# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch

from mmdet.models.dense_heads import LADHead, lad_head
from mmdet.models.dense_heads.lad_head import levels_to_images


def test_lad_head_loss():
    """Tests lad head loss when truth is empty and non-empty."""

    class mock_skm:

        def GaussianMixture(self, *args, **kwargs):
            return self

        def fit(self, loss):
            pass

        def predict(self, loss):
            components = np.zeros_like(loss, dtype=np.long)
            return components.reshape(-1)

        def score_samples(self, loss):
            scores = np.random.random(len(loss))
            return scores

    lad_head.skm = mock_skm()

    s = 256
    img_metas = [{
        'img_shape': (s, s, 3),
        'scale_factor': 1,
        'pad_shape': (s, s, 3)
    }]
    train_cfg = mmcv.Config(
        dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.1,
                neg_iou_thr=0.1,
                min_pos_iou=0,
                ignore_iof_thr=-1),
            allowed_border=-1,
            pos_weight=-1,
            debug=False))
    # since Focal Loss is not supported on CPU
    self = LADHead(
        num_classes=4,
        in_channels=1,
        train_cfg=train_cfg,
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5))
    teacher_model = LADHead(
        num_classes=4,
        in_channels=1,
        train_cfg=train_cfg,
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5))
    feat = [
        torch.rand(1, 1, s // feat_size, s // feat_size)
        for feat_size in [4, 8, 16, 32, 64]
    ]
    self.init_weights()
    teacher_model.init_weights()

    # Test that empty ground truth encourages the network to predict background
    gt_bboxes = [torch.empty((0, 4))]
    gt_labels = [torch.LongTensor([])]
    gt_bboxes_ignore = None

    outs_teacher = teacher_model(feat)
    label_assignment_results = teacher_model.get_label_assignment(
        *outs_teacher, gt_bboxes, gt_labels, img_metas, gt_bboxes_ignore)

    outs = teacher_model(feat)
    empty_gt_losses = self.loss(*outs, gt_bboxes, gt_labels, img_metas,
                                gt_bboxes_ignore, label_assignment_results)
    # When there is no truth, the cls loss should be nonzero but there should
    # be no box loss.
    empty_cls_loss = empty_gt_losses['loss_cls']
    empty_box_loss = empty_gt_losses['loss_bbox']
    empty_iou_loss = empty_gt_losses['loss_iou']
    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
    assert empty_box_loss.item() == 0, (
        'there should be no box loss when there are no true boxes')
    assert empty_iou_loss.item() == 0, (
        'there should be no box loss when there are no true boxes')

    # When truth is non-empty then both cls and box loss should be nonzero for
    # random inputs
    gt_bboxes = [
        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
    ]
    gt_labels = [torch.LongTensor([2])]

    label_assignment_results = teacher_model.get_label_assignment(
        *outs_teacher, gt_bboxes, gt_labels, img_metas, gt_bboxes_ignore)

    one_gt_losses = self.loss(*outs, gt_bboxes, gt_labels, img_metas,
                              gt_bboxes_ignore, label_assignment_results)
    onegt_cls_loss = one_gt_losses['loss_cls']
    onegt_box_loss = one_gt_losses['loss_bbox']
    onegt_iou_loss = one_gt_losses['loss_iou']
    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
    assert onegt_iou_loss.item() > 0, 'box loss should be non-zero'
    n, c, h, w = 10, 4, 20, 20
    mlvl_tensor = [torch.ones(n, c, h, w) for i in range(5)]
    results = levels_to_images(mlvl_tensor)
    assert len(results) == n
    assert results[0].size() == (h * w * 5, c)
    assert self.with_score_voting

    self = LADHead(
        num_classes=4,
        in_channels=1,
        train_cfg=train_cfg,
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            octave_base_scale=8,
            scales_per_octave=1,
            strides=[8]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5))
    cls_scores = [torch.ones(2, 4, 5, 5)]
    bbox_preds = [torch.ones(2, 4, 5, 5)]
    iou_preds = [torch.ones(2, 1, 5, 5)]
    cfg = mmcv.Config(
        dict(
            nms_pre=1000,
            min_bbox_size=0,
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.6),
            max_per_img=100))
    rescale = False
    self.get_bboxes(
        cls_scores, bbox_preds, iou_preds, img_metas, cfg, rescale=rescale)