_base_ = [
    '../_base_/models/faster_rcnn_r50_fpn.py',
    '../_base_/datasets/coco_detection.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]


data_root = '/host/home/yxliu/uav_workshop_project/prepare_detection_dataset/coco_uav_flow/'
dataset_type = 'CocoDataset'

classes = ( 'static car', 'moving car', 'parking car')
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile', load_flow=True),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(960, 540), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'flow']),
]
test_pipeline = [
    dict(type='LoadImageFromFile', load_flow=True),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(960, 540),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img', 'flow']),
            dict(type='Collect', keys=['img', 'flow']),
        ])
]

data = dict(
    samples_per_gpu=1, #8
    workers_per_gpu=0, #8
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_train2017.json',
        img_prefix=data_root + 'train2017/',
        pipeline=train_pipeline,
        classes=classes),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        pipeline=test_pipeline,
        classes=classes),
    test=dict(
        type=dataset_type,
        #ann_file=data_root + 'annotations/instances_test2017.json',
        ann_file="/host/home/yxliu/uav_workshop_project/prepare_detection_dataset/coco/annotations/instances_train2017.json",
        img_prefix="/host/home/yxliu/uav_workshop_project/prepare_detection_dataset/coco/train2017",
        #img_prefix=data_root + 'test2017/',
        pipeline=test_pipeline,
        classes=classes)
)

model = dict(
    type='FasterRCNNFlow',
    pretrained='torchvision://resnet50',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=False),
        norm_eval=True,
        style='pytorch'),
    neck=dict(
        type='FPNFlow',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='StandardRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', out_size=7, sample_num=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=3,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0., 0., 0., 0.],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=True,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0, class_weight=[1, 1, 1, 8]),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0))))

test_cfg = dict(
    rcnn=dict(
        score_thr=0.05, nms=dict(type='nms', iou_thr=0.05, class_agnostic=True), max_per_img=100)
)

optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=2.0, norm_type=2), _delete_=True)
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=300,
    warmup_ratio=0.001,
    step=[25, 28])
total_epochs = 30
evaluation = dict(interval=5, metric='bbox')

checkpoint_config = dict(interval=5)
