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#
# 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.
import paddle
import paddle.nn as nn
from ppdet.core.workspace import register
from ..shape_spec import ShapeSpec
__all__ = ['HarDNet']
def ConvLayer(in_channels,
out_channels,
kernel_size=3,
stride=1,
bias_attr=False):
layer = nn.Sequential(
('conv', nn.Conv2D(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=kernel_size // 2,
groups=1,
bias_attr=bias_attr)), ('norm', nn.BatchNorm2D(out_channels)),
('relu', nn.ReLU6()))
return layer
def DWConvLayer(in_channels,
out_channels,
kernel_size=3,
stride=1,
bias_attr=False):
layer = nn.Sequential(
('dwconv', nn.Conv2D(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=1,
groups=out_channels,
bias_attr=bias_attr)), ('norm', nn.BatchNorm2D(out_channels)))
return layer
def CombConvLayer(in_channels, out_channels, kernel_size=1, stride=1):
layer = nn.Sequential(
('layer1', ConvLayer(
in_channels, out_channels, kernel_size=kernel_size)),
('layer2', DWConvLayer(
out_channels, out_channels, stride=stride)))
return layer
class HarDBlock(nn.Layer):
def __init__(self,
in_channels,
growth_rate,
grmul,
n_layers,
keepBase=False,
residual_out=False,
dwconv=False):
super().__init__()
self.keepBase = keepBase
self.links = []
layers_ = []
self.out_channels = 0
for i in range(n_layers):
outch, inch, link = self.get_link(i + 1, in_channels, growth_rate,
grmul)
self.links.append(link)
if dwconv:
layers_.append(CombConvLayer(inch, outch))
else:
layers_.append(ConvLayer(inch, outch))
if (i % 2 == 0) or (i == n_layers - 1):
self.out_channels += outch
self.layers = nn.LayerList(layers_)
def get_out_ch(self):
return self.out_channels
def get_link(self, layer, base_ch, growth_rate, grmul):
if layer == 0:
return base_ch, 0, []
out_channels = growth_rate
link = []
for i in range(10):
dv = 2**i
if layer % dv == 0:
k = layer - dv
link.append(k)
if i > 0:
out_channels *= grmul
out_channels = int(int(out_channels + 1) / 2) * 2
in_channels = 0
for i in link:
ch, _, _ = self.get_link(i, base_ch, growth_rate, grmul)
in_channels += ch
return out_channels, in_channels, link
def forward(self, x):
layers_ = [x]
for layer in range(len(self.layers)):
link = self.links[layer]
tin = []
for i in link:
tin.append(layers_[i])
if len(tin) > 1:
x = paddle.concat(tin, 1)
else:
x = tin[0]
out = self.layers[layer](x)
layers_.append(out)
t = len(layers_)
out_ = []
for i in range(t):
if (i == 0 and self.keepBase) or (i == t - 1) or (i % 2 == 1):
out_.append(layers_[i])
out = paddle.concat(out_, 1)
return out
@register
class HarDNet(nn.Layer):
def __init__(self, depth_wise=False, return_idx=[1, 3, 8, 13], arch=85):
super(HarDNet, self).__init__()
assert arch in [68, 85], "HarDNet-{} is not supported.".format(arch)
if arch == 85:
first_ch = [48, 96]
second_kernel = 3
ch_list = [192, 256, 320, 480, 720]
grmul = 1.7
gr = [24, 24, 28, 36, 48]
n_layers = [8, 16, 16, 16, 16]
elif arch == 68:
first_ch = [32, 64]
second_kernel = 3
ch_list = [128, 256, 320, 640]
grmul = 1.7
gr = [14, 16, 20, 40]
n_layers = [8, 16, 16, 16]
else:
raise ValueError("HarDNet-{} is not supported.".format(arch))
self.return_idx = return_idx
self._out_channels = [96, 214, 458, 784]
avg_pool = True
if depth_wise:
second_kernel = 1
avg_pool = False
blks = len(n_layers)
self.base = nn.LayerList([])
# First Layer: Standard Conv3x3, Stride=2
self.base.append(
ConvLayer(
in_channels=3,
out_channels=first_ch[0],
kernel_size=3,
stride=2,
bias_attr=False))
# Second Layer
self.base.append(
ConvLayer(
first_ch[0], first_ch[1], kernel_size=second_kernel))
# Avgpooling or DWConv3x3 downsampling
if avg_pool:
self.base.append(nn.AvgPool2D(kernel_size=3, stride=2, padding=1))
else:
self.base.append(DWConvLayer(first_ch[1], first_ch[1], stride=2))
# Build all HarDNet blocks
ch = first_ch[1]
for i in range(blks):
blk = HarDBlock(ch, gr[i], grmul, n_layers[i], dwconv=depth_wise)
ch = blk.out_channels
self.base.append(blk)
if i != blks - 1:
self.base.append(ConvLayer(ch, ch_list[i], kernel_size=1))
ch = ch_list[i]
if i == 0:
self.base.append(
nn.AvgPool2D(
kernel_size=2, stride=2, ceil_mode=True))
elif i != blks - 1 and i != 1 and i != 3:
self.base.append(nn.AvgPool2D(kernel_size=2, stride=2))
def forward(self, inputs):
x = inputs['image']
outs = []
for i, layer in enumerate(self.base):
x = layer(x)
if i in self.return_idx:
outs.append(x)
return outs
@property
def out_shape(self):
return [ShapeSpec(channels=self._out_channels[i]) for i in range(4)]
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