def get_faster_rcnn_config(num_classes=80):
h = Config()
h.detector = "FasterRCNN"
h.dtype = "float16"
h.num_classes = num_classes
h.backbone = dict(backbone="ResNet50V1D",
convolution="conv2d",
dropblock=None,
normalization=dict(normalization="batch_norm",
momentum=0.997,
epsilon=1e-4,
trainable=False),
activation=dict(activation="relu"),
strides=[2, 2, 2, 2, 2],
dilation_rates=[1, 1, 1, 1, 1],
output_indices=[2, 3, 4, 5],
frozen_stages=[
-1,
])
h.neck = dict(neck="FPN",
convolution="conv2d",
feat_dims=256,
input_dims=[256, 512, 1024, 2048],
min_level=2,
max_level=6,
add_extra_convs=False)
h.anchors = dict(scales=[[32], [64], [128], [256], [512]],
aspect_ratios=[0.5, 1., 2.0],
strides=[4, 8, 16, 32, 64],
num_anchors=3)
h.rpn_head = dict(head="RPNHead",
convolution="conv2d",
normalization=None,
activation=dict(activation="relu"),
feat_dims=256,
dropblock=None,
num_classes=2,
min_level=2,
max_level=6,
use_sigmoid=False,
bbox_encoder=dict(encoder="Box2Delta", weights=None),
bbox_decoder=dict(decoder="Delta2Box", weights=None),
assigner=dict(assigner="MaxIoUAssigner",
pos_iou_thresh=0.7,
neg_iou_thresh=0.3),
sampler=dict(sampler="RandomSampler",
num_proposals=256,
pos_fraction=0.5,
add_gt_as_proposals=False),
label_loss=dict(loss="CrossEntropy",
label_smoothing=0.01,
weight=1.,
from_logits=True,
reduction="none"),
bbox_loss=dict(loss="SmoothL1Loss",
delta=1. / 9.,
weight=1.,
reduction="none"))
h.roi_head = dict(
num_stages=3,
roi_pooling=dict(roi_pooling="MultiLevelAlignedRoIPooling",
cropped_size=7,
strides=(4, 8, 16, 32, 64)),
bbox_head=[
dict(head="Shared2FCRCNNHead",
activation=dict(activation="relu"),
dropblock=None,
fc_dims=1024,
num_classes=num_classes,
bbox_encoder=dict(encoder="Box2Delta",
weights=[10., 10., 5., 5.]),
bbox_decoder=dict(decoder="Delta2Box",
weights=[10., 10., 5., 5.]),
assigner=dict(
assigner="MaxIoUAssigner",
pos_iou_thresh=0.5,
neg_iou_thresh=0.5,
),
sampler=dict(sampler="RandomSampler",
num_proposals=512,
pos_fraction=0.25,
add_gt_as_proposals=True),
label_loss=dict(loss="CrossEntropy",
label_smoothing=0.0,
weight=1.,
from_logits=True,
reduction="none"),
bbox_loss=dict(loss="SmoothL1Loss",
delta=1.,
weight=1.,
reduction="none"),
use_sigmoid=False,
reg_class_agnostic=True),
dict(head="Shared2FCRCNNHead",
activation=dict(activation="relu"),
dropblock=None,
fc_dims=1024,
num_classes=num_classes,
bbox_encoder=dict(encoder="Box2Delta",
weights=[20., 20., 10., 10.]),
bbox_decoder=dict(decoder="Delta2Box",
weights=[20., 20., 10., 10.]),
assigner=dict(
assigner="MaxIoUAssigner",
pos_iou_thresh=0.6,
neg_iou_thresh=0.6,
),
sampler=dict(sampler="RandomSampler",
num_proposals=512,
pos_fraction=0.25,
add_gt_as_proposals=True),
label_loss=dict(loss="CrossEntropy",
label_smoothing=0.01,
weight=1.,
from_logits=True,
reduction="none"),
bbox_loss=dict(loss="SmoothL1Loss",
delta=1.,
weight=1.,
reduction="none"),
use_sigmoid=False,
reg_class_agnostic=True),
dict(head="Shared2FCRCNNHead",
activation=dict(activation="relu"),
dropblock=None,
fc_dims=1024,
num_classes=num_classes,
bbox_encoder=dict(
encoder="Box2Delta",
weights=[1. / 0.033, 1. / 0.022, 1. / 0.067, 1. / 0.067]),
bbox_decoder=dict(
decoder="Delta2Box",
weights=[1. / 0.033, 1. / 0.022, 1. / 0.067, 1. / 0.067]),
assigner=dict(assigner="MaxIoUAssigner",
pos_iou_thresh=0.7,
neg_iou_thresh=0.7),
sampler=dict(sampler="RandomSampler",
num_proposals=512,
pos_fraction=0.25,
add_gt_as_proposals=True),
label_loss=dict(loss="CrossEntropy",
label_smoothing=0.01,
weight=1.,
from_logits=True,
reduction="none"),
bbox_loss=dict(loss="SmoothL1Loss",
delta=1.,
weight=1.,
reduction="none"),
use_sigmoid=False,
reg_class_agnostic=True)
])
h.weight_decay = 1e-4
h.train = dict(
proposal_layer=dict(pre_nms_size=12000,
post_nms_size=2000,
max_total_size=2000,
iou_threshold=0.7,
min_size=0),
input_size=(1024, 1024),
dataset=dict(dataset="COCODataset",
batch_size=2,
dataset_dir="/data/bail/COCO",
training=True,
augmentations=[
dict(FlipLeftToRight=dict(probability=0.5)),
dict(RandomDistortColor=dict(probability=1.)),
dict(Resize=dict(size=(1024, 1024),
strides=128,
min_scale=1.0,
max_scale=1.0)),
],
num_samples=118287,
num_classes=num_classes),
pretrained_weights_path="/data/bail/pretrained_weights/resnet50_v1d.h5",
optimizer=dict(optimizer="SGD", momentum=0.9),
mixed_precision=dict(
loss_scale=None
), # The loss scale in mixed precision training. If None, use dynamic.
gradient_clip_norm=10.0,
scheduler=dict(train_epochs=12,
learning_rate_scheduler=dict(
scheduler="PiecewiseConstantDecay",
boundaries=[8, 11],
values=[0.02, 0.002, 0.0002]),
warmup=dict(warmup_learning_rate=0.0001, steps=8000)),
checkpoint_dir="checkpoints/faster_rcnn",
summary_dir="logs/faster_rcnn",
log_every_n_steps=100,
save_ckpt_steps=5000)
h.val = dict(dataset=dict(
dataset="COCODataset",
batch_size=2,
dataset_dir="/data/bail/COCO",
training=False,
augmentations=[
dict(Resize=dict(
size=(1024, 1024), strides=128, min_scale=1.0, max_scale=1.0))
]),
input_size=(1024, 1024),
samples=5000,
val_every_n_steps=250)
h.test = dict(
proposal_layer=dict(pre_nms_size=6000,
post_nms_size=1000,
max_total_size=1000,
iou_threshold=0.7,
min_size=0),
pre_nms_size=1000, # select top_k high confident detections for nms
post_nms_size=100,
iou_threshold=0.5,
score_threshold=0.05)
return h
def get_faster_rcnn_config(num_classes=80):
h = Config()
h.detector = "FasterRCNN"
h.dtype = "float16"
h.data_format = "channels_last"
h.num_classes = num_classes
h.backbone = dict(backbone="CaffeResNet50",
dropblock=None,
normalization=dict(normalization="batch_norm",
momentum=0.997,
epsilon=1e-4,
trainable=True),
activation=dict(activation="relu"),
strides=[2, 2, 2, 2, 2],
dilation_rates=[1, 1, 1, 1, 1],
output_indices=[2, 3, 4, 5],
frozen_stages=[
-1,
])
h.neck = dict(neck="FPN",
feat_dims=256,
min_level=2,
max_level=5,
num_output_levels=5,
add_extra_convs=False)
h.anchors = dict(generator="AnchorGenerator",
scales=[[32], [64], [128], [256], [512]],
aspect_ratios=[0.5, 1., 2.0],
strides=[4, 8, 16, 32, 64],
num_anchors=3)
h.rpn_head = dict(head="RPNHead",
normalization=None,
activation=dict(activation="relu"),
feat_dims=256,
dropblock=None,
num_classes=1,
min_level=2,
max_level=6,
use_sigmoid=True,
train_proposal=dict(pre_nms_size=2000,
post_nms_size=1000,
iou_threshold=0.7,
min_box_size=0.),
test_proposal=dict(pre_nms_size=1000,
post_nms_size=1000,
iou_threshold=0.7,
min_box_size=0.1),
bbox_encoder=dict(encoder="Box2Delta", weights=None),
bbox_decoder=dict(decoder="Delta2Box", weights=None),
assigner=dict(assigner="MaxIoUAssigner",
pos_iou_thresh=0.7,
neg_iou_thresh=0.3,
min_pos_iou=0.3),
sampler=dict(sampler="RandomSampler",
num_proposals=256,
pos_fraction=0.5,
add_gt_as_proposals=False),
label_loss=dict(loss="CrossEntropy",
label_smoothing=0.0,
weight=1.,
from_logits=True,
reduction="none"),
bbox_loss=dict(loss="SmoothL1Loss",
delta=1. / 9.,
weight=1.,
reduction="none"))
h.roi_head = dict(head="StandardRoIHead",
bbox_head=dict(roi_pooling=dict(
roi_pooling="MultiLevelAlignedRoIPooling",
pooled_size=7,
feat_dims=256),
normalization=None,
activation=dict(activation="relu"),
dropblock=None,
num_convs=0,
conv_dims=256,
num_fc=2,
fc_dims=1024,
feat_dims=256),
min_level=2,
max_level=5,
class_agnostic=False,
use_sigmoid=False,
bbox_encoder=dict(encoder="Box2Delta",
weights=[10., 10., 5., 5.]),
bbox_decoder=dict(decoder="Delta2Box",
weights=[10., 10., 5., 5.]),
assigner=dict(assigner="MaxIoUAssigner",
pos_iou_thresh=0.5,
neg_iou_thresh=0.5,
min_pos_iou=0.5),
sampler=dict(sampler="RandomSampler",
num_proposals=512,
pos_fraction=0.25,
add_gt_as_proposals=True),
label_loss=dict(loss="CrossEntropy",
label_smoothing=0.0,
weight=1.,
from_logits=True,
reduction="none"),
bbox_loss=dict(loss="SmoothL1Loss",
delta=1.,
weight=1.,
reduction="none"),
reg_class_agnostic=True)
h.weight_decay = 1e-4
h.train = dict(
dataset=dict(dataset="COCODataset",
batch_size=2,
dataset_dir="/data/bail/COCO",
training=True,
augmentations=[
dict(augmentation="FlipLeftToRight", probability=0.5),
dict(augmentation="RandomDistortColor"),
dict(augmentation="Resize",
img_scale=[(1333, 800)],
keep_ratio=True),
dict(augmentation="Pad", size_divisor=32),
],
num_samples=118287,
num_classes=num_classes),
pretrained_weights_path=
"/data/bail/pretrained_weights/resnet50/resnet50.ckpt",
optimizer=dict(optimizer="SGD", momentum=0.9),
mixed_precision=dict(
loss_scale=None
), # The loss scale in mixed precision training. If None, use dynamic.
gradient_clip_norm=10.0,
scheduler=dict(train_epochs=12,
learning_rate_scheduler=dict(
scheduler="PiecewiseConstantDecay",
boundaries=[8, 11],
values=[0.01, 0.001, 0.0001]),
warmup=dict(warmup_learning_rate=0.00001, steps=2000)),
checkpoint_dir="checkpoints/faster_rcnn",
summary_dir="logs/faster_rcnn",
log_every_n_steps=100,
save_ckpt_steps=5000)
h.val = dict(dataset=dict(dataset="COCODataset",
batch_size=1,
dataset_dir="/data/bail/COCO",
training=False,
augmentations=[
dict(augmentation="Resize",
img_scale=[(1333, 800)],
keep_ratio=True),
dict(augmentation="Pad", size_divisor=32),
]),
input_size=(1024, 1024),
samples=5000)
h.test = dict(nms="CombinedNonMaxSuppression",
pre_nms_size=5000,
post_nms_size=100,
iou_threshold=0.6,
score_threshold=0.5)
return h
