support to test mmdet inference with mmcls backbone

requirements/tests.txt

@@ -2,6 +2,7 @@ codecov
 flake8
 interrogate
 isort==4.3.21
+mmdet
 pytest
 xdoctest >= 0.10.0
 yapf

setup.cfg

@@ -14,6 +14,6 @@ line_length = 79
 multi_line_output = 0
 known_standard_library = pkg_resources,setuptools
 known_first_party = mmcls
-known_third_party = PIL,cv2,matplotlib,mmcv,numpy,onnxruntime,pytest,seaborn,torch,torchvision,ts
+known_third_party = PIL,cv2,matplotlib,mmcv,mmdet,numpy,onnxruntime,pytest,seaborn,torch,torchvision,ts
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY

tests/data/retinanet.py

@@ -0,0 +1,60 @@
+# model settings
+model = dict(
+    type='RetinaNet',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_input',
+        num_outs=5),
+    bbox_head=dict(
+        type='RetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100))

tests/test_mmdet_inference.py

@@ -0,0 +1,60 @@
+from mmdet.models import build_detector
+
+from mmcls.models import (MobileNetV2, MobileNetV3, RegNet, ResNeSt, ResNet,
+                          ResNeXt, SEResNet, SEResNeXt, SwinTransformer)
+
+backbone_configs = dict(
+    mobilenetv2=dict(
+        backbone=dict(
+            type='mmcls.MobileNetV2',
+            widen_factor=1.0,
+            norm_cfg=dict(type='GN', num_groups=2, requires_grad=True),
+            out_indices=(4, 7))),
+    mobilenetv3=dict(
+        backbone=dict(
+            type='mmcls.MobileNetV3',
+            norm_cfg=dict(type='GN', num_groups=2, requires_grad=True),
+            out_indices=range(7, 12))),
+    regnet=dict(backbone=dict(type='mmcls.RegNet', arch='regnetx_400mf')),
+    resnext=dict(
+        backbone=dict(
+            type='mmcls.ResNeXt', depth=50, groups=32, width_per_group=4)),
+    resnet=dict(backbone=dict(type='mmcls.ResNet', depth=50)),
+    seresnet=dict(backbone=dict(type='mmcls.SEResNet', depth=50)),
+    seresnext=dict(
+        backbone=dict(
+            type='mmcls.SEResNeXt', depth=50, groups=32, width_per_group=4)),
+    resnest=dict(
+        backbone=dict(
+            type='mmcls.ResNeSt',
+            depth=50,
+            radix=2,
+            reduction_factor=4,
+            out_indices=(0, 1, 2, 3))),
+    swin=dict(
+        backbone=dict(
+            type='mmcls.SwinTransformer', arch='small', drop_path_rate=0.2)))
+
+module_mapping = {
+    'mobilenetv2': MobileNetV2,
+    'mobilenetv3': MobileNetV3,
+    'regnet': RegNet,
+    'resnext': ResNeXt,
+    'resnet': ResNet,
+    'seresnext': SEResNeXt,
+    'seresnet': SEResNet,
+    'resnest': ResNeSt,
+    'swin': SwinTransformer
+}
+
+
+def test_mmdet_inference():
+    from mmcv import Config
+    config_path = './tests/data/retinanet.py'
+    config = Config.fromfile(config_path)
+
+    for module_name, backbone_config in backbone_configs.items():
+        config.model.backbone = backbone_config['backbone']
+        model = build_detector(config.model)
+        module = module_mapping[module_name]
+        assert isinstance(model.backbone, module)