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])