_base_ = [
    '../../_base_/models/faster_rcnn_r50_fpn.py',
    '../../_base_/datasets/coco_detection_pl.py',
    '../../_base_/schedules/schedule_1x.py', '../../_base_/default_runtime.py'
]
classes = ('09', '10', '11', '12', '13', '16', '17', '18')
num_classes = 8
# model settings
model = dict(
    roi_head=dict(
        bbox_head=dict(
            num_classes=num_classes)))

dataset_type = 'CocoDataset'
data_root = 'data/coco/'
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'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1333, 800), 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']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        #img_scale=(1333, 800),
        #flip=False,
        img_scale=[(667, 400), (833, 500), (1000, 600), (1167, 700), (1333, 800), (1500, 900), (1667, 1000),
            (1833, 1100), (2000, 1200)],
        flip=True, # 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']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    samples_per_gpu=5,
    workers_per_gpu=16,
    train=dict(
        type=dataset_type,
        classes = classes,
        ann_file='/data/dibe/data/train.json',
        img_prefix='/root/.darwin/datasets/si/construction-equipment/images',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        classes = classes,
        ann_file='/data/dibe/data/test.json',
        img_prefix='/root/.darwin/datasets/si/construction-equipment/images',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        classes = classes,
        ann_file='/data/dibe/data/test.json',
        img_prefix='/root/.darwin/datasets/si/construction-equipment/images',
        pipeline=test_pipeline))
evaluation = dict(interval=1, metric='bbox')