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, num_max_boxes=50, mixup_epoch=250): 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) self.num_max_boxes = num_max_boxes self.mixup_epoch = mixup_epoch self.mode = 'TRAIN'
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), ColorDistort(), RandomExpand(fill_value=[123.675, 116.28, 103.53]), RandomCrop(), RandomFlipImage(is_normalized=False), Resize(target_dim=608, interp='random'), NormalizePermute( mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375]), NormalizeBox(), ], 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'
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'
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'