import torch

from mmdet.core import MaxIoUAssigner
from mmdet.core.bbox.samplers import OHEMSampler, RandomSampler


def test_random_sampler():
    assigner = MaxIoUAssigner(
        pos_iou_thr=0.5,
        neg_iou_thr=0.5,
        ignore_iof_thr=0.5,
        ignore_wrt_candidates=False,
    )
    bboxes = torch.FloatTensor([
        [0, 0, 10, 10],
        [10, 10, 20, 20],
        [5, 5, 15, 15],
        [32, 32, 38, 42],
    ])
    gt_bboxes = torch.FloatTensor([
        [0, 0, 10, 9],
        [0, 10, 10, 19],
    ])
    gt_labels = torch.LongTensor([1, 2])
    gt_bboxes_ignore = torch.Tensor([
        [30, 30, 40, 40],
    ])
    assign_result = assigner.assign(
        bboxes,
        gt_bboxes,
        gt_bboxes_ignore=gt_bboxes_ignore,
        gt_labels=gt_labels)

    sampler = RandomSampler(
        num=10, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=True)

    sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)

    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)


def test_random_sampler_empty_gt():
    assigner = MaxIoUAssigner(
        pos_iou_thr=0.5,
        neg_iou_thr=0.5,
        ignore_iof_thr=0.5,
        ignore_wrt_candidates=False,
    )
    bboxes = torch.FloatTensor([
        [0, 0, 10, 10],
        [10, 10, 20, 20],
        [5, 5, 15, 15],
        [32, 32, 38, 42],
    ])
    gt_bboxes = torch.empty(0, 4)
    gt_labels = torch.empty(0, ).long()
    assign_result = assigner.assign(bboxes, gt_bboxes, gt_labels=gt_labels)

    sampler = RandomSampler(
        num=10, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=True)

    sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)

    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)


def test_random_sampler_empty_pred():
    assigner = MaxIoUAssigner(
        pos_iou_thr=0.5,
        neg_iou_thr=0.5,
        ignore_iof_thr=0.5,
        ignore_wrt_candidates=False,
    )
    bboxes = torch.empty(0, 4)
    gt_bboxes = torch.FloatTensor([
        [0, 0, 10, 9],
        [0, 10, 10, 19],
    ])
    gt_labels = torch.LongTensor([1, 2])
    assign_result = assigner.assign(bboxes, gt_bboxes, gt_labels=gt_labels)

    sampler = RandomSampler(
        num=10, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=True)

    sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)

    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)


def _context_for_ohem():
    try:
        from test_forward import _get_detector_cfg
    except ImportError:
        # Hack: grab testing utils from test_forward to make a context for ohem
        import sys
        from os.path import dirname
        sys.path.insert(0, dirname(__file__))
        from test_forward import _get_detector_cfg
    model, train_cfg, test_cfg = _get_detector_cfg(
        'faster_rcnn_ohem_r50_fpn_1x.py')
    model['pretrained'] = None
    # torchvision roi align supports CPU
    model['bbox_roi_extractor']['roi_layer']['use_torchvision'] = True
    from mmdet.models import build_detector
    context = build_detector(model, train_cfg=train_cfg, test_cfg=test_cfg)
    return context


def test_ohem_sampler():

    assigner = MaxIoUAssigner(
        pos_iou_thr=0.5,
        neg_iou_thr=0.5,
        ignore_iof_thr=0.5,
        ignore_wrt_candidates=False,
    )
    bboxes = torch.FloatTensor([
        [0, 0, 10, 10],
        [10, 10, 20, 20],
        [5, 5, 15, 15],
        [32, 32, 38, 42],
    ])
    gt_bboxes = torch.FloatTensor([
        [0, 0, 10, 9],
        [0, 10, 10, 19],
    ])
    gt_labels = torch.LongTensor([1, 2])
    gt_bboxes_ignore = torch.Tensor([
        [30, 30, 40, 40],
    ])
    assign_result = assigner.assign(
        bboxes,
        gt_bboxes,
        gt_bboxes_ignore=gt_bboxes_ignore,
        gt_labels=gt_labels)

    context = _context_for_ohem()

    sampler = OHEMSampler(
        num=10,
        pos_fraction=0.5,
        context=context,
        neg_pos_ub=-1,
        add_gt_as_proposals=True)

    feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]]
    sample_result = sampler.sample(
        assign_result, bboxes, gt_bboxes, gt_labels, feats=feats)

    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)


def test_ohem_sampler_empty_gt():

    assigner = MaxIoUAssigner(
        pos_iou_thr=0.5,
        neg_iou_thr=0.5,
        ignore_iof_thr=0.5,
        ignore_wrt_candidates=False,
    )
    bboxes = torch.FloatTensor([
        [0, 0, 10, 10],
        [10, 10, 20, 20],
        [5, 5, 15, 15],
        [32, 32, 38, 42],
    ])
    gt_bboxes = torch.empty(0, 4)
    gt_labels = torch.LongTensor([])
    gt_bboxes_ignore = torch.Tensor([])
    assign_result = assigner.assign(
        bboxes,
        gt_bboxes,
        gt_bboxes_ignore=gt_bboxes_ignore,
        gt_labels=gt_labels)

    context = _context_for_ohem()

    sampler = OHEMSampler(
        num=10,
        pos_fraction=0.5,
        context=context,
        neg_pos_ub=-1,
        add_gt_as_proposals=True)

    feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]]

    sample_result = sampler.sample(
        assign_result, bboxes, gt_bboxes, gt_labels, feats=feats)

    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)


def test_ohem_sampler_empty_pred():
    assigner = MaxIoUAssigner(
        pos_iou_thr=0.5,
        neg_iou_thr=0.5,
        ignore_iof_thr=0.5,
        ignore_wrt_candidates=False,
    )
    bboxes = torch.empty(0, 4)
    gt_bboxes = torch.FloatTensor([
        [0, 0, 10, 10],
        [10, 10, 20, 20],
        [5, 5, 15, 15],
        [32, 32, 38, 42],
    ])
    gt_labels = torch.LongTensor([1, 2, 2, 3])
    gt_bboxes_ignore = torch.Tensor([])
    assign_result = assigner.assign(
        bboxes,
        gt_bboxes,
        gt_bboxes_ignore=gt_bboxes_ignore,
        gt_labels=gt_labels)

    context = _context_for_ohem()

    sampler = OHEMSampler(
        num=10,
        pos_fraction=0.5,
        context=context,
        neg_pos_ub=-1,
        add_gt_as_proposals=True)

    feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]]

    sample_result = sampler.sample(
        assign_result, bboxes, gt_bboxes, gt_labels, feats=feats)

    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)
