def test_resnest_stem():
# Test default stem_channels
model = ResNet(50)
assert model.stem_channels == 64
assert model.conv1.out_channels == 64
assert model.norm1.num_features == 64
# Test default stem_channels, with base_channels=32
model = ResNet(50, base_channels=32)
assert model.stem_channels == 32
assert model.conv1.out_channels == 32
assert model.norm1.num_features == 32
assert model.layer1[0].conv1.in_channels == 32
# Test stem_channels=64
model = ResNet(50, stem_channels=64)
assert model.stem_channels == 64
assert model.conv1.out_channels == 64
assert model.norm1.num_features == 64
assert model.layer1[0].conv1.in_channels == 64
# Test stem_channels=64, with base_channels=32
model = ResNet(50, stem_channels=64, base_channels=32)
assert model.stem_channels == 64
assert model.conv1.out_channels == 64
assert model.norm1.num_features == 64
assert model.layer1[0].conv1.in_channels == 64
# Test stem_channels=128
model = ResNet(depth=50, stem_channels=128)
model.init_weights()
model.train()
assert model.conv1.out_channels == 128
assert model.layer1[0].conv1.in_channels == 128
# Test V1d stem_channels
model = ResNetV1d(depth=50, stem_channels=128)
model.init_weights()
model.train()
assert model.stem[0].out_channels == 64
assert model.stem[1].num_features == 64
assert model.stem[3].out_channels == 64
assert model.stem[4].num_features == 64
assert model.stem[6].out_channels == 128
assert model.stem[7].num_features == 128
assert model.layer1[0].conv1.in_channels == 128
def test_resnest_stem():
# Test default stem_channels
model = ResNet(50)
assert model.stem_channels == 64
assert model.conv1.out_channels == 64
assert model.norm1.num_features == 64
# Test default stem_channels, with base_channels=3
model = ResNet(50, base_channels=3)
assert model.stem_channels == 3
assert model.conv1.out_channels == 3
assert model.norm1.num_features == 3
assert model.layer1[0].conv1.in_channels == 3
# Test stem_channels=3
model = ResNet(50, stem_channels=3)
assert model.stem_channels == 3
assert model.conv1.out_channels == 3
assert model.norm1.num_features == 3
assert model.layer1[0].conv1.in_channels == 3
# Test stem_channels=3, with base_channels=2
model = ResNet(50, stem_channels=3, base_channels=2)
assert model.stem_channels == 3
assert model.conv1.out_channels == 3
assert model.norm1.num_features == 3
assert model.layer1[0].conv1.in_channels == 3
# Test V1d stem_channels
model = ResNetV1d(depth=50, stem_channels=6)
model.train()
assert model.stem[0].out_channels == 3
assert model.stem[1].num_features == 3
assert model.stem[3].out_channels == 3
assert model.stem[4].num_features == 3
assert model.stem[6].out_channels == 6
assert model.stem[7].num_features == 6
assert model.layer1[0].conv1.in_channels == 6
def __init__(self):
super(Net3, self).__init__()
cfg = mmcv.Config.fromfile('/home/liuziming/mmdetection/configs/rpn_r50_fpn_1x.py')
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
self.RPN = builder.build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
self.backbone = ResNet(50,4,frozen_stages=1,)
self.init_weights(pretrained='modelzoo://resnet50')
self.relation = SelfAttention(2,256,256,256)
self.fc = nn.Linear(256*2,40)
self.avgpool = nn.AdaptiveAvgPool2d((1,1))