TinaFace for CocoDataset

[gd1925]

Hi all,

I am trying to train the TinaFace model for a dataset in the form of Coco with .json annotation files. (Dataset has annotations for face. number of class is '1')

On running the command :

CUDA_VISIBLE_DEVICES="0" python tools/trainval.py configs/trainval/tinaface/tinaface_r50_fpn_gn_dcn.py

But I have been running into the following error:

Traceback (most recent call last):
File "tools/trainval.py", line 65, in
main()
File "tools/trainval.py", line 61, in main
trainval(cfg, distributed, logger)
File "/home/gd/Desktop/vedadet/vedadet/assembler/trainval.py", line 19, in trainval
dataset = build_dataset(cfg.data.train)
File "/home/gd/Desktop/vedadet/vedadet/datasets/builder.py", line 57, in build_dataset
dataset = build_from_cfg(cfg, registry, 'dataset', default_args)
File "/home/gd/Desktop/vedadet/vedacore/misc/registry.py", line 102, in build_from_cfg
return obj_cls(**args)
TypeError: init() got an unexpected keyword argument 'min_size'

My config file (vedadet/configs/trainval/tinaface/tinaface_r50_fpn_gn_dcn.py) is as follows:

1. data

dataset_type = 'CocoDataset'
data_root = '/media/gd/Dataset/Synth/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
size_divisor = 32

data = dict(
samples_per_gpu=4,
workers_per_gpu=2,
train=dict(
typename=dataset_type,
ann_file=data_root + 'annotations/train.json',
img_prefix=data_root + 'train/',
min_size=1,
offset=0,
pipeline=[
dict(typename='LoadImageFromFile', to_float32=True),
dict(typename='LoadAnnotations', with_bbox=True),
dict(typename='RandomSquareCrop',
crop_choice=[0.3, 0.45, 0.6, 0.8, 1.0]),
dict(
typename='PhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(typename='RandomFlip', flip_ratio=0.5),
dict(typename='Resize', img_scale=(640, 640), keep_ratio=False),
dict(typename='Normalize', **img_norm_cfg),
dict(typename='DefaultFormatBundle'),
dict(typename='Collect', keys=['img', 'gt_bboxes',
'gt_labels', 'gt_bboxes_ignore']),
]),
val=dict(
typename=dataset_type,
ann_file=data_root + 'annotations/val.json',
img_prefix=data_root + 'val/',
min_size=1,
offset=0,
pipeline=[
dict(typename='LoadImageFromFile'),
dict(
typename='MultiScaleFlipAug',
img_scale=(1100, 1650),
flip=False,
transforms=[
dict(typename='Resize', keep_ratio=True),
dict(typename='RandomFlip', flip_ratio=0.0),
dict(typename='Normalize', **img_norm_cfg),
dict(typename='Pad', size_divisor=32, pad_val=0),
dict(typename='ImageToTensor', keys=['img']),
dict(typename='Collect', keys=['img'])
])
]),
)

2. model

num_classes = 1
strides = [4, 8, 16, 32, 64, 128]
use_sigmoid = True
scales_per_octave = 3
ratios = [1.3]
num_anchors = scales_per_octave * len(ratios)

model = dict(
typename='SingleStageDetector',
backbone=dict(
typename='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
norm_cfg=dict(typename='GN', num_groups=32, requires_grad=True),
norm_eval=False,
dcn=dict(typename='DCN', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, False, True, True),
style='pytorch'),
neck=[
dict(
typename='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=0,
add_extra_convs='on_input',
num_outs=6,
norm_cfg=dict(typename='GN', num_groups=32, requires_grad=True),
upsample_cfg=dict(mode='bilinear')),
dict(
typename='Inception',
in_channel=256,
num_levels=6,
norm_cfg=dict(typename='GN', num_groups=32, requires_grad=True),
share=True)
],
head=dict(
typename='IoUAwareRetinaHead',
num_classes=num_classes,
num_anchors=num_anchors,
in_channels=256,
stacked_convs=4,
feat_channels=256,
norm_cfg=dict(typename='GN', num_groups=32, requires_grad=True),
use_sigmoid=use_sigmoid))

3. engines

meshgrid = dict(
typename='BBoxAnchorMeshGrid',
strides=strides,
base_anchor=dict(
typename='BBoxBaseAnchor',
octave_base_scale=2**(4 / 3),
scales_per_octave=scales_per_octave,
ratios=ratios,
base_sizes=strides))

bbox_coder = dict(
typename='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[0.1, 0.1, 0.2, 0.2])

train_engine = dict(
typename='TrainEngine',
model=model,
criterion=dict(
typename='IoUBBoxAnchorCriterion',
num_classes=num_classes,
meshgrid=meshgrid,
bbox_coder=bbox_coder,
loss_cls=dict(
typename='FocalLoss',
use_sigmoid=use_sigmoid,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
reg_decoded_bbox=True,
loss_bbox=dict(typename='DIoULoss', loss_weight=2.0),
loss_iou=dict(
typename='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
train_cfg=dict(
assigner=dict(
typename='MaxIoUAssigner',
pos_iou_thr=0.35,
neg_iou_thr=0.35,
min_pos_iou=0.35,
ignore_iof_thr=-1,
gpu_assign_thr=100),
allowed_border=-1,
pos_weight=-1,
debug=False)),
optimizer=dict(typename='SGD', lr=3.75e-3, momentum=0.9, weight_decay=5e-4)) # 3 GPUS

3.2 val engine

val_engine = dict(
typename='ValEngine',
model=model,
meshgrid=meshgrid,
converter=dict(
typename='IoUBBoxAnchorConverter',
num_classes=num_classes,
bbox_coder=bbox_coder,
nms_pre=-1,
use_sigmoid=use_sigmoid),
num_classes=num_classes,
test_cfg=dict(
min_bbox_size=0,
score_thr=0.01,
nms=dict(typename='lb_nms', iou_thr=0.45),
max_per_img=-1),
use_sigmoid=use_sigmoid,
eval_metric=None)

hooks = [
dict(typename='OptimizerHook'),
dict(
typename='CosineRestartLrSchedulerHook',
periods=[30] * 21,
restart_weights=[1] * 21,
warmup='linear',
warmup_iters=500,
warmup_ratio=1e-1,
min_lr_ratio=1e-2),
dict(typename='EvalHook'),
dict(typename='SnapshotHook', interval=1),
dict(typename='LoggerHook', interval=100)
]

5. work modes

modes = ['train']#, 'val']
max_epochs = 630

6. checkpoint

weights = dict(
filepath='torchvision://resnet50',
prefix='backbone')

optimizer = dict(filepath='workdir/retinanet_mini/epoch_3_optim.pth')

meta = dict(filepath='workdir/retinanet_mini/epoch_3_meta.pth')

7. misc

seed = 1234
dist_params = dict(backend='nccl')
log_level = 'INFO'

Could someone provide me with some direction?
Thank you.