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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 (CGNet, FastSCNN, MobileNetV3, ResNeSt,
ResNet, ResNetV1d, ResNeXt)
from mmseg.models.backbones.cgnet import (ContextGuidedBlock,
GlobalContextExtractor)
from mmseg.models.backbones.resnest import Bottleneck as BottleneckS
from mmseg.models.backbones.resnet import BasicBlock, Bottleneck
from mmseg.models.backbones.resnext import Bottleneck as BottleneckX
from mmseg.models.utils import ResLayer
def is_block(modules):
"""Check if is ResNet building block."""
if isinstance(modules, (BasicBlock, Bottleneck, BottleneckX)):
return True
return False
def is_norm(modules):
"""Check if is one of the norms."""
if isinstance(modules, (GroupNorm, _BatchNorm)):
return True
return False
def all_zeros(modules):
"""Check if the weight(and bias) is all zero."""
weight_zero = torch.allclose(modules.weight.data,
torch.zeros_like(modules.weight.data))
if hasattr(modules, 'bias'):
bias_zero = torch.allclose(modules.bias.data,
torch.zeros_like(modules.bias.data))
else:
bias_zero = True
return weight_zero and bias_zero
def check_norm_state(modules, train_state):
"""Check if norm layer is in correct train state."""
for mod in modules:
if isinstance(mod, _BatchNorm):
if mod.training != train_state:
return False
return True
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)
model.init_weights(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)
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)
model.init_weights('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)
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])
def test_renext_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
BottleneckX(64, 64, groups=32, base_width=4, style='tensorflow')
# Test ResNeXt Bottleneck structure
block = BottleneckX(
64, 64, groups=32, base_width=4, stride=2, style='pytorch')
assert block.conv2.stride == (2, 2)
assert block.conv2.groups == 32
assert block.conv2.out_channels == 128
# Test ResNeXt Bottleneck with DCN
dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
with pytest.raises(AssertionError):
# conv_cfg must be None if dcn is not None
BottleneckX(
64,
64,
groups=32,
base_width=4,
dcn=dcn,
conv_cfg=dict(type='Conv'))
BottleneckX(64, 64, dcn=dcn)
# Test ResNeXt Bottleneck forward
block = BottleneckX(64, 16, groups=32, base_width=4)
x = torch.randn(1, 64, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size([1, 64, 56, 56])
def test_resnext_backbone():
with pytest.raises(KeyError):
# ResNeXt depth should be in [50, 101, 152]
ResNeXt(depth=18)
# Test ResNeXt with group 32, base_width 4
model = ResNeXt(depth=50, groups=32, base_width=4)
print(model)
for m in model.modules():
if is_block(m):
assert m.conv2.groups == 32
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])
def test_fastscnn_backbone():
with pytest.raises(AssertionError):
# Fast-SCNN channel constraints.
FastSCNN(
3, (32, 48),
64, (64, 96, 128), (2, 2, 1),
global_out_channels=127,
higher_in_channels=64,
lower_in_channels=128)
# Test FastSCNN Standard Forward
model = FastSCNN()
model.init_weights()
model.train()
batch_size = 4
imgs = torch.randn(batch_size, 3, 512, 1024)
feat = model(imgs)
assert len(feat) == 3
# higher-res
assert feat[0].shape == torch.Size([batch_size, 64, 64, 128])
# lower-res
assert feat[1].shape == torch.Size([batch_size, 128, 16, 32])
# FFM output
assert feat[2].shape == torch.Size([batch_size, 128, 64, 128])
def test_resnest_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
BottleneckS(64, 64, radix=2, reduction_factor=4, style='tensorflow')
# Test ResNeSt Bottleneck structure
block = BottleneckS(
64, 256, radix=2, reduction_factor=4, stride=2, style='pytorch')
assert block.avd_layer.stride == 2
assert block.conv2.channels == 256
# Test ResNeSt Bottleneck forward
block = BottleneckS(64, 16, radix=2, reduction_factor=4)
x = torch.randn(2, 64, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size([2, 64, 56, 56])
def test_resnest_backbone():
with pytest.raises(KeyError):
# ResNeSt depth should be in [50, 101, 152, 200]
ResNeSt(depth=18)
# Test ResNeSt with radix 2, reduction_factor 4
model = ResNeSt(
depth=50, radix=2, reduction_factor=4, out_indices=(0, 1, 2, 3))
model.init_weights()
model.train()
imgs = torch.randn(2, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == torch.Size([2, 256, 56, 56])
assert feat[1].shape == torch.Size([2, 512, 28, 28])
assert feat[2].shape == torch.Size([2, 1024, 14, 14])
assert feat[3].shape == torch.Size([2, 2048, 7, 7])
def test_cgnet_GlobalContextExtractor():
block = GlobalContextExtractor(16, 16, with_cp=True)
x = torch.randn(2, 16, 64, 64, requires_grad=True)
x_out = block(x)
assert x_out.shape == torch.Size([2, 16, 64, 64])
def test_cgnet_context_guided_block():
with pytest.raises(AssertionError):
# cgnet ContextGuidedBlock GlobalContextExtractor channel and reduction
# constraints.
ContextGuidedBlock(8, 8)
# test cgnet ContextGuidedBlock with checkpoint forward
block = ContextGuidedBlock(
16, 16, act_cfg=dict(type='PReLU'), with_cp=True)
assert block.with_cp
x = torch.randn(2, 16, 64, 64, requires_grad=True)
x_out = block(x)
assert x_out.shape == torch.Size([2, 16, 64, 64])
# test cgnet ContextGuidedBlock without checkpoint forward
block = ContextGuidedBlock(32, 32)
assert not block.with_cp
x = torch.randn(3, 32, 32, 32)
x_out = block(x)
assert x_out.shape == torch.Size([3, 32, 32, 32])
# test cgnet ContextGuidedBlock with down sampling
block = ContextGuidedBlock(32, 32, downsample=True)
assert block.conv1x1.conv.in_channels == 32
assert block.conv1x1.conv.out_channels == 32
assert block.conv1x1.conv.kernel_size == (3, 3)
assert block.conv1x1.conv.stride == (2, 2)
assert block.conv1x1.conv.padding == (1, 1)
assert block.f_loc.in_channels == 32
assert block.f_loc.out_channels == 32
assert block.f_loc.kernel_size == (3, 3)
assert block.f_loc.stride == (1, 1)
assert block.f_loc.padding == (1, 1)
assert block.f_loc.groups == 32
assert block.f_loc.dilation == (1, 1)
assert block.f_loc.bias is None
assert block.f_sur.in_channels == 32
assert block.f_sur.out_channels == 32
assert block.f_sur.kernel_size == (3, 3)
assert block.f_sur.stride == (1, 1)
assert block.f_sur.padding == (2, 2)
assert block.f_sur.groups == 32
assert block.f_sur.dilation == (2, 2)
assert block.f_sur.bias is None
assert block.bottleneck.in_channels == 64
assert block.bottleneck.out_channels == 32
assert block.bottleneck.kernel_size == (1, 1)
assert block.bottleneck.stride == (1, 1)
assert block.bottleneck.bias is None
x = torch.randn(1, 32, 32, 32)
x_out = block(x)
assert x_out.shape == torch.Size([1, 32, 16, 16])
# test cgnet ContextGuidedBlock without down sampling
block = ContextGuidedBlock(32, 32, downsample=False)
assert block.conv1x1.conv.in_channels == 32
assert block.conv1x1.conv.out_channels == 16
assert block.conv1x1.conv.kernel_size == (1, 1)
assert block.conv1x1.conv.stride == (1, 1)
assert block.conv1x1.conv.padding == (0, 0)
assert block.f_loc.in_channels == 16
assert block.f_loc.out_channels == 16
assert block.f_loc.kernel_size == (3, 3)
assert block.f_loc.stride == (1, 1)
assert block.f_loc.padding == (1, 1)
assert block.f_loc.groups == 16
assert block.f_loc.dilation == (1, 1)
assert block.f_loc.bias is None
assert block.f_sur.in_channels == 16
assert block.f_sur.out_channels == 16
assert block.f_sur.kernel_size == (3, 3)
assert block.f_sur.stride == (1, 1)
assert block.f_sur.padding == (2, 2)
assert block.f_sur.groups == 16
assert block.f_sur.dilation == (2, 2)
assert block.f_sur.bias is None
x = torch.randn(1, 32, 32, 32)
x_out = block(x)
assert x_out.shape == torch.Size([1, 32, 32, 32])
def test_cgnet_backbone():
with pytest.raises(AssertionError):
# check invalid num_channels
CGNet(num_channels=(32, 64, 128, 256))
with pytest.raises(AssertionError):
# check invalid num_blocks
CGNet(num_blocks=(3, 21, 3))
with pytest.raises(AssertionError):
# check invalid dilation
CGNet(num_blocks=2)
with pytest.raises(AssertionError):
# check invalid reduction
CGNet(reductions=16)
with pytest.raises(AssertionError):
# check invalid num_channels and reduction
CGNet(num_channels=(32, 64, 128), reductions=(64, 129))
# Test CGNet with default settings
model = CGNet()
model.init_weights()
model.train()
imgs = torch.randn(2, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 3
assert feat[0].shape == torch.Size([2, 35, 112, 112])
assert feat[1].shape == torch.Size([2, 131, 56, 56])
assert feat[2].shape == torch.Size([2, 256, 28, 28])
# Test CGNet with norm_eval True and with_cp True
model = CGNet(norm_eval=True, with_cp=True)
with pytest.raises(TypeError):
# check invalid pretrained
model.init_weights(pretrained=8)
model.init_weights()
model.train()
imgs = torch.randn(2, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 3
assert feat[0].shape == torch.Size([2, 35, 112, 112])
assert feat[1].shape == torch.Size([2, 131, 56, 56])
assert feat[2].shape == torch.Size([2, 256, 28, 28])
def test_mobilenet_v3():
with pytest.raises(AssertionError):
# check invalid arch
MobileNetV3('big')
with pytest.raises(AssertionError):
# check invalid reduction_factor
MobileNetV3(reduction_factor=0)
with pytest.raises(ValueError):
# check invalid out_indices
MobileNetV3(out_indices=(0, 1, 15))
with pytest.raises(ValueError):
# check invalid frozen_stages
MobileNetV3(frozen_stages=15)
with pytest.raises(TypeError):
# check invalid pretrained
model = MobileNetV3()
model.init_weights(pretrained=8)
# Test MobileNetV3 with default settings
model = MobileNetV3()
model.init_weights()
model.train()
imgs = torch.randn(2, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 3
assert feat[0].shape == (2, 16, 112, 112)
assert feat[1].shape == (2, 16, 56, 56)
assert feat[2].shape == (2, 576, 28, 28)
# Test MobileNetV3 with arch = 'large'
model = MobileNetV3(arch='large', out_indices=(1, 3, 16))
model.init_weights()
model.train()
imgs = torch.randn(2, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 3
assert feat[0].shape == (2, 16, 112, 112)
assert feat[1].shape == (2, 24, 56, 56)
assert feat[2].shape == (2, 960, 28, 28)
# Test MobileNetV3 with norm_eval True, with_cp True and frozen_stages=5
model = MobileNetV3(norm_eval=True, with_cp=True, frozen_stages=5)
with pytest.raises(TypeError):
# check invalid pretrained
model.init_weights(pretrained=8)
model.init_weights()
model.train()
imgs = torch.randn(2, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 3
assert feat[0].shape == (2, 16, 112, 112)
assert feat[1].shape == (2, 16, 56, 56)
assert feat[2].shape == (2, 576, 28, 28)
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