset) super(MaskRCNNTestFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, num_workers=num_workers, use_process=use_process, use_padded_im_info=use_padded_im_info) self.mode = 'TEST' @register class SSDTrainFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset=VocDataSet().__dict__, fields=['image', 'gt_box', 'gt_label'], image_shape=[3, 300, 300], sample_transforms=[ DecodeImage(to_rgb=True, with_mixup=False), NormalizeBox(), RandomDistort(brightness_lower=0.875, brightness_upper=1.125, is_order=True), ExpandImage(max_ratio=4, prob=0.5), CropImage(batch_sampler=[[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]], satisfy_all=False, avoid_no_bbox=False), ResizeImage(target_size=300, use_cv2=False, interp=1), RandomFlipImage(is_normalized=True), Permute(), NormalizeImage(mean=[127.5, 127.5, 127.5], std=[127.502231, 127.502231, 127.502231], is_scale=False) ], batch_transforms=[], batch_size=32, shuffle=True, samples=-1, drop_last=True, num_workers=8, bufsize=10, use_process=True, memsize=None): sample_transforms.append(ArrangeSSD()) super(SSDTrainFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, num_workers=num_workers, bufsize=bufsize, use_process=use_process, memsize=None) self.mode = 'TRAIN' @register class SSDEvalFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__( self, dataset=VocDataSet(VOC_VAL_ANNOTATION).__dict__, fields=['image', 'im_shape', 'im_id', 'gt_box', 'gt_label', 'is_difficult'], image_shape=[3, 300, 300], sample_transforms=[ DecodeImage(to_rgb=True, with_mixup=False), NormalizeBox(), ResizeImage(target_size=300, use_cv2=False, interp=1), Permute(), NormalizeImage( mean=[127.5, 127.5, 127.5], std=[127.502231, 127.502231, 127.502231], is_scale=False) ], batch_transforms=[], batch_size=64, shuffle=False, samples=-1, drop_last=True, num_workers=8, bufsize=10, use_process=False, memsize=None): sample_transforms.append(ArrangeEvalSSD(fields)) super(SSDEvalFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, num_workers=num_workers, bufsize=bufsize, use_process=use_process, memsize=memsize) self.mode = 'VAL' @register class SSDTestFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset=SimpleDataSet(VOC_VAL_ANNOTATION).__dict__, fields=['image', 'im_id', 'im_shape'], image_shape=[3, 300, 300], sample_transforms=[ DecodeImage(to_rgb=True), ResizeImage(target_size=300, use_cv2=False, interp=1), Permute(), NormalizeImage( mean=[127.5, 127.5, 127.5], std=[127.502231, 127.502231, 127.502231], is_scale=False) ], batch_transforms=[], batch_size=1, shuffle=False, samples=-1, drop_last=False, num_workers=8, bufsize=10, use_process=False, memsize=None): sample_transforms.append(ArrangeTestSSD()) if isinstance(dataset, dict): dataset = SimpleDataSet(**dataset) super(SSDTestFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, num_workers=num_workers, bufsize=bufsize, use_process=use_process, memsize=memsize) self.mode = 'TEST' @register class YoloTrainFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset=CocoDataSet().__dict__, fields=['image', 'gt_box', 'gt_label', 'gt_score'], image_shape=[3, 608, 608], sample_transforms=[ DecodeImage(to_rgb=True, with_mixup=True), MixupImage(alpha=1.5, beta=1.5), NormalizeBox(), RandomDistort(), ExpandImage(max_ratio=4., prob=.5, mean=[123.675, 116.28, 103.53]), CropImage([[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 1.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 1.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 1.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 1.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 1.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]]), RandomInterpImage(target_size=608), RandomFlipImage(is_normalized=True), NormalizeImage( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_scale=True, is_channel_first=False), Permute(to_bgr=False), ], batch_transforms=[ RandomShape(sizes=[ 320, 352, 384, 416, 448, 480, 512, 544, 576, 608 ]) ], batch_size=8, shuffle=True, samples=-1, drop_last=True, with_background=False, num_workers=8, bufsize=128, use_process=True, memsize=None, num_max_boxes=50, mixup_epoch=250, class_aware_sampling=False): sample_transforms.append(ArrangeYOLO()) super(YoloTrainFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, with_background=with_background, num_workers=num_workers, bufsize=bufsize, use_process=use_process, memsize=memsize, class_aware_sampling=class_aware_sampling) self.num_max_boxes = num_max_boxes self.mixup_epoch = mixup_epoch self.mode = 'TRAIN' @register class YoloEvalFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset=CocoDataSet(COCO_VAL_ANNOTATION, COCO_VAL_IMAGE_DIR).__dict__, fields=['image', 'im_size', 'im_id', 'gt_box', 'gt_label', 'is_difficult'], image_shape=[3, 608, 608], sample_transforms=[ DecodeImage(to_rgb=True), ResizeImage(target_size=608, interp=2), NormalizeImage( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_scale=True, is_channel_first=False), Permute(to_bgr=False), ], batch_transforms=[], batch_size=8, shuffle=False, samples=-1, drop_last=False, with_background=False, num_workers=8, num_max_boxes=50, use_process=False, memsize=None): sample_transforms.append(ArrangeEvalYOLO()) super(YoloEvalFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, with_background=with_background, num_workers=num_workers, use_process=use_process, memsize=memsize) self.num_max_boxes = num_max_boxes self.mode = 'VAL' self.bufsize = 128 # support image shape config, resize image with image_shape for i, trans in enumerate(sample_transforms): if isinstance(trans, ResizeImage): sample_transforms[i] = ResizeImage( target_size=self.image_shape[-1], interp=trans.interp) @register class YoloTestFeed(DataFeed): __doc__ = DataFeed.__doc__ def __init__(self, dataset=SimpleDataSet(COCO_VAL_ANNOTATION, COCO_VAL_IMAGE_DIR).__dict__, fields=['image', 'im_size', 'im_id'], image_shape=[3, 608, 608], sample_transforms=[ DecodeImage(to_rgb=True), ResizeImage(target_size=608, interp=2), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_scale=True, is_channel_first=False), Permute(to_bgr=False), ], batch_transforms=[], batch_size=1, shuffle=False, samples=-1, drop_last=False, with_background=False, num_workers=8, num_max_boxes=50, use_process=False, memsize=None): sample_transforms.append(ArrangeTestYOLO()) if isinstance(dataset, dict): dataset = SimpleDataSet(**dataset) super(YoloTestFeed, self).__init__( dataset, fields, image_shape, sample_transforms, batch_transforms, batch_size=batch_size, shuffle=shuffle, samples=samples, drop_last=drop_last, with_background=with_background, num_workers=num_workers, use_process=use_process, memsize=memsize) self.mode = 'TEST' self.bufsize = 128 # support image shape config, resize image with image_shape for i, trans in enumerate(sample_transforms): if isinstance(trans, ResizeImage): sample_transforms[i] = ResizeImage( target_size=self.image_shape[-1], interp=trans.interp) # yapf: enable