# 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/1904.01169
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 = {
"Res2Net50_26w_4s":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_26w_4s_pretrained.pdparams",
"Res2Net50_14w_8s":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_14w_8s_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer):
def __init__(
self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None, ):
super(ConvBNLayer, self).__init__()
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)
if name == "conv1":
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
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):
y = self._conv(inputs)
y = self._batch_norm(y)
return y
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels1,
num_channels2,
num_filters,
stride,
scales,
shortcut=True,
if_first=False,
name=None):
super(BottleneckBlock, self).__init__()
self.stride = stride
self.scales = scales
self.conv0 = ConvBNLayer(
num_channels=num_channels1,
num_filters=num_filters,
filter_size=1,
act='relu',
name=name + "_branch2a")
self.conv1_list = []
for s in range(scales - 1):
conv1 = self.add_sublayer(
name + '_branch2b_' + str(s + 1),
ConvBNLayer(
num_channels=num_filters // scales,
num_filters=num_filters // scales,
filter_size=3,
stride=stride,
act='relu',
name=name + '_branch2b_' + str(s + 1)))
self.conv1_list.append(conv1)
self.pool2d_avg = AvgPool2D(kernel_size=3, stride=stride, padding=1)
self.conv2 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_channels2,
filter_size=1,
act=None,
name=name + "_branch2c")
if not shortcut:
self.short = ConvBNLayer(
num_channels=num_channels1,
num_filters=num_channels2,
filter_size=1,
stride=stride,
name=name + "_branch1")
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
xs = paddle.split(y, self.scales, 1)
ys = []
for s, conv1 in enumerate(self.conv1_list):
if s == 0 or self.stride == 2:
ys.append(conv1(xs[s]))
else:
ys.append(conv1(paddle.add(xs[s], ys[-1])))
if self.stride == 1:
ys.append(xs[-1])
else:
ys.append(self.pool2d_avg(xs[-1]))
conv1 = paddle.concat(ys, axis=1)
conv2 = self.conv2(conv1)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(x=short, y=conv2)
y = F.relu(y)
return y
class Res2Net(nn.Layer):
def __init__(self, layers=50, scales=4, width=26, class_num=1000):
super(Res2Net, self).__init__()
self.layers = layers
self.scales = scales
self.width = width
basic_width = self.width * self.scales
supported_layers = [50, 101, 152, 200]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(
supported_layers, layers)
if layers == 50:
depth = [3, 4, 6, 3]
elif layers == 101:
depth = [3, 4, 23, 3]
elif layers == 152:
depth = [3, 8, 36, 3]
elif layers == 200:
depth = [3, 12, 48, 3]
num_channels = [64, 256, 512, 1024]
num_channels2 = [256, 512, 1024, 2048]
num_filters = [basic_width * t for t in [1, 2, 4, 8]]
self.conv1 = ConvBNLayer(
num_channels=3,
num_filters=64,
filter_size=7,
stride=2,
act='relu',
name="conv1")
self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
for block in range(len(depth)):
shortcut = False
for i in range(depth[block]):
if layers in [101, 152] and block == 2:
if i == 0:
conv_name = "res" + str(block + 2) + "a"
else:
conv_name = "res" + str(block + 2) + "b" + str(i)
else:
conv_name = "res" + str(block + 2) + chr(97 + i)
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
num_channels1=num_channels[block]
if i == 0 else num_channels2[block],
num_channels2=num_channels2[block],
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
scales=scales,
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name))
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="fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
def forward(self, inputs):
y = self.conv1(inputs)
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 Res2Net50_26w_4s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net(layers=50, scales=4, width=26, **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["Res2Net50_26w_4s"], use_ssld=use_ssld)
return model
def Res2Net50_14w_8s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net(layers=50, scales=8, width=14, **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["Res2Net50_14w_8s"], use_ssld=use_ssld)
return model