Merge branch 'dev_shufflenetv1' into dev_shufflenetv2
lixiaojie
commit
e1f39f003e
|
|
@ -0,0 +1,3 @@
|
|||
from .shufflenet_v1 import ShuffleNetv1
|
||||
|
||||
__all__ = ['ShuffleNetv1']
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
import logging
|
||||
from abc import ABCMeta, abstractmethod
|
||||
|
||||
import torch.nn as nn
|
||||
from mmcv.runner import load_checkpoint
|
||||
|
||||
|
||||
class BaseBackbone(nn.Module, metaclass=ABCMeta):
|
||||
|
||||
def __init__(self):
|
||||
super(BaseBackbone, self).__init__()
|
||||
|
||||
def init_weights(self, pretrained=None):
|
||||
if isinstance(pretrained, str):
|
||||
logger = logging.getLogger()
|
||||
load_checkpoint(self, pretrained, strict=False, logger=logger)
|
||||
elif pretrained is None:
|
||||
pass
|
||||
else:
|
||||
raise TypeError('pretrained must be a str or None')
|
||||
|
||||
@abstractmethod
|
||||
def forward(self, x):
|
||||
pass
|
||||
|
||||
def train(self, mode=True):
|
||||
super(BaseBackbone, self).train(mode)
|
||||
|
|
@ -0,0 +1,317 @@
|
|||
import logging
|
||||
from collections import OrderedDict
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.utils.checkpoint as cp
|
||||
from mmcv.runner import load_checkpoint
|
||||
|
||||
from .base_backbone import BaseBackbone
|
||||
from .weight_init import constant_init, kaiming_init
|
||||
|
||||
|
||||
def conv3x3(inplanes, planes, stride=1, padding=1, bias=False, groups=1):
|
||||
"""3x3 convolution with padding
|
||||
"""
|
||||
return nn.Conv2d(
|
||||
inplanes,
|
||||
planes,
|
||||
kernel_size=3,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
bias=bias,
|
||||
groups=groups)
|
||||
|
||||
|
||||
def conv1x1(inplanes, planes, groups=1):
|
||||
"""1x1 convolution with padding
|
||||
- Normal pointwise convolution when groups == 1
|
||||
- Grouped pointwise convolution when groups > 1
|
||||
"""
|
||||
return nn.Conv2d(inplanes, planes, kernel_size=1, groups=groups, stride=1)
|
||||
|
||||
|
||||
def channel_shuffle(x, groups):
|
||||
batchsize, num_channels, height, width = x.data.size()
|
||||
assert (num_channels % groups == 0)
|
||||
channels_per_group = num_channels // groups
|
||||
|
||||
# reshape
|
||||
x = x.view(batchsize, groups, channels_per_group, height, width)
|
||||
|
||||
# transpose
|
||||
# - contiguous() required if transpose() is used before view().
|
||||
# See https://github.com/pytorch/pytorch/issues/764
|
||||
x = torch.transpose(x, 1, 2).contiguous()
|
||||
|
||||
# flatten
|
||||
x = x.view(batchsize, -1, height, width)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def _make_divisible(v, divisor, min_value=None):
|
||||
if min_value is None:
|
||||
min_value = divisor
|
||||
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
||||
# Make sure that round down does not go down by more than 10%.
|
||||
if new_v < 0.9 * v:
|
||||
new_v += divisor
|
||||
return new_v
|
||||
|
||||
|
||||
class ShuffleUnit(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
inplanes,
|
||||
planes,
|
||||
groups=3,
|
||||
first_block=True,
|
||||
combine='add',
|
||||
with_cp=False):
|
||||
|
||||
super(ShuffleUnit, self).__init__()
|
||||
self.inplanes = inplanes
|
||||
self.planes = planes
|
||||
self.first_block = first_block
|
||||
self.combine = combine
|
||||
self.groups = groups
|
||||
self.bottleneck_channels = self.planes // 4
|
||||
self.with_cp = with_cp
|
||||
|
||||
if self.combine == 'add':
|
||||
self.depthwise_stride = 1
|
||||
self._combine_func = self._add
|
||||
elif self.combine == 'concat':
|
||||
self.depthwise_stride = 2
|
||||
self._combine_func = self._concat
|
||||
self.planes -= self.inplanes
|
||||
else:
|
||||
raise ValueError("Cannot combine tensors with \"{}\" "
|
||||
"Only \"add\" and \"concat\" are "
|
||||
"supported".format(self.combine))
|
||||
|
||||
if combine == 'add':
|
||||
assert inplanes == planes, \
|
||||
'inplanes must be equal to outplanes when combine is add'
|
||||
|
||||
self.first_1x1_groups = self.groups if first_block else 1
|
||||
self.g_conv_1x1_compress = self._make_grouped_conv1x1(
|
||||
self.inplanes,
|
||||
self.bottleneck_channels,
|
||||
self.first_1x1_groups,
|
||||
batch_norm=True,
|
||||
relu=True)
|
||||
|
||||
self.depthwise_conv3x3 = conv3x3(
|
||||
self.bottleneck_channels,
|
||||
self.bottleneck_channels,
|
||||
stride=self.depthwise_stride,
|
||||
groups=self.bottleneck_channels)
|
||||
self.bn_after_depthwise = \
|
||||
nn.BatchNorm2d(self.bottleneck_channels)
|
||||
|
||||
self.g_conv_1x1_expand = \
|
||||
self._make_grouped_conv1x1(self.bottleneck_channels,
|
||||
self.planes,
|
||||
self.groups,
|
||||
batch_norm=True,
|
||||
relu=False)
|
||||
self.avgpool = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
|
||||
@staticmethod
|
||||
def _add(x, out):
|
||||
# residual connection
|
||||
return x + out
|
||||
|
||||
@staticmethod
|
||||
def _concat(x, out):
|
||||
# concatenate along channel axis
|
||||
return torch.cat((x, out), 1)
|
||||
|
||||
@staticmethod
|
||||
def _make_grouped_conv1x1(inplanes,
|
||||
planes,
|
||||
groups,
|
||||
batch_norm=True,
|
||||
relu=False):
|
||||
|
||||
modules = OrderedDict()
|
||||
|
||||
conv = conv1x1(inplanes, planes, groups=groups)
|
||||
modules['conv1x1'] = conv
|
||||
|
||||
if batch_norm:
|
||||
modules['batch_norm'] = nn.BatchNorm2d(planes)
|
||||
if relu:
|
||||
modules['relu'] = nn.ReLU()
|
||||
if len(modules) > 1:
|
||||
return nn.Sequential(modules)
|
||||
else:
|
||||
return conv
|
||||
|
||||
def forward(self, x):
|
||||
|
||||
def _inner_forward(x):
|
||||
residual = x
|
||||
|
||||
if self.combine == 'concat':
|
||||
residual = self.avgpool(residual)
|
||||
|
||||
out = self.g_conv_1x1_compress(x)
|
||||
out = channel_shuffle(out, self.groups)
|
||||
out = self.depthwise_conv3x3(out)
|
||||
out = self.bn_after_depthwise(out)
|
||||
out = self.g_conv_1x1_expand(out)
|
||||
|
||||
out = self._combine_func(residual, out)
|
||||
|
||||
return out
|
||||
|
||||
if self.with_cp and x.requires_grad:
|
||||
out = cp.checkpoint(_inner_forward, x)
|
||||
else:
|
||||
out = _inner_forward(x)
|
||||
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class ShuffleNetv1(BaseBackbone):
|
||||
"""ShuffleNetv1 backbone.
|
||||
|
||||
Args:
|
||||
groups (int): number of groups to be used in grouped
|
||||
1x1 convolutions in each ShuffleUnit. Default is 3 for best
|
||||
performance according to original paper.
|
||||
widen_factor (float): Config of widen_factor.
|
||||
out_indices (Sequence[int]): Output from which stages.
|
||||
frozen_stages (int): Stages to be frozen (all param fixed). -1 means
|
||||
not freezing any parameters.
|
||||
bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze
|
||||
running stats (mean and var).
|
||||
bn_frozen (bool): Whether to freeze weight and bias of BN layers.
|
||||
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
|
||||
memory while slowing down the training speed.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
groups=3,
|
||||
widen_factor=1.0,
|
||||
out_indices=(0, 1, 2, 3),
|
||||
frozen_stages=-1,
|
||||
bn_eval=True,
|
||||
bn_frozen=False,
|
||||
with_cp=False):
|
||||
super(ShuffleNetv1, self).__init__()
|
||||
blocks = [3, 7, 3]
|
||||
self.groups = groups
|
||||
self.out_indices = out_indices
|
||||
self.frozen_stages = frozen_stages
|
||||
self.bn_eval = bn_eval
|
||||
self.bn_frozen = bn_frozen
|
||||
self.with_cp = with_cp
|
||||
|
||||
if groups == 1:
|
||||
channels = [144, 288, 576]
|
||||
elif groups == 2:
|
||||
channels = [200, 400, 800]
|
||||
elif groups == 3:
|
||||
channels = [240, 480, 960]
|
||||
elif groups == 4:
|
||||
channels = [272, 544, 1088]
|
||||
elif groups == 8:
|
||||
channels = [384, 768, 1536]
|
||||
else:
|
||||
raise ValueError("{} groups is not supported for "
|
||||
"1x1 Grouped Convolutions".format(groups))
|
||||
channels = [_make_divisible(ch * widen_factor, 8) for ch in channels]
|
||||
|
||||
self.inplanes = int(24 * widen_factor)
|
||||
self.conv1 = conv3x3(3, self.inplanes, stride=2)
|
||||
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
||||
|
||||
self.layer1 = self._make_layer(
|
||||
channels[0], blocks[0], first_block=False, with_cp=with_cp)
|
||||
self.layer2 = self._make_layer(channels[1], blocks[1], with_cp=with_cp)
|
||||
self.layer3 = self._make_layer(channels[2], blocks[2], with_cp=with_cp)
|
||||
|
||||
def init_weights(self, pretrained=None):
|
||||
if isinstance(pretrained, str):
|
||||
logger = logging.getLogger()
|
||||
load_checkpoint(self, pretrained, strict=False, logger=logger)
|
||||
elif pretrained is None:
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
kaiming_init(m)
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
constant_init(m, 1)
|
||||
else:
|
||||
raise TypeError('pretrained must be a str or None')
|
||||
|
||||
def _make_layer(self, outplanes, blocks, first_block=True, with_cp=False):
|
||||
layers = []
|
||||
for i in range(blocks):
|
||||
if i == 0:
|
||||
layers.append(
|
||||
ShuffleUnit(
|
||||
self.inplanes,
|
||||
outplanes,
|
||||
groups=self.groups,
|
||||
first_block=first_block,
|
||||
combine='concat',
|
||||
with_cp=with_cp))
|
||||
else:
|
||||
layers.append(
|
||||
ShuffleUnit(
|
||||
self.inplanes,
|
||||
outplanes,
|
||||
groups=self.groups,
|
||||
first_block=True,
|
||||
combine='add',
|
||||
with_cp=with_cp))
|
||||
self.inplanes = outplanes
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.maxpool(x)
|
||||
|
||||
outs = []
|
||||
x = self.layer1(x)
|
||||
if 0 in self.out_indices:
|
||||
outs.append(x)
|
||||
x = self.layer2(x)
|
||||
if 1 in self.out_indices:
|
||||
outs.append(x)
|
||||
x = self.layer3(x)
|
||||
if 2 in self.out_indices:
|
||||
outs.append(x)
|
||||
|
||||
outs.append(x)
|
||||
|
||||
if len(outs) == 1:
|
||||
return outs[0]
|
||||
else:
|
||||
return tuple(outs)
|
||||
|
||||
def train(self, mode=True):
|
||||
super(ShuffleNetv1, self).train(mode)
|
||||
if self.bn_eval:
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.BatchNorm2d):
|
||||
m.eval()
|
||||
if self.bn_frozen:
|
||||
for params in m.parameters():
|
||||
params.requires_grad = False
|
||||
if mode and self.frozen_stages >= 0:
|
||||
for param in self.conv1.parameters():
|
||||
param.requires_grad = False
|
||||
for i in range(1, self.frozen_stages + 1):
|
||||
mod = getattr(self, 'layer{}'.format(i))
|
||||
mod.eval()
|
||||
for param in mod.parameters():
|
||||
param.requires_grad = False
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
# Copyright (c) Open-MMLab. All rights reserved.
|
||||
import numpy as np
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
def constant_init(module, val, bias=0):
|
||||
if hasattr(module, 'weight') and module.weight is not None:
|
||||
nn.init.constant_(module.weight, val)
|
||||
if hasattr(module, 'bias') and module.bias is not None:
|
||||
nn.init.constant_(module.bias, bias)
|
||||
|
||||
|
||||
def xavier_init(module, gain=1, bias=0, distribution='normal'):
|
||||
assert distribution in ['uniform', 'normal']
|
||||
if distribution == 'uniform':
|
||||
nn.init.xavier_uniform_(module.weight, gain=gain)
|
||||
else:
|
||||
nn.init.xavier_normal_(module.weight, gain=gain)
|
||||
if hasattr(module, 'bias') and module.bias is not None:
|
||||
nn.init.constant_(module.bias, bias)
|
||||
|
||||
|
||||
def normal_init(module, mean=0, std=1, bias=0):
|
||||
nn.init.normal_(module.weight, mean, std)
|
||||
if hasattr(module, 'bias') and module.bias is not None:
|
||||
nn.init.constant_(module.bias, bias)
|
||||
|
||||
|
||||
def uniform_init(module, a=0, b=1, bias=0):
|
||||
nn.init.uniform_(module.weight, a, b)
|
||||
if hasattr(module, 'bias') and module.bias is not None:
|
||||
nn.init.constant_(module.bias, bias)
|
||||
|
||||
|
||||
def kaiming_init(module,
|
||||
a=0,
|
||||
mode='fan_out',
|
||||
nonlinearity='relu',
|
||||
bias=0,
|
||||
distribution='normal'):
|
||||
assert distribution in ['uniform', 'normal']
|
||||
if distribution == 'uniform':
|
||||
nn.init.kaiming_uniform_(
|
||||
module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
|
||||
else:
|
||||
nn.init.kaiming_normal_(
|
||||
module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
|
||||
if hasattr(module, 'bias') and module.bias is not None:
|
||||
nn.init.constant_(module.bias, bias)
|
||||
|
||||
|
||||
def caffe2_xavier_init(module, bias=0):
|
||||
# `XavierFill` in Caffe2 corresponds to `kaiming_uniform_` in PyTorch
|
||||
# Acknowledgment to FAIR's internal code
|
||||
kaiming_init(
|
||||
module,
|
||||
a=1,
|
||||
mode='fan_in',
|
||||
nonlinearity='leaky_relu',
|
||||
distribution='uniform')
|
||||
|
||||
|
||||
def bias_init_with_prob(prior_prob):
|
||||
""" initialize conv/fc bias value according to giving probablity"""
|
||||
bias_init = float(-np.log((1 - prior_prob) / prior_prob))
|
||||
return bias_init
|
||||
|
|
@ -0,0 +1,157 @@
|
|||
import pytest
|
||||
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.shufflenet_v1 import ShuffleUnit
|
||||
|
||||
|
||||
def is_block(modules):
|
||||
"""Check if is ResNet building block."""
|
||||
if isinstance(modules, (ShuffleUnit, )):
|
||||
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_shufflenetv1_shuffleuint():
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
# combine must be in ['add', 'concat']
|
||||
ShuffleUnit(24, 16, groups=3, first_block=True, combine='test')
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
# in_channels must be divisible by groups
|
||||
ShuffleUnit(64, 64, groups=3, first_block=True, combine='add')
|
||||
|
||||
with pytest.raises(AssertionError):
|
||||
# inplanes must be equal tp = outplanes when combine='add'
|
||||
ShuffleUnit(64, 24, groups=3, first_block=True, combine='add')
|
||||
|
||||
# Test ShuffleUnit with combine='add'
|
||||
block = ShuffleUnit(24, 24, groups=3, first_block=True, combine='add')
|
||||
x = torch.randn(1, 24, 56, 56)
|
||||
x_out = block(x)
|
||||
assert x_out.shape == torch.Size([1, 24, 56, 56])
|
||||
|
||||
# Test ShuffleUnit with combine='concat'
|
||||
block = ShuffleUnit(24, 240, groups=3, first_block=True, combine='concat')
|
||||
x = torch.randn(1, 24, 56, 56)
|
||||
x_out = block(x)
|
||||
assert x_out.shape == torch.Size([1, 240, 28, 28])
|
||||
|
||||
# Test ShuffleUnit with checkpoint forward
|
||||
block = ShuffleUnit(
|
||||
24, 24, groups=3, first_block=True, combine='add', with_cp=True)
|
||||
x = torch.randn(1, 24, 56, 56)
|
||||
x_out = block(x)
|
||||
assert x_out.shape == torch.Size([1, 24, 56, 56])
|
||||
|
||||
|
||||
def test_shufflenetv1_backbone():
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
# groups must in [1, 2, 3, 4, 8]
|
||||
ShuffleNetv1(groups=10)
|
||||
|
||||
# Test ShuffleNetv1 norm state
|
||||
model = ShuffleNetv1()
|
||||
model.init_weights()
|
||||
model.train()
|
||||
assert check_norm_state(model.modules(), False)
|
||||
|
||||
# Test ShuffleNetv1 with first stage frozen
|
||||
frozen_stages = 1
|
||||
model = ShuffleNetv1(frozen_stages=frozen_stages)
|
||||
model.init_weights()
|
||||
model.train()
|
||||
for layer in [model.conv1]:
|
||||
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 ShuffleNetv1 with bn frozen
|
||||
model = ShuffleNetv1(bn_frozen=True)
|
||||
model.init_weights()
|
||||
model.train()
|
||||
|
||||
for i in range(1, 4):
|
||||
layer = getattr(model, f'layer{i}')
|
||||
|
||||
for mod in layer.modules():
|
||||
if isinstance(mod, _BatchNorm):
|
||||
assert mod.training is False
|
||||
for params in mod.parameters():
|
||||
params.requires_grad = False
|
||||
|
||||
# Test ShuffleNetv1 forward with groups=3
|
||||
model = ShuffleNetv1(groups=3)
|
||||
model.init_weights()
|
||||
model.train()
|
||||
|
||||
for m in model.modules():
|
||||
if is_norm(m):
|
||||
assert isinstance(m, _BatchNorm)
|
||||
|
||||
imgs = torch.randn(1, 3, 224, 224)
|
||||
feat = model(imgs)
|
||||
assert len(feat) == 4
|
||||
assert feat[0].shape == torch.Size([1, 240, 28, 28])
|
||||
assert feat[1].shape == torch.Size([1, 480, 14, 14])
|
||||
assert feat[2].shape == torch.Size([1, 960, 7, 7])
|
||||
assert feat[3].shape == torch.Size([1, 960, 7, 7])
|
||||
|
||||
# Test ShuffleNetv1 forward with layers 1, 2 forward
|
||||
model = ShuffleNetv1(groups=3, out_indices=(1, 2))
|
||||
model.init_weights()
|
||||
model.train()
|
||||
|
||||
for m in model.modules():
|
||||
if is_norm(m):
|
||||
assert isinstance(m, _BatchNorm)
|
||||
|
||||
imgs = torch.randn(1, 3, 224, 224)
|
||||
feat = model(imgs)
|
||||
assert len(feat) == 3
|
||||
assert feat[0].shape == torch.Size([1, 480, 14, 14])
|
||||
assert feat[1].shape == torch.Size([1, 960, 7, 7])
|
||||
assert feat[2].shape == torch.Size([1, 960, 7, 7])
|
||||
|
||||
# Test ShuffleNetv1 forward with checkpoint forward
|
||||
model = ShuffleNetv1(groups=3, with_cp=True)
|
||||
model.init_weights()
|
||||
model.train()
|
||||
|
||||
for m in model.modules():
|
||||
if is_norm(m):
|
||||
assert isinstance(m, _BatchNorm)
|
||||
|
||||
imgs = torch.randn(1, 3, 224, 224)
|
||||
feat = model(imgs)
|
||||
assert len(feat) == 4
|
||||
assert feat[0].shape == torch.Size([1, 240, 28, 28])
|
||||
assert feat[1].shape == torch.Size([1, 480, 14, 14])
|
||||
assert feat[2].shape == torch.Size([1, 960, 7, 7])
|
||||
assert feat[3].shape == torch.Size([1, 960, 7, 7])
|
||||
Loading…
Reference in New Issue