code: def test_resnet_backbone():
      """Test resnet backbone."""
      with pytest.raises(KeyError):
          # ResNet depth should be in [18, 34, 50, 101, 152]
          ResNet(20)
  
      with pytest.raises(AssertionError):
          # In ResNet: 1 <= num_stages <= 4
          ResNet(50, num_stages=0)
  
      with pytest.raises(AssertionError):
          # len(stage_with_dcn) == num_stages
          dcn = dict(
              type="DCN", deform_groups=1, fallback_on_stride=False
          )
          ResNet(50, dcn=dcn, stage_with_dcn=(True,))
  
      with pytest.raises(AssertionError):
          # len(stage_with_plugin) == num_stages
          plugins = [
              dict(
                  cfg=dict(type="ContextBlock", ratio=1.0 / 16),
                  stages=(False, True, True),
                  position="after_conv3",
              )
          ]
          ResNet(50, plugins=plugins)
  
      with pytest.raises(AssertionError):
          # In ResNet: 1 <= num_stages <= 4
          ResNet(50, num_stages=5)
  
      with pytest.raises(AssertionError):
          # len(strides) == len(dilations) == num_stages
          ResNet(50, strides=(1,), dilations=(1, 1), num_stages=3)
  
      with pytest.raises(TypeError):
          # pretrained must be a string path
          model = ResNet(50, pretrained=0)
  
      with pytest.raises(AssertionError):
          # Style must be in ['pytorch', 'caffe']
          ResNet(50, style="tensorflow")
  
      # Test ResNet50 norm_eval=True
      model = ResNet(50, norm_eval=True, base_channels=1)
      model.train()
      assert check_norm_state(model.modules(), False)
  
      # Test ResNet50 with torchvision pretrained weight
      model = ResNet(
          depth=50, norm_eval=True, pretrained="torchvision://resnet50"
      )
      model.train()
      assert check_norm_state(model.modules(), False)
  
      # Test ResNet50 with first stage frozen
      frozen_stages = 1
      model = ResNet(50, frozen_stages=frozen_stages, base_channels=1)
      model.train()
      assert model.norm1.training is False
      for layer in [model.conv1, model.norm1]:
          for param in layer.parameters():
              assert param.requires_grad is False
      for i in range(1, frozen_stages + 1):
          layer = getattr(model, f"layer{i}")
          for mod in layer.modules():
              if isinstance(mod, _BatchNorm):
                  assert mod.training is False
          for param in layer.parameters():
              assert param.requires_grad is False
  
      # Test ResNet50V1d with first stage frozen
      model = ResNetV1d(
          depth=50, frozen_stages=frozen_stages, base_channels=2
      )
      assert len(model.stem) == 9
      model.train()
      assert check_norm_state(model.stem, False)
      for param in model.stem.parameters():
          assert param.requires_grad is False
      for i in range(1, frozen_stages + 1):
          layer = getattr(model, f"layer{i}")
          for mod in layer.modules():
              if isinstance(mod, _BatchNorm):
                  assert mod.training is False
          for param in layer.parameters():
              assert param.requires_grad is False
  
      # Test ResNet18 forward
      model = ResNet(18)
      model.train()
  
      imgs = torch.randn(1, 3, 32, 32)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 64, 8, 8])
      assert feat[1].shape == torch.Size([1, 128, 4, 4])
      assert feat[2].shape == torch.Size([1, 256, 2, 2])
      assert feat[3].shape == torch.Size([1, 512, 1, 1])
  
      # Test ResNet18 with checkpoint forward
      model = ResNet(18, with_cp=True)
      for m in model.modules():
          if is_block(m):
              assert m.with_cp
  
      # Test ResNet50 with BatchNorm forward
      model = ResNet(50, base_channels=1)
      for m in model.modules():
          if is_norm(m):
              assert isinstance(m, _BatchNorm)
      model.train()
  
      imgs = torch.randn(1, 3, 32, 32)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 4, 8, 8])
      assert feat[1].shape == torch.Size([1, 8, 4, 4])
      assert feat[2].shape == torch.Size([1, 16, 2, 2])
      assert feat[3].shape == torch.Size([1, 32, 1, 1])
  
      # Test ResNet50 with layers 1, 2, 3 out forward
      model = ResNet(50, out_indices=(0, 1, 2), base_channels=1)
      model.train()
  
      imgs = torch.randn(1, 3, 32, 32)
      feat = model(imgs)
      assert len(feat) == 3
      assert feat[0].shape == torch.Size([1, 4, 8, 8])
      assert feat[1].shape == torch.Size([1, 8, 4, 4])
      assert feat[2].shape == torch.Size([1, 16, 2, 2])
  
      # Test ResNet50 with checkpoint forward
      model = ResNet(50, with_cp=True, base_channels=1)
      for m in model.modules():
          if is_block(m):
              assert m.with_cp
      model.train()
  
      imgs = torch.randn(1, 3, 32, 32)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 4, 8, 8])
      assert feat[1].shape == torch.Size([1, 8, 4, 4])
      assert feat[2].shape == torch.Size([1, 16, 2, 2])
      assert feat[3].shape == torch.Size([1, 32, 1, 1])
  
      # Test ResNet50 with GroupNorm forward
      model = ResNet(
          50,
          base_channels=4,
          norm_cfg=dict(type="GN", num_groups=2, requires_grad=True),
      )
      for m in model.modules():
          if is_norm(m):
              assert isinstance(m, GroupNorm)
      model.train()
  
      imgs = torch.randn(1, 3, 32, 32)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 16, 8, 8])
      assert feat[1].shape == torch.Size([1, 32, 4, 4])
      assert feat[2].shape == torch.Size([1, 64, 2, 2])
      assert feat[3].shape == torch.Size([1, 128, 1, 1])
  
      # Test ResNet50 with 1 GeneralizedAttention after conv2, 1 NonLocal2D
      # after conv2, 1 ContextBlock after conv3 in layers 2, 3, 4
      plugins = [
          dict(
              cfg=dict(
                  type="GeneralizedAttention",
                  spatial_range=-1,
                  num_heads=8,
                  attention_type="0010",
                  kv_stride=2,
              ),
              stages=(False, True, True, True),
              position="after_conv2",
          ),
          dict(cfg=dict(type="NonLocal2d"), position="after_conv2"),
          dict(
              cfg=dict(type="ContextBlock", ratio=1.0 / 16),
              stages=(False, True, True, False),
              position="after_conv3",
          ),
      ]
      model = ResNet(50, plugins=plugins, base_channels=8)
      for m in model.layer1.modules():
          if is_block(m):
              assert not hasattr(m, "context_block")
              assert not hasattr(m, "gen_attention_block")
              assert m.nonlocal_block.in_channels == 8
      for m in model.layer2.modules():
          if is_block(m):
              assert m.nonlocal_block.in_channels == 16
              assert m.gen_attention_block.in_channels == 16
              assert m.context_block.in_channels == 64
  
      for m in model.layer3.modules():
          if is_block(m):
              assert m.nonlocal_block.in_channels == 32
              assert m.gen_attention_block.in_channels == 32
              assert m.context_block.in_channels == 128
  
      for m in model.layer4.modules():
          if is_block(m):
              assert m.nonlocal_block.in_channels == 64
              assert m.gen_attention_block.in_channels == 64
              assert not hasattr(m, "context_block")
      model.train()
  
      imgs = torch.randn(1, 3, 32, 32)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 32, 8, 8])
      assert feat[1].shape == torch.Size([1, 64, 4, 4])
      assert feat[2].shape == torch.Size([1, 128, 2, 2])
      assert feat[3].shape == torch.Size([1, 256, 1, 1])
  
      # Test ResNet50 with 1 ContextBlock after conv2, 1 ContextBlock after
      # conv3 in layers 2, 3, 4
      plugins = [
          dict(
              cfg=dict(type="ContextBlock", ratio=1.0 / 16, postfix=1),
              stages=(False, True, True, False),
              position="after_conv3",
          ),
          dict(
              cfg=dict(type="ContextBlock", ratio=1.0 / 16, postfix=2),
              stages=(False, True, True, False),
              position="after_conv3",
          ),
      ]
  
      model = ResNet(50, plugins=plugins, base_channels=8)
      for m in model.layer1.modules():
          if is_block(m):
              assert not hasattr(m, "context_block")
              assert not hasattr(m, "context_block1")
              assert not hasattr(m, "context_block2")
      for m in model.layer2.modules():
          if is_block(m):
              assert not hasattr(m, "context_block")
              assert m.context_block1.in_channels == 64
              assert m.context_block2.in_channels == 64
  
      for m in model.layer3.modules():
          if is_block(m):
              assert not hasattr(m, "context_block")
              assert m.context_block1.in_channels == 128
              assert m.context_block2.in_channels == 128
  
      for m in model.layer4.modules():
          if is_block(m):
              assert not hasattr(m, "context_block")
              assert not hasattr(m, "context_block1")
              assert not hasattr(m, "context_block2")
      model.train()
  
      imgs = torch.randn(1, 3, 32, 32)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 32, 8, 8])
      assert feat[1].shape == torch.Size([1, 64, 4, 4])
      assert feat[2].shape == torch.Size([1, 128, 2, 2])
      assert feat[3].shape == torch.Size([1, 256, 1, 1])
  
      # Test ResNet50 zero initialization of residual
      model = ResNet(50, zero_init_residual=True, base_channels=1)
      model.init_weights()
      for m in model.modules():
          if isinstance(m, Bottleneck):
              assert assert_params_all_zeros(m.norm3)
          elif isinstance(m, BasicBlock):
              assert assert_params_all_zeros(m.norm2)
      model.train()
  
      imgs = torch.randn(1, 3, 32, 32)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 4, 8, 8])
      assert feat[1].shape == torch.Size([1, 8, 4, 4])
      assert feat[2].shape == torch.Size([1, 16, 2, 2])
      assert feat[3].shape == torch.Size([1, 32, 1, 1])
  
      # Test ResNetV1d forward
      model = ResNetV1d(depth=50, base_channels=2)
      model.train()
  
      imgs = torch.randn(1, 3, 32, 32)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 8, 8, 8])
      assert feat[1].shape == torch.Size([1, 16, 4, 4])
      assert feat[2].shape == torch.Size([1, 32, 2, 2])
      assert feat[3].shape == torch.Size([1, 64, 1, 1])
  
file_path: mmdetection/tests/test_models/test_backbones/test_resnet.py
imports: import pytest
  import torch
  from mmcv.ops import DeformConv2dPack
  from torch.nn.modules import AvgPool2d, GroupNorm
  from torch.nn.modules.batchnorm import _BatchNorm
  from mmdet.models.backbones import ResNet, ResNetV1d
  from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
  from mmdet.models.layers import ResLayer, SimplifiedBasicBlock
  from .utils import check_norm_state, is_block, is_norm
label_desc: Use image node_id 7 for calling a global function with example usage: test_resnet_backbone() without return types