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- # Copyright (c) OpenMMLab. All rights reserved.
- import pytest
- import torch
- from mmcv import assert_params_all_zeros
- 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.utils import ResLayer, SimplifiedBasicBlock
- from .utils import 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 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])
-
- # Test BasicBlock with checkpoint forward
- block = BasicBlock(64, 64, 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])
-
-
- 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_simplied_basic_block():
- with pytest.raises(AssertionError):
- # Not implemented yet.
- dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
- SimplifiedBasicBlock(64, 64, dcn=dcn)
-
- with pytest.raises(AssertionError):
- # Not implemented yet.
- plugins = [
- dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16),
- position='after_conv3')
- ]
- SimplifiedBasicBlock(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')
- ]
- SimplifiedBasicBlock(64, 64, plugins=plugins)
-
- with pytest.raises(AssertionError):
- # Not implemented yet
- SimplifiedBasicBlock(64, 64, with_cp=True)
-
- # test SimplifiedBasicBlock structure and forward
- block = SimplifiedBasicBlock(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])
-
- # test SimplifiedBasicBlock without norm
- block = SimplifiedBasicBlock(64, 64, norm_cfg=None)
- assert block.norm1 is None
- assert block.norm2 is None
- 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 BasicBlock with stride=2 and downsample_first=False
- layer = ResLayer(BasicBlock, 64, 64, 3, stride=2, downsample_first=False)
- assert layer[2].downsample[0].out_channels == 64
- assert layer[2].downsample[0].stride == (2, 2)
- for i in range(len(layer) - 1):
- 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, 28, 28])
-
-
- 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 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)
-
- 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. / 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. / 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, 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])
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