def default_configs(phi, batch_size=4, image_size=512, num_classes=3): h = Config() h.dtype = "float32" # backbone h.model = dict( model="EfficientNetB%d" % phi, convolution="depthwise_conv2d", dropblock=None, # dropblock=dict(keep_prob=None, # block_size=None) normalization=dict(normalization="batch_norm", momentum=0.99, epsilon=1e-3, axis=-1, trainable=True), activation=dict(activation="swish"), strides=[2, 1, 2, 2, 2, 1, 2, 1], dilation_rates=[1, 1, 1, 1, 1, 1, 1, 1], output_indices=[ -1, ], frozen_stages=[ -1, ], num_classes=num_classes) # loss h.use_sigmoid = False h.loss = dict(loss="CrossEntropy", weight=1., from_logits=True, reduction="none") h.weight_decay = 4e-5 # dataset h.num_classes = num_classes h.train = dict(dataset=dict( dataset="ShuaidaoDataset", batch_size=batch_size, dataset_dir= "/home/bail/Data/data1/Dataset/RongChuangActions/shuaidao/shuaidaoThree/train", training=True, augmentations=[ dict(Resize=dict(input_size=image_size)), # dict(RandomDistortColor=dict()), dict(RandAugment=dict(num_layers=2, magnitude=10., cutout_const=40., translate_const=100.)) ])) h.val = dict(dataset=dict( dataset="ShuaidaoDataset", batch_size=batch_size, dataset_dir= "/home/bail/Data/data1/Dataset/RongChuangActions/shuaidao/shuaidaoThree/val", training=False, augmentations=[dict(Resize=dict(input_size=image_size))])) # train h.pretrained_weights_path = "/data/bail/pretrained_weights/efficientnet-b%d.h5" % phi h.optimizer = dict(optimizer="SGD", momentum=0.9) h.lookahead = None h.learning_rate_scheduler = dict(scheduler="CosineDecay", initial_learning_rate=0.01, warmup_steps=1200, warmup_learning_rate=0.001, train_steps=32001) h.checkpoint_dir = "checkpoints/efficientnet_b%d_shuaidao" % phi h.summary_dir = "logs/efficientnet_b%d_shuaidao" % phi h.gradient_clip_norm = 0. h.log_every_n_steps = 100 h.save_ckpt_steps = 1000 h.val_every_n_steps = 1000 return h
def default_detection_configs(phi, min_level=3, max_level=7, fpn_filters=64, neck_repeats=3, head_repeats=3, anchor_scale=4, num_scales=3, batch_size=4, image_size=512, fusion_type="weighted_sum"): h = Config() # model name h.detector = "efficientdet-d%d" % phi h.min_level = min_level h.max_level = max_level h.dtype = "float16" # backbone h.backbone = dict(backbone="efficientnet-b%d" % phi, convolution="depthwise_conv2d", dropblock=None, # dropblock=dict(keep_prob=None, # block_size=None) normalization=dict(normalization="batch_norm", momentum=0.99, epsilon=1e-3, axis=-1, trainable=False), activation=dict(activation="swish"), strides=[2, 1, 2, 2, 2, 1, 2, 1], dilation_rates=[1, 1, 1, 1, 1, 1, 1, 1], output_indices=[3, 4, 5], frozen_stages=[-1]) # neck h.neck = dict(neck="bifpn", repeats=neck_repeats, convolution="separable_conv2d", dropblock=None, # dropblock=dict(keep_prob=None, # block_size=None) feat_dims=fpn_filters, normalization=dict(normalization="batch_norm", momentum=0.99, epsilon=1e-3, axis=-1, trainable=False), activation=dict(activation="swish"), add_extra_conv=False, # Add extra convolution for neck fusion_type=fusion_type, use_multiplication=False) # head h.head = dict(head="RetinaNetHead", repeats=head_repeats, convolution="separable_conv2d", dropblock=None, # dropblock=dict(keep_prob=None, # block_size=None) feat_dims=fpn_filters, normalization=dict(normalization="batch_norm", momentum=0.99, epsilon=1e-3, axis=-1, trainable=False), activation=dict(activation="swish"), prior=0.01) # anchors parameters strides = [2 ** l for l in range(min_level, max_level + 1)] h.anchor = dict(aspect_ratios=[[1., 0.5, 2.]] * (max_level - min_level + 1), scales=[ [2 ** (i / num_scales) * s * anchor_scale for i in range(num_scales)] for s in strides ], num_anchors=9) # assigner h.assigner = dict(assigner="max_iou_assigner", pos_iou_thresh=0.5, neg_iou_thresh=0.5) # sampler h.sampler = dict(sampler="pseudo_sampler") # loss h.use_sigmoid = True h.label_loss=dict(loss="focal_loss", alpha=0.25, gamma=1.5, label_smoothing=0., weight=1., from_logits=True, reduction="none") h.bbox_loss=dict(loss="smooth_l1_loss", weight=50., # 50. delta=.1, # .1 reduction="none") # h.box_loss=dict(loss="giou_loss", # weight=10., # reduction="none") h.weight_decay = 4e-5 h.bbox_mean = None # [0., 0., 0., 0.] h.bbox_std = None # [0.1, 0.1, 0.2, 0.2] # dataset h.num_classes = 90 h.skip_crowd_during_training = True h.dataset = "objects365" h.batch_size = batch_size h.input_size = [image_size, image_size] h.train_dataset_dir = "/home/bail/Data/data1/Dataset/Objects365/train" h.val_dataset_dir = "/home/bail/Data/data1/Dataset/Objects365/train" h.augmentation = [ dict(ssd_crop=dict(patch_area_range=(0.3, 1.), aspect_ratio_range=(0.5, 2.0), min_overlaps=(0.1, 0.3, 0.5, 0.7, 0.9), max_attempts=100, probability=.5)), # dict(data_anchor_sampling=dict(anchor_scales=(16, 32, 64, 128, 256, 512), # overlap_threshold=0.7, # max_attempts=50, # probability=.5)), dict(flip_left_to_right=dict(probability=0.5)), dict(random_distort_color=dict(probability=1.)) ] # train h.pretrained_weights_path = "/home/bail/Workspace/pretrained_weights/efficientdet-d%d" % phi h.optimizer = dict(optimizer="sgd", momentum=0.9) h.lookahead = None h.train_steps = 240000 h.learning_rate_scheduler = dict(scheduler="cosine", initial_learning_rate=0.002) h.warmup = dict(warmup_learning_rate = 0.00001, steps = 24000) h.checkpoint_dir = "checkpoints/efficientdet_d%d" % phi h.summary_dir = "logs/efficientdet_d%d" % phi h.gradient_clip_norm = .0 h.log_every_n_steps = 500 h.save_ckpt_steps = 10000 h.val_every_n_steps = 4000 h.postprocess = dict(pre_nms_size=5000, # select top_k high confident detections for nms post_nms_size=100, iou_threshold=0.5, score_threshold=0.2) return h
def default_configs(name, batch_size=4, image_size=512): h = Config() h.dtype = "float32" # backbone h.model = dict( model=name, convolution="conv2d", dropblock=None, # dropblock=dict(keep_prob=None, # block_size=None) normalization=dict(normalization="batch_norm", momentum=0.99, epsilon=1e-3, axis=-1, trainable=True), activation=dict(activation="relu"), strides=[2, 1, 2, 2, 2, 1, 2, 1], dilation_rates=[1, 1, 1, 1, 1, 1, 1, 1], output_indices=[ -1, ], frozen_stages=[ -1, ], num_classes=1) # loss h.use_sigmoid = True h.loss = dict(loss="BinaryCrossEntropy", weight=1., from_logits=True, reduction="none") h.weight_decay = 4e-5 # dataset h.num_classes = 1 h.train = dict(dataset=dict( dataset="SmokingDataset", batch_size=batch_size, dataset_dir="/data/bail/smoking/train", training=True, augmentations=[ dict(Resize=dict(input_size=image_size)), # dict(RandAugment=dict(num_layers=2, # magnitude=10., # cutout_const=40., # translate_const=100.)) ])) h.val = dict( dataset=dict(dataset="SmokingDataset", batch_size=batch_size, dataset_dir="/data/bail/smoking/val", training=False, augmentations=[dict(Resize=dict(input_size=image_size))])) # train h.pretrained_weights_path = "/data/bail/pretrained_weights/efficientnet-b%d.h5" % phi h.optimizer = dict(optimizer="SGD", momentum=0.9) h.lookahead = None h.learning_rate_scheduler = dict(scheduler="CosineDecay", initial_learning_rate=0.016, warmup_steps=800, warmup_learning_rate=0.001, train_steps=40001) h.checkpoint_dir = "checkpoints/%s" % name h.summary_dir = "logs/%s" % name h.gradient_clip_norm = 0. h.log_every_n_steps = 100 h.save_ckpt_steps = 2000 h.val_every_n_steps = 2000 return h