mmdeploy/tests/test_codebase/test_mmdet/test_mmdet_models.py

# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os
import random
from typing import Dict, List

import mmcv
import numpy as np
import pytest
import torch

from mmdeploy.codebase import import_codebase
from mmdeploy.utils import Backend, Codebase
from mmdeploy.utils.config_utils import get_ir_config
from mmdeploy.utils.test import (WrapModel, check_backend, get_model_outputs,
                                 get_rewrite_outputs)

import_codebase(Codebase.MMDET)


def seed_everything(seed=1029):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU.
        torch.backends.cudnn.benchmark = False
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.enabled = False


def convert_to_list(rewrite_output: Dict, output_names: List[str]) -> List:
    """Converts output from a dictionary to a list.

    The new list will contain only those output values, whose names are in list
    'output_names'.
    """
    outputs = [
        value for name, value in rewrite_output.items() if name in output_names
    ]
    return outputs


def get_anchor_head_model():
    """AnchorHead Config."""
    test_cfg = mmcv.Config(
        dict(
            deploy_nms_pre=0,
            min_bbox_size=0,
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100))

    from mmdet.models.dense_heads import AnchorHead
    model = AnchorHead(num_classes=4, in_channels=1, test_cfg=test_cfg)
    model.requires_grad_(False)

    return model


def get_ssd_head_model():
    """SSDHead Config."""
    test_cfg = mmcv.Config(
        dict(
            nms_pre=1000,
            nms=dict(type='nms', iou_threshold=0.45),
            min_bbox_size=0,
            score_thr=0.02,
            max_per_img=200))

    from mmdet.models import SSDHead
    model = SSDHead(
        in_channels=(96, 1280, 512, 256, 256, 128),
        num_classes=4,
        use_depthwise=True,
        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
        act_cfg=dict(type='ReLU6'),
        init_cfg=dict(type='Normal', layer='Conv2d', std=0.001),
        anchor_generator=dict(
            type='SSDAnchorGenerator',
            scale_major=False,
            strides=[16, 32, 64, 107, 160, 320],
            ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]],
            min_sizes=[48, 100, 150, 202, 253, 304],
            max_sizes=[100, 150, 202, 253, 304, 320]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[0.1, 0.1, 0.2, 0.2]),
        test_cfg=test_cfg)

    model.requires_grad_(False)

    return model


def get_fcos_head_model():
    """FCOS Head Config."""
    test_cfg = mmcv.Config(
        dict(
            deploy_nms_pre=0,
            min_bbox_size=0,
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100))

    from mmdet.models.dense_heads import FCOSHead
    model = FCOSHead(num_classes=4, in_channels=1, test_cfg=test_cfg)

    model.requires_grad_(False)
    return model


def get_focus_backbone_model():
    """Backbone Focus Config."""
    from mmdet.models.backbones.csp_darknet import Focus
    model = Focus(3, 32)

    model.requires_grad_(False)
    return model


def get_l2norm_forward_model():
    """L2Norm Neck Config."""
    from mmdet.models.necks.ssd_neck import L2Norm
    model = L2Norm(16)
    torch.nn.init.uniform_(model.weight)

    model.requires_grad_(False)
    return model


def get_rpn_head_model():
    """RPN Head Config."""
    test_cfg = mmcv.Config(
        dict(
            deploy_nms_pre=0,
            nms_pre=0,
            max_per_img=100,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0))
    from mmdet.models.dense_heads import RPNHead
    model = RPNHead(in_channels=1, test_cfg=test_cfg)

    model.requires_grad_(False)
    return model


def get_reppoints_head_model():
    """Reppoints Head Config."""
    test_cfg = mmcv.Config(
        dict(
            deploy_nms_pre=0,
            min_bbox_size=0,
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100))

    from mmdet.models.dense_heads import RepPointsHead
    model = RepPointsHead(num_classes=4, in_channels=1, test_cfg=test_cfg)

    model.requires_grad_(False)
    return model


def get_single_roi_extractor():
    """SingleRoIExtractor Config."""
    from mmdet.models.roi_heads import SingleRoIExtractor
    roi_layer = dict(type='RoIAlign', output_size=7, sampling_ratio=2)
    out_channels = 1
    featmap_strides = [4, 8, 16, 32]
    model = SingleRoIExtractor(roi_layer, out_channels, featmap_strides).eval()

    return model


def get_gfl_head_model():
    test_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))
    anchor_generator = dict(
        type='AnchorGenerator',
        scales_per_octave=1,
        octave_base_scale=8,
        ratios=[1.0],
        strides=[8, 16, 32, 64, 128])
    from mmdet.models.dense_heads import GFLHead
    model = GFLHead(
        num_classes=3,
        in_channels=256,
        reg_max=3,
        test_cfg=test_cfg,
        anchor_generator=anchor_generator)
    model.requires_grad_(False)
    return model


@pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME, Backend.NCNN])
def test_focus_forward(backend_type):
    check_backend(backend_type)
    focus_model = get_focus_backbone_model()
    focus_model.cpu().eval()
    s = 128
    seed_everything(1234)
    x = torch.rand(1, 3, s, s)
    model_outputs = [focus_model.forward(x)]
    wrapped_model = WrapModel(focus_model, 'forward')
    rewrite_inputs = {
        'x': x,
    }
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(input_shape=None)))
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)
    for model_output, rewrite_output in zip(model_outputs[0], rewrite_outputs):
        model_output = model_output.squeeze()
        rewrite_output = rewrite_output.squeeze()
        torch.testing.assert_allclose(
            model_output, rewrite_output, rtol=1e-03, atol=1e-05)


@pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME])
def test_l2norm_forward(backend_type):
    check_backend(backend_type)
    l2norm_neck = get_l2norm_forward_model()
    l2norm_neck.cpu().eval()
    s = 128
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(input_shape=None)))
    seed_everything(1234)
    feat = torch.rand(1, 16, s, s)
    model_outputs = [l2norm_neck.forward(feat)]
    wrapped_model = WrapModel(l2norm_neck, 'forward')
    rewrite_inputs = {
        'x': feat,
    }
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)

    if is_backend_output:
        for model_output, rewrite_output in zip(model_outputs[0],
                                                rewrite_outputs[0]):
            model_output = model_output.squeeze().cpu().numpy()
            rewrite_output = rewrite_output.squeeze()
            assert np.allclose(
                model_output, rewrite_output, rtol=1e-03, atol=1e-05)
    else:
        for model_output, rewrite_output in zip(model_outputs[0],
                                                rewrite_outputs[0]):
            model_output = model_output.squeeze().cpu().numpy()
            rewrite_output = rewrite_output.squeeze()
            assert np.allclose(
                model_output[0], rewrite_output, rtol=1e-03, atol=1e-05)


def test_get_bboxes_of_fcos_head_ncnn():
    backend_type = Backend.NCNN
    check_backend(backend_type)
    fcos_head = get_fcos_head_model()
    fcos_head.cpu().eval()
    s = 128
    img_metas = [{
        'scale_factor': np.ones(4),
        'pad_shape': (s, s, 3),
        'img_shape': (s, s, 3)
    }]

    output_names = ['detection_output']
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(output_names=output_names, input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                model_type='ncnn_end2end',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.5,
                    max_output_boxes_per_class=200,
                    pre_top_k=5000,
                    keep_top_k=100,
                    background_label_id=-1,
                ))))

    # the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16),
    # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2).
    # the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16),
    # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2)
    seed_everything(1234)
    cls_score = [
        torch.rand(1, fcos_head.num_classes, pow(2, i), pow(2, i))
        for i in range(5, 0, -1)
    ]
    seed_everything(5678)
    bboxes = [torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1)]

    seed_everything(9101)
    centernesses = [
        torch.rand(1, 1, pow(2, i), pow(2, i)) for i in range(5, 0, -1)
    ]

    # to get outputs of onnx model after rewrite
    img_metas[0]['img_shape'] = torch.Tensor([s, s])
    wrapped_model = WrapModel(
        fcos_head, 'get_bboxes', img_metas=img_metas, with_nms=True)
    rewrite_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
        'centernesses': centernesses
    }
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)

    # output should be of shape [1, N, 6]
    if is_backend_output:
        assert rewrite_outputs[0].shape[-1] == 6
    else:
        assert rewrite_outputs.shape[-1] == 6


@pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME, Backend.NCNN])
def test_get_bboxes_of_rpn_head(backend_type: Backend):
    check_backend(backend_type)
    head = get_rpn_head_model()
    head.cpu().eval()
    s = 4
    img_metas = [{
        'scale_factor': np.ones(4),
        'pad_shape': (s, s, 3),
        'img_shape': (s, s, 3)
    }]

    output_names = ['dets']
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(output_names=output_names, input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.5,
                    max_output_boxes_per_class=200,
                    pre_top_k=5000,
                    keep_top_k=100,
                    background_label_id=-1,
                ))))

    # the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16),
    # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2).
    # the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16),
    # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2)
    seed_everything(1234)
    cls_score = [
        torch.rand(1, 9, pow(2, i), pow(2, i)) for i in range(5, 0, -1)
    ]
    seed_everything(5678)
    bboxes = [torch.rand(1, 36, pow(2, i), pow(2, i)) for i in range(5, 0, -1)]

    # to get outputs of onnx model after rewrite
    img_metas[0]['img_shape'] = torch.Tensor([s, s])
    wrapped_model = WrapModel(
        head, 'get_bboxes', img_metas=img_metas, with_nms=True)
    rewrite_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
    }
    # do not run with ncnn backend
    run_with_backend = False if backend_type in [Backend.NCNN] else True
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg,
        run_with_backend=run_with_backend)
    assert rewrite_outputs is not None


@pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME])
def test_get_bboxes_of_gfl_head(backend_type):
    check_backend(backend_type)
    head = get_gfl_head_model()
    head.cpu().eval()
    s = 4
    img_metas = [{
        'scale_factor': np.ones(4),
        'pad_shape': (s, s, 3),
        'img_shape': (s, s, 3)
    }]
    output_names = ['dets']
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(output_names=output_names, input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                model_type='ncnn_end2end',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.5,
                    max_output_boxes_per_class=200,
                    pre_top_k=5000,
                    keep_top_k=100,
                    background_label_id=-1,
                ))))

    seed_everything(1234)
    cls_score = [
        torch.rand(1, 3, pow(2, i), pow(2, i)) for i in range(5, 0, -1)
    ]
    seed_everything(5678)
    bboxes = [torch.rand(1, 16, pow(2, i), pow(2, i)) for i in range(5, 0, -1)]

    # to get outputs of onnx model after rewrite
    img_metas[0]['img_shape'] = torch.Tensor([s, s])
    wrapped_model = WrapModel(
        head, 'get_bboxes', img_metas=img_metas, with_nms=True)
    rewrite_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
    }
    # do not run with ncnn backend
    run_with_backend = False if backend_type in [Backend.NCNN] else True
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg,
        run_with_backend=run_with_backend)
    assert rewrite_outputs is not None


@pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME])
def test_forward_of_gfl_head(backend_type):
    check_backend(backend_type)
    head = get_gfl_head_model()
    head.cpu().eval()
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(input_shape=None)))
    feats = [torch.rand(1, 256, pow(2, i), pow(2, i)) for i in range(5, 0, -1)]
    model_outputs = [head.forward(feats)]
    wrapped_model = WrapModel(head, 'forward')
    rewrite_inputs = {
        'feats': feats,
    }
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)
    model_outputs[0] = [*model_outputs[0][0], *model_outputs[0][1]]
    for model_output, rewrite_output in zip(model_outputs[0],
                                            rewrite_outputs[0]):
        model_output = model_output.squeeze().cpu().numpy()
        rewrite_output = rewrite_output.squeeze()
        assert np.allclose(
            model_output, rewrite_output, rtol=1e-03, atol=1e-05)


def _replace_r50_with_r18(model):
    """Replace ResNet50 with ResNet18 in config."""
    model = copy.deepcopy(model)
    if model.backbone.type == 'ResNet':
        model.backbone.depth = 18
        model.backbone.base_channels = 2
        model.neck.in_channels = [2, 4, 8, 16]
    return model


@pytest.mark.parametrize('backend', [Backend.ONNXRUNTIME])
@pytest.mark.parametrize('model_cfg_path', [
    'tests/test_codebase/test_mmdet/data/single_stage_model.json',
    'tests/test_codebase/test_mmdet/data/mask_model.json'
])
def test_forward_of_base_detector(model_cfg_path, backend):
    check_backend(backend)
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend.value),
            onnx_config=dict(
                output_names=['dets', 'labels'], input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.5,
                    max_output_boxes_per_class=200,
                    pre_top_k=-1,
                    keep_top_k=100,
                    background_label_id=-1,
                ))))

    model_cfg = mmcv.Config(dict(model=mmcv.load(model_cfg_path)))
    model_cfg.model = _replace_r50_with_r18(model_cfg.model)
    from mmdet.apis import init_detector
    model = init_detector(model_cfg, None, 'cpu')

    img = torch.randn(1, 3, 64, 64)
    rewrite_inputs = {'img': img}
    rewrite_outputs, _ = get_rewrite_outputs(
        wrapped_model=model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)

    assert rewrite_outputs is not None


@pytest.mark.parametrize('backend_type',
                         [Backend.ONNXRUNTIME, Backend.OPENVINO])
def test_single_roi_extractor(backend_type: Backend):
    check_backend(backend_type)

    single_roi_extractor = get_single_roi_extractor()
    output_names = ['roi_feat']
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(output_names=output_names, input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
            )))

    seed_everything(1234)
    out_channels = single_roi_extractor.out_channels
    feats = [
        torch.rand((1, out_channels, 200, 336)),
        torch.rand((1, out_channels, 100, 168)),
        torch.rand((1, out_channels, 50, 84)),
        torch.rand((1, out_channels, 25, 42)),
    ]
    seed_everything(5678)
    rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]])

    model_inputs = {
        'feats': feats,
        'rois': rois,
    }
    model_outputs = get_model_outputs(single_roi_extractor, 'forward',
                                      model_inputs)

    backend_outputs, _ = get_rewrite_outputs(
        wrapped_model=single_roi_extractor,
        model_inputs=model_inputs,
        deploy_cfg=deploy_cfg)
    if isinstance(backend_outputs, dict):
        backend_outputs = backend_outputs.values()
    for model_output, backend_output in zip(model_outputs[0], backend_outputs):
        model_output = model_output.squeeze().cpu().numpy()
        backend_output = backend_output.squeeze()
        assert np.allclose(
            model_output, backend_output, rtol=1e-03, atol=1e-05)


def get_cascade_roi_head(is_instance_seg=False):
    """CascadeRoIHead Config."""
    num_stages = 3
    stage_loss_weights = [1, 0.5, 0.25]
    bbox_roi_extractor = {
        'type': 'SingleRoIExtractor',
        'roi_layer': {
            'type': 'RoIAlign',
            'output_size': 7,
            'sampling_ratio': 0
        },
        'out_channels': 64,
        'featmap_strides': [4, 8, 16, 32]
    }
    all_target_stds = [[0.1, 0.1, 0.2, 0.2], [0.05, 0.05, 0.1, 0.1],
                       [0.033, 0.033, 0.067, 0.067]]
    bbox_head = [{
        'type': 'Shared2FCBBoxHead',
        'in_channels': 64,
        'fc_out_channels': 1024,
        'roi_feat_size': 7,
        'num_classes': 80,
        'bbox_coder': {
            'type': 'DeltaXYWHBBoxCoder',
            'target_means': [0.0, 0.0, 0.0, 0.0],
            'target_stds': target_stds
        },
        'reg_class_agnostic': True,
        'loss_cls': {
            'type': 'CrossEntropyLoss',
            'use_sigmoid': False,
            'loss_weight': 1.0
        },
        'loss_bbox': {
            'type': 'SmoothL1Loss',
            'beta': 1.0,
            'loss_weight': 1.0
        }
    } for target_stds in all_target_stds]

    mask_roi_extractor = {
        'type': 'SingleRoIExtractor',
        'roi_layer': {
            'type': 'RoIAlign',
            'output_size': 14,
            'sampling_ratio': 0
        },
        'out_channels': 64,
        'featmap_strides': [4, 8, 16, 32]
    }
    mask_head = {
        'type': 'FCNMaskHead',
        'num_convs': 4,
        'in_channels': 64,
        'conv_out_channels': 64,
        'num_classes': 80,
        'loss_mask': {
            'type': 'CrossEntropyLoss',
            'use_mask': True,
            'loss_weight': 1.0
        }
    }

    test_cfg = mmcv.Config(
        dict(
            score_thr=0.05,
            nms=mmcv.Config(dict(type='nms', iou_threshold=0.5)),
            max_per_img=100,
            mask_thr_binary=0.5))

    args = [num_stages, stage_loss_weights, bbox_roi_extractor, bbox_head]
    kwargs = {'test_cfg': test_cfg}
    if is_instance_seg:
        args += [mask_roi_extractor, mask_head]

    from mmdet.models.roi_heads import CascadeRoIHead
    model = CascadeRoIHead(*args, **kwargs).eval()
    return model


@pytest.mark.parametrize('backend_type',
                         [Backend.ONNXRUNTIME, Backend.OPENVINO])
def test_cascade_roi_head(backend_type: Backend):
    check_backend(backend_type)

    cascade_roi_head = get_cascade_roi_head()
    seed_everything(1234)
    x = [
        torch.rand((1, 64, 200, 304)),
        torch.rand((1, 64, 100, 152)),
        torch.rand((1, 64, 50, 76)),
        torch.rand((1, 64, 25, 38)),
    ]
    proposals = torch.tensor([[587.8285, 52.1405, 886.2484, 341.5644, 0.5]])
    img_metas = {
        'img_shape': torch.tensor([800, 1216]),
        'ori_shape': torch.tensor([800, 1216]),
        'scale_factor': torch.tensor([1, 1, 1, 1])
    }

    model_inputs = {
        'x': x,
        'proposal_list': [proposals],
        'img_metas': [img_metas]
    }
    output_names = ['results']
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(output_names=output_names, input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.5,
                    max_output_boxes_per_class=200,
                    pre_top_k=-1,
                    keep_top_k=100,
                    background_label_id=-1))))
    model_inputs = {'x': x, 'proposals': proposals.unsqueeze(0)}
    wrapped_model = WrapModel(
        cascade_roi_head, 'simple_test', img_metas=[img_metas])
    backend_outputs, _ = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=model_inputs,
        deploy_cfg=deploy_cfg)

    assert backend_outputs is not None


def get_fovea_head_model():
    """FoveaHead Config."""
    test_cfg = mmcv.Config(
        dict(
            deploy_nms_pre=0,
            min_bbox_size=0,
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100))

    from mmdet.models.dense_heads import FoveaHead
    model = FoveaHead(num_classes=4, in_channels=1, test_cfg=test_cfg)

    model.requires_grad_(False)
    return model


@pytest.mark.parametrize('backend_type',
                         [Backend.ONNXRUNTIME, Backend.OPENVINO])
def test_get_bboxes_of_fovea_head(backend_type: Backend):
    check_backend(backend_type)
    fovea_head = get_fovea_head_model()
    fovea_head.cpu().eval()
    s = 128
    img_metas = [{
        'scale_factor': np.ones(4),
        'pad_shape': (s, s, 3),
        'img_shape': (s, s, 3)
    }]

    output_names = ['dets', 'labels']
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(output_names=output_names, input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.5,
                    max_output_boxes_per_class=200,
                    pre_top_k=-1,
                    keep_top_k=100,
                    background_label_id=-1,
                ))))

    # the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16),
    # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2).
    # the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16),
    # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2)
    seed_everything(1234)
    cls_score = [
        torch.rand(1, fovea_head.num_classes, pow(2, i), pow(2, i))
        for i in range(5, 0, -1)
    ]
    seed_everything(5678)
    bboxes = [torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1)]

    model_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
        'img_metas': img_metas
    }
    model_outputs = get_model_outputs(fovea_head, 'get_bboxes', model_inputs)

    # to get outputs of onnx model after rewrite
    img_metas[0]['img_shape'] = torch.Tensor([s, s])
    wrapped_model = WrapModel(fovea_head, 'get_bboxes', img_metas=img_metas)
    rewrite_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
    }
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)
    if is_backend_output:
        if isinstance(rewrite_outputs, dict):
            rewrite_outputs = convert_to_list(rewrite_outputs, output_names)
        for model_output, rewrite_output in zip(model_outputs[0],
                                                rewrite_outputs):
            model_output = model_output.squeeze().cpu().numpy()
            rewrite_output = rewrite_output.squeeze()
            # hard code to make two tensors with the same shape
            # rewrite and original codes applied different nms strategy
            assert np.allclose(
                model_output[:rewrite_output.shape[0]],
                rewrite_output,
                rtol=1e-03,
                atol=1e-05)
    else:
        assert rewrite_outputs is not None


@pytest.mark.parametrize('backend_type', [Backend.OPENVINO])
def test_cascade_roi_head_with_mask(backend_type: Backend):
    check_backend(backend_type)

    cascade_roi_head = get_cascade_roi_head(is_instance_seg=True)
    seed_everything(1234)
    x = [
        torch.rand((1, 64, 200, 304)),
        torch.rand((1, 64, 100, 152)),
        torch.rand((1, 64, 50, 76)),
        torch.rand((1, 64, 25, 38)),
    ]
    proposals = torch.tensor([[587.8285, 52.1405, 886.2484, 341.5644, 0.5]])
    img_metas = {
        'img_shape': torch.tensor([800, 1216]),
        'ori_shape': torch.tensor([800, 1216]),
        'scale_factor': torch.tensor([1, 1, 1, 1])
    }

    output_names = ['bbox_results', 'segm_results']
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(output_names=output_names, input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.5,
                    max_output_boxes_per_class=200,
                    pre_top_k=-1,
                    keep_top_k=100,
                    background_label_id=-1))))
    model_inputs = {'x': x, 'proposals': proposals.unsqueeze(0)}
    wrapped_model = WrapModel(
        cascade_roi_head, 'simple_test', img_metas=[img_metas])
    backend_outputs, _ = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=model_inputs,
        deploy_cfg=deploy_cfg)
    bbox_results = backend_outputs[0]
    segm_results = backend_outputs[1]
    assert bbox_results is not None
    assert segm_results is not None


def get_yolov3_head_model():
    """yolov3 Head Config."""
    test_cfg = mmcv.Config(
        dict(
            nms_pre=1000,
            min_bbox_size=0,
            score_thr=0.05,
            conf_thr=0.005,
            nms=dict(type='nms', iou_threshold=0.45),
            max_per_img=100))
    from mmdet.models.dense_heads import YOLOV3Head
    model = YOLOV3Head(
        num_classes=4,
        in_channels=[16, 8, 4],
        out_channels=[32, 16, 8],
        test_cfg=test_cfg)

    model.requires_grad_(False)
    return model


@pytest.mark.parametrize('backend_type',
                         [Backend.ONNXRUNTIME, Backend.OPENVINO])
def test_yolov3_head_get_bboxes(backend_type):
    """Test get_bboxes rewrite of yolov3 head."""
    check_backend(backend_type)
    yolov3_head = get_yolov3_head_model()
    yolov3_head.cpu().eval()
    s = 128
    img_metas = [{
        'scale_factor': np.ones(4),
        'pad_shape': (s, s, 3),
        'img_shape': (s, s, 3)
    }]

    output_names = ['dets', 'labels']
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(output_names=output_names, input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.45,
                    confidence_threshold=0.005,
                    max_output_boxes_per_class=200,
                    pre_top_k=-1,
                    keep_top_k=100,
                    background_label_id=-1,
                ))))

    seed_everything(1234)
    pred_maps = [
        torch.rand(1, 27, 5, 5),
        torch.rand(1, 27, 10, 10),
        torch.rand(1, 27, 20, 20)
    ]
    # to get outputs of pytorch model
    model_inputs = {'pred_maps': pred_maps, 'img_metas': img_metas}
    model_outputs = get_model_outputs(yolov3_head, 'get_bboxes', model_inputs)

    # to get outputs of onnx model after rewrite
    wrapped_model = WrapModel(
        yolov3_head, 'get_bboxes', img_metas=img_metas, with_nms=True)
    rewrite_inputs = {
        'pred_maps': pred_maps,
    }
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)

    if is_backend_output:
        if isinstance(rewrite_outputs, dict):
            rewrite_outputs = convert_to_list(rewrite_outputs, output_names)
        for model_output, rewrite_output in zip(model_outputs[0],
                                                rewrite_outputs):
            model_output = model_output.squeeze().cpu().numpy()
            rewrite_output = rewrite_output.squeeze()
            # hard code to make two tensors with the same shape
            # rewrite and original codes applied different nms strategy
            assert np.allclose(
                model_output[:rewrite_output.shape[0]],
                rewrite_output,
                rtol=1e-03,
                atol=1e-05)
    else:
        assert rewrite_outputs is not None


def test_yolov3_head_get_bboxes_ncnn():
    """Test get_bboxes rewrite of yolov3 head."""
    backend_type = Backend.NCNN
    check_backend(backend_type)
    yolov3_head = get_yolov3_head_model()
    yolov3_head.cpu().eval()
    s = 128
    img_metas = [{
        'scale_factor': np.ones(4),
        'pad_shape': (s, s, 3),
        'img_shape': (s, s, 3)
    }]

    output_names = ['detection_output']
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(output_names=output_names, input_shape=None),
            codebase_config=dict(
                type='mmdet',
                model_type='ncnn_end2end',
                task='ObjectDetection',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.45,
                    confidence_threshold=0.005,
                    max_output_boxes_per_class=200,
                    pre_top_k=-1,
                    keep_top_k=10,
                    background_label_id=-1,
                ))))

    seed_everything(1234)
    pred_maps = [
        torch.rand(1, 27, 5, 5),
        torch.rand(1, 27, 10, 10),
        torch.rand(1, 27, 20, 20)
    ]

    # to get outputs of onnx model after rewrite
    wrapped_model = WrapModel(
        yolov3_head, 'get_bboxes', img_metas=img_metas[0], with_nms=True)
    rewrite_inputs = {
        'pred_maps': pred_maps,
    }
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)
    # output should be of shape [1, N, 6]
    if is_backend_output:
        assert rewrite_outputs[0].shape[-1] == 6
    else:
        assert rewrite_outputs.shape[-1] == 6


def get_yolox_head_model():
    """YOLOX Head Config."""
    test_cfg = mmcv.Config(
        dict(
            deploy_nms_pre=0,
            min_bbox_size=0,
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100))

    from mmdet.models.dense_heads import YOLOXHead
    model = YOLOXHead(num_classes=4, in_channels=1, test_cfg=test_cfg)

    model.requires_grad_(False)
    return model


@pytest.mark.parametrize('backend_type',
                         [Backend.ONNXRUNTIME, Backend.OPENVINO])
def test_yolox_head_get_bboxes(backend_type: Backend):
    """Test get_bboxes rewrite of YOLOXHead."""
    check_backend(backend_type)
    yolox_head = get_yolox_head_model()
    yolox_head.cpu().eval()
    s = 128
    img_metas = [{
        'scale_factor': np.ones(4),
        'pad_shape': (s, s, 3),
        'img_shape': (s, s, 3)
    }]
    output_names = ['dets', 'labels']
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(output_names=output_names, input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.5,
                    max_output_boxes_per_class=20,
                    pre_top_k=-1,
                    keep_top_k=10,
                    background_label_id=-1,
                ))))
    seed_everything(1234)
    cls_scores = [
        torch.rand(1, yolox_head.num_classes, pow(2, i), pow(2, i))
        for i in range(3, 0, -1)
    ]
    seed_everything(5678)
    bbox_preds = [
        torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(3, 0, -1)
    ]
    seed_everything(9101)
    objectnesses = [
        torch.rand(1, 1, pow(2, i), pow(2, i)) for i in range(3, 0, -1)
    ]

    # to get outputs of pytorch model
    model_inputs = {
        'cls_scores': cls_scores,
        'bbox_preds': bbox_preds,
        'objectnesses': objectnesses,
        'img_metas': img_metas
    }
    model_outputs = get_model_outputs(yolox_head, 'get_bboxes', model_inputs)

    # to get outputs of onnx model after rewrite
    wrapped_model = WrapModel(
        yolox_head, 'get_bboxes', img_metas=img_metas, with_nms=True)
    rewrite_inputs = {
        'cls_scores': cls_scores,
        'bbox_preds': bbox_preds,
        'objectnesses': objectnesses,
    }
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)

    if is_backend_output:
        if isinstance(rewrite_outputs, dict):
            rewrite_outputs = convert_to_list(rewrite_outputs, output_names)
        for model_output, rewrite_output in zip(model_outputs[0],
                                                rewrite_outputs):
            model_output = model_output.squeeze().cpu().numpy()
            rewrite_output = rewrite_output.squeeze().cpu().numpy()
            # hard code to make two tensors with the same shape
            # rewrite and original codes applied different nms strategy
            min_shape = min(model_output.shape[0], rewrite_output.shape[0], 5)
            assert np.allclose(
                model_output[:min_shape],
                rewrite_output[:min_shape],
                rtol=1e-03,
                atol=1e-05)
    else:
        assert rewrite_outputs is not None


def test_yolox_head_get_bboxes_ncnn():
    """Test get_bboxes rewrite of yolox head for ncnn."""
    backend_type = Backend.NCNN
    check_backend(backend_type)
    yolox_head = get_yolox_head_model()
    yolox_head.cpu().eval()
    s = 128
    img_metas = [{
        'scale_factor': np.ones(4),
        'pad_shape': (s, s, 3),
        'img_shape': (s, s, 3)
    }]

    output_names = ['detection_output']
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(output_names=output_names, input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.5,
                    max_output_boxes_per_class=20,
                    pre_top_k=5000,
                    keep_top_k=10,
                    background_label_id=0,
                ))))

    seed_everything(1234)
    cls_scores = [
        torch.rand(1, yolox_head.num_classes, pow(2, i), pow(2, i))
        for i in range(3, 0, -1)
    ]
    seed_everything(5678)
    bbox_preds = [
        torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(3, 0, -1)
    ]
    seed_everything(9101)
    objectnesses = [
        torch.rand(1, 1, pow(2, i), pow(2, i)) for i in range(3, 0, -1)
    ]

    # to get outputs of onnx model after rewrite
    wrapped_model = WrapModel(yolox_head, 'get_bboxes', img_metas=img_metas)
    rewrite_inputs = {
        'cls_scores': cls_scores,
        'bbox_preds': bbox_preds,
        'objectnesses': objectnesses,
    }
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)
    # output should be of shape [1, N, 6]
    if is_backend_output:
        assert rewrite_outputs[0].shape[-1] == 6
    else:
        assert rewrite_outputs.shape[-1] == 6


def get_vfnet_head_model():
    """VFNet Head Config."""
    test_cfg = mmcv.Config(
        dict(
            deploy_nms_pre=0,
            min_bbox_size=0,
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100))
    from mmdet.models.dense_heads import VFNetHead
    model = VFNetHead(num_classes=4, in_channels=1, test_cfg=test_cfg)

    model.requires_grad_(False)
    model.cpu().eval()
    return model


@pytest.mark.parametrize('backend_type',
                         [Backend.OPENVINO, Backend.ONNXRUNTIME])
def test_get_bboxes_of_vfnet_head(backend_type: Backend):
    """Test get_bboxes rewrite of VFNet head."""
    check_backend(backend_type)
    vfnet_head = get_vfnet_head_model()
    vfnet_head.cpu().eval()
    s = 16
    img_metas = [{
        'scale_factor': np.ones(4),
        'pad_shape': (s, s, 3),
        'img_shape': (s, s, 3)
    }]
    output_names = ['dets', 'labels']
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(output_names=output_names, input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.5,
                    max_output_boxes_per_class=200,
                    pre_top_k=-1,
                    keep_top_k=100,
                    background_label_id=-1,
                ))))

    seed_everything(1234)
    cls_score = [
        torch.rand(1, vfnet_head.num_classes, pow(2, i), pow(2, i))
        for i in range(5, 0, -1)
    ]
    seed_everything(5678)
    bboxes = [torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1)]
    seed_everything(9101)

    model_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
        'img_metas': img_metas
    }
    model_outputs = get_model_outputs(vfnet_head, 'get_bboxes', model_inputs)

    img_metas[0]['img_shape'] = torch.Tensor([s, s])
    wrapped_model = WrapModel(
        vfnet_head, 'get_bboxes', img_metas=img_metas, with_nms=True)
    rewrite_inputs = {'cls_scores': cls_score, 'bbox_preds': bboxes}
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)

    if is_backend_output:
        if isinstance(rewrite_outputs, dict):
            rewrite_outputs = convert_to_list(rewrite_outputs, output_names)
        for model_output, rewrite_output in zip(model_outputs[0],
                                                rewrite_outputs):
            model_output = model_output.squeeze().cpu().numpy()
            rewrite_output = rewrite_output.squeeze()
            min_shape = min(model_output.shape[0], rewrite_output.shape[0])
            assert np.allclose(
                model_output[:min_shape],
                rewrite_output[:min_shape],
                rtol=1e-03,
                atol=1e-05)
    else:
        assert rewrite_outputs is not None


def get_deploy_cfg(backend_type: Backend, ir_type: str):
    return mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(
                type=ir_type,
                output_names=['dets', 'labels'],
                input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.5,
                    max_output_boxes_per_class=200,
                    pre_top_k=5000,
                    keep_top_k=100,
                    background_label_id=-1,
                ))))


@pytest.mark.parametrize('backend_type, ir_type',
                         [(Backend.ONNXRUNTIME, 'onnx'),
                          (Backend.OPENVINO, 'onnx'),
                          (Backend.TORCHSCRIPT, 'torchscript')])
def test_base_dense_head_get_bboxes(backend_type: Backend, ir_type: str):
    """Test get_bboxes rewrite of base dense head."""
    check_backend(backend_type)
    anchor_head = get_anchor_head_model()
    anchor_head.cpu().eval()
    s = 128
    img_metas = [{
        'scale_factor': np.ones(4),
        'pad_shape': (s, s, 3),
        'img_shape': (s, s, 3)
    }]

    deploy_cfg = get_deploy_cfg(backend_type, ir_type)
    output_names = get_ir_config(deploy_cfg).get('output_names', None)

    # the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16),
    # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2).
    # the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16),
    # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2)
    seed_everything(1234)
    cls_score = [
        torch.rand(1, 36, pow(2, i), pow(2, i)) for i in range(5, 0, -1)
    ]
    seed_everything(5678)
    bboxes = [torch.rand(1, 36, pow(2, i), pow(2, i)) for i in range(5, 0, -1)]

    # to get outputs of pytorch model
    model_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
        'img_metas': img_metas
    }
    model_outputs = get_model_outputs(anchor_head, 'get_bboxes', model_inputs)

    # to get outputs of onnx model after rewrite
    img_metas[0]['img_shape'] = torch.Tensor([s, s])
    wrapped_model = WrapModel(
        anchor_head, 'get_bboxes', img_metas=img_metas, with_nms=True)
    rewrite_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
    }
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)

    if is_backend_output:
        if isinstance(rewrite_outputs, dict):
            rewrite_outputs = convert_to_list(rewrite_outputs, output_names)
        for model_output, rewrite_output in zip(model_outputs[0],
                                                rewrite_outputs):
            model_output = model_output.squeeze().cpu().numpy()
            rewrite_output = rewrite_output.squeeze()
            # hard code to make two tensors with the same shape
            # rewrite and original codes applied different nms strategy
            assert np.allclose(
                model_output[:rewrite_output.shape[0]],
                rewrite_output,
                rtol=1e-03,
                atol=1e-05)
    else:
        assert rewrite_outputs is not None


def test_base_dense_head_get_bboxes__ncnn():
    """Test get_bboxes rewrite of base dense head."""
    backend_type = Backend.NCNN
    check_backend(backend_type)
    anchor_head = get_anchor_head_model()
    anchor_head.cpu().eval()
    s = 128
    img_metas = [{
        'scale_factor': np.ones(4),
        'pad_shape': (s, s, 3),
        'img_shape': (s, s, 3)
    }]

    output_names = ['output']
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(output_names=output_names, input_shape=None),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                model_type='ncnn_end2end',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.5,
                    max_output_boxes_per_class=200,
                    pre_top_k=5000,
                    keep_top_k=100,
                    background_label_id=-1,
                ))))

    # the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16),
    # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2).
    # the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16),
    # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2)
    seed_everything(1234)
    cls_score = [
        torch.rand(1, 36, pow(2, i), pow(2, i)) for i in range(5, 0, -1)
    ]
    seed_everything(5678)
    bboxes = [torch.rand(1, 36, pow(2, i), pow(2, i)) for i in range(5, 0, -1)]

    # to get outputs of onnx model after rewrite
    img_metas[0]['img_shape'] = torch.Tensor([s, s])
    wrapped_model = WrapModel(
        anchor_head, 'get_bboxes', img_metas=img_metas, with_nms=True)
    rewrite_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
    }
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)

    # output should be of shape [1, N, 6]
    if is_backend_output:
        rewrite_outputs = rewrite_outputs[0]

    assert rewrite_outputs.shape[-1] == 6


@pytest.mark.parametrize('is_dynamic', [True, False])
def test_ssd_head_get_bboxes__ncnn(is_dynamic: bool):
    """Test get_bboxes rewrite of ssd head for ncnn."""
    check_backend(Backend.NCNN)
    ssd_head = get_ssd_head_model()
    ssd_head.cpu().eval()
    s = 128
    img_metas = [{
        'scale_factor': np.ones(4),
        'pad_shape': (s, s, 3),
        'img_shape': (s, s, 3)
    }]
    output_names = ['output']
    input_names = []
    for i in range(6):
        input_names.append('cls_scores_' + str(i))
        input_names.append('bbox_preds_' + str(i))
    dynamic_axes = None
    if is_dynamic:
        dynamic_axes = {
            output_names[0]: {
                1: 'num_dets',
            }
        }
        for input_name in input_names:
            dynamic_axes[input_name] = {2: 'height', 3: 'width'}
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=Backend.NCNN.value),
            onnx_config=dict(
                input_names=input_names,
                output_names=output_names,
                input_shape=None,
                dynamic_axes=dynamic_axes),
            codebase_config=dict(
                type='mmdet',
                task='ObjectDetection',
                model_type='ncnn_end2end',
                post_processing=dict(
                    score_threshold=0.05,
                    iou_threshold=0.5,
                    max_output_boxes_per_class=200,
                    pre_top_k=5000,
                    keep_top_k=100,
                    background_label_id=-1,
                ))))

    # For the ssd_head:
    # the cls_score's size: (1, 30, 20, 20), (1, 30, 10, 10),
    # (1, 30, 5, 5), (1, 30, 3, 3), (1, 30, 2, 2), (1, 30, 1, 1)
    # the bboxes's size: (1, 24, 20, 20), (1, 24, 10, 10),
    # (1, 24, 5, 5), (1, 24, 3, 3), (1, 24, 2, 2), (1, 24, 1, 1)
    feat_shape = [20, 10, 5, 3, 2, 1]
    num_prior = 6
    seed_everything(1234)
    cls_score = [
        torch.rand(1, 30, feat_shape[i], feat_shape[i])
        for i in range(num_prior)
    ]
    seed_everything(5678)
    bboxes = [
        torch.rand(1, 24, feat_shape[i], feat_shape[i])
        for i in range(num_prior)
    ]

    # to get outputs of onnx model after rewrite
    img_metas[0]['img_shape'] = torch.tensor([s, s]) if is_dynamic else [s, s]
    wrapped_model = WrapModel(
        ssd_head, 'get_bboxes', img_metas=img_metas, with_nms=True)
    rewrite_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
    }
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)

    # output should be of shape [1, N, 6]
    if is_backend_output:
        rewrite_outputs = rewrite_outputs[0]

    assert rewrite_outputs.shape[-1] == 6


@pytest.mark.parametrize('backend_type, ir_type', [(Backend.OPENVINO, 'onnx')])
def test_reppoints_head_get_bboxes(backend_type: Backend, ir_type: str):
    """Test get_bboxes rewrite of base dense head."""
    check_backend(backend_type)
    dense_head = get_reppoints_head_model()
    dense_head.cpu().eval()
    s = 128
    img_metas = [{
        'scale_factor': np.ones(4),
        'pad_shape': (s, s, 3),
        'img_shape': (s, s, 3)
    }]

    deploy_cfg = get_deploy_cfg(backend_type, ir_type)
    output_names = get_ir_config(deploy_cfg).get('output_names', None)

    # the cls_score's size: (1, 4, 32, 32), (1, 4, 16, 16),
    # (1, 4, 8, 8), (1, 4, 4, 4), (1, 4, 2, 2).
    # the bboxes's size: (1, 4, 32, 32), (1, 4, 16, 16),
    # (1, 4, 8, 8), (1, 4, 4, 4), (1, 4, 2, 2)
    seed_everything(1234)
    cls_score = [
        torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1)
    ]
    seed_everything(5678)
    bboxes = [torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1)]

    # to get outputs of pytorch model
    model_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
        'img_metas': img_metas
    }
    model_outputs = get_model_outputs(dense_head, 'get_bboxes', model_inputs)

    # to get outputs of onnx model after rewrite
    img_metas[0]['img_shape'] = torch.Tensor([s, s])
    wrapped_model = WrapModel(
        dense_head, 'get_bboxes', img_metas=img_metas, with_nms=True)
    rewrite_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
    }
    rewrite_outputs, is_backend_output = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)

    if is_backend_output:
        if isinstance(rewrite_outputs, dict):
            rewrite_outputs = convert_to_list(rewrite_outputs, output_names)
        for model_output, rewrite_output in zip(model_outputs[0],
                                                rewrite_outputs):
            model_output = model_output.squeeze().cpu().numpy()
            rewrite_output = rewrite_output.squeeze()
            # hard code to make two tensors with the same shape
            # rewrite and original codes applied different nms strategy
            assert np.allclose(
                model_output[:rewrite_output.shape[0]],
                rewrite_output,
                rtol=1e-03,
                atol=1e-05)
    else:
        assert rewrite_outputs is not None


@pytest.mark.parametrize('backend_type, ir_type', [(Backend.OPENVINO, 'onnx')])
def test_reppoints_head_points2bbox(backend_type: Backend, ir_type: str):
    """Test get_bboxes rewrite of base dense head."""
    check_backend(backend_type)
    dense_head = get_reppoints_head_model()
    dense_head.cpu().eval()
    output_names = ['output']

    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type.value),
            onnx_config=dict(
                input_shape=None,
                input_names=['pts'],
                output_names=output_names)))

    # the cls_score's size: (1, 4, 32, 32), (1, 4, 16, 16),
    # (1, 4, 8, 8), (1, 4, 4, 4), (1, 4, 2, 2).
    # the bboxes's size: (1, 4, 32, 32), (1, 4, 16, 16),
    # (1, 4, 8, 8), (1, 4, 4, 4), (1, 4, 2, 2)
    seed_everything(1234)
    pts = torch.rand(1, 18, 16, 16)

    # to get outputs of onnx model after rewrite
    wrapped_model = WrapModel(dense_head, 'points2bbox', y_first=True)
    rewrite_inputs = {'pts': pts}
    _ = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)


@pytest.mark.skipif(
    reason='Only support GPU test', condition=not torch.cuda.is_available())
@pytest.mark.parametrize('backend_type', [(Backend.TENSORRT)])
def test_windows_msa(backend_type: Backend):
    check_backend(backend_type)
    from mmdet.models.backbones.swin import WindowMSA
    model = WindowMSA(96, 3, (7, 7))
    model.cuda().eval()
    output_names = ['output']

    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(
                type=backend_type.value,
                common_config=dict(fp16_mode=True, max_workspace_size=1 << 20),
                model_inputs=[
                    dict(
                        input_shapes=dict(
                            x=dict(
                                min_shape=[12, 49, 96],
                                opt_shape=[12, 49, 96],
                                max_shape=[12, 49, 96]),
                            mask=dict(
                                min_shape=[12, 49, 49],
                                opt_shape=[12, 49, 49],
                                max_shape=[12, 49, 49])))
                ]),
            onnx_config=dict(
                input_shape=None,
                input_names=['x', 'mask'],
                output_names=output_names)))

    x = torch.randn([12, 49, 96]).cuda()
    mask = torch.randn([12, 49, 49]).cuda()
    wrapped_model = WrapModel(model, 'forward')
    rewrite_inputs = {'x': x, 'mask': mask}
    _ = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg)


@pytest.mark.skipif(
    reason='Only support GPU test', condition=not torch.cuda.is_available())
@pytest.mark.parametrize('backend_type', [(Backend.TENSORRT)])
def test_shift_windows_msa(backend_type: Backend):
    check_backend(backend_type)
    from mmdet.models.backbones.swin import ShiftWindowMSA
    model = ShiftWindowMSA(96, 3, 7)
    model.cuda().eval()
    output_names = ['output']

    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(
                type=backend_type.value,
                model_inputs=[
                    dict(
                        input_shapes=dict(
                            query=dict(
                                min_shape=[1, 60800, 96],
                                opt_shape=[1, 60800, 96],
                                max_shape=[1, 60800, 96])))
                ]),
            onnx_config=dict(
                input_shape=None,
                input_names=['query'],
                output_names=output_names)))

    query = torch.randn([1, 60800, 96]).cuda()
    hw_shape = (torch.tensor(200), torch.tensor(304))

    wrapped_model = WrapModel(model, 'forward')
    rewrite_inputs = {'query': query, 'hw_shape': hw_shape}
    _ = get_rewrite_outputs(
        wrapped_model=wrapped_model,
        model_inputs=rewrite_inputs,
        deploy_cfg=deploy_cfg,
        run_with_backend=False)