text: # Copyright (c) OpenMMLab. All rights reserved.
  import pytest
  import torch
  from mmcv.ops import DeformConv2dPack
  from mmcv.utils.parrots_wrapper import _BatchNorm
  from torch.nn.modules import AvgPool2d, GroupNorm
  
  from mmseg.models.backbones import ResNet, ResNetV1d
  from mmseg.models.backbones.resnet import BasicBlock, Bottleneck
  from mmseg.models.utils import ResLayer
  from .utils import all_zeros, check_norm_state, is_block, is_norm
  
  
  def test_resnet_basic_block():
      with pytest.raises(AssertionError):
          # Not implemented yet.
          dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
          BasicBlock(64, 64, dcn=dcn)
  
      with pytest.raises(AssertionError):
          # Not implemented yet.
          plugins = [
              dict(
                  cfg=dict(type='ContextBlock', ratio=1. / 16),
                  position='after_conv3')
          ]
          BasicBlock(64, 64, plugins=plugins)
  
      with pytest.raises(AssertionError):
          # Not implemented yet
          plugins = [
              dict(
                  cfg=dict(
                      type='GeneralizedAttention',
                      spatial_range=-1,
                      num_heads=8,
                      attention_type='0010',
                      kv_stride=2),
                  position='after_conv2')
          ]
          BasicBlock(64, 64, plugins=plugins)
  
      # Test BasicBlock with checkpoint forward
      block = BasicBlock(16, 16, with_cp=True)
      assert block.with_cp
      x = torch.randn(1, 16, 56, 56)
      x_out = block(x)
      assert x_out.shape == torch.Size([1, 16, 56, 56])
  
      # test BasicBlock structure and forward
      block = BasicBlock(64, 64)
      assert block.conv1.in_channels == 64
      assert block.conv1.out_channels == 64
      assert block.conv1.kernel_size == (3, 3)
      assert block.conv2.in_channels == 64
      assert block.conv2.out_channels == 64
      assert block.conv2.kernel_size == (3, 3)
      x = torch.randn(1, 64, 56, 56)
      x_out = block(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
  
  def test_resnet_bottleneck():
      with pytest.raises(AssertionError):
          # Style must be in ['pytorch', 'caffe']
          Bottleneck(64, 64, style='tensorflow')
  
      with pytest.raises(AssertionError):
          # Allowed positions are 'after_conv1', 'after_conv2', 'after_conv3'
          plugins = [
              dict(
                  cfg=dict(type='ContextBlock', ratio=1. / 16),
                  position='after_conv4')
          ]
          Bottleneck(64, 16, plugins=plugins)
  
      with pytest.raises(AssertionError):
          # Need to specify different postfix to avoid duplicate plugin name
          plugins = [
              dict(
                  cfg=dict(type='ContextBlock', ratio=1. / 16),
                  position='after_conv3'),
              dict(
                  cfg=dict(type='ContextBlock', ratio=1. / 16),
                  position='after_conv3')
          ]
          Bottleneck(64, 16, plugins=plugins)
  
      with pytest.raises(KeyError):
          # Plugin type is not supported
          plugins = [dict(cfg=dict(type='WrongPlugin'), position='after_conv3')]
          Bottleneck(64, 16, plugins=plugins)
  
      # Test Bottleneck with checkpoint forward
      block = Bottleneck(64, 16, with_cp=True)
      assert block.with_cp
      x = torch.randn(1, 64, 56, 56)
      x_out = block(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
      # Test Bottleneck style
      block = Bottleneck(64, 64, stride=2, style='pytorch')
      assert block.conv1.stride == (1, 1)
      assert block.conv2.stride == (2, 2)
      block = Bottleneck(64, 64, stride=2, style='caffe')
      assert block.conv1.stride == (2, 2)
      assert block.conv2.stride == (1, 1)
  
      # Test Bottleneck DCN
      dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
      with pytest.raises(AssertionError):
          Bottleneck(64, 64, dcn=dcn, conv_cfg=dict(type='Conv'))
      block = Bottleneck(64, 64, dcn=dcn)
      assert isinstance(block.conv2, DeformConv2dPack)
  
      # Test Bottleneck forward
      block = Bottleneck(64, 16)
      x = torch.randn(1, 64, 56, 56)
      x_out = block(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
      # Test Bottleneck with 1 ContextBlock after conv3
      plugins = [
          dict(
              cfg=dict(type='ContextBlock', ratio=1. / 16),
              position='after_conv3')
      ]
      block = Bottleneck(64, 16, plugins=plugins)
      assert block.context_block.in_channels == 64
      x = torch.randn(1, 64, 56, 56)
      x_out = block(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
      # Test Bottleneck with 1 GeneralizedAttention after conv2
      plugins = [
          dict(
              cfg=dict(
                  type='GeneralizedAttention',
                  spatial_range=-1,
                  num_heads=8,
                  attention_type='0010',
                  kv_stride=2),
              position='after_conv2')
      ]
      block = Bottleneck(64, 16, plugins=plugins)
      assert block.gen_attention_block.in_channels == 16
      x = torch.randn(1, 64, 56, 56)
      x_out = block(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
      # Test Bottleneck with 1 GeneralizedAttention after conv2, 1 NonLocal2d
      # after conv2, 1 ContextBlock after conv3
      plugins = [
          dict(
              cfg=dict(
                  type='GeneralizedAttention',
                  spatial_range=-1,
                  num_heads=8,
                  attention_type='0010',
                  kv_stride=2),
              position='after_conv2'),
          dict(cfg=dict(type='NonLocal2d'), position='after_conv2'),
          dict(
              cfg=dict(type='ContextBlock', ratio=1. / 16),
              position='after_conv3')
      ]
      block = Bottleneck(64, 16, plugins=plugins)
      assert block.gen_attention_block.in_channels == 16
      assert block.nonlocal_block.in_channels == 16
      assert block.context_block.in_channels == 64
      x = torch.randn(1, 64, 56, 56)
      x_out = block(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
      # Test Bottleneck with 1 ContextBlock after conv2, 2 ContextBlock after
      # conv3
      plugins = [
          dict(
              cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1),
              position='after_conv2'),
          dict(
              cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2),
              position='after_conv3'),
          dict(
              cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=3),
              position='after_conv3')
      ]
      block = Bottleneck(64, 16, plugins=plugins)
      assert block.context_block1.in_channels == 16
      assert block.context_block2.in_channels == 64
      assert block.context_block3.in_channels == 64
      x = torch.randn(1, 64, 56, 56)
      x_out = block(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
  
  def test_resnet_res_layer():
      # Test ResLayer of 3 Bottleneck w\o downsample
      layer = ResLayer(Bottleneck, 64, 16, 3)
      assert len(layer) == 3
      assert layer[0].conv1.in_channels == 64
      assert layer[0].conv1.out_channels == 16
      for i in range(1, len(layer)):
          assert layer[i].conv1.in_channels == 64
          assert layer[i].conv1.out_channels == 16
      for i in range(len(layer)):
          assert layer[i].downsample is None
      x = torch.randn(1, 64, 56, 56)
      x_out = layer(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
      # Test ResLayer of 3 Bottleneck with downsample
      layer = ResLayer(Bottleneck, 64, 64, 3)
      assert layer[0].downsample[0].out_channels == 256
      for i in range(1, len(layer)):
          assert layer[i].downsample is None
      x = torch.randn(1, 64, 56, 56)
      x_out = layer(x)
      assert x_out.shape == torch.Size([1, 256, 56, 56])
  
      # Test ResLayer of 3 Bottleneck with stride=2
      layer = ResLayer(Bottleneck, 64, 64, 3, stride=2)
      assert layer[0].downsample[0].out_channels == 256
      assert layer[0].downsample[0].stride == (2, 2)
      for i in range(1, len(layer)):
          assert layer[i].downsample is None
      x = torch.randn(1, 64, 56, 56)
      x_out = layer(x)
      assert x_out.shape == torch.Size([1, 256, 28, 28])
  
      # Test ResLayer of 3 Bottleneck with stride=2 and average downsample
      layer = ResLayer(Bottleneck, 64, 64, 3, stride=2, avg_down=True)
      assert isinstance(layer[0].downsample[0], AvgPool2d)
      assert layer[0].downsample[1].out_channels == 256
      assert layer[0].downsample[1].stride == (1, 1)
      for i in range(1, len(layer)):
          assert layer[i].downsample is None
      x = torch.randn(1, 64, 56, 56)
      x_out = layer(x)
      assert x_out.shape == torch.Size([1, 256, 28, 28])
  
      # Test ResLayer of 3 Bottleneck with dilation=2
      layer = ResLayer(Bottleneck, 64, 16, 3, dilation=2)
      for i in range(len(layer)):
          assert layer[i].conv2.dilation == (2, 2)
      x = torch.randn(1, 64, 56, 56)
      x_out = layer(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
      # Test ResLayer of 3 Bottleneck with dilation=2, contract_dilation=True
      layer = ResLayer(Bottleneck, 64, 16, 3, dilation=2, contract_dilation=True)
      assert layer[0].conv2.dilation == (1, 1)
      for i in range(1, len(layer)):
          assert layer[i].conv2.dilation == (2, 2)
      x = torch.randn(1, 64, 56, 56)
      x_out = layer(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
      # Test ResLayer of 3 Bottleneck with dilation=2, multi_grid
      layer = ResLayer(Bottleneck, 64, 16, 3, dilation=2, multi_grid=(1, 2, 4))
      assert layer[0].conv2.dilation == (1, 1)
      assert layer[1].conv2.dilation == (2, 2)
      assert layer[2].conv2.dilation == (4, 4)
      x = torch.randn(1, 64, 56, 56)
      x_out = layer(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
  
  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. / 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)
          model.init_weights()
  
      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)
      model.init_weights()
      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.init_weights()
      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)
      model.init_weights()
      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, 'layer{}'.format(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)
      assert len(model.stem) == 9
      model.init_weights()
      model.train()
      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, 'layer{}'.format(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.init_weights()
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 64, 56, 56])
      assert feat[1].shape == torch.Size([1, 128, 28, 28])
      assert feat[2].shape == torch.Size([1, 256, 14, 14])
      assert feat[3].shape == torch.Size([1, 512, 7, 7])
  
      # Test ResNet50 with BatchNorm forward
      model = ResNet(50)
      for m in model.modules():
          if is_norm(m):
              assert isinstance(m, _BatchNorm)
      model.init_weights()
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 256, 56, 56])
      assert feat[1].shape == torch.Size([1, 512, 28, 28])
      assert feat[2].shape == torch.Size([1, 1024, 14, 14])
      assert feat[3].shape == torch.Size([1, 2048, 7, 7])
  
      # Test ResNet50 with layers 1, 2, 3 out forward
      model = ResNet(50, out_indices=(0, 1, 2))
      model.init_weights()
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 3
      assert feat[0].shape == torch.Size([1, 256, 56, 56])
      assert feat[1].shape == torch.Size([1, 512, 28, 28])
      assert feat[2].shape == torch.Size([1, 1024, 14, 14])
  
      # Test ResNet18 with checkpoint forward
      model = ResNet(18, with_cp=True)
      for m in model.modules():
          if is_block(m):
              assert m.with_cp
      model.init_weights()
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 64, 56, 56])
      assert feat[1].shape == torch.Size([1, 128, 28, 28])
      assert feat[2].shape == torch.Size([1, 256, 14, 14])
      assert feat[3].shape == torch.Size([1, 512, 7, 7])
  
      # Test ResNet50 with checkpoint forward
      model = ResNet(50, with_cp=True)
      for m in model.modules():
          if is_block(m):
              assert m.with_cp
      model.init_weights()
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 256, 56, 56])
      assert feat[1].shape == torch.Size([1, 512, 28, 28])
      assert feat[2].shape == torch.Size([1, 1024, 14, 14])
      assert feat[3].shape == torch.Size([1, 2048, 7, 7])
  
      # Test ResNet50 with GroupNorm forward
      model = ResNet(
          50, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))
      for m in model.modules():
          if is_norm(m):
              assert isinstance(m, GroupNorm)
      model.init_weights()
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 256, 56, 56])
      assert feat[1].shape == torch.Size([1, 512, 28, 28])
      assert feat[2].shape == torch.Size([1, 1024, 14, 14])
      assert feat[3].shape == torch.Size([1, 2048, 7, 7])
  
      # 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. / 16),
              stages=(False, True, True, False),
              position='after_conv3')
      ]
      model = ResNet(50, plugins=plugins)
      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 == 64
      for m in model.layer2.modules():
          if is_block(m):
              assert m.nonlocal_block.in_channels == 128
              assert m.gen_attention_block.in_channels == 128
              assert m.context_block.in_channels == 512
  
      for m in model.layer3.modules():
          if is_block(m):
              assert m.nonlocal_block.in_channels == 256
              assert m.gen_attention_block.in_channels == 256
              assert m.context_block.in_channels == 1024
  
      for m in model.layer4.modules():
          if is_block(m):
              assert m.nonlocal_block.in_channels == 512
              assert m.gen_attention_block.in_channels == 512
              assert not hasattr(m, 'context_block')
      model.init_weights()
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 256, 56, 56])
      assert feat[1].shape == torch.Size([1, 512, 28, 28])
      assert feat[2].shape == torch.Size([1, 1024, 14, 14])
      assert feat[3].shape == torch.Size([1, 2048, 7, 7])
  
      # Test ResNet50 with 1 ContextBlock after conv2, 1 ContextBlock after
      # conv3 in layers 2, 3, 4
      plugins = [
          dict(
              cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1),
              stages=(False, True, True, False),
              position='after_conv3'),
          dict(
              cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2),
              stages=(False, True, True, False),
              position='after_conv3')
      ]
  
      model = ResNet(50, plugins=plugins)
      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 == 512
              assert m.context_block2.in_channels == 512
  
      for m in model.layer3.modules():
          if is_block(m):
              assert not hasattr(m, 'context_block')
              assert m.context_block1.in_channels == 1024
              assert m.context_block2.in_channels == 1024
  
      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.init_weights()
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 256, 56, 56])
      assert feat[1].shape == torch.Size([1, 512, 28, 28])
      assert feat[2].shape == torch.Size([1, 1024, 14, 14])
      assert feat[3].shape == torch.Size([1, 2048, 7, 7])
  
      # Test ResNet50 zero initialization of residual
      model = ResNet(50, zero_init_residual=True)
      model.init_weights()
      for m in model.modules():
          if isinstance(m, Bottleneck):
              assert all_zeros(m.norm3)
          elif isinstance(m, BasicBlock):
              assert all_zeros(m.norm2)
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 256, 56, 56])
      assert feat[1].shape == torch.Size([1, 512, 28, 28])
      assert feat[2].shape == torch.Size([1, 1024, 14, 14])
      assert feat[3].shape == torch.Size([1, 2048, 7, 7])
  
      # Test ResNetV1d forward
      model = ResNetV1d(depth=50)
      model.init_weights()
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, 256, 56, 56])
      assert feat[1].shape == torch.Size([1, 512, 28, 28])
      assert feat[2].shape == torch.Size([1, 1024, 14, 14])
      assert feat[3].shape == torch.Size([1, 2048, 7, 7])
  