312 lines
10 KiB
Python
312 lines
10 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
# reference: https://arxiv.org/abs/1611.05431 & https://arxiv.org/abs/1812.01187 & https://arxiv.org/abs/1709.01507
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
import paddle
|
|
from paddle import ParamAttr
|
|
import paddle.nn as nn
|
|
import paddle.nn.functional as F
|
|
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
|
|
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
|
|
from paddle.nn.initializer import Uniform
|
|
|
|
import math
|
|
|
|
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
|
|
|
|
MODEL_URLS = {
|
|
"SE_ResNeXt50_vd_32x4d":
|
|
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams",
|
|
"SENet154_vd":
|
|
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SENet154_vd_pretrained.pdparams",
|
|
}
|
|
|
|
__all__ = list(MODEL_URLS.keys())
|
|
|
|
|
|
class ConvBNLayer(nn.Layer):
|
|
def __init__(self,
|
|
num_channels,
|
|
num_filters,
|
|
filter_size,
|
|
stride=1,
|
|
groups=1,
|
|
is_vd_mode=False,
|
|
act=None,
|
|
name=None):
|
|
super(ConvBNLayer, self).__init__()
|
|
|
|
self.is_vd_mode = is_vd_mode
|
|
self._pool2d_avg = AvgPool2D(
|
|
kernel_size=2, stride=2, padding=0, ceil_mode=True)
|
|
self._conv = Conv2D(
|
|
in_channels=num_channels,
|
|
out_channels=num_filters,
|
|
kernel_size=filter_size,
|
|
stride=stride,
|
|
padding=(filter_size - 1) // 2,
|
|
groups=groups,
|
|
weight_attr=ParamAttr(name=name + "_weights"),
|
|
bias_attr=False)
|
|
bn_name = name + '_bn'
|
|
self._batch_norm = BatchNorm(
|
|
num_filters,
|
|
act=act,
|
|
param_attr=ParamAttr(name=bn_name + '_scale'),
|
|
bias_attr=ParamAttr(bn_name + '_offset'),
|
|
moving_mean_name=bn_name + '_mean',
|
|
moving_variance_name=bn_name + '_variance')
|
|
|
|
def forward(self, inputs):
|
|
if self.is_vd_mode:
|
|
inputs = self._pool2d_avg(inputs)
|
|
y = self._conv(inputs)
|
|
y = self._batch_norm(y)
|
|
return y
|
|
|
|
|
|
class BottleneckBlock(nn.Layer):
|
|
def __init__(self,
|
|
num_channels,
|
|
num_filters,
|
|
stride,
|
|
cardinality,
|
|
reduction_ratio,
|
|
shortcut=True,
|
|
if_first=False,
|
|
name=None):
|
|
super(BottleneckBlock, self).__init__()
|
|
|
|
self.conv0 = ConvBNLayer(
|
|
num_channels=num_channels,
|
|
num_filters=num_filters,
|
|
filter_size=1,
|
|
act='relu',
|
|
name='conv' + name + '_x1')
|
|
self.conv1 = ConvBNLayer(
|
|
num_channels=num_filters,
|
|
num_filters=num_filters,
|
|
filter_size=3,
|
|
groups=cardinality,
|
|
stride=stride,
|
|
act='relu',
|
|
name='conv' + name + '_x2')
|
|
self.conv2 = ConvBNLayer(
|
|
num_channels=num_filters,
|
|
num_filters=num_filters * 2 if cardinality == 32 else num_filters,
|
|
filter_size=1,
|
|
act=None,
|
|
name='conv' + name + '_x3')
|
|
self.scale = SELayer(
|
|
num_channels=num_filters * 2 if cardinality == 32 else num_filters,
|
|
num_filters=num_filters * 2 if cardinality == 32 else num_filters,
|
|
reduction_ratio=reduction_ratio,
|
|
name='fc' + name)
|
|
|
|
if not shortcut:
|
|
self.short = ConvBNLayer(
|
|
num_channels=num_channels,
|
|
num_filters=num_filters * 2
|
|
if cardinality == 32 else num_filters,
|
|
filter_size=1,
|
|
stride=1,
|
|
is_vd_mode=False if if_first else True,
|
|
name='conv' + name + '_prj')
|
|
|
|
self.shortcut = shortcut
|
|
|
|
def forward(self, inputs):
|
|
y = self.conv0(inputs)
|
|
conv1 = self.conv1(y)
|
|
conv2 = self.conv2(conv1)
|
|
scale = self.scale(conv2)
|
|
|
|
if self.shortcut:
|
|
short = inputs
|
|
else:
|
|
short = self.short(inputs)
|
|
y = paddle.add(x=short, y=scale)
|
|
y = F.relu(y)
|
|
return y
|
|
|
|
|
|
class SELayer(nn.Layer):
|
|
def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
|
|
super(SELayer, self).__init__()
|
|
|
|
self.pool2d_gap = AdaptiveAvgPool2D(1)
|
|
|
|
self._num_channels = num_channels
|
|
|
|
med_ch = int(num_channels / reduction_ratio)
|
|
stdv = 1.0 / math.sqrt(num_channels * 1.0)
|
|
self.squeeze = Linear(
|
|
num_channels,
|
|
med_ch,
|
|
weight_attr=ParamAttr(
|
|
initializer=Uniform(-stdv, stdv), name=name + "_sqz_weights"),
|
|
bias_attr=ParamAttr(name=name + '_sqz_offset'))
|
|
self.relu = nn.ReLU()
|
|
stdv = 1.0 / math.sqrt(med_ch * 1.0)
|
|
self.excitation = Linear(
|
|
med_ch,
|
|
num_filters,
|
|
weight_attr=ParamAttr(
|
|
initializer=Uniform(-stdv, stdv), name=name + "_exc_weights"),
|
|
bias_attr=ParamAttr(name=name + '_exc_offset'))
|
|
self.sigmoid = nn.Sigmoid()
|
|
|
|
def forward(self, input):
|
|
pool = self.pool2d_gap(input)
|
|
pool = paddle.squeeze(pool, axis=[2, 3])
|
|
squeeze = self.squeeze(pool)
|
|
squeeze = self.relu(squeeze)
|
|
excitation = self.excitation(squeeze)
|
|
excitation = self.sigmoid(excitation)
|
|
excitation = paddle.unsqueeze(excitation, axis=[2, 3])
|
|
out = paddle.multiply(input, excitation)
|
|
return out
|
|
|
|
|
|
class ResNeXt(nn.Layer):
|
|
def __init__(self, layers=50, class_num=1000, cardinality=32):
|
|
super(ResNeXt, self).__init__()
|
|
|
|
self.layers = layers
|
|
self.cardinality = cardinality
|
|
self.reduction_ratio = 16
|
|
supported_layers = [50, 101, 152]
|
|
assert layers in supported_layers, \
|
|
"supported layers are {} but input layer is {}".format(
|
|
supported_layers, layers)
|
|
supported_cardinality = [32, 64]
|
|
assert cardinality in supported_cardinality, \
|
|
"supported cardinality is {} but input cardinality is {}" \
|
|
.format(supported_cardinality, cardinality)
|
|
if layers == 50:
|
|
depth = [3, 4, 6, 3]
|
|
elif layers == 101:
|
|
depth = [3, 4, 23, 3]
|
|
elif layers == 152:
|
|
depth = [3, 8, 36, 3]
|
|
num_channels = [128, 256, 512, 1024]
|
|
num_filters = [128, 256, 512,
|
|
1024] if cardinality == 32 else [256, 512, 1024, 2048]
|
|
|
|
self.conv1_1 = ConvBNLayer(
|
|
num_channels=3,
|
|
num_filters=64,
|
|
filter_size=3,
|
|
stride=2,
|
|
act='relu',
|
|
name="conv1_1")
|
|
self.conv1_2 = ConvBNLayer(
|
|
num_channels=64,
|
|
num_filters=64,
|
|
filter_size=3,
|
|
stride=1,
|
|
act='relu',
|
|
name="conv1_2")
|
|
self.conv1_3 = ConvBNLayer(
|
|
num_channels=64,
|
|
num_filters=128,
|
|
filter_size=3,
|
|
stride=1,
|
|
act='relu',
|
|
name="conv1_3")
|
|
|
|
self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
|
|
|
|
self.block_list = []
|
|
n = 1 if layers == 50 or layers == 101 else 3
|
|
for block in range(len(depth)):
|
|
n += 1
|
|
shortcut = False
|
|
for i in range(depth[block]):
|
|
bottleneck_block = self.add_sublayer(
|
|
'bb_%d_%d' % (block, i),
|
|
BottleneckBlock(
|
|
num_channels=num_channels[block] if i == 0 else
|
|
num_filters[block] * int(64 // self.cardinality),
|
|
num_filters=num_filters[block],
|
|
stride=2 if i == 0 and block != 0 else 1,
|
|
cardinality=self.cardinality,
|
|
reduction_ratio=self.reduction_ratio,
|
|
shortcut=shortcut,
|
|
if_first=block == 0,
|
|
name=str(n) + '_' + str(i + 1)))
|
|
self.block_list.append(bottleneck_block)
|
|
shortcut = True
|
|
|
|
self.pool2d_avg = AdaptiveAvgPool2D(1)
|
|
|
|
self.pool2d_avg_channels = num_channels[-1] * 2
|
|
|
|
stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
|
|
|
|
self.out = Linear(
|
|
self.pool2d_avg_channels,
|
|
class_num,
|
|
weight_attr=ParamAttr(
|
|
initializer=Uniform(-stdv, stdv), name="fc6_weights"),
|
|
bias_attr=ParamAttr(name="fc6_offset"))
|
|
|
|
def forward(self, inputs):
|
|
y = self.conv1_1(inputs)
|
|
y = self.conv1_2(y)
|
|
y = self.conv1_3(y)
|
|
y = self.pool2d_max(y)
|
|
for block in self.block_list:
|
|
y = block(y)
|
|
y = self.pool2d_avg(y)
|
|
y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
|
|
y = self.out(y)
|
|
return y
|
|
|
|
|
|
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
|
|
if pretrained is False:
|
|
pass
|
|
elif pretrained is True:
|
|
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
|
|
elif isinstance(pretrained, str):
|
|
load_dygraph_pretrain(model, pretrained)
|
|
else:
|
|
raise RuntimeError(
|
|
"pretrained type is not available. Please use `string` or `boolean` type."
|
|
)
|
|
|
|
|
|
def SE_ResNeXt50_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
|
|
model = ResNeXt(layers=50, cardinality=32, **kwargs)
|
|
_load_pretrained(
|
|
pretrained,
|
|
model,
|
|
MODEL_URLS["SE_ResNeXt50_vd_32x4d"],
|
|
use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def SENet154_vd(pretrained=False, use_ssld=False, **kwargs):
|
|
model = ResNeXt(layers=152, cardinality=64, **kwargs)
|
|
_load_pretrained(
|
|
pretrained, model, MODEL_URLS["SENet154_vd"], use_ssld=use_ssld)
|
|
return model
|