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resnet.py
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resnet.py
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# coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
from collections import OrderedDict
from numbers import Integral
from paddle import fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.framework import Variable
from paddle.fluid.regularizer import L2Decay
from paddle.fluid.initializer import Constant
from .nonlocal_helper import add_space_nonlocal
from .name_adapter import NameAdapter
__all__ = ['ResNet', 'ResNetC5']
class ResNet(object):
"""
Residual Network, see https://arxiv.org/abs/1512.03385
Args:
depth (int): ResNet depth, should be 34, 50.
freeze_at (int): freeze the backbone at which stage
norm_type (str): normalization type, 'bn'/'sync_bn'/'affine_channel'
freeze_norm (bool): freeze normalization layers
norm_decay (float): weight decay for normalization layer weights
variant (str): ResNet variant, supports 'a', 'b', 'c', 'd' currently
feature_maps (list): index of stages whose feature maps are returned
dcn_v2_stages (list): index of stages who select deformable conv v2
nonlocal_stages (list): index of stages who select nonlocal networks
"""
__shared__ = ['norm_type', 'freeze_norm', 'weight_prefix_name']
def __init__(self,
depth=50,
freeze_at=0,
norm_type='sync_bn',
freeze_norm=False,
norm_decay=0.,
variant='b',
feature_maps=[3, 4, 5],
dcn_v2_stages=[],
weight_prefix_name='',
nonlocal_stages=[],
get_prediction=False,
class_dim=1000):
super(ResNet, self).__init__()
if isinstance(feature_maps, Integral):
feature_maps = [feature_maps]
assert depth in [34, 50], \
"depth {} not in [34, 50]"
assert variant in ['a', 'b', 'c', 'd'], "invalid ResNet variant"
assert 0 <= freeze_at <= 4, "freeze_at should be 0, 1, 2, 3 or 4"
assert len(feature_maps) > 0, "need one or more feature maps"
assert norm_type in ['bn', 'sync_bn', 'affine_channel']
assert not (len(nonlocal_stages)>0 and depth<50), \
"non-local is not supported for resnet18 or resnet34"
self.depth = depth
self.freeze_at = freeze_at
self.norm_type = norm_type
self.norm_decay = norm_decay
self.freeze_norm = freeze_norm
self.variant = variant
self._model_type = 'ResNet'
self.feature_maps = feature_maps
self.dcn_v2_stages = dcn_v2_stages
self.depth_cfg = {
34: ([3, 4, 6, 3], self.basicblock),
50: ([3, 4, 6, 3], self.bottleneck),
}
self.stage_filters = [64, 128, 256, 512]
self._c1_out_chan_num = 64
self.na = NameAdapter(self)
self.prefix_name = weight_prefix_name
self.nonlocal_stages = nonlocal_stages
self.nonlocal_mod_cfg = {
50: 2,
101: 5,
152: 8,
200: 12,
}
self.get_prediction = get_prediction
self.class_dim = class_dim
def _conv_offset(self,
input,
filter_size,
stride,
padding,
act=None,
name=None):
out_channel = filter_size * filter_size * 3
out = fluid.layers.conv2d(
input,
num_filters=out_channel,
filter_size=filter_size,
stride=stride,
padding=padding,
param_attr=ParamAttr(initializer=Constant(0.0), name=name + ".w_0"),
bias_attr=ParamAttr(initializer=Constant(0.0), name=name + ".b_0"),
act=act,
name=name)
return out
def _conv_norm(self,
input,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None,
dcn_v2=False):
_name = self.prefix_name + name if self.prefix_name != '' else name
if not dcn_v2:
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=_name + "_weights"),
bias_attr=False,
name=_name + '.conv2d.output.1')
else:
# select deformable conv"
offset_mask = self._conv_offset(
input=input,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
act=None,
name=_name + "_conv_offset")
offset_channel = filter_size**2 * 2
mask_channel = filter_size**2
offset, mask = fluid.layers.split(
input=offset_mask,
num_or_sections=[offset_channel, mask_channel],
dim=1)
mask = fluid.layers.sigmoid(mask)
conv = fluid.layers.deformable_conv(
input=input,
offset=offset,
mask=mask,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
deformable_groups=1,
im2col_step=1,
param_attr=ParamAttr(name=_name + "_weights"),
bias_attr=False,
name=_name + ".conv2d.output.1")
bn_name = self.na.fix_conv_norm_name(name)
bn_name = self.prefix_name + bn_name if self.prefix_name != '' else bn_name
norm_lr = 0. if self.freeze_norm else 1.
norm_decay = self.norm_decay
pattr = ParamAttr(
name=bn_name + '_scale',
learning_rate=norm_lr,
regularizer=L2Decay(norm_decay))
battr = ParamAttr(
name=bn_name + '_offset',
learning_rate=norm_lr,
regularizer=L2Decay(norm_decay))
if self.norm_type in ['bn', 'sync_bn']:
global_stats = True if self.freeze_norm else False
out = fluid.layers.batch_norm(
input=conv,
act=act,
name=bn_name + '.output.1',
param_attr=pattr,
bias_attr=battr,
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance',
use_global_stats=global_stats)
scale = fluid.framework._get_var(pattr.name)
bias = fluid.framework._get_var(battr.name)
elif self.norm_type == 'affine_channel':
scale = fluid.layers.create_parameter(
shape=[conv.shape[1]],
dtype=conv.dtype,
attr=pattr,
default_initializer=fluid.initializer.Constant(1.))
bias = fluid.layers.create_parameter(
shape=[conv.shape[1]],
dtype=conv.dtype,
attr=battr,
default_initializer=fluid.initializer.Constant(0.))
out = fluid.layers.affine_channel(
x=conv, scale=scale, bias=bias, act=act)
if self.freeze_norm:
scale.stop_gradient = True
bias.stop_gradient = True
return out
def _shortcut(self, input, ch_out, stride, is_first, name):
max_pooling_in_short_cut = self.variant == 'd'
ch_in = input.shape[1]
# the naming rule is same as pretrained weight
name = self.na.fix_shortcut_name(name)
std_senet = getattr(self, 'std_senet', False)
if ch_in != ch_out or stride != 1 or (self.depth < 50 and is_first):
if std_senet:
if is_first:
return self._conv_norm(input, ch_out, 1, stride, name=name)
else:
return self._conv_norm(input, ch_out, 3, stride, name=name)
if max_pooling_in_short_cut and not is_first:
input = fluid.layers.pool2d(
input=input,
pool_size=2,
pool_stride=2,
pool_padding=0,
ceil_mode=True,
pool_type='avg')
return self._conv_norm(input, ch_out, 1, 1, name=name)
return self._conv_norm(input, ch_out, 1, stride, name=name)
else:
return input
def bottleneck(self,
input,
num_filters,
stride,
is_first,
name,
dcn_v2=False):
if self.variant == 'a':
stride1, stride2 = stride, 1
else:
stride1, stride2 = 1, stride
# ResNeXt
groups = getattr(self, 'groups', 1)
group_width = getattr(self, 'group_width', -1)
if groups == 1:
expand = 4
elif (groups * group_width) == 256:
expand = 1
else: # FIXME hard code for now, handles 32x4d, 64x4d and 32x8d
num_filters = num_filters // 2
expand = 2
conv_name1, conv_name2, conv_name3, \
shortcut_name = self.na.fix_bottleneck_name(name)
std_senet = getattr(self, 'std_senet', False)
if std_senet:
conv_def = [[
int(num_filters / 2), 1, stride1, 'relu', 1, conv_name1
], [num_filters, 3, stride2, 'relu', groups, conv_name2],
[num_filters * expand, 1, 1, None, 1, conv_name3]]
else:
conv_def = [[num_filters, 1, stride1, 'relu', 1, conv_name1],
[num_filters, 3, stride2, 'relu', groups, conv_name2],
[num_filters * expand, 1, 1, None, 1, conv_name3]]
residual = input
for i, (c, k, s, act, g, _name) in enumerate(conv_def):
residual = self._conv_norm(
input=residual,
num_filters=c,
filter_size=k,
stride=s,
act=act,
groups=g,
name=_name,
dcn_v2=(i == 1 and dcn_v2))
short = self._shortcut(
input,
num_filters * expand,
stride,
is_first=is_first,
name=shortcut_name)
# Squeeze-and-Excitation
if callable(getattr(self, '_squeeze_excitation', None)):
residual = self._squeeze_excitation(
input=residual, num_channels=num_filters, name='fc' + name)
return fluid.layers.elementwise_add(
x=short, y=residual, act='relu', name=name + ".add.output.5")
def basicblock(self,
input,
num_filters,
stride,
is_first,
name,
dcn_v2=False):
assert dcn_v2 is False, "Not implemented yet."
conv0 = self._conv_norm(
input=input,
num_filters=num_filters,
filter_size=3,
act='relu',
stride=stride,
name=name + "_branch2a")
conv1 = self._conv_norm(
input=conv0,
num_filters=num_filters,
filter_size=3,
act=None,
name=name + "_branch2b")
short = self._shortcut(
input, num_filters, stride, is_first, name=name + "_branch1")
return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
def layer_warp(self, input, stage_num):
"""
Args:
input (Variable): input variable.
stage_num (int): the stage number, should be 2, 3, 4, 5
Returns:
The last variable in endpoint-th stage.
"""
assert stage_num in [2, 3, 4, 5]
stages, block_func = self.depth_cfg[self.depth]
count = stages[stage_num - 2]
ch_out = self.stage_filters[stage_num - 2]
is_first = False if stage_num != 2 else True
dcn_v2 = True if stage_num in self.dcn_v2_stages else False
nonlocal_mod = 1000
if stage_num in self.nonlocal_stages:
nonlocal_mod = self.nonlocal_mod_cfg[
self.depth] if stage_num == 4 else 2
# Make the layer name and parameter name consistent
# with ImageNet pre-trained model
conv = input
for i in range(count):
conv_name = self.na.fix_layer_warp_name(stage_num, count, i)
if self.depth < 50:
is_first = True if i == 0 and stage_num == 2 else False
conv = block_func(
input=conv,
num_filters=ch_out,
stride=2 if i == 0 and stage_num != 2 else 1,
is_first=is_first,
name=conv_name,
dcn_v2=dcn_v2)
# add non local model
dim_in = conv.shape[1]
nonlocal_name = "nonlocal_conv{}".format(stage_num)
if i % nonlocal_mod == nonlocal_mod - 1:
conv = add_space_nonlocal(conv, dim_in, dim_in,
nonlocal_name + '_{}'.format(i),
int(dim_in / 2))
return conv
def c1_stage(self, input):
out_chan = self._c1_out_chan_num
conv1_name = self.na.fix_c1_stage_name()
if self.variant in ['c', 'd']:
conv_def = [
[out_chan // 2, 3, 2, "conv1_1"],
[out_chan // 2, 3, 1, "conv1_2"],
[out_chan, 3, 1, "conv1_3"],
]
else:
conv_def = [[out_chan, 7, 2, conv1_name]]
for (c, k, s, _name) in conv_def:
input = self._conv_norm(
input=input,
num_filters=c,
filter_size=k,
stride=s,
act='relu',
name=_name)
output = fluid.layers.pool2d(
input=input,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
return output
def __call__(self, input):
assert isinstance(input, Variable)
assert not (set(self.feature_maps) - set([2, 3, 4, 5])), \
"feature maps {} not in [2, 3, 4, 5]".format(self.feature_maps)
res_endpoints = []
res = input
feature_maps = self.feature_maps
severed_head = getattr(self, 'severed_head', False)
if not severed_head:
res = self.c1_stage(res)
feature_maps = range(2, max(self.feature_maps) + 1)
for i in feature_maps:
res = self.layer_warp(res, i)
if i in self.feature_maps:
res_endpoints.append(res)
if self.freeze_at >= i:
res.stop_gradient = True
if self.get_prediction:
pool = fluid.layers.pool2d(
input=res, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
size=self.class_dim,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv)))
out = fluid.layers.softmax(out)
return out
return OrderedDict([('res{}_sum'.format(self.feature_maps[idx]), feat)
for idx, feat in enumerate(res_endpoints)])
class ResNetC5(ResNet):
def __init__(self,
depth=50,
freeze_at=2,
norm_type='affine_channel',
freeze_norm=True,
norm_decay=0.,
variant='b',
feature_maps=[5],
weight_prefix_name=''):
super(ResNetC5, self).__init__(depth, freeze_at, norm_type, freeze_norm,
norm_decay, variant, feature_maps)
self.severed_head = True