# copyright (c) 2021 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.
import paddle
import paddle.nn as nn
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
'HarDNet39_ds':
'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet39_ds_pretrained.pdparams',
'HarDNet68_ds':
'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_ds_pretrained.pdparams',
'HarDNet68':
'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_pretrained.pdparams',
'HarDNet85':
'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet85_pretrained.pdparams'
}
__all__ = MODEL_URLS.keys()
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 # if upsample else in_channels
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
# print("Blk out =",self.out_channels)
self.layers = nn.LayerList(layers_)
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
class HarDNet(nn.Layer):
def __init__(self,
depth_wise=False,
arch=85,
class_num=1000,
with_pool=True):
super().__init__()
first_ch = [32, 64]
second_kernel = 3
max_pool = True
grmul = 1.7
drop_rate = 0.1
# HarDNet68
ch_list = [128, 256, 320, 640, 1024]
gr = [14, 16, 20, 40, 160]
n_layers = [8, 16, 16, 16, 4]
downSamp = [1, 0, 1, 1, 0]
if arch == 85:
# HarDNet85
first_ch = [48, 96]
ch_list = [192, 256, 320, 480, 720, 1280]
gr = [24, 24, 28, 36, 48, 256]
n_layers = [8, 16, 16, 16, 16, 4]
downSamp = [1, 0, 1, 0, 1, 0]
drop_rate = 0.2
elif arch == 39:
# HarDNet39
first_ch = [24, 48]
ch_list = [96, 320, 640, 1024]
grmul = 1.6
gr = [16, 20, 64, 160]
n_layers = [4, 16, 8, 4]
downSamp = [1, 1, 1, 0]
if depth_wise:
second_kernel = 1
max_pool = False
drop_rate = 0.05
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))
# Maxpooling or DWConv3x3 downsampling
if max_pool:
self.base.append(nn.MaxPool2D(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 and arch == 85:
self.base.append(nn.Dropout(0.1))
self.base.append(ConvLayer(ch, ch_list[i], kernel_size=1))
ch = ch_list[i]
if downSamp[i] == 1:
if max_pool:
self.base.append(nn.MaxPool2D(kernel_size=2, stride=2))
else:
self.base.append(DWConvLayer(ch, ch, stride=2))
ch = ch_list[blks - 1]
layers = []
if with_pool:
layers.append(nn.AdaptiveAvgPool2D((1, 1)))
if class_num > 0:
layers.append(nn.Flatten())
layers.append(nn.Dropout(drop_rate))
layers.append(nn.Linear(ch, class_num))
self.base.append(nn.Sequential(*layers))
def forward(self, x):
for layer in self.base:
x = layer(x)
return x
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 HarDNet39_ds(pretrained=False, **kwargs):
model = HarDNet(arch=39, depth_wise=True, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["HarDNet39_ds"])
return model
def HarDNet68_ds(pretrained=False, **kwargs):
model = HarDNet(arch=68, depth_wise=True, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["HarDNet68_ds"])
return model
def HarDNet68(pretrained=False, **kwargs):
model = HarDNet(arch=68, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["HarDNet68"])
return model
def HarDNet85(pretrained=False, **kwargs):
model = HarDNet(arch=85, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["HarDNet85"])
return model