mmdeploy/tests/test_mmdet/test_mmdet_models.py

import copy
import importlib
import os
import random
import tempfile

import mmcv
import numpy as np
import pytest
import torch

from mmdeploy.utils.constants import Backend, Codebase
from mmdeploy.utils.test import (WrapModel, get_model_outputs,
                                 get_rewrite_outputs)


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 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 import AnchorHead
    model = AnchorHead(num_classes=4, in_channels=1, 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 import FCOSHead
    model = FCOSHead(num_classes=4, in_channels=1, test_cfg=test_cfg)

    model.requires_grad_(False)
    return model


def get_rpn_head_model():
    """RPN 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 import RPNHead
    model = RPNHead(in_channels=1, test_cfg=test_cfg)

    model.requires_grad_(False)
    return model


@pytest.mark.parametrize('backend_type', ['onnxruntime', 'ncnn'])
def test_anchor_head_get_bboxes(backend_type):
    """Test get_bboxes rewrite of anchor head."""
    pytest.importorskip(backend_type, reason=f'requires {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 = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type),
            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,
                ))))

    # 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[0], with_nms=True)
    rewrite_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
    }
    rewrite_outputs = 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().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)


@pytest.mark.parametrize('backend_type', ['onnxruntime', 'ncnn'])
def test_get_bboxes_of_fcos_head(backend_type):
    pytest.importorskip(backend_type, reason=f'requires {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)
    }]

    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type=backend_type),
            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,
                ))))

    # 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 pytorch model
    model_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
        'centernesses': centernesses,
        'img_metas': img_metas
    }
    model_outputs = get_model_outputs(fcos_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(
        fcos_head, 'get_bboxes', img_metas=img_metas[0], with_nms=True)
    rewrite_inputs = {
        'cls_scores': cls_score,
        'bbox_preds': bboxes,
        'centernesses': centernesses
    }
    rewrite_outputs = 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().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)


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('model_cfg_path', [
    'tests/test_mmdet/data/single_stage_model.json',
    'tests/test_mmdet/data/mask_model.json'
])
@pytest.mark.skipif(
    not importlib.util.find_spec('onnxruntime'), reason='requires onnxruntime')
def test_forward_of_base_detector_and_visualize(model_cfg_path):
    deploy_cfg = mmcv.Config(
        dict(
            backend_config=dict(type='onnxruntime'),
            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)

    from mmdeploy.apis.utils import visualize
    output_file = tempfile.NamedTemporaryFile(suffix='.jpg').name
    model.CLASSES = [''] * 80
    visualize(
        Codebase.MMDET,
        img.squeeze().permute(1, 2, 0).numpy(),
        result=[torch.rand(0, 5).numpy()] * 80,
        model=model,
        output_file=output_file,
        backend=Backend.ONNXRUNTIME,
        show_result=False)

    assert rewrite_outputs is not None