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

from mmdet.models.dense_heads import YOLOFHead


def test_yolof_head_loss():
    """Tests yolof head loss when truth is empty and non-empty."""
    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='UniformAssigner',
                pos_ignore_thr=0.15,
                neg_ignore_thr=0.7),
            allowed_border=-1,
            pos_weight=-1,
            debug=False))
    self = YOLOFHead(
        num_classes=4,
        in_channels=1,
        reg_decoded_bbox=True,
        train_cfg=train_cfg,
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            scales=[1, 2, 4, 8, 16],
            strides=[32]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1., 1., 1., 1.],
            add_ctr_clamp=True,
            ctr_clamp=32),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=1.0))
    feat = [torch.rand(1, 1, s // 32, s // 32)]
    cls_scores, bbox_preds = self.forward(feat)

    # 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
    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
                                img_metas, gt_bboxes_ignore)
    # 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']
    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')

    # 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])]
    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
                              img_metas, gt_bboxes_ignore)
    onegt_cls_loss = one_gt_losses['loss_cls']
    onegt_box_loss = one_gt_losses['loss_bbox']
    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'

import mmcv
import torch

from mmdet.models.dense_heads import YOLOFHead


def test_yolof_head_loss():
    """Tests yolof head loss when truth is empty and non-empty."""
    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='UniformAssigner',
                pos_ignore_thr=0.15,
                neg_ignore_thr=0.7),
            allowed_border=-1,
            pos_weight=-1,
            debug=False))
    self = YOLOFHead(
        num_classes=4,
        in_channels=1,
        reg_decoded_bbox=True,
        train_cfg=train_cfg,
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            scales=[1, 2, 4, 8, 16],
            strides=[32]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1., 1., 1., 1.],
            add_ctr_clamp=True,
            ctr_clamp=32),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=1.0))
    feat = [torch.rand(1, 1, s // 32, s // 32)]
    cls_scores, bbox_preds = self.forward(feat)

    # 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
    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
                                img_metas, gt_bboxes_ignore)
    # 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']
    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')

    # 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])]
    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
                              img_metas, gt_bboxes_ignore)
    onegt_cls_loss = one_gt_losses['loss_cls']
    onegt_box_loss = one_gt_losses['loss_bbox']
    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
