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Merge branch 'dev_mobilenetv2' into 'master' 

add mobilenetv2

See merge request open-mmlab/mmclassification!4
pull/2/head
chenkai 2020-06-16 14:37:03 +08:00
parent 9435eecd46 deee5d61d7
commit 3a5b25162e
7 changed files with 590 additions and 69 deletions
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13
mmcls/models/backbones/__init__.py
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@ -1,13 +1,10 @@
from .mobilenet_v2 import MobileNetV2
from .resnet import ResNet, ResNetV1d
from .resnext import ResNeXt
from .shufflenet_v1 import ShuffleNetv1
from .shufflenet_v2 import ShuffleNetv2
from .shufflenet_v1 import ShuffleNetV1
from .shufflenet_v2 import ShuffleNetV2
__all__ = [
'ResNet',
'ResNeXt',
'ResNetV1d',
'ResNetV1d',
'ShuffleNetv1',
'ShuffleNetv2',
'ResNet', 'ResNeXt', 'ResNetV1d', 'ResNetV1d', 'ShuffleNetV1',
'ShuffleNetV2', 'MobileNetV2'
]

265
mmcls/models/backbones/mobilenet_v2.py 100644
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@ -0,0 +1,265 @@
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule, constant_init, kaiming_init
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.utils import make_divisible
from ..builder import BACKBONES
from .base_backbone import BaseBackbone
class InvertedResidual(nn.Module):
"""InvertedResidual block for MobileNetV2.
Args:
inplanes (int): The input channels of the InvertedResidual block.
planes (int): The output channels of the InvertedResidual block.
stride (int): Stride of the middle (first) 3x3 convolution.
expand_ratio (int): adjusts number of channels of the hidden layer
in InvertedResidual by this amount.
conv_cfg (dict): Config dict for convolution layer.
Default: None, which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN').
act_cfg (dict): Config dict for activation layer.
Default: dict(type='ReLU6').
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed. Default: False.
Returns:
Tensor: The output tensor
"""
def __init__(self,
inplanes,
planes,
stride,
expand_ratio,
conv_cfg=None,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU6'),
with_cp=False):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2], f'stride must in [1, 2]. ' \
f'But received {stride}.'
self.with_cp = with_cp
self.use_res_connect = self.stride == 1 and inplanes == planes
hidden_dim = int(round(inplanes * expand_ratio))
layers = []
if expand_ratio != 1:
layers.append(
ConvModule(
in_channels=inplanes,
out_channels=hidden_dim,
kernel_size=3,
stride=1,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
layers.extend([
ConvModule(
in_channels=hidden_dim,
out_channels=hidden_dim,
kernel_size=3,
stride=stride,
padding=1,
groups=hidden_dim,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg),
ConvModule(
in_channels=hidden_dim,
out_channels=planes,
kernel_size=1,
stride=1,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None)
])
self.conv = nn.Sequential(*layers)
def forward(self, x):
def _inner_forward(x):
if self.use_res_connect:
return x + self.conv(x)
else:
return self.conv(x)
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
return out
@BACKBONES.register_module()
class MobileNetV2(BaseBackbone):
"""MobileNetV2 backbone.
Args:
widen_factor (float): Width multiplier, multiply number of
channels in each layer by this amount. Default: 1.0.
out_indices (None or Sequence[int]): Output from which stages.
Default: None
frozen_stages (int): Stages to be frozen (all param fixed).
Default: -1, which means not freezing any parameters.
conv_cfg (dict): Config dict for convolution layer.
Default: None, which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN').
act_cfg (dict): Config dict for activation layer.
Default: dict(type='ReLU6').
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only. Default: False.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed. Default: False.
"""
# Parameters to build layers. 4 parameters are needed to construct a
# layer, from left to right: expand_ratio, channel, num_blocks, stride.
arch_settings = [[1, 16, 1, 1], [6, 24, 2, 2], [6, 32, 3, 2],
[6, 64, 4, 2], [6, 96, 3, 1], [6, 160, 3, 2],
[6, 320, 1, 1]]
def __init__(self,
widen_factor=1.,
out_indices=None,
frozen_stages=-1,
conv_cfg=None,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU6'),
norm_eval=False,
with_cp=False):
super(MobileNetV2, self).__init__()
self.widen_factor = widen_factor
self.out_indices = out_indices
if out_indices is not None:
assert max(out_indices) < len(self.arch_settings)
self.frozen_stages = frozen_stages
assert frozen_stages < len(self.arch_settings)
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.norm_eval = norm_eval
self.with_cp = with_cp
self.inplanes = make_divisible(32 * widen_factor, 8)
self.conv1 = ConvModule(
in_channels=3,
out_channels=self.inplanes,
kernel_size=3,
stride=2,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.inverted_res_layers = []
for i, layer_cfg in enumerate(self.arch_settings):
expand_ratio, channel, num_blocks, stride = layer_cfg
planes = make_divisible(channel * widen_factor, 8)
inverted_res_layer = self.make_layer(
planes=planes,
num_blocks=num_blocks,
stride=stride,
expand_ratio=expand_ratio)
layer_name = f'layer{i + 1}'
self.add_module(layer_name, inverted_res_layer)
self.inverted_res_layers.append(layer_name)
if widen_factor > 1.0:
self.out_channel = int(1280 * widen_factor)
else:
self.out_channel = 1280
self.conv2 = ConvModule(
in_channels=self.inplanes,
out_channels=self.out_channel,
kernel_size=1,
stride=1,
padding=0,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
def make_layer(self, planes, num_blocks, stride, expand_ratio):
""" Stack InvertedResidual blocks to build a layer for MobileNetV2.
Args:
planes (int): planes of block.
num_blocks (int): number of blocks.
stride (int): stride of the first block. Default: 1
expand_ratio (int): Expand the number of channels of the
hidden layer in InvertedResidual by this ratio. Default: 6.
"""
layers = []
for i in range(num_blocks):
if i >= 1:
stride = 1
layers.append(
InvertedResidual(
self.inplanes,
planes,
stride,
expand_ratio=expand_ratio,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
with_cp=self.with_cp))
self.inplanes = planes
return nn.Sequential(*layers)
def init_weights(self, pretrained=None):
if pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
else:
raise TypeError('pretrained must be a str or None')
def forward(self, x):
x = self.conv1(x)
outs = []
for i, layer_name in enumerate(self.inverted_res_layers):
inverted_res_layer = getattr(self, layer_name)
x = inverted_res_layer(x)
if self.out_indices is not None and i in self.out_indices:
outs.append(x)
x = self.conv2(x)
if self.out_indices is None:
return x
else:
return tuple(outs)
def _freeze_stages(self):
if self.frozen_stages >= 0:
for param in self.conv1.parameters():
param.requires_grad = False
for i in range(1, self.frozen_stages + 1):
layer = getattr(self, f'layer{i}')
layer.eval()
for param in layer.parameters():
param.requires_grad = False
def train(self, mode=True):
super(MobileNetV2, self).train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval()

10
mmcls/models/backbones/shufflenet_v1.py
View File

@ -6,6 +6,7 @@ from mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer,
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.utils import channel_shuffle, make_divisible
from ..builder import BACKBONES
from .base_backbone import BaseBackbone
@ -139,8 +140,9 @@ class ShuffleUnit(nn.Module):
return out
class ShuffleNetv1(BaseBackbone):
"""ShuffleNetv1 backbone.
@BACKBONES.register_module()
class ShuffleNetV1(BaseBackbone):
"""ShuffleNetV1 backbone.
Args:
groups (int, optional): The number of groups to be used in grouped 1x1
@ -174,7 +176,7 @@ class ShuffleNetv1(BaseBackbone):
act_cfg=dict(type='ReLU'),
norm_eval=False,
with_cp=False):
super(ShuffleNetv1, self).__init__()
super(ShuffleNetV1, self).__init__()
self.stage_blocks = [3, 7, 3]
self.groups = groups
@ -294,7 +296,7 @@ class ShuffleNetv1(BaseBackbone):
return tuple(outs)
def train(self, mode=True):
super(ShuffleNetv1, self).train(mode)
super(ShuffleNetV1, self).train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():

10
mmcls/models/backbones/shufflenet_v2.py
View File

@ -5,6 +5,7 @@ from mmcv.cnn import ConvModule, constant_init, kaiming_init
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.utils import channel_shuffle
from ..builder import BACKBONES
from .base_backbone import BaseBackbone
@ -125,8 +126,9 @@ class InvertedResidual(nn.Module):
return out
class ShuffleNetv2(BaseBackbone):
"""ShuffleNetv2 backbone.
@BACKBONES.register_module()
class ShuffleNetV2(BaseBackbone):
"""ShuffleNetV2 backbone.
Args:
groups (int): The number of groups to be used in grouped 1x1
@ -160,7 +162,7 @@ class ShuffleNetv2(BaseBackbone):
act_cfg=dict(type='ReLU'),
norm_eval=False,
with_cp=False):
super(ShuffleNetv2, self).__init__()
super(ShuffleNetV2, self).__init__()
self.stage_blocks = [4, 8, 4]
self.groups = groups
self.out_indices = out_indices
@ -273,7 +275,7 @@ class ShuffleNetv2(BaseBackbone):
return tuple(outs)
def train(self, mode=True):
super(ShuffleNetv2, self).train(mode)
super(ShuffleNetV2, self).train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():

255
tests/test_backbones/test_mobilenet_v2.py 100644
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@ -0,0 +1,255 @@
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import MobileNetV2
from mmcls.models.backbones.mobilenet_v2 import InvertedResidual
def is_block(modules):
"""Check if is ResNet building block."""
if isinstance(modules, (InvertedResidual, )):
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 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_mobilenetv2_invertedresidual():
with pytest.raises(AssertionError):
# stride must be in [1, 2]
InvertedResidual(16, 24, stride=3, expand_ratio=6)
# Test InvertedResidual with checkpoint forward, stride=1
block = InvertedResidual(16, 24, stride=1, expand_ratio=6)
x = torch.randn(1, 16, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size((1, 24, 56, 56))
# Test InvertedResidual with expand_ratio=1
block = InvertedResidual(16, 16, stride=1, expand_ratio=1)
assert len(block.conv) == 2
# Test InvertedResidual with use_res_connect
block = InvertedResidual(16, 16, stride=1, expand_ratio=6)
x = torch.randn(1, 16, 56, 56)
x_out = block(x)
assert block.use_res_connect is True
assert x_out.shape == torch.Size((1, 16, 56, 56))
# Test InvertedResidual with checkpoint forward, stride=2
block = InvertedResidual(16, 24, stride=2, expand_ratio=6)
x = torch.randn(1, 16, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size((1, 24, 28, 28))
# Test InvertedResidual with checkpoint forward
block = InvertedResidual(16, 24, stride=1, expand_ratio=6, 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, 24, 56, 56))
# Test InvertedResidual with act_cfg=dict(type='ReLU')
block = InvertedResidual(
16, 24, stride=1, expand_ratio=6, act_cfg=dict(type='ReLU'))
x = torch.randn(1, 16, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size((1, 24, 56, 56))
def test_mobilenetv2_backbone():
with pytest.raises(TypeError):
# pretrained must be a string path
model = MobileNetV2()
model.init_weights(pretrained=0)
with pytest.raises(AssertionError):
# frozen_stages must less than 7
MobileNetV2(frozen_stages=8)
with pytest.raises(AssertionError):
# the max value in out_indices must less than 7
MobileNetV2(out_indices=[8])
# Test MobileNetV2 with first stage frozen
frozen_stages = 1
model = MobileNetV2(frozen_stages=frozen_stages)
model.init_weights()
model.train()
for mod in model.conv1.modules():
for param in mod.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 MobileNetV2 with norm_eval=True
model = MobileNetV2(norm_eval=True)
model.init_weights()
model.train()
assert check_norm_state(model.modules(), False)
# Test MobileNetV2 forward with widen_factor=1.0
model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 7))
model.init_weights()
model.train()
assert check_norm_state(model.modules(), True)
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 7
assert feat[0].shape == torch.Size((1, 16, 112, 112))
assert feat[1].shape == torch.Size((1, 24, 56, 56))
assert feat[2].shape == torch.Size((1, 32, 28, 28))
assert feat[3].shape == torch.Size((1, 64, 14, 14))
assert feat[4].shape == torch.Size((1, 96, 14, 14))
assert feat[5].shape == torch.Size((1, 160, 7, 7))
assert feat[6].shape == torch.Size((1, 320, 7, 7))
# Test MobileNetV2 forward with widen_factor=0.5
model = MobileNetV2(widen_factor=0.5, out_indices=range(0, 7))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 7
assert feat[0].shape == torch.Size((1, 8, 112, 112))
assert feat[1].shape == torch.Size((1, 16, 56, 56))
assert feat[2].shape == torch.Size((1, 16, 28, 28))
assert feat[3].shape == torch.Size((1, 32, 14, 14))
assert feat[4].shape == torch.Size((1, 48, 14, 14))
assert feat[5].shape == torch.Size((1, 80, 7, 7))
assert feat[6].shape == torch.Size((1, 160, 7, 7))
# Test MobileNetV2 forward with widen_factor=2.0
model = MobileNetV2(widen_factor=2.0, out_indices=None)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert feat.shape == torch.Size((1, 2560, 7, 7))
# Test MobileNetV2 forward with out_indices=None
model = MobileNetV2(widen_factor=1.0, out_indices=None)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert feat.shape == torch.Size((1, 1280, 7, 7))
# Test MobileNetV2 forward with dict(type='ReLU')
model = MobileNetV2(
widen_factor=1.0, act_cfg=dict(type='ReLU'), out_indices=range(0, 7))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 7
assert feat[0].shape == torch.Size((1, 16, 112, 112))
assert feat[1].shape == torch.Size((1, 24, 56, 56))
assert feat[2].shape == torch.Size((1, 32, 28, 28))
assert feat[3].shape == torch.Size((1, 64, 14, 14))
assert feat[4].shape == torch.Size((1, 96, 14, 14))
assert feat[5].shape == torch.Size((1, 160, 7, 7))
assert feat[6].shape == torch.Size((1, 320, 7, 7))
# Test MobileNetV2 with GroupNorm forward
model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 7))
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) == 7
assert feat[0].shape == torch.Size((1, 16, 112, 112))
assert feat[1].shape == torch.Size((1, 24, 56, 56))
assert feat[2].shape == torch.Size((1, 32, 28, 28))
assert feat[3].shape == torch.Size((1, 64, 14, 14))
assert feat[4].shape == torch.Size((1, 96, 14, 14))
assert feat[5].shape == torch.Size((1, 160, 7, 7))
assert feat[6].shape == torch.Size((1, 320, 7, 7))
# Test MobileNetV2 with BatchNorm forward
model = MobileNetV2(
widen_factor=1.0,
norm_cfg=dict(type='GN', num_groups=2, requires_grad=True),
out_indices=range(0, 7))
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) == 7
assert feat[0].shape == torch.Size((1, 16, 112, 112))
assert feat[1].shape == torch.Size((1, 24, 56, 56))
assert feat[2].shape == torch.Size((1, 32, 28, 28))
assert feat[3].shape == torch.Size((1, 64, 14, 14))
assert feat[4].shape == torch.Size((1, 96, 14, 14))
assert feat[5].shape == torch.Size((1, 160, 7, 7))
assert feat[6].shape == torch.Size((1, 320, 7, 7))
# Test MobileNetV2 with layers 1, 3, 5 out forward
model = MobileNetV2(widen_factor=1.0, out_indices=(0, 2, 4))
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, 16, 112, 112))
assert feat[1].shape == torch.Size((1, 32, 28, 28))
assert feat[2].shape == torch.Size((1, 96, 14, 14))
# Test MobileNetV2 with checkpoint forward
model = MobileNetV2(
widen_factor=1.0, with_cp=True, out_indices=range(0, 7))
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) == 7
assert feat[0].shape == torch.Size((1, 16, 112, 112))
assert feat[1].shape == torch.Size((1, 24, 56, 56))
assert feat[2].shape == torch.Size((1, 32, 28, 28))
assert feat[3].shape == torch.Size((1, 64, 14, 14))
assert feat[4].shape == torch.Size((1, 96, 14, 14))
assert feat[5].shape == torch.Size((1, 160, 7, 7))
assert feat[6].shape == torch.Size((1, 320, 7, 7))

58
tests/test_backbones/test_shufflenet_v1.py
View File

@ -3,7 +3,7 @@ import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import ShuffleNetv1
from mmcls.models.backbones import ShuffleNetV1
from mmcls.models.backbones.shufflenet_v1 import ShuffleUnit
@ -66,30 +66,30 @@ def test_shufflenetv1_backbone():
with pytest.raises(ValueError):
# frozen_stages must be in range(-1, 4)
ShuffleNetv1(frozen_stages=10)
ShuffleNetV1(frozen_stages=10)
with pytest.raises(ValueError):
# the item in out_indices must be in range(0, 4)
ShuffleNetv1(out_indices=[5])
ShuffleNetV1(out_indices=[5])
with pytest.raises(ValueError):
# groups must be in [1, 2, 3, 4, 8]
ShuffleNetv1(groups=10)
ShuffleNetV1(groups=10)
with pytest.raises(TypeError):
# pretrained must be str or None
model = ShuffleNetv1()
model = ShuffleNetV1()
model.init_weights(pretrained=1)
# Test ShuffleNetv1 norm state
model = ShuffleNetv1()
# Test ShuffleNetV1 norm state
model = ShuffleNetV1()
model.init_weights()
model.train()
assert check_norm_state(model.modules(), True)
# Test ShuffleNetv1 with first stage frozen
# Test ShuffleNetV1 with first stage frozen
frozen_stages = 1
model = ShuffleNetv1(frozen_stages=frozen_stages)
model = ShuffleNetV1(frozen_stages=frozen_stages)
model.init_weights()
model.train()
for param in model.conv1.parameters():
@ -102,8 +102,8 @@ def test_shufflenetv1_backbone():
for param in layer.parameters():
assert param.requires_grad is False
# Test ShuffleNetv1 forward with groups=1
model = ShuffleNetv1(groups=1)
# Test ShuffleNetV1 forward with groups=1
model = ShuffleNetV1(groups=1)
model.init_weights()
model.train()
@ -118,8 +118,8 @@ def test_shufflenetv1_backbone():
assert feat[1].shape == torch.Size((1, 288, 14, 14))
assert feat[2].shape == torch.Size((1, 576, 7, 7))
# Test ShuffleNetv1 forward with groups=2
model = ShuffleNetv1(groups=2)
# Test ShuffleNetV1 forward with groups=2
model = ShuffleNetV1(groups=2)
model.init_weights()
model.train()
@ -134,8 +134,8 @@ def test_shufflenetv1_backbone():
assert feat[1].shape == torch.Size((1, 400, 14, 14))
assert feat[2].shape == torch.Size((1, 800, 7, 7))
# Test ShuffleNetv1 forward with groups=3
model = ShuffleNetv1(groups=3)
# Test ShuffleNetV1 forward with groups=3
model = ShuffleNetV1(groups=3)
model.init_weights()
model.train()
@ -150,8 +150,8 @@ def test_shufflenetv1_backbone():
assert feat[1].shape == torch.Size((1, 480, 14, 14))
assert feat[2].shape == torch.Size((1, 960, 7, 7))
# Test ShuffleNetv1 forward with groups=4
model = ShuffleNetv1(groups=4)
# Test ShuffleNetV1 forward with groups=4
model = ShuffleNetV1(groups=4)
model.init_weights()
model.train()
@ -166,8 +166,8 @@ def test_shufflenetv1_backbone():
assert feat[1].shape == torch.Size((1, 544, 14, 14))
assert feat[2].shape == torch.Size((1, 1088, 7, 7))
# Test ShuffleNetv1 forward with groups=8
model = ShuffleNetv1(groups=8)
# Test ShuffleNetV1 forward with groups=8
model = ShuffleNetV1(groups=8)
model.init_weights()
model.train()
@ -182,8 +182,8 @@ def test_shufflenetv1_backbone():
assert feat[1].shape == torch.Size((1, 768, 14, 14))
assert feat[2].shape == torch.Size((1, 1536, 7, 7))
# Test ShuffleNetv1 forward with GroupNorm forward
model = ShuffleNetv1(
# Test ShuffleNetV1 forward with GroupNorm forward
model = ShuffleNetV1(
groups=3, norm_cfg=dict(type='GN', num_groups=2, requires_grad=True))
model.init_weights()
model.train()
@ -199,8 +199,8 @@ def test_shufflenetv1_backbone():
assert feat[1].shape == torch.Size((1, 480, 14, 14))
assert feat[2].shape == torch.Size((1, 960, 7, 7))
# Test ShuffleNetv1 forward with layers 1, 2 forward
model = ShuffleNetv1(groups=3, out_indices=(1, 2))
# Test ShuffleNetV1 forward with layers 1, 2 forward
model = ShuffleNetV1(groups=3, out_indices=(1, 2))
model.init_weights()
model.train()
@ -214,8 +214,8 @@ def test_shufflenetv1_backbone():
assert feat[0].shape == torch.Size((1, 480, 14, 14))
assert feat[1].shape == torch.Size((1, 960, 7, 7))
# Test ShuffleNetv1 forward with layers 2 forward
model = ShuffleNetv1(groups=3, out_indices=(2, ))
# Test ShuffleNetV1 forward with layers 2 forward
model = ShuffleNetV1(groups=3, out_indices=(2, ))
model.init_weights()
model.train()
@ -228,14 +228,14 @@ def test_shufflenetv1_backbone():
assert isinstance(feat, torch.Tensor)
assert feat.shape == torch.Size((1, 960, 7, 7))
# Test ShuffleNetv1 forward with checkpoint forward
model = ShuffleNetv1(groups=3, with_cp=True)
# Test ShuffleNetV1 forward with checkpoint forward
model = ShuffleNetV1(groups=3, with_cp=True)
for m in model.modules():
if is_block(m):
assert m.with_cp
# Test ShuffleNetv1 with norm_eval
model = ShuffleNetv1(norm_eval=True)
# Test ShuffleNetV1 with norm_eval
model = ShuffleNetV1(norm_eval=True)
model.init_weights()
model.train()

48
tests/test_backbones/test_shufflenet_v2.py
View File

@ -3,7 +3,7 @@ import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import ShuffleNetv2
from mmcls.models.backbones import ShuffleNetV2
from mmcls.models.backbones.shufflenet_v2 import InvertedResidual
@ -59,26 +59,26 @@ def test_shufflenetv2_backbone():
with pytest.raises(ValueError):
# groups must be in 0.5, 1.0, 1.5, 2.0]
ShuffleNetv2(widen_factor=3.0)
ShuffleNetV2(widen_factor=3.0)
with pytest.raises(AssertionError):
# frozen_stages must be in [0, 1, 2]
ShuffleNetv2(widen_factor=3.0, frozen_stages=3)
ShuffleNetV2(widen_factor=3.0, frozen_stages=3)
with pytest.raises(TypeError):
# pretrained must be str or None
model = ShuffleNetv2()
model = ShuffleNetV2()
model.init_weights(pretrained=1)
# Test ShuffleNetv2 norm state
model = ShuffleNetv2()
# Test ShuffleNetV2 norm state
model = ShuffleNetV2()
model.init_weights()
model.train()
assert check_norm_state(model.modules(), True)
# Test ShuffleNetv2 with first stage frozen
# Test ShuffleNetV2 with first stage frozen
frozen_stages = 1
model = ShuffleNetv2(frozen_stages=frozen_stages)
model = ShuffleNetV2(frozen_stages=frozen_stages)
model.init_weights()
model.train()
for param in model.conv1.parameters():
@ -91,15 +91,15 @@ def test_shufflenetv2_backbone():
for param in layer.parameters():
assert param.requires_grad is False
# Test ShuffleNetv2 with norm_eval
model = ShuffleNetv2(norm_eval=True)
# Test ShuffleNetV2 with norm_eval
model = ShuffleNetV2(norm_eval=True)
model.init_weights()
model.train()
assert check_norm_state(model.modules(), False)
# Test ShuffleNetv2 forward with widen_factor=0.5
model = ShuffleNetv2(widen_factor=0.5)
# Test ShuffleNetV2 forward with widen_factor=0.5
model = ShuffleNetV2(widen_factor=0.5)
model.init_weights()
model.train()
@ -114,8 +114,8 @@ def test_shufflenetv2_backbone():
assert feat[1].shape == torch.Size((1, 96, 14, 14))
assert feat[2].shape == torch.Size((1, 192, 7, 7))
# Test ShuffleNetv2 forward with widen_factor=1.0
model = ShuffleNetv2(widen_factor=1.0)
# Test ShuffleNetV2 forward with widen_factor=1.0
model = ShuffleNetV2(widen_factor=1.0)
model.init_weights()
model.train()
@ -130,8 +130,8 @@ def test_shufflenetv2_backbone():
assert feat[1].shape == torch.Size((1, 232, 14, 14))
assert feat[2].shape == torch.Size((1, 464, 7, 7))
# Test ShuffleNetv2 forward with widen_factor=1.5
model = ShuffleNetv2(widen_factor=1.5)
# Test ShuffleNetV2 forward with widen_factor=1.5
model = ShuffleNetV2(widen_factor=1.5)
model.init_weights()
model.train()
@ -146,8 +146,8 @@ def test_shufflenetv2_backbone():
assert feat[1].shape == torch.Size((1, 352, 14, 14))
assert feat[2].shape == torch.Size((1, 704, 7, 7))
# Test ShuffleNetv2 forward with widen_factor=2.0
model = ShuffleNetv2(widen_factor=2.0)
# Test ShuffleNetV2 forward with widen_factor=2.0
model = ShuffleNetV2(widen_factor=2.0)
model.init_weights()
model.train()
@ -162,8 +162,8 @@ def test_shufflenetv2_backbone():
assert feat[1].shape == torch.Size((1, 488, 14, 14))
assert feat[2].shape == torch.Size((1, 976, 7, 7))
# Test ShuffleNetv2 forward with layers 3 forward
model = ShuffleNetv2(widen_factor=1.0, out_indices=(2, ))
# Test ShuffleNetV2 forward with layers 3 forward
model = ShuffleNetV2(widen_factor=1.0, out_indices=(2, ))
model.init_weights()
model.train()
@ -176,8 +176,8 @@ def test_shufflenetv2_backbone():
assert isinstance(feat, torch.Tensor)
assert feat.shape == torch.Size((1, 464, 7, 7))
# Test ShuffleNetv2 forward with layers 1 2 forward
model = ShuffleNetv2(widen_factor=1.0, out_indices=(1, 2))
# Test ShuffleNetV2 forward with layers 1 2 forward
model = ShuffleNetV2(widen_factor=1.0, out_indices=(1, 2))
model.init_weights()
model.train()
@ -191,8 +191,8 @@ def test_shufflenetv2_backbone():
assert feat[0].shape == torch.Size((1, 232, 14, 14))
assert feat[1].shape == torch.Size((1, 464, 7, 7))
# Test ShuffleNetv2 forward with checkpoint forward
model = ShuffleNetv2(widen_factor=1.0, with_cp=True)
# Test ShuffleNetV2 forward with checkpoint forward
model = ShuffleNetV2(widen_factor=1.0, with_cp=True)
for m in model.modules():
if is_block(m):
assert m.with_cp